Genetic variants affect diurnal glucose levels throughout the day.

Circadian rhythms not only coordinate the timing of wake and sleep but also regulate homeostasis within the body, including glucose metabolism. However, the genetic variants that contribute to temporal control of glucose levels have not been previously examined. Using data from 420,000 individuals from the UK Biobank and replicating our findings in 100,000 individuals from the Estonian Biobank, we show that diurnal serum glucose is under genetic control. We discover a robust temporal association of glucose levels at the Melatonin receptor 1B (MTNR1B) (rs10830963, P = 1e-22) and a canonical circadian pacemaker gene Cryptochrome 2 (CRY2) loci (rs12419690, P = 1e-16). Furthermore, we show that sleep modulates serum glucose levels and the genetic variants have a separate mechanism of diurnal control. Finally, we show that these variants independently modulate risk of type 2 diabetes. Our findings, together with earlier genetic and epidemiological evidence, show a clear connection between sleep and metabolism and highlight variation at MTNR1B and CRY2 as temporal regulators for glucose levels.

Genetic and functional analysis of Raynaud's syndrome implicates loci in vasculature and immunity.

Raynaud's syndrome is a dysautonomia where exposure to cold causes vasoconstriction and hypoxia, particularly in the extremities. We performed meta-analysis in four cohorts and discovered eight loci (ADRA2A, IRX1, NOS3, ACVR2A, TMEM51, PCDH10-DT, HLA, and RAB6C) where ADRA2A, ACVR2A, NOS3, TMEM51, and IRX1 co-localized with expression quantitative trait loci (eQTLs), particularly in distal arteries. CRISPR gene editing further showed that ADRA2A and NOS3 loci modified gene expression and in situ RNAscope clarified the specificity of ADRA2A in small vessels and IRX1 around small capillaries in the skin. A functional contraction assay in the cold showed lower contraction in ADRA2A-deficient and higher contraction in ADRA2A-overexpressing smooth muscle cells. Overall, our study highlights the power of genome-wide association testing with functional follow-up as a method to understand complex diseases. The results indicate temperature-dependent adrenergic signaling through ADRA2A, effects at the microvasculature by IRX1, endothelial signaling by NOS3, and immune mechanisms by the HLA locus in Raynaud's syndrome.

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.

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.

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.

Melatonin decreases human adipose tissue insulin sensitivity.

Melatonin is a pineal hormone that modulates the circadian system and exerts soporific and phase-shifting effects. It is also involved in many other physiological processes, such as those implicated in cardiovascular, endocrine, immune, and metabolic functions. However, the role of melatonin in glucose metabolism remains contradictory, and its action on human adipose tissue (AT) explants has not been demonstrated. We aimed to assess whether melatonin (a pharmacological dose) influences insulin sensitivity in human AT. This will help better understand melatonin administration's effect on glucose metabolism. Abdominal AT (subcutaneous and visceral) biopsies were obtained from 19 participants with severe obesity (age: 42.84 ± 12.48 years; body mass index: 43.14 ± 8.26 kg/m2) who underwent a laparoscopic gastric bypass. AT biopsies were exposed to four different treatments: control (C), insulin alone (I) (10 nM), melatonin alone (M) (5000 pg/mL), and insulin plus melatonin combined (I + M). All four conditions were repeated in both subcutaneous and visceral AT, and all were performed in the morning at 8 a.m. (n = 19) and the evening at 8 p.m. (in a subsample of n = 12). We used western blot analysis to determine insulin signaling (using the pAKT/tAKT ratio). Furthermore, RNAseq analyses were performed to better understand the metabolic pathways involved in the effect of melatonin on insulin signaling. As expected, insulin treatment (I) increased the pAKT/tAKT ratio compared with control (p < .0001). Furthermore, the addition of melatonin (I + M) resulted in a decrease in insulin signaling as compared with insulin alone (I); this effect was significant only during the evening time (not in the morning time). Further, RNAseq analyses in visceral AT during the evening condition (at 8 p.m.) showed that melatonin resulted in a prompt transcriptome response (around 1 h after melatonin addition), particularly by downregulating the insulin signaling pathway. Our results show that melatonin reduces insulin sensitivity in human AT during the evening. These results may partly explain the previous studies showing a decrease in glucose tolerance after oral melatonin administration in the evening or when eating late when endogenous melatonin is present.

