SLEEP

Cucchiara, Federico; Frumento, Paolo; Banfi, Tommaso; Sesso, Gianluca; Di Galante, Marco; D’Ascanio, Paola; Valvo, Giulia; Sicca, Federico; Faraguna, Ugo

doi: 10.1093/sleep/zsz255pmid: 31722434

Abstract Study Objectives Recently, a role for gain-of-function (GoF) mutations of the astrocytic potassium channel Kir4.1 (KCNJ10 gene) has been proposed in subjects with Autism–Epilepsy phenotype (AEP). Epilepsy and autism spectrum disorder (ASD) are common and complexly related to sleep disorders. We tested whether well characterized mutations in KCNJ10 could result in specific sleep electrophysiological features, paving the way to the discovery of a potentially relevant biomarker for Kir4.1-related disorders. Methods For this case–control study, we recruited seven children with ASD either comorbid or not with epilepsy and/or EEG paroxysmal abnormalities (AEP) carrying GoF mutations of KCNJ10 and seven children with similar phenotypes but wild-type for the same gene, comparing period-amplitude features of slow waves detected by fronto-central bipolar EEG derivations (F3-C3, F4-C4, and Fz-Cz) during daytime naps. Results Children with Kir4.1 mutations displayed longer slow waves periods than controls, in Fz-Cz (mean period = 112,617 ms ± SE = 0.465 in mutated versus mean period = 105,249 ms ± SE = 0.375 in controls, p < 0.001). An analog result was found in F3-C3 (mean period = 125,706 ms ± SE = 0.397 in mutated versus mean period = 120,872 ms ± SE = 0.472 in controls, p < 0.001) and F4-C4 (mean period = 127,914 ms ± SE = 0.557 in mutated versus mean period = 118,174 ms ± SE = 0.442 in controls, p < 0.001). Conclusion This preliminary finding suggests that period-amplitude slow wave features are modified in subjects carrying Kir4.1 GoF mutations. Potential clinical applications of this finding are discussed. children, autism, epilepsy, EEG, nap, sleep homeostasis, tripartite synapse, K+ buffering, Kir4.1, KCNJ10 Statement of significance An increasing body of experimental evidence suggests an association between sleep and neurodevelopmental disorders such as autism spectrum disorders (ASDs) and epilepsy/EEG paroxysmal abnormalities. Furthermore, a recent study has highlighted a possible link between the concurrence of these two disorders (AEP, Autism–Epilepsy phenotype) and mutations in the KCNJ10 gene coding for the astrocytic potassium channel Kir4.1. This preliminary study reports on electrophysiological sleep features observed in children with ASD or AEP and gain-of-function mutations of Kir4.1, which can be used as possible biomarkers of Kir4.1 dysfunction in these disorders. Introduction Autism–epilepsy phenotype (AEP) Autism spectrum disorders (ASDs) are a broad group of neurodevelopmental disorders characterized by impaired communication and social interactions and repetitive and restrictive behaviors [1] that begin in childhood, with a variable degree of severity, and are associated with variable impairments in cognitive functioning [2]. Etiologies and neural alterations underlying ASD are not clear and are still highly debated [3–7]. ASDs are strongly associated with epilepsy and/or paroxysmal EEG abnormalities. Epidemiologically, people with ASD (especially females with lower verbal and nonverbal abilities [8]) have a significantly higher risk of developing epilepsy, with a prevalence ratio of around 30%; conversely ASD features can be found in 5% of patients suffering from epilepsy [8, 9]. Yet, individuals with autism and comorbid epilepsy have lower intellectual, speech, and language abilities as compared to those without epilepsy [8]. The relationship between ASD and epilepsy/paroxysmal EEG abnormalities has long been known, but common pathophysiological aspects have only recently been systematically described [10] in a subgroup of individuals displaying AEP [11, 12]. Astrocytic Kir4.1 channels gain-of-function defects are related to autism–epilepsy comorbidity A recent study [11] has revealed that the dysfunction of the astrocytic inwardly-rectifying potassium channels Kir4.1 (encoded by the KCNJ10 gene) represents a possible pathogenic mechanism contributing to ASD and epilepsy comorbidity. Three KCNJ10 gain-of-function (GoF) mutations (p.R18Q, p.V84M, and p.R348H) have showed a significant correlation with a relatively benign outcome of seizures, lower frequency of stereotyped behaviors, and greater alteration of sensory perception in AEP children. In vitro studies [13–17] clarified the effects of Kir4.1 mutations on channel function: p.R18Q mutation (the most frequent variant found in AEP patients) resulted in an increase in channel expression on astrocytic membranes, whereas the rarer p.R348H and p.V84M variants were instead associated with lower sensitivity to intracellular acidification and increased single channel conductance, respectively. The gain of Kir4.1 function ultimately led to an increase in K+ buffering by astrocytic cells, especially when exposed to high extracellular K+ concentration. K+ buffering in astrocytes is an essential mechanism for the maintenance of optimal extracellular K+ concentration and consequently represents a crucial process for correct synaptic functioning [18–20] and regulation of neuronal excitability [21]. Kir4.1 activity at the tripartite synapse also plays an important role in synaptic plasticity by mediating neurovascular coupling [22] and promoting synaptic maturation and remodeling [23] that occur during sleep and across neurodevelopment. Altered synaptic homeostasis due to dysfunction of the Kir4.1 channel may therefore contribute to epileptic seizures and to cognitive and behavioral deficits [24, 25]. Sleep and AEP A high prevalence of sleep disorders has been found in ASD patients with significant consequences on daytime behavior [26, 27] and overall quality of life and a major impact on externalizing symptoms [10]. Among the most commonly reported sleep problems, initial, intermediate, and terminal insomnias, and different parasomnias such as sleepwalking, REM sleep disorders, and sleep paralysis are described [25, 26]. Also, epilepsy and sleep have a significant mutual influence, such that sleep states can affect epilepsy, and in turn epilepsy can affect sleep [28, 29]. Indeed changes in membrane potential and in ion channels activity at the cellular level that occur in wakefulness-to-sleep transitions constitute a fertile ground for the expression of epileptic phenomena [29]. Moreover, sleep deprivation has a strong epileptogenic effect especially in association with physical or emotional stress. Finally, seizures and anticonvulsant therapy can alter sleep architecture [28, 30]. Within sleep deepening, while cerebral cortex disconnects from the external environment, the most distinctive electrophysiological feature is the occurrence of slow waves (0.5–4 Hz) in all or most cortical areas [31]: they are negative deflections followed by positive peaks, which cross the x-axis with a certain inclination [32–34]. Every wave originates in small different regions of the cortex and travels uniquely to other cortical areas with its own speed and pattern of propagation, often originating in anterior cortical regions and propagating throughout the mesial highway to posterior regions [32, 33, 35]. Slow waves are the consequence of the activity of million bistable neurons around cortical layer 5 [36, 37] that nearly every second (<1 Hz) [35] generate synchronized membrane potentials slow oscillations, from hyperpolarized “down-states” during which neurons remain silent for a few hundred milliseconds, to “up-states” characterized by prolonged depolarization and irregular firing [38–41]. Mechanisms that trigger and terminate up- and down-states remain unclear, but the importance of depolarization-dependent K+ currents [42, 43] is wellknown: since K+ is necessary to modulate neuronal excitability both at the presynaptic and postsynaptic levels, the ability of neuroglia to buffer local increases of extracellular K+ confer to these cells an active role in the modulation and spreading of cortical oscillatory activities [44]. Changes in slow wave parameters point out changes in synchrony of neuronal firing which in turn reflect changes in synaptic strength and efficiency of cortico-cortical connections [33, 45, 46]. While slow wave amplitude correlates to the number of neurons entering synchronously in the up- or down-state, slow wave slope reflects the speed of neuronal recruitment [41, 43]. Thus, slow waves amplitude and slope act as indirect but reliable EEG markers of circuit dynamics, which in turn may be expression of synaptic plasticity [33, 42, 47, 48]. During neuronal development, changes in slow wave parameters reflect unique aspects of brain connectivity and adaptation during synaptic maturation [46, 49, 50]. Throughout adolescence, a progressive weakening of short-range connections occurs in association with a strengthening of long-range ones [51], resulting in a significant decline of the slow wave activity (SWA) [52–54]. Instead, in individuals with autism, circuits involving nearby neurons are more frequently activated than those involving neurons among distant cortical areas [55], so that the former would be consolidated while the latter would have been lost permanently [56]. This impairment of down-selection during critical developmental periods could have irreversible consequences on wiring and function of neural circuits [57–60] and could reflect in major sleep abnormalities [27, 46]. Altered sleep-wake cycles have implications on astrocytic plasticity, too [23, 61, 62]. Astrocytes are responsive to changes in wake-promoting neuromodulators, not only by regulating extracellular volume but also by influencing composition and glymphatic drainage of interstitial fluid. These events are accompanied by changes in astrocyte–neuron interactions and neurovascular coupling, synaptic maturation and remodeling, and especially neuronal discharge patterns [63]. Reduced astrocytic coverage during sleep may favor glutamate spill-over and increase K+ buffering, which promote neuronal synchronization during NREM sleep [61, 63, 64] and characterize EEG slow wave sleep pattern [21, 44, 65]. In this study, we compared several parameters of slow waves in a group of seven mutated children and in seven sex- and age-matched control subjects, to test our hypothesis that a well characterized mutation in Kir4.1 channels and consequent and persistent K+ buffering could result in specific electrophysiological features of sleep (amplitude, period, and down and up slope), paving the way to the discovery of potentially clinically relevant biomarkers that may help in distinguishing children with ASD/AEP and GoF mutations of astrocytic Kir4.1 channel from those with similar phenotypes but no Kir4.1 dysfunction. Methods Subjects Fourteen subjects with ASD were included in the present study, 12 males and 2 females ranging from 3 to 13 years of age (8.25 mean ± 3.54 SD). Twelve of them (10 males and 2 females) had a history of epileptic seizures and/or EEG abnormalities, and among them, 5 (4 males) were taking sodium valproate (VPA) during EEG recording. Seven subjects of our cohort (6 males), carried a GoF mutation of the Kir4.1 channel gene. Among them, the R18Q variant was found in 6 males, while the R348H variant was found only in one female. Mutated and control subjects were matched for phenotype, age, and gender (Table 1). Table 1. Sample characteristics ID . Gender: 1=Fml, 2=Ml . Age . Treatment at the time of the EEG under investigation . KCNJ10 mutation . History of seizures 0=No, 1=Yes . Type of seizures . Seizure outcome . Eeg abnormalities 0=No, 1=Yes . 1 2 12 years 8 months VPA R18Q 1 absences Remission under VPA 1 2 2 13 years 5 months - R18Q 0 1 3 1 9 years 7 months - R348H 1 spasms Spontaneous remission after a febrile illness 1 4 1 8 years 7 months VPA WT 1 absences Remission under VPA 1 5 2 12 years 8 months VPA WT 1 focal Remission under VPA 1 6 2 3 years 8 months - R18Q 0 0 7 2 3 years 9 months - WT 0 0 8 2 6 years 3 months - R18Q 1 Spasms + focal Remission under ACTH, VPA, TPM 1 9 2 6 years 0 months - R18Q 1 spasms Remission under ACTH and VPA 1 10 2 5 years 6 months - R18Q 0 1 11 2 6 years 4 months VPA WT 1 focal Still active epilepsy 1 12 2 6 years 9 months VPA WT 1 tonic-clonic Remission under VPA 1 13 2 13 years 3 months - WT 0 1 14 2 5 years 8 months - WT 0 1 ID . Gender: 1=Fml, 2=Ml . Age . Treatment at the time of the EEG under investigation . KCNJ10 mutation . History of seizures 0=No, 1=Yes . Type of seizures . Seizure outcome . Eeg abnormalities 0=No, 1=Yes . 1 2 12 years 8 months VPA R18Q 1 absences Remission under VPA 1 2 2 13 years 5 months - R18Q 0 1 3 1 9 years 7 months - R348H 1 spasms Spontaneous remission after a febrile illness 1 4 1 8 years 7 months VPA WT 1 absences Remission under VPA 1 5 2 12 years 8 months VPA WT 1 focal Remission under VPA 1 6 2 3 years 8 months - R18Q 0 0 7 2 3 years 9 months - WT 0 0 8 2 6 years 3 months - R18Q 1 Spasms + focal Remission under ACTH, VPA, TPM 1 9 2 6 years 0 months - R18Q 1 spasms Remission under ACTH and VPA 1 10 2 5 years 6 months - R18Q 0 1 11 2 6 years 4 months VPA WT 1 focal Still active epilepsy 1 12 2 6 years 9 months VPA WT 1 tonic-clonic Remission under VPA 1 13 2 13 years 3 months - WT 0 1 14 2 5 years 8 months - WT 0 1 ID= identification number; Fml= female; Ml= male; VPA= sodium valproate; WT= Wild Type. Open in new tab Table 1. Sample characteristics ID . Gender: 1=Fml, 2=Ml . Age . Treatment at the time of the EEG under investigation . KCNJ10 mutation . History of seizures 0=No, 1=Yes . Type of seizures . Seizure outcome . Eeg abnormalities 0=No, 1=Yes . 1 2 12 years 8 months VPA R18Q 1 absences Remission under VPA 1 2 2 13 years 5 months - R18Q 0 1 3 1 9 years 7 months - R348H 1 spasms Spontaneous remission after a febrile illness 1 4 1 8 years 7 months VPA WT 1 absences Remission under VPA 1 5 2 12 years 8 months VPA WT 1 focal Remission under VPA 1 6 2 3 years 8 months - R18Q 0 0 7 2 3 years 9 months - WT 0 0 8 2 6 years 3 months - R18Q 1 Spasms + focal Remission under ACTH, VPA, TPM 1 9 2 6 years 0 months - R18Q 1 spasms Remission under ACTH and VPA 1 10 2 5 years 6 months - R18Q 0 1 11 2 6 years 4 months VPA WT 1 focal Still active epilepsy 1 12 2 6 years 9 months VPA WT 1 tonic-clonic Remission under VPA 1 13 2 13 years 3 months - WT 0 1 14 2 5 years 8 months - WT 0 1 ID . Gender: 1=Fml, 2=Ml . Age . Treatment at the time of the EEG under investigation . KCNJ10 mutation . History of seizures 0=No, 1=Yes . Type of seizures . Seizure outcome . Eeg abnormalities 0=No, 1=Yes . 1 2 12 years 8 months VPA R18Q 1 absences Remission under VPA 1 2 2 13 years 5 months - R18Q 0 1 3 1 9 years 7 months - R348H 1 spasms Spontaneous remission after a febrile illness 1 4 1 8 years 7 months VPA WT 1 absences Remission under VPA 1 5 2 12 years 8 months VPA WT 1 focal Remission under VPA 1 6 2 3 years 8 months - R18Q 0 0 7 2 3 years 9 months - WT 0 0 8 2 6 years 3 months - R18Q 1 Spasms + focal Remission under ACTH, VPA, TPM 1 9 2 6 years 0 months - R18Q 1 spasms Remission under ACTH and VPA 1 10 2 5 years 6 months - R18Q 0 1 11 2 6 years 4 months VPA WT 1 focal Still active epilepsy 1 12 2 6 years 9 months VPA WT 1 tonic-clonic Remission under VPA 1 13 2 13 years 3 months - WT 0 1 14 2 5 years 8 months - WT 0 1 ID= identification number; Fml= female; Ml= male; VPA= sodium valproate; WT= Wild Type. Open in new tab Recruitment criteria A cohort of children with ASD/AEP was recruited at the Stella Maris Foundation Hospital from 2009 to 2017. Among them, subjects with a history of seizures and/or EEG abnormalities were included in the autism–epilepsy phenotype subgroup. Phenotype characterization was carried out through clinical and laboratory assessments, EEG study, and brain magnetic resonance imaging. A family history for epilepsy or ASD was investigated up to the 4th degree of kinship. Clinical and EEG assessments, and KCNJ10 sequencing, were performed after having obtained an informed consent from parents or caregivers, and complying with the ethical principles outlined by the Declaration of Helsinki. EEG recording and sleep scoring Digital video-EEG-polysomnographic (PSG) systems (Grass Technologies, Rhode Island, United States; Micromed, Mogliano Veneto, Italy) were used to obtain sleep recordings in children during 1-h-long daytime naps on average (Table 2). PSG measurements included 19 electrodes EEG in a standard 10–20 location, deltoid muscle electromyogram (EMGs), and electrocardiogram (ECG). Table 2. Sleep measures for entire nap . Wild-Type . Mutated . P-value . Total time in bed, min 54.14 ± 3.776 60.57 ± 3.884 0.258 Total sleep time (TST), min 24.71 ± 2.286 22.00 ± 2.177 0.407 NREM1, min 1.64 ± 0.497 1.86 ± 0.652 0.798 NREM2, min 8.57 ± 1.706 8.86 ± 2.593 0.928 NREM3, min 13.50 ± 3.677 10.43 ± 2.494 0.503 Waking period 1.36 ± 0.322 0.86 ± 0.210 0.218 . Wild-Type . Mutated . P-value . Total time in bed, min 54.14 ± 3.776 60.57 ± 3.884 0.258 Total sleep time (TST), min 24.71 ± 2.286 22.00 ± 2.177 0.407 NREM1, min 1.64 ± 0.497 1.86 ± 0.652 0.798 NREM2, min 8.57 ± 1.706 8.86 ± 2.593 0.928 NREM3, min 13.50 ± 3.677 10.43 ± 2.494 0.503 Waking period 1.36 ± 0.322 0.86 ± 0.210 0.218 Data are expressed as mean ± SEM (n = 7). Two subjects do not have NREM 3 sleep stage. None of all presents REM sleep stage. min, minutes; NREM, nonrapid eye movement; SEM, standard error of the mean; n, number of subjects per group. Open in new tab Table 2. Sleep measures for entire nap . Wild-Type . Mutated . P-value . Total time in bed, min 54.14 ± 3.776 60.57 ± 3.884 0.258 Total sleep time (TST), min 24.71 ± 2.286 22.00 ± 2.177 0.407 NREM1, min 1.64 ± 0.497 1.86 ± 0.652 0.798 NREM2, min 8.57 ± 1.706 8.86 ± 2.593 0.928 NREM3, min 13.50 ± 3.677 10.43 ± 2.494 0.503 Waking period 1.36 ± 0.322 0.86 ± 0.210 0.218 . Wild-Type . Mutated . P-value . Total time in bed, min 54.14 ± 3.776 60.57 ± 3.884 0.258 Total sleep time (TST), min 24.71 ± 2.286 22.00 ± 2.177 0.407 NREM1, min 1.64 ± 0.497 1.86 ± 0.652 0.798 NREM2, min 8.57 ± 1.706 8.86 ± 2.593 0.928 NREM3, min 13.50 ± 3.677 10.43 ± 2.494 0.503 Waking period 1.36 ± 0.322 0.86 ± 0.210 0.218 Data are expressed as mean ± SEM (n = 7). Two subjects do not have NREM 3 sleep stage. None of all presents REM sleep stage. min, minutes; NREM, nonrapid eye movement; SEM, standard error of the mean; n, number of subjects per group. Open in new tab Sleep recordings were scored by a trained scorer according to American Academy of Sleep Medicine (AASM) standardized criteria [28]: Alice Sleepwear software was used for manual visual epoch-by-epoch scoring on 30-s-long segments of neurologic layout of PSG channels. Slow wave detection and analysis of wave parameters For slow wave sleep detection a single EEG signal channel was used, as performed in a recent study [66]. The choice was based both on methodological and electrophysiological rationales. Each derivation was preliminarily assessed for every subject. For each derived signal we performed temporal and spectral analyses and a score, from 0 to 10, was then assigned to judge the quality and cleanliness of each subject’s signal. Thus, for each signal, the scores ranged from 0 to 140 given that there were 14 subjects in total. F3-C3 (107 points), F4-C4 (103 points) and Fz-Cz (108 points) were the most eligible channels for evaluation. First, the Fz-Cz channel was selected since it was the one with few artifacts and the most evident waves and it is considered the most suitable for evaluating slow waves [67, 68]; then, to enrich the dataset and support the results, the same analyses were also re-run separately for F3-C3 and F4-C4 since they were of almost equivalent quality and equivalent artifacts-free. Sleep slow waves were automatically detected using a re-edited EEGLAB toolbox (MatLab, The Math Works Inc., Natick, MA). First, 200 Hz sampled signal was high-pass filtered (0.1 Hz) and band-pass filtered (0.3–30 Hz) to remove artifacts and background noise. Second, using a Chebyshev Type II filter (band-pass 0.5–4.0 Hz, stopband 0.1 and 10 Hz) signal was re-filtered for slow wave best detection. The filter parameters were visually optimized on the EEG signal to achieve minimal wave shape and amplitude distortion while allowing the least high-frequency contamination. Finally, individual half-waves were detected by defining a half-wave as negative deflections between 2 zero-crossings [69]. The zero-crossing detection was chosen for the analysis due to the high degree of variability in the positive signal deflections compared to the stability of the negative deflections. Within an artifact- and epileptic abnormalities-free NREM epoch, only half-waves whose period and maximal negative amplitude were bigger than the mode of all half-waves detected were considered as slow waves. The use of the mode as a threshold was empiric, carefully assessed by analyzing each subject after applying the same criteria. The mode was assessed for period and amplitude as the highest peak of a histogram, which in turn is the frequency distribution in classes of a parameter. Moreover, to assess whether differences in slow wave amplitude are related to difference in slow wave period, we detected sleep slow waves based only on a duration criterion, for instance, 0.25–1 s negative-to-positive zero crossing. This detection procedure described above has been found to be similar to that employed in a previous work on period-amplitude analysis [33, 70] and allowed a specific detection of slow waves during NREM2 and NREM3 stages sleep. Identified slow waves were then plotted to visually assess the reliability of the automatic detection for each subject. For every detected slow oscillation, we collected several single-wave parameters that could be used to compare the two groups: (1) half-waves number, (2) half-waves period, (3) amplitudes of the maximal negative peaks, and (4) maximal half-wave down and up slope. Maximal slopes were defined as the maximum of the signal derivative following the negative zero crossing but before the most negative peak (down slope), or after the most negative peak but prior to the positive-going zero crossing (up slope). To improve slow waves detection and to avoid detection of large epileptic waves like slow waves (EEG abnormalities highlighted in 12/14 subject, 64.29%), MatLab scripts have been implemented based on a previous work by Dr. Brady Riedner of University of Wisconsin (Madison, WI) [33] (Figure 1). Figure 1. Open in new tabDownload slide Slow waves detection. A MatLab® EEG plot of a NREM3 sleep epoch is here reported. Amplitude detected half-slow waves are highlighted with a thick black line, whereas the background rhythm is marked with a fine one. Half-waves were defined as negative deflections between 2 zero-crossings. Slow waves were automatically detected using MatLab EEGLAB toolbox, editing a previous detection algorithm described by Riedner et al. [33]. Finally, since the vast majority of the recorded waves had an amplitude <100 µV (95.76% of wild-type subjects’ waves, 98.32% of mutated subjects’ ones), we focused on slow wave parameters within this amplitude range and subdivided detected waves showing an amplitude <100 µV into 10 groups of 10 µV of width. However, since subject 13’s data showed multiple artifactual epileptic graphoelements that affected the majority of the EEG tracing, we excluded subject 13 and the paired control subject 2 from the analysis of slow waves. Statistical analysis Statistical analyses were performed using Sigma Plot software to compare ASD/AEP children with astrocytic Kir4.1 mutated channels to a control group of ASD/AEP wild-type (WT) children. Each comparison was performed between all mutations and all WT, then between mutated and WT subgroups matched by age, to evaluate the specific effect of this variable on sleep. For each parameter, comparisons were first performed between the two groups of mutated and WT children; then, age- and sex-matched pairs of mutated and WT children were individually compared to estimate the effect size of related clinical variables. A nonparametric Mann–Whitney U-test was performed to assess differences in slow wave parameters. Finally, to take into account the effect of potential confounders, we estimated longitudinal linear regression using two different methods: random-effects (RE) and generalized estimating equations (GEE) models. Results First, we compared total sleep and sleep stage durations between mutated children group and controls (Table 2). There were no significant differences in total time in bed, total sleep time (TST), NREM1, NREM2, NREM3 and in waking epochs occurring during nap, between WT and mutated subjects. Astrocyte Kir4.1 channel mutation is associated with longer slow waves period In order to examine changes in individual slow wave parameters between WT and mutated subjects, we analyzed a statistically comparable number of automatically detected slow waves in the Fz-Cz derivation (median number of detected slow waves in mutated subjects = 1724 versus median number of detected slow waves in control subjects = 1309.5, Wilcoxon Test p-value = 0.313). A significant difference was observed in the period of the half-negative waves between cases and controls. First, we found that the slow waves period in mutated subjects was longer than the wild types’ one (mean period = 112,617 ms ± SE = 0.465 in mutated versus mean period = 105,249 ms ± SE = 0.375 in controls, p < 0.001). Then, age- and sex-matched pairs of mutated and WT children, ranging from 3 to 13 years, were created to compare sleep slow waves parameters and avoid confounding factors due to changes across neurodevelopment (Figure 2). Figure 2. Open in new tabDownload slide Slow wave period in a case–control analysis. (A) Period (ms) is shown on y-axis. WT (n = 10,390 waves) bar is reported in anthracite/black while M (n = 7,696 waves) one is in gray. Error bars are also represented. (B) Period (ms) is shown on y-axis. WT box-plots are reported in anthracite/black while M ones are in gray. Mutated subjects have slow waves with longer periods than WT and also in all age-matched comparisons differences are greater than would be expected by chance. *p < 0.05; **p < 0.001; WT, wild type; M, mutated; ms, milliseconds; n, number of waves. For each couple we normalized the interval duration of detection window of slow waves on the shortest one between the two subjects within the couple. So, a difference in slow wave form is shown between cases and controls (Figure 3). Figure 3. Open in new tabDownload slide Wave forms. Here is a graphical representation of the comparison between the average waveform in WT children (anthracite/black) and the average waveform in Kir4.1-mutated children (gray). Period (ms) and amplitude (μV) are, respectively, shown on the x-axis and on the y-axis. The standard errors are also shown for mean amplitude of the negative peaks, mean time of the negative peaks and mean period. Also, we found that the difference between mutated and WT subjects is further enhanced in the range from −10 µV to −40 µV. Within this range, the period was significantly longer in subjects with astrocyte Kir4.1 mutated channels compared to controls. Conversely, higher amplitude slow waves have shown no statistically significant difference between cases and controls (Figure 4). Figure 4. Open in new tabDownload slide Slow wave period in an amplitude-dependent case–control analysis. (A) Amplitude (μV) and period (ms) are, respectively, shown on x-axis and y-axis. WT bars are reported in anthracite/black while M bars are in gray. Error bars are represented, too. We analyzed the trend of the period according to amplitude of the maximal negative peaks of the detected half- slow waves. We analyzed the data by evaluating an amplitude range between 0 and 100 μV in which the greatest number of waves is recorded (as shown in B). This range has been further subdivided into subranges of 10 μV. We found statistically significant differences between the two groups in the ranges between 0 and 40 μV and from 50 μV to 60 μV. * p < 0.05; ** p < 0.001; WT, Wild Type; M, mutated; ms, milliseconds; μV, microvolts; Wvs N, waves number. An analogue result was obtained for F3-C3 (mean period = 125,706 ms ± SE = 0.397 in mutated versus mean period = 120,872 ms ± SE = 0.472 in controls, p < 0.001) and F4-C4 (mean period = 127,914 ms ± SE = 0.557 in mutated versus mean period = 118,174 ms ± SE = 0.442 in controls, p < 0.001) (Figures 5 and 6). Figure 5. Open in new tabDownload slide Slow wave period in a case–control analysis; F3-C3 derivation. (A) Period (ms) is shown on y-axis. WT (n = 7,740 waves) bar is reported in anthracite/black while M (n = 13,346 waves) one is in gray. Error bars are also represented. (B) Period (ms) is shown on y-axis. WT box-plots are reported in anthracite/black while M ones are in gray. Mutated subjects have slow waves with longer periods than WT and also in all age-matched comparisons differences are greater than would be expected by chance. * p < 0.05; ** p < 0.001; WT, wild type; M, mutated; ms, milliseconds; n, number of waves. Figure 6. Open in new tabDownload slide Slow wave period in a case–control analysis; F4-C4 derivation. (A) Period (ms) is shown on y-axis. WT (n = 9,217 waves) bar is reported in anthracite/black while M (n = 7,028 waves) one is in gray. Error bars are also represented. (B) Period (ms) is shown on y-axis. WT box-plots are reported in anthracite/black while M ones are in gray. Mutated subjects have slow waves with longer periods than WT and also in all age-matched comparisons differences are greater than would be expected by chance. * p < 0.05; ** p < 0.001; WT, wild type; M, mutated; ms, milliseconds; n, number of waves. Astrocyte Kir4.1 channel mutation is not associated with other changes in slow waves parameters Similarly, we analyzed down and up slope of slow waves detected in Fz-Cz signals, both between groups and among age-matched couples. Significant differences emerged in slow wave slopes between age-matched couples of mutated and WT subjects (mean down-slope = 500,669 μV/ms ± SE = 3.643 in mutated versus mean down-slope = 519,857 μV/ms ± SE = 4.306 in controls, p ≤ 0.001; mean up-slope = 493,835 μV/ms ± SE = 3.591 in mutated versus mean up-slope = 491,824 μV/ms ± SE = 3.644 in controls, p ≤ 0.001); however, the distribution of slow waves slopes mean values is highly variable depending on the age and no meaningful trend is observed between cases and controls (Figures 7 and 8). Figure 7. Open in new tabDownload slide Down-slope in a case–control analysis. (A) Down-slope (μV/ms) is shown on y-axis. WT (n = 10,390 waves) bar is reported in anthracite/black while M (n = 7,696 waves) one is in gray. Error bars are also represented. (B) Down-slope (μV/ms) is shown on y-axis. WT box-plots are reported in anthracite/black while M ones are in gray. Using Mann–Whitney rank-sum test we found statistically significant differences between slow waves. Down-slopes of every subject with mutated Kir4.1 channels compared with its own WT control matched by age. **p < 0.001; WT, wild type; M, mutated; ms, milliseconds; μV, microvolts; n, number of