Association of genetic risk, lifestyle, and their interaction with MRI-based brain aging

Individual brain aging is shaped by both genetic and environmental factors, yet their combined influence remains poorly understood. Using multimodal MRI, genomic data, and lifestyle measures from 46,843 participants in the UK Biobank, we developed and validated a multimodal brain age prediction model (R² = 0.72-0.75). Genome-wide association analyses identified 19 independent loci and 122 mapped genes associated with brain aging.

Through cross-sectional and longitudinal analyses, we found that higher genetic liability and unhealthy lifestyle predicted the acceleration of brain aging. Moreover, linkage disequilibrium score regression revealed a significant negative genetic correlation between lifestyle and brain aging. A genome-wide gene–lifestyle interaction analysis identified a significant locus at rs62287096 upstream of HRASLS, where risk-allele carriers showed amplified effects of poor lifestyle but reduced brain aging under healthy lifestyle conditions.

Supporting a regulatory role for this locus, dual-luciferase reporter assays demonstrated significant enhancer activity with allele-specific effects at rs62287096. These findings highlight modifiable pathways that may offset genetic vulnerability and inform precision strategies for healthy brain aging. We gratefully acknowledge the UK Biobank participants and coordinating team for providing access to this invaluable resource, which made this study possible.

This work uses data provided by patients and collected by the NHS as part of their care and support. This research was conducted using the UK Biobank Resource under Application Number 432214 and 85139. Genetic analyses were performed under application 85139, while other data aspects were conducted under application 432214.

We thank the developers of the publicly available software and tools used in this study for their contributions to open and reproducible science. We also thank colleagues for their constructive discussions and valuable suggestions that helped improve the analyses and interpretation of the results. S.L. was supported by the Pioneer Hundred Talents Program of the Chinese Academy of Sciences and the Yunnan Talent Support Plan.

These authors contributed equally: Yu Zhang, Quanzhen Zheng, Yanyu Zhou. State Key Laboratory of Genetic Evolution & Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China Yu Zhang, Quanzhen Zheng, Yanyu Zhou, Dengfeng Zhang & Shu Liu National Resource Center for Non-Human Primates, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China University of Chinese Academy of Sciences, Beijing, China The authors declare no competing interest. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.