Research Questions:
1) How do complex interactions among genetic susceptibility, anthropometric profiles (including imaging-derived body composition and muscle mass), and multidimensional lifestyle factors drive the onset and progression of coronary artery disease (CAD)?
2) Can we construct a robust, multi-modal risk prediction model for the full trajectory of CAD and its adverse outcomes?
Objectives:
This project constitutes the core of my doctoral dissertation. The primary objectives are:
1) To systematically evaluate the joint effects of polygenic/monogenic traits (using WES/WGS data) and comprehensive lifestyle factors on CAD incidence.
2) To exploit imaging data (e.g., cardiac MRI, abdominal MRI) to explore the prognostic value of body composition (such as fat distribution and skeletal muscle mass/sarcopenia indices) in CAD patients.
3) To develop and validate an integrated predictive model combining clinical, genetic, and imaging parameters to stratify cardiovascular risk, guiding early intervention.
Scientific Rationale:
While traditional risk factors are well-established, a considerable proportion of cardiovascular events still occur in individuals with varying risk profiles, highlighting the limitations of current assessment strategies. Previous investigations often focused on isolated dimensions, such as individual lifestyle components or basic standard modifiable cardiovascular risk factors (SMuRFs). However, CAD is a systemic metabolic syndrome. By leveraging the full UK Biobank cohort, this doctoral project will integrate multi-dimensional data-spanning whole-genome sequencing, extensive biochemical assays, physical measurements, linked health records, and sophisticated imaging data -to decode the hidden pathways of cardiovascular disease. This holistic approach will not only map the longitudinal trajectory of CAD but also identify novel therapeutic targets and subgroups, fundamentally supporting the completion of my doctoral thesis.
