This project aims to leverage UK Biobank’s extensive clinical, demographic, biochemical, and imaging data to investigate the full spectrum of coronary artery disease (CAD) and its associated cardiometabolic comorbidities including diabetes, chronic kidney disease, and systemic inflammatory conditions. Our objectives include:
To explore how baseline metabolic, inflammatory, and imaging markers correlate with the incidence and progression of CAD, from stable angina to myocardial infarction and heart failure.
To identify predictive markers of treatment response in patients receiving novel therapies such as GLP-1 receptor agonists, PCSK9 inhibitors, and SGLT2 inhibitors.
To evaluate long-term prognosis and clinical outcomes such as recurrent cardiovascular events, hospitalization, and mortality, using medication exposure, procedural history, and imaging-derived phenotypes (e.g., echocardiography, cardiac MRI).
To apply machine learning algorithms to the integrated dataset to develop risk stratification tools that can improve precision medicine strategies in cardiology.
The scientific rationale stems from the need to move beyond traditional LDL-centric models and incorporate multi-dimensional data (including novel biomarkers and imaging) into CAD risk assessment and management. UK Biobank’s rich dataset offers a unique opportunity to bridge this translational gap.