Metabolomic Analyses for the Prognosis of Acute Coronary Syndrome

项目概要（研究项目3） 急性冠状动脉综合征（ACS）是一种危及生命的冠心病，是一种主要的 在美国死亡和残疾的原因。ACS患者的复发事件非常常见， 面临着产生不利后果的巨大过度风险，导致巨大的经济和社会负担。准确 ACS患者的风险预测对于帮助临床医生做出治疗决策至关重要， 建议进行更积极的干预和强化随访。然而，ACS的风险分层 患者仍然具有挑战性，识别新的预测因子对于改善患者的预后是必要的。 ACS患者的预后预测。高通量代谢组学技术的最新进展使得 新的代谢生物标志物或这些生物标志物的模式用于更好的风险分层是非常有希望的， ACS患者中，将被识别。因此，拟议研究的总体目标是确定 使用最先进的代谢组学平台预测ACS预后的代谢生物标志物， 集成LC-MS和GC-MS方法。我们将招募478名ACS患者，他们将在3个主要医院住院， 大新奥尔良地区的医院将在24小时内收集患者的基线数据。 入场时间。血浆样本将用于代谢组学分析。研究患者将 平均随访1.5年。主要心血管不良事件（MACE）随访数据 研究患者将每6个月收集一次，并由研究心脏病专家确定。严格的质量控制 程序将应用于代谢物的实验室测量和随后的代谢组学数据 装卸.我们将使用生存分析方法来检查代谢组学特征之间的关联 ACS患者的MACE。除了单个代谢物分析外，我们还将使用多变量方法 （包括主成分分析和偏最小二乘判别分析），以确定代谢组学 在随访期间，可以区分ACS患者有无MACE的模式。我们 将进一步检查所鉴定的代谢物或代谢组学模式是否会提供额外的 与现有风险评分（如GRACE和TIMI评分）相比， 预测ACS患者。这项拟议中的研究将是第一项全面调查 与ACS患者复发事件和死亡相关的代谢生物标志物。它有很大的潜力， 确定新的代谢生物标志物，以更好地预测结果并改善患者的风险预测 ACS。这可能对转化医学在改善ACS临床管理方面产生重大影响。它 也可能促进我们对动脉粥样硬化进展中所涉及的途径的理解， ACS的新型治疗分子靶点。这个COBRE研究项目和资金将帮助赵博士 过渡到一个成功的竞争独立NIH资助的研究者。

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.