Multi-dimensional sleep and mortality: The Multi-Ethnic Study of Atherosclerosis

Exploring nightly variability and clinical influences on sleep measures: insights from a digital brain health platform

BACKGROUND

Digital technology offers a convenient way to continuously monitor sleep and assess night-to-night variability, particularly in aging populations where traditional self-reported sleep assessments may be limited.

AIMS

This study aimed to investigate nightly variability in sleep measures obtained via a ring oximeter sensor in older adults and to explore the influence of demographic and cognitive factors on the stability of these metrics.

METHODS

The study included 62 participants (mean age 74, 67.7 % women, 90.3 % White) from the Boston University Alzheimer's Disease Research Center (BU ADRC) cohort. Each participant wore a SleepImage Ring for at least three consecutive nights. Thirty-four continuous sleep measures, such as mean SpO2 and apnea-hypopnea index within unstable sleep, were analyzed. Night-to-night variability was assessed using intraclass correlation coefficients (ICC) based on a two-way random-effects model. Subgroup analyses examined variability by sex, age, and cognitive status. Group-level changes were assessed using one-way repeated measures ANOVA.

RESULTS

Seven sleep measures demonstrated high stability across nights (ICC: 0.70-0.88), with average heart rate being the most stable, followed by mean SpO2 and apnea-hypopnea indices. Sleep latency exhibited the highest variability. Stability improved between the second and third nights compared to the first and second nights. Women and participants under 75 years old showed greater stability in several metrics, while cognitively intact individuals exhibited more consistent breathing-related measures.

CONCLUSION

At least three nights of monitoring are required for reliable estimates of key sleep metrics. Expanding studies with larger samples and extended monitoring periods could further elucidate sleep variability as a potential non-invasive marker for general health.

The Association between All-Cause Mortality and Obstructive Sleep Apnea in Adults: A U-Shaped Curve

Rationale: The relationship between sleep apnea (SA) and mortality remains a topic of debate. Objectives: We explored the relationship between the severity of SA and mortality and the effect of age on this association. Methods: Using a veterans' database, we extracted an apnea-hypopnea index (AHI) from physician interpretations of sleep studies by developing a natural language processing pipeline (with 944 manually annotated notes), which achieved more than 85% accuracy. We categorized the participants into no SA (n-SA; AHI, <5), mild to moderate SA (m-SA; 5 ⩽ AHI < 30), and severe SA (s-SA; AHI, ⩾30). We propensity-matched the m-SA and s-SA categories with n-SA on the basis of age, sex, race, ethnicity, body mass index, and 38 components of the Elixhauser Comorbidity Index. Using logistic regression, we estimated the odds ratio (OR) for all-cause mortality using m-SA as a reference. Also, we stratified the findings on the basis of age: young, ⩽40; middle aged, >40 and <65; and older, ⩾65 adults. Results: We extracted the AHI on 179,121 propensity-matched participants (mean age = 45.85 [SD = 14.1]; BMI = 30.15 ± 5.37 kg/m2; male, 79.09%; White, 64.5%). All-cause mortality rates among three AHI categories showed a U-shaped curve (11.55%, 7.07%, and 8.15% for n-SA, m-SA, and s-SA, respectively), regardless of age group. Compared with m-SA, the odds of all-cause mortality in n-SA (OR, 1.72; 95% confidence interval = 1.65-1.79) and s-SA (OR, 1.17; 95% confidence interval = 1.12-1.22) were higher. Stratifying by age yielded consistent findings. Conclusions: All-cause mortality showed a U-shaped association with the AHI. Further investigations to understand the underlying mechanisms of this phenomenon are warranted.

