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.

Genetic predisposition to macronutrient preference and workplace food choices.

Prior research identified genetic variants influencing macronutrient preference, but whether genetic differences underlying nutrient preference affect long-term food choices is unknown. Here we examined the associations of polygenic scores for carbohydrate, fat, and protein preference with 12 months' workplace food purchases among 397 hospital employees from the ChooseWell 365 study. Food purchases were obtained retrospectively from the hospital's cafeteria sales data for the 12 months before participants were enrolled in the ChooseWell 365 study. Traffic light labels, visible to employees when making purchases, measured the quality of workplace purchases. During the 12-month study period, there were 215,692 cafeteria purchases. Each SD increase in the polygenic score for carbohydrate preference was associated with 2.3 additional purchases/month (95%CI, 0.2 to 4.3; p = 0.03) and a higher number of green-labeled purchases (ß = 1.9, 95%CI, 0.5-3.3; p = 0.01). These associations were consistent in subgroup and sensitivity analyses accounting for additional sources of bias. There was no evidence of associations between fat and protein polygenic scores and cafeteria purchases. Findings from this study suggest that genetic differences in carbohydrate preference could influence long-term workplace food purchases and may inform follow-up experiments to enhance our understanding of the molecular mechanisms underlying food choice behavior.

Interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank.

BACKGROUND: Genetic and lifestyle factors have considerable effects on obesity and related diseases, yet their effects in a clinical cohort are unknown. This study in a patient biobank examined associations of a BMI polygenic risk score (PRS), and its interactions with lifestyle risk factors, with clinically measured BMI and clinical phenotypes. METHODS: The Mass General Brigham (MGB) Biobank is a hospital-based cohort with electronic health record, genetic, and lifestyle data. A PRS for obesity was generated using 97 genetic variants for BMI. An obesity lifestyle risk index using survey responses to obesogenic lifestyle risk factors (alcohol, education, exercise, sleep, smoking, and shift work) was used to dichotomize the cohort into high and low obesogenic index based on the population median. Height and weight were measured at a clinical visit. Multivariable linear cross-sectional associations of the PRS with BMI and interactions with the obesity lifestyle risk index were conducted. In phenome-wide association analyses (PheWAS), similar logistic models were conducted for 675 disease outcomes derived from billing codes. RESULTS: Thirty-three thousand five hundred eleven patients were analyzed (53.1% female; age 60.0 years; BMI 28.3 kg/m2), of which 17,040 completed the lifestyle survey (57.5% female; age: 60.2; BMI: 28.1 (6.2) kg/m2). Each standard deviation increment in the PRS was associated with 0.83 kg/m2 unit increase in BMI (95% confidence interval (CI) =0.76, 0.90). There was an interaction between the obesity PRS and obesity lifestyle risk index on BMI. The difference in BMI between those with a high and low obesogenic index was 3.18 kg/m2 in patients in the highest decile of PRS, whereas that difference was only 1.55 kg/m2 in patients in the lowest decile of PRS. In PheWAS, the obesity PRS was associated with 40 diseases spanning endocrine/metabolic, circulatory, and 8 other disease groups. No interactions were evident between the PRS and the index on disease outcomes. CONCLUSIONS: In this hospital-based clinical biobank, obesity risk conferred by common genetic variants was associated with elevated BMI and this risk was attenuated by a healthier patient lifestyle. Continued consideration of the role of lifestyle in the context of genetic predisposition in healthcare settings is necessary to quantify the extent to which modifiable lifestyle risk factors may moderate genetic predisposition and inform clinical action to achieve personalized medicine.

Genetics of Sleep and Insights into Its Relationship with Obesity.

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.

Associations of sleep duration and sleep-wake rhythm with lung parenchymal abnormalities on computed tomography: The MESA study.

