RetiMap: Automated Retinal Vascular Measures Link Microvascular Structure to Metabolic Health and Predict Cardiovascular Risk.

Fundus imaging enables noninvasive, high-resolution visualization of the retinal microvasculature. Advances in artificial intelligence (AI) now allow for extraction of quantitative vascular metrics from retinal images, offering new opportunities for identifying systemic health biomarkers. This study sought to characterize retinal microvascular features in a large healthy population and assess their associations with diverse clinical phenotypes and evaluate their ability to predict incident cardiovascular events. We analyzed fundus photographs from 8,467 healthy individuals aged 40-70 years enrolled in the Human Phenotype Project. For external validation we used fundus images from 16,249 participants from UK Biobank. Using an automated AI-based tool (AutoMorph), we extracted 12 quantitative vascular metrics, such as vessel density, average width, fractal dimension, distance tortuosity, and curvature tortuosity, separately for arteries and veins. We derived age- and sex-stratified reference values and evaluated associations with clinical parameters spanning cardiometabolic, respiratory, and behavioral domains. Retinal vascular features demonstrated strong age- and sex-related patterns. Multiple significant associations were observed between microvascular metrics and systemic traits. Arterial features were particularly associated with cardiometabolic factors including blood pressure, lipid profiles, glycemic indices, and body composition (body mass index, fat mass), as well as sleep apnea parameters. Findings replicated in UK Biobank and demonstrated prognostic value for incident cardiovascular events. This large-scale, AI-driven study provides normative data on retinal vascular traits and supports the utility of fundus imaging for systemic risk stratification and prediction of cardiovascular events. Our findings highlight the potential of retinal biomarkers for early detection and monitoring of cardiometabolic and sleep-related conditions, reinforcing the emerging role of oculomics in predictive and preventive health care.