Personal light exposure patterns and incidence of type 2 diabetes: analysis of 13 million hours of light sensor data and 670,000 person-years of prospective observation.

Background: Light at night disrupts circadian rhythms, and circadian disruption is a risk factor for type 2 diabetes. Whether personal light exposure predicts diabetes risk has not been demonstrated in a large prospective cohort. We therefore assessed whether personal light exposure patterns predicted risk of incident type 2 diabetes in UK Biobank participants, using ∼13 million hours of light sensor data. Methods: Participants (N = 84,790, age (M ± SD) = 62.3 ± 7.9 years, 58% female) wore light sensors for one week, recording day and night light exposure. Circadian amplitude and phase were modeled from weekly light data. Incident type 2 diabetes was recorded (1997 cases; 7.9 ± 1.2 years follow-up; excluding diabetes cases prior to light-tracking). Risk of incident type 2 diabetes was assessed as a function of day and night light, circadian phase, and circadian amplitude, adjusting for age, sex, ethnicity, socioeconomic and lifestyle factors, and polygenic risk. Findings: Compared to people with dark nights (0-50th percentiles), diabetes risk was incrementally higher across brighter night light exposure percentiles (50-70th: multivariable-adjusted HR = 1.29 [1.14-1.46]; 70-90th: 1.39 [1.24-1.57]; and 90-100th: 1.53 [1.32-1.77]). Diabetes risk was higher in people with lower modeled circadian amplitude (aHR = 1.07 [1.03-1.10] per SD), and with early or late circadian phase (aHR range: 1.06-1.26). Night light and polygenic risk independently predicted higher diabetes risk. The difference in diabetes risk between people with bright and dark nights was similar to the difference between people with low and moderate genetic risk. Interpretation: Type 2 diabetes risk was higher in people exposed to brighter night light, and in people exposed to light patterns that may disrupt circadian rhythms. Avoidance of light at night could be a simple and cost-effective recommendation that mitigates risk of diabetes, even in those with high genetic risk. Funding: Australian Government Research Training Program.

Impact of insomnia on ovarian cancer risk and survival: a Mendelian randomization study.

BACKGROUND: Insomnia is the most common sleep disorder in patients with epithelial ovarian cancer (EOC). We investigated the causal association between genetically predicted insomnia and EOC risk and survival through a two-sample Mendelian randomization (MR) study. METHODS: Insomnia was proxied using genetic variants identified in a genome-wide association study (GWAS) meta-analysis of UK Biobank and 23andMe. Using genetic associations with EOC risk and overall survival from the Ovarian Cancer Association Consortium (OCAC) GWAS in 66,450 women (over 11,000 cases with clinical follow-up), we performed Iterative Mendelian Randomization and Pleiotropy (IMRP) analysis followed by a set of sensitivity analyses. Genetic associations with survival and response to treatment in ovarian cancer study of The Cancer Genome Atlas (TCGA) were estimated controlling for chemotherapy and clinical factors. FINDINGS: Insomnia was associated with higher risk of endometrioid EOC (OR = 1.60, 95% CI 1.05-2.45) and lower risk of high-grade serous EOC (HGSOC) and clear cell EOC (OR = 0.79 and 0.48, 95% CI 0.63-1.00 and 0.27-0.86, respectively). In survival analysis, insomnia was associated with shorter survival of invasive EOC (OR = 1.45, 95% CI 1.13-1.87) and HGSOC (OR = 1.4, 95% CI 1.04-1.89), which was attenuated after adjustment for body mass index and reproductive age. Insomnia was associated with reduced survival in TCGA HGSOC cases who received standard chemotherapy (OR = 2.48, 95% CI 1.13-5.42), but was attenuated after adjustment for clinical factors. INTERPRETATION: This study supports the impact of insomnia on EOC risk and survival, suggesting treatments targeting insomnia could be pivotal for prevention and improving patient survival. FUNDING: National Institutes of Health, National Cancer Institute. Full funding details are provided in acknowledgments.

The role of accelerometer-derived sleep traits on glycated haemoglobin and glucose levels: a Mendelian randomization study.

