SLEEP

Perlis, Michael, L;Vargas,, Ivan;Ellis, Jason, G;Grandner, Michael, A;Morales, Knashawn, H;Gencarelli,, Amy;Khader,, Waliuddin;Kloss, Jaqueline, D;Gooneratne, Nalaka, S;Thase, Michael, E

doi: 10.1093/sleep/zsz299pmid: 31848629

Abstract Study Objectives The primary aim of the present study was to estimate the incidence per annum of acute insomnia and to what extent those that develop acute insomnia recover good sleep or develop chronic insomnia. Unlike prior studies, a dense-sampling approach was used here (i.e. daily diaries) and this allowed for a more precise detection of acute insomnia and the follow-on states (the transitions to either recovery or chronic insomnia). Methods Good sleeper subjects (n = 1,248; 67% female) that were at least 35 years old participated in this prospective study on the natural history of insomnia. Subjects were recruited nationwide and completed online assessments for 1 year. The online measures consisted primarily of daily sleep diaries, as well as weekly/bi-weekly and monthly measures of sleep, stress, and psychological and physical health. Results The 1-year incidence rate of acute insomnia was 27.0% (n = 337). The incidence rate of chronic insomnia was 1.8% (n = 23). Of those that developed acute insomnia, 72.4% (n = 244) went on to recover good sleep. 19.3% (n = 65) of the acute insomnia sample continued to experience persistent poor sleep, but did not meet criteria for chronic insomnia. Conclusions The incidence rate of acute insomnia (3 or more nights a week for between 2 and 12 weeks) is remarkably high. This said, most incident cases resolve within a few days to weeks. Incident chronic insomnia only occurs in about 2 in 100 individuals. insomnia, aging, natural history, acute insomnia, incidence Statement of Significance While there are many studies on the prevalence of insomnia, only a few have evaluated new-onset incidence rates; none of which have adopted a high density sampling approach to determine the incidence of (1) good sleep to acute insomnia, (2) acute insomnia to the recovery of good sleep, and (3) acute insomnia to chronic insomnia. The present study addresses this gap. The remarkably high incidence rate of acute insomnia observed in this study suggests that this phenomenon, in contrast to chronic insomnia, may be normative (i.e. sleeplessness may be an inherent part of the fight-flight response). The next step is to evaluate the factors that mediate the transition from good sleep to acute insomnia and from acute to chronic insomnia. Introduction There is a wealth of data regarding insomnia prevalence [1–4]. In general, chronic insomnia (CI) has been found to occur in 6%–10% of the population. This said, prevalence rates as high as 30% have been reported when: duration of illness is not taken into account, qualitative criteria are used for insomnia frequency; and/or when daytime impairment criteria are not used to define “caseness” [2, 4]. Data on the prevalence of acute insomnia (AI) have only recently been published and suggest that the prevalence of AI is between 7.9% and 9.5% [5]. Little is known, however, about incidence rates for the transitions from Good Sleep (GS) to AI and from AI to either CI or Remission/Recovery. This lack of data makes it difficult to know (1) how common AI is, (2) empirically define the thresholds for AI/CI, and (3) identify the factors that mediate risk for (or protect from) developing CI. As part of the National Institute of Mental Health Epidemiologic Catchment Area study, Ford and Kamerow were among the first to estimate the incidence of insomnia. They reported that the annual incidence rate of new-onset insomnia was 6.2% [6]. A decade later, a similar study in older adults (≥65 years old) suggested that the 3-year incidence rate of insomnia was 15% (or an annual incidence rate of approximately 5%) [7]. Aside from the age of the sample and the time interval between measurement points (i.e. 1 vs. 3 years), an important difference between this and the earlier study was the assessment of insomnia. While both studies used retrospective reports, one focused on duration of symptoms (e.g. “have you ever had a period of two weeks or more when you had trouble falling asleep, staying asleep, or with waking up too early?”) [6], whereas the other focused on frequency of symptoms (e.g. “how often do you have trouble falling asleep”) [7]. Two more recent studies provide convergent data suggesting that the annual incidence of insomnia is between 26% and 31% for the occurrence of insomnia “symptoms” and 7%–8% for insomnia “syndrome” [8, 9]. Subjects who were classified as “symptomatic” reported symptoms of initial, maintenance, or late insomnia at least three nights per week, without fulfilling all the diagnostic criteria of an insomnia syndrome (i.e. they could be satisfied with their sleep [not report distress or daytime consequences], or their sleep difficulties lasted for less than one month). Subjects who were classified as “syndromic” endorsed symptoms of insomnia at least three nights per week for a minimum duration of 1 month and reported dissatisfaction with their sleep. These rates decreased to 28.8% and 3.9% for those without a prior lifetime episode of insomnia [8]. To date, only one study has explicitly assessed the incidence rate of AI [5]. As part of a larger mixed model study, 412 “normal sleepers” from the United Kingdom (UK) were surveyed longitudinally to determine incidence, transition, and remission rates for AI. Individuals categorized as normal sleepers did not meet the minimum criterion for insomnia (i.e. did not report insomnia symptoms and sleep-related daytime impairment). Subjects completed three evaluations via telephone at baseline, 1 month, and 3 months. Sleep status was ascertained using the following questions: (1) “Have you ever had a problem with your sleep?”; (2) “Is this an ongoing problem at the moment?”; (3) “For how long has this been going on?”; and (4) “What is the nature of your sleep problem?.” The third question was used to differentiate AI (i.e. 3 days to 3 months) from CI (i.e. 3 months or longer). A final question pertained to whether subjects’ principal sleep complaint resulted in impairment in daytime functioning. According to the study findings, the annual incidence of AI was between 31% and 37% (depending on whether DSM-5 criteria with or without additional case criteria [SL or WASO of 30 min or longer and a self-reported increase in daytime impairment] was used). In sum, the two early studies suggest that the annual incidence of insomnia ranges between 5% and 7% [6, 7]. The three later studies suggest that the annual incidence of insomnia ranges between 26% and 37% [5, 8, 9]. While the studies had different methods, measures, sampling rates and time frames, the difference between the early and late studies is potentially ascribable to how new onset insomnia was defined. The two early studies were describing the new onset of CI while the three latter studies were specifically profiling the incidence of acute and sub-CI. Of the studies that assessed recovery, it was found that 47%–78% of those that experience AI exhibit a resolution of their insomnia by the follow-up assessment [5, 7, 9]. While there are a number of limitations of the prior research (e.g. heterogeneity in sample demographics and methodology), the primary limitations (and those addressed by the present study) were that (1) the two to three time point assessment strategy lacks the temporal resolution needed to identify the onset and offset of AI; (2) only one of the studies adopted a quantitative approach to the assessment of sleep continuity (e.g. sleep latency [SL] and wake after sleep onset [WASO] defined in minutes); (3) the reported incident rates, for at least the two earlier studies [6, 7], are not truly the incidence of AI but rather the identification of new onset insomnia of any duration (includes both AI and CI). The present study was undertaken as a natural history study of insomnia. Good sleeper subjects were recruited over three successive cohorts and were tracked for changes in sleep patterns for 1 year using online daily sleep diaries. The a priori aims for the study were to track incidence rates for transitions from GS to AI and from AI to either CI or Recovery back to GS (AI-REC). Please note this is the first of series of planned publications, which ultimately have the intent of (1) estimating the incidence rate of acute and CI, (2) defining, on an empirical basis, what is acute and CI, and what level of morbidity is associated with daytime dysfunction, and (3) exploration of what factors account for the transition from acute to CI. Methods Subjects and procedure Adult good sleeper subjects (>35 years of age) were recruited from two nationwide platforms over three recruitment intervals, separated by approximately 1 year’s time. Recruitment did not include individuals from 18 to 35 years of age because the study was focused on insomnia in middle-aged and older adults. Subjects were recruited from Zogby Analytics [10] (an international polling agency) and ResearchMatch [11]. The study was conducted in two phases, described as follows. Phase-1 For subjects recruited by Zogby, age-appropriate good sleepers were identified and screened via a preliminary survey administered to panel members. Appropriate individuals were referred on to the study website. For ResearchMatch, age-appropriate individuals without sleep disorders were identified via an internal poll. Interested subjects were then referred to a screening questionnaire hosted on a Redcap server. In both cases (Zogby and ResearchMatch) potential study candidates provided a yes response to the following statement: “Are you a good sleeper? That is, do you reliably (5 or more nights per week) take less than 15 min to fall asleep and are awake during the night for less than 15 min? Has this been true for you for at least the last 6 months?.” The screening criteria (i.e. 15 min or less) was rigorous to enhance the likelihood that the study recruited enduringly good sleepers. No other inclusion or exclusion criteria were applied. Eligible subjects who expressed an interest in participating in the study were then referred to the study website where they (1) reviewed HIPAA forms and provided their informed consent, (2) completed an intake survey (profiling sleep, health, and mental health status and history), and (3) completed 2 weeks of online sleep diaries (baseline assessment) to corroborate their status as good sleepers. Phase-2 Subjects that entered Phase-2 were monitored for a year and completed a number of assessments via the study website. The online questionnaires included: daily morning and evening sleep diaries; weekly and bi-weekly instruments (e.g. medical symptoms checklist, perceived stress scale, etc.); and monthly instruments (including one additional instrument regarding menstruation for women). Subjects that transitioned to acute or CI also completed an additional measure that was specific to insomnia (i.e. insomnia severity index). Instruments that have or are based on specific time frames (e.g. “in the last week, did you…”) were given according to the prescribed time frames. Instruments that were retrospective but without a specific time frame were given according to a schedule that we developed; where the guiding principle was to reduce subject costs by administering the instrument less versus more frequently. Accordingly, the majority of instruments were given monthly in order to assess changes over the 1-year study period. See Table 1 for a list of all the questionnaires included in this study. While a comprehensive list of measures is included here, between-subjects analyses on all of these questionnaires is beyond the scope of this paper and will be the focus of future work. Note, as participation in this study was reliant on the completion of online measures, participants were withdrawn from the study for noncompliance if their adherence rate dropped below 60% across 14 days at any point during the study (see Subject Attrition section). This adherence cut-off was empirically determined to maximize the number of subjects that could be retained for the final analyses. Table 1. Study instruments Schedule . Instrument . Baseline only* Dysfunctional Beliefs and Attitudes About Sleep Scale (DBAS-16)[12] Ford Insomnia Response to Stress Test (FIRST) [13] Daily AM daily sleep diary PM daily sleep diary Weekly Health and Medical Symptoms Checklist (MHS-CL) Daily Hassles Scale (DHS) [14] Perceived Stress Scale (PSS) [15] Social Readjustment Rating Scale (SRRS) [16] Brief Fatigue Inventory (BFI) [17] Epworth Sleepiness Scale (ESS) [18] Insomnia Severity Index (ISI) [19, 20] Glasgow Sleep Effort Scale (GSES) [21] Sleep Preoccupation Scale (SPS) [22] Sleep Hygiene Index (SHI) [23] Biweekly Patient Health Questionnaire (PHQ-9)[24] Monthly Medical History Form (MHF) Sleep Medication History Form Psychiatric Health Screen Alcohol Use Disorders Identification Test (AUDIT) [25] Drug Abuse Screening Test (DAST-20)[26] Menstrual Cycle Questionnaire Sleep Associated Monitoring Index (SAMI) [27] Schedule . Instrument . Baseline only* Dysfunctional Beliefs and Attitudes About Sleep Scale (DBAS-16)[12] Ford Insomnia Response to Stress Test (FIRST) [13] Daily AM daily sleep diary PM daily sleep diary Weekly Health and Medical Symptoms Checklist (MHS-CL) Daily Hassles Scale (DHS) [14] Perceived Stress Scale (PSS) [15] Social Readjustment Rating Scale (SRRS) [16] Brief Fatigue Inventory (BFI) [17] Epworth Sleepiness Scale (ESS) [18] Insomnia Severity Index (ISI) [19, 20] Glasgow Sleep Effort Scale (GSES) [21] Sleep Preoccupation Scale (SPS) [22] Sleep Hygiene Index (SHI) [23] Biweekly Patient Health Questionnaire (PHQ-9)[24] Monthly Medical History Form (MHF) Sleep Medication History Form Psychiatric Health Screen Alcohol Use Disorders Identification Test (AUDIT) [25] Drug Abuse Screening Test (DAST-20)[26] Menstrual Cycle Questionnaire Sleep Associated Monitoring Index (SAMI) [27] *It was the case that “baseline only” measures were only given at baseline, and that the daily, weekly, etc. measures were given at baseline and according to their corresponding schedule. Open in new tab Table 1. Study instruments Schedule . Instrument . Baseline only* Dysfunctional Beliefs and Attitudes About Sleep Scale (DBAS-16)[12] Ford Insomnia Response to Stress Test (FIRST) [13] Daily AM daily sleep diary PM daily sleep diary Weekly Health and Medical Symptoms Checklist (MHS-CL) Daily Hassles Scale (DHS) [14] Perceived Stress Scale (PSS) [15] Social Readjustment Rating Scale (SRRS) [16] Brief Fatigue Inventory (BFI) [17] Epworth Sleepiness Scale (ESS) [18] Insomnia Severity Index (ISI) [19, 20] Glasgow Sleep Effort Scale (GSES) [21] Sleep Preoccupation Scale (SPS) [22] Sleep Hygiene Index (SHI) [23] Biweekly Patient Health Questionnaire (PHQ-9)[24] Monthly Medical History Form (MHF) Sleep Medication History Form Psychiatric Health Screen Alcohol Use Disorders Identification Test (AUDIT) [25] Drug Abuse Screening Test (DAST-20)[26] Menstrual Cycle Questionnaire Sleep Associated Monitoring Index (SAMI) [27] Schedule . Instrument . Baseline only* Dysfunctional Beliefs and Attitudes About Sleep Scale (DBAS-16)[12] Ford Insomnia Response to Stress Test (FIRST) [13] Daily AM daily sleep diary PM daily sleep diary Weekly Health and Medical Symptoms Checklist (MHS-CL) Daily Hassles Scale (DHS) [14] Perceived Stress Scale (PSS) [15] Social Readjustment Rating Scale (SRRS) [16] Brief Fatigue Inventory (BFI) [17] Epworth Sleepiness Scale (ESS) [18] Insomnia Severity Index (ISI) [19, 20] Glasgow Sleep Effort Scale (GSES) [21] Sleep Preoccupation Scale (SPS) [22] Sleep Hygiene Index (SHI) [23] Biweekly Patient Health Questionnaire (PHQ-9)[24] Monthly Medical History Form (MHF) Sleep Medication History Form Psychiatric Health Screen Alcohol Use Disorders Identification Test (AUDIT) [25] Drug Abuse Screening Test (DAST-20)[26] Menstrual Cycle Questionnaire Sleep Associated Monitoring Index (SAMI) [27] *It was the case that “baseline only” measures were only given at baseline, and that the daily, weekly, etc. measures were given at baseline and according to their corresponding schedule. Open in new tab Daily sleep diary The prospective assessment of sleep continuity disturbance (i.e. difficulty initiating or maintaining sleep) was conducted via online daily sleep diaries through a dedicated web-portal (all questionnaires were completed on this study-specific site). Items included in the online sleep diary were based on the Consensus Sleep Diary [28]. Primarily, the diary was used to quantify daily variations in SL, wake after sleep onset (WASO), early morning awakenings (EMA), nocturnal awakenings (NWAK), total sleep time (TST), and time in bed (TIB). Participant received daily email reminders to complete their entries. Participants were also emailed if they missed a diary entry and send a “warning” email if their adherence dropped below 60% across 14 days at any point during the study. Subject compensation In order to encourage high adherence rates (i.e. the completion of daily sleep diaries), a novel compensation strategy was used: a lottery. All study participants were automatically enrolled in the study lottery. The lottery was conducted once a month where each questionnaire that a subject completed was automatically counted as an “entry” into the lottery. Each subject accumulated entries over the course of each month. At the end of each month, a drawing was conducted where winners were randomly selected from all the submitted entries. Each subject was eligible to win one prize per month, and one prize of each dollar value over the year. The prizes for the first cohort were as follows: 2 of $750, 4 of $500, 10 of $250, and 20 of $100 (total of 36 awards/month). At the end of the year, a final lottery was conducted for all the participants that completed the study. In this case, 22 awards of $1000 were randomly awarded. Identification of Cohort. While the study recruited good sleepers by self-report (5 or more nights per week taking 15 min or less to fall asleep and awake during the night for 15 min or less [including EMA]), extra steps were taken to confirm stable GS in the analysis stage. First, using moving 7-day windows (successive, overlapping 7-day segments; i.e. first window consists of days 1–7, the second window consists of days 2–8, the third window consists of days 3–9, etc.) each week was determined to be a good sleeping week or poor sleeping week. A poor sleeping week consisted of 3 or more nights with SL ≥ 30 min and/or WASO ≥ 30 min and/or EMA ≥ 30 min. Notably, while frequency and chronicity are defined in the diagnostic criteria, severity is not. It is, however, common in research criteria to use 30 min as a severity threshold (e.g. a SL ≥ 30 min is considered clinically significant), and therefore this is the criteria used here [5, 29]. Provided diary responses were given for at least 4 days of the week, the remaining weeks were good sleeping weeks. A stable good sleeper had to have 10 of the first 12 weeks classified as good sleeping weeks. Identification of transitions Each subjects’ sleep diary data were used to identify instances of sleep initiation and/or maintenance difficulties and to determine if such difficulties persisted. Acute insomnia was defined as two consecutive weeks with a frequency of at least 3 nights per week of sleep initiation and/or maintenance complaints. For the definitions of CI and recovery, we chose 3 months since it is consistent with current diagnostic criteria for defining a new and enduring state. Because we are applying these rules to prospective, high frequency sampled data (i.e. daily sleep diaries), one has to adopt a more quantitatively precise definition. The current definition was selected to allow for some normal variation in good and bad sleep (takes into account that insomnia severity varies from night-to-night and week-to-week), but to also be, by anyone’s definition, consistent with what most would define as CI. The definition for recovery from CI was more lenient (greater than 50% “good sleep” during a 12-week period) in order to “catch” people early in recovery, but we also wanted to be sure that this definition captured an enduring state change (hence, requiring the last 4 weeks be “good sleep”). See also Table 2 for specific definitions). Additionally, standard quantitative criteria for insomnia severity (≥30 min) were used to identify excessively long SL and/or WASO and/or early morning awakening (EMA) times. In contrast to DSM-5’s criteria for Insomnia Disorder, qualitative assessments of distress and/or impairment in daytime functioning were not included in these definitions so that post hoc analyses could be conducted to empirically determine what levels of sleep continuity severity, frequency and chronicity are associated with daytime complaints (to be reported elsewhere). It is possible or even likely that, by excluding impairments in daytime functioning from our definition of insomnia, the incidence rate of insomnia may be overestimated. For example, Ohayon reported that the prevalence of insomnia, when only considering quantitative criteria or nocturnal symptoms, was 30%–48%. The prevalence rate dropped to 9%–15% when daytime consequences were included in the criteria [4]. This said, the present study assesses incidence not prevalence and uses a more dense sampling approach (i.e. daily diaries), which may maximize the ability to detect new onset cases. Table 2. Definitions for state transitions* Acute insomnia (AI) Two or more consecutive weeks with a frequency of ≥3 nights/week of sleep latency and/or wake after sleep onset (WASO) severity ≥30 min. Recovery (REC) Within a 12-week period, 7 or more weeks of good sleep after AI and/or CI episodes where the final 4 weeks in the period must be designated as good sleep. Persistent poor sleep (PPS) Recurring bouts of AI without transition to CI or REC Chronic insomnia (CI) 10 or more weeks in a 12-week period with same frequency and severity criteria as AI Acute insomnia (AI) Two or more consecutive weeks with a frequency of ≥3 nights/week of sleep latency and/or wake after sleep onset (WASO) severity ≥30 min. Recovery (REC) Within a 12-week period, 7 or more weeks of good sleep after AI and/or CI episodes where the final 4 weeks in the period must be designated as good sleep. Persistent poor sleep (PPS) Recurring bouts of AI without transition to CI or REC Chronic insomnia (CI) 10 or more weeks in a 12-week period with same frequency and severity criteria as AI *Participants that did not meet either transition criteria were considered continuous good sleepers. Open in new tab Table 2. Definitions for state transitions* Acute insomnia (AI) Two or more consecutive weeks with a frequency of ≥3 nights/week of sleep latency and/or wake after sleep onset (WASO) severity ≥30 min. Recovery (REC) Within a 12-week period, 7 or more weeks of good sleep after AI and/or CI episodes where the final 4 weeks in the period must be designated as good sleep. Persistent poor sleep (PPS) Recurring bouts of AI without transition to CI or REC Chronic insomnia (CI) 10 or more weeks in a 12-week period with same frequency and severity criteria as AI Acute insomnia (AI) Two or more consecutive weeks with a frequency of ≥3 nights/week of sleep latency and/or wake after sleep onset (WASO) severity ≥30 min. Recovery (REC) Within a 12-week period, 7 or more weeks of good sleep after AI and/or CI episodes where the final 4 weeks in the period must be designated as good sleep. Persistent poor sleep (PPS) Recurring bouts of AI without transition to CI or REC Chronic insomnia (CI) 10 or more weeks in a 12-week period with same frequency and severity criteria as AI *Participants that did not meet either transition criteria were considered continuous good sleepers. Open in new tab Statistical analyses Demographic characteristics were compared between the good sleeper cohort and those excluded from the analysis using t-tests, Wilcoxon rank-sum tests for skewed data, or Fisher’s exact tests for binary variables. Statistical analyses were performed in SAS v9.4 (SAS; Institute Cary, NC) and Stata v15.1 (College Station, TX). Results Subject attrition A total of 3,287 subjects were positively screened for GS and entered into Phase-1 (consent, baseline questionnaires and two weeks of daily sleep diaries). Eighty-five subjects (2.6%) were disenrolled due to non-compliance during the 2-week baseline assessment (daily diaries). Of the remaining 3,202 subjects that completed the baseline assessment and entered into Phase-2 of the study, 1,954 subjects (59.4%) were excluded from our final analyses if the subject (1) did not meet the 60% adherence threshold to the daily sleep diaries (minimum threshold to confirm GS and assess incidence over time) and/or (2) met criteria for AI during the first 12 weeks of the study (i.e. had 2 or more consecutive poor sleeping weeks during this baseline period; see Figure 1 for subject flow). Of the 1,954 subjects that were excluded in Phase-2, 926 subjects were excluded for not meeting the 60% adherence threshold and 1,028 subjects were excluded for meeting criteria for at least AI (i.e. they did not enter the study as good sleepers). A total of 1,248 subjects (38.0%) entered into, and completed Phase-2. Table 3 provides a statistical comparison of the final sample (n = 1,248; i.e. included) relative to those subjects that were excluded (n = 2,039). The final sample was slightly older, had lower BMIs, and had a greater proportion of subjects who were white and male. While significant differences between the samples were observed, the magnitudes of the differences were small (see Table 3). For example, the included sample had a mean age of 53.2 years, while the excluded sample had a mean age of 52.2 years (p = 0.02). The final sample also reported lower scores on baseline sleep and clinical profile measures. Table 3. Between-participants comparisons between the final sample and the excluded sample on baseline measures Variable . Excluded (N = 2039) . . Included (N = 1248) . . p* . . Mean or % . SD . Mean or % . SD . . Age (years) 52.2 11.6 53.2 11.0 0.02 BMI (kg/m2) 29.6 7.6 28.9 7.4 <0.01 AUDIT 9.0 3.6 8.9 3.6 0.52 DBAS-16 43.1 19.0 38.6 17.9 <0.001  PSS 14.0 6.1 12.4 5.6 <0.001 % Education (>HS) 86.4 87.9 0.24 % Female 75.8 67.4 <0.001 % Income (≥$30,000) 75.0 77.8 0.07 % Ethnic minority 23.1 17.5 <0.001 % PHQ-9 ≥ 5 25.2 15.2 <0.001 Variable Median Min, Max Median Min, Max p† DHS 27.0 0, 310 20.0 0, 296 <0.001 FIRST 18.0 9, 36 16.0 9, 36 <0.001 SAMI 21.0 11, 55 19.0 11, 55 <0.001 SPS 24.0 10, 64 21.0 10, 63 <0.001 ESS 6.0 0, 24 6.0 0, 24 <0.001 Variable . Excluded (N = 2039) . . Included (N = 1248) . . p* . . Mean or % . SD . Mean or % . SD . . Age (years) 52.2 11.6 53.2 11.0 0.02 BMI (kg/m2) 29.6 7.6 28.9 7.4 <0.01 AUDIT 9.0 3.6 8.9 3.6 0.52 DBAS-16 43.1 19.0 38.6 17.9 <0.001  PSS 14.0 6.1 12.4 5.6 <0.001 % Education (>HS) 86.4 87.9 0.24 % Female 75.8 67.4 <0.001 % Income (≥$30,000) 75.0 77.8 0.07 % Ethnic minority 23.1 17.5 <0.001 % PHQ-9 ≥ 5 25.2 15.2 <0.001 Variable Median Min, Max Median Min, Max p† DHS 27.0 0, 310 20.0 0, 296 <0.001 FIRST 18.0 9, 36 16.0 9, 36 <0.001 SAMI 21.0 11, 55 19.0 11, 55 <0.001 SPS 24.0 10, 64 21.0 10, 63 <0.001 ESS 6.0 0, 24 6.0 0, 24 <0.001 AUDIT = Alcohol Use Disorders Identification Test, BMI = body mass index, DBAS-16 = Dysfunctional Beliefs and Attitudes about Sleep Scale, DHS = Daily Hassles Scale, ESS = Epworth Sleepiness Scale, FIRST = Ford Insomnia Response to Stress Test, PHQ-9 = Patient Health Questionnaire, PSS = Perceived Stress Scale, SAMI = Sleep Associated Monitoring Index, SPS = Sleep Preoccupation Scale. *ANOVA (or Fisher’s exact) p-value from test of equal means (or %) across groups. †All of the variables arrayed above as median values were arrayed in this manner owing to skewed distributions with p-values from Kruskal–Wallis test. Open in new tab Table 3. Between-participants comparisons between the final sample and the excluded sample on baseline measures Variable . Excluded (N = 2039) . . Included (N = 1248) . . p* . . Mean or % . SD . Mean or % . SD . . Age (years) 52.2 11.6 53.2 11.0 0.02 BMI (kg/m2) 29.6 7.6 28.9 7.4 <0.01 AUDIT 9.0 3.6 8.9 3.6 0.52 DBAS-16 43.1 19.0 38.6 17.9 <0.001  PSS 14.0 6.1 12.4 5.6 <0.001 % Education (>HS) 86.4 87.9 0.24 % Female 75.8 67.4 <0.001 % Income (≥$30,000) 75.0 77.8 0.07 % Ethnic minority 23.1 17.5 <0.001 % PHQ-9 ≥ 5 25.2 15.2 <0.001 Variable Median Min, Max Median Min, Max p† DHS 27.0 0, 310 20.0 0, 296 <0.001 FIRST 18.0 9, 36 16.0 9, 36 <0.001 SAMI 21.0 11, 55 19.0 11, 55 <0.001 SPS 24.0 10, 64 21.0 10, 63 <0.001 ESS 6.0 0, 24 6.0 0, 24 <0.001 Variable . Excluded (N = 2039) . . Included (N = 1248) . . p* . . Mean or % . SD . Mean or % . SD . . Age (years) 52.2 11.6 53.2 11.0 0.02 BMI (kg/m2) 29.6 7.6 28.9 7.4 <0.01 AUDIT 9.0 3.6 8.9 3.6 0.52 DBAS-16 43.1 19.0 38.6 17.9 <0.001  PSS 14.0 6.1 12.4 5.6 <0.001 % Education (>HS) 86.4 87.9 0.24 % Female 75.8 67.4 <0.001 % Income (≥$30,000) 75.0 77.8 0.07 % Ethnic minority 23.1 17.5 <0.001 % PHQ-9 ≥ 5 25.2 15.2 <0.001 Variable Median Min, Max Median Min, Max p† DHS 27.0 0, 310 20.0 0, 296 <0.001 FIRST 18.0 9, 36 16.0 9, 36 <0.001 SAMI 21.0 11, 55 19.0 11, 55 <0.001 SPS 24.0 10, 64 21.0 10, 63 <0.001 ESS 6.0 0, 24 6.0 0, 24 <0.001 AUDIT = Alcohol Use Disorders Identification Test, BMI = body mass index, DBAS-16 = Dysfunctional Beliefs and Attitudes about Sleep Scale, DHS = Daily Hassles Scale, ESS = Epworth Sleepiness Scale, FIRST = Ford Insomnia Response to Stress Test, PHQ-9 = Patient Health Questionnaire, PSS = Perceived Stress Scale, SAMI = Sleep Associated Monitoring Index, SPS = Sleep Preoccupation Scale. *ANOVA (or Fisher’s exact) p-value from test of equal means (or %) across groups. †All of the variables arrayed above as median values were arrayed in this manner owing to skewed distributions with p-values from Kruskal–Wallis test. Open in new tab Figure 1. Open in new tabDownload slide Subject flow. Figure 1. Open in new tabDownload slide Subject flow. Incidence rates As can be seen in Figure 2, the 1-year incidence rate of AI was 27.0% (n = 337). Of these, 72.4% (n = 244) of subjects recovered GS (AI-REC), and 6.8% (n = 23) developed CI. Notably, 19.3% (n = 65) neither recovered nor went on to develop CI. This group exhibited what might be best referred to as persistent poor sleep (PPS; problems with sleep initiation or maintenance [SL or WASO or EMA > 30 min] that did not meet or exceed frequency (3 or more days per week) or chronicity criteria [3 or more months in duration]). Of those that developed CI, 5 subjects (1.5% of AI) recovered GS (CI-REC). Note: the definition of new onset insomnia (AI or CI) was based solely on quantitative criteria for severity, frequency and chronicity. As noted above, daytime impairment was not included as a criterion but rather was assessed as a dependent variable. Figure 2. Open in new tabDownload slide One-year incident rate of acute insomnia (AI), persistent poor sleep (PPS), chronic insomnia (CI), and recovery (REC; from both AI and CI). Figure 2. Open in new tabDownload slide One-year incident rate of acute insomnia (AI), persistent poor sleep (PPS), chronic insomnia (CI), and recovery (REC; from both AI and CI). Discussion The data from the present study suggest that AI is common (affects about 27% of the population per annum) and that for most people, sleep continuity disturbance is self-limiting (about 72% of those with incident insomnia recover). Of those that develop AI, only about 7% developed CI, but it appears that up to another 19% exhibit an intermediate form of insomnia (i.e. persistent poor sleep or a form of poor sleep that is neither acute nor chronic). Incidence of (and recovery from) AI The observed incidence of AI is remarkably high, although not as common as was found by Ellis and colleagues (31%–37%) [5]. The difference in estimates is possibly ascribable to methodological differences between the studies, if not cultural differences between the study samples. For example, differences may exist in terms of occupational response to insomnia—people from the UK may be more likely to be absent from work whereas people in the United State (US) more likely to exhibit “presenteeism.” As such, it may be that those in the UK ascribe a specific sequelae event to their insomnia, aiding recall, whereas those in the US may not. This is, however, just one example of a potential cultural difference. The methodological differences include: (1) the current cohort did not contain subjects between 18 and 35 years of age (the mean age of the UK sample was 27.7 + 11.4 years); (2) The UK sample was not assessed with prospective sampling (daily sleep diaries [arguably a more refined method for the detection AI]); (3) the current study used quantitative criteria for the identification of insomnia (i.e. 3 or more nights with SL ≥ 30 min and/or WASO ≥ 30 min and/or EMA ≥ 30 min). This said, the annual incidence rate of insomnia in both studies (27%–37%) is so common that it suggests that the occurrence of AI may not be, as is the case with CI, necessarily pathological (i.e. of or relating to a true disease or disorder). More than this, as previously noted by Ellis and colleagues [30], it is possible that AI may be normative (i.e. expected as part of the natural rhythm of sleep/insomnia) , if not adaptive. One way this might be true is that the AI that occurs with stress may be an unrecognized part of the fight-flight response; a necessary override to the normal two process regulation of sleep timing, depth and/or duration [31]. Put differently, stress induced insomnia may prohibit the systematic imperative for sleep under unsafe conditions. While this idea awaits empirical validation, it has been proffered on multiple occasions [30, 32, 33]. This perspective was presaged by Spielman and colleagues when they suggested that “No matter how important sleep may be, it was adaptively deferred when the mountain lion entered the cave” (p.3) [34] A similar perspective was advanced by others, including D. Handley (personal communication, 2005) “we live with insomnia today, because at some point in our evolutionary history, insomnia allowed us to live.” Conceived of this way, it is not surprising that at least 27% of the population experience AI per year. Given this perspective, it is also not surprising that 72% of the population do not go on to develop either “persistent poor sleep” or CI. That is, as the threat (or stress) abates, the AI resolves. The question that arises here is “what factors mediate the transition from AI to GS?.” While it is possible that this occurs simply with the absence of behavioral adaptations to sleep loss (e.g. sleep extension), it is also possible that there are factors that are uniquely prophylactic. While these issues are speculative and under further investigation, possible mediators include individual differences with respect to: basal sleep need (i.e. whether incident insomnia results in TSTs that are below sleep need) [35]; resilience (i.e. how robust the individual’s capacity is to avoid sleep loss) [13]; psychosocial demand (i.e. the intensity of daytime work and/or social requirements) [36]; and context (i.e. the significance of sleep loss in the context of the individual’s cumulative medical, psychological, daily hassles, and/or life stress impacts). Incidence of CI In marked contrast to the incidence of AI, a remarkably small number of subjects transition from AI to CI on an annual basis (~7% of AI subjects and 1.8% of the initial good sleeper sample). While the exact explanation(s) for this transition are unknown, it is possible that, for individuals that do transition from AI to CI, behavioral adaptations to sleep loss and/or nocturnal wakefulness (e.g. sleep extension) may serve to perpetuate insomnia (mediate the transition from AI to CI) [37]. Alternatively, the transition from AI to CI may be mediated by other factors, some of which may be related to more cognitively driven phenomenon (such as sleep reactivity, monitoring, and/or preoccupation) [13, 38, 39], the failure to engage in naturalistic sleep restriction (i.e. if one can only sleep 6 h, then only try for 6 h) [34, 37], conditioned aberrant neurobiologic control and regulation of sleep ability (i.e. conditioned local wakefulness during non-REM sleep) [40, 41], or inherent and enduring decrements to sleep ability (e.g. permanent alterations in dopaminergic, orexinergic and/or GABAnergic tone) [42, 43]. Moreover, it is possible that all of these factors to one extent or another contribute to the development of CI, and therefore, should be studied further. Incidence of persistent poor sleep While presaged by other’s prior work with the natural history of insomnia (i.e. the identification of a subsyndromal insomnia) [8], it remains surprising that more than 19% of individuals that experience AI neither recover nor develop CI; they simply exhibit a variable (by day and week), but nonetheless persistent (over the courses of months), level of sleep continuity disturbance. To date, this group was not found to differ on demographic or baseline variables (with the exception of having slightly higher BMIs). While it is possible the unique factors account for this “middle of the road” outcome, it is also possible that the same factors (but to different degrees) that are responsible for pathological outcomes are also responsible for prophylaxis. For example, those that engage in sleep extension develop CI, those that maintain stable times in bed develop persistent poor sleep (i.e. intermediate insomnia), and those that naturally engage in sleep restriction (e.g. awaken early and start their day) recover. Alternatively, those with persistent poor sleep may be on the way to developing CI but are doing so at a slower rate of progression. An extended follow-up or repeat assessment would be required to address this issue. Study limitations and strengths The current study had both important limitations and strengths. The primary limitation was the high exclusion rate (i.e. loss of subjects owing to ineligibility, non-compliance, subject withdrawal, and disenrollment). This issue is important as it may potentially limit the generalizability of the findings. More, while we believe that the results are generalizable to the population at large, our sample was marginally different than the general population (based on 2018 U.S. Census data), such that, our sample had a greater proportion of persons that identified as female (67% as compared to 51%) and white (82.5% as compared to 76.5%). Perhaps the larger concern pertains to the limitations on our ability to detect new-onset CI, and this limitation will adversely affect (in terms of statistical power) future efforts to assess the factors that mediate the transition to CI. In addition, it is important to note that the present study did not include adults between the ages of 18 and 35 years. The incidence rates reported here may therefore not generalize to this age group. More, as mentioned above, the definition of insomnia used in the present study included nocturnal symptoms only, and it’s possible that the annual incidence rate of insomnia with daytime consequences is lower. It’s also possible that, given the prospective assessment of sleep continuity disturbance (i.e. daily sleep diaries), the incidence rates of insomnia reported here are more accurate, particularly for AI. Traditional diagnostic criteria (i.e. DSM-5 and ICSD-3) do not require daily diaries and rely on retrospective assessments of sleep continuity, which are less burdensome to the client but are less likely to capture brief episodes of sleep continuity disturbance due to cyclical nature of insomnia. Along these lines, while the present study took multiple steps to exclude individuals with a prior history of insomnia (i.e. at recruitment, include only those with GS during the past 6 months, require a 12-week baseline without insomnia to continue in the study), it is still possible that some participants did have a prior history of insomnia and were in a good phase when the baseline assessment was completed. It is important that future studies take into consideration the tendency for insomnia symptoms to fluctuate over time and how these fluctuations may influence the estimation of new-onset acute or CI incidence rates. The present study also had a number of important strengths. To our knowledge, this is the first study to prospectively assess the incidence of acute and CI using a dense-sampling approach (i.e. daily sleep diaries) in a large sample of good sleepers. This assessment strategy was particularly advantageous because it offered (1) the opportunity to more accurately determine whether a transition occurred and (2) the temporal resolution to identify when the transition(s) occurred. 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Hibberd,, Charlotte;Charman,, Tony;Bhatoa, Raj, Seraya;Tekes,, Sinem;Hedderly,, Tammy;Gringras,, Paul;Robinson,, Sally

