Metabolomic Analyses for the Prognosis of Acute Coronary Syndrome

急性冠脉综合征预后的代谢组学分析

• 批准号: 8813116
• 负责人: Qi Zhao
• 金额: $ 22.56万
• 依托单位: TULANE UNIVERSITY OF LOUISIANA
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8813116
• 关键词: Admission activity; Applications Grants; Area; Atherosclerosis; Biological Markers; Cardiology; Cardiovascular Diseases; Cardiovascular system; Career Choice; Cause of Death; Centers of Research Excellence; Cessation of life; Classification; Clinical; Clinical Management; Clinical Research; Consult; Coronary; Coronary Care Units; Coronary heart disease; Data; Data Collection; Diagnosis; Discriminant Analysis; Disease; Economics; Ensure; Event; Face; Funding; Goals; Hospitals; Hour; Hyperglycemia; Hyperlipidemia; Individual; Intervention; Knowledge; Laboratories; Least-Squares Analysis; Life; Mass Fragmentography; Measurement; Mentors; Mentorship; Metabolic; Methods; Modeling; Molecular Target; Pathway interactions; Patients; Pattern; Performance; Plasma; Predictive Value; Procedures; Prospective Studies; Quality Control; Recruitment Activity; Recurrence; Research; Research Design; Research Personnel; Research Project Grants; Resources; Risk; Risk Factors; Sampling; Staging; Stratification; Survival Analysis; Survivors; Technology; Testing; Therapeutic; Time; Training; Translational Research; United States National Institutes of Health; acute coronary syndrome; adverse outcome; base; career; career development; cost; design; disability; experience; follow-up; high risk; improved; improved outcome; metabolomics; mortality; multidisciplinary; novel; novel marker; novel therapeutics; outcome forecast; patient stratification; prognostic; programs; protocol development; research and development; social; tool; translational medicine

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.

项目摘要（研究项目3） 急性冠状动脉综合征（ACS）是一种威胁生命的冠心病，这是一种主要 美国的死亡和残疾原因。 ACS患者的复发事件非常普遍，幸存者 面临不利结果的大量过剩风险，导致巨大的经济和社会负担。准确的 ACS患者的风险预测对于帮助临床医生做出治疗决定至关重要，例如 建议进行更积极的干预和深入的随访。但是，ACS的风险分层 患者仍然具有挑战性，新型预测因子的识别对于改善 ACS患者的预后预测。高通量代谢组技术的最新进展使得 非常有希望的是，新型的代谢生物标志物或这些生物标志物的模式以更好的风险分层 在ACS患者中，将被确定。因此，拟议研究的总体目标是确定 使用最先进的代谢组平台预测AC的预后的代谢生物标志物，该平台 集成LC-MS和GC-MS方法。我们将招募478名ACS患者，他们将在3个专业中住院 为大新奥尔良地区服务的医院。来自患者的基线数据将在24中收集 入学时间。血浆样品将用于代谢组学分析。研究患者将 平均遵循1。5年。有关主要不良心血管事件（MACE）的后续数据 研究患者将每6个月收集一次，并由研究心脏病学家确定。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数据 处理。我们将使用生存分析方法来检查代谢组特征之间的关联 和ACS患者的狼牙棒。除了单个代谢物分析外，我们还将使用多元方法 （包括主要成分分析和部分最小二乘判别分析）以识别代谢组学 在随访期间，可以区分有和无MACE的ACS患者的模式。我们 将进一步检查确定的代谢物或代谢组模式是否会提供额外 与预后的现有风险评分（例如恩典和TIMI评分）相比，预测价值 ACS患者的预测。拟议的研究将是第一个全面研究的研究 ACS患者的复发事件和死亡相关的代谢生物标志物。它有很大的潜力 确定新型的代谢生物标志物，以更好地预测结果并改善患者的风险预测 ACS。这可能会对改善ACS临床管理的转化医学产生重大影响。它 也可能会促进我们对动脉粥样硬化进展涉及的途径的理解，提供 ACS的新型治疗分子靶标。这个毛病研究项目和资金将帮助赵博士 过渡到成功的独立NIH资助的研究者。