Self-reported shorter/longer sleep duration, insomnia, and evening preference are associated with hyperglycaemia in observational analyses, with similar observations in small studies using accelerometer-derived sleep traits. Mendelian randomization (MR) studies support an effect of self-reported insomnia, but not others, on glycated haemoglobin (HbA1c). To explore potential effects, we used MR methods to assess effects of accelerometer-derived sleep traits (duration, mid-point least active 5-h, mid-point most active 10-h, sleep fragmentation, and efficiency) on HbA1c/glucose in European adults from the UK Biobank (UKB) (n = 73,797) and the MAGIC consortium (n = 146,806). Cross-trait linkage disequilibrium score regression was applied to determine genetic correlations across accelerometer-derived, self-reported sleep traits, and HbA1c/glucose. We found no causal effect of any accelerometer-derived sleep trait on HbA1c or glucose. Similar MR results for self-reported sleep traits in the UKB sub-sample with accelerometer-derived measures suggested our results were not explained by selection bias. Phenotypic and genetic correlation analyses suggested complex relationships between self-reported and accelerometer-derived traits indicating that they may reflect different types of exposure. These findings suggested accelerometer-derived sleep traits do not affect HbA1c. Accelerometer-derived measures of sleep duration and quality might not simply be 'objective' measures of self-reported sleep duration and insomnia, but rather captured different sleep characteristics.

Genome-wide association analysis of composite sleep health scores in 413,904 individuals.

Recent genome-wide association studies (GWASs) of several individual sleep traits have identified hundreds of genetic loci, suggesting diverse mechanisms. Moreover, sleep traits are moderately correlated, and together may provide a more complete picture of sleep health, while also illuminating distinct domains. Here we construct novel sleep health scores (SHSs) incorporating five core self-report measures: sleep duration, insomnia symptoms, chronotype, snoring, and daytime sleepiness, using additive (SHS-ADD) and five principal components-based (SHS-PCs) approaches. GWASs of these six SHSs identify 28 significant novel loci adjusting for multiple testing on six traits (p<8.3e-9), along with 341 previously reported loci (p<5e-08). The heritability of the first three SHS-PCs equals or exceeds that of SHS-ADD (SNP-h2=0.094), while revealing sleep-domain-specific genetic discoveries. Significant loci enrich in multiple brain tissues and in metabolic and neuronal pathways. Post GWAS analyses uncover novel genetic mechanisms underlying sleep health and reveal connections to behavioral, psychological, and cardiometabolic traits.

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 ±â€…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 ±â€…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.

Genetics of sleep medication purchases suggests causality from sleep problems to psychiatric traits.

STUDY OBJECTIVES: Over 10% of the population in Europe and in the United States use sleep medication to manage sleep problems. Our objective was to elucidate genetic risk factors and clinical correlates that contribute to sleep medication purchase and estimate the comorbid impact of sleep problems. METHODS: We performed epidemiological analysis for psychiatric diagnoses, and genetic association studies of sleep medication purchase in 797 714 individuals from FinnGen Release 7 (N = 311 892) and from the UK Biobank (N = 485 822). Post-association analyses included genetic correlation, co-localization, Mendelian randomization (MR), and polygenic risk estimation. RESULTS: In a GWAS we identified 27 genetic loci significantly associated with sleep medication, located in genes associated with sleep; AUTS2, CACNA1C, MEIS1, KIRREL3, PAX8, GABRA2, psychiatric traits; CACNA1C, HIST1H2BD, NUDT12. TOPAZ1 and TSNARE1. Co-localization and expression analysis emphasized effects on the KPNA2, GABRA2, and CACNA1C expression in the brain. Sleep medications use was epidemiologically related to psychiatric traits in FinnGen (OR [95% (CI)] = 3.86 [3.78 to 3.94], p < 2 × 10-16), and the association was accentuated by genetic correlation and MR; depression (rg = 0.55 (0.027), p = 2.86 × 10-89, p MR = 4.5 × 10-5), schizophrenia (rg = 0.25 (0.026), p = 2.52 × 10-21, p MR = 2 × 10-4), and anxiety (rg = 0.44 (0.047), p = 2.88 × 10-27, p MR = 8.6 × 10-12). CONCLUSIONS: These results demonstrate the genetics behind sleep problems and the association between sleep problems and psychiatric traits. Our results highlight the scientific basis for sleep management in treating the impact of psychiatric diseases.