doi: 10.1093/sleep/zsz308pmid: 31859345

Abstract Sleep difficulties are common in children and young people with Tourette syndrome and chronic tic disorders (TS/CTD). However, it is unclear whether sleep problems can be considered typical of the TS/CTD phenotype or whether they reflect concomitant factors such as individual patient characteristics (e.g. medication use), underlying neurodevelopmental disorders and/or co-occurring psychiatric symptoms. To help address this question, this review systematically explored types and frequency of sleep problems in children and young people with TS/CTD, while also examining the heterogeneity and methodological quality of studies. Psycinfo, Ovid Medline, Embase, and Web of Science databases were searched using a range of terms relating to tics, sleep and co-occurring psychopathology. Studies were considered that included a sample of children with TS/CTD (n > 5) for whom sleep difficulties were measured. Eighteen studies met criteria for inclusion in the review. Findings supported the high prevalence of sleep difficulties in children with TS/CTD, though estimates of sleep difficulties ranged from 9.7% to 80.4%. Twelve studies reported on other factors affecting sleep in this patient group including tic severity, comorbid psychopathological or neurodevelopmental disorders and medication use. Studies varied in terms of methodology, sample characteristics and research quality, but most concluded that children with TS/CTD experienced high levels of sleep difficulties with children with co-occurring anxiety most at risk. The current review highlights the need for further empirical investigation of sleep in children with TS/CTS, with a view to informing understanding and clinical management. sleep, Tourette syndrome, systematic review Statement of Significance This systematic review provides the first comprehensive overview of studies exploring sleep in children and young people with Tourette syndrome and chronic tic disorders (TS/CTD). The high rates of sleep problems identified in this group as part of this review, which was based on a small yet heterogeneous sample of papers, highlights the need for continued research in this area. The findings support the implementation of routine screening for sleep difficulties in this clinical group, particularly in children and young people with comorbid anxiety, as this group may be most at risk of poor sleep. Suggestions are made for possible future research directions, including studies exploring the efficacy of tailored sleep interventions with children with TS/CTD and their families. Introduction During typical development aspects of sleep, including its duration, patterns, architecture, and stage distribution change in a maturational process that depends on age and central nervous system development [1]. As might be expected, sleep difficulties are often reported in the context of neurodevelopmental and neuropsychiatric disorders that involve altered neurological development [2]. Whilst clinically significant conditions may be present (e.g. hypersomnolence or circadian rhythm disorders), sub-clinical disturbances, such as behavioral sleep problems are more commonly reported [3]. Sleep difficulties affect daytime functioning and are predictive of emotional and behavioral difficulties, as well as cognitive and academic performance [4–6]. Identifying and managing sleep difficulties promptly is, therefore, of clinical importance to minimize their impact on psychosocial functioning and on-going development. When sleep problems co-occur in the context of neurodevelopmental and/or emotional disorders their presentation is often thought to be disorder-specific, possibly reflecting varying etiological factors [7]. The purpose of this article is to review the available evidence for children and young people with Tourette syndrome and chronic tic disorders (TS/CTD), with the aim of identifying the nature of sleep problems in this population to help inform theoretical understanding and the development of tailored clinical interventions. Tourette syndrome and chronic tic disorders Gilles de la Tourette syndrome (TS) is a complex neuropsychiatric condition characterized by the presence of two or more motor tics and at least one phonic tic that have been present for at least 1 year, begin before 18 years of age and are not caused by medications or health conditions (DSM-V [8]). People with either motor or phonic tics who meet these criteria are diagnosed with CTD [8]. Overall prevalence estimates for TS/CTDs in childhood are around 0.77%, with a male to female predominance of ratio of 4:1 [9, 10]. Co-occurring psychiatric problems are well-documented in TS/CTD with Attention Deficit Hyperactivity Disorder (ADHD), Obsessive Compulsive Disorder (OCD) and other anxiety disorders commonly reported and identified as significant predictors of functional disability [11]. Sleep difficulties are also commonly reported however, in line with research into other disorders, their frequency, nature, and etiology is unclear [12]. Disorder-specific hypotheses draw on observations of shared neurobiological changes between sleep difficulties and TS/CTD. For instance, certain genes implicated in the etiology of TS/CTD also cause sleep-related movement problems (e.g. PLMS [13]). Furthermore, reduced intracortical inhibition (i.e. increased arousal) of motor pathways is a common factor in TS/CTD and sleep disturbance [14]. In addition, evidence that the neurotransmitter dopamine is implicated in the etiology of both TS/CTD and sleep difficulties, and findings that patients with TS/CTD respond well to pharmacological agents that block dopaminergic systems, suggest a possible common dopaminergic basis for sleep- and movement-related problems in this population [15]. However, in samples of children with TS/CTD and comorbid ADHD, sleep difficulties have been reported to be almost entirely accounted for by ADHD symptoms, suggesting they may reflect comorbid factors rather than being characteristic of TS/CTD per se [16]. Individual characteristics have also been reported to influence sleep difficulties in TS/CTD. For example, a reduced prevalence of sleep problems has been reported for adults relative to children, though whether this is linked to changes in tic-related pathophysiology (i.e. hormones, neurological structure or function), improved management of other factors (i.e. receiving treatment for anxiety) or behavioral changes (i.e. better sleep hygiene) is unclear [16]. Likewise, whilst it has also been suggested that sleep problems may be more common in females than males, it is unclear whether this reflects hormonal processes in the development of sleep difficulties or gender-specific patterns of comorbidity or tic severity [17, 18]. Alternatively, sleep difficulties in TS/CTD may be related to medication-use, with the most frequently prescribed medications (Clonidine, Aripiprazole, and Risperidone) all reported to impact on sleep [19]. This picture is further complicated by the way in which studies assess sleep. Physiological and behavioral changes and can be assessed in various ways, broadly categorized into objective and subjective methods [20]. Objective measures, including Polysomnography (PSG) and actigraphy provide a range of sleep parameters including total sleep time, duration, number of stages, cycles, and sleep efficiency (i.e. the amount of time in bed spent asleep). However, they do not generate information regarding children- or parent-perception of sleep difficulties. For this, subjective measures such as sleep diaries and questionnaires should be used. Despite offering information on perceptions of sleep, sole use of subjective sleep measures is considered insufficient as they can be affected by expectations, psychological influences, and responder bias [21]. Thus, the use of multiple assessment methods is recommended to produce a comprehensive overview of sleep difficulties and their functional impact [22]. Review aims Evidence suggests that people with TS/CTD experience sleep difficulties; however, it is unclear whether sleep difficulties are linked directly with TS/CTD pathology (at a hormonal, genetic or neuroanatomical level), whether they are caused by comorbid neurodevelopmental difficulties, pharmacological treatments or result from a combination of these factors. The methodological quality of research is also unclear and may contribute to disparate findings. To date, no formal systematic review has yet been undertaken to fully summarize this area. The current review aims to provide this overview and has two main aims: To evaluate the types and frequency of sleep difficulties in children with TS/CTD. To consider factors that may contribute to sleep difficulties in children with TS/CTD. Method Literature search This review was based on a systematic search of papers published before February 2019. The search was conducted in line with the guidelines of PRISMA (the Preferred Reporting Items for Systematic reviews and Meta-Analyses [23]), which provides an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses. Firstly, a computer search was conducted of the Psycinfo, Medline (ovid), Embase, and Web of Science online databases. This search combined four separate components: 1. Tourette* Syndrome (OR) Tourette* (OR) tic* (OR) tic disorder (OR) chronic tics (OR) chronic tic disorder (OR) transient tic disorder (AND) 2. Sleep (OR) sleep disorder (OR) sleep behavi?r (OR) sleep disturbance (OR) sleep difficult* (AND) 3. Actigraph* (OR) polysomnograph* (OR) diary (OR) questionnaire (OR) self-report (OR) parent-report (OR) objective (OR) subjective (AND) 4. Comorbid* (OR) Psycholog* (OR) Psychiat* (OR) disorder* (OR) mental health (OR) mood disorder* (OR) anx* (OR) dep* (OR) neurodevelopment* Secondly, relevant journals were hand searched, along with reference lists of relevant papers and the bibliographies of frequently cited researchers. Figure 1 shows the PRISMA study flowchart. Figure 1. Open in new tabDownload slide PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart. Figure 1. Open in new tabDownload slide PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart. Each subsequent stage of the review was conducted independently by two researchers (C.H. and either S.T. or R.S.B.) to reduce bias, with disputes raised with a third member of the review team if necessary. After the removal of duplicate papers, 1088 abstracts were screened for the following inclusion criteria: (1) sample size ≥5 participants; (2) mean age of participants <18 years; (3) inclusion of participants with Tourette syndrome or chronic tics; (4) inclusion of a measure of sleep difficulty; (5) empirically based (i.e. not literature reviews) and peer-reviewed (i.e. not dissertations or conference proceedings); (6) published in English. Based on the inclusion criteria, 95 studies were identified for more in-depth review. Of these, 77 papers were excluded with the following justifications: (1) no clear measurement of sleep difficulty; (2) sample size <5; (3) mean age >18; (4) not being empirically based or published in a peer-reviewed format; (5) not being written in English. Ten papers could not be accessed by the researcher. Eighteen papers fulfilled criteria for inclusion in the final review. Data extraction For eligible articles, data was extracted regarding the sample characteristics (number of TS/CTD participants, details of control groups, recruitment site, age, gender, and medication status), study methods and results. Quality assessment Included articles were assessed for their methodological quality. As there is no agreed quality assessment tool for evaluating observational and prevalence studies [24], a novel 12-item measure was developed drawing on criteria outlined by Loney, Chambers, Bennett, Roberts & Stratford [25], Downs and Black [26] and the Centre for Reviews and Dissemination guidance [27]. This tool assessed the methodology of the study (e.g. aims/hypothesis, validated measures, power calculations, matched control group, statistical analysis/significance levels), the participant (e.g. recruitment processes, participant characteristics, confirmed diagnosis) and possible confounding factors (e.g. co-occurring conditions). Results Details of eligible studies are presented in Tables 1 and 2. Table 1 details the sample characteristics of included studies. Table 1. Study characteristics Paper reference . N with TS . Number and details of comparison groups . Sample type/ recruitment site . Age range (mean/SD-if avail) . % males/females . Medication status of Pps . Quality rating (/14) . Papers using objective and subjective sleep measures  Kostanecka-Endress et al. [28] TS = 17 HC = 16 TS: Specialist clinic HC: convenience method TS = 7.11–15.5 (11.10) HC = 11.78 TS = 70.58% M; 29.42 % F HC = 75% M; 25% F 58.83% of pps had been medicated; all had at least 14 medication-free days prior to study. 11 Papers using objective sleep measures  Hashimoto et al. [29] TS = 9 HC = number unclear N/R 4–12 TS group = 77.78% M; 22.22% F HC = N/R N/R 7  Kirov et al. [30] TS + ADHD = 19 HC = 19 TS + ADHD: Specialist clinic HC: friends and relatives of staff TS + ADHD = 8.2–16.2 (11.07/2.26) HC-8–15 (11.09/2.23) TS + ADHD = 94.74% M; 5.26% F HC = 89.47% M; 10.53% F 63.16% of TS + ADHD pps took medication; all had 7 medication-free days prior to study. 11  Kirov et al. [31] TS-only = 18 TS + ADHD = 18 ADHD-only = 18 HC = 18 TS + ADHD: Specialist clinic HC: friends and relatives of staff TS-only = 8–15.7 (11.74/2.31) TS + ADHD = 8.2–16.4 (11.10/2.33) ADHD-only = 8.2–14.9 (10.94/1.99) HC = 8–15.6 (11.58/2.25) TS-only = 88.89% M; 11.11% F TS + ADHD = 94.44% M; 5.56 % F ADHD-only = 94.44% M; 5.56 % F HC = 88.89% M; 11.11% F 72.22% of TS + ADHD & ADHD- only and 61.11% of TS-only children were medicated; all had 5–14 medication-free days prior to study. 11  Kirov et al. [32] TS-only = 21 TS + ADHD = 21 HC = 22 ADHD = 24 Specialist Clinic TS-only = 8–15.8 (11.79/1.78) TS + ADHD = 8–16 (11.04/2.32) ADHD = 8–15 (11.19/2.05) HC = 8–15.6 (11.42/2.28) TS-only = 90.48% M; 9.52% F TS + ADHD = 90.48% M; 9.52% F ADHD = 83.33% M; 16.67% F HC = 86.36% M; 13.64% F 42.86% of TS-only, 33.33% of TS + ADHD and 41.67% of ADHD were medicated; all had 5–14 medication-free days prior to study. 10  Stephens et al. [33] TS = 20 TS + ADHD = 21 ADHD = 33 HC = 16 TS = Specialist clinic ADHD & HC = community pediatricians, general hospital, family doctors and community posters Total sample = 6–16 (10.8) Total sample = 83.3% M; 16.7% F All pps were at least 6 weeks free of medication and 72.4% = medication naïve 11 Papers using subjective sleep measures  Allen et al. [34] TS only = 57 TS + ADHD = 89 ADHD-only = 21 HC = 146 TS: Specialist clinic & TS associations HC: Local schools Overall range = 7–14 HC= (10.8/1.8) TS-only = (II.6/2.0) TS + ADHD = (10.9/1.9) ADHD-only = (10.4/2.0) 100% M 1 medication: 58% of TS pps (27/57 TS only; 57/89 TS + ADHD) 2/>: 4/27 TS-only; 12/57 TS + ADHD 10  Ghosh et al. [35] TS-only = 48 TS + ADHD = 75 N/A Specialist clinic Overall range = 6–21 (13.6/3.8) TS-only = 14.4 (3.6) TS + ADHD = 13.2 (3.9) TS-only = 80% M; 20% F TS + ADHD = 66.7% M; 33.3% F N/R 3  Groth et al. [36] TS = 218 (T1, 2005– 2007) and 174 (T2, 2011–2013) N/A Specialist Clinic T1: 5.3–19.8 (12.4/2.8) T2: 11.1–25.9 (18.5/2.8) T1 & T2 combined: 82% M; 18% F T1 & T2 combined: 18.1% for tics; 4.8% for OCD; 28.8% for ADHD; 5.2% for sleep (T2 only) 8  Lee et al. [37] TS = 1124 HC = 3372 National Database 0–17 TS = 75.98% M; 24.02% F HC = 75.98% M; 24.02% F N/R 8  Modafferi et al. [38] TS = 36 78%=TS 22%=Chronic motor/phonic tic disorder HC = 266 TS: Specialist clinic HC: schools TS = 8–16.3 (11.7) HC mean = 11.5 years TS = 83.30% M; 16.70% F HC = 71.80% M; 28.20% F 30.56% of TS pps were taking medication for tic management at the time of the study 8  Mol Debes et al. (2008) [39] TS = 314 N/A Specialist clinic 5.3–20 (12.4) 81.9% M; 18.1% F 7.7% = methylphenidate 1.6% “treated medically for OCD” 7  Ricketts et al. [40] TS = 298 History of TS = 122 HC = 254 National Database 6–17 TS = (12.51/3.17) History of TS = (13.21/3.28) HC = (12.48/2.93) TS = 80.2% M; 19.8% FHistory of TS = 80.2% M; 19.8% F HC = 80.3% M; 19.7% F TS = 66.0% History of TS = 48.8% HC = 17.3% 9  Saccomani et al. [41] TS = 48 CTD = 48 HC = 30 TS = Specialist clinic HC: convenience method TS = 4.6–17.8 (11.2) CTD = 5.10–20 (12.1) HC = 6.4–13.10 (10.8) TS = 75% M; 25% F CTD = 68.75% M; 31.25% F HC = 66.67% M; 33.33% F N/R 6  Storch et al. [42] TS/CTD = 56 N/A Specialist clinic 7–17 (11.46 ± 2.63) 71.42% M; 28.58% F N/R 7  Teive et al. [43] TS = 33 Chronic tics = 10 Transitory tics = 1 N/A Specialist clinic 3–60 (13.5) 63.6% M; 36.4% F N/R 4  Wand et al. [44] TS = 446 overall; 245 < 18 years N/A Charitable organization Of group <18, mean = 11.9 (2.9) Of group <18 84.8% M; 15.2% F N/R 6 Not stated  Barabas and Matthews [45] TS-only = 27 (group 2) TS + patient migraine = 18 (group 1a) TS + primary relative with migraine = 20 (group 1b) N/A Specialist clinic Group 2 = 12.1 Group 1a = 11.6 Group 1b = 9.7 Group 2 = 77.78% M; 22.22% F Group 1a = 77.78% M; 22.22% F Group 1b = 95% M; 5% F N/R 4 Paper reference . N with TS . Number and details of comparison groups . Sample type/ recruitment site . Age range (mean/SD-if avail) . % males/females . Medication status of Pps . Quality rating (/14) . Papers using objective and subjective sleep measures  Kostanecka-Endress et al. [28] TS = 17 HC = 16 TS: Specialist clinic HC: convenience method TS = 7.11–15.5 (11.10) HC = 11.78 TS = 70.58% M; 29.42 % F HC = 75% M; 25% F 58.83% of pps had been medicated; all had at least 14 medication-free days prior to study. 11 Papers using objective sleep measures  Hashimoto et al. [29] TS = 9 HC = number unclear N/R 4–12 TS group = 77.78% M; 22.22% F HC = N/R N/R 7  Kirov et al. [30] TS + ADHD = 19 HC = 19 TS + ADHD: Specialist clinic HC: friends and relatives of staff TS + ADHD = 8.2–16.2 (11.07/2.26) HC-8–15 (11.09/2.23) TS + ADHD = 94.74% M; 5.26% F HC = 89.47% M; 10.53% F 63.16% of TS + ADHD pps took medication; all had 7 medication-free days prior to study. 11  Kirov et al. [31] TS-only = 18 TS + ADHD = 18 ADHD-only = 18 HC = 18 TS + ADHD: Specialist clinic HC: friends and relatives of staff TS-only = 8–15.7 (11.74/2.31) TS + ADHD = 8.2–16.4 (11.10/2.33) ADHD-only = 8.2–14.9 (10.94/1.99) HC = 8–15.6 (11.58/2.25) TS-only = 88.89% M; 11.11% F TS + ADHD = 94.44% M; 5.56 % F ADHD-only = 94.44% M; 5.56 % F HC = 88.89% M; 11.11% F 72.22% of TS + ADHD & ADHD- only and 61.11% of TS-only children were medicated; all had 5–14 medication-free days prior to study. 11  Kirov et al. [32] TS-only = 21 TS + ADHD = 21 HC = 22 ADHD = 24 Specialist Clinic TS-only = 8–15.8 (11.79/1.78) TS + ADHD = 8–16 (11.04/2.32) ADHD = 8–15 (11.19/2.05) HC = 8–15.6 (11.42/2.28) TS-only = 90.48% M; 9.52% F TS + ADHD = 90.48% M; 9.52% F ADHD = 83.33% M; 16.67% F HC = 86.36% M; 13.64% F 42.86% of TS-only, 33.33% of TS + ADHD and 41.67% of ADHD were medicated; all had 5–14 medication-free days prior to study. 10  Stephens et al. [33] TS = 20 TS + ADHD = 21 ADHD = 33 HC = 16 TS = Specialist clinic ADHD & HC = community pediatricians, general hospital, family doctors and community posters Total sample = 6–16 (10.8) Total sample = 83.3% M; 16.7% F All pps were at least 6 weeks free of medication and 72.4% = medication naïve 11 Papers using subjective sleep measures  Allen et al. [34] TS only = 57 TS + ADHD = 89 ADHD-only = 21 HC = 146 TS: Specialist clinic & TS associations HC: Local schools Overall range = 7–14 HC= (10.8/1.8) TS-only = (II.6/2.0) TS + ADHD = (10.9/1.9) ADHD-only = (10.4/2.0) 100% M 1 medication: 58% of TS pps (27/57 TS only; 57/89 TS + ADHD) 2/>: 4/27 TS-only; 12/57 TS + ADHD 10  Ghosh et al. [35] TS-only = 48 TS + ADHD = 75 N/A Specialist clinic Overall range = 6–21 (13.6/3.8) TS-only = 14.4 (3.6) TS + ADHD = 13.2 (3.9) TS-only = 80% M; 20% F TS + ADHD = 66.7% M; 33.3% F N/R 3  Groth et al. [36] TS = 218 (T1, 2005– 2007) and 174 (T2, 2011–2013) N/A Specialist Clinic T1: 5.3–19.8 (12.4/2.8) T2: 11.1–25.9 (18.5/2.8) T1 & T2 combined: 82% M; 18% F T1 & T2 combined: 18.1% for tics; 4.8% for OCD; 28.8% for ADHD; 5.2% for sleep (T2 only) 8  Lee et al. [37] TS = 1124 HC = 3372 National Database 0–17 TS = 75.98% M; 24.02% F HC = 75.98% M; 24.02% F N/R 8  Modafferi et al. [38] TS = 36 78%=TS 22%=Chronic motor/phonic tic disorder HC = 266 TS: Specialist clinic HC: schools TS = 8–16.3 (11.7) HC mean = 11.5 years TS = 83.30% M; 16.70% F HC = 71.80% M; 28.20% F 30.56% of TS pps were taking medication for tic management at the time of the study 8  Mol Debes et al. (2008) [39] TS = 314 N/A Specialist clinic 5.3–20 (12.4) 81.9% M; 18.1% F 7.7% = methylphenidate 1.6% “treated medically for OCD” 7  Ricketts et al. [40] TS = 298 History of TS = 122 HC = 254 National Database 6–17 TS = (12.51/3.17) History of TS = (13.21/3.28) HC = (12.48/2.93) TS = 80.2% M; 19.8% FHistory of TS = 80.2% M; 19.8% F HC = 80.3% M; 19.7% F TS = 66.0% History of TS = 48.8% HC = 17.3% 9  Saccomani et al. [41] TS = 48 CTD = 48 HC = 30 TS = Specialist clinic HC: convenience method TS = 4.6–17.8 (11.2) CTD = 5.10–20 (12.1) HC = 6.4–13.10 (10.8) TS = 75% M; 25% F CTD = 68.75% M; 31.25% F HC = 66.67% M; 33.33% F N/R 6  Storch et al. [42] TS/CTD = 56 N/A Specialist clinic 7–17 (11.46 ± 2.63) 71.42% M; 28.58% F N/R 7  Teive et al. [43] TS = 33 Chronic tics = 10 Transitory tics = 1 N/A Specialist clinic 3–60 (13.5) 63.6% M; 36.4% F N/R 4  Wand et al. [44] TS = 446 overall; 245 < 18 years N/A Charitable organization Of group <18, mean = 11.9 (2.9) Of group <18 84.8% M; 15.2% F N/R 6 Not stated  Barabas and Matthews [45] TS-only = 27 (group 2) TS + patient migraine = 18 (group 1a) TS + primary relative with migraine = 20 (group 1b) N/A Specialist clinic Group 2 = 12.1 Group 1a = 11.6 Group 1b = 9.7 Group 2 = 77.78% M; 22.22% F Group 1a = 77.78% M; 22.22% F Group 1b = 95% M; 5% F N/R 4 N/A = not applicable; N/R = not reported; HC = healthy controls TS = Tourette syndrome; CTD = chronic tic disorder; ADHD = attention deficit hyperactivity disorder. Open in new tab Table 1. Study characteristics Paper reference . N with TS . Number and details of comparison groups . Sample type/ recruitment site . Age range (mean/SD-if avail) . % males/females . Medication status of Pps . Quality rating (/14) . Papers using objective and subjective sleep measures  Kostanecka-Endress et al. [28] TS = 17 HC = 16 TS: Specialist clinic HC: convenience method TS = 7.11–15.5 (11.10) HC = 11.78 TS = 70.58% M; 29.42 % F HC = 75% M; 25% F 58.83% of pps had been medicated; all had at least 14 medication-free days prior to study. 11 Papers using objective sleep measures  Hashimoto et al. [29] TS = 9 HC = number unclear N/R 4–12 TS group = 77.78% M; 22.22% F HC = N/R N/R 7  Kirov et al. [30] TS + ADHD = 19 HC = 19 TS + ADHD: Specialist clinic HC: friends and relatives of staff TS + ADHD = 8.2–16.2 (11.07/2.26) HC-8–15 (11.09/2.23) TS + ADHD = 94.74% M; 5.26% F HC = 89.47% M; 10.53% F 63.16% of TS + ADHD pps took medication; all had 7 medication-free days prior to study. 11  Kirov et al. [31] TS-only = 18 TS + ADHD = 18 ADHD-only = 18 HC = 18 TS + ADHD: Specialist clinic HC: friends and relatives of staff TS-only = 8–15.7 (11.74/2.31) TS + ADHD = 8.2–16.4 (11.10/2.33) ADHD-only = 8.2–14.9 (10.94/1.99) HC = 8–15.6 (11.58/2.25) TS-only = 88.89% M; 11.11% F TS + ADHD = 94.44% M; 5.56 % F ADHD-only = 94.44% M; 5.56 % F HC = 88.89% M; 11.11% F 72.22% of TS + ADHD & ADHD- only and 61.11% of TS-only children were medicated; all had 5–14 medication-free days prior to study. 11  Kirov et al. [32] TS-only = 21 TS + ADHD = 21 HC = 22 ADHD = 24 Specialist Clinic TS-only = 8–15.8 (11.79/1.78) TS + ADHD = 8–16 (11.04/2.32) ADHD = 8–15 (11.19/2.05) HC = 8–15.6 (11.42/2.28) TS-only = 90.48% M; 9.52% F TS + ADHD = 90.48% M; 9.52% F ADHD = 83.33% M; 16.67% F HC = 86.36% M; 13.64% F 42.86% of TS-only, 33.33% of TS + ADHD and 41.67% of ADHD were medicated; all had 5–14 medication-free days prior to study. 10  Stephens et al. [33] TS = 20 TS + ADHD = 21 ADHD = 33 HC = 16 TS = Specialist clinic ADHD & HC = community pediatricians, general hospital, family doctors and community posters Total sample = 6–16 (10.8) Total sample = 83.3% M; 16.7% F All pps were at least 6 weeks free of medication and 72.4% = medication naïve 11 Papers using subjective sleep measures  Allen et al. [34] TS only = 57 TS + ADHD = 89 ADHD-only = 21 HC = 146 TS: Specialist clinic & TS associations HC: Local schools Overall range = 7–14 HC= (10.8/1.8) TS-only = (II.6/2.0) TS + ADHD = (10.9/1.9) ADHD-only = (10.4/2.0) 100% M 1 medication: 58% of TS pps (27/57 TS only; 57/89 TS + ADHD) 2/>: 4/27 TS-only; 12/57 TS + ADHD 10  Ghosh et al. [35] TS-only = 48 TS + ADHD = 75 N/A Specialist clinic Overall range = 6–21 (13.6/3.8) TS-only = 14.4 (3.6) TS + ADHD = 13.2 (3.9) TS-only = 80% M; 20% F TS + ADHD = 66.7% M; 33.3% F N/R 3  Groth et al. [36] TS = 218 (T1, 2005– 2007) and 174 (T2, 2011–2013) N/A Specialist Clinic T1: 5.3–19.8 (12.4/2.8) T2: 11.1–25.9 (18.5/2.8) T1 & T2 combined: 82% M; 18% F T1 & T2 combined: 18.1% for tics; 4.8% for OCD; 28.8% for ADHD; 5.2% for sleep (T2 only) 8  Lee et al. [37] TS = 1124 HC = 3372 National Database 0–17 TS = 75.98% M; 24.02% F HC = 75.98% M; 24.02% F N/R 8  Modafferi et al. [38] TS = 36 78%=TS 22%=Chronic motor/phonic tic disorder HC = 266 TS: Specialist clinic HC: schools TS = 8–16.3 (11.7) HC mean = 11.5 years TS = 83.30% M; 16.70% F HC = 71.80% M; 28.20% F 30.56% of TS pps were taking medication for tic management at the time of the study 8  Mol Debes et al. (2008) [39] TS = 314 N/A Specialist clinic 5.3–20 (12.4) 81.9% M; 18.1% F 7.7% = methylphenidate 1.6% “treated medically for OCD” 7  Ricketts et al. [40] TS = 298 History of TS = 122 HC = 254 National Database 6–17 TS = (12.51/3.17) History of TS = (13.21/3.28) HC = (12.48/2.93) TS = 80.2% M; 19.8% FHistory of TS = 80.2% M; 19.8% F HC = 80.3% M; 19.7% F TS = 66.0% History of TS = 48.8% HC = 17.3% 9  Saccomani et al. [41] TS = 48 CTD = 48 HC = 30 TS = Specialist clinic HC: convenience method TS = 4.6–17.8 (11.2) CTD = 5.10–20 (12.1) HC = 6.4–13.10 (10.8) TS = 75% M; 25% F CTD = 68.75% M; 31.25% F HC = 66.67% M; 33.33% F N/R 6  Storch et al. [42] TS/CTD = 56 N/A Specialist clinic 7–17 (11.46 ± 2.63) 71.42% M; 28.58% F N/R 7  Teive et al. [43] TS = 33 Chronic tics = 10 Transitory tics = 1 N/A Specialist clinic 3–60 (13.5) 63.6% M; 36.4% F N/R 4  Wand et al. [44] TS = 446 overall; 245 < 18 years N/A Charitable organization Of group <18, mean = 11.9 (2.9) Of group <18 84.8% M; 15.2% F N/R 6 Not stated  Barabas and Matthews [45] TS-only = 27 (group 2) TS + patient migraine = 18 (group 1a) TS + primary relative with migraine = 20 (group 1b) N/A Specialist clinic Group 2 = 12.1 Group 1a = 11.6 Group 1b = 9.7 Group 2 = 77.78% M; 22.22% F Group 1a = 77.78% M; 22.22% F Group 1b = 95% M; 5% F N/R 4 Paper reference . N with TS . Number and details of comparison groups . Sample type/ recruitment site . Age range (mean/SD-if avail) . % males/females . Medication status of Pps . Quality rating (/14) . Papers using objective and subjective sleep measures  Kostanecka-Endress et al. [28] TS = 17 HC = 16 TS: Specialist clinic HC: convenience method TS = 7.11–15.5 (11.10) HC = 11.78 TS = 70.58% M; 29.42 % F HC = 75% M; 25% F 58.83% of pps had been medicated; all had at least 14 medication-free days prior to study. 11 Papers using objective sleep measures  Hashimoto et al. [29] TS = 9 HC = number unclear N/R 4–12 TS group = 77.78% M; 22.22% F HC = N/R N/R 7  Kirov et al. [30] TS + ADHD = 19 HC = 19 TS + ADHD: Specialist clinic HC: friends and relatives of staff TS + ADHD = 8.2–16.2 (11.07/2.26) HC-8–15 (11.09/2.23) TS + ADHD = 94.74% M; 5.26% F HC = 89.47% M; 10.53% F 63.16% of TS + ADHD pps took medication; all had 7 medication-free days prior to study. 11  Kirov et al. [31] TS-only = 18 TS + ADHD = 18 ADHD-only = 18 HC = 18 TS + ADHD: Specialist clinic HC: friends and relatives of staff TS-only = 8–15.7 (11.74/2.31) TS + ADHD = 8.2–16.4 (11.10/2.33) ADHD-only = 8.2–14.9 (10.94/1.99) HC = 8–15.6 (11.58/2.25) TS-only = 88.89% M; 11.11% F TS + ADHD = 94.44% M; 5.56 % F ADHD-only = 94.44% M; 5.56 % F HC = 88.89% M; 11.11% F 72.22% of TS + ADHD & ADHD- only and 61.11% of TS-only children were medicated; all had 5–14 medication-free days prior to study. 11  Kirov et al. [32] TS-only = 21 TS + ADHD = 21 HC = 22 ADHD = 24 Specialist Clinic TS-only = 8–15.8 (11.79/1.78) TS + ADHD = 8–16 (11.04/2.32) ADHD = 8–15 (11.19/2.05) HC = 8–15.6 (11.42/2.28) TS-only = 90.48% M; 9.52% F TS + ADHD = 90.48% M; 9.52% F ADHD = 83.33% M; 16.67% F HC = 86.36% M; 13.64% F 42.86% of TS-only, 33.33% of TS + ADHD and 41.67% of ADHD were medicated; all had 5–14 medication-free days prior to study. 10  Stephens et al. [33] TS = 20 TS + ADHD = 21 ADHD = 33 HC = 16 TS = Specialist clinic ADHD & HC = community pediatricians, general hospital, family doctors and community posters Total sample = 6–16 (10.8) Total sample = 83.3% M; 16.7% F All pps were at least 6 weeks free of medication and 72.4% = medication naïve 11 Papers using subjective sleep measures  Allen et al. [34] TS only = 57 TS + ADHD = 89 ADHD-only = 21 HC = 146 TS: Specialist clinic & TS associations HC: Local schools Overall range = 7–14 HC= (10.8/1.8) TS-only = (II.6/2.0) TS + ADHD = (10.9/1.9) ADHD-only = (10.4/2.0) 100% M 1 medication: 58% of TS pps (27/57 TS only; 57/89 TS + ADHD) 2/>: 4/27 TS-only; 12/57 TS + ADHD 10  Ghosh et al. [35] TS-only = 48 TS + ADHD = 75 N/A Specialist clinic Overall range = 6–21 (13.6/3.8) TS-only = 14.4 (3.6) TS + ADHD = 13.2 (3.9) TS-only = 80% M; 20% F TS + ADHD = 66.7% M; 33.3% F N/R 3  Groth et al. [36] TS = 218 (T1, 2005– 2007) and 174 (T2, 2011–2013) N/A Specialist Clinic T1: 5.3–19.8 (12.4/2.8) T2: 11.1–25.9 (18.5/2.8) T1 & T2 combined: 82% M; 18% F T1 & T2 combined: 18.1% for tics; 4.8% for OCD; 28.8% for ADHD; 5.2% for sleep (T2 only) 8  Lee et al. [37] TS = 1124 HC = 3372 National Database 0–17 TS = 75.98% M; 24.02% F HC = 75.98% M; 24.02% F N/R 8  Modafferi et al. [38] TS = 36 78%=TS 22%=Chronic motor/phonic tic disorder HC = 266 TS: Specialist clinic HC: schools TS = 8–16.3 (11.7) HC mean = 11.5 years TS = 83.30% M; 16.70% F HC = 71.80% M; 28.20% F 30.56% of TS pps were taking medication for tic management at the time of the study 8  Mol Debes et al. (2008) [39] TS = 314 N/A Specialist clinic 5.3–20 (12.4) 81.9% M; 18.1% F 7.7% = methylphenidate 1.6% “treated medically for OCD” 7  Ricketts et al. [40] TS = 298 History of TS = 122 HC = 254 National Database 6–17 TS = (12.51/3.17) History of TS = (13.21/3.28) HC = (12.48/2.93) TS = 80.2% M; 19.8% FHistory of TS = 80.2% M; 19.8% F HC = 80.3% M; 19.7% F TS = 66.0% History of TS = 48.8% HC = 17.3% 9  Saccomani et al. [41] TS = 48 CTD = 48 HC = 30 TS = Specialist clinic HC: convenience method TS = 4.6–17.8 (11.2) CTD = 5.10–20 (12.1) HC = 6.4–13.10 (10.8) TS = 75% M; 25% F CTD = 68.75% M; 31.25% F HC = 66.67% M; 33.33% F N/R 6  Storch et al. [42] TS/CTD = 56 N/A Specialist clinic 7–17 (11.46 ± 2.63) 71.42% M; 28.58% F N/R 7  Teive et al. [43] TS = 33 Chronic tics = 10 Transitory tics = 1 N/A Specialist clinic 3–60 (13.5) 63.6% M; 36.4% F N/R 4  Wand et al. [44] TS = 446 overall; 245 < 18 years N/A Charitable organization Of group <18, mean = 11.9 (2.9) Of group <18 84.8% M; 15.2% F N/R 6 Not stated  Barabas and Matthews [45] TS-only = 27 (group 2) TS + patient migraine = 18 (group 1a) TS + primary relative with migraine = 20 (group 1b) N/A Specialist clinic Group 2 = 12.1 Group 1a = 11.6 Group 1b = 9.7 Group 2 = 77.78% M; 22.22% F Group 1a = 77.78% M; 22.22% F Group 1b = 95% M; 5% F N/R 4 N/A = not applicable; N/R = not reported; HC = healthy controls TS = Tourette syndrome; CTD = chronic tic disorder; ADHD = attention deficit hyperactivity disorder. Open in new tab Table 2. Methods and results Paper reference . Method(s) of sleep assessment (Informant/ assessment duration) . Types of sleep difficulty assessed . Main sleep-related findings . Additional findings or associations . Papers using objective and subjective sleep measures  Kostanecka-Endress et al. [28] • Sleep items of CBCL (parents) • PSG (2 consecutive nights) Wide range of physiological sleep variables TS Pps vs HC > time in bed, sleep period, wakefulness after sleep onset and time awake during night <sleep efficiency and sleep stage 2 duration No significant differences Amount of REM, SWS or stage 1, total sleep time, numbers of sleep stage shifts, numbers of stages, duration or stage latencies of each cycle. 1 pp = PLMS and 0 Pps = sleep apnea Medication use: No difference in sleep parameters of medication-naïve and previously medicated Pps. Tic severity: No correlations with sleep parameters Psychopathology: No correlations with sleep parameters Papers using objective sleep measures  Hashimoto et al. [29] • Sleep polygram (1 night) Twitch movements (TM) Total sleep REM sleep non-REM sleep Body movements (BM) Total sleep REM sleep Non-REM sleep 6/9 TS Pps = EEG changes during sleep but no consistent effect across Pps. TS vs HC Significantly > BMs and different frequency of movements across sleep stages. Significantly > TM/min during REM sleep, but not during non-REM, or for total sleep time. TS Pps within-group Significantly > TM = REM vs non-REM. N/A  Kirov et al. [30] • PSG (2 consecutive nights) Wide range of physiological sleep variables TS ± ADHD vs HC >time in bed, sleep time, REM sleep %, microarousals in light and REM sleep and short motor-related arousals <REM sleep latency. negative correlation = REM sleep latency and REM % No significant differences Sleep efficiency, onset, SWS latency, duration of wake, light sleep and SWS, % of movement and number of microarousals in SWS. No PLMS or SDB in either group. ADHD symptoms: Conner’s scores determined changes in REM sleep latency and REM duration.  Kirov et al. [31] • PSG (2 consecutive nights) Wide range of physiological sleep variables TS-only and TS ± ADHD vs ADHD-only and HC <sleep efficiency >latency of sleep stages 1, 2 and 3, microarousals in REM sleep and short motor-related arousals. ADHD-only and TS ± ADHD vs TS-only and HC >time in bed, sleep period, total sleep time, REM % and number of sleep cycles. <latency of sleep stage 1, 2 and REM sleep Only Pps with ADHD showed evidence of PLMS (11%) or SDB (5%). Tic severity: Associated with < sleep efficiency, >sleep onset, >SWS latency and >microarousals in REM sleep. Tic severity + psychopathology (CBCL score): Determined > short motor-related arousals. Attentional problems: Determined > REM duration and <sleep onset latency. Hyperactivity: Determined > sleep cycles.  Kirov et al. [32] PSG (2 consecutive nights) Wide range of physiological sleep variables TS only > Sleep Onset Latency (SOL) vs ADHD and HC. >SWS latency vs ADHD and HC. TS-only, TS ± ADHD and ADHD vs HC > REM sleep duration + ve correlation inattention (CBCL) >REM sleep duration –ve correlations PIQ >REM sleep latency –ve correlation inattention (CBCL) HC only >REM sleep duration –ve correlation inattention >REM sleep duration + ve correlation PIQ Medications: No medication effects on PSG findings for all groups.  Stephens et al. [33] • PSG (2 consecutive nights) Wide range of physiological sleep variables TS ± ADHD vs other groups >PLMS TS ± ADHD and ADHD-only vs other groups >leg movements in sleep ADHD-only vs other groups >movements during REM sleep, total arousals from sleep and arousals from SWS Hyperactivity: ADHD-only (low- and high-hyperactivity)=>total arousals than TS-only, TS + ADHD low-hyperactivity and controls. TS + ADHD high-hyperactivity group fell within the middle Behavior: CBCL delinquency = correlated with number of movements during REM sleep. Conduct disorder scale and measures of hyperactivity/immaturity and restless/disorganized behavior = correlated with number of total arousals and arousals from SWS. Pubertal status: Total sleep time and % SWS = differed between levels of puberty. Papers using subjective sleep measures  Allen et al. [34] • Questionnaire (parents) Modified version of Sleep Behavior Questionnaire [46] Wide range of sleep behaviors Sleep and behavior complaints were significantly more common in TS and/or ADHD than controls. Overall “poor sleep” was reported in: 26% = TS-only 41% = TS + ADHD 48% = ADHD-only 10% = HC Medications: Antidepressants = associated with unpleasant dreams; Stimulants = associated with enuresis  Ghosh et al. [35] • Questionnaire (young person and at least one parent/guardian) Novel Wide range of sleep problems assessed and prevalence of DSM-IV coded sleep disorders TS + ADHD vs TS-only >Sleep maintenance problems and abnormal sleep behaviors. No significant differences Sleep initiation. Both groups = high impact on daily functioning secondary to sleep disturbances, but no significant difference between groups. DSM-V sleep disorders TS-only = 65%; TS + ADHD = 64% Primary Insomnia TS-only = 32%; TS + ADHD = 42% Medications: Hypersomnia secondary to medication 3%=TS-only 4%=TS-+ADHD insomnia secondary to medication 0%=TS-only 33%=TS + ADHD  Groth et al. [36] • Questionnaire (parents) Sleep items of CBCL “Sleep disturbances” (>6 on CBCL scale) 9.7% scored above cutoff for sleep disturbance Yearly increase in sleep disturbances on CBCL N/A  Lee et al. [37] • Review of clinic records Incidence rates of ICD-9-CM coded sleep disorders TS Pps vs HC > incidence rate of sleep disorders in TS (e.g. sleep apnea, hypersomnia, sleep wake cycle, other sleep disturbance) TS Pps Risk of developing sleep disorders higher at 1-Year and 2–4 Years follow-up >prevalence rate for “unspecified sleep disorders” N/A  Modafferi et al. [38] • Questionnaire (parents) Novel Range of sleep behaviors and difficulties during last 6 months Significant differences on 15 of 45 questions. TS Pps >sleep duration <8 hours, sleep difficulties, anxiety/fear around sleep, reluctance to go to bed, hypnic jerks, use of sleep aids (e.g. fluids, medications, light, tv), transitional objects, parasomnias, restless sleep, bruxism, snoring and daytime sleepiness. Tic severity: >sleep latency, hypnagogic hallucinations, sleep talking and nightmares. Medication use: no difference, medicated vs unmedicated TS Pps but unmedicated TS Pps vs HC ≥ sleep breathing problems and hypnagogic hallucinations. Psychopathology: TS Pps with borderline/pathological SAFA-A or SAFA-D=>abnormal movements before sleep. TS Pps with borderline/pathological SAFA-O=>problems falling asleep and <sleep duration.  Mol Debes et al. [39] • Questionnaire (parents) Sleep items of CBCL “Sleep disturbances”(>6 on CBCL scale) 17% of Pps = score >6 Psychopathology: TS + ADHD+OCD = significantly > likely to have sleep disturbances than other groups (TS + ADHD, TS/OCD or TS-only).  Ricketts et al. [40] • Parent report/interview Parent-reported number of “sufficient nights child has slept” in past week TS <sufficient sleep per week vs HC TS <sufficient sleep per week vs history of TS Age: Older adolescent males with mild TD > sleep vs children and early adolescents  Saccomani et al. [41] • Parent report/interview Degree of “sleep problems,” based on DSM-IV-TR criteria. Present in: TS group = 27.1% CTD group = 16.7% HC = 0% N/A  Storch et al. [42] • Questionnaire (parents and children) Items from CBCL (n = 6) and MASC (n = 1) were combined to make composite measure Sleep-related problems (SRP) 19.6% of sample = no sleep-related problems 19.7% of sample = 4 or more SRPs Most common = nightmares and being overtired upon waking Gender: Females ≥ total SRP Age: younger children ≥ total SRP Tic severity: Overall severity = not associated with SRP, but YGTSS motor scale = negatively correlated with total SRP QoL, internalizing and externalizing and anxiety = Significantly related to number of total SRP Psychopathology: TS + anxiety dx ≥ SRP(“sleeping less” and “trouble sleeping”)  Teive et al. [43] • Review of clinic records DSM-IV sleep problems 4 Pps = “sleep problems” N/A  Wand et al. [44] • Questionnaire (Parents/parents + children/children) Novel, modeled after the Ohio study of Tourette Syndrome Frequency of sleep disturbance Ratings of “often” or “sometimes” 66.4% = problems getting to sleep 31.3% = problems staying asleep 23.5% = sleepwalking N/A Not stated  Barabas and Matthews [45] • Not stated Somnambulism Night terrors Enuresis TS + Migraine = a significantly greater prevalence of disorders of arousal than TS-only. Highest prevalence = TS + patient migraine. N/A Paper reference . Method(s) of sleep assessment (Informant/ assessment duration) . Types of sleep difficulty assessed . Main sleep-related findings . Additional findings or associations . Papers using objective and subjective sleep measures  Kostanecka-Endress et al. [28] • Sleep items of CBCL (parents) • PSG (2 consecutive nights) Wide range of physiological sleep variables TS Pps vs HC > time in bed, sleep period, wakefulness after sleep onset and time awake during night <sleep efficiency and sleep stage 2 duration No significant differences Amount of REM, SWS or stage 1, total sleep time, numbers of sleep stage shifts, numbers of stages, duration or stage latencies of each cycle. 1 pp = PLMS and 0 Pps = sleep apnea Medication use: No difference in sleep parameters of medication-naïve and previously medicated Pps. Tic severity: No correlations with sleep parameters Psychopathology: No correlations with sleep parameters Papers using objective sleep measures  Hashimoto et al. [29] • Sleep polygram (1 night) Twitch movements (TM) Total sleep REM sleep non-REM sleep Body movements (BM) Total sleep REM sleep Non-REM sleep 6/9 TS Pps = EEG changes during sleep but no consistent effect across Pps. TS vs HC Significantly > BMs and different frequency of movements across sleep stages. Significantly > TM/min during REM sleep, but not during non-REM, or for total sleep time. TS Pps within-group Significantly > TM = REM vs non-REM. N/A  Kirov et al. [30] • PSG (2 consecutive nights) Wide range of physiological sleep variables TS ± ADHD vs HC >time in bed, sleep time, REM sleep %, microarousals in light and REM sleep and short motor-related arousals <REM sleep latency. negative correlation = REM sleep latency and REM % No significant differences Sleep efficiency, onset, SWS latency, duration of wake, light sleep and SWS, % of movement and number of microarousals in SWS. No PLMS or SDB in either group. ADHD symptoms: Conner’s scores determined changes in REM sleep latency and REM duration.  Kirov et al. [31] • PSG (2 consecutive nights) Wide range of physiological sleep variables TS-only and TS ± ADHD vs ADHD-only and HC <sleep efficiency >latency of sleep stages 1, 2 and 3, microarousals in REM sleep and short motor-related arousals. ADHD-only and TS ± ADHD vs TS-only and HC >time in bed, sleep period, total sleep time, REM % and number of sleep cycles. <latency of sleep stage 1, 2 and REM sleep Only Pps with ADHD showed evidence of PLMS (11%) or SDB (5%). Tic severity: Associated with < sleep efficiency, >sleep onset, >SWS latency and >microarousals in REM sleep. Tic severity + psychopathology (CBCL score): Determined > short motor-related arousals. Attentional problems: Determined > REM duration and <sleep onset latency. Hyperactivity: Determined > sleep cycles.  Kirov et al. [32] PSG (2 consecutive nights) Wide range of physiological sleep variables TS only > Sleep Onset Latency (SOL) vs ADHD and HC. >SWS latency vs ADHD and HC. TS-only, TS ± ADHD and ADHD vs HC > REM sleep duration + ve correlation inattention (CBCL) >REM sleep duration –ve correlations PIQ >REM sleep latency –ve correlation inattention (CBCL) HC only >REM sleep duration –ve correlation inattention >REM sleep duration + ve correlation PIQ Medications: No medication effects on PSG findings for all groups.  Stephens et al. [33] • PSG (2 consecutive nights) Wide range of physiological sleep variables TS ± ADHD vs other groups >PLMS TS ± ADHD and ADHD-only vs other groups >leg movements in sleep ADHD-only vs other groups >movements during REM sleep, total arousals from sleep and arousals from SWS Hyperactivity: ADHD-only (low- and high-hyperactivity)=>total arousals than TS-only, TS + ADHD low-hyperactivity and controls. TS + ADHD high-hyperactivity group fell within the middle Behavior: CBCL delinquency = correlated with number of movements during REM sleep. Conduct disorder scale and measures of hyperactivity/immaturity and restless/disorganized behavior = correlated with number of total arousals and arousals from SWS. Pubertal status: Total sleep time and % SWS = differed between levels of puberty. Papers using subjective sleep measures  Allen et al. [34] • Questionnaire (parents) Modified version of Sleep Behavior Questionnaire [46] Wide range of sleep behaviors Sleep and behavior complaints were significantly more common in TS and/or ADHD than controls. Overall “poor sleep” was reported in: 26% = TS-only 41% = TS + ADHD 48% = ADHD-only 10% = HC Medications: Antidepressants = associated with unpleasant dreams; Stimulants = associated with enuresis  Ghosh et al. [35] • Questionnaire (young person and at least one parent/guardian) Novel Wide range of sleep problems assessed and prevalence of DSM-IV coded sleep disorders TS + ADHD vs TS-only >Sleep maintenance problems and abnormal sleep behaviors. No significant differences Sleep initiation. Both groups = high impact on daily functioning secondary to sleep disturbances, but no significant difference between groups. DSM-V sleep disorders TS-only = 65%; TS + ADHD = 64% Primary Insomnia TS-only = 32%; TS + ADHD = 42% Medications: Hypersomnia secondary to medication 3%=TS-only 4%=TS-+ADHD insomnia secondary to medication 0%=TS-only 33%=TS + ADHD  Groth et al. [36] • Questionnaire (parents) Sleep items of CBCL “Sleep disturbances” (>6 on CBCL scale) 9.7% scored above cutoff for sleep disturbance Yearly increase in sleep disturbances on CBCL N/A  Lee et al. [37] • Review of clinic records Incidence rates of ICD-9-CM coded sleep disorders TS Pps vs HC > incidence rate of sleep disorders in TS (e.g. sleep apnea, hypersomnia, sleep wake cycle, other sleep disturbance) TS Pps Risk of developing sleep disorders higher at 1-Year and 2–4 Years follow-up >prevalence rate for “unspecified sleep disorders” N/A  Modafferi et al. [38] • Questionnaire (parents) Novel Range of sleep behaviors and difficulties during last 6 months Significant differences on 15 of 45 questions. TS Pps >sleep duration <8 hours, sleep difficulties, anxiety/fear around sleep, reluctance to go to bed, hypnic jerks, use of sleep aids (e.g. fluids, medications, light, tv), transitional objects, parasomnias, restless sleep, bruxism, snoring and daytime sleepiness. Tic severity: >sleep latency, hypnagogic hallucinations, sleep talking and nightmares. Medication use: no difference, medicated vs unmedicated TS Pps but unmedicated TS Pps vs HC ≥ sleep breathing problems and hypnagogic hallucinations. Psychopathology: TS Pps with borderline/pathological SAFA-A or SAFA-D=>abnormal movements before sleep. TS Pps with borderline/pathological SAFA-O=>problems falling asleep and <sleep duration.  Mol Debes et al. [39] • Questionnaire (parents) Sleep items of CBCL “Sleep disturbances”(>6 on CBCL scale) 17% of Pps = score >6 Psychopathology: TS + ADHD+OCD = significantly > likely to have sleep disturbances than other groups (TS + ADHD, TS/OCD or TS-only).  Ricketts et al. [40] • Parent report/interview Parent-reported number of “sufficient nights child has slept” in past week TS <sufficient sleep per week vs HC TS <sufficient sleep per week vs history of TS Age: Older adolescent males with mild TD > sleep vs children and early adolescents  Saccomani et al. [41] • Parent report/interview Degree of “sleep problems,” based on DSM-IV-TR criteria. Present in: TS group = 27.1% CTD group = 16.7% HC = 0% N/A  Storch et al. [42] • Questionnaire (parents and children) Items from CBCL (n = 6) and MASC (n = 1) were combined to make composite measure Sleep-related problems (SRP) 19.6% of sample = no sleep-related problems 19.7% of sample = 4 or more SRPs Most common = nightmares and being overtired upon waking Gender: Females ≥ total SRP Age: younger children ≥ total SRP Tic severity: Overall severity = not associated with SRP, but YGTSS motor scale = negatively correlated with total SRP QoL, internalizing and externalizing and anxiety = Significantly related to number of total SRP Psychopathology: TS + anxiety dx ≥ SRP(“sleeping less” and “trouble sleeping”)  Teive et al. [43] • Review of clinic records DSM-IV sleep problems 4 Pps = “sleep problems” N/A  Wand et al. [44] • Questionnaire (Parents/parents + children/children) Novel, modeled after the Ohio study of Tourette Syndrome Frequency of sleep disturbance Ratings of “often” or “sometimes” 66.4% = problems getting to sleep 31.3% = problems staying asleep 23.5% = sleepwalking N/A Not stated  Barabas and Matthews [45] • Not stated Somnambulism Night terrors Enuresis TS + Migraine = a significantly greater prevalence of disorders of arousal than TS-only. Highest prevalence = TS + patient migraine. N/A N/A = not applicable; Pps = participants; TS = Tourette syndrome; CTD = chronic tic disorder ADHD = attention deficit hyperactivity disorder; PLMS = periodic limb movements in sleep; SDB = sleep disordered breathing; REM = rapid eye movement sleep; SWS = slow wave sleep; CBCL = Child Behavior Checklist; MASC = Multidimensional Anxiety Scale for Children; ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification. Open in new tab Table 2. Methods and results Paper reference . Method(s) of sleep assessment (Informant/ assessment duration) . Types of sleep difficulty assessed . Main sleep-related findings . Additional findings or associations . Papers using objective and subjective sleep measures  Kostanecka-Endress et al. [28] • Sleep items of CBCL (parents) • PSG (2 consecutive nights) Wide range of physiological sleep variables TS Pps vs HC > time in bed, sleep period, wakefulness after sleep onset and time awake during night <sleep efficiency and sleep stage 2 duration No significant differences Amount of REM, SWS or stage 1, total sleep time, numbers of sleep stage shifts, numbers of stages, duration or stage latencies of each cycle. 1 pp = PLMS and 0 Pps = sleep apnea Medication use: No difference in sleep parameters of medication-naïve and previously medicated Pps. Tic severity: No correlations with sleep parameters Psychopathology: No correlations with sleep parameters Papers using objective sleep measures  Hashimoto et al. [29] • Sleep polygram (1 night) Twitch movements (TM) Total sleep REM sleep non-REM sleep Body movements (BM) Total sleep REM sleep Non-REM sleep 6/9 TS Pps = EEG changes during sleep but no consistent effect across Pps. TS vs HC Significantly > BMs and different frequency of movements across sleep stages. Significantly > TM/min during REM sleep, but not during non-REM, or for total sleep time. TS Pps within-group Significantly > TM = REM vs non-REM. N/A  Kirov et al. [30] • PSG (2 consecutive nights) Wide range of physiological sleep variables TS ± ADHD vs HC >time in bed, sleep time, REM sleep %, microarousals in light and REM sleep and short motor-related arousals <REM sleep latency. negative correlation = REM sleep latency and REM % No significant differences Sleep efficiency, onset, SWS latency, duration of wake, light sleep and SWS, % of movement and number of microarousals in SWS. No PLMS or SDB in either group. ADHD symptoms: Conner’s scores determined changes in REM sleep latency and REM duration.  Kirov et al. [31] • PSG (2 consecutive nights) Wide range of physiological sleep variables TS-only and TS ± ADHD vs ADHD-only and HC <sleep efficiency >latency of sleep stages 1, 2 and 3, microarousals in REM sleep and short motor-related arousals. ADHD-only and TS ± ADHD vs TS-only and HC >time in bed, sleep period, total sleep time, REM % and number of sleep cycles. <latency of sleep stage 1, 2 and REM sleep Only Pps with ADHD showed evidence of PLMS (11%) or SDB (5%). Tic severity: Associated with < sleep efficiency, >sleep onset, >SWS latency and >microarousals in REM sleep. Tic severity + psychopathology (CBCL score): Determined > short motor-related arousals. Attentional problems: Determined > REM duration and <sleep onset latency. Hyperactivity: Determined > sleep cycles.  Kirov et al. [32] PSG (2 consecutive nights) Wide range of physiological sleep variables TS only > Sleep Onset Latency (SOL) vs ADHD and HC. >SWS latency vs ADHD and HC. TS-only, TS ± ADHD and ADHD vs HC > REM sleep duration + ve correlation inattention (CBCL) >REM sleep duration –ve correlations PIQ >REM sleep latency –ve correlation inattention (CBCL) HC only >REM sleep duration –ve correlation inattention >REM sleep duration + ve correlation PIQ Medications: No medication effects on PSG findings for all groups.  Stephens et al. [33] • PSG (2 consecutive nights) Wide range of physiological sleep variables TS ± ADHD vs other groups >PLMS TS ± ADHD and ADHD-only vs other groups >leg movements in sleep ADHD-only vs other groups >movements during REM sleep, total arousals from sleep and arousals from SWS Hyperactivity: ADHD-only (low- and high-hyperactivity)=>total arousals than TS-only, TS + ADHD low-hyperactivity and controls. TS + ADHD high-hyperactivity group fell within the middle Behavior: CBCL delinquency = correlated with number of movements during REM sleep. Conduct disorder scale and measures of hyperactivity/immaturity and restless/disorganized behavior = correlated with number of total arousals and arousals from SWS. Pubertal status: Total sleep time and % SWS = differed between levels of puberty. Papers using subjective sleep measures  Allen et al. [34] • Questionnaire (parents) Modified version of Sleep Behavior Questionnaire [46] Wide range of sleep behaviors Sleep and behavior complaints were significantly more common in TS and/or ADHD than controls. Overall “poor sleep” was reported in: 26% = TS-only 41% = TS + ADHD 48% = ADHD-only 10% = HC Medications: Antidepressants = associated with unpleasant dreams; Stimulants = associated with enuresis  Ghosh et al. [35] • Questionnaire (young person and at least one parent/guardian) Novel Wide range of sleep problems assessed and prevalence of DSM-IV coded sleep disorders TS + ADHD vs TS-only >Sleep maintenance problems and abnormal sleep behaviors. No significant differences Sleep initiation. Both groups = high impact on daily functioning secondary to sleep disturbances, but no significant difference between groups. DSM-V sleep disorders TS-only = 65%; TS + ADHD = 64% Primary Insomnia TS-only = 32%; TS + ADHD = 42% Medications: Hypersomnia secondary to medication 3%=TS-only 4%=TS-+ADHD insomnia secondary to medication 0%=TS-only 33%=TS + ADHD  Groth et al. [36] • Questionnaire (parents) Sleep items of CBCL “Sleep disturbances” (>6 on CBCL scale) 9.7% scored above cutoff for sleep disturbance Yearly increase in sleep disturbances on CBCL N/A  Lee et al. [37] • Review of clinic records Incidence rates of ICD-9-CM coded sleep disorders TS Pps vs HC > incidence rate of sleep disorders in TS (e.g. sleep apnea, hypersomnia, sleep wake cycle, other sleep disturbance) TS Pps Risk of developing sleep disorders higher at 1-Year and 2–4 Years follow-up >prevalence rate for “unspecified sleep disorders” N/A  Modafferi et al. [38] • Questionnaire (parents) Novel Range of sleep behaviors and difficulties during last 6 months Significant differences on 15 of 45 questions. TS Pps >sleep duration <8 hours, sleep difficulties, anxiety/fear around sleep, reluctance to go to bed, hypnic jerks, use of sleep aids (e.g. fluids, medications, light, tv), transitional objects, parasomnias, restless sleep, bruxism, snoring and daytime sleepiness. Tic severity: >sleep latency, hypnagogic hallucinations, sleep talking and nightmares. Medication use: no difference, medicated vs unmedicated TS Pps but unmedicated TS Pps vs HC ≥ sleep breathing problems and hypnagogic hallucinations. Psychopathology: TS Pps with borderline/pathological SAFA-A or SAFA-D=>abnormal movements before sleep. TS Pps with borderline/pathological SAFA-O=>problems falling asleep and <sleep duration.  Mol Debes et al. [39] • Questionnaire (parents) Sleep items of CBCL “Sleep disturbances”(>6 on CBCL scale) 17% of Pps = score >6 Psychopathology: TS + ADHD+OCD = significantly > likely to have sleep disturbances than other groups (TS + ADHD, TS/OCD or TS-only).  Ricketts et al. [40] • Parent report/interview Parent-reported number of “sufficient nights child has slept” in past week TS <sufficient sleep per week vs HC TS <sufficient sleep per week vs history of TS Age: Older adolescent males with mild TD > sleep vs children and early adolescents  Saccomani et al. [41] • Parent report/interview Degree of “sleep problems,” based on DSM-IV-TR criteria. Present in: TS group = 27.1% CTD group = 16.7% HC = 0% N/A  Storch et al. [42] • Questionnaire (parents and children) Items from CBCL (n = 6) and MASC (n = 1) were combined to make composite measure Sleep-related problems (SRP) 19.6% of sample = no sleep-related problems 19.7% of sample = 4 or more SRPs Most common = nightmares and being overtired upon waking Gender: Females ≥ total SRP Age: younger children ≥ total SRP Tic severity: Overall severity = not associated with SRP, but YGTSS motor scale = negatively correlated with total SRP QoL, internalizing and externalizing and anxiety = Significantly related to number of total SRP Psychopathology: TS + anxiety dx ≥ SRP(“sleeping less” and “trouble sleeping”)  Teive et al. [43] • Review of clinic records DSM-IV sleep problems 4 Pps = “sleep problems” N/A  Wand et al. [44] • Questionnaire (Parents/parents + children/children) Novel, modeled after the Ohio study of Tourette Syndrome Frequency of sleep disturbance Ratings of “often” or “sometimes” 66.4% = problems getting to sleep 31.3% = problems staying asleep 23.5% = sleepwalking N/A Not stated  Barabas and Matthews [45] • Not stated Somnambulism Night terrors Enuresis TS + Migraine = a significantly greater prevalence of disorders of arousal than TS-only. Highest prevalence = TS + patient migraine. N/A Paper reference . Method(s) of sleep assessment (Informant/ assessment duration) . Types of sleep difficulty assessed . Main sleep-related findings . Additional findings or associations . Papers using objective and subjective sleep measures  Kostanecka-Endress et al. [28] • Sleep items of CBCL (parents) • PSG (2 consecutive nights) Wide range of physiological sleep variables TS Pps vs HC > time in bed, sleep period, wakefulness after sleep onset and time awake during night <sleep efficiency and sleep stage 2 duration No significant differences Amount of REM, SWS or stage 1, total sleep time, numbers of sleep stage shifts, numbers of stages, duration or stage latencies of each cycle. 1 pp = PLMS and 0 Pps = sleep apnea Medication use: No difference in sleep parameters of medication-naïve and previously medicated Pps. Tic severity: No correlations with sleep parameters Psychopathology: No correlations with sleep parameters Papers using objective sleep measures  Hashimoto et al. [29] • Sleep polygram (1 night) Twitch movements (TM) Total sleep REM sleep non-REM sleep Body movements (BM) Total sleep REM sleep Non-REM sleep 6/9 TS Pps = EEG changes during sleep but no consistent effect across Pps. TS vs HC Significantly > BMs and different frequency of movements across sleep stages. Significantly > TM/min during REM sleep, but not during non-REM, or for total sleep time. TS Pps within-group Significantly > TM = REM vs non-REM. N/A  Kirov et al. [30] • PSG (2 consecutive nights) Wide range of physiological sleep variables TS ± ADHD vs HC >time in bed, sleep time, REM sleep %, microarousals in light and REM sleep and short motor-related arousals <REM sleep latency. negative correlation = REM sleep latency and REM % No significant differences Sleep efficiency, onset, SWS latency, duration of wake, light sleep and SWS, % of movement and number of microarousals in SWS. No PLMS or SDB in either group. ADHD symptoms: Conner’s scores determined changes in REM sleep latency and REM duration.  Kirov et al. [31] • PSG (2 consecutive nights) Wide range of physiological sleep variables TS-only and TS ± ADHD vs ADHD-only and HC <sleep efficiency >latency of sleep stages 1, 2 and 3, microarousals in REM sleep and short motor-related arousals. ADHD-only and TS ± ADHD vs TS-only and HC >time in bed, sleep period, total sleep time, REM % and number of sleep cycles. <latency of sleep stage 1, 2 and REM sleep Only Pps with ADHD showed evidence of PLMS (11%) or SDB (5%). Tic severity: Associated with < sleep efficiency, >sleep onset, >SWS latency and >microarousals in REM sleep. Tic severity + psychopathology (CBCL score): Determined > short motor-related arousals. Attentional problems: Determined > REM duration and <sleep onset latency. Hyperactivity: Determined > sleep cycles.  Kirov et al. [32] PSG (2 consecutive nights) Wide range of physiological sleep variables TS only > Sleep Onset Latency (SOL) vs ADHD and HC. >SWS latency vs ADHD and HC. TS-only, TS ± ADHD and ADHD vs HC > REM sleep duration + ve correlation inattention (CBCL) >REM sleep duration –ve correlations PIQ >REM sleep latency –ve correlation inattention (CBCL) HC only >REM sleep duration –ve correlation inattention >REM sleep duration + ve correlation PIQ Medications: No medication effects on PSG findings for all groups.  Stephens et al. [33] • PSG (2 consecutive nights) Wide range of physiological sleep variables TS ± ADHD vs other groups >PLMS TS ± ADHD and ADHD-only vs other groups >leg movements in sleep ADHD-only vs other groups >movements during REM sleep, total arousals from sleep and arousals from SWS Hyperactivity: ADHD-only (low- and high-hyperactivity)=>total arousals than TS-only, TS + ADHD low-hyperactivity and controls. TS + ADHD high-hyperactivity group fell within the middle Behavior: CBCL delinquency = correlated with number of movements during REM sleep. Conduct disorder scale and measures of hyperactivity/immaturity and restless/disorganized behavior = correlated with number of total arousals and arousals from SWS. Pubertal status: Total sleep time and % SWS = differed between levels of puberty. Papers using subjective sleep measures  Allen et al. [34] • Questionnaire (parents) Modified version of Sleep Behavior Questionnaire [46] Wide range of sleep behaviors Sleep and behavior complaints were significantly more common in TS and/or ADHD than controls. Overall “poor sleep” was reported in: 26% = TS-only 41% = TS + ADHD 48% = ADHD-only 10% = HC Medications: Antidepressants = associated with unpleasant dreams; Stimulants = associated with enuresis  Ghosh et al. [35] • Questionnaire (young person and at least one parent/guardian) Novel Wide range of sleep problems assessed and prevalence of DSM-IV coded sleep disorders TS + ADHD vs TS-only >Sleep maintenance problems and abnormal sleep behaviors. No significant differences Sleep initiation. Both groups = high impact on daily functioning secondary to sleep disturbances, but no significant difference between groups. DSM-V sleep disorders TS-only = 65%; TS + ADHD = 64% Primary Insomnia TS-only = 32%; TS + ADHD = 42% Medications: Hypersomnia secondary to medication 3%=TS-only 4%=TS-+ADHD insomnia secondary to medication 0%=TS-only 33%=TS + ADHD  Groth et al. [36] • Questionnaire (parents) Sleep items of CBCL “Sleep disturbances” (>6 on CBCL scale) 9.7% scored above cutoff for sleep disturbance Yearly increase in sleep disturbances on CBCL N/A  Lee et al. [37] • Review of clinic records Incidence rates of ICD-9-CM coded sleep disorders TS Pps vs HC > incidence rate of sleep disorders in TS (e.g. sleep apnea, hypersomnia, sleep wake cycle, other sleep disturbance) TS Pps Risk of developing sleep disorders higher at 1-Year and 2–4 Years follow-up >prevalence rate for “unspecified sleep disorders” N/A  Modafferi et al. [38] • Questionnaire (parents) Novel Range of sleep behaviors and difficulties during last 6 months Significant differences on 15 of 45 questions. TS Pps >sleep duration <8 hours, sleep difficulties, anxiety/fear around sleep, reluctance to go to bed, hypnic jerks, use of sleep aids (e.g. fluids, medications, light, tv), transitional objects, parasomnias, restless sleep, bruxism, snoring and daytime sleepiness. Tic severity: >sleep latency, hypnagogic hallucinations, sleep talking and nightmares. Medication use: no difference, medicated vs unmedicated TS Pps but unmedicated TS Pps vs HC ≥ sleep breathing problems and hypnagogic hallucinations. Psychopathology: TS Pps with borderline/pathological SAFA-A or SAFA-D=>abnormal movements before sleep. TS Pps with borderline/pathological SAFA-O=>problems falling asleep and <sleep duration.  Mol Debes et al. [39] • Questionnaire (parents) Sleep items of CBCL “Sleep disturbances”(>6 on CBCL scale) 17% of Pps = score >6 Psychopathology: TS + ADHD+OCD = significantly > likely to have sleep disturbances than other groups (TS + ADHD, TS/OCD or TS-only).  Ricketts et al. [40] • Parent report/interview Parent-reported number of “sufficient nights child has slept” in past week TS <sufficient sleep per week vs HC TS <sufficient sleep per week vs history of TS Age: Older adolescent males with mild TD > sleep vs children and early adolescents  Saccomani et al. [41] • Parent report/interview Degree of “sleep problems,” based on DSM-IV-TR criteria. Present in: TS group = 27.1% CTD group = 16.7% HC = 0% N/A  Storch et al. [42] • Questionnaire (parents and children) Items from CBCL (n = 6) and MASC (n = 1) were combined to make composite measure Sleep-related problems (SRP) 19.6% of sample = no sleep-related problems 19.7% of sample = 4 or more SRPs Most common = nightmares and being overtired upon waking Gender: Females ≥ total SRP Age: younger children ≥ total SRP Tic severity: Overall severity = not associated with SRP, but YGTSS motor scale = negatively correlated with total SRP QoL, internalizing and externalizing and anxiety = Significantly related to number of total SRP Psychopathology: TS + anxiety dx ≥ SRP(“sleeping less” and “trouble sleeping”)  Teive et al. [43] • Review of clinic records DSM-IV sleep problems 4 Pps = “sleep problems” N/A  Wand et al. [44] • Questionnaire (Parents/parents + children/children) Novel, modeled after the Ohio study of Tourette Syndrome Frequency of sleep disturbance Ratings of “often” or “sometimes” 66.4% = problems getting to sleep 31.3% = problems staying asleep 23.5% = sleepwalking N/A Not stated  Barabas and Matthews [45] • Not stated Somnambulism Night terrors Enuresis TS + Migraine = a significantly greater prevalence of disorders of arousal than TS-only. Highest prevalence = TS + patient migraine. N/A N/A = not applicable; Pps = participants; TS = Tourette syndrome; CTD = chronic tic disorder ADHD = attention deficit hyperactivity disorder; PLMS = periodic limb movements in sleep; SDB = sleep disordered breathing; REM = rapid eye movement sleep; SWS = slow wave sleep; CBCL = Child Behavior Checklist; MASC = Multidimensional Anxiety Scale for Children; ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification. Open in new tab Methods and results Table 2 describes each study in more details and outlines the main sleep-related findings for each. Twelve of the 18 studies (66.7%) used subjective methods to assess sleep, while the remaining six (33.3%) used the objective method of PSG. One study used both objective and subjective methods [28]. All 11 studies that included healthy controls found that participants with TS/CTD had more sleep difficulties in comparison (of which, five were subjective, five were objective and one was mixed methods). For the subjective studies, estimates of sleep difficulty in TS/CTD-only participants ranged from 9.7 to 80.4% (IQR = 40.43–14.95), while estimates for controls were 0%–10% [34, 36, 39, 41, 42]. Rates of DSM/ICD-9-CM [47] coded sleep disorders in TS/CTD ranged from 7.24% to 65% [35, 37, 43]. Studies directly comparing clinical samples found that sleep difficulties were most common in ADHD-only, followed by TS + ADHD, then TS-only [34, 35]. For the objective studies, participants with ADHD, regardless of TS-status (i.e. TS + ADHD or ADHD-only) showed a particular sleep pattern featuring more time in bed, a longer total sleep time, higher percentage of REM sleep, more sleep cycles, shorter latency of stages one, two and REM sleep and more leg movements during sleep compared to those without ADHD (i.e. TS-only or healthy controls [31, 33]). One study also reported a longer sleep onset (latency) and more prolonged slow-wave sleep (latency) for those with TS compared to ADHD and HC; however, no significant differences in REM sleep parameters were reported across TS, ADHD, and TS + ADHD groups [32]. Sleep-related associations In 14 of the 18 studies (77.7%, of which four used objective measures, nine used subjective measures and one used mixed measures), information was included about links between sleep and participant characteristics, tic severity, medications and comorbid internalizing and externalizing difficulties. Three subjective papers found that f sleep difficulties increased over time in TS [36, 37, 40]. In contrast, two papers (one objective, one subjective) found younger children appeared to have more total sleep problems than their older peers [33, 42]. Pubertal stage also appeared to be linked with total sleep time and percentage of SWS, although the direction of this effect was unclear in both objective and subjective studies [33, 42]. Two studies (both subjective) reported that females had more sleep difficulties than males [40, 42]. Reported relationships between tic severity and sleep outcomes were inconsistent, with two studies (one objective, one subjective) concluding that increased tic severity was associated with poorer sleep [31, 38] and two studies (one subjective and one mixed measures) observing no such association [28, 42]. Findings related to medication-use and sleep in TS as assessed via subjective measure were similarly inconsistent, with two studies reporting increased prevalence of sleep-related difficulties characterized by insomnia and hypersomnia [35] and unpleasant dreams and enuresis [34], while three other studies reported no such associations [28, 38, 40]. Six studies (33%, of which, two used subjective measures and four used objective measures) considered the relationships between mood and sleep [28, 31, 37–39, 42]. One objective paper reported sleep difficulties in TS, ADHD, and TS + ADHD groups positively correlating with inattention problems and negatively correlating with performance IQ [32]. Regardless of study type, significant correlations between sleep-related problems and quality of life, internalizing symptoms and/or anxiety were reported in all but four studies [28, 32, 36, 40]. Methodological quality Total methodological quality ratings are provided in Table 1. Of a maximum of 14, scores ranged from 3 [35] to 11 [28, 30, 31, 33]. The average quality rating was 7.8, which is just over 50% of the maximum score. This suggests that the available research is of moderate quality, although highly heterogeneous. Items on which particularly low ratings were received included not adequately representing the TS population (i.e. single recruitment site or only specialist clinics; 14 studies), not justifying or commenting on sample size (16 studies) and not stating number to refuse participation (14 studies). Discussion This review aimed to summarize existing studies of sleep difficulty in children with TS/CTD by focusing on the types and frequencies of sleep problems in this population and considering other factors that might affect sleep. Overall, a high prevalence of sleep difficulties in this group was identified in studies using both objective and subjective measures, with sleep affected by factors such as comorbidity, medication use, age and gender. The studies varied in terms of methodology, sample characteristics, and quality, with current findings based on a small and heterogeneous set of papers. The significance of these findings for understanding sleep problems in children with TS/CTD will be considered, along with suggestions for future research and potential clinical implications. Types and frequency of sleep difficulties in children with TS/CTD The results presented here support previous suggestions that children with TS/CTD experience more sleep-related difficulties and have a higher prevalence of clinically significant sleep problems than typically developing control participants [35, 40, 41, 43]. However, findings were less consistent about the type of difficulties experienced. Regardless of study type, sleep initiation was not found to be a specific problem [31, 35], although parents reported that before sleep children with TS/CTD felt anxious and required reassurance [38]. Furthermore, while evidence for objective difficulties with sleep maintenance was inconclusive, parents tended to report that children with TS/CTD experienced fragmented sleep and sleep-interfering behaviors (e.g. nightmares, sleepwalking, bruxism, sleep-talking [38, 42]. Once asleep, PSG studies found that children with TS/CTD appeared to sleep less efficiently than healthy controls, although no relationship was reported between sleep efficiency and tic severity [28, 31]. Nevertheless, increased levels of arousal and movement have been reported for some children with TS/CTD compared to healthy controls [29]. Across a number of objective and subjective studies no increased prevalence of specific movement disorders was found (e.g. PLMS [28, 30, 31, 33]), though one study found an increased risk of unspecified sleep disorders (ICD-9-CM diagnosis) in TS/CTD relative to healthy controls [37]. Thus, although this group clearly do experience a high level of sleep difficulty, the current evidence base does not seem to highlight a clear pattern of sleep difficulties in children with TS/CTD. To date, there are also no studies that explicitly explore the relationship between specific daytime tics and movements or arousals exhibited during sleep, which would be a helpful direction for future research. Association between sample characteristics and sleep problems The impact of age on sleep problem severity was mixed. Some objective papers suggested younger participants being most at risk [33, 42], whereas others reported increased sleep difficulties over time [36, 37]. Gender also appeared to impact on sleep problem severity with females being most at risk [42], in which one study reflected early adolescent females with moderate/severe TS/CTD having significantly fewer nights of sufficient sleep compared to males [40]. It is unclear why this is, it may reflect methodological differences [48], though consideration also needs to be given to pubertal changes that have been reported to contribute to an increase in sleep problems and alterations in sleep patterns for girls during typical development [49, 50]. These relationships might be partially mediated by a third variable, such as tic severity and/or co-occurring psychopathology. The evidence for a relationship between tic severity and sleep was inconclusive overall [28]; however, increased motor tic severity was associated with poorer sleep in one subjective study [42]. Psychopathology also appeared related to sleep difficulties with some evidence that children with co-occurring anxiety or obsessive-compulsive symptoms might be most at risk [37, 38, 42]. However, other studies found that number, rather than type of co-occurring diagnosis, was related to sleep difficulty, such that an increase in co-occurring difficulties of any type were linked to increased sleep-related problems [39]. In terms of externalizing and behavioral difficulties, in objective studies, it appeared that difficulties with sleep maintenance, increased sleep stage latencies and REM sleep changes were more clearly linked with ADHD than TS [30, 31]. Notably, no significant changes in latency or duration of sleep stages or number of sleep cycles were associated with “pure” tic disorders [28]. However, REM sleep proportion was found to be positively correlated with inattention scores in TS-only, as well as ADHD and TS + ADHD, respectively [32]. Nevertheless, children with TS/CTD + ADHD showed changes to REM sleep latency and duration, and these differences were directly associated with severity and type of ADHD symptoms [30, 31]. Externalizing behaviors were positively associated with sleep problems in TS based on scores for ADHD characteristics such as hyperactivity and delinquency [33]. Furthermore, sleep-related movement disorders (PLMS and SDB) did not tend to present in children with TS-only, mostly occurring in those with comorbid ADHD [28, 30, 31, 33]. This may further support the link between ADHD and sleep in the context of TS/CTD. Evidence for associations between sleep-related problems and medication-usage were unclear from the available evidence. This may partially be due to the lack of full disclosure of medication status in 64.3% of included studies [29, 35, 37, 39, 41–45]. A possible hypothesis might be that links between sleep and medication-use in TS/CTD are mediated by pre-existing psychopathology. For instance, the association between anti-depressants and nightmares [34] could be due to the high levels of insomnia and nightmares in people with depression [51], rather than TS/CTD specifically. Methodologies of previous studies The quality of included studies was mostly moderate; however, scores were highly heterogeneous. Only one study followed suggested best practice by using both an objective and subjective measure of sleep [28]. More studies used subjective than objective approaches (66.7% vs 33.3%), which may have biased the results through factors commonly affecting questionnaire studies (e.g. expectations [21]). In support of this, differences were identified between studies using different types of measures. For instance, while children with TS/CTD did not experience objective difficulties with sleep maintenance [35], subjectively, parents reported that children with TS/CTD had significant difficulties staying asleep [44]. Limitations of previous research The reviewed studies had various limitations. There was significant heterogeneity in all aspects of the studies discussed here so the present conclusions should be considered tentatively. Furthermore, the average quality rating for included studies was moderate, with the majority of studies individually scored as moderate or lower. This further impacts on the strength of the present conclusions. Many included studies used novel scales to assess sleep (sometimes referred to as “franken-scales” [22]), which limits generalisability and comparability of findings across studies. Additionally, 77.7% of included studies (of which, five used objective measures and seven used subjective measures) recruited either partially or entirely through specialist clinics, which may mean that these participants have more severe TS/CTD than is seen in the general community [11]. Recruitment bias may, therefore, increase the chance and severity of difficulties and thus potentially result in overestimation of rates of sleep difficulties compared to the wider population of children with TS/CTD. Finally, most studies were correlational in nature which does not allow consideration of the causality of sleep difficulties in TS/CTD. Areas for further study The limited evidence meeting criteria for the present review suggests a need for more research considering the nature of sleep difficulties in children with TS/CTD. It has been suggested that such a complex construct as sleep cannot be adequately assessed using a single approach [22]. Future studies should aim to use both subjective and objective measures, along with questionnaires with multiple informants, where possible. The use of standardized measures, such as the parent-report Children’s Sleep Habits Questionnaire (CSHQ [52]), or the self-report Dysfunctional Beliefs about Sleep questionnaire (DBAS [53]) may be particularly useful to support comparison across studies and provide information of direct relevance to clinical practice. Furthermore, only one study explicitly assessed perceptions of the impact of sleep difficulties on everyday functioning [35]. Characterizing daytime consequences of sleep problems is of relevance to clinical practice, as a better understanding of functional domains that may be at risk in children with poor sleep would support the development of more targeted interventions for these children. Of note, none of the included studies used actigraphy. Six of the 18 studies included in this review used PSG; however, this provides a less naturalistic reflection of sleep [54]. Children with TS/CTD were reported to move more during the night than controls [29], therefore using actigraphy to measure movements during sleep might provide valuable information about sleep quality and nighttime disturbance in children with TS/CTD. Recruitment bias was also identified as a problem for many studies included in this review, with reliance on tertiary level clinical samples potentially overestimating the nature and degree of sleep difficulties. Future studies should aim to explore sleep difficulties using more varied cohorts of TS/CTD patients, for instance by recruiting through community clinics or charities. Furthermore, in line with the general male predominance of TS/CTD, females were under-represented in all samples. Although clinically-representative, this limits the strength of gender comparisons, which is of interest given that two studies in the current review found females to be at increased risk of sleep problems than males [40, 42]. Samples including a higher proportion of females would also allow hypotheses regarding the influence of hormonal changes on sleep in TS/CTD to be evaluated. Finally, although many studies in the present review noted ADHD prevalence, few considered other psychopathological comorbidities that are known to contribute to sleep problems in other clinical samples (e.g. depression [55]). Thus, further evaluation of the potentially mediating effects of psychopathology would be of interest to help determine whether different patterns of sleep difficulties are seen in patients with different TS presentations (e.g. pure TS vs TS-plus vs full-blown TS). Limitations of the current review The current findings should be considered within the context of various limitations. Although broad search criteria were applied, it is possible that some eligible studies could have been missed. Inclusion criteria was also fairly strict, excluding studies that were not peer-reviewed or written in English. This led to exclusion of a number of potentially-relevant abstracts, conference proceedings and posters, with a further 10 papers excluded that could not be accessed by the researchers. This may have biased the included sample towards articles in higher-impact or more popular journals subscribed to by the researchers’ organizations. However, it is highly likely that excluded studies were unpublished or published in lesser known journals due to being of low quality and thus may have had limited relevance for the present review. Search criteria included “sleep disturbance” or “sleep disorder” but did not include specific names of sleep disorders (e.g. insomnia, RLS, OSA, PLMS, etc.), possibly resulting in some papers being missed from the review. Having used a different reviewer (R.S.B.) to conduct the systematic review for papers between 2016 and 2019, this may have affected the inter-rater reliability of the review. However, every effort was made to make sure that procedures were standardized and the Chief Investigator was involved in both phases. Clinical implications Despite the aforementioned limitations, it was consistently found that children with TS/CTD (irrespective of co-occurring diagnoses) were frequently reported to experience significant sleep-related difficulties. This highlights the need for the routine screening of sleep problems during clinical assessments, with consideration of the type of problems reported (e.g. onset, maintenance) and contributing factors (e.g. behavior, mood, tics). The routine provision of information about good sleep hygiene would help support the management of sleep-related problems, with consideration of pharmacological interventions (e.g. melatonin) if this is ineffective. Children with TS/CTD were reported to experience pre-sleep anxiety [38], with children with TS/CTD and comorbid anxiety at particular risk of sleep problems [37, 42]. Again, behavioral or cognitive-behavioral interventions are likely to be most effective, which could include training in relaxation strategies to help manage pre-sleep anxiety. Clinicians also need to consider the impact of tics on sleep and whether this may contribute to problems with sleep onset, for which children may require more targeted tic-specific support. Conclusions This review has summarized the current evidence regarding sleep difficulties in children with TS/CTD to explore whether any specific difficulties could be considered disorder-specific. The available research is limited, highly heterogeneous, and mostly of moderate quality, but does suggest increased prevalence of sleep difficulties in TS/CTD compared to healthy controls. Some differences appear to be present between this and other clinical groups (e.g. ADHD), which may be indicative of different etiology of sleep difficulties between conditions. 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Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Arazi,, Ayelet;Meiri,, Gal;Danan,, Dor;Michaelovski,, Analya;Flusser,, Hagit;Menashe,, Idan;Tarasiuk,, Ariel;Dinstein,, Ilan

