Optimal medical treatment in patients with stable coronary artery disease (CAD) reduced morbidity and mortality but left a substantial residual risk (RR) of disease progression and events. According to recent evidence, insulin resistance or pre-diabetes together with elevated levels of triglycerides, low levels, and functionality of HDL-cholesterol, often associated with a chronic inflammatory state, are deemed to be relevant components of cardiometabolic and vascular RR. In the present project, we aim at discovering specific individual genetic/molecular profiles subtending emerging cardiometabolic and vascular risk patterns and associated with more severe stable CAD phenotypes. To this end, we will analyse clinical data, blood samples, and imaging data already gathered in a retrospective population of 561 patients with suspected stable coronary disease and will develop integrated predictive models of severity and extent of disease defined by qualitative and quantitative analysis of coronary plaques by cardiac computed tomography. The new predictive models, which will incorporate relevant clinical and genetic/molecular variables associated with more severe coronary atherosclerosis, will be validated in a similar prospective population of patients and extended to the prediction of progression (at 1 year follow-up) of coronary disease phenotypes, occurring despite optimal medical treatment.