Research Questions and Objectives:
Cardiovascular disease (CVD) remains a major global health burden with complex underlying mechanisms. While risk factors like aging, obesity, metabolic dysfunction, and chronic inflammation are established, precise molecular pathways and interactions with environmental exposures require further elucidation. This study leverages the UK Biobank’s multi-omics data (genomics, proteomics, metabolomics, imaging), lifestyle indicators, and clinical records to identify key molecules and pathways driving CVD pathogenesis through multi-omics integration. We will investigate the roles of metabolic dysregulation, immune-inflammatory activation, and environmental factors (e.g., air pollution, diet) in CVD development and progression. Furthermore, we aim to develop AI prediction models for early CVD risk stratification and personalized preventive strategies.
Scientific Rationale:
Circulating proteins and metabolites reflect systemic alterations in CVD pathophysiology and reveal critical molecular pathways. Evidence indicates that metabolic disorders (e.g., insulin resistance, dyslipidemia) and chronic inflammation promote CVD via oxidative stress, endothelial dysfunction, and vascular remodeling. However, previous studies often focused on single-omics layers or lacked granular environmental exposure assessment. The UK Biobank’s unique resources, including longitudinal multi-omics data, accelerometer-measured physical activity, and geocoded environmental data, enable comprehensive investigation. This study will integrate genomics (SNPs, polygenic risk scores), proteomics (inflammatory/vascular markers), metabolomics (lipid/energy metabolites), and imaging to delineate disease mechanisms. Causal inference and machine learning (ensemble models) will distinguish causal biomarkers from confounders and optimize predictive performance. Findings will be validated using nested case-control designs and external cohorts.