Cardiovascular diseases (CVD) remain the leading cause of death worldwide, accounting for nearly 30% of global mortality. With non!cardiac multimorbidities increasingly complicating the clinical scenario-especially among older adults-the overall burden is rising. As a PhD student in cardiovascular research, I am motivated to leverage the extensive multi-omics and clinical datasets from the UK Biobank to better understand the molecular basis of CVD and its related conditions.
My research will address three key questions. First, can integrated analyses of genomics, proteomics, metabolomics, and imaging data reveal critical genetic variants, biomarkers, and pathogenic pathways associated with CVD and its comorbidities? Second, how do these molecular features relate to epidemiological trends such as disease prevalence and mortality? Third, can advanced machine learning approaches build reliable models for early diagnosis and risk stratification?
To answer these, I will (1) perform genome-wide association studies (GWAS) to uncover genetic variants linked to CVD and co-occurring disorders; (2) integrate proteomic, metabolomic, and imaging data to construct molecular networks that highlight key biological pathways; (3) correlate these findings with epidemiological indicators; (4) apply machine learning algorithms to develop and validate predictive models for early detection and proactive risk management; and (5) verify the biomarkers and pathways using independent cohorts and experimental data. This multidimensional approach not only addresses the limits of single-dataset studies but also offers insights that can guide targeted prevention strategies and improve clinical decision making.
