Integration of novel contextual and genomic blood pressure measures to enhance cardiovascular disease prediction and management in young adults

PROJECT SUMMARY/ABSTRACT High blood pressure (BP), referred to as hypertension, is a major risk factor for cardiovascular disease (CVD), the leading cause of death worldwide. Despite BP carrying a greater population-attributable risk for CVD earlier in life, the detection and management of hypertension are poor among young adults. Furthermore, due to the lack of event-based trials investigating benefit-to-harm of antihypertensive prescriptions on young adults with low presumed risk, existing risk prediction frameworks are not applicable below age 30 years. Nevertheless, the process leading to atherosclerosis results from the accumulation of risk early in life, contributing to an unacceptably high premature CVD rate in this underexplored population. Hence, the goal of this proposal is to identify novel contextual and genomic BP measures toward efficient hypertension management and enhanced CVD prevention among young adults aged 18 to 39 years. In Aim 1, we will identify novel ambulatory (out-of-clinic setting) BP patterns capturing dynamic BP responses to everyday extrinsic factors and environment based on machine learning approaches. In Aim 2, we will derive a multi-ancestry BP genetic risk score and assess its ability to project long-term BP trajectory to inform antihypertensive initiation timing across ancestrally diverse populations. In Aim 3, we will develop an individualized CVD risk prediction model integrating genomic and non-genomic risk factors, accounting for time-updated treatment status (e.g., BP-lowering medications), and applicable beginning at age 18 years. Successful completion of the research will leverage multiethnic population-based cohorts, large-scale genomics, and real-world healthcare utilization data to accurately estimate short-and long-term CVD risk and facilitate clinical actionability earlier in life. This project will be led by Dr. Cho, a cardiovascular epidemiologist with extensive field experience in biomedical cohorts and analytical expertise in hypertension and lifetime appraisal of cardiovascular health. Dr. Cho has developed a comprehensive plan to complete the above research aims and further her training in machine learning, genetic epidemiology, and advanced statistical modeling techniques. Dr. Cho's exceptional advisory team will directly support her career development. Her primary mentor, Dr. Pradeep Natarajan, is a prominent leader in cardiovascular genomics. The members of Dr. Cho's advisory team include physicians and scientists with expertise in hypertension management, epidemiological methods, health outcomes research, machine learning, and genetics analyses of complex traits. Dr. Cho's strategic advisory team and training plan, coupled with the collaborative environment and extensive resources at the Broad Institute of MIT and Harvard and Massachusetts General Hospital, will facilitate her transition into an independent research career.