ArticleJapanese journal of ophthalmology2025
Association between the retinal age gap and systemic diseases in the Japanese population: the Nagahama study.
Article in Japanese journal of ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Retinal age gap in unilateral retinal diseases with unaffected fellow eye: phakia and vascular pathology impact retinal age prediction.Japanese journal of ophthalmology · 2026Article
- Deep learning-derived retinal age gap and its associations with lifestyle, systemic, and ocular health in a health screening cohort.GeroScience · 2026Article
- Clinical determinants of retinal age gap estimated from fundus photographs in glaucoma patients.Scientific reports · 2026Observational
- Replication of the Association between Retinal Aging Clock Susceptibility Genes and Retinal Age Gap in an Asian Population: The Nagahama Study.Ophthalmology science · 2026Article
- Detection of pediatric cataracts through non-invasive facial photography using deep convolutional neural networks for early diagnosis.BMC ophthalmology · 2026Observational
- Systemic and local vascular features in branch retinal vein occlusion: analysis of the retinal age gap and crossing pattern.BMJ open ophthalmology · 2026Observational
- Age-Related Associations of Blood Pressure and Arterial Stiffness with Retinal Arterial Structure.Ophthalmology science · 2026Article
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9 authors.
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Abstract
purposeTo investigate the retinal age gap, defined as the difference between deep learning-predicted retinal age and chronological age, as a potential biomarker of systemic health in the Japanese population. STUDY
designProspective cohort study.
methodsData from the Nagahama Study, a large-scale Japanese cohort study, were used. Participants were divided into fine-tuning (n=2,261) and analysis (n=6,070) cohorts based on their visit status across the two periods. The fine-tuning cohort only included individuals without a history of systemic or cardiovascular diseases. A deep learning model, originally released in the Japan Ocular Imaging Registry, was fine-tuned using a fine-tuning cohort to predict retinal age from images. This refined model was then applied to the analysis cohort to calculate retinal age gaps. We conducted cross-sectional and longitudinal analyses to examine the association of these gaps with systemic and cardiovascular diseases.
resultsThe retinal age-prediction model achieved a mean absolute error of 3.00-3.42 years. Cross-sectional analysis revealed significant associations between the retinal age gap and a history of diabetes (β = 1.08, p < 0.001) and hyperlipidemia (β = -0.67, p < 0.001). Longitudinal analysis showed no significant association between the baseline retinal age gap and disease onset. However, onset of hypertension (β = 0.35, p = 0.049) and hyperlipidemia (β = 0.34, p = 0.035) showed marginal associations with an increase in retinal age gap over time.
conclusionThe retinal age gap is a promising biomarker for systemic health, particularly in relation to diabetes, hypertension, and hyperlipidemia.
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