Frontiers in molecular biosciences · 2025
Urinary metabolomics analysis based on LC-MS for the diagnosis and monitoring of acute coronary syndrome.
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Liu Jiaqi, Wang Aiwei, Qi Feng, Liu Xiaoyan, Guo Zhengguang, Sun Haidan, Zhao Mindi, Li Tingmiao, Xue Fei, Wang Hai, Sun Wei, He Chengyan
Аннотация
BACKGROUND: Acute coronary syndrome (ACS) is a cardiovascular disease caused by acute myocardial ischemia. The aim of this study was to use urine metabolomics to explore potential biomarkers for the diagnosis of ACS and the changes in metabolites during the development of this disease.
METHODS: Urine samples were collected from 81 healthy controls and 130 ACS patients (103 UA and 27 AMI). Metabolomics based on liquid chromatography-mass spectrometry (LC-MS) was used to analyze urine samples. Statistical analysis and functional annotation were applied to identify potential metabolite panels and altered metabolic pathways between ACS patients and healthy controls, unstable angina (UA), and acute myocardial infarction (AMI) patients.
RESULTS: There were significant differences in metabolic profiles among the UA, AMI and control groups. A total of 512 differential metabolites were identified in this study. Functional annotation revealed that changes in arginine biosynthesis, cysteine and methionine metabolism, galactose metabolism, sulfur metabolism and steroid hormone biosynthesis pathways occur in ACS. In addition, a panel composed of guanidineacetic acid, S-adenosylmethionine, oxindole was able to distinguish ACS patients from healthy controls. The AUC values were 0.8339 (UA VS HCs) and 0.8617 (AMI VS HCs). Moreover, DL-homocystine has the ability to distinguish between UA and AMI, and the area under the ROC curve is 0.8789. The metabolites whose levels increased with disease severity the disease were involved mainly in cysteine and methionine metabolism and the galactose metabolism pathway. Metabolites that decrease with disease severity are related mainly to tryptophan metabolism.
CONCLUSION: The results of this study suggest that urinary metabolomics studies can reveal differences between ACS patients and healthy controls, which may help in understanding its mechanisms and the discovery of related biomarkers.
METHODS: Urine samples were collected from 81 healthy controls and 130 ACS patients (103 UA and 27 AMI). Metabolomics based on liquid chromatography-mass spectrometry (LC-MS) was used to analyze urine samples. Statistical analysis and functional annotation were applied to identify potential metabolite panels and altered metabolic pathways between ACS patients and healthy controls, unstable angina (UA), and acute myocardial infarction (AMI) patients.
RESULTS: There were significant differences in metabolic profiles among the UA, AMI and control groups. A total of 512 differential metabolites were identified in this study. Functional annotation revealed that changes in arginine biosynthesis, cysteine and methionine metabolism, galactose metabolism, sulfur metabolism and steroid hormone biosynthesis pathways occur in ACS. In addition, a panel composed of guanidineacetic acid, S-adenosylmethionine, oxindole was able to distinguish ACS patients from healthy controls. The AUC values were 0.8339 (UA VS HCs) and 0.8617 (AMI VS HCs). Moreover, DL-homocystine has the ability to distinguish between UA and AMI, and the area under the ROC curve is 0.8789. The metabolites whose levels increased with disease severity the disease were involved mainly in cysteine and methionine metabolism and the galactose metabolism pathway. Metabolites that decrease with disease severity are related mainly to tryptophan metabolism.
CONCLUSION: The results of this study suggest that urinary metabolomics studies can reveal differences between ACS patients and healthy controls, which may help in understanding its mechanisms and the discovery of related biomarkers.