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.
Sleep Apnea and COVID-19 Mortality and Hospitalization Cade, Brian E; Dashti, Hassan S; Hassan, Syed M ...
American journal of respiratory and critical care medicine,
11/2020, Letnik:
202, Številka:
10
Journal Article
To examine the effects of past and current night shift work and genetic type 2 diabetes vulnerability on type 2 diabetes odds.
In the UK Biobank, we examined associations of current (
= 272,214) and ...lifetime (
= 70,480) night shift work exposure with type 2 diabetes risk (6,770 and 1,191 prevalent cases, respectively). For 180,704 and 44,141 unrelated participants of European ancestry (4,002 and 726 cases, respectively) with genetic data, we assessed whether shift work exposure modified the relationship between a genetic risk score (comprising 110 single-nucleotide polymorphisms) for type 2 diabetes and prevalent diabetes.
Compared with day workers, all current night shift workers were at higher multivariable-adjusted odds for type 2 diabetes (none or rare night shifts: odds ratio OR 1.15 95% CI 1.05-1.26; some nights: OR 1.18 95% CI 1.05-1.32; and usual nights: OR 1.44 95% CI 1.19-1.73), except current permanent night shift workers (OR 1.09 95% CI 0.93-1.27). Considering a person's lifetime work schedule and compared with never shift workers, working more night shifts per month was associated with higher type 2 diabetes odds (<3/month: OR 1.24 95% CI 0.90-1.68; 3-8/month: OR 1.11 95% CI 0.90-1.37; and >8/month: OR 1.36 95% CI 1.14-1.62;
= 0.001). The association between genetic type 2 diabetes predisposition and type 2 diabetes odds was not modified by shift work exposure.
Our findings show that night shift work, especially rotating shift work including night shifts, is associated with higher type 2 diabetes odds and that the number of night shifts worked per month appears most relevant for type 2 diabetes odds. Also, shift work exposure does not modify genetic risk for type 2 diabetes, a novel finding that warrants replication.
Sleep Duration and Myocardial Infarction Daghlas, Iyas; Dashti, Hassan S.; Lane, Jacqueline ...
Journal of the American College of Cardiology,
09/2019, Letnik:
74, Številka:
10
Journal Article
Recenzirano
Odprti dostop
Observational studies suggest associations between extremes of sleep duration and myocardial infarction (MI), but the causal contribution of sleep to MI and its potential to mitigate genetic ...predisposition to coronary disease is unclear.
This study sought to investigate associations between sleep duration and incident MI, accounting for joint effects with other sleep traits and genetic risk of coronary artery disease, and to assess causality using Mendelian randomization (MR).
In 461,347 UK Biobank (UKB) participants free of relevant cardiovascular disease, the authors estimated multivariable adjusted hazard ratios (HR) for MI (5,128 incident cases) across habitual self-reported short (<6 h) and long (>9 h) sleep duration, and examined joint effects with sleep disturbance traits and a coronary artery disease genetic risk score. The authors conducted 2-sample MR for short (24 single nucleotide polymorphisms) and continuous (71 single nucleotide polymorphisms) sleep duration with MI (n = 43,676 cases/128,199 controls), and replicated results in UKB (n = 12,111/325,421).
Compared with sleeping 6 to 9 h/night, short sleepers had a 20% higher multivariable-adjusted risk of incident MI (HR: 1.20; 95% confidence interval CI: 1.07 to 1.33), and long sleepers had a 34% higher risk (HR: 1.34; 95% CI: 1.13 to 1.58); associations were independent of other sleep traits. Healthy sleep duration mitigated MI risk even among individuals with high genetic liability (HR: 0.82; 95% CI: 0.68 to 0.998). MR was consistent with a causal effect of short sleep duration on MI in CARDIoGRAMplusC4D (Coronary ARtery DIsease Genome wide Replication and Meta-analysis plus Coronary Artery Disease Genetics Consortium) (HR: 1.19; 95% CI: 1.09 to 1.29) and UKB (HR: 1.21; 95% CI: 1.08 to 1.37).
Prospective observational and MR analyses support short sleep duration as a potentially causal risk factor for MI. Investigation of sleep extension to prevent MI may be warranted.
