Retinal BioAge Reveals Indicators of Cardiovascular-Kidney-Metabolic Syndrome in US and UK Populations

Background: There is a growing recognition of the divergence between biological and chronological age, as well as the interaction among cardiovascular, kidney, and metabolic (CKM) diseases, known as CKM syndrome, in shortening both lifespan and healthspan. Detecting indicators of CKM syndrome can prompt lifestyle and risk-factor management to prevent progression to adverse clinical events. In this study, we tested a novel deep-learning model, retinal BioAge, to determine whether it could identify individuals with a higher prevalence of CKM indicators compared to their peers of similar chronological age. Methods: Retinal images and health records were analyzed from both the UK Biobank population health study and the US-based EyePACS 10K dataset of persons living with diabetes. 77,887 retinal images from 44,731 unique participants were used to train the retinal BioAge model. For validation, separate test sets of 10,976 images (5,476 individuals) from UK Biobank and 19,856 retinal images (9,786 individuals) from EyePACS 10K were analyzed. Retinal AgeGap (retinal BioAge — chronological age) was calculated for each participant, and those in the top and bottom retinal AgeGap quartiles were compared for prevalence of abnormal blood pressure, cholesterol, kidney function, and hemoglobin A1c. Results: In UK Biobank, participants in the top retinal AgeGap quartile had significantly higher prevalence of hypertension compared to the bottom quartile (36.3% vs. 29.0%, p<0.001), while the prevalence was similar for elevated non-HDL cholesterol (77.9% vs. 78.4%, p=0.80), impaired kidney function (4.8% vs. 4.2%, p=0.60), and diabetes (3.1% vs. 2.2%, p=0.24). In contrast, EyePACS 10K individuals in the top retinal AgeGap quartile had higher prevalence of elevated non-HDL cholesterol (49.9% vs. 43.0%, p<0.001), impaired kidney function (36.7% vs. 23.1%, p<0.001), suboptimally controlled diabetes (76.5% vs. 60.0%, p<0.001), and diabetic retinopathy (52.9% vs. 8.0%, p<0.001), but not hypertension (53.8% vs. 55.4%, p=0.33). Conclusion: A deep-learning retinal BioAge model identified individuals who had a higher prevalence of underlying indicators of CKM syndrome compared to their peers, particularly in a diverse US dataset of persons living with diabetes.