Research Paper|Volume 17, Issue 8|pp 2126—2151

Evaluating the nonlinear effects of sleep duration on biological aging across phenotypic, genomic, and epigenomic data

Xueyao Wu¹, Xunying Zhao¹, Aaron Ge², Zhitong Han³, Can Hou⁴, Yu Hao¹, Jinyu Xiao¹, Mengyu Fan¹, Stephen Burgess^5,⁶, Jiayuan Li¹, Xia Jiang^1,^7,⁸

¹Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
²University of Maryland School of Medicine, Baltimore, MD 21201, USA
³School of Life Sciences, Sichuan University, Chengdu, China
⁴West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
⁵MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, United Kingdom
⁶British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0BD, United Kingdom
⁷Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Solna, Stockholm, Sweden
⁸Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China

* Equal contribution

# Senior author

Received: March 3, 2025Accepted: July 28, 2025Published: August 25, 2025

https://doi.org/10.18632/aging.206306

Copyright: © 2025 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Short and long sleep durations have been inconsistently linked to aging and health outcomes, potentially due to underexplored nonlinear associations. Using phenotypic and genomic data from the UK Biobank (n=442,664), we applied multivariable linear regression, restricted cubic splines, and Mendelian randomization (MR) to analyze nonlinear relationships between self-reported sleep duration and biomarkers of accelerated aging: PhenoAge acceleration (PhenoAgeAccel), BioAge acceleration (BioAgeAccel), and leukocyte telomere length (LTL). Functional annotation analyses were performed to assess potential shared biological pathways using epigenomic profiles. Observational analyses supported U-shaped phenotypic associations between sleep duration and PhenoAgeAccel/BioAgeAccel, with optimal sleep around 7 h/d. For LTL, linear models suggested a U-shape, while spline models indicated an inverted reverse J-pattern. MR analyses corroborated the deleterious impacts of insufficient, but not excessive, sleep, by revealing a threshold nonlinear relationship between increasing genetically-predicted sleep duration up to 7 h/d and lower PhenoAgeAccel/BioAgeAccel, and a linear relationship with longer LTL. Cell-type enrichment analyses connected short sleep to BioAgeAccel/LTL through pathways related to muscle maintenance and immune function. These findings suggest that extending sleep may mitigate accelerated aging, though further research is needed to clarify the underlying biological mechanisms and whether excessive sleep also contributes causally to biological aging.