A good night's rest: A contemporary review of sleep and cardiovascular health

Sleep is increasingly recognized as a significant contributor to the development of cardiovascular disease (CVD). Recent American Heart Association guidelines incorporate sleep duration into the "Life's Essential Eight" framework of ideal cardiovascular health. This article will review the evidence relating sleep duration, regularity, and quality with all-cause and cardiovascular mortality, cardiometabolic syndrome, and coronary artery disease in adults. Short sleep duration is strongly associated with cardiovascular mortality, cardiometabolic risk factors, and coronary artery disease. Limited studies also suggest a possible U-shaped association, with long sleep duration also associated with greater cardiovascular risk. Sleep regularity has emerged as a strong and independent risk factor for CVD-related mortality, cardiometabolic syndrome, and subclinical atherosclerosis. Less is known about the impact of sleep quality on CVD, though a number of observational studies suggest a possible association with metabolic syndrome and subclinical atherosclerosis. This review provides an update of the literature on the cardiovascular impact of sleep for the everyday clinician and highlights gaps in knowledge that warrant future research.

Untargeted metabolome atlas for sleep-related phenotypes in the Hispanic community health study/study of Latinos

BACKGROUND

Sleep is essential to maintaining health and wellbeing of individuals, influencing a variety of outcomes from mental health to cardiometabolic disease. This study aims to assess the relationships between various sleep-related phenotypes and blood metabolites.

METHODS

Utilising data from the Hispanic Community Health Study/Study of Latinos, we performed association analyses between 40 sleep-related phenotypes, grouped in several domains (sleep disordered breathing (SDB), sleep duration, sleep timing, self-reported insomnia symptoms, excessive daytime sleepiness (EDS), and heart rate during sleep), and 768 metabolites measured via untargeted metabolomics profiling. Network analysis was employed to visualise and interpret the associations between sleep phenotypes and metabolites.

FINDINGS

The patterns of statistically significant associations between sleep phenotypes and metabolites differed by superpathways, and highlighted subpathways of interest for future studies. For example, primary bile acid metabolism showed the highest cumulative percentage of statistically significant associations across all sleep phenotype domains except for SDB and EDS phenotypes. Several metabolites were associated with multiple sleep phenotypes, from a few domains. Glycochenodeoxycholate, vanillyl mandelate (VMA) and 1-stearoyl-2-oleoyl-GPE (18:0/18:1) were associated with the highest number of sleep phenotypes, while pregnenolone sulfate was associated with all sleep phenotype domains except for sleep duration. N-lactoyl amino acids such as N-lactoyl phenylalanine (lac-Phe), were associated with sleep duration, SDB, sleep timing and heart rate during sleep.

INTERPRETATION

This atlas of sleep-metabolite associations will facilitate hypothesis generation and further study of the metabolic underpinnings of sleep health.

FUNDING

R01HL161012, R35HL135818, R01AG80598.

Sleep Health Disparities

Sleep is an important and potentially modifiable determinant of many severe health outcomes. Sleep health disparities exist and are exemplified by reported differential rates of prevalence, severity, and outcomes among minority groups and low-socioeconomic-status backgrounds. In this review we highlight the concept of sleep health, review the evidence for disparities in sleep health, examine risk factors and consequences of poor sleep health, and discuss policy implications.

Influence of sleep on physiological systems in atherosclerosis

Sleep is a fundamental requirement of life and is integral to health. Deviation from optimal sleep associates with numerous diseases including those of the cardiovascular system. Studies, spanning animal models to humans, show that insufficient, disrupted or inconsistent sleep contribute to poor cardiovascular health by disrupting body systems. Fundamental experiments have begun to uncover the molecular and cellular links between sleep and heart health while large-scale human studies have associated sleep with cardiovascular outcomes in diverse populations. Here, we review preclinical and clinical findings that demonstrate how sleep influences the autonomic nervous, metabolic and immune systems to affect atherosclerotic cardiovascular disease.

Age modifies the association between severe sleep apnea and all-cause mortality

PURPOSE

While sleep apnea (SA) gets more prevalent with advancing age, the impact of age on the association between SA and health outcomes is not well known. We assessed the association between the severity of SA and all-cause mortality in different age groups using large longitudinal data.

