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Circadian rhythms and sleep are fundamental biological processes integral to human health. Their disruption is associated with detrimental physiological consequences, including cognitive, metabolic, cardiovascular and immunological dysfunctions. Yet many of the molecular underpinnings of sleep regulation in health and disease have remained elusive. Given the moderate heritability of circadian and sleep traits, genetics offers an opportunity that complements insights from model organism studies to advance our fundamental molecular understanding of human circadian and sleep physiology and linked chronic disease biology. Here, we review recent discoveries of the genetics of circadian and sleep physiology and disorders with a focus on those that reveal causal contributions to complex diseases.The circadian system and sleep physiology are linked to myriad biological processes, the disruption of which is detrimental to human health. Here, the authors review insights from genetic studies of human circadian and sleep phenotypes and disorders, with a focus on those with causal contributions to other complex diseases.

Abstract Traditionally, the presence and severity of obstructive sleep apnea (OSA) have been defined by the apnea–hypopnea index (AHI). Continuous positive airway pressure is generally first-line therapy despite low adherence, because it reliably reduces the AHI when used, and the response to other therapies is variable. However, there is growing appreciation that the underlying etiology (i.e., endotype) and clinical manifestation (i.e., phenotype) of OSA in an individual are not well described by the AHI. We define and review the important progress made in understanding and measuring physiological mechanisms (or endotypes) that help define subtypes of OSA and identify the potential use of genetics to further refine disease classification. This more detailed understanding of OSA pathogenesis should influence clinical treatment decisions as well as help inform research priorities and clinical study design. In short, treatments could be individualized on the basis of the underlying cause of OSA; patients could better understand which symptoms and outcomes will respond to OSA treatment and by how much; and researchers could select populations most likely to benefit from specific treatment approaches for OSA.

Abstract Study Objectives We aimed to detect cross-sectional phenotype and polygenic risk score (PRS) associations between sleep duration and prevalent diseases using the Partners Biobank, a hospital-based cohort study linking electronic medical records (EMR) with genetic information. Methods Disease prevalence was determined from EMR, and sleep duration was self-reported. A PRS for sleep duration was derived using 78 previously associated SNPs from genome-wide association studies (GWAS) for self-reported sleep duration. We tested for associations between (1) self-reported sleep duration and 22 prevalent diseases (n = 30 251), (2) the PRS and self-reported sleep duration (n = 6903), and (3) the PRS and the 22 prevalent diseases (n = 16 033). For observed PRS-disease associations, we tested causality using two-sample Mendelian randomization (MR). Results In the age-, sex-, and race-adjusted model, U-shaped associations were observed for sleep duration and asthma, depression, hypertension, insomnia, obesity, obstructive sleep apnea, and type 2 diabetes, where both short and long sleepers had higher odds for these diseases than normal sleepers (p < 2.27 × 10−3). Next, we confirmed associations between the PRS and longer sleep duration (0.65 ± 0.19 SD minutes per effect allele; p = 7.32 × 10−04). The PRS collectively explained 1.4% of the phenotypic variance in sleep duration. After adjusting for age, sex, genotyping array, and principal components of ancestry, we observed that the PRS was also associated with congestive heart failure (CHF; p = 0.015), obesity (p = 0.019), hypertension (p = 0.039), restless legs syndrome (RLS; p = 0.041), and insomnia (p = 0.049). Associations were maintained following additional adjustment for obesity status, except for hypertension and insomnia. For all diseases, except RLS, carrying a higher genetic burden of the 78 sleep duration-increasing alleles (i.e. higher sleep duration PRS) associated with lower odds for prevalent disease. In MR, we estimated causal associations between genetically defined longer sleep duration with decreased risk of CHF (inverse variance weighted [IVW] OR per minute of sleep [95% CI] = 0.978 [0.961–0.996]; p = 0.019) and hypertension (IVW OR [95% CI] = 0.993 [0.986–1.000]; p = 0.049), and increased risk of RLS (IVW OR [95% CI] = 1.018 [1.000–1.036]; p = 0.045). Conclusions By validating the PRS for sleep duration and identifying cross-phenotype associations, we lay the groundwork for future investigations on the intersection between sleep, genetics, clinical measures, and diseases using large EMR datasets.

