Yong Fan1, Yong Li2, Yan Chen3, Yi-Jing Zhao1, Li-Wei Liu1, Jin Li1, Shi-Lei Wang1, Raphael N Alolga1, Yin Yin4, Xiang-Ming Wang5, Dong-Sheng Zhao6, Jian-Hua Shen7, Fan-Qi Meng8, Xin Zhou9, Hao Xu2, Guo-Ping He2, Mao-De Lai1, Ping Li10, Wei Zhu11, Lian-Wen Qi12. 1. State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, Jiangsu, China. 2. Department of Cardiology, the Affiliated Wujin Hospital of Jiangsu University, Changzhou, Jiangsu, China. 3. Department of Emergency Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China; Medical Department, Women and Children Health Care Hospital of Jiangsu Province, Nanjing, Jiangsu, China. 4. Department of Gynecology and Obstetrics, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China. 5. Department of Geriatric Cardiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China. 6. Department of Cardiology, the Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, China. 7. Department of Cardiology, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, China. 8. Department of Cardiology, Xiamen Cardiovascular Hospital, Xiamen, Fujian, China. 9. Department of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China. 10. State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, Jiangsu, China. Electronic address: liping2004@126.com. 11. Department of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China. Electronic address: zhuwei@njmu.edu.cn. 12. State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, Jiangsu, China. Electronic address: Qilw@cpu.edu.cn.
Abstract
BACKGROUND: 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.
BACKGROUND: 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.
Authors: Hossein Maleki; Ahmad K Karanji; Sandra Majuta; Megan M Maurer; Stephen J Valentine Journal: J Am Soc Mass Spectrom Date: 2017-09-27 Impact factor: 3.109
Authors: Xuesi Dong; Zhaozhong Zhu; Yongyue Wei; Debby Ngo; Ruyang Zhang; Mulong Du; Hui Huang; Lijuan Lin; Paula Tejera; Li Su; Feng Chen; Amy M Ahasic; B Taylor Thompson; Nuala J Meyer; David C Christiani Journal: Chest Date: 2020-11-12 Impact factor: 9.410