doi: 10.1093/sleep/zsz309pmid: 31848619

Abstract Study Objectives Sleep disturbances and insomnia are highly prevalent in children with Autism Spectrum Disorder (ASD). Sleep homeostasis, a fundamental mechanism of sleep regulation that generates pressure to sleep as a function of wakefulness, has not been studied in children with ASD so far, and its potential contribution to their sleep disturbances remains unknown. Here, we examined whether slow-wave activity (SWA), a measure that is indicative of sleep pressure, differs in children with ASD. Methods In this case-control study, we compared overnight electroencephalogram (EEG) recordings that were performed during Polysomnography (PSG) evaluations of 29 children with ASD and 23 typically developing children. Results Children with ASD exhibited significantly weaker SWA power, shallower SWA slopes, and a decreased proportion of slow-wave sleep in comparison to controls. This difference was largest during the first 2 hours following sleep onset and decreased gradually thereafter. Furthermore, SWA power of children with ASD was significantly negatively correlated with the time of their sleep onset in the lab and at home, as reported by parents. Conclusions These results suggest that children with ASD may have a dysregulation of sleep homeostasis that is manifested in reduced sleep pressure. The extent of this dysregulation in individual children was apparent in the amplitude of their SWA power, which was indicative of the severity of their individual sleep disturbances. We, therefore, suggest that disrupted homeostatic sleep regulation may contribute to sleep disturbances in children with ASD. Autism Spectrum Disorder, sleep disorders, slow-wave activity, sleep homeostasis, EEG Statement of Significance Sleep disturbances are apparent in 40%–80% of children with autism. Homeostatic sleep regulation, a mechanism that increases the pressure to sleep as a function of prior wakefulness, has not been studied in children with autism. Here, we compared Polysomnography exams of 29 children with autism and 23 matched controls. We found that children with autism exhibited reduced slow-wave-activity power and shallower slopes, particularly during the first 2 hours of sleep. This suggests that they develop less pressure to sleep. Furthermore, the reduction in slow-wave-activity was associated with the severity of sleep disturbances as observed in the laboratory and as reported by parents. We, therefore, suggest that disrupted homeostatic sleep regulation may contribute to sleep disturbances of children with autism. Introduction Autism Spectrum Disorders (ASDs) are a family of heterogeneous neurodevelopmental disorders characterized by impairments in social interaction and by restricted interests and repetitive behaviors [1]. Sleep disturbances appear in 40%–80% of children with ASD [2–5], as compared with 20%–40% of typically developing children [6, 7]. Symptoms include prolonged sleep latency, shorter sleep duration, and increased wake periods during the night, as reported by both subjective parental questionnaires and actigraphy measures [8–14]. Poor sleep in children with ASD is associated with increased sensory sensitivities [15–18] and increased aberrant behaviors [18–21], which impair the quality of life of affected families [14]. Polysomnography (PSG) studies have corroborated the existence of sleep disturbances in children with ASD [22–28], but have yielded mixed results regarding potential abnormalities in sleep architecture. While some have reported that children with ASD exhibit decreased slow-wave sleep (SWS, stage N3) [22–25] or rapid eye movement (REM) [11, 22, 28] durations, others have not [23, 26–28]. It has been proposed that the sleep disturbances of children with ASD are caused by an interaction of several behavioral and physiological factors including anxiety [21], poor sleep hygiene [29], sensory hyper-sensitivities [15], abnormalities with the melatonin system (i.e. circadian rhythm) [30], and obstructive sleep apnea (OSA) [31]. To date, the potential contribution of disrupted sleep homeostasis to the emergence of insomnia in children with ASD has not been examined. Sleep homeostasis is a critical mechanism of sleep regulation that increases the pressure to sleep as a function of time spent awake [32, 33]. Larger sleep pressure generates deeper SWS, which can be quantified by the power of slow-wave activity (SWA, electroencephalogram [EEG] power in the Delta band, 0.75–4 Hz) [34]. Deep sleep is essential for proper cognitive function [35], stabilizing synaptic plasticity [36, 37], and enabling learning and memory consolidation [38]. To our knowledge, only three studies to date have quantified the amplitude of SWA in ASD and all were performed with high-functioning adolescents and adults [39–41]. Two of these studies reported significantly weaker SWA in the ASD group [39, 40], a difference that was particularly large during the first 2–3 hours of sleep. To evaluate potential impairments in the SWA of children with ASD, we examined PSG recordings from 29 children with ASD and 23 typically developing controls. We quantified SWA power, SWA slope, and traditional sleep staging in 1-hour segments from sleep onset, and assessed whether significant differences were apparent across groups during specific segments of sleep. In addition, we examined whether individual differences in SWA could explain differences in the severity of sleep disturbances as observed in the sleep laboratory and as reported by the parents at home. Methods Subjects We recruited 34 children with ASD (eight females), mean age 4.6 years old (range 1.9–7.8), from the National Autism Research Center of Israel [42], which is part of Ben Gurion University of the Negev, and located in Soroka University Medical Center (SUMC). Approximately 200 children are referred to SUMC from the community (i.e. education system, primary care, etc.) annually with suspicion of ASD and approximately 80% of these children receive a positive diagnosis. We randomly approached 70 families who received a positive diagnosis between April 2017 to August 2018, and 34 of them agreed to participate in the study. Families were recruited regardless of specific symptom severities. Informed consent was obtained from all parents of ASD children who were reimbursed for their participation. All children with ASD were referred by the research team to a PSG evaluation at the Sleep-Wake Disorder Unit of SUMC. We excluded five children with ASD from the final analysis due to poor PSG quality (n = 4) or evidence of Obstructive Sleep Apnea (OSA, n = 1). All children with ASD were diagnosed with autism, independently, by a physician and a developmental psychologist, according to DSM-5 [1] criteria. Of the 29 children in the final analysis, 26 children with ASD also completed the autism diagnostic observation schedule (ADOS) [43], the remaining three children did not complete the ADOS due to lack of availability. Mean ADOS scores were: Social Affect 14.9 ± 5, Restricted and Repetitive Behaviors 4.3 ± 1.7, and total score 19.2 ± 6.1. Parents of 27 children with ASD completed the Hebrew version of the child sleep habit questionnaire (CSHQ) [44, 45]. Parents of two additional children did not complete the questionnaire. CSHQ scores were compared to the mean scores of a large cohort of typically developing children without sleep problems [44]. The biggest sleep concerns for the ASD children involved bedtime resistance, sleep anxiety, and excessive daytime sleepiness (Table 1). Table 1. Sleep concerns in children with autism . Above mean . Below mean . Bedtime resistance 24 (89%) 3 (11%) Sleep anxiety 23 (85%) 4 (15%) Excessive daytime sleepiness 22 (81%) 5 (19%) Night waking 21 (78%) 6 (22%) Sleep onset delay 20 (74%) 7 (26%) Parasomnias 19 (70%) 8 (30%) Sleep duration 13 (48%) 14 (52%) Sleep disordered breathing 10 (37%) 17 (43%) . Above mean . Below mean . Bedtime resistance 24 (89%) 3 (11%) Sleep anxiety 23 (85%) 4 (15%) Excessive daytime sleepiness 22 (81%) 5 (19%) Night waking 21 (78%) 6 (22%) Sleep onset delay 20 (74%) 7 (26%) Parasomnias 19 (70%) 8 (30%) Sleep duration 13 (48%) 14 (52%) Sleep disordered breathing 10 (37%) 17 (43%) Sleep concerns as reported by parents of the 27 children with ASD who completed the CSHQ. The CSHQ scores sleep disturbances in 8 domains. We present the number of ASD cases (and percentage) who had scores that were above/below the mean score of previously published CSHQ scores from a large population of typically developing children [44]. Open in new tab Table 1. Sleep concerns in children with autism . Above mean . Below mean . Bedtime resistance 24 (89%) 3 (11%) Sleep anxiety 23 (85%) 4 (15%) Excessive daytime sleepiness 22 (81%) 5 (19%) Night waking 21 (78%) 6 (22%) Sleep onset delay 20 (74%) 7 (26%) Parasomnias 19 (70%) 8 (30%) Sleep duration 13 (48%) 14 (52%) Sleep disordered breathing 10 (37%) 17 (43%) . Above mean . Below mean . Bedtime resistance 24 (89%) 3 (11%) Sleep anxiety 23 (85%) 4 (15%) Excessive daytime sleepiness 22 (81%) 5 (19%) Night waking 21 (78%) 6 (22%) Sleep onset delay 20 (74%) 7 (26%) Parasomnias 19 (70%) 8 (30%) Sleep duration 13 (48%) 14 (52%) Sleep disordered breathing 10 (37%) 17 (43%) Sleep concerns as reported by parents of the 27 children with ASD who completed the CSHQ. The CSHQ scores sleep disturbances in 8 domains. We present the number of ASD cases (and percentage) who had scores that were above/below the mean score of previously published CSHQ scores from a large population of typically developing children [44]. Open in new tab The control group included 23 children who were identified retrospectively from children who were referred to PSG evaluation at the same SUMC unit and their discharge letter indicated that they did not have any clinical findings. Reasons for initial referral of these children included snoring (n = 18) or other sleep problems such as bedwetting and daytime sleepiness (n = 5). All of the control children were screened negative for neurological, psychiatric, or developmental disorders, using a detailed clinical history questionnaire that was completed by the parents. The Helsinki committee at SUMC approved this study, which was carried out under the guidelines of the Helsinki declaration. Polysomnography All parents were instructed to keep a regular sleep-wake schedule on the day of the PSG evaluation. The PSG study started at 08:30 pm and ended at 06:00 am on the following morning. Children were connected to a clinical PSG system (SomniPro 19 PSG, Deymed Diagnostic, Hronov, Czech Republic) by a technician with over 5 years of experience. All participants were connected to six EEG electrodes, (C3, C4, O1, O2, A1, and A2 according to the international 10–20 system; sampling frequency: 128Hz; resolution: 16 bit), EOG, EMG and ECG electrodes, abdomen and chest effort belts to measure respiratory activity, and an oxygen saturation sensor. In some cases, where the child did not cooperate, this procedure was completed after the child fell asleep, typically within 10 minutes of sleep onset. Four children with ASD were being treated with Melatonin, but did not take Melatonin on the day of the exam. Derivations C3/A2 and C4/A1 were used for sleep-stage scoring, which was determined blindly by one of the investigators (AT) according to the American Academy of Sleep Medicine criteria [46]. An Apnea-Hypopnea Index (AHI) was calculated as the number of respiratory events resulting in either arousal or oxygen desaturation of >4%, per hour of sleep [47]. EEG analysis Preprocessing Data were analyzed offline using MATLAB (Mathworks Inc. United States) and the EEGLAB toolbox [48]. EEG data was re-referenced to the bilateral mastoids, filtered using a 0.75 Hz FIR high-pass filter (cutoff frequency at −6 db: 0.37 Hz, transition bandwidth: 0.75 Hz) and 20 Hz FIR low-pass filter (cutoff frequency at −6 db: 22.5, transition bandwidth: 5 Hz), and then divided into consecutive 30-second epochs. Epochs with manually identified artifacts such as movement or muscles contractions were removed (mean percentage of removed epochs in the ASD group: 18.9%, and in controls: 16.5%). EEG data analysis Each 30-second epoch was subdivided into consecutive 4-second segments with a 2-second overlap, using a hamming window. The power spectrum was computed for each 4-second segment using the FFT function as implemented in MATLAB (frequency resolution of 0.25 Hz) and then averaged across segments of each 30-second epoch. Absolute power was calculated for the Delta (1–4 Hz), Theta (4–8 Hz), Alpha (8–13 Hz), and Beta (13–20 Hz) frequency bands as the sum of power across these frequencies. To compute the slope of SWA, we re-filtered the EEG data using a 0.75–4 Hz band-pass filter. Slow waves were identified in each 30-second epoch as negative peaks, with subsequent zero crossing, that were separated by 0.25–1 second. We calculated the slope of each wave as the amplitude of the negative peak divided by time to the next zero crossing (i.e. ascending slope) and then computed the mean slope across all waves in each 30-second epoch. Descending slopes were also computed, as the negative peak divided by the time to the previous zero crossing. Both ascending and descending slope measures yielded equivalent results and only ascending slopes are reported in the manuscript. Statistical analysis Statistical analysis was performed using MATLAB (Mathworks Inc. United States). SWA power, SWA slope, or the percentage of sleep at each sleep stage (i.e. N2, N3, or REM) were analyzed using 2-way ANOVAs with group as one factor and time (i.e. hour since sleep onset) as a second factor. Additional comparisons across ASD and control groups were performed using two-tailed t-tests with unequal variance. We also computed the effect size for each comparison using Cohen’s d. When performing comparisons across groups for each frequency or each 1-hour segment of the night (from sleep onset), we used the false discovery rate (FDR) correction [49] to control for the multiple comparisons problem. The relationship between CSHQ scores and EEG power was assessed using Pearson’s correlations. The statistical significance of the correlation coefficients was tested with a randomization test where we shuffled the labels of the subjects before computing the correlation. We performed this procedure 10,000 times to generate a null distribution for each relationship and assessed whether the true correlation coefficient was higher than the 97.5th percentile or lower than the 2.5th percentile of this null distribution (equivalent to p = 0.05 in a two-tailed t-test). Results Children with ASD spent significantly less time in bed, and their total sleep time was shorter than that of controls (Table 2). Sleep efficiency, percentage of wake after sleep onset (WASO), sleep latency, and arousal index did not differ across groups. The mean apnea-hypopnea index (AHI) was <1 in both groups, demonstrating that none of the participating children had OSA. Note that ~50% of the ASD children and 17% of the controls were connected to the PSG apparatus only after sleep onset. As a result, these PSG recordings began approximately 10 minutes after the children fell asleep. This is a common issue in PSG studies with ASD children, where measures of time in bed, sleep latency, and sleep efficiency are often distorted due to poor cooperation at the initiation of the exam. Table 2. Sleep characteristics in children with ASD and controls . Control (mean ± SD) . ASD (mean ± SD) . p-value . N 23 (8 females) 29 (8 females) Age (years) 5.3 ± 1.5 4.6 ± 1.7 0.16 Time in bed (minutes) 427 ± 40 391 ± 70 0.02* Total sleep time (minutes) 403 ± 38 362 ± 72 0.03* Sleep efficiency (%) 94.3 ± 3 92.5 ± 7 0.24 Sleep latency (minutes) 9.8 ± 8.5 7.5 ± 8.4 0.33 WASO (%) 3.7 ± 2.6 6.9 ± 10.1 0.16 Arousal index (events\hour) 10.7 ± 4.4 11.5 ± 3.6 0.44 AHI (events\hour) 0.43 ± 0.39 0.39 ± 0.35 0.69 Sleep stage N2 (%) – whole night 52.8 ± 7.3 57.8 ± 8.7 0.03* Sleep stage N3 (%) – whole night 29.2 ± 5.2 27.1 ± 9.1 0.33 Sleep stage REM (%) – whole night 17.5 ± 3.9 14.9 ± 5.4 0.06 Sleep stage N2 (%) – first half 40.1 ± 8.8 49.8 ± 12.5 0.003* Sleep stage N2 (%) – second half 64.2 ± 10.5 65.8 ± 9.6 0.66 Sleep stage N3 (%) – first half 50.5 ± 10.1 39.4 ± 12.4 0.001* Sleep stage N3 (%) – second half 10.1 ± 7.9 15.2 ± 10.6 0.06 Sleep stage REM (%) – first half 8.5 ± 5.6 10.6 ± 5.5 0.18 Sleep stage REM (%) – second half 25.6 ± 8 19.1 ± 8.5 0.007* . Control (mean ± SD) . ASD (mean ± SD) . p-value . N 23 (8 females) 29 (8 females) Age (years) 5.3 ± 1.5 4.6 ± 1.7 0.16 Time in bed (minutes) 427 ± 40 391 ± 70 0.02* Total sleep time (minutes) 403 ± 38 362 ± 72 0.03* Sleep efficiency (%) 94.3 ± 3 92.5 ± 7 0.24 Sleep latency (minutes) 9.8 ± 8.5 7.5 ± 8.4 0.33 WASO (%) 3.7 ± 2.6 6.9 ± 10.1 0.16 Arousal index (events\hour) 10.7 ± 4.4 11.5 ± 3.6 0.44 AHI (events\hour) 0.43 ± 0.39 0.39 ± 0.35 0.69 Sleep stage N2 (%) – whole night 52.8 ± 7.3 57.8 ± 8.7 0.03* Sleep stage N3 (%) – whole night 29.2 ± 5.2 27.1 ± 9.1 0.33 Sleep stage REM (%) – whole night 17.5 ± 3.9 14.9 ± 5.4 0.06 Sleep stage N2 (%) – first half 40.1 ± 8.8 49.8 ± 12.5 0.003* Sleep stage N2 (%) – second half 64.2 ± 10.5 65.8 ± 9.6 0.66 Sleep stage N3 (%) – first half 50.5 ± 10.1 39.4 ± 12.4 0.001* Sleep stage N3 (%) – second half 10.1 ± 7.9 15.2 ± 10.6 0.06 Sleep stage REM (%) – first half 8.5 ± 5.6 10.6 ± 5.5 0.18 Sleep stage REM (%) – second half 25.6 ± 8 19.1 ± 8.5 0.007* Sleep characteristics in children with ASD and controls. WASO: wake after sleep onset, AHI: apnea-hypopnea index. REM: repaid eye-movement. *Significant difference as assessed by a two-tailed t-test (p < 0.05). Open in new tab Table 2. Sleep characteristics in children with ASD and controls . Control (mean ± SD) . ASD (mean ± SD) . p-value . N 23 (8 females) 29 (8 females) Age (years) 5.3 ± 1.5 4.6 ± 1.7 0.16 Time in bed (minutes) 427 ± 40 391 ± 70 0.02* Total sleep time (minutes) 403 ± 38 362 ± 72 0.03* Sleep efficiency (%) 94.3 ± 3 92.5 ± 7 0.24 Sleep latency (minutes) 9.8 ± 8.5 7.5 ± 8.4 0.33 WASO (%) 3.7 ± 2.6 6.9 ± 10.1 0.16 Arousal index (events\hour) 10.7 ± 4.4 11.5 ± 3.6 0.44 AHI (events\hour) 0.43 ± 0.39 0.39 ± 0.35 0.69 Sleep stage N2 (%) – whole night 52.8 ± 7.3 57.8 ± 8.7 0.03* Sleep stage N3 (%) – whole night 29.2 ± 5.2 27.1 ± 9.1 0.33 Sleep stage REM (%) – whole night 17.5 ± 3.9 14.9 ± 5.4 0.06 Sleep stage N2 (%) – first half 40.1 ± 8.8 49.8 ± 12.5 0.003* Sleep stage N2 (%) – second half 64.2 ± 10.5 65.8 ± 9.6 0.66 Sleep stage N3 (%) – first half 50.5 ± 10.1 39.4 ± 12.4 0.001* Sleep stage N3 (%) – second half 10.1 ± 7.9 15.2 ± 10.6 0.06 Sleep stage REM (%) – first half 8.5 ± 5.6 10.6 ± 5.5 0.18 Sleep stage REM (%) – second half 25.6 ± 8 19.1 ± 8.5 0.007* . Control (mean ± SD) . ASD (mean ± SD) . p-value . N 23 (8 females) 29 (8 females) Age (years) 5.3 ± 1.5 4.6 ± 1.7 0.16 Time in bed (minutes) 427 ± 40 391 ± 70 0.02* Total sleep time (minutes) 403 ± 38 362 ± 72 0.03* Sleep efficiency (%) 94.3 ± 3 92.5 ± 7 0.24 Sleep latency (minutes) 9.8 ± 8.5 7.5 ± 8.4 0.33 WASO (%) 3.7 ± 2.6 6.9 ± 10.1 0.16 Arousal index (events\hour) 10.7 ± 4.4 11.5 ± 3.6 0.44 AHI (events\hour) 0.43 ± 0.39 0.39 ± 0.35 0.69 Sleep stage N2 (%) – whole night 52.8 ± 7.3 57.8 ± 8.7 0.03* Sleep stage N3 (%) – whole night 29.2 ± 5.2 27.1 ± 9.1 0.33 Sleep stage REM (%) – whole night 17.5 ± 3.9 14.9 ± 5.4 0.06 Sleep stage N2 (%) – first half 40.1 ± 8.8 49.8 ± 12.5 0.003* Sleep stage N2 (%) – second half 64.2 ± 10.5 65.8 ± 9.6 0.66 Sleep stage N3 (%) – first half 50.5 ± 10.1 39.4 ± 12.4 0.001* Sleep stage N3 (%) – second half 10.1 ± 7.9 15.2 ± 10.6 0.06 Sleep stage REM (%) – first half 8.5 ± 5.6 10.6 ± 5.5 0.18 Sleep stage REM (%) – second half 25.6 ± 8 19.1 ± 8.5 0.007* Sleep characteristics in children with ASD and controls. WASO: wake after sleep onset, AHI: apnea-hypopnea index. REM: repaid eye-movement. *Significant difference as assessed by a two-tailed t-test (p < 0.05). Open in new tab Small differences in sleep architecture across groups were found when examining the proportion of sleep stages throughout the entire night. Children with ASD exhibited a significantly higher percentage of N2 sleep (p = 0.03, Cohen’s d = 0.61; Table 2), and a marginally significant reduction in the percentage of REM sleep (p = 0.06, Cohen’s d = 0.54). Larger differences across groups were revealed when dividing the sleep period of each child into two equal halves. Children with ASD had a significantly larger percentage of N2 sleep (p = 0.003, Cohen’s d = 0.87) and lower percentage of N3 sleep (p = 0.001, Cohen’s d = 0.96) during the first half of the sleep period as well as a lower percentage of REM sleep (p = 0.007, Cohen’s d = 0.79) during the second half of the sleep period. Differences in Delta power across groups We computed the spectral power of each frequency band in all artifact-free epochs. We then computed the mean power across all epochs from each sleep stage (i.e. N2, N3, or REM), and compared the findings across children from the two groups. We focused our analyses of EEG power on occipital electrodes given that SWA at the examined ages is maximal in occipital cortex [50]. Children with ASD exhibited significantly weaker power in the Delta (i.e. SWA) and Beta bands during epochs of N3 sleep (Figure 1D). Spectral power in N2 and REM epochs did not differ significantly across groups (Figure 1, B and F). Performing the same analyses with the central electrodes did not reveal any significant differences across groups in any of the sleep stages. Figure 1. Open in new tabDownload slide Absolute EEG power for ASD and control groups. Each panel represents the mean power across subjects for the control (blue) and ASD (red) groups during sleep stage N2 (A and B) sleep stage N3 (C and D) and REM sleep (E and F). Power was computed as the mean across all artifact-free epochs from each sleep stage and plotted in 0.25Hz bins (A, C, and E) or averaged within the Delta, Theta, Alpha, and Beta frequency bands (B, D, and F). Error bar: standard error of the mean across subjects. *Significant difference across groups (two-tailed t-test, p < 0.05, FDR correction). Figure 1. Open in new tabDownload slide Absolute EEG power for ASD and control groups. Each panel represents the mean power across subjects for the control (blue) and ASD (red) groups during sleep stage N2 (A and B) sleep stage N3 (C and D) and REM sleep (E and F). Power was computed as the mean across all artifact-free epochs from each sleep stage and plotted in 0.25Hz bins (A, C, and E) or averaged within the Delta, Theta, Alpha, and Beta frequency bands (B, D, and F). Error bar: standard error of the mean across subjects. *Significant difference across groups (two-tailed t-test, p < 0.05, FDR correction). SWA dynamics across the night The power and slope of SWA (i.e. activity in the Delta band, 1–4 Hz) decreased gradually during the night in a manner that corresponded to changes in the proportion of the different sleep stages (Figure 2). These dynamics were similarly apparent in individuals of both groups, yet children with ASD exhibited weaker SWA power, particularly in the first 2 hours of sleep. Figure 2. Open in new tabDownload slide Examples of slow-wave-activity dynamics across the night from one child with ASD (red, right column) and one child from the control group (blue, left column). (A and B) Hypnogram with manual scoring of the different sleep stages in 30-second epochs. (C and D) Time courses of SWA power throughout the night. (E and F) SWA slopes throughout the night. Traces in C–F represent the mean across the two occipital electrodes (O1 and O2). Figure 2. Open in new tabDownload slide Examples of slow-wave-activity dynamics across the night from one child with ASD (red, right column) and one child from the control group (blue, left column). (A and B) Hypnogram with manual scoring of the different sleep stages in 30-second epochs. (C and D) Time courses of SWA power throughout the night. (E and F) SWA slopes throughout the night. Traces in C–F represent the mean across the two occipital electrodes (O1 and O2). To quantify differences in SWA across groups during specific sleep segments, we divided the night into 1-hour segments, from sleep onset to the end of the PSG evaluation/recording (Figure 3, A and B). SWA power and slope were computed in each 1-hour segment using all non-REM sleep epochs. A two-way ANOVA analysis revealed that SWA power differed significantly across groups (p = 0.0008) and over time (p = 0.5 × 10−30), with a significant interaction across the two (p = 0.02). SWA slope also differed significantly across groups (p = 0.001) and over time (p = 0.1 × 10−29), with a significant interaction across the two (p = 0.02). Follow-up comparisons within specific sleep segments using two-tailed t-tests (FDR corrected for multiple comparisons) revealed that children with ASD exhibited significantly weaker SWA power (first hour: p = 0.018, Cohen’s d = 0.83, second hour: p = 0.017, Cohen’s d = 0.87) and shallower SWA slopes (first hour: p = 0.022, Cohen’s d = 0.8, second hour: p = 0.022, Cohen’s d = 0.85) during the first 2 hours of sleep. No significant differences were found during the rest of the sleep period. We found similar trends when analyzing data from the central electrodes, but the differences across groups were not statistically significant. Figure 3. Open in new tabDownload slide Differences in SWA and percentage of sleep stages across ASD and control groups in 1-hour segments from sleep onset. Power (A) and slope (B) of SWA were computed for each hour of the sleep period, starting at sleep onset, for children with ASD (red) and controls (blue). Percentage of each sleep stage were also computed for each hour (C). Error bars: standard error of the mean across subjects. *Significant differences across groups (two-tailed t-test; p < 0.05, FDR correction). Figure 3. Open in new tabDownload slide Differences in SWA and percentage of sleep stages across ASD and control groups in 1-hour segments from sleep onset. Power (A) and slope (B) of SWA were computed for each hour of the sleep period, starting at sleep onset, for children with ASD (red) and controls (blue). Percentage of each sleep stage were also computed for each hour (C). Error bars: standard error of the mean across subjects. *Significant differences across groups (two-tailed t-test; p < 0.05, FDR correction). In a complementary analysis, we compared the percentage of sleep stages in the same 1-hour sleep segments (Figure 3C) using two-tailed t-tests (FDR corrected). Children with ASD exhibited significantly less N3 sleep during the first hour of sleep (p = 0.05, Cohen’s d = 0.78), and marginally significant differences during the second hour (p = 0.12, Cohen’s d = 0.54) as well as a corresponding increase in stage N2 sleep which was marginally significant during the first 2 hours of sleep (first hour: p = 0.1, Cohen’s d = 0.63; second hour: p = 0.1, Cohen’s d = 0.65). Interestingly, ASD children exhibited significantly more REM sleep during the first hour of sleep (p = 0.03, Cohen’s d = 0.84) and a reversed marginally significant trend during the end of the sleep period, where they exhibited relatively less REM sleep than controls. Previous PSG studies in children and adolescents have reported that SWA power increases with age from early childhood to adolescence and then declines throughout adulthood [34, 51]. To control for differences in the power and slope of SWA, which may arise from differences in the mean age of the control and ASD groups, we performed an identical analysis with a subset of children who were tightly matched for age. This included 21 children with autism (mean age: 5 ± 1.5 years) and 21 controls (mean age: 5 ± 1.3 years). A two-way ANOVA analysis revealed that SWA power differed significantly across groups (p = 0.0001) and over time (p = 0.2 × 10−24), with a marginally significant interaction across the two (p = 0.06). Similarly, SWA slope differed significantly across groups (p = 0.0002) and over time (p = 0.2 × 10−23), with a significant interaction across the two (p = 0.05). Follow up comparisons of each 1-hour sleep segment using two-tailed t-tests (FDR corrected for multiple comparisons) revealed that children with ASD exhibited significantly weaker SWA power (first hour: p = 0.034, Cohen’s d = 0.78; second hour: p = 0.035, Cohen’s d = 0.83; third hour: p = 0.035, Cohen’s d = 0.78) and shallower SWA slopes (first hour: p = 0.033, Cohen’s d = 0.87; second hour: p = 0.033, Cohen’s d = 0.84; third hour: p = 0.034, Cohen’s d = 0.79) during the first 3 hours of sleep (Figure 4, A and B). Figure 4. Open in new tabDownload slide Differences in SWA across groups when tightly matching the children’s age (A and B) and when including only children whose PSG recording started before sleep onset (C and D). Power (A and C) and slope (B and D) of SWA were computed for each hour of the sleep period, starting at sleep onset, for children with ASD (red) and controls (blue). Error bars: standard error of the mean across subjects. *Significant differences across groups (two-tailed t-test; p < 0.05, FDR corrected). Figure 4. Open in new tabDownload slide Differences in SWA across groups when tightly matching the children’s age (A and B) and when including only children whose PSG recording started before sleep onset (C and D). Power (A and C) and slope (B and D) of SWA were computed for each hour of the sleep period, starting at sleep onset, for children with ASD (red) and controls (blue). Error bars: standard error of the mean across subjects. *Significant differences across groups (two-tailed t-test; p < 0.05, FDR corrected). In 15 children with ASD and four controls, PSG recording began after the children fell asleep due to difficulties in connecting the EEG electrodes before sleep onset. As a result, a certain period of sleep was missing from the recordings of these children. To control for this potential bias, we performed identical analyses with the 14 ASD children (mean age: 5.6 ± 1.8 years old) and 19 controls (mean age: 5.6 ± 1.4 years old) whose recording began before they fell asleep (Figure 4, C and D). A two-way ANOVA analysis revealed significant differences of both SWA power and slope across groups (power: p = 0.003; slope: p = 0.004) and over time (power and slope: p = 0.8 × 10−22) with a marginally significant interaction across the two factors (power: p = 0.064; slope: p = 0.065). Subsequent comparisons using two-tailed t-test revealed significantly weaker SWA power and shallower slopes in children with ASD during the second hour of sleep (power: p = 0.01, Cohen’s d = 1.2; slope: p = 0.012, Cohen’s d = 1.2) and a marginally significant difference during the first hour of sleep (power: p = 0.08, Cohen’s d = 0.84; slope: p = 0.09, Cohen’s d = 0.82). Relationship between SWA and sleep disturbances in children with ASD We computed the correlation between SWA power in the first hour of sleep and the behavioral sleep scores that parents reported using the CSHQ. Note that this analysis examines the relationship between a single night of sleep in the lab and the parent reports of sleep problems at home. A significant negative correlation was found between bedtime resistance and SWA power (r(27) = −0.49, p = 0.01; Figure 5B) as well as a significant negative correlation between total sleep disturbances and SWA power (r(27) = −0.38, p = 0.05; Figure 5A). Furthermore, there was a significant negative correlation between the absolute time that children fell asleep in the lab and SWA power (r(29) = −0.42, p = 0.02, Figure 5D) as well as a negative nonsignificant trend between the child’s sleep onset time at home (according to parent report) and SWA power (r(27) = −0.31, p = 0.11, Figure 5C). These results suggest that weaker SWA power in children with ASD at the beginning of the night can partially explain individual problems of initiating sleep, resulting in late sleep onset. Figure 5. Open in new tabDownload slide Relationship between SWA power during the first hour of sleep and (A) total sleep disturbance score (B) bedtime resistance score (C) parental report of sleep onset time at home and (D) sleep onset time in the sleep lab. Each point represents a single subject. Pearson’s correlation coefficients and p-values are noted in each panel. Figure 5. Open in new tabDownload slide Relationship between SWA power during the first hour of sleep and (A) total sleep disturbance score (B) bedtime resistance score (C) parental report of sleep onset time at home and (D) sleep onset time in the sleep lab. Each point represents a single subject. Pearson’s correlation coefficients and p-values are noted in each panel. Discussion Our results reveal that a considerable number of young children with ASD exhibit weaker SWA power, shallower SWA slopes, and less N3 sleep during the first 2 hours of sleep (Figures 3 and 4). We interpret these findings as evidence for a reduction in the pressure to sleep in the ASD children. Moreover, SWA power during the first hour of sleep was significantly correlated with the severity of individual sleep disturbances and especially with sleep-onset difficulties (Figure 5). We, therefore, suggest that a disruption in sleep homeostasis may reduce sleep pressure in children with ASD and exacerbate difficulties with sleep initiation and sleep maintenance. Sleep disturbances in children with ASD Clinical sleep disturbances are apparent in the majority of ASD cases [2, 3, 5]. While some have reported that sleep problems are more common in children with lower IQ [52] or higher autism severity [21, 52], others have not [16, 20]. More consistent reports have shown that sleep problems are associated with increased self-injury, anxiety, and aggression [18–21] as well as with sensory sensitivities [15–18], thereby generating considerable challenges and difficulties for ASD children and their families [53]. Previous studies have proposed that these sleep disturbances are caused by heightened levels of anxiety [21], poor sleep hygiene [29], abnormalities with the melatonin system (i.e. circadian rhythm) [30], and obstructive sleep apnea (OSA) [31]. Our results suggest that disrupted sleep homeostasis may further contribute to sleep disturbances in children with ASD who do not have OSA. Note that similar disruptions of sleep homeostasis have also been proposed to underlie sleep problems in other populations with insomnia, such as aging adults [54]. In particular, our results reveal that quantifying SWA power during the first hour of sleep is informative for identifying children with reduced sleep pressure. Like children with OSA who are commonly identified with overnight PSG evaluations, and can benefit from effective targeted treatments [55], weak SWA during the first 2 hours of sleep may act as an indicator of children with ASD who may benefit from specific interventions with behavioral and pharmacological treatments that can increase the pressure to sleep [54, 56]. Quantifying SWA and percentage of sleep stages in children with ASD We believe that previous PSG studies in children with ASD have not reported the reduced SWA described in the current study for several reasons. First, previous studies did not quantify the amplitude of SWA but rather relied on manual sleep staging, which is a categorical measure of sleep depth with a very limited range. Our results showed that differences across groups were larger and clearer when quantifying SWA (Figure 3). Nevertheless, one may expect that reduced SWA would be apparent in smaller proportions of SWS (stage N3). This was indeed reported by some PSG studies [22–25], but not others [26–28]. Note that in our results, the proportion of SWS was not significantly different across groups when examining the entire sleep period (Table 2). It was only after we split the night into 1-hour segments following sleep onset, that considerable differences in the proportion of SWS emerged in the first 2 hours after sleep onset (Figure 3). This emphasizes a second key point: the importance of quantifying SWA and percentage of sleep stages in separate segments relative to sleep onset rather than averaging the measures across the entire sleep period. SWA power and the percentage of sleep stages change dramatically throughout the night [32] (Figure 2). Meaningful abnormalities in sleep regulation may, therefore, appear in specific sleep segments and be obscured by averaging measures throughout the entire sleep period. Moreover, since children with ASD often sleep less during PSG evaluations [28], the relative proportion of sleep stages across the entire night will be biased by their shorter sleep duration, which varies across studies. Taken together, these findings motivate using quantification of SWA power, rather than traditional sleep staging, and focusing on the first hour following sleep onset. This measure is not biased by potential differences in overall sleep duration during the PSG and represents an objective and quantitative marker of initial sleep pressure in individual children. The importance of deep SWS Contemporary sleep research highlights the importance of SWS for regulating the strength (i.e. the number and efficacy) of cortical synapses [36, 38, 57, 58], which is critical for proper cognitive function including learning and memory consolidation [34, 38, 56, 59, 60]. In typically developing individuals, the amplitude of SWS (i.e. SWA power) increases as a function of time spent awake as demonstrated by studies with sleep deprivation [32]. A remarkable finding in our study is that children with ASD did not exhibit this canonical relationship. Indeed, children with ASD who fell asleep later in the night exhibited weaker SWA (Figure 5). This suggests that the ASD children with the larger sleep onset disturbances had greater sleep homeostasis impairments, and these particular children are likely to benefit most from targeted therapy. Limitations An important limitation of the current study is that the ASD and control samples were not recruited in an identical manner. The ASD children were recruited prospectively from a clinical cohort of a large tertiary medical center and referred by the research team to PSG regardless of potential sleep concerns (Table 1). In contrast, the control PSG recordings were extracted retrospectively from the same tertiary medical center and included typically developing children who were referred to PSG with sleep concerns. For the current study, we only selected PSG recordings of control children who were referred with minor sleep concerns (mostly snoring problems) and completed the PSG without any clinical findings. Nevertheless, it would be important to validate the reported findings with prospectively recruited control children who do not have any sleep concerns. Conclusions The children with ASD who participated in the current study exhibited reduced SWA, which we interpret as an indication of weak sleep pressure. Moreover, the magnitude of this reduction was associated with individual severity of sleep disturbances and particularly with sleep-onset difficulties. A variety of existing behavioral and pharmacological interventions are available for enhancing sleep pressure (i.e. increasing sleep depth) [54], including mild interventions such as increased exercise [56]. Since improving sleep quality is likely to reduce aberrant behaviors [18–21] in children with ASD and reduce parental stress [53], the initiation of clinical trials with these interventions is highly warranted. Funding This work was supported by a SFARI explorer grant 439370, Israel Science Foundation grant 961/14, and an Israel Academy of Sciences Adams Fellowship to A.A. Conflict of interest statement. None declared. The manuscript has been posted on a preprint server (https://www.biorxiv.org/content/10.1101/706135v2l; doi:https://doi.org/10.1101/706135). References 1. American Psychiatric Association . Diagnostic and Statistical Manual of Mental Disorders . 5th ed. Washington, DC : American Psychiatric Publishing ; 2013 . 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Old brains come uncoupled in sleep: slow wave-spindle synchrony, brain atrophy, and forgetting . Neuron. 2018 ; 97 ( 1 ): 221 – 230.e4 . Google Scholar Crossref Search ADS PubMed WorldCat Author notes Contributed equally as first authors. © Sleep Research Society 2019. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Dergacheva,, Olga;Fleury-Curado,, Thomaz;Polotsky, Vsevolod, Y;Kay,, Matthew;Jain,, Vivek;Mendelowitz,, David

doi: 10.1093/sleep/zsz301pmid: 31832664

Abstract Obstructive sleep apnea (OSA) is a common disorder characterized by repetitive sleep-related losses of upper airway patency that occur most frequently during rapid eye movement (REM) sleep. Hypoglossal motoneurons play a key role in regulating upper airway muscle tone and patency during sleep. REM sleep activates GABA and glycine neurons in the ventral medulla (VM) to induce cortical desynchronization and skeletal muscle atonia during REM sleep; however, the role of this brain region in modulating hypoglossal motor activity is unknown. We combined optogenetic and chemogenetic approaches with in-vitro and in-vivo electrophysiology, respectfully, in GAD2-Cre mice of both sexes to test the hypothesis that VM GABA/glycine neurons control the activity of hypoglossal motoneurons and tongue muscles. Here, we show that there is a pathway originating from GABA/glycine neurons in the VM that monosynaptically inhibits brainstem hypoglossal motoneurons innervating both tongue protruder genioglossus (GMNs) and retractor (RMNs) muscles. Optogenetic activation of ChR2-expressing fibers induced a greater postsynaptic inhibition in RMNs than in GMNs. In-vivo chemogenetic activation of VM GABA/glycine neurons produced an inhibitory effect on tongue electromyographic (EMG) activity, decreasing both the amplitude and duration of inspiratory-related EMG bursts without any change in respiratory rate. These results indicate that activation of GABA/glycine neurons from the VM inhibits tongue muscles via a direct pathway to both GMNs and RMNs. This inhibition may play a role in REM sleep associated upper airway obstructions that occur in patients with OSA. hypoglossal motoneurons, GABA, glycine, ventral medulla REM sleep, obstructive apnea Statement of Significance Obstructive sleep apnea (OSA) is a sleep-related breathing disorder. Determining the mechanisms underlying the reduction of upper airway muscle activity during rapid eye movement (REM) sleep is pivotal to understanding the pathophysiological mechanisms and developing new targets to alleviate REM sleep-associated obstructive apneas in OSA patients. We found that activation of the GABA/glycine medullary region, known to play a key role in REM sleep control, inhibits tongue muscles via a direct pathway to both protruder- and retractor-innervating hypoglossal motoneurons in the brainstem and this inhibition may play a role in REM sleep-associated upper airway obstructions that occur in patients with OSA. These findings provide a foundation for developing novel pharmacological strategies to improve upper airway potency in OSA patients particularly during REM sleep. Introduction Obstructive sleep apnea (OSA) is a highly prevalent breathing disorder, characterized by repetitive airway obstructions during sleep [1, 2]. Rapid eye movement (REM) sleep is strongly associated with a higher incidence of obstructive events that also have a longer duration, more severe desaturations of oxyhemoglobin, greater hypoxemia, and increased severity of adverse effects on the cardiovascular system when compared to non-REM sleep [3–5]. REM sleep-related reduction of upper-airway muscle activity [6–8] is a likely major contributor to the increased severity of OSA during REM sleep [9]. Both protruder genioglossus and retractor (styloglossus and hyoglossus) muscles of the tongue increase their activity during inspiratory phase of the respiratory cycle and play an important role in maintaining upper airway patency [10–12]. The tongue muscles are innervated by the hypoglossal nerve, which contains axons of motoneurons located in the hypoglossal nucleus (12N) [10]. Neuromodulation of hypoglossal motoneurons during sleep is complex [13]. Recent studies demonstrated an important role of withdrawal of serotonergic [14] and noradrenergic [15] excitation as well as cholinergic inhibition [16] in the depression of hypoglossal motoneuron activity during REM sleep. In addition, an active inhibition of hypoglossal motoneurons has been shown during REM sleep via release of inhibitory neurotransmitters including gamma-aminobutyric acid (GABA) and glycine [17–19]. Previous studies showed that the ventral medulla (VM) contains REM sleep active neurons [20–22] and these neurons play a crucial role in skeletal muscle atonia [23] and cortical desynchronization [22] associated with REM sleep. In particular, GABA neurons in the VM have been reported to discharge in all behavioral states, including REM sleep, non-REM sleep and wakefulness (with increased activity during eating and grooming), however, these neurons exhibited the highest firing rate during REM sleep [22]. Thus, the inhibitory neurons in the VM play an important role in REM sleep control; however, the role of GABA/glycine neurons located in the VM that overlaps with REM sleep-controlling neurons, in modulating hypoglossal motor activity is unknown. In this study, we combined optogenetic and chemogenetic approaches with in-vivo and in-vitro electrophysiology to tests the hypothesis that inhibitory neurons from the VM suppress tongue muscle activity via direct GABAergic and glycinergic inhibitory synaptic pathways to hypoglossal motoneurons innervating tongue protruder genioglossus (GMNs) and retractor (RMNs) muscles. Methods Animals and ethnical approval Experiments were conducted in male and female Gad2-IRES-Cre knock-in mice (Gad2-cre, Jackson Lab). Animals were housed in the George Washington University animal care facility under standard environmental conditions. All animal procedures were performed in compliance with the institutional guidelines at George Washington University and are in accordance with the recommendations of the Panel on Euthanasia of the American Veterinary Medical Association and the National Institutes of Health Guide for the Care and Use of Laboratory Animals. All procedures were approved by the George Washington University Institutional Animal Care and Use Committee. HMNs labeling and viral injections into the VM Gad2-Cre mice (postnatal day 5) were anesthetized with hypothermia. The tongue was maximally exposed and the genioglossus muscles were injected with the retrograde tracer cholera toxin subunit B conjugated Alexa Fluor 555 (CTB, 20 nl, Invitrogen, Carisban, CA). CTB delivery to the genioglossus was performed by accessing the muscle in the posterior tongue dorsum (caudally to the intermolar eminence), in a close proximity to the midline, 0.3 mm deep. CTB delivery to the retractor muscles (styloglossus and hyoglossus) was performed by an injection of CTB (20 nl) into the posterior-lateral aspects of the tongue, 0.3 mm deep. Consistent with previous publications [24, 25], CTB injections into the retractor muscles and genioglossus muscles resulted in differential fluorescent expression in motoneurons located in the dorsal compartment and ventrolateral compartment of the 12N, respectively. Viral vectors used in this study included AAV1.EF1a.DIO.hChR2(H134R)-EYFP.WPRE.hGH, Penn Vector Core, Philadelphia, PA (ChR2-EYFP) and pAAV-hSyn-DIO-hM3D(Gq)-mCherry, Addgene, Watertown, MA (hM3D-mCherry). Stereotaxic surgeries were performed in Gad2-Cre mice (P5) anesthetized with hypothermia and mounted in a stereotactic apparatus with a neonatal adapter (Stoelting). The skull was exposed and a small burr hole was made to position a pulled calibrated pipette (VWR, Radnor, PA) containing viral vector. Viral vectors were injected into the VM at the following coordinates: 3.15–3.22 mm posterior and 0.37 mm lateral relative to Lambda. The pipette tip was lowered 2.8–3.2 mm from the dorsal surface of the brain and 30 nl of the viral vector was slowly injected. The stereotactic coordinates were verified after each electrophysiological experiment for accuracy of the injection sites. Animals with ChR2-EYFP or hM3D-mCherry expression outside of the VM were eliminated from the study. A Representative image of ChR2-EYFP expression limited to the VM is shown in Figure 1A, while a schematic drawing of injection sites is illustrated in Figure 1B. Figure 1. Open in new tabDownload slide Identification and optogenetic stimulation of GABA neurons in the VM. (A) Fluorescent image of brainstem slice shows the site of viral injection. EYFP labeling (green) was expressed exclusively within the VM following stereotaxic microinjection of AVV1-ChR2-EYFP (30 nl) in a Gad2-Cre mouse pup (P5). LPGi, lateral paragigantocellular nucleus; py, pyramidal tract; Amb, nucleus ambiguus; 12N, hypoglossal nucleus; 4V, fourth ventricle (here and in B). Scale bar, 0.5 mm. (B) Schematic drawing of viral injection sites shown in sagittal plane. DPGi, dorsal paragigantocellular nucleus; Gi, gigantocellular reticular nucleus; GiA, gigantocellular retic, alpha; GiV, gigantocellular retic, vent; MdV, medullary reticular nucleus, vent. (C) Individual GABA neuron identified by EYFP expression. Scale bar, 10 µm. (D) Optogenetic photostimulation (3 ms, 1 Hz) of a ChR2-EYFP expressing GABA neuron in the VM generated reliable action potential firing. Figure 1. Open in new tabDownload slide Identification and optogenetic stimulation of GABA neurons in the VM. (A) Fluorescent image of brainstem slice shows the site of viral injection. EYFP labeling (green) was expressed exclusively within the VM following stereotaxic microinjection of AVV1-ChR2-EYFP (30 nl) in a Gad2-Cre mouse pup (P5). LPGi, lateral paragigantocellular nucleus; py, pyramidal tract; Amb, nucleus ambiguus; 12N, hypoglossal nucleus; 4V, fourth ventricle (here and in B). Scale bar, 0.5 mm. (B) Schematic drawing of viral injection sites shown in sagittal plane. DPGi, dorsal paragigantocellular nucleus; Gi, gigantocellular reticular nucleus; GiA, gigantocellular retic, alpha; GiV, gigantocellular retic, vent; MdV, medullary reticular nucleus, vent. (C) Individual GABA neuron identified by EYFP expression. Scale bar, 10 µm. (D) Optogenetic photostimulation (3 ms, 1 Hz) of a ChR2-EYFP expressing GABA neuron in the VM generated reliable action potential firing. Slice preparation and electrophysiology On the day of experiments, animals (25–30 days old) were anesthetized with isoflurane and killed by exsanguination/cardiac perfusion with a glycerol-based artificial cerebrospinal fluid (aCSF). The glycerol-based aCSF (4°C) contained (in mM): 252 glycerol, 1.6 KCl, 1.2 NaH2PO4, 1.2 MgCl, 2.4 CaCl2, 26 NaHCO3, and 11 glucose. The brain was carefully removed, and 300-μm-thick coronal slices of the medulla were obtained with a vibratome. The slices were transferred to a solution of the following composition (in mM) 110 N-methyl-d-glucamine (NMDG), 2.5 KCl, 1.2 NaH2PO4, 25 NaHCO3, 25 glucose, 0.5 CaCl2, and 10 mM MgSO4 equilibrated with 95% O2-5% CO2 (pH 7.4) at 34°C for 15 min. The slices were then transferred from NMDG-based aCSF to a recording chamber, which allowed perfusion (5–10 ml/min) of aCSF at room temperature (25°C) containing (in mM) 125 NaCl, 3 KCl, 2 CaCl2, 26 NaHCO3, 5 glucose, and 5 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) equilibrated with 95% O2-5% CO2 (pH 7.4) for at least 30 min before experiments were conducted. Individual GMNS and RMNs were identified in the 12N by the presence of the fluorescent tracer CTB. To examine light-triggered postsynaptic currents in hypoglossal motoneurons, patch pipettes (2.5–3.5 MΩ) were filled with a solution consisting of (in mM) 150 KCl, 2 MgCl2, 2 ethylene glycol-bis(β-aminoethyl ether)-N,N,N′,N′-tetraacetic acid (EGTA), 10 HEPES, and 2 Mg-ATP (pH 7.3) and guided to the surface of individual GMNs or RMNs. Voltage clamp whole cell recordings were made at a holding potential of −80 mV with an Axopatch 200 B and pClamp 8 software (Axon Instruments, Union City, CA). Action potential firing in hypoglossal motoneurons and GABA neurons were recorded with the patched pipets filled with a solution consisting of 135 mM K-gluconic acid, 10 mM HEPES, 10 mM EGTA, 1 mM CaCl2, and 1 mM MgCl2, pH 7.3. Drugs used in this electrophysiological study were purchased from Sigma-Aldrich Chemical (St. Louis, MO). Selective photostimulation of ChR2-expressing fibers surrounding HMNs was performed by using a 473-nm blue laser light (CrystaLaser, Reno, NV). A series of 60 consecutive single stimulations (3-ms duration, at 1 Hz) were applied to each neuron. Laser light intensity was kept constant across all experiments at an output of 10 mW. Anesthesia technique In-vivo chemogenetic experiments assessing the VM inhibitory region mediated changes in tongue EMG activity were performed in anesthetized animals 8–9 weeks after microinjection of the hM3D-mCherry virus into the VM. Anesthesia was maintained with isoflurane in spontaneously breathing mice in a mixture of isoflurance (1.7%) with supplemental oxygen at a constant flow of 0.8 L/min. To assess the effect of isoflurane on tonic and inspiratory-related activity of the tongue muscles, we performed a control recording of tongue EMG for at least 4 h in five mice. All isoflurane-anesthetized animals examined in this study demonstrated minimal or absent tonic EMG, while phasic activity of the tongue muscles was robust and synchronized with respiratory efforts. These experiments indicated that the inspiratory-related burst frequency typically gradually decreased, whereas inspiratory-related burst amplitude typically gradually increased during isoflurane administration. Tongue EMG In anesthetized animals, lying supine, a small midline incision was made along the submentum. Two wire hook electrodes (0.008 inches teflon-coated stainless steel wire, A-M Systems, Carlsborg, WA) with bared ends (0.5 mm) were inserted in the targeted genioglossus muscle from the base of the tongue using 27G needles to direct the intramuscular placement of the wire hooks. The EMG signal was amplified, filtered between 30 Hz and 1,000 Hz using Bio Amplifier 8/35 (ADInstruments Inc., Colorado Springs CO) and digitized at a sampling rate of 1,000 Hz (LabChart 8, ADInstruments). In addition to raw EMG signal, the moving average was obtained (100 ms time constant) using LabChart 8, ADInstuments. Experiments examining the relative contribution of the protrudor and retractor muscles in tongue EMG were conducted in four mice. Bilateral dissection of the medial branch of hypoglossal nerve 1–2 mm proximal to the bifurcation point abolished 70%–80% of respiratory-related EMG suggesting that the protrudor was a major contributor to the tongue EMG. Bilateral dissection of both medial and lateral branches of hypoglossal nerve nearly abolished tongue electromyographic activity, suggesting that 20%–30% of tongue EMG represented the retractor muscle activity. All mice selected for pharmacological experiments demonstrated normal food and water intake, weight gain, and normal behavior. Animals that received hM3D-mCherry injection into the VM were randomly divided into two groups. Animals from the first group were administrated clozapine N-oxide (CNO, intraperitoneally [IP], 1 mg/kg) while animals from the second group were given injections of saline. Tongue EMG was monitored for at least 4 h post-CNO or saline injection in all animals studied. Immunohistochemistry To determine the specificity of hM3D-mCherry expression in GABA neurons, immunostaining was used. Medulla slices (100 µm) were prepared and soaked for 3 h in 10% formalin 6–8 weeks following hM3D-mCherry viral injections into the VM of Gad2-Cre mice (P5). The slices were then incubated with 0.2% Triton X-100 and processed for GABA immunoreactivity by using rabbit anti-GABA as a primary antibody (350:1,000 dilution, overnight incubation, Sigma-Aldrich, St Louis, MO) and anti-rabbit Alexa Fluor 488 as a secondary antibody (1:200, 4 h, Life Technologies, Carlsbad, CA). For analysis of colocalization of GABA immunoreactivity and hM3D-mCherry expression, the Zeiss 710 confocal imaging system was used. Data analysis and statistics ChR2 photostimulation evoked postsynaptic currents in hypoglossal motoneurons were measured using pClamp 8 software (Molecular Devices, Sunnyvale, CA). The responses to a series of 60 consecutive photostimulations in each neuron were averaged. The mean value from each neuron in the population was averaged for the population of neurons to create a summary of results for each condition. Analysis of neuronal firing activity was performed by using Mini Analysis (version 5.6.12; Synaptosoft, Decatur, GA). Inspiratory-related bursting activity of tongue muscles was analyzed using LabChart 8 (ADInstuments) from 1.5 to 2 h post-CNO or saline injections. Results are presented as means ± SE. Prior to statistical comparison, all data were tested for the normality using D’Agostino-Pearson normality test. To compare normally distributed data, we used either Student’s t-test or analysis of variance (ANOVA) with repeated-measures and Dunnett’s post-test, as appropriate. When the data were not normally distributed, we used either nonparametric Mann–Whitney test (for two groups) or nonparametric Friedman test followed by Dunn’s post-test, as an alternative to the one-way ANOVA with repeated measures. Significant difference was set at p < 0.05. Results In-vitro optogenetic activation of GABA neurons in the VM Stereotaxic injection of AAV1-ChR2-EYFP into the VM of Gad2-Cre mice resulted in robust ChR2-EYFP expression limited to the VM (see Figure 1, A and B). Optogenetic photostimulation of CHR2-containing GABA neurons, identified by EYFP expression (see Figure 1C), generated a reliable action potential firing (n = 6 neurons, Figure 1D) indicating sufficient expression of ChR2-EYFP in GABA neurons to consistently depolarize and evoke action potential firing in these neurons. In-vitro optogenetic activation of ChR2-EYFP-expressing VM terminals in the hypoglossal nucleus (12N) inhibits hypoglossal motoneurons In addition to ChR2-EYFP expression in the VM, a dense network of ChR2-EYFP-expressing fibers was detected in the 12N (Figure 2A). Retrogradely labeled hypoglossal motoneurons in the 12N were surrounded by REM-VM ChR2-EYFP-expressing fibers and presumptive terminals (Figure 2B). Photostimulation of ChR2-EYFP fibers in the 12N with brief light pulses (3 ms) evoked hyperpolarization and inhibition of action potential firing (Figure 2C) in both RMNs (n = 6) and GMNs (n = 7). Figure 2. Open in new tabDownload slide Optogenetic stimulation of ChR2-EYFP expressing axons projecting from the VM to hypoglossal motoneurons. (A) Dense ChR2-EYFP fiber expression (green) in the hypoglossal nucleus 20 days after AVV1-ChR2-EVFP injection into the ventral medulla of a GAD2-Cre mouse pup (P5). CTB-expressing GMNs (red) were located in the ventrolateral compartment of the 12N. Scale bar, 100 µm. (B) Typical network of ChR2-EYFP-expressing fibers (green) that surround fluorescently labeled (red) GMN in the 12N. Scale bar, 30 µm. (C) Photostimulation (3 ms, at 1 Hz) of ChR2-EYFP-expressing fibers in the 12N resulted in membrane hyperpolarization and action potential firing suppression in identified GMN. Arrow represents photostimulation (in this and all subsequent figures). (D) Photostimulation of ChR2-EYFP fibers evoked large postsynaptic current in all RMNs and GMNs, studied. This panel shows representative experiment conducted on identified RMN. Photostimulation-evoked current was diminished by application of gabazine and nearly blocked by co application of strychnine. (E) This panel shows summary data from 23 motoneurons, including 12 RMNs and 11 GMNs. ** denotes p < 0.01 and *** denotes p < 0.001. (F) A typical experiment demonstrates GABA/glycine postsynaptic current in identified GMN evoked by photostimulation of ChR2-EYFP-expressing fibers. This response was eliminated by TTX, reinstated by coapplication of 4-AP, and blocked by adding gabazine and strychnine. Figure 2. Open in new tabDownload slide Optogenetic stimulation of ChR2-EYFP expressing axons projecting from the VM to hypoglossal motoneurons. (A) Dense ChR2-EYFP fiber expression (green) in the hypoglossal nucleus 20 days after AVV1-ChR2-EVFP injection into the ventral medulla of a GAD2-Cre mouse pup (P5). CTB-expressing GMNs (red) were located in the ventrolateral compartment of the 12N. Scale bar, 100 µm. (B) Typical network of ChR2-EYFP-expressing fibers (green) that surround fluorescently labeled (red) GMN in the 12N. Scale bar, 30 µm. (C) Photostimulation (3 ms, at 1 Hz) of ChR2-EYFP-expressing fibers in the 12N resulted in membrane hyperpolarization and action potential firing suppression in identified GMN. Arrow represents photostimulation (in this and all subsequent figures). (D) Photostimulation of ChR2-EYFP fibers evoked large postsynaptic current in all RMNs and GMNs, studied. This panel shows representative experiment conducted on identified RMN. Photostimulation-evoked current was diminished by application of gabazine and nearly blocked by co application of strychnine. (E) This panel shows summary data from 23 motoneurons, including 12 RMNs and 11 GMNs. ** denotes p < 0.01 and *** denotes p < 0.001. (F) A typical experiment demonstrates GABA/glycine postsynaptic current in identified GMN evoked by photostimulation of ChR2-EYFP-expressing fibers. This response was eliminated by TTX, reinstated by coapplication of 4-AP, and blocked by adding gabazine and strychnine. GABA/glycine synaptic projections from the VM to hypoglossal motoneurons In addition to inhibition of firing activity in hypoglossal motoneurons, photostimulation of ChR2-EYFP fibers in the 12N generated an inhibitory postsynaptic current (IPSC) in all hypoglossal motoneurons studied (n = 23, including 12 RMNs and 11 GMNs). These IPSCs were significantly diminished by application of the GABA(A) receptor antagonist gabazine (25 µM, peak amplitude diminished from 1,460 ± 211 pA to 650 ± 95 pA, p < 0.01, Friedman test and Dunn’s post-test, Figure 2, D and E). This postsynaptic current was nearly blocked by coapplication of the glycine receptor antagonist strychnine (1 µM, peak amplitude diminished to 51 ± 9 pA, p < 0.001, Friedman test and Dunn’s post-test, Figure 2, D and E). These data are consistent with the previous report indicating that GABA and glycine are colocalized in the same presynaptic vesicles and are coreleased from presynaptic terminals opposed to the hypoglossal motoneurons [26]. GABA/glycine input from the VM to hypoglossal motoneurons is monosynaptic We next assessed whether the GABA/glycine synaptic pathway from the VM to GMNs and RMNs were mono- or polysynaptic. We first applied the sodium channel blocker tetrodotoxin (TTX, 1 µM) to eliminate action potential-depended neurotransmission and polysynaptic activity. As shown in Figure 2E, application of TTX abolished light-triggered postsynaptic currents in hypoglossal motoneurons. We next coapplied the potassium channel blocker 4-aminopyridine (4-AP, 200 µM). Application of 4-AP facilitates the direct depolarization of ChR2-containing fibers [27] and enhances the local release of GABA and glycine neurotransmission in hypoglossal motoneurons during photostimulation. As shown in Figure 2E, adding 4-AP to the bath solution reinstated the light-triggered inhibitory response in hypoglossal motoneurons. Since postsynaptic responses in hypoglossal motoneurons occurred in the presence of TTX and 4-AP, it is likely these responses represent a monosynaptic connection between GABA/glycine neurons and hypoglossal motoneurons. This pharmacological criteria of coapplication of TTX and 4-AP has been used in previous studies to isolate monosynaptic release of neurotransmitter during photostimulation [27, 28]. Comparison GABA/glycine synaptic transmission in GMNs and RMNs To examine if this neurotransmission was different in protruder and retractor hypoglossal motoneurons, we compared the IPSC responses from GMNs to RMNs upon photostimulation of ChR2-EYFP fibers. Consistent with previously published work [24, 25], RMNs (demonstrated in Figure 3A, left) were located in the dorsal compartment of the 12N, whereas GMNs (shown in Figure 3 right) were located in the ventrolateral part of the 12N. The amplitude of IPSCs in RMNs was significantly greater (p < 0.05, Mann–Whitney test) in RMNs than the amplitude of IPSCs in GMNs (postsynaptic current amplitude in RMNs, 1,879 ± 327 pA, n = 12 vs postsynaptic current amplitude in RMNs, 1,193 ± 192 pA, n = 11, see Figure 3, B and C). Figure 3. Open in new tabDownload slide Comparison of GABA/glycine synaptic transmission in GMNs and RMNs. (A) Confocal microscopy image demonstrates fluorescent labeling of RMNs (left) and GMNs (right) after CTB injection into the corresponding muscles of the tongue. Scale bar, 100 µm. (B) As shown in these representative traces, the amplitude of GABA/glycine postsynaptic current evoked by ChR2-EYFP fibers photostimulation in RMN (left) was greater than the amplitude of the response evoked in GMN (right). (C) Summary data analysis revealed a significant difference in the amplitude of GABA/glycine postsynaptic current between RMNs (n = 12) and GMNs (n = 11). *denotes p < 0.05. Figure 3. Open in new tabDownload slide Comparison of GABA/glycine synaptic transmission in GMNs and RMNs. (A) Confocal microscopy image demonstrates fluorescent labeling of RMNs (left) and GMNs (right) after CTB injection into the corresponding muscles of the tongue. Scale bar, 100 µm. (B) As shown in these representative traces, the amplitude of GABA/glycine postsynaptic current evoked by ChR2-EYFP fibers photostimulation in RMN (left) was greater than the amplitude of the response evoked in GMN (right). (C) Summary data analysis revealed a significant difference in the amplitude of GABA/glycine postsynaptic current between RMNs (n = 12) and GMNs (n = 11). *denotes p < 0.05. In vitro chemogenetic activation of VM GABA/glycine neurons To assess the effects of VM GABA/glycine neurons chemical activation, we injected a Cre-dependent vector expressing hM3D-mCherry into the ventral medulla of GAD2-Cre mice (Figure 4A, left). Three weeks postinjection hM3D-containing neurons in the ventral medulla were identified by m-Cherry fluorescent expression (Figure 4A, right) and studied using the whole-cell patch-clamp method. CNO application evoked a significant increase in the action potential firing frequency of REM-VM hM3D-containing neurons (from 1.5 ± 0.05 Hz to 3.9 ± 0.09 Hz; n = 6; p < 0.01; Student’s paired t-test; see Figure 4C). Figure 4. Open in new tabDownload slide In-vitro activation of hM3D-mCherry-expressing inhibitory neurons in the VM. (A) In left panel, confocal microscopy image shows fluorescence expression (red) in the VM in a Gad2-Cre mouse injected with AVV-hM3D-mCherry viral vector. LPGi, lateral paragigantocellular nucleus; py, pyramidal tract. Scale bar, 200 µm. In right panel, enlarged view of the VM demonstrates hM3D-mCherry expression in individual GABA neurons (red). Scale bar, 20 µm. (B) As shown in representative in-vitro experiment, CNO application increases action potential firing activity in fluorescently identified GABA neuron. (C) Summary data analysis (n = 6 neurons) indicates a significant difference in the firing activity frequency before and after CNO application. ** denotes p < 0.01. Figure 4. Open in new tabDownload slide In-vitro activation of hM3D-mCherry-expressing inhibitory neurons in the VM. (A) In left panel, confocal microscopy image shows fluorescence expression (red) in the VM in a Gad2-Cre mouse injected with AVV-hM3D-mCherry viral vector. LPGi, lateral paragigantocellular nucleus; py, pyramidal tract. Scale bar, 200 µm. In right panel, enlarged view of the VM demonstrates hM3D-mCherry expression in individual GABA neurons (red). Scale bar, 20 µm. (B) As shown in representative in-vitro experiment, CNO application increases action potential firing activity in fluorescently identified GABA neuron. (C) Summary data analysis (n = 6 neurons) indicates a significant difference in the firing activity frequency before and after CNO application. ** denotes p < 0.01. In-vivo effects of chemogenetic activation of VM GABA/glycine neurons on tongue EMG In-vivo experiments assessing the effect of VM GABA/glycine neurons activation on tongue EMG, as well as the immunohistochemical experiments, were performed 6–8 weeks after hM3D-mCherry injection into the VM. Immunohistochemical results indicate that a majority (81%, 124/154) of hM3D-mCherry-expressing neurons in the VM were GABA positive (n = 2 mice, see Figure 5A). IP injections of CNO (1mg/kg), or saline in another group of animals, were performed 1 h after isoflurane anesthesia onset. Neither the tonic muscle activity from the tongue, nor the frequency of inspiratory related bursts was altered with CNO administration. However, as shown in Figure 5, CNO induced a significant decrease (p < 0.01, Student’s unpaired t-test) in the inspiratory-related tongue EMG amplitude during bursts (saline, 17.2 ± 2.5 a.u., n = 6 mice vs CNO, 6.6. ± 1.8 a.u., n = 6 mice) as well as significant decrease (p < 0.05, Student’s unpaired t-test) and in the inspiratory-related bursting duration (saline, 0.18 ± 0.01 s, n = 6 mice vs CNO, 0.12 ± 0.02 s, n = 6 mice). There were no significant differences in either amplitude, frequency, or duration of inspiratory-related bursts between the two groups of animals prior to CNO or saline administration. Since CNO has been recently reported to exert off-target effects in particular in mice [29], we tested for any effects of CNO in a control group of mice which did not received any viral injections. Comparison between this group and the “saline” group demonstrated no significant differences (p > 0.05, Student’s unpaired t-test) in either amplitude (saline, 17.2 ± 2.5 a.u., n=6 mice vs CNO, 26.0 ± 7.0 a.u., n = 6 mice), frequency (saline, 0.45 ± 0.07 Hz, n = 6 mice vs CNO, 0.52 ± 0.05 Hz, n = 6 mice) or duration (saline, 0.18 ± 0.01 s, n = 6 mice vs CNO, 0.21 ± 0.02 s, n = 6 mice) of inspiratory-related bursts post-CNO/saline injections (data not shown). These data indicate that CNO did not exert any significant off-target effects that may affect the results of this study. Figure 5. Open in new tabDownload slide Immunostaining and effect of pharmacological activation of GABA/glycine neurons in the VM. (A) Representative example of colocalization of hM3D-mCherry expression (red) with GABA immunoreactivity (green) in the VM of Gad2-Cre mouse. Scale bar, 10 µm. (B) Tongue EMG and moving average (∫EMG) are shown in AVV-hM3D-mCherry-administrated animals after saline (top) or CNO (in another animal, bottom) IP injection. Enlarged view of the inspiratory bursts, outlined on the right, is presented on the left. (C) Summary data demonstrate significant difference between CNO- and saline-administrated groups of animals. CNO-administrated mice (n = 6) had diminished amplitude (right) and duration (left) of inspiratory bursts when compared to saline-administrated group (n = 6). There was no difference in the burst frequency between groups (middle). * denoted p < 0.05; ** denoted p < 0.01. Figure 5. Open in new tabDownload slide Immunostaining and effect of pharmacological activation of GABA/glycine neurons in the VM. (A) Representative example of colocalization of hM3D-mCherry expression (red) with GABA immunoreactivity (green) in the VM of Gad2-Cre mouse. Scale bar, 10 µm. (B) Tongue EMG and moving average (∫EMG) are shown in AVV-hM3D-mCherry-administrated animals after saline (top) or CNO (in another animal, bottom) IP injection. Enlarged view of the inspiratory bursts, outlined on the right, is presented on the left. (C) Summary data demonstrate significant difference between CNO- and saline-administrated groups of animals. CNO-administrated mice (n = 6) had diminished amplitude (right) and duration (left) of inspiratory bursts when compared to saline-administrated group (n = 6). There was no difference in the burst frequency between groups (middle). * denoted p < 0.05; ** denoted p < 0.01. Discussion To the best of our knowledge, this is the first report identifying and characterizing the in-vitro and in-vivo inhibition of GMNs and RMNs in the 12N from GABA/glycine neurons located in the VM that overlaps with REM sleep-controlling neurons. The major findings from this study include the following: (1) A dense network of inhibitory fibers surrounding both RMNs and GMNs originates from neurons in the VM area. (2) These projections are monosynaptic and include both GABA and glycine neurotransmission to both RMNs and GMNs. (3) Optogenetic activation of this GABA/glycine pathway evokes greater inhibition in RMNs than GMNs. (4) Pharmacogenetic activation of GABA/glycine ventral medullary region results in selective reduction in the amplitude and duration of inspiratory-related bursts in the tongue muscles. Previous studies have found a population of neurons specifically active during REM within the VM, particularly in the lateral paragigantocellular nucleus [20]. Furthermore, identified GABA neurons in the VM exhibited maximal firing rate during REM sleep, when compared to both non-REM sleep and wakefulness [22]. REM sleep active neurons in the VM send projections to important brain regions that contribute to REM sleep generation and/or maintenance, including the spinal cord [23, 30, 31], the locus coeruleus [20, 21] and the ventrolateral periaqueductal gray [22]. This study shows there are direct GABA/glycine axonal projections from the VM to both GMNs and RMNs, which selectively inhibit tongue muscles during respiratory bursts, providing a possible neuronal substrate for REM sleep-mediated losses of upper airway patency that occur in OSA. The networks responsible for REM sleep-related suppression of upper airway muscle tone are complex and the relative roles played by disfacilitatory and inhibitory mechanisms are not fully investigated [13]. Cholinergic and noradrenergic neuromodulation has been implicated in REM sleep-related control of tongue muscle function [13, 16, 32, 33]. Supporting the role of GABA and glycine neuromodulation in upper airway muscle control in REM sleep previous studies indicate that systemic administration of GABA and glycine agonists produces an inhibition of hypoglossal nerve activity [34]. In addition, during REM sleep firing activity of hypoglossal motoneurons is suppressed via glycine receptor-mediated postsynaptic inhibition [18]. Accordingly, glycine receptor antagonism at the 12N in REM sleep evoked an increase in inspiratory-related bursting activity of genioglossus muscle in naturally sleeping animals [35]. However, in carbachol-induced REM sleep injections of glycine or GABA receptor antagonists into the 12N failed to reduce suppression of hypoglossal nerve activity [14]. The lack of effects of GABA/glycine receptor antagonisms on hypoglossal nerve activity observed in latter studies may be partially explained by a nonspecific inhibition of all GABA or glycine receptors at the 12N including the blockage of GABA/glycine presynaptic hetero- and/or auto-receptors that play an important regulatory role in the 12N [36]. In contrast to the previous studies, the present study identified and characterized a very selective synaptic pathway from GABA/glycine neurons located in the VM that overlaps with REM sleep-controlling neurons, to hypoglossal motoneurons in the 12N. Activation of this pathway may contribute to a sleep state-dependent suppression of upper-airway muscle activity and patency [6–8, 33, 35]. Importantly, our data indicate that although both GMNs and RMNs are inhibited with optogenetic stimulation of VM GABA/glycine fibers in the 12N, there is a greater inhibitory effect on RMNs than GMNs. Previous studies suggest that both protrudor and retractor muscles of the tongue play a significant role in maintenance of upper airway potency [37–39]. Airway occlusion as well as hypoxia and hypercapnia exposures normally activates both tongue protrudor and retractor muscles and this coactivation have been proposed to play a significant role in protecting upper airway patency [38, 40, 41]. However, this coactivation is compromised in sleeping or anesthetized OSA patients due to deficient retractor muscle activation and this asynchrony increases pharyngeal collapsibility during obstructive breathing [37, 39, 42]. It is possible that the greater inhibition of RMNs compared to GMNs exerted by REM sleep-controlling medullary pathway contributes to more prolonged and severe obstructive episodes during REM sleep than during non REM sleep in OSA patients [4]. The limitations of this study include the following: (1) Conducting in-vivo experiments on anesthetized animals did not allow assessing the effects of pharmacological activation of VM inhibitory region on the tonic muscle activity from the tongue. As tonic tongue EMG is minimal or absent in isoflurane-anesthetized animals [43], it is anticipated that this activity could not be diminished further by pharmacological activation of GABA and glycine fibers from the VM to the hypoglossal motoneurons. Inspiratory-related EMG of the tongue, however, is robust in anesthetized animals and was the focus of the VM mediated inhibition of hypoglossal motoneurons in-vivo. (2) VM GABA/glycine neurons that project to hypoglossal motoneurons are contained within the brainstem area known to contain REM sleep-controlling neurons, however the extent of overlap of these populations is not known. 3) Since the incidence of OSA increases with age, the results from in-vitro experiments on sexually mature juvenile mice should be cautiously extrapolating to adult patients. In conclusion, this study demonstrates that activation of GABA/glycine neurons from REM sleep-controlling VM inhibits hypoglossal motoneurons as well as tongue EMG activity via a direct pathway to both GMNs and RMNs in the 12N. This important pathway may play a role in REM sleep-related upper airway obstruction in OSA patients. Funding This study was supported by National Institutes of Health, National Heart, Lung, and Blood Institute 133862 and National Institutes of Health, National Heart, Lung, and Blood Institute 146169. Conflict of interest statement. None declared. References 1. 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, de Zambotti, Massimiliano;Goldstone,, Aimee;Forouzanfar,, Mohamad;Javitz,, Harold;Claudatos,, Stephanie;Colrain, Ian, M;Baker, Fiona, C

