| Title: | A cross population study of retinal aging biomarkers with longitudinal pre-training and label distribution learning |
| Journal: | npj Digital Medicine |
| Published: | 10 Jun 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40494933/ |
| DOI: | https://doi.org/10.1038/s41746-025-01751-7 |
| URL: | https://www.nature.com/articles/s41746-025-01751-7.pdf |
Publication 15032
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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.</p>
17 Authors
- Zhen Yu
- Ruiye Chen
- Peng Gui
- Wei Wang
- Imran Razzak
- Hamid Alinejad-Rokny
- Xiaomin Zeng
- Xianwen Shang
- Lei Zhang
- Xiaohong Yang
- Honghua Yu
- Wenyong Huang
- Huimin Lu
- Peter van Wijngaarden
- Mingguang He
- Zhuoting Zhu
- Zongyuan Ge
Enabling scientific discoveries that improve human health