Causal Association Between Subtypes of Excessive Daytime Sleepiness and Risk of Cardiovascular Diseases.

BACKGROUND: Excessive daytime sleepiness (EDS), experienced in 10% to 20% of the population, has been associated with cardiovascular disease and death. However, the condition is heterogeneous and is prevalent in individuals having short and long sleep duration. We sought to clarify the relationship between sleep duration subtypes of EDS with cardiovascular outcomes, accounting for these subtypes. METHODS AND RESULTS: We defined 3 sleep duration subtypes of excessive daytime sleepiness: normal (6-9 hours), short (<6 hours), and long (>9 hours), and compared these with a nonsleepy, normal-sleep-duration reference group. We analyzed their associations with incident myocardial infarction (MI) and stroke using medical records of 355 901 UK Biobank participants and performed 2-sample Mendelian randomization for each outcome. Compared with healthy sleep, long-sleep EDS was associated with an 83% increased rate of MI (hazard ratio, 1.83 [95% CI, 1.21-2.77]) during 8.2-year median follow-up, adjusting for multiple health and sociodemographic factors. Mendelian randomization analysis provided supporting evidence of a causal role for a genetic long-sleep EDS subtype in MI (inverse-variance weighted ß=1.995, P=0.001). In contrast, we did not find evidence that other subtypes of EDS were associated with incident MI or any associations with stroke (P>0.05). CONCLUSIONS: Our study suggests the previous evidence linking EDS with increased cardiovascular disease risk may be primarily driven by the effect of its long-sleep subtype on higher risk of MI. Underlying mechanisms remain to be investigated but may involve sleep irregularity and circadian disruption, suggesting a need for novel interventions in this population.

A Novel Home-Based Study of Circadian Rhythms: Design, Rationale, and Methods for the Circadia Study.

The Circadia Study (Circadia) is a novel "direct-to-participant" research study investigating the genetics of circadian rhythm disorders of advanced and delayed sleep phase and non-24 hour rhythms. The goals of the Circadia Study are twofold: (i) to create an easy-to-use toolkit for at-home circadian phase assessment for patients with circadian rhythm disorders through the use of novel in-home based surveys, tests, and collection kits; and (ii) create a richly phenotyped patient resource for genetic studies that will lead to new genetic loci associated with circadian rhythm disorders revealing possible loci of interest to target in the development of therapeutics for circadian rhythm disorders. Through these goals, we aim to broaden our understanding and elucidate the genetics of circadian rhythm disorders across a diverse patient population while increasing accessibility to circadian rhythm disorder diagnostics reducing health disparities through self-directed at-home dim light melatonin onset (DLMO) collections.

Self-Directed Home-Based Dim-Light Melatonin Onset Collection: The Circadia Pilot Study.

Study Objectives: To test the feasibility of a novel at-home salivary Dim Light Melatonin Onset (DLMO) assessment protocol to measure the endogenous circadian phase of 10 individuals ( 1 Advanced Sleep-Wake Phase Disorder patient (ASWPD), 4 Delayed Sleep-Wake Phase Disorder patients (DSWPD), and 5 controls). Methods: The study involved 10 participants (sex at birth: females = 9; male= 1), who ranged between 27 to 63 years old, with an average age of 38 years old. Our study population consisted of 7 individuals who identified as white and 3 who identified as Asian. Our participants were diverse in gender identity (woman = 7, male = 1, transgender = 1, nonbinary = 1, none = 1).The study tracked the sleep and activity patterns of 10 individuals over a 5-6 week period using self-reported online sleep diaries and objective actigraphy data. Participants completed two self-directed DLMO assessments, approximately one week apart, adhering to objective compliance measures. Participants completed the study entirely remotely: they completed all sleep diaries and other evaluations online and were mailed a kit with all materials needed to perform the actigraphy and at-home sample collections. Results: Salivary DLMO times were calculated for 8/10 participants using the Hockeystick method. DLMO times were on average 3 hours and 18 minutes earlier than self-reported sleep onset times (DSPD: 12:04 AM, controls: 9:55 PM.) Among the 6 participants for whom we calculated two separate DLMO times, DLMOs 1 and 2 were 96% correlated (p<0.0005.). Conclusions: Our results indicate that self-directed, at-home DLMO assessments are feasible and accurate. The current protocol may serve as a framework to reliably assess circadian phase in both clinical and general populations.

