Genetic and Environmental Determinants of Antihypertensive Response in a Real-World Longitudinal Cohort

PROJECT SUMMARY Hypertension is a leading modifiable risk factor for cardiovascular disease (CVD) and chronic kidney disease (CKD), contributing to over 10 million deaths worldwide each year. Although multiple classes of antihypertensive therapies (e.g., ACE inhibitors, ARBs, diuretics, CCBs) exist, fewer than half of all treated patients achieve adequate blood pressure (BP) control. Growing evidence suggests that both genetic variation (e.g., polygenic risk scores, known pharmacogenomic variants) and environmental factors (e.g., dietary sodium/potassium intake, physical activity) play key roles in shaping drug response, yet real-world data on how these elements jointly affect treatment effectiveness remain limited. This proposal aims to address these gaps by leveraging longitudinal electronic health records (EHRs) from the Kaiser Permanente Research Bank, which integrates genetic data, prescription records (including dose and strength), and lifestyle measures in a large real-world cohort. In Aim 1, we will evaluate the real-world effectiveness and dose-response relationships of both monotherapy and combination antihypertensive regimens, identifying “responders” and “non-responders” and modeling linear and non-linear dose effects. In Aim 2, we will use robust genome-wide approaches to identify genetic variants associated with antihypertensive drug response, constructing and validating polygenic risk scores (PRS) that incorporate relevant covariates. Finally, Aim 3 will map gene–environment interactions, investigating how genetic predisposition intersects with factors such as sodium intake, physical activity, and age to influence BP control and CVD risk. By focusing on hypertension as a case study, this research will advance our understanding of the complex interplay between genetics, environment, and medication use, providing insights that can be generalized to other chronic conditions and informing more reliable, data-driven precision medicine strategies.