Deep learning aging marker from retinal images unveils sex-specific clinical and genetic signatures
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.
Authors
- Yating Pan (ORCID: https://orcid.org/0009-0007-3098-4521)
- Bart Liefers (ORCID: https://orcid.org/0000-0002-2763-6426)
- Caroline C. W. Klaver (ORCID: https://orcid.org/0000-0002-2355-5258)
- Sofía Ortín Vela (ORCID: https://orcid.org/0000-0002-2268-9772)
- Olga Trofimova (ORCID: https://orcid.org/0009-0000-0328-4676)
- Dennis Bontempi (ORCID: https://orcid.org/0000-0003-0775-5679)
- Ilaria Iuliani (ORCID: https://orcid.org/0000-0003-1758-0497)
- Michael J. Beyeler (ORCID: https://orcid.org/0000-0001-6199-4879)
- Victor A. de Vries (ORCID: https://orcid.org/0000-0002-2425-3841)
- Ihor Kuras
- Janna Hastings (ORCID: https://orcid.org/0000-0002-3469-4923)
- Ciara Bergin (ORCID: https://orcid.org/0000-0002-5837-7308)
- Mattia Tomasoni (ORCID: https://orcid.org/0000-0001-8775-2384)
- Sven Bergmann (ORCID: https://orcid.org/0000-0002-6785-9034)
- Adham Elwakil (ORCID: https://orcid.org/0009-0009-4881-6643)
- Ilenia Meloni
- Sacha Bors
- Leah Böttger
- Ian Quintas
- Bogdan Draganski
- Györgyi V. Hamvas
- Marc Schindewolf
- Jose D. Vargas-Quiros
- Reinier O. Schlingemann
- David M. Presby
Institutions
- University of Bern (CH)
- SIB Swiss Institute of Bioinformatics (CH)
- Radboud University Nijmegen (NL)
- University of Cape Town (ZA)
- University of Basel (CH)
- University of Zurich (CH)
- University of St.Gallen (CH)
- University Hospital of Bern (CH)
- Erasmus MC (NL)
- Radboud University Medical Center (NL)
- Fondation Asile des Aveugles (CH)
- Max Planck Institute for Human Cognitive and Brain Sciences (DE)
- Amsterdam University Medical Centers (NL)
- Institute of Molecular and Clinical Ophthalmology Basel (CH)
- Idiap Research Institute (CH)
- University of Amsterdam (NL)
- Erasmus University Rotterdam (NL)
- University of Lausanne (CH)
Publication Details
- Journal
- Nature Communications
- Published
- 2026-08-26
- DOI
- https://doi.org/10.1038/s41467-026-77102-1
- Citations
- 1
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 6.26
Funders
- National Science Foundation
- European Commission
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- ZonMw
- Erasmus Medisch Centrum