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Divergent biological pathways linking short and long sleep durations to mental and physical health

Nature Mental Health volume 3, pages 429–443 (2025)Cite this article

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Abstract

Short and long sleep durations are associated with multiple physical, psychiatric and neurodegenerative diseases, yet their potentially shared and distinct biological mechanisms remain unclear. Here, using data from UK Biobank participants aged 38–73 years, we have characterized the in-depth genetic architecture of short (≤7 h) and long (≥7 h) sleep groups, along with their associations with behaviors, neuroimaging and blood biomarkers. The two sleep groups exhibited independent genetic architectures and distinct immunometabolic and proteomic profiles. Notably, long sleep showed more significant associations with cardiovascular-related biomarkers (for example, cholesterol), brain structures (for example, hippocampus) and plasma proteins (for example, GDF15), whereas short sleep demonstrated greater genetic overlap with psychiatric conditions, particularly depression. Mendelian randomization further supported this dissociation by showing that long sleep duration is probably a consequence of multiple brain disorders and cardiovascular diseases, whereas short sleep duration has a potential causal effect on various brain and physical illnesses. Our findings advance our understanding of the relationship between sleep and health conditions by revealing distinct biological origins and genetic mechanisms underlying short and long sleep duration.

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Fig. 1: Common and specific phenotypic correlation patterns of short and long sleep groups.
Fig. 2: Specific blood and neuroimaging biomarker association pattern of short and long sleep groups.
Fig. 3: Manhattan plot for sleep duration and genetic correlations between short and long sleep groups with health-related phenotypes.
Fig. 4: Distinct protein profiles of the short and long sleep groups.
Fig. 5: Causal associations between short and long sleep groups with multiple health conditions.

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Data availability

This project corresponds to UK Biobank application ID 19542. Neuroimaging, genotype and behavioral data from UK Biobank dataset are available from https://biobank.ndph.ox.ac.uk/ by application. The variables utilized in this study are detailed in Supplementary Tables 1–3. Previous published GWASs of psychiatric disorders, including depression, bipolar disorder and schizophrenia, were provided by the Psychiatric Genomics Consortium, which can be downloaded from https://pgc.unc.edu/for-researchers/download-results/. GWAS summary statistics of immunometabolic phenotypes, obesity and aging disease are available in the MRC IEU OpenGWAS database (https://gwas.mrcieu.ac.uk), and the detailed PubMed identifiers (PMIDs) for the GWAS summary data are provided in Supplementary Table 10 (https://pubmed.ncbi.nlm.nih.gov/). European ancestral background LD scores from the 1000 Genomes Project were downloaded from https://alkesgroup.broadinstitute.org/LDSCORE/. The GRCh37 coordinates can be accessed via http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/.

Code availability

R version 4.2.0 was used to perform phenotype-wide association analysis. Matlab 2018b was used to perform linear association analysis. Freesurfer v6.0 was used to process the imaging data. PLINK 2.0 was used to perform GWAS analysis. R version 4.2.0 GenomicSEM version 0.0.3 was utilized to calculate heritability and genetic correlations. TwoSampleMR version 0.5.6 was utilized to measure the causal association. Scripts used to perform the analyses are available at https://github.com/yuzhulineu/UKB_short_longsleep/.

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Acknowledgements

This study utilized the UK Biobank resource under application no. 19542. We thank all the participants and researchers from UK Biobank. This work received support from the following sources: National Key R&D Program of China (2021YFC2501400 to T.J., 2019YFA0709502 to J.F., 2018YFC1312904 to J.F., 2022CSJGG1000 to T.J., 2019YFA0709501 to T.J., 2018YFC1312900 to T.J., 2023YFE0199700 to T.J. and 2023YFC3605400 to W.C.), the National Natural Science Foundation of China (T2122005 to T.J., 82472055 to W.C., 82071997 to W.C. and 81801773 to T.J.), the 111 Project (B18015 to J.F.), the key project of Shanghai Science and Technology (16JC1420402 to J.F.), Shanghai Municipal Science and Technology Major Project (2018SHZDZX01 to J.F.), Zhangjiang Lab (to J.F.), Shanghai Center for Brain Science and Brain-Inspired Technology (to J.F.), Shanghai Rising-Star Program (21QA1408700 to W.C.), Shanghai Pujiang Project (18PJ1400900 to T.J.), Shanghai Yangfan Project (24YF2738400 to Y.L.) and Postdoctoral Fellowship Program (Grade B) of China Postdoctoral Science Foundation (GZB20230164 to Y.L. and 2023M730697 to Y.L.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