waves. Figure 8. Open in new tabDownload slide Up-slope in a case–control analysis. (A) Up-slope (μV/ms) is shown on y-axis. WT (n = 10,390 waves) bar is reported in anthracite/black while M (n = 7,696 waves) one is in gray. Error bars are also represented. (B) Up-slope (μV/ms) is shown on y-axis. WT box-plots are reported in anthracite/black while M ones are in gray. Using Mann–Whitney Rank Sum Test we found statistically significant differences between slow waves Down-slopes of every subject with mutated Kir4.1 channels compared with its own WT control matched by age. **p < 0.001; WT, wild type; M, mutated; ms, milliseconds; μV, microvolts; n, number of waves. In the end, although we found that in the fronto-central medial bipolar derivation (Fz-Cz) the overall mean of slow waves amplitude was significantly higher in mutated subjects than controls, the result does not reflect a marked difference between the slow waves amplitudes detected in KCNJ10 mutated and WT subjects (mean amplitude = −30,371 μV ± SE = 4.4282 in mutated versus mean amplitude = −27,854 μV ± SE = 7.167 in controls, p ≤ 0.001) and no meaningful trend is observed between cases and controls across ages (Figure 9). Figure 9. Open in new tabDownload slide Amplitude case–control analysis. (A) Amplitude (µV) is shown on y-axis. Wild Type (WT, n = 10,390 peaks) bar is reported in anthracite/black while mutated (M, n = 7,696 peaks) is in gray. Error bars are also represented. Amplitude evaluation in a stand-alone manner shows a statistically significant difference between the 2 groups. (B) Amplitude (μV) is shown on y-axis. WT box-plots are reported in anthracite/black while M ones are in gray. Using Mann–Whitney rank-sum test we found statistically significant differences between slow waves Amplitudes of every subject with mutated Kir4.1 channels compared with its own WT control matched by age. **p < 0.001; WT, wild type; M, mutated; μV, microvolts; n, number of waves. The results seem to be confirmed also estimating longitudinal linear regression using both the RE and GEE methods (Table 3). Table 3. Longitudinal linear regression estimation using both the RE and GEE methods . Crude (RE) . Adjusted (RE) . Crude (GEE) . Adjusted (GEE) . Amplitude 0.834 0.827 0.819 0.773 Down-slope 0.816 0.753 0.799 0.690 Up-slope 0.814 0.810 0.796 0.756 Period 0.037 0.025 0.022 0.015 . Crude (RE) . Adjusted (RE) . Crude (GEE) . Adjusted (GEE) . Amplitude 0.834 0.827 0.819 0.773 Down-slope 0.816 0.753 0.799 0.690 Up-slope 0.814 0.810 0.796 0.756 Period 0.037 0.025 0.022 0.015 In this table, the p-values associated to subjects with mutated KCNJ10 are shown. Two models have been made: one “Crude” including only KCNJ10, and one “Adjusted” in which gender, age and VPA were also included. RE, random effects model; GEE, generalized estimating equations model; VPA, sodium valproate. Open in new tab Table 3. Longitudinal linear regression estimation using both the RE and GEE methods . Crude (RE) . Adjusted (RE) . Crude (GEE) . Adjusted (GEE) . Amplitude 0.834 0.827 0.819 0.773 Down-slope 0.816 0.753 0.799 0.690 Up-slope 0.814 0.810 0.796 0.756 Period 0.037 0.025 0.022 0.015 . Crude (RE) . Adjusted (RE) . Crude (GEE) . Adjusted (GEE) . Amplitude 0.834 0.827 0.819 0.773 Down-slope 0.816 0.753 0.799 0.690 Up-slope 0.814 0.810 0.796 0.756 Period 0.037 0.025 0.022 0.015 In this table, the p-values associated to subjects with mutated KCNJ10 are shown. Two models have been made: one “Crude” including only KCNJ10, and one “Adjusted” in which gender, age and VPA were also included. RE, random effects model; GEE, generalized estimating equations model; VPA, sodium valproate. Open in new tab Discussion Our study aimed at evaluating electrophysiological features of sleep in a cohort of ASD/AEP children with GoF mutation of the astrocytic Kir4.1 channel, compared to age- and sex-matched children with similar phenotypes, but not carrying Kir4.1 mutations. Although the ASD/AEP children cohort was small, subjects were recruited over a long period of time (from 2009 to 2017) and are the only ones who are recognized—at least to date—to have an association between ASD/AEP and GoF mutation of the astrocytic Kir4.1 channels. This study, to the best of our knowledge the first investigating sleep EEG features in this cohort, reveals that the slow waves detected in sleep EEG recordings during daytime naps behave differently when associated with mutations in Kir4.1 channels and consequent dysfunction of astrocytic K+ buffering [11]. Subjects with Kir4.1 mutations have indeed a significantly longer slow waves period than controls. This difference, which is particularly relevant for slow waves in the low-amplitude range, appears to be independent from the developmental stage since it remains stable from childhood to adolescence, thus providing a potential electrophysiologically grounded noninvasive biomarker for these mutations. One might further speculate on the pathogenic mechanism underlying these alterations. Indeed, the local increase in extracellular K+ has an active role in the homeostasis of neuronal excitability [21, 44] but the mutations of KCNJ10 might prevent this increase by favoring a strongest or more continuous K+ buffering that is collected from astrocytes and carried to other distant brain areas [20]. Therefore, in subjects with mutated Kir4.1 channels the increase in period duration might reflect a longer time during slow wave sleep in a state of hyperpolarization (down-state) which is in turn reflected by a longer negative half-wave period [71]. This difference becomes more evident when a small number of neurons is recruited in the oscillatory activities resulting in small amplitude slow waves [45, 46], which are proportionally more sensitive to changes in the extracellular ions concentration. Thus, the period of slow waves, which is generally modulated by extracellular factors such as the persistent Na+ current and the depolarization-activated K+ current [33], could be reliably detected in ASD/AEP children carrying GoF Kir4.1 mutations. Moreover, we could not find any difference between mutated and WT subjects in the slow wave down and up slopes. This is likely due to the evidence that slow wave slope is deeply influenced by the synaptic strength while being less affected by the surrounding extracellular environment [33, 47], notably the extracellular K+ concentration, mostly in the high-amplitude range of slow waves [33, 41, 43]: in fact, slow waves amplitude is supposed to be closely linked to the number of neurons recruited in slow oscillations between up and down states [41, 43]. In other words, the greater the number of neurons recruited in the oscillation, the lower the influence of extracellular ion concentration changes on slow wave amplitude; on the other hand, with fewer recruited neurons, changes in astrocyte K+ buffering activity is proportionally more influential. Finally, high variability was found also in the average of slow waves amplitude, and no meaningful trend was observed between mutated and WT subjects. The lack of any statistical difference between cases and controls in terms of slow wave parameters other than the period supports the specificity and distinctive role of this electrophysiological feature in the discrimination between mutated and WT subjects. Furthermore, we could not detect any significant difference between groups in TST, sleep stage duration, and other macrostructural sleep parameters. This further supports the hypothesis that period-amplitude differences in slow wave sleep parameters between cases and controls are likely due to the specific genetic pattern, and consequent K+ buffering efficiency, but not to differences in sleep time measures. Our preliminary results have promising clinical implications since they provide a useful biomarker of Kir4.1 dysfunction in ASD/AEP children harboring KCNJ10 mutations in brief EEG recordings of daytime naps, without needing therefore prolonged EEG monitoring of the sleep. This would avoid the commitment of both medical equipment and technical and health personnel for the entire duration of night EEG recording to highlight sleep disturbance and confirm the phenotype. The main limit of our study remains the small size of our sample. However, an appropriate statistical approach was used to avoid type 1 errors. We also remark that our sample was heterogeneous with respect to current symptoms and medications which could create a bias effect in data analysis. As an example, improved Kir4.1 function may decrease seizure propensity [11, 12, 72] and the precise history of seizures could also lead to major changes in synaptic plasticity that may possibly explain the differences observed. We were aware that there were some differences in the two groups analyzed, for instance one patient in the control group (ID 11) still had active epilepsy at the time of the EEG, while all those with Kir4.1 mutations were under remission. Nevertheless, we did our best to match the samples with regard to gender, age, and clinical histories in order to minimize possible biases due to nongenetic features. Furthermore, the results obtained after evaluating the longitudinal linear regression using both the RE and GEE methods are also quite similar and suggest no significant differences for down slope, up slope and amplitude: intuitively, for these parameters, the differences shown in Figures 7, 8 and 9 could be explained by systematic differences between individuals, whereas a KCNJ10 mutation would not be an important predictor. On the contrary, this is not true for the period, which is confirmed to be significantly influenced by the presence of this mutation (Table 3). We also tested the strength of the result in the face of a different filter setting (1–3 Hz): re-performing the analyses with these subtle alterations in the choice of filters, we confirmed differences between cases and controls for period (mean period = 130,720 ms ± SE = 0.418 in mutated versus mean period = 124,515 ms ± SE = 0.412 in controls, p-value = <0.001), down-slope (mean down-slope = 293,699 μV/ms ± SE = 2.354 in mutated versus mean down-slope = 304,718 μV/ms ± SE = 2.762 in controls, p = 0.003), Up-slope (mean up-slope = 302,264 μV/ms ± SE = 2.326 in mutated versus mean up-slope = 288,422 μV/ms ± SE = 2.731 in controls, p ≤ 0.001) and amplitude (mean amplitude = −23,707 μV ± SE = 4.552 in mutated versus mean amplitude = −22,851 μV ± SE = 6.108 in controls, p = 0.001). Given the subtle nature of the results and the low number of subjects, we were careful to repeat the analyses, testing altered half-wave detection criteria too. In estimating longitudinal linear regression using the RE models for slow waves detected with three different setting thresholds, from the least inclusive to the most—mean, median and 95th percentile—we found that while amplitude, down-slope and up-slope are not influenced by the presence of the mutation, differences in the period between cases and controls seem to have a progressive tendency to significance from the mean (“Crude” model p-value = 0.824; “Adjusted” model p-value = 0.238) to the 95th percentile (“Crude” model p-value = 0.032; “Adjusted” model p-value = 0.039), and through the median (“Crude” model p-value = 0.191; “Adjusted” model p-value = 0.077). This is probably due to two reasons: mean and median are most affected by the distribution of values and above all, aberrant or extreme values of the distribution, with respect to the mode; in addition they are less inclusive criteria and many of the waves on which the difference between mutated and wild type is played, are excluded. Similar results were obtained across all fronto-central derivations (Fz-Cz and also F3-C3, F4-C4, data not shown). The differences in slow wave features should be confirmed by further studies on larger samples of affected subjects in order to verify the reproducibility, specificity, sensitivity and robustness of the present results. ASD and epilepsy are very common worldwide and are clinically important and relevant for public health and socioeconomic burden. Moreover, sleep has recently emerged as a potential marker of autism and other neurodevelopmental disorders and a close association between epilepsy and sleep has been repeatedly confirmed; sleep disorders could be so relevant that they might play a causal role in the development of some neuropsychiatric disorders. Specific sleep abnormalities, repeatedly reported in ASD patients with an epileptic phenotype both clinically and polysomnographically, may play a role for diagnostic purposes. In our case–control study between ASD/AEP children with mutations of KCNJ10 gene (coding for potassium channels Kir4.1 abundantly expressed in astrocytes) and a control group consisting of children WT for the same gene and similar clinical phenotypes, we compared characteristic parameters of slow waves detected during daytime sleep EEG recordings. Our hypothesis was that astrocytic Kir4.1 channels GoF mutations and consequent dysfunctional and persistent K+ buffering could influence neurons activity, slowing down the achievement of their action potential. This abnormality underlies changes in slow waves features and particularly in period, which is longer in subjects with KCNJ10 mutations compared to WT. This difference in slow wave features could have interesting clinical implications. Evaluation of a short period of a daytime nap is sufficient and more compatible with clinic practice than an entire night sleep recording, providing an instrumental and therefore objective diagnostic support. The peculiar electrophysiological features of sleep slow waves identified in Kir4.1 mutated children could represent a clinically relevant readout of astrocytic dysfunctions and provide a noninvasive biomarker suitable for monitoring targeted drug therapies in these ASD/AEP patients [11]. Acknowledgments We thank Chiara Cirelli, Giulio Tononi and Brady Riedner for helpful advice and discussion. Funding This study was partially supported by Ministero della Salute (Ricerca Corrente to FS and Ricerca Corrente to UF) and the ARPA Foundation (SONNOLab Grant to UF). This was not an industry supported study. Conflict of interest statement. There are no financial conflicts of interest. References 1. Biondi M, et al. [The Italian edition of DSM-5]. Riv Psichiatr. 2014;49(2):57–60. doi:10.1708/1461.16137 2. Matson JL , et al. Comorbid psychopathology with autism spectrum disorder in children: an overview . Res Dev Disabil. 2007 ; 28 ( 4 ): 341 – 352 . Google Scholar Crossref Search ADS PubMed WorldCat 3. Billeci L , et al. On the application of quantitative EEG for characterizing autistic brain: a systematic review . Front Hum Neurosci. 2013 ; 7 : 442 . Google Scholar Crossref Search ADS PubMed WorldCat 4. Maximo JO , et al. 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Published by Oxford University Press [on behalf of the Sleep Research Society].

de Boer, M; Nijdam, M J; Jongedijk, R A; Bangel, K A; Olff, M; Hofman, W F; Talamini, Lucia M

doi: 10.1093/sleep/zsz269pmid: 31702010

Abstract Study Objectives Sleep problems are a core feature of post-traumatic stress disorder (PTSD). The aim of this study was to find a robust objective measure for the sleep disturbance in patients having PTSD. Methods The current study assessed EEG power across a wide frequency range and multiple scalp locations, in matched trauma-exposed individuals with and without PTSD, during rapid eye movement (REM) and non-REM (NREM) sleep. In addition, a full polysomnographical evaluation was performed, including sleep staging and assessment of respiratory function, limb movements, and heart rate. The occurrence of sleep disorders was also assessed. Results In patients having PTSD, NREM sleep shows a substantial loss of slow oscillation power and increased higher frequency activity compared with controls. The change is most pronounced over right-frontal sensors and correlates with insomnia. PTSD REM sleep shows a large power shift in the opposite direction, with increased slow oscillation power over occipital areas, which is strongly related to nightmare activity and to a lesser extent with insomnia. These pronounced spectral changes occur in the context of severe subjective sleep problems, increased occurrence of various sleep disorders and modest changes in sleep macrostructure. Conclusions This is the first study to show pronounced changes in EEG spectral topologies during both NREM and REM sleep in PTSD. Importantly, the observed power changes reflect the hallmarks of PTSD sleep problems: insomnia and nightmares and may thus be specific for PTSD. A spectral index derived from these data distinguishes patients from controls with high effect size, bearing promise as a candidate biomarker. post-traumatic stress disorder, sleep, polysomnography, quantitative electroencephalography, spectral analysis Statement of Significance Here, we investigate sleep disturbances in post-traumatic stress disorder (PTSD) and present evidence for an objective neural marker for PTSD sleep problems. Sleep EEG spectral power across the brain and polysomnographical results are compared in matched trauma-exposed individuals with and without PTSD. During non-REM sleep, PTSD patients show a substantial loss of slow oscillation power, yet increased power for higher frequencies. These effects appear over frontal areas and correlate with insomnia. PTSD REM sleep shows increased slow oscillation power over occipital areas. The latter effects positively correlate to nightmare activity and insomnia. A spectral index derived from these data distinguishes patients from controls with high effect size, bearing promise as a candidate biomarker for PTSD. Introduction Post-traumatic stress disorder (PTSD) is a highly debilitating disorder with a lifetime prevalence of 7%–8% [1, 2]. Sleep problems are the most prevalent symptoms of PTSD with roughly 70% of patients experiencing co-occurring sleep disorders [3, 4]. The sleep problems typically include nightmares, distressed awakenings, nocturnal panic attacks, sleep terrors, and insomnia [5]. Sleep abnormalities following trauma are a strong predictor for future development of PTSD [6–9]. However, sleep abnormalities may also pre-date trauma [10] and then also predict subsequent PTSD [9]. These and other findings suggest that sleep disturbances play an important role in the development and maintenance of PTSD [11]. This role may be related to sleep’s crucial involvement in memory consolidation [12, 13], reduction of memories’ emotional tone [14–17] and emotional regulation in general [14, 18, 19]. While the subjective sleep impairments in PTSD are well established, the underlying physiological abnormalities are less clear. Several studies have focused on the macrostructure of sleep, measuring variables such as the amount of time slept, periods of wakefulness after sleep-onset and the amount of time spent in the different sleep stages. A meta-analysis of such studies reports an increase in stage 1 sleep (the lightest sleep stage), decreased slow-wave sleep (SWS) or deep sleep, and increased density of rapid eye movements (REMs) in PTSD patients [20]. The reported average effect sizes are modest, however, with small effects for stage 1 (d = 0.24) and SWS (d = −0.28), and a small to medium effect for REM density (d = 0.43). While sleep stages are informative about the temporal organization of sleep, they have limited value for quantifying the spectral content of the sleep electroencephalography (EEG). Sleep staging is by definition a categorical analysis in which a large amount of variance with regard to the frequency content of the signal goes undetected. Moreover, sleep staging regards neuronal population dynamics as a whole-brain state, resulting in a spatially globalized analysis. However, EEG-recorded activity varies substantially across different regions of the brain, during sleeping and waking alike. This spatial variance is largely disregarded in standard polysomnography. A more precise way to quantify the frequency content of the EEG entails the analysis of spectral power. A few controlled studies of PTSD sleep report on such analyses, albeit only for recordings on single, often central, derivations [21–26]. The most consistent observation in these studies is reduced non-REM (NREM) delta (0.5–4 Hz) activity in patients [23–25]. Activity in this frequency range indexes sleep depth, suggesting reduced sleep depth in PTSD, in line with the findings of reduced SWS. However, as with the macroscopic findings, reduced delta activity is not consistently observed in all studies [22, 27]. Thus, none of the physiological sleep measures assessed thus far provides a reliable correlate of the severe subjective sleep problems in PTSD. The primary aim of the current study was to obtain a better understanding of the neural underpinnings of PTSD sleep disturbances, through the assessment of spatially distributed brain activity. Secondly, we hoped to find a robust neural correlate of PTSD sleep problems, leading the way toward the development of a biomarker. Such a biomarker would importantly facilitate further research on PTSD, including the objective evaluation of (sleep-oriented) therapeutic strategies. With these goals in mind, we performed a comprehensive investigation of sleep in patients with PTSD and matched, traumatized participants without PTSD. Central to the investigation was a spectral analysis of the sleep EEG captured over four, widely spaced, electrode locations (as compared to the most common method in sleep EEG, which captures only a single location). We considered a broad frequency range (0.5–50 Hz) and adopted a division into frequency bands in line with state-of-the-art knowledge on the neural underpinnings of oscillatory population dynamics. In particular, we separated the 0.5–4 Hz delta band considered in previous studies, into slow oscillations (SOs: 0.5–1.5 Hz), which have a cortical origin [28–30], and higher delta frequencies (1.5–4 Hz), which have a thalamic origin [31]. The range of the SO band was based on the maxima in EEG power spectra of young adult humans during normal nocturnal sleep, which show a broad peak centered around 0.7–0.8 Hz [32]. As an a priori hypothesis, we considered that SO dynamics might be disrupted in PTSD. Indeed, SOs [33] orchestrate sleep-related cortical communication [34] and have been shown to play an important role in (emotional) memory consolidation [35, 36] and emotional resilience [14]. As such, their disruption might lead to abnormal trauma memory consolidation and impaired recovery from emotional trauma. Furthermore, SO dynamics are most pronounced in EEG recordings over frontal cortical areas. The moderate reductions of 0.5–4 Hz power, observed over central locations previously, might actually reflect a more frontally centered SO power deficit, picked up in the diluted form. While the above hypothesis concerns NREM sleep, the subjective sleep complaints associated with PTSD are likely associated with both NREM and REM sleep. Insomnia, commonly found in PTSD patients, has been associated with disruptions in NREM sleep and sometimes REM sleep (although the changes in polysomnography derived variables tend to be less pronounced than expected based on subjective reports [37]). Nightmares primarily occur during REM sleep [3, 4], although they have also been described to occur during N2 in patients with PTSD [38]. In addition to the EEG spectral analysis, a full sleep physiological evaluation (polysomnography) was performed, including visual sleep staging, assessments of limb movements, respiratory, and cardiac function. Furthermore, we evaluated subjective sleep quality and the presence of sleep disorders by means of validated questionnaires. These measures allowed us to relate any abnormalities in brain activity to sleep symptomatology. Method Participants Participants in the experiment were traumatized police officers and military veterans, with (N = 16) or without (N = 14) PTSD. Participants with PTSD were recruited at ARQ Centrum‘45, the Dutch national center for diagnostics and treatment of PTSD, part of ARQ National Psychotrauma Centre. Participants all met the criteria for chronic PTSD and five participants met the criteria for delayed-onset PTSD. Participants in the PTSD and control group were matched on age, gender, education level, and professional background (Table 1). No differences in alcohol-related disorders and use were found between groups (Table 1). Participants were asked to refrain from medication use prior to the experiment. However, in seven PTSD patients medication (SSRIs, antipsychotics, anti-depressants, sedatives or hypnotics) could, for medical ethical reasons, not be interrupted. All participants gave written informed consent. For additional details see Supplementary materials. Table 1. Sociodemographic and clinical characteristics of participants with PTSD and trauma-exposed controls Characteristics . PTSD group . Control group . Professional background (n, %)  Police 12 (75%) 10 (71%)  Veteran 4 (25%) 4 (29%) Mean age (SD) 45.6 (7.9) 44.4 (8.7) Gender (n, %)  Male 15 (94%) 13 (93%)  Female 1 (6%) 1 (7%) Educational level (n, %)  Lower vocational education 3 (19%) 0 (0%)  Middle vocational education 10 (63%) 11 (79%)  Higher vocational education 3 (19%) 3 (21%) Clinical characteristics  CAPS score (mean, SD) 82.8 (11.6) 5.3 (4.7)  Alcohol dependence (n, %) 0 (0%) 0 (0%)  Alcohol abuse (n, %) 0 (0%) 1 (7%)  Number of drinks per week (mean, SD) 2.4 (3.3) 4.6 (4.5) Characteristics . PTSD group . Control group . Professional background (n, %)  Police 12 (75%) 10 (71%)  Veteran 4 (25%) 4 (29%) Mean age (SD) 45.6 (7.9) 44.4 (8.7) Gender (n, %)  Male 15 (94%) 13 (93%)  Female 1 (6%) 1 (7%) Educational level (n, %)  Lower vocational education 3 (19%) 0 (0%)  Middle vocational education 10 (63%) 11 (79%)  Higher vocational education 3 (19%) 3 (21%) Clinical characteristics  CAPS score (mean, SD) 82.8 (11.6) 5.3 (4.7)  Alcohol dependence (n, %) 0 (0%) 0 (0%)  Alcohol abuse (n, %) 0 (0%) 1 (7%)  Number of drinks per week (mean, SD) 2.4 (3.3) 4.6 (4.5) CAPS, clinical-administered PTSD scale. Open in new tab Table 1. Sociodemographic and clinical characteristics of participants with PTSD and trauma-exposed controls Characteristics . PTSD group . Control group . Professional background (n, %)  Police 12 (75%) 10 (71%)  Veteran 4 (25%) 4 (29%) Mean age (SD) 45.6 (7.9) 44.4 (8.7) Gender (n, %)  Male 15 (94%) 13 (93%)  Female 1 (6%) 1 (7%) Educational level (n, %)  Lower vocational education 3 (19%) 0 (0%)  Middle vocational education 10 (63%) 11 (79%)  Higher vocational education 3 (19%) 3 (21%) Clinical characteristics  CAPS score (mean, SD) 82.8 (11.6) 5.3 (4.7)  Alcohol dependence (n, %) 0 (0%) 0 (0%)  Alcohol abuse (n, %) 0 (0%) 1 (7%)  Number of drinks per week (mean, SD) 2.4 (3.3) 4.6 (4.5) Characteristics . PTSD group . Control group . Professional background (n, %)  Police 12 (75%) 10 (71%)  Veteran 4 (25%) 4 (29%) Mean age (SD) 45.6 (7.9) 44.4 (8.7) Gender (n, %)  Male 15 (94%) 13 (93%)  Female 1 (6%) 1 (7%) Educational level (n, %)  Lower vocational education 3 (19%) 0 (0%)  Middle vocational education 10 (63%) 11 (79%)  Higher vocational education 3 (19%) 3 (21%) Clinical characteristics  CAPS score (mean, SD) 82.8 (11.6) 5.3 (4.7)  Alcohol dependence (n, %) 0 (0%) 0 (0%)  Alcohol abuse (n, %) 0 (0%) 1 (7%)  Number of drinks per week (mean, SD) 2.4 (3.3) 4.6 (4.5) CAPS, clinical-administered PTSD scale. Open in new tab Clinical assessments and sleep-related questionnaires The presence or absence of PTSD according to DSM-IV criteria was diagnosed with the Clinician Administered PTSD Scale (CAPS), following assessment of trauma history with the Life Events Checklist [39]. We used the CAPS score on item B2 as an indicator of nightmare severity (combined score of frequency and intensity). Past and present comorbid psychiatric disorders according to DSM-IV criteria were assessed with the MINI-PLUS clinical interview [40]. The presence of sleep disorders that were not physiologically evaluated (see next section) was assessed with the SLEEP-50 [41]. The subjective sleep quality on the night of the polysomnography was assessed the morning after, with the Dutch Sleep Quality Scale [42]. Polysomnography and general procedure All experimentation, including sleep recording, was performed at the in-patient facility of ARQ Centrum’45. The current study is part of a larger registered clinical project ([43]; for details see Supplementary Materials). Participants slept at the department twice, in the context of a broad diagnostic assessment before treatment onset. Data for the current study was recorded on one of the two nights, with the order of the nights counterbalanced over participants and disease status (PTSD, trauma-control) through semi-randomization. Subjective sleep quality did not differ significantly between the two nights, either in participants with PTSD or in trauma-controls (p’s > 0.1). All reported clinical and sleep diagnostics were obtained within six to eight weeks of the sleep recording. Polysomnographic data was recorded successfully for 13 participants with and 14 without PTSD. Participants were given the opportunity to sleep undisturbed for 9 hours during a lights-off period starting between 11 and 12 pm, depending on habitual sleep times. Polysomnography, using ambulatory 16-channel Porti amplifiers (TMS-i) and Galaxy sleep analysis software (PHI-international), consisted of EEG recording (F3, F4, C4 and O2, referenced to average mastoids), two EOG electrodes monitoring eye-movements, and two for submental EMG. Further sensors were ECG monitoring heart rate, plethysmography monitoring blood oxygenation, piezo leg sensors to detect leg movements, probes measuring nasal airflow, and a piezo respiratory band for the thoracic respiratory effort to monitor breathing and sleep apnea. The sample rate for all signals was 512 Hz. Data analysis Sleep stages were scored visually according to the American Academy of Sleep Medicine (AASM) criteria [44]. For each recording, we calculated total sleep time, sleep latency, REM latency, wake after sleep onset, and sleep efficiency. We also determined the amounts of light sleep (N1+N2), SWS (N3) and REM sleep, in minutes and as a percentage of total sleep time. The frequency content of the EEG was analyzed using fast Fourier transform-based spectral analysis (4s time windows with 50% overlap, 0.25Hz bin size; Hamming window), on each electrode (F3, F4, C4 and O2) for NREM sleep and REM sleep separately. Through visual inspection of the data, EEG epochs containing artifacts were removed. Next, for each frequency bin, the power per 30s epoch was computed and summated over all epochs in the same sleep stage. Normalized power for each sleep stage was calculated dividing power per frequency bin by total power in the 0.5-50Hz range. Finally, normalized power bins were merged across frequencies in each of the following bands: SOs (0.5–1.5Hz), delta (1.5–4Hz), theta (4–8Hz), alpha (8–12Hz), sigma (12–16Hz), beta (16–30Hz) and gamma (30–50Hz). Apneas and hypopneas, oxygen desaturations, leg movements