doi: 10.1093/sleep/zsz312pmid: 31872251

Abstract Study Objectives To investigate the pre-sleep psychophysiological state and the arousal deactivation process across the sleep onset (SO) transition in adolescents. Methods Data were collected from a laboratory overnight recording in 102 healthy adolescents (48 girls, 12–20 years old). Measures included pre-sleep self-reported cognitive/somatic arousal, and cortical electroencephalographic (EEG) and electrocardiographic activity across the SO transition. Results Adolescent girls, compared with boys, reported higher pre-sleep cognitive activation (p = 0.025) and took longer to fall asleep (p < 0.05), as defined with polysomnography. Girls also showed a less smooth progression from wake-to-sleep compared with boys (p = 0.022). In both sexes, heart rate (HR) dropped at a rate of ~0.52 beats per minute in the 5 minutes preceding SO, and continued to drop, at a slower rate, during the 5 minutes following SO (p < 0.05). Older girls had a higher HR overall in the pre-sleep period and across SO, compared to younger girls and boys (p < 0.05). The EEG showed a progressive cortical synchronization, with increases in Delta relative power and reductions in Alpha, Sigma, Beta1, and Beta2 relative powers (p < 0.05) in the approach to sleep, in both sexes. Delta relative power was lower and Theta, Alpha, and Sigma relative powers were higher in older compared to younger adolescents at bedtime and across SO (p < 0.05). Conclusions Our findings show the dynamics of the cortical-cardiac de-arousing process across the SO transition in a non-clinical sample of healthy adolescents. Findings suggest a female-specific vulnerability to inefficient sleep initiation, which may contribute to their greater risk for developing insomnia. falling asleep, adolescence, arousal, heart rate, EEG, sex differences Statement of Significance Sleep difficulties frequently emerge in adolescence on the backdrop of profound sleep maturation and biopsychosocial change, with older girls being particularly vulnerable to the development of insomnia symptoms. It is unknown whether the process of falling asleep, which involves the reorganization of multiple biological domains across the wake-to-sleep transition, differs across adolescence and in girls compared with boys. Our findings highlight the dynamics of the falling asleep process in healthy adolescents and suggest a female-specific vulnerability to inefficient sleep initiation, which may contribute to a higher risk for developing sleep disturbances in girls during adolescence as well as later in life. Introduction Falling asleep is a complex and frequently overlooked process in which a cascade of psychophysiological changes characterize the transition from wakefulness to sleep [1]. It is characterized by a progressive reduction in responsiveness to external stimuli and an increase in behavioral quiescence. The processing of sensory inputs changes together with the level and content of consciousness. When falling asleep, slow eye movements initially occur (reflecting drowsiness) and usually disappear at the first manifestation of specific cortical electroencephalographic (EEG) markers of consolidated sleep (e.g. spindles and K-complexes) [1]. Cortical activity shows a progressive spatiotemporal re-organization toward an overall decrease in complexity and increase in high-voltage synchronized EEG activity [1, 2]. Heart rate (HR) progressively drops, and the cardiac autonomic nervous system (ANS) modulation moves towards vagal dominance [3]. A cascade of other physiological changes like a drop in core body temperature, an increase in distal temperature, and a reduction in respiration rate also occur across the sleep onset (SO) transition, each with their own specific temporal dynamics [1]. Limited empirical evidence exists in explaining the fundamental process of SO, and its role in health and pathology. Many studies have focused on insomnia, in which difficulty falling asleep is a major diagnostic criterion and frequently a cardinal symptom of the disorder. Within this framework, abnormally up-regulated levels of cortical, somatic and cognitive activity (hyperarousal) play a key role in the pathophysiology of the disorder [4]. Hyperarousal is magnified under insomnia-specific circumstances such as when an individual is trying to sleep and interferes with the falling asleep and sleep maintenance processes. Insomnia sufferers exhibit high levels of pre-sleep anxiety, worry, intrusive thoughts [5], accompanied by evidence of central and autonomic nervous system hyperarousal around SO. Hyperarousal may manifest as elevated EEG Beta activity [6] and elevated HR and cardiac sympathetic ANS activity [7]. There may also be specific abnormalities in the normal falling asleep de-arousing processes. These include smaller declines in Alpha and Beta powers in the approach to SO [8], a slower increase in cortical synchronization [9] and the absence of a drop in cardiac ANS sympathetic activity [7] across the SO transition, in insomnia sufferers compared to controls. There is a paucity of data exploring the falling asleep process in adolescents despite evidence that falling asleep difficulties are common during adolescence [10], and with historical trends showing that the prevalence of SO difficulties in adolescents has increased over time, being about 8% more common in 2005 than in 1983 [11]. Longer sleep onset latency (SOL) contributes to adolescents’ perceived sleep problems [12] and clinically, difficulty falling asleep is a cardinal symptom of insomnia disorder in adolescents [13]. Difficulty falling asleep is also evident in several other common conditions during adolescence, such as major depressive disorder [14]. Altered pre-sleep somatic arousal and cognitive processes such as catastrophizing, elevated worry, anxiety, and rumination are critical, potentially interfering with the processes underlying falling asleep and maintaining sleep in adolescence [15–17]. In the context of insomnia development, Fernandez-Mendoza and colleagues [18] found that adolescents complaining of insomnia symptoms such as self-reported difficulty in falling and staying asleep, had greater EEG beta power than controls before and during the first 5 minutes of NREM sleep after SO (first epoch of N2 sleep after lights-out). This greater cortical activation at SO was then maintained during NREM sleep across the night. In a second study [19], they found that greater cortical hyperarousal (high-NREM EEG beta power) in childhood (6–11 years) was associated with greater incidence of insomnia symptoms 8 years later (13–20 years). These results suggest that hyperarousal may be present even years before the manifestation of insomnia. Findings in adolescents need to be contextualized within the profound normal developmental changes that occur during adolescence. Adolescence is accompanied by a dramatic age-related drop in slow-wave sleep and its activity (SWA; 0.3–4 Hz), reflecting changes in brain reorganization (e.g. synaptic pruning) [20, 21]. Changes in circadian and homeostatic biological maturation processes that delay SO times, coupled with social constraints such as early school start times, lead to changes in adolescents’ sleep timing and duration. Also, adolescent-specific stressors (e.g. academic pressure, peer-stress) and maladaptive behaviors (e.g. bedtime technology use, excessive caffeine consumption) may also interfere with the adolescents’ sleep pattern, and particularly with the falling asleep process, delaying the onset of sleep and contributing to poor sleep [13]. The incidence of insomnia disorder changes across adolescence, being higher in post-pubertal girls compared to pre-pubertal girls and compared with boys [13, 22]. This female-prevalence for insomnia is then maintained at all ages [23], although PSG measures tend to show that females have more N3 (or slow-wave sleep) and less light N1 sleep than males [24]. One possible reason for the sex difference in insomnia that has not received much attention is that the falling asleep process could differ in girls, making them vulnerable to insomnia. Longer self-reported SOL in girls than boys has been reported [25]; however, a detailed investigation of sex differences in the falling asleep process in adolescents is lacking. The purpose of the current study is to investigate the pre-sleep psychophysiological state and the physiology of the falling asleep de-arousing process in a non-clinical population of healthy male and female adolescents. Data were analyzed as a function of age and sex, using multidimensional data reflecting cognitive, cortical EEG, and cardiac ANS functioning. We hypothesized that girls would show greater difficulties in the arousal deactivation process across the SO transition than boys, potentially reflecting a female-specific “vulnerability” to sleep initiation problems and that the sex difference would be particularly evident in older adolescents. Method Participants Healthy adolescents (n = 102, 48 girls, age range: 12–20 years old) who were in the SRI International baseline cohort of the longitudinal National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study constituted the final sample. The study was approved by the SRI International Institutional Review Board. All adult participants consented to participate, with minors providing written assent along with consent from a parent/legal guardian. Details about the NCANDA methodology and sample characteristics [26], nocturnal PSG sleep [27], and ANS functioning during sleep [28] are described elsewhere. Sample characteristics are from data release version NCANDA_DATA_00010_V2. A phone interview and a subsequent in-lab screening session were used to determine eligibility. Participants were free from severe current/past major DSM-IV disorders and medical conditions and were not currently using medications known to affect the central nervous system and/or the cardiovascular system. None of the participants had major sleep disorders as confirmed by in-lab clinical polysomnography (PSG). Sample demographics are provided in Table 1. Ethnicity was self-identified. Pubertal status was determined using the Pubertal Development Scale (PDS) [29]. Self-reported sleep quality and depression symptoms were assessed with the Pittsburgh Sleep Quality Index (PSQI) [30], and the Center for Epidemiologic Studies Depression Scale (CES-D) [31], respectively. Height and weight were objectively measured and body mass index (BMI; kg × m-2) calculated. The number of adolescents drinking at levels above age-matched national norms is reported (see [26], for details). Table 1. Sample demographics . Boys Mean (SD) . Girls Mean (SD) . Sample, No. 54 48 Ethnicity  Caucasian, No. 42 36  Asian, No. 9 9  African-American, No. 1 3  Other/undeclared, No. 2 0 Age, years 15.2 (2.0) 15.4 (2.1) PDS, scorea 2.8 (0.7) 3.3 (0.7) BMI, kg × m−2 21.5 (4.2) 21.1 (4.3) PSQI, total scoreb 3.8 (2.3) 4.1 (1.9) CES-D, total scorec 5.2 (3.9) 6.8 (6.8) Exceeding drinking criteria, No. 8 10 . Boys Mean (SD) . Girls Mean (SD) . Sample, No. 54 48 Ethnicity  Caucasian, No. 42 36  Asian, No. 9 9  African-American, No. 1 3  Other/undeclared, No. 2 0 Age, years 15.2 (2.0) 15.4 (2.1) PDS, scorea 2.8 (0.7) 3.3 (0.7) BMI, kg × m−2 21.5 (4.2) 21.1 (4.3) PSQI, total scoreb 3.8 (2.3) 4.1 (1.9) CES-D, total scorec 5.2 (3.9) 6.8 (6.8) Exceeding drinking criteria, No. 8 10 Ethnicity was self-identified. The number of adolescents drinking at levels above age-matched national norms is reported (see [26], for details). Data are provided as numbers (No.), percentage, or mean and SD when appropriate. aData unavailable for three girls and one boy. bData unavailable for two girls and one boy. cData unavailable for eight girls and seven boys. Pubertal Development Scale (PDS); Pittsburgh Sleep Quality Index (PSQI); Center for Epidemiologic Studies Depression Scale (CES-D); Body Mass Index (BMI). Open in new tab Table 1. Sample demographics . Boys Mean (SD) . Girls Mean (SD) . Sample, No. 54 48 Ethnicity  Caucasian, No. 42 36  Asian, No. 9 9  African-American, No. 1 3  Other/undeclared, No. 2 0 Age, years 15.2 (2.0) 15.4 (2.1) PDS, scorea 2.8 (0.7) 3.3 (0.7) BMI, kg × m−2 21.5 (4.2) 21.1 (4.3) PSQI, total scoreb 3.8 (2.3) 4.1 (1.9) CES-D, total scorec 5.2 (3.9) 6.8 (6.8) Exceeding drinking criteria, No. 8 10 . Boys Mean (SD) . Girls Mean (SD) . Sample, No. 54 48 Ethnicity  Caucasian, No. 42 36  Asian, No. 9 9  African-American, No. 1 3  Other/undeclared, No. 2 0 Age, years 15.2 (2.0) 15.4 (2.1) PDS, scorea 2.8 (0.7) 3.3 (0.7) BMI, kg × m−2 21.5 (4.2) 21.1 (4.3) PSQI, total scoreb 3.8 (2.3) 4.1 (1.9) CES-D, total scorec 5.2 (3.9) 6.8 (6.8) Exceeding drinking criteria, No. 8 10 Ethnicity was self-identified. The number of adolescents drinking at levels above age-matched national norms is reported (see [26], for details). Data are provided as numbers (No.), percentage, or mean and SD when appropriate. aData unavailable for three girls and one boy. bData unavailable for two girls and one boy. cData unavailable for eight girls and seven boys. Pubertal Development Scale (PDS); Pittsburgh Sleep Quality Index (PSQI); Center for Epidemiologic Studies Depression Scale (CES-D); Body Mass Index (BMI). Open in new tab Procedures Participants had a clinical/adaptation PSG night followed by a nonconsecutive standard PSG overnight recording (except for three participants who had consecutive nights due to schedule constraints). The standard PSG from which data were analyzed was, on average, 23 ± 19 days after the adaptation night, with four participants scheduled more than 2 months later (maximum, 118 days). All recordings were made in sound-attenuated and temperature-controlled bedrooms at the Human Sleep Research Program at SRI International. Participants were instructed to avoid consuming beverages containing alcohol and/or caffeine after 3:00 p.m. on the day of the recording night. In the evening, once they arrived in the lab, participants had a breath alcohol test (S75 Pro, BACtrack Breathalyzers) and urine drug test (10 Panel iCup drug test, Instant Technologies, Inc.) to confirm the absence of recent alcohol or drug use. After the recording sensors were attached, they were allowed to engage in quiet activities (e.g. watching TV, reading) within the laboratory main areas. When ready to sleep, they went to their bedrooms and room lights were dimmed for the resting state recording. Right before bedtime (i.e. before lights-out) they had a 5-minute resting-state recording of EEG and electrocardiographic (ECG) activity while lying in bed in a supine position, awake and with their eyes closed (resting state), followed by a brief self-assessment of sleepiness (evaluated using a 0–100 mm visual analog scale [VAS], with higher scores indicating higher sleepiness), and cognitive and somatic arousal with the Pre-sleep Arousal Scale (PSAS) [32]. The PSAS evaluates the intensity of an individual’s symptoms (e.g. worry, depressive or anxious thoughts, muscle tension) when attempting to fall asleep. Greater scores indicate greater perceived arousal. The scale has 16 items with a 5-point Likert scale scoring ranging from “not at all” to “extremely.” A total score was calculated for the cognitive and somatic sub-scales (eight items each). Lights were then turned out and participants were allowed to sleep. EEG and ECG signals were continuously recorded as part of the standard PSG assessment across the whole night (Figure 1). Figure 1. Open in new tabDownload slide Timeline for the assessment and evaluation of (1) the resting state before lights-out (bin 1-to-10 of the 5-minute wake eyes-closed period) and pre-sleep perceived sleepiness, cognitive and somatic arousal, and of (2) the changes in EEG and ECG activity across SO. The physiology of the falling asleep process (EEG power spectral analysis and ECG heart rate) is analyzed across the 5 minutes preceding (bin 1-to-10) and following (bin 11-to-20) the first epoch of four consecutive epochs of stable sleep (N2/N3) (SO-C) after bedtime, which we consider reflecting “how” an individual first approaches consolidated sleep. Each bin represents 30 seconds of data. Figure 1. Open in new tabDownload slide Timeline for the assessment and evaluation of (1) the resting state before lights-out (bin 1-to-10 of the 5-minute wake eyes-closed period) and pre-sleep perceived sleepiness, cognitive and somatic arousal, and of (2) the changes in EEG and ECG activity across SO. The physiology of the falling asleep process (EEG power spectral analysis and ECG heart rate) is analyzed across the 5 minutes preceding (bin 1-to-10) and following (bin 11-to-20) the first epoch of four consecutive epochs of stable sleep (N2/N3) (SO-C) after bedtime, which we consider reflecting “how” an individual first approaches consolidated sleep. Each bin represents 30 seconds of data. Participants were instructed not to use their phones or any electronic devices in bed; they were specifically told to turn devices off or to activate the silent mode, and to remove any alarms. All participants complied with the instructions. Before turning out the lights, participants did not receive any specific instructions. Participants self-selected their bedtimes and wake-up times. Girls who were post-menarche were studied irrespective of their menstrual cycle phase. However, as previously reported in the whole NCANDA sleep project baseline cohort, only five girls had detectable progesterone levels in saliva consistent with the luteal phase of their menstrual cycle [28]. Assessment of cortical and cardiac ANS activity before and across the falling asleep period PSG was performed according to the American Academy of Sleep Medicine (AASM) guidelines [33], and included the recommended EEG derivations (F3, F4, C3, C4, O1, O2 referenced to the contralateral mastoid; 256 Hz sampled), ECG (512 Hz sampled), bipolar electrooculography, and submental electromyography (see ref. [27], for details about the NCANDA PSG data collection and outcomes). PSG signals were recorded during the resting state period and across the whole night. All signals were recorded using Compumedics Grael HD-PSG systems (Compumedics, Abbotsford, Victoria, Australia). PSG records were scored (wake, N1, N2, N3, rapid-eye-movement [REM] sleep) in 30-second epochs by experienced scorers following the AASM rules. Standard overnight PSG sleep parameters were calculated (see ref. [27], for details). Spectral EEG analysis was performed on frontal EEG derivations (F3 and F4) based on evidence that anterior areas show earlier synchronization at SO [2], using EEGLAB toolbox [34] for MATLAB (MathWorks, Natick, MA), as previously described [27, 35]. EEG was re-referenced to the averaged mastoids and filtered at 0.3–36 Hz with half-amplitude cutoffs at 0.15 and 36.15 Hz. Fast Fourier Transform analysis was conducted on 2-second epochs, to facilitate artifact identification and removal (see below), using a sliding Hanning window to calculate power density values with 1 Hz resolution. Power density (µV2/Hz) values were then averaged across Delta (1 Hz to ≤4 Hz), Theta (>4 Hz to ≤8 Hz), Alpha (>8 Hz to ≤12 Hz), Sigma (>12 Hz to ≤15 Hz), Beta1 (>15 Hz to ≤23 Hz), and Beta2 (>23 Hz to ≤30 Hz) frequencies, and EEG relative power (expressed as ratios, ranging from 0 to 1) calculated for each of the frequency bands as a function of the EEG total power (1 Hz to ≤30 Hz), and then averaged between F3 and F4. To minimize the potential impact of eye-movements and other artifacts (e.g. muscle artifacts), artifact rejection algorithms were implemented. For each 30-second window (equivalent to the 30-second PSG epochs), power density values (calculated at 2-second epochs) were assessed for artifact: in the time domain, if the maximum value of any epoch exceeded the 30-second window median by 10 times the median absolute deviation, that epoch was removed. In the frequency domain, for each band, if the power of any epoch exceeded the 30-second window median by 10 times the median absolute deviation, that epoch was also removed. Further, epochs containing scored arousals were removed from the analysis. All remaining non-artifact epochs were then averaged to create a 30-second power-density average that aligned with the PSG epochs. The proportion of rejected 2-second bins within each 30-second epoch was 37% ± 14% of the pre-SO period and 13% ± 11% of the post-SO period. This difference is attributed to greater variability in the individuals’ behavioral pattern in the pre-sleep phase compared to post-stable SO and has been shown elsewhere (in a similar analysis of the EEG changes across the SO transition [2]: the proportion of data rejected in the 5-minute preceding SO was 41% ± 16%, while 21% ± 16% of data were rejected post SO). The ECG signal was digitally filtered with a fourth-order Butterworth bandpass filter (upper cutoff: 0.5 Hz; lower cutoff: 35 Hz). Customized algorithms were used to automatically detect R peaks, derive normal beat-to-beat HR, and compute standard heart rate variability (HRV) time-domain indices (SD of normal to normal R-R intervals [SDNN, ms], and root mean square of the successive differences in normal to normal R-R intervals [RMSSD, ms]), according to [36]. ECG signal samples with a value more than 10 SDs away from the mean were identified as outliers and were replaced by interpolating the remainder of ECG samples. Average R‐R interval was then estimated using power spectral analysis. An automatic peak detection algorithm available in MATLAB R2018a software (MathWorks, Inc., Natick, MA) was adopted to detect the ECG R peaks using half of the average R‐R interval as the minimum distance criterion between two successive peaks. To intensify the R peaks and minimize the effect of T waves with amplitudes higher than normal, the automatic peak detection algorithm was applied on the ECG signal derivative. R‐R intervals with a value more than 10 SDs away from their mean were identified as outliers [37]. Those cardiac cycles that happened to have an out‐of‐range ECG signal level or R‐R value were identified as invalid beats (corrupted by noise and artifacts or ectopic beats) and were replaced by interpolating the remainder of HR data. Beat-to-beat HR was averaged in 30-second epochs, to match the PSG epochs, and was analyzed across the resting state window and SO, while HRV analysis was performed on the 5-minute resting state window only (Figure 1). All the algorithms were developed in MATLAB R2018a (MathWorks, Inc., Natick, MA). PSG SO characterization There is no scientific consensus on the precise moment when we fall asleep, which is considered more a process rather than a moment in time, and multiple definitions of the process have been previously used [1]. We characterized SO using multiple definitions based on 30-second PSG staging across the wake-to-sleep transition (Figure 1): SO-A: SO defined as the occurrence of the first PSG epoch of any sleep stage (usually N1 sleep) after bedtime (standard AASM definition [33]); SO-B: SO defined as the occurrence of the first epoch of N2 sleep after bedtime, which has been previously used in analyses focused on physiological changes occurring across the SO transition [2, 38]; SO-C: SO defined as the occurrence of the first epoch of four consecutive epochs of stable sleep (N2/N3) after bedtime, which we consider a more conservative definition reflecting “how an individual approaches consolidated sleep.” To analyze the cortical and cardiac changes occurring across the SO transition, we considered SO-C as the reference point to align the data. Differences between SO-B and SO-C were minimal, with SO-B and SO-C being coincident in 82% of the sample, with a difference of 0.88 ± 2.7 minutes (mean ± SD). Statistical analyses PSAS-cognitive, PSAS-somatic, SOL-A, SOL-B, SOL-C, SDNN, and RMSSD were log-transformed to improve normality. Sex was originally coded as 0 for boys and 1 for girls, and then centered at the proportion of girls in the sample (0.471). Age was measured in years and centered at the average age (15.26). Linear regression models were used for the analysis of the resting state period. Dependent variables were: resting-state EEG relative power in Delta, Theta, Alpha, Sigma, Beta1 and Beta2 frequency bands, HR and HRV SDNN and RMSSD measures (calculated across the 5-minute resting-state period), self-reported measures of sleepiness, cognitive (PSAS-cognitive) and somatic (PSAS-somatic) arousal, and SO latencies from lights-out to SO-A, SO-B, and SO-C. Independent variables included sex, age, and the interaction term, sex × age. The formula for the regression was Yi = c + β 1 Age + β 2 Sex + β 3 Age × Sex + ei where Sex and Age are mean-centered, c is a constant, and i indexes participants. To analyze the cortical and cardiac measures across the falling asleep process (after lights-out), separate linear regressions were performed to analyze changes across the 5-minute preceding (observations in bins 1-to-10) and following (observations in bins 11-to-21) SO-C (Figure 1). The dependent variables in these regressions were EEG relative power in the Delta, Theta, Alpha, Sigma, Beta1 and Beta2 frequency bands, and HR. The independent variables were bin (centered at the midpoint of the bin range and considered as an interval-level variable), sex (as described above), age (as described above), and all two- and three-factor interactions. Variances for the regression coefficients were estimated using the Huber and White robust method as implemented in Stata [39, 40], which adjusts for within-cluster correlation and heteroskedasticity and provides accurate assessments of the sample-to-sample variability of the parameter estimates even when the model is misspecified. The model was given by Yij = c + β 1 Sex + β 2 Bin + β 3 Age + β 4 Sex × Bin + β 5 Sex x Age + β 6 Bin × Age +β 7 Bin × Sex × Age + eij, where i indexes participant and j indexes bin, and the eij are not assumed independent nor homoskedastic. The amount of PSG wake was only analyzed in the 5 minutes preceding SO-C (there were no wake epochs in the post SO-C period) using a multi-level mixed-effects logistic model. The model was logit(pij) = c + β 1 Sex + β 2 Bin + β 3 Age + β 4 Sex × Bin + β 5 Sex × Age + β 6 Bin × Age +β 7 Bin × Sex × Age + ei, ei is the variance component attributable to person i, pij is the logit of the probability of the j-th observation on person i being 1, the probability of Wake being equal to 1 is given by exp(r)/(1+exp(r)) where r is the right-hand side of the equation for logit(pij) and the individual observations are from a Bernoulli distribution with probability pij. Effects were considered significant at p < 0.05. Regression coefficients (β), standard errors (SE) and degree of freedom are reported for the significant models. All analyses were performed using Stata/SE 14.1 for Windows. Results Pre-sleep psychophysiological state: bedtime cortical, cardiac ANS, and cognitive activity Means and SDs for the pre-sleep psychophysiological measures are provided in Table 2. Before bedtime, girls, as compared to boys, reported greater cognitive arousal (β = 0.065, SE = 0.029, t87 = 2.28, p = 0.025), regardless of age. No sex differences or sex × age interactions were found in pre-sleep somatic arousal or sleepiness. Table 2. Physiological (cardiac autonomic and cortical activity) and self-reported (sleepiness, cognitive, and somatic arousal) pre-sleep measures (mean (SD)), in boys and girls . . Boys Mean (SD) . Girls Mean (SD) . Self-reported Cognitive arousal, PSAS cognitive scoresa 12.0 (4.8) 14.0 (4.2)* Somatic arousal, PSAS somatic scoresa 8.8 (1.8) 9.2 (2.0) Sleepiness, 0–100 mm VASa 68.9 (18.9) 74.7 (17.1) Autonomic ECG Heart rate, bpm 66.9 (9.2) 68.5 (9.2)* HRV RMSSD, ms 76.4 (46.5) 76.3(45.2) HRV SDNN, ms 76.8 (46.5) 72.7 (32.1)* Cortical EEG Delta, relative power 0.43 (0.13) 0.42 (0.12) EEG Theta, relative power 0.17 (0.05) 0.18 (0.05) EEG Alpha, relative power 0.22 (0.10) 0.22 (0.10) EEG Sigma, relative power 0.05 (0.02) 0.06 (0.02) EEG Beta1, relative power 0.03(0.01) 0.03 (0.02) EEG Beta2, relative power 0.01 (0.01) 0.01 (0.01) . . Boys Mean (SD) . Girls Mean (SD) . Self-reported Cognitive arousal, PSAS cognitive scoresa 12.0 (4.8) 14.0 (4.2)* Somatic arousal, PSAS somatic scoresa 8.8 (1.8) 9.2 (2.0) Sleepiness, 0–100 mm VASa 68.9 (18.9) 74.7 (17.1) Autonomic ECG Heart rate, bpm 66.9 (9.2) 68.5 (9.2)* HRV RMSSD, ms 76.4 (46.5) 76.3(45.2) HRV SDNN, ms 76.8 (46.5) 72.7 (32.1)* Cortical EEG Delta, relative power 0.43 (0.13) 0.42 (0.12) EEG Theta, relative power 0.17 (0.05) 0.18 (0.05) EEG Alpha, relative power 0.22 (0.10) 0.22 (0.10) EEG Sigma, relative power 0.05 (0.02) 0.06 (0.02) EEG Beta1, relative power 0.03(0.01) 0.03 (0.02) EEG Beta2, relative power 0.01 (0.01) 0.01 (0.01) aData unavailable for eight males and three females. RMSSD = Root mean square of the successive differences in normal to normal R-R intervals; SDNN = SD of normal to normal R-R intervals; VAS = Visual analogic scale. *Significant sex or sex × age interactions. Open in new tab Table 2. Physiological (cardiac autonomic and cortical activity) and self-reported (sleepiness, cognitive, and somatic arousal) pre-sleep measures (mean (SD)), in boys and girls . . Boys Mean (SD) . Girls Mean (SD) . Self-reported Cognitive arousal, PSAS cognitive scoresa 12.0 (4.8) 14.0 (4.2)* Somatic arousal, PSAS somatic scoresa 8.8 (1.8) 9.2 (2.0) Sleepiness, 0–100 mm VASa 68.9 (18.9) 74.7 (17.1) Autonomic ECG Heart rate, bpm 66.9 (9.2) 68.5 (9.2)* HRV RMSSD, ms 76.4 (46.5) 76.3(45.2) HRV SDNN, ms 76.8 (46.5) 72.7 (32.1)* Cortical EEG Delta, relative power 0.43 (0.13) 0.42 (0.12) EEG Theta, relative power 0.17 (0.05) 0.18 (0.05) EEG Alpha, relative power 0.22 (0.10) 0.22 (0.10) EEG Sigma, relative power 0.05 (0.02) 0.06 (0.02) EEG Beta1, relative power 0.03(0.01) 0.03 (0.02) EEG Beta2, relative power 0.01 (0.01) 0.01 (0.01) . . Boys Mean (SD) . Girls Mean (SD) . Self-reported Cognitive arousal, PSAS cognitive scoresa 12.0 (4.8) 14.0 (4.2)* Somatic arousal, PSAS somatic scoresa 8.8 (1.8) 9.2 (2.0) Sleepiness, 0–100 mm VASa 68.9 (18.9) 74.7 (17.1) Autonomic ECG Heart rate, bpm 66.9 (9.2) 68.5 (9.2)* HRV RMSSD, ms 76.4 (46.5) 76.3(45.2) HRV SDNN, ms 76.8 (46.5) 72.7 (32.1)* Cortical EEG Delta, relative power 0.43 (0.13) 0.42 (0.12) EEG Theta, relative power 0.17 (0.05) 0.18 (0.05) EEG Alpha, relative power 0.22 (0.10) 0.22 (0.10) EEG Sigma, relative power 0.05 (0.02) 0.06 (0.02) EEG Beta1, relative power 0.03(0.01) 0.03 (0.02) EEG Beta2, relative power 0.01 (0.01) 0.01 (0.01) aData unavailable for eight males and three females. RMSSD = Root mean square of the successive differences in normal to normal R-R intervals; SDNN = SD of normal to normal R-R intervals; VAS = Visual analogic scale. *Significant sex or sex × age interactions. Open in new tab There was a sex × age interaction for resting-state HR (β = 2.773, SE = 0.864, t98 = 3.21, p = 0.002) and SDNN (total HRV; β = −0.037, SE = 0.018, t98 = −2.04, p = 0.044) indicating higher HR and lower SDNN (total HRV) during the resting state in older girls compared to boys. Resting-state HR was lower in older than younger boys, while it was similar or slightly higher in older than younger girls. In contrast, SDNN during resting state showed a greater age-related reduction in girls, compared to boys. Quantitative EEG analyses showed that resting-state EEG Delta relative power (β = −0.020, SE = 0.006, t98 = −3.48, p = 0.001) was lower and EEG Alpha relative power (β = 0.019, SE = 0.005, t98 = 4.11, p < 0.001) was higher in older versus younger adolescents. PSG SO latency and sleep/wake stage shifting in the approach to SO Table 3 shows PSG-defined SO parameters in boys and girls. Girls took longer than boys to fall asleep, evident across different SO definitions: SOL-A (β = 0.159, SE = 0.076, t98 = 2.09, p = 0.039), SOL-B (β = 0.139, SE = 0.065, t98 = 2.12, p = 0.036), SOL-C (β = 0.168, SE = 0.066, t98 = 2.55, p = 0.012) (Table 3). 13% of girls and 6% of boys spent ≥ 30 minutes to fall asleep (SOL-A, AASM definition [33]), with 67% of girls and 78% of boys falling asleep within 15 minutes. Bedtimes did not significantly differ between boys and girls. Table 3. Polysomnographic-defined sleep onset measures (mean (SD)) derived from a single night recording in adolescent boys and girls . . Boys Mean (SD) . Girls Mean (SD) . Sleep onset Bedtime, hour:minutes 22:50 (00:46) 22:52 (00:43) SOL-A, minutes 11.9 (13.6) 16.1 (16.5)* SOL-B, minutes 14.5 (14.5) 19.8 (17.3)* SOL-C, minutes 14.8 (14.5) 21.3 (17.4)* . . Boys Mean (SD) . Girls Mean (SD) . Sleep onset Bedtime, hour:minutes 22:50 (00:46) 22:52 (00:43) SOL-A, minutes 11.9 (13.6) 16.1 (16.5)* SOL-B, minutes 14.5 (14.5) 19.8 (17.3)* SOL-C, minutes 14.8 (14.5) 21.3 (17.4)* Sleep onset latency (SOL; -A, first epoch of any sleep stage; -B, first epoch of N2 sleep; -C, first epoch of first four consecutive epochs of stable sleep [N2/N3]). *Significant sex main effects on sleep onset outcomes. Open in new tab Table 3. Polysomnographic-defined sleep onset measures (mean (SD)) derived from a single night recording in adolescent boys and girls . . Boys Mean (SD) . Girls Mean (SD) . Sleep onset Bedtime, hour:minutes 22:50 (00:46) 22:52 (00:43) SOL-A, minutes 11.9 (13.6) 16.1 (16.5)* SOL-B, minutes 14.5 (14.5) 19.8 (17.3)* SOL-C, minutes 14.8 (14.5) 21.3 (17.4)* . . Boys Mean (SD) . Girls Mean (SD) . Sleep onset Bedtime, hour:minutes 22:50 (00:46) 22:52 (00:43) SOL-A, minutes 11.9 (13.6) 16.1 (16.5)* SOL-B, minutes 14.5 (14.5) 19.8 (17.3)* SOL-C, minutes 14.8 (14.5) 21.3 (17.4)* Sleep onset latency (SOL; -A, first epoch of any sleep stage; -B, first epoch of N2 sleep; -C, first epoch of first four consecutive epochs of stable sleep [N2/N3]). *Significant sex main effects on sleep onset outcomes. Open in new tab In approaching stable sleep (SOL-C) there was a progressive non-linear reduction in the proportion of PSG wake epochs (β = −0.831, SE = 0.062, z = −13.50, p < 0.001). In addition, a sex × bin interaction effect (β = 0.117, SE = 0.051, z = 2.30, p = 0.022) indicated that the proportion of wake epochs declined more rapidly in boys compared to girls, reflecting a smoother progression from wake-to-sleep in boys. No sex × age interactions or age main effects were found in any of the models. Changes in cardiac and cortical activities across the SO transition The rate of change in HR across SO was similar between sexes and across age. In the five minutes preceding SO-C, HR progressively slowed (β = −0.533, SE = 0.058, t101 = −9.14, p < 0.001) in both boys and girls (HR drop of ~0.53 bpm every 30 seconds in the 5 minutes preceding SO-C). In the 5 minutes after SO-C, HR continued to drop in both sexes (β = −0.088, SE = 0.025, t101 = −3.56, p = 0.001), at a lower rate (~0.09 bpm drop every 30 seconds in the 5 minutes after SO-C) (Figure 2). Figure 2. Open in new tabDownload slide Mean (±SEs) heart rate plotted every 30 seconds across the 5 minutes preceding and 5 minutes following SO, defined as the first epoch of four consecutive epochs of stable sleep (N2/N3) (SO-C), separately for girls (green rhombuses) and boys (blue circles). For illustrative purpose only, the sample was split in thirds according to the age distribution: young (<14 years; N = 34), mid (≥14 years, <17 years; N = 34), and older (≥17 years; N = 34) age categories. However, age was included as a continuous factor in the statistical models. Figure 2. Open in new tabDownload slide Mean (±SEs) heart rate plotted every 30 seconds across the 5 minutes preceding and 5 minutes following SO, defined as the first epoch of four consecutive epochs of stable sleep (N2/N3) (SO-C), separately for girls (green rhombuses) and boys (blue circles). For illustrative purpose only, the sample was split in thirds according to the age distribution: young (<14 years; N = 34), mid (≥14 years, <17 years; N = 34), and older (≥17 years; N = 34) age categories. However, age was included as a continuous factor in the statistical models. As for the resting state data, there was a sex × age interaction effect for HR for both pre- (β = 1.307, SE = 0.046, t101 = 2.84, p = 0.005) and post-SO-C (β = 1.289, SE = 0.428, t101 = 3.01, p = 0.003), showing faster HR in older girls than older boys. HR was slower in older than younger boys, while it remained constant or was slightly faster in older than younger girls. EEG Delta relative power progressively increased (β = 0.0176, SE = 0.0017, t101 = 10.19, p < 0.001) at a rate of ~1.8% every 30 seconds in the 5 minutes preceding SO-C (an overall cumulative increase of ~17.6%), in both boys and girls. In contrast, EEG Alpha (β = −0.0114, SE = 0.0012, t101 = −9.54, p < 0.001), Sigma (β = −0.0024, SE = 0.0028, t101 = −8.50, p < 0.001), Beta1 (β = −0.0012, SE = 0.0002, t101 = −7.29, p < 0.001) and Beta2 (β = −0.0017, SE = 0.0003, t101 = −6.04, p < 0.001) relative powers progressively decreased in the approach to SO-C, in boys and girls. No changes were found in EEG Theta relative power in the approach to SO-C. Figure 3 illustrates the patterns of change for cortical frontal EEG relative powers in Delta, Theta, Alpha, Sigma, Beta1, and Beta2 frequency bands across the SO period. Once reaching SO-C, EEG Delta relative power (β = −0.0023, SE = 0.0008, t101 = −2.78, p = 0.007) started declining slightly across the 5 minutes post SO. Similarly, once reaching SO-C, EEG Theta relative power (β = −0.0024, SE = 0.0005, t101 = −4.93, p < 0.001) started declining across the 5 minutes post SO. An inverted trend is evident in EEG Sigma (β = 0.0018, SE = 0.0003, t101 = 6.38, p < 0.001) and Alpha (β = 0.0035, SE = 0.0004, t101 = 9.53, p < 0.001) relative powers, which started rising across the 5-minute period after SO-C. Beta1 (β = −0.0005, SE = 0.0001, t101 = −4.81, p < 0.001) continued progressively dropping across the 5 minutes post SO-C. Figure 3. Open in new tabDownload slide Patterns of change (mean values) in EEG relative power (averaged for F3 and F4) in Delta, Theta, Alpha, Sigma, Beta1, and Beta2 frequencies bands across the 5 minutes preceding and 5 minutes periods following SO, defined as the first epoch of four consecutive epochs of stable sleep (N2/N3) (SO-C). Data are plotted for boys (circles) and girls (rhombuses). For illustrative purpose only, the sample was split in thirds according to the age distribution: young (<14 years, red; N = 34), mid (≥14 years, <17 years, yellow; N = 34) and older (≥17 years, blue; N = 34) age categories. However, age was included as a continuous factor in the statistical models. Figure 3. Open in new tabDownload slide Patterns of change (mean values) in EEG relative power (averaged for F3 and F4) in Delta, Theta, Alpha, Sigma, Beta1, and Beta2 frequencies bands across the 5 minutes preceding and 5 minutes periods following SO, defined as the first epoch of four consecutive epochs of stable sleep (N2/N3) (SO-C). Data are plotted for boys (circles) and girls (rhombuses). For illustrative purpose only, the sample was split in thirds according to the age distribution: young (<14 years, red; N = 34), mid (≥14 years, <17 years, yellow; N = 34) and older (≥17 years, blue; N = 34) age categories. However, age was included as a continuous factor in the statistical models. There was a significant main effect of age for EEG Delta power in the period before SO-C (β = −0.0099, SE = 0.0039, t101 = −2.53, p = 0.013), as well as for EEG Delta (β = −0.0119, SE = 0.0008, t101 = −4.78, p < 0.001), Theta (β = 0.0057, SE = 0.0016, t101 = 3.48, p < 0.001), Alpha (β = 0.0032, SE = 0.0013, t101 = 2.49, p = 0.015) and Sigma (β = 0.0026, SE = 0.0012, t101 = 2.23, p = 0.028) relative powers post SO-C, indicating lower EEG Delta relative power (drop-rate of ~1% every year), and higher EEG Theta, Alpha and Sigma relative powers in older compared with younger boys and girls. No sex × age, bins × age or bins × sex interactions were significant for any of the EEG frequency bands. Average EEG absolute power in Delta, Theta, Alpha, Sigma, Beta1, and Beta2 frequency bands across SO are provided in Supplementary Table 1, separately for boys and girls, and the time-course of single Hz EEG absolute power across the SO transition is provided in Supplementary Figure 1. Changes in absolute EEG power bands across SO were similar to the patterns seen for relative power. Discussion To our knowledge, this is the first study showing the dynamics of the pre-sleep psychophysiological state and cortical and cardiac de-arousing processes across the wake-to-sleep transition in a non-clinical sample of healthy adolescent boys and girls. In our study, girls approached sleep in a greater state of pre-sleep negative cognition (elevated worry, rumination, depressing, or anxious thoughts) than boys, and had a delayed onset to consolidated sleep, with a less smooth progression from wake-to-sleep than boys. These findings support the existence of a female-specific vulnerability to sleep initiation, which may potentially set girls at high risk to develop sleep disturbances in adolescence as well as later in life. The finding of elevated pre-sleep cognitive arousal in girls supports existing population-based evidence of sex differences in sleep-related cognitive processing. Catastrophic worry (e.g. worry about school, relation with others) was common in a community sample of young (11–12 years) adolescent females [41], higher in older (16–18 years) adolescent girls compared to boys [42], and associated with poor self-reported sleep quality [41]. Also, catastrophic worry partially mediated the relationship between adolescents’ sleep disturbances and the occurrence of depressive symptoms [42]. Interestingly, a meta-analytic review highlighted the fact that adolescent girls have a greater tendency for ruminating than adolescent boys, which does not seem to be evident in children [43], suggesting potential sex differences in developmental changes in pre-sleep-related cognition. Further studies are needed to comprehensively evaluate bedtime cognition in adolescent boys and girls, its relationship with mood and anxiety, and its interference with the physiological de-arousing process. Our results of prolonged PSG-defined SOL in girls confirmed our previous laboratory finding of longer latencies to N2 sleep in girls compared to boys based on an extended NCANDA sample [27], and results from large population-based studies based on self-reported SOL (see ref. [25], for example). In addition, in our study, 13% of girls versus 6% of boys spent ≥ 30 minutes to fall asleep (using the AASM standard definition of SOL, SOL-A), that is considered a clinical cutoff for difficulties falling asleep in the insomnia literature, further supporting the evidence of greater perceived SO difficulties in girls compared to boys [11]. Of interest, girls displayed a less clear wake-to-sleep transition than boys, which was reflected in a blunted reduction in the proportion of PSG wake time in approaching stable sleep (SO-C). While boys seemed to have less trouble in proceeding from wake-to-sleep, girls had more switches from “wake–brief period of sleep (N1/N2)–wake,” before ultimately reaching consolidated sleep, possibly reflecting less efficiency in reaching stable sleep. The significant sex effects that we found for pre-sleep cognitive arousal, SOL, and the wake-to-sleep transition were equally apparent in younger and older age groups, which does not support our hypothesis that effects would be more evident in older girls. Our results, therefore, show that adolescent girls, regardless of age, differ from boys in pre-sleep arousal and reaching consolidated sleep. However, girls and boys showed a similar pattern of change in cortical EEG and cardiac activities around the onset of stable sleep. In both sexes, HR dropped rapidly in the 5 minutes preceding stable sleep at a rate of ~0.53 bpm every 30 seconds and continued to drop at a slower rate once stable sleep was reached. A progressive cortical synchronization (increases in EEG Delta relative power) and a reduction in EEG high-frequency activity characterized the approach to sleep in both sexes. As shown in Supplementary Figure 1, our findings are similar to those reported by De Gennaro et al. [38] in healthy young male adults. We found no evidence of age- or sex-specific differences in the time-course of EEG changes across SO in this adolescent group. However, Spiess and colleagues [44] by employing high-density EEG recordings and specific analyses of EEG oscillatory rhythms, highlighted several differences in the falling asleep process between children (8–11 years) and young adults (20–25 years) (the sample consisted of 67% males, and sex differences or sex × age interactions were not examined). For example, changes in slow-wave density and amplitude showed a similar temporal course across the SO transition in children, while they were dissociated in adults. Also, adults and children showed different EEG patterns across different cortical regions (see ref. [44] for details), which we did not examine. Future work should compare the spatiotemporal cortical reorganization that occurs across the SO transition in male and female adolescents and adults. Although we did not find sex or age differences in CNS or ANS variables underlying the falling asleep de-arousing processes, significant age and age × sex interactions in basal cortical and cardiac activities were evident. Overall, older girls exhibited higher HR before and across SO, compared to younger girls and boys. This effect was accompanied by an age-related reduction in total HRV, as measured during the resting state, in girls, which was not evident in boys. These findings extend those of our previous investigation [28] showing sex × age interactions in HR and cardiac ANS function during nighttime sleep, and appears to reflect normal sex differences in ANS maturation rather than SO-specific sex- and age-dependent shifting in ANS control. We only investigated a 5-minute resting period before lights-out and the 10-minute period across SO, and found no sex × age interaction effects in the falling asleep process. Possibly, a longer time period of analysis, such as across the hour before bedtime, might reveal sex-age divergence in the preparation to sleep, if it does exist. Also, similar to the converging findings of age-related changes in sleep EEG across adolescence (e.g. strong reduction in sleep EEG Delta power in older compared to younger adolescence) [21, 27, 45], our study showed that EEG Delta relative power was lower (~1% lower with every additional year of age), and EEG Theta, Alpha and Sigma relative powers were higher in older compared to younger adolescents, most likely reflecting a trait signature of the brain maturation processes occurring in adolescence, rather than age-related differences in SO-specific cortical activity. Indeed, we and others have shown dramatic changes in EEG delta power with age across adolescence, which is evident during wake, NREM sleep, and REM sleep [27, 46–48]. Also, we found that age-related changes in brain structure (reduction in gray matter volume and cortical thickness, likely suggesting changes in synaptic pruning and myelination [20]) explained between 3% and 9% of variance, and partially mediated the relationship between age and delta activity during NREM sleep [21]. Our study has some limitations, which need to be acknowledged. We did not account for next-day stressful situations (e.g. school exams) or current stress levels, which may have confounded the assessments of pre-sleep psychophysiological state and sleep processes. For example, Wang et al. [49] reported that 78.3% of adolescents preparing for their College Entrance Exam, reported spending more than 30 minutes to fall asleep in the month preceding the exam. Also, despite no differences in SOL being found, Dewald et al. [50] reported greater actigraphic sleep fragmentation in adolescents (12–17 years old; 71% girls) when sleeping during exam weeks (high stress) versus regular school weeks (low stress). Further investigations should account for potential effects of stress. On the other hand, we controlled for several other factors (e.g. caffeine, bedtime electronic media use, alcohol and drug use), which could potentially affect pre-sleep arousal and falling asleep processes ([51, 52]). Participants self-selected their bedtimes and there were no sex, age or sex × age interactions in the timing of lights-out or self-reported sleepiness levels. However, we cannot completely exclude potential sex and age differences in endogenous circadian timing, which was not assessed in the current study. Future studies should include experimental manipulations of the pre-sleep psychophysiological state (e.g. up-regulation) and longitudinal evaluations of adolescents’ falling asleep processes to provide new insight into potential age- and sex-related vulnerabilities for sleep initiation, as well as relationships between different pre-sleep psychophysiological states and the subsequent falling asleep process. In conclusion, here we have described the cognitive, cortical, and cardiac autonomic dynamics of the falling asleep process in healthy adolescents. Our findings suggest a female-specific vulnerability to inefficient sleep initiation which may underly sex-specific risks (greater in girls) for the development of sleep disturbances in adolescence as well as later in life. Supplementary material Supplementary material is available at SLEEP online. Supplementary Figure 1. Time-course of single Hz electroencephalographic (EEG) absolute power (mean values averaged between F3 and F4; log µV2/Hz) across the 5 min preceding and following SO, defined as the first epoch of four consecutive epochs of stable sleep (N2/N3) (SO-C), in boys (right) and girls (left). Funding This study was supported by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) grant AA021696 (I.M.C. + F.C.B.) and 3U01AA021696-07S1 supplement grant (FCB), and by the National Heart, Lung and Blood Institute (NHLBI) grant R01 HL139652 (M.d.Z.). The content is solely the responsibility of the authors and does not necessarily represent the official views the National Institutes of Health. Conflict of interest statement. We declare no conflict of interest related to the current work. 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Dauvilliers,, Yves;Barateau,, Lucie;Lopez,, Regis;Rassu, Anna, Laura;Chenini,, Sofiene;Beziat,, Severine;Jaussent,, Isabelle

