ArticleSleep2019
Polygenic risk score identifies associations between sleep duration and diseases determined from an electronic medical record biobank.
Article in Sleep, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 3 syntheses or guidelines pooled it, 73 citations in OpenAlex.
- Multi-ancestry genome-wide analysis identifies shared genetic effects and common genetic variants for self-reported sleep duration.Human molecular genetics · 2023Pooled it
- Association of sleep disorders with asthma: a meta-analysis.BMJ open respiratory research · 2023Pooled it
- Associations between sleep duration and cardiovascular diseases: A meta-review and meta-analysis of observational and Mendelian randomization studies.Frontiers in cardiovascular medicine · 2022Pooled it
- Polygenic Index for Sleep Duration and Brain Changes over Time.Medical sciences (Basel, Switzerland) · 2026Article
- Polygenic score for sleep duration in relation to the risk of Alzheimer's disease: results from the UK biobank.Frontiers in dementia · 2026Article
- Association between clock genes polygenic risk score for depression and obesity in a population with high burden of obesity.Nutrition & metabolism · 2025Article
- Associations of a multidimensional polygenic sleep health score and a sleep lifestyle index with disease outcomes and their interaction in a clinical biobank.Sleep health · 2025Article
- Sleep and schizophrenia polygenic scores in non-affective and affective psychotic disorders.Psychological medicine · 2025Article
- Causal relationship of sleep duration on risks for metabolic syndrome: a Mendelian randomization study.Diabetology & metabolic syndrome · 2025Article
- Patient-centred outcomes in severe asthma: fatigue, sleep, physical activity and work.European respiratory review : an official journal of the European Respiratory Society · 2025Review
- The impact of sleep quality on asthma incidence in middle-aged and older adults: evidence from a prospective cohort study.Frontiers in public health · 2025Article
- Case Identification of Depression in Inpatient Electronic Medical Records: Scoping Review.JMIR medical informatics · 2024Article
- Changes in sleep patterns in people with a history of depression during the COVID-19 pandemic: a natural experiment.BMJ mental health · 2024Article
- Associations of a multidimensional polygenic sleep health score and a sleep lifestyle index on health outcomes and their interaction in a clinical biobank.medRxiv : the preprint server for health sciences · 2024Article
- Navigating Mendelian Randomization in Sleep Medicine: Challenges, Opportunities, and Best Practices.Nature and science of sleep · 2024Article
- Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review.Biosensors · 2023Review
- Selection of genetic instruments in Mendelian randomisation studies of sleep traits.Sleep medicine · 2023Review
- Stronger Associations Between Sleep and Mental Health in Adults with Autism: A UK Biobank Study.Journal of autism and developmental disorders · 2023Article
- Highlighting the importance of healthy sleep patterns in the risk of adult asthma under the combined effects of genetic susceptibility: a large-scale prospective cohort study of 455 405 participants.BMJ open respiratory research · 2023Article
- Association between sleep duration and metabolic syndrome: linear and nonlinear Mendelian randomization analyses.Journal of translational medicine · 2023Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
STUDY
objectivesWe 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.
methodsDisease 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).
resultsIn 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).
conclusionsBy 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.
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