Metabolomic Analyses for the Prognosis of Acute Coronary Syndrome

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PROJECT SUMMARY (Research Project 3) Acute coronary syndrome (ACS) is a life-threatening form of coronary heart disease, which is a major cause of death and disability in the US. Recurrent events in patients with ACS are very common, and survivors face a substantial excess risk of adverse outcomes, leading to a great economic and social burden. Accurate risk prediction in ACS patients is critically important for helping clinicians make therapeutic decisions, such as recommending a more aggressive intervention and intensive follow-up. However, risk stratification in ACS patients remains challenging, and the identification of novel predictors is necessary for improving the prognostic prediction in patients with ACS. Recent advances in high-throughput metabolomic technology make it highly promising that novel metabolic biomarkers or patterns of these biomarkers for better risk stratification in patients with ACS will be identified. Therefore, the overall objective of the proposed study is to identify metabolic biomarkers for predicting the prognosis of ACS using a state-of-the-art metabolomic platform which integrates LC-MS and GC-MS methods. We will recruit 478 ACS patients who will be hospitalized in 3 major hospitals serving the Greater New Orleans area. Baseline data from the patients will be collected within 24 hours of admission. Blood plasma samples will be used for the metabolomic analysis. The study patients will be followed for 1.5 years, on average. Follow-up data on major adverse cardiovascular events (MACE) in the study patients will be collected every 6 months and ascertained by study cardiologists. Rigorous quality control procedures will be applied to the laboratory measurements of metabolites and subsequent metabolomic data handling. We will use the survival analysis method to examine the associations between metabolomic features and MACE in ACS patients. In addition to individual metabolite analysis, we will use multivariate methods (including principle component analysis and partial least squares discriminant analysis) to identify metabolomic patterns which can discriminate between ACS patients with and without MACE during the follow-up period. We will further examine whether the identified metabolites or metabolomic patterns will provide additional predictive value compared to existing risk scores (such as GRACE and TIMI scores) for the prognostic prediction in patients with ACS. The proposed research will be the first study to comprehensively investigate metabolic biomarkers associated with recurrent events and death in patients with ACS. It has great potential to identify novel metabolic biomarkers for better predicting outcomes and improving risk prediction in patients with ACS. It may have a significant impact on translational medicine in improving clinical management of ACS. It may also advance our understanding of the pathways involved in the progression of atherosclerosis, providing novel therapeutic molecular targets for ACS. This COBRE research project and funding will help Dr. Zhao to transition into a successful competitive independent NIH-funded investigator.