METHOD

We applied a Natural Language Processing pipeline to extract the apnea-hypopnea index (AHI) from the physicians' interpretation of sleep studies performed at the Veteran Health Administration (FY 1999-2022). We categorized the participants as no SA (n-SA, AHI< 5) and severe SA (s-SA, AHI≥30). We grouped the cohort based on age: Young≤40; Middle-aged:40-65; and Older adults≥65; and calculated the odds ratio (aOR) of mortality adjusted for age, sex, race, ethnicity, BMI, and Charlson-Comorbidity Index (CCI) using n-SA as the reference.

RESULTS

We identified 146,148 participants (age 52.23 ± 15.02; BMI 32.11 ± 6.05; male 86.7 %; White 66 %). Prevalence of s-SA increased with age. All-cause mortality was lower in s-SA compared to n-SA in the entire cohort (aOR,0.56; 95%CI: 0.54,0.58). Comparing s-SA to n-SA, the all-cause mortality rates (Young 1.86 % vs 1.49 %; Middle-aged 12.07 % vs 13.34 %; and Older adults 26.35 % vs 40.18 %) and the aOR diminished as the age increased (Young: 1.11, 95%CI: 0.93-1.32; Middle-aged: 0.64, 95%CI: 0.61-0.67; and Older adults: 0.44, 95%CI: 0.41-0.46).

CONCLUSION

The prevalence of severe SA increased while the odds of all-cause mortality compared to n-SA diminished with age. SA may exert less harmful effects on the aged population. A causality analysis is warranted to assess the relationship between SA, aging, and all-cause mortality.

Development and validation of a novel sleep health score in the sleep heart health study

BACKGROUND

There is a lack of consensus in evaluating multidimensional sleep health, especially concerning its implication for mortality. A validated multidimensional sleep health score is the foundation of effective interventions.

METHODS

We obtained data from 5706 participants in the Sleep Heart Health Study. First, random forest-recursive feature elimination algorithm was used to select potential predictive variables. Second, a sleep composite score was developed based on the regression coefficients from a Cox proportional hazards model evaluating the associations between selected sleep-related variables and mortality. Last, we validated the score by constructing Cox proportional hazards models to assess its association with mortality.

RESULTS

The mean age of participants was 63.2 years old, and 47.6% (2715/5706) were male. Six sleep variables, including average oxygen saturation (%), spindle density (C3), sleep efficiency (%), spindle density (C4), percentage of fast spindles (%) and percentage of rapid eye movement (%) were selected to construct this multidimensional sleep health score. The average sleep composite score in participants was 6.8 of 22 (lower is better). Participants with a one-point increase in sleep composite score had an 10% higher risk of death (hazard ratio = 1.10, 95% confidence interval: 1.08-1.12).

CONCLUSIONS

This study constructed and validated a novel multidimensional sleep health score to better predict death based on sleep, with significant associations between sleep composite score and all-cause mortality. Integrating questionnaire information and sleep microstructures, our sleep composite score is more appropriately applied for mortality risk stratification.

Revolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine

Personalized sleep medicine represents a transformative shift in healthcare, emphasizing individualized approaches to optimizing sleep health, considering the bidirectional relationship between sleep and health. This field moves beyond conventional methods, tailoring care to the unique physiological and psychological needs of individuals to improve sleep quality and manage disorders. Key to this approach is the consideration of diverse factors like genetic predispositions, lifestyle habits, environmental factors, and underlying health conditions. This enables more accurate diagnoses, targeted treatments, and proactive management. Technological advancements play a pivotal role in this field: wearable devices, mobile health applications, and advanced diagnostic tools collect detailed sleep data for continuous monitoring and analysis. The integration of machine learning and artificial intelligence enhances data interpretation, offering personalized treatment plans based on individual sleep profiles. Moreover, research on circadian rhythms and sleep physiology is advancing our understanding of sleep's impact on overall health. The next generation of wearable technology will integrate more seamlessly with IoT and smart home systems, facilitating holistic sleep environment management. Telemedicine and virtual healthcare platforms will increase accessibility to specialized care, especially in remote areas. Advancements will also focus on integrating various data sources for comprehensive assessments and treatments. Genomic and molecular research could lead to breakthroughs in understanding individual sleep disorders, informing highly personalized treatment plans. Sophisticated methods for sleep stage estimation, including machine learning techniques, are improving diagnostic precision. Computational models, particularly for conditions like obstructive sleep apnea, are enabling patient-specific treatment strategies. The future of personalized sleep medicine will likely involve cross-disciplinary collaborations, integrating cognitive behavioral therapy and mental health interventions. Public awareness and education about personalized sleep approaches, alongside updated regulatory frameworks for data security and privacy, are essential. Longitudinal studies will provide insights into evolving sleep patterns, further refining treatment approaches. In conclusion, personalized sleep medicine is revolutionizing sleep disorder treatment, leveraging individual characteristics and advanced technologies for improved diagnosis, treatment, and management. This shift towards individualized care marks a significant advancement in healthcare, enhancing life quality for those with sleep disorders.

