Deep learning-derived retinal age gap and its associations with lifestyle, systemic, and ocular health in a health screening cohort.

Authors

Boa Jang, Richul Oh, Tae-Hoon Lee, Chang Ki Yoon, Hyuk Jin Choi, Jinwook Choi, Young-Gon Kim, Kunho Bae

Published in

GeroScience. Sep 17, 2026. Epub Sep 17, 2026.

Abstract

Individuals of the same chronological age differ in biological aging, and scalable, noninvasive markers are needed. The deep learning-derived retinal age gap (RAG) is a promising measure of retinal aging, but its associations with real-world health determinants remain unclear. We developed a multi-task model to predict retinal age using 29,530 fundus images from 7535 participants in a health screening cohort and evaluated RAG in cohort A for lifestyle, socioeconomic, and systemic factors (n = 5606) and cohort B for ocular diseases (n = 1810). The bias-corrected multi-task model trained on mixed data achieved the best performance, with a mean absolute error of 2.656 years and a Pearson correlation of 0.921 in cohort A and 2.529 years and 0.938 in cohort B. Higher RAG was significantly associated with smoking (ex-smokers, β = +0.46 years; current smokers, β = +0.50 years) and with clinical diabetes (+2.52 years); both survived false discovery rate (FDR) and Bonferroni correction, and the diabetes association persisted across all sequential covariate-adjustment sets. Married participants had lower RAG (β = -0.46 years), significant after FDR correction only. Hypertension and hyperlipidemia were not associated with RAG. In cohort B, RAG was significantly higher in eyes with age-related macular degeneration (β = +0.60 years) and cataract (β = +1.86 years) than in normal controls, both surviving corrections. RAG, an imaging-derived age-prediction residual, is therefore associated with lifestyle, systemic, and ocular health. Whether it reflects biological aging requires longitudinal validation against established aging biomarkers; at present, RAG suits population-level characterization better than individual-level risk stratification.

PMID:
42753030
Bibliographic data and abstract were imported from PubMed on 18 Sep 2026.

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