2026-10-12T07:30:19Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33903191 2026-09-17T17:22:37Z category_7 category_15 category_16 category_21 category_64 category_122 portal_5 item_type_3 month_year_09_2026

<p>Baseline characteristics of study participants.</p> Jingsong Luo (6517541) Ruiyi Liu (9504251) Jie Yin (266670) Wangnan Cao (7149731) Shengzhi Sun (7149728) Rui Chen (95310) Medicine Neuroscience Physiology Biotechnology Cancer Mental Health widespread clinical vulnerabilities restricted cubic spline rapid eye movement 049 health outcomes sleep onset using poor sleep quality favorable sleep architecture rem ], n1 derived key metrics world sleep patterns world sleep stages including sleep stages greater sleep irregularity 559 biobank participants total sleep duration wide association analysis worn accelerometer data study characterizes real lower disease risk sleep patterns sleep stages sleep irregularity rem sleep sleep duration deep sleep specific analysis lower risks reported data physiological data derived real xlink "> sleepnet algorithm residual confounding remains susceptible older adults offering insights observational design median follow measured objectively main limitation linear relationships lifestyle factors increased waso higher amount first occurrence findings underscore <div> <p>Background</p><p>Previous studies utilizing self-reported data or polysomnography have found that poor sleep quality is associated with increased risk of morbidity and mortality. However, the association between real-world sleep patterns, including sleep stages, duration, and fragmentation, measured objectively with physiological data and the risk of incident disease among middle-aged and older adults has not yet been systematically evaluated.</p> <p>Methods and findings</p><p>In this cohort study, we analyzed wrist-worn accelerometer data from 95,559 Biobank participants and derived key metrics of real-world sleep patterns: rapid eye movement [REM], N1, N2, and N3; total sleep duration; sleep irregularity; and wakefulness after sleep onset using the SleepNet algorithm. Participants were followed up from the date of accelerometer wear until the first occurrence of disease, death, or April 1, 2024 with median follow-up length of 8.9 years. Phenome-wide association analysis and restricted cubic spline (RCS) analyses were performed using Cox proportional hazard regression, with adjustment for demographic characteristics, lifestyle factors, and environmental exposures, to map an atlas of associations between real-world sleep patterns and the incidence of 1,049 health outcomes. We found that variations in sleep patterns were associated with 156 incidences of diseases. Specifically, higher amount of REM sleep and deep sleep were associated with lower risks of 83 and 7 diseases, respectively, while greater sleep irregularity and increased WASO were linked to elevated risks of 3 and 6 diseases, respectively. RCS analyses revealed significant non-linear relationships between sleep duration and 86 disease phenotypes (<i>P</i> for nonlinear <0.05), with the minimum-risk hours for 69 phenotypes predominantly concentrated within the 6–8 hours window. In the category-specific analysis, individuals with extreme short sleep (<5 hours) exhibited the most widespread clinical vulnerabilities, accounting for 37 of the 41 identified significant adverse associations compared to the 6–8 hours reference group. The main limitation of this study was its observational design, which remains susceptible to residual confounding and precludes causal inference.</p> <p>Conclusion</p><p>In summary, this study characterizes real-world sleep patterns and their links to disease risk across multiple systems, highlighting the differential contributions of sleep stages and sleep duration. The findings underscore that maintaining 6–8 hours of sleep is associated with a more favorable sleep architecture and lower disease risk, offering insights for prevention and health promotion.</p></div> 2026-09-17T17:22:03Z Dataset Dataset 10.1371/journal.pmed.1005213.t001 https://figshare.com/articles/dataset/_p_Baseline_characteristics_of_study_participants_p_/33903191 CC BY 4.0