Martins Aline M A, Paiva Mariana U B, Paiva Diego V N, de Oliveira Raphaela M, Machado Henrique L, Alves Leonardo J S R, Picossi Carolina R C, Faccio Andréa T, Tavares Marina F M, Barbas Coral, Giraldez Viviane Z R, Santos Raul D, Monte Guilherme U, Atik Fernando A
Centre of Metabolomics and Bioanalysis (CEMBIO), San Pablo CEU University, Madrid, Spain.
School of Medicine, University of Brasilia, Brasilia, Brazil.
Front Cardiovasc Med. 2021 Dec 22;8:788062. doi: 10.3389/fcvm.2021.788062. eCollection 2021.
Current risk stratification strategies for coronary artery disease (CAD) have low predictive value in asymptomatic subjects classified as intermediate cardiovascular risk. This is relevant because not all coronary events occur in individuals with traditional multiple risk factors. Most importantly, the first manifestation of the disease may be either sudden cardiac death or acute coronary syndrome, after rupture and thrombosis of an unstable non-obstructive atherosclerotic plaque, which was previously silent. The inaccurate stratification using the current models may ultimately subject the individual to excessive or insufficient preventive therapies. A breakthrough in the comprehension of the molecular mechanisms governing the atherosclerosis pathology has driven many researches toward the necessity for a better risk stratification. In this Review, we discuss how metabolomics screening integrated with traditional risk assessments becomes a powerful approach to improve non-invasive CAD subclinical diagnostics. In addition, this Review highlights the findings of metabolomics studies performed by two relevant analytical platforms in current use-mass spectrometry (MS) hyphenated to separation techniques and nuclear magnetic resonance spectroscopy (NMR) -and evaluates critically the challenges for further clinical implementation of metabolomics data. We also discuss the modern understanding of the pathophysiology of atherosclerosis and the limitations of traditional analytical methods. Our aim is to show how discriminant metabolites originated from metabolomics approaches may become promising candidate molecules to aid intermediate risk patient stratification for cardiovascular events and how these tools could successfully meet the demands to translate cardiovascular metabolic biomarkers into clinical settings.
目前针对冠状动脉疾病(CAD)的风险分层策略,对于被归类为心血管中危的无症状受试者,预测价值较低。这一点很重要,因为并非所有冠心病事件都发生在具有传统多种危险因素的个体身上。最重要的是,该疾病的首发表现可能是心源性猝死或急性冠状动脉综合征,这是在先前无症状的不稳定非阻塞性动脉粥样硬化斑块破裂和血栓形成之后发生的。使用当前模型进行的不准确分层最终可能使个体接受过度或不足的预防性治疗。在理解动脉粥样硬化病理的分子机制方面取得的突破,促使许多研究认为有必要进行更好的风险分层。在本综述中,我们讨论了代谢组学筛查与传统风险评估相结合如何成为改善CAD亚临床无创诊断的有力方法。此外,本综述重点介绍了目前使用的两种相关分析平台——与分离技术联用的质谱(MS)和核磁共振波谱(NMR)——所进行的代谢组学研究结果,并批判性地评估了代谢组学数据进一步临床应用面临的挑战。我们还讨论了对动脉粥样硬化病理生理学的现代理解以及传统分析方法的局限性。我们的目的是展示源自代谢组学方法的判别性代谢物如何可能成为有助于对心血管事件中危患者进行分层的有前景的候选分子,以及这些工具如何能够成功满足将心血管代谢生物标志物转化为临床应用的需求。