Identification of biomarkers for unstable angina by plasma metabolomic profiling
Identification of biomarkers for unstable angina by plasma metabolomic profiling
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通过血浆代谢组学分析鉴定不稳定型心绞痛的生物标志物
DOI：
10.1039/c3mb70216b
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发表时间：
2013-01-01
影响因子：
--
通讯作者：
Yu, Bo
中科院分区：
文献类型：
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作者：
Sun, Meng;Gao, Xueqin;Yu, Bo
不稳定的心绞痛（UA）是冠心病最危险的类型之一，在全球范围内具有高死亡率和发病率。但是，在临床实践中，UA的诊断准确性不令人满意。在这项研究中，我们研究了血浆代谢组学在发现UA诊断的潜在生物标志物中的应用。收集来自45个UA和43个动脉粥样硬化（AS）住院患者的血浆样品，并使用正阳性和负离子模式中的快速分辨率液相色谱量子级质谱（RRLC-QTOF/MS）分析。在UA患者和对照组之间观察到良好的分离。进行了串联质谱实验，以识别对歧视最大的生物标志物候选者（VIP> 1.2和p <0.05）。确定了16个潜在的UA潜在内源性生物标志物，这些生物标志物可以在UA和患者之间歧视这些诊断精度令人满意（AUC = 0.9143）。在UA患者中，与AS对照组相比，血浆浓度的12个代谢产物较高，而四个代谢产物的浓度较低。总之，我们的研究表明，由RRLC-QTOF/MS分析的血浆代谢组学在UA的生物标志物发现中具有很大的潜力。这些生物标志物不仅有助于诊断UA患者，而且还提供了更多信息，以进一步了解UA的代谢过程。
Unstable angina (UA) is one of the most dangerous types of coronary heart disease and has high mortality and morbidity rates worldwide. However, the diagnostic accuracy for UA is unsatisfactory in clinical practice. In this study, we investigated the application of plasma metabolomics in discovering potential biomarkers for the diagnosis of UA. Plasma samples from 45 UA and 43 atherosclerosis (AS) in-patients were collected and analyzed using rapid resolution liquid chromatography quadrupole time-of-flight mass spectrometry (RRLC-QTOF/MS) in both positive and negative ion modes. Good separations were observed between the UA patients and AS controls. Tandem mass spectrometry experiments were carried out to identify biomarker candidates that contributed most to the discrimination (VIP > 1.2 and p < 0.05). Sixteen potential endogenous biomarkers for UA were identified, and those could perform a satisfactory diagnostic accuracy for discrimination between UA and AS patients (AUC = 0.9143). In the UA patients compared to the AS controls, the plasma concentrations of 12 metabolites were higher while the concentrations of four metabolites were lower. In conclusion, our study demonstrated that plasma metabolomics analyzed by RRLC-QTOF/MS had great potential in biomarker discovery for UA. These biomarkers could not only be helpful for the diagnosis of patients with UA, but also provide more information for further understanding of the metabolic processes of UA.