This project aims to advance the precision medicine approach for Cardiovascular Diseases (CVD) across the entire clinical spectrum, from initial risk prediction to treatment response and long-term prognosis. We will address three key areas:
Prediction & Prevention: Can integrated models combining polygenic risk scores (PRS), multi-omics data (proteomics/metabolomics), and clinical factors improve the identification of high-risk individuals for primary prevention beyond current risk scores?
Treatment & Management: Can we identify genetic and biomarker predictors of response to common CVD treatments (e.g., statins, antihypertensants, antiplatelets) and understand the molecular basis of drug efficacy and side effects?
Prognosis & Secondary Prevention: In individuals with established CVD, what genetic and omics profiles are associated with disease progression, recurrence of major adverse cardiac events (MACE), and all-cause mortality?
Our objectives are: to develop a CVD-specific PRS; to discover omics biomarkers for CVD incidence, treatment response, and prognosis using machine learning; to assess the utility of these markers in risk stratification; and to investigate causal pathways via Mendelian Randomization. The UK Biobank’s genetic, omics, rich phenotypic, linked prescription, and longitudinal outcome data provide an unparalleled resource to address these questions, ultimately guiding more personalized and effective CVD care.
Scientific Rationale: CVD persists as a leading cause of death, hampered by imprecise risk prediction and variable treatment responses. This project will leverage the UK Biobank’s unique data to transcend these “one-size-fits-all” limitations. By developing a CVD-specific PRS, discovering omics biomarkers via machine learning, and investigating causal pathways, we aim to enable earlier risk identification, guide optimal drug selection, and improve prognostication, thereby paving the way for personalized CVD care.
