Joint Associations of Device-Measured Sleep Duration and Efficiency With All-Cause and Cause-Specific Mortality: A Prospective Cohort Study of 90 398 UK Biobank Participants

Sleep disorders and risk of alzheimer's disease: A two-way road

Substantial sleep impairment in patients with Alzheimer's disease (AD) is one of the emerging points for continued efforts to better understand the disease. Individuals without cognitive decline, an important marker of the clinical phase of AD, may show early alterations in the sleep-wake cycle. The objective of this critical narrative review is to explore the bidirectional pathophysiological correlation between sleep disturbances and Alzheimer's Disease. Specifically, it examines how the disruption of sleep homeostasis in individuals without dementia could contribute to the pathogenesis of AD, and conversely, how neurodegeneration in individuals with Alzheimer's Disease might lead to dysregulation of the sleep-wake cycle. Recent scientific results indicate that sleep disturbances, particularly those related to impaired glymphatic clearance, may act as an important mechanism associated with the genesis of Alzheimer's Disease. Additionally, amyloid deposition and tau protein hyperphosphorylation, along with astrocytic hyperactivation, appear to trigger changes in neurotransmission dynamics in areas related to sleep, which may explain the onset of sleep disturbances in individuals with AD. Disruption of sleep homeostasis appears to be a modifiable risk factor in Alzheimer's disease. Whenever possible, the use of non-pharmacological strategies becomes important in this context. From a different perspective, additional research is needed to understand and treat the dysfunction of the sleep-wake cycle in individuals already affected by AD. Early recognition and correction of sleep disturbances in this population could potentially mitigate the progression of dementia and improve the quality of life for those with AD.

Brighter nights and darker days predict higher mortality risk: A prospective analysis of personal light exposure in >88,000 individuals

Light enhances or disrupts circadian rhythms, depending on the timing of exposure. Circadian disruption contributes to poor health outcomes that increase mortality risk. Whether personal light exposure predicts mortality risk has not been established. We therefore investigated whether personal day and night light, and light patterns that disrupt circadian rhythms, predicted mortality risk. UK Biobank participants (N = 88,905, 62.4 ± 7.8 y, 57% female) wore light sensors for 1 wk. Day and night light exposures were defined by factor analysis of 24-h light profiles. A computational model of the human circadian pacemaker was applied to model circadian amplitude and phase from light data. Cause-specific mortality was recorded in 3,750 participants across a mean (±SD) follow-up period of 8.0 ± 1.0 y. Individuals with brighter day light had incrementally lower all-cause mortality risk (adjusted-HR ranges: 0.84 to 0.90 [50 to 70th light exposure percentiles], 0.74 to 0.84 [70 to 90th], and 0.66 to 0.83 [90 to 100th]), and those with brighter night light had incrementally higher all-cause mortality risk (aHR ranges: 1.15 to 1.18 [70 to 90th], and 1.21 to 1.34 [90 to 100th]), compared to individuals in darker environments (0 to 50th percentiles). Individuals with lower circadian amplitude (aHR range: 0.90 to 0.96 per SD), earlier circadian phase (aHR range: 1.16 to 1.30), or later circadian phase (aHR range: 1.13 to 1.20) had higher all-cause mortality risks. Day light, night light, and circadian amplitude predicted cardiometabolic mortality, with larger hazard ratios than for mortality by other causes. Findings were robust to adjustment for age, sex, ethnicity, photoperiod, and sociodemographic and lifestyle factors. Minimizing night light, maximizing day light, and keeping regular light-dark patterns that enhance circadian rhythms may promote cardiometabolic health and longevity.

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality

Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion, 1448 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 34.7 min (95% limits of agreement (LoA): -37.8-107.2 min) for total sleep duration, 2.6 min for REM duration (95% LoA: -68.4-73.4 min) and 32.1 min (95% LoA: -54.4-118.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with 100,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66,214 UK Biobank participants, 1642 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration of 6-7.9 h, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.58; 95% confidence intervals (CIs): 1.19-2.11) or high sleep efficiency (HRs: 1.45; 95% CIs: 1.16-1.81). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.

