Research Questions:
How do genetic predispositions, environmental exposures, lifestyle factors, and comorbidities interact to determine the risk and trajectory of cardiovascular diseases (CVD)?
What are the key imaging-derived phenotypes, molecular biomarkers, and pathways that can be leveraged for early detection, prognosis, and personalized intervention in CVD?
Research Objectives:
Risk Modelling: To develop an integrated risk prediction framework for CVD by incorporating genetic, proteomic, imaging, lifestyle, and environmental data.
Mechanistic Insight: To identify key biomarkers and elucidate pathological mechanisms driving CVD onset and progression through comprehensive multi-omics and multimodal imaging analyses.
Prognostic Modelling: To apply advanced statistical and machine learning methods to identify predictors of clinical outcomes and build accurate prognostic tools for personalized CVD management.
Scientific Rationale for the Research:
Cardiovascular disease remains a leading cause of global morbidity and mortality, driven by a complex interplay of genetic, environmental, and lifestyle factors. Multimodal imaging (e.g., cardiac MRI, CT) provides detailed characterization of cardiovascular structure and function, revealing subclinical disease phenotypes. Genomic, proteomic, and metabolomic data offer complementary molecular insights: identifying susceptibility loci, revealing disease-specific protein signatures, and highlighting metabolic alterations. Furthermore, environmental exposures (e.g., air pollution) and lifestyle choices interact with genetic susceptibility to modulate CVD risk. The integration of the UK Biobank’s rich multimodal data-spanning imaging, multi-omics, lifestyle, and environmental exposures-provides an unprecedented opportunity to advance our understanding of CVD mechanisms, improve risk stratification, and develop more precise prevention and management strategies.