doi: 10.1093/sleep/zsaa009pmid: 31993661

Abstract Study Objectives To define clinically relevant Narcolepsy Severity Scale (NSS) score ranges, confirm its main performances and sensitivity to medications, and determine whether items need to be weighted. Methods One hundred and forty-three consecutive untreated and 238 treated adults with narcolepsy type 1 (NT1) completed the NSS, a 15-item self-administered questionnaire (score: 0–57) that assesses the severity and consequences of the five major narcolepsy symptoms such as daytime sleepiness, cataplexy, hallucinations, sleep paralysis, and disturbed nighttime sleep (DNS). They also completed the Epworth Sleepiness scale (ESS; daytime sleepiness), Beck Depression Inventory (BDI; depressive symptoms), and EQ5D (quality of life). Results The mean symptom number (4.3 vs 3.5), NSS total score (33.3 ± 9.4 vs 24.3 ± 10.2), and number of narcolepsy symptoms (five symptoms: 53.1% vs 24.8%; four symptoms: 26.6% vs 22.7%; three symptoms: 15.4% vs 32.4%; two symptoms: 4.9% vs 20.2%) were significantly different between untreated and treated patients (p < 0.0001). DNS was often the third symptom (95.5 per cent). The symptom number was associated with diagnosis delay, age at onset, and ESS and BDI scores. Comparisons with ESS, BDI and EQ5D showed that NSS item weighting was not necessary to highlight between-group differences. Four NSS severity levels were defined (mild, moderate, severe, and very severe) with between-group differences related to treatment. The probability of having ESS ≥ 16, BDI ≥ 20, and EQ-5D < 60 increased with the severity level. Conclusion NSS is valid, reliable, and responsive to treatment in patients with NT1, with four clinically relevant severity score ranges provided. NSS has adequate clinimetric properties for broadening its use for both clinic and research. narcolepsy, scale, severity, cataplexy, sleepiness, treatment Statement of Significance In a large population of untreated and treated patients with narcolepsy type I, we confirmed that the Narcolepsy Severity Scale is valid, reliable, and responsive to changes in narcolepsy severity, with different number of symptoms and total scores. The 15 different NSS items do not need to be weighted to improve its performances. The NSS seems useful clinically and experimentally and has adequate clinimetric properties for continued use, with four clinically relevant severity score ranges (mild, moderate, severe, and very severe). We advocate its increased use in clinical settings and in future narcolepsy studies. Introduction Narcolepsy type 1 (NT1, or hypocretin-deficiency syndrome) is a rare disabling chronic neurologic disorder classically characterized by two major symptoms: excessive daytime sleepiness (EDS) and cataplexy [1, 2]. EDS is often the first and also the most frequent and disabling symptom, and is required for NT1 diagnosis. Cataplexy, either partial or generalized, is the most specific NT1 symptom. However, cataplexy is often underdiagnosed due to the large variability in terms of age of onset, clinical presentation, triggering factors, frequency, and intensity of attacks [3]. Among other symptoms, hypnagogic hallucinations and sleep paralysis as well as disrupted nighttime sleep (DNS) are frequently reported, also with large variability in frequency and intensity [4]. Although nonspecific to NT1, DNS (i.e. disrupted and unsatisfactory sleep) is more frequent than sleep paralysis and hallucinations and should be considered as part of a pentad of NT1 symptoms [4]. The clinical experience suggests that only a minority of patients with NT1 have all five symptoms; however, few studies evaluated NT1 symptom frequency and intensity altogether in a standardized way [4–6]. Moreover, it is not clear whether and how the global functioning and quality of life of patients with NT1 are affected by these different symptoms. We recently developed the Narcolepsy Severity Scale (NSS), a self-administered 15-item scale that evaluates the severity, frequency, and impact of the five main narcolepsy symptoms (EDS, cataplexy, hypnagogic hallucinations, sleep paralysis, and DNS), with higher scores indicating more severe symptoms [7]. The NSS shows satisfactory psychometric properties with a good item convergent validity and reliability. Its internal consistency, content validity, reproducibility, and responsiveness to medication (the 10-point difference between untreated and treated groups) indicate that NSS is a valid, reliable, and informative tool for assessing narcolepsy symptom severity. The different NSS items are assumed to be equal, and all items are severity indicators. The total score (0–57) is the sum of each item score. However, the number of items per symptom is not identical: seven items for EDS, three for cataplexy, two for hallucinations, two for sleep paralysis, and one for DNS. In addition, six items assess symptoms frequency using a 6-point Likert scale, and nine items evaluate the symptom-related functional impairment on a 4-point Likert scale. Therefore, it is reasonable to ask whether the different NSS items should be weighted to best assess the narcolepsy severity spectrum, and whether the global score is influenced by age, disease duration, and sex. As the NSS evaluates the severity of narcolepsy symptoms and their impact on daily functioning, it is also important to compare its scores with other clinical measures of EDS, depressive symptoms, and quality of life due to the potential global health consequences of narcolepsy symptoms [8–11]. The aims of present study were: (1) to confirm the main NSS performances, (2) to test whether NSS items need to be weighted to improve its performances, (3) to define clinically relevant severity score ranges, and finally (4) to reassess its responsiveness to narcolepsy medications in a sample of adult untreated and treated patients with NT1. Methods Patients For this study, 381 consecutive adult patients with NT1 (179 women and 202 men; mean age 38.93 ± 17.09 years, 143 drug-free and 238 treated) recruited from the National Reference Center for Narcolepsy of Montpellier, France, completed the NSS. The sample included 219 new patients (80 untreated and 139 treated) who did not participate in the first NSS study [7]. NT1 diagnosis was made using the ICSD-3 criteria [12]: EDS lasting for at least 3 months, history of clear-cut cataplexy, mean sleep latency on the Multiple Sleep Latency Test (MSLT) ≤8 min with ≥2 sleep-onset rapid eye movement periods (SOREMPs), and/or cerebrospinal fluid (CSF) hypocretin-1 level <110 pg/mL. In the subgroup of patients who had a lumbar puncture (n = 177), CSF hypocretin-1 level was lower than 110 pg/mL. Measures All patients completed the NSS on the basis of the last month experience. The physician must first ensure that the patient understands the different symptoms of narcolepsy in order to be able to interpret and quantify them, and clarified any misunderstanding the patient may have had about the questions included in the scale. The different NSS items provide a basis for examining the patient’s perception on symptoms severity and on how symptoms impact daily life and functioning. The NSS is completed in 8–10 min and is restricted to the five main narcolepsy symptoms: EDS (seven items), cataplexy (three items), hallucinations (two items), sleep paralysis (two items), and DNS (one item). Questions concern symptom frequency and intensity (six items rated from 0 to 5), and the impact of symptoms on daily life aspects (nine items rated from 0 to 3). The presence of the five narcolepsy symptoms was defined by a positive answer (score ≥ 1) to at least one of the items related to that symptom. A standardized clinical evaluation was performed at the time of NSS completion by a sleep expert physician. This included questions on: (1) demographic characteristics, (2) education level, dichotomized as <12 years or ≥12 years of education, (3) body mass index (BMI, dichotomized as nonobese <30 kg/m2 and obese ≥30 kg/m2), and (4) diagnosis delay (i.e. interval between symptom onset and NT1 diagnosis). The depressive symptom severity was assessed with the Beck Depression Inventory (BDI-II) [13] and was categorized as “no or mild” (score ≤ 19) and “moderate to severe” (score: 20–63). EDS presence and severity were assessed with the Epworth Sleepiness Scale (ESS) [14] and categorized as no EDS (≤10), mild (11–15), and severe (≥16). Patients filled in the Insomnia Severity Index (ISI) [15] to assess sleep disruption and inadequacy. Patients were classified as having no insomnia (score: ≤7), subthreshold (8–14), and moderate to severe insomnia (>14). Finally, the quality of life was assessed with the EuroQoL five-dimension questionnaire (EQ-5D-5L) [16] in a subgroup of 156 consecutive patients (i.e. the filling of the EuroQoL began a few years after that of NSS). This instrument includes a health self-classification system with five dimensions (EQ-5D utility values) and a visual analog scale (EQ-5D VAS). The EQ-5D VAS score was categorized into tertiles, and the lowest tertile (score = 60) was compared with the other two tertiles. Data on total sleep time, sleep efficiency on the polysomnography, mean sleep latency, and the number of SOREMP on the MSLT were recorded in drug-free patients, and on mean sleep latency on the Maintenance of Wakefulness Test (MWT) in treated patients (n = 109). The use of drugs to treat EDS (modafinil, methylphenidate, pitolisant, mazindol, and sodium oxybate) and cataplexy (antidepressants and sodium oxybate) was reported for all treated patients at the time of the evaluation. All patients with narcolepsy medication had stable dosages for at least 1 month prior to NSS completion. In this study, 143 patients were drug-free, and 94 (65.7 per cent) were drug-naïve (never exposed to medication against narcolepsy) while the others discontinued treatments with an effect on sleep or cataplexy at least 2 months prior the NSS completion. Among this untreated group, 67 patients (31 females and 36 males, 39.28 ± 14.65 years) underwent a second evaluation under medication for narcolepsy after a median interval of 0.5 years [0.08–7.9]. This study was approved by the Institutional Review Board of Montpellier University, France. All participants signed the informed consent. Statistical analysis Demographic characteristics and clinical data were described using means and standard deviations for continuous variables, and numbers and percentages for categorical variables. To confirm the scale performances, sampling adequacy was assessed by calculating the Kaiser–Meyer–Olkin (KMO) index, and the internal consistency (reliability) of the item scores was estimated using the Cronbach coefficient α. The independent Student’s t-test and ANOVA were used to compare continuous variables in two groups (e.g. untreated vs treated patients, ESS ≥ 16 vs <16) and in three groups (e.g. comparisons of 1–3 vs 4 vs 5 NSS symptoms) respectively, and the Chi-square and Fisher’s exact tests for categorical variables. The dependent t-test was used to compare the differences between continuous variables at two different time points or in two different conditions, and the Mc Nemar’s test or McNemar–Bowker test of symmetry for paired categorical data. Associations between continuous variables were assessed with the Pearson correlation coefficient. In univariate analysis, logistic regression models were used to compare the different NSS items with the ESS score. For scoring, all the NSS items associated in the univariate analysis at p < 0.05 were included in a logistic regression model. From the final logistic regression model, the weighted item total score was assigned to each item with the respective β coefficients. To make the score approach an integer and be more intuitive, all β coefficients were standardized in such a way that the lowest one had a value of 1. As the lowest β value was −0.0618, it was multiplied by 16 and was rounded to the closest integer. The weighted total score for each item was obtained by summing the scores for the appropriate level of each item. The same methodology was used for depressive symptoms (BDI ≥ 20) and bad health (EQ-5D VAS < 60). Statistical significance was set at p < 0.05. Statistical analyses were performed using SAS version 9.4 and the Stata 11 software (StataCorp 2007; Stata Statistical Software: Release 11. College Station, TX: StataCorp LP). Results One hundred forty-three consecutive drug-free patients (67 women and 76 men, mean age 38.96 ± 15.33 years) assessed for a diagnosis of NT1, and 238 treated adult patients (112 women and 126 men, mean age 38.92 ± 18.10 years) assessed for a follow-up of NT1 completed the NSS at least once (independent sample). Among the treated patients, 135 were taking stimulant (modafinil, methylphenidate, pitolisant, mazindol) and anticataplectic (sodium oxybate or antidepressant [i.e. venlafaxine, duloxetine, clomipramine]) drugs, 88 only a stimulant, 3 only an anticataplectic drug, and 2 only sodium oxybate. Age, sex, education level, and BMI were not different between treated and untreated patients (Table 1). In the untreated group, 67 patients completed the NSS a second time after starting treatment (29 treated with stimulant and anticataplectic drugs, 31 with stimulants, 2 with antidepressants, and 2 with sodium oxybate; dependent sample). The sociodemographic, clinical, and polysomnographic features of all patients are reported in Table 1. Table 1. Sociodemographic and clinical characteristics of drug-free and treated patients with narcolepsy type 1 Variables . Independent sample . . . . . Dependent sample . . . . . . Drug-free patients N = 143 . . Treated patients N = 238 . . P-value . Drug-free patients N = 67 . . Treated patients N = 67 . . P-value . . n . % . n . % . . n . % . n . % . . Sociodemographic characteristics  Sex, men 76 53.15 126 52.94 0.97 36 53.73 —  Age at NSS completion (years)† 38.96 (15.33) 38.92 (18.10) 0.98 39.28 (14.65) 40.84 (14.97) <0.0001  Education level (years), ≥12 96 69.06 127 64.80 0.41 45 68.18 43 71.67 0.32 Clinical characteristics  BMI, kg/m2† 26.23 (5.21) 26.57 (5.95) 0.57 26.70 (4.63) 26.51 (5.28) 0.15  BMI (kg/m2), ≥30 27 19.57 53 23.56 0.37 13 20.63 13 20.97 0.71  Age at NT1 onset (years)† 32.23 (12.66) 28.96 (14.61) 0.03 33.88 (12.00) —  Diagnosis delay (years)† 8.10 (10.08) 7.77 (10.43) 0.76 6.57 (8.28) —  Disease duration (years)† 14.92 (15.21) 17.32 (15.24) 0.14 11.97 (13.52) 13.52 (14.19) <0.0001  Sleep paralysis, Yes 74 58.73 63 44.06 0.02 31 55.74 1 50.00 NA  Hypnagogic hallucinations, Yes 100 80.00 76 53.52 <0.0001 51 83.61 1 50.00 NA Self-reported rating scales  ESS total score† 18.07 (3.74) 15.11 (4.62) <0.0001 18.22 (4.11) 15.49 (4.88) <0.0001  ESS total score   <11 8 5.67 42 17.80 <0.0001 5 7.46 9 14.29 0.002   [11–16] 21 14.89 70 29.66 10 14.93 21 33.33   ≥16 112 79.43 124 52.54 52 77.61 33 52.38  EQ-5D VAS† 59.70 (20.19) 69.10 (16.74) 0.002 57.24 (23.34) 65.87 (19.32) 0.01  EQ-5D Utility† 0.77 (0.16) 0.81 (0.17) 0.11 0.76 (0.18) 0.79 (0.19) 0.91  BDI-II total score† 16.34 (10.03) 11.94 (9.29) <0.0001 17.45 (9.79) 13.48 (9.38) 0.0002  BDI-II total score, >19 40 32.26 42 21.99 0.04 19 34.55 15 25.00 0.13  ISI total score† 14.76 (4.94) 11.29 (4.88) <0.0001 14.54 (5.43) 13.08 (6.30) 0.01  ISI total score, >14 76 57.58 52 23.53 <0.0001 33 55.93 24 38.10 0.003 Polysomnography measurements  Sleep efficiency (%)† 81.54 (11.45) 82.57 (11.90) 0.48 80.03 (12.40) 81.51 (10.40) 0.58  Total sleep time (min)† 409.60 (84.55) 425.89 (89.37) 0.14 393.25 (99.53) 397.68 (59.78) 0.58  Sleep latency (min)† 7.43 (21.90) 9.99 (11.88) 0.26 5.03 (7.46) 4.52 (6.23) 0.71 MSLT and MWT measurements  MSLT sleep latency (min)† 4.91 (3.76) — 4.84 (3.79) —  MWT sleep latency (min)† — 23.64 (13.52) 25.62 (11.22) — Variables . Independent sample . . . . . Dependent sample . . . . . . Drug-free patients N = 143 . . Treated patients N = 238 . . P-value . Drug-free patients N = 67 . . Treated patients N = 67 . . P-value . . n . % . n . % . . n . % . n . % . . Sociodemographic characteristics  Sex, men 76 53.15 126 52.94 0.97 36 53.73 —  Age at NSS completion (years)† 38.96 (15.33) 38.92 (18.10) 0.98 39.28 (14.65) 40.84 (14.97) <0.0001  Education level (years), ≥12 96 69.06 127 64.80 0.41 45 68.18 43 71.67 0.32 Clinical characteristics  BMI, kg/m2† 26.23 (5.21) 26.57 (5.95) 0.57 26.70 (4.63) 26.51 (5.28) 0.15  BMI (kg/m2), ≥30 27 19.57 53 23.56 0.37 13 20.63 13 20.97 0.71  Age at NT1 onset (years)† 32.23 (12.66) 28.96 (14.61) 0.03 33.88 (12.00) —  Diagnosis delay (years)† 8.10 (10.08) 7.77 (10.43) 0.76 6.57 (8.28) —  Disease duration (years)† 14.92 (15.21) 17.32 (15.24) 0.14 11.97 (13.52) 13.52 (14.19) <0.0001  Sleep paralysis, Yes 74 58.73 63 44.06 0.02 31 55.74 1 50.00 NA  Hypnagogic hallucinations, Yes 100 80.00 76 53.52 <0.0001 51 83.61 1 50.00 NA Self-reported rating scales  ESS total score† 18.07 (3.74) 15.11 (4.62) <0.0001 18.22 (4.11) 15.49 (4.88) <0.0001  ESS total score   <11 8 5.67 42 17.80 <0.0001 5 7.46 9 14.29 0.002   [11–16] 21 14.89 70 29.66 10 14.93 21 33.33   ≥16 112 79.43 124 52.54 52 77.61 33 52.38  EQ-5D VAS† 59.70 (20.19) 69.10 (16.74) 0.002 57.24 (23.34) 65.87 (19.32) 0.01  EQ-5D Utility† 0.77 (0.16) 0.81 (0.17) 0.11 0.76 (0.18) 0.79 (0.19) 0.91  BDI-II total score† 16.34 (10.03) 11.94 (9.29) <0.0001 17.45 (9.79) 13.48 (9.38) 0.0002  BDI-II total score, >19 40 32.26 42 21.99 0.04 19 34.55 15 25.00 0.13  ISI total score† 14.76 (4.94) 11.29 (4.88) <0.0001 14.54 (5.43) 13.08 (6.30) 0.01  ISI total score, >14 76 57.58 52 23.53 <0.0001 33 55.93 24 38.10 0.003 Polysomnography measurements  Sleep efficiency (%)† 81.54 (11.45) 82.57 (11.90) 0.48 80.03 (12.40) 81.51 (10.40) 0.58  Total sleep time (min)† 409.60 (84.55) 425.89 (89.37) 0.14 393.25 (99.53) 397.68 (59.78) 0.58  Sleep latency (min)† 7.43 (21.90) 9.99 (11.88) 0.26 5.03 (7.46) 4.52 (6.23) 0.71 MSLT and MWT measurements  MSLT sleep latency (min)† 4.91 (3.76) — 4.84 (3.79) —  MWT sleep latency (min)† — 23.64 (13.52) 25.62 (11.22) — BDI, Beck Depression Inventory; BMI, body mass index; EQ- 5D, EuroQol five-dimensional questionnaire; ESS, Epworth Severity Scale; ISI, Insomnia Severity Scale; MSLT, Multiple Sleep Latency Test; MWT, Maintenance of Wakefulness Test; NA, test not applicable; SOREMP, sleep-onset rapid eye movement Periods; VAS, Visual Analog Scale. †Continuous variables are expressed as mean (SD). Open in new tab Table 1. Sociodemographic and clinical characteristics of drug-free and treated patients with narcolepsy type 1 Variables . Independent sample . . . . . Dependent sample . . . . . . Drug-free patients N = 143 . . Treated patients N = 238 . . P-value . Drug-free patients N = 67 . . Treated patients N = 67 . . P-value . . n . % . n . % . . n . % . n . % . . Sociodemographic characteristics  Sex, men 76 53.15 126 52.94 0.97 36 53.73 —  Age at NSS completion (years)† 38.96 (15.33) 38.92 (18.10) 0.98 39.28 (14.65) 40.84 (14.97) <0.0001  Education level (years), ≥12 96 69.06 127 64.80 0.41 45 68.18 43 71.67 0.32 Clinical characteristics  BMI, kg/m2† 26.23 (5.21) 26.57 (5.95) 0.57 26.70 (4.63) 26.51 (5.28) 0.15  BMI (kg/m2), ≥30 27 19.57 53 23.56 0.37 13 20.63 13 20.97 0.71  Age at NT1 onset (years)† 32.23 (12.66) 28.96 (14.61) 0.03 33.88 (12.00) —  Diagnosis delay (years)† 8.10 (10.08) 7.77 (10.43) 0.76 6.57 (8.28) —  Disease duration (years)† 14.92 (15.21) 17.32 (15.24) 0.14 11.97 (13.52) 13.52 (14.19) <0.0001  Sleep paralysis, Yes 74 58.73 63 44.06 0.02 31 55.74 1 50.00 NA  Hypnagogic hallucinations, Yes 100 80.00 76 53.52 <0.0001 51 83.61 1 50.00 NA Self-reported rating scales  ESS total score† 18.07 (3.74) 15.11 (4.62) <0.0001 18.22 (4.11) 15.49 (4.88) <0.0001  ESS total score   <11 8 5.67 42 17.80 <0.0001 5 7.46 9 14.29 0.002   [11–16] 21 14.89 70 29.66 10 14.93 21 33.33   ≥16 112 79.43 124 52.54 52 77.61 33 52.38  EQ-5D VAS† 59.70 (20.19) 69.10 (16.74) 0.002 57.24 (23.34) 65.87 (19.32) 0.01  EQ-5D Utility† 0.77 (0.16) 0.81 (0.17) 0.11 0.76 (0.18) 0.79 (0.19) 0.91  BDI-II total score† 16.34 (10.03) 11.94 (9.29) <0.0001 17.45 (9.79) 13.48 (9.38) 0.0002  BDI-II total score, >19 40 32.26 42 21.99 0.04 19 34.55 15 25.00 0.13  ISI total score† 14.76 (4.94) 11.29 (4.88) <0.0001 14.54 (5.43) 13.08 (6.30) 0.01  ISI total score, >14 76 57.58 52 23.53 <0.0001 33 55.93 24 38.10 0.003 Polysomnography measurements  Sleep efficiency (%)† 81.54 (11.45) 82.57 (11.90) 0.48 80.03 (12.40) 81.51 (10.40) 0.58  Total sleep time (min)† 409.60 (84.55) 425.89 (89.37) 0.14 393.25 (99.53) 397.68 (59.78) 0.58  Sleep latency (min)† 7.43 (21.90) 9.99 (11.88) 0.26 5.03 (7.46) 4.52 (6.23) 0.71 MSLT and MWT measurements  MSLT sleep latency (min)† 4.91 (3.76) — 4.84 (3.79) —  MWT sleep latency (min)† — 23.64 (13.52) 25.62 (11.22) — Variables . Independent sample . . . . . Dependent sample . . . . . . Drug-free patients N = 143 . . Treated patients N = 238 . . P-value . Drug-free patients N = 67 . . Treated patients N = 67 . . P-value . . n . % . n . % . . n . % . n . % . . Sociodemographic characteristics  Sex, men 76 53.15 126 52.94 0.97 36 53.73 —  Age at NSS completion (years)† 38.96 (15.33) 38.92 (18.10) 0.98 39.28 (14.65) 40.84 (14.97) <0.0001  Education level (years), ≥12 96 69.06 127 64.80 0.41 45 68.18 43 71.67 0.32 Clinical characteristics  BMI, kg/m2† 26.23 (5.21) 26.57 (5.95) 0.57 26.70 (4.63) 26.51 (5.28) 0.15  BMI (kg/m2), ≥30 27 19.57 53 23.56 0.37 13 20.63 13 20.97 0.71  Age at NT1 onset (years)† 32.23 (12.66) 28.96 (14.61) 0.03 33.88 (12.00) —  Diagnosis delay (years)† 8.10 (10.08) 7.77 (10.43) 0.76 6.57 (8.28) —  Disease duration (years)† 14.92 (15.21) 17.32 (15.24) 0.14 11.97 (13.52) 13.52 (14.19) <0.0001  Sleep paralysis, Yes 74 58.73 63 44.06 0.02 31 55.74 1 50.00 NA  Hypnagogic hallucinations, Yes 100 80.00 76 53.52 <0.0001 51 83.61 1 50.00 NA Self-reported rating scales  ESS total score† 18.07 (3.74) 15.11 (4.62) <0.0001 18.22 (4.11) 15.49 (4.88) <0.0001  ESS total score   <11 8 5.67 42 17.80 <0.0001 5 7.46 9 14.29 0.002   [11–16] 21 14.89 70 29.66 10 14.93 21 33.33   ≥16 112 79.43 124 52.54 52 77.61 33 52.38  EQ-5D VAS† 59.70 (20.19) 69.10 (16.74) 0.002 57.24 (23.34) 65.87 (19.32) 0.01  EQ-5D Utility† 0.77 (0.16) 0.81 (0.17) 0.11 0.76 (0.18) 0.79 (0.19) 0.91  BDI-II total score† 16.34 (10.03) 11.94 (9.29) <0.0001 17.45 (9.79) 13.48 (9.38) 0.0002  BDI-II total score, >19 40 32.26 42 21.99 0.04 19 34.55 15 25.00 0.13  ISI total score† 14.76 (4.94) 11.29 (4.88) <0.0001 14.54 (5.43) 13.08 (6.30) 0.01  ISI total score, >14 76 57.58 52 23.53 <0.0001 33 55.93 24 38.10 0.003 Polysomnography measurements  Sleep efficiency (%)† 81.54 (11.45) 82.57 (11.90) 0.48 80.03 (12.40) 81.51 (10.40) 0.58  Total sleep time (min)† 409.60 (84.55) 425.89 (89.37) 0.14 393.25 (99.53) 397.68 (59.78) 0.58  Sleep latency (min)† 7.43 (21.90) 9.99 (11.88) 0.26 5.03 (7.46) 4.52 (6.23) 0.71 MSLT and MWT measurements  MSLT sleep latency (min)† 4.91 (3.76) — 4.84 (3.79) —  MWT sleep latency (min)† — 23.64 (13.52) 25.62 (11.22) — BDI, Beck Depression Inventory; BMI, body mass index; EQ- 5D, EuroQol five-dimensional questionnaire; ESS, Epworth Severity Scale; ISI, Insomnia Severity Scale; MSLT, Multiple Sleep Latency Test; MWT, Maintenance of Wakefulness Test; NA, test not applicable; SOREMP, sleep-onset rapid eye movement Periods; VAS, Visual Analog Scale. †Continuous variables are expressed as mean (SD). Open in new tab Replication of NSS construct validity NSS construct validity was confirmed in 219 (139 treated and 80 untreated) patients who did not participate in the first NSS study. Reliability tests gave a Cronbach α value of 0.86 for the entire NSS, indicating a good internal consistency, and sampling was adequate (KMO = 0.79). Concerning the convergent validity, the NSS total score was positively correlated with the ESS and BDI-II scores and was negatively correlated with the EQ-5D VAS score in both untreated and treated patients (p < 0.03 for all comparisons). Number of symptoms and relationship with the NSS score in treated and untreated patients In the independent sample, the NSS total score was higher in the untreated (n = 143) than treated group (n = 238) (33.34 ± 9.40 vs 24.26 ± 10.24, p < 0.0001), and all NSS items, except items #2 and #5, were less severe in treated than in untreated patients (Table 2). Moreover, treated patients had fewer symptoms than untreated patients (p < 0.0001). Specifically, 20.2 per cent of treated patients reported two symptoms (EDS and cataplexy), 32.4 per cent three, 22.7 per cent four, and 24.8 per cent all five symptoms (Table 2). Among untreated patients, 4.9 per cent had only two symptoms, 15.4 per cent three symptoms, 26.6 per cent four symptoms, and 53.1 per cent five symptoms. DNS was the third symptom in 95.5 per cent of untreated patients with three symptoms. Moreover, in untreated patients, the NSS score of the DNS-related single item was positively correlated with the ISI score (r = 0.62, p < 0.0001). DNS and hallucinations were reported by 71.1 per cent of untreated patients with four symptoms. Table 2. Number of narcolepsy symptoms based on the NSS and NSS item scores in drug-free and treated patients with narcolepsy type 1 Variables . Independent sample . . . . . Dependent sample . . . . . . Drug-free patients N = 143 . . Treated patients N = 238 . . P-value . Drug-free patients N = 67 . . Treated patients N = 67 . . P-value . . n . % . n . % . . n . % . n . % . . Number of symptoms  1–2 7 4.90 48 20.17 <0.0001 4 5.97 7 10.45 0.04  3 22 15.38 77 32.35 10 14.93 20 29.85  4 38 26.57 54 22.69 18 26.87 7 10.45  5 76 53.15 59 24.79 35 52.24 33 49.25 Number of symptoms† 4.28 (0.90) 3.49 (1.14) <0.0001 4.25 (0.93) 3.96 (1.17) 0.03 Irresistible need to sleep during the day (Item 1)  Less than 1 episode per day 36 25.17 94 39.50 0.005 15 22.39 33 49.25 0.0007  More than 1 episode per day 107 74.83 144 60.50 52 77.61 34 50.75 Worried about falling asleep during the day (Item 2)  Not Worried at all/Not very worried 66 46.15 126 52.94 0.20 29 43.28 39 58.21 0.04  Worried/Very worried 77 53.85 112 47.06 38 56.72 28 41.79 Disruption of work/activities caused by daytime sleep attacks (Item 3)  Not important at all/Moderately important 47 32.87 127 53.36 0.0001 20 29.85 28 41.79 0.06  Important/Very important 96 67.13 111 46.64 47 70.15 39 58.21 Disruption of social and family life by daytime sleep attacks (Item 4)  Not important at all/Moderately important 40 27.97 125 52.52 <0.0001 17 25.37 28 41.79 0.02  Important/Very important 103 72.03 113 47.48 50 74.63 39 58.21 Feeling after one of such daytime sleep attacks (Item 5)  Very refreshed/Refreshed 94 65.73 149 62.61 0.54 46 68.66 46 68.66 0.99  Tired/Very tired 49 34.27 89 37.39 21 31.34 21 31.34 Time passed before the next episode of daytime sleep attack (Item 6)  More than 3 h 76 53.15 167 70.17 0.0009 40 59.70 43 64.18 0.53  Less than 3 h 67 46.85 71 29.83 27 40.30 24 35.82 Impact of daytime sleep episodes on the ability to drive a car (Item 7)  Not at all/Not too much 51 35.66 147 61.76 <0.0001 23 34.33 34 50.75 0.03  Much/Very much 92 64.34 91 38.24 44 65.67 33 49.25 Frequency of generalized cataplexy episodes (Item 8)  Less than 1 per month 40 27.97 122 51.26 <0.0001 16 23.88 33 49.25 0.001  More than 1 per month 103 72.03 116 48.74 51 76.12 34 50.75 Frequency of partial cataplexy episodes (Item 9)  Less than 1 per week 51 35.66 148 62.18 <0.0001 20 29.85 37 55.22 0.004  More than 1 per week 92 64.34 90 37.82 47 70.15 30 44.78 Impact of cataplexy episodes on work, social or family life (Item 10)  Not at all/Not very much 53 37.06 165 69.33 <0.0001 22 32.84 40 59.70 0.0002  Much/Very much 90 62.94 73 30.67 45 67.16 27 40.30 Frequency of hallucinations when falling asleep/waking up (Item 11)  Less than 1 per week 90 62.94 195 81.93 <0.0001 40 59.70 52 77.61 0.007  More than 1 per week 53 37.06 43 18.07 27 40.30 15 22.39 Hallucination bothering (Item 12)  Not bothered at all/Not very bothered 91 63.64 200 84.03 <0.0001 39 58.21 53 79.10 0.002  Bothered/Very bothered 52 36.36 38 15.97 28 41.79 14 20.90 Frequency of sleep paralysis when falling asleep/waking up (Item 13)  Less than 1 per week 103 72.03 202 84.87 0.003 49 73.13 54 80.60 0.23  More than 1 per week 40 27.97 36 15.13 18 26.87 13 19.40 Sleep paralysis bothering (Item 14)  Not bothered at all/Not very bothered 101 70.63 198 83.19 0.004 49 73.13 53 79.10 0.32  Bothered/Very bothered 42 29.37 40 16.81 18 26.87 14 20.90 Nighttime sleep disturbance (Item 15)  Not at all/Not too much 56 39.16 142 59.66 0.0001 25 37.31 37 55.22 0.01  Much/Very much 87 60.84 96 40.34 42 62.69 30 44.78 NSS total score† 33.34 (9.40) 24.26 (10.24) <0.0001 33.93 (9.86) 27.69 (10.83) <0.0001 Variables . Independent sample . . . . . Dependent sample . . . . . . Drug-free patients N = 143 . . Treated patients N = 238 . . P-value . Drug-free patients N = 67 . . Treated patients N = 67 . . P-value . . n . % . n . % . . n . % . n . % . . Number of symptoms  1–2 7 4.90 48 20.17 <0.0001 4 5.97 7 10.45 0.04  3 22 15.38 77 32.35 10 14.93 20 29.85  4 38 26.57 54 22.69 18 26.87 7 10.45  5 76 53.15 59 24.79 35 52.24 33 49.25 Number of symptoms† 4.28 (0.90) 3.49 (1.14) <0.0001 4.25 (0.93) 3.96 (1.17) 0.03 Irresistible need to sleep during the day (Item 1)  Less than 1 episode per day 36 25.17 94 39.50 0.005 15 22.39 33 49.25 0.0007  More than 1 episode per day 107 74.83 144 60.50 52 77.61 34 50.75 Worried about falling asleep during the day (Item 2)  Not Worried at all/Not very worried 66 46.15 126 52.94 0.20 29 43.28 39 58.21 0.04  Worried/Very worried 77 53.85 112 47.06 38 56.72 28 41.79 Disruption of work/activities caused by daytime sleep attacks (Item 3)  Not important at all/Moderately important 47 32.87 127 53.36 0.0001 20 29.85 28 41.79 0.06  Important/Very important 96 67.13 111 46.64 47 70.15 39 58.21 Disruption of social and family life by daytime sleep attacks (Item 4)  Not important at all/Moderately important 40 27.97 125 52.52 <0.0001 17 25.37 28 41.79 0.02  Important/Very important 103 72.03 113 47.48 50 74.63 39 58.21 Feeling after one of such daytime sleep attacks (Item 5)  Very refreshed/Refreshed 94 65.73 149 62.61 0.54 46 68.66 46 68.66 0.99  Tired/Very tired 49 34.27 89 37.39 21 31.34 21 31.34 Time passed before the next episode of daytime sleep attack (Item 6)  More than 3 h 76 53.15 167 70.17 0.0009 40 59.70 43 64.18 0.53  Less than 3 h 67 46.85 71 29.83 27 40.30 24 35.82 Impact of daytime sleep episodes on the ability to drive a car (Item 7)  Not at all/Not too much 51 35.66 147 61.76 <0.0001 23 34.33 34 50.75 0.03  Much/Very much 92 64.34 91 38.24 44 65.67 33 49.25 Frequency of generalized cataplexy episodes (Item 8)  Less than 1 per month 40 27.97 122 51.26 <0.0001 16 23.88 33 49.25 0.001  More than 1 per month 103 72.03 116 48.74 51 76.12 34 50.75 Frequency of partial cataplexy episodes (Item 9)  Less than 1 per week 51 35.66 148 62.18 <0.0001 20 29.85 37 55.22 0.004  More than 1 per week 92 64.34 90 37.82 47 70.15 30 44.78 Impact of cataplexy episodes on work, social or family life (Item 10)  Not at all/Not very much 53 37.06 165 69.33 <0.0001 22 32.84 40 59.70 0.0002  Much/Very much 90 62.94 73 30.67 45 67.16 27 40.30 Frequency of hallucinations when falling asleep/waking up (Item 11)  Less than 1 per week 90 62.94 195 81.93 <0.0001 40 59.70 52 77.61 0.007  More than 1 per week 53 37.06 43 18.07 27 40.30 15 22.39 Hallucination bothering (Item 12)  Not bothered at all/Not very bothered 91 63.64 200 84.03 <0.0001 39 58.21 53 79.10 0.002  Bothered/Very bothered 52 36.36 38 15.97 28 41.79 14 20.90 Frequency of sleep paralysis when falling asleep/waking up (Item 13)  Less than 1 per week 103 72.03 202 84.87 0.003 49 73.13 54 80.60 0.23  More than 1 per week 40 27.97 36 15.13 18 26.87 13 19.40 Sleep paralysis bothering (Item 14)  Not bothered at all/Not very bothered 101 70.63 198 83.19 0.004 49 73.13 53 79.10 0.32  Bothered/Very bothered 42 29.37 40 16.81 18 26.87 14 20.90 Nighttime sleep disturbance (Item 15)  Not at all/Not too much 56 39.16 142 59.66 0.0001 25 37.31 37 55.22 0.01  Much/Very much 87 60.84 96 40.34 42 62.69 30 44.78 NSS total score† 33.34 (9.40) 24.26 (10.24) <0.0001 33.93 (9.86) 27.69 (10.83) <0.0001 †Continuous variables are expressed as mean (SD). Open in new tab Table 2. Number of narcolepsy symptoms based on the NSS and NSS item scores in drug-free and treated patients with narcolepsy type 1 Variables . Independent sample . . . . . Dependent sample . . . . . . Drug-free patients N = 143 . . Treated patients N = 238 . . P-value . Drug-free patients N = 67 . . Treated patients N = 67 . . P-value . . n . % . n . % . . n . % . n . % . . Number of symptoms  1–2 7 4.90 48 20.17 <0.0001 4 5.97 7 10.45 0.04  3 22 15.38 77 32.35 10 14.93 20 29.85  4 38 26.57 54 22.69 18 26.87 7 10.45  5 76 53.15 59 24.79 35 52.24 33 49.25 Number of symptoms† 4.28 (0.90) 3.49 (1.14) <0.0001 4.25 (0.93) 3.96 (1.17) 0.03 Irresistible need to sleep during the day (Item 1)  Less than 1 episode per day 36 25.17 94 39.50 0.005 15 22.39 33 49.25 0.0007  More than 1 episode per day 107 74.83 144 60.50 52 77.61 34 50.75 Worried about falling asleep during the day (Item 2)  Not Worried at all/Not very worried 66 46.15 126 52.94 0.20 29 43.28 39 58.21 0.04  Worried/Very worried 77 53.85 112 47.06 38 56.72 28 41.79 Disruption of work/activities caused by daytime sleep attacks (Item 3)  Not important at all/Moderately important 47 32.87 127 53.36 0.0001 20 29.85 28 41.79 0.06  Important/Very important 96 67.13 111 46.64 47 70.15 39 58.21 Disruption of social and family life by daytime sleep attacks (Item 4)  Not important at all/Moderately important 40 27.97 125 52.52 <0.0001 17 25.37 28 41.79 0.02  Important/Very important 103 72.03 113 47.48 50 74.63 39 58.21 Feeling after one of such daytime sleep attacks (Item 5)  Very refreshed/Refreshed 94 65.73 149 62.61 0.54 46 68.66 46 68.66 0.99  Tired/Very tired 49 34.27 89 37.39 21 31.34 21 31.34 Time passed before the next episode of daytime sleep attack (Item 6)  More than 3 h 76 53.15 167 70.17 0.0009 40 59.70 43 64.18 0.53  Less than 3 h 67 46.85 71 29.83 27 40.30 24 35.82 Impact of daytime sleep episodes on the ability to drive a car (Item 7)  Not at all/Not too much 51 35.66 147 61.76 <0.0001 23 34.33 34 50.75 0.03  Much/Very much 92 64.34 91 38.24 44 65.67 33 49.25 Frequency of generalized cataplexy episodes (Item 8)  Less than 1 per month 40 27.97 122 51.26 <0.0001 16 23.88 33 49.25 0.001  More than 1 per month 103 72.03 116 48.74 51 76.12 34 50.75 Frequency of partial cataplexy episodes (Item 9)  Less than 1 per week 51 35.66 148 62.18 <0.0001 20 29.85 37 55.22 0.004  More than 1 per week 92 64.34 90 37.82 47 70.15 30 44.78 Impact of cataplexy episodes on work, social or family life (Item 10)  Not at all/Not very much 53 37.06 165 69.33 <0.0001 22 32.84 40 59.70 0.0002  Much/Very much 90 62.94 73 30.67 45 67.16 27 40.30 Frequency of hallucinations when falling asleep/waking up (Item 11)  Less than 1 per week 90 62.94 195 81.93 <0.0001 40 59.70 52 77.61 0.007  More than 1 per week 53 37.06 43 18.07 27 40.30 15 22.39 Hallucination bothering (Item 12)  Not bothered at all/Not very bothered 91 63.64 200 84.03 <0.0001 39 58.21 53 79.10 0.002  Bothered/Very bothered 52 36.36 38 15.97 28 41.79 14 20.90 Frequency of sleep paralysis when falling asleep/waking up (Item 13)  Less than 1 per week 103 72.03 202 84.87 0.003 49 73.13 54 80.60 0.23  More than 1 per week 40 27.97 36 15.13 18 26.87 13 19.40 Sleep paralysis bothering (Item 14)  Not bothered at all/Not very bothered 101 70.63 198 83.19 0.004 49 73.13 53 79.10 0.32  Bothered/Very bothered 42 29.37 40 16.81 18 26.87 14 20.90 Nighttime sleep disturbance (Item 15)  Not at all/Not too much 56 39.16 142 59.66 0.0001 25 37.31 37 55.22 0.01  Much/Very much 87 60.84 96 40.34 42 62.69 30 44.78 NSS total score† 33.34 (9.40) 24.26 (10.24) <0.0001 33.93 (9.86) 27.69 (10.83) <0.0001 Variables . Independent sample . . . . . Dependent sample . . . . . . Drug-free patients N = 143 . . Treated patients N = 238 . . P-value . Drug-free patients N = 67 . . Treated patients N = 67 . . P-value . . n . % . n . % . . n . % . n . % . . Number of symptoms  1–2 7 4.90 48 20.17 <0.0001 4 5.97 7 10.45 0.04  3 22 15.38 77 32.35 10 14.93 20 29.85  4 38 26.57 54 22.69 18 26.87 7 10.45  5 76 53.15 59 24.79 35 52.24 33 49.25 Number of symptoms† 4.28 (0.90) 3.49 (1.14) <0.0001 4.25 (0.93) 3.96 (1.17) 0.03 Irresistible need to sleep during the day (Item 1)  Less than 1 episode per day 36 25.17 94 39.50 0.005 15 22.39 33 49.25 0.0007  More than 1 episode per day 107 74.83 144 60.50 52 77.61 34 50.75 Worried about falling asleep during the day (Item 2)  Not Worried at all/Not very worried 66 46.15 126 52.94 0.20 29 43.28 39 58.21 0.04  Worried/Very worried 77 53.85 112 47.06 38 56.72 28 41.79 Disruption of work/activities caused by daytime sleep attacks (Item 3)  Not important at all/Moderately important 47 32.87 127 53.36 0.0001 20 29.85 28 41.79 0.06  Important/Very important 96 67.13 111 46.64 47 70.15 39 58.21 Disruption of social and family life by daytime sleep attacks (Item 4)  Not important at all/Moderately important 40 27.97 125 52.52 <0.0001 17 25.37 28 41.79 0.02  Important/Very important 103 72.03 113 47.48 50 74.63 39 58.21 Feeling after one of such daytime sleep attacks (Item 5)  Very refreshed/Refreshed 94 65.73 149 62.61 0.54 46 68.66 46 68.66 0.99  Tired/Very tired 49 34.27 89 37.39 21 31.34 21 31.34 Time passed before the next episode of daytime sleep attack (Item 6)  More than 3 h 76 53.15 167 70.17 0.0009 40 59.70 43 64.18 0.53  Less than 3 h 67 46.85 71 29.83 27 40.30 24 35.82 Impact of daytime sleep episodes on the ability to drive a car (Item 7)  Not at all/Not too much 51 35.66 147 61.76 <0.0001 23 34.33 34 50.75 0.03  Much/Very much 92 64.34 91 38.24 44 65.67 33 49.25 Frequency of generalized cataplexy episodes (Item 8)  Less than 1 per month 40 27.97 122 51.26 <0.0001 16 23.88 33 49.25 0.001  More than 1 per month 103 72.03 116 48.74 51 76.12 34 50.75 Frequency of partial cataplexy episodes (Item 9)  Less than 1 per week 51 35.66 148 62.18 <0.0001 20 29.85 37 55.22 0.004  More than 1 per week 92 64.34 90 37.82 47 70.15 30 44.78 Impact of cataplexy episodes on work, social or family life (Item 10)  Not at all/Not very much 53 37.06 165 69.33 <0.0001 22 32.84 40 59.70 0.0002  Much/Very much 90 62.94 73 30.67 45 67.16 27 40.30 Frequency of hallucinations when falling asleep/waking up (Item 11)  Less than 1 per week 90 62.94 195 81.93 <0.0001 40 59.70 52 77.61 0.007  More than 1 per week 53 37.06 43 18.07 27 40.30 15 22.39 Hallucination bothering (Item 12)  Not bothered at all/Not very bothered 91 63.64 200 84.03 <0.0001 39 58.21 53 79.10 0.002  Bothered/Very bothered 52 36.36 38 15.97 28 41.79 14 20.90 Frequency of sleep paralysis when falling asleep/waking up (Item 13)  Less than 1 per week 103 72.03 202 84.87 0.003 49 73.13 54 80.60 0.23  More than 1 per week 40 27.97 36 15.13 18 26.87 13 19.40 Sleep paralysis bothering (Item 14)  Not bothered at all/Not very bothered 101 70.63 198 83.19 0.004 49 73.13 53 79.10 0.32  Bothered/Very bothered 42 29.37 40 16.81 18 26.87 14 20.90 Nighttime sleep disturbance (Item 15)  Not at all/Not too much 56 39.16 142 59.66 0.0001 25 37.31 37 55.22 0.01  Much/Very much 87 60.84 96 40.34 42 62.69 30 44.78 NSS total score† 33.34 (9.40) 24.26 (10.24) <0.0001 33.93 (9.86) 27.69 (10.83) <0.0001 †Continuous variables are expressed as mean (SD). Open in new tab In untreated patients, the number of symptoms was negatively associated with the diagnosis delay (11.5 ± 12.2 years, 10.8 ± 12.9 years, and 5.4 ± 6.1 years for two [EDS and cataplexy] or three, four, and five symptoms, respectively, p = 0.009) and age at NT1 onset (36.4 ± 14.0 years, 34.0 ± 14.3 years, and 29.7 ± 10.6 years, p = 0.04), without any effect of age at the study time, sex, BMI, and CSF hypocretin-1 levels. The number of symptoms was also associated with higher ESS (16.6 ± 4.3 for two or three symptoms, 17.7 ± 4.1 for four symptoms, and 18.8 ± 3.1 for five symptoms, p = 0.03), BDI-II (12.3 ± 8.7, 15.0 ± 9.7, 18.5 ± 10.2, p = 0.03), and ISI (12.4 ± 5.9, 14.2 ± 5.4, 16.0 ± 3.8, p < 0.008) scores, as well as with the mean sleep latency on the MSLT (6.8 ± 4.5, 4.5 ± 3.0, 4.3 ± 3.6, p = 0.02). In treated patients, the number of symptoms was associated with the ESS (12.9 ± 5.3 for one or two symptoms, 15.1 ± 4.1 for three symptoms, and 16.0±4.4 for four or five symptoms, p = 0.001), BDI-II (6.8 ± 5.6, 12.1 ± 9.7, 14.1 ± 9.5, p = 0.0006), and ISI (8.3 ± 4.3, 11.3 ± 4.9, 12.7 ± 4.6, p < 0.0001) scores, but not with sex, age at the study time, age at disease onset, disease duration, mean sleep latency on the MWT, and CSF hypocretin-1 levels. In the dependent sample (n = 67), NSS total score was higher in the untreated than in the treated condition (33.93 ± 9.86 vs 27.69 ± 10.83, p < 0.0001) (Table 2). Similarly, each NSS item was less severe after treatment starts except for five items (#3, #5, #6, #13, and #14). Crude and weighted NSS total scores Among untreated and treated patients, 62.8 per cent had severe EDS (ESS score ≥ 16), 24.5 per cent had moderate/severe depressive symptoms (BDI-II score ≥ 20), and 30 per cent reported low health status (EQ-5D VAS < 60). More precisely, in untreated patients, 79.6 per cent had ESS ≥ 16, 30.4 per cent BDI-II ≥ 20, and 40.5 per cent EQ-5D VAS < 60 while in treated patients 52.7 per cent had ESS ≥ 16, 20.8 per cent BDI-II ≥ 20, and 13.7 per cent EQ-5D VAS < 60. Univariate analysis of the relationships of the crude and weighted NSS scores with the EDS, BDI-II, and EQ-5D scores showed that the scores of all NSS items were higher in patients with severe EDS, moderate/severe depressive symptoms or low health status, with the exception of item #5 for EDS, item #7 for depressive symptoms, and items #1 and #5 for health status. Multivariate analysis including all the NSS items associated in the univariate analysis at p < 0.05 (Table 3) showed that items #1, #2, and #7 were independently associated with severe EDS, and items #2, #5, #8, and #11 with moderate/severe depressive symptoms. Conversely, no item was independently associated with low health status. When patients were divided into two groups in function of the ESS (<16 and ≥16), BDI II (<20 and ≥20), and EQ5D-VAS scores (≥60 and <60), the NSS total score and total weighted score were significantly associated with severe EDS, moderate/severe depressive symptoms, and low health status in unadjusted (Table 4) and in adjusted models (p < 0.0001 after adjustment for age and treatment group, or disease duration and treatment group; data not shown). The weight of the association strength of each item with the ESS, BDI-II, and EQ-5D VAS scores largely varied in the different comparisons (for instance, item 2 had a weighted score of 10, 21, and −9 for ESS, BDI-II, and EQ-5D, respectively). This precluded the establishment of a robust weighted NSS score (Table 3). Table 3. Multivariate logistic regression model for excessive daytime sleepiness, depressive symptoms, and low health status according to the NSS items and the risk scores derived from the β coefficients Variables . ESS score ≥ 16 . . . . BDI score ≥ 20 . . . . EQ-5D VAS score < 60 . . . . . β coefficient . P . OR [95% CI] . Score . β coefficient . P . OR [95% CI] . Score . β coefficient . P . OR [95% CI] . Score . Irresistible need to sleep during the day  Less than 1 episode per day 0 (reference) 0.001 1 0 0 (reference) 0.73 1 0  More than 1 episode per day 0.9051 2.47 [1.44 to 4.24] 14 0.1224 1.13 [0.56 to 2.26] 3 Worried about falling asleep during the day  Not Worried at all/ Not very worried 0 (reference) 0.02 1 0 0 (reference) 0.02 1 0 0 (reference) 0.34 1 0  Worried/Very Worried 0.6293 1.88 [1.10 to 3.21] 10 0.7451 2.11 [1.13 to 3.92] 21 −0.4546 0.63 [0.25 to 1.62] -9 Disruption of your work/activities caused by these daytime sleep attacks  Not important at all/Moderately important 0 (reference) 0.75 1 0 0 (reference) 0.09 1 0 0 (reference) 0.10 1 0  Important/Very important −0.0973 0.91 [0.50 to 1.65] −2 0.6085 1.84 [0.91 to 3.70] 17 0.8406 2.32 [0.85 to 6.32] 17 Disruption of social and family life by these daytime sleep attacks  Not important/ Moderately important 0 (reference) 0.05 1 0 0 (reference) 0.15 1 0 0 (reference) 0.16 1 0  Important/Very important 0.5635 1.76 [0.99 to 3.12] 9 0.5254 1.69 [0.83 to 3.45] 15 0.7964 2.22 [0.74 to 6.68] 16 Feeling after one of such daytime sleep attacks —  Very refreshed/ Refreshed — 0 (reference) 0.03 1 0  Tired/Very tired — −0.6302 1.88 [1.05 to 3.34] −18 Time passed before the next episode of daytime sleep attack  More than 3 h 0 (reference) 0.08 1 0 0 (reference) 0.17 1 0 0 (reference) 0.13 1 0  Less than 3 h 0.5108 1.67 [0.95 to 2.94] 8 0.4040 1.50 [0.84 to 2.68] 11 0.6634 1.94 [0.83 to 4.56] 13 Impact of sudden daytime sleep episodes on the ability to drive a car  Not at all/Not too much 0 (reference) <0.0001 1 0 0 (reference) 0.58 1 0  Much/Very much 1.0905 2.98 [1.6 to 5.03] 17 0.2507 1.28 [0.53 to 3.10] 5 Frequency of generalized cataplexy episodes when experiencing emotions  Less than 1 per month 0 (reference) 0.23 1 0 0 (reference) 0.005 1 0 0 (reference) 0.70 1 0  More than 1 per month 0.3470 1.41 [0.80 to 2.51] 6 1.0351 2.82 [1.37 to 5.80] 29 0.2045 1.23 [0.44 to 3.43] 4 Frequency of partial cataplexy episodes when experiencing emotions  Less than 1 per week 0 (reference) 0.84 1 0 0 (reference) 0.10 1 0 0 (reference) 0.06 1 0  More than 1 per week −0.0618 0.94 [0.52 to 1.70] −1 −0.5445 0.58 [0.30 to 1.12] −15 0.9181 2.50 [0.95 to 6.59] 18 Impact of cataplexy episodes on work, social or family life  Not at all /Not very much 0 (reference) 0.46 1 0 0 (reference) 0.92 1 0 0 (reference) 0.37 1 0  Much/Very much 0.2413 1.27 [0.67 to 2.43] 4 −0.0356 0.97 [0.48 to 1.94] −1 0.4272 1.53 [0.60 to 3.91] 9 Frequency of hallucinations when falling asleep or waking up  Less than 1 per week 0 (reference) 0.38 1 0 0 (reference) 0.04 1 0 0 (reference) 0.69 1 0  More than 1 per week 0.3758 1.46 [0.63 to 3.34] 6 0.7670 2.15 [1.02 to 4.52] 21 0.2265 1.25 [0.42 to 3.77] 5 Hallucination bothering  Not bothered at all/Not very bothered 0 (reference) 0.20 1 0 0 (reference) 0.61 1 0 0 (reference) 0.10 1 0  Bothered/Very bothered −0.5313 0.59 [0.26 to 1.32] −9 0.2019 1.22 [0.57 to 2.65] 6 1.0329 2.81 [0.81 to 9.73] 21 Frequency of sleep paralysis when falling asleep or waking up  Less than 1 per week 0 (reference) 0.82 1 0 0 (reference) 0.54 1 0 0 (reference) 0.64 1 0  More than 1 per week −0.0961 0.91 [0.40 to 2.09] −2 −0.2465 0.78 [0.36 to 1.70] −7 −0.2562 0.77 [0.26 to 2.28] −5 Sleep paralysis bothering  Not bothered at all/Not very bothered 0 (reference) 0.41 1 0 0 (reference) 0.09 1 0 0 (reference) 0.87 1 0  Bothered/Very bothered 0.3344 1.40 [0.63 to 3.10] 5 0.6705 1.96 [0.91 to 4.20] 19 −0.0943 0.91 [0.29 to 2.90] −2 Nighttime sleep disturbance  Not at all/Not too much 0 (reference) 0.09 1 0 0 (reference) 0.35 1 0 0 (reference) 0.92 1 0  Much/Very much 0.4667 1.59 [0.93 to 2.72] 7 0.2904 1.34 [0.73 to 2.46] 8 −0.0497 0.95 [0.36 to 2.50] −1 Variables . ESS score ≥ 16 . . . . BDI score ≥ 20 . . . . EQ-5D VAS score < 60 . . . . . β coefficient . P . OR [95% CI] . Score . β coefficient . P . OR [95% CI] . Score . β coefficient . P . OR [95% CI] . Score . Irresistible need to sleep during the day  Less than 1 episode per day 0 (reference) 0.001 1 0 0 (reference) 0.73 1 0  More than 1 episode per day 0.9051 2.47 [1.44 to 4.24] 14 0.1224 1.13 [0.56 to 2.26] 3 Worried about falling asleep during the day  Not Worried at all/ Not very worried 0 (reference) 0.02 1 0 0 (reference) 0.02 1 0 0 (reference) 0.34 1 0  Worried/Very Worried 0.6293 1.88 [1.10 to 3.21] 10 0.7451 2.11 [1.13 to 3.92] 21 −0.4546 0.63 [0.25 to 1.62] -9 Disruption of your work/activities caused by these daytime sleep attacks  Not important at all/Moderately important 0 (reference) 0.75 1 0 0 (reference) 0.09 1 0 0 (reference) 0.10 1 0  Important/Very important −0.0973 0.91 [0.50 to 1.65] −2 0.6085 1.84 [0.91 to 3.70] 17 0.8406 2.32 [0.85 to 6.32] 17 Disruption of social and family life by these daytime sleep attacks  Not important/ Moderately important 0 (reference) 0.05 1 0 0 (reference) 0.15 1 0 0 (reference) 0.16 1 0  Important/Very important 0.5635 1.76 [0.99 to 3.12] 9 0.5254 1.69 [0.83 to 3.45] 15 0.7964 2.22 [0.74 to 6.68] 16 Feeling after one of such daytime sleep attacks —  Very refreshed/ Refreshed — 0 (reference) 0.03 1 0  Tired/Very tired — −0.6302 1.88 [1.05 to 3.34] −18 Time passed before the next episode of daytime sleep attack  More than 3 h 0 (reference) 0.08 1 0 0 (reference) 0.17 1 0 0 (reference) 0.13 1 0  Less than 3 h 0.5108 1.67 [0.95 to 2.94] 8 0.4040 1.50 [0.84 to 2.68] 11 0.6634 1.94 [0.83 to 4.56] 13 Impact of sudden daytime sleep episodes on the ability to drive a car  Not at all/Not too much 0 (reference) <0.0001 1 0 0 (reference) 0.58 1 0  Much/Very much 1.0905 2.98 [1.6 to 5.03] 17 0.2507 1.28 [0.53 to 3.10] 5 Frequency of generalized cataplexy episodes when experiencing emotions  Less than 1 per month 0 (reference) 0.23 1 0 0 (reference) 0.005 1 0 0 (reference) 0.70 1 0  More than 1 per month 0.3470 1.41 [0.80 to 2.51] 6 1.0351 2.82 [1.37 to 5.80] 29 0.2045 1.23 [0.44 to 3.43] 4 Frequency of partial cataplexy episodes when experiencing emotions  Less than 1 per week 0 (reference) 0.84 1 0 0 (reference) 0.10 1 0 0 (reference) 0.06 1 0  More than 1 per week −0.0618 0.94 [0.52 to 1.70] −1 −0.5445 0.58 [0.30 to 1.12] −15 0.9181 2.50 [0.95 to 6.59] 18 Impact of cataplexy episodes on work, social or family life  Not at all /Not very much 0 (reference) 0.46 1 0 0 (reference) 0.92 1 0 0 (reference) 0.37 1 0  Much/Very much 0.2413 1.27 [0.67 to 2.43] 4 −0.0356 0.97 [0.48 to 1.94] −1 0.4272 1.53 [0.60 to 3.91] 9 Frequency of hallucinations when falling asleep or waking up  Less than 1 per week 0 (reference) 0.38 1 0 0 (reference) 0.04 1 0 0 (reference) 0.69 1 0  More than 1 per week 0.3758 1.46 [0.63 to 3.34] 6 0.7670 2.15 [1.02 to 4.52] 21 0.2265 1.25 [0.42 to 3.77] 5 Hallucination bothering  Not bothered at all/Not very bothered 0 (reference) 0.20 1 0 0 (reference) 0.61 1 0 0 (reference) 0.10 1 0  Bothered/Very bothered −0.5313 0.59 [0.26 to 1.32] −9 0.2019 1.22 [0.57 to 2.65] 6 1.0329 2.81 [0.81 to 9.73] 21 Frequency of sleep paralysis when falling asleep or waking up  Less than 1 per week 0 (reference) 0.82 1 0 0 (reference) 0.54 1 0 0 (reference) 0.64 1 0  More than 1 per week −0.0961 0.91 [0.40 to 2.09] −2 −0.2465 0.78 [0.36 to 1.70] −7 −0.2562 0.77 [0.26 to 2.28] −5 Sleep paralysis bothering  Not bothered at all/Not very bothered 0 (reference) 0.41 1 0 0 (reference) 0.09 1 0 0 (reference) 0.87 1 0  Bothered/Very bothered 0.3344 1.40 [0.63 to 3.10] 5 0.6705 1.96 [0.91 to 4.20] 19 −0.0943 0.91 [0.29 to 2.90] −2 Nighttime sleep disturbance  Not at all/Not too much 0 (reference) 0.09 1 0 0 (reference) 0.35 1 0 0 (reference) 0.92 1 0  Much/Very much 0.4667 1.59 [0.93 to 2.72] 7 0.2904 1.34 [0.73 to 2.46] 8 −0.0497 0.95 [0.36 to 2.50] −1 Open in new tab Table 3. Multivariate logistic regression model for excessive daytime sleepiness, depressive symptoms, and low health status according to the NSS items and the risk scores derived from the β coefficients Variables . ESS score ≥ 16 . . . . BDI score ≥ 20 . . . . EQ-5D VAS score < 60 . . . . . β coefficient . P . OR [95% CI] . Score . β coefficient . P . OR [95% CI] . Score . β coefficient . P . OR [95% CI] . Score . Irresistible need to sleep during the day  Less than 1 episode per day 0 (reference) 0.001 1 0 0 (reference) 0.73 1 0  More than 1 episode per day 0.9051 2.47 [1.44 to 4.24] 14 0.1224 1.13 [0.56 to 2.26] 3 Worried about falling asleep during the day  Not Worried at all/ Not very worried 0 (reference) 0.02 1 0 0 (reference) 0.02 1 0 0 (reference) 0.34 1 0  Worried/Very Worried 0.6293 1.88 [1.10 to 3.21] 10 0.7451 2.11 [1.13 to 3.92] 21 −0.4546 0.63 [0.25 to 1.62] -9 Disruption of your work/activities caused by these daytime sleep attacks  Not important at all/Moderately important 0 (reference) 0.75 1 0 0 (reference) 0.09 1 0 0 (reference) 0.10 1 0  Important/Very important −0.0973 0.91 [0.50 to 1.65] −2 0.6085 1.84 [0.91 to 3.70] 17 0.8406 2.32 [0.85 to 6.32] 17 Disruption of social and family life by these daytime sleep attacks  Not important/ Moderately important 0 (reference) 0.05 1 0 0 (reference) 0.15 1 0 0 (reference) 0.16 1 0  Important/Very important 0.5635 1.76 [0.99 to 3.12] 9 0.5254 1.69 [0.83 to 3.45] 15 0.7964 2.22 [0.74 to 6.68] 16 Feeling after one of such daytime sleep attacks —  Very refreshed/ Refreshed — 0 (reference) 0.03 1 0  Tired/Very tired — −0.6302 1.88 [1.05 to 3.34] −18 Time passed before the next episode of daytime sleep attack  More than 3 h 0 (reference) 0.08 1 0 0 (reference) 0.17 1 0 0 (reference) 0.13 1 0  Less than 3 h 0.5108 1.67 [0.95 to 2.94] 8 0.4040 1.50 [0.84 to 2.68] 11 0.6634 1.94 [0.83 to 4.56] 13 Impact of sudden daytime sleep episodes on the ability to drive a car  Not at all/Not too much 0 (reference) <0.0001 1 0 0 (reference) 0.58 1 0  Much/Very much 1.0905 2.98 [1.6 to 5.03] 17 0.2507 1.28 [0.53 to 3.10] 5 Frequency of generalized cataplexy episodes when experiencing emotions  Less than 1 per month 0 (reference) 0.23 1 0 0 (reference) 0.005 1 0 0 (reference) 0.70 1 0  More than 1 per month 0.3470 1.41 [0.80 to 2.51] 6 1.0351 2.82 [1.37 to 5.80] 29 0.2045 1.23 [0.44 to 3.43] 4 Frequency of partial cataplexy episodes when experiencing emotions  Less than 1 per week 0 (reference) 0.84 1 0 0 (reference) 0.10 1 0 0 (reference) 0.06 1 0  More than 1 per week −0.0618 0.94 [0.52 to 1.70] −1 −0.5445 0.58 [0.30 to 1.12] −15 0.9181 2.50 [0.95 to 6.59] 18 Impact of cataplexy episodes on work, social or family life  Not at all /Not very much 0 (reference) 0.46 1 0 0 (reference) 0.92 1 0 0 (reference) 0.37 1 0  Much/Very much 0.2413 1.27 [0.67 to 2.43] 4 −0.0356 0.97 [0.48 to 1.94] −1 0.4272 1.53 [0.60 to 3.91] 9 Frequency of hallucinations when falling asleep or waking up  Less than 1 per week 0 (reference) 0.38 1 0 0 (reference) 0.04 1 0 0 (reference) 0.69 1 0  More than 1 per week 0.3758 1.46 [0.63 to 3.34] 6 0.7670 2.15 [1.02 to 4.52] 21 0.2265 1.25 [0.42 to 3.77] 5 Hallucination bothering  Not bothered at all/Not very bothered 0 (reference) 0.20 1 0 0 (reference) 0.61 1 0 0 (reference) 0.10 1 0  Bothered/Very bothered −0.5313 0.59 [0.26 to 1.32] −9 0.2019 1.22 [0.57 to 2.65] 6 1.0329 2.81 [0.81 to 9.73] 21 Frequency of sleep paralysis when falling asleep or waking up  Less than 1 per week 0 (reference) 0.82 1 0 0 (reference) 0.54 1 0 0 (reference) 0.64 1 0  More than 1 per week −0.0961 0.91 [0.40 to 2.09] −2 −0.2465 0.78 [0.36 to 1.70] −7 −0.2562 0.77 [0.26 to 2.28] −5 Sleep paralysis bothering  Not bothered at all/Not very bothered 0 (reference) 0.41 1 0 0 (reference) 0.09 1 0 0 (reference) 0.87 1 0  Bothered/Very bothered 0.3344 1.40 [0.63 to 3.10] 5 0.6705 1.96 [0.91 to 4.20] 19 −0.0943 0.91 [0.29 to 2.90] −2 Nighttime sleep disturbance  Not at all/Not too much 0 (reference) 0.09 1 0 0 (reference) 0.35 1 0 0 (reference) 0.92 1 0  Much/Very much 0.4667 1.59 [0.93 to 2.72] 7 0.2904 1.34 [0.73 to 2.46] 8 −0.0497 0.95 [0.36 to 2.50] −1 Variables . ESS score ≥ 16 . . . . BDI score ≥ 20 . . . . EQ-5D VAS score < 60 . . . . . β coefficient . P . OR [95% CI] . Score . β coefficient . P . OR [95% CI] . Score . β coefficient . P . OR [95% CI] . Score . Irresistible need to sleep during the day  Less than 1 episode per day 0 (reference) 0.001 1 0 0 (reference) 0.73 1 0  More than 1 episode per day 0.9051 2.47 [1.44 to 4.24] 14 0.1224 1.13 [0.56 to 2.26] 3 Worried about falling asleep during the day  Not Worried at all/ Not very worried 0 (reference) 0.02 1 0 0 (reference) 0.02 1 0 0 (reference) 0.34 1 0  Worried/Very Worried 0.6293 1.88 [1.10 to 3.21] 10 0.7451 2.11 [1.13 to 3.92] 21 −0.4546 0.63 [0.25 to 1.62] -9 Disruption of your work/activities caused by these daytime sleep attacks  Not important at all/Moderately important 0 (reference) 0.75 1 0 0 (reference) 0.09 1 0 0 (reference) 0.10 1 0  Important/Very important −0.0973 0.91 [0.50 to 1.65] −2 0.6085 1.84 [0.91 to 3.70] 17 0.8406 2.32 [0.85 to 6.32] 17 Disruption of social and family life by these daytime sleep attacks  Not important/ Moderately important 0 (reference) 0.05 1 0 0 (reference) 0.15 1 0 0 (reference) 0.16 1 0  Important/Very important 0.5635 1.76 [0.99 to 3.12] 9 0.5254 1.69 [0.83 to 3.45] 15 0.7964 2.22 [0.74 to 6.68] 16 Feeling after one of such daytime sleep attacks —  Very refreshed/ Refreshed — 0 (reference) 0.03 1 0  Tired/Very tired — −0.6302 1.88 [1.05 to 3.34] −18 Time passed before the next episode of daytime sleep attack  More than 3 h 0 (reference) 0.08 1 0 0 (reference) 0.17 1 0 0 (reference) 0.13 1 0  Less than 3 h 0.5108 1.67 [0.95 to 2.94] 8 0.4040 1.50 [0.84 to 2.68] 11 0.6634 1.94 [0.83 to 4.56] 13 Impact of sudden daytime sleep episodes on the ability to drive a car  Not at all/Not too much 0 (reference) <0.0001 1 0 0 (reference) 0.58 1 0  Much/Very much 1.0905 2.98 [1.6 to 5.03] 17 0.2507 1.28 [0.53 to 3.10] 5 Frequency of generalized cataplexy episodes when experiencing emotions  Less than 1 per month 0 (reference) 0.23 1 0 0 (reference) 0.005 1 0 0 (reference) 0.70 1 0  More than 1 per month 0.3470 1.41 [0.80 to 2.51] 6 1.0351 2.82 [1.37 to 5.80] 29 0.2045 1.23 [0.44 to 3.43] 4 Frequency of partial cataplexy episodes when experiencing emotions  Less than 1 per week 0 (reference) 0.84 1 0 0 (reference) 0.10 1 0 0 (reference) 0.06 1 0  More than 1 per week −0.0618 0.94 [0.52 to 1.70] −1 −0.5445 0.58 [0.30 to 1.12] −15 0.9181 2.50 [0.95 to 6.59] 18 Impact of cataplexy episodes on work, social or family life  Not at all /Not very much 0 (reference) 0.46 1 0 0 (reference) 0.92 1 0 0 (reference) 0.37 1 0  Much/Very much 0.2413 1.27 [0.67 to 2.43] 4 −0.0356 0.97 [0.48 to 1.94] −1 0.4272 1.53 [0.60 to 3.91] 9 Frequency of hallucinations when falling asleep or waking up  Less than 1 per week 0 (reference) 0.38 1 0 0 (reference) 0.04 1 0 0 (reference) 0.69 1 0  More than 1 per week 0.3758 1.46 [0.63 to 3.34] 6 0.7670 2.15 [1.02 to 4.52] 21 0.2265 1.25 [0.42 to 3.77] 5 Hallucination bothering  Not bothered at all/Not very bothered 0 (reference) 0.20 1 0 0 (reference) 0.61 1 0 0 (reference) 0.10 1 0  Bothered/Very bothered −0.5313 0.59 [0.26 to 1.32] −9 0.2019 1.22 [0.57 to 2.65] 6 1.0329 2.81 [0.81 to 9.73] 21 Frequency of sleep paralysis when falling asleep or waking up  Less than 1 per week 0 (reference) 0.82 1 0 0 (reference) 0.54 1 0 0 (reference) 0.64 1 0  More than 1 per week −0.0961 0.91 [0.40 to 2.09] −2 −0.2465 0.78 [0.36 to 1.70] −7 −0.2562 0.77 [0.26 to 2.28] −5 Sleep paralysis bothering  Not bothered at all/Not very bothered 0 (reference) 0.41 1 0 0 (reference) 0.09 1 0 0 (reference) 0.87 1 0  Bothered/Very bothered 0.3344 1.40 [0.63 to 3.10] 5 0.6705 1.96 [0.91 to 4.20] 19 −0.0943 0.91 [0.29 to 2.90] −2 Nighttime sleep disturbance  Not at all/Not too much 0 (reference) 0.09 1 0 0 (reference) 0.35 1 0 0 (reference) 0.92 1 0  Much/Very much 0.4667 1.59 [0.93 to 2.72] 7 0.2904 1.34 [0.73 to 2.46] 8 −0.0497 0.95 [0.36 to 2.50] −1 Open in new tab Table 4. Associations between NSS total score and weighted NSS total score with excessive daytime sleepiness, depressive symptoms, and low health status Variables . ESS score . . . . . BDI score . . . . . EQ-5D VAS . . . . . . <16 N = 141 . . ≥16 N = 238 . . P . <20 N = 271 . . ≥20 N = 88 . . P . ≥60 N = 109 . . <60 N = 47 . . P . . n . % . n . % . . n . % . n . % . . n . % . n . % . . Sex, men 74 52.48 127 53.36 0.86 142 52.40 49 55.68 0.59 65 59.63 22 46.81 0.14 Age at NSS completion (years)† 34.27 (16.72) 41.45 (16.57) <0.0001 39.53 (17.14) 37.56 (15.65) <0.0001 36.76 (16.25) 41.91 (16.29) 0.07 Disease duration (years)† 12.85 (12.97) 18.31 (15.94) 0.0009 17.47 (16.03) 13.49 (11.89) 0.0009 15.51 (1346) 16.66 (16.53) 0.65 Treated, Yes 112 79.43 125 52.52 <0.0001 175 64.58 46 52.27 0.04 62 56.88 15 31.91 0.005 NSS total score† 21.80 (9.92) 31.10 (9.86) <0.0001 25.41 (10.17) 35.56 (8.96) <0.0001 26.03 (10.16) 35.83 (9.01) <0.0001 Weighted NSS total score† 23.13 (18.36) 45.75 (17.62) <0.0001 41.53 (32.87) 77.10 (30.74) <0.0001 33.15 (25.31) 64.17 (24.14) <0.0001 Variables . ESS score . . . . . BDI score . . . . . EQ-5D VAS . . . . . . <16 N = 141 . . ≥16 N = 238 . . P . <20 N = 271 . . ≥20 N = 88 . . P . ≥60 N = 109 . . <60 N = 47 . . P . . n . % . n . % . . n . % . n . % . . n . % . n . % . . Sex, men 74 52.48 127 53.36 0.86 142 52.40 49 55.68 0.59 65 59.63 22 46.81 0.14 Age at NSS completion (years)† 34.27 (16.72) 41.45 (16.57) <0.0001 39.53 (17.14) 37.56 (15.65) <0.0001 36.76 (16.25) 41.91 (16.29) 0.07 Disease duration (years)† 12.85 (12.97) 18.31 (15.94) 0.0009 17.47 (16.03) 13.49 (11.89) 0.0009 15.51 (1346) 16.66 (16.53) 0.65 Treated, Yes 112 79.43 125 52.52 <0.0001 175 64.58 46 52.27 0.04 62 56.88 15 31.91 0.005 NSS total score† 21.80 (9.92) 31.10 (9.86) <0.0001 25.41 (10.17) 35.56 (8.96) <0.0001 26.03 (10.16) 35.83 (9.01) <0.0001 Weighted NSS total score† 23.13 (18.36) 45.75 (17.62) <0.0001 41.53 (32.87) 77.10 (30.74) <0.0001 33.15 (25.31) 64.17 (24.14) <0.0001 ESS, Epworth Severity Scale; BDI, Beck depression inventory; EQ-5D VAS, EuroQol five-dimensions questionnaire Visual Analog Scale. †Continuous variables are expressed as mean (SD). Open in new tab Table 4. Associations between NSS total score and weighted NSS total score with excessive daytime sleepiness, depressive symptoms, and low health status Variables . ESS score . . . . . BDI score . . . . . EQ-5D VAS . . . . . . <16 N = 141 . . ≥16 N = 238 . . P . <20 N = 271 . . ≥20 N = 88 . . P . ≥60 N = 109 . . <60 N = 47 . . P . . n . % . n . % . . n . % . n . % . . n . % . n . % . . Sex, men 74 52.48 127 53.36 0.86 142 52.40 49 55.68 0.59 65 59.63 22 46.81 0.14 Age at NSS completion (years)† 34.27 (16.72) 41.45 (16.57) <0.0001 39.53 (17.14) 37.56 (15.65) <0.0001 36.76 (16.25) 41.91 (16.29) 0.07 Disease duration (years)† 12.85 (12.97) 18.31 (15.94) 0.0009 17.47 (16.03) 13.49 (11.89) 0.0009 15.51 (1346) 16.66 (16.53) 0.65 Treated, Yes 112 79.43 125 52.52 <0.0001 175 64.58 46 52.27 0.04 62 56.88 15 31.91 0.005 NSS total score† 21.80 (9.92) 31.10 (9.86) <0.0001 25.41 (10.17) 35.56 (8.96) <0.0001 26.03 (10.16) 35.83 (9.01) <0.0001 Weighted NSS total score† 23.13 (18.36) 45.75 (17.62) <0.0001 41.53 (32.87) 77.10 (30.74) <0.0001 33.15 (25.31) 64.17 (24.14) <0.0001 Variables . ESS score . . . . . BDI score . . . . . EQ-5D VAS . . . . . . <16 N = 141 . . ≥16 N = 238 . . P . <20 N = 271 . . ≥20 N = 88 . . P . ≥60 N = 109 . . <60 N = 47 . . P . . n . % . n . % . . n . % . n . % . . n . % . n . % . . Sex, men 74 52.48 127 53.36 0.86 142 52.40 49 55.68 0.59 65 59.63 22 46.81 0.14 Age at NSS completion (years)† 34.27 (16.72) 41.45 (16.57) <0.0001 39.53 (17.14) 37.56 (15.65) <0.0001 36.76 (16.25) 41.91 (16.29) 0.07 Disease duration (years)† 12.85 (12.97) 18.31 (15.94) 0.0009 17.47 (16.03) 13.49 (11.89) 0.0009 15.51 (1346) 16.66 (16.53) 0.65 Treated, Yes 112 79.43 125 52.52 <0.0001 175 64.58 46 52.27 0.04 62 56.88 15 31.91 0.005 NSS total score† 21.80 (9.92) 31.10 (9.86) <0.0001 25.41 (10.17) 35.56 (8.96) <0.0001 26.03 (10.16) 35.83 (9.01) <0.0001 Weighted NSS total score† 23.13 (18.36) 45.75 (17.62) <0.0001 41.53 (32.87) 77.10 (30.74) <0.0001 33.15 (25.31) 64.17 (24.14) <0.0001 ESS, Epworth Severity Scale; BDI, Beck depression inventory; EQ-5D VAS, EuroQol five-dimensions questionnaire Visual Analog Scale. †Continuous variables are expressed as mean (SD). Open in new tab Probability of having severe EDS, depressive symptoms, or bad health in the function of the total NSS score range NSS total score was divided in four equal ranks to define different severity levels: mild 0–14 (n = 51, 13.5 per cent), moderate 15–28 (n = 149, 39.3 per cent), severe 29–42 (n = 146, 38.5 per cent), and very severe 43–57 (n = 33, 8.7 per cent). The patient distribution in the four groups was significantly different in the treated and untreated groups (p < 0.0001), with 68.3 per cent and 34.6 per cent of untreated and treated patients in the severe/very severe groups. The probability of having an ESS score ≥ 16, BDI-II score ≥ 20, and/or EQ-5D VAS < 60 increased with the NSS score rank in the whole sample, with a risk of 87.9 per cent, 62.5 per cent, and 80 per cent, respectively, in the very severe NSS group. Similar results were obtained in untreated and treated patients (Table 5). Table 5. Probability of having excessive daytime sleepiness, depressive symptoms, and low health status according to the NSS total score severity range NSS score . Whole sample . . . . Drug-free patients . . . . Treated patients . . . . . N . % . ESS ≥16 (n) . Risk% [95% CI] . N . % . ESS ≥16 (n) . Risk% [95% CI] . N . % . ESS ≥16 (n) . Risk% [95% CI] . 0–14 51 13.46 13 25.49 [23.82 to 37.45] 6 4.23 2 33.33 [17.93 to 71.05] 45 18.99 11 24.44 [22.57 to 37.00] 15–28 149 39.31 82 55.03 [54.38 to 63.02] 39 27.46 29 74.36 [72.16 to 88.06] 110 46.41 53 48.18 [47.29 to 57.52] 29–42 146 38.52 114 78.08 [77.53 to 84.79] 74 52.11 62 83.78 [82.81 to 92.18] 72 30.38 52 72.22 [71.00 to 82.57] 43–57 33 8.71 29 87.88 [85.94 to 99.01] 23 16.20 20 86.96 [84.08 to 100.00] 10 4.22 9 90.00 [84.12 to 100.00] NSS score N % BDI ≥20 (n) Risk% [95% CI] N % BDI ≥20 (n) Risk% [95% CI] N % BDI ≥20 (n) Risk% [95% CI] 0–14 48 13.37 2 4.17 [3.35 to 9.82] 6 4.35 1 16.67 [4.49 to 46.49] 42 19.00 1 2.38 [1.67 to 6.99] 15–28 140 39.00 17 12.14 [11.69 to 17.55] 38 27.54 8 21.05 [18.95 to 34.02] 102 46.15 9 8.82 [8.28 to 14.32] 29–42 139 38.72 49 35.25 [34.58 to 43.19] 72 52.17 20 27.78 [26.56 to 38.12] 67 30.32 29 43.28 [41.83 to 55.15] 43–57 32 8.91 20 62.50 [59.53 to 79.27] 22 15.94 13 59.09 [54.71 to 79.64] 10 4.53 7 70.00 [61.02 to 98.40] NSS score N % EQ5-D <60 (n) Risk% [95% CI] N % EQ5-D <60 (n) Risk% [95% CI] N % EQ5-D <60 (n) Risk% [95% CI] 0–14 20 12.82 1 5.00 [2.86–14.55] 4 5.06 0 — 16 20.00 1 6.25 [3.28 to 18.11] 15–28 53 33.97 10 18.87 [17.42–29.40] 19 24.06 4 21.05 [16.85] 37 46.25 6 17.65 [15.45 to 30.46] 29–42 68 43.59 24 35.29 [33.92 to 46.65] 45 56.96 20 44.44 [42.28 to 58.96] 23 28.75 4 17.39 [14.16 to 32.88] 43–57 15 9.62 12 80.00 [74.77 to 100.00] 11 13.92 8 72.73 [64.79 to 99.05] 4 5.00 0 — NSS score . Whole sample . . . . Drug-free patients . . . . Treated patients . . . . . N . % . ESS ≥16 (n) . Risk% [95% CI] . N . % . ESS ≥16 (n) . Risk% [95% CI] . N . % . ESS ≥16 (n) . Risk% [95% CI] . 0–14 51 13.46 13 25.49 [23.82 to 37.45] 6 4.23 2 33.33 [17.93 to 71.05] 45 18.99 11 24.44 [22.57 to 37.00] 15–28 149 39.31 82 55.03 [54.38 to 63.02] 39 27.46 29 74.36 [72.16 to 88.06] 110 46.41 53 48.18 [47.29 to 57.52] 29–42 146 38.52 114 78.08 [77.53 to 84.79] 74 52.11 62 83.78 [82.81 to 92.18] 72 30.38 52 72.22 [71.00 to 82.57] 43–57 33 8.71 29 87.88 [85.94 to 99.01] 23 16.20 20 86.96 [84.08 to 100.00] 10 4.22 9 90.00 [84.12 to 100.00] NSS score N % BDI ≥20 (n) Risk% [95% CI] N % BDI ≥20 (n) Risk% [95% CI] N % BDI ≥20 (n) Risk% [95% CI] 0–14 48 13.37 2 4.17 [3.35 to 9.82] 6 4.35 1 16.67 [4.49 to 46.49] 42 19.00 1 2.38 [1.67 to 6.99] 15–28 140 39.00 17 12.14 [11.69 to 17.55] 38 27.54 8 21.05 [18.95 to 34.02] 102 46.15 9 8.82 [8.28 to 14.32] 29–42 139 38.72 49 35.25 [34.58 to 43.19] 72 52.17 20 27.78 [26.56 to 38.12] 67 30.32 29 43.28 [41.83 to 55.15] 43–57 32 8.91 20 62.50 [59.53 to 79.27] 22 15.94 13 59.09 [54.71 to 79.64] 10 4.53 7 70.00 [61.02 to 98.40] NSS score N % EQ5-D <60 (n) Risk% [95% CI] N % EQ5-D <60 (n) Risk% [95% CI] N % EQ5-D <60 (n) Risk% [95% CI] 0–14 20 12.82 1 5.00 [2.86–14.55] 4 5.06 0 — 16 20.00 1 6.25 [3.28 to 18.11] 15–28 53 33.97 10 18.87 [17.42–29.40] 19 24.06 4 21.05 [16.85] 37 46.25 6 17.65 [15.45 to 30.46] 29–42 68 43.59 24 35.29 [33.92 to 46.65] 45 56.96 20 44.44 [42.28 to 58.96] 23 28.75 4 17.39 [14.16 to 32.88] 43–57 15 9.62 12 80.00 [74.77 to 100.00] 11 13.92 8 72.73 [64.79 to 99.05] 4 5.00 0 — Open in new tab Table 5. Probability of having excessive daytime sleepiness, depressive symptoms, and low health status according to the NSS total score severity range NSS score . Whole sample . . . . Drug-free patients . . . . Treated patients . . . . . N . % . ESS ≥16 (n) . Risk% [95% CI] . N . % . ESS ≥16 (n) . Risk% [95% CI] . N . % . ESS ≥16 (n) . Risk% [95% CI] . 0–14 51 13.46 13 25.49 [23.82 to 37.45] 6 4.23 2 33.33 [17.93 to 71.05] 45 18.99 11 24.44 [22.57 to 37.00] 15–28 149 39.31 82 55.03 [54.38 to 63.02] 39 27.46 29 74.36 [72.16 to 88.06] 110 46.41 53 48.18 [47.29 to 57.52] 29–42 146 38.52 114 78.08 [77.53 to 84.79] 74 52.11 62 83.78 [82.81 to 92.18] 72 30.38 52 72.22 [71.00 to 82.57] 43–57 33 8.71 29 87.88 [85.94 to 99.01] 23 16.20 20 86.96 [84.08 to 100.00] 10 4.22 9 90.00 [84.12 to 100.00] NSS score N % BDI ≥20 (n) Risk% [95% CI] N % BDI ≥20 (n) Risk% [95% CI] N % BDI ≥20 (n) Risk% [95% CI] 0–14 48 13.37 2 4.17 [3.35 to 9.82] 6 4.35 1 16.67 [4.49 to 46.49] 42 19.00 1 2.38 [1.67 to 6.99] 15–28 140 39.00 17 12.14 [11.69 to 17.55] 38 27.54 8 21.05 [18.95 to 34.02] 102 46.15 9 8.82 [8.28 to 14.32] 29–42 139 38.72 49 35.25 [34.58 to 43.19] 72 52.17 20 27.78 [26.56 to 38.12] 67 30.32 29 43.28 [41.83 to 55.15] 43–57 32 8.91 20 62.50 [59.53 to 79.27] 22 15.94 13 59.09 [54.71 to 79.64] 10 4.53 7 70.00 [61.02 to 98.40] NSS score N % EQ5-D <60 (n) Risk% [95% CI] N % EQ5-D <60 (n) Risk% [95% CI] N % EQ5-D <60 (n) Risk% [95% CI] 0–14 20 12.82 1 5.00 [2.86–14.55] 4 5.06 0 — 16 20.00 1 6.25 [3.28 to 18.11] 15–28 53 33.97 10 18.87 [17.42–29.40] 19 24.06 4 21.05 [16.85] 37 46.25 6 17.65 [15.45 to 30.46] 29–42 68 43.59 24 35.29 [33.92 to 46.65] 45 56.96 20 44.44 [42.28 to 58.96] 23 28.75 4 17.39 [14.16 to 32.88] 43–57 15 9.62 12 80.00 [74.77 to 100.00] 11 13.92 8 72.73 [64.79 to 99.05] 4 5.00 0 — NSS score . Whole sample . . . . Drug-free patients . . . . Treated patients . . . . . N . % . ESS ≥16 (n) . Risk% [95% CI] . N . % . ESS ≥16 (n) . Risk% [95% CI] . N . % . ESS ≥16 (n) . Risk% [95% CI] . 0–14 51 13.46 13 25.49 [23.82 to 37.45] 6 4.23 2 33.33 [17.93 to 71.05] 45 18.99 11 24.44 [22.57 to 37.00] 15–28 149 39.31 82 55.03 [54.38 to 63.02] 39 27.46 29 74.36 [72.16 to 88.06] 110 46.41 53 48.18 [47.29 to 57.52] 29–42 146 38.52 114 78.08 [77.53 to 84.79] 74 52.11 62 83.78 [82.81 to 92.18] 72 30.38 52 72.22 [71.00 to 82.57] 43–57 33 8.71 29 87.88 [85.94 to 99.01] 23 16.20 20 86.96 [84.08 to 100.00] 10 4.22 9 90.00 [84.12 to 100.00] NSS score N % BDI ≥20 (n) Risk% [95% CI] N % BDI ≥20 (n) Risk% [95% CI] N % BDI ≥20 (n) Risk% [95% CI] 0–14 48 13.37 2 4.17 [3.35 to 9.82] 6 4.35 1 16.67 [4.49 to 46.49] 42 19.00 1 2.38 [1.67 to 6.99] 15–28 140 39.00 17 12.14 [11.69 to 17.55] 38 27.54 8 21.05 [18.95 to 34.02] 102 46.15 9 8.82 [8.28 to 14.32] 29–42 139 38.72 49 35.25 [34.58 to 43.19] 72 52.17 20 27.78 [26.56 to 38.12] 67 30.32 29 43.28 [41.83 to 55.15] 43–57 32 8.91 20 62.50 [59.53 to 79.27] 22 15.94 13 59.09 [54.71 to 79.64] 10 4.53 7 70.00 [61.02 to 98.40] NSS score N % EQ5-D <60 (n) Risk% [95% CI] N % EQ5-D <60 (n) Risk% [95% CI] N % EQ5-D <60 (n) Risk% [95% CI] 0–14 20 12.82 1 5.00 [2.86–14.55] 4 5.06 0 — 16 20.00 1 6.25 [3.28 to 18.11] 15–28 53 33.97 10 18.87 [17.42–29.40] 19 24.06 4 21.05 [16.85] 37 46.25 6 17.65 [15.45 to 30.46] 29–42 68 43.59 24 35.29 [33.92 to 46.65] 45 56.96 20 44.44 [42.28 to 58.96] 23 28.75 4 17.39 [14.16 to 32.88] 43–57 15 9.62 12 80.00 [74.77 to 100.00] 11 13.92 8 72.73 [64.79 to 99.05] 4 5.00 0 — Open in new tab Analysis of untreated patients divided in three groups based on the NSS score severity (mild-moderate: 31.7 per cent, severe: 52.1 per cent, and very severe: 16.2 per cent) indicated that men reported more frequently severe symptoms than women (79.2 per cent of men had a very severe score and 20.8 per cent of women, p = 0.03), with no effect of age, age at disease onset, disease duration, BMI, mean sleep latency on the MSLT, and CSF hypocretin-1 levels. The same analysis in the treated group did not highlight any significant difference. Discussion Our study confirmed that NSS is a reliable tool and responsive to treatment-related changes, as indicated by the significant different number of symptoms and total scores in untreated and treated patients with NT1. Our results also demonstrated that weighting the different NSS items is not mandatory to show between-group severity differences. Moreover, the four different levels of NSS severity allowed confirming the differences in symptom severity in the function of the treatment status and their association with EDS, depression, and health status. Altogether, our findings further support the use of NSS in patients with NT1 in clinical settings and trials. Narcolepsy severity can be variable in terms of symptom intensity and frequency. In NT1, the presence of EDS and cataplexy is required for the diagnosis, whereas hallucinations are observed in 30%–80% of patients, sleep paralysis in 25%–50%, and DNS in 30%–95% [1, 2, 4, 5], in the function of the study and the used definition. NSS allows to evaluate the severity/frequency of the narcolepsy spectrum in a comprehensive way. Few other disease-specific measures have been developed, such as the Ullanlinna narcolepsy scale and the Swiss narcolepsy scale, but they were designed to discriminate patients with narcolepsy from controls [17–20]. Conversely, the NSS aim is not to screen for NT1, but rather to quantify and monitor symptom severity, after confirmation of NT1 diagnosis. After multiple inputs from narcolepsy experts and patients during the development process, a final version of 15 questions was created and validated [7]. In the present study that included 219 patients with NT1 who did not participate in the first NSS validation study, we confirmed the good psychometric properties of the scale. Specifically, we found that 53.1 per cent of untreated patients with NT1 had the five major narcolepsy symptoms, 26.6 per cent four symptoms, 15.4 per cent three symptoms, and 4.9 per cent two symptoms (EDS and cataplexy). Moreover, patients with more symptoms had shorter diagnosis delay and younger age at onset, suggesting a more rapid diagnosis in younger individuals with more severe disease. This relationship between young age at onset and the shortest diagnostic delay was previously reported using the patient database of the European Narcolepsy Network [6]. The number of symptoms was also associated with subjective and objective daytime sleepiness, and depressive and insomnia symptoms. DNS was the most frequent symptom (in 95 per cent of cases) after EDS and cataplexy, more often than hallucination or sleep paralysis. These results are largely consistent with the clinical experience. However, DNS was assessed through a single NSS item (item #15) as often not considered specific to narcolepsy. The different NSS items are considered to be equal. However, some of them could be rated as worse by many patients, and this could question the ordinality of the NSS dimension scores. Therefore, in this study, we assessed whether the different NSS items should be weighted to best define narcolepsy impacts. Accordingly, we tested their weight on the strength of the association of each item with the values of other clinical outcomes (i.e. EDS, depressive symptoms, and health status assessed with the ESS, BDI-II, and EQ-5D VAS, respectively). Indeed, EDS is often the primary complaint and ESS the endpoint of many narcolepsy studies [21–23]. Moreover, mood symptoms (BDI-II) and quality of life (EQ-5D VAS) can be affected by the severity of narcolepsy symptoms. Previous studies already reported that patients with narcolepsy had a social stigma, educational and professional problems, and altered quality of life that may cause substantial socioeconomic consequences [24, 25]. The NSS score (total score and total weighted score) correlated well with these clinical scores, as indicated by the significant differences between groups categorized according to EDS severity, depressive symptom severity, and health status, in unadjusted models and after adjustment for age, disease duration, and treatment group. We could not identify robust weighted items that best defined the disease severity relative to the different outcomes. As an example, DNS, which was frequently reported by patients and was defined by a single NSS item, did not affect significantly patient functioning compared with other items. In conclusion, this analysis did not identify any specific item, and thus a weighted NSS is not necessary to best describe NT1 severity. The development of the NSS score and its total score easy to compute using the Likert method (i.e. sum of all ratings) were already described [7]. NSS is restricted to the five main narcolepsy symptoms, does not include other features that may be part of the narcolepsy spectrum (i.e. fatigue, obesity, attentional problems, depression, brain fog, automatic behaviors, and parasomnia), and did not include potential for side effects of medication that may also be part of the impacts of the disease [5, 8–11, 26]. Indeed, we decided to focus on the five disease-specific symptoms because treatment choices and regimen adjustments are often based on their severity. New patient-reported outcome scales may be developed to capture these other aspects to better describe the whole heterogeneous narcolepsy spectrum [27]. Our present analysis indicated that the number of symptoms was lower in treated than untreated patients (both in the independent and dependent samples), with an NSS total score significantly lower (by nine points) in the treated group. Moreover, the score of all NSS items, except two, was lower in treated than untreated patients. The impact of medications on the different symptoms could be under- or over-estimated depending on which symptoms were effectively treated and assessed [28, 29]. For this reason, we should not support the validity of a subset of items from NSS but favor again the global NSS assessment. We defined four different levels of NSS severity: mild (0–14), moderate (15–28), severe (29–42), and very severe (43–57). The proportion of patients in these groups differed in the treated and untreated groups in both the dependent and independent samples, with more patients in the severe and very severe levels when untreated. The probability of having severe sleepiness, moderate-to-severe depressive symptoms, and low health status increased with the NSS level in the whole sample. As for many chronic disorders, disease comprehension and adequate adjustment of behaviors are key components to coping with narcolepsy. Pharmacological medications are really useful, but they do not fully resolve the symptoms of narcolepsy in most cases. We hope that with NSS as a self-reported questionnaire the patient becomes an active participant in the assessment/quantification of the main symptomatic complaints and in treatment decisions and goals. Future long-term studies using NSS should be encouraged to assess changes in the frequency, severity, and consequences of NT1 symptoms over the years. Altogether, our results confirmed, in typical patients with NT1 who had undergone a complete evaluation in a single reference expert center, that NSS is sensitive to changes in symptom severity. Although other narcolepsy centers should test it in patients with narcolepsy before considering its global use, NSS could potentially be useful for the initial and follow-up clinical evaluations, and to monitor and optimize NT1 management. However, we did not include the patient global impression or clinical global impression ratings, and it is not clear whether NSS will have a large placebo effect or not. Globally, its responsiveness to treatment has not been validated formally in a randomized controlled trial; however, future narcolepsy trials could use the NSS instead of the classical ESS that assesses only EDS. The NSS has been translated into various languages by the MAPI Research Institute that hosts and distributes the scale and provides a central clearinghouse for all current and future translations. Finally, as NT1 often occurs in children and adolescents, a Pediatric NSS is currently under development. In conclusion, the NSS is valid, reliable, and responsive to changes in narcolepsy severity in patients with NT1, and the different NSS items do not need to be weighted. The NSS seems useful clinically and experimentally with four clinically relevant severity score ranges and has adequate clinimetric properties for continued use. We advocate its increased use in clinical settings and in future narcolepsy studies. Funding None reported. Conflict of interest statement. This was not an industry-supported study. Y.D. received funds for seminars, board engagements, and travel to conferences by UCB Pharma, Jazz, Theranexus, Flamel, and Bioprojet. R.L. received funds for speaking with UCB Pharma and Shire. S.C., A.L.R., S.B., L.B., and I.J. reported nonfinancial disclosures. References 1. Dauvilliers Y , et al. Narcolepsy with cataplexy . Lancet. 2007 ; 369 ( 9560 ): 499 – 511 . Google Scholar Crossref Search ADS PubMed WorldCat 2. Scammell TE . Narcolepsy . 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Sowho,, Mudiaga;Sgambati,, Francis;Guzman,, Michelle;Schneider,, Hartmut;Schwartz,, Alan

