Evidence map›Paper›PMID 42786167›Full record

ArticleNature communications2026

Deep learning aging marker from retinal images unveils sex-specific clinical and genetic signatures.

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 and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Olga TrofimovaDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland. olga.trofimova@unil.ch.ORCID http://orcid.org/0009-0000-0328-4676
Leah BöttgerDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland.ORCID http://orcid.org/0009-0009-0253-8772
Sacha BorsDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland.ORCID http://orcid.org/0009-0007-0478-0808
Yating PanDepartment of Computational Linguistics, University of Zurich, Zurich, Switzerland.
Bart LiefersDepartment of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands.
Jose D Vargas-QuirosDepartment of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands.
Victor A de VriesDepartment of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands.ORCID http://orcid.org/0000-0002-2425-3841
Michael J BeyelerDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland.ORCID http://orcid.org/0000-0001-6199-4879
David M PresbyDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland.
Dennis BontempiDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland.
Janna HastingsSwiss Institute of Bioinformatics, Lausanne, Switzerland.ORCID http://orcid.org/0000-0002-3469-4923
Caroline C W KlaverDepartment of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands.ORCID http://orcid.org/0000-0002-2355-5258
VascX Consortium
Sven BergmannDepartment of Computational Biology, University of Lausanne, Lausanne, Switzerland. sven.bergmann@unil.ch.ORCID http://orcid.org/0000-0002-6785-9034

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation) CRSII5 209510
6 · The paper itself

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.

Indexed as

AgingDeep LearningRetinaAgedFemaleFundus OculiGenome-Wide Association StudyHumansMaleMiddle AgedSex CharacteristicsSex FactorsUK Biobank

Identifiers

PMID42786167
PMCPMC13612558

What OpenQuestion holds

Textmetadata
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.