Characterization of response to lipid-modifying regimens for atherosclerotic cardiovascular disease using electronic health records
Recent pharmacological advancements have reduced the burden of atherosclerotic cardiovascular disease (ASCVD), the leading cause of mortality in the United States, but more work is necessary to further improve patient outcomes. In particular, we do not thoroughly understand how individual patients respond to lipid lowering therapies over time including the demographic, clinical and genetic variables which modify response. Data from randomized controlled trials (RCTs) have largely shaped the evidence base for the clinical management of lipid-modifying therapeutic regimens, but answer narrow questions in participants who may not be representative of the broader population at risk for ASCVD. Thus, we cannot rely on RCTs to comprehensively guide pharmaceutical care. Electronic health records (EHRs) are a valuable and efficient resource for evaluating lipid-modifying therapies. Results from EHRs can complement RCT findings to provide more comprehensive treatment recommendations.
The overall objective of this proposal is to advance our understanding of how patients respond to lipid-modifying agents for the prevention of ASCVD. We anticipate that novel markers can identify responders, non-responders, and adverse-effect responders to different lipid-modifying regimens. We will use EHRs from Kaiser Permanente Northern California (KPNC) to characterize therapy response. Members of KPNC (~3.5 million members) represent a broad and diverse background of patients with ASCVD or at risk for ASCVD. In addition, KPNC EHRs represent health records from an integrated healthcare delivery system with an exceptionally long period of follow-up (1996-present), allowing for the application of innovative methodologies to evaluate therapy response.
In Aim 1, Dr. Akinyemi Oni-Orisan (Principal Investigator) will model longitudinal non-HDL-C levels for the development of a lipid-modifying drug dosing algorithm using non- linear mixed effects modeling.
In Aim 2, Dr. Oni-Orisan will characterize the efficacy and safety of lipid- modifying regimens for ASCVD using Cox regression.
In Aim 3, Dr. Oni-Orisan will identify genetic predictors of statin response using genome wide association studies.
Findings from this proposal will (1) identify predictors of response to lipid-modifying drug regimens, (2) uncover key biological pathways important to lipid-modifying drug response, and (3) aid clinicians in providing individualized care that will reduce the public health burden of ASCVD. Dr. Oni-Orisan will be mentored by a team of experienced researchers with expertise in mathematical modeling, biostatistics, epidemiology, genomics, and lipidology.
The University of California, San Francisco is one of the leading biomedical research centers in the world. Dr. Oni-Orisan will take advantage of the rich resources within this research environment to complete the proposal. Overall, the research, training, and institutional environment described in this proposal will aid Dr. Oni-Orisan in his long-term career goals of (1) conducting high-impact translational research that advances pharmaceutical care and directly benefits the cardiovascular health of the nation and (2) becoming a successful independent investigator.