Untargeted Metabolome Atlas for Sleep Phenotypes in the Hispanic Community Health Study/Study of Latinos

Sleep is essential to maintaining health and wellbeing of individuals, influencing a variety of outcomes from mental health to cardiometabolic disease. This study aims to assess the relationships between various sleep phenotypes and blood metabolites. Utilizing data from the Hispanic Community Health Study/Study of Latinos, we performed association analyses between 40 sleep phenotypes, grouped in several domains (i.e., sleep disordered breathing (SDB), sleep duration, timing, insomnia symptoms, and heart rate during sleep), and 768 metabolites measured via untargeted metabolomics profiling. Network analysis was employed to visualize and interpret the associations between sleep phenotypes and metabolites. The patterns of statistically significant associations between sleep phenotypes and metabolites differed by superpathways, and highlighted subpathways of interest for future studies. For example, some xenobiotic metabolites were associated with sleep duration and heart rate phenotypes (e.g. 1H-indole-7-acetic acid, 4-allylphenol sulfate), while ketone bodies and fatty acid metabolism metabolites were associated with sleep timing measures (e.g. 3-hydroxybutyrate (BHBA), 3-hydroxyhexanoylcarnitine (1)). Heart rate phenotypes had the overall largest number of detected metabolite associations. Many of these associations were shared with both SDB and with sleep timing phenotypes, while SDB phenotypes shared relatively few metabolite associations with sleep duration measures. A number of metabolites were associated with multiple sleep phenotypes, from a few domains. The amino acids vanillylmandelate (VMA) and 1-carboxyethylisoleucine were associated with the greatest number of sleep phenotypes, from all domains other than insomnia. This atlas of sleep-metabolite associations will facilitate hypothesis generation and further study of the metabolic underpinnings of sleep health.

Multidimensional Sleep Health Problems Across Middle and Older Adulthood Predict Early Mortality

BACKGROUND

Having multiple sleep problems is common in adulthood. Yet, most studies have assessed single sleep variables at one timepoint, potentially misinterpreting health consequences of co-occurring sleep problems that may change over time. We investigated the relationship between multidimensional sleep health across adulthood and mortality.

METHODS

Participants from the Midlife in the United States Study reported sleep characteristics in 2004-2006 (MIDUS-2; M2) and in 2013-2014 (MIDUS-3; M3). We calculated a composite score of sleep health problems across 5 dimensions: Regularity, Satisfaction, Alertness, Efficiency, and Duration (higher = more problems). Two separate models for baseline sleep health (n = 5 140; median follow-up time = 15.3 years) and change in sleep health (n = 2 991; median follow-up time = 6.4 years) to mortality were conducted. Cox regression models controlled for sociodemographics and key health risk factors (body mass index, smoking, depressive symptoms, diabetes, and hypertension).

RESULTS

On average, 88% of the sample reported having one or more sleep health problems at M2. Each additional sleep health problem at M2 was associated with 12% greater risk of all-cause mortality (hazard ratio [HR] = 1.12, 95% confidence interval [CI] = 1.04-1.21), but not heart disease-related mortality (HR = 1.14, 95% CI = 0.99-1.31). An increase in sleep health problems from M2 to M3 was associated with 27% greater risk of all-cause mortality (HR = 1.27, 95% CI = 1.005-1.59), and 153% greater risk of heart disease mortality (HR = 2.53, 95% CI = 1.37-4.68).

