Cardiovascular disease remains a leading cause of morbidity and mortality worldwide with Coronary Artery Disease (CAD) representing a significant burden on patients and healthcare systems. The current treatment guidelines base recommendations off of the burden of CAD. The overall goal of this proposal is to determine the influence of metabolic, proteomic, and clinical markers in the prediction of coronary artery disease (CAD) severity and the subsequent risk of major adverse cardiac events (MACE). By utilizing comprehensive UK Biobank data, we aim to address the following research questions:
1) Metabolomics:
a. Can specific metabolic profiles derived from blood metabolite measurements predict the onset and progression of CAD?
b. Do these metabolic markers differ in their prognostic capabilities between patients with and without subsequent MACE?
2) Proteomics:
a. How do plasma proteomic signatures correlate with the phenotypic severity of CAD and the occurrence of MACE?
b. Which proteomic markers can robustly predict adverse outcomes independently or in conjunction with other clinical data?
3) Genetics
a. Is clonal hematopoiesis of indeterminate potential (CHIP) associated with CAD and MACE, and do CHIP-associated proteomic profiles mediate the relationship between CHIP and CAD?
b. What is the burden of undiagnosed monogenic cardiovascular diseases including FH (a strong risk factor for CAD)?
4) Clinical Data:
a. How effectively can integrated models that combine clinical, metabolomic, proteomic and genetic data predict MACE in CAD patients compared to traditional risk factors alone?
b. Can these integrated biomarker models identify subgroups of patients at particularly high risk for MACE?