and R-peaks in the ECG were automatically scored (Galaxy, PHI-international) and manually checked. From these measures an apnea/hypopnea index, oxygen saturation index, periodic leg movement index, leg movement index, and heart rate were calculated (details in Supplementary materials). Sleep macrostructure and non-EEG physiological variables were statistically analyzed using independent samples t-tests (two-tailed) or Mann–Whitney U tests. Relative spectral power was log-transformed to achieve a Gaussian distribution and analyzed taking into account unequal variances between power variables for different frequency bands. The full data set was analyzed through mixed design MANOVA with planned comparisons. Factors in the design were Diagnosis (PTSD, trauma-control), Sleep State (NREM, REM), and Electrode (F3, F4, C4, O2), entering power in each frequency band as a separate dependent variable (thus avoiding sphericity violations) and using Roy’s largest root as test statistic. This first-level analysis was followed by ANOVA on the individual dependent variables and analyses of planned contrasts for significant ANOVA effects. The model contained all main and interaction effects of the factor Diagnosis. Correlation analyses were performed using Spearman’s rho. Finally, effect sizes were calculated as Glass’ delta [45]. Results Sleep quality and sleep disorders Means and standard deviations of subjectively assessed sleep quality and sleep disorders are given in Table 2. As expected, PTSD patients rated their sleep quality as extremely poor (t = −4.9, p < 0.0005) and scored very high on insomnia (insomnia: t = 9.3, p < 0.0005) and nightmares (W = 142.5, p < 0.0005) compared to trauma-controls. Furthermore, symptoms of periodic limb movement disorder were increased in patients (t = 2.4, p = 0.026). Considering diagnostic threshold criteria, out of 16 PTSD patients 13 met criteria for insomnia, 11 for nightmare disorder, 8 for periodic limb movement disorder, and 1 for a circadian rhythm sleep disorder. In the control sample, the number of participants crossing a diagnostic threshold ranged between 0 and 3 across all scales. Finally, PTSD patients’ daily functioning complaints associated with sleep problems were much higher than trauma-controls’ (W = 105, p < 0.0005). Table 2. Questionnaire-based sleep variables Variable . Group . Mean . STD . P . Sleep quality PTSD 5.9 3.8 *** CTRL 11.6 2.6 Insomnia PTSD 23.3 4.9 *** CTRL 10.9 1.7 Nightmares PTSD 3.8 2.8 *** CTRL 0.1 0.5 PLMD PTSD 7.1 2.9 * CTRL 5.1 1.3 CRSD PTSD 5.2 2.3 CTRL 5.2 2.0 Sleepwalking PTSD 3.3 0.8 CTRL 3.5 1.7 Daily functioning PTSD 22.0 4.1 *** CTRL 9.9 2.5 Variable . Group . Mean . STD . P . Sleep quality PTSD 5.9 3.8 *** CTRL 11.6 2.6 Insomnia PTSD 23.3 4.9 *** CTRL 10.9 1.7 Nightmares PTSD 3.8 2.8 *** CTRL 0.1 0.5 PLMD PTSD 7.1 2.9 * CTRL 5.1 1.3 CRSD PTSD 5.2 2.3 CTRL 5.2 2.0 Sleepwalking PTSD 3.3 0.8 CTRL 3.5 1.7 Daily functioning PTSD 22.0 4.1 *** CTRL 9.9 2.5 CRSD, circadian rhythm sleep disorder; PLMD, periodic limb movement disorder. *p < 0.05. ***p < 0.0005. Open in new tab Table 2. Questionnaire-based sleep variables Variable . Group . Mean . STD . P . Sleep quality PTSD 5.9 3.8 *** CTRL 11.6 2.6 Insomnia PTSD 23.3 4.9 *** CTRL 10.9 1.7 Nightmares PTSD 3.8 2.8 *** CTRL 0.1 0.5 PLMD PTSD 7.1 2.9 * CTRL 5.1 1.3 CRSD PTSD 5.2 2.3 CTRL 5.2 2.0 Sleepwalking PTSD 3.3 0.8 CTRL 3.5 1.7 Daily functioning PTSD 22.0 4.1 *** CTRL 9.9 2.5 Variable . Group . Mean . STD . P . Sleep quality PTSD 5.9 3.8 *** CTRL 11.6 2.6 Insomnia PTSD 23.3 4.9 *** CTRL 10.9 1.7 Nightmares PTSD 3.8 2.8 *** CTRL 0.1 0.5 PLMD PTSD 7.1 2.9 * CTRL 5.1 1.3 CRSD PTSD 5.2 2.3 CTRL 5.2 2.0 Sleepwalking PTSD 3.3 0.8 CTRL 3.5 1.7 Daily functioning PTSD 22.0 4.1 *** CTRL 9.9 2.5 CRSD, circadian rhythm sleep disorder; PLMD, periodic limb movement disorder. *p < 0.05. ***p < 0.0005. Open in new tab Non-EEG physiological measures to assess movement and breathing-related sleep disorders (Table 3) showed an increased Limb movement index in PTSD patients compared to trauma-controls (W = 158.0, p = 0.039). Heart rate and respiratory variables did not differ significantly between groups (p’s > 0.1), but one PTSD participant was diagnosed with sleep apnea. Table 3. Non-EEG physiological variables Variable . Group . Mean . STD . P . Limb movement index PTSD 58.1 42.9 * CTRL 33.8 13.6 Respiratory disturbance index PTSD 1.4 2.2 CTRL 1.6 1.4 SpO2 desaturation 3% index PTSD 3.3 2.5 CTRL 4.3 3.5 SpO2 desaturation 4% index PTSD 1.4 1.1 CTRL 1.8 2.0 Mean SpO2 PTSD 94.0 1.6 CTRL 94.6 1.2 Variable . Group . Mean . STD . P . Limb movement index PTSD 58.1 42.9 * CTRL 33.8 13.6 Respiratory disturbance index PTSD 1.4 2.2 CTRL 1.6 1.4 SpO2 desaturation 3% index PTSD 3.3 2.5 CTRL 4.3 3.5 SpO2 desaturation 4% index PTSD 1.4 1.1 CTRL 1.8 2.0 Mean SpO2 PTSD 94.0 1.6 CTRL 94.6 1.2 SpO2, blood oxygen saturation level. *p < 0.05. Open in new tab Table 3. Non-EEG physiological variables Variable . Group . Mean . STD . P . Limb movement index PTSD 58.1 42.9 * CTRL 33.8 13.6 Respiratory disturbance index PTSD 1.4 2.2 CTRL 1.6 1.4 SpO2 desaturation 3% index PTSD 3.3 2.5 CTRL 4.3 3.5 SpO2 desaturation 4% index PTSD 1.4 1.1 CTRL 1.8 2.0 Mean SpO2 PTSD 94.0 1.6 CTRL 94.6 1.2 Variable . Group . Mean . STD . P . Limb movement index PTSD 58.1 42.9 * CTRL 33.8 13.6 Respiratory disturbance index PTSD 1.4 2.2 CTRL 1.6 1.4 SpO2 desaturation 3% index PTSD 3.3 2.5 CTRL 4.3 3.5 SpO2 desaturation 4% index PTSD 1.4 1.1 CTRL 1.8 2.0 Mean SpO2 PTSD 94.0 1.6 CTRL 94.6 1.2 SpO2, blood oxygen saturation level. *p < 0.05. Open in new tab Sleep macrostructure Sleep macrostructural variables (Table 4) also differed between groups. Participants with PTSD displayed significantly more awakenings during sleep, both in terms of the absolute number (t = 2.4, p = 0.025) and the frequency of awakenings (awakenings/TST: t = 3.0, p = 0.006), increased wake after sleep onset (t = 2.3, p = 0.037), a tendency toward longer sleep latency (W = 164.5, p = 0.077) and, consequently, reduced sleep efficiency (t = −2.5, p = 0.025). Furthermore, the PTSD group showed trend-level changes in sleep stage composition compared to trauma-controls: N1 percentage was somewhat increased (t = 2.0, p = 0.056), while there was a decrease in N3 percentage (t = −1.9, p = 0.067) and time spent in N3 (t = −2.0, p = 0.057). Finally, REM latency in the PTSD group was significantly increased (t = 2.2, p = 0.043). For other variables no significant differences were found (p’s > 0.1). Table 4. Sleep macrostructure Variable . Group . Mean . STD . P . Total sleep time (min) PTSD 393.0 82.6 CTRL 429.6 45.9 Sleep latency (min) PTSD 21.1 24.1 CTRL 10.0 5.3 REM latency (min) PTSD 130.8 81.0 * CTRL 79.3 36.3 Sleep efficiency PTSD 82.9 11.5 * CTRL 90.9 3.6 Wake after sleep onset (min) PTSD 56.5 34.9 * CTRL 33.3 16.6 WASO/TST PTSD 0.2 0.1 * CTRL 0.1 0.0 Awakenings (#) PTSD 31.3 8.5 * CTRL 24.1 7.5 Awakenings/TST (#/min) PTSD 4.9 1.7 ** CTRL 3.4 1.0 Arousal index (#/min) PTSD 23.8 6.8 CTRL 21.4 7.2 N1 (min) PTSD 67.3 26.2 CTRL 57.8 22.1 N1 (%) PTSD 17.3 5.5 CTRL 13.4 4.8 N2 (min) PTSD 238.0 45.6 CTRL 248.7 21.3 N2 (%) PTSD 61.1 6.6 CTRL 58.3 5.7 N3 (min) PTSD 18.2 19.2 CTRL 35.5 26.1 N3 (%) PTSD 4.4 4.2 CTRL 8.3 6.3 REM (min) PTSD 68.6 36.7 CTRL 87.6 29.3 REM (%) PTSD 17.1 7.3 CTRL 20.0 4.6 Variable . Group . Mean . STD . P . Total sleep time (min) PTSD 393.0 82.6 CTRL 429.6 45.9 Sleep latency (min) PTSD 21.1 24.1 CTRL 10.0 5.3 REM latency (min) PTSD 130.8 81.0 * CTRL 79.3 36.3 Sleep efficiency PTSD 82.9 11.5 * CTRL 90.9 3.6 Wake after sleep onset (min) PTSD 56.5 34.9 * CTRL 33.3 16.6 WASO/TST PTSD 0.2 0.1 * CTRL 0.1 0.0 Awakenings (#) PTSD 31.3 8.5 * CTRL 24.1 7.5 Awakenings/TST (#/min) PTSD 4.9 1.7 ** CTRL 3.4 1.0 Arousal index (#/min) PTSD 23.8 6.8 CTRL 21.4 7.2 N1 (min) PTSD 67.3 26.2 CTRL 57.8 22.1 N1 (%) PTSD 17.3 5.5 CTRL 13.4 4.8 N2 (min) PTSD 238.0 45.6 CTRL 248.7 21.3 N2 (%) PTSD 61.1 6.6 CTRL 58.3 5.7 N3 (min) PTSD 18.2 19.2 CTRL 35.5 26.1 N3 (%) PTSD 4.4 4.2 CTRL 8.3 6.3 REM (min) PTSD 68.6 36.7 CTRL 87.6 29.3 REM (%) PTSD 17.1 7.3 CTRL 20.0 4.6 Arousal index, number of arousals/total sleep time; TST, total sleep time; WASO, wake after sleep onset. *p < 0.05, **p < 0.01. Open in new tab Table 4. Sleep macrostructure Variable . Group . Mean . STD . P . Total sleep time (min) PTSD 393.0 82.6 CTRL 429.6 45.9 Sleep latency (min) PTSD 21.1 24.1 CTRL 10.0 5.3 REM latency (min) PTSD 130.8 81.0 * CTRL 79.3 36.3 Sleep efficiency PTSD 82.9 11.5 * CTRL 90.9 3.6 Wake after sleep onset (min) PTSD 56.5 34.9 * CTRL 33.3 16.6 WASO/TST PTSD 0.2 0.1 * CTRL 0.1 0.0 Awakenings (#) PTSD 31.3 8.5 * CTRL 24.1 7.5 Awakenings/TST (#/min) PTSD 4.9 1.7 ** CTRL 3.4 1.0 Arousal index (#/min) PTSD 23.8 6.8 CTRL 21.4 7.2 N1 (min) PTSD 67.3 26.2 CTRL 57.8 22.1 N1 (%) PTSD 17.3 5.5 CTRL 13.4 4.8 N2 (min) PTSD 238.0 45.6 CTRL 248.7 21.3 N2 (%) PTSD 61.1 6.6 CTRL 58.3 5.7 N3 (min) PTSD 18.2 19.2 CTRL 35.5 26.1 N3 (%) PTSD 4.4 4.2 CTRL 8.3 6.3 REM (min) PTSD 68.6 36.7 CTRL 87.6 29.3 REM (%) PTSD 17.1 7.3 CTRL 20.0 4.6 Variable . Group . Mean . STD . P . Total sleep time (min) PTSD 393.0 82.6 CTRL 429.6 45.9 Sleep latency (min) PTSD 21.1 24.1 CTRL 10.0 5.3 REM latency (min) PTSD 130.8 81.0 * CTRL 79.3 36.3 Sleep efficiency PTSD 82.9 11.5 * CTRL 90.9 3.6 Wake after sleep onset (min) PTSD 56.5 34.9 * CTRL 33.3 16.6 WASO/TST PTSD 0.2 0.1 * CTRL 0.1 0.0 Awakenings (#) PTSD 31.3 8.5 * CTRL 24.1 7.5 Awakenings/TST (#/min) PTSD 4.9 1.7 ** CTRL 3.4 1.0 Arousal index (#/min) PTSD 23.8 6.8 CTRL 21.4 7.2 N1 (min) PTSD 67.3 26.2 CTRL 57.8 22.1 N1 (%) PTSD 17.3 5.5 CTRL 13.4 4.8 N2 (min) PTSD 238.0 45.6 CTRL 248.7 21.3 N2 (%) PTSD 61.1 6.6 CTRL 58.3 5.7 N3 (min) PTSD 18.2 19.2 CTRL 35.5 26.1 N3 (%) PTSD 4.4 4.2 CTRL 8.3 6.3 REM (min) PTSD 68.6 36.7 CTRL 87.6 29.3 REM (%) PTSD 17.1 7.3 CTRL 20.0 4.6 Arousal index, number of arousals/total sleep time; TST, total sleep time; WASO, wake after sleep onset. *p < 0.05, **p < 0.01. Open in new tab Spectral analysis The sleep EEG spectral analysis revealed a striking pattern of differences between PTSD patients and controls. An overview of these differences, across frequencies and spatial positions, is shown in Figure 1 in terms of the power deviations in the PTSD group from control group values (individual group means and SDs are given in Supplementary Table S1). As can be seen, there is a selective loss of SO power in PTSD NREM sleep and a power increase across higher frequency bands (Figure 1A). The pattern is apparent across all derivations, but is the most pronounced over frontal electrodes, especially in the right hemisphere. PTSD REM sleep (Figure 1B) shows a more or less opposite pattern, with increased SO power and power loss in higher frequency bands. This pattern is the most pronounced on the occipital electrode. Figure 1. Open in new tabDownload slide Mean differences in normalized power between patients with PTSD and trauma-controls in NREM (upper panel) and REM sleep (lower panel), across derivations and EEG frequency bands. Difference values were calculated as [Mean power value PTSD group − Mean power value control group]. SO, slow oscillations. Figure 1. Open in new tabDownload slide Mean differences in normalized power between patients with PTSD and trauma-controls in NREM (upper panel) and REM sleep (lower panel), across derivations and EEG frequency bands. Difference values were calculated as [Mean power value PTSD group − Mean power value control group]. SO, slow oscillations. MANOVA with factors Diagnosis (PTSD, trauma-control), Sleep State (NREM, REM), and Electrode (F3, F4, C4, O2) showed a non-significant effect of Diagnosis (Θ = 0.64, F = 2.0, df 7.20, p = 0.11), a significant interaction effect of Diagnosis and Sleep State (Θ = 0.92, F = 2.6, df 7.20, p = 0.04), reflecting that group differences are different for NREM and REM sleep, and a significant Diagnosis by Electrode interaction (Θ = 0.32, F = 3.4, df 7.74, p = 0.003), which indicates that group differences differ across electrodes. The 3-way interaction (Diagnosis*Sleep State*Electrode) was not significant (Θ = 0.15, F = 1.6, df 7.74, p = 0.14). In the follow-up ANOVAs, the Diagnosis by Sleep State interaction was highly significant for all frequency bands, with p-values ranging between p = 0.001 (in the SO band: F = 15.2, df 1.25, p = 0.001) and p = 0.008 (gamma band: F = 8.2, df 1.25, p = 0.008). Further analyses were thus conducted for the two sleep states separately, through repeated measures ANOVA with factors Diagnosis (PTSD, trauma-control) and Electrode (F3, F4, C4, O2) (Table 5). Table 5. Results of repeated measures ANOVA’s per sleep state and frequency band Frequency band . Effect . NREM . . . REM . . . . . F . df . P . F . df . P . Slow waves 0.5–1.5 Hz Group 9.3 1.25 0.005* 7.4 1.25 0.01* Group * electrode 1.4 1.75 0.25 3.3 1.75 0.025* Delta 1.5–4 Hz Group 6.2 1.25 0.019* 6.8 1.25 0.015* Group * electrode 2.2 1.75 0.01* 0.5 1.75 0.66 Theta 4–8 Hz Group 4.7 1.25 0.040* 5.1 1.25 0.033* Group * electrode 1.8 1.75 0.16 0.6 1.75 0.63 Alpha 8–12 Hz Group 6.7 1.25 0.016* 3.8 1.25 0.061 Group * electrode 2.5 1.75 0.088 0.5 1.75 0.65 Sigma 12–16 Hz Group 7.0 1.25 0.014* 3.7 1.25 0.065 Group * electrode 2.4 1.75 0.08 0.6 1.75 0.62 Beta 16–30 Hz Group 7.5 1.25 0.011* 2.9 1.25 0.10 Group * electrode 2.0 1.75 0.12 0.6 1.75 0.65 Gamma 30–50 Hz Group 5.3 1.25 0.030* 3.7 1.25 0.066 Group * electrode 1.5 1.75 0.23 0.5 1.75 0.70 Frequency band . Effect . NREM . . . REM . . . . . F . df . P . F . df . P . Slow waves 0.5–1.5 Hz Group 9.3 1.25 0.005* 7.4 1.25 0.01* Group * electrode 1.4 1.75 0.25 3.3 1.75 0.025* Delta 1.5–4 Hz Group 6.2 1.25 0.019* 6.8 1.25 0.015* Group * electrode 2.2 1.75 0.01* 0.5 1.75 0.66 Theta 4–8 Hz Group 4.7 1.25 0.040* 5.1 1.25 0.033* Group * electrode 1.8 1.75 0.16 0.6 1.75 0.63 Alpha 8–12 Hz Group 6.7 1.25 0.016* 3.8 1.25 0.061 Group * electrode 2.5 1.75 0.088 0.5 1.75 0.65 Sigma 12–16 Hz Group 7.0 1.25 0.014* 3.7 1.25 0.065 Group * electrode 2.4 1.75 0.08 0.6 1.75 0.62 Beta 16–30 Hz Group 7.5 1.25 0.011* 2.9 1.25 0.10 Group * electrode 2.0 1.75 0.12 0.6 1.75 0.65 Gamma 30–50 Hz Group 5.3 1.25 0.030* 3.7 1.25 0.066 Group * electrode 1.5 1.75 0.23 0.5 1.75 0.70 *p < 0.05. Open in new tab Table 5. Results of repeated measures ANOVA’s per sleep state and frequency band Frequency band . Effect . NREM . . . REM . . . . . F . df . P . F . df . P . Slow waves 0.5–1.5 Hz Group 9.3 1.25 0.005* 7.4 1.25 0.01* Group * electrode 1.4 1.75 0.25 3.3 1.75 0.025* Delta 1.5–4 Hz Group 6.2 1.25 0.019* 6.8 1.25 0.015* Group * electrode 2.2 1.75 0.01* 0.5 1.75 0.66 Theta 4–8 Hz Group 4.7 1.25 0.040* 5.1 1.25 0.033* Group * electrode 1.8 1.75 0.16 0.6 1.75 0.63 Alpha 8–12 Hz Group 6.7 1.25 0.016* 3.8 1.25 0.061 Group * electrode 2.5 1.75 0.088 0.5 1.75 0.65 Sigma 12–16 Hz Group 7.0 1.25 0.014* 3.7 1.25 0.065 Group * electrode 2.4 1.75 0.08 0.6 1.75 0.62 Beta 16–30 Hz Group 7.5 1.25 0.011* 2.9 1.25 0.10 Group * electrode 2.0 1.75 0.12 0.6 1.75 0.65 Gamma 30–50 Hz Group 5.3 1.25 0.030* 3.7 1.25 0.066 Group * electrode 1.5 1.75 0.23 0.5 1.75 0.70 Frequency band . Effect . NREM . . . REM . . . . . F . df . P . F . df . P . Slow waves 0.5–1.5 Hz Group 9.3 1.25 0.005* 7.4 1.25 0.01* Group * electrode 1.4 1.75 0.25 3.3 1.75 0.025* Delta 1.5–4 Hz Group 6.2 1.25 0.019* 6.8 1.25 0.015* Group * electrode 2.2 1.75 0.01* 0.5 1.75 0.66 Theta 4–8 Hz Group 4.7 1.25 0.040* 5.1 1.25 0.033* Group * electrode 1.8 1.75 0.16 0.6 1.75 0.63 Alpha 8–12 Hz Group 6.7 1.25 0.016* 3.8 1.25 0.061 Group * electrode 2.5 1.75 0.088 0.5 1.75 0.65 Sigma 12–16 Hz Group 7.0 1.25 0.014* 3.7 1.25 0.065 Group * electrode 2.4 1.75 0.08 0.6 1.75 0.62 Beta 16–30 Hz Group 7.5 1.25 0.011* 2.9 1.25 0.10 Group * electrode 2.0 1.75 0.12 0.6 1.75 0.65 Gamma 30–50 Hz Group 5.3 1.25 0.030* 3.7 1.25 0.066 Group * electrode 1.5 1.75 0.23 0.5 1.75 0.70 *p < 0.05. Open in new tab For NREM sleep, a significant effect of Diagnosis was found for all frequency bands (p’s < 0.05), supporting that in PTSD power is consistently decreased in the SO range and significantly increased in all other bands, with respect to control. The anterior–posterior gradient in the power change was tested through the Diagnosis*Electrode interaction, assessing the contrast between anterior F4 and posterior O2. The contrast was statistically significant for the delta, alpha, sigma, and beta bands (p < 0.05) and reached trend-level significance (p < 0.1) for all other bands (SO, theta, beta, and gamma), confirming larger changes in anterior than posterior region. For REM sleep, the main effect of Diagnosis was significant for the SO, delta, and theta bands and reached trend-level significance in all remaining bands (alpha, sigma, beta, gamma). These results reflect the power increase in the SO band and decrease for higher frequencies in PTSD compared to control. The posterior–anterior gradient in this effect was again assessed through the Diagnosis*Electrode O2 to F4 contrast. The contrast was only significant for the SO band (p < 0.05), suggesting that only the SO power increase is significantly localized over posterior brain areas. To assess possible effects of medication on these results, we compared patients with and without medication on SO power, which showed the largest abnormalities across both NREM and REM sleep. Given the small numbers, this comparison has very low power to detect effects. Having said this, no differences were found either during NREM (t12 = 0.53, p = 0.61) or REM sleep (t12 = 0.84, p = 0.42). More importantly, the differences in SO power between patients and trauma-controls were still statistically significant if the patients on medication were removed from the analysis (NREM: t20 = −2.3, p = 0.03; REM: t14.6 = 2.7, p = 0.01), suggesting medication effects did not notably drive the abnormalities in patients. Correlation of power changes in PTSD with experienced sleep problems To investigate the relation of abnormalities in oscillatory sleep dynamics with experienced sleep problems in PTSD, we focused on the largest power changes in the investigated space-frequency domain. That is, SO power, the most strongly affected band across the combined sleep states, on right-frontal F4 for NREM sleep and on occipital O2 for REM sleep. Each variable was correlated with the two hallmark sleep problems in PTSD: insomnia and nightmares (Supplementary Figure S1). Reduced right-frontal SO power in NREM sleep was related to increased insomnia (r = −0.46, p = 0.017), but was not related at all to nightmare severity (p > 0.1). Conversely, occipital SO power in REM sleep showed a large positive correlation with nightmare severity (r = 0.64, p = 0.048), as well as a significant correlation with insomnia (r = 0.50, p = 0.007). A candidate biomarker for PTSD-sleep problems We explored the extent to which a single variable, reflecting both the NREM and REM sleep spectral abnormalities, might be used as a biomarker. To this purpose, we calculated a “PTSD spectral sleep index” (PSSI) as the ratio between right-frontal NREM SO power and occipital REM sleep SO power. The clinical relevance of this index was assessed through the effect size of disease status (PTSD vs trauma-control). We observed an effect size of 3.4, which is considered very large [46, 47]. Importantly, a biomarker should correlate with diagnostic measures obtained with standardized diagnostic instruments. Accordingly, the PSSI shows a highly significant correlation with participants’ CAPS scores (r = 0.60, p = 0.001; Supplementary Figure S2). Discussion In the present study, we investigated EEG power in PTSD over a large frequency domain at multiple positions across the scalp, in both REM and NREM sleep. Our findings reveal substantial power differences between patients with PTSD and traumatized controls. Specifically, patients show a strong shift away from the lowest frequency band (SO band) toward the higher frequencies during NREM sleep, in particular over the right-frontal cortex. On the other hand, during REM sleep SO power is increased at the expense of higher frequency power, over the occipital part of the brain. The latter abnormality is strongly related to nightmare activity, while both REM and NREM abnormalities show a robust relation to insomnia. Abnormalities in PTSD sleep macrostructure were also observed but were much less pronounced than the power abnormalities. These changes in sleep macro- and microstructure co-occur with severe sleep pathology, apparent from subjective as well as physiological assessments. The findings in NREM sleep support the hypothesis of deregulated SO dynamics in PTSD, showing a preferential reduction of SO power in patients over frontal areas, where SOs are most frequently generated. Interestingly, a recent study recording from a forehead location produced a similar observation, suggesting a reduced amount of NREM sleep with dominant power in the slow oscillation (0.1–1 Hz) range in participants with PTSD compared to healthy subjects ([26; note limitations with regard to age and gender matching of participant groups). Notably, slow oscillations have been implicated in orchestrating sleep-related information processing [34], including memory reactivation and consolidation [48–53]. Activity in the spindle and beta/gamma bands, which is increased in our patient group, has been shown to be more directly associated with memory reprocessing [54], with local spindles reflecting the reactivation of specific memory content. Our findings, therefore, suggest a possible deregulation of these processes in PTSD, perhaps involving exaggerated reprocessing and consolidation of trauma memories. This, in turn, could lead to the overgeneralized, intrusive trauma memories that constitute a key symptom of PTSD [55, 56]. Further research into this putative relation is currently ongoing in our lab. Importantly, the frontal loss of SO power also entails a reduction in sleep depth. Sleep depth is traditionally indexed by low-frequency power over a central electrode, but recent studies have shown that SO power, as well as other oscillatory brain dynamics, are also regulated locally, in relation with brain regions’ wake-time activity [57]. Deep sleep is essential for sleep’s homeostatic recovery function and the resolution of sleep pressure [58]. It is, moreover, crucial for a myriad of anabolic and restorative processes, including build-up of energy molecules and the immune system [59–61]. Thus, chronic deficiencies in deep sleep have important health consequences [62–64]. Turning to REM sleep, we surprisingly found a power shift toward the SO band, which was most prominent on the occipital channel. The abnormality is highly correlated with nightmare severity. Previous studies have reported increased REM sleep delta (0.5–4 Hz) in nightmare disorder [65], and in association with sleep onset hypnagogic imagery [66]. Furthermore, delta power is higher in phasic compared to tonic REM sleep [67], the former being the part of REM sleep marked by REMs and hosting the most vivid dreaming [68]. The combined findings suggest that increased REM sleep SO power in PTSD patients may be related to the visual imagery and/or REMs associated with nightmares. Accordingly, the posterior focus of the power increase might reflect a neural source in the visual cortex, or in posterior areas controlling eye movements (e.g. posterior temporoparietal areas or cerebellum [69]). It would be interesting to assess these possibilities through high-density EEG, which allows a more precise estimation of EEG activity’s neural sources. Note that the neural mechanisms underlying SO power abnormalities in PTSD REM and NREM sleep may not be related. NREM slow oscillations in the EEG reflect synchronized transitions of bistable cortical neurons between so-called up-states and down-states. This dynamic and the resulting EEG slow oscillations are not compatible with the REM sleep state in the same network. However, given local components to sleep–wake regulation [57] and considering recent findings in rodents [70], such slow oscillations might occur locally, while other parts of the cortical mantle display REM sleep-like activity. Still, given the posterior location and the correlation with nightmare activity, it is perhaps more likely that REM sleep SO power reflects neural activity unrelated to the typical NREM slow oscillation dynamic. REM sleep has also been related to memory reprocessing, in particular regarding emotional memories [14, 19, 71, 72]. A comprehensive account of how observed physiological changes during REM and NREM sleep in PTSD might lead to erratic memory consolidation is given in a recent review [73]. Our findings regarding sleep macrostructure in PTSD are generally in line with previous findings [20]; we observed a notable decrease in sleep efficiency, with increased awakenings and wake-time after sleep onset and a tendency toward longer sleep latency. Patients also showed trend-level changes in sleep stage composition, involving decreased N3 and increased N1, as well as increased REM latency, which was not reported previously. Of interest is a comparison of these results with those of spectral analysis. In fact, the pronounced spectral abnormalities in patients’ sleep EEG are only marginally (for NREM sleep) or not at all (for REM sleep) captured by the analysis of sleep stage composition. Given the nature and limitations of the method, this should not be surprising. Nevertheless, many clinical studies assessing brain activity during sleep still revert to sleep staging as the method of choice. We would like to advocate that spectral analysis across multiple brain locations presents a more sensitive and more generic method to assess oscillatory brain activity during sleep. The pronounced spectral abnormalities in PTSD sleep, described above, occur in the context of pronounced sleep pathology. As expected, the large majority of PTSD participants experienced severe insomnia (81%) and nightmare pathology (69%). More interestingly, we observed excessive limb movements during sleep in a large percentage of patients (50% reached the diagnostic threshold for periodic limb movement disorder). These findings might, in part, be related to the reduced NREM sleep depth in patients, as movement normally diminishes with sleep depth. However, the polysomnographical recordings showed that limb movements also often occurred during both phasic and tonic REM sleep. This is highly abnormal, as REM-associated hypotonia normally prevents such movements. Indeed, this observation points in the direction of REM sleep behavior disorder, symptoms of which, such as acting out dreams, have clinically been noted in patients with PTSD [74]. Interestingly, enhanced leg movements in both REM and NREM sleep have also been observed in nightmare sufferers with and without PTSD [75]. Therefore, the REM sleep movements might be related to the intense negative dreaming and related high arousal, which is experienced by PTSD patients and nightmare sufferers alike. Besides contributing to our understanding of sleep disturbance in PTSD, our findings may have practical implications. Indeed, the spectral fingerprint of PTSD sleep presents a pattern of abnormalities that has not been observed before. These abnormalities, moreover, appear to reflect the main proponents of PTSD sleep pathology, namely insomnia and nightmares, the combination of which has some specificity for PTSD. This fosters the exciting notion that a spectral biomarker of PTSD sleep problems might be obtained. A reliable biomarker would be highly useful in PTSD diagnostic practice and would importantly facilitate further research. Our first steps toward exploring this idea are encouraging, showing that a combined NREM-REM spectral index distinguishes PSTD patients from trauma-controls with strikingly high effect size. However, further research should investigate whether our findings can be replicated in a larger and more diverse sample of patients and, subsequently, whether the candidate biomarker has specificity versus other sleep and affective disorders. As a final consideration, and limiting generalization of our results, the patients in this study have severe and chronic PTSD. As such, physiological abnormalities might be particularly pronounced. Also, as the sample consisted of treatment-seeking police officers and veterans, some caution is warranted in extrapolating to other PTSD populations. In conclusion, our findings reveal substantial, abnormalities in the microarchitecture of PTSD sleep, including altered SO dynamics in both sleep states. These changes are likely to affect sleep’s homeostatic recovery function and memory reprocessing during sleep. The right frontal hotspot of abnormalities during NREM sleep may be related to reprocessing of negative memories, while the occipital REM sleep abnormalities are related to nightmare activity. Studies involving emotional memory measures, high-density EEG sleep recordings and larger samples, needed to confirm and extend these initial findings, are ongoing in our lab. These findings provide new insights into the neural basis of PTSD-related sleep disorder and its role in PTSD etiology. Furthermore, given their robustness and potential specificity, the sleep microstructural deficits bear the promise of delivering a biomarker. Neuroscientifically informed treatment interventions aimed at targeting specific PTSD symptoms will be essential to future research agendas [76]. The objective disease marker for typical sleep problems in PTSD, found in the present study, could importantly enhance such interventions. In particular, those aimed at the debilitating sleep problems associated with PTSD. Funding We thank Amsterdam Brain and Cognition (ABC), University of Amsterdam, for financially supporting this work and Marnus Witte for assisting in part of the analyses. Conflictof interest statement: none clared. Acknowledgments We thank the staff of ARQ Centrum‘45 for their involvement in patient recruitment and varied practical support that has been crucial to the realization of this study. References 1. de Vries GJ , et al. 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Google Scholar Crossref Search ADS PubMed WorldCat 74. Roepke S , et al. Nightmares that mislead to diagnosis of reactivation of PTSD . Eur J Psychotraumatol . 2013 ; 4 (1). doi:10.3402/ejpt.v4i0.18714. Google Scholar OpenURL Placeholder Text WorldCat 75. Germain A , et al. Sleep pathophysiology in posttraumatic stress disorder and idiopathic nightmare sufferers . Biol Psychiatry. 2003 ; 54 ( 10 ): 1092 – 1098 . Google Scholar Crossref Search ADS PubMed WorldCat 76. Lanius RA , et al. Restoring large-scale brain networks in PTSD and related disorders: a proposal for neuroscientifically-informed treatment interventions . Eur J Psychotraumatol. 2015 ; 6 : 27313 . Google Scholar Crossref Search ADS PubMed WorldCat Author notes These authors contributed equally to this work. © Sleep Research Society 2019. Published by Oxford University Press [on behalf of the Sleep Research Society]. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact [email protected] © Sleep Research Society 2019. Published by Oxford University Press [on behalf of the Sleep Research Society].

