ArticleProceedings. Biological sciences2025
The retinal age gap: an affordable and highly accessible biomarker for population-wide disease screening across the globe.
Article in Proceedings. Biological sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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.
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Who cites it
4 citing papers in PubMed.
- Clinical determinants of retinal age gap estimated from fundus photographs in glaucoma patients.Scientific reports · 2026Observational
- The retinal age gap: an affordable and highly accessible biomarker for population-wide disease screening across the globe.Proceedings. Biological sciences · 2025Article
- Biological heart and brain ageing in subjects with cardiovascular diseases.Frontiers in cardiovascular medicine · 2025Article
- Assessment of demographic bias in retinal age prediction machine learning models.Frontiers in artificial intelligence · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Traditional biomarkers, such as those obtained from blood tests, are essential for early disease detection, improving health outcomes and reducing healthcare costs. However, they often involve invasive procedures, specialized laboratory equipment or special handling of biospecimens. The retinal age gap (RAG) has emerged as a promising new biomarker that can overcome these limitations, making it particularly suitable for disease screening in low- and middle-income countries. This study aimed to evaluate the potential of the RAG as a biomarker for broad disease screening across a vast spectrum of diseases. Fundus images were collected from 86 522 UK Biobank participants aged 40-83 (mean age: 56.2 ± 8.3 years). A deep learning model was trained to predict retinal age using 17 791 images from healthy participants. The remaining images were categorized into disease/injury groups based on clinical codes. Additionally, 8524 participants from the Brazilian Multilabel Ophthalmological Dataset (BRSET) were used for external validation. Among the 159 disease/injury groups from the 2019 Global Burden of Disease Study, 56 groups (35.2%) exhibited RAG distributions significantly different from healthy controls. Notable examples included chronic kidney disease, cardiovascular disease, blindness, vision loss and diabetes. Overall, the RAG shows great promise as a cost-effective, non-invasive biomarker for early disease screening.
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Registered trials
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