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Updated: May 10, 2025

Digital PCR for Quantifying Circulating MicroRNAs in Acute Myocardial Infarction and Cardiovascular Disease
Published on: July 3, 2018
Metabolic Profiling Reveals Diagnostic Biomarkers for Distinguishing Myocarditis From Acute Myocardial Infarction
Yuting Chen1, Xiu Liu1, Chengying Hong1
1Department of Critical Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China.
Abstract:
Background: Distinguishing between myocarditis (MC) and acute myocardial infarction (AMI) in the early stages is crucial due to their similar symptoms yet vastly different treatment protocols. This study seeks to utilize metabolomics techniques to differentiate between MC and AMI. Methods: Plasma samples from 15 patients with MC and 12 patients with AMI were collected. Metabolic profiles of plasma from the two groups of patients were obtained using ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS), identifying metabolites with significant differences. Results: We identified 30 significantly different metabolites in both diseases. In patients with MC, 17 metabolites were upregulated, including 5-hydroxy-L-tryptophan and LysoPC (18:2(9Z,12Z)), while 13 metabolites were downregulated, such as 11-cis-retinol, L-glutamate, and hydroxynicotinic acid. KEGG enrichment analysis revealed that the altered metabolites were enriched in tryptophan metabolism, linoleic acid metabolism, primary bile acid biosynthesis, nitrogen metabolism, and retinol metabolism. Biomarker analysis via receiver-operating characteristic curves highlighted 11-cis-retinol as the predominant biomarker, with an AUC value of 0.917. Conclusions: In conclusion, patients experiencing AMI and MC undergo significant metabolic reprogramming. Metabolites exhibiting abnormal expression in peripheral blood hold diagnostic value for distinguishing between AMI and MC in clinical settings. 11-cis-retinol proved to be the pivotal biomarker for AMI, potentially aiding in the development of a robust predictive model for distinguishing between MC and AMI in clinical settings.
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