Abstract Study Objectives To identify systematic biases across groups in objectively and subjectively measured sleep duration. Methods We investigated concordance of self-reported habitual sleep duration compared with actigraphy- and single-night in-home polysomnography (PSG) across white, black, Hispanic, and Chinese participants in the Multi-Ethnic Study of Atherosclerosis. Results Among 1910 adults, self-reported sleep duration, determined by differences between bed and wake times, was overestimated in all racial groups compared with PSG and actigraphy. Compared with whites (ρ = 0.45), correlations were significantly lower only in blacks (ρ = 0.28). Self-reporting bias for total sleep time compared with wrist actigraphy was 66 min (95% confidence interval [CI]: 61–71) for whites, 58 min (95% CI: 48–69) for blacks, 66 min (95% CI: 57–74) for Hispanics, and 60 min (95% CI: 49–70) for Chinese adults. Compared with PSG, self-reporting bias in whites at 73 min (95% CI: 67–79) was higher than in blacks (54 min [95% CI: 42–65]) and Chinese (49 min [95% CI: 37–61]) but not different from Hispanics (67 min [95% CI: 56–78]). Slight agreement/concordance was observed between self-reported and actigraphy-based total sleep time (kw = 0.14 for whites, 0.10 for blacks, 0.17 for Hispanics, and 0.11 for Chinese) and PSG (kw = 0.08 for whites, 0.04 for blacks, 0.05 for Hispanics, and 0.01 for Chinese) across race/ethnicity. Conclusions Self-reported sleep duration overestimated objectively measured sleep across all races, and compared with PSG, overestimation is significantly greater in whites compared with blacks. Larger reporting bias reduces the ability to identify significant associations between sleep duration and health among blacks compared with whites. Sleep measurement property differences should be considered when comparing sleep indices across racial/ethnic groups.

In a randomized trial, over 2700 patients with obstructive sleep apnea and cardiovascular disease were assigned to CPAP plus usual care or to usual care alone. At a mean of 3.7 years, the rate of adverse cardiovascular events did not differ significantly between the groups. Obstructive sleep apnea causes episodic hypoxemia and nocturnal sympathetic nervous system activation 1 and elevates blood pressure 2 and markers of oxidative stress, inflammation, and hypercoagulation. 3 , 4 Large negative intrathoracic pressure swings also impose mechanical stress on the heart and great vessels. 5 – 7 Population-based and sleep-clinic–based cohort studies have shown an association between obstructive sleep apnea and cardiovascular events, 8 – 16 particularly stroke. 17 Randomized, controlled trials have shown that treatment with continuous positive airway pressure (CPAP) lowers systolic blood pressure by 2 to 3 mm Hg in patients with normotensive obstructive sleep apnea 18 and by 6 to 7 mm Hg in patients with . . .

Abstract Study Objectives Most epidemiological studies assess sleep duration using questionnaires. Interpreting this information requires understanding the extent to which self-reported habitual sleep reflects objectively assessed sleep duration, particularly among African Americans, who disproportionately experience poor sleep health. Methods Among African-American participants of the Jackson Heart Sleep Study, we investigated differences in questionnaire-based self-assessed average sleep duration and self-assessed wake-bed time differences compared to actigraphy-based assessments of total sleep time (TST) and average time in bed (TIB). Linear regression models provided estimates of concordance between actigraphy-based and self-reported sleep duration. Results Among 821 adults, self-assessed average sleep duration was lower than self-assessed wake-bed time differences (6.4 ± 1.4 vs. 7.5 ± 1.7 h, p < 0.0001). Mean actigraphy-based TST was 6.6 ± 1.2 h, and actigraphy-based average TIB was 7.6 ± 1.2 h. Self-assessed average sleep duration and actigraphy-based TST were moderately correlated (r = 0.28, p < 0.0001). Self-assessed average sleep duration underestimated actigraphy-based TST by −30.7 min (95% confidence intervals [CI]: −36.5 to −24.9). In contrast, self-assessed wake-bed time differences overestimated actigraphy-based TST by 45.1 min (95% CI: 38.6–51.5). In subgroup analyses, self-assessed average sleep duration underestimated actigraphy-based measures most strongly among participants with insomnia symptoms. Conclusions Among African Americans, self-assessed average sleep duration underestimated objectively measured sleep while self-assessed wake-bed time differences overestimated objectively measured sleep. Sleep measurement property differences should be considered when investigating disparities in sleep and evaluating their associations with health outcomes.

Air pollution may influence sleep through airway inflammation or autonomic nervous system pathway alterations. Epidemiological studies may provide evidence of relationships between chronic air pollution exposure and sleep apnea. To determine whether ambient-derived pollution exposure is associated with obstructive sleep apnea and objective sleep disruption. We analyzed data from a sample of participants in MESA (Multi-Ethnic Study of Atherosclerosis) who participated in both the Sleep and Air studies. Mean annual and 5-year exposure levels to nitrogen dioxide (NO ) and particulate matter ≤ 2.5 μm in aerodynamic diameter (PM ) were estimated at participants' homes using spatiotemporal models based on cohort-specific monitoring. Participants completed in-home full polysomnography and 7 days of wrist actigraphy. We used multivariate models, adjusted for demographics, comorbidities, socioeconomic factors, and site, to assess whether air pollution was associated with sleep apnea (apnea-hypopnea index ≥ 15) and actigraphy-measured sleep efficiency. The participants (n = 1,974) were an average age of 68 (±9) years, 46% male, 36% white, 24% Hispanic, 28% black, and 12% Asian; 48% had sleep apnea and 25% had a sleep efficiency of ≤88%. A 10 ppb annual increase in NO exposure was associated with 39% greater adjusted odds of sleep apnea (95% confidence interval [CI], 1.03-1.87). A 5 μg/m greater annual PM exposure was also associated with 60% greater odds of sleep apnea (95% CI, 0.98-2.62). Sleep efficiency was not associated with air pollution levels in fully adjusted models. Individuals with higher annual NO and PM exposure levels had a greater odds of sleep apnea. These data suggest that in addition to individual risk factors, environmental factors also contribute to the variation of sleep disorders across groups, possibly contributing to health disparities.