Impairment of the circadian rhythm promotes lung inflammation and fibrosis in pre-clinical models. We aimed to examine whether short and/or long sleep duration and other markers of sleep-wake patterns are associated with a greater burden of lung parenchymal abnormalities on computed tomography among adults. We cross-sectionally examined associations of sleep duration captured by actigraphy with interstitial lung abnormalities (n = 1111) and high attenuation areas (n = 1416) on computed tomography scan in the Multi-Ethnic Study of Atherosclerosis at Exam 5 (2010-2013). We adjusted for potential confounders in logistic and linear regression models for interstitial lung abnormalities and high attenuation area, respectively. High attenuation area models were also adjusted for study site, lung volume imaged, radiation dose and stratified by body mass index. Secondary exposures were self-reported sleep duration, sleep fragmentation index, sleep midpoint and chronotype. The mean age of those with longer sleep duration (≥ 8 hr) was 70 years and the prevalence of interstitial lung abnormalities was 14%. Increasing actigraphy-based sleep duration among participants with ≥ 8 hr of sleep was associated with a higher adjusted odds of interstitial lung abnormalities (odds ratio of 2.66 per 1-hr increment, 95% confidence interval 1.42-4.99). Longer sleep duration and higher sleep fragmentation index were associated with greater high attenuation area on computed tomography among participants with a body mass index < 25 kg m-2 (p-value for interaction < 0.02). Self-reported sleep duration, later sleep midpoint and evening chronotype were not associated with outcomes. Actigraphy-based longer sleep duration and sleep fragmentation were associated with a greater burden of lung abnormalities on computed tomography scan.

Night shift work is associated with an increased risk of asthma.

INTRODUCTION: Shift work causes misalignment between internal circadian time and the external light/dark cycle and is associated with metabolic disorders and cancer. Approximately 20% of the working population in industrialised countries work permanent or rotating night shifts, exposing this large population to the risk of circadian misalignment-driven disease. Analysis of the impact of shift work on chronic inflammatory diseases is lacking. We investigated the association between shift work and asthma. METHODS: We describe the cross-sectional relationship between shift work and prevalent asthma in >280000 UK Biobank participants, making adjustments for major confounding factors (smoking history, ethnicity, socioeconomic status, physical activity, body mass index). We also investigated chronotype. RESULTS: Compared with day workers, 'permanent' night shift workers had a higher likelihood of moderate-severe asthma (OR 1.36 (95% CI 1.03 to 1.8)) and all asthma (OR 1.23 (95% CI 1.03 to 1.46)). Individuals doing any type of shift work had higher adjusted odds of wheeze/whistling in the chest. Shift workers who never or rarely worked on nights and people working permanent nights had a higher adjusted likelihood of having reduced lung function (FEV1 <80% predicted). We found an increase in the risk of moderate-severe asthma in morning chronotypes working irregular shifts, including nights (OR 1.55 (95% CI 1.06 to 2.27)). CONCLUSIONS: The public health implications of these findings are far-reaching due to the high prevalence and co-occurrence of both asthma and shift work. Future longitudinal follow-up studies are needed to determine if modifying shift work schedules to take into account chronotype might present a public health measure to reduce the risk of developing inflammatory diseases such as asthma.

Polygenic risk score for obesity and the quality, quantity, and timing of workplace food purchases: A secondary analysis from the ChooseWell 365 randomized trial.

BACKGROUND: 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. METHODS AND FINDINGS: 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 (BMIGPS) 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 × 10-8): (1) BMI97 (97 loci), (2) BMICNS (54 loci near genes related to central nervous system [CNS]), and (3) BMInon-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 BMIGPS 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 BMIGPS quartile. Compared with the lowest quartile, the highest BMICNS quartile was associated with fewer items purchased (P = 0.04), and the highest BMInon-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. CONCLUSIONS: 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. TRIAL REGISTRATION: Clinicaltrials.gov NCT02660086.

Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium.

Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with significant familial aggregation. Previous studies have identified two genetic loci for macronutrient intake, but incomplete coverage of genetic variation and modest sample sizes have hindered the discovery of additional loci. Here, we expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P < 1 × 10-6) associated with intake of any macronutrient in 91,114 European ancestry participants. Four loci replicated and reached genome-wide significance in a combined meta-analysis including 123,659 European descent participants, unraveling two novel loci; a common variant in RARB locus for carbohydrate intake and a rare variant in DRAM1 locus for protein intake, and corroborating earlier FGF21 and FTO findings. In additional analysis of 144,770 participants from the UK Biobank, all identified associations from the two-stage analysis were confirmed except for DRAM1. Identified loci might have implications in brain and adipose tissue biology and have clinical impact in obesity-related phenotypes. Our findings provide new insight into biological functions related to macronutrient intake.

Sugar-sweetened beverage intake associations with fasting glucose and insulin concentrations are not modified by selected genetic variants in a ChREBP-FGF21 pathway: a meta-analysis.

