Cardiovascular diseases (CVDs) remain a leading cause of global mortality. While numerous risk factors are known, a substantial portion of individual risk and disease mechanisms remains unexplained. This project aims to leverage the large-scale multi-omics data in UK Biobank-including genomics, proteomics, and metabolomics-to gain a deeper, systems-level understanding of CVD pathogenesis.
Our key research questions are: 1) What are the novel genetic, protein, and metabolic markers associated with incident CVDs (e.g., coronary artery disease, heart failure, and atrial fibrillation)? 2) How do these multi-omics layers interact to influence CVD risk? 3) Can a multi-omics integrated model significantly improve upon traditional risk factors for CVD prediction and stratification?
We will employ advanced statistical and machine learning methods to integrate these diverse omics data. Objectives include: conducting genome-wide and proteome/metabolome-wide association studies; investigating causal relationships using Mendelian randomization; and developing a polygenic and multi-omics risk score. The scientific rationale is that a holistic, multi-omics approach can uncover novel biological pathways, reveal key drivers of disease, and ultimately enable more precise prevention and early intervention strategies for CVD.