Cardiometabolic diseases remain leading causes of morbidity and mortality worldwide, with complex etiologies involving both genetic and environmental factors (i.e., social determinants of health and lifestyle factors). While genome-wide association studies (GWAS) have identified numerous loci associated with cardiometabolic traits, the biological mechanisms and contextual modifiers remain poorly understood. Multi-omic data, including proteomic and metabolomic profiles, offer a powerful lens to bridge this gap. Moreover, incorporating social determinants and lifestyle factors enables a more holistic understanding of disease risk and progression, especially in the context of health differences across populations. UK Biobank’s rich, multi-dimensional dataset provides an unparalleled opportunity to investigate these questions at scale, especially when coupled with similar data available in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the Trans-Omics for Precision Medicine (TOPMed) program. Advanced statistical and machine learning approaches will be developed and applied to leverage longitudinal phenotype data, model interactions, and identify predictive patterns. The integration of these data types will enable a systems-level understanding of cardiometabolic health.
The objectives of the project aim to address the following research questions:
How do genetic variants and multi-omic profiles contribute to longitudinal variation and trajectories in cardiometabolic traits, such as lipid levels, blood pressure, glycemic control, subclinical atherosclerosis, cardiac function, vascular function, pulmonary function, and incidence of cardiometabolic conditions including cardiovascular diseases?
What roles do social determinants of health (e.g., income, education, neighborhood structure), and lifestyle factors (e.g., diet, physical activity, smoking) play beyond sex, race/ethnicity, and age in modulating these associations?