Association of daytime napping and nighttime sleep with all-cause mortality: A prospective cohort study of China Health and Retirement Longitudinal Study

STUDY OBJECTIVES

The correlation of daytime napping and nighttime sleep duration on mortality was inconsistent. We aimed to explore their separate links to all-cause/premature mortality, and evaluate their combined impact on all-cause mortality risk.

METHODS

All of 20617 (mean age: 56.90 ± 10.19, 52.18 % females) participants from China Health and Retirement Longitudinal Study were followed for a median of 7 years (interquartile range: 4-7) to detect death status. Baseline self-reported napping and sleep duration was categorized: napping as none, <60 min, 60-90 min, and ≥90 min, sleep as <6 h/night, 6-8 h/night, and ≥8 h/night. Death event was tracked, and premature death was defined using 2015 China's average life expectancy (73.64 years for men, and 79.43 years for women). Cox regression models analyzed the data.

RESULTS

During follow-up, 1621 participants (7.86 %) died, including 985 (4.78 %) premature deaths. Compared to none nappers, napping ≥90 min associated with a higher risk of all-cause mortality (Hazard ratio, [HR] 1.23, 95 % confidence interval [CI] 1.06-1.42) and premature mortality (HR 1.23, 95 % CI 1.02-1.49), while napping <60 min correlated with a lower risk of premature mortality (HR 0.71, 95 % CI 0.54-0.95), after adjustment. Compared to sleep 6-8 h/night, nighttime sleep ≥8 h was associated with an increased risk of all-cause mortality (HR 1.20, 95 % CI 1.04-1.37) and premature mortality (HR 1.28, 95 % CI 1.08-1.52). Participants napping ≥90 min and sleeping ≥8 h had a multi-adjusted HR (95%CI) of 1.50 (95 % CI 1.17-1.92) for all-cause mortality, versus no napping and 6-8 h/night sleep.

CONCLUSIONS

Prolonged napping and extended nighttime sleep linked to increased mortality risk, particularly in combination. Optimizing sleep patterns may have potential implication in mortality prevention.

Sleep regularity is a stronger predictor of mortality risk than sleep duration: A prospective cohort study

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.

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality

Background

Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes.

Methods

We developed and validated a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry data from three countries (Australia, the UK, and the USA). The model was validated within-cohort using subject-wise five-fold cross-validation for sleep-wake classification and in a three-class setting for sleep stage classification wake, rapid-eye-movement sleep (REM), non-rapid-eye-movement sleep (NREM) and by external validation. We assessed the face validity of our model for population inference by applying the model to the UK Biobank with 100,000 participants, each of whom wore a wristband for up to seven days. The derived sleep parameters were used in a Cox regression model to study the association of sleep duration and sleep efficiency with all-cause mortality.

Findings

After exclusion, 1,448 participant nights of data were used to train the sleep classifier. The difference between polysomnography and the model classifications on the external validation was 34.7 minutes (95% limits of agreement (LoA): -37.8 to 107.2 minutes) for total sleep duration, 2.6 minutes for REM duration (95% LoA: -68.4 to 73.4 minutes) and 32.1 minutes (95% LoA: -54.4 to 118.5 minutes) for NREM duration. The derived sleep architecture estimate in the UK Biobank sample showed good face validity. Among 66,214 UK Biobank participants, 1,642 mortality events were observed. Short sleepers (<6 hours) had a higher risk of mortality compared to participants with normal sleep duration (6 to 7.9 hours), regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.69; 95% confidence intervals (CIs): 1.28 to 2.24 ) or high sleep efficiency (HRs: 1.42; 95% CIs: 1.14 to 1.77).

Interpretation

Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.