Molecular predictors of cardiovascular events and resilience in chronic coronary artery disease

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PROJECT ABSTRACT State-of-the-art risk assessments in chronic coronary artery disease (CAD) only partially capture risk for cardiovascular events (CVEs), leaving substantial ‘residual risk’ unaddressed. Current risk assessments also incompletely capture resilience to CAD, defined as those at high risk by contemporary algorithms—but without disease. This ‘residual protection’ highlights novel resiliency factors protective against the development of CAD. In this context, it is crucial to understand factors related to both residual risk and resiliency to personalize risk prediction and help clinicians and patients make better treatment decisions. Our overarching hypothesis is that a multi ‘omics’ approach can identify molecular features of residual risk and resilience in CAD. Historically, omics studies of CAD were limited by 1) phenotypic heterogeneity—reliance on variable definitions of CAD and CVEs, biasing results and limiting prediction; and 2) risk homogeneity—constraining identification of novel pathways and limiting generalizability. We overcome these limitations by leveraging unique access to landmark NHLBI CAD strategy trials and a cohort study with aligned core-lab confirmed testing, molecular data, and adjudicated CVEs. Collectively, these studies span the CAD risk continuum—a feature critical to assessing performance of biomarkers and molecular features and overcoming prior limitations. Preliminary data supporting our hypothesis show: 1) substantial, unexplained residual risk (>30%) for death/myocardial infarction with a clinical model of risk factors and CAD severity, 2) biomarkers of inflammation, myocyte injury and distension improve model performance, and 3) novel transcriptome modules of inflammation and interferon signaling further improve prediction. New preliminary data from the imputed transcriptome of ‘resilient’ patients without CAD demonstrates dysregulated pathways and genes of fatty acid metabolism. Our overall goal is to leverage well-phenotyped participants from these landmark studies to improve CVE prediction and better understand resilience to CAD. We propose the following specific aims. Aim 1: Improve prediction of CVEs in patients with established CAD. We will test and validate (1a) candidate biomarkers, polygenic risk scores for CAD and (1b) transcriptomics to improve CVE prediction beyond a clinical model of risk factors and state-of-the-art testing (core-lab confirmed severity of CAD and ischemia). Aim 2: Identify biomarkers and molecular features of resilience to CAD. We will test the association of (2a) candidate biomarkers and (2b) transcriptomics among resilient patients without CAD despite a high probability of disease by clinical and polygenic risk scores for CAD. In the applicant’s opinion, this proposal is innovative and departs from the status quo by using meticulously adjudicated CVEs and phenotype from patients across the CAD risk spectrum and is significant because it will accelerate personalized risk stratification and treatment—especially for the large number of patients at intermediate risk for CAD and CVEs. Ultimately, knowledge generated from this application has the potential to improve the care and outcomes for millions of Americans with CAD.