Donovan, Lucas, M;Kapur, Vishesh, K

doi: 10.1093/sleep/zsaa043pmid: 32221540

Obstructive sleep apnea (OSA) is a prevalent disorder that frequently causes excessive daytime sleepiness and lapses in attention, raising risks for impaired driving [1, 2]. Accordingly, individuals with OSA have almost 2.5 times greater risk of motor vehicle collisions [3]. Due to overlapping demographic risk factors including age, sex, and sedentary occupation, OSA is particularly common among commercial drivers. According to recent estimates, 21%–28% of commercial drivers have OSA [4, 5], a majority of whom are likely undiagnosed [4]. Collisions involving commercial large truck drivers are a public health concern. Commercial large truck drivers drive up to 11 h/day, and large trucks require substantially greater stopping distance than other vehicles, over 200 yards when traveling at 65 miles/h [6, 7]. While the 3.5 million large truck commercial drivers compose just 1.5% of all US drivers, they account for nearly 10% of traffic fatalities [8, 9]. Each year, approximately 4,000–4,500 individuals in the United States are killed and 100–110,000 are injured by accidents involving large trucks, nearly three-quarters of whom are passengers of other vehicles [8]. It is unclear how many of these deaths are due to drowsy driving, but it is likely that a meaningful number of these deaths each year are attributable to OSA. Given the human and monetary costs of motor vehicle collisions [10], substantial attention has been paid to mitigating the risks posed by OSA to vigilance and alertness among commercial drivers. Fortunately, the adverse impacts of OSA on driving performance are reversible. Individuals with OSA treated with continuous positive airway pressure (CPAP) experience a more than 50% reduction in the rate of motor vehicle collisions [11], and OSA treatment reduces risks of near-misses on driving simulators by 75% [11]. Tools are also available to screen undiagnosed individuals. Sensitive instruments such as Somni-Sage and STOP [12, 13] can prioritize individuals for confirmatory sleep testing. The feasibility of OSA screening and its potential benefits in reducing fatal and nonfatal crashes prompted both the Federal Motor Carrier Safety Administration and the National Transportation Safety Board to endorse OSA screening among commercial drivers as early as 2007. However, the benefits of screening have faced scrutiny relative to its costs. These costs potentially affect drivers, trucking companies, and society. Drivers face the possibility of lost wages and employment opportunity, prompting frequent underreporting of symptoms [14, 15]. Trucking corporations face costs associated with testing and treatment of OSA and opportunity costs from a labor force of drivers who often cannot work while screening is ongoing. Finally, there are costs to society, as commercial trucking carries over 70% of the nation’s freight products [9]. Even temporary declines in trucking capacity from OSA screening are likely to be felt by society at large through reductions in interstate commerce. These concerns prompted trucking firms to successfully lobby policymakers to prevent implementation of universal OSA screening. Counteracting these concerns is the notion that screening and treating OSA may actually reduce medical costs paid by trucking companies. Patients with untreated OSA have substantially higher medical costs than age- and sex-matched patients without OSA [16]. Conceivably medical costs decrease after therapy prevents or resolves some negative consequences of OSA. Observational studies have attempted to demonstrate cost savings, though most have been limited by their design and analysis [17]. A recent high-quality analysis of the association between PAP adherence and cost in Medicare enrollees found costs increased in both PAP adherent and non-adherent groups over time, but the group with highest PAP adherence had the smallest increase in costs [18]. Perhaps medical cost savings over time make the economics of OSA screening and treatment more favorable for trucking companies. In this context, Burks et al. [19] provide much-needed evidence regarding medical costs from the trucking company’s perspective. To inform this issue, they analyze data from drivers impacted by a large trucking firm’s OSA screening, diagnosis, and treatment program. In this program, about one-half of drivers between 2006 and 2009 were screened (n = 17,098). A selected sample of those classified as “high priority” by OSA screening was referred for laboratory polysomnography (PSG), ultimately leading to 2,186 individuals undergoing PSGs. Drivers diagnosed with OSA were provided objectively monitored PAP therapy and standard follow-up. Drivers with OSA who remained non-adherent, eventually had their employment terminated. For the purposes of this quasi-experimental study, 1,516 drivers with PSG data (1,224 with OSA and 292 without OSA) and medical insurance data were matched to a similar screened driver who had not yet had a PSG based on similar experience at hire and job tenure. Those with PSG diagnosis of OSA (n = 1224) were further subdivided into those with some PAP usage (n = 932; 510 meeting Medicare adherence criteria) and those with no recorded usage (n = 292). The cost incurred by the carrier’s medical insurance plan, excluding costs related to OSA management, was tallied up to 18 months before and after the PSG date or matching date for screen-positive control. Importantly, the authors compared groups using a difference-in-differences approach [20]. This analytic technique compares the difference in treatment effect between the intervention and control groups, potentially mitigating the effects of time-dependent trends and bias introduced by screening and diagnostic procedures and PAP adherence. This approach is superior to simply comparing costs in the post-period between groups, which neglects the possibility of important cost differences associated with adherence that predate the index date. On the other hand, the difference-in-difference technique assumes that the slope of costs over time in the groups is the same prior to intervention [20]. It is not clear whether this assumption is valid for this study as non-adherent drivers may not have similar cost trends to untreated drivers who eventually become PAP adherent. Within this context, the authors found mean per member per month (PMPM) costs increased from 18 months pre to 18 months post for all groups (unadjusted differences $41–211) but the increase was only statistically significant in screen-positive controls ($211). The magnitude of increase was least in the adherent and OSA-negative groups ($41–48) and intermediate in the non-adherence group ($145). The model adjusted difference in PMPM costs that compared a typical driver with diagnosed OSA who was at least partially adherent to CPAP to non-adherent driver showed a $441 statistically significant savings. Overall, these results suggest that while costs do not decrease among drivers adherent with CPAP, they do not rise as much as they do in non-adherent drivers. This study advances our understanding of the economic implications of OSA treatment. Most notably, the authors mitigated threats to validity by incorporating control groups, utilizing a longitudinal cohort design, and comparing changes in costs rather than relying only on costs in the posttreatment period. Furthermore, unlike a recent study of medical costs among commercial drivers screened for OSA, the authors avoided biases incurred by including the costs of OSA diagnosis and treatment which naturally decline over time, leading to overestimation of the cost savings attributed to therapy [21]. The study findings echo results reported by a prior economic evaluation of a sleep-disordered breathing education program that increased the number of railroad employees using PAP therapy [22]. Though the current study findings indicate that OSA screening and treatment may reduce the non-OSA medical expenditures for trucking companies, a more holistic economic evaluation is warranted. For example, some of the medical savings were likely attributable to selective driver attrition. Drivers who were non-adherent to CPAP eventually had their employment terminated; job tenure after PSG was significantly lower for this group. They were also less likely to be healthy and adhere to other therapies [23]. While the trucking firm no longer incurred medical costs for these drivers once they left employment, they bore new costs from hiring and training replacements. The medical costs of the terminated driver were then borne by that individual and society at large. In addition, there may have been significant cost savings experienced by the trucking firm and from a societal perspective from reduced property damage and liability from motor vehicle collisions that were not assessed in this study [10]. Future work would ideally more comprehensively assess costs from a societal perspective and incorporate more robust study designs that are not vulnerable to biases inherent to comparing groups defined by adherence. Furthermore, we need to understand ways we can support drivers with untreated OSA in ways that reduce threats to their health and livelihood. It is important to note that monetary costs represent only one component of what is relevant. As with costs, a societal perspective regarding benefits is most pertinent to establishing public policy. Important societal benefits include improved driver quality of life and health, as well as reduced fatalities and injuries from motor vehicle collisions. Notably, the cost-effectiveness of OSA diagnosis and therapy in individuals with symptomatic moderate-to-severe OSA is well established from a societal perspective [24]. Though further study of these issues is welcome, given the impact on public health, inaction is not a rational option. It is time to save lives through the implementation of mandatory OSA screening for commercial truck drivers. Funding LMD is supported by a Career Development Award from VA Health Services Research & Development (CDA 18–187). The views expressed here are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs. None of the funding sources was involved in the design, conduct, or analysis of this project. Conflicts of interest statement. The authors report no other conflicts. Prior publication: None. References 1. Benjafield AV , et al. . Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis . Lancet Respir Med. 2019 ; 7 ( 8 ): 687 – 698 . Google Scholar Crossref Search ADS PubMed WorldCat 2. Durmer JS , et al. . Neurocognitive consequences of sleep deprivation . Semin Neurol. 2005 ; 25 ( 1 ): 117 – 129 . Google Scholar Crossref Search ADS PubMed WorldCat 3. Tregear S , et al. . 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Reports, Trends & Statistics . https://www.trucking.org/News_and_Information_Reports_Industry_Data.aspx. February 20, 2020. 10. Federal Motor Carrier Safety Administration . Unit Costs of Medium and Heavy Truck Crashes 2007 . https://www.fmcsa.dot.gov/sites/fmcsa.dot.gov/files/docs/UnitCostsTruck%20Crashes2007.pdf. 11. Antonopoulos CN , et al. . Nasal continuous positive airway pressure (nCPAP) treatment for obstructive sleep apnea, road traffic accidents and driving simulator performance: a meta-analysis . Sleep Med Rev. 2011 ; 15 ( 5 ): 301 – 310 . Google Scholar Crossref Search ADS PubMed WorldCat 12. Kales SN , et al. . Obstructive sleep apnea in North American commercial drivers . Ind Health. 2014 ; 52 ( 1 ): 13 – 24 . Google Scholar Crossref Search ADS PubMed WorldCat 13. Popević MB , et al. . Screening commercial drivers for obstructive sleep apnea: validation of STOP-Bang questionnaire . Int J Occup Med Environ Health. 2017 ; 30 ( 5 ): 751 – 761 . 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Association between adherence to continuous positive airway pressure treatment and cost among Medicare enrollees . Sleep . 2020 ; 43 ( 1 ). doi:10.1093/sleep/zsz188 OpenURL Placeholder Text WorldCat 19. Burks SV , et al. Employer-mandated obstructive sleep apnea treatment and healthcare cost savings among truckers . Sleep . 2019 ; 43 ( 4 ). doi:10.1093/sleep/zsz262 OpenURL Placeholder Text WorldCat 20. Dimick JB , et al. . Methods for evaluating changes in health care policy: the difference-in-differences approach . JAMA. 2014 ; 312 ( 22 ): 2401 – 2402 . Google Scholar Crossref Search ADS PubMed WorldCat 21. Hoffman B , et al. . The long-term health plan and disability cost benefit of obstructive sleep apnea treatment in a commercial motor vehicle driver population . J Occup Environ Med. 2010 ; 52 ( 5 ): 473 – 477 . Google Scholar Crossref Search ADS PubMed WorldCat 22. Potts KJ , et al. . Cost savings associated with an education campaign on the diagnosis and management of sleep-disordered breathing: a retrospective, claims-based US study . Popul Health Manag. 2013 ; 16 ( 1 ): 7 – 13 . Google Scholar Crossref Search ADS PubMed WorldCat 23. Platt AB , et al. Adherence to sleep apnea therapy and use of lipid-lowering drugs: a study of the healthy-user effect . Chest . 2010 ; 137 ( 1 ): 102 – 108 . Google Scholar Crossref Search ADS PubMed WorldCat 24. CPAP Treatment for Adults with Obstructive Sleep Apnea: Review of the Clinical and Cost-Effectiveness and Guidelines [Internet] . Ottawa, ON : Canadian Agency for Drugs and Technologies in Health. Available from http://www.ncbi.nlm.nih.gov/books/NBK195805/. November 18, 2013 . © Sleep Research Society 2020. Published by Oxford University Press [on behalf of the Sleep Research Society]. All rights reserved. For permissions, please email: [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)

Setnik, Beatrice; McDonnell, Michael; Mills, Catherine; Scart-Grès, Catherine; Robert, Philippe; Dayno, Jeffrey M; Schwartz, Jean-Charles

doi: 10.1093/sleep/zsz252pmid: 31626696

Abstract Objectives To evaluate the human abuse potential of pitolisant, a selective histamine 3 (H3)-receptor antagonist/inverse agonist recently approved by the US Food and Drug Administration for the treatment of excessive daytime sleepiness in adult patients with narcolepsy. Methods Nondependent, recreational stimulant users able to distinguish phentermine HCl 60 mg from placebo in a drug discrimination test were randomized in a four-period, double-blind, crossover design to receive single doses of pitolisant 35.6 mg (therapeutic dose), pitolisant 213.6 mg (supratherapeutic dose), phentermine HCl 60 mg, and placebo. The primary endpoint was maximum effect (Emax) on the 100-point Drug Liking (“at this moment”) visual analog scale. Results In 38 study completers (73.7% male; 65.8% white; mean age, 33.3 years), mean Drug Liking Emax was significantly greater for phentermine versus pitolisant 35.6 mg (mean difference, 21.4; p < 0.0001) and pitolisant 213.6 mg (mean difference, 19.7; p < 0.0001). Drug Liking Emax was similar for pitolisant (both doses) and placebo. Similarly, for key secondary measures of Overall Drug Liking and willingness to Take Drug Again, mean Emax scores were significantly greater for phentermine versus pitolisant (both doses) and similar for pitolisant (both doses) versus placebo. The incidence of adverse events was 82.1% after phentermine HCl 60 mg, 72.5% after pitolisant 213.6 mg, 47.5% after pitolisant 35.6 mg, and 48.8% after placebo administration. Conclusions In this study, pitolisant demonstrated significantly lower potential for abuse compared with phentermine and an overall profile similar to placebo; this suggests a low risk of abuse for pitolisant. Clinical Trial Registration ClinicalTrials.gov NCT03152123. Determination of the abuse potential of pitolisant in healthy, nondependent recreational stimulant users. https://clinicaltrials.gov/ct2/show/NCT03152123. drug abuse, narcolepsy, pitolisant Statement of Significance Except for pitolisant, all medications approved in the United States for the treatment of narcolepsy are controlled substances due to their potential for abuse. Pitolisant, a selective histamine 3 (H3)-receptor antagonist/inverse agonist that enhances the activity of histaminergic neurons in the brain, was recently approved by the US Food and Drug Administration (FDA) for the treatment of excessive daytime sleepiness in adult patients with narcolepsy. This study shows that pitolisant has an abuse potential profile similar to that of placebo and significantly lower than that of phentermine (a mild stimulant), with no findings suggestive of a risk of abuse. Based on these findings, along with preclinical data, pitolisant was approved by the FDA without being scheduled as a controlled substance. Introduction Narcolepsy is a chronic, debilitating, rare neurological disorder of sleep–wake state instability that is characterized by excessive daytime sleepiness (EDS), cataplexy, and other manifestations of rapid eye movement (REM) sleep dysregulation [1, 2]. Narcolepsy imposes substantial psychosocial and economic burdens that include reductions in work productivity and quality of life, disruptions to interpersonal relationships, and increases in healthcare resource usage and associated costs [3–6]. For most patients, the management of narcolepsy requires lifelong pharmacological treatment [7, 8]. Stimulants and wake-promoting agents, approved for the treatment of EDS, act primarily via dopaminergic mechanisms [9–12]; these agents are prone to abuse and thus are classified as controlled substances. Stimulant abuse continues to be a public health concern. In the United States (2017), an estimated 1.8 million persons aged 12 or older (including 715,000 young adults aged 18 to 25 years) reported misusing prescription stimulants in the previous month [13]. Abuse of stimulants has been linked to multiple health concerns including cardiovascular disease and psychiatric symptoms, with methamphetamine abuse leading to particularly severe consequences (e.g. increased risk of stroke) [14–18]. Sodium oxybate, a central nervous system depressant approved for the treatment of EDS and cataplexy, works through an unknown mechanism of action, but the therapeutic effect is thought to be mediated by activity at gamma-aminobutyric acid B receptors [19, 20]. Sodium oxybate has a boxed warning regarding its risk of abuse (Schedule III controlled substance) and is available only via a restricted access Risk Evaluation and Mitigation Strategy (REMS) program [19]. Hence, there is a need for narcolepsy medications with novel mechanisms of action that are associated with minimal or no abuse potential. Pitolisant is a first-in-class medication for the treatment of narcolepsy, with a novel mechanism of action [21, 22]. As a potent, highly selective histamine 3 (H3) receptor antagonist/inverse agonist, pitolisant enhances the activity of histaminergic neurons in the brain and activates the release of other wake-promoting neurotransmitters [21, 23, 24]. In contrast to known drugs of abuse (e.g. amphetamines, opioids, ethanol, nicotine, and cocaine) [25, 26] and wake-promoting agents (i.e. modafinil and armodafinil) [11], pitolisant does not work primarily through dopamine and does not cause an increase in dopamine levels in the nucleus accumbens (the brain’s main reward center) [27]. No findings suggestive of abuse potential have been observed in preclinical or clinical studies of pitolisant [27, 28]. The efficacy of pitolisant in the treatment of EDS and cataplexy in patients with narcolepsy has been demonstrated in randomized, placebo-controlled trials [28, 29]. Pitolisant is approved by the European Medicines Agency for the treatment of narcolepsy with or without cataplexy in adults [22, 30], and is not considered a controlled substance in the European Union. Pitolisant was recently approved by the US Food and Drug Administration (FDA) for the treatment of EDS in adult patients with narcolepsy [31]. As pitolisant is the first agent in a new pharmacological class that has central nervous system activity, it was necessary in the United States to evaluate the abuse potential of the drug in accordance with FDA requirements [32]. For example, assessment of the abuse potential of an investigational medication requires comparison with both an active drug with known abuse potential and placebo [32]. In the case of pitolisant, although it is not a psychostimulant, it has demonstrated wake-promoting effects [28, 29]; therefore, a moderately active psychostimulant (i.e. phentermine HCl; Schedule IV) would serve as an appropriate active comparator. The current study was conducted in accordance with the 2017 FDA guidance on performing human abuse potential studies (Supplementary Table S1) [32]. The primary objective was to assess the abuse potential of pitolisant relative to phentermine and placebo after single-dose administration in nondependent, recreational stimulant users. The secondary objectives were to evaluate the pharmacokinetic profile of pitolisant and assess the safety and tolerability of a single dose of the drug. Methods Study design This was a single-dose, randomized, double-blind, active- and placebo-controlled, four-sequence, four-period crossover study conducted at one clinical site in Toronto, Canada, between March 2017 and October 2017. The study was conducted in accordance with the ethical principles that have their origins in the Declaration of Helsinki (1964); the guidelines for Good Clinical Practice, as outlined by the International Conference on Harmonisation; and FDA guidance [32] on the assessment of the abuse potential of drugs. The study protocol was submitted to the FDA (Controlled Substance Staff) before conduct of the study (with no comments received) and was approved by the institutional review board IRB Services. All participants provided written informed consent before study-related procedures began. Participants This study enrolled healthy males and females aged 18 to 55 years (inclusive) with body mass index (BMI) in the range of 18.0 to 33.0 kg/m2 (inclusive). All participants were recreational stimulant users, defined as users of stimulants for nontherapeutic purposes (i.e. psychoactive effects) at least 10 times in the past year and at least 1 time in the previous 8 weeks. Potential participants were excluded if they had a history of alcohol or other drug dependence (excluding caffeine and nicotine) within the previous 2 years, as defined by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision criteria [33], and/or a history of attendance or a plan to participate in a rehabilitation program to treat alcohol or other substance dependence. Other exclusion criteria included clinically significant medical conditions, any history of suicidal ideation or behavior, and heavy use of tobacco products (>20 cigarettes per day and/or inability to abstain from nicotine-containing products for ≥6 hr per day). Use of concomitant medications or natural health products was prohibited, except for nicotine-containing substances, acetaminophen, vitamin or mineral supplements, selected contraceptives, and hormone replacement therapy. Negative alcohol breath test and negative urine drug screen results were required before admission for each study visit, although positive results for tetrahydrocannabinol (THC) were permitted at the discretion of the investigator, provided that residual levels were not indicative of recent use. Procedure Following a screening visit, a drug discrimination test was first conducted to ensure that enrolled participants could discern the effects of the active comparator (phentermine HCl 60 mg) compared with placebo (Figure 1). After an overnight fast, participants received, in randomized order, phentermine or placebo, administered in a double-blind crossover manner, with each dose separated by approximately 24 hr. Ability to discriminate phentermine from placebo was based on the 100-point, bipolar Drug Liking visual analog scale (VAS), anchored with “strong disliking” at 0, “neither like nor dislike” at 50, and “strong liking” at 100, and defined by all of the following: peak score for phentermine of at least 65 points, peak score for placebo between 40 and 60 points (inclusive), and score difference of at least +15 points for phentermine relative to placebo. Consistent with the FDA guidance [32], only those participants who “report drug liking in response to the positive control and demonstrate a meaningfully different response from that produced by placebo…should participate in the Treatment Phase.” This methodology was used because a study population that is able to discriminate between the positive control (in the class of drug being tested) and placebo is necessary to assess for drug liking of the investigational agent. Participants were eligible to continue into the double-blind treatment phase if they met the discrimination criteria and were able to tolerate phentermine HCl 60 mg (e.g. no emesis within 4 hr postdose). Figure 1. Open in new tabDownload slide Study design and participant disposition. HCl = hydrochloride. During the double-blind treatment phase, each participant was to receive all four treatments: pitolisant 35.6 mg (optimal therapeutic dose), pitolisant 213.6 mg (supratherapeutic dose), phentermine HCl 60 mg (active comparator), and placebo. Participants were assigned to receive one of four treatment sequences via computer-generated randomization according to a 4 x 4 Williams square randomization design. Study medication was administered in a randomized, double-blind, crossover manner, with a minimum 7 day washout period between treatments. To maintain the integrity of the double-blind administration, participants received four matching capsules containing active drug and/or placebo at each treatment visit. Study medication was administered in the morning after a fasting period of at least 8 hr, and participants were required to fast for at least 4 hr postdose. Participants were admitted to the clinical research unit the day before each treatment was administered and remained until approximately 24 hr after dosing or until discharge was deemed safe by the investigator. Pharmacodynamic measures Pharmacodynamic measures used to assess subjective drug effects were consistent with FDA guidance for abuse potential studies (Table 1) [32]. The primary endpoint was the maximum effect (Emax) for Drug Liking (“at this moment”) assessed on a bipolar (0 to 100) VAS. Key secondary endpoints included Emax for global drug effects (i.e. Overall Drug Liking, Take Drug Again) and positive drug effects (i.e. Good Drug Effects, High). Additional VAS scores assessed other drug effects (e.g. Any Drug Effects, Bad Drug Effects, Relaxation/Agitation Effects, Drug Similarity). A modified version of the Addiction Research Center Inventory (ARCI) [34] consisted of items from four subscales: the Morphine-Benzedrine Group (MBG) scale, a measure of euphoria; the Amphetamine and Benzedrine Group scales, to assess stimulant effects; and the Lysergic Acid Diethylamide (LSD) scale, a measure of dysphoria. Table 1. Pharmacodynamic measures used to assess drug liking and other drug effects Measure . Description . Time(s) administered . Drug Effects VAS Drug Liking (“At this moment, my liking for this drug is”), scored on a bipolar 100-point VAS anchored with “strong disliking” at 0, “neither like nor dislike” at 50, and “strong liking” at 100 Good Drug Effects (“At this moment, I feel good drug effects”), High (“At this moment, I feel high”), Bad Drug Effects (“At this moment, I feel bad drug effects”), and Any Drug Effects (“At this moment, I feel any drug effect”), scored on a unipolar 100-point VAS anchored with “not at all” at 0 and “extremely” at 100 Relaxation/Agitation (“At this moment, my mood is”), scored on a bipolar 100-point VAS anchored with “very relaxed” at 0, “neither relaxed nor agitated” at 50, and “very agitated” at 100 Predosea and 0.5, 1, 1.5, 2, 3, 4, 5, 6, 8, 10, 12, and 24 hr postdose Overall Drug Liking VAS “Overall, my liking for this drug is,” scored on a bipolar 100-point VAS anchored with “strong disliking” at 0, “neither like nor dislike” at 50, and “strong liking” at 100 8, 12, and 24 hr postdose Take Drug Again VAS “I would take this drug again,” scored on a bipolar 100-point VAS anchored with “definitely not” at 0, “neutral” at 50, and “definitely so” at 100 8, 12, and 24 hr postdose Drug Similarity VAS “How similar is the drug you most recently received to [drug name]?” scored on a unipolar 100-point VAS anchored with “not at all similar” at 0 and “very similar” at 100 Similarity was rated for the study medication compared with a variety of drugs (eg, caffeine, cocaine, heroin, LSD, methadone, nicotine, d-amphetamine [“speed”], methamphetamine, THC) Item for Overall Familiarity (“How familiar was the effect of the drug you most recently received?”) scored on a bipolar 100-point VAS anchored with “very unfamiliar” at 0 and “very familiar” at 100 24 hr postdose Addiction Research Center Inventory [34] 39 true–false statements comprising 4 subscales that assess euphoria (MBG), stimulant effects (BG, Amphetamine), and dysphoria (LSD) Predose and 0.5, 1, 1.5, 2, 3, 4, 5, 6, 8, 10, 12, and 24 hr postdose Measure . Description . Time(s) administered . Drug Effects VAS Drug Liking (“At this moment, my liking for this drug is”), scored on a bipolar 100-point VAS anchored with “strong disliking” at 0, “neither like nor dislike” at 50, and “strong liking” at 100 Good Drug Effects (“At this moment, I feel good drug effects”), High (“At this moment, I feel high”), Bad Drug Effects (“At this moment, I feel bad drug effects”), and Any Drug Effects (“At this moment, I feel any drug effect”), scored on a unipolar 100-point VAS anchored with “not at all” at 0 and “extremely” at 100 Relaxation/Agitation (“At this moment, my mood is”), scored on a bipolar 100-point VAS anchored with “very relaxed” at 