Association of Rest-Activity Rhythm and Risk of Developing Dementia or Mild Cognitive Impairment in the Middle-Aged and Older Population: Prospective Cohort Study.

BACKGROUND: The relationship between 24-hour rest-activity rhythms (RARs) and risk for dementia or mild cognitive impairment (MCI) remains an area of growing interest. Previous studies were often limited by small sample sizes, short follow-ups, and older participants. More studies are required to fully explore the link between disrupted RARs and dementia or MCI in middle-aged and older adults. OBJECTIVE: We leveraged the UK Biobank data to examine how RAR disturbances correlate with the risk of developing dementia and MCI in middle-aged and older adults. METHODS: We analyzed the data of 91,517 UK Biobank participants aged between 43 and 79 years. Wrist actigraphy recordings were used to derive nonparametric RAR metrics, including the activity level of the most active 10-hour period (M10) and its midpoint, the activity level of the least active 5-hour period (L5) and its midpoint, relative amplitude (RA) of the 24-hour cycle [RA=(M10-L5)/(M10+L5)], interdaily stability, and intradaily variability, as well as the amplitude and acrophase of 24-hour rhythms (cosinor analysis). We used Cox proportional hazards models to examine the associations between baseline RAR and subsequent incidence of dementia or MCI, adjusting for demographic characteristics, comorbidities, lifestyle factors, shiftwork status, and genetic risk for Alzheimer's disease. RESULTS: During the follow-up of up to 7.5 years, 555 participants developed MCI or dementia. The dementia or MCI risk increased for those with lower M10 activity (hazard ratio [HR] 1.28, 95% CI 1.14-1.44, per 1-SD decrease), higher L5 activity (HR 1.15, 95% CI 1.10-1.21, per 1-SD increase), lower RA (HR 1.23, 95% CI 1.16-1.29, per 1-SD decrease), lower amplitude (HR 1.32, 95% CI 1.17-1.49, per 1-SD decrease), and higher intradaily variability (HR 1.14, 95% CI 1.05-1.24, per 1-SD increase) as well as advanced L5 midpoint (HR 0.92, 95% CI 0.85-0.99, per 1-SD advance). These associations were similar in people aged <70 and >70 years, and in non-shift workers, and they were independent of genetic and cardiovascular risk factors. No significant associations were observed for M10 midpoint, interdaily stability, or acrophase. CONCLUSIONS: Based on findings from a large sample of middle-to-older adults with objective RAR assessment and almost 8-years of follow-up, we suggest that suppressed and fragmented daily activity rhythms precede the onset of dementia or MCI and may serve as risk biomarkers for preclinical dementia in middle-aged and older adults.

Sleep and circadian biomarkers of postoperative delirium (SLEEP-POD): protocol for a prospective and observational cohort study.

INTRODUCTION: Surgical patients over 70 experience postoperative delirium (POD) complications in up to 50% of procedures. Sleep/circadian disruption has emerged as a potential risk factor for POD in epidemiological studies. This protocol presents a single-site, prospective observational study designed to examine the relationship between sleep/circadian regulation and POD and how this association could be moderated or mediated by Alzheimer's disease (AD) pathology and genetic risk for AD. METHODS AND ANALYSIS: Study staff members will screen for eligible patients (age ≥70) seeking joint replacement or spinal surgery at Massachusetts General Hospital (MGH). At the inclusion visit, patients will be asked a series of questionnaires related to sleep and cognition, conduct a four-lead ECG recording and be fitted for an actigraphy watch to wear for 7 days before surgery. Blood samples will be collected preoperatively and postoperatively and will be used to gather information about AD variant genes (APOE-ε4) and AD-related pathology (total and phosphorylated tau). Confusion Assessment Method-Scale and Montreal Cognitive Assessment will be completed twice daily for 3 days after surgery. Seven-day actigraphy assessments and Patient-Reported Outcomes Measurement Information System questionnaires will be performed 1, 3 and 12 months after surgery. Relevant patient clinical data will be monitored and recorded throughout the study. ETHICS AND DISSEMINATION: This study is approved by the IRB at MGH, Boston, and it is registered with the US National Institutes of Health on ClinicalTrials.gov (NCT06052397). Plans for dissemination include conference presentations at a variety of scientific institutions. Results from this study are intended to be published in peer-reviewed journals. Relevant updates will be made available on ClinicalTrials.gov. TRIAL REGISTRATION NUMBER: NCT06052397.

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.

