Association of Retinal Age Gap and Risk of Kidney Failure: A UK Biobank Study

Abstract

Rationale & objective: The incidence of kidney failure is known to increase with age. We have previously developed and validated the use of retinal age based on fundus images as a biomarker of aging. However, the association of retinal age with kidney failure is not clear. We investigated the association of retinal age gap (the difference between retinal age and chronological age) with future risk of kidney failure.

Study design: Prospective cohort study.

Setting & participants: 11,052 UK Biobank study participants without any reported disease for characterizing retinal age in a deep learning algorithm. 35,864 other participants with retinal images and no kidney failure were followed to assess the association between retinal age gap and the risk of kidney failure.

Exposure: Retinal age gap, defined as the difference between model-based retinal age and chronological age.

Outcome: Incident kidney failure.

Analytical approach: A deep learning prediction model used to characterize retinal age based on retinal images and chronological age, and Cox proportional hazards regression models to investigate the association of retinal age gap with incident kidney failure.

Results: After a median follow-up period of 11 (IQR, 10.89-11.14) years, 115 (0.32%) participants were diagnosed with incident kidney failure. Each 1-year greater retinal age gap at baseline was independently associated with a 10% increase in the risk of incident kidney failure (HR, 1.10 [95% CI, 1.03-1.17]; P=0.003). Participants with retinal age gaps in the fourth (highest) quartile had a significantly higher risk of incident kidney failure compared with those in the first quartile (HR, 2.77 [95% CI, 1.29-5.93]; P=0.009).

Limitations: Limited generalizability related to the composition of participants in the UK Biobank study.

Conclusions: Retinal age gap was significantly associated with incident kidney failure and may be a promising noninvasive predictive biomarker for incident kidney failure.

Keywords: Aging; biological age; biomarker; chronological age; end-stage renal disease (ESRD); fundus photography; imaging; kidney failure; machine learning (ML); microvascular changes; optical coherence tomography; renal replacement therapy (RRT); retinal age gap.