Display omitted
Links between short sleep duration and obesity, type 2 diabetes, hypertension, and cardiovascular disease may be mediated through changes in dietary intake. This review provides an overview of recent ...epidemiologic studies on the relations between habitual short sleep duration and dietary intake in adults from 16 cross-sectional studies. The studies have observed consistent associations between short sleep duration and higher total energy intake and higher total fat intake, and limited evidence for lower fruit intake, and lower quality diets. Evidence also suggests that short sleepers may have irregular eating behavior deviating from the traditional 3 meals/d to fewer main meals and more frequent, smaller, energy-dense, and highly palatable snacks at night. Although the impact of short sleep duration on dietary intake tends to be small, if chronic, it may contribute to an increased risk of obesity and related chronic disease. Mechanisms mediating the associations between sleep duration and dietary intake are likely to be multifactorial and include differences in the appetite-related hormones leptin and ghrelin, hedonic pathways, extended hours for intake, and altered time of intake. Taking into account these epidemiologic relations and the evidence for causal relations between sleep loss and metabolism and cardiovascular function, health promotion strategies should emphasize improved sleep as an additional factor in health and weight management. Moreover, future sleep interventions in controlled studies and sleep extension trials in chronic short sleepers are imperative for establishing whether there is a causal relation between short sleep duration and changes in dietary intake.
Considerable recent advancements in elucidating the genetic architecture of sleep traits and sleep disorders may provide insight into the relationship between sleep and obesity. Despite the ...involvement of the circadian clock in sleep and metabolism, few shared genes, including
FTO
, were implicated in genome-wide association studies (GWASs) of sleep and obesity. Polygenic scores composed of signals from GWASs of sleep traits show largely null associations with obesity, suggesting lead variants are unique to sleep. Modest genome-wide genetic correlations are observed between many sleep traits and obesity and are largest for snoring. Notably, U-shaped positive genetic correlations with body mass index (BMI) exist for both short and long sleep durations. Findings from Mendelian randomization suggest robust causal effects of insomnia on higher BMI and, conversely, of higher BMI on snoring and daytime sleepiness. In addition, bidirectional effects between sleep duration and daytime napping with obesity may also exist. Limited gene-sleep interaction studies suggest that achieving favorable sleep, as part of a healthy lifestyle, may attenuate genetic predisposition to obesity,but whether these improvements produce clinically meaningful reductions in obesity risk remains unclear. Investigations of the genetic link between sleep and obesity for sleep disorders other than insomnia and in populations of non-European ancestry are currently limited.
Poor dietary choices may underlie known associations between having an evening diurnal preference and cardiometabolic diseases. Assessing causal links between diurnal preference and food intake is ...now possible in Mendelian randomization (MR) analyses.
We aimed to use a 2-sample MR to determine potential causal effects of genetic liability to a morning preference on food intake. We also examined potential causal effects of a morning preference on objectively captured response performances to email-administered 24-h diet recalls.
We used genetic variants associated with a morning preference from a published genome-wide association meta-analysis. Our outcomes included 61 food items with estimates from a food-frequency questionnaire in the UK Biobank (n = 361,194). For significant findings, we repeated the analysis using intake estimates from modified 24-h diet recalls in a subset of overlapping participants (n = 146,086). In addition, we examined 7 response performance outcomes, including the time and duration of responses to 24-h diet recalls (n = 123,035). MR effects were estimated using an inverse-variance weighted analysis.
Genetic liability to a morning preference was associated with increased intake of 6 food items (fresh fruit, alcohol with meals, bran cereal, cereals, dried fruit, and water), decreased intake of 4 food items (beer plus cider, processed meat, other cereals e.g., corn or frosted flakes, and full cream milk), increased temperature of hot drinks, and decreased variation in diet (PFalse Discovery Rate < 0.05). There was no evidence for an effect on coffee or tea intake. Findings for fresh fruit, beer plus cider, bran cereal, and cereal were consistent when intakes were estimated by 24-h diet recalls (P < 0.05). We also identified potential causal links between a morning preference with earlier timing and a shorter duration for completing email-administered 24-h diet recalls.
Our findings provide evidence for a potentially causal effect of a morning preference with the increased intake of foods known to constitute a healthy diet, suggesting possible health benefits of adopting a more morning diurnal preference.
There is a paucity of evidence regarding the role of food timing on cardiometabolic health and weight loss in adults.
To determine whether late eating is cross-sectionally associated with obesity and ...cardiometabolic risk factors at baseline; and whether late eating is associated with weight loss rate and success following a weight loss intervention protocol. Also, to identify obesogenic behaviors and weight loss barriers associated with late eating.
