Department of Genetics & Molecular Medicine, Faculty of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran.
Chemistry Group, Faculty of Basic Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.
Clin Chim Acta. 2019 Aug;495:43-53. doi: 10.1016/j.cca.2019.03.1632. Epub 2019 Mar 27.
Acute coronary syndrome (ACS) is one of the most dangerous types of coronary heart disease (CHD) and contributes to significant mortality and morbidity worldwide. Outcomes in these patients remain a challenge despite improvements in diagnosis and treatment. Risk stratification continues to be problematic and the identification of novel predictors is crucial for improved outcomes. As such, there is a strong need for the development of novel analytical methods as well as the characterization of better predictive and prognostic biomarkers to enable more personalized treatment. Metabolite profile analysis may greatly assist in interpreting altered pathway dynamics, especially when combined with other 'omics' technologies such as transcriptomics and proteomics. In this review, we describe ACS pathophysiology and recent advances in the role of metabolomics in the diagnosis and the molecular pathogenesis of ACS. We briefly describe key technologies used in metabolomics research and statistical approaches for data reduction and pathway analysis and discuss their application to CHD.
急性冠状动脉综合征（ACS）是最危险的冠心病（CHD）类型之一，在全球范围内导致了大量的死亡和发病。尽管在诊断和治疗方面有所改善，但这些患者的结局仍然是一个挑战。风险分层仍然存在问题，确定新的预测因素对于改善结局至关重要。因此，迫切需要开发新的分析方法，以及更好的预测和预后生物标志物的特征，以实现更个性化的治疗。代谢组学分析可以极大地帮助解释改变的通路动态，特别是当与转录组学和蛋白质组学等其他“组学”技术结合使用时。在这篇综述中，我们描述了 ACS 的病理生理学以及代谢组学在 ACS 的诊断和分子发病机制中的最新进展。我们简要描述了代谢组学研究中使用的关键技术和用于数据减少和通路分析的统计方法，并讨论了它们在 CHD 中的应用。