AIMS/HYPOTHESIS: Sugar-sweetened beverages (SSBs) are a major dietary contributor to fructose intake. A molecular pathway involving the carbohydrate responsive element-binding protein (ChREBP) and the metabolic hormone fibroblast growth factor 21 (FGF21) may influence sugar metabolism and, thereby, contribute to fructose-induced metabolic disease. We hypothesise that common variants in 11 genes involved in fructose metabolism and the ChREBP-FGF21 pathway may interact with SSB intake to exacerbate positive associations between higher SSB intake and glycaemic traits. METHODS: Data from 11 cohorts (six discovery and five replication) in the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium provided association and interaction results from 34,748 adults of European descent. SSB intake (soft drinks, fruit punches, lemonades or other fruit drinks) was derived from food-frequency questionnaires and food diaries. In fixed-effects meta-analyses, we quantified: (1) the associations between SSBs and glycaemic traits (fasting glucose and fasting insulin); and (2) the interactions between SSBs and 18 independent SNPs related to the ChREBP-FGF21 pathway. RESULTS: In our combined meta-analyses of discovery and replication cohorts, after adjustment for age, sex, energy intake, BMI and other dietary covariates, each additional serving of SSB intake was associated with higher fasting glucose (ß ± SE 0.014 ± 0.004 [mmol/l], p = 1.5 × 10-3) and higher fasting insulin (0.030 ± 0.005 [log e pmol/l], p = 2.0 × 10-10). No significant interactions on glycaemic traits were observed between SSB intake and selected SNPs. While a suggestive interaction was observed in the discovery cohorts with a SNP (rs1542423) in the ß-Klotho (KLB) locus on fasting insulin (0.030 ± 0.011 log e pmol/l, uncorrected p = 0.006), results in the replication cohorts and combined meta-analyses were non-significant. CONCLUSIONS/INTERPRETATION: In this large meta-analysis, we observed that SSB intake was associated with higher fasting glucose and insulin. Although a suggestive interaction with a genetic variant in the ChREBP-FGF21 pathway was observed in the discovery cohorts, this observation was not confirmed in the replication analysis. TRIAL REGISTRATION: Trials related to this study were registered at clinicaltrials.gov as NCT00005131 (Atherosclerosis Risk in Communities), NCT00005133 (Cardiovascular Health Study), NCT00005121 (Framingham Offspring Study), NCT00005487 (Multi-Ethnic Study of Atherosclerosis) and NCT00005152 (Nurses' Health Study).

Macronutrient Intakes in Infancy Are Associated with Sleep Duration in Toddlerhood.

BACKGROUND: Dietary composition has been associated with sleep indexes. However, most of the evidence is based on cross-sectional data, and studies in young children are lacking. OBJECTIVE: The aim of this study was to explore the longitudinal associations of macronutrient composition of the diet with sleep duration and consolidation (number of awakenings) in infancy and early childhood. METHODS: The study was performed in 3465 children from the Generation R Study, a population-based cohort study in the Netherlands. Mothers reported their child's food intake at 13 mo of age by using a validated food-frequency questionnaire and their child's sleep patterns at 2 and 3 y of age. We used nutrient substitution models to assess the associations of relative macronutrient intakes with sleep indexes and adjusted the models for sociodemographic and lifestyle factors. RESULTS: Isocaloric substitution of fat intake by protein or carbohydrate in infancy was associated with longer total sleep duration at 2 but not 3 y of age. For each 5% increase in energy intake of either protein or carbohydrate at the expense of fat, sleep duration at 2 y of age was longer by 6 min (95% CI: 0.4, 12 min) and 4 min (95% CI: 2, 6 min), respectively. Further exploration of macronutrient subtypes indicated no consistent differences between saturated or unsaturated fat and that intake of plant compared with animal protein or Trp did not explain the association of higher total protein intake with longer sleep duration at 2 y of age. Replacing unsaturated with saturated fat was associated with 7 min (95% CI: -13, -1 min) shorter total sleep duration at 3 y of age. Macronutrient intakes were not associated with sleep consolidation. CONCLUSIONS: Our results suggest that the macronutrient composition of the diet is associated with sleep duration in young children. Future research should further study the causality of this association and explore the underlying mechanisms.

Actigraphic sleep fragmentation, efficiency and duration associate with dietary intake in the Rotterdam Study.

