Identification for High Risk of Coronary Heart Disease Based on Quantitative Analysis of Fundus Vessels and Metabolic Molecules

This ambidirectional observational cohort study builds upon and expands an existing coronary artery disease (CAD) cohort based on retinal fundus imaging (ChiCTR2200066429). A comprehensive baseline database will be established, including demographic characteristics, laboratory test results, coronary angiography findings, quantitative retinal vascular features, and multi-omics data.Artificial intelligence methods will be used to quantify retinal vascular characteristics and to develop and validate models for identifying and predicting CAD. In addition, targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) assays will be developed to measure previously identified CAD-related biomarkers, including metabolites and lipids, in baseline biological samples. Clinical, retinal imaging, and multi-omics data will subsequently be integrated to develop a risk stratification model for identifying individuals at high risk of CAD. Participants will be followed for cardiovascular events to evaluate the predictive performance of the model. The findings may support the development of improved strategies for CAD detection, risk assessment, and clinical management. No treatment or clinical intervention is assigned by the study protocol, and all medical decisions are made as part of routine clinical care.