Participants were recruited from a weight-loss program in Spain. Upon recruitment, the midpoint of meal intake was determined by calculating the midway point between breakfast and dinner times, and dietary composition was determined from diet recall. Population median for the midpoint of meal intake was used to stratify participants into early (before 14:54) and late (after 14:54) eaters. Cardiometabolic and satiety hormonal profiles were determined from fasting blood samples collected prior to intervention. Weekly weight loss and barriers were evaluated during the ∼19-wk program. Linear and logistic regression models were used to assess differences between late and early eaters in cardiometabolic traits, satiety hormones, obesogenic behaviors, and weight loss, adjusted for age, sex, clinic site, year of recruitment, and baseline BMI.
A total of 3362 adults mean (SD): age: 41 (14) y; 79.2% women, BMI: 31.05 (5.58) kg/m2 were enrolled. At baseline, no differences were observed in energy intake or physical activity levels between early and late eaters (P >0.05). Late eaters had higher BMI, higher concentrations of triglycerides, and lower insulin sensitivity compared with early eaters (all P <0.05) prior to intervention. In addition, late eaters had higher concentrations of the satiety hormone leptin in the morning (P = 0.001). On average, late eaters had an average 80 g lower weekly rate of weight loss early, 585 (667) g/wk; late, 505 (467) g/wk; P = 0.008, higher odds of having weight-loss barriers OR (95% CI): 1.22 (1.03, 1.46); P = 0.025, and lower odds of motivation for weight loss 0.81 (0.66, 0.99); P = 0.044 compared with early eaters.
Our results suggest that late eating is associated with cardiometabolic risk factors and reduced efficacy of a weight-loss intervention. Insights into the characteristics and behaviors related to late eating may be useful in the development of future interventions aimed at advancing the timing of food intake.
The influence of genetic risk for obesity on food choice behaviors is unknown and may be in the causal pathway between genetic risk and weight gain. The aim of this study was to examine associations ...between genetic risk for obesity and food choice behaviors using objectively assessed workplace food purchases. This study is a secondary analysis of baseline data collected prior to the start of the "ChooseWell 365" health-promotion intervention randomized control trial. Participants were employees of a large hospital in Boston, MA, who enrolled in the study between September 2016 and February 2018. Cafeteria sales data, collected retrospectively for 3 months prior to enrollment, were used to track the quantity (number of items per 3 months) and timing (median time of day) of purchases, and participant surveys provided self-reported behaviors, including skipping meals and preparing meals at home. A previously validated Healthy Purchasing Score was calculated using the cafeteria traffic-light labeling system (i.e., green = healthy, yellow = less healthy, red = unhealthy) to estimate the healthfulness (quality) of employees' purchases (range, 0%-100% healthy). DNA was extracted and genotyped from blood samples. A body mass index (BMI) genome-wide polygenic score (BMI.sub.GPS) was generated by summing BMI-increasing risk alleles across the genome. Additionally, 3 polygenic risk scores (PRSs) were generated with 97 BMI variants previously identified at the genome-wide significance level (P < 5 x 10.sup.-8 ): (1) BMI.sub.97 (97 loci), (2) BMI.sub.CNS (54 loci near genes related to central nervous system CNS), and (3) BMI.sub.non-CNS (43 loci not related to CNS). Multivariable linear and logistic regression tested associations of genetic risk score quartiles with workplace purchases, adjusted for age, sex, seasonality, and population structure. Associations were considered significant at P < 0.05. In 397 participants, mean age was 44.9 years, and 80.9% were female. Higher genetic risk scores were associated with higher BMI. The highest quartile of BMI.sub.GPS was associated with lower Healthy Purchasing Score (-4.8 percentage points 95% CI -8.6 to -1.0; P = 0.02), higher quantity of food purchases (14.4 more items 95% CI -0.1 to 29.0; P = 0.03), later time of breakfast purchases (15.0 minutes later 95% CI 1.5-28.5; P = 0.03), and lower likelihood of preparing dinner at home (Q4 odds ratio OR = 0.3 95% CI 0.1-0.9; P = 0.03) relative to the lowest BMI.sub.GPS quartile. Compared with the lowest quartile, the highest BMI.sub.CNS quartile was associated with fewer items purchased (P = 0.04), and the highest BMI.sub.non-CNS quartile was associated with purchasing breakfast at a later time (P = 0.01), skipping breakfast (P = 0.03), and not preparing breakfast (P = 0.04) or lunch (P = 0.01) at home. A limitation of this study is our data come from a relatively small sample of healthy working adults of European ancestry who volunteered to enroll in a health-promotion study, which may limit generalizability. In this study, genetic risk for obesity was associated with the quality, quantity, and timing of objectively measured workplace food purchases. These findings suggest that genetic risk for obesity may influence eating behaviors that contribute to weight and could be targeted in personalized workplace wellness programs in the future.
PDF