0, “neither relaxed nor agitated” at 50, and “very agitated” at 100 Predosea and 0.5, 1, 1.5, 2, 3, 4, 5, 6, 8, 10, 12, and 24 hr postdose Overall Drug Liking VAS “Overall, my liking for this drug is,” scored on a bipolar 100-point VAS anchored with “strong disliking” at 0, “neither like nor dislike” at 50, and “strong liking” at 100 8, 12, and 24 hr postdose Take Drug Again VAS “I would take this drug again,” scored on a bipolar 100-point VAS anchored with “definitely not” at 0, “neutral” at 50, and “definitely so” at 100 8, 12, and 24 hr postdose Drug Similarity VAS “How similar is the drug you most recently received to [drug name]?” scored on a unipolar 100-point VAS anchored with “not at all similar” at 0 and “very similar” at 100 Similarity was rated for the study medication compared with a variety of drugs (eg, caffeine, cocaine, heroin, LSD, methadone, nicotine, d-amphetamine [“speed”], methamphetamine, THC) Item for Overall Familiarity (“How familiar was the effect of the drug you most recently received?”) scored on a bipolar 100-point VAS anchored with “very unfamiliar” at 0 and “very familiar” at 100 24 hr postdose Addiction Research Center Inventory [34] 39 true–false statements comprising 4 subscales that assess euphoria (MBG), stimulant effects (BG, Amphetamine), and dysphoria (LSD) Predose and 0.5, 1, 1.5, 2, 3, 4, 5, 6, 8, 10, 12, and 24 hr postdose aHigh and Relaxation/Agitation scales only. BG = Benzedrine Group; LSD = lysergic acid diethylamide; MBG = Morphine–Benzedrine Group; THC = tetrahydrocannabinol; VAS = visual analog scale. Open in new tab Table 1. Pharmacodynamic measures used to assess drug liking and other drug effects Measure . Description . Time(s) administered . Drug Effects VAS Drug Liking (“At this moment, my liking for this drug is”), scored on a bipolar 100-point VAS anchored with “strong disliking” at 0, “neither like nor dislike” at 50, and “strong liking” at 100 Good Drug Effects (“At this moment, I feel good drug effects”), High (“At this moment, I feel high”), Bad Drug Effects (“At this moment, I feel bad drug effects”), and Any Drug Effects (“At this moment, I feel any drug effect”), scored on a unipolar 100-point VAS anchored with “not at all” at 0 and “extremely” at 100 Relaxation/Agitation (“At this moment, my mood is”), scored on a bipolar 100-point VAS anchored with “very relaxed” at 0, “neither relaxed nor agitated” at 50, and “very agitated” at 100 Predosea and 0.5, 1, 1.5, 2, 3, 4, 5, 6, 8, 10, 12, and 24 hr postdose Overall Drug Liking VAS “Overall, my liking for this drug is,” scored on a bipolar 100-point VAS anchored with “strong disliking” at 0, “neither like nor dislike” at 50, and “strong liking” at 100 8, 12, and 24 hr postdose Take Drug Again VAS “I would take this drug again,” scored on a bipolar 100-point VAS anchored with “definitely not” at 0, “neutral” at 50, and “definitely so” at 100 8, 12, and 24 hr postdose Drug Similarity VAS “How similar is the drug you most recently received to [drug name]?” scored on a unipolar 100-point VAS anchored with “not at all similar” at 0 and “very similar” at 100 Similarity was rated for the study medication compared with a variety of drugs (eg, caffeine, cocaine, heroin, LSD, methadone, nicotine, d-amphetamine [“speed”], methamphetamine, THC) Item for Overall Familiarity (“How familiar was the effect of the drug you most recently received?”) scored on a bipolar 100-point VAS anchored with “very unfamiliar” at 0 and “very familiar” at 100 24 hr postdose Addiction Research Center Inventory [34] 39 true–false statements comprising 4 subscales that assess euphoria (MBG), stimulant effects (BG, Amphetamine), and dysphoria (LSD) Predose and 0.5, 1, 1.5, 2, 3, 4, 5, 6, 8, 10, 12, and 24 hr postdose Measure . Description . Time(s) administered . Drug Effects VAS Drug Liking (“At this moment, my liking for this drug is”), scored on a bipolar 100-point VAS anchored with “strong disliking” at 0, “neither like nor dislike” at 50, and “strong liking” at 100 Good Drug Effects (“At this moment, I feel good drug effects”), High (“At this moment, I feel high”), Bad Drug Effects (“At this moment, I feel bad drug effects”), and Any Drug Effects (“At this moment, I feel any drug effect”), scored on a unipolar 100-point VAS anchored with “not at all” at 0 and “extremely” at 100 Relaxation/Agitation (“At this moment, my mood is”), scored on a bipolar 100-point VAS anchored with “very relaxed” at 0, “neither relaxed nor agitated” at 50, and “very agitated” at 100 Predosea and 0.5, 1, 1.5, 2, 3, 4, 5, 6, 8, 10, 12, and 24 hr postdose Overall Drug Liking VAS “Overall, my liking for this drug is,” scored on a bipolar 100-point VAS anchored with “strong disliking” at 0, “neither like nor dislike” at 50, and “strong liking” at 100 8, 12, and 24 hr postdose Take Drug Again VAS “I would take this drug again,” scored on a bipolar 100-point VAS anchored with “definitely not” at 0, “neutral” at 50, and “definitely so” at 100 8, 12, and 24 hr postdose Drug Similarity VAS “How similar is the drug you most recently received to [drug name]?” scored on a unipolar 100-point VAS anchored with “not at all similar” at 0 and “very similar” at 100 Similarity was rated for the study medication compared with a variety of drugs (eg, caffeine, cocaine, heroin, LSD, methadone, nicotine, d-amphetamine [“speed”], methamphetamine, THC) Item for Overall Familiarity (“How familiar was the effect of the drug you most recently received?”) scored on a bipolar 100-point VAS anchored with “very unfamiliar” at 0 and “very familiar” at 100 24 hr postdose Addiction Research Center Inventory [34] 39 true–false statements comprising 4 subscales that assess euphoria (MBG), stimulant effects (BG, Amphetamine), and dysphoria (LSD) Predose and 0.5, 1, 1.5, 2, 3, 4, 5, 6, 8, 10, 12, and 24 hr postdose aHigh and Relaxation/Agitation scales only. BG = Benzedrine Group; LSD = lysergic acid diethylamide; MBG = Morphine–Benzedrine Group; THC = tetrahydrocannabinol; VAS = visual analog scale. Open in new tab Pharmacokinetic evaluation During each treatment period, a 6-mL blood sample was collected for pharmacokinetic analysis at each of the following time points: predose and at 0.5, 1, 1.5, 2, 3, 4, 5, 6, 8, 10, 12, 16, and 24 hr postdose. The pharmacokinetic parameters calculated were maximum observed serum concentration (Cmax), time to reach maximum serum concentration (tmax), and area under the concentration-time curve from time 0 to the last measurable concentration (AUC0-last). Safety evaluation The incidence and severity of adverse events were assessed during the drug discrimination and treatment phases, from the time of study drug administration until approximately 24 hr postdose. Other safety assessments included vital signs measurements, physical examination findings, clinical laboratory test results, electrocardiogram parameters, and Columbia-Suicide Severity Rating Scale (C-SSRS) score [35]. Statistical analyses The study was planned to randomize 44 participants into the treatment phase to ensure that at least 36 participants completed the study. As determined by a published algorithm [36], adjusting for four periods and four sequences, with a 1-sided significance level of 0.05 and a phentermine-placebo difference of 5 points, a sample size of 36 participants was estimated to have at least 90% power to detect a difference in Drug Liking VAS Emax, assuming within-group standard deviations of 17.5 for phentermine and 10.2 for placebo (based on unpublished data collected at the study site). The analysis population for the pharmacodynamic endpoints consisted of participants who completed all four treatment periods and had at least one assessment on the Drug Liking VAS within 2 hr of tmax for each treatment (completers population). The pharmacokinetic analysis population included participants who received at least one dose of pitolisant in the treatment phase and had at least one evaluable serum pitolisant concentration postdose. The safety population included participants who received at least one dose of any study medication in the treatment phase. Pharmacodynamic endpoints were analyzed using a mixed-effects model for a crossover study. The model included treatment, period, treatment sequence, and first-order carryover effect (if applicable) as fixed effects; baseline (predose) measurement as a covariate (if applicable); and participant nested within treatment sequence as a random effect. The residuals from the mixed-effects model were investigated for normality, and parametric (e.g. paired t-test) or nonparametric (e.g. sign test) analyses were conducted, as appropriate. Comparisons were 1-tailed for the primary and key secondary endpoints and 2-tailed for all other secondary endpoints; the significance level was set at 0.05 without adjustment for multiplicity. For the primary endpoint (Drug Liking Emax), statistical analyses evaluated three discrete questions: (1) did phentermine produce reliable abuse-related responses compared with placebo (to assess study validity); (2) did pitolisant produce abuse-related responses that were smaller than those of phentermine (to assess the relative abuse potential of pitolisant); and (3) did pitolisant produce abuse-related responses that were similar to placebo (to assess the absolute abuse potential of pitolisant). For the first question, study validity was evaluated by comparing Drug Liking Emax between phentermine (the active control) and placebo; statistical testing evaluated the hypothesis that the mean score for phentermine was at least 5 points higher than the mean score for placebo. A statistically significant result would indicate that phentermine differed from placebo. Second, for comparisons of pitolisant (35.6 and 213.6 mg) to phentermine on Drug Liking Emax, statistical testing evaluated the hypothesis that the mean score for phentermine was higher (by any amount) than the mean score for pitolisant. A statistically significant result would indicate that phentermine differed from pitolisant. Third, for comparisons of pitolisant (35.6 and 213.6 mg) to placebo on Drug Liking Emax, statistical testing evaluated the hypothesis that the mean score for pitolisant was less than 11 points greater than the mean score for placebo. The margin of 11 points was based on recommendations from a meta-analysis of eight abuse potential studies [37]. For the third hypothesis, a statistically significant result would mean that pitolisant was similar to placebo; this hypothesis testing is in contrast to the statistical evaluation used in efficacy studies but is appropriate for measures of safety (such as abuse potential). Evaluation of key secondary endpoints was based on the same hypothesis testing conducted for Drug Liking Emax. For other pharmacodynamic endpoints, statistical testing evaluated the hypothesis that the difference between treatments was not equal to 0, with the absence of a statistically significant between-group difference indicating that mean Emax values were not different. Pharmacokinetic parameters for pitolisant (Cmax, tmax, AUC0-last) were calculated from serum concentration data using standard noncompartmental analysis and were summarized using descriptive statistics. SAS version 9.3 (SAS Institute Inc., Cary, NC) was used for all statistical analyses. Results Participants A total of 95 participants were enrolled in the drug discrimination phase (Figure 1). Of 43 randomized participants, 38 completed the study and constitute the primary analysis (completers) population. Participants in the completers population were predominantly male (73.7%) and white (65.8%), with a mean (range) age of 33.3 (21 to 54) years and a mean (range) BMI of 24.6 (18.9 to 32.5) kg/m2. In addition to recreational stimulant use (required for study participation, per protocol), almost all randomized participants (95.3%) reported a history of alcohol use. Recreational drug use also included cannabinoids (95.3%), morphine or other opioids (58.1%), central nervous system depressants (55.8%), hallucinogens (39.5%), and dissociative anesthetics (25.6%). Primary pharmacodynamic outcome measure Study validity and sensitivity were confirmed by the statistically significant difference in mean Drug Liking Emax for phentermine (78.7) compared with placebo (56.1; p < 0.0001; Table 2). Phentermine also differed significantly from placebo in the maximum effect (Emax) on all other VAS and ARCI scales, except for Bad Drug Effects (Table 3). Table 2. Analysis results for Drug Liking (“at this moment”) VAS Emax (primary endpoint) in the completers population (N = 38) Comparison . Mean (SE) or median (Q1-Q3) of the paired difference . P . Phentermine HCl 60 mg vs placeboa 22.7 (2.86) < 0.0001 Phentermine HCl 60 mg vs pitolisant 35.6 mga 21.4 (3.16) < 0.0001 Phentermine HCl 60 mg vs pitolisant 213.6 mga 19.7 (3.52) < 0.0001 Pitolisant 35.6 mg vs placebob 0.0 (0.0 to 6.0) < 0.0001 Pitolisant 213.6 mg vs placebob 0.0 (0.0 to 11.0) 0.0013 Comparison . Mean (SE) or median (Q1-Q3) of the paired difference . P . Phentermine HCl 60 mg vs placeboa 22.7 (2.86) < 0.0001 Phentermine HCl 60 mg vs pitolisant 35.6 mga 21.4 (3.16) < 0.0001 Phentermine HCl 60 mg vs pitolisant 213.6 mga 19.7 (3.52) < 0.0001 Pitolisant 35.6 mg vs placebob 0.0 (0.0 to 6.0) < 0.0001 Pitolisant 213.6 mg vs placebob 0.0 (0.0 to 11.0) 0.0013 aPaired t-test was used to assess the difference between the two treatments. For these comparisons, the null hypothesis was that the mean difference for phentermine vs placebo was ≤5 and that the mean difference for phentermine vs pitolisant was ≤0. bSign test (which evaluated statistical significance based on the proportion of patients for whom the pitolisant–placebo difference in Drug Liking Emax exceeded the prespecified threshold of 11) was used to assess the difference between the two treatments, because the paired differences were not normally distributed or quite symmetric. For these comparisons, the null hypothesis was that the difference between medians for pitolisant vs placebo was ≥11. Therefore, the significant results indicate that the null hypothesis was rejected and the Drug Liking Emax scores were similar for pitolisant and placebo. Emax = peak maximum effect; Q1 = 25th percentile; Q3 = 75th percentile; SE = standard error; VAS = visual analog scale. Open in new tab Table 2. Analysis results for Drug Liking (“at this moment”) VAS Emax (primary endpoint) in the completers population (N = 38) Comparison . Mean (SE) or median (Q1-Q3) of the paired difference . P . Phentermine HCl 60 mg vs placeboa 22.7 (2.86) < 0.0001 Phentermine HCl 60 mg vs pitolisant 35.6 mga 21.4 (3.16) < 0.0001 Phentermine HCl 60 mg vs pitolisant 213.6 mga 19.7 (3.52) < 0.0001 Pitolisant 35.6 mg vs placebob 0.0 (0.0 to 6.0) < 0.0001 Pitolisant 213.6 mg vs placebob 0.0 (0.0 to 11.0) 0.0013 Comparison . Mean (SE) or median (Q1-Q3) of the paired difference . P . Phentermine HCl 60 mg vs placeboa 22.7 (2.86) < 0.0001 Phentermine HCl 60 mg vs pitolisant 35.6 mga 21.4 (3.16) < 0.0001 Phentermine HCl 60 mg vs pitolisant 213.6 mga 19.7 (3.52) < 0.0001 Pitolisant 35.6 mg vs placebob 0.0 (0.0 to 6.0) < 0.0001 Pitolisant 213.6 mg vs placebob 0.0 (0.0 to 11.0) 0.0013 aPaired t-test was used to assess the difference between the two treatments. For these comparisons, the null hypothesis was that the mean difference for phentermine vs placebo was ≤5 and that the mean difference for phentermine vs pitolisant was ≤0. bSign test (which evaluated statistical significance based on the proportion of patients for whom the pitolisant–placebo difference in Drug Liking Emax exceeded the prespecified threshold of 11) was used to assess the difference between the two treatments, because the paired differences were not normally distributed or quite symmetric. For these comparisons, the null hypothesis was that the difference between medians for pitolisant vs placebo was ≥11. Therefore, the significant results indicate that the null hypothesis was rejected and the Drug Liking Emax scores were similar for pitolisant and placebo. Emax = peak maximum effect; Q1 = 25th percentile; Q3 = 75th percentile; SE = standard error; VAS = visual analog scale. Open in new tab Table 3. Summary of pharmacodynamic measures: mean (SE) Emax scores in the completers population (N = 38)a Measure . Placebo . Pitolisant 35.6 mg . Pitolisant 213.6 mg . Phentermine HCl 60 mg . Primary endpointb  Drug Liking (“at this moment”) 56.1 (2.1) 57.3 (2.1)**** ,††††,c,d 59.0 (2.1)**** ,††,c,d 78.7 (2.8)†††† Key secondary endpointsb  Overall Drug Liking 54.4 (2.2) 52.7 (2.1)**** ,††††,c,d 49.2 (4.3)** ,†††,c,d 77.4 (3.8)††††  Take Drug Again 51.0 (2.9) 49.4 (3.4)**** ,††††,c,d 44.5 (4.9)**** ,†††,c,d 78.7 (4.3)††††  Good Drug Effects 15.3 (4.4) 15.7 (4.0)**** ,†,c,d 26.3 (4.8)****c 62.9 (5.2)††††  High 12.3 (3.9) 15.7 (4.1)**** ,c 35.3 (5.6)*** ,c 58.6 (5.0)†††† Other secondary endpointse  Bad Drug Effects 7.4 (3.2) 6.7 (2.9) 28.2 (5.7)* ,†† 14.7 (3.5)  Relaxation/Agitation 52.4 (2.1) 53.2 (2.8) 60.8 (2.9)†† 62.9 (3.2)††  Any Drug Effects 17.2 (4.9) 19.3 (4.5)**** 41.6 (6.1)** ,††† 63.6 (5.0)††††  ARCI/MBG score 3.4 (0.7) 3.3 (0.7)**** 3.9 (0.7)**** 9.6 (0.9)††††  ARCI/Amphetamine score 2.7 (0.4) 2.3 (0.4)**** 3.1 (0.5)**** 6.4 (0.5)††††  ARCI/BG score 6.0 (0.3) 5.9 (0.3)**** 6.1 (0.3)**** 8.2 (0.4)††††  ARCI/LSD score 4.4 (0.3) 4.5 (0.3)**** 5.8 (0.4)†† 6.1 (0.4)†††† Measure . Placebo . Pitolisant 35.6 mg . Pitolisant 213.6 mg . Phentermine HCl 60 mg . Primary endpointb  Drug Liking (“at this moment”) 56.1 (2.1) 57.3 (2.1)**** ,††††,c,d 59.0 (2.1)**** ,††,c,d 78.7 (2.8)†††† Key secondary endpointsb  Overall Drug Liking 54.4 (2.2) 52.7 (2.1)**** ,††††,c,d 49.2 (4.3)** ,†††,c,d 77.4 (3.8)††††  Take Drug Again 51.0 (2.9) 49.4 (3.4)**** ,††††,c,d 44.5 (4.9)**** ,†††,c,d 78.7 (4.3)††††  Good Drug Effects 15.3 (4.4) 15.7 (4.0)**** ,†,c,d 26.3 (4.8)****c 62.9 (5.2)††††  High 12.3 (3.9) 15.7 (4.1)**** ,c 35.3 (5.6)*** ,c 58.6 (5.0)†††† Other secondary endpointse  Bad Drug Effects 7.4 (3.2) 6.7 (2.9) 28.2 (5.7)* ,†† 14.7 (3.5)  Relaxation/Agitation 52.4 (2.1) 53.2 (2.8) 60.8 (2.9)†† 62.9 (3.2)††  Any Drug Effects 17.2 (4.9) 19.3 (4.5)**** 41.6 (6.1)** ,††† 63.6 (5.0)††††  ARCI/MBG score 3.4 (0.7) 3.3 (0.7)**** 3.9 (0.7)**** 9.6 (0.9)††††  ARCI/Amphetamine score 2.7 (0.4) 2.3 (0.4)**** 3.1 (0.5)**** 6.4 (0.5)††††  ARCI/BG score 6.0 (0.3) 5.9 (0.3)**** 6.1 (0.3)**** 8.2 (0.4)††††  ARCI/LSD score 4.4 (0.3) 4.5 (0.3)**** 5.8 (0.4)†† 6.1 (0.4)†††† ap values are based on a mixed-effects model for a crossover study and included treatment, period, treatment sequence, baseline, and carryover effects. bBecause of the structure of the statistical hypothesis testing for the primary and key secondary endpoints, significant results for pitolisant compared with placebo indicate that mean Emax values were similar for pitolisant and placebo. cStatistically significant comparison with phentermine indicates difference. dStatistically significant comparison with placebo indicates similarity. eFor other secondary endpoint comparisons, statistical significance indicates difference. *p < 0.05 versus phentermine; **p < 0.01 versus phentermine; ***p < 0.001 versus phentermine; ****p < 0.0001 versus phentermine; †p < 0.05 versus placebo; ††p < 0.01 versus placebo; †††p < 0.001 versus placebo; ††††p < 0.0001 versus placebo. ARCI = Addiction Research Center Inventory; BG = Benzedrine Group; Emax = peak maximum effect; LSD = lysergic acid diethylamine; MBG = Morphine-Benzedrine Group; SE = standard error. Open in new tab Table 3. Summary of pharmacodynamic measures: mean (SE) Emax scores in the completers population (N = 38)a Measure . Placebo . Pitolisant 35.6 mg . Pitolisant 213.6 mg . Phentermine HCl 60 mg . Primary endpointb  Drug Liking (“at this moment”) 56.1 (2.1) 57.3 (2.1)**** ,††††,c,d 59.0 (2.1)**** ,††,c,d 78.7 (2.8)†††† Key secondary endpointsb  Overall Drug Liking 54.4 (2.2) 52.7 (2.1)**** ,††††,c,d 49.2 (4.3)** ,†††,c,d 77.4 (3.8)††††  Take Drug Again 51.0 (2.9) 49.4 (3.4)**** ,††††,c,d 44.5 (4.9)**** ,†††,c,d 78.7 (4.3)††††  Good Drug Effects 15.3 (4.4) 15.7 (4.0)**** ,†,c,d 26.3 (4.8)****c 62.9 (5.2)††††  High 12.3 (3.9) 15.7 (4.1)**** ,c 35.3 (5.6)*** ,c 58.6 (5.0)†††† Other secondary endpointse  Bad Drug Effects 7.4 (3.2) 6.7 (2.9) 28.2 (5.7)* ,†† 14.7 (3.5)  Relaxation/Agitation 52.4 (2.1) 53.2 (2.8) 60.8 (2.9)†† 62.9 (3.2)††  Any Drug Effects 17.2 (4.9) 19.3 (4.5)**** 41.6 (6.1)** ,††† 63.6 (5.0)††††  ARCI/MBG score 3.4 (0.7) 3.3 (0.7)**** 3.9 (0.7)**** 9.6 (0.9)††††  ARCI/Amphetamine score 2.7 (0.4) 2.3 (0.4)**** 3.1 (0.5)**** 6.4 (0.5)††††  ARCI/BG score 6.0 (0.3) 5.9 (0.3)**** 6.1 (0.3)**** 8.2 (0.4)††††  ARCI/LSD score 4.4 (0.3) 4.5 (0.3)**** 5.8 (0.4)†† 6.1 (0.4)†††† Measure . Placebo . Pitolisant 35.6 mg . Pitolisant 213.6 mg . Phentermine HCl 60 mg . Primary endpointb  Drug Liking (“at this moment”) 56.1 (2.1) 57.3 (2.1)**** ,††††,c,d 59.0 (2.1)**** ,††,c,d 78.7 (2.8)†††† Key secondary endpointsb  Overall Drug Liking 54.4 (2.2) 52.7 (2.1)**** ,††††,c,d 49.2 (4.3)** ,†††,c,d 77.4 (3.8)††††  Take Drug Again 51.0 (2.9) 49.4 (3.4)**** ,††††,c,d 44.5 (4.9)**** ,†††,c,d 78.7 (4.3)††††  Good Drug Effects 15.3 (4.4) 15.7 (4.0)**** ,†,c,d 26.3 (4.8)****c 62.9 (5.2)††††  High 12.3 (3.9) 15.7 (4.1)**** ,c 35.3 (5.6)*** ,c 58.6 (5.0)†††† Other secondary endpointse  Bad Drug Effects 7.4 (3.2) 6.7 (2.9) 28.2 (5.7)* ,†† 14.7 (3.5)  Relaxation/Agitation 52.4 (2.1) 53.2 (2.8) 60.8 (2.9)†† 62.9 (3.2)††  Any Drug Effects 17.2 (4.9) 19.3 (4.5)**** 41.6 (6.1)** ,††† 63.6 (5.0)††††  ARCI/MBG score 3.4 (0.7) 3.3 (0.7)**** 3.9 (0.7)**** 9.6 (0.9)††††  ARCI/Amphetamine score 2.7 (0.4) 2.3 (0.4)**** 3.1 (0.5)**** 6.4 (0.5)††††  ARCI/BG score 6.0 (0.3) 5.9 (0.3)**** 6.1 (0.3)**** 8.2 (0.4)††††  ARCI/LSD score 4.4 (0.3) 4.5 (0.3)**** 5.8 (0.4)†† 6.1 (0.4)†††† ap values are based on a mixed-effects model for a crossover study and included treatment, period, treatment sequence, baseline, and carryover effects. bBecause of the structure of the statistical hypothesis testing for the primary and key secondary endpoints, significant results for pitolisant compared with placebo indicate that mean Emax values were similar for pitolisant and placebo. cStatistically significant comparison with phentermine indicates difference. dStatistically significant comparison with placebo indicates similarity. eFor other secondary endpoint comparisons, statistical significance indicates difference. *p < 0.05 versus phentermine; **p < 0.01 versus phentermine; ***p < 0.001 versus phentermine; ****p < 0.0001 versus phentermine; †p < 0.05 versus placebo; ††p < 0.01 versus placebo; †††p < 0.001 versus placebo; ††††p < 0.0001 versus placebo. ARCI = Addiction Research Center Inventory; BG = Benzedrine Group; Emax = peak maximum effect; LSD = lysergic acid diethylamine; MBG = Morphine-Benzedrine Group; SE = standard error. Open in new tab At early time points and continuing for up to 10 hr, mean scores for the primary outcome, Drug Liking (“at this moment”) VAS, were greater for phentermine compared with the other treatments; from 3 to 24 hr postdose, scores were similar for pitolisant 35.6 mg and placebo and consistently lower for pitolisant 213.6 mg relative to placebo (Figure 2). Mean Drug Liking Emax was significantly greater for phentermine (78.7) compared with pitolisant 35.6 mg (57.3) and pitolisant 213.6 mg (59.0; both p < 0.0001) and similar for pitolisant (both doses) compared with placebo (56.1). Because of the structure of the statistical hypothesis testing (difference between pitolisant and placebo < 11), the significant results for pitolisant 35.6 and 213.6 mg compared with placebo (Table 2) indicate that Drug Liking Emax scores were similar for pitolisant and placebo. Figure 2. Open in new tabDownload slide Primary pharmacodynamic measure, Drug Liking VAS, over time (completers population, N = 38). Error bars represent ±1 SE. SE = standard error; VAS = visual analog scale. Key secondary pharmacodynamic outcome measures For the Overall Drug Liking VAS and Take Drug Again VAS, mean scores were greater for phentermine compared with both pitolisant treatments at all assessments (8, 12, and 24 hr postdose; Figure 3A and B); Emax scores were significantly greater for phentermine relative to pitolisant (both doses) and similar for pitolisant (both doses) compared with placebo (Table 3). Mean scores for the Good Drug Effects VAS were markedly greater for phentermine compared with pitolisant (both doses) between 1.5 and 3 hr postdose and remained higher until approximately 10 hr postdose (Figure 3C); mean Emax scores were significantly greater for phentermine relative to pitolisant (both doses) (Table 3). Pitolisant 35.6 mg was similar to placebo on Good Drug Effects Emax, but pitolisant 213.6 mg did not meet statistical criteria for similarity (<11-point difference from placebo). Mean scores for the High VAS were greatest for phentermine, intermediate for pitolisant 213.6 mg, and lowest for pitolisant 35.6 mg and placebo (Figure 3D). Mean Emax scores for High VAS were significantly greater for phentermine relative to pitolisant (both doses); neither dose of pitolisant met the statistical criteria for similarity (<11-point difference) to placebo (Table 3). Figure 3. Open in new tabDownload slide Key secondary pharmacodynamic measures over time (completers population, N = 38) for (A) Overall Drug Liking, (B) Take Drug Again, (C) Good Drug Effects, and (D) High VAS. Error bars represent ±1 SE. SE = standard error; VAS = visual analog scale. Other secondary pharmacodynamic outcome measures On the other secondary VAS measures (Bad Drug Effects, Relaxation/Agitation, Any Drug Effects), mean Emax for pitolisant 35.6 mg was not different from placebo (Table 3). Emax on the Bad Drug Effects VAS was significantly greater for pitolisant 213.6 mg compared with both phentermine and placebo. On the Relaxation/Agitation VAS, Emax for pitolisant 213.6 mg was significantly different from placebo (in the direction of agitation) but not significantly different from phentermine. On the Drug Similarity VAS, pitolisant was rated as dissimilar (median score near 0) to all drug classes including stimulants, central nervous system (CNS) depressants, opioids, THC, hallucinogens, and dissociative drugs. For the stimulant and hallucinogen drug classes, median Drug Similarity VAS scores for phentermine were markedly greater than those for pitolisant or placebo. Median scores on the Overall Familiarity item were comparable (and near the neutral point) for pitolisant 35.6 mg (52.5) and placebo (52.0); median scores were in the direction of more familiar for phentermine (78.5) and less familiar for pitolisant 213.6 mg (19.0). Peak scores on the ARCI scales were significantly different for pitolisant (both doses) compared with phentermine and not different for pitolisant relative to placebo (Table 3), with the exception of the dysphoria (LSD) scale, on which mean Emax for pitolisant 213.6 mg was significantly greater than that for placebo and not different from phentermine. Pharmacokinetic parameters Mean (standard deviation [SD]) Cmax for pitolisant 35.6 mg was 54.5 (27.7) ng/mL and mean (SD) AUC0-last was 501.2 (308.4) ng·hr/mL (Figure 4); these parameters are consistent with the established pharmacokinetic profile of pitolisant. For pitolisant 213.6 mg (supratherapeutic dose), mean (SD) Cmax (425.0 [198.4] ng/mL) and AUC0-last (4001.5 [1523.9] ng·hr/mL) were approximately 7.8 times higher and 8.0 times higher, respectively, compared with pitolisant 35.6 mg. Median tmax was 3.0 hr for pitolisant 35.6 mg and 2.0 hr for pitolisant 213.6 mg. No pharmacokinetic and pharmacodynamic correlations or trends were identified between Cmax and the maximum pharmacodynamic effects (Emax) of pitolisant; in fact, higher plasma concentrations were associated with lower scores on the Drug Liking scale. Figure 4. Open in new tabDownload slide Serum concentration of pitolisant over time (pharmacokinetic population, N = 40). SD = standard deviation. Safety and tolerability The overall rate of adverse events was similar for placebo and pitolisant 35.6 mg and somewhat higher for pitolisant 213.6 mg (supratherapeutic dose) and phentermine (Table 4). Headache was the most common adverse event for pitolisant, and the incidence appeared to be dose-related. The majority of adverse events were rated as mild in intensity by the investigator; adverse events rated as moderate were headache (3 participants after receiving placebo and 2 after receiving pitolisant 213.6 mg), inability to concentrate (1 participant after receiving phentermine), and vomiting (1 participant after receiving pitolisant 35.6 mg). There were no deaths, serious adverse events, or severe adverse events during the study, and no participants discontinued from the study because of adverse events. Table 4. Adverse events (safety population)a Adverse event, n (%)b . Placebo (N = 41) . Pitolisant 35.6 mg (N = 40) . Pitolisant 213.6 mg (N = 40) . Phentermine HCl 60 mg (N = 39) . Any adverse event 20 (48.8) 19 (47.5) 29 (72.5) 32 (82.1) Headache 5 (12.2) 6 (15.0) 10 (25.0) 4 (10.3) Euphoric mood 4 (9.8) 4 (10.0) 7 (17.5) 16 (41.0) Somnolence 6 (14.6) 4 (10.0) 3 (7.5) 3 (7.7) Hypervigilance 1 (2.4) 3 (7.5) 5 (12.5) 13 (33.3) Nausea 1 (2.4) 2 (5.0) 4 (10.0) 0 (0.0) Dizziness 0 (0.0) 1 (2.5) 4 (10.0) 1 (2.6) Feeling hot 0 (0.0) 0 (0.0) 4 (10.0) 2 (5.1) Adverse event, n (%)b . Placebo (N = 41) . Pitolisant 35.6 mg (N = 40) . Pitolisant 213.6 mg (N = 40) . Phentermine HCl 60 mg (N = 39) . Any adverse event 20 (48.8) 19 (47.5) 29 (72.5) 32 (82.1) Headache 5 (12.2) 6 (15.0) 10 (25.0) 4 (10.3) Euphoric mood 4 (9.8) 4 (10.0) 7 (17.5) 16 (41.0) Somnolence 6 (14.6) 4 (10.0) 3 (7.5) 3 (7.7) Hypervigilance 1 (2.4) 3 (7.5) 5 (12.5) 13 (33.3) Nausea 1 (2.4) 2 (5.0) 4 (10.0) 0 (0.0) Dizziness 0 (0.0) 1 (2.5) 4 (10.0) 1 (2.6) Feeling hot 0 (0.0) 0 (0.0) 4 (10.0) 2 (5.1) aOccurring in >5% of participants for either pitolisant dose. bCoded using the Medical Dictionary for Regulatory Activities version 20.0. Open in new tab Table 4. Adverse events (safety population)a Adverse event, n (%)b . Placebo (N = 41) . Pitolisant 35.6 mg (N = 40) . Pitolisant 213.6 mg (N = 40) . Phentermine HCl 60 mg (N = 39) . Any adverse event 20 (48.8) 19 (47.5) 29 (72.5) 32 (82.1) Headache 5 (12.2) 6 (15.0) 10 (25.0) 4 (10.3) Euphoric mood 4 (9.8) 4 (10.0) 7 (17.5) 16 (41.0) Somnolence 6 (14.6) 4 (10.0) 3 (7.5) 3 (7.7) Hypervigilance 1 (2.4) 3 (7.5) 5 (12.5) 13 (33.3) Nausea 1 (2.4) 2 (5.0) 4 (10.0) 0 (0.0) Dizziness 0 (0.0) 1 (2.5) 4 (10.0) 1 (2.6) Feeling hot 0 (0.0) 0 (0.0) 4 (10.0) 2 (5.1) Adverse event, n (%)b . Placebo (N = 41) . Pitolisant 35.6 mg (N = 40) . Pitolisant 213.6 mg (N = 40) . Phentermine HCl 60 mg (N = 39) . Any adverse event 20 (48.8) 19 (47.5) 29 (72.5) 32 (82.1) Headache 5 (12.2) 6 (15.0) 10 (25.0) 4 (10.3) Euphoric mood 4 (9.8) 4 (10.0) 7 (17.5) 16 (41.0) Somnolence 6 (14.6) 4 (10.0) 3 (7.5) 3 (7.7) Hypervigilance 1 (2.4) 3 (7.5) 5 (12.5) 13 (33.3) Nausea 1 (2.4) 2 (5.0) 4 (10.0) 0 (0.0) Dizziness 0 (0.0) 1 (2.5) 4 (10.0) 1 (2.6) Feeling hot 0 (0.0) 0 (0.0) 4 (10.0) 2 (5.1) aOccurring in >5% of participants for either pitolisant dose. bCoded using the Medical Dictionary for Regulatory Activities version 20.0. Open in new tab Six participants experienced clinically significant vital sign values that were recorded as adverse events (i.e. hypertension, blood pressure increased, and tachycardia), all after administration of phentermine. No participants exhibited any suicidal ideation or behavior during the study, as assessed by the C-SSRS. Discussion In this randomized, double-blind study involving recreational stimulant users, maximal scores on the primary endpoint of maximum Drug Liking (Emax) were significantly greater for phentermine HCl (60 mg) compared with pitolisant (single doses, 35.6 and 213.6 mg) and similar for pitolisant relative to placebo, with no apparent dose–response effect for pitolisant on Drug Liking. Results on the key secondary endpoints of Overall Drug Liking and willingness to Take Drug Again were supportive of the primary endpoint (Drug Liking “at this moment”). Across outcome measures, the profile for pitolisant 35.6 mg (the maximum therapeutic dose for the treatment of narcolepsy) was consistent with that for placebo, except for the High VAS, on which the maximal rating for pitolisant did not meet the statistical criteria for similarity with placebo. Differences between phentermine and the supratherapeutic dose of pitolisant (213.6 mg) were evident on some pharmacodynamic measures. Maximal responses on the Good Drug Effects VAS and High VAS were significantly greater for phentermine compared with pitolisant 213.6 mg; peak scores for the supratherapeutic dose of pitolisant were greater than those for placebo. In addition, pitolisant 213.6 mg was associated with negative subjective effects, including significantly higher maximal scores than phentermine or placebo on the Bad Drug Effects VAS and significantly greater dysphoric effects than placebo on the ARCI-LSD scale, indicating that the higher dose of pitolisant produces aversive drug effects. Neither dose of pitolisant was associated with increased scores on the ARCI Amphetamine, Benzedrine Group, or MBG scales. By contrast, phentermine produced increased scores on all ARCI scales, indicative of significantly greater stimulant and euphoric effects compared with pitolisant (both doses) and placebo. Pitolisant was perceived as dissimilar to all drug classes including stimulants, CNS depressants, opioids, THC, hallucinogens, and dissociative agents. The clinical dose of pitolisant (35.6 mg) was rated as more similar to placebo than any other drug class. Study participants, all recreational stimulant abusers, were relatively familiar with the effects of phentermine but unfamiliar with the effects of high-dose pitolisant (213.6 mg), which supports the lack of similarity between pitolisant and stimulant medications. Overall, results from this study showed that pitolisant has significantly less abuse potential compared with phentermine (a known stimulant) and an overall profile that is similar to placebo, with no signal suggestive of abuse even at the supratherapeutic dose. Study design and methodology were consistent with the latest FDA guidance for assessing the abuse potential of drugs [32]. Because recreational drug users often exceed medically recommended doses to enhance the subjective experience produced by a drug, FDA guidance recommends evaluating a supratherapeutic dose 2 to 3 times greater than the therapeutic dose [32]. In this study, a larger supratherapeutic dose was selected (pitolisant 213.6 mg, 6 times the therapeutic dose) because it exceeded the FDA recommendation yet was well tolerated by healthy volunteers in previous studies (data on file). Phentermine was chosen as the active comparator for pitolisant because it has wake-promoting effects, has some common adverse events (e.g. insomnia), and is a scheduled drug (Schedule IV controlled substance) with demonstrated abuse potential. Using a Schedule IV psychostimulant (e.g. phentermine) as the active comparator provides a more sensitive evaluation of abuse potential than using a Schedule II psychostimulant (e.g. methylphenidate and amphetamine), because it is more difficult to demonstrate significantly lower drug liking compared with a Schedule IV drug than to a Schedule II drug. The dose of phentermine used in this study (60 mg), although larger than the generally prescribed clinical doses (15 or 30 mg), was selected because it can be expected to reliably produce psychoactive effects, is in a range that can be safely administered, and is within the range (45–90 mg) used in previous abuse potential studies [38–40]. The sensitivity of the methodology and validity of the study were established by the statistically significant difference between phentermine and placebo on the primary endpoint of Drug Liking Emax (and also on secondary pharmacodynamic measures). These findings were consistent with results for phentermine reported in previous studies [38–40]. Human abuse potential studies utilize validated measures of subjective drug effects that evaluate the propensity of drug liking, positive and reinforcing effects, willingness to take drug again, perceived drug value, and pharmacological effects that are similar in nature to drugs with known abuse potential [41]. These measures have been routinely used by regulatory agencies (i.e. FDA, the US Drug Enforcement Administration [DEA], Health Canada) to make drug controlling and scheduling decisions based on a drug’s abuse potential profile. Furthermore, these measures are recognized surrogates to approximate potential real-world abuse of a drug and are recommended in the FDA’s 2017 guidance for the assessment of abuse potential [32]. Approximately 10-point difference between drugs was reported to be clinically meaningful for Drug Liking [42] and High Emax scores [43]. In a study of opioid medications, reductions in Overall Drug Liking Emax were significantly associated with lower rates of nonmedical use [44]. With the exception of pitolisant, all medications currently approved by the FDA for the treatment of narcolepsy (i.e. stimulants, wake-promoting agents, and sodium oxybate) are controlled substances [7]; thus, there has been a need for effective pharmacologic options to treat narcolepsy that have reduced potential for abuse. Methylphenidate [45], dextroamphetamine [46], and mixed amphetamine salts [47] are Schedule II controlled substances (high potential for abuse) and have long been recognized as drugs of abuse [34]. Modafinil [48] and armodafinil [49] are Schedule IV controlled substances (lower potential for abuse). Modafinil was shown to induce behavioral sensitization upon repeated administration in a preclinical animal study [27]. In addition, a positron emission tomography study in healthy volunteers showed modafinil to increase dopamine release in the human nucleus accumbens, a brain region associated with rewarding effects and abuse [50]. In an abuse potential study that included men with a history of polysubstance abuse (including cocaine), drug liking was significantly greater for modafinil (200, 400, or 800 mg) compared with placebo, but the overall pharmacologic profile (including responses on the ARCI Amphetamine scale) indicated lower abuse potential for modafinil compared with methylphenidate [51]. In a clinical trial in which modafinil was compared with pitolisant, 10% of patients in the modafinil group (but none in the pitolisant group) displayed amphetamine-like withdrawal symptoms [28]. Another dopaminergic agent—solriamfetol, a dopamine, and norepinephrine reuptake inhibitor—was recently approved by the FDA for the treatment of EDS in patients with narcolepsy or obstructive sleep apnea [52]. In an abuse potential study of adults with a history of recreational polydrug use (including a stimulant), drug liking was significantly greater for solriamfetol (300, 600, and 1200 mg) compared with placebo [40]. Overall, the abuse potential of solriamfetol appeared to be similar to or lower than that of phentermine [40]. Solriamfetol is classified as a Schedule IV controlled substance [52]. The sodium oxybate oral solution approved for the treatment of narcolepsy is a Schedule III controlled substance (abuse potential less than Schedule I or II but more than Schedule IV); it is the sodium salt of gamma-hydroxybutyrate (GHB), a Schedule I controlled substance (high or very high potential for abuse) [19]. In a study of men and women with a history of sedative/hypnotic abuse, drug liking was significantly greater for sodium oxybate (at doses up to 18 g/70 kg) compared with placebo; overall, the abuse potential of sodium oxybate was found to be greater than that of triazolam but less than that of pentobarbital [53]. In a study of nondependent, recreational users of both ethanol and other sedative-hypnotics, drug liking was significantly greater for sodium oxybate (at doses up to 10 g/70 kg) compared with placebo; drug liking for sodium oxybate was similar to that for alcohol, whereas willingness to take the drug again was significantly greater for sodium oxybate [54]. By contrast, the results of this study (which found that drug liking was similar for pitolisant and placebo) indicate that pitolisant was not associated with a profile suggesting a potential for recreational drug use or abuse. Based on these findings, along with preclinical data, pitolisant was approved by the FDA without being scheduled as a controlled substance by the DEA. The minimal abuse potential observed for pitolisant, showing similarity to placebo, is consistent with its mechanism of action, which differs from that of other products used in the treatment of narcolepsy [55]. Pitolisant functions as an antagonist/inverse agonist at H3 autoreceptors, which increases the synthesis and release of histamine and enhances histaminergic signaling in the brain [56]. Studies in animals have shown that histaminergic signaling in the brain promotes wakefulness and suppresses non-REM and REM sleep [57]. Histamine activates wake-promoting neurons including norepinephrine neurons in the locus coeruleus, acetylcholine neurons in the pons and forebrain, and serotonin neurons in the dorsal raphe nucleus [58]; however, as noted above, histamine does not increase dopamine release in the nucleus accumbens [24, 27]. No cases of drug withdrawal syndrome were reported in the clinical development program for pitolisant in narcolepsy (8 studies), including during the 1 week placebo washout phase at the end of the pivotal trials [28, 29] or in patients who discontinued from the 1 year, open-label, long-term extension study (data on file). Furthermore, pitolisant partially counteracts the stimulant properties of cocaine, as demonstrated in an animal study [27]. Considering the preclinical and clinical evidence, pitolisant has a different profile than psychostimulants. The pharmacokinetic characteristics of pitolisant observed in this study of recreational stimulant users were consistent with those reported in previous studies [59]. Pitolisant is rapidly absorbed after oral administration and reaches peak serum concentration in approximately 3 hr [59]; median tmax was 3.0 hr for pitolisant 35.6 mg and 2.0 hr for pitolisant 213.6 mg in this study. Pitolisant exposure increases in a dose-dependent fashion [30] and was approximately 8 times greater for the 213.6 mg dose relative to the 35.6 mg dose in this study. Consistent with the absence of any correlation or trend between the pharmacokinetic concentrations (i.e. Cmax) and pharmacodynamic effects (e.g. Drug Liking Emax) of pitolisant, no concentration-dependent abuse potential was observed in this study. Pitolisant was well-tolerated in the study population of recreational stimulant users. The overall incidence of adverse events was comparable for pitolisant 35.6 mg and placebo, and there were no study discontinuations due to adverse events. Headache was the most common adverse event reported after administration of pitolisant in this study; in studies of patients with narcolepsy, the most common adverse events associated with pitolisant were headache, insomnia, and nausea [22, 31]. Unlike phentermine, pitolisant did not produce any clinically significant increases in blood pressure or heart rate. Limitations of this study primarily concern the generalizability of the results. This study involved single-dose administration of pitolisant in a highly controlled setting to a relatively small population of nondependent stimulant users. Although this design is consistent with FDA guidance and results are considered an accurate predictor of abuse potential, generalizability to other populations or settings may be limited. Conclusions Pitolisant, a first-in-class medication with a novel mechanism of action, increases histaminergic signaling in the brain and has demonstrated efficacy in the treatment of adult patients with narcolepsy, with or without cataplexy. In this study of recreational stimulant users, pitolisant (at the recommended therapeutic dose of 35.6 mg and a supratherapeutic dose of 213.6 mg) produced pharmacodynamic responses that demonstrated significantly lower abuse potential compared with phentermine. In addition, the response profile of pitolisant for maximum Drug Liking (“at this moment”), Overall Drug Liking, and willingness to Take Drug Again were similar to placebo, demonstrating a lack of reinforcing drug effects. Pitolisant, especially at the therapeutic dose of 35.6 mg, showed no signals suggestive of abuse, which contrasts with the other classes of drugs currently available to treat patients with narcolepsy. Given the public health crisis related to abuse, misuse, and diversion of prescription drugs (which includes both opioids and stimulants), a new treatment with minimal risk of abuse is an important therapeutic option for healthcare professionals to treat patients with narcolepsy. Acknowledgments Technical editorial and medical writing assistance was provided under the direction of the authors by Nancy Holland, PhD, Synchrony Medical Communications, LLC, West Chester, PA, and funded by Harmony Biosciences, LLC, Plymouth Meeting, PA. Funding This study was funded by Bioprojet Pharma, Paris, France. Conflict of interest statement. Dr Setnik was an employee of INC Research/inVentiv Health (now Syneos Health) at the time this study was conducted. Dr McDonnell and Ms Mills are employees of Syneos Health. Dr Scart-Grès is an employee of Bioprojet Pharma. Dr Schwartz is a cofounder of Bioprojet Pharma. Dr Robert is an employee of Bioprojet Biotech. Dr Dayno is an employee of Harmony Biosciences, LLC. Work Performed: INC Research Toronto, Inc., Toronto, Ontario, Canada (now part of Syneos Health) References 1. American Academy of Sleep Medicine. The International Classification of Sleep Disorders . 3rd ed. Darien, IL : American Academy of Sleep Medicine ; 2014 . 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Webster, Lucy; Costafreda Gonzalez, Sergi; Stringer, Aisling; Lineham, Amy; Budgett, Jessica; Kyle, Simon; Barber, Julie; Livingston, Gill

doi: 10.1093/sleep/zsz251pmid: 31633188

Abstract Study Objectives Sleep disturbances are a feature in people living with dementia, including getting up during the night, difficulty falling asleep, and excessive daytime sleepiness and may precipitate a person with dementia moving into residential care. There are varying estimates of the frequency of sleep disturbances, and it is unknown whether they are a problem for the individual. We conducted the first systematic review and meta-analysis on the prevalence and associated factors of sleep disturbances in the care home population with dementia. Methods We searched Embase, MEDLINE, and PsycINFO (29/04/2019) for studies of the prevalence or associated factors of sleep disturbances in people with dementia living in care homes. We computed meta-analytical estimates of the prevalence of sleep disturbances and used meta-regression to investigate the effects of measurement methods, demographics, and study characteristics. Results We included 55 studies of 22,780 participants. The pooled prevalence on validated questionnaires of clinically significant sleep disturbances was 20% (95% confidence interval, CI 16% to 24%) and of any symptom of sleep disturbance was 38% (95% CI 33% to 44%). On actigraphy using a cutoff sleep efficiency of <85% prevalence was 70% (95% CI 55% to 85%). Staff distress, resident agitation, and prescription of psychotropic medications were associated with sleep disturbances. Studies with a higher percentage of males had a higher prevalence of sleep disturbance. Conclusions Clinically significant sleep disturbances are less common than those measured on actigraphy and are associated with residents and staff distress and the increased prescription of psychotropics. Actigraphy appears to offer no benefit over proxy reports in this population. dementia, actigraphy, aging; insomnia Statement of Significance Our findings show that 20% of care home residents with dementia are having clinically significant sleep problems when measured on validated informant questionnaires, but that this goes up to 70% when sleep disturbances are measured using actigraphy. This highlights that the prevalence of sleep disturbances varies greatly depending on how they are measured, highlighting the need for improvement of measurement in this population. In addition, sleep disturbances seem to be more common in men with dementia. These disturbances do seem to affect the individuals themselves with dementia in terms of being related to increased prescriptions of psychotropic medications and agitation, and they distress the staff who care for them, and therefore need evidence-based recommendations on how they should be managed. Introduction There are currently 50 million people worldwide living with dementia, and this is projected to increase to 152 million by 2050 [1]. People living with dementia often have sleep disturbances including difficulty falling asleep, nighttime awakening and wandering, and excessive daytime sleepiness [2]. Sleep disturbances impact family carers, who report that being woken during the night is the most distressing sleep disturbance [3]. Families may be unable to continue caring at home, and thus people with dementia who have disturbed sleep are more likely to move into a care home [4, 5], which in turn increases the cost of care [6]. Sleep disturbances, therefore, may be highly prevalent in people with dementia who live in care homes, although individual studies vary markedly in their findings. A previous systematic review on the prevalence of neuropsychiatric symptoms in dementia [7] included three studies of sleep disturbance prevalence in nursing homes [8–10], but no meta-analysis has been conducted. A second systematic review and meta-analysis reported the prevalence of sleep disturbances in people with Alzheimer’s disease as 39% when measured via validated questionnaires [11]; however, most participants in the included studies lived in the community. Sleep disturbances in people with dementia are often measured by validated proxy questionnaires as dementia can impact an individual’s ability to accurately recall their sleep, particularly in the care home population where dementia is often more severe than in the community [12]. More recently, actigraphy has been used where an accelerometer, typically worn on the wrist, measures the intensity of body movement to infer sleep and wake states [13]. There is no consensus on the best way to measure sleep disturbances in people with dementia, with previous studies comparing both methods in community-dwelling people with dementia and finding differing results [14, 15]. Comparing them may help to illuminate their meaning when measuring sleep disturbances in the care home population with dementia. There is, to our knowledge, no previous systematic review focusing on sleep disturbances in the care home population with dementia. Thus, we aimed to produce the first systematic review and meta-analysis of the prevalence of sleep disturbances in people with dementia living in care homes and to explore what factors are associated with these sleep disturbances. Method Search strategy and selection criteria We followed the PRISMA guidelines [16] and registered the protocol on PROSPERO (CRD42017080312). We searched Embase, MEDLINE, and PsycINFO from database inception to November 2, 2017, and updated the search until April 29, 2019. We used the search terms (“Dementia” OR “Alzheimer*”) AND (“sleep*” OR “insomnia” OR “circadian” OR “night*” OR “neuropsychiatric”) AND (“care home*” OR “residential” “home*” OR “nursing home*” OR “residential care” OR “long-term care” OR “long term care” OR “institution”), with no restriction on language. We searched reference lists of included papers and relevant systematic reviews and emailed authors of included papers for further relevant papers. We included quantitative studies that reported: □ the prevalence of, or factors associated with, sleep disturbances in people with dementia living in care homes; □ results reported separately if they included community-dwelling people with dementia or people without dementia; □ sleep disturbances by validated questionnaires or actigraphy measures (e.g. nighttime sleep efficiency); □ cross-sectional data (in longitudinal studies we used baseline data only). We excluded studies if: □ sleep disturbances were an inclusion or exclusion criteria; □ study participants were reported as having sleep-related breathing or movement disorders rather than sleep disturbances; □ the study reported only rest activity or circadian rhythm variables rather than sleep disturbances; □ data were collected during admission to a care home. We contacted authors if we needed additional data or information to include a study. We defined a care home as a long-term residential or nursing care facility in the community, which provides 24-hour personal or nursing care for people with illness, disability, or dependence [17]. We characterized sleep disturbances as any well-defined disturbance in the sleep process, including difficulty falling asleep, reduced duration of sleep, waking or getting up during the nighttime, and excessive daytime sleepiness [2]. One researcher (LW) screened all titles and abstracts, and two researchers (LW and AL or AS) independently screened full texts and reached a consensus on included papers. We extracted data including country, setting, study design, sample size, dementia type, dementia severity, how dementia was defined, mean age, percentage of females, measure of sleep disturbances, measures of potential associated factors, and reported statistical results of prevalence and/or associated factors, and if any analyses were adjusted. Two researchers (LW and JBu) independently assessed the quality of studies using the Mixed Methods Appraisal Tool (MMAT)—Version 2011 [18] criteria for quantitative descriptive studies. This assesses studies on four elements with scores ranging from 0 to 4 and higher scores indicating higher quality studies: Is the sampling strategy relevant to address the quantitative research question (standardized method of sampling)? Is the sample representative of the population under study (e.g. whole care home population with dementia)? Are measurements appropriate (clear origin, or validity known, or standard instrument)? Is there an acceptable response rate (≥60%)? Data analysis We separated prevalence data into three methods of measurement: (1) informant rated validated questionnaires for clinically significant cases of sleep disturbances, (2) informant rated validated questionnaire for any symptoms of sleep disturbances, and (3) actigraphy measured sleep disturbances. We used STATA version 14 and the Metaprop command [19] that uses inverse-variance weights to conduct random effect meta-analyses of pooled prevalence. We conducted meta-analyses separately for each category of measurement, calculated confidence intervals (CI) using the exact method [20], and used the I2 statistic to assess heterogeneity (≥75% indicating high heterogeneity). We conducted post hoc random effects meta-regressions using the Metareg command to investigate if study characteristics and participant demographics could explain the high heterogeneity in prevalence estimates. We combined data from all three meta-analyses and explored the category of prevalence measurement as a single covariate, and then as the first covariate in a further five meta-regressions, which included looking at the study covariates of age, percentage of females, dementia type (Alzheimer’s disease vs mixed/not specified), publication year, and study quality. We assessed publication bias in the studies where data were meta-analyzed using funnel plots. We deemed studies that did not have an acceptable response rate (of 60% and above as defined by the MMAT), or who did not report the response rate, as lower quality studies and used this as criteria for sensitivity analyses. We provide a narrative synthesis for factors associated with sleep disturbances reported in individual studies. Results We screened 7901 references (Figure 1, PRISMA diagram), of which 58 papers comprising 55 studies met the inclusion criteria (Table 1). Fifty-one studies reported the prevalence of sleep disturbances [9, 10, 21–69], and 20 studies reported factors associated with sleep disturbances [24, 27, 30, 32, 35, 37, 38, 40, 41, 45, 49, 54, 61, 62, 64, 69–73]. Sixteen studies provided extra data when contacted [21, 28, 34–36, 39, 41, 47, 48, 50, 57–60, 62, 63]. Forty of the studies took place in Europe including studies in Denmark [60], France [26–28, 39], Germany [36, 47, 48, 64, 69], Italy [33, 43, 67], Netherlands [10, 22, 23, 30, 31, 45, 68], Norway [9, 21, 24, 25, 29, 34, 35, 44, 61], Poland [41, 71, 72], Portugal [50], Spain [32, 52], and Sweden [56, 57, 63, 66]. Others took place in Australia [42, 53, 54, 62], China [65, 73], Japan [40, 59, 70], South Korea [38, 49], Taiwan [37, 51], the United States [46], and Brazil [55]. Table 1. Study characteristics and quality ratings Study reference . N (N sleep disturbances measured in) . Number of care homes . Dementia type . Dementia severity . Females (%) . Mean age . Measure of sleep disturbances . Study quality . 1 . 2 . 3 . 4 . Total . Aasmul et al. 2014 [35, 96] 352 (341) 18 Not specified Advanced 74.4 86.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Appelhof et al. 2019 [68] 274 (227) 13 All young onset; AD 43.8%; VaD 10.6%; FTD 29.2%; mixed AD/VaD 5.1%; LBD/PPD 1.8%; alcohol-related dementia 2.2%; other 7.3% GDS mild 15.7%; moderate 20.8%; severe 62.8% 49.6 63.8 NPI sleep item ✓ ✘ ✓ ? 2 Aupperle et al. 2004 [46] 173 (134) 29 All AD Moderate to severe 81.5 82.6 NPI sleep item ✓ ✘ ✓ ? 2 Balzotti et al. 2018 [67] 30 (30) 1 57% AD, 43% VaD Mean MMSE score 7.6 83.3 85.7 NPI sleep item ✓ ✘ ✓ ? 3 Bergh et al. 2011 [9] 169 (169) 7 Not specified CDR mild 20.71%; moderate 37.27%; severe 42.01% 69.2 84.9 NPI sleep item ✓ ✓ ✓ ✓ 4 Bergh et al. 2012 [21] 620 (619) 32 Not specified CDR mild 22%; moderate 29%; severe 50% 71.0 84.7 NPI sleep item ✓ ✓ ✓ ✓ 4 Bidzan et al. 2006 [71] 31 (31) 2 All AD Mean MMSE score 14.8 Not specified 79.2 NPI sleep item ✓ ✘ ✓ ✓ 3 Bidzan et al. 2008 [72] 58 (58) 3 All AD MMSE score between 11 and 23 Not specified 77.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Bidzan et al. 2014 [41] 48 (48) 1 All AD Mean MMSE score 15.96 Not specified 70.0 NPI sleep item ✓ ✘ ✓ ? 2 Bjork et al. 2018 [63] 2827 (2827) not specified Not specified Mild 37.7%; moderate 38.6%; severe 23.6% 69.9 85.6 NPI sleep item ✓ ✓ ✓ ✓ 4 Blytt et al. 2017 [24] 1535 (1535) 64 Not specified Mild 35%; moderate 29%; severe 36% 75.7 85.3 NPI sleep item ✓ ✓ ✓ ✓ 4 Blytt et al. 2018 [25] 106 (106) 47 Not specified Mean MMSE score 7.6 76.0 85.5 Actigraphy ✓ ✘ ✓ ✓ 3 Boada et al. 2006 [52] 79 (79) 2 All AD Mild 26.6%; moderate 35.4%; moderately severe 19%; severe 19% 73.4 83.7 BEHAVE-AD diurnal rhythm disturbance item ✓ ✓ ✓ ? 3 Brodaty et al. 2001 [53] 505 (505) 11 All AD Not specified 74.1 83.4 BEHAVE-AD diurnal rhythm disturbance item ✓ ✓ ✓ ✓ 4 Brown et al. 2015 [62] 22 (22) 4 Not specified Not specified 73.0 85.6 Actigraphy ✓ ✘ ✓ ✘ 2 Castineiras et al. 2012 [32] 212 (212) 6 AD 26.9%; VaD 18.9%; mixed 7.1%; DLB 0.9%; FTD 0.5%; unknown 45.8% Mild 14.6%; moderate 16.5%; moderately severe 35.4%; severe 33.5% 73.1 85.7 NPI sleep item ✓ ✓ ✓ ? 3 Chen et al 2018 [65] 112 (112) 1 Not specified CDR mild 10.7%; moderate 39.3%; severe 50.0% 63.4 81.2 NPI sleep item ✓ ✘ ✓ ✘ 2 Cheng et al. 2009 [51] 63 (63) not specified All AD Mean MMSE 10.3 60.7 81.9 BEHAVE-AD diurnal rhythm disturbance ✓ ✘ ✓ ? 