Associations between sleep health and grey matter volume in the UK Biobank cohort (n = 33 356).

As suggested by previous research, sleep health is assumed to be a key determinant of future morbidity and mortality. In line with this, recent studies have found that poor sleep is associated with impaired cognitive function. However, to date, little is known about brain structural abnormalities underlying this association. Although recent findings link sleep health deficits to specific alterations in grey matter volume, evidence remains inconsistent and reliant on small sample sizes. Addressing this problem, the current preregistered study investigated associations between sleep health and grey matter volume (139 imaging-derived phenotypes) in the UK Biobank cohort (33 356 participants). Drawing on a large sample size and consistent data acquisition, sleep duration, insomnia symptoms, daytime sleepiness, chronotype, sleep medication and sleep apnoea were examined. Our main analyses revealed that long sleep duration was systematically associated with larger grey matter volume of basal ganglia substructures. Insomnia symptoms, sleep medication and sleep apnoea were not associated with any of the 139 imaging-derived phenotypes. Short sleep duration, daytime sleepiness as well as late and early chronotype were associated with solitary imaging-derived phenotypes (no recognizable pattern, small effect sizes). To our knowledge, this is the largest study to test associations between sleep health and grey matter volume. Clinical implications of the association between long sleep duration and larger grey matter volume of basal ganglia are discussed. Insomnia symptoms as operationalized in the UK Biobank do not translate into grey matter volume findings.

Mobile Apps for Dietary and Food Timing Assessment: Evaluation for Use in Clinical Research.

BACKGROUND: Over the last decade, health mobile apps have become an increasingly popular tool used by clinicians and researchers to track food consumption and exercise. However, many consumer apps lack the technological features for facilitating the capture of critical food timing details. OBJECTIVE: This study aimed to introduce users to 11 apps from US app stores that recorded both dietary intake and food timing to establish which one would be the most appropriate for clinical research. METHODS: To determine a viable app that recorded both dietary intake and food timing for use in a food timing-related clinical study, we evaluated the time stamp data, usability, privacy policies, the accuracy of nutrient estimates, and general features of 11 mobile apps for dietary assessment that were available on US app stores. The following apps were selected using a keyword search of related terms and reviewed: text entry apps-Cronometer, DiaryNutrition, DietDiary, FoodDiary, Macros, and MyPlate; image entry apps-FoodView and MealLogger; and text plus image entry apps-Bitesnap, myCircadianClock, and MyFitnessPal. RESULTS: Our primary goal was to identify apps that recorded food time stamps, which 8 (73%) of the 11 reviewed apps did. Of the 11 apps, only 4 (36%) allowed users to edit the time stamps. Next, we sought to evaluate the usability of the apps using the System Usability Scale across 2 days, and 82% (9/11) of the apps received favorable scores for usability. To enable use in research and clinical settings, the privacy policies of each app were systematically reviewed using common criteria, with 1 (9%) Health Insurance Portability and Accountability Act-compliant app (Cronometer). Furthermore, protected health information was collected by 9 (82%) of the 11 apps. Finally, to assess the accuracy of the nutrient estimates generated by these apps, we selected 4 sample food items and a 3-day dietary record to input into each app. The caloric and macronutrient estimates of the apps were compared with the nutrient estimates provided by a registered dietitian using the Nutrition Data System for Research database. In terms of the 3-day food record, the apps were found to consistently underestimate daily calories and macronutrients compared with the Nutrition Data System for Research output. CONCLUSIONS: Overall, we found that the Bitesnap app provided flexible dietary and food timing functionality capable of being used in research and clinical settings, whereas most other apps lacked in the necessary food timing functionality or user privacy.

The public health impact of poor sleep on severe COVID-19, influenza and upper respiratory infections.