CONCLUSIONS

More sleep health problems may increase the risk of early mortality. Sleep health in middle and older adulthood is a vital sign that can be assessed at medical checkups to identify those at greater risk.

Are we getting enough sleep? Frequent irregular sleep found in an analysis of over 11 million nights of objective in-home sleep data

OBJECTIVES

Evidence-based guidelines recommend that adults should sleep 7-9 h/night for optimal health and function. This study used noninvasive, multinight, objective sleep monitoring to determine average sleep duration and sleep duration variability in a large global community sample, and how often participants met the recommended sleep duration range.

METHODS

Data were analyzed from registered users of the Withings under-mattress Sleep Analyzer (predominantly located in Europe and North America) who had ≥28 nights of sleep recordings, averaging ≥4 per week. Sleep durations (the average and standard deviation) were assessed across a ∼9-month period. Associations between age groups, sex, and sleep duration were assessed using linear and logistic regressions, and proportions of participants within (7-9 hours) or outside (<7 hours or >9 hours) the recommended sleep duration range were calculated.

RESULTS

The sample consisted of 67,254 adults (52,523 males, 14,731 females; aged mean ± SD 50 ± 12 years). About 30% of adults demonstrated an average sleep duration outside the recommended 7-9 h/night. Even in participants with an average sleep duration within 7-9 hours, about 40% of nights were outside this range. Only 15% of participants slept between 7 and 9 hours for at least 5 nights per week. Female participants had significantly longer sleep durations than male participants, and middle-aged participants had shorter sleep durations than younger or older participants.

CONCLUSIONS

These findings indicate that a considerable proportion of adults are not regularly sleeping the recommended 7-9 h/night. Even among those who do, irregular sleep is prevalent. These novel data raise several important questions regarding sleep requirements and the need for improved sleep health policy and advocacy.

Objectively regular sleep patterns and mortality in a prospective cohort: The Multi-Ethnic Study of Atherosclerosis

Irregular sleep and non-optimal sleep duration separately have been shown to be associated with increased disease and mortality risk. We used data from the prospective cohort Multi-Ethnic Study of Atherosclerosis sleep study (2010-2013) to investigate: do aging adults whose sleep is objectively high in regularity in timing and duration, and of sufficient duration tend to have increased survival compared with those whose sleep is lower in regularity and duration, in a diverse US sample? At baseline, sleep was measured by 7-day wrist actigraphy, concurrent with at-home polysomnography and questionnaires. Objective metrics of sleep regularity and duration from actigraphy were used for statistical clustering using sparse k-means clustering. Two sleep patterns were identified: "regular-optimal" (average duration: 7.0 ± 1.0 hr obtained regularly) and "irregular-insufficient" (duration: 5.8 ± 1.4 hr obtained with twice the irregularity). Using proportional hazard models with multivariate adjustment, we estimated all-cause mortality hazard ratios. Among 1759 participants followed for a median of 7.0 years (Q1-Q3, 6.4-7.4 years), 176 deaths were recorded. The "regular-optimal" group had a 39% lower mortality hazard than did the "irregular-insufficient" sleep group (hazard ratio [95% confidence interval]: 0.61 [0.45, 0.83]) after adjusting for socio-demographics, lifestyle, medical comorbidities and sleep disorders. In conclusion, a "regular-optimal" sleep pattern was significantly associated with a lower hazard of all-cause mortality. The regular-optimal phenotype maps behaviourally to regular bed and wake times, suggesting sleep benefits of adherence to recommended healthy sleep practices, with further potential benefits for longevity.

Another benefit of regular sleep

A large observational study has found that irregular sleep-wake patterns are associated with a higher risk of overall mortality, and also mortality from cancers and cardiovascular disease.