Background Machine learning tools such as random forests provide important opportunities for modeling large, complex modern data generated in medicine. Unfortunately, when it comes to understanding why machine learning models are predictive, applied research continues to rely on ‘out of bag’ (OOB) variable importance metrics (VIMPs) that are known to have considerable shortcomings within the statistics community. After explaining the limitations of OOB VIMPs – including bias towards correlated features and limited interpretability – we describe a modern approach called ‘knockoff VIMPs’ and explain its advantages. Methods We first evaluate current VIMP practices through an in-depth literature review of 50 recent random forest manuscripts. Next, we recommend organized and interpretable strategies for analysis with knockoff VIMPs, including computing them for groups of features and considering multiple model performance metrics. To demonstrate methods, we develop a random forest to predict 5-year incident stroke in the Sleep Heart Health Study and compare results based on OOB and knockoff VIMPs. Results Nearly all papers in the literature review contained substantial limitations in their use of VIMPs. In our demonstration, using OOB VIMPs for individual variables suggested two highly correlated lung function variables (forced expiratory volume, forced vital capacity) as the best predictors of incident stroke, followed by age and height. Using an organized analytic approach that considered knockoff VIMPs of both groups of features and individual features, the largest contributions to model sensitivity were medications (especially cardiovascular) and measured medical risk factors, while the largest contributions to model specificity were age, diastolic blood pressure, self-reported medical risk factors, polysomnography features, and pack-years of smoking. Thus, we reach very different conclusions about stroke risk factors using OOB VIMPs versus knockoff VIMPs. Conclusions The near-ubiquitous reliance on OOB VIMPs may provide misleading results for researchers who use such methods to guide their research. Given the rapid pace of scientific inquiry using machine learning, it is essential to bring modern knockoff VIMPs that are interpretable and unbiased into widespread applied practice to steer researchers using random forest machine learning toward more meaningful results.

This randomized trial showed no effect of early adenotonsillectomy, as compared with watchful waiting, on the primary outcome of attention and executive functioning in children with obstructive sleep apnea. Many secondary outcomes favored early surgery. The childhood obstructive sleep apnea syndrome is associated with numerous adverse health outcomes, including cognitive and behavioral deficits. 1 The most commonly identified risk factor for the childhood obstructive sleep apnea syndrome is adenotonsillar hypertrophy. Thus, the primary treatment is adenotonsillectomy, which accounts for more than 500,000 procedures annually in the United States alone. 2 Nevertheless, there has been no controlled study evaluating the benefits and risks of adenotonsillectomy, as compared with watchful waiting, for the management of the obstructive sleep apnea syndrome. The Childhood Adenotonsillectomy Trial (CHAT) was designed to evaluate the efficacy of early adenotonsillectomy versus watchful waiting with supportive . . .

Abstract Study Objectives The bases for sex disparities in obstructive sleep apnea (OSA), is poorly understood. We quantified the influences of event definitions, sleep-state, and body position on apnea–hypopnea indices (AHIs) in men and women, and evaluated sex differences in pathophysiological endotypes. Methods Polysomnography (PSG) data were analyzed from 2057 participants from the multi-ethnic study of atherosclerosis. Alternative AHIs were compared using various desaturation and arousal criteria. Endotypes (loop gain, airway collapsibility, arousal threshold) were derived using breath-by-breath analysis of PSG signals. Regression models estimated the extent to which endotypes explained sex differences in AHI. Results The sample (mean 68.5 ± 9.2 years) included 54% women. OSA (AHI4P ≥15/h, defined by events with ≥4% desaturations) was found in 41.1% men and 21.8% women. Compared to AHI4P, male/female AHI ratios decreased by 5%–10% when using 3%-desaturation and/or arousal criteria; p < 0.05. REM-OSA (REM-AHI ≥15/h) was similar in men and women regardless of event desaturation criteria. REM-AHI4P ≥15/h was observed in 57% of men and women each. In NREM, AHI4P in men was 2.49 (CI95: 2.25, 2.76) of that in women. Women demonstrated lower loop gain, less airway collapsibility, and lower arousal threshold in NREM (ps < 0.0005). Endotypes explained 30% of the relative sex differences in NREM-AHI4P. Conclusions There are significant sex differences in NREM-AHI levels and in physiological endotypes. Physiological endotypes explained a significant portion of the relative sex differences in NREM-AHI. Definitions that use 4%-desaturation criteria under-estimate AHI in women. Combining NREM and REM events obscures OSA prevalence in REM in women.