BACKGROUND: Poor sleep is associated with an increased risk of infections and all-cause mortality but the causal direction between poor sleep and respiratory infections has remained unclear. We examined if poor sleep contributes as a causal risk factor to respiratory infections. METHODS: We used data on insomnia, influenza and upper respiratory infections (URIs) from primary care and hospital records in the UK Biobank (N ≈ 231,000) and FinnGen (N ≈ 392,000). We computed logistic regression to assess association between poor sleep and infections, disease free survival hazard ratios, and performed Mendelian randomization analyses to assess causality. FINDINGS: Utilizing 23 years of registry data and follow-up, we discovered that insomnia diagnosis associated with increased risk for infections (FinnGen influenza Cox's proportional hazard (CPH) HR = 4.34 [3.90, 4.83], P = 4.16 × 10-159, UK Biobank influenza CPH HR = 1.54 [1.37, 1.73], P = 2.49 × 10-13). Mendelian randomization indicated that insomnia causally predisposed to influenza (inverse-variance weighted (IVW) OR = 1.65, P = 5.86 × 10-7), URI (IVW OR = 1.94, P = 8.14 × 10-31), COVID-19 infection (IVW OR = 1.08, P = 0.037) and risk of hospitalization from COVID-19 (IVW OR = 1.47, P = 4.96 × 10-5). INTERPRETATION: Our findings indicate that chronic poor sleep is a causal risk factor for contracting respiratory infections, and in addition contributes to the severity of respiratory infections. These findings highlight the role of sleep in maintaining sufficient immune response against pathogens. FUNDING: Instrumentarium Science Foundation, Academy of Finland, Signe and Ane Gyllenberg Foundation, National Institutes of Health.

Circadian Rest-Activity Rhythms, Delirium Risk, and Progression to Dementia.

OBJECTIVE: Delirium is a complex neurocognitive syndrome suspected to be bidirectionally linked to dementia. Circadian rhythm disturbances likely contribute to dementia pathogenesis, but whether these disturbances are related to delirium risk and progression to all-cause dementia is unknown. METHODS: We analyzed continuous actigraphy data from 53,417 middle-aged or older UK Biobank participants during a median 5 years of follow-up. Four measures were used to characterize the 24-hour daily rest-activity rhythms (RARs): normalized amplitude, acrophase representing the peak activity time, interdaily stability, and intradaily variability (IV) for fragmentation of the rhythm. Cox proportional hazards models examined whether RARs predicted incident delirium (n = 551) and progression to dementia (n = 61). RESULTS: Suppressed 24-hour amplitude, lowest (Q1) versus highest (Q4) quartile (hazard ratio [HR]Q1 vs Q4 = 1.94, 95% confidence interval [CI] = 1.53-2.46, p < 0.001), and more fragmented (higher IV: HRQ4 vs Q1 = 1.49, 95% CI = 1.18-1.88, p < 0.001) rhythms predicted higher delirium risk, after adjusting for age, sex, education, cognitive performance, sleep duration/disturbances, and comorbidities. In those free from dementia, each hour of delayed acrophase was associated with delirium risk (HR = 1.13, 95% CI = 1.04-1.23, p = 0.003). Suppressed 24-hour amplitude was associated with increased risk of progression from delirium to new onset dementia (HR = 1.31, 95% CI = 1.03-1.67, p = 0.03 for each 1-standard deviation decrease). INTERPRETATION: Twenty-four-hour daily RAR suppression, fragmentation, and potentially delayed acrophase were associated with delirium risk. Subsequent progression to dementia was more likely in delirium cases with suppressed rhythms. The presence of RAR disturbances before delirium and prior to progression to dementia suggests that these disturbances may predict higher risk and be involved in early disease pathogenesis. ANN NEUROL 2023;93:1145-1157.

Genome-wide gene by environment study of time spent in daylight and chronotype identifies emerging genetic architecture underlying light sensitivity.