Nightmares share genetic risk factors with sleep and psychiatric traits.

Nightmares are vivid, extended, and emotionally negative or negative dreams that awaken the dreamer. While sporadic nightmares and bad dreams are common and generally harmless, frequent nightmares often reflect underlying pathologies of emotional regulation. Indeed, insomnia, depression, anxiety, or alcohol use have been associated with nightmares in epidemiological and clinical studies. However, the connection between nightmares and their comorbidities are poorly understood. Our goal was to examine the genetic risk factors for nightmares and estimate correlation or causality between nightmares and comorbidities. We performed a genome-wide association study (GWAS) in 45,255 individuals using a questionnaire-based assessment on the frequency of nightmares during the past month and genome-wide genotyping data. While the GWAS did not reveal individual risk variants, heritability was estimated at 5%. In addition, the genetic correlation analysis showed a robust correlation (rg > 0.4) of nightmares with anxiety (rg = 0.671, p = 7.507e-06), depressive (rg = 0.562, p = 1.282e-07) and posttraumatic stress disorders (rg = 0.4083, p = 0.0152), and personality trait neuroticism (rg = 0.667, p = 4.516e-07). Furthermore, Mendelian randomization suggested causality from insomnia to nightmares (beta = 0.027, p = 0.0002). Our findings suggest that nightmares share genetic background with psychiatric traits and that insomnia may increase an individual's liability to experience frequent nightmares. Given the significant correlations with psychiatric and psychological traits, it is essential to grow awareness of how nightmares affect health and disease and systematically collect information about nightmares, especially from clinical samples and larger cohorts.

Associations of a multidimensional polygenic sleep health score and a sleep lifestyle index on health outcomes and their interaction in a clinical biobank.

Sleep is a complex behavior regulated by genetic and environmental factors, and is known to influence health outcomes. However, the effect of multidimensional sleep encompassing several sleep dimensions on diseases has yet to be fully elucidated. Using the Mass General Brigham Biobank, we aimed to examine the association of multidimensional sleep with health outcomes and investigate whether sleep behaviors modulate genetic predisposition to unfavorable sleep on mental health outcomes. First, we generated a Polygenic Sleep Health Score using previously identified single nucleotide polymorphisms for sleep health and constructed a Sleep Lifestyle Index using data from self-reported sleep questions and electronic health records; second, we performed phenome-wide association analyses between these indexes and clinical phenotypes; and third, we analyzed the interaction between the indexes on prevalent mental health outcomes. Fifteen thousand eight hundred and eighty-four participants were included in the analysis (mean age 54.4; 58.6% female). The Polygenic Sleep Health Score was associated with the Sleep Lifestyle Index (ß=0.050, 95%CI=0.032, 0.068) and with 114 disease outcomes spanning 12 disease groups, including obesity, sleep, and substance use disease outcomes (p<3.3×10-5). The Sleep Lifestyle Index was associated with 458 disease outcomes spanning 17 groups, including sleep, mood, and anxiety disease outcomes (p<5.1×10-5). No interactions were found between the indexes on prevalent mental health outcomes. These findings suggest that favorable sleep behaviors and genetic predisposition to healthy sleep may independently be protective of disease outcomes. This work provides novel insights into the role of multidimensional sleep on population health and highlights the need to develop prevention strategies focused on healthy sleep habits.

Impact of polygenic score for BMI on weight loss effectiveness and genome-wide association analysis.

BACKGROUND: While environmental factors play an important role in weight loss effectiveness, genetics may also influence its success. We examined whether a genome-wide polygenic score for BMI was associated with weight loss effectiveness and aimed to identify common genetic variants associated with weight loss. METHODS: Participants in the ONTIME study (n = 1210) followed a uniform, multimodal behavioral weight-loss intervention. We first tested associations between a genome-wide polygenic score for higher BMI and weight loss effectiveness (total weight loss, rate of weight loss, and attrition). We then conducted a genome-wide association study (GWAS) for weight loss in the ONTIME study and performed the largest weight loss meta-analysis with earlier studies (n = 3056). Lastly, we ran exploratory GWAS in the ONTIME study for other weight loss outcomes and related factors. RESULTS: We found that each standard deviation increment in the polygenic score was associated with a decrease in the rate of weight loss (Beta (95% CI) = -0.04 kg per week (-0.06, -0.01); P = 3.7 × 10-03) and with higher attrition after adjusting by treatment duration. No associations reached genome-wide significance in meta-analysis with previous GWAS studies for weight loss. However, associations in the ONTIME study showed effects consistent with published studies for rs545936 (MIR486/NKX6.3/ANK1), a previously noted weight loss locus. In the meta-analysis, each copy of the minor A allele was associated with 0.12 (0.03) kg/m2 higher BMI at week five of treatment (P = 3.9 × 10-06). In the ONTIME study, we also identified two genome-wide significant (P < 5×10-08) loci for the rate of weight loss near genes implicated in lipolysis, body weight, and metabolic regulation: rs146905606 near NFIP1/SPRY4/FGF1; and rs151313458 near LSAMP. CONCLUSION: Our findings are expected to help in developing personalized weight loss approaches based on genetics. CLINICAL TRIAL REGISTRATION: Obesity, Nutrigenetics, Timing, and Mediterranean (ONTIME; clinicaltrials.gov: NCT02829619) study.

