Plasma Quantitative Lipid Profiles: Identification of CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1) as Novel Biomarkers for Pre-warning and Prognosis in Acute Myocardial Infarction

Abstract

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.

Gut-microbiome-based predictive model for ST-elevation myocardial infarction in young male patients

“…For decades, common clinical parameters and novel biomarkers, including lipid metabolites and exosomal miRNA, have been used to predict AMI. However, the predictive value of these biomarkers is moderate (Eichler et al, 2007;Su et al, 2020;Liu et al, 2022). In our study, the analysis of the clinical parameters revealed a correlation between parameters like BMI, SBP, AST, and STEMI in young males.…”

A novel circulating biomarker lnc‐MALAT1 for acute myocardial infarction: Its relationship with disease risk, features, cytokines, and major adverse cardiovascular events

“…15 However, the relationship of lnc-MALAT1 with disease characteristics in AMI patients is unclear. According to previous studies, lipid profile, cTnI, infarct size, inflammation, and adhesion molecules are viewed as hallmarks of AMI [22][23][24] ; thus, we explored the association of lnc-MALAT1 with them among AMI patients, which showed that lnc-MALAT1 was positively linked with lipid dysregulation (reflected by LDL-C), the marker of myocardial injury (reflected by cTnI), disease severity indicator (reflected by infarct size), systematic inflammation (reflected by CRP, TNFα, IL-6, and IL-17A), and adhesion cytokines (reflected by VCAM-1 and ICAM-1). The potential explanations might be that In addition, we also discovered that higher lnc-MALAT1 was linked with a history of DM among AMI patients.…”

“…However, the relationship of lnc‐MALAT1 with disease characteristics in AMI patients is unclear. According to previous studies, lipid profile, cTnI, infarct size, inflammation, and adhesion molecules are viewed as hallmarks of AMI 22–24 ; thus, we explored the association of lnc‐MALAT1 with them among AMI patients, which showed that lnc‐MALAT1 was positively linked with lipid dysregulation (reflected by LDL‐C), the marker of myocardial injury (reflected by cTnI), disease severity indicator (reflected by infarct size), systematic inflammation (reflected by CRP, TNF‐α, IL‐6, and IL‐17A), and adhesion cytokines (reflected by VCAM‐1 and ICAM‐1). The potential explanations might be that (1) lnc‐MALAT1 could accelerate lipid metabolism and lipid oxidation in AMI, which resulted in elevated LDL‐C 25 ; (2) lnc‐MALAT1 might promote cardiomyocyte injury through several mechanisms, such as regulating autophagy‐related 3, as well as phosphatase and tensin homolog deleted on chromosome 10, which led to increased cTnI and infarct size in AMI 26,27 ; (3) lnc‐MALAT1 might be able to accelerate inflammation by regulating several signaling pathways, including Wnt/β‐catenin and nuclear factor‐kappa B signaling; thereby, lnc‐MALAT1 was positively associated with inflammation in AMI patients 28,29 ; and (4) lnc‐MALAT1 could accelerate the production of adhesion molecules through several methods, such as microRNA‐590 and the signal transducer and activator of transcription 3 pathway 21 ; thus, a positive relation was found in lnc‐MALAT1 with adhesion cytokines among AMI patients.…”

Biomarker discovery for tuberculosis using metabolomics

“…In recent years, biomarker discovery has been greatly facilitated by the advance of the modern multi-omics strategies, including metabolomics and lipidomics, based on quantitative liquid/gas chromatography and mass spectrometry technology. These omics technologies have been applied in the various biomedical studies, including studies on TB ( Pitaloka et al, 2022 ), myocardial infarction ( Liu et al, 2022 ), ischemic stroke ( Montaner et al, 2020 ), depressive disorders ( Sethi and Brietzke, 2015 ; Yu et al, 2021 ) and other diseases ( Tounta et al, 2021 ).…”