Comprehensive Metabolomic Characterization of Coronary Artery Diseases

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• 作者：Yong Fan ; MS^a ; Yong Li ; MD ; PhD^b ; Yan Chen ; MD ; PhD^c ; ^d ; Yi-Jing Zhao ; BS^a ; Li-Wei Liu ; BS^a ; Jin Li ; BS^a ; Shi-Lei Wang ; BS^a ; Raphael N. Alolga ; MS^a ; Yin Yin ; MD ; PhD^e ; Xiang-Ming Wang ; MD ; PhD^f ; Dong-Sheng Zhao ; MD^g ; Jian-Hua Shen ; MD ; PhD^h ; Fan-Qi Meng ; MD ; PhDⁱ ; Xin Zhou ; MD ; PhD^j ; Hao Xu ; MS^b ; Guo-Ping He ; MS^b ; Mao-De Lai ; MD ; PhD^a ; Ping Li ; PhD^a ; ^{liping2004@126.com" class="auth_mail" title="E-mail the corresponding author} ; Wei Zhu ; MD ; PhD^j ; ^{zhuwei@njmu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Lian-Wen Qi ; PhD^a ; ^{Qilw@cpu.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：angina ; diagnostic biomarkers ; disturbed metabolic pathways ; metabolite ; plasma
• 刊名：Journal of the American College of Cardiology
• 出版年：2016
• 出版时间：20 September 2016
• 年：2016
• 卷：68
• 期：12
• 页码：1281-1293
• 全文大小：1937 K

文摘

Pathogenesis and diagnostic biomarkers for diseases can be discovered by metabolomic profiling of human fluids. If the various types of coronary artery disease (CAD) can be accurately characterized by metabolomics, effective treatment may be targeted without using unnecessary therapies and resources.

Objectives

The authors studied disturbed metabolic pathways to assess the diagnostic value of metabolomics-based biomarkers in different types of CAD.

Methods

A cohort of 2,324 patients from 4 independent centers was studied. Patients underwent coronary angiography for suspected CAD. Groups were divided as follows: normal coronary artery (NCA), nonobstructive coronary atherosclerosis (NOCA), stable angina (SA), unstable angina (UA), and acute myocardial infarction (AMI). Plasma metabolomic profiles were determined by liquid chromatography–quadrupole time-of-flight mass spectrometry and were analyzed by multivariate statistics.

Results

We made 12 cross-comparisons to and within CAD to characterize metabolic disturbances. We focused on comparisons of NOCA versus NCA, SA versus NOCA, UA versus SA, and AMI versus UA. Other comparisons were made, including SA versus NCA, UA versus NCA, AMI versus NCA, UA versus NOCA, AMI versus NOCA, AMI versus SA, significant CAD (SA/UA/AMI) versus nonsignificant CAD (NCA/NOCA), and acute coronary syndrome (UA/AMI) versus SA. A total of 89 differential metabolites were identified. The altered metabolic pathways included reduced phospholipid catabolism, increased amino acid metabolism, increased short-chain acylcarnitines, decrease in tricarboxylic acid cycle, and less biosynthesis of primary bile acid. For differential diagnosis, 12 panels of specific metabolomics-based biomarkers provided areas under the curve of 0.938 to 0.996 in the discovery phase (n = 1,086), predictive values of 89.2% to 96.0% in the test phase (n = 933), and 85.3% to 96.4% in the 3-center external sets (n = 305).

Conclusions

Plasma metabolomics are powerful for characterizing metabolic disturbances. Differences in small-molecule metabolites may reflect underlying CAD and serve as biomarkers for CAD progression.