Research questions
-What are the interactions between traditional (e.g. hypertension), and novel (e.g. inflammatory markers) and genomic factors in driving transitions along the cardiovascular disease (CVD) continuum?
-Can causal relationships between exposures and CVDs along with their mediators be elucidated using Mendelian randomization (MR), thereby informing preventive and therapeutic interventions?
-How do genetic variants and metabolic signatures modulate individual responses to cardiovascular pharmacotherapy?
-Can an integrated model combining clinical, genetic, metabolomic, and pharmacological data predict individual trajectories and identify preclinical stages in CV continuum?
Objectives
-Map the cardiovascular continuum across the UK Biobank cohort using multimodal data, including imaging, clinical biomarkers, hospital records, and mortality data.
-Uncover and quantify causal impacts of risk factors using MR and polygenic risk scoring, stratified across disease stages.
-Identify genotype-phenotype-drug response interactions using prescribing data, pharmacogenomic markers, and longitudinal outcomes.
-Develop and validate multimodal predictive models using machine learning to stratify individual risk, predict transitions along the CV continuum, and personalize treatments.
Scientific rationale
CVD remains the leading cause of morbidity and mortality, yet prevention and treatment are not tailored to individual risk. The “cardiovascular continuum” concept acknowledges that risk accumulates over time, often silently, before culminating in major events. A deeper understanding of this continuum is essential to move from reactive to proactive, precision care.
The UK Biobank’s extensive genetic, metabolic, clinical, and imaging data across a diverse population uniquely supports this integrative research, which will elucidate disease mechanisms and yield actionable tools for prevention, pharmacogenomics-guided prescribing, and clinical decision support.
