SLEEP

Lucey, Brendan P

doi: 10.1093/sleep/zsad301pmid: 38011628

Vanderveken, Olivier M; Gagnadoux, Frédéric

doi: 10.1093/sleep/zsad290pmid: 37948610

Windred, Daniel P; Burns, Angus C; Lane, Jacqueline M; Saxena, Richa; Rutter, Martin K; Cain, Sean W; Phillips, Andrew J K

doi: 10.1093/sleep/zsad253pmid: 37738616

Abnormally short and long sleep are associated with premature mortality, and achieving optimal sleep duration has been the focus of sleep health guidelines. Emerging research demonstrates that sleep regularity, the day-to-day consistency of sleep–wake timing, can be a stronger predictor for some health outcomes than sleep duration. The role of sleep regularity in mortality, however, has not been investigated in a large cohort with objective data. We therefore aimed to compare how sleep regularity and duration predicted risk for all-cause and cause-specific mortality. We calculated Sleep Regularity Index (SRI) scores from > 10 million hours of accelerometer data in 60 977 UK Biobank participants (62.8 ± 7.8 years, 55.0% female, median[IQR] SRI: 81.0[73.8–86.3]). Mortality was reported up to 7.8 years after accelerometer recording in 1859 participants (4.84 deaths per 1000 person-years, mean (±SD) follow-up of 6.30 ± 0.83 years). Higher sleep regularity was associated with a 20%–48% lower risk of all-cause mortality (p < .001 to p = 0.004), a 16%–39% lower risk of cancer mortality (p < 0.001 to p = 0.017), and a 22%–57% lower risk of cardiometabolic mortality (p < 0.001 to p = 0.048), across the top four SRI quintiles compared to the least regular quintile. Results were adjusted for age, sex, ethnicity, and sociodemographic, lifestyle, and health factors. Sleep regularity was a stronger predictor of all-cause mortality than sleep duration, by comparing equivalent mortality models, and by comparing nested SRI-mortality models with and without sleep duration (p = 0.14–0.20). These findings indicate that sleep regularity is an important predictor of mortality risk and is a stronger predictor than sleep duration. Sleep regularity may be a simple, effective target for improving general health and survival.

Chang, Chia-Shuan; Chang, Ling-Yin; Wu, Chi-Chen; Chang, Hsing-Yi

doi: 10.1093/sleep/zsad270pmid: 37855456

Study ObjectivesThis study employed longitudinal data collected repeatedly from individuals over the course of several years to examine the trajectories of social jetlag from ages 11 to 22 years and their associations with subsequent body mass index (BMI). Potential sex differences were also investigated.MethodsData were obtained from two longitudinal studies conducted in Taiwan (N = 4287). Social jetlag was defined as ≥ 2 hours of absolute difference in sleep midpoint between weekdays and weekends. BMI was calculated using weight (kg)/height(m)2 and categorized as underweight (<18 kg/m2), normal weight (18 kg/m2 ≤ BMI < 24 kg/m2), overweight (24 kg/m2 ≤ BMI < 27 kg/m2), and obese (≥27 kg/m2). Group-based trajectory modeling and multinomial logistic regression were applied to investigate study objectives.ResultsFour distinct trajectories of social jetlag throughout the adolescent years were identified, with corresponding proportions as follows: low-stable (42%), moderate-decreasing (19%), low-increasing (22%), and chronic (17%) trajectories. Among males, the risk of being underweight (aOR, 1.96; 95% CI: 1.35 to 2.84) or obese (aOR, 1.40; 95% CI: 1.02 to 1.92) was higher in individuals with a low-increasing trajectory than in those with a low-stable trajectory. Among females, those with a low-increasing (aOR, 1.61; 95% CI: 1.02 to 2.54) or chronic (aOR, 2.04; 95% CI: 1.27 to 3.25) trajectory were at a higher risk of being obese relative to those with a low-stable trajectory.ConclusionsAddressing the development of increasing or chronic social jetlag during adolescence can help prevent abnormal BMI in young adulthood. Practitioners should consider sex differences in treatment or consultation.

Dawson, Andrew; Avraam, Joanne; Nicholas, Christian L; Kay, Amanda; Thornton, Therese; Feast, Nicole; Fridgant, Monika D; O’Donoghue, Fergal J; Trinder, John; Jordan, Amy S

doi: 10.1093/sleep/zsad202pmid: 37503934

Study ObjectivesTransient arousal from sleep has been shown to elicit a prolonged increase in genioglossus muscle activity that persists following the return to sleep and which may protect against subsequent airway collapse. We hypothesized that this increased genioglossal activity following return to sleep after an arousal is due to persistent firing of inspiratory-modulated motor units (MUs) that are recruited during the arousal.MethodsThirty-four healthy participants were studied overnight while wearing a nasal mask with pneumotachograph to measure ventilation and with 4 intramuscular genioglossus EMG electrodes. During stable N2 and N3 sleep, auditory tones were played to induce brief (3-15s) AASM arousals. Ventilation and genioglossus MUs were quantified before the tone, during the arousal and for 10 breaths after the return to sleep.ResultsA total of 1089 auditory tones were played and gave rise to 239 MUs recorded across arousal and the return to sleep in 20 participants (aged 23 ± 4.2 years and BMI 22.5 ± 2.2 kg/m2). Ventilation was elevated above baseline during arousal and the first post-arousal breath (p < .001). Genioglossal activity was elevated for five breaths following the return to sleep, due to increased firing rate and recruitment of inspiratory modulated MUs, as well as a small increase in tonic MU firing frequency.ConclusionsThe sustained increase in genioglossal activity that occurs on return to sleep after arousal is primarily a result of persistent activity of inspiratory-modulated MUs, with a slight contribution from tonic units. Harnessing genioglossal activation following arousal may potentially be useful for preventing obstructive respiratory events.

