A cross population study of retinal aging biomarkers with longitudinal pre-training and label distribution learning.

Journal: NPJ digital medicine
Published Date: Jun 10, 2025

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 2.79 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.

Authors

  • Zhen Yu
  • Ruiye Chen
    Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia.
  • Peng Gui
    School of Computer Science and Engineering Artificial Intelligence, Wuhan Institute of Technology, Wuhan, China.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Imran Razzak
  • Hamid Alinejad-Rokny
    Systems Biology and Health Data Analytics Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, 2052 Sydney, Australia; School of Computer Science and Engineering, The University of New South Wales (UNSW Sydney), 2052 Sydney, Australia; Health Data Analytics Program Leader, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney 2109, Australia.
  • Xiaomin Zeng
    Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, the Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
  • Xianwen Shang
    Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia.
  • Lei Zhang
    Division of Gastroenterology, Union Hospital, Tongji Medical College Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xiaohong Yang
    Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China.
  • Honghua Yu
    Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, the Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
  • Wenyong Huang
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
  • Huimin Lu
  • Peter van Wijngaarden
    Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, VIC.
  • Mingguang He
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China; Centre for Eye Research Australia; Departments of Ophthalmology and Surgery, University of Melbourne, Melbourne, Australia. Electronic address: mingguang.he@unimelb.edu.au.
  • Zhuoting Zhu
    Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China.
  • Zongyuan Ge
    AIM for Health Lab, Faculty of IT, Monash University, Clayton, Victoria, Australia; Monash-Airdoc Research Lab, Faculty of IT, Monash University, Clayton, Victoria, Australia.

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