ArticleInternational journal of general medicine2025
The Diagnostic Value of Bile Acids and Amino Acids in Differentiating Acute Coronary Syndromes.
Article in International journal of general medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Causal Effects of Hydrophilic Bile Acids on Carfilzomib-Related Cardiovascular Events in Multiple Myeloma: A Mendelian Randomization Study.Clinical pharmacology and therapeutics · 2026Article
- Emerging insights of bile acids in cardiovascular physiology and disease: from molecular mechanisms to therapeutic potential.Frontiers in pharmacology · 2026Review
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Authors and funding
11 authors.
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Abstract
Purpose: Acute coronary syndrome (ACS), comprising unstable angina and acute myocardial infarction, is the most dangerous and fatal form of coronary heart disease. This study evaluates serum bile acids (BAs) and amino acids (AAs) as potential predictors of AMI in UA patients. Patients and Methods: A total of 72 Non-Coronary Artery Disease (NCAD) patients, 157 UA patients, and 79 AMI patients were analyzed. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) measured 15 bile acids and 19 amino acids. The data was split into training and validation sets (7:3). Univariate and multivariate analyses were performed. Diagnostic value and clinical benefits were assessed using receiver operating characteristic (ROC) curves, decision curve analysis, and metrics such as the area under the curve (AUC), integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results: Orthogonal partial least squares discriminant analysis (OPLS-DA) of serum BAs and AAs effectively differentiated NCAD, UA, and AMI groups. The differences in serum BA and AA profiles between UA and AMI patients were primarily driven by four metabolites: deoxycholic acid (DCA), histidine (His), lysine (Lys), and phenylalanine (Phe). Together, they had an AUC of 0.830 (0.768 in the validation cohort) for predicting AMI in UA patients. After adjusting for multiple confounding factors, DCA, His, Lys, and Phe were independent predictors distinguishing UA from AMI. The results of AUC, IDI, and NRI showed that adding these four biomarkers to a model with clinical variables significantly improved predictive value, which was confirmed in the validation cohort. Conclusion: These findings highlight the association of DCA, His, Lys, and Phe with AMI, suggesting their potential role in AMI pathogenesis.
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