Year: 2026
Type: preprint
Abstract: Biological ageing markers have attracted growing interest, with models estimating age from organ imaging or blood biomarkers. An estimated age above chronological age or the age-specific population expectation is assumed to reflect accelerated ageing and poorer health. Previous research has supported this assumption through positive associations between disease and age gaps or acceleration. However, in this study, we identified a widespread health-dependent prediction bias in ageing markers that affects their key interpretation and application. Specifically, we investigated five ageing markers derived from retinal images, brain MRI, chest radiographs, abdominal CT, and blood tests, and evaluated them using association analyses. We observed the well-recognised phenomenon of regression to the mean (RTM) in the four organ-image based markers, whereby estimated ages were shifted towards the mean age of the training cohort. More importantly, we revealed that the strength of RTM varied with health status, with stronger RTM in unhealthy than in healthy individuals. This differential RTM introduced a health-dependent prediction bias that persisted after calibration and systematically altered associations across age subgroups, suggesting that whole cohort associations may not reflect those observed within individual age subgroups. Additionally, we showed that the tested ageing markers, including PhenoAge derived from blood biomarkers, had limited ability to distinguish health status at the individual level. These findings call for careful interpretation of biological ageing markers and their use in clinical studies, and highlight the need for further development and validation before these ageing markers can reliably inform individual health assessments.
Source: arXiv (Cornell University)
Authors: Lin, Yiqun, Ong, Ariel Yuhan, Wong, Matthew Yu Heng, Wu, Yilan, Hu, Wenyi, Verma, Shubhank Shobhit, Poschkamp, Broder, Ju, Lie, Narayan, Akshay, Martin, Sophie A., Kohli, Maitrei, Antaki, Fares, Heatley, Alexander C., Huemer, Josef, Wang, Meng, Zou, Ke, Wang, Zheyuan, Ruffell, Eden, Williamson, Dominic, Engelmann, Justin, Kim, Hyunmin, Ninomiya, Takahiro, Jonas, Rahul A., Liu, Yansong, Cheng, Zijie, Zhang, Hanyuan, Cui, Hejie, Xu, Jiayang, Wang, Hualiang, Liu, Zuozhu, Pu, Bin, Liu, Chubo, Li, Kenli, Ding, Xinpeng, Zhang, Guokai, Wang, Weiru, Wang, Jiaxiang, Fu, Huazhu, Ge, Zongyuan, Liu, Xujia, Eslami, Mohammad, Raytchev, Milen, Elze, Tobias, Morley, Michael G., Oxtoby, Neil P., Alexander, Daniel C., Khawaja, Anthony P., Cheng, Ching-Yu, Zhu, Lisa Zhuoting, Tham, Yih Chung, Cole, James H., Cheung, Carol Y., Keane, Pearse A., Wagner, Siegfried K., Zhou, Yukun
Institutions: University of Cambridge, The University of Melbourne, Centre for Eye Research Australia, University College London, Zhejiang University, National University of Singapore, Shanghai Jiao Tong University, Stanford University, Hong Kong University of Science and Technology, Agency for Science, Technology and Research, Central South University, Xiangya Hospital Central South University, Hunan University, Chinese University of Hong Kong, Monash University, Singapore National Eye Center, Singapore Eye Research Institute
Language: English
Topic: Frailty in Older Adults
Subfield: Geriatrics and Gerontology
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: Wellcome Trust, National Institute for Health and Care Research, Department of Health and Social Care, Moorfields Eye Hospital NHS Foundation Trust, Moorfields Eye Charity, Engineering and Physical Sciences Research Council, National Health and Medical Research Council, National Medical Research Council, Alcon Research Institute, Medical Research Future Fund
Awards: G1064243852, G2165123084, G30712838, Medical image computing for next-generation healthcare technology, G3454835369, Assessing Placental Structure and Function by Unified Fluid Mechanical Modelling and in-vivo MRI, G4114730015, Advancing Medical Artificial Intelligence with Foundation Models, G5737906405, G6842936429, Enabling Clinical Decisions From Low-power MRI In Developing Nations Through Image Quality Transfer, G902693135
OpenAlex ID: W7214625823