Abstract 14794: Stratification of Cardiovascular Risk Using an Unbiased Proteomic Approach

Abstract

Individuals with stable coronary heart disease (CHD) have widely divergent risk for cardiovascular (CV) events. Current methods for CV risk stratification are imprecise, and may lead to both over-treatment and under-treatment of individuals, with downstream burdens on the health care system. To develop a biomarker-based risk-assessment tool, we performed proteomic analysis on blood samples from 987 individuals with stable CHD and a median 6 years of follow-up, using a novel affinity reagent proteomics platform which allows quantification of ~1000 proteins from a small volume of blood (SOMAscan^TM). From this unbiased search, we derived a 10 protein classifier that yields a hazard ratio of 12 between the highest and lowest quintiles for all subsequent CV events (myocardial infarction, heart failure, stroke, and transient ischemic attack) (Figure). Notably, these 10 proteins are novel for CV risk stratification. Although our primary model predicts the risk of combined CV events of any type, we have also considered CV end-points individually. Proteins associated with heart failure partly overlap those associated with thrombotic events (defined as myocardial infarction, stroke, and transient ischemic attack); however some proteins are related only to one specific type of CV event. The relationships between CV event type and proteomic pattern can be interpreted in the light of biological processes such as angiogenesis, CV signaling, inflammation, extracellular matrix maintenance and renal function. Each individual’s plasma proteome can be compared to others within these biologically motivated proteomic subspaces, delivering a richer picture than a single aggregate risk score can provide. In sum, high-content proteomic analysis leads to markedly improved risk assessment and may enable targeting of preventive therapies, enhance patient management, enrich enrollment for CV events clinical trials, and identify potential targets for therapeutic discovery.