Retinal age gap as a predictive biomarker for future risk of clinically significant diabetic retinopathy

Abstract

Aims: Retinal age derived from fundus images has been verified as a novel ageing biomarker. We aim to explore the association between retinal age gap (retinal age minus chronological age) and incident diabetic retinopathy (DR).

Methods: Retinal age prediction was performed by a deep learning model, trained and validated based on 19,200 fundus images of 11,052 disease-free participants. Retinal age gaps were determined for 2311 patients with diabetes who had no history of diabetic retinopathy at baseline. DR events were ascertained by data linkage to hospital admissions. Cox proportional hazards regression models were performed to evaluate the association between retinal age gaps and incident DR.

Results: During the median follow-up period of 11.0 (interquartile range: 10.8-11.1) years, 183 of 2311 participants with diabetes developed incident DR. Each additional year of the retinal age gap was associated with a 7% increase in the risk of incident DR (hazard ratio [HR] = 1.07, 95% confidence interval [CI] 1.02-1.12, P = 0.004), after adjusting for confounding factors. Participants with retinal age gaps in the fourth quartile had a significantly higher DR risk compared to participants with retinal age gaps in the lowest quartile (HR = 2.88, 95% CI 1.61-5.15, P < 0.001).

Conclusions: We found that higher retinal age gap was associated with an increased risk of incident DR. As an easy and non-invasive biomarker, the retinal age gap may serve as an informative tool to facilitate the individualized risk assessment and personalized screening protocol for DR.

Keywords: Biomarker; Diabetic retinopathy; Prediction; Retinal age.