The data requested from the UK Biobank will be used solely and exclusively for the purpose of my PhD dissertation research, as detailed in this proposal.
Research Questions and Objectives:
This project will assess whether integrated models that combine polygenic risk scores, plasma proteomics, imaging-derived phenotypes, and clinical-questionnaire data can improve prediction of major cardiovascular diseases compared with traditional risk factors alone. It will also identify biomarkers across proteomic, imaging, and clinical domains that are most strongly associated with adverse outcomes such as mortality, heart failure hospitalization, and recurrent events. Finally, the study will explore interactions between different biological layers-genetic predisposition, protein expression, and subclinical organ damage-to clarify how these factors jointly shape cardiovascular risk and prognosis.
Scientific Rationale
Cardiovascular disease prediction is transitioning from a reliance on traditional risk factors towards a more holistic, systems-based approach. The UK Biobank is one of the few resources globally that enables this paradigm shift, as it concurrently houses deep genomic, proteomic, imaging, and phenotypic data on a vast scale. Genomics provides a lifetime risk estimate through PRS, identifying high-risk individuals early in life. Proteomics reflects the current pathophysiological state, potentially revealing active disease processes long before clinical symptoms manifest. Imaging (e.g., cardiac MRI) offers a direct, in-vivo assessment of subclinical organ damage, a crucial step between risk exposure and overt disease. Clinical & Questionnaire Data form the established foundation of risk assessment. The central hypothesis is that the integration of these complementary data layers will capture the entire spectrum of disease development.