The Role of Circadian Rhythms and Sleep in Anorexia Nervosa.

Importance: Observational studies have associated anorexia nervosa with circadian rhythms and sleep traits. However, the direction of causality and the extent of confounding by psychosocial comorbidities in these associations are unknown. Objectives: To investigate the association between anorexia nervosa and circadian and sleep traits through mendelian randomization and to test the associations between a polygenic risk score (PRS) for anorexia nervosa and sleep disorders in a clinical biobank. Design, Setting, and Participants: This genetic association study used bidirectional 2-sample mendelian randomization with summary-level genetic associations between anorexia nervosa (from the Psychiatric Genomics Consortium) and chronotype and sleep traits (primarily from the UK Biobank). The inverse-variance weighted method, in addition to other sensitivity approaches, was used. From the clinical Mass General Brigham (MGB) Biobank (n = 47 082), a PRS for anorexia nervosa was calculated for each patient and associations were tested with prevalent sleep disorders derived from electronic health records. Patients were of European ancestry. All analyses were performed between February and August 2023. Exposures: Genetic instruments for anorexia nervosa, chronotype, daytime napping, daytime sleepiness, insomnia, and sleep duration. Main Outcomes and Measures: Chronotype, sleep traits, risk of anorexia nervosa, and sleep disorders derived from a clinical biobank. Results: The anorexia nervosa genome-wide association study included 16 992 cases (87.7%-97.4% female) and 55 525 controls (49.6%-63.4% female). Genetic liability for anorexia nervosa was associated with a more morning chronotype (ß = 0.039; 95% CI, 0.006-0.072), and conversely, genetic liability for morning chronotype was associated with increased risk of anorexia nervosa (ß = 0.178; 95% CI, 0.042-0.315). Associations were robust in sensitivity and secondary analyses. Genetic liability for insomnia was associated with increased risk of anorexia nervosa (ß = 0.369; 95% CI, 0.073-0.666); however, sensitivity analyses indicated bias due to horizontal pleiotropy. The MGB Biobank analysis included 47 082 participants with a mean (SD) age of 60.4 (17.0) years and 25 318 (53.8%) were female. A PRS for anorexia nervosa was associated with organic or persistent insomnia in the MGB Biobank (odds ratio, 1.10; 95% CI, 1.03-1.17). No associations were evident for anorexia nervosa with other sleep traits. Conclusions and Relevance: The results of this study suggest that in contrast to other metabo-psychiatric diseases, anorexia nervosa is a morningness eating disorder and further corroborate findings implicating insomnia in anorexia nervosa. Future studies in diverse populations and with subtypes of anorexia nervosa are warranted.

Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores From Clinical and Genetic Risk Factors in Early and Late Pregnancies.

BACKGROUND: Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20-weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at risk are needed. METHODS: We identified a cohort of N=1125 pregnant individuals who delivered between May 2015 and May 2022 at Mass General Brigham Hospitals with available electronic health record data and linked genetic data. Using clinical electronic health record data and systolic blood pressure polygenic risk scores derived from a large genome-wide association study, we developed machine learning (XGBoost) and logistic regression models to predict preeclampsia risk. RESULTS: Pregnant individuals with a systolic blood pressure polygenic risk score in the top quartile had higher blood pressures throughout pregnancy compared with patients within the lowest quartile systolic blood pressure polygenic risk score. In the first trimester, the most predictive model was XGBoost, with an area under the curve of 0.74. In late pregnancy, with data obtained up to the delivery admission, the best-performing model was XGBoost using clinical variables, which achieved an area under the curve of 0.91. Adding the systolic blood pressure polygenic risk score to the models did not improve the performance significantly based on De Long test comparing the area under the curve of models with and without the polygenic score. CONCLUSIONS: Integrating clinical factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes.

