Parkinson's Disease and Retinal Age Gap: A Cross‐Sectional Analysis

Background Deep‐learning models are capable of predicting age from retinal scans and the difference between this and chronological age, retinal age gap, has been shown to be significantly associated with risk of mortality, cardiovascular diseases, and kidney failure. Objective Our aim was to investigate the association between Parkinson's disease (PD) and retinal age gap in a large, real‐world population. Methods The deep‐learning model was trained and validated using 19,200 fundus images from 11,052 participants in the UK Biobank study. This cross‐sectional analysis used data from the AlzEye study, a retrospective cohort of 154,830 patients aged 40 years and older. Retinal age gap was calculated as the difference between the retina‐predicted age by the model and chronological age. Adjusted linear mixed‐effects models were used to assess the association between PD and retinal age gap. Results A total of 776 individuals with PD (mean [standard deviation] age, 69.6 [8.9] years; 42.9% female) and 93,944 without PD (mean age, 57.6 [13.7] years; 51.6% female) were included. After adjusting for confounders, retinal age gap was significantly greater in those with PD ( β , 5.92, 95% confidence interval: 5.33–6.51; P < 0.001). Stratified analyses showed a larger retinal age gap across all age deciles ≥50 years. Within the PD group, males and individuals with diabetes had greater retinal age gaps than females and individuals without diabetes. Conclusions Individuals with PD aged ≥50 years demonstrate older retinal age relative to chronological age. Retinal age gap may serve as a biomarker of accelerated aging in PD.