Cardiometabolic Syndromes (CMSs)-including coronary artery disease (CAD), stroke, hypertension, and type 2 diabetes (T2D)-are among the leading global causes of morbidity and mortality. While traditional risk factors such as genetic predisposition, lifestyle choices, and environmental exposures are well-established, the biological mechanisms through which these factors contribute to disease onset and progression remain incompletely understood. In particular, the role of circulating metabolites and proteins as molecular intermediaries is underexplored, yet may be critical for early detection, prevention, and intervention.
This project aims to leverage the comprehensive genomic, metabolomic, proteomic, imaging, and epidemiological resources of the UK Biobank (UKB) to systematically dissect the molecular pathways that link established risk factors to CMS outcomes. The specific objectives are:
i). To quantitatively assess the impact of genetic predisposition, environmental exposures, and lifestyle behaviors on the risk of major CMSs.
ii). To identify and evaluate circulating metabolites and plasma proteins that mediate the associations between upstream risk factors (e.g., body mass index [BMI], smoking, diet, physical activity) and CMS outcomes.
iii). To apply robust causal inference methodologies, including Mendelian Randomization (MR) and mediation analysis, to distinguish direct effects from those mediated by intermediate molecular phenotypes.
iv). To integrate multi-omics data (genomics, metabolomics, proteomics) using advanced statistical and machine learning approaches to identify novel biomarkers and predictive signatures for CMS onset and progression.
Overall, by capitalizing on the extensive multi-modal data within UKB and applying rigorous causal inference and integrative modeling frameworks, this project will go beyond traditional association analyses to generate mechanistic insights into CMS pathogenesis.