2 Cunha et al. 1985 [55] 227 (227) 10 Not specified Severe 77.5%; mild 22.5% 84.7 75.6 CGBRS sleep problem item ✓ ✘ ✓ ✓ 3 Dechamps et al. 2008 [26] 109 (109) 4 Not specified MMSE ≥24 9%, MMSE between 10 and 23 61.5% MMSE<10 29.5% 76.1 83.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Dichter et al. 2015 [48] 154 (154) 9 Not specified FAST stages 2-6 63.6%; stage 7 36.4% 83.1 83.1 NPI sleep item ✓ ✓ ✓ ✓ 4 Ferreira et al. 2016 [50] 97 (97) 3 Not specified Mean MMSE score 22 90.0 81.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Fetveit et al. 2002 [61, 97] 29 (25) 1 Majority AD, number not specified Mean MMSE score 13.4 not specified 85.4 Actigraphy ✓ ✓ ✓ ✓ 4 Gustafsson et al. 2016 [56] 3482 (3482) not specified Not specified Mean GCS score 11.8 in 2007; 12.4 in 2013 69.4 84.8 MDDAS interrupted night sleep item ✓ ✘ ✓ ✓ 3 Hsieh et al. 2009 [37] 103 (103) 10 AD 50.5%; VaD 49.5% Mean CDR score 1.38 AD; 1.33 VD 47.6 72.2 NPI sleep item ✓ ✘ ✓ ? 2 Koopmans et al. 2009 [23] 39 (39) 2 70% AD; 10% VaD; 20% not specified Advanced 90.0 83.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Krolak‐Salmon et al. 2016 [28] 211 (211) not specified All AD Not specified 61.1 84.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Kume et al. 2016 [59] 17 (17) 4 AD 58.8%; VaD 41.2% Mean CDR score 1.4 58.8 82.2 Actigraphy ✓ ✘ ✓ ? 2 Lam et al. 2006 [73] 125 (125) 3 AD 43.2%; VaD 24.8%; not specified 32.0% Not specified 58.4 82.0 NPI sleep item ✓ ✘ ✓ ? 2 Lee et al. 2015 [38] 529 (529) 20 Not specified Not specified 77.5 81.2 NPI sleep item ✓ ? ✓ ? 2 Lichtwarck et al. 2018 [34] 229 (229) 33 Not specified Mild 4.4%; moderate 25.8; severe 69.5% 60.3 83.2 NPI sleep item ✓ ✘ ✓ ✓ 3 Lövheim et al. 2009 [57] 1826 (1826) not specified Not specified Mean GCS score 11.5 68.9 82.8 MDDAS interrupted night sleep item ✓ ✓ ✓ ✓ 4 Malara et al. 2016 [43] 201 (201) 10 VaD 61.9%; AD 29.3% Mild 11.1%; moderate 27.1%; severe 61.9% 66.3 83.9 NPI sleep item ✓ ✓ ✓ ✓ 4 Melander et al. 2018 [66] 14 (14) 5 VaD 50%, AD 14.3%, FTD 14.3%, mixed 14.3%, LBD 7.1% All GDS score 6 78.6 81.5 NPI sleep item ✓ ✘ ✓ ✓ 3 Mulders et al. 2016 [22] 230 (225) 8 All young onset; AD 32.0%; VaD 12.9%; FTD 16.0%; AlcD 17.8%; Other 21.3% GDS score 2-4 17.3%; score 5 24.4%; score 6 30.2%; score 7 28.0% 46.7 60.1 NPI sleep item ✓ ✘ ✓ ✓ 3 Ozaki et al. 2017 [40] 312 (200) 10 AD 35.9%; VaD 19.6%; other 9.9%; not specified 34.6% Mild 28.8%; moderate 54.8%; severe 16.3% 82.4 87.6 NPI sleep item ✓ ✓ ✓ ✘ 3 Palm et al. 2018 [64] 1132 (1132) 140 Not specified DSS mean score 9.5 79.2 83.4 NPI sleep item ✓ ✓ ✓ ✘ 3 Prado-Jean et al. 2010 [27] 319 (319) 17 Not specified Mild 24.4%; moderate 50.2%; severe 25.4% 76.5 85.6 NPI sleep item ✓ ✘ ✓ ✓ 3 Reuther et al. 2016 [47] 840 (840) 40 Not specified FAST scale mild 3.8%; moderate 63.5%; severe 32.7% 76.0 85.0 NPI sleep item ✓ ✘ ✓ ? 2 Ricci et al. 2009 [33] 173 (157) 1 AD 44.5%; VaD 30.6%; mixed 17.3%; ns 10.8%; DLB 1.7%; PDD 1.2%; PPA 1.2% Not specified 74.9 79.9 NPI sleep item ✓ ? ✓ ? 2 Ruths et al. 2008 [44] 55 (55) 13 Not specified Not specified 78.2 84.1 NPI sleep item ✓ ✘ ✓ ? 2 Schüssler et al. 2015 [58] 178 (178) 175 AD 52%; VaD 15.8%; other 19.2%; ns 13% Mean MMSE 16.5 83.1 83.5 CDS Day-/night pattern item ✓ ✘ ✓ ✓ 3 Seidl et al. 2007 [36] 128 (128) not specified AD 77.3%; VaD or mixed 17.2%; Other 5.5% GDS score ≤3 26%; score 4 14%; score 5 19%; score 6 30%; score 7 11% 81.4 84.8 NPI sleep item ✓ ? ✓ ? 2 Selbaek et al. 2014 [29] 931 (931) 26 Not specified CDR 1 25%, 2 33%, 3 42% 74.0 84.5 NPI sleep item ✓ ✓ ✓ ✓ 4 Song et al. 2015 [49] 423 (423) 6 Not specified Mild 9.1%; moderate 21.7%; severe 69.2% 82.0 83.3 NPI sleep item ✓ ✘ ✓ ? 2 Suzuki et al. 2017 [70] 226 (226) not specified AD 47.0%; VaD 14.0%; LBD 1.0%; FTD 1.5%; mixed 15.5%; other 7.5%; not specified 13.5% Mean MMSE score 9.53 76.6 85.1 NPI sleep item ? ✓ ✓ ✘ 2 Tan et al. 2015 [54] 169 (169) 6 Not specified Not specified 77.5 87.5 ESS ✓ ✘ ✓ ✓ 3 Thodberg et al. 2016 [60] 100 (70) 4 Not specified Not specified 69.0 85.5 Actigraphy ✓ ? ✓ ? 2 Tournier et al. 2017 [39] 13 (11) 1 Not specified 36% moderate; 64% severe 90.9 82.9 NPI sleep item ✓ ✓ ✓ ✓ 4 Wetzels et al. 2010 [10] 290 (117) 9 AD 35.0%; VaD 11.1%; mixed AD/VaD 1.7%; other 52.1 GDS score 4 11.1; score 5 26.5; score 6 33.3; score 7 29.1% 71.7 81.7 NPI sleep item ✓ ✘ ✓ ? 2 Wilfling et al. 2019 [69] 1187 (1187) 38 Not specified Not specified 74.0 83.0 SDI ✓ ✓ ✓ ✓ 4 Wu et al. 2009 [42] 93 (93) 7 Not specified GDS score 4 2.2%; Score 5 12.9%; score 6 55.9%; score 7 29.0% 76.3 88.6 NPI sleep item ✓ ✘ ✓ ✘ 2 Zuidema et al. 2006 [31] 59 (59) 2 Not specified Not specified 83.0 82.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Zuidema et al. 2007 [30, 98] 1437 (1437) 27 Not specified Mild 4%; moderate 20%; moderately severe 51%; severe 26% 81.0 83.0 NPI sleep item ✓ ✓ ✓ ✓ 4 Zwijsen et al. 2014 [45] 432 (432) 17 AD 47.7%; VaD 19.0%; mixed AD/VaD 15.5%; DLB 3.7%; FTD 2.5%; other 8.6% GDS score ≤3 1%; score 4 4%; score 5 21%; score 6 62%; score 7 12% 69.9 83.3 NPI sleep item ✓ ✓ ✓ ✓ 4 Study reference . N (N sleep disturbances measured in) . Number of care homes . Dementia type . Dementia severity . Females (%) . Mean age . Measure of sleep disturbances . Study quality . 1 . 2 . 3 . 4 . Total . Aasmul et al. 2014 [35, 96] 352 (341) 18 Not specified Advanced 74.4 86.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Appelhof et al. 2019 [68] 274 (227) 13 All young onset; AD 43.8%; VaD 10.6%; FTD 29.2%; mixed AD/VaD 5.1%; LBD/PPD 1.8%; alcohol-related dementia 2.2%; other 7.3% GDS mild 15.7%; moderate 20.8%; severe 62.8% 49.6 63.8 NPI sleep item ✓ ✘ ✓ ? 2 Aupperle et al. 2004 [46] 173 (134) 29 All AD Moderate to severe 81.5 82.6 NPI sleep item ✓ ✘ ✓ ? 2 Balzotti et al. 2018 [67] 30 (30) 1 57% AD, 43% VaD Mean MMSE score 7.6 83.3 85.7 NPI sleep item ✓ ✘ ✓ ? 3 Bergh et al. 2011 [9] 169 (169) 7 Not specified CDR mild 20.71%; moderate 37.27%; severe 42.01% 69.2 84.9 NPI sleep item ✓ ✓ ✓ ✓ 4 Bergh et al. 2012 [21] 620 (619) 32 Not specified CDR mild 22%; moderate 29%; severe 50% 71.0 84.7 NPI sleep item ✓ ✓ ✓ ✓ 4 Bidzan et al. 2006 [71] 31 (31) 2 All AD Mean MMSE score 14.8 Not specified 79.2 NPI sleep item ✓ ✘ ✓ ✓ 3 Bidzan et al. 2008 [72] 58 (58) 3 All AD MMSE score between 11 and 23 Not specified 77.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Bidzan et al. 2014 [41] 48 (48) 1 All AD Mean MMSE score 15.96 Not specified 70.0 NPI sleep item ✓ ✘ ✓ ? 2 Bjork et al. 2018 [63] 2827 (2827) not specified Not specified Mild 37.7%; moderate 38.6%; severe 23.6% 69.9 85.6 NPI sleep item ✓ ✓ ✓ ✓ 4 Blytt et al. 2017 [24] 1535 (1535) 64 Not specified Mild 35%; moderate 29%; severe 36% 75.7 85.3 NPI sleep item ✓ ✓ ✓ ✓ 4 Blytt et al. 2018 [25] 106 (106) 47 Not specified Mean MMSE score 7.6 76.0 85.5 Actigraphy ✓ ✘ ✓ ✓ 3 Boada et al. 2006 [52] 79 (79) 2 All AD Mild 26.6%; moderate 35.4%; moderately severe 19%; severe 19% 73.4 83.7 BEHAVE-AD diurnal rhythm disturbance item ✓ ✓ ✓ ? 3 Brodaty et al. 2001 [53] 505 (505) 11 All AD Not specified 74.1 83.4 BEHAVE-AD diurnal rhythm disturbance item ✓ ✓ ✓ ✓ 4 Brown et al. 2015 [62] 22 (22) 4 Not specified Not specified 73.0 85.6 Actigraphy ✓ ✘ ✓ ✘ 2 Castineiras et al. 2012 [32] 212 (212) 6 AD 26.9%; VaD 18.9%; mixed 7.1%; DLB 0.9%; FTD 0.5%; unknown 45.8% Mild 14.6%; moderate 16.5%; moderately severe 35.4%; severe 33.5% 73.1 85.7 NPI sleep item ✓ ✓ ✓ ? 3 Chen et al 2018 [65] 112 (112) 1 Not specified CDR mild 10.7%; moderate 39.3%; severe 50.0% 63.4 81.2 NPI sleep item ✓ ✘ ✓ ✘ 2 Cheng et al. 2009 [51] 63 (63) not specified All AD Mean MMSE 10.3 60.7 81.9 BEHAVE-AD diurnal rhythm disturbance ✓ ✘ ✓ ? 2 Cunha et al. 1985 [55] 227 (227) 10 Not specified Severe 77.5%; mild 22.5% 84.7 75.6 CGBRS sleep problem item ✓ ✘ ✓ ✓ 3 Dechamps et al. 2008 [26] 109 (109) 4 Not specified MMSE ≥24 9%, MMSE between 10 and 23 61.5% MMSE<10 29.5% 76.1 83.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Dichter et al. 2015 [48] 154 (154) 9 Not specified FAST stages 2-6 63.6%; stage 7 36.4% 83.1 83.1 NPI sleep item ✓ ✓ ✓ ✓ 4 Ferreira et al. 2016 [50] 97 (97) 3 Not specified Mean MMSE score 22 90.0 81.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Fetveit et al. 2002 [61, 97] 29 (25) 1 Majority AD, number not specified Mean MMSE score 13.4 not specified 85.4 Actigraphy ✓ ✓ ✓ ✓ 4 Gustafsson et al. 2016 [56] 3482 (3482) not specified Not specified Mean GCS score 11.8 in 2007; 12.4 in 2013 69.4 84.8 MDDAS interrupted night sleep item ✓ ✘ ✓ ✓ 3 Hsieh et al. 2009 [37] 103 (103) 10 AD 50.5%; VaD 49.5% Mean CDR score 1.38 AD; 1.33 VD 47.6 72.2 NPI sleep item ✓ ✘ ✓ ? 2 Koopmans et al. 2009 [23] 39 (39) 2 70% AD; 10% VaD; 20% not specified Advanced 90.0 83.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Krolak‐Salmon et al. 2016 [28] 211 (211) not specified All AD Not specified 61.1 84.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Kume et al. 2016 [59] 17 (17) 4 AD 58.8%; VaD 41.2% Mean CDR score 1.4 58.8 82.2 Actigraphy ✓ ✘ ✓ ? 2 Lam et al. 2006 [73] 125 (125) 3 AD 43.2%; VaD 24.8%; not specified 32.0% Not specified 58.4 82.0 NPI sleep item ✓ ✘ ✓ ? 2 Lee et al. 2015 [38] 529 (529) 20 Not specified Not specified 77.5 81.2 NPI sleep item ✓ ? ✓ ? 2 Lichtwarck et al. 2018 [34] 229 (229) 33 Not specified Mild 4.4%; moderate 25.8; severe 69.5% 60.3 83.2 NPI sleep item ✓ ✘ ✓ ✓ 3 Lövheim et al. 2009 [57] 1826 (1826) not specified Not specified Mean GCS score 11.5 68.9 82.8 MDDAS interrupted night sleep item ✓ ✓ ✓ ✓ 4 Malara et al. 2016 [43] 201 (201) 10 VaD 61.9%; AD 29.3% Mild 11.1%; moderate 27.1%; severe 61.9% 66.3 83.9 NPI sleep item ✓ ✓ ✓ ✓ 4 Melander et al. 2018 [66] 14 (14) 5 VaD 50%, AD 14.3%, FTD 14.3%, mixed 14.3%, LBD 7.1% All GDS score 6 78.6 81.5 NPI sleep item ✓ ✘ ✓ ✓ 3 Mulders et al. 2016 [22] 230 (225) 8 All young onset; AD 32.0%; VaD 12.9%; FTD 16.0%; AlcD 17.8%; Other 21.3% GDS score 2-4 17.3%; score 5 24.4%; score 6 30.2%; score 7 28.0% 46.7 60.1 NPI sleep item ✓ ✘ ✓ ✓ 3 Ozaki et al. 2017 [40] 312 (200) 10 AD 35.9%; VaD 19.6%; other 9.9%; not specified 34.6% Mild 28.8%; moderate 54.8%; severe 16.3% 82.4 87.6 NPI sleep item ✓ ✓ ✓ ✘ 3 Palm et al. 2018 [64] 1132 (1132) 140 Not specified DSS mean score 9.5 79.2 83.4 NPI sleep item ✓ ✓ ✓ ✘ 3 Prado-Jean et al. 2010 [27] 319 (319) 17 Not specified Mild 24.4%; moderate 50.2%; severe 25.4% 76.5 85.6 NPI sleep item ✓ ✘ ✓ ✓ 3 Reuther et al. 2016 [47] 840 (840) 40 Not specified FAST scale mild 3.8%; moderate 63.5%; severe 32.7% 76.0 85.0 NPI sleep item ✓ ✘ ✓ ? 2 Ricci et al. 2009 [33] 173 (157) 1 AD 44.5%; VaD 30.6%; mixed 17.3%; ns 10.8%; DLB 1.7%; PDD 1.2%; PPA 1.2% Not specified 74.9 79.9 NPI sleep item ✓ ? ✓ ? 2 Ruths et al. 2008 [44] 55 (55) 13 Not specified Not specified 78.2 84.1 NPI sleep item ✓ ✘ ✓ ? 2 Schüssler et al. 2015 [58] 178 (178) 175 AD 52%; VaD 15.8%; other 19.2%; ns 13% Mean MMSE 16.5 83.1 83.5 CDS Day-/night pattern item ✓ ✘ ✓ ✓ 3 Seidl et al. 2007 [36] 128 (128) not specified AD 77.3%; VaD or mixed 17.2%; Other 5.5% GDS score ≤3 26%; score 4 14%; score 5 19%; score 6 30%; score 7 11% 81.4 84.8 NPI sleep item ✓ ? ✓ ? 2 Selbaek et al. 2014 [29] 931 (931) 26 Not specified CDR 1 25%, 2 33%, 3 42% 74.0 84.5 NPI sleep item ✓ ✓ ✓ ✓ 4 Song et al. 2015 [49] 423 (423) 6 Not specified Mild 9.1%; moderate 21.7%; severe 69.2% 82.0 83.3 NPI sleep item ✓ ✘ ✓ ? 2 Suzuki et al. 2017 [70] 226 (226) not specified AD 47.0%; VaD 14.0%; LBD 1.0%; FTD 1.5%; mixed 15.5%; other 7.5%; not specified 13.5% Mean MMSE score 9.53 76.6 85.1 NPI sleep item ? ✓ ✓ ✘ 2 Tan et al. 2015 [54] 169 (169) 6 Not specified Not specified 77.5 87.5 ESS ✓ ✘ ✓ ✓ 3 Thodberg et al. 2016 [60] 100 (70) 4 Not specified Not specified 69.0 85.5 Actigraphy ✓ ? ✓ ? 2 Tournier et al. 2017 [39] 13 (11) 1 Not specified 36% moderate; 64% severe 90.9 82.9 NPI sleep item ✓ ✓ ✓ ✓ 4 Wetzels et al. 2010 [10] 290 (117) 9 AD 35.0%; VaD 11.1%; mixed AD/VaD 1.7%; other 52.1 GDS score 4 11.1; score 5 26.5; score 6 33.3; score 7 29.1% 71.7 81.7 NPI sleep item ✓ ✘ ✓ ? 2 Wilfling et al. 2019 [69] 1187 (1187) 38 Not specified Not specified 74.0 83.0 SDI ✓ ✓ ✓ ✓ 4 Wu et al. 2009 [42] 93 (93) 7 Not specified GDS score 4 2.2%; Score 5 12.9%; score 6 55.9%; score 7 29.0% 76.3 88.6 NPI sleep item ✓ ✘ ✓ ✘ 2 Zuidema et al. 2006 [31] 59 (59) 2 Not specified Not specified 83.0 82.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Zuidema et al. 2007 [30, 98] 1437 (1437) 27 Not specified Mild 4%; moderate 20%; moderately severe 51%; severe 26% 81.0 83.0 NPI sleep item ✓ ✓ ✓ ✓ 4 Zwijsen et al. 2014 [45] 432 (432) 17 AD 47.7%; VaD 19.0%; mixed AD/VaD 15.5%; DLB 3.7%; FTD 2.5%; other 8.6% GDS score ≤3 1%; score 4 4%; score 5 21%; score 6 62%; score 7 12% 69.9 83.3 NPI sleep item ✓ ✓ ✓ ✓ 4 AD, Alzheimer’s Disease; CGBRS, Crichton Geriatric Behavioural Rating Scale; DSS, Dementia Screening Scale; ESS, Epworth Sleepiness Scale; FAST, Functional Assessment Staging Test; FTD, Frontotemporal dementia; GCS, Gottfries Cognitive Scale; GDS, Global Deterioration Scale; LBD, Lewy Body dementia; MMSE, Mini Mental State Examination; NPI, Neuropsychiatric Inventory; PDD, Parkinson’s disease dementia; PPA, Primary progressive aphasia; SDI, Sleep Disorder Inventory; VaD, Vascular dementia. Open in new tab Table 1. Study characteristics and quality ratings Study reference . N (N sleep disturbances measured in) . Number of care homes . Dementia type . Dementia severity . Females (%) . Mean age . Measure of sleep disturbances . Study quality . 1 . 2 . 3 . 4 . Total . Aasmul et al. 2014 [35, 96] 352 (341) 18 Not specified Advanced 74.4 86.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Appelhof et al. 2019 [68] 274 (227) 13 All young onset; AD 43.8%; VaD 10.6%; FTD 29.2%; mixed AD/VaD 5.1%; LBD/PPD 1.8%; alcohol-related dementia 2.2%; other 7.3% GDS mild 15.7%; moderate 20.8%; severe 62.8% 49.6 63.8 NPI sleep item ✓ ✘ ✓ ? 2 Aupperle et al. 2004 [46] 173 (134) 29 All AD Moderate to severe 81.5 82.6 NPI sleep item ✓ ✘ ✓ ? 2 Balzotti et al. 2018 [67] 30 (30) 1 57% AD, 43% VaD Mean MMSE score 7.6 83.3 85.7 NPI sleep item ✓ ✘ ✓ ? 3 Bergh et al. 2011 [9] 169 (169) 7 Not specified CDR mild 20.71%; moderate 37.27%; severe 42.01% 69.2 84.9 NPI sleep item ✓ ✓ ✓ ✓ 4 Bergh et al. 2012 [21] 620 (619) 32 Not specified CDR mild 22%; moderate 29%; severe 50% 71.0 84.7 NPI sleep item ✓ ✓ ✓ ✓ 4 Bidzan et al. 2006 [71] 31 (31) 2 All AD Mean MMSE score 14.8 Not specified 79.2 NPI sleep item ✓ ✘ ✓ ✓ 3 Bidzan et al. 2008 [72] 58 (58) 3 All AD MMSE score between 11 and 23 Not specified 77.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Bidzan et al. 2014 [41] 48 (48) 1 All AD Mean MMSE score 15.96 Not specified 70.0 NPI sleep item ✓ ✘ ✓ ? 2 Bjork et al. 2018 [63] 2827 (2827) not specified Not specified Mild 37.7%; moderate 38.6%; severe 23.6% 69.9 85.6 NPI sleep item ✓ ✓ ✓ ✓ 4 Blytt et al. 2017 [24] 1535 (1535) 64 Not specified Mild 35%; moderate 29%; severe 36% 75.7 85.3 NPI sleep item ✓ ✓ ✓ ✓ 4 Blytt et al. 2018 [25] 106 (106) 47 Not specified Mean MMSE score 7.6 76.0 85.5 Actigraphy ✓ ✘ ✓ ✓ 3 Boada et al. 2006 [52] 79 (79) 2 All AD Mild 26.6%; moderate 35.4%; moderately severe 19%; severe 19% 73.4 83.7 BEHAVE-AD diurnal rhythm disturbance item ✓ ✓ ✓ ? 3 Brodaty et al. 2001 [53] 505 (505) 11 All AD Not specified 74.1 83.4 BEHAVE-AD diurnal rhythm disturbance item ✓ ✓ ✓ ✓ 4 Brown et al. 2015 [62] 22 (22) 4 Not specified Not specified 73.0 85.6 Actigraphy ✓ ✘ ✓ ✘ 2 Castineiras et al. 2012 [32] 212 (212) 6 AD 26.9%; VaD 18.9%; mixed 7.1%; DLB 0.9%; FTD 0.5%; unknown 45.8% Mild 14.6%; moderate 16.5%; moderately severe 35.4%; severe 33.5% 73.1 85.7 NPI sleep item ✓ ✓ ✓ ? 3 Chen et al 2018 [65] 112 (112) 1 Not specified CDR mild 10.7%; moderate 39.3%; severe 50.0% 63.4 81.2 NPI sleep item ✓ ✘ ✓ ✘ 2 Cheng et al. 2009 [51] 63 (63) not specified All AD Mean MMSE 10.3 60.7 81.9 BEHAVE-AD diurnal rhythm disturbance ✓ ✘ ✓ ? 2 Cunha et al. 1985 [55] 227 (227) 10 Not specified Severe 77.5%; mild 22.5% 84.7 75.6 CGBRS sleep problem item ✓ ✘ ✓ ✓ 3 Dechamps et al. 2008 [26] 109 (109) 4 Not specified MMSE ≥24 9%, MMSE between 10 and 23 61.5% MMSE<10 29.5% 76.1 83.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Dichter et al. 2015 [48] 154 (154) 9 Not specified FAST stages 2-6 63.6%; stage 7 36.4% 83.1 83.1 NPI sleep item ✓ ✓ ✓ ✓ 4 Ferreira et al. 2016 [50] 97 (97) 3 Not specified Mean MMSE score 22 90.0 81.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Fetveit et al. 2002 [61, 97] 29 (25) 1 Majority AD, number not specified Mean MMSE score 13.4 not specified 85.4 Actigraphy ✓ ✓ ✓ ✓ 4 Gustafsson et al. 2016 [56] 3482 (3482) not specified Not specified Mean GCS score 11.8 in 2007; 12.4 in 2013 69.4 84.8 MDDAS interrupted night sleep item ✓ ✘ ✓ ✓ 3 Hsieh et al. 2009 [37] 103 (103) 10 AD 50.5%; VaD 49.5% Mean CDR score 1.38 AD; 1.33 VD 47.6 72.2 NPI sleep item ✓ ✘ ✓ ? 2 Koopmans et al. 2009 [23] 39 (39) 2 70% AD; 10% VaD; 20% not specified Advanced 90.0 83.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Krolak‐Salmon et al. 2016 [28] 211 (211) not specified All AD Not specified 61.1 84.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Kume et al. 2016 [59] 17 (17) 4 AD 58.8%; VaD 41.2% Mean CDR score 1.4 58.8 82.2 Actigraphy ✓ ✘ ✓ ? 2 Lam et al. 2006 [73] 125 (125) 3 AD 43.2%; VaD 24.8%; not specified 32.0% Not specified 58.4 82.0 NPI sleep item ✓ ✘ ✓ ? 2 Lee et al. 2015 [38] 529 (529) 20 Not specified Not specified 77.5 81.2 NPI sleep item ✓ ? ✓ ? 2 Lichtwarck et al. 2018 [34] 229 (229) 33 Not specified Mild 4.4%; moderate 25.8; severe 69.5% 60.3 83.2 NPI sleep item ✓ ✘ ✓ ✓ 3 Lövheim et al. 2009 [57] 1826 (1826) not specified Not specified Mean GCS score 11.5 68.9 82.8 MDDAS interrupted night sleep item ✓ ✓ ✓ ✓ 4 Malara et al. 2016 [43] 201 (201) 10 VaD 61.9%; AD 29.3% Mild 11.1%; moderate 27.1%; severe 61.9% 66.3 83.9 NPI sleep item ✓ ✓ ✓ ✓ 4 Melander et al. 2018 [66] 14 (14) 5 VaD 50%, AD 14.3%, FTD 14.3%, mixed 14.3%, LBD 7.1% All GDS score 6 78.6 81.5 NPI sleep item ✓ ✘ ✓ ✓ 3 Mulders et al. 2016 [22] 230 (225) 8 All young onset; AD 32.0%; VaD 12.9%; FTD 16.0%; AlcD 17.8%; Other 21.3% GDS score 2-4 17.3%; score 5 24.4%; score 6 30.2%; score 7 28.0% 46.7 60.1 NPI sleep item ✓ ✘ ✓ ✓ 3 Ozaki et al. 2017 [40] 312 (200) 10 AD 35.9%; VaD 19.6%; other 9.9%; not specified 34.6% Mild 28.8%; moderate 54.8%; severe 16.3% 82.4 87.6 NPI sleep item ✓ ✓ ✓ ✘ 3 Palm et al. 2018 [64] 1132 (1132) 140 Not specified DSS mean score 9.5 79.2 83.4 NPI sleep item ✓ ✓ ✓ ✘ 3 Prado-Jean et al. 2010 [27] 319 (319) 17 Not specified Mild 24.4%; moderate 50.2%; severe 25.4% 76.5 85.6 NPI sleep item ✓ ✘ ✓ ✓ 3 Reuther et al. 2016 [47] 840 (840) 40 Not specified FAST scale mild 3.8%; moderate 63.5%; severe 32.7% 76.0 85.0 NPI sleep item ✓ ✘ ✓ ? 2 Ricci et al. 2009 [33] 173 (157) 1 AD 44.5%; VaD 30.6%; mixed 17.3%; ns 10.8%; DLB 1.7%; PDD 1.2%; PPA 1.2% Not specified 74.9 79.9 NPI sleep item ✓ ? ✓ ? 2 Ruths et al. 2008 [44] 55 (55) 13 Not specified Not specified 78.2 84.1 NPI sleep item ✓ ✘ ✓ ? 2 Schüssler et al. 2015 [58] 178 (178) 175 AD 52%; VaD 15.8%; other 19.2%; ns 13% Mean MMSE 16.5 83.1 83.5 CDS Day-/night pattern item ✓ ✘ ✓ ✓ 3 Seidl et al. 2007 [36] 128 (128) not specified AD 77.3%; VaD or mixed 17.2%; Other 5.5% GDS score ≤3 26%; score 4 14%; score 5 19%; score 6 30%; score 7 11% 81.4 84.8 NPI sleep item ✓ ? ✓ ? 2 Selbaek et al. 2014 [29] 931 (931) 26 Not specified CDR 1 25%, 2 33%, 3 42% 74.0 84.5 NPI sleep item ✓ ✓ ✓ ✓ 4 Song et al. 2015 [49] 423 (423) 6 Not specified Mild 9.1%; moderate 21.7%; severe 69.2% 82.0 83.3 NPI sleep item ✓ ✘ ✓ ? 2 Suzuki et al. 2017 [70] 226 (226) not specified AD 47.0%; VaD 14.0%; LBD 1.0%; FTD 1.5%; mixed 15.5%; other 7.5%; not specified 13.5% Mean MMSE score 9.53 76.6 85.1 NPI sleep item ? ✓ ✓ ✘ 2 Tan et al. 2015 [54] 169 (169) 6 Not specified Not specified 77.5 87.5 ESS ✓ ✘ ✓ ✓ 3 Thodberg et al. 2016 [60] 100 (70) 4 Not specified Not specified 69.0 85.5 Actigraphy ✓ ? ✓ ? 2 Tournier et al. 2017 [39] 13 (11) 1 Not specified 36% moderate; 64% severe 90.9 82.9 NPI sleep item ✓ ✓ ✓ ✓ 4 Wetzels et al. 2010 [10] 290 (117) 9 AD 35.0%; VaD 11.1%; mixed AD/VaD 1.7%; other 52.1 GDS score 4 11.1; score 5 26.5; score 6 33.3; score 7 29.1% 71.7 81.7 NPI sleep item ✓ ✘ ✓ ? 2 Wilfling et al. 2019 [69] 1187 (1187) 38 Not specified Not specified 74.0 83.0 SDI ✓ ✓ ✓ ✓ 4 Wu et al. 2009 [42] 93 (93) 7 Not specified GDS score 4 2.2%; Score 5 12.9%; score 6 55.9%; score 7 29.0% 76.3 88.6 NPI sleep item ✓ ✘ ✓ ✘ 2 Zuidema et al. 2006 [31] 59 (59) 2 Not specified Not specified 83.0 82.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Zuidema et al. 2007 [30, 98] 1437 (1437) 27 Not specified Mild 4%; moderate 20%; moderately severe 51%; severe 26% 81.0 83.0 NPI sleep item ✓ ✓ ✓ ✓ 4 Zwijsen et al. 2014 [45] 432 (432) 17 AD 47.7%; VaD 19.0%; mixed AD/VaD 15.5%; DLB 3.7%; FTD 2.5%; other 8.6% GDS score ≤3 1%; score 4 4%; score 5 21%; score 6 62%; score 7 12% 69.9 83.3 NPI sleep item ✓ ✓ ✓ ✓ 4 Study reference . N (N sleep disturbances measured in) . Number of care homes . Dementia type . Dementia severity . Females (%) . Mean age . Measure of sleep disturbances . Study quality . 1 . 2 . 3 . 4 . Total . Aasmul et al. 2014 [35, 96] 352 (341) 18 Not specified Advanced 74.4 86.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Appelhof et al. 2019 [68] 274 (227) 13 All young onset; AD 43.8%; VaD 10.6%; FTD 29.2%; mixed AD/VaD 5.1%; LBD/PPD 1.8%; alcohol-related dementia 2.2%; other 7.3% GDS mild 15.7%; moderate 20.8%; severe 62.8% 49.6 63.8 NPI sleep item ✓ ✘ ✓ ? 2 Aupperle et al. 2004 [46] 173 (134) 29 All AD Moderate to severe 81.5 82.6 NPI sleep item ✓ ✘ ✓ ? 2 Balzotti et al. 2018 [67] 30 (30) 1 57% AD, 43% VaD Mean MMSE score 7.6 83.3 85.7 NPI sleep item ✓ ✘ ✓ ? 3 Bergh et al. 2011 [9] 169 (169) 7 Not specified CDR mild 20.71%; moderate 37.27%; severe 42.01% 69.2 84.9 NPI sleep item ✓ ✓ ✓ ✓ 4 Bergh et al. 2012 [21] 620 (619) 32 Not specified CDR mild 22%; moderate 29%; severe 50% 71.0 84.7 NPI sleep item ✓ ✓ ✓ ✓ 4 Bidzan et al. 2006 [71] 31 (31) 2 All AD Mean MMSE score 14.8 Not specified 79.2 NPI sleep item ✓ ✘ ✓ ✓ 3 Bidzan et al. 2008 [72] 58 (58) 3 All AD MMSE score between 11 and 23 Not specified 77.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Bidzan et al. 2014 [41] 48 (48) 1 All AD Mean MMSE score 15.96 Not specified 70.0 NPI sleep item ✓ ✘ ✓ ? 2 Bjork et al. 2018 [63] 2827 (2827) not specified Not specified Mild 37.7%; moderate 38.6%; severe 23.6% 69.9 85.6 NPI sleep item ✓ ✓ ✓ ✓ 4 Blytt et al. 2017 [24] 1535 (1535) 64 Not specified Mild 35%; moderate 29%; severe 36% 75.7 85.3 NPI sleep item ✓ ✓ ✓ ✓ 4 Blytt et al. 2018 [25] 106 (106) 47 Not specified Mean MMSE score 7.6 76.0 85.5 Actigraphy ✓ ✘ ✓ ✓ 3 Boada et al. 2006 [52] 79 (79) 2 All AD Mild 26.6%; moderate 35.4%; moderately severe 19%; severe 19% 73.4 83.7 BEHAVE-AD diurnal rhythm disturbance item ✓ ✓ ✓ ? 3 Brodaty et al. 2001 [53] 505 (505) 11 All AD Not specified 74.1 83.4 BEHAVE-AD diurnal rhythm disturbance item ✓ ✓ ✓ ✓ 4 Brown et al. 2015 [62] 22 (22) 4 Not specified Not specified 73.0 85.6 Actigraphy ✓ ✘ ✓ ✘ 2 Castineiras et al. 2012 [32] 212 (212) 6 AD 26.9%; VaD 18.9%; mixed 7.1%; DLB 0.9%; FTD 0.5%; unknown 45.8% Mild 14.6%; moderate 16.5%; moderately severe 35.4%; severe 33.5% 73.1 85.7 NPI sleep item ✓ ✓ ✓ ? 3 Chen et al 2018 [65] 112 (112) 1 Not specified CDR mild 10.7%; moderate 39.3%; severe 50.0% 63.4 81.2 NPI sleep item ✓ ✘ ✓ ✘ 2 Cheng et al. 2009 [51] 63 (63) not specified All AD Mean MMSE 10.3 60.7 81.9 BEHAVE-AD diurnal rhythm disturbance ✓ ✘ ✓ ? 2 Cunha et al. 1985 [55] 227 (227) 10 Not specified Severe 77.5%; mild 22.5% 84.7 75.6 CGBRS sleep problem item ✓ ✘ ✓ ✓ 3 Dechamps et al. 2008 [26] 109 (109) 4 Not specified MMSE ≥24 9%, MMSE between 10 and 23 61.5% MMSE<10 29.5% 76.1 83.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Dichter et al. 2015 [48] 154 (154) 9 Not specified FAST stages 2-6 63.6%; stage 7 36.4% 83.1 83.1 NPI sleep item ✓ ✓ ✓ ✓ 4 Ferreira et al. 2016 [50] 97 (97) 3 Not specified Mean MMSE score 22 90.0 81.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Fetveit et al. 2002 [61, 97] 29 (25) 1 Majority AD, number not specified Mean MMSE score 13.4 not specified 85.4 Actigraphy ✓ ✓ ✓ ✓ 4 Gustafsson et al. 2016 [56] 3482 (3482) not specified Not specified Mean GCS score 11.8 in 2007; 12.4 in 2013 69.4 84.8 MDDAS interrupted night sleep item ✓ ✘ ✓ ✓ 3 Hsieh et al. 2009 [37] 103 (103) 10 AD 50.5%; VaD 49.5% Mean CDR score 1.38 AD; 1.33 VD 47.6 72.2 NPI sleep item ✓ ✘ ✓ ? 2 Koopmans et al. 2009 [23] 39 (39) 2 70% AD; 10% VaD; 20% not specified Advanced 90.0 83.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Krolak‐Salmon et al. 2016 [28] 211 (211) not specified All AD Not specified 61.1 84.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Kume et al. 2016 [59] 17 (17) 4 AD 58.8%; VaD 41.2% Mean CDR score 1.4 58.8 82.2 Actigraphy ✓ ✘ ✓ ? 2 Lam et al. 2006 [73] 125 (125) 3 AD 43.2%; VaD 24.8%; not specified 32.0% Not specified 58.4 82.0 NPI sleep item ✓ ✘ ✓ ? 2 Lee et al. 2015 [38] 529 (529) 20 Not specified Not specified 77.5 81.2 NPI sleep item ✓ ? ✓ ? 2 Lichtwarck et al. 2018 [34] 229 (229) 33 Not specified Mild 4.4%; moderate 25.8; severe 69.5% 60.3 83.2 NPI sleep item ✓ ✘ ✓ ✓ 3 Lövheim et al. 2009 [57] 1826 (1826) not specified Not specified Mean GCS score 11.5 68.9 82.8 MDDAS interrupted night sleep item ✓ ✓ ✓ ✓ 4 Malara et al. 2016 [43] 201 (201) 10 VaD 61.9%; AD 29.3% Mild 11.1%; moderate 27.1%; severe 61.9% 66.3 83.9 NPI sleep item ✓ ✓ ✓ ✓ 4 Melander et al. 2018 [66] 14 (14) 5 VaD 50%, AD 14.3%, FTD 14.3%, mixed 14.3%, LBD 7.1% All GDS score 6 78.6 81.5 NPI sleep item ✓ ✘ ✓ ✓ 3 Mulders et al. 2016 [22] 230 (225) 8 All young onset; AD 32.0%; VaD 12.9%; FTD 16.0%; AlcD 17.8%; Other 21.3% GDS score 2-4 17.3%; score 5 24.4%; score 6 30.2%; score 7 28.0% 46.7 60.1 NPI sleep item ✓ ✘ ✓ ✓ 3 Ozaki et al. 2017 [40] 312 (200) 10 AD 35.9%; VaD 19.6%; other 9.9%; not specified 34.6% Mild 28.8%; moderate 54.8%; severe 16.3% 82.4 87.6 NPI sleep item ✓ ✓ ✓ ✘ 3 Palm et al. 2018 [64] 1132 (1132) 140 Not specified DSS mean score 9.5 79.2 83.4 NPI sleep item ✓ ✓ ✓ ✘ 3 Prado-Jean et al. 2010 [27] 319 (319) 17 Not specified Mild 24.4%; moderate 50.2%; severe 25.4% 76.5 85.6 NPI sleep item ✓ ✘ ✓ ✓ 3 Reuther et al. 2016 [47] 840 (840) 40 Not specified FAST scale mild 3.8%; moderate 63.5%; severe 32.7% 76.0 85.0 NPI sleep item ✓ ✘ ✓ ? 2 Ricci et al. 2009 [33] 173 (157) 1 AD 44.5%; VaD 30.6%; mixed 17.3%; ns 10.8%; DLB 1.7%; PDD 1.2%; PPA 1.2% Not specified 74.9 79.9 NPI sleep item ✓ ? ✓ ? 2 Ruths et al. 2008 [44] 55 (55) 13 Not specified Not specified 78.2 84.1 NPI sleep item ✓ ✘ ✓ ? 2 Schüssler et al. 2015 [58] 178 (178) 175 AD 52%; VaD 15.8%; other 19.2%; ns 13% Mean MMSE 16.5 83.1 83.5 CDS Day-/night pattern item ✓ ✘ ✓ ✓ 3 Seidl et al. 2007 [36] 128 (128) not specified AD 77.3%; VaD or mixed 17.2%; Other 5.5% GDS score ≤3 26%; score 4 14%; score 5 19%; score 6 30%; score 7 11% 81.4 84.8 NPI sleep item ✓ ? ✓ ? 2 Selbaek et al. 2014 [29] 931 (931) 26 Not specified CDR 1 25%, 2 33%, 3 42% 74.0 84.5 NPI sleep item ✓ ✓ ✓ ✓ 4 Song et al. 2015 [49] 423 (423) 6 Not specified Mild 9.1%; moderate 21.7%; severe 69.2% 82.0 83.3 NPI sleep item ✓ ✘ ✓ ? 2 Suzuki et al. 2017 [70] 226 (226) not specified AD 47.0%; VaD 14.0%; LBD 1.0%; FTD 1.5%; mixed 15.5%; other 7.5%; not specified 13.5% Mean MMSE score 9.53 76.6 85.1 NPI sleep item ? ✓ ✓ ✘ 2 Tan et al. 2015 [54] 169 (169) 6 Not specified Not specified 77.5 87.5 ESS ✓ ✘ ✓ ✓ 3 Thodberg et al. 2016 [60] 100 (70) 4 Not specified Not specified 69.0 85.5 Actigraphy ✓ ? ✓ ? 2 Tournier et al. 2017 [39] 13 (11) 1 Not specified 36% moderate; 64% severe 90.9 82.9 NPI sleep item ✓ ✓ ✓ ✓ 4 Wetzels et al. 2010 [10] 290 (117) 9 AD 35.0%; VaD 11.1%; mixed AD/VaD 1.7%; other 52.1 GDS score 4 11.1; score 5 26.5; score 6 33.3; score 7 29.1% 71.7 81.7 NPI sleep item ✓ ✘ ✓ ? 