Cardiovascular diseases, particularly Coronary Artery Disease (CAD), remain leading causes of mortality and morbidity worldwide. While traditional risk factors provide valuable insights into disease susceptibility, they often fall short in predicting individual disease trajectories. Recent advances in genomics have highlighted the polygenic nature of CAD, where multiple genetic variants collectively influence disease risk and progression. Polygenic Risk Scores (PRS) have emerged as promising tools for aggregating genetic information into clinically meaningful metrics.
Current research has primarily focused on using PRS for disease onset prediction, with limited exploration of their utility in predicting disease progression. This gap is particularly significant for CAD, where understanding progression patterns could inform therapeutic strategies and resource allocation. Previous studies have demonstrated that genetic factors influence not only disease onset but also its progression rate and complications. However, the relationship between PRS and disease trajectory remains inadequately characterized.
The UK Biobank’s comprehensive dataset, combining genetic information with detailed clinical outcomes and longitudinal follow-up, provides an unprecedented opportunity to investigate this relationship. By leveraging this resource, we aim to evaluate whether PRS can enhance our ability to predict disease progression in CAD patients, potentially enabling more personalized risk stratification and treatment approaches.
Assuming that we can implement a successful model for improved prediction of disease onset in CAD, we intend to explore similar models in other common disease phenotypes.