doi: 10.1093/sleep/zsz305pmid: 31837267

Abstract Snoring is a highly prevalent condition associated with obstructive sleep apnea (OSA) and sleep disturbance in bed partners. Objective measurements of snoring in the community, however, are limited. The present study was designed to measure sound levels produced by self-reported habitual snorers in a single night. Snorers were excluded if they reported nocturnal gasping or had severe obesity (BMI > 35 kg/m2). Sound was measured by a monitor mounted 65 cm over the head of the bed on an overnight sleep study. Snoring was defined as sound ≥40 dB(A) during flow limited inspirations. The apnea hypopnea index (AHI) and breath-by-breath peak decibel levels were measured. Snore breaths were tallied to determine the frequency and intensity of snoring. Regression models were used to determine the relationship between objective measures of snoring and OSA (AHI ≥ 5 events/h). The area under the curve (AUC) for the receiver operating characteristic (ROC) was used to predict OSA. Snoring intensity exceeded 45 dB(A) in 66% of the 162 participants studied, with 14% surpassing the 53 dB(A) threshold for noise pollution. Snoring intensity and frequency were independent predictors of OSA. AUCs for snoring intensity and frequency were 77% and 81%, respectively, and increased to 87% and 89%, respectively, with the addition of age and sex as predictors. Snoring represents a source of noise pollution in the bedroom and constitutes an important target for mitigating sound and its adverse effects on bed partners. Precise breath-by-breath identification and quantification of snoring also offers a way to risk stratify otherwise healthy snorers for OSA. habitual snoring, sleep disturbance, sleep apnea, cardiovascular stress Statement of Significance Snoring is a potential source of noise pollution in the bedroom that can degrade the quality of sleep in bed partners and may also be an indicator of obstructive sleep apnea (OSA) in the snorer. Both noise exposure and OSA are known risk factors for adverse health events. Precise characterization of snoring provides a means to identify otherwise healthy habitual snorers at risk for OSA and their bed partners who can have exposure to unhealthy sound levels. Introduction Snoring is highly prevalent in the community and reported to be between 20% and 40% of the population [1–3]. As an auditory environmental exposure, it is a potential source of noise pollution that can disturb the sleep of bed partners. It is a form of upper airway obstruction (UAO) that may also be indicative of the presence of obstructive sleep apnea (OSA) in the snorer [4, 5]. Snoring and associated OSA, may have important health consequences for both the bed partner and snorer alike. Snoring and OSA are recognized risk factors for cardiovascular disease, which may be mitigated by therapy [6]. Similarly, noise pollution in excess of 53 dB(A) has been associated with adverse cardiovascular events [7, 8] in exposed populations. Current evidence suggests that accumulated nocturnal exposure to snoring can thus contribute to the development and/or progression of cardiovascular disease in both the snorer [9] and bed partner. Cardiovascular stress is related to increased sympathetic activation, leading to surges in heart rate and sustained elevations in blood pressure during sleep [10]. Nonetheless, objective measures of snoring severity and its association with OSA have not been well characterized in the general community. The major goal of this paper was to characterize snoring objectively and its association with OSA in a community sample of self-reported habitual snorers. Recognizing that snoring severity can vary widely, we hypothesized that snoring exceeds standards associated with noise pollution and predicts concomitant OSA. To address this hypothesis, we monitored sound levels objectively in a group of healthy habitual snorers without other OSA symptoms, and quantified snoring frequency and intensity in a single night. Methods Study design Self-reported snorers were recruited from the communities surrounding the study sites (Johns Hopkins, Baltimore, MD, Neurotrials Research Inc., Atlanta, GA, and Doctors Community Hospital, Lanham, MD) through flyers, advertisements in community newspapers, social media, and brochures made available in participating medical clinics. Six hundred and eleven self-reported snorers were screened by telephone and 447 persons were excluded. Participants with witnessed apneas, gasping/choking and severe obesity were excluded because these factors are well-recognized risk factors for OSA, and in of themselves would represent a sound indication for sleep apnea testing (Figure 1). Those with co-morbidities such as COPD, asthma, emphysema, or chronic bronchitis, a history of heart disease and heart failure, were also excluded because breathing difficulties in these disorders may lead to noisy breathing, for example, wheezing, not related to UAO. Participants were consented into the study, underwent general medical examination and an in-laboratory polysomnography. All studies were conducted in sound attenuated sleep laboratories at all three study sites. Two recordings were excluded due to continuous sound artifact. Sleep staging and respiratory analyses were done using the American Academy of Sleep Medicine (AASM) criteria [11] and OSA was defined as an AHI ≥5 events/h. The study was approved by Institutional Review Boards of all study sites and was registered on www.clinicaltrials.gov (#NCT01949584) [12]. Figure 1. Open in new tabDownload slide Study flow chart. Figure 1. Open in new tabDownload slide Study flow chart. Measurement and analyses of snoring Self-reported Participants completed a short survey to evaluate for loud, habitual snoring that bothered bed partners and drove them from the bedroom. Response to survey questions were graded on a five-point Likert scale. Objective Snoring frequency and intensity were captured with a high-accuracy class 2 digital sound pressure level meter with an accuracy ± 1.4 dB (DT-8851, Ruby Electronics, Saratoga, CA) in adherence with IEC 61672–1 standards. The device was A-frequency weighted with a fast-time response (125 ms), the settings used in most sound monitors. A-frequency weighting ensured that sound captured was within the acoustic and frequency range of human hearing, while the fast-time response determined the speed of sound capture [13–15]. The system was calibrated using an industrial sound level calibrator (SC-05, Reed, Inc., Wilmington, NC) with an accuracy ± 0.5 dB in adherence with IEC 942. A DC analog sound level output of 10 mV/dB was digitized by the RemLogic (Pleasanton, CA) data acquisition system and sound pressure measurements in decibels (dB(A)) were recorded continuously throughout each sleep study and synchronized with the airflow signal. Pink noise was applied for 10-s intervals to calibrate (Figure 2) the sound signal [16, 17]. Sleep studies were conducted in closed sound-attenuated laboratory bedrooms where background ambient noise levels were ≤35 dB(A). The sound pressure level meter was affixed 65 cm above the head position of the bed during the study night to approximate the distance between the head of the bed partner and snorer. We defined snoring as inspiration during sleep with peak sound ≥40 dB(A), given that background ambient sound levels were ≤35 dB(A). Custom software was deployed to identify inspiratory periods on the airflow signal and facilitate the capture of sound during inspiration (Figure 3). Accuracy of inspiration detection was ensured by visually inspecting the airflow channel for all 162 recordings, and manually adjusting the respiratory tags to align with inspiration when necessary. Figure 2. Open in new tabDownload slide Sound meter calibration. Figure 2. Open in new tabDownload slide Sound meter calibration. Figure 3. Open in new tabDownload slide Snoring sound and airflow characteristics. Figure 3. Open in new tabDownload slide Snoring sound and airflow characteristics. To confirm that breaths with inspiratory peak sound ≥40 dB(A) were actually snores, we assessed breaths for other features of UAO during sleep, viz., inspiratory flow limitation (IFL) [5, 18, 19]. Specifically, we formulated a two-step analyses to examine (1) the frequency of IFL in breaths with peak sound ≥40 dB(A) and (2) the association between a key marker of UAO, viz., inspiratory duty cycle and peak sound level as described in the Methods and Results section. Breaths were randomly analyzed for 3 min samples every 20 min throughout the night from 85 sleep studies, which were evenly drawn from the three study sites. An experienced person was designated to visually identify IFL breaths based on flattening of the inspiratory contour and high frequency oscillations [20, 21]. The scorer was blinded to the sound level signal to prevent bias. A total of 61,739 breaths were sampled from the sleep studies. After IFL scoring, we found that 16,787 of the 61,739 breaths had inspiratory sound level ≥40 dB(A). 94% of these 16,787 breaths met the IFL criteria, suggesting that most breaths with sound ≥40 dB(A) were associated with UAO during sleep. Indeed, a sound threshold of ≥40 dB(A) indicates that the upper airway is dynamically collapsing in the vast majority of breaths during sleep. The sound level signal was exported from RemLogic in European Data Format (EDF) to MatLab (Natick, MA) [22] for analysis. These data were used to calculate snoring severity metrics including snore latency, frequency and intensity as follows: Snore latency Time from sleep onset to the first snore breath. Snoring frequency The percentage of inspiratory breaths during sleep with sound peaks ≥40 dB(A). Snoring intensity (mean peak inspiratory sound) The maximum sound produced during each inspiration (Figure 3) was first converted from a logarithmic scale (decibels) to a linear scale (Pascals) (Equation). Then the arithmetic mean value for sound pressure level in Pascals was calculated, before reconverting the mean in Pascals to decibels [23]. In the equation below, decibels is denoted as dB(A) and Pascals as Pa. Equation Conversion of decibels to Pascals Pa=10 ^(dB(A)/20)∗0.00002 The calculated mean peak inspiratory sound in decibels was defined as snoring intensity. Sound threshold for adverse health events To estimate the proportion of persons that may impose a health risk on their bed partners, we differentiated snorers based on their snoring intensity. We used noise thresholds of 45 and 53 dB(A) which are traffic noise levels known to be associated with sleep disruption and adverse cardiovascular events, respectively [24]. Statistical analyses To characterize snoring metrics in our population of habitual snorers, we first described the distribution of snoring frequency and intensity at specific thresholds by sleep stage and body position. We examined the prevalence of snoring at intensities ≥45 and ≥53 dB(A), and characterized the association between snoring intensity and frequency using Pearson’s correlation coefficient. The Mann–Whitney’s t-test was used to compare anthropometric, demographic, sleep study, and snore characteristics between sub-groups above and below the snoring intensity threshold of 53 dB(A) and between those with and without OSA. Data are presented as mean ± SD or median (IQR) where appropriate. The association between snoring severity and OSA was examined in two ways. First, a Fisher exact method was used to test the dependence of OSA on snoring above or below a snoring intensity of 53 dB(A). Second, we used a logistic regression analyses to model the relationship between snoring intensity as a continuous predictor of OSA. The accuracy of the logistic regression model was examined by calculating the area under the curve (AUC) for the receiver operating characteristic (ROC). For our sample size calculation, we assumed a confidence level of 95% and a 90% probability of success, that is, 10% of respondents who said they snore, would not be objective snorers. All statistical analyses were performed using R and MatLab. Two-tailed p values of less than 0.05 were considered to indicate statistical significance. Post hoc analysis was performed to examine the association between breath-by-breath peak inspiratory sound and UAO severity. We used a quantifiable surrogate of UAO, the “inspiratory duty cycle,” which is the ratio between inspiratory time and total respiratory time denoted as Ti/TTOT [25, 26]. The Ti/TTOT was estimated with the start and end times of the inspiratory tags described above. The inspiratory duty cycle is usually about one-third of the respiratory period during un-obstructed breathing [25, 26]. In UAO, however, duty cycle increases as a compensatory response that helps maintain ventilation [25, 26]. The association between Ti/TTOT and peak inspiratory sound was examined using a mixed effects linear regression model to account for repeated measures within individuals. Results Participant characteristics For our population of habitual snorers, the distribution of snore intensity and frequency are presented in Figure 4 and the association between snoring frequency and intensity is included in the supplement. The proportion of persons with snoring intensity ≥45 and ≥53 dB(A) was 66% and 14%, respectively, and the snoring intensity and frequency were correlated (r = 0.71, p < 0.0001, see Supplementary Figure S1). Stratifying the distribution of snoring severity by presence of OSA, we found that snoring severity was a greater proportion in the OSA compared no OSA groups (see Supplementary Figure S2a and b). Figure 4. Open in new tabDownload slide Distribution of snoring intensity and snoring breath frequency. Figure 4. Open in new tabDownload slide Distribution of snoring intensity and snoring breath frequency. Anthropometric, demographic and sleep study characteristics are shown in Table 1 for the entire group and for those above and below snoring intensity of 53 dB(A). No significant between-group differences were noted in anthropometry, demographics and sleep architecture except for a reduced sleep latency and increased supine sleep time in the group with snoring intensity ≥53 dB(A). In those with elevated snoring intensity, AHI was greater compared to those with snoring intensity <53 dB(A), resulting from elevations in AI and HI. As expected, self-reported snore scores and snore frequency were significantly higher in persons with snoring intensity ≥53 dB(A), and the latency to snore onset was lower. Table 1. Participant characteristics by snoring intensity. . All . <53 dB(A) . ≥53 dB(A) . p . Demographics  Sex (F:M) 74:88 66:74 8:14 0.46  Age (years) 47.4 ± 13.9 47.6 ± 14.2 46.0 ± 12.6 0.54 Anthropometrics  BMI (kg/m2) 27.8 ± 4.5 27.7 ± 4.7 28.3 ± 3.3 0.77  Weight (kg) 181.3 ± 34.7 180.3 ± 35.2 187.6 ± 31.6 0.57  Neck (cm) 38.2 ± 3.8 38.0 ± 3.9 39.5 ± 3.1 0.05  Waist (cm) 95.2 ± 11.1 94.7 ± 11.2 98.8 ± 10.0 0.11  Hip (cm) 106.8 ± 8.8 107.0 ± 9.0 105.7 ± 7.6 0.34 Sleep architecture  Total sleep time (min) 364.3 (328.6–401.1) 363.1 (329.9–397.6) 385.7 (328.8–416.5) 0.27  Sleep efficiency (%) 85.7 (76.7–91.8) 85.6 (76.8–91.2) 88.0 (76.7–93.4) 0.30  Sleep latency (min) 5.9 (2.2–13.8) 7.1 (2.3–14.3) 3.3 (1.8–9.1) 0.04  Slow wave sleep (%) 14.9 (6.2–23.7) 16.5 (6.6–24.1) 12.0 (2.4–20.7) 0.11  Supine sleep (min) 283.9 (162.5–355.6) 267.2 (145.7–345.8) 342.0 (295.1–385.8) <0.001 AHI and sleepiness  ESS 6.0 (4.0–8.0) 6.0 (4.0–8.0) 5.0 (3.0–6.0) 0.07  AHI (events/h) 12.8 (5.4–24.1) 10.5 (4.9–20.0) 32.6 (14.4–61.0) <0.001  AI (events/h) 3.2 (0.7–8.2) 2.7 (0.6–6.0) 15.2 (1.5–37.4) <0.001  HI (events/h) 10.3 (4.9–17.0) 8.7 (4.5–16.3) 14.8 (9.9–23.0) 0.01 Snore parameters  Self-reported snore score 10.0 (7.0–13.0) 6.8 (9.0–12.0) 13.0 (9.3–14.8) 0.003  Snore latency (min) 4.1 (1.3–11.5) 4.5 (1.5–12.5) 2.0 (0.5–6.5) 0.02  Snoring frequency (%) 18.9 (5.8–44.3) 14.7 (3.8–37.2) 59.5 (44.3–70.2) <0.001  Snoring Intensity (dB(A)) 45.4 (43.2–47.7) 45.9 (43.7–48.8) 56.7 (55.2–58.0) <0.001 . All . <53 dB(A) . ≥53 dB(A) . p . Demographics  Sex (F:M) 74:88 66:74 8:14 0.46  Age (years) 47.4 ± 13.9 47.6 ± 14.2 46.0 ± 12.6 0.54 Anthropometrics  BMI (kg/m2) 27.8 ± 4.5 27.7 ± 4.7 28.3 ± 3.3 0.77  Weight (kg) 181.3 ± 34.7 180.3 ± 35.2 187.6 ± 31.6 0.57  Neck (cm) 38.2 ± 3.8 38.0 ± 3.9 39.5 ± 3.1 0.05  Waist (cm) 95.2 ± 11.1 94.7 ± 11.2 98.8 ± 10.0 0.11  Hip (cm) 106.8 ± 8.8 107.0 ± 9.0 105.7 ± 7.6 0.34 Sleep architecture  Total sleep time (min) 364.3 (328.6–401.1) 363.1 (329.9–397.6) 385.7 (328.8–416.5) 0.27  Sleep efficiency (%) 85.7 (76.7–91.8) 85.6 (76.8–91.2) 88.0 (76.7–93.4) 0.30  Sleep latency (min) 5.9 (2.2–13.8) 7.1 (2.3–14.3) 3.3 (1.8–9.1) 0.04  Slow wave sleep (%) 14.9 (6.2–23.7) 16.5 (6.6–24.1) 12.0 (2.4–20.7) 0.11  Supine sleep (min) 283.9 (162.5–355.6) 267.2 (145.7–345.8) 342.0 (295.1–385.8) <0.001 AHI and sleepiness  ESS 6.0 (4.0–8.0) 6.0 (4.0–8.0) 5.0 (3.0–6.0) 0.07  AHI (events/h) 12.8 (5.4–24.1) 10.5 (4.9–20.0) 32.6 (14.4–61.0) <0.001  AI (events/h) 3.2 (0.7–8.2) 2.7 (0.6–6.0) 15.2 (1.5–37.4) <0.001  HI (events/h) 10.3 (4.9–17.0) 8.7 (4.5–16.3) 14.8 (9.9–23.0) 0.01 Snore parameters  Self-reported snore score 10.0 (7.0–13.0) 6.8 (9.0–12.0) 13.0 (9.3–14.8) 0.003  Snore latency (min) 4.1 (1.3–11.5) 4.5 (1.5–12.5) 2.0 (0.5–6.5) 0.02  Snoring frequency (%) 18.9 (5.8–44.3) 14.7 (3.8–37.2) 59.5 (44.3–70.2) <0.001  Snoring Intensity (dB(A)) 45.4 (43.2–47.7) 45.9 (43.7–48.8) 56.7 (55.2–58.0) <0.001 Data are presented as mean ± SD and median (IQR) as appropriate. AHI = apnea–hypopnea index, AI = apnea index, ESS = Epworth sleepiness scale, HI = hypopnea index. Open in new tab Table 1. Participant characteristics by snoring intensity. . All . <53 dB(A) . ≥53 dB(A) . p . Demographics  Sex (F:M) 74:88 66:74 8:14 0.46  Age (years) 47.4 ± 13.9 47.6 ± 14.2 46.0 ± 12.6 0.54 Anthropometrics  BMI (kg/m2) 27.8 ± 4.5 27.7 ± 4.7 28.3 ± 3.3 0.77  Weight (kg) 181.3 ± 34.7 180.3 ± 35.2 187.6 ± 31.6 0.57  Neck (cm) 38.2 ± 3.8 38.0 ± 3.9 39.5 ± 3.1 0.05  Waist (cm) 95.2 ± 11.1 94.7 ± 11.2 98.8 ± 10.0 0.11  Hip (cm) 106.8 ± 8.8 107.0 ± 9.0 105.7 ± 7.6 0.34 Sleep architecture  Total sleep time (min) 364.3 (328.6–401.1) 363.1 (329.9–397.6) 385.7 (328.8–416.5) 0.27  Sleep efficiency (%) 85.7 (76.7–91.8) 85.6 (76.8–91.2) 88.0 (76.7–93.4) 0.30  Sleep latency (min) 5.9 (2.2–13.8) 7.1 (2.3–14.3) 3.3 (1.8–9.1) 0.04  Slow wave sleep (%) 14.9 (6.2–23.7) 16.5 (6.6–24.1) 12.0 (2.4–20.7) 0.11  Supine sleep (min) 283.9 (162.5–355.6) 267.2 (145.7–345.8) 342.0 (295.1–385.8) <0.001 AHI and sleepiness  ESS 6.0 (4.0–8.0) 6.0 (4.0–8.0) 5.0 (3.0–6.0) 0.07  AHI (events/h) 12.8 (5.4–24.1) 10.5 (4.9–20.0) 32.6 (14.4–61.0) <0.001  AI (events/h) 3.2 (0.7–8.2) 2.7 (0.6–6.0) 15.2 (1.5–37.4) <0.001  HI (events/h) 10.3 (4.9–17.0) 8.7 (4.5–16.3) 14.8 (9.9–23.0) 0.01 Snore parameters  Self-reported snore score 10.0 (7.0–13.0) 6.8 (9.0–12.0) 13.0 (9.3–14.8) 0.003  Snore latency (min) 4.1 (1.3–11.5) 4.5 (1.5–12.5) 2.0 (0.5–6.5) 0.02  Snoring frequency (%) 18.9 (5.8–44.3) 14.7 (3.8–37.2) 59.5 (44.3–70.2) <0.001  Snoring Intensity (dB(A)) 45.4 (43.2–47.7) 45.9 (43.7–48.8) 56.7 (55.2–58.0) <0.001 . All . <53 dB(A) . ≥53 dB(A) . p . Demographics  Sex (F:M) 74:88 66:74 8:14 0.46  Age (years) 47.4 ± 13.9 47.6 ± 14.2 46.0 ± 12.6 0.54 Anthropometrics  BMI (kg/m2) 27.8 ± 4.5 27.7 ± 4.7 28.3 ± 3.3 0.77  Weight (kg) 181.3 ± 34.7 180.3 ± 35.2 187.6 ± 31.6 0.57  Neck (cm) 38.2 ± 3.8 38.0 ± 3.9 39.5 ± 3.1 0.05  Waist (cm) 95.2 ± 11.1 94.7 ± 11.2 98.8 ± 10.0 0.11  Hip (cm) 106.8 ± 8.8 107.0 ± 9.0 105.7 ± 7.6 0.34 Sleep architecture  Total sleep time (min) 364.3 (328.6–401.1) 363.1 (329.9–397.6) 385.7 (328.8–416.5) 0.27  Sleep efficiency (%) 85.7 (76.7–91.8) 85.6 (76.8–91.2) 88.0 (76.7–93.4) 0.30  Sleep latency (min) 5.9 (2.2–13.8) 7.1 (2.3–14.3) 3.3 (1.8–9.1) 0.04  Slow wave sleep (%) 14.9 (6.2–23.7) 16.5 (6.6–24.1) 12.0 (2.4–20.7) 0.11  Supine sleep (min) 283.9 (162.5–355.6) 267.2 (145.7–345.8) 342.0 (295.1–385.8) <0.001 AHI and sleepiness  ESS 6.0 (4.0–8.0) 6.0 (4.0–8.0) 5.0 (3.0–6.0) 0.07  AHI (events/h) 12.8 (5.4–24.1) 10.5 (4.9–20.0) 32.6 (14.4–61.0) <0.001  AI (events/h) 3.2 (0.7–8.2) 2.7 (0.6–6.0) 15.2 (1.5–37.4) <0.001  HI (events/h) 10.3 (4.9–17.0) 8.7 (4.5–16.3) 14.8 (9.9–23.0) 0.01 Snore parameters  Self-reported snore score 10.0 (7.0–13.0) 6.8 (9.0–12.0) 13.0 (9.3–14.8) 0.003  Snore latency (min) 4.1 (1.3–11.5) 4.5 (1.5–12.5) 2.0 (0.5–6.5) 0.02  Snoring frequency (%) 18.9 (5.8–44.3) 14.7 (3.8–37.2) 59.5 (44.3–70.2) <0.001  Snoring Intensity (dB(A)) 45.4 (43.2–47.7) 45.9 (43.7–48.8) 56.7 (55.2–58.0) <0.001 Data are presented as mean ± SD and median (IQR) as appropriate. AHI = apnea–hypopnea index, AI = apnea index, ESS = Epworth sleepiness scale, HI = hypopnea index. Open in new tab Association between snoring and OSA Anthropometric, demographic, sleep study, and snore characteristics are shown in Table 2 for the entire group and for those with and without OSA. Older persons, males, greater neck, and waist size, but not BMI increased the likelihood of OSA, consistent with link between central adiposity and OSA [3]. As expected, sleep efficiency and slow wave sleep were diminished in the OSA group. In contrast, both groups reported relatively low levels of daytime sleepiness, as reflected by the Epworth sleepiness scores (ESS) in a community rather than sleep clinic population. The latency to snore onset, however, was shorter in OSA vs no OSA groups, and snoring was more frequent and more intense in the OSA population. Nevertheless, participants had similar reports of self-reported snoring regardless of whether they had OSA. Table 2. Participant characteristics by presence of OSA . All . No OSA . OSA . p . Demographics  Sex (F:M) 74:88 25:12 49:76 <0.001  Age (years) 47.4 ± 13.9 36.7 ± 11.6 50.6 ± 13.0 <0.001 Anthropometrics  BMI (kg/m2) 27.8 ± 4.5 27.6 ± 4.3 27.9 ± 4.6 0.44  Weight (kg) 181.3 ± 34.7 175.1 ± 32.3 183.1 ± 35.3 0.13  Neck (cm) 38.2 ± 3.8 36.2 ± 3.1 38.7 ± 3.8 <0.001  Waist (cm) 95.2 ± 11.1 89.2 ± 8.7 97.0 ± 11.1 <0.001  Hip (cm) 106.8 ± 8.8 105.9 ± 10.6 107.1 ± 8.3 0.72 Sleep architecture  Total sleep time (min) 364.3 (328.6–401.1) 378.3 (346.3–401.5) 358.0 (323.0–400.0) 0.10  Sleep efficiency (%) 85.7 (76.7–91.8) 88.3 (82.0–93.5) 84.8 (75.5–90.9) 0.02  Sleep latency (min) 5.9 (2.2–13.8) 7.4 (2.5–12.9) 5.5 (2.2–13.8) 0.98  Slow wave sleep (%) 14.9 (6.2–23.7) 23.7 (11.1–28.0) 13.4 (5.7–22.3) <0.001  Supine sleep (min) 283.9 (162.5–355.6) 309.5 (191.5–362.5) 268.4 (156.1–344.0) 0.16 AHI and sleepiness  ESS 6.0 (4.0–8.0) 5.0 (3.0–8.3) 6.0 (4.0–8.0) 0.43  AHI (events/h) 12.8 (5.4–24.1) 2.4 (1.4–4.0) 15.6 (9.0–29.9) <0.001  AI (events/h) 3.2 (0.7–8.2) 0.4 (0.2–0.9) 4.6 (1.7–12.9) <0.001  HI (events/h) 10.3 (4.9–17.0) 2.3 (1.1–4.8) 13.7 (7.9–19.7) <0.001 Snore parameters  Self-reported snore score 10.0 (7.0–13.0) 9.0 (5.0–12.0) 10.0 (7.0–13.0) 0.13  Snore latency (min) 4.1 (1.3–11.5) 11.5 (3.6–21.2) 3.4 (0.9–7.7) <0.001  Snoring frequency (%) 18.9 (5.8–44.3) 3.3 (0.4–9.0) 23.6 (10.1–46.9) <0.001  Snoring intensity (dB(A)) 45.4 (43.2–47.7) 43.0 (41.8–46.0) 48.1 (45.0–51.7) <0.001 . All . No OSA . OSA . p . Demographics  Sex (F:M) 74:88 25:12 49:76 <0.001  Age (years) 47.4 ± 13.9 36.7 ± 11.6 50.6 ± 13.0 <0.001 Anthropometrics  BMI (kg/m2) 27.8 ± 4.5 27.6 ± 4.3 27.9 ± 4.6 0.44  Weight (kg) 181.3 ± 34.7 175.1 ± 32.3 183.1 ± 35.3 0.13  Neck (cm) 38.2 ± 3.8 36.2 ± 3.1 38.7 ± 3.8 <0.001  Waist (cm) 95.2 ± 11.1 89.2 ± 8.7 97.0 ± 11.1 <0.001  Hip (cm) 106.8 ± 8.8 105.9 ± 10.6 107.1 ± 8.3 0.72 Sleep architecture  Total sleep time (min) 364.3 (328.6–401.1) 378.3 (346.3–401.5) 358.0 (323.0–400.0) 0.10  Sleep efficiency (%) 85.7 (76.7–91.8) 88.3 (82.0–93.5) 84.8 (75.5–90.9) 0.02  Sleep latency (min) 5.9 (2.2–13.8) 7.4 (2.5–12.9) 5.5 (2.2–13.8) 0.98  Slow wave sleep (%) 14.9 (6.2–23.7) 23.7 (11.1–28.0) 13.4 (5.7–22.3) <0.001  Supine sleep (min) 283.9 (162.5–355.6) 309.5 (191.5–362.5) 268.4 (156.1–344.0) 0.16 AHI and sleepiness  ESS 6.0 (4.0–8.0) 5.0 (3.0–8.3) 6.0 (4.0–8.0) 0.43  AHI (events/h) 12.8 (5.4–24.1) 2.4 (1.4–4.0) 15.6 (9.0–29.9) <0.001  AI (events/h) 3.2 (0.7–8.2) 0.4 (0.2–0.9) 4.6 (1.7–12.9) <0.001  HI (events/h) 10.3 (4.9–17.0) 2.3 (1.1–4.8) 13.7 (7.9–19.7) <0.001 Snore parameters  Self-reported snore score 10.0 (7.0–13.0) 9.0 (5.0–12.0) 10.0 (7.0–13.0) 0.13  Snore latency (min) 4.1 (1.3–11.5) 11.5 (3.6–21.2) 3.4 (0.9–7.7) <0.001  Snoring frequency (%) 18.9 (5.8–44.3) 3.3 (0.4–9.0) 23.6 (10.1–46.9) <0.001  Snoring intensity (dB(A)) 45.4 (43.2–47.7) 43.0 (41.8–46.0) 48.1 (45.0–51.7) <0.001 Data is presented as mean ± SD and median (IQR) as appropriate. AHI = apnea–hypopnea index, AI = apnea index, ESS = Epworth sleepiness scale, HI = hypopnea index. Open in new tab Table 2. Participant characteristics by presence of OSA . All . No OSA . OSA . p . Demographics  Sex (F:M) 74:88 25:12 49:76 <0.001  Age (years) 47.4 ± 13.9 36.7 ± 11.6 50.6 ± 13.0 <0.001 Anthropometrics  BMI (kg/m2) 27.8 ± 4.5 27.6 ± 4.3 27.9 ± 4.6 0.44  Weight (kg) 181.3 ± 34.7 175.1 ± 32.3 183.1 ± 35.3 0.13  Neck (cm) 38.2 ± 3.8 36.2 ± 3.1 38.7 ± 3.8 <0.001  Waist (cm) 95.2 ± 11.1 89.2 ± 8.7 97.0 ± 11.1 <0.001  Hip (cm) 106.8 ± 8.8 105.9 ± 10.6 107.1 ± 8.3 0.72 Sleep architecture  Total sleep time (min) 364.3 (328.6–401.1) 378.3 (346.3–401.5) 358.0 (323.0–400.0) 0.10  Sleep efficiency (%) 85.7 (76.7–91.8) 88.3 (82.0–93.5) 84.8 (75.5–90.9) 0.02  Sleep latency (min) 5.9 (2.2–13.8) 7.4 (2.5–12.9) 5.5 (2.2–13.8) 0.98  Slow wave sleep (%) 14.9 (6.2–23.7) 23.7 (11.1–28.0) 13.4 (5.7–22.3) <0.001  Supine sleep (min) 283.9 (162.5–355.6) 309.5 (191.5–362.5) 268.4 (156.1–344.0) 0.16 AHI and sleepiness  ESS 6.0 (4.0–8.0) 5.0 (3.0–8.3) 6.0 (4.0–8.0) 0.43  AHI (events/h) 12.8 (5.4–24.1) 2.4 (1.4–4.0) 15.6 (9.0–29.9) <0.001  AI (events/h) 3.2 (0.7–8.2) 0.4 (0.2–0.9) 4.6 (1.7–12.9) <0.001  HI (events/h) 10.3 (4.9–17.0) 2.3 (1.1–4.8) 13.7 (7.9–19.7) <0.001 Snore parameters  Self-reported snore score 10.0 (7.0–13.0) 9.0 (5.0–12.0) 10.0 (7.0–13.0) 0.13  Snore latency (min) 4.1 (1.3–11.5) 11.5 (3.6–21.2) 3.4 (0.9–7.7) <0.001  Snoring frequency (%) 18.9 (5.8–44.3) 3.3 (0.4–9.0) 23.6 (10.1–46.9) <0.001  Snoring intensity (dB(A)) 45.4 (43.2–47.7) 43.0 (41.8–46.0) 48.1 (45.0–51.7) <0.001 . All . No OSA . OSA . p . Demographics  Sex (F:M) 74:88 25:12 49:76 <0.001  Age (years) 47.4 ± 13.9 36.7 ± 11.6 50.6 ± 13.0 <0.001 Anthropometrics  BMI (kg/m2) 27.8 ± 4.5 27.6 ± 4.3 27.9 ± 4.6 0.44  Weight (kg) 181.3 ± 34.7 175.1 ± 32.3 183.1 ± 35.3 0.13  Neck (cm) 38.2 ± 3.8 36.2 ± 3.1 38.7 ± 3.8 <0.001  Waist (cm) 95.2 ± 11.1 89.2 ± 8.7 97.0 ± 11.1 <0.001  Hip (cm) 106.8 ± 8.8 105.9 ± 10.6 107.1 ± 8.3 0.72 Sleep architecture  Total sleep time (min) 364.3 (328.6–401.1) 378.3 (346.3–401.5) 358.0 (323.0–400.0) 0.10  Sleep efficiency (%) 85.7 (76.7–91.8) 88.3 (82.0–93.5) 84.8 (75.5–90.9) 0.02  Sleep latency (min) 5.9 (2.2–13.8) 7.4 (2.5–12.9) 5.5 (2.2–13.8) 0.98  Slow wave sleep (%) 14.9 (6.2–23.7) 23.7 (11.1–28.0) 13.4 (5.7–22.3) <0.001  Supine sleep (min) 283.9 (162.5–355.6) 309.5 (191.5–362.5) 268.4 (156.1–344.0) 0.16 AHI and sleepiness  ESS 6.0 (4.0–8.0) 5.0 (3.0–8.3) 6.0 (4.0–8.0) 0.43  AHI (events/h) 12.8 (5.4–24.1) 2.4 (1.4–4.0) 15.6 (9.0–29.9) <0.001  AI (events/h) 3.2 (0.7–8.2) 0.4 (0.2–0.9) 4.6 (1.7–12.9) <0.001  HI (events/h) 10.3 (4.9–17.0) 2.3 (1.1–4.8) 13.7 (7.9–19.7) <0.001 Snore parameters  Self-reported snore score 10.0 (7.0–13.0) 9.0 (5.0–12.0) 10.0 (7.0–13.0) 0.13  Snore latency (min) 4.1 (1.3–11.5) 11.5 (3.6–21.2) 3.4 (0.9–7.7) <0.001  Snoring frequency (%) 18.9 (5.8–44.3) 3.3 (0.4–9.0) 23.6 (10.1–46.9) <0.001  Snoring intensity (dB(A)) 45.4 (43.2–47.7) 43.0 (41.8–46.0) 48.1 (45.0–51.7) <0.001 Data is presented as mean ± SD and median (IQR) as appropriate. AHI = apnea–hypopnea index, AI = apnea index, ESS = Epworth sleepiness scale, HI = hypopnea index. Open in new tab Modeling the association between measures of snoring severity and OSA The relationship between AHI and measures of snoring severity for participants with and without OSA is illustrated in Figure 5. We observed that persons without OSA had a snoring intensity below 53 dB(A) and all but one with snoring intensity ≥53 dB(A) had OSA (Panel A). Similarly, persons without OSA had a snoring frequency below 25% and all but three persons with frequency >25% had OSA (Panel B). OSA severity was associated with snoring intensity and frequency (r2 of 0.23 p < 0.0001 and r2 of 0.11 p < 0.0001, respectively). Figure 5. Open in new tabDownload slide Scatter plot of objective snoring metrics vs OSA severity. Figure 5. Open in new tabDownload slide Scatter plot of objective snoring metrics vs OSA severity. We confirmed that the presence of OSA was dependent on snoring intensity and frequency (see Fischer exact tests,Supplementary Table S1), suggesting that snoring sound levels conferred greater likelihood of having OSA. The univariate logistic regression models revealed that snoring intensity and frequency were associated with the presence of OSA. These relationships were strengthened after incorporating age and sex in the models (see Tables 3 and 4). Table 3. Odds ratios of the univariate logistic regression models Outcome: OSA . Odds ratios (95% CI) . p . Model A  Snoring intensity 1.296 (1.146–1.467) <0.0001 Model B  Snoring frequency 1.071 (1.036–1.106) <0.0001 Outcome: OSA . Odds ratios (95% CI) . p . Model A  Snoring intensity 1.296 (1.146–1.467) <0.0001 Model B  Snoring frequency 1.071 (1.036–1.106) <0.0001 Open in new tab Table 3. Odds ratios of the univariate logistic regression models Outcome: OSA . Odds ratios (95% CI) . p . Model A  Snoring intensity 1.296 (1.146–1.467) <0.0001 Model B  Snoring frequency 1.071 (1.036–1.106) <0.0001 Outcome: OSA . Odds ratios (95% CI) . p . Model A  Snoring intensity 1.296 (1.146–1.467) <0.0001 Model B  Snoring frequency 1.071 (1.036–1.106) <0.0001 Open in new tab Table 4. Adjusted odds ratios of the multivariate logistic regression models Outcome: OSA . Adjusted odds ratios (95% CI) . p . Model C  Snoring Intensity 1.23 (1.09–1.40) 0.001  Age 1.10 (1.05–1.14) <0.0001  Sex 3.10 (1.13–8.54) 0.028 Model D  Snoring frequency 1.06 (1.03–1.10) 0.000  Age 1.10 (1.05–1.14) <0.0001  Sex 3.91 (1.38–11.05) 0.010 Outcome: OSA . Adjusted odds ratios (95% CI) . p . Model C  Snoring Intensity 1.23 (1.09–1.40) 0.001  Age 1.10 (1.05–1.14) <0.0001  Sex 3.10 (1.13–8.54) 0.028 Model D  Snoring frequency 1.06 (1.03–1.10) 0.000  Age 1.10 (1.05–1.14) <0.0001  Sex 3.91 (1.38–11.05) 0.010 Open in new tab Table 4. Adjusted odds ratios of the multivariate logistic regression models Outcome: OSA . Adjusted odds ratios (95% CI) . p . Model C  Snoring Intensity 1.23 (1.09–1.40) 0.001  Age 1.10 (1.05–1.14) <0.0001  Sex 3.10 (1.13–8.54) 0.028 Model D  Snoring frequency 1.06 (1.03–1.10) 0.000  Age 1.10 (1.05–1.14) <0.0001  Sex 3.91 (1.38–11.05) 0.010 Outcome: OSA . Adjusted odds ratios (95% CI) . p . Model C  Snoring Intensity 1.23 (1.09–1.40) 0.001  Age 1.10 (1.05–1.14) <0.0001  Sex 3.10 (1.13–8.54) 0.028 Model D  Snoring frequency 1.06 (1.03–1.10) 0.000  Age 1.10 (1.05–1.14) <0.0001  Sex 3.91 (1.38–11.05) 0.010 Open in new tab The ROC curves for snoring intensity and frequency are shown in Figure 6 with and without age and sex as predictors of OSA. Measures of snoring severity yielded AUCs that were substantially greater than chance alone (see dashed diagonal line). AUCs increased with the addition of age and sex as predictors of OSA. Figure 6. Open in new tabDownload slide AUCs for the ROC curves of snoring intensity and frequency with and without age and sex as predictors. The AUCs were 77% (p < .0001), 87% (p < .0001), and 81% (p < .0001), 89% (p < .0001) for the univariate and multivariate models of snoring intensity and snoring frequency respectively. Figure 6. Open in new tabDownload slide AUCs for the ROC curves of snoring intensity and frequency with and without age and sex as predictors. The AUCs were 77% (p < .0001), 87% (p < .0001), and 81% (p < .0001), 89% (p < .0001) for the univariate and multivariate models of snoring intensity and snoring frequency respectively. Association between peak inspiratory sound and UAO severity In our post hoc analysis, the linear mixed effects regression model demonstrated (1) a positive association (see Supplementary Table S2) between peak inspiratory sound and Ti/TTOT, indicating that sound production was tightly linked to the severity of UAO during sleep. In addition, our multivariate model accounted for differences in peak inspiratory sound by sleep stage and body position, and demonstrated (2) the highest peak inspiratory sound during N3 sleep in the supine position. Relative to supine N3 sleep, (3) N1, N2, REM, and non-supine sleep were associated with reductions in peak inspiratory sound (see Supplementary Table S2). This finding together with the fact that the studies were done in sound attenuated laboratories, indicate that phasic peak inspiratory sounds ≥40 dB(A) are emblematic of UAO during sleep. Discussion This study generated several novel findings that characterized overnight snoring objectively relative to noise pollution standards. First, snoring severity can be characterized by its frequency and intensity, which are well correlated. Second, more than half of our self-reported habitual snorers produced sound levels that exceeded noise thresholds for sleep disturbance, with some who actually surpassed the noise thresholds associated with adverse cardiovascular events [27, 28]. Third, despite the fact that our habitual snorers were asymptomatic, they still demonstrated a high prevalence of OSA. Fourth, self-reported habitual snoring spans a spectrum from negligible to severe noise production throughout the night. Finally, both snoring frequency and intensity predicted the presence of OSA and accuracy improved even further when age and sex were incorporated in the models. These findings suggest that objective measures of habitual snoring constitute a health risk for both snorers and bed partners alike, and that strategies to reduce the snoring impacts can decrease the risk of adverse health consequences. Snore exposure as noise pollution Snoring is a potential form of noise pollution with attendant health consequences. Using accepted methods for quantifying noise exposure, we characterized the intensity and frequency of nocturnal snoring among a group of habitual snorers without overt symptoms of OSA. On a single study night, a substantial proportion of these snorers produced sound levels that exceeded the thresholds for nocturnal noise pollution. Specifically, the World Health Organization (WHO) guidelines and empiric data caution [29] that sleep disruption commonly occurs at sound levels greater than 45 dB(A) [30, 31], which we found in 66% of our cohort. Further increases in sound intensity from road traffic exceeding a 53 dB(A) threshold have been associated with adverse cardiovascular events [27, 28] possibly due to surges in sympathetic activity [10, 32]. We found that measurements of snoring frequency correlated well with calibrated measures of snoring intensity, suggesting that commonly available measures of snoring frequency (i.e. phone applications) may offer reasonable surrogates for bedroom noise pollution. Of note, objective sound recordings in our study indicated little to no snoring in approximately 35% of our cohort (Figure 4B). In those without objective snoring, therapeutic efforts can be redirected to focus on identifying a primary sleep disturbance in the bed partner rather than noise pollution from the putative snorer per se. Nonetheless, our findings indicate that bed partners of habitual snorers are exposed to noise at or above thresholds for a healthy environment, putting them at risk for chronic sleep disturbance and adverse health effects. Objective snoring and OSA Even after excluding participants with overt symptoms of OSA, we still found a high prevalence of this disorder in otherwise asymptomatic habitual snorers. This finding is consistent with previous epidemiologic studies that demonstrated a similarly high prevalence in the general population [3, 33]. Epidemiologic risk factors for OSA including age, male sex and BMI are known to increase pharyngeal collapsibility in humans and animal models [34, 35]. The present study demonstrates that objective snoring is associated with OSA severity, suggesting that snoring is a surrogate for marked elevations in airway collapsibility during sleep [5, 36]. Nonetheless, we acknowledge that symptomatic OSA confers greater cardiovascular risk than asymptomatic OSA, particularly in those with relatively mild disease. The present study documents strong associations between snoring severity and OSA, suggesting that health risks be taken seriously in loud snorers. Health risks may be due to nocturnal hemodynamic stresses resulting from intermittent hypoxia, recurrent arousals and widening pleural pressure swings [37, 38] during periods of UAO. Several lines of evidence suggest that snoring can predict the presence of OSA from the data in the present study population. First, we demonstrated that OSA was dependent on snoring severity using the Fischer exact test. Specifically, the Fischer exact tests showed that snoring intensity ≥53 dB(A) and snoring frequency ≥25% were both significantly associated with the presence of OSA in our population. Second, we accounted for potential covariates of this relationship including age and sex by applying a multivariate regression logistic model to predict the presence of OSA based on snoring severity, and found that snoring intensity and frequency were independent predictors of the presence of OSA. Third, having demonstrated significant odds of OSA in logistic models, we generated ROC curves to determine the accuracy in classifying (diagnosing) participants from snoring parameters. The ROC curves discriminated those with and without OSA with a high degree of accuracy. Taken together, multiple lines of evidence offer a compelling case for using snoring to predict OSA. Limitations A few limitations should be considered when interpreting our results. First, the decibel meter was placed vertically above the pillow. Participants who slept supine may appear to produce louder snores compared to those who slept on their side, leading to an underestimation of snore intensity in these participants. Second, in calculating snoring intensity, we only used the sound data points associated with inspiration. Sound decay during the ensuing expiratory period or expiratory snoring was not included given our definition of snoring for this project, which have led us to underestimate overall noise pollution. Third, a pertinent factor for the perceived sound level is the distance from the noise source. In this study, sound meters were placed at 25.5 inches (65 cm) above the pillow. Halving the distance would increase perceived sound levels by 6 dB (A) [23] and vice versa. Fourth, although the snoring intensity was related to snore frequency (see Supplementary Figure S1), it does not account for the temporal distribution of snore exposure. For example, a snorer with 30% snore breaths would produce approximately 2000 snore sounds over the course of the night. The snores could either be equally spaced or clustered. It remains unclear if the temporal distribution of snoring plays a role for adverse health effects. Fifth, we acknowledge that night to night variability in snoring severity may introduce some inaccuracies in our objective snoring measurement in a single night. Finally, our current semi-automated procedure for detecting inspiration and characterizing breath-by-breath snoring is painstaking and time consuming. To streamline this process, structured development and cross validation of the custom algorithm are required, especially if the process is to become fully automated. Implications Objective measurements suggest that snoring is a significant environmental noise pollutant with potential implications for public and personal health in snorers and bed partners alike. First, objective measures of snoring severity constitute a strong predictor for concomitant OSA after adjusting for risk factors such as age and sex. Increased availability of home-based assessments of snoring can facilitate OSA screening strategies in the community at large, although further work will be required to account for ambient domiciliary noise, and to standardize and streamline the process of accurately characterizing inspiratory snoring for the purpose of OSA screening. Second, in those who do not have objective evidence of snoring, self-reported snoring may reflect an underlying discord or a primary sleep disturbance in the bed partner, and offer a cautionary note in snoring management. Finally, sleep disruption leading to intermittent surges in sympathetic activity and elevations in blood pressure has been suggested as a potential mechanism for noise-induced cardiovascular morbidity [32]. 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Thurston, Rebecca, C;Wu,, Minjie;Aizenstein, Howard, J;Chang,, Yuefang;Barinas Mitchell,, Emma;Derby, Carol, A;Maki, Pauline, M