Comparison between self-reported and actigraphy-derived sleep measures in patients receiving home parenteral nutrition: Secondary analysis of observational data.

BACKGROUND: Patients receiving home parenteral nutrition (HPN) frequently report disrupted sleep. However, there are often inconsistencies between objectively measured and questionnaire-derived sleep measures. We compared sleep measures estimated from wrist actigraphy and self-report in adults receiving HPN. METHODS: In this secondary analysis, we pooled data from two sleep-related studies enrolling adults receiving habitual HPN. We compared measures from 7-day averages of wrist actigraphy against comparable responses to a sleep questionnaire. Sleep measures included bedtime, wake time, time in bed, total sleep time, and sleep onset latency (SOL). Spearman correlation coefficients, Bland-Altman plots, and linear regression models for each set of sleep measures provided estimates of agreement. RESULTS: Participants (N = 35) had a mean age of 52 years, body mass index of 21.6 kg/m2 , and 77% identified as female. Correlation coefficients ranged from 0.35 to 0.90, were highest for wake time (r = 0.90) and bedtime (r = 0.74), and lowest for total sleep time (r = 0.35). Actigraphy overestimated self-reported bedtime, wake time, and total sleep time and underestimated self-reported time in bed and SOL. Regression coefficients indicated the highest calibration for bedtime and wake time and lower calibration for time in bed, total sleep time, and SOL. CONCLUSION: We observed strong-to-moderate agreement between sleep measures derived from wrist actigraphy and self-report in adults receiving HPN. Weaker correlations for total sleep time and SOL may indicate low wrist actigraphy sensitivity. Low-quality sleep resulting from sleep disruptions may have also contributed to an underreporting of perceived sleep quantity and lower concordance.

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.

Glycemic and sleep effects of daytime compared with those of overnight infusions of home parenteral nutrition in adults with short bowel syndrome: A quasi-experimental pilot trial.

BACKGROUND: Patients with short bowel syndrome (SBS) dependent on home parenteral nutrition (HPN) commonly cycle infusions overnight, likely contributing to circadian misalignment and sleep disruption. METHODS: The objective of this quasi-experimental, single-arm, controlled, pilot trial was to examine the feasibility, safety, and efficacy of daytime infusions of HPN in adults with SBS without diabetes. Enrolled patients were fitted with a continuous glucose monitor and wrist actigraph and were instructed to cycle their infusions overnight for 1 wk, followed by daytime for another week. The 24-h average blood glucose, the time spent >140 mg/dL or <70 mg/dL, and sleep fragmentation were derived for each week and compared using Wilcoxon signed-rank test. Patient-reported quality-of-life outcomes were also compared between the weeks. RESULTS: Twenty patients (mean age, 51.7 y; 75% female; mean body mass index, 21.5 kg/m2) completed the trial. Overnight infusions started at 21:00 and daytime infusions at 09:00. No serious adverse events were noted. There were no differences in 24-h glycemia (daytime-median: 93.00 mg/dL; 95% CI: 87.7-99.9 mg/dL, compared with overnight-median: 91.1 mg/dL; 95% CI: 89.6-99.0 mg/dL; P = 0.922). During the day hours (09:00-21:00), the mean glucose concentrations were 13.5 (5.7-22.0) mg/dL higher, and the time spent <70 mg/dL was 15.0 (-170.0, 22.5) min lower with daytime than with overnight HPN. Conversely, during the night hours (21:00-09:00), the glucose concentrations were 16.6 (-23.1, -2.2) mg/dL lower with daytime than with overnight HPN. There were no differences in actigraphy-derived measures of sleep and activity rhythms; however, sleep timing was later, and light at night exposure was lower with daytime than with overnight HPN. Patients reported less sleep disruptions due to urination and fewer episodes of uncontrollable diarrhea or ostomy output with daytime HPN. CONCLUSIONS: Daytime HPN was feasible and safe in adults with SBS and, compared with overnight HPN, improved subjective sleep without increasing 24-h glucose concentrations. This trial was registered at clinicaltrials.gov as NCT04743960 (https://classic. CLINICALTRIALS: gov/ct2/show/NCT04743960).