| Title: | A predictive atlas of disease onset from retinal fundus photographs: a modelling study using data from population-based cohorts |
| Journal: | The Lancet Digital Health |
| Published: | 18 Mar 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41856874/ |
| DOI: | https://doi.org/10.1016/j.landig.2025.100962 |
| URL: | http://dx.doi.org/10.1016/j.landig.2025.100962 |
Publication 17351
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Abstract
BACKGROUND: Early detection of individuals at high risk of disease onset is crucial for health-care systems to cope with changing demographics and an ever-increasing patient population. Images of the retinal fundus are a non-invasive, low-cost examination routinely collected and potentially scalable beyond ophthalmology. Previous work showed the potential of retinal images for risk assessment for some diseases, but it remains unclear whether this potential extends to a broader range of human diseases. We aimed to systematically assess the extent to which retinal fundus photographs can predict incident disease onset across the human phenome, and to benchmark their added value beyond readily available patient characteristics.</p>
METHODS: In this modelling study using data from population-based cohorts, we extended a retinal foundation model (RETFound) to systematically explore the predictive potential of retinal images as a screening strategy for disease onset across 752 incident diseases in 61 256 individuals (33 285 [54%] females and 27 971 [46%] males; median age 58 years [IQR 50-63]) from the UK Biobank cohort. Participants had retinal images collected at baseline (Dec 7, 2009, to July 21, 2010) and were linked to routinely collected hospital and death records in the UK. External validation was performed in 7248 individuals (median age 67 years [IQR 62-73]) from the EPIC-Norfolk Eye Study. Predictive improvements were investigated by extracting image attributions from risk models and performing genome-wide association studies.</p>
FINDINGS: We showed improved discriminative performance compared with readily available patient characteristics for 306 (41%) of the 752 investigated disease endpoints, including 280 outside of ophthalmology. Retinal information did not improve the prediction for the onset of cardiovascular diseases compared with established primary prevention scores. Predictive improvements were attributable to retinal vascularisation patterns and less obvious features, such as eye colour or lens morphology. Genetic findings highlighted commonalities between eye-derived risk estimates and complex disorders: across 84 retinal risk phenotypes, we identified 1385 genome-wide statistically significant variant associations across 178 loci, including a low-frequency missense variant in IMPA1 (rs204781; minor allele frequency 2·0%) associated with decreased risk estimates across 48 diseases, with the strongest association observed for iron deficiency anaemia (β=-0·16; p<7·2 × 10-16).</p>
INTERPRETATION: We present one of the first comprehensive evaluations of predictive information derived from retinal fundus photographs, illustrating the potential and limitations of readily accessible and low-cost retinal images for risk assessment across common and rare diseases. Our findings show the potential of retinal images to complement screening strategies more widely, but also demonstrate the need for rigorous benchmarking and disease-agnostic efforts to design cost-efficient screening strategies to improve population health.</p>
FUNDING: Charité - Universitätsmedizin Berlin.</p>
19 Authors
- Thore Buergel
- Lukas Loock
- Jakob Steinfeldt
- Noel Kronenberg
- Laura Hoffmann
- Steffen Emil Künzel
- Julius Upmeier Zu Belzen
- Lucas Arnoldt
- Anthony P Khawaja
- Robert Luben
- Paul J Foster
- Claudia Langenberg
- Ulf Landmesser
- John Deanfield
- Oliver Zeitz
- Antonia M Joussen
- Maik Pietzner
- Benjamin Wild
- Roland Eils
3 Applications
| Application ID | Title |
|---|---|
| 44448 | The genetic and environmental determinants of cardiometabolic and related disorders. |
| 49966 | Learning Intergenic Interactions Shaping Human Genotype-Phenotype Relations |
| 51157 | Stratification and modelling of competing risks in cardiovascular disease events based on multi-level patient characteristics and genetics |
Enabling scientific discoveries that improve human health