doi: 10.1093/sleep/zsz298pmid: 31863110

Abstract Study Objectives Sleep disturbance is common among midlife women. Poor self-reported sleep characteristics have been linked to cerebrovascular disease and dementia risk. However, little work has considered the relation of objectively assessed sleep characteristics and white matter hyperintensities (WMHs), a marker of small vessel disease in the brain. Among 122 midlife women, we tested whether women with short or disrupted sleep would have greater WMH, adjusting for cardiovascular disease (CVD) risk factors, estradiol, and physiologically assessed sleep hot flashes. Methods We recruited 122 women (mean age = 58 years) without a history of stroke or dementia who underwent 72 h of actigraphy to quantify sleep, 24 h of physiologic monitoring to quantify hot flashes; magnetic resonance imaging to assess WMH; phlebotomy, questionnaires, and physical measures (blood pressure, height, and weight). Associations between actigraphy-assessed sleep (wake after sleep onset and total sleep time) and WMH were tested in linear regression models. Covariates included demographics, CVD risk factors (blood pressure, lipids, and diabetes), estradiol, mood, and sleep hot flashes. Results Greater actigraphy-assessed waking after sleep onset was associated with more WMH [B(SE) = .008 (.002), p = 0.002], adjusting for demographics, CVD risk factors, and sleep hot flashes. Findings persisted adjusting for estradiol and mood. Neither total sleep time nor subjective sleep quality was related to WMH. Conclusions Greater actigraphy-assessed waking after sleep onset but not subjective sleep was related to greater brain WMH among midlife women. Poor sleep may be associated with brain small vessel disease at midlife, which can increase the risk for brain disorders. white matter hyperintensities, brain, sleep, actigraphy Statement of Significance Disrupted sleep is common for midlife and older women. An increasing literature links poorer self-reported sleep to dementia and stroke risk. However, little is known about how sleep is related to cerebrovascular disease risk using actigraphy assessments of sleep. In this investigation of 122 midlife women, greater actigraphy-assessed wake after sleep onset was associated with greater white matter hyperintensities in the brain, an indicator of cerebrovascular disease. Associations persisted adjusting for CVD risk factors, estradiol, mood, and sleep hot flashes. Poor sleep may be associated with brain small vessel disease at midlife, which can increase the risk for future brain disorders. Future work should consider any cerebrovascular benefit of treating women’s sleep problems as they age. Introduction Sleep disturbance is common among women in midlife and older ages. In fact, some estimates indicate that a third to 60% of peri- and postmenopausal women in the United States report poor sleep [1–5]. These sleep problems have been attributed to chronologic aging as well as to the menopause transition and symptoms such as hot flashes during sleep, which most women experience during this transition [6]. Poor sleep among midlife women can persist well into their later postmenopausal years [7] and cause considerable distress, functional impairment, and poor quality of life [8]. Sleep problems can also have implications for women’s physical health as they age. For example, a growing body of literature links poor sleep to cardiovascular disease (CVD) risk [9, 10]. However, more limited research has examined the relation between sleep and cerebrovascular health, and particularly lesions in the white matter (WM) that appear as white matter hyperintensities (WMH) on T2-weighted magnetic resonance imaging (MRI) scans. WMH are a subclinical marker of cerebral small vessel damage thought to develop due to small vessel disease in the brain [11]. WMH are linked to later stroke, cognitive decline and dementia, and mortality [12]. As women rarely have overt cerebrovascular disease or dementia at midlife [13, 14], WMH can serve as an early risk marker for the development of these brain disorders and a target for future intervention studies. The limited work that has examined relationships between sleep and WMH have relied on self-reported indices of sleep. For example, a study of middle aged adults has found short self-reported sleep duration associated with greater WMH volume in the parietal lobe [15]. Other research with older adults has found long self-reported sleep duration or poor subjective sleep quality linked to more global WMH [16, 17]. Other work did not find significant associations between self-reported sleep and WMH [18]. While self-reported sleep does have clinical utility, precise reporting on sleep characteristics can be challenging for participants, and objectively assessed sleep features (e.g. via actigraphy and polysomnography) can yield important information on an individual’s sleep characteristics beyond subjective reports [19]. Moreover, most studies of sleep and WMH have been conducted on older adults, with the literature on midlife sleep and WMH limited. Notably, an important consideration for midlife women is hot flashes. The majority of midlife women experience menopausal hot flashes, many of which occur during sleep [6] and can disrupt sleep [20]. We have previously linked sleep hot flashes to WMH [21]; thus, for midlife women, sleep hot flashes should be taken into account when exploring sleep–WMH relationships. To address these gaps in the literature, the goal of the present study was to determine whether objectively assessed sleep is linked to greater WMH among midlife women after accounting for the occurrence of hot flashes. We examined the relationship between sleep and WMH among a sample of 122 women aged 45–66 who were free of clinical CVD, stroke, or dementia. Women underwent actigraphic assessments of their sleep and brain MRI to assess WMH, as well as objective monitoring of physiologic hot flashes using ambulatory skin conductance monitoring [22, 23]. We tested the hypothesis that short or disrupted sleep would be associated with greater WMH, after adjustment for sleep hot flashes and a range of other potential confounders. Materials and Methods Sample Participants were recruited from a cohort of non-smoking late perimenopausal and postmenopausal midlife women who had participated in a study on menopausal hot flashes and cardiovascular health (MsHeart) between 2012 and 2015. MsHeart exclusion criteria included: current smoking; reported history of CVD/stroke/cerebrovascular accident; insulin-dependent diabetes; Parkinson’s disease; hysterectomy and/or bilateral oophorectomy; current pregnancy; and use of hormone therapy (oral or transdermal estrogen and/or progesterone), select cardiovascular medications (beta blockers, calcium channel blockers, and alpha-2 adrenergic agonists), selective estrogen receptor modulators, aromatase inhibitors, selective serotonin reuptake inhibitors, or serotonin norepinephrine reuptake inhibitors. For the present study, 126 of the MsHeart participants were recruited between 2017 and 2019 to participate in the MsBrain Study, a study of menopause and brain aging. MsBrain exclusion criteria included: a reported history of stroke/cerebrovascular accident; dementia; seizure disorder; brain tumor; Parkinson’s Disease; a history of head trauma with loss of consciousness; contraindications to MRI (e.g. metal in the body); current chemotherapy; active substance use; pregnancy; and current use of medications including hormone therapy (oral or transdermal estrogen and/or progesterone), selective estrogen receptor modulators, aromatase inhibitors, selective serotonin reuptake inhibitors, or serotonin norepinephrine reuptake inhibitors. Of the 126 women, one woman was excluded due to brain tumor, two women were excluded due to suspected stroke, one woman was excluded due to seizure disorder, yielding a sample of 122 women with actigraphy and MRI data. Due to missing values, an additional five women were excluded from multivariable models which included low-density lipoprotein (LDL) cholesterol (N = 3) and physiologic hot flashes (N = 2). Participants underwent telephone and in-person screening procedures, physical measurements and questionnaire completion; 3 days of ambulatory monitoring, including sleep measurement by actigraphy and hot flashes by skin conductance; and MRI brain imaging. Procedures were approved by the University of Pittsburgh Institutional Review Board. Participants provided written, informed consent. Sleep Women wore an Actiwatch 2 wrist actigraph unit on the wrist of the non-dominant hand (Respironics, Inc., Murrysville, PA) [19] and completed a sleep diary [24] for 3 consecutive days. Actigraphy data were collected in 1-min epochs and analyzed with Philips Actiware v6.0.0 software, with a wake threshold of 40 and number of epochs of sleep/wake for sleep onset/offset of 10. Bedtime (time tried to go to sleep) and rise time (final wake time) were determined via sleep diary reports. Wake after sleep onset (WASO; minutes of wakefulness between actigraphy-defined sleep onset time and actigraphy-defined final wake time) was our primary sleep outcome given its specific relevance to menopause, and to circulating markers of endothelial function in our prior work [25]. Total sleep time [(difference between actigraphy-defined sleep onset and actigraphy-defined final wake time) − (actigraphy-defined WASO)] was also considered. Total sleep time was considered primarily as a continuous variable given that there were few long sleepers (e.g. only two women had sleep times ≥9 h/night) in the sample, but it was also considered categorized according to its distribution (<6 h, 6–7 h, and >7 h) in additional analyses. Women completed the Pittsburgh Sleep Quality Index (PSQI), a widely used and well-validated measure of subjective sleep quality [26]. Women also reported a history of sleep apnea and completed the Berlin Questionnaire, a validated inventory assessing sleep apnea symptoms [27]. WMH MRI scanning was performed at the MR Research Center of the University of Pittsburgh. MRI scanning was performed an average of 12 days (standard deviation = 4.9; range 4–30) from the sleep measurements. A 3T Siemens Prisma MR scanner was used, with a Siemens 64-channel head coil. Two series of MR images were analyzed for the current study: A magnetization-prepared rapid gradient echo (MPRAGE) T1-weighted sequence and T2-weighted (T2w) fluid-attenuated inversion recovery (FLAIR) sequence. MPRAGE images were acquired in the sagittal plane using the following parameters: TR = 2400 ms; TE = 2.22 ms; TI = 1000 ms; flip angle = 8°; FOV = 240*256 mm; slice thickness = 0.8 mm; voxel size = 0.8 mm*0.8 mm; matrix size = 300*320; and number of slices = 208. FLAIR images were acquired in the axial plane using the following parameters: TR = 9690 or 10000 ms; TE = 91 ms; TI = 2500 ms; flip angle = 135°; FOV = 256 × 256 mm; matrix = 320 × 320; slice thickness = 1.6 mm; voxel size = 0.8 mm*0.8 mm; and number of slices = 104. The small change in TR from 9699 to 1000 was performed 1 year into the study to meet Specific Absorption Rate human safety guidelines for participants with a higher body mass index (BMI). This change slightly increased the time of acquisition but had minimal effect on image contrast. An automated pipeline was used to segment WMH on the T2w FLAIR images using previously documented methods [28]. Cerebral and cerebellar WM were segmented in individual T2w FLAIR image space using SPM12 (Welcome Trust Centre for Neuroimaging, http://www.fil.ion.ucl.ac.uk/spm/). Given that there were very few lesions in the cerebellum in our participants, the mean and standard deviation of the cerebellar WM on the FLAIR image were used to Z-transform the FLAIR image (Z-T2w FLAIR). On the Z-transformed FLAIR images, voxels greater than or equal to 2 and within the cerebral WM mask were identified as WMH. This method uses individual mean and standard deviation from normal cerebellar WM to standardize individual FLAIR images, which avoids systematic bias in seed selection between participants with significant cerebral WMH versus those with few WMH. Z-transformation also reduces variations in FLAIR images. The total WMH volume (in cubic centimeters) was normalized by total gray matter (GM) and WM volumes [nWMH = WMH/(GM + WM)] and log transformed for analysis. Covariates Height was measured via fixed stadiometer and weight via Detecto Apex scale, and BMI was calculated [weight (kg)/height2 (m)]. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were the average of three seated measurements taken via a Dinamap v100. Demographics, medical history, medication use, and health behaviors were assessed by questionnaires and interview. Race/ethnicity was self-reported. Educational attainment was assessed as years of completed education and classified as less than or greater than a college degree for analysis. Depressive symptoms were assessed via the Center for Epidemiologic Studies Depression Survey [29]. Consistent with prior work, physiologic hot flashes were assessed via the VU-AMS monitor (Amsterdam, The Netherlands), a portable device that measures sternal skin conductance sampled at 1 Hz from the sternum via a 0.5 V constant voltage circuit passed between two Ag/AgCl electrodes (UFI) filled with 0.05 M KCl Velvachol/glycol paste [30]. Physiologic hot flashes were classified via standard methods, with skin conductance rise of 2 µmho in 30 s [22] flagged by UFI software (DPSv3.6; Morro Bay, CA), and edited for artifact [23]. Physiologic hot flashes were classified as occurring during sleep or wake hours as defined by the sleep diary, with sleep physiologic hot flashes considered here. Participants provided a morning fasting blood sample for the assessment of glucose, insulin, lipids, and estradiol. Glucose, total cholesterol, high-density lipoprotein cholesterol, and triglycerides were determined using an enzymatic assay; insulin was determined by immunoturbidimetric assay in serum (ACE Axcel, Alfa Wasserman; West Caldwell, NJ). LDL was calculated using the Friedewald equation [31]. Homeostatic model assessment, an index reflecting insulin resistance, was calculated [(fasting insulin*fasting glucose)/22.5] [32]. Estradiol was measured at the University of Pittsburgh’s Small Biomarker Core using liquid chromatography–tandem mass spectrometry, the reference method for the measurement of estradiol as it can quantify the low levels of estradiol observed in postmenopausal women with high precision and accuracy [33, 34]. This assay employs liquid–liquid extraction, derivatization, and detection with a triple quad mass spectrometer [35]. The lower limit of quantitation was 1.0 pg/mL. Intraday statistics showed errors below 8.1% and relative standard deviations (RSDs) below 10.4%; interday statistics showed errors below 5.0% with RSD below 7.4%. Standards, blanks, calibrators, and control pools were run simultaneously with all samples. Statistical analysis Variables were examined for distributions, outliers, and cell sizes. WMH and estradiol were log transformed to conform to model assumptions of normality. One observation was excluded as an outlier: a WASO value that was greater than 6 standard deviations from the mean. Bivariate relations between study variables and WMH were examined via Pearson and Spearman correlation coefficients. We tested relations between sleep variables and WMH in separate linear regression models. Age, BMI, and sleep apnea (current sleep apnea, which was the sleep apnea most strongly related to WMH) were included in models as covariates a priori. All other covariates were included based upon their relationship with the outcome at p < 0.10. The main analysis included three models. The first model was unadjusted for covariates. The second model included age, BMI, race/ethnicity, education, sleep apnea (current sleep apnea), sleep medication, diabetes, DBP, and LDL cholesterol. The third model also included physiologic hot flashes. In additional analyses, we adjusted for Berlin scores, estradiol, and depressive symptoms; excluded a nightshift worker; considered PSQI subscales; and considered total sleep time as a categorical variable. All tests were two tailed with an alpha set to 0.05. Analyses were conducted using SAS v9.4 (SAS Institute, Cary, NC). Results Women were on average 58.8 years old, overweight, and normotensive (Table 1). Over a quarter of the women in the sample (27%) were black, with the remainder non-Hispanic white. Half of the women had PSQI scores indicating poor subjective sleep quality. The average actigraphy sleep time was 6.5 h, and the average minutes of wakening after sleep onset detected on actigraphy were 42 min. Factors associated with increased WMH at p < 0.10 were non-white race/ethnicity (vs. white: r = −0.29, p = 0.001), lower education (<college vs. ≥college, r = −0.26, p = 0.004), higher DBP (r = 0.24, p = 0.007), and higher LDL cholesterol (r = 0.19, p = 0.04), and lower sleep physiologic hot flashes (r = 0.16, p = 0.09). Table 1. Sample characteristics N 122 Age, M (SD) 58.86 (4.14) Race/ethnicity, N (%)  White 89 (72.95)  Black 33 (27.05) Education, N (%)  High school/some college/vocational 36 (29.51)  College or higher 86 (70.49) BMI, M (SD) 29.58 (6.51) SBP, mmHg, M (SD) 117.34 (13.21) DBP, mmHg, M (SD) 67.93 (8.78) LDL cholesterol, mg/dL, M (SD) 113.77 (34.05) HDL cholesterol, mg/dL, M (SD) 61.90 (16.80) Estradiol, pg/mL, median (IQR) 2 (2, 6) Triglycerides, mg/dL, median (IQR) 96.00 (70.00, 123.00) Homeostatic model assessment, median (IQR) 4.49 (2.94, 5.36) Women reporting hot flashes, N (%) 65 (53.28) Physiologic overnight hot flashes, number, median (IQR)* 1 (0, 3) Sleep medication use, N (%) 2 (1.64) Antihypertensive medication use, N (%) 30 (24.59) Depressive symptoms (CESD), median (IQR) 6.00 (3.00, 10.00) Subjective sleep quality (PSQI), M (SD) 5.88 (3.06) Total sleep time (actigraphy), min, M (SD) 395.59 (62.08) WASO (actigraphy), min, M (SD) 42.73 (24.50) N 122 Age, M (SD) 58.86 (4.14) Race/ethnicity, N (%)  White 89 (72.95)  Black 33 (27.05) Education, N (%)  High school/some college/vocational 36 (29.51)  College or higher 86 (70.49) BMI, M (SD) 29.58 (6.51) SBP, mmHg, M (SD) 117.34 (13.21) DBP, mmHg, M (SD) 67.93 (8.78) LDL cholesterol, mg/dL, M (SD) 113.77 (34.05) HDL cholesterol, mg/dL, M (SD) 61.90 (16.80) Estradiol, pg/mL, median (IQR) 2 (2, 6) Triglycerides, mg/dL, median (IQR) 96.00 (70.00, 123.00) Homeostatic model assessment, median (IQR) 4.49 (2.94, 5.36) Women reporting hot flashes, N (%) 65 (53.28) Physiologic overnight hot flashes, number, median (IQR)* 1 (0, 3) Sleep medication use, N (%) 2 (1.64) Antihypertensive medication use, N (%) 30 (24.59) Depressive symptoms (CESD), median (IQR) 6.00 (3.00, 10.00) Subjective sleep quality (PSQI), M (SD) 5.88 (3.06) Total sleep time (actigraphy), min, M (SD) 395.59 (62.08) WASO (actigraphy), min, M (SD) 42.73 (24.50) *Number divided by sleep monitoring time and standardized to 7-h sleep time. Open in new tab Table 1. Sample characteristics N 122 Age, M (SD) 58.86 (4.14) Race/ethnicity, N (%)  White 89 (72.95)  Black 33 (27.05) Education, N (%)  High school/some college/vocational 36 (29.51)  College or higher 86 (70.49) BMI, M (SD) 29.58 (6.51) SBP, mmHg, M (SD) 117.34 (13.21) DBP, mmHg, M (SD) 67.93 (8.78) LDL cholesterol, mg/dL, M (SD) 113.77 (34.05) HDL cholesterol, mg/dL, M (SD) 61.90 (16.80) Estradiol, pg/mL, median (IQR) 2 (2, 6) Triglycerides, mg/dL, median (IQR) 96.00 (70.00, 123.00) Homeostatic model assessment, median (IQR) 4.49 (2.94, 5.36) Women reporting hot flashes, N (%) 65 (53.28) Physiologic overnight hot flashes, number, median (IQR)* 1 (0, 3) Sleep medication use, N (%) 2 (1.64) Antihypertensive medication use, N (%) 30 (24.59) Depressive symptoms (CESD), median (IQR) 6.00 (3.00, 10.00) Subjective sleep quality (PSQI), M (SD) 5.88 (3.06) Total sleep time (actigraphy), min, M (SD) 395.59 (62.08) WASO (actigraphy), min, M (SD) 42.73 (24.50) N 122 Age, M (SD) 58.86 (4.14) Race/ethnicity, N (%)  White 89 (72.95)  Black 33 (27.05) Education, N (%)  High school/some college/vocational 36 (29.51)  College or higher 86 (70.49) BMI, M (SD) 29.58 (6.51) SBP, mmHg, M (SD) 117.34 (13.21) DBP, mmHg, M (SD) 67.93 (8.78) LDL cholesterol, mg/dL, M (SD) 113.77 (34.05) HDL cholesterol, mg/dL, M (SD) 61.90 (16.80) Estradiol, pg/mL, median (IQR) 2 (2, 6) Triglycerides, mg/dL, median (IQR) 96.00 (70.00, 123.00) Homeostatic model assessment, median (IQR) 4.49 (2.94, 5.36) Women reporting hot flashes, N (%) 65 (53.28) Physiologic overnight hot flashes, number, median (IQR)* 1 (0, 3) Sleep medication use, N (%) 2 (1.64) Antihypertensive medication use, N (%) 30 (24.59) Depressive symptoms (CESD), median (IQR) 6.00 (3.00, 10.00) Subjective sleep quality (PSQI), M (SD) 5.88 (3.06) Total sleep time (actigraphy), min, M (SD) 395.59 (62.08) WASO (actigraphy), min, M (SD) 42.73 (24.50) *Number divided by sleep monitoring time and standardized to 7-h sleep time. Open in new tab In analyses of the relation between actigraphic sleep and WMH, greater WASO was associated with greater WMH (Figure 1). Associations persisted when covarying for a range of covariates, including sleep physiologic hot flashes, DBP and LDL cholesterol (Table 2). Neither total sleep time nor subjective sleep quality [PSQI: B(SE)= −02 (.02), p = 0.26, age, race, education, BMI, sleep medications, sleep apnea, diabetes, DBP, LDL cholesterol, and physiologic hot flashes] was related to WMH. Table 2. Relationship of actigraphy-assessed WASO and sleep time to WMH . WMH . . . . Model 1 . Model 2 . Model 3 . . B(SE) . B(SE) . B(SE) . WASO .007 (.003)** .008 (.002)** .008 (.002)** Total sleep time −.03 (.05) .03 (.05) .03 (.05) . WMH . . . . Model 1 . Model 2 . Model 3 . . B(SE) . B(SE) . B(SE) . WASO .007 (.003)** .008 (.002)** .008 (.002)** Total sleep time −.03 (.05) .03 (.05) .03 (.05) **p < 0.01; WMH log transformed. Model 1: unadjusted; Model 2: adjusted for age, race/ethnicity, education, BMI, sleep apnea, sleep medication, diabetes, DBP, and LDL cholesterol; Model 3: Model 2 covariates + physiologic hot flashes. Open in new tab Table 2. Relationship of actigraphy-assessed WASO and sleep time to WMH . WMH . . . . Model 1 . Model 2 . Model 3 . . B(SE) . B(SE) . B(SE) . WASO .007 (.003)** .008 (.002)** .008 (.002)** Total sleep time −.03 (.05) .03 (.05) .03 (.05) . WMH . . . . Model 1 . Model 2 . Model 3 . . B(SE) . B(SE) . B(SE) . WASO .007 (.003)** .008 (.002)** .008 (.002)** Total sleep time −.03 (.05) .03 (.05) .03 (.05) **p < 0.01; WMH log transformed. Model 1: unadjusted; Model 2: adjusted for age, race/ethnicity, education, BMI, sleep apnea, sleep medication, diabetes, DBP, and LDL cholesterol; Model 3: Model 2 covariates + physiologic hot flashes. Open in new tab Figure 1. Open in new tabDownload slide Scatterplot showing (A) the raw association between WASO and WMH (B) representative T2 FLAIR images from individuals with low, medium, and high WMH (red dots in A). Figure 1. Open in new tabDownload slide Scatterplot showing (A) the raw association between WASO and WMH (B) representative T2 FLAIR images from individuals with low, medium, and high WMH (red dots in A). In additional models, although estradiol was not significantly related to WMH (r = 0.03, p = 0.75), we considered estradiol as a covariate in models and findings were unchanged (data not shown). We also considered PSQI subscales in relation to WMH, with all subscales nonsignificant with the exception of sleep efficiency, which was marginally related to fewer WMH [B(SE)= −.09(.05), p = 0.08, adjusted for age, race, education, BMI, sleep medications, sleep apnea, diabetes, DBP, LDL cholesterol, and physiologic hot flashes]. We also considered total sleep time as a categorical variable (<6 h, 6–7 h, and >7 h), and conclusions were unchanged (data not shown). We also considered models with Berlin scores instead of reported sleep apnea, with conclusions unchanged (data not shown). Further, we excluded the one shift worker in the sample, with findings unchanged (data not shown). Moreover, although depressive symptoms were not significantly related to WMH, we considered models adjusting for these symptoms and findings were unchanged (data not shown). Discussion In this study of midlife and older women, we found more awakening during the night related to greater brain WMH. These associations persisted adjusting for a range of covariates, including standard risk factors for cerebrovascular disease as well as monitor-assessed sleep hot flashes. These findings indicate that more awakening during the night is associated with indicators of poorer cerebrovascular health at midlife. A growing literature points to the importance of sleep for cerebrovascular health. A more limited body of research links poorer self-reported sleep to greater WMH [15–17]. However, we found objective (actigraphy) sleep measures associated with WMH. Notably, objective indices can be particularly useful in investigating sleep as they avoid the need for individuals to precisely report on their sleep characteristics. A small literature has linked actigraphic indices of poor sleep to diffusion tensor imaging indices of WM microstructure [36, 37], which can reflect a range of processes, such as changes in myelination, changes in the alignment of fibers, or disease states [38–40]. Further, the present findings complement our prior work which finds that poorer actigaphically assessed sleep was associated with greater carotid atherosclerosis among midlife women [10]. The present work represents an important contribution to the literature on sleep and cerebrovascular health, indicating more objectively assessed nighttime awakening is associated with more WMH as early as midlife in women. Midlife and early old age are particularly important times to study how sleep related to the brain health of women. Poor sleep is prevalent in women during midlife and early old age [41]. For women, midlife includes the menopause transition, when poor sleep and hot flashes are common; these symptoms can persist well into the later postmenopausal years [3, 6]. In prior work, we found a relationship between hot flashes accompanied by awakening during the night [20] and greater subclinical CVD [42]. We have also found sleep hot flashes linked to greater WMH [21]. However, state-of-the-art-measured hot flashes did not account for the present relationships between sleep and WMH. Notably, poor sleep is common among midlife women even in the absence of hot flashes [43]. Further, midlife and early old age are important windows of opportunity to lower the risk of cerebrovascular disease [13], and sleep may be a modifiable risk factor for adverse brain health. Associations with WMH were observed for wakening during the night rather than for sleep duration or subjective sleep quality. Notably, the limited prior research on sleep duration and WMH, which relied exclusively on self-reported sleep, has produced highly mixed findings [15, 16]. Our work is consistent with our prior work linking more nighttime wakening to a more proinflammatory/coagulant profile [25]. We did not observe associations between global subjective sleep quality and WMH, with the exception of the PSQI subscale measuring poorer subjective sleep continuity. These findings further suggest the potential specificity of poorer sleep continuity, rather than sleep duration or global sleep quality, related to greater WMH among midlife and older women. Multiple mechanisms can underlie associations between poor sleep and WMH. We considered a range of standard CVD risk factors in these associations, such as blood pressure, obesity, diabetes, and lipids, and findings persisted. We controlled for several indices of sleep apnea, and associations remained. We carefully considered the role of overnight hot flashes, assessed via physiologic monitoring, important given difficulties in accurately estimating hot flashes occurring during sleep [44]. Although hot flashes did not explain the observed associations here, their role is important for future work to consider when examining relations between sleep and WMH during the menopause transition. Future work should also consider other potential mechanisms such as genetic [45] or epigenetic [46] processes that may underlie links between sleep and WMH. Study findings should be interpreted in light of several limitations. First, in this cross-sectional study we cannot make conclusions about the causal nature of or directionality of findings. It is plausible that the WMH cause poor or disrupted sleep [47]. Further, while actigraphy provides a strong proxy measure sleep, we did not use the more burdensome and costly polysomnography indices which include more direct measures of sleep/wake state. We conducted 3 days of actigraphy monitoring, which is less ideal than more extended monitoring durations for obtaining estimates of typical sleep patterns (e.g. weekends vs. weekdays, intra–inter day stability) [19]. We had several self-reported indices of sleep apnea, and findings persisted even when controlling for these factors, yet we did not have direct measures of sleep disordered breathing. Moreover, while WMH are commonly interpreted as reflecting components of small vessel disease, they can reflect a range of pathophysiologic processes, including demyelination, mild gliosis, and axonal loss [12]. Finally, the study sample was comprised of non-Hispanic white and black women, and conclusions may not apply to other racial/ethnic groups of women or to men. This study has several strengths. This analysis was based on a well-characterized sample of midlife and older women. Women underwent detailed, actigraphic assessments of sleep and brain MRI indices of WMH. Multiple covariates were considered, including CVD risk factors, estradiol, and sleep hot flashes measured via state-of-the-art means. In conclusion, this study indicated that midlife women with more awakening during the night had greater brain WMH, a marker of small vessel disease associated with risk for future stroke and dementia. Findings indicate the importance of poor sleep continuity not only to quality of life, but also to brain health. These findings can also point to sleep as an important modifiable risk factor, that if causally related to WMH, can be targeted to improve brain health as women age. Acknowledgments This research was supported by the National Institutes of Health (NIH), National Institute on Aging (RF1AG053504 to Thurston and Maki) and the National Institutes of Health (NIH), National Heart, Lung, and Blood Institute (2K24HL123565 to Thurston). This work was also supported by the University of Pittsburgh Clinical and Translational Science Institute (NIH Grant UL1TR000005). This project used the University of Pittsburgh Small Molecule Biomarker Core (NIH Grant S10RR023461-01). Conflict of interest statement: R Thurston: Astellas, Pfizer, and Procter and Gamble (consulting). No other authors have financial disclosures to declare. References 1. NIH . State-of-the-Science Conference statement. Management of menopause-related symptoms . Ann Intern Med. 2005 ; 142 : 1003 – 1013 . Crossref Search ADS PubMed WorldCat 2. Kravitz HM , et al. Sleep difficulty in women at midlife: a community survey of sleep and the menopausal transition . 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Gharib, Sina, A;Hurley, Amanda, L;Rosen, Michael, J;Spilsbury, James, C;Schell, Amy, E;Mehra,, Reena;Patel, Sanjay, R

doi: 10.1093/sleep/zsz314pmid: 31872261

Abstract Obstructive sleep apnea (OSA) has been linked to dysregulated metabolic states, and treatment of sleep apnea may improve these conditions. Subcutaneous adipose tissue is a readily samplable fat depot that plays an important role in regulating metabolism. However, neither the pathophysiologic consequences of OSA nor the effects of continuous positive airway pressure (CPAP) in altering this compartment’s molecular pathways are understood. This study aimed to systematically identify subcutaneous adipose tissue transcriptional programs modulated in OSA and in response to its effective treatment with CPAP. Two subject groups were investigated: Study Group 1 was comprised of 10 OSA and 8 controls; Study Group 2 included 24 individuals with OSA studied at baseline and following CPAP. For each subject, genome-wide gene expression measurement of subcutaneous fat was performed. Differentially activated pathways elicited by OSA (Group 1) and in response to its treatment (Group 2) were determined using network and Gene Set Enrichment Analysis (GSEA). In Group 2, treatment of OSA with CPAP improved apnea-hypopnea index, daytime sleepiness, and blood pressure, but not anthropometric measures. In Group 1, GSEA revealed many up-regulated gene sets in OSA subjects, most of which were involved in immuno-inflammatory (e.g. interferon-γ signaling), transcription, and metabolic processes such as adipogenesis. Unexpectedly, CPAP therapy in Group 2 subjects was also associated with up-regulation of several immune pathways as well as cholesterol biosynthesis. Collectively, our findings demonstrate that OSA alters distinct inflammatory and metabolic programs in subcutaneous fat, but these transcriptional signatures are not reversed with short-term effective therapy. sleep apnea, subcutaneous adipose, gene expression, CPAP, microarray, pathway Statement of Significance Obstructive sleep apnea is linked to metabolic abnormalities and subcutaneous adipose tissue is an important regulator of metabolism. In this study, we investigated how the presence of sleep apnea changes gene expression in subcutaneous fat and whether CPAP therapy influences this signal. Identifying the genes and pathways altered in adipose tissue due to obstructive sleep apnea and in response to its effective treatment can help elucidate molecular mechanisms driving metabolic and cardiovascular morbidities associated with this complex disorder. Introduction Obstructive sleep apnea (OSA) is commonly associated with insulin resistance and type 2 diabetes independent of obesity [1, 2], and emerging data suggest that OSA may be an independent risk factor for incident type 2 diabetes [3, 4] The mechanisms by which OSA may impact diabetes risk are not completely clear, but effects on adipose tissue represent one potential pathway. Growing evidence suggests adipose tissue hypoxia through an outstripping of blood supply plays a key role in mediating the development of inflammation and insulin resistance in the setting of obesity [5, 6], and clearly, the recurrent breathing stoppages seen in OSA may further exacerbate adipose tissue hypoxia. Given the known importance of adipose tissue in regulating inflammation and insulin resistance [7], it is highly likely that many of the systemic effects of OSA are due to changes in adipocyte biology. Adipose tissue is a major site of production of pro-inflammatory mediators and modulators of insulin resistance such as adiponectin [8, 9]. In addition, the regulatory role of adipose tissue on free fatty acid levels can importantly affect insulin resistance [10, 11]. We have previously demonstrated that OSA is associated with an inflammatory and insulin-resistant expression phenotype in visceral adipose tissue [12]. However, reversibility of this phenotype cannot be easily demonstrated given the difficulties in accessing visceral adipose tissue in a longitudinal fashion. In contrast, subcutaneous fat can be repeatedly sampled through percutaneous biopsies. In this study, we assessed the cross-sectional association of OSA with the transcriptome in subcutaneous adipose tissue as well as the impact of OSA therapy using continuous positive airway pressure (CPAP) in order to better define the causal effects of OSA on adipose tissue function. Methods Protocol for Study Group 1 Adult patients with a body mass index (BMI) ≥ 20 kg/m2 and on no OSA treatment scheduled to undergo ventral hernia repair surgery were recruited from a general surgery clinic to undergo sleep apnea screening and abdominal subcutaneous fat biopsy under general anesthesia at the time of hernia repair [12]. Study procedures were approved by the University Hospitals Case Medical Center institutional review board and informed consent was obtained from each subject. OSA severity was assessed using the ARES Unicorder (Watermark Medical, Boca Raton, FL) a previously validated portable sleep monitor worn two consecutive nights prior to surgery [13, 14]. Respiratory events were defined as a ≥50% decrease in airflow for ≥ 10 seconds as assessed by nasal pressure associated with a ≥3% decrease in oxygen saturation. The respiratory disturbance index (RDI) was calculated as the total number of respiratory events divided by total recording time in hours averaged over both nights of recording. Subcutaneous fat biopsies were obtained at the initiation of surgery using the incisions made for the procedure. Biopsy tissue was rinsed in PBS to remove excess blood, minced, and immediately frozen in liquid nitrogen. Protocol for Study Group 2 Adult patients with a BMI ≥ 25 kg/m2 diagnosed with severe OSA as evidenced by an apnea-hypopnea index (AHI) ≥30 event/hour on overnight polysomnography and ≥2% of total sleep time spent with oxyhemoglobin saturation <90%, planning treatment with CPAP and not taking medications (e.g. thiazolidinediones, glucocorticoids) that alter adipose tissue function were recruited from a sleep disorders clinic [15]. Study procedures were approved by the University Hospitals Case Medical Center institutional review board and informed consent was obtained from each subject. All participants underwent in-laboratory overnight polysomnography (Compumedics, Abbottsford AU) with lights off at 10:30 pm and lights on at 06:30 am using oronasal thermocouple and nasal pressure to assess airflow and inductive plethysmography to assess respiratory effort. Apneas were defined as complete cessation of airflow, and hypopneas were defined as a ≥ 30% decrease in airflow ≥ 10 seconds associated with a ≥3% decrease in oxygen saturation. The AHI was computed by dividing the total number of apneas and hypopneas by total sleep time. A percutaneous biopsy of the subcutaneous adipose tissue was performed at 07:00 am in the peri-umbilical region using a 13 g needle. The tissue was immediately rinsed with PBS to remove excess blood and then frozen in liquid nitrogen and stored at −80°C. Following the biopsy, patients were initiated on CPAP therapy. After at least 2 weeks of self-reported CPAP usage greater than 4 hours per night, a follow-up overnight visit was performed identical to the initial visit except the subject used CPAP during the overnight PSG. The follow-up biopsy was performed on the opposite side of the abdomen from the initial site. CPAP adherence data were downloaded from the subject’s machine and nightly usage was averaged over the 2 weeks prior to the follow-up visit. RNA isolation and microarray experiments Total RNA was isolated from subcutaneous adipose tissue in both experiments using RNeasy Lipid Tissue Mini Kit with DNase treatment (Qiagen, Valencia, California) according to the manufacturer’s protocol. The integrity of purified total RNA samples was assessed qualitatively on an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA) and verified by the presence of two discrete electropherogram peaks corresponding to the 28S and 18S rRNA at a ratio approaching 2:1. Of note, in Study Group 2, RNA extraction was only performed from samples of patients who had maintained an average of > 4 hours of CPAP use per night over the 2 weeks prior to the second biopsy. RNA samples were processed using sense target GeneChip Human Gene 1.0 ST arrays (Affymetrix, Inc., Santa Clara, CA), which analyze the expression levels of 36 079 transcripts from 21 014 annotated genes. For each sample, 100 ng of total RNA was labeled using Affymetrix GeneChip Whole Transcript (WT) Sense Target Labeling Assay, which included cRNA synthesis and generation of amplified and biotinylated sense-strand DNA targets, covering the entire expression genome. After hybridization and scanning, image acquisition was performed using the GeneChip Operating System (GCOS). Gene expression levels from probe intensities were estimated using a Robust Multiarray Analysis (RMA) method with quantile normalization and background correction [16]. Detailed information meeting Minimum Information About a Microarray Experiment (MIAME) requirements has been deposited for both experiments at Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/, GSE135917). Data analysis Multidimensional scaling. To assess whether the presence of OSA influenced global transcriptional responses, we applied both unsupervised [17] (correspondence analysis) and supervised [18] (partial least squares discriminant analysis, PLSDA) multidimensional scaling on subcutaneous fat transcriptome in Study Group 1 subjects (n = 10 OSA, n = 8 no OSA). Gene-based statistical testing. We applied empirical Bayes moderated t-statistics algorithm as implemented in the R package “Limma” [19] to test for differential gene expression between OSA vs. no OSA (Study 1) and paired analysis for pre vs. post-CPAP in OSA (Study 2). Probe intensities were log-transformed and a false discovery rate (FDR) < 0.05 cutoff was used to designate statistical significance. Gene set enrichment analysis. For each study group (OSA vs. no OSA; OSA: pre- vs. post-CPAP) we performed identical Gene Set Enrichment Analysis (GSEA) separately, using 1379 curated canonical biological pathways (e.g. Hallmark, Reactome, KEGG, Biocarta, etc.) [20]. We applied an FDR threshold < 0.05 to identify significantly enriched gene sets. We performed leading edge analysis in GSEA to identify the genes with the largest contribution to pathway enrichment [21]. Network-based visualization of GSEA results was achieved using Enrichment Map [22], a plug-in application within the bioinformatics software platform, Cytoscape [23]. Interaction network analysis. We implemented gene product interaction analysis using Ingenuity Pathway Analysis software [24] (Qiagen Bioinformatics, Redwood City, CA) by combining leading edge gene members of “Interferon-γ” associated gene sets enriched in subcutaneous fat of OSA subjects relative to those without OSA (Study 1). To enhance biological relevance, we limited nodal relationships to those based on high confidence, experimentally verified direct interactions. Results Study subject demographics and CPAP adherence There were 18 subjects in Study Group 1 (n = 10 OSA and n = 8 no OSA) as has been previously described [12]. As presented in Table 1, this cohort was middle-aged, primarily female and obese with mean (SD) BMI 35.7 (7.7) kg/m2 with no significant differences in age, sex, or BMI between groups. Of note, three of the control subjects and two of the OSA subjects were morbidly obese with BMI > 40 kg/m2. As expected, those found to have OSA on screening had greater mean RDI (19.2 vs. 0.6 events/hour, p = 0.05) and lower minimum O2 saturation (79.7% vs. 87.8%, p = 0.001). Table 1. Subject characteristics at baseline . Study Group 1 . . Study Group 2 . . Control (n = 8) . OSA (n = 10) . OSA (n = 24) . Age (years) 54.5 ± 11.6 56.1 ± 10.8 49.4 ± 10.9 Male 1 (13%) 3 (30%) 13 (54%) Body mass index (kg/m2) 35.2 ± 5.8 36.1 ± 9.3 42.6 ± 9.4 RDI or AHI (events/hour) 0.6 ± 0.5 19.2 ± 25.9 41.5 ± 23.8 Diabetes 0 (0%) 3 (30%) 5 (21%) Hypertension 3 (38%) 3 (30%) 9 (38%) Heart disease 0 (0%) 0 (0%) 2 (8%) . Study Group 1 . . Study Group 2 . . Control (n = 8) . OSA (n = 10) . OSA (n = 24) . Age (years) 54.5 ± 11.6 56.1 ± 10.8 49.4 ± 10.9 Male 1 (13%) 3 (30%) 13 (54%) Body mass index (kg/m2) 35.2 ± 5.8 36.1 ± 9.3 42.6 ± 9.4 RDI or AHI (events/hour) 0.6 ± 0.5 19.2 ± 25.9 41.5 ± 23.8 Diabetes 0 (0%) 3 (30%) 5 (21%) Hypertension 3 (38%) 3 (30%) 9 (38%) Heart disease 0 (0%) 0 (0%) 2 (8%) Study Group 1 consisted of individuals undergoing ventral hernia repair surgery. Study Group 2 consisted of individuals diagnosed with severe obstructive sleep apnea (OSA) getting initiated on continuous positive airway pressure (CPAP) therapy. Data reported as mean ± standard deviation or N (%). Respiratory disturbance index (RDI) reported for Study Group 1 and apnea hypopnea index (AHI) reported for Study Group 2. Open in new tab Table 1. Subject characteristics at baseline . Study Group 1 . . Study Group 2 . . Control (n = 8) . OSA (n = 10) . OSA (n = 24) . Age (years) 54.5 ± 11.6 56.1 ± 10.8 49.4 ± 10.9 Male 1 (13%) 3 (30%) 13 (54%) Body mass index (kg/m2) 35.2 ± 5.8 36.1 ± 9.3 42.6 ± 9.4 RDI or AHI (events/hour) 0.6 ± 0.5 19.2 ± 25.9 41.5 ± 23.8 Diabetes 0 (0%) 3 (30%) 5 (21%) Hypertension 3 (38%) 3 (30%) 9 (38%) Heart disease 0 (0%) 0 (0%) 2 (8%) . Study Group 1 . . Study Group 2 . . Control (n = 8) . OSA (n = 10) . OSA (n = 24) . Age (years) 54.5 ± 11.6 56.1 ± 10.8 49.4 ± 10.9 Male 1 (13%) 3 (30%) 13 (54%) Body mass index (kg/m2) 35.2 ± 5.8 36.1 ± 9.3 42.6 ± 9.4 RDI or AHI (events/hour) 0.6 ± 0.5 19.2 ± 25.9 41.5 ± 23.8 Diabetes 0 (0%) 3 (30%) 5 (21%) Hypertension 3 (38%) 3 (30%) 9 (38%) Heart disease 0 (0%) 0 (0%) 2 (8%) Study Group 1 consisted of individuals undergoing ventral hernia repair surgery. Study Group 2 consisted of individuals diagnosed with severe obstructive sleep apnea (OSA) getting initiated on continuous positive airway pressure (CPAP) therapy. Data reported as mean ± standard deviation or N (%). Respiratory disturbance index (RDI) reported for Study Group 1 and apnea hypopnea index (AHI) reported for Study Group 2. Open in new tab There were 31 subjects initially recruited to Study Group 2. Of these, seven subjects had suboptimal CPAP adherence so follow-up fat biopsy was either not performed or the tissue was not analyzed. The 24 subjects included in this analysis were middle-aged, 45.8% female, and had severe OSA (Table 1). Mean BMI did not change between baseline and follow-up biopsies (42.6 vs. 42.3 kg/m2, p = 0.23). The follow-up biopsy occurred a mean of 44 (26) days after initiating CPAP therapy and mean adherence for the 2 weeks prior to biopsy was 6.6 (1.1) hours/night. Subject-specific demographics for both Study Groups are provided in Supplementary Table S1. OSA elicits immuno-inflammatory and metabolic signatures in subcutaneous fat To evaluate the global consequences of OSA in subcutaneous adipose tissue transcriptome, we applied unsupervised and supervised multidimensional scaling to Study Group 1 (n = 10 OSA, n = 8 no OSA). As depicted in Figure 1, we found segregation between the subject groups, indicating that the presence of sleep apnea is associated with widespread changes in gene expression. Since the supervised algorithm (PLSDA) incorporates a priori information about the two subgroups, it outperforms the unsupervised (correspondence analysis) method in discriminating between subjects but may be susceptible to overfitting. Nevertheless, both classification approaches confirmed that OSA influences subcutaneous fat transcriptome. Figure 1. Open in new tabDownload slide Multidimensional scaling of subcutaneous fat gene expression in subjects with OSA (n = 10) and those without OSA (n = 8) assesses whether sleep apnea alters global transcriptional signals. (A) Unsupervised correspondence analysis (COA) of subcutaneous fat transcriptome. (B) Supervised partial least squares discriminant analysis (PLSDA) of subcutaneous fat transcriptome improves separation between subjects with and without OSA. Figure 1. Open in new tabDownload slide Multidimensional scaling of subcutaneous fat gene expression in subjects with OSA (n = 10) and those without OSA (n = 8) assesses whether sleep apnea alters global transcriptional signals. (A) Unsupervised correspondence analysis (COA) of subcutaneous fat transcriptome. (B) Supervised partial least squares discriminant analysis (PLSDA) of subcutaneous fat transcriptome improves separation between subjects with and without OSA. We next performed gene-based testing using the “Limma” statistical algorithm but did not find any differentially expressed genes after adjustment for multiple hypothesis testing, indicating modest changes in any single gene’s expression. However, most biological processes and complex diseases are the result of coordinated effects of multiple interacting genes [25, 26]. To map the landscape of pathways altered in OSA, we applied GSEA using the entire subcutaneous fat tissue transcriptome. We limited the enrichment analysis to well-curated canonical pathways (n = 1379) and identified 361 up-regulated and 22 down-regulated gene sets in OSA patients relative to subjects without OSA (complete list in Supplementary Tables S2 and S3). To visualize this large information, we performed network enrichment analysis by grouping gene sets that shared at least 25% of their member genes into larger biological modules with common functional themes (Figure 2). “Immunity and Inflammation” and “Cell Cycle and Transcription” were the largest up-regulated biological modules in OSA, but metabolism-associated processes such as “Adipogenesis” and “Diabetes” were also prominent, as was “TGF-β signaling.” Down-regulated modules included “Olfactory Signaling,” “Cytochrome P450,” and “G-protein Coupled Receptors.” Taken together, these results indicate that the presence of sleep apnea affects diverse pathways in subcutaneous fat tissue, with prominent up-regulation of immuno-inflammatory, cell cycle/transcription, and metabolic signals. Figure 2. Open in new tabDownload slide Network-based visual depiction of gene set enrichment analysis (GSEA) of subcutaneous fat tissue in OSA subjects vs. those without OSA. Red spheres correspond to gene sets up-regulated and blue spheres to gene sets down-regulated in OSA. Connectivity between the gene sets is based on 25% or greater overlap among their member genes. Note that the topology of the network is characterized by the emergence of biological modules comprises of highly interconnected gene sets with similar functional themes. Notable modules up-regulated in OSA include “Immunity and Inflammation,” “Cell Cycle and Transcription,” “Adipogenesis,” “Diabetes,” TGF-β Signaling,” whereas down-regulated modules include “Olfactory Signaling,” “GPCR Binding,” and “Cytochrome P450.” Complete list of enriched gene sets in included in Supplementary Tables S2 and S3. Figure 2. Open in new tabDownload slide Network-based visual depiction of gene set enrichment analysis (GSEA) of subcutaneous fat tissue in OSA subjects vs. those without OSA. Red spheres correspond to gene sets up-regulated and blue spheres to gene sets down-regulated in OSA. Connectivity between the gene sets is based on 25% or greater overlap among their member genes. Note that the topology of the network is characterized by the emergence of biological modules comprises of highly interconnected gene sets with similar functional themes. Notable modules up-regulated in OSA include “Immunity and Inflammation,” “Cell Cycle and Transcription,” “Adipogenesis,” “Diabetes,” TGF-β Signaling,” whereas down-regulated modules include “Olfactory Signaling,” “GPCR Binding,” and “Cytochrome P450.” Complete list of enriched gene sets in included in Supplementary Tables S2 and S3. To gain more insight into specific altered processes, we performed “leading edge” analysis on the “Adipogenesis” gene set to identify the genes that were significant contributors to its enrichment. Figure 3A summarizes this analysis using a heatmap to display the expression patterns of adipogenesis-associated leading edge genes in subjects with and without OSA. These genes include several well-known regulators of adipogenesis such as leptin, adiponectin, retinol saturase, and fatty acid-binding protein 4 (Supplementary Table S4). However, not every member of the “Adipogenesis” gene set was up-regulated in OSA; for example, the expression of apolipoprotein E (APOE)—a key carrier of fat molecules—was down-regulated (Figure 3B). Figure 3. Open in new tabDownload slide (A) Heatmap of the leading edge members of “Adipogenesis,” an up-regulated gene set in OSA subjects. Several representative genes have been highlighted (details provided in Supplementary Table S4). (B) However, some members of the “Adipogenesis” pathway, such as APOE, were down-regulated in OSA. Figure 3. Open in new tabDownload slide (A) Heatmap of the leading edge members of “Adipogenesis,” an up-regulated gene set in OSA subjects. Several representative genes have been highlighted (details provided in Supplementary Table S4). (B) However, some members of the “Adipogenesis” pathway, such as APOE, were down-regulated in OSA. The “Immune and Inflammation” module represented the largest grouping of up-regulated gene sets and included many canonical processes such as “FAS pathway,” “T-cell receptor,” “Cytokine signaling in immune system,” “TNF pathway,” “Toll receptor cascade,” as well as several gene sets involved in interferon-γ signaling. To better elucidate the components of interferon-γ signaling cascade that are activated in the subcutaneous fat of OSA patients, we performed gene product interaction network analysis on the “leading edge” members of this gene set. The resultant relational network (Figure 4) demonstrated complex molecular interactions among its gene members, and was characterized by several highly connected nodes. These hubs may represent critical regulators of interferon-γ signaling in subcutaneous adipose tissue of individuals with OSA and included IFNG, FAS, STAT1, STAT3, TNFSF10, CASP3, and CASP8 (Supplementary Table S5). Figure 4. Open in new tabDownload slide Gene product interaction network of “Interferon-γ” gene set—an up-regulated pathway in subcutaneous fat of OSA subjects relative to those without OSA. A number of the most highly connected nodes (known as hubs) have been labeled, including interferon-γ (IFNG), FAS, STAT1, STAT3, CASP3, CASP8, and TNFSF10. Note that the interactions among these densely connected nodes (shown as green lines) capture the majority of the network’s overall connectivity (green and gray lines). These hubs represent key drivers of OSA-induced activation of interferon-γ signaling in subcutaneous adipose tissue. Full list of network nodes is provided in Supplementary Table S5. Figure 4. Open in new tabDownload slide Gene product interaction network of “Interferon-γ” gene set—an up-regulated pathway in subcutaneous fat of OSA subjects relative to those without OSA. A number of the most highly connected nodes (known as hubs) have been labeled, including interferon-γ (IFNG), FAS, STAT1, STAT3, CASP3, CASP8, and TNFSF10. Note that the interactions among these densely connected nodes (shown as green lines) capture the majority of the network’s overall connectivity (green and gray lines). These hubs represent key drivers of OSA-induced activation of interferon-γ signaling in subcutaneous adipose tissue. Full list of network nodes is provided in Supplementary Table S5. Subcutaneous and visceral fat share similar pathway enrichment patterns in sleep apnea We previously reported on the transcriptional effects of OSA in visceral adipose tissue in the same subject cohort (Study Group 1) [12]. To compare those findings with the present data generated from subcutaneous fat, we re-analyzed the visceral fat gene expression data using identical pathway enrichment program (GSEA) and curated gene set databases. We identified 285 up-regulated gene sets (FDR < 0.05) in visceral adipose tissue of OSA patients, of which 228 (80%) were also up-regulated in subcutaneous fat of the same subjects, including many of the same immuno-inflammatory pathways such as interferon-γ signaling. Similarly, we found 32 down-regulated gene sets (FDR < 0.05) in visceral fat of OSA subjects, of which 15 (47%) were identical to pathways down-regulated in subcutaneous adipose tissue (including the top four most significant gene sets). The complete lists are provided in Supplementary Tables S6 and S7. Taken together, our results indicate that the transcriptional responses induced by OSA are broadly similar in adipose and subcutaneous fat tissue, although differences do exist. For example, the peroxisome proliferator-activated receptor (PPAR) pathway—a key regulator of metabolism—was down-regulated in visceral but not subcutaneous fat. CPAP therapy alters distinct transcriptional programs in subcutaneous fat of OSA patients We evaluated the transcriptional consequences of effective sleep apnea treatment on subcutaneous adipose tissue of 24 subjects with OSA at baseline (pre-CPAP) and after CPAP (Study Group 2). Using a paired gene-specific statistical analysis (“Limma”), we did not observe any differentially expressed genes in pre- vs. post-CPAP comparison after adjustment for multiple hypothesis testing. We next applied GSEA to identify pathways enriched in response to CPAP therapy. We found 88 gene sets up-regulated in subcutaneous fat of OSA patients after therapeutic CPAP (FDR < 0.05), but no significantly down-regulated gene sets (Supplementary Table S8). Using the same network-based approach used for Study Group 1, we grouped enriched gene sets following CPAP use into larger functional aggregates representing biological modules (Figure 5). Surprisingly, several of the modules previously found to be up-regulated in the OSA vs. no OSA cohort (Study Group 1) were also enriched after CPAP treatment of OSA in Study Group 2, including “Cell Cycle,” “Transcription,” and “Immunity and Inflammation.” This observation suggests that immuno-inflammatory signals in subcutaneous fat are not moderated within the first few months of effective OSA therapy, but rather enhanced in response to CPAP. Figure 5. Open in new tabDownload slide Network-based visualization of gene set enrichment analysis of subcutaneous fat tissue in OSA subjects following treatment with CPAP. Red spheres correspond to pathways up-regulated following CPAP therapy. No pathway was significantly down-regulated post-CPAP. Note the emergence of several biological modules in response to CPAP, including “Immunity and Inflammation,” “Cell Cycle,” “Transcription,” “Unfolded Protein Response,” and “Cholesterol.” Complete list of enriched gene sets in included in Supplementary Table S8. Figure 5. Open in new tabDownload slide Network-based visualization of gene set enrichment analysis of subcutaneous fat tissue in OSA subjects following treatment with CPAP. Red spheres correspond to pathways up-regulated following CPAP therapy. No pathway was significantly down-regulated post-CPAP. Note the emergence of several biological modules in response to CPAP, including “Immunity and Inflammation,” “Cell Cycle,” “Transcription,” “Unfolded Protein Response,” and “Cholesterol.” Complete list of enriched gene sets in included in Supplementary Table S8. Several gene sets involved in cholesterol biosynthesis were also up-regulated after CPAP therapy in Study Group 2 (Figure 5). We performed “leading edge” analysis to identify the main drivers of this network and integrated our results within the canonical cholesterol biosynthesis pathway, as depicted in Figure 6. We found that up-regulated “leading edge” genes mapped to 24 key steps of the multi-component processes involved in steroid synthesis (Supplementary Table S9). Figure 6. Open in new tabDownload slide Schematic diagram of cholesterol biosynthesis—a pathway that was up-regulated in OSA subjects treated with CPAP. Leading edge members of the “cholesterol biosynthesis” gene set have been highlighted in yellow background and populate 24 critical steps for steroid synthesis and are detailed in Supplementary Table S9. Adapted from Reactome database. Figure 6. Open in new tabDownload slide Schematic diagram of cholesterol biosynthesis—a pathway that was up-regulated in OSA subjects treated with CPAP. Leading edge members of the “cholesterol biosynthesis” gene set have been highlighted in yellow background and populate 24 critical steps for steroid synthesis and are detailed in Supplementary Table S9. Adapted from Reactome database. Discussion To our knowledge, this study is the first comprehensive evaluation of the transcriptional profile of subcutaneous adipose tissue in OSA and its response to CPAP therapy. Leveraging two different subject cohorts, we found that while gene-specific expression changes were modest, pathway-based analyses revealed a diverse repertoire of differentially activated processes in both untreated OSA and following its short-term effective treatment. Adipose tissue is a key regulator of metabolic state: increasing evidence implicates OSA as a disruptor of this homeostasis, with an important role in insulin resistance, diabetes, and obesity [27]. While much of the research efforts to date have focused on the visceral fat compartment, subcutaneous adipose tissue is also an important contributor to metabolism [28, 29]. Furthermore, repeated sampling of subcutaneous fat in human subjects is much more feasible than invasive biopsies of visceral adipose tissue and allowed us to assess the molecular consequences of pre- vs. post-CPAP therapy in OSA patients. We initially compared subjects with versus without OSA and found global alterations in subcutaneous fat transcriptome due to the presence of sleep apnea. When applying gene-specific statistical models with multiple comparisons adjustments, we did not find significant differential gene expression, an observation that has been previously reported in genome-wide transcriptional profiling of adipose tissue [30]. However, gene set enrichment analysis revealed up-regulation of inflammatory and immune pathways in OSA, which is consistent with other reports of elevated circulating pro-inflammatory markers in sleep apnea and our previous study in visceral fat tissue [12, 31–33]. Activation of immuno-inflammatory signals in fat tissue has been well-characterized in obesity, insulin resistance and diabetes. Our study extends these findings and demonstrates that among subjects with similar BMI, those with OSA have profound enrichment of immune-related programs in their subcutaneous fat. As an example, interferon-γ-associated gene sets were highly enriched in our OSA cohort, and gene interaction network analysis revealed several putative drivers of interferon-γ signaling, such as STAT1/STAT3, in subcutaneous fat tissue of OSA subjects. Interestingly, interferon-γ activation in adipose has been recently implicated as a contributor to metabolic abnormalities in human obesity [34], in vitro studies have shown that interferon-γ suppresses insulin signaling and lipid storage in human adipocytes via STAT pathway [35], and animal experiments have established a critical role for interferon-γ in regulating fat inflammation [36]. Other up-regulated gene sets in OSA included those involved in transforming growth factor-β (TGF-β) signaling, which plays a multifaceted role in fat cells and obesity [37], as well as gene sets mapping to metabolic processes such as diabetes and adipogenesis. A number of well-known regulators of adipocyte biology and metabolism such as leptin [38], adiponectin [9, 39], and fatty acid-binding protein 4 [40], were up-regulated in OSA, indicating that the effects of sleep apnea in subcutaneous fat depots may have system-wide metabolic consequences. We also found that a number of biological modules were down-regulated in OSA patients, with the topmost significant gene sets being involved in “Olfactory Signaling.” While the sensory role of smell has been shown to influence metabolic state [41], there is emerging evidence that olfactory receptors are also present in adipose tissue and may be directly involved in regulating adipogenesis, energy metabolism and obesity [42, 43]. Our findings, therefore, suggest another potential signaling mechanism by which OSA can alter fat tissue homeostasis. We compared pathway enrichment between subcutaneous and visceral adipose tissue in Study Group 1 subjects (OSA vs. control), and observed generally similar up- and down-regulated patterns between the two fat depots. This finding implies that the more readily accessible subcutaneous fat may be a reasonable surrogate for sampling visceral adipose tissue in sleep apnea. In a second set of experiments, we applied a similar transcriptomics analysis to subcutaneous fat tissue biopsies of 24 patients with OSA at baseline (pre-CPAP) and after effective therapy (post-CPAP). Unexpectedly, we observed that pathway enrichment after treatment with CPAP was characterized by up-regulation of immuno-inflammatory programs, as well as processes involved in transcription, cell cycle, and cholesterol synthesis. Activation of immune-associated gene sets such as “TNF signaling via NF-κB,” “AP1 pathway,” and several cytokine signaling processes following CPAP therapy indicates that the inflammatory signal associated with OSA in subcutaneous fat tissue is not attenuated with treatment, but maybe even enhanced. There may a number of explanations for this finding. First, the duration of therapy was relatively short, and the transcriptional effects of long-term CPAP use were not captured in our study. In this context, obtaining additional time points during CPAP therapy will be important in future studies. It is also possible that chronic disease exposure moderated the OSA-induced inflammatory state of adipose tissue and short-term treatment with CPAP elicited a paradoxical re-setting of pro-inflammatory programs. There is conflicting evidence on whether CPAP reduces inflammatory markers in sleep apnea [44–46]; for example, we did not find down-regulation of pro-inflammatory transcriptional signals in circulating leukocytes of OSA patients after effective CPAP therapy [15], and a more recent re-analysis of our original study using different statistical methods also did not find alteration of immune or inflammatory processes following CPAP therapy [47]. Furthermore, our study, as well as several other reports, have found that treatment of sleep apnea with CPAP does not result in weight loss [48, 49], and does not lead to reduced volume of visceral or subcutaneous fat content [50]. Since adipose tissue inflammation is a prominent characteristic of obesity, it is possible that the pro-inflammatory state of fat depots persists despite CPAP therapy. Interestingly, recent reports suggest that even with weight loss, the inflammatory profile of subcutaneous fat in humans may not become attenuated [51, 52]. Another prominent up-regulated module after CPAP therapy in OSA was “Cholesterol Biosynthesis.” When we mapped the “leading edge” genes that drive the enrichment of these cholesterol-associated pathways, we found that almost every step of steroid synthesis included one such gene member, highlighting the widespread effects of OSA treatment in modulating cholesterol production. Our study has a number of limitations. The sample size for each Study Group was relatively small, and our findings need to be validated in larger cohorts before they can be generalized. Three of the OSA subjects in Study Group 1 had diabetes, whereas none of the controls were diabetic. It is possible that the presence of diabetes in the OSA subjects may have influenced the transcriptional profile of subcutaneous adipose tissue and confounded our results. Subcutaneous fat is a complex mixture of several cell types including adipocytes, macrophages, and connective tissue—each of which likely contributed to the overall gene expression that was measured in our study. Obtaining cell-specific transcriptional profiles of this fat compartment can provide more granular information and deeper insight into the role of subcutaneous adipose tissue constituents in OSA. Differences between the two Study Groups may also have impacted findings. Although both groups were obese, the CPAP intervention group was conducted in a population with morbid obesity (mean BMI 42.6 kg/m2). It is possible that this severity of obesity led to severe tissue hypoxia independent of OSA such that the impact of CPAP therapy was minimal. The duration of CPAP therapy in Study Group 2 was brief and only one time point after initiation of treatment was captured, precluding assessment of long-term consequences. Therefore, temporal changes in gene expression and pathway enrichment, and chronic molecular effects of CPAP therapy on subcutaneous fat were not investigated. We also did not have a “control” group of OSA subjects treated with sham CPAP in Study Group 2 to compare with subjects undergoing effective therapy. Finally, it is possible that the level of CPAP adherence achieved (mean 6.6 hours/night) was insufficient to fully eliminate the ongoing exposure to intermittent hypoxia and other OSA-associated physiologic insults. Nevertheless, this level of CPAP adherence does reflect what is typically observed in clinical populations initiated on CPAP and therefore reflects the impact of current approaches to OSA therapy. Despite these limitations, our study represents the largest effort to date to investigate the transcriptional consequences of OSA and its treatment in subcutaneous fat tissue. We found that compared to subjects without sleep apnea, OSA patients have a pro-inflammatory and dysregulated metabolic transcriptional state in this important fat depot. However, the immuno-inflammatory signal associated with sleep apnea was not reversed with effective but short-term CPAP therapy, highlighting a complex relationship between the treatment of OSA and its molecular sequelae in adipose tissue. Funding This work was funded by the American Thoracic Society (S.R.P.), NIH HL081385 (S.R.P.), HL127307 (S.R.P.), HL079114 (R.M.), HL125177 (R.M.), and AI137111 (S.A.G.). Acknowledgments We would like to thank all subject participants in this study. Conflict of interest statement. This was not an industry-supported study. 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Fischer,, Dorothee;McHill, Andrew, W;Sano,, Akane;Picard, Rosalind, W;Barger, Laura, K;Czeisler, Charles, A;Klerman, Elizabeth, B;Phillips, Andrew, Jk

