Loading...
A cross population study of retinal aging biomarkers with longitudinal pre-training and label distribution learning
Yu, Zhen Chen, Ruiye Gui, Peng Wang, Wei Razzak, Imran Alinejad-Rokny, Hamid Zeng, Xiaomin Shang, Xianwen Zhang, Lei Yang, Xiaohong
Yu, Zhen
Chen, Ruiye
Gui, Peng
Wang, Wei
Razzak, Imran
Alinejad-Rokny, Hamid
Zeng, Xiaomin
Shang, Xianwen
Zhang, Lei
Yang, Xiaohong
Supervisor
Department
Computational Biology
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Retinal age has emerged as a promising biomarker of aging, offering a non-invasive and accessible assessment tool We developed a deep learning model to estimate retinal age with enhanced accuracy, leveraging retinal images from diverse populations Our approach integrates self-supervised learning to capture chronological information from both snapshot and sequential images, alongside a progressive label distribution learning module to model biological aging variability Trained and validated on healthy cohorts (34,433 participants from the UK Biobank and three Chinese cohorts), the model achieved a mean absolute error of 279 years, surpassing previous methods When applied to broader populations, analysis of the retinal age gap—the difference between retina-predicted and chronological age—revealed associations with increased risks of all-cause mortality and multiple age-related diseases These findings highlight the potential of retinal age as a reliable biomarker for predicting survival and aging outcomes, supporting targeted risk management and precision health interventions © The Author(s) 2025
Citation
Z. Yu et al., “A cross population study of retinal aging biomarkers with longitudinal pre-training and label distribution learning,” NPJ Digit Med, vol. 8, no. 1, pp. 1–14, Dec. 2025, doi: 10.1038/S41746-025-01751-7
Source
npj Digital Medicine
Conference
Keywords
Subjects
Source
Publisher
Springer Nature