STUDY OBJECTIVES: Light is the primary stimulus for synchronizing the circadian clock in humans. There are very large interindividual differences in the sensitivity of the circadian clock to light. Little is currently known about the genetic basis for these interindividual differences. METHODS: We performed a genome-wide gene-by-environment interaction study (GWIS) in 280 897 individuals from the UK Biobank cohort to identify genetic variants that moderate the effect of daytime light exposure on chronotype (individual time of day preference), acting as "light sensitivity" variants for the impact of daylight on the circadian system. RESULTS: We identified a genome-wide significant SNP mapped to the ARL14EP gene (rs3847634; p < 5 × 10-8), where additional minor alleles were found to enhance the morningness effect of daytime light exposure (ßGxE = -.03, SE = 0.005) and were associated with increased gene ARL14EP expression in brain and retinal tissues. Gene-property analysis showed light sensitivity loci were enriched for genes in the G protein-coupled glutamate receptor signaling pathway and genes expressed in Per2+ hypothalamic neurons. Linkage disequilibrium score regression identified Bonferroni significant genetic correlations of greater light sensitivity GWIS with later chronotype and shorter sleep duration. Greater light sensitivity was nominally genetically correlated with insomnia symptoms and risk for post-traumatic stress disorder (PTSD). CONCLUSIONS: This study is the first to assess light as an important exposure in the genomics of chronotype and is a critical first step in uncovering the genetic architecture of human circadian light sensitivity and its links to sleep and mental health.

Genetics of circadian rhythms and sleep in human health and disease.

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 impact of Mendelian sleep and circadian genetic variants in a population setting.

Rare variants in ten genes have been reported to cause Mendelian sleep conditions characterised by extreme sleep duration or timing. These include familial natural short sleep (ADRB1, DEC2/BHLHE41, GRM1 and NPSR1), advanced sleep phase (PER2, PER3, CRY2, CSNK1D and TIMELESS) and delayed sleep phase (CRY1). The association of variants in these genes with extreme sleep conditions were usually based on clinically ascertained families, and their effects when identified in the population are unknown. We aimed to determine the effects of these variants on sleep traits in large population-based cohorts. We performed genetic association analysis of variants previously reported to be causal for Mendelian sleep and circadian conditions. Analyses were performed using 191,929 individuals with data on sleep and whole-exome or genome-sequence data from 4 population-based studies: UK Biobank, FINRISK, Health-2000-2001, and the Multi-Ethnic Study of Atherosclerosis (MESA). We identified sleep disorders from self-report, hospital and primary care data. We estimated sleep duration and timing measures from self-report and accelerometery data. We identified carriers for 10 out of 12 previously reported pathogenic variants for 8 of the 10 genes. They ranged in frequency from 1 individual with the variant in CSNK1D to 1,574 individuals with a reported variant in the PER3 gene in the UK Biobank. No carriers for variants reported in NPSR1 or PER2 were identified. We found no association between variants analyzed and extreme sleep or circadian phenotypes. Using sleep timing as a proxy measure for sleep phase, only PER3 and CRY1 variants demonstrated association with earlier and later sleep timing, respectively; however, the magnitude of effect was smaller than previously reported (sleep midpoint ~7 mins earlier and ~5 mins later, respectively). We also performed burden tests of protein truncating (PTVs) or rare missense variants for the 10 genes. Only PTVs in PER2 and PER3 were associated with a relevant trait (for example, 64 individuals with a PTV in PER2 had an odds ratio of 4.4 for being "definitely a morning person", P = 4x10-8; and had a 57-minute earlier midpoint sleep, P = 5x10-7). Our results indicate that previously reported variants for Mendelian sleep and circadian conditions are often not highly penetrant when ascertained incidentally from the general population.

Associations Between Sleep Health and Amygdala Reactivity to Negative Facial Expressions in the UK Biobank Cohort.

BACKGROUND: Sleep health (SH) is considered a key determinant of human physiological and psychological well-being. In line with this, previous studies have found that poor sleep is associated with various psychiatric disorders, in particular, with anxiety and depression. Although little is known about the neural mechanisms underlying these associations, recent findings suggest that essential dimensions of SH are associated with altered amygdala reactivity (AR); however, evidence to date is inconsistent and reliant on small sample sizes. METHODS: To address this problem, the current preregistered study investigated associations between SH and AR to negative facial expressions in the UK Biobank cohort (25,758 participants). Drawing on a large sample size and consistent data acquisition, 5 dimensions of SH (insomnia symptoms, sleep duration, daytime sleepiness, chronotype, and sleep medication) were examined. RESULTS: Exploratory analyses revealed that short sleep duration was associated with decreased AR. The remaining SH dimensions and a composite measure of all SH dimensions were not associated with AR. CONCLUSIONS: To our knowledge, this is the largest study to test associations between SH and AR. Habitual short sleep duration may be associated with decreased AR, possibly indicating compensation for impaired prefrontal processes and hampered emotion regulation.