doi: 10.1093/sleep/zsz300pmid: 31837266

Abstract Study Objectives Sleep regularity, in addition to duration and timing, is predictive of daily variations in well-being. One possible contributor to changes in these sleep dimensions are early morning scheduled events. We applied a composite metric—the Composite Phase Deviation (CPD)—to assess mistiming and irregularity of both sleep and event schedules to examine their relationship with self-reported well-being in US college students. Methods Daily well-being, actigraphy, and timing of sleep and first scheduled events (academic/exercise/other) were collected for approximately 30 days from 223 US college students (37% females) between 2013 and 2016. Participants rated well-being daily upon awakening on five scales: Sleepy–Alert, Sad–Happy, Sluggish–Energetic, Sick–Healthy, and Stressed–Calm. A longitudinal growth model with time-varying covariates was used to assess relationships between sleep variables (i.e. CPDSleep, sleep duration, and midsleep time) and daily and average well-being. Cluster analysis was used to examine relationships between CPD for sleep vs. event schedules. Results CPD for sleep was a significant predictor of average well-being (e.g. Stressed–Calm: b = −6.3, p < 0.01), whereas sleep duration was a significant predictor of daily well-being (Stressed–Calm, b = 1.0, p < 0.001). Although cluster analysis revealed no systematic relationship between CPD for sleep vs. event schedules (i.e. more mistimed/irregular events were not associated with more mistimed/irregular sleep), they interacted upon well-being: the poorest well-being was reported by students for whom both sleep and event schedules were mistimed and irregular. Conclusions Sleep regularity and duration may be risk factors for lower well-being in college students. Stabilizing sleep and/or event schedules may help improve well-being. Clinical Trial Registration NCT02846077. mental health, public health, sleep and stress, stress, intra-individual variability, social jet lag, sleep regularity, mood, well-being Statement of Significance With mental health problems rising among college students, negative well-being is a cause of concern. Emerging evidence suggests a link between irregular sleep and worse well-being. A potential driver of irregular sleep is irregularly scheduled morning-events (e.g. classes), but this relationship has not been carefully investigated. We studied sleep, scheduled events, and well-being in undergraduates using a recently developed metric for quantifying irregular patterns on different timescales (daily, monthly). We discovered that irregular sleep predicts lower average well-being and short sleep predicts lower daily well-being. Surprisingly, we found no association of irregular sleep with irregular events but both factors combined associate with worse well-being. Future work should investigate whether stabilizing sleep and/or events can improve well-being in undergraduates. Introduction Adverse effects of short sleep duration on health are well-documented in the scientific literature [1]. Emerging findings suggest that sleep timing (i.e. sleep onset and offset) and sleep regularity (consistency in sleep timing from one day to the next) may be just as important for health and performance outcomes as sleep duration. Although the body of research on consequences of mistimed sleep is large and growing [2–4], a recent systematic review on sleep regularity—labeled intraindividual variability—concluded that “this body of literature is still at its infancy” [5]. Consistent associations have been reported between irregular sleep and adverse mental and physical health outcomes, including higher BMI, weight gain, affective disorders, insomnia, and generally poor sleep despite inconsistent methodologies used in available studies [5, 6]. In college students, sleep patterns are often highly variable as many students for the first time establish their own schedules and develop independent social rhythms while also facing academic demands and personal challenges. Irregular sleep patterns in this population have been associated with reduced physical health, including increased blood pressure [7], poorer psychomotor performance [8], lower academic performance [9, 10], higher BMI [11], and weight gain [12]. Numerous studies have linked sleep regularity to mental health in college students, using self-reports/questionnaires or average metrics (i.e. within-individual standard deviation) to quantify sleep regularity. An early study by Taub and Hawkins found that self-reported irregular sleepers scored lower on several personality dimensions (e.g. sociability, self-acceptance) than regular sleepers, despite no differences in average sleep duration [13]. Similarly, students at risk for bipolar disorder had less regular sleep patterns based on four weeks of diary data [14], which was confirmed by another study using seven days of objective actimetry recordings [15]. Self-reported irregular sleepers have fewer positive mood states and greater negative affect [8], as well as lower self-reported mental health scores and higher depression ratings [7, 16]. A recent study examining depressed mood and suicidal ideation at three time points over the course of 21 days in undergraduates at high-suicide risk found that irregular sleep patterns at baseline predicted suicidal and depressive symptoms at 7-day and 21-day follow-ups, and that sleep irregularity was a stronger predictor of acute suicidal ideation than depression severity [17]. Previous research has studied the effects of class schedules on college students’ alcohol consumption and academic performance [18, 19], assigning sleep timing a mediating role [20, 21]. Hershner and Chervin named “variable class schedules from day to day” a challenge in college students for good sleep hygiene and consequently for learning and mood [22]. To the best of our knowledge, no prior studies have examined the relationship between the regularity of sleep vs. event schedules and their relationship with well-being in undergraduates. All of these studies used self-reports/questionnaires or average metrics (i.e. within-individual standard deviation) to quantify sleep regularity. In fact, the overwhelming majority of studies on sleep regularity, including all 53 studies in the systematic review by Bei and colleagues [5], used metrics that quantify overall variability in sleep after averaging across days, rather than quantifying the degree to which sleep patterns differ between consecutive days (i.e. on a circadian timescale). However, rapid day-to-day changes in sleep patterns are important to quantify because they would be expected to cause misalignment between the circadian system and sleep–wake cycles, since the circadian clock takes time to adjust to schedule changes. Such misalignment may be an underlying mechanism for the association between irregular sleep patterns and adverse health and performance outcomes. Metrics that quantify overall regularity, such as within-individual standard deviations or the widely used interdaily stability [23], do not specifically capture day-to-day (circadian) changes: e.g. if sleep times in a time series were randomly re-ordered, these metrics would return the identical value, even though the sleep regularity for a given interval within that time series, e.g. sleep on days 3–5, may have dramatically changed. Two metrics have been recently developed to specifically capture day-to-day changes in sleep patterns: the Composite Phase Deviation (CPD [24]) and the Sleep Regularity Index (SRI [9]). The CPD metric combines sleep irregularity and sleep mistiming by quantifying (i) how different the midsleep times are compared to those on the previous day, and (ii) how far away midsleep times occur from an individual’s preferred sleep timing (chronotype, as measured by midsleep time on weekends [25]). The SRI calculates the probability of an individual being in the same state (asleep vs. awake) at any two time-points 24 h apart, scaled to values between 0 (completely irregular) and 100 (completely regular). Although CPD and SRI both capture day-to-day changes in sleep patterns, CPD may be complementary to SRI due to its composite nature, combining features of irregularity with mistiming. For example in shift work, sleep during the daytime for several consecutive night shifts tends to be highly regular (as captured by SRI and the irregularity component of the CPD metric) but as it occurs during the day it is largely mistimed for the majority of shift workers [26]. This mistiming is captured by the second component of the CPD metric but not by the SRI. Using the recently developed CPD metric to specifically capture day-to-day changes in sleep patterns and event schedules, we examined the relationships among mistiming/irregularity of sleep schedules, event schedules, and self-reported well-being in 223 US college students studied during approximately 30 days. We formulated three study aims: (1) We aimed to test whether CPD for sleep is associated with well-being in college students and hypothesized that high-CPD (mistimed/irregular) sleep patterns would be associated with poor well-being upon awakening, on both a daily and an average (entire month) basis. (2) Previous studies have shown that sleep regularity can be experimentally manipulated by enforcing regular sleep schedules, yet found inconsistent results as to whether the manipulation leads to improvements in health- and performance-related outcomes [27–29]. We aimed to quantify the relationship between the timing and regularity of students’ sleep schedules and the timing and regularity of their event schedules, using continuous regression analysis and cluster analysis. We hypothesized that CPD for sleep and event schedules would be positively associated, resulting in two main clusters, i.e. mistimed/irregular sleepers on mistimed/irregular event schedules and regular/aligned sleepers on regular/aligned event schedules. (3) We aimed to quantify whether sleep and event schedules would have combined effects on well-being and hypothesized that event schedules would exacerbate the effects of sleep, i.e. mistimed/irregular sleepers on mistimed/irregular event schedules would report the poorest well-being. Although we did not formulate explicit hypotheses regarding the daily and average effects of sleep duration and sleep timing, both variables were included in the analysis to test that any potential impact of CPD was independent from these aspects of sleep. Hypotheses were not formulated for each well-being scale. Methods Participants and study protocol Daily well-being and actimetry data were collected for approximately 30 days from 223 fulltime undergraduates at one midsize private Massachusetts university during fall and spring semesters between 2013 and 2016. The approximately 30 days of data collection began within the first weeks of start of semester so that they would end before a scheduled multiday vacation (e.g. thanksgiving holiday or spring break). Participants were excluded if pregnant or traveling more than one time zone 1 week before or during the study. Students were aged 18–27 (mean ± sd, 19.4 ± 1.5 y) and 37% were females (n = 83). Students wore the actigraphy device Motionlogger-L (Ambulatory Monitoring, Inc., Ardsley, NY) on the wrist of their nondominant hand for approximately 30 days (6–34 days, median 29 days). Students also completed a daily online diary after awakening that included self-reports of: (i) sleep onset and offset times that were later used to assist determination of sleep onsets and offsets from actigraphy; (ii) the time of their first scheduled event (FSE, note: the event could be any academic, exercise, or extracurricular activity including meeting a friend); and (iii) their well-being on five visual nonnumerical scales (described below under Well-being section). Figure 1a illustrates the study design, showing daily sleep episodes, FSEs, and morning assessments of well-being for 30 days for one college student. Figure 1. Open in new tabDownload slide Example Composite Phase Deviation for sleep (CPDSleep) and for first scheduled event (FSE) timing (CPDFSE). (a) Raster plot of one individual showing daily sleep episodes, FSEs, and well-being assessments over 30 days. Days 1 and 7 are missing sleep data (gray bars from left to right). MSFsc = chronotype (midsleep on weekends, corrected for sleep loss on weekdays). Panels b and d show an enlarged section of panel a with only midsleep and FSE information. (b) Enlarged section of panel a (days 1–7) for midsleep times with ΔChronotype (ΔCT) and ΔDay-to-Day (ΔDD). Note that days 1 and 7 are missing, resulting in missing CPD data. (c) CPD plot for sleep. The arrows exemplify vectors from the origin to a data point. The CPD value of this data point is quantified by the length of the corresponding vector. Colored contour lines connect areas of equal data point density. (d) As in panel b but for FSEs. (e) As in panel c but for FSEs with ΔEvent and ΔDD. Figure 1. Open in new tabDownload slide Example Composite Phase Deviation for sleep (CPDSleep) and for first scheduled event (FSE) timing (CPDFSE). (a) Raster plot of one individual showing daily sleep episodes, FSEs, and well-being assessments over 30 days. Days 1 and 7 are missing sleep data (gray bars from left to right). MSFsc = chronotype (midsleep on weekends, corrected for sleep loss on weekdays). Panels b and d show an enlarged section of panel a with only midsleep and FSE information. (b) Enlarged section of panel a (days 1–7) for midsleep times with ΔChronotype (ΔCT) and ΔDay-to-Day (ΔDD). Note that days 1 and 7 are missing, resulting in missing CPD data. (c) CPD plot for sleep. The arrows exemplify vectors from the origin to a data point. The CPD value of this data point is quantified by the length of the corresponding vector. Colored contour lines connect areas of equal data point density. (d) As in panel b but for FSEs. (e) As in panel c but for FSEs with ΔEvent and ΔDD. The study was in adherence with the Declaration of Helsinki and approved by the Committee on the Use of Humans as Experimental Subjects (Couhes) at Massachusetts Institute of Technology. The study was registered on ClinicalTrials.gov: NCT02846077. All participants provided written informed consent. Data processing and study variables Well-being Students reported their well-being online on five visual analog (nonnumerical) scales every morning. Seventy-five percent of entries were within 3 h after awakening; entries more than 6 h after awakening (6%) were excluded from this analysis. The five scales Sleepy–Alert, Sad–Happy, Sluggish–Energetic, Sick–Healthy, and Stressed–Calm were later scored 0–100 with higher scores representing better well-being. Actigraphy Sleep onsets and offsets were determined using a combination of actigraphy and online sleep diaries [30, 31]. Students reported any actiwatch removals, and these were marked as missing data. Chronotype An individual’s chronotype reflects how the circadian system embeds itself into the 24 h day with rhythms in physiology, cognition, and (sleep–wake) behavior occurring accordingly earlier or later [32]. Chronotype can be assessed from sleep–wake behavior using midsleep times on non-workdays to minimize the influence of external demands during the work week, such as forced wake-ups. Here, chronotype was calculated as the midpoint of the major sleep episode on weekends, corrected for over-sleeping due to sleep loss on weekdays (MSFsc) (equations 1–3) [25, 33]: MSF=SONweekends+12SDurweekends (1) if SDurweekends> SDurweekday, MSFsc=MSF− SDurweekends− SDurweekly2, (2) else MSFsc=MSF (3) where MSF is midsleep on weekends; SONweekends sleep onset on weekends; SDurweekends sleep duration on weekends; SDurweekdays sleep duration on weekdays; MSFsc midsleep on weekends, corrected for sleep loss on weekdays (chronotype); SDurweekly denotes weighted weekly average of sleep duration. We chose to use midsleep on weekends rather than nonevent days, because (i) 25% of students had 4 or less nonevent days per 30 days and we wanted to maximize the number of days used to calculate chronotype; (ii) irrespective of scheduled events, weekdays are usually socially different from weekends, and we aimed to increase comparability. This means that for some students the chronotype calculation included days with scheduled events, but we found fewer events were scheduled on weekends (37%) vs. weekdays (93%), and they began systematically later on weekends: median FSE time on weekends was 12:00 with 50% of events between 10:00 and 15:00, whereas median FSE time on weekdays was 10:00 with 50% of events between 9:30 and 11:00. Composite Phase Deviation using midsleep times (CPDSleep) The CPDSleep metric quantifies day-to-day changes in sleep (irregularity component) and the extent of sleeping at the wrong time (mistiming component). The latter assumes that sleep during an internal sleep window provided by the circadian clock is optimal and sleeping outside or misaligned with this window is considered mistimed. The internal sleep window can be estimated by an individual’s chronotype and MSFsc is therefore used as the reference to quantify mistimed sleep [24]. CPDSleep calculates how far away (in hours) sleep occurs from (i) the individual’s chronotype (ΔChronotype), and (ii) the previous sleep episode (ΔDay-to-Day). The ΔChronotype (ΔCT) component reflects the mistiming of sleep (i.e. whether sleep occurs close to its optimal time), whereas ΔDay-to-Day (ΔDD) reflects irregularity of sleep timing (Figure 1b) (equations 4 and 5). CPDSleep plots have ΔCT on the horizontal axis and ΔDD on the vertical axis. In these plots (Figure 1c), the origin represents an “ideal state,” where sleep occurs at the individual’s optimal sleep time (chronotype) and at the same time every day. The deviation of any data point from the origin is quantified by the length of its corresponding vector; the CPDSleep metric is the average vector length (Figure 1c) (equation 6). For example in Figure 1c, a few data points are gathered in the upper-right quadrant of the plot, where sleep is advanced relative to chronotype (positive ΔCT) and advanced relative to the previous sleep episode (positive ΔDD). These data points are from Sundays to Mondays: after a delay in sleep times over the weekend, sleep is advanced due to class or other scheduled events. Missing midsleeps, such as from an “all-nighter,” result in missing CPDSleep data (10% of days had no sleep data). ΔChronotypet (ΔCTt)=MSFsc−Midsleept (4) ΔDay-to-Dayt (ΔDDt)=Midsleept−1− Midsleept (5) CPDtSleep= ΔCTt2+ΔDDt2 (6) where subscript t denotes a given day in the time series. Composite Phase Deviation using FSE times (CPDFSE) We applied the CPD approach described above to FSE times to assess the mistiming and irregularity of students’ event schedules. CPDFSE quantifies how far an event occurs (i) from the average event start time (ΔEvent), and (ii) from the previous event (ΔDay-to-Day) (Figure 1d). Accordingly, missing events result in missing CPDFSE data (21% of days had no FSE). ΔEvent and ΔDay-to-Day are then plotted against each other (Figure 1e). CPDFSE is quantified using the length of the vector from the origin (i.e. both perfectly aligned and regular events) to each data point. Statistics All variables were tested for normality of distribution. Sleep duration was the only normally distributed variable (Shapiro–Wilk W = 0.99, p = 0.45). We therefore used nonparametric tests. Rank correlations (Spearman’s rho) were computed to test associations between well-being and sleep variables. Mann–Whitney U tests were used for two-group comparisons (males vs. females) of well-being. Effect size r was calculated as Z/N for sex differences. Kruskal–Wallis tests were computed for more than two-group comparisons among chronotype categories (moderate: MSFsc < 5:00, late: 5:00–7:00, very late: >7:00) and among clusters (outcome variables: average sleep duration, MSFsc, standard deviations of midsleep and scheduled event times, average time of FSE, average well-being; note: cluster analysis is described below). Effect size ε 2 was computed for cluster differences: 0.01–<0.08 small effect, 0.08–<0.26 medium effect, ≥0.26 large effect. A χ 2-test was performed to examine sex distributions by cluster. Circular means and standard deviations were calculated for midsleeps and FSEs. In order to use linear statistics, midsleep was linearized by transforming midsleeps between 20:00 and 24:00 into values between −4.00 and 0.00. Significance level was set to α = 0.05. No meaningful auto- or cross-correlation components were detected in the time series and data were found to be stationary. To check the appropriateness of linear regression vs. logistic regression, Q–Q plots and Shapiro–Wilk tests were used to test for normal distribution of model residuals; no violations were detected. Residuals vs. fitted values-plots and Breusch–Pagan tests were computed to test for heteroscedasticity; results were nonsignificant. Because 11% of all observations were missing data (n = 759, missing sleep or well-being or both on a given day) with a range of 0%–81% among students (median = 6.67%, IQR = 3.33%–10%), we conducted sensitivity analyses, excluding students with more than 10% of missing data (remaining sample: n = 173 students and 5289 observations). Regression analysis We were interested in both daily (within-person) and average (between-person) effects of CPDSleep on students’ well-being. To disaggregate the two types of effects (between/average vs. within/daily), we computed a longitudinal growth model with time-varying covariates (TVCs) [34]. After checking that the TVCs in our dataset (CPDSleep, sleep duration, midsleep) were unrelated to time, we followed the traditional approach [35] to disaggregate between-person and within-person effects by calculating the person-specific mean (⁠ z¯i ⁠) and daily deviations from the person-specific mean (⁠ z˙ti ⁠) for all three TVCs. Both z¯i and z˙ti were then used as predictors in a random-intercept model: yti=(y00+y01z¯i+y10z˙ti)+(rti+u0i) (7) where y00 is intercept (or grand mean); y01 direct estimate of the between-person effect (average effects); y10 direct estimate of the within-person effect (daily effects); rti residual term (i.e. time-specific deviation from person-specific mean); u0i is random residual term (i.e. unexplained (“random”) differences among individuals). The model thus captures the relation between average levels of, e.g. CPDSleep and average levels of well-being across all individuals (via the estimate y01 ⁠). It also captures the relation between a given student’s daily deviation in CPDSleep (relative to the overall level of CPDSleep) and the student’s daily well-being (via the estimate y10 ⁠). We report the unstandardized coefficient b and standard errors in the main text, and provide full model information in Supplementary Table S1A–E). To test for combined effects of sleep and event schedules on well-being, the interaction term CPDSleep*CPDFSE was included both for daily and average effects. Two additional models were calculated separately for the two CPDSleep components to determine whether potential effects of CPDSleep were driven by sleep mistiming (ΔCT) vs. sleep irregularity (ΔDD), using absolute values of ΔCT and ΔDD. The same type of longitudinal growth model with TVCs was computed to test daily and average effects of event schedules (CPDFSE) (i) on sleep variables (CPDSleep, sleep duration, and midsleep), and (ii) on well-being scores of all five scales. All models were adjusted for sex, whereas age was not included due to its narrow range (75% of students were between 18 and 20). Study year was not included as a covariate, since it did not improve model AIC. To specifically examine the relationship between CPDFSE and CPDSleep, we furthermore conducted cluster analysis (see below for details). Regular linear models were used to compare average well-being among clusters with cluster, average sleep duration, chronotype (MSFsc), and sex in the same model. Methods for clustering are described below. For these models, reference group was Cluster 4: mistimed/irregular sleepers (high-CPDSleep) on mistimed/irregular event schedules (high-CPDFSE). The multiplicative term cluster*chronotype was added to test for interactions. Cluster analysis We performed divisive hierarchical clustering in R (DIvisive ANAlysis, DIANA [36]) to examine the relationship between sleep schedules (CPDSleep) and event schedules (CPDFSE). Hierarchical clustering is an alternative approach to partitioning clustering (e.g. k-means clustering); it does not require the optimal number of clusters to be specified a priori. Agglomerative hierarchical clustering is a bottom-up approach starting with as many clusters as observations whereas DIANA is a top-down approach. In DIANA, all observations are initially in one cluster, the observation with maximum average dissimilarity is then moved to a new cluster, and this process is iterated until every observation is in a separate cluster. Divisive clustering is good at detecting large clusters, which is why we chose to perform DIANA. Results were compared with partitioning clustering using k-mediods. CPDSleep was positively skewed and thus log-transformed. Log-transforming CPDFSE resulted in a heavily skewed distribution; CPDFSE was thus not log-transformed. Both CPDSleep and CPDFSE were scaled (mean = 0, sd = 1). R packages “cluster” [37], “factoextra” [38], and “fpc” [39] were used to perform the cluster analyses. The Jaccard Index was calculated to assess stability of clusters using the clusterboot function in R. Clusters with a stability value less than 0.6 should be considered unstable; values between 0.6 and 0.75 indicate that the cluster is measuring a pattern in the data with moderate certainty about which points should be clustered together; values above 0.85 can be considered highly stable. Results Later chronotype is associated with more mistimed/irregular sleep but not with poorer well-being The average chronotype (MSFsc) was 6:35 ± 1.20 h (interquartile range = 5:49–7:27). Only six students had a chronotype of MSFsc < 4:00. Cut-offs for chronotype categories as shown in Table 1 were therefore chosen as: moderate types (MSFsc < 5:00), late types (5:00–7:00), and very late types (>7:00), in line with previous studies [25, 40]. Table 1. Sample demographics and study variables in the total sample, by sex and chronotype categories . Total sample (n = 223) . Females (n = 83) . Males (n = 140) . Moderate chronotype (<5:00, n = 19) . Late chronotype (5:00–7:00, n = 122) . Very late chronotype (>7:00, n = 82) . Age (years) 19.4 ± 1.5 19.4 ± 1.5 19.4 ± 1.5 19.6 ± 1.0 19.4 ± 1.5 19.4 ± 1.4 (18–27) (18–27) (18–27) (18–22) (18–27) (18–24) Sex (% female (n)) 37% 100% 0% 46% 79% 39% (83) (83) (0) (6) (54) (23) Sleep duration (h) 6.8 ± 0.78 6.8 ± 0.7 6.8 ± 0.8 6.8 ± 0.7 6.8 ± 0.7 6.8 ± 0.8 (5.2–9.1) (5.2–8.6) (5.2–9.1) (5.3–8.1) (5.3–9.1) (5.2–8.6) Chronotype (MSFsc) (h) 6:35 ± 1.2 6:26 ± 1.2 6:40 ± 1.2 4:20 ± 0.5 6:05 ± 0.5 7:50 ± 0.6 (3:29–10:43) (3:52–10:43) (3:29–9:02) (3:29–4:57) (5:01–6:58) (7:01–10:43) *CPDSleep (h) 1.8 ± 0.6 1.7 ± 0.5 1.8 ± 0.6 1.5 ± 0.4 1.7 ± 0.6 2.1 ± 0.5 (0.8–4.0) (0.8–3.5) (0.8–4.0) (0.9–2.5) (0.8–4.0) (1.0–3.6) CPDFSE (h) 2.4 ± 1.3 2.6 ± 1.2 2.2 ± 1.4 2.5 ± 1.2 2.3 ± 1.3 2.5 ± 1.4 (0.00–6.0) (0.00–6.0) (0.1–5.6) (0.7–4.3) (0.00–5.6) (0.00–6.0) #Sleepy–Alert (0–100) 50.6 ± 18.6 47.1 ± 17.2 52.8 ± 19.2 55.9 ± 17.0 50.7 ± 18.6 49.3 ± 19.0 (5.7–95.8) (16.4–89.4) (5.7–95.8) (26.6–84.8) (5.7–94.6) (10.4–95.8) #Sad–Happy (0–100) 60.9 ± 15.8 57.7 ± 13.7 62.9 ± 16.6 64.3 ± 13.5 60.5 ± 16.0 60.8 ± 15.9 (14.8–97.8) (22.2–94.8) (14.8–97.8) (44.9–81.9) (14.8–97.8) (22.2–96.2) #Sluggish–Energetic (0–100) 51.1 ± 17.6 47.7 ± 15.8 53.1 ± 18.4 55.0 ± 15.7 51.6 ± 17.7 49.3 ± 18.0 (5.3–95.0) (14.6–92.9) (5.3–95.0) (32.4–81.3) (5.3–93.7) (7.9–95.0) Sick–Healthy (0–100) 64.6 ± 18.4 62.3 ± 17.5 66.0 ± 18.9 63.2 ± 20.1 65.2 ± 18.1 64.1 ± 18.7 (4.3–100.0) (4.3–97.9) (22.3–100.0) (22.4–90.6) (22.3–100.0) (4.3–100.0) #Stressed–Calm (0–100) 53.1 ± 18.4 46.5 ± 16.1 57.0 ± 18.5 55.8 ± 14.1 53.2 ± 18.4 52.4 ± 19.2 (3.1–96.2) (14.1–92.7) (3.1–96.2) (32.5–81.2) (3.1–96.2) (10.8–95.7) . Total sample (n = 223) . Females (n = 83) . Males (n = 140) . Moderate chronotype (<5:00, n = 19) . Late chronotype (5:00–7:00, n = 122) . Very late chronotype (>7:00, n = 82) . Age (years) 19.4 ± 1.5 19.4 ± 1.5 19.4 ± 1.5 19.6 ± 1.0 19.4 ± 1.5 19.4 ± 1.4 (18–27) (18–27) (18–27) (18–22) (18–27) (18–24) Sex (% female (n)) 37% 100% 0% 46% 79% 39% (83) (83) (0) (6) (54) (23) Sleep duration (h) 6.8 ± 0.78 6.8 ± 0.7 6.8 ± 0.8 6.8 ± 0.7 6.8 ± 0.7 6.8 ± 0.8 (5.2–9.1) (5.2–8.6) (5.2–9.1) (5.3–8.1) (5.3–9.1) (5.2–8.6) Chronotype (MSFsc) (h) 6:35 ± 1.2 6:26 ± 1.2 6:40 ± 1.2 4:20 ± 0.5 6:05 ± 0.5 7:50 ± 0.6 (3:29–10:43) (3:52–10:43) (3:29–9:02) (3:29–4:57) (5:01–6:58) (7:01–10:43) *CPDSleep (h) 1.8 ± 0.6 1.7 ± 0.5 1.8 ± 0.6 1.5 ± 0.4 1.7 ± 0.6 2.1 ± 0.5 (0.8–4.0) (0.8–3.5) (0.8–4.0) (0.9–2.5) (0.8–4.0) (1.0–3.6) CPDFSE (h) 2.4 ± 1.3 2.6 ± 1.2 2.2 ± 1.4 2.5 ± 1.2 2.3 ± 1.3 2.5 ± 1.4 (0.00–6.0) (0.00–6.0) (0.1–5.6) (0.7–4.3) (0.00–5.6) (0.00–6.0) #Sleepy–Alert (0–100) 50.6 ± 18.6 47.1 ± 17.2 52.8 ± 19.2 55.9 ± 17.0 50.7 ± 18.6 49.3 ± 19.0 (5.7–95.8) (16.4–89.4) (5.7–95.8) (26.6–84.8) (5.7–94.6) (10.4–95.8) #Sad–Happy (0–100) 60.9 ± 15.8 57.7 ± 13.7 62.9 ± 16.6 64.3 ± 13.5 60.5 ± 16.0 60.8 ± 15.9 (14.8–97.8) (22.2–94.8) (14.8–97.8) (44.9–81.9) (14.8–97.8) (22.2–96.2) #Sluggish–Energetic (0–100) 51.1 ± 17.6 47.7 ± 15.8 53.1 ± 18.4 55.0 ± 15.7 51.6 ± 17.7 49.3 ± 18.0 (5.3–95.0) (14.6–92.9) (5.3–95.0) (32.4–81.3) (5.3–93.7) (7.9–95.0) Sick–Healthy (0–100) 64.6 ± 18.4 62.3 ± 17.5 66.0 ± 18.9 63.2 ± 20.1 65.2 ± 18.1 64.1 ± 18.7 (4.3–100.0) (4.3–97.9) (22.3–100.0) (22.4–90.6) (22.3–100.0) (4.3–100.0) #Stressed–Calm (0–100) 53.1 ± 18.4 46.5 ± 16.1 57.0 ± 18.5 55.8 ± 14.1 53.2 ± 18.4 52.4 ± 19.2 (3.1–96.2) (14.1–92.7) (3.1–96.2) (32.5–81.2) (3.1–96.2) (10.8–95.7) Chronotype categories were chosen based on previous studies [25, 40]. Mean values ± sd (range) are shown. MSFsc = midsleep on weekends, corrected for sleep loss on weekdays. CPDSleep = Composite Phase Deviation using midsleeps. CPDFSE = Composite Phase Deviation using first scheduled event (FSE) times. Well-being scales range from 0 to 100, with higher values representing better well-being. *Significantly different among chronotype groups (Kruskal–Wallis, p < 0.001). #Significantly different between males and females (Mann–Whitney U, p < 0.05). Open in new tab Table 1. Sample demographics and study variables in the total sample, by sex and chronotype categories . Total sample (n = 223) . Females (n = 83) . Males (n = 140) . Moderate chronotype (<5:00, n = 19) . Late chronotype (5:00–7:00, n = 122) . Very late chronotype (>7:00, n = 82) . Age (years) 19.4 ± 1.5 19.4 ± 1.5 19.4 ± 1.5 19.6 ± 1.0 19.4 ± 1.5 19.4 ± 1.4 (18–27) (18–27) (18–27) (18–22) (18–27) (18–24) Sex (% female (n)) 37% 100% 0% 46% 79% 39% (83) (83) (0) (6) (54) (23) Sleep duration (h) 6.8 ± 0.78 6.8 ± 0.7 6.8 ± 0.8 6.8 ± 0.7 6.8 ± 0.7 6.8 ± 0.8 (5.2–9.1) (5.2–8.6) (5.2–9.1) (5.3–8.1) (5.3–9.1) (5.2–8.6) Chronotype (MSFsc) (h) 6:35 ± 1.2 6:26 ± 1.2 6:40 ± 1.2 4:20 ± 0.5 6:05 ± 0.5 7:50 ± 0.6 (3:29–10:43) (3:52–10:43) (3:29–9:02) (3:29–4:57) (5:01–6:58) (7:01–10:43) *CPDSleep (h) 1.8 ± 0.6 1.7 ± 0.5 1.8 ± 0.6 1.5 ± 0.4 1.7 ± 0.6 2.1 ± 0.5 (0.8–4.0) (0.8–3.5) (0.8–4.0) (0.9–2.5) (0.8–4.0) (1.0–3.6) CPDFSE (h) 2.4 ± 1.3 2.6 ± 1.2 2.2 ± 1.4 2.5 ± 1.2 2.3 ± 1.3 2.5 ± 1.4 (0.00–6.0) (0.00–6.0) (0.1–5.6) (0.7–4.3) (0.00–5.6) (0.00–6.0) #Sleepy–Alert (0–100) 50.6 ± 18.6 47.1 ± 17.2 52.8 ± 19.2 55.9 ± 17.0 50.7 ± 18.6 49.3 ± 19.0 (5.7–95.8) (16.4–89.4) (5.7–95.8) (26.6–84.8) (5.7–94.6) (10.4–95.8) #Sad–Happy (0–100) 60.9 ± 15.8 57.7 ± 13.7 62.9 ± 16.6 64.3 ± 13.5 60.5 ± 16.0 60.8 ± 15.9 (14.8–97.8) (22.2–94.8) (14.8–97.8) (44.9–81.9) (14.8–97.8) (22.2–96.2) #Sluggish–Energetic (0–100) 51.1 ± 17.6 47.7 ± 15.8 53.1 ± 18.4 55.0 ± 15.7 51.6 ± 17.7 49.3 ± 18.0 (5.3–95.0) (14.6–92.9) (5.3–95.0) (32.4–81.3) (5.3–93.7) (7.9–95.0) Sick–Healthy (0–100) 64.6 ± 18.4 62.3 ± 17.5 66.0 ± 18.9 63.2 ± 20.1 65.2 ± 18.1 64.1 ± 18.7 (4.3–100.0) (4.3–97.9) (22.3–100.0) (22.4–90.6) (22.3–100.0) (4.3–100.0) #Stressed–Calm (0–100) 53.1 ± 18.4 46.5 ± 16.1 57.0 ± 18.5 55.8 ± 14.1 53.2 ± 18.4 52.4 ± 19.2 (3.1–96.2) (14.1–92.7) (3.1–96.2) (32.5–81.2) (3.1–96.2) (10.8–95.7) . Total sample (n = 223) . Females (n = 83) . Males (n = 140) . Moderate chronotype (<5:00, n = 19) . Late chronotype (5:00–7:00, n = 122) . Very late chronotype (>7:00, n = 82) . Age (years) 19.4 ± 1.5 19.4 ± 1.5 19.4 ± 1.5 19.6 ± 1.0 19.4 ± 1.5 19.4 ± 1.4 (18–27) (18–27) (18–27) (18–22) (18–27) (18–24) Sex (% female (n)) 37% 100% 0% 46% 79% 39% (83) (83) (0) (6) (54) (23) Sleep duration (h) 6.8 ± 0.78 6.8 ± 0.7 6.8 ± 0.8 6.8 ± 0.7 6.8 ± 0.7 6.8 ± 0.8 (5.2–9.1) (5.2–8.6) (5.2–9.1) (5.3–8.1) (5.3–9.1) (5.2–8.6) Chronotype (MSFsc) (h) 6:35 ± 1.2 6:26 ± 1.2 6:40 ± 1.2 4:20 ± 0.5 6:05 ± 0.5 7:50 ± 0.6 (3:29–10:43) (3:52–10:43) (3:29–9:02) (3:29–4:57) (5:01–6:58) (7:01–10:43) *CPDSleep (h) 1.8 ± 0.6 1.7 ± 0.5 1.8 ± 0.6 1.5 ± 0.4 1.7 ± 0.6 2.1 ± 0.5 (0.8–4.0) (0.8–3.5) (0.8–4.0) (0.9–2.5) (0.8–4.0) (1.0–3.6) CPDFSE (h) 2.4 ± 1.3 2.6 ± 1.2 2.2 ± 1.4 2.5 ± 1.2 2.3 ± 1.3 2.5 ± 1.4 (0.00–6.0) (0.00–6.0) (0.1–5.6) (0.7–4.3) (0.00–5.6) (0.00–6.0) #Sleepy–Alert (0–100) 50.6 ± 18.6 47.1 ± 17.2 52.8 ± 19.2 55.9 ± 17.0 50.7 ± 18.6 49.3 ± 19.0 (5.7–95.8) (16.4–89.4) (5.7–95.8) (26.6–84.8) (5.7–94.6) (10.4–95.8) #Sad–Happy (0–100) 60.9 ± 15.8 57.7 ± 13.7 62.9 ± 16.6 64.3 ± 13.5 60.5 ± 16.0 60.8 ± 15.9 (14.8–97.8) (22.2–94.8) (14.8–97.8) (44.9–81.9) (14.8–97.8) (22.2–96.2) #Sluggish–Energetic (0–100) 51.1 ± 17.6 47.7 ± 15.8 53.1 ± 18.4 55.0 ± 15.7 51.6 ± 17.7 49.3 ± 18.0 (5.3–95.0) (14.6–92.9) (5.3–95.0) (32.4–81.3) (5.3–93.7) (7.9–95.0) Sick–Healthy (0–100) 64.6 ± 18.4 62.3 ± 17.5 66.0 ± 18.9 63.2 ± 20.1 65.2 ± 18.1 64.1 ± 18.7 (4.3–100.0) (4.3–97.9) (22.3–100.0) (22.4–90.6) (22.3–100.0) (4.3–100.0) #Stressed–Calm (0–100) 53.1 ± 18.4 46.5 ± 16.1 57.0 ± 18.5 55.8 ± 14.1 53.2 ± 18.4 52.4 ± 19.2 (3.1–96.2) (14.1–92.7) (3.1–96.2) (32.5–81.2) (3.1–96.2) (10.8–95.7) Chronotype categories were chosen based on previous studies [25, 40]. Mean values ± sd (range) are shown. MSFsc = midsleep on weekends, corrected for sleep loss on weekdays. CPDSleep = Composite Phase Deviation using midsleeps. CPDFSE = Composite Phase Deviation using first scheduled event (FSE) times. Well-being scales range from 0 to 100, with higher values representing better well-being. *Significantly different among chronotype groups (Kruskal–Wallis, p < 0.001). #Significantly different between males and females (Mann–Whitney U, p < 0.05). Open in new tab Later chronotype (MSFsc) was associated with higher CPDSleep (r = 0.48, p < 0.001) and younger age (r = −0.15, p = 0.03) (Figure 2a and b). Males had marginally later chronotypes than females (p = 0.06). No significant correlations were observed between chronotype and sleep duration (r = 0.07, p = 0.33) or chronotype and any of the five scales (−0.002 < r < −0.09, p > 0.17). Age was not associated with any of the five scales (−0.06 < r < 0.04), CPDSleep (r = −0.10), or sleep duration (r = 0.07), all p > 0.33. The five well-being scales were correlated with each other, with coefficients ranging from r = 0.56 (Sick–Healthy and Stressed–Calm) to r = 0.90 (Sleepy–Alert and Sluggish–Energetic), all p < 0.001 (Supplementary Table S2 and Figure S1). Figure 2. Open in new tabDownload slide Associations of well-being and CPDSleep with chronotype and sex. A late chronotype (MSFsc) was associated with (a) higher Composite Phase Deviation (CPDSleep) and (b) younger age. Males scored higher (“better”) on (c) Sleepy–Alert, (d) Sad–Happy, (e) Sluggish–Energetic, and (f) Stressed–Calm. r = rank correlation coefficient Spearman’s rho. Sex comparisons in panels c–f are based on nonparametric Mann–Whitney U tests. Horizontal lines denote significant group differences: *p < 0.05, ***p < 0.001. Figure 2. Open in new tabDownload slide Associations of well-being and CPDSleep with chronotype and sex. A late chronotype (MSFsc) was associated with (a) higher Composite Phase Deviation (CPDSleep) and (b) younger age. Males scored higher (“better”) on (c) Sleepy–Alert, (d) Sad–Happy, (e) Sluggish–Energetic, and (f) Stressed–Calm. r = rank correlation coefficient Spearman’s rho. Sex comparisons in panels c–f are based on nonparametric Mann–Whitney U tests. Horizontal lines denote significant group differences: *p < 0.05, ***p < 0.001. Although sex was not associated with CPDSleep (Mann–Whitney U, r = −0.12, p = 0.33) or sleep duration (r = 0.06, p = 0.69), males did report significantly higher (i.e. “better”) scores on four of the five scales (Table 1, Figure 2c–f): Sleepy–Alert (r = −0.16, p = 0.02), Sad–Happy (r = −0.18, p = 0.006), Sluggish–Energetic (r = −0.16, p = 0.02), and Stressed–Calm (r = −0.30, p < 0.001). Males and females did not significantly differ on the scale Sick–Healthy (r = −0.09, p = 0.15). Weak to moderate correlations were observed between percentage of missing data (11% overall, range 0–81%, 75%-quartile = 10%) and CPDSleep (r = 0.19, p = 0.004) or chronotype (r = 0.19, p = 0.004). No significant correlations with percentage of missing data were observed for sleep duration (r = 0.05, p = 0.42) or any of the five scales (−0.01 < r < 0.05, p > 0.46). Composite Phase Deviation (CPDSleep) and sleep duration predict college students’ well-being on different timescales (Hypothesis 1) Daily well-being (within-person effects) Daily sleep duration was the strongest significant predictor of daily well-being on four of five scales: with every additional hour of sleep, daily Sleepy–Alert, Sad–Happy, Sluggish–Energetic, and Stressed–Calm improved by 2.6, 0.5, 1.6, and 1.0 units, respectively, on a scale from 0 (poor) to 100 (good) (Table 2). Predictive power of sleep duration was highest for daily Sleepy–Alert, whereas sleep duration had no significant effect on daily Sick–Healthy. Midsleep predicted Stressed–Calm on a daily basis: with every hour that midsleep was later, students reported feeling calmer by 0.7 units. CPDSleep was not a significant predictor of any of the five scales on a daily basis. Table 2. Longitudinal growth model with time-varying covariates (random intercept) . Sleepy–Alert . Sad–Happy . Sluggish–Energetic . Sick–Healthy . Stressed–Calm . . Estimate . SE . Estimate . SE . Estimate . SE . Estimate . SE . Estimate . SE . Intercept 70.1 13.9 54.9 11.3 69.5 13.0 67.2 13.6 31.5 12.8 CPDSleep (h, daily) −0.03 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.2 Sleep duration (h, daily) 2.6*** 0.2 0.5*** 0.2 1.6*** 0.2 0.1 0.2 1.0*** 0.2 Midsleep (h, daily) 0.4 0.2 0.2 0.2 −0.1 0.2 −0.3 0.2 0.7*** 0.2 CPDSleep (h, average) −2.6 2.5 −5.6** 2.0 −3.2 2.3 −5.9* 2.4 −6.3** 2.3 Sleep duration (h, average) −1.8 1.8 1.9 1.5 −1.2 1.7 0.5 1.8 3.8* 1.6 Midsleep (h, average) −0.9 1.2 −0.02 0.9 −1.2 2.5 0.2 1.2 −0.01 1.1 . Sleepy–Alert . Sad–Happy . Sluggish–Energetic . Sick–Healthy . Stressed–Calm . . Estimate . SE . Estimate . SE . Estimate . SE . Estimate . SE . Estimate . SE . Intercept 70.1 13.9 54.9 11.3 69.5 13.0 67.2 13.6 31.5 12.8 CPDSleep (h, daily) −0.03 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.2 Sleep duration (h, daily) 2.6*** 0.2 0.5*** 0.2 1.6*** 0.2 0.1 0.2 1.0*** 0.2 Midsleep (h, daily) 0.4 0.2 0.2 0.2 −0.1 0.2 −0.3 0.2 0.7*** 0.2 CPDSleep (h, average) −2.6 2.5 −5.6** 2.0 −3.2 2.3 −5.9* 2.4 −6.3** 2.3 Sleep duration (h, average) −1.8 1.8 1.9 1.5 −1.2 1.7 0.5 1.8 3.8* 1.6 Midsleep (h, average) −0.9 1.2 −0.02 0.9 −1.2 2.5 0.2 1.2 −0.01 1.1 Person-specific means and daily deviations from person-specific mean were calculated to estimate between-person (average) and within-person (daily) effects of sleep variables on well-being. An unstandardized regression coefficient of e.g. b = −5.6 for CPDSleep means that with every additional hour of mistimed/irregular sleep, well-being worsens by 5.6 units on a scale from 0 (poor) to 100 (good). CPDSleep = Composite Phase Deviation using midsleeps. Estimate = unstandardized regression coefficient b. SE = standard error. All estimates are sex-adjusted. **p < 0.01. ***p < 0.001. Open in new tab Table 2. Longitudinal growth model with time-varying covariates (random intercept) . Sleepy–Alert . Sad–Happy . Sluggish–Energetic . Sick–Healthy . Stressed–Calm . . Estimate . SE . Estimate . SE . Estimate . SE . Estimate . SE . Estimate . SE . Intercept 70.1 13.9 54.9 11.3 69.5 13.0 67.2 13.6 31.5 12.8 CPDSleep (h, daily) −0.03 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.2 Sleep duration (h, daily) 2.6*** 0.2 0.5*** 0.2 1.6*** 0.2 0.1 0.2 1.0*** 0.2 Midsleep (h, daily) 0.4 0.2 0.2 0.2 −0.1 0.2 −0.3 0.2 0.7*** 0.2 CPDSleep (h, average) −2.6 2.5 −5.6** 2.0 −3.2 2.3 −5.9* 2.4 −6.3** 2.3 Sleep duration (h, average) −1.8 1.8 1.9 1.5 −1.2 1.7 0.5 1.8 3.8* 1.6 Midsleep (h, average) −0.9 1.2 −0.02 0.9 −1.2 2.5 0.2 1.2 −0.01 1.1 . Sleepy–Alert . Sad–Happy . Sluggish–Energetic . Sick–Healthy . Stressed–Calm . . Estimate . SE . Estimate . SE . Estimate . SE . Estimate . SE . Estimate . SE . Intercept 70.1 13.9 54.9 11.3 69.5 13.0 67.2 13.6 31.5 12.8 CPDSleep (h, daily) −0.03 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.2 Sleep duration (h, daily) 2.6*** 0.2 0.5*** 0.2 1.6*** 0.2 0.1 0.2 1.0*** 0.2 Midsleep (h, daily) 0.4 0.2 0.2 0.2 −0.1 0.2 −0.3 0.2 0.7*** 0.2 CPDSleep (h, average) −2.6 2.5 −5.6** 2.0 −3.2 2.3 −5.9* 2.4 −6.3** 2.3 Sleep duration (h, average) −1.8 1.8 1.9 1.5 −1.2 1.7 0.5 1.8 3.8* 1.6 Midsleep (h, average) −0.9 1.2 −0.02 0.9 −1.2 2.5 0.2 1.2 −0.01 1.1 Person-specific means and daily deviations from person-specific mean were calculated to estimate between-person (average) and within-person (daily) effects of sleep variables on well-being. An unstandardized regression coefficient of e.g. b = −5.6 for CPDSleep means that with every additional hour of mistimed/irregular sleep, well-being worsens by 5.6 units on a scale from 0 (poor) to 100 (good). CPDSleep = Composite Phase Deviation using midsleeps. Estimate = unstandardized regression coefficient b. SE = standard error. All estimates are sex-adjusted. **p < 0.01. ***p < 0.001. Open in new tab Average well-being (between-person effects) In contrast, average CPDSleep was the strongest significant predictor of average (across ~30 days) well-being on three of five scales: with every additional hour of CPDSleep (i.e. more mistimed/irregular sleep), Sad–Happy, Sick–Healthy, and Stressed–Calm worsened by 5.6, 5.9, and 6.2 units, respectively (Table 2). Predictive power of CPDSleep was highest for average Stressed–Calm, whereas CPDSleep had no effect on average ratings of Sleepy–Alert and Sluggish–Energetic. Average sleep duration was a significant predictor of average Stressed–Calm, which improved by 3.8 units with every additional hour of sleep. Average midsleep had no effect on average well-being on any of the five scales. Given that the CPDSleep metric combines two components—(i) the sleep mistiming component ΔChronotype (ΔCT) and (ii) the sleep irregularity component ΔDay-to-Day (ΔDD)—we were interested in their individual predictive contributions to well-being. We therefore re-computed the longitudinal growth model replacing CPDSleep with ΔCT or ΔDD. ΔDD (irregularity), but not ΔCT (mistiming), was a significant predictor for average well-being on four of five scales: Sad–Happy, Sluggish–Energetic, Sick–Healthy, and Stressed–Calm. With every additional hour of day-to-day irregularity (ΔDD), students reported feeling overall less happy (b = −9.1, SE = 2.8, p = 0.002), less energetic (b = −7.0, SE = 3.3, p = 0.03), less healthy (b = −8.1, SE = 3.5, p = 0.02), and less calm (b = −9.6, SE = 3.2, p = 0.002). ΔCT (mistiming) was a significant predictor on the scale Stressed–Calm: with every additional hour of mistimed sleep, students reported feeling less calm (b = −6.2, SE = 3.0, p = 0.04). Daily ΔCT or ΔDD were not significant predictors of daily well-being (all p > 0.60). Results of the longitudinal growth models did not change when variables were entered stepwise (beginning with CPDSleep). To test the influence of missing data, we excluded students with more than 10% of missing data and conducted a sensitivity analysis with the remaining sample (n = 173 students with 5289 observations, 22% excluded data). Results were the same for both daily and average well-being scores. Event schedules (CPDFSE) are associated with sleep schedules (CPDSleep) on a daily but not average basis (Hypothesis 2) Daily basis (within-person effects) CPDFSE was associated with CPDSleep, sleep duration, and midsleep on a daily basis: with every additional hour of CPDFSE (i.e. more mistimed/irregular events), CPDSleep increased by 2.4 min (b = 0.04, SE = 0.01, p < 0.001), sleep duration decreased by 2.4 min (b = −0.04, SE = 0.01, p < 0.001), and midsleep delayed by 3.0 min (b = 0.05, SE = 0.01, p < 0.001), indicating that event schedules are more variable than sleep schedules (e.g. a difference of 5 h in CPDFSE results in a difference of 12 min in CPDSleep and sleep duration and 15 min in midsleep). Average basis (between-person effects) CPDFSE was not associated with CPDSleep, sleep duration, or midsleep on an average level (all b < 0.08, all p > 0.19). We furthermore specifically checked that there was no relationship between CPDFSE and chronotype: event schedules of late types were on average not more mistimed/irregular than event schedules of earlier types (b = 0.02, SE = 0.06, p = 0.75). Using hierarchical cluster analysis, the DIANA dendrogram (a tree diagram showing the number of clusters at different levels) identified approximately equally partitioned two-cluster and four-cluster solutions, when cut close to the top (Figure 3a). If there were a relationship between event schedules and sleep schedules, e.g. such that high-CPD event schedules promote high-CPD sleep schedules, we would expect higher density of data points in the upper-right (sleep and event schedules both high-CPD) and bottom-left (sleep and event schedules both low-CPD) quadrants, along the diagonal axis of the cluster plot. The two-clusters solution (Figure 3b) instead separated the data along the vertical axis into low-CPDFSE (aligned/regular event schedules) and high-CPDFSE (mistimed/irregular event schedules) clusters, illustrating that mistimed and irregular sleep occurs on either type of event schedule. This is consistent with the lack of association between CPDFSE and CPDSleep on an average level. Figure 3. Open in new tabDownload slide Cluster analysis. Divisive hierarchical clustering [36] was used to examine the relationship between sleep schedules and event schedules. Sleep schedules were assessed by Composite Phase Deviation using midsleeps (CPDSleep), whereas event schedules were assessed by Composite Phase Deviation using FSE times (CPDFSE). (a) The dendrogram shows a two-clusters and a four-clusters solution, depending on where the dendrogram is cut. (b) The two-clusters solution groups the data into low-CPDFSE (aligned and regular event schedules, Cluster 1) and high-CPDFSE (mistimed and irregular event schedules, Cluster 2) clusters. Axes show z-scaled CPDSleep and CPDFSE values, i.e. a value of −1 equals 1 sd below the sample mean. (c) The four-clusters solution further splits the data along the horizontal axis: aligned/regular sleepers on aligned/regular schedules (low-CPDSleep/low-CPDFSE) (Cluster 1, n = 48), aligned/regular sleepers on mistimed/irregular schedules (low-CPDSleep/high-CPDFSE) (Cluster 2, n = 73), mistimed/irregular sleepers on aligned/regular schedules (high-CPDSleep/low-CPDFSE) (Cluster 3, n = 61), and mistimed/irregular sleepers on mistimed/irregular schedules (high-CPDSleep/high-CPDFSE) (Cluster 4, n = 41). The four colored circles mark the four individuals shown in panels e–h. (d) Characteristics of the four clusters by sleep duration (SDur), chronotype (MSFsc, sleep loss-corrected midsleep on weekends), standard deviation of midsleeps (MS (sd)), and standard deviation of first scheduled events (FSE (sd)). Colored boxes (gray and red) mark statistical differences between clusters (Kruskal–Wallis, p < 0.05). Effect sizes (ε 2) for cluster comparisons were as follows: SDε 2 = 0.02, MSFsc ε 2 = 0.20, MS (sd) ε 2 = 0.60, and FSE (sd) ε 2 = 0.67. Raster plots are shown of one individual from each cluster (note that individuals were selected to illustrate differences): (e) Cluster 3, (f) Cluster 4, (g) Cluster 1, (h) Cluster 2. Black bars = sleep episodes. Red dots = midsleeps. Red line = chronotype (MSFsc, sleep loss-corrected midsleep on weekends). Blue dots = first scheduled events (FSEs). Blue line = average start time of FSE. Figure 3. Open in new tabDownload slide Cluster analysis. Divisive hierarchical clustering [36] was used to examine the relationship between sleep schedules and event schedules. Sleep schedules were assessed by Composite Phase Deviation using midsleeps (CPDSleep), whereas event schedules were assessed by Composite Phase Deviation using FSE times (CPDFSE). (a) The dendrogram shows a two-clusters and a four-clusters solution, depending on where the dendrogram is cut. (b) The two-clusters solution groups the data into low-CPDFSE (aligned and regular event schedules, Cluster 1) and high-CPDFSE (mistimed and irregular event schedules, Cluster 2) clusters. Axes show z-scaled CPDSleep and CPDFSE values, i.e. a value of −1 equals 1 sd below the sample mean. (c) The four-clusters solution further splits the data along the horizontal axis: aligned/regular sleepers on aligned/regular schedules (low-CPDSleep/low-CPDFSE) (Cluster 1, n = 48), aligned/regular sleepers on mistimed/irregular schedules (low-CPDSleep/high-CPDFSE) (Cluster 2, n = 73), mistimed/irregular sleepers on aligned/regular schedules (high-CPDSleep/low-CPDFSE) (Cluster 3, n = 61), and mistimed/irregular sleepers on mistimed/irregular schedules (high-CPDSleep/high-CPDFSE) (Cluster 4, n = 41). The four colored circles mark the four individuals shown in panels e–h. (d) Characteristics of the four clusters by sleep duration (SDur), chronotype (MSFsc, sleep loss-corrected midsleep on weekends), standard deviation of midsleeps (MS (sd)), and standard deviation of first scheduled events (FSE (sd)). Colored boxes (gray and red) mark statistical differences between clusters (Kruskal–Wallis, p < 0.05). Effect sizes (ε 2) for cluster comparisons were as follows: SDε 2 = 0.02, MSFsc ε 2 = 0.20, MS (sd) ε 2 = 0.60, and FSE (sd) ε 2 = 0.67. Raster plots are shown of one individual from each cluster (note that individuals were selected to illustrate differences): (e) Cluster 3, (f) Cluster 4, (g) Cluster 1, (h) Cluster 2. Black bars = sleep episodes. Red dots = midsleeps. Red line = chronotype (MSFsc, sleep loss-corrected midsleep on weekends). Blue dots = first scheduled events (FSEs). Blue line = average start time of FSE. The four-clusters solution (Figure 3c) further split the low- and high-CPDFSE clusters along the horizontal axis into low- and high-CPDSleep clusters. The four resulting clusters can thus be characterized as: aligned/regular sleepers on aligned/regular schedules (low-CPDSleep/low-CPDFSE) (Cluster 1, n = 48), aligned/regular sleepers on mistimed/irregular schedules (low-CPDSleep/high-CPDFSE) (Cluster 2, n = 73), mistimed/irregular sleepers on aligned/regular schedules (high-CPDSleep/low-CPDFSE) (Cluster 3, n = 61), and mistimed/irregular sleepers on mistimed/irregular schedules (high-CPDSleep/high-CPDFSE) (Cluster 4, n = 41). Other differences include earlier chronotypes in Clusters 1 and 2 (aligned/regular sleepers) than in Clusters 3 and 4 (mistimed/irregular sleepers) (Kruskal–Wallis, p < 0.001, ε 2 = 0.20) (Figure 3d). Age and sex distributions did not differ among clusters (Kruskal–Wallis, p = 0.31, respectively χ 2, p = 0.17). Figure 3e–h shows exemplary sleep and event schedules for one individual from each cluster (note that these individuals were selected to show individual differences as clearly as possible). Comparison with partitioning clustering using k-mediods as well as excluding students with more than 10% missing data yielded virtually identical clusters. The Jaccard Index yielded values of 0.71 (Cluster 1), 0.77 (Cluster 2), 0.70 (Cluster 3), and 0.76 (Cluster 4), indicating that all clusters were moderately stable, with Clusters 2 and 4 being slightly more stable than Clusters 1 and 3. Poor well-being reported by mistimed/irregular sleepers (high-CPDSleep) is exacerbated by mistimed/irregular event schedules (high-CPDFSE) (Hypothesis 3) To test for combined effects of sleep and event schedules on well-being, we included the interaction term CPDSleep*CPDFSE for both daily and average effects. Whereas the interaction terms did not reach significance, the coefficients indicated that the combination of high-CPDSleep and high-CPDFSE further lowered students’ daily and average well-being on all five scales (daily/average: Sleepy–Alert: binteraction = −0.05/–0.48; Sad–Happy: binteraction = −0.04/−1.67; Sluggish–Energetic: binteraction = −0.07/−0.96; Sick–Healthy: binteraction = −0.06/−1.66; Stressed–Calm: binteraction = −0.06/−1.60; all p > 0.22). We also tested for combined effects of CPDSleep and CPDFSE by comparing students’ average well-being among clusters using linear regression models. Across scales, the poorest average well-being was reported by students who were on both mistimed/irregular sleep and event schedules (Cluster 4) (Figure 4a–e). These students reported feeling significantly less happy (b = −8.92, SE = 3.15, p = 0.005), less energetic (b = −7.53, SE = 3.55, p = 0.03), less healthy (b = −8.69, SE = 3.74, p = 0.02), and less calm (b = −10.82, SE = 3.53, p = 0.002) than students who were also on mistimed/irregular schedules but whose sleep was (relatively) aligned and regular (Cluster 2). They also felt less calm/more stressed than students in Cluster 3 (Figure 4e), whose sleep was mistimed/irregular but who were on aligned/regular event schedules (Stressed–Calm: b = −7.25, SE = 3.50, p = 0.04), suggesting that for mistimed/irregular sleepers feeling stressed may decrease on aligned/regular event schedules. Effect sizes (ε 2) for comparisons of well-being among clusters were as follows: Sleepy–Alert ε 2 = 0.11, Sad–Happy ε 2 = 0.13, Sluggish–Energetic ε 2 = 0.12, Sick–Healthy ε 2 = 0.13, and Stressed–Calm ε 2 = 0.14. Figure 4. Open in new tabDownload slide Well-being by clusters. Scores (mean ± SE) were compared among the four clusters on scales (a) Sleepy–Alert, (b) Sad–Happy, (c) Sluggish–Energetic, (d) Sick–Healthy, and (e) Stressed–Calm. Cluster 1: aligned/regular sleepers on aligned/regular event schedules (low-CPDSleep/low-CPDFSE). Cluster 2: aligned/regular sleepers on mistimed/irregular event schedules (low-CPDSleep/high-CPDFSE). Cluster 3: mistimed/irregular sleepers on aligned/regular event schedules (high-CPDSleep/low-CPDFSE). Cluster 4: mistimed/irregular sleepers on mistimed/irregular event schedules (high-CPDSleep/high-CPDFSE). Mistimed/irregular sleepers on mistimed/irregular event schedules (Cluster 4) reported the poorest well-being, whereas aligned/regular sleepers on mistimed/irregular event schedules (Cluster 2) reported the best well-being. The latter may be explained by later average start times of first scheduled events on mistimed/irregular event schedules (Clusters 2 and 4) compared to aligned/regular event schedules (Clusters 1 and 3), as shown in panel f. Horizontal lines denote significant group differences with *p < 0.05 and **p < 0.01, derived from linear regression models with Cluster 4 as reference. Figure 4. Open in new tabDownload slide Well-being by clusters. Scores (mean ± SE) were compared among the four clusters on scales (a) Sleepy–Alert, (b) Sad–Happy, (c) Sluggish–Energetic, (d) Sick–Healthy, and (e) Stressed–Calm. Cluster 1: aligned/regular sleepers on aligned/regular event schedules (low-CPDSleep/low-CPDFSE). Cluster 2: aligned/regular sleepers on mistimed/irregular event schedules (low-CPDSleep/high-CPDFSE). Cluster 3: mistimed/irregular sleepers on aligned/regular event schedules (high-CPDSleep/low-CPDFSE). Cluster 4: mistimed/irregular sleepers on mistimed/irregular event schedules (high-CPDSleep/high-CPDFSE). Mistimed/irregular sleepers on mistimed/irregular event schedules (Cluster 4) reported the poorest well-being, whereas aligned/regular sleepers on mistimed/irregular event schedules (Cluster 2) reported the best well-being. The latter may be explained by later average start times of first scheduled events on mistimed/irregular event schedules (Clusters 2 and 4) compared to aligned/regular event schedules (Clusters 1 and 3), as shown in panel f. Horizontal lines denote significant group differences with *p < 0.05 and **p < 0.01, derived from linear regression models with Cluster 4 as reference. Contrary to our hypothesis, students with aligned/regular sleep and event schedules (Cluster 1) did not have the highest well-being scores; students in Cluster 2 whose sleep was aligned/regular but who were on mistimed/irregular event schedules reported (nonsignificantly) better well-being. We tested whether average FSE time differed among clusters as a potential explanation: FSEs started on average approximately 30–45 min later for students on irregular event schedules (Cluster 2: 10:55, Cluster 4: 11:00) than for students on regular event schedules (Cluster 1: 10:21, Cluster 3: 10:26) (Kruskal–Wallis, p < 0.01, ε 2 = 0.12) (Figure 4f). Well-being differences among clusters were not driven by chronotype (all p > 0.28): earlier chronotypes did not report worse well-being than late chronotypes on mistimed/irregular event schedules, and among mistimed/irregular sleepers early chronotypes did not report worse well-being than late chronotypes. Mistimed/irregular sleepers (Clusters 3 and 4) reported on average poorer well-being than aligned/regular sleepers (Clusters 1 and 2) (e.g. Sad–Happy: b = −4.52, SE = 2.35, p = 0.02), whereas well-being of students on mistimed/irregular event schedules (Clusters 2 and 4) did not differ from students on aligned/regular event schedules (Clusters 1 and 3), suggesting that sleep schedules may be more important for well-being than event schedules. This finding was confirmed using longitudinal growth models, yielding CPDSleep but not CPDFSE as a significant predictor of average well-being: Sad–Happy (CPDSleep: b = −5.78, p < 0.01 vs. CPDFSE: b = −0.40, p = 0.63), Sick–Healthy (CPDSleep: b = −5.92, p = 0.02 vs. CPDFSE: b = −0.59, p = 0.55), and Stressed–Calm (CPDSleep: b = −6.27, p < 0.01 vs. CPDFSE: b = 0.40, p = 0.67). Discussion In this study, we used a recently developed metric—the Composite Phase Deviation (CPD)—to quantify the mistiming and irregularity of sleep and FSEs in undergraduate students. This extension of CPD from its original application of sleep timing to other events generated novel insights into the relationship between sleep and event schedules and allowed us to quantify whether these schedules are predictive of self-reported well-being upon awakening, either at the daily level or the average level (across ~30 days). Our hypothesis that mistimed and irregular sleep patterns (i.e. high-CPDSleep) would be associated with poorer well-being was confirmed for average well-being but rejected for daily well-being. Contrary to our expectations, CPD for sleep and CPD for events were found to be (weakly) positively associated with one another at the daily level, and not at the average level in this population, i.e. generally mistimed and irregular event schedules were not associated with overall mistimed and irregular sleep. CPDSleep and CPDFSE did interact on well-being, such that effects of mistimed/irregular sleep on well-being were exacerbated by mistimed/irregular event schedules. A recent systematic review of sleep regularity [5] identified the need to study differential associations between various sleep dimensions and outcomes so as to develop a cohesive theoretical framework for the effect of sleep regularity. We report here such differential associations, namely for duration, timing, and regularity of sleep, as well as for different timescales (daily vs. average). Specifically, we conclude that CPDSleep is a useful predictor of inter-individual differences in average well-being, whereas sleep duration is a useful predictor of intra-individual daily variations in well-being relative to an individual’s average. Moreover, we conclude that the relationship between CPDSleep and average well-being is driven by day-to-day differences in sleep timing (ΔDD) but not its misalignment (relative to an individual’s preferred sleep timing, ΔCT). A study by Whiting and Murdock [41] found that consistently short sleep had adverse effects on attention whereas occasionally short sleep did not, supporting our finding that irregular sleep timing was associated with poorer well-being after some exposure time (~30 days). Although we cannot determine causality of these associations from these data, the findings suggest that chronic exposure to irregular sleep patterns may be a specific risk factor for lowered average well-being. Whereas some previous studies have associated later sleep timing (chronotype) with worse mood and well-being [42], we found no such association on an average level. This may be due to the absence of early chronotypes in this cohort; since the sleep timing in our sample of college students was overall delayed compared with the US population [25], we could only compare moderate and late chronotypes. On a daily level, midsleep timing was related to well-being on one scale: students reported feeling more calm (less stressed) when their midsleep was delayed. A later midsleep might indicate that no class or other events were forcing students to get up early, thus reducing feelings of stress. Although the finding that average CPDSleep was associated with average well-being may indicate that irregular sleep–wake behavior could be trait-like (i.e. some individuals may tend to be irregular sleepers irrespective of circumstances), a recent study using a physiological mathematical model of sleep–wake regulation showed that irregular sleep–wake patterns can result from the interaction of endogenous characteristics (e.g. circadian period) with external factors (i.e. ability to control light–dark cycles) [43]. Our findings overall demonstrate that sleep and event schedules are very loosely coupled in these undergraduate college students. At the average level, there was no association between sleep and event schedules. At the daily level, the association was significant but weak; hours of difference in event times resulted in a difference of only minutes in sleep. This may be due to the late start times of most FSEs in this population, meaning they do not face the same sleep curtailment pressures as other populations, such as high school students with very early start times [44]. The type of FSE may also influence sleep (e.g. team athletic practice vs. class or extracurricular events). We do not know the type of FSE from these data; further studies can explore this. Since our analysis focuses on FSEs, we may also be overlooking impacts of other scheduled events on sleep, such as late social events that may delay sleep onsets (which were not collected in this sample). Similar to chronotype, our findings may also be influenced by the absence of individuals with very regular (rigid) schedules. Although some students were relatively regular compared to others, only n = 11 (5%) had CPDFSE < 1 h. Extending this work to other cohorts that include more rigid work and/or sleep schedules would be predicted to reveal a stronger coupling between sleep timing and work constraints [43, 45]. Self-reported well-being has been shown to largely depend on mood [46], which in turn shows a circadian rhythmicity [47]. Here, we have examined the impact of preceding sleep on well-being upon awakening but it is also possible that well-being before bedtime might affect the following sleep episode. Future work could investigate (i) the impact of preceding sleep (CPD, duration, timing) on well-being depending on the time of the assessment (morning vs. evening) and (ii) the impact of evening well-being on the following sleep episode (CPD, duration, timing). We did not see any effects of CPDSleep nor its components ΔCT (mistiming) and ΔDD (irregularity) on daily well-being. It is important to note that we used absolute values of ΔCT and ΔDD, thereby ignoring the direction of the deviation (i.e. advance vs. delay). Future analyses could examine daily effects of ΔCT and ΔDD using relative values to distinguish between effects that are potentially different for advances vs. delays in sleep timing. Among the limitations of this study is the fact that CPDSleep does not consider naps and treats nights with no sleep (all-nighters) as missing data, since CPDSleep is based on mid-sleep time of the major sleep episode. This may have resulted in an underestimation of the effect of irregular sleep/wake behavior on well-being. Other metrics, such as the SRI, are better equipped to deal with fragmented sleep/wake patterns. In addition, whereas the amount of missing data did not appear to impact the results based on sensitivity analyses (i.e. excluding those with the most missing data did not change the relationship between well-being and sleep), we cannot establish whether it impacted the calculation of CPDSleep and other sleep variables. It is plausible that extreme sleep (very short/early/rigid or very long/late/irregular) may be more likely to be missing data, e.g. students may take off their actiwatches or forget to fill out the sleep diary when there are special occasions (e.g. birthdays, parties, stressful events) that are more likely to result in atypical sleep. We therefore assume that the missing values in our dataset are missing not at random. There is no universal method to properly handle data that are missing not at random, and we therefore did not impute missing values. Although we may have biased toward the null and underestimated the relationships between mistiming/irregularity and other variables, it is important to note that missing data would affect both the estimates and the standard deviations of the estimates of those relationships [48]. Another limitation includes the fact that cluster analyses can result in unstable groups, and our finding of four clusters needs to be replicated in an external sample. In their review, Bei et al. pointed out that no study justified the number of days to employ the sleep regularity method [5]. Future work should empirically determine in various populations the minimum number of days required for (i) reliable estimates of sleep regularity metrics, including CPDSleep and SRI; and (ii) reliable prediction of specific outcomes from these metrics. We found here that 30 days is sufficient to determine average well-being using CPDSleep, but it may be possible to use shorter time-spans. Sano et al. previously demonstrated, e.g. that SRI computed across 4–5 previous nights is sufficient to predict daily self-reported well-being [49]. Future work should perform a detailed comparison of CPD to other metrics of sleep regularity, including the SRI. Future studies could also use the CPD metric to assess variability in other dimensions of sleep, including sleep latency, sleep efficiency, and sleep fragmentation. In conclusion, we find that mistimed/irregular sleep patterns are largely independent of mistimed/irregular FSEs in these college students; however, both of these factors are associated with worse average well-being over a period of approximately 30 days. Future work should extend this to other populations. These findings suggest that interventions to stabilize and align sleep and/or FSEs have potential to improve well-being. Acknowledgments We thank the participating students and research staff. Funding National Institutes of Health (F32DK107146, T32HL007901, KL2TR002370, K24HL105664, R01HL114088, R01GM105018, R01HL128538, P01AG009975, R21HD086392, R00HL119618, R01DK099512, R01DK105072, R01HL118601, R01OH07567, R01OH010300) and National Space Biomedical Research Institute (HFP02802, HFP04201, HFP0006). D.F. was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—FI 2275/1-1. This work was conducted with support from Harvard Catalyst, The Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences, National Institutes of Health Award UL 1TR002541), and financial contributions from Harvard University and its affiliated academic healthcare centers. The content is solely the responsibility of the authors and does not necessarily represent the official views of Harvard Catalyst, Harvard University, and its affiliated academic healthcare centers, or the National Institutes of Health. Conflict of interest statement. A.W.M. reports speaker honorarium or travel reimbursement fees from the Utah Sleep Research Society and the California Precast Concrete Association. A.S. has received travel reimbursement or honorarium payments from Philips Research, Apple, Gordon Research Conferences, Pola Chemical Industries, Leuven Mindgate, and American Epilepsy Society. R.W.P. is a cofounder of and shareholder in Empatica Inc and Affectiva Inc and serves on the board of Empatica. She is inventor or coinventor on over two dozen patents, mostly in the field of affective computing and physiological measurement. She has received royalty payments from MIT for patents licensed to Affectiva, consulting and honorarium payments from Merck, Samsung, Analog Devices, and fees for serving as an expert witness in cases involving wearable sensors from Apple and Intel. Her research is funded in part by a consortium that includes over 70 companies who fund the MIT Media Lab (up to date list is kept online at http://media.mit.edu) and includes project funding supporting her team’s work from Robert Wood Johnson Foundation, The Simons Foundation, The SDSC Global Foundation, NEC, LKK, Cisco, Deloitte, Steelcase, and Medimmune. She has received travel reimbursement from Apple, Future of Storytelling, Mattel/Fisher-Price, Microsoft, MindCare, Motorola, Planetree, Profectum, Sentiment Symposium, Seoul Digital, Silicon Valley Entrepreneurs Network, and Wired. L.K.B. is on the scientific advisory board for CurAegis Technologies and has received consulting fees from University of Pittsburgh, Sygma, Insight, and Puget Sound Pilots. C.A.C. reports grants from Cephalon Inc., Ganesco Inc., Jazz Pharmaceuticals Pic., Inc., National Football League Charities, Optum, Philips Respironics, Inc., Regeneron Pharmaceuticals, ResMed Foundation, San Francisco Bar Pilots, Sanofi S.A., Sanofi-Aventis, Inc, Schneider Inc., Sepracor, Inc, Mary Ann & Stanley Snider via Combined Jewish Philanthropies, Sysco, Takeda Pharmaceuticals, Teva Pharmaceuticals Industries, Ltd., and Wake Up Narcolepsy; and personal fees from Bose Corporation, Boston Celtics, Boston Red Sox, Cephalon, Inc., Columbia River Bar Pilots, Institute of Digital Media and Child Development, Klarman Family Foundation, Samsung Electronics, Quest Diagnostics, Inc, Teva Pharma Australia, Yanda Pharmaceuticals, Washington State Board of Pilotage Commissioners, Zurich Insurance Company, Ltd. In addition, C.A.C. holds a number of process patents in the field of sleep/circadian rhythms (e.g. photic resetting of the human circadian pacemaker), and holds an equity interest in Yanda Pharmaceuticals, Inc. Since 1985, C.A.C. has also served as an expert on various legal and technical cases related to sleep and/or circadian rhythms including those involving the following commercial entities: Casper Sleep Inc., Comair/Delta Airlines, Complete General Construction Company, FedEx, Greyhound, HG Energy LLC, Purdue Pharma, LP, South Carolina Central Railroad Co., Steel Warehouse Inc., Stric-Lan Companies LLC, Texas Premier Resource LLC and United Parcel Service (UPS). C.A.C. receives royalties from the New England Journal of Medicine; McGraw Hill; Houghton Mifflin Harcomi/Penguin; and Philips Respironics, Inc. for the Actiwatch-2 and Actiwatch-Spectrum devices. 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