| Title: | Deep learning aging marker from retinal images unveils sex-specific clinical and genetic signatures |
| Journal: | Nature Communications |
| Published: | 26 Aug 2026 |
| DOI: | https://doi.org/10.1038/s41467-026-77102-1 |
| URL: | http://dx.doi.org/10.1038/s41467-026-77102-1 |
Publication 20883
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Abstract
Retinal fundus images offer a non-invasive window into systemic aging. Here, we fine-tune a foundation model (RETFound) to predict chronological age from color fundus images in 71,343 participants from the UK Biobank, achieving a mean absolute error of 2.85 years. The resulting retinal age gap, i.e. the difference between predicted and chronological age, is associated with cardiometabolic traits, inflammation, cognitive performance, all-cause mortality, dementia, cancer, and incident cardiovascular disease. Genome-wide analyses identify genes related to longevity, metabolism, neurodegeneration, and age-related eye diseases. Sex-stratified models reveal consistent performance but divergent biological signatures: males have stronger links to metabolic syndrome, while in females, both model attention and genetics point to a greater involvement of retinal vasculature. Additional analyses indicate that retinal aging patterns in females vary across the menopausal transition, with postmenopausal females exhibiting higher retinal age gap values and clinical associations that more closely resemble those observed in males. Our study positions the retinal age gap as a biologically relevant and sex-specific phenotype associated with multiple aging-related diseases and outcomes beyond conventional risk factors, including chronological age.</p>
13 Authors
- Olga Trofimova
- Leah Böttger
- Sacha Bors
- Yating Pan
- Bart Liefers
- Jose D. Vargas-Quiros
- Victor A. de Vries
- Michael J. Beyeler
- David M. Presby
- Dennis Bontempi
- Janna Hastings
- Caroline C. W. Klaver
- Sven Bergmann
1 Application
| Application ID | Title |
|---|---|
| 90947 | Connecting properties of the micro- and macro-vasculature from multimodal imaging through genetics and deep learning to better understand vascular pathomechanisms and predict disease risk |
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