2 Wilfling et al. 2019 [69] 1187 (1187) 38 Not specified Not specified 74.0 83.0 SDI ✓ ✓ ✓ ✓ 4 Wu et al. 2009 [42] 93 (93) 7 Not specified GDS score 4 2.2%; Score 5 12.9%; score 6 55.9%; score 7 29.0% 76.3 88.6 NPI sleep item ✓ ✘ ✓ ✘ 2 Zuidema et al. 2006 [31] 59 (59) 2 Not specified Not specified 83.0 82.0 NPI sleep item ✓ ✘ ✓ ✓ 3 Zuidema et al. 2007 [30, 98] 1437 (1437) 27 Not specified Mild 4%; moderate 20%; moderately severe 51%; severe 26% 81.0 83.0 NPI sleep item ✓ ✓ ✓ ✓ 4 Zwijsen et al. 2014 [45] 432 (432) 17 AD 47.7%; VaD 19.0%; mixed AD/VaD 15.5%; DLB 3.7%; FTD 2.5%; other 8.6% GDS score ≤3 1%; score 4 4%; score 5 21%; score 6 62%; score 7 12% 69.9 83.3 NPI sleep item ✓ ✓ ✓ ✓ 4 AD, Alzheimer’s Disease; CGBRS, Crichton Geriatric Behavioural Rating Scale; DSS, Dementia Screening Scale; ESS, Epworth Sleepiness Scale; FAST, Functional Assessment Staging Test; FTD, Frontotemporal dementia; GCS, Gottfries Cognitive Scale; GDS, Global Deterioration Scale; LBD, Lewy Body dementia; MMSE, Mini Mental State Examination; NPI, Neuropsychiatric Inventory; PDD, Parkinson’s disease dementia; PPA, Primary progressive aphasia; SDI, Sleep Disorder Inventory; VaD, Vascular dementia. Open in new tab Figure 1. Open in new tabDownload slide PRISMA flow diagram. Figure 1. Open in new tabDownload slide PRISMA flow diagram. The majority of studies (n = 45) used the Neuropsychiatric Inventory (NPI) [74] sleep item, which measures sleep disturbances during the nighttime and excessive daytime sleepiness. Clinically significant cases are those who score ≥4 for the frequency times severity of the item [75]. Five studies [21, 34–36, 65] reported estimates of prevalence of both clinically significant cases and symptoms on the NPI and were included in two meta-analyses. One study used the Sleep Disorders Inventory [76], which is based on the NPI sleep item and the item subquestions. Other measures used include the Epworth Sleepiness Scale (ESS) [77], a scale used in many different populations that reports daytime sleepiness and defines a clinically significant case by a score of ≥10 [78]. There were three measures of nighttime sleep disturbances (Behavioural Pathology in Alzheimer’s Disease [79] diurnal rhythm disturbance item, Multi-Dimensional Dementia Assessment Scale [80] interrupted night sleep item, and Crichton Geriatric Behavioural Rating Scale [81] sleep item) and one measure of sleep disturbances during the day and at night (Care Dependency Scale [82] Day/night pattern item). Five studies measured sleep disturbances via wrist worn actigraphy; measures recorded included time spent asleep and awake at night, and sleep efficiency, which is the percentage of time spent asleep of the total time spent in bed. Sleep disturbance is often defined by a sleep efficiency of <85% [83–86]. In the five included studies, sleep efficiency was averaged over the nights the acti-watch was worn for, which varied from 1 night, 3 nights, 7 nights (in 2 studies), and 14 nights. Study quality Quality scores across studies on the MMAT ranged from 2 to 4, out of a possible 4 (see Table 1). Thirteen studies were of higher quality scoring 4, 23 studies scored 3, and 19 studies scored 2. Nineteen studies did not report the proportion of responders, and for five studies, <60% of potential participants participated. Prevalence of sleep disturbances Nineteen studies on 7,026 participants reported the prevalence of clinically significant cases from validated questionnaires. Individual study prevalence ranged from 5% to 53%. Pooled prevalence was 20% (95% CI 16% to 24%; Figure 2). Heterogeneity was high (I2 = 95%). One of the studies reported only daytime sleepiness [54], whereas the others reported both excessive daytime sleepiness and nighttime sleep disturbances. Removing this study did not markedly alter the pooled estimate (19%; 95% CI 15% to 23%). We conducted a sensitivity analysis by removing six lower quality studies [10, 32, 33, 36, 65, 68], as assessed by MMATS, but the pooled estimate prevalence remained essentially unchanged (21%; 95% CI 16% and 26%). Figure 2. Open in new tabDownload slide Forest plot of the prevalence of clinically significant sleep disturbances in people with dementia living in care homes measured by validated questionnaires. ES, effect size. Figure 2. Open in new tabDownload slide Forest plot of the prevalence of clinically significant sleep disturbances in people with dementia living in care homes measured by validated questionnaires. ES, effect size. Thirty-two studies on 16,503 participants reported the presence of any sleep symptoms on validated questionnaires. Individual study prevalence ranged from 13% to 86%. Pooled prevalence was 38% (95% CI 33% to 44%; Figure 3). Heterogeneity was high (I2 = 98%). In sensitivity analysis, 15 lower quality studies [36–38, 40–42, 44, 46, 47, 49, 51, 52, 64, 65, 67] were removed and pooled prevalence increased slightly from 38% to 43% (95% CI 36% to 51%). Figure 3. Open in new tabDownload slide Forest plot of the prevalence of symptoms of sleep disturbances in people with dementia living in care homes measured by validated questionnaires. ES, effect size. Figure 3. Open in new tabDownload slide Forest plot of the prevalence of symptoms of sleep disturbances in people with dementia living in care homes measured by validated questionnaires. ES, effect size. Five studies on 240 participants reported sleep disturbances as measured by a sleep efficiency of <85% on actigraphy. Across the individual studies prevalence ranged from 32% to 84%. Pooled prevalence was 70% (95% CI 55% to 85%; Figure 4). Heterogeneity was high (I2 = 84%). Three studies [59, 60, 62] of lower quality were removed in sensitivity analysis and the pooled prevalence increased to 82% (95% CI 76% to 89%). Figure 4. Open in new tabDownload slide Forest plot of the prevalence of sleep disturbances on actigraphy in people with dementia living in care homes measure. ES, effect size. Figure 4. Open in new tabDownload slide Forest plot of the prevalence of sleep disturbances on actigraphy in people with dementia living in care homes measure. ES, effect size. Meta-regressions showed that the method of measurement employed was a statistically significant moderator of prevalence (F2,48 = 16.00, p < 0.0001), with estimates of prevalence markedly increasing from clinically significant cases, symptoms, and then on actigraphy. After taking the method of measurement into account pooled meta-regressions also revealed that a higher percentage of males was associated with higher estimates of prevalence of sleep disturbances (t48 = −2.42, p = 0.020), though neither year of publication, study quality, average age of participants, or dementia type moderated the estimates of prevalence (all p > 0.10). We investigated publication bias by funnel plots for each meta-analysis, all of which appeared asymmetrical (Supplementary material Figures S1–S3), which could indicate publication bias. Associated factors Overall, six factors were tested for association in more than one of the included studies (Table 2). Increased staff distress was overall consistently associated with sleep disturbances as measured on questionnaires, with three studies finding evidence for an association between sleep disturbances and staff distress in both nurses and care workers [35, 45, 49]. For residents, being agitated, including subtypes of verbal and physical agitations, was also consistently associated with having sleep disturbances reported on questionnaires [64, 71, 73]. When measured on actigraphy, having sleep disturbances was associated with physical agitation, but there was no evidence of an association with verbal agitation [62]. Table 2. Associates of sleep disturbance (considered in >1 study) Factor . Measure of sleep disturbances . Study . Factors investigated for association with sleep disturbances . Number of significant relationships/times measured (%) . [35] Nurses and care workers distress (sleep disturbance mean score**) Staff distress about sleep disturbance NPI [49] Nurses distress (sleep disturbance severity**) Nurse distress (sleep disturbance symptoms) Care worker distress (sleep disturbance symptoms** and severity**) 6/8 (75%) [45] Staff distress in nurses (sleep disturbance severity,* mean score,* and frequency) Resident agitation NPI [71] Agitation (sleep disturbance frequency* and severity*) Agitation—physical nonaggressive (sleep disturbance frequency* or severity*) Agitation—verbal aggressive (sleep disturbance frequency* or severity*) Agitation—physical aggressive (sleep disturbance frequency* or severity*) Agitation—verbal nonaggressive (sleep disturbance frequency or severity) 13/15 (87%) [73] Incidence of challenging behaviors (Sleep mean score*) Frequency of challenging behaviors (Sleep mean score*) Difficulty of challenging behaviors (Sleep mean score*) Total challenging behaviors (Sleep mean score*) [64] Agitation (Sleep symptoms**) Actigraphy 62 Agitation—physical nonaggressive (amount of nighttime sleep*) Agitation—verbal aggressive or nonaggressive (amount of nighttime sleep) 1/2 (50%) Psychotropics NPI [35] Antipsychotics (clinically significant sleep disturbance**) Antidepressants (clinically significant sleep disturbance*) 7/10 (70%) [32] Antipsychotics (clinically significant sleep disturbance) [38] Antipsychotics (sleep disturbance symptoms or severity) [30] Any psychotropic (clinically significant sleep disturbance*) Hypnotics/sedatives (clinically significant sleep disturbance*) Antipsychotics (clinically significant sleep disturbance*) Anxiolytics (clinically significant sleep disturbance*) Antidepressants (clinically significant sleep disturbance) SDI [69] Any psychotropic (sleep disturbance symptoms) Resident age NPI [32] Age (clinically significant sleep disturbance) 0/3 (0%) [70] Age (sleep disturbance mean score) SDI [69] Age (sleep disturbance symptoms) Resident sex NPI [32] Sex (clinically significant sleep disturbance) 1/2 (50%) SDI [69] Male sex (sleep disturbance symptoms*) Dementia severity NPI [72] More severe dementia (sleep disturbance frequency* or severity*) 2/4 (50%) [32] Dementia severity (clinically significant sleep disturbance) [70] Dementia severity (sleep disturbance mean score) Actigraphy [61] Less severe dementia (duration of nighttime awakenings*) Dementia severity (% of sleep efficiency) Dementia severity (Amount of nighttime sleep) 1/3 (33%) Factor . Measure of sleep disturbances . Study . Factors investigated for association with sleep disturbances . Number of significant relationships/times measured (%) . [35] Nurses and care workers distress (sleep disturbance mean score**) Staff distress about sleep disturbance NPI [49] Nurses distress (sleep disturbance severity**) Nurse distress (sleep disturbance symptoms) Care worker distress (sleep disturbance symptoms** and severity**) 6/8 (75%) [45] Staff distress in nurses (sleep disturbance severity,* mean score,* and frequency) Resident agitation NPI [71] Agitation (sleep disturbance frequency* and severity*) Agitation—physical nonaggressive (sleep disturbance frequency* or severity*) Agitation—verbal aggressive (sleep disturbance frequency* or severity*) Agitation—physical aggressive (sleep disturbance frequency* or severity*) Agitation—verbal nonaggressive (sleep disturbance frequency or severity) 13/15 (87%) [73] Incidence of challenging behaviors (Sleep mean score*) Frequency of challenging behaviors (Sleep mean score*) Difficulty of challenging behaviors (Sleep mean score*) Total challenging behaviors (Sleep mean score*) [64] Agitation (Sleep symptoms**) Actigraphy 62 Agitation—physical nonaggressive (amount of nighttime sleep*) Agitation—verbal aggressive or nonaggressive (amount of nighttime sleep) 1/2 (50%) Psychotropics NPI [35] Antipsychotics (clinically significant sleep disturbance**) Antidepressants (clinically significant sleep disturbance*) 7/10 (70%) [32] Antipsychotics (clinically significant sleep disturbance) [38] Antipsychotics (sleep disturbance symptoms or severity) [30] Any psychotropic (clinically significant sleep disturbance*) Hypnotics/sedatives (clinically significant sleep disturbance*) Antipsychotics (clinically significant sleep disturbance*) Anxiolytics (clinically significant sleep disturbance*) Antidepressants (clinically significant sleep disturbance) SDI [69] Any psychotropic (sleep disturbance symptoms) Resident age NPI [32] Age (clinically significant sleep disturbance) 0/3 (0%) [70] Age (sleep disturbance mean score) SDI [69] Age (sleep disturbance symptoms) Resident sex NPI [32] Sex (clinically significant sleep disturbance) 1/2 (50%) SDI [69] Male sex (sleep disturbance symptoms*) Dementia severity NPI [72] More severe dementia (sleep disturbance frequency* or severity*) 2/4 (50%) [32] Dementia severity (clinically significant sleep disturbance) [70] Dementia severity (sleep disturbance mean score) Actigraphy [61] Less severe dementia (duration of nighttime awakenings*) Dementia severity (% of sleep efficiency) Dementia severity (Amount of nighttime sleep) 1/3 (33%) NPI, Neuropsychiatric Inventory; SD, sleep disturbance; SDI, Sleep Disorders Inventory. *p < 0.05; **p < 0.001. Open in new tab Table 2. Associates of sleep disturbance (considered in >1 study) Factor . Measure of sleep disturbances . Study . Factors investigated for association with sleep disturbances . Number of significant relationships/times measured (%) . [35] Nurses and care workers distress (sleep disturbance mean score**) Staff distress about sleep disturbance NPI [49] Nurses distress (sleep disturbance severity**) Nurse distress (sleep disturbance symptoms) Care worker distress (sleep disturbance symptoms** and severity**) 6/8 (75%) [45] Staff distress in nurses (sleep disturbance severity,* mean score,* and frequency) Resident agitation NPI [71] Agitation (sleep disturbance frequency* and severity*) Agitation—physical nonaggressive (sleep disturbance frequency* or severity*) Agitation—verbal aggressive (sleep disturbance frequency* or severity*) Agitation—physical aggressive (sleep disturbance frequency* or severity*) Agitation—verbal nonaggressive (sleep disturbance frequency or severity) 13/15 (87%) [73] Incidence of challenging behaviors (Sleep mean score*) Frequency of challenging behaviors (Sleep mean score*) Difficulty of challenging behaviors (Sleep mean score*) Total challenging behaviors (Sleep mean score*) [64] Agitation (Sleep symptoms**) Actigraphy 62 Agitation—physical nonaggressive (amount of nighttime sleep*) Agitation—verbal aggressive or nonaggressive (amount of nighttime sleep) 1/2 (50%) Psychotropics NPI [35] Antipsychotics (clinically significant sleep disturbance**) Antidepressants (clinically significant sleep disturbance*) 7/10 (70%) [32] Antipsychotics (clinically significant sleep disturbance) [38] Antipsychotics (sleep disturbance symptoms or severity) [30] Any psychotropic (clinically significant sleep disturbance*) Hypnotics/sedatives (clinically significant sleep disturbance*) Antipsychotics (clinically significant sleep disturbance*) Anxiolytics (clinically significant sleep disturbance*) Antidepressants (clinically significant sleep disturbance) SDI [69] Any psychotropic (sleep disturbance symptoms) Resident age NPI [32] Age (clinically significant sleep disturbance) 0/3 (0%) [70] Age (sleep disturbance mean score) SDI [69] Age (sleep disturbance symptoms) Resident sex NPI [32] Sex (clinically significant sleep disturbance) 1/2 (50%) SDI [69] Male sex (sleep disturbance symptoms*) Dementia severity NPI [72] More severe dementia (sleep disturbance frequency* or severity*) 2/4 (50%) [32] Dementia severity (clinically significant sleep disturbance) [70] Dementia severity (sleep disturbance mean score) Actigraphy [61] Less severe dementia (duration of nighttime awakenings*) Dementia severity (% of sleep efficiency) Dementia severity (Amount of nighttime sleep) 1/3 (33%) Factor . Measure of sleep disturbances . Study . Factors investigated for association with sleep disturbances . Number of significant relationships/times measured (%) . [35] Nurses and care workers distress (sleep disturbance mean score**) Staff distress about sleep disturbance NPI [49] Nurses distress (sleep disturbance severity**) Nurse distress (sleep disturbance symptoms) Care worker distress (sleep disturbance symptoms** and severity**) 6/8 (75%) [45] Staff distress in nurses (sleep disturbance severity,* mean score,* and frequency) Resident agitation NPI [71] Agitation (sleep disturbance frequency* and severity*) Agitation—physical nonaggressive (sleep disturbance frequency* or severity*) Agitation—verbal aggressive (sleep disturbance frequency* or severity*) Agitation—physical aggressive (sleep disturbance frequency* or severity*) Agitation—verbal nonaggressive (sleep disturbance frequency or severity) 13/15 (87%) [73] Incidence of challenging behaviors (Sleep mean score*) Frequency of challenging behaviors (Sleep mean score*) Difficulty of challenging behaviors (Sleep mean score*) Total challenging behaviors (Sleep mean score*) [64] Agitation (Sleep symptoms**) Actigraphy 62 Agitation—physical nonaggressive (amount of nighttime sleep*) Agitation—verbal aggressive or nonaggressive (amount of nighttime sleep) 1/2 (50%) Psychotropics NPI [35] Antipsychotics (clinically significant sleep disturbance**) Antidepressants (clinically significant sleep disturbance*) 7/10 (70%) [32] Antipsychotics (clinically significant sleep disturbance) [38] Antipsychotics (sleep disturbance symptoms or severity) [30] Any psychotropic (clinically significant sleep disturbance*) Hypnotics/sedatives (clinically significant sleep disturbance*) Antipsychotics (clinically significant sleep disturbance*) Anxiolytics (clinically significant sleep disturbance*) Antidepressants (clinically significant sleep disturbance) SDI [69] Any psychotropic (sleep disturbance symptoms) Resident age NPI [32] Age (clinically significant sleep disturbance) 0/3 (0%) [70] Age (sleep disturbance mean score) SDI [69] Age (sleep disturbance symptoms) Resident sex NPI [32] Sex (clinically significant sleep disturbance) 1/2 (50%) SDI [69] Male sex (sleep disturbance symptoms*) Dementia severity NPI [72] More severe dementia (sleep disturbance frequency* or severity*) 2/4 (50%) [32] Dementia severity (clinically significant sleep disturbance) [70] Dementia severity (sleep disturbance mean score) Actigraphy [61] Less severe dementia (duration of nighttime awakenings*) Dementia severity (% of sleep efficiency) Dementia severity (Amount of nighttime sleep) 1/3 (33%) NPI, Neuropsychiatric Inventory; SD, sleep disturbance; SDI, Sleep Disorders Inventory. *p < 0.05; **p < 0.001. Open in new tab For psychotropic medications, overall, there was consistent evidence for an association with sleep disturbances reported on validated questionnaires; however, the evidence for individual psychotropics was mixed. In two studies, the prescription of antipsychotics was associated with having sleep disturbances [30, 35]; however, in two other studies, there was no evidence of an association [32, 38]. Similarly, antidepressants were associated with sleep disturbances in one out of two studies [30, 35]. Taking any psychotropic medication was associated with increased sleep disturbances in two studies [30, 69], as were hypnotics/sedatives, or anxiolytics in one study [30]. Resident sex had mixed results for an association with sleep disturbances, with an association with more males and increased prevalence of sleep disturbances [69], and no evidence for an association in one study [32]. The evidence for an association between dementia severity and sleep disturbances was mixed, both when measured by questionnaires and on actigraphy. On questionnaires more severe dementia was associated with more severe and frequent sleep disturbances in one study [72], but there is no evidence of an association in two other studies [32, 70]. On actigraphy, less severe dementia was associated with an increased duration of nighttime awakenings, but dementia severity was not associated with percentage of sleep efficiency or amount of nighttime sleep [61]. In three studies, age was not associated with sleep disturbances [32, 69, 70]. No other associated factors were reported across more than one study (all associates reported in Supplementary Table S1). Discussion This is the first systematic review and meta-analysis investigating the measurement and prevalence of sleep disturbances in people with dementia living in care homes. We found that the pooled prevalence of clinically significant sleep disturbance was 20%; this was less common than having any symptom of sleep disturbance, which occurred in 38%. Actigraphy-determined sleep disturbance was much higher (70%). In meta-regressions, the method of sleep disturbance measurement was a highly statistically significant moderator of outcome, and the confidence intervals for the different methods did not overlap. It seems that these different methods are measuring different phenomena, or potentially different groups of people living in care homes. In addition, the percentage of males within a study was important, as a higher percentage of males was associated with a higher prevalence of sleep disturbances, and this association was also found in one of the individual studies [69]. This finding was robust to adjustment by method of measurement. There were a variety of other demographic and illness related factors tested within individual studies for their association with sleep disturbances with overall consistent findings for staff distress, resident agitation and prescription of psychotropic medications. A previous meta-analysis investigated questionnaire rated prevalence of sleep disturbances in people with Alzheimer’s disease, most of whom lived in the community [11]. Of the studies included in the previous meta-analysis, most (16/17) measured sleep as any symptoms of sleep disturbance, with one study measuring clinically significant sleep disturbances. They found a pooled estimate of 39%, similar to the figure found in our meta-analysis of symptoms of sleep disturbance in care homes. We found that the prevalence of sleep disturbances varied greatly dependent on the measurement method, and disagreements between actigraphy and questionnaires has been found in previous studies of people with dementia living in the community [14, 15, 87–89]. A recent cross-sectional study compared reports of sleep disturbances on proxy questionnaires with actigraphy in care home residents with and without dementia [85]. Similar to our findings, they found that 20.5% of residents were classified as having clinically significant cases of sleep disturbance on the NPI sleep item, and that 89.2% of the same residents had a sleep efficiency of less than 85% on actigraphy. The authors of that direct comparison argue that the large discrepancies in rates of sleep disturbance between actigraphy and proxy questionnaires implies that care home staff are unaware of many residents being disturbed during the night, and people are not receiving treatment when they should be [85]. However, questionnaires report broader sleep disturbances than actigraphy, such as daytime sleepiness, and when answered by an informant they reflect the impact of sleep disturbance on both family and paid carers. On the other hand, actigraphy may overestimate sleep disturbances. As people get older sleep efficiency significantly decreases, with a 3% decrease every decade of age [90]. Therefore, it is possible that a sleep efficiency threshold of 85% that was developed in healthy adults [83–86], may not be applicable to older adults who have dementia, though it is still used. Residents in care homes often spend a long time in bed over the nighttime [85, 91], which could also lead to lower sleep efficiency without sleep being disturbed as the sleep window, the period between when someone goes to bed and when they get up to start the day, is longer. One of the studies mentioned that residents could decide their bedtime, but rising time was influenced by the care homes routine [61], so someone going early to bed and then waking before the staff helped them get ready for the day could have been classed as sleep disordered. However, spending an extended time in bed itself often fragments and disturbs sleep [92]. Similarly, care home residents may spend some of the daytime napping, which could also fragment sleep as the nocturnal drive for sleep is reduced [25, 93]. Of the five actigraphy studies included in this review, three were of lower quality, which may also account for some of the differences in prevalence estimates between actigraphy and questionnaires. Inclusion criteria for participating in an actigraphy study were generally more restrictive than for other measurement methods, which could have biased the discrepancies in prevalence between questionnaires and actigraphy. However, we do not think this potential bias is likely to account for the significant differences in prevalence of sleep disorders between actigraphy and clinical questionnaires. This is because the actigraphy studies excluded those with severe aggression or pain [25], immobile and bed-bound participants, as they could not define rising and bedtime for these people [25, 61], or those who had been recently hospitalized [59, 62], or used benzodiazepines within 1 month [59]. This more severely ill population would be likely to have had a higher level of sleep disturbances, so that its exclusion in the actigraphy studies would have potentially led to a lower, not higher, prevalence. We found that a higher percentage of men living in a care home was associated with a higher prevalence of sleep disturbances in this population. It is unknown whether this finding might be associated with concurrent additional neuropsychiatric symptoms that might differ between men and women. A previous meta-analysis that found no sex differences in the prevalence of sleep disturbances in people with dementia living the community [11] also found no sex differences in the prevalence of other neuropsychiatric symptoms on the NPI. Additional studies are needed to examine this issue. We also explored whether age, publication year, study quality, and dementia subtype influenced the prevalence of sleep disturbances, though we found these characteristics did not. With dementia subtypes, the accuracy of the diagnoses can be unreliable [94], and most of the included studies did not specify dementia type, hence why we compared studies with only Alzheimer’s disease compared to mixed or not specified. Sleep disturbances were also associated with increased prescription of psychotropics across individual studies. Residents in the studies may be receiving psychotropic medication for reasons that could be contributing to the development of sleep disturbances, such as anxiety, depression, or psychosis. Similarly, as studies tended to record what medications were prescribed, and not what medications were taken, this may be unreliable information. Sleep disturbances were also associated with increased agitation, but it is unclear if agitation is a cause or consequence, or potentially both, of sleep disturbances. There was high heterogeneity in the estimates of prevalence across individual studies, which may be explained because included studies were heterogeneous in several aspects: they had been published across many years, in various countries with varying admission criteria for care homes, and using different study designs. One factor that varied substantially was the sample size of included studies, and questionnaire studies often had larger samples. Those using questionnaires ranged from 11 to 3,482 participants, to those using actigraphy ranging from 17 to 106 participants. Studies also used different measures of sleep disturbances. In one study, a small minority (9.3%) of residents with dementia self-reported their daytime sleepiness via the ESS [54]; in all other studies, a care home staff member reported sleep disturbances. Some studies had stricter exclusion or inclusion criteria, e.g. excluding those with a life expectancy of less than 6 months, or only including those with clinically significant agitation or those referred for management of neuropsychiatric symptoms, which may further explain the heterogeneity within the estimates across individual studies. Strengths and weaknesses of the review To our knowledge, this is the most comprehensive systematic review to date on the prevalence of sleep disturbances in dementia. We systematically searched three databases and contacted the authors of included studies for further papers and additional data. We were consequently able to add 16 studies providing unpublished reports of prevalence. However, we only included published studies and did not search the gray literature. While two reviewers screened all full texts for inclusion, and agreements were reached by consensus, only one reviewer screened all abstracts and titles. We had no restriction on language and included nine studies published in languages other than English, including studies taking place across five continents. A limitation of our review is that we only used cross-sectional baseline data from all studies; therefore, longitudinal changes in the prevalence of sleep disturbances and the causal mechanisms of any significantly associated factors are unclear. Many studies did not adjust for confounding variables in analyses of associated factors, and studies may have been less likely to report nonsignificant associations. Treatment implications of our findings As our findings indicate a large discrepancy between prevalence by method of measurement, this could have implications for if sleep disturbances are treated as actigraphy may classify an individual as having a sleep problem when a questionnaire does not, or vice versa. For example, actigraphy may be overestimating sleep problems, and this could lead to care home residents with dementia being treated for disturbances that they do not have. This could have further implications as hypnotic medications prescribed for sleep disturbances can increase risk of falls and other undesirable outcomes in this population [95] and would have no benefit for those who are wrongly classified as sleep disturbed. However, on the other hand, questionnaires may be underestimating sleep problems in this population, possibly because care home staff may not always know someone is awake, and therefore residents may not be adequately treated for these disturbances that could be having a negative effect on them. In conclusion, sleep disturbances are prevalent in care home residents with dementia, with large discrepancies between estimates of prevalence on validated questionnaires and on actigraphy. Those seem to be measuring different concepts of disturbed sleep. It is important that sleep disturbances are measured accurately as identification is necessary for treatment. Future research is needed to understand the precision of actigraphy and questionnaires in people with dementia. Questionnaires are currently advantageous as they are quicker, cheaper, and more feasible to measure sleep disturbances in all residents with dementia and are clinically important as they measure a phenomenon associated with resident’s agitation, being prescribed psychotropic drugs and staff distress. Further longitudinal research is needed to illustrate the direction of these associations. Acknowledgment We would like to thank all study authors who provided us with extra information and data. Funding Our review was funded by the Economic and Social Research Council. The funders had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. Conflict of interest statement. None declared. References 1. World Health Organization . Dementia fact sheet no. 362. 2017 . http://www.who.int/mediacentre/factsheets/fs362/en/. Accessed August 20, 2018 . 2. McCleery J , et al. . 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