    Yuzhu Li, Barbara J. Sahakian, Wei Zhang, Yujie Zhao, Tianye Jia, Wei Cheng & Jianfeng Feng

  2. Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China

    Yuzhu Li, Wei Zhang, Yujie Zhao, Tianye Jia, Wei Cheng & Jianfeng Feng

  3. Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China

    Yuzhu Li

  4. Shool of Data Science, Fudan University, Shanghai, China

    Weikang Gong & Jianfeng Feng

  5. Department of Psychiatry, University of Cambridge, Cambridge, UK

    Barbara J. Sahakian

  6. Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK

    Barbara J. Sahakian

  7. Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China

    Shuyi Huang, Jintai Yu & Wei Cheng

  8. Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Liang Ma

  9. School of Psychology, The University of Sydney, Sydney, New South Wales, Australia

    Sharon Naismith

  10. Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King’s College London, London, UK

    Tianye Jia

  11. School of Psychology, Southampton University, Southampton, UK

    Tianye Jia

  12. Fudan ISTBI—ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, Zhejiang, China

    Wei Cheng

  13. Zhangjiang Fudan International Innovation Center, Shanghai, China

    Jianfeng Feng

  14. Department of Computer Science, University of Warwick, Coventry, UK

    Jianfeng Feng

Authors
  1. Yuzhu Li
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Contributions

J.F., W.C. and T.J. proposed the study. Y.L. and W.G. analyzed the data. W.Z. and S.H. preprocessed the data. T.J., S.N. and J.Y. contributed to interpretation of the results. Y.L. drafted the paper and T.J., S.N., B.J.S. and W.C. edited it. Y.L. and W.C. were responsible for visualization. Y.Z. and L.M. provided feedback to improve the paper. All authors contributed to discussions on data analysis and approved the final version of the paper.

Corresponding authors

Correspondence to Tianye Jia, Wei Cheng or Jianfeng Feng.

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The authors declare no competing interests.

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Nature Mental Health thanks Julian Mutz, Arezu Najafi, Masoud Tahmasian and Angeliki Tsapanou for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Guideline of the study.

Top, UK biobank data used in this study included environmental and behavioral measures, blood and imaging biomarkers, genomics, proteins, and sleep duration. Middle, A phenotype-wide association study (PheWAS) was performed to explore the associations of short and long sleep duration with these phenotypes. Biological blood biomarkers, neuroimaging biomarkers, and proteins were utilized to characterize distinct association profiles for short and long sleep duration. Bottom, Distinct genetic architectures and roles in association with health conditions were identified for short and long sleep duration. Finally, Mendelian randomization (MR) analysis showed distinct causal associations between short and long sleep duration and health-related phenotypes.

Supplementary information

Supplementary Information

Supplementary Tables 1–27 and Figs. 1–13.

Reporting Summary

Supplementary Data 1–6

Supplementary Data 1 Phenotype-wide associations study of sleep <= 7 h. Supplementary Data 2 Phenotype-wide associations study of sleep >= 7 h. Supplementary Data 3 Genetic correlations between sleep duration and health-related phenotypes. Supplementary Data 4 Phenotype-wide associations study of stratified short sleep (sleep < 7 versus sleep = 7/8 h). Supplementary Data 5 Phenotype-wide associations study of stratified long sleep (sleep > 8 versus sleep = 7/8 h). Supplementary Data 6 Genetic correlations between stratified sleep duration and health-related phenotypes.

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Li, Y., Gong, W., Sahakian, B.J. et al. Divergent biological pathways linking short and long sleep durations to mental and physical health. Nat. Mental Health 3, 429–443 (2025). https://doi.org/10.1038/s44220-025-00395-6

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