Key result
Plasma levels of CarnitineC18:1-OH, CarnitineC18:2-OH, and FFA (20:1) and their combined diagnostic model accurately discriminated acute myocardial infarction from unstable angina with an AUC of 0.9.
Why the study?
To determine the association between potential plasma lipid biomarkers and the early screening and prognosis of acute myocardial infarction.
Do plasma lipid biomarkers (CarnitineC18:1-OH, CarnitineC18:2-OH, and FFA (20:1)) accurately diagnose acute myocardial infarction and predict 1-year cardiovascular re-admission?
Case-Control (n=150)
No
Do plasma lipid biomarkers (CarnitineC18:1-OH, CarnitineC18:2-OH, and FFA (20:1)) accurately diagnose acute myocardial infarction and predict 1-year cardiovascular re-admission?
Effect estimate: AUC 0.9
Plasma lipid biomarkers CarnitineC18:1-OH, CarnitineC18:2-OH, and FFA (20:1) show strong potential for the early diagnosis of acute myocardial infarction and the prediction of 1-year cardiovascular re-admission.
This study was aimed to determine the association between potential plasma lipid biomarkers and early screening and prognosis of Acute myocardial infarction (AMI). In the present study, a total of 795 differentially expressed lipid metabolites were detected based on ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Out of these metabolites, 25 lipid metabolites were identified which showed specifical expression in the AMI group compared with the healthy control (HC) group and unstable angina (UA) group. Then, we applied the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) methods to obtain three lipid molecules, including CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1). The three lipid metabolites and the diagnostic model exhibited well predictive ability in discriminating between AMI patients and UA patients in both the discovery and validation sets with an area under the curve (AUC) of 0.9. Univariate and multivariate logistic regression analyses indicated that the three lipid metabolites may serve as potential biomarkers for diagnosing AMI. A subsequent 1-year follow-up analysis indicated that the three lipid biomarkers also had prominent performance in predicting re-admission of patients with AMI due to cardiovascular events. In summary, we used quantitative lipid technology to delineate the characteristics of lipid metabolism in patients with AMI, and identified potential early diagnosis biomarkers of AMI via machine learning approach.
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Liu et al. (2022) conducted a case-control in Acute Myocardial Infarction (n=150). Plasma lipid biomarkers (CarnitineC18:1-OH, CarnitineC18:2-OH, FFA (20:1)) vs. Unstable angina patients and healthy controls was evaluated on Discrimination between AMI and UA patients (AUC 0.9). Plasma levels of CarnitineC18:1-OH, CarnitineC18:2-OH, and FFA (20:1) and their combined diagnostic model accurately discriminated acute myocardial infarction from unstable angina with an AUC of 0.9.
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