Assessing the Causal Role of Sleep Traits on Glycated Hemoglobin: A Mendelian Randomization Study.

OBJECTIVE: To examine the effects of sleep traits on glycated hemoglobin (HbA1c). RESEARCH DESIGN AND METHODS: This study triangulated evidence across multivariable regression (MVR) and one- (1SMR) and two-sample Mendelian randomization (2SMR) including sensitivity analyses on the effects of five self-reported sleep traits (i.e., insomnia symptoms [difficulty initiating or maintaining sleep], sleep duration, daytime sleepiness, napping, and chronotype) on HbA1c (in SD units) in adults of European ancestry from the UK Biobank (for MVR and 1SMR analyses) (n = 336,999; mean [SD] age 57 [8] years; 54% female) and in the genome-wide association studies from the Meta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC) (for 2SMR analysis) (n = 46,368; 53 [11] years; 52% female). RESULTS: Across MVR, 1SMR, 2SMR, and their sensitivity analyses, we found a higher frequency of insomnia symptoms (usually vs. sometimes or rarely/never) was associated with higher HbA1c (MVR 0.05 SD units [95% CI 0.04-0.06]; 1SMR 0.52 [0.42-0.63]; 2SMR 0.24 [0.11-0.36]). Associations remained, but point estimates were somewhat attenuated after excluding participants with diabetes. For other sleep traits, there was less consistency across methods, with some but not all providing evidence of an effect. CONCLUSIONS: Our results suggest that frequent insomnia symptoms cause higher HbA1c levels and, by implication, that insomnia has a causal role in type 2 diabetes. These findings could have important implications for developing and evaluating strategies that improve sleep habits to reduce hyperglycemia and prevent diabetes.

The public health impact of poor sleep on severe COVID-19, influenza and upper respiratory infections.

Background: Poor sleep is associated with an increased risk of infections and all-cause mortality, and acute sleep loss and disruption have been linked with inflammation and poorer immune control. Previous studies, however, have been unable to evidence causality between the chronic effects of poor sleep and respiratory infection risk. In light of the ongoing COVID-19 pandemic and potential future disease outbreaks, understanding the risk factors for these infections is of great importance. Aim: Our goal was to understand if chronic poor sleep could be identified as a causal risk factor for respiratory infections including influenza, upper respiratory infections and COVID-19. Methods: We used population cohorts from the UK Biobank (N ≈ 231,000) and FinnGen (N ≈ 327,000) with ICD-10 based electronic health records and obtained diagnoses of insomnia, influenza and upper respiratory infections (URIs) from primary care and hospital settings. We computed logistic regression to assess association between poor sleep and infections, disease free survival hazard ratios, and used summary statistics from genome-wide association studies of insomnia, influenza, URI and COVID-19 to perform Mendelian randomization analyses and assess causality. Findings: Utilizing 23 years of registry data and follow-up, we saw that insomnia diagnosis associated with increased risk for infections in FinnGen and in UK Biobank (FinnGen influenza HR = 5.32 [4.09, 6.92], P = 1.02×10-35, UK Biobank influenza HR = 1.54 [1.37, 1.73], P = 2.49×10-13). Mendelian randomization indicated that insomnia causally predisposed to influenza (OR = 1.59, P = 6.23×10-4), upper respiratory infections (OR = 1.71, P = 7.60×10-13), COVID-19 infection (OR = 1.08, P = 0.037) and risk of hospitalization from COVID-19 (OR = 1.47, P = 4.96×10-5). Conclusions: Our findings indicate that chronic poor sleep is a causal risk factor for contracting respiratory infections, and in addition contributes to the severity of respiratory infections. These findings highlight the role of sleep in maintaining sufficient immune response against pathogens as suggested by earlier work. As the current COVID-19 pandemic has increased the number of people suffering from poor sleep, safe interventions such as sleep management and treating individuals with insomnia could be promoted to reduce infections and save lives.