Version 1
: Received: 7 May 2024 / Approved: 7 May 2024 / Online: 8 May 2024 (11:25:42 CEST)
How to cite:
Shibata, M.; Sugimoto, M.; Watanabe, N.; Namiki, A. Exploring Novel Biomarkers for an Acute Coronary Syndrome Diagnosis Utilizing Plasma Metabolomics. Preprints2024, 2024050426. https://doi.org/10.20944/preprints202405.0426.v1
Shibata, M.; Sugimoto, M.; Watanabe, N.; Namiki, A. Exploring Novel Biomarkers for an Acute Coronary Syndrome Diagnosis Utilizing Plasma Metabolomics. Preprints 2024, 2024050426. https://doi.org/10.20944/preprints202405.0426.v1
Shibata, M.; Sugimoto, M.; Watanabe, N.; Namiki, A. Exploring Novel Biomarkers for an Acute Coronary Syndrome Diagnosis Utilizing Plasma Metabolomics. Preprints2024, 2024050426. https://doi.org/10.20944/preprints202405.0426.v1
APA Style
Shibata, M., Sugimoto, M., Watanabe, N., & Namiki, A. (2024). Exploring Novel Biomarkers for an Acute Coronary Syndrome Diagnosis Utilizing Plasma Metabolomics. Preprints. https://doi.org/10.20944/preprints202405.0426.v1
Chicago/Turabian Style
Shibata, M., Norikazu Watanabe and Atsuo Namiki. 2024 "Exploring Novel Biomarkers for an Acute Coronary Syndrome Diagnosis Utilizing Plasma Metabolomics" Preprints. https://doi.org/10.20944/preprints202405.0426.v1
Abstract
Acute coronary syndrome (ACS) is a life-threatening condition that requires a prompt diagnosis and therapeutic intervention. Although serum troponin T and creatinine kinase-MB (CK-MB) are established biomarkers for ACS, it may take several hours to reach diagnostic values for ACS. In this study, we attempted to explore novel biomarkers for ACS with higher sensitivity than that of troponin T and CK-MB. The metabolomic profiles of 18 patients with ACS upon hospital arrival and those of the age-matched control (HC) group consisted of 24 healthy volunteers were analyzed using liquid chromatography-time-of-flight mass spectrometry. Volcano plots showed 24 metabolites whose concentrations differed significantly between the ACS and HC groups. Using these data, we developed a multiple logistic regression model for the ACS diagnosis, in which lysine, isocitrate, and tryptophan were selected as minimum-independent metabolites. The area under the receiver operating characteristic curve value for discriminating ACS from HC was 1.00 (95% confidence interval [CI]: 1.00-1.00). In contrast, those for troponin T and CK-MB were 0.917 (95% confidence interval [CI]: 0.812-1.00) and 0.988 (95% CI: 0.966-1.00), respectively. This study showed the potential of combining three plasma metabolites to discriminate ACS from HC with a higher sensitivity than that of troponin T and CK-MB.
Keywords
acute coronary syndrome; biomarker; metabolomics; multiple logistic regression model
Subject
Medicine and Pharmacology, Cardiac and Cardiovascular Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
