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

被引：12

作者：

Liu, Jun ^{[1
,2
]}

Tang, Liangqiu ^{[1
,2
]}

Lu, Qiqi ^{[1
,2
]}

Yu, Yi ^{[1
,2
,3
]}

Xu, Qiu-Gui ^{[3
]}

Zhang, Shanqiang ^{[1
,2
]}

Chen, Yun-Xian ^{[1
,2
]}

Dai, Wen-Jie ^{[1
,2
]}

Li, Ji-Cheng ^{[1
,2
,3
,4
,5
]}

机构：

[1] Shantou Univ, Yue Bei Peoples Hosp, Med Res Ctr, Med Coll, Shaoguan, Peoples R China

[2] Shantou Univ, Yue Bei Peoples Hosp, Dept Cardiol, Med Coll, Shaoguan, Peoples R China

[3] Yangjiang Peoples Hosp, Cent Lab, Yangjiang, Peoples R China

[4] Shaoguan Univ, Dept Histol & Embryol, Sch Med, Shaoguan, Peoples R China

[5] Zhejiang Univ, Inst Cell Biol, Hangzhou, Peoples R China

来源：

FRONTIERS IN CARDIOVASCULAR MEDICINE | 2022年 / 9卷

关键词：

lipid metabolites; UPLC-MS; MS; machine learning; quantitative lipid profile; AMI; ACUTE CORONARY SYNDROMES; CARDIAC BIOMARKERS; DIABETES-MELLITUS; CREATINE-KINASE; METABOLOMICS; ASSOCIATION; METABOLITES; MORTALITY; DIAGNOSIS; SELECTION;

D O I：

10.3389/fcvm.2022.848840

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

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.

引用

页数：13