Title: Methods to Improve Personalized Cardiovascular Disease Prevention Across the Life Course Abstract: Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality. The overall objective of this proposal is to develop methods for improving personalized CVD prevention across the life course. CVD risk prediction plays a central role in clinical CVD prevention strategies, by aiding decision making for lifestyle modification and/or to match the intensity of therapy to the absolute risk of a given patient. The current risk prediction algorithms are generally based on the risk factors measured at a single time. Recently, we and others have shown that cumulative burden and trajectories of CV risk factors are independently associated with incident CVD. As risk factors like blood pressure are regularly collected in clinical practice, we propose to develop dynamic personalized prediction models for (1) short-term (e.g., 10-year) and lifetime risk of CVD and (2) life expectancy lived free of CVD and life expectancy lived with different subtypes of CVD across the life course using the history of time-varying CV risk factors. In addition, we will develop robust methods to improve the prediction of personalized blood pressure-lowering and cholesterol-lowering benefit with respect to CVD risk reduction as well as life expectancy lived free of CVD and life expectancy lived with CVD across the life course. The investigator team of this proposal has pooled the data from 20 community-based CVD cohorts through the Lifetime Risk Pooling Project (LRPP), which now has in excess of 25 years of follow-up data with repeated measured CVD risk factors, detailed information about medication use (including blood pressure- lowering and cholesterol-lowering therapy), nearly 100% follow-up for vital status, and detailed CVD event adjudication. Therefore, the LRPP provides a unique data source for our objective. We will validate the estimates for short-term personalized blood pressure-lowering and cholesterol-lowering treatment effects using the data from RCTs through our collaborations with the Blood Pressure Lowering Treatment Trialists' Collaboration and the Cholesterol Treatment Trialists' Collaboration, respectively. The consistency of the results would suggest adequate confounding adjustment and support the long-term personalized treatment effect estimates from LRPP which cannot otherwise be derived from RCTs data due to relatively short follow up.
Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality. The findings from our study will promote precision medicine in CVD prevention by improving personalized risk prediction and personalized treatment benefit estimation with patient-centered outcomes, which ultimately could improve risk communication and personalized prevention strategies, as well as treatment adherence.