Short self-reported sleep duration is associated with dietary intake and this association may partly mediate the link between short sleep and metabolic abnormalities. Subjective sleep measures, however, may be inaccurate and biased. The objective of this study was to evaluate the associations between actigraphic measures of sleep fragmentation, efficiency and duration and energy and macronutrient intakes. We used data from a subgroup of 439 participants of the population-based cohort, Rotterdam Study. Sleep was assessed using 7-day actigraphy and sleep diaries, and dietary data with a validated food frequency questionnaire. We assessed the associations of actigraphic sleep parameters with dietary intake using multivariable linear regression models. Higher sleep fragmentation was associated with 4.19 g lower carbohydrate intake per standard deviation of fragmentation {ß [95% confidence interval (CI) = -4.19 (-8.0, -0.3)]; P = 0.03}. Each additional percentage increase in sleep efficiency was associated with 11.1 kcal lower energy intake [ß (95% CI) = -11.1 (-20.6, -1.7); P = 0.02]. Furthermore, very short sleep duration (<5.5 h) was associated with 218.1 kcal higher energy intake [ß (95% CI = 218.06 (33.3, 402.8), P = 0.02], relative to the reference group (≥6.5 to <7.5 h). We observed associations between higher sleep fragmentation with lower carbohydrate intake, and both lower sleep efficiency and very short sleep duration (<5 h) with higher energy intake. The association between sleep and higher energy intake could mediate, in part, the link between short sleep or sleep fragmentation index and metabolic abnormalities.

Sleep and home parenteral nutrition in adults: A narrative review.

Infusions of home parenteral nutrition (HPN) are often cycled at night coinciding with sleep episodes. Adult consumers of HPN are known to experience poor sleep attributed to frequent awakenings and long durations of wakefulness after falling asleep. Consequently, most consumers do not meet recommendations for sleep duration and quality or daytime napping. The primary underlying pathophysiology resulting in sleep problems is nocturia; however, other factors also exist, including disruptions caused by medical equipment (ie, pump alarms), comorbid conditions, dysglycemia, and medication use. Early guidance on sleep is imperative because of the central role of sleep in physical health and wellbeing, including mitigating complications, such as infection risk, gastrointestinal problems, pain sensitivity, and fatigue. Clinicians should routinely inquire about the sleep of their patients and address factors known to perturb sleep. Nonpharmacologic opportunities to mitigate sleep problems include education on healthy sleep practices (ie, sleep hygiene); changes in infusion schedules, volumes, rates, and equipment; and, possibly, behavioral interventions, which have yet to be examined in this population. Addressing comorbid conditions, such as mood disorders, and nutrition deficiencies may also help. Pharmacologic interventions and technological advancement in HPN delivery are also needed. Research on sleep in this population is considered a priority, yet it remains limited at this time.

Home parenteral nutrition, sleep patterns, and depressive symptoms: Secondary analysis of cross-sectional data.

BACKGROUND: Patients receiving home parenteral nutrition (HPN) are known to experience psychological distress and have profoundly disrupted sleep. The aim of this analysis was to examine the relationship between sleep patterns with depressive symptoms and HPN characteristics. METHODS: The study was a secondary analysis of cross-sectional data examining sleep patterns using subjective and objective measures. Sleep was assessed by surveys and 7-day actigraphy. The Patient Health Questionnaire-8 was used to evaluate depressive symptoms. Participants provided information on HPN. Spearman correlations were calculated between sleep measures with depressive symptoms and HPN characteristics. Correlations were further examined in multivariable linear regression models. RESULTS: Thirty-two adults (age = 53 years; 75% female; 94% White) were included. Lower sleep quality (r = 0.54-0.60; P < 0.001) and later sleep timing (r = -0.35; P = 0.049) were correlated with higher depressive symptoms. Sleep patterns were also correlated with several HPN characteristics (r = -0.47 to 0.51). In linear regression models, rate of infusion was associated with sleep duration (ß = -0.004 [0.002] h; P = 0.046) in which each 100 mL/h was associated with 24-min shorter duration. Higher total energy was associated with lower sleep quality (ß = 0.0004 [0.0002] log-unit; P = 0.042), and higher volume was associated with longer sleep onset latency (ß = 0.0006 [0.0003] log-min; P = 0.049). CONCLUSIONS: We provide evidence supporting the link between poor and later sleep with higher depressive symptoms and identify potentially modifiable infusion characteristics (notably, slower rate of infusion and lower total energy and volume) that, on further verification, may support sleep among those receiving HPN.

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.

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.