Prediction bias in biological ageing markers

Yiqun Lin; Ariel Yuhan Ong; Matthew Yu Heng Wong; Yilan Wu; Wenyi Hu; Shubhank Shobhit Verma; Broder Poschkamp; Lie Ju; Akshay Narayan; Sophie A. Martin; Maitrei Kohli; Fares Antaki; Alexander C. Heatley; Josef Huemer; Meng Wang; Ke Zou; Zheyuan Wang; Eden Ruffell; Dominic Williamson; Justin Engelmann; Hyunmin Kim; Takahiro Ninomiya; Rahul A. Jonas; Yansong Liu; Zijie Cheng; Hanyuan Zhang; Hejie Cui; Jiayang Xu; Hualiang Wang; Zuozhu Liu; Bin Pu; Chubo Liu; Kenli Li; Xinpeng Ding; Guokai Zhang; Weiru Wang; Jiaxiang Wang; Huazhu Fu; Zongyuan Ge; Xujia Liu; Mohammad Eslami; Milen Raytchev; Tobias Elze; Michael G. Morley; Neil P. Oxtoby; Daniel C. Alexander; Anthony P. Khawaja; Ching-Yu Cheng; Lisa Zhuoting Zhu; Yih Chung Tham; James H. Cole; Carol Y. Cheung; Pearse A. Keane; Siegfried K. Wagner; Yukun Zhou

arXiv · 2609.30322

Prediction bias in biological ageing markers

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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.

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Yiqun Lin, Ariel Yuhan Ong, Matthew Yu Heng Wong, Yilan Wu, Wenyi Hu, Shubhank Shobhit Verma, Broder Poschkamp, Lie Ju, Akshay Narayan, Sophie A. Martin, Maitrei Kohli, Fares Antaki, Alexander C. Heatley, Josef Huemer, Meng Wang, Ke Zou, Zheyuan Wang, Eden Ruffell, Dominic Williamson, Justin Engelmann, Hyunmin Kim, Takahiro Ninomiya, Rahul A. Jonas, Yansong Liu, Zijie Cheng, Hanyuan Zhang, Hejie Cui, Jiayang Xu, Hualiang Wang, Zuozhu Liu, Bin Pu, Chubo Liu, Kenli Li, Xinpeng Ding, Guokai Zhang, Weiru Wang, Jiaxiang Wang, Huazhu Fu, Zongyuan Ge, Xujia Liu, Mohammad Eslami, Milen Raytchev, Tobias Elze, Michael G. Morley, Neil P. Oxtoby, Daniel C. Alexander, Anthony P. Khawaja, Ching-Yu Cheng, Lisa Zhuoting Zhu, Yih Chung Tham, James H. Cole, Carol Y. Cheung, Pearse A. Keane, Siegfried K. Wagner, Yukun Zhou. 2026-09-29. Prediction bias in biological ageing markers. https://arxiv.org/abs/2609.30322

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