doi: 10.1093/sleep/zsac299pmid: 36938602

Lima Santos, João Paulo; Hayes, Rebecca; Franzen, Peter L; Goldstein, Tina R; Hasler, Brant P; Buysse, Daniel J; Siegle, Greg J; Dahl, Ronald E; Forbes, Erika E; Ladouceur, Cecile D; McMakin, Dana L; Ryan, Neal D; Silk, Jennifer S; Jalbrzikowski, Maria; Soehner, Adriane M

Zapata, Ignacio A; Wen, Peng; Jones, Evan; Fjaagesund, Shauna; Li, Yan

doi: 10.1093/sleep/zsad159pmid: 37294908

Sleep spindles are isolated transient surges of oscillatory neural activity present during sleep stages 2 and 3 in the nonrapid eye movement (NREM). They can indicate the mechanisms of memory consolidation and plasticity in the brain. Spindles can be identified across cortical areas and classified as either slow or fast. There are spindle transients across different frequencies and power, yet most of their functions remain a mystery. Using several electroencephalogram (EEG) databases, this study presents a new method, called the “spindles across multiple channels” (SAMC) method, for identifying and categorizing sleep spindles in EEGs during the NREM sleep. The SAMC method uses a multitapers and convolution (MT&C) approach to extract the spectral estimation of different frequencies present in sleep EEGs and graphically identify spindles across multiple channels. The characteristics of spindles, such as duration, power, and event areas, are also extracted by the SAMC method. Comparison with other state-of-the-art spindle identification methods demonstrated the superiority of the proposed method with an agreement rate, average positive predictive value, and sensitivity of over 90% for spindle classification across the three databases used in this paper. The computing cost was found to be, on average, 0.004 seconds per epoch. The proposed method can potentially improve the understanding of the behavior of spindles across the scalp and accurately identify and categories sleep spindles.

Denisova, Kristina

doi: 10.1093/sleep/zsad219pmid: 37616382

This is the first English translation of the work Periodic phenomena in the sleep in children, published in 1926 in the Journal Novoe v refleksologii i fiziologii nervnoi sistemy (Vol. 2, pp. 338–345) by Maria Denisova and Nicholai Figurin; it is the first study to report data on what is currently termed rapid eye movement (REM) sleep. The authors acquired continuous quantitative respiration data, as well as, eye and body movements during sleep in children for up to 6 hours, and discovered several novel features of sleep cycles in healthy infants from birth to about 1 year of age. First, the study reports cyclical periods of increased respiration and eye and body movements, with rapid ocular movements visible under relaxed eyelids (separation: 0.5–1 mm). These observations suggest atonia of REM sleep. Second, the length of the complete cycle (alternating active and quiet sleep phases or states) is about 50 minutes, an estimate that is consistent with later work. Third, the study identifies infant-specific ordering of sleep states, with the active phase beginning after sleep onset, followed by the quiescence phase. Importantly, these published data on sleep cycles precede all published studies related to the state now termed REM sleep by about 30 years (i.e. publishing in Science and in the Journal of Applied Physiology in the 1950s by Eugene Aserinski and Nathaniel Kleitman). In the historical commentary accompanying this translation, the findings of those later works are carefully compared to the original data on respiration and ocular and body motility cycles during sleep in infants, first reported and published by Denisova and Figurin (1926).

Lee, Minki P; Hoang, Kien; Park, Sungkyu; Song, Yun Min; Joo, Eun Yeon; Chang, Won; Kim, Jee Hyun; Kim, Jae Kyoung

doi: 10.1093/sleep/zsad266pmid: 37819273

Sleep is a critical component of health and well-being but collecting and analyzing accurate longitudinal sleep data can be challenging, especially outside of laboratory settings. We propose a simple neural network model titled SOMNI (Sleep data restOration using Machine learning and Non-negative matrix factorIzation [NMF]) for imputing missing rest-activity data from actigraphy, which can enable clinicians to better handle missing data and monitor sleep–wake cycles of individuals with highly irregular sleep–wake patterns. The model consists of two hidden layers and uses NMF to capture hidden longitudinal sleep–wake patterns of individuals with disturbed sleep–wake cycles. Based on this, we develop two approaches: the individual approach imputes missing data based on the data from only one participant, while the global approach imputes missing data based on the data across multiple participants. Our models are tested with shift and non-shift workers' data from three independent hospitals. Both approaches can accurately impute missing data up to 24 hours of long dataset (>50 days) even for shift workers with extremely irregular sleep–wake patterns (AUC > 0.86). On the other hand, for short dataset (~15 days), only the global model is accurate (AUC > 0.77). Our approach can be used to help clinicians monitor sleep–wake cycles of patients with sleep disorders outside of laboratory settings without relying on sleep diaries, ultimately improving sleep health outcomes.