Received:22 November 2020,
Published:30 May 2021
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Wenxin RU, Han HU, Ting ZHOU, et al. Characteristics of plasma lipid metabolism and potential biomarkers in patients with acute myocardial infarction[J]. Journal of Third Military Medical University, 2021, 43(10): 970-981.
Wenxin RU, Han HU, Ting ZHOU, et al. Characteristics of plasma lipid metabolism and potential biomarkers in patients with acute myocardial infarction[J]. Journal of Third Military Medical University, 2021, 43(10): 970-981. DOI: 10.16016/j.1000-5404.202011212.
Objective
2
To compare the differences in plasma lipid metabolites between the patients with acute myocardial infarction (AMI) and those with normal coronary artery in order to explore the pathophysiologic mechanism and search for possible new biomarkers of AMI.
Methods
2
The patients undergoing coronary angiography in our hospital from September 20
2019 to January 20
2020 were enrolled in this study. According to the results
they were divided into myocardial infarction group (AMI group
n
=32) and control group (age- and sex-matched with the AMI group
n
=32). Non-targeted lipidomics analysis was performed on the blood samples to find the differences of lipid metabolites betw
een the 2 groups. Univariate and multivariate statistical analyses were applied to explore the meaningful differential metabolites
and receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the selected differential metabolites. Finally
how to disturb metabolic pathways was investigated in the AMI patients.
Results
2
① The orthogonal projection to latent structure discriminant analysis (OPLS-DA) and cluster heat map showed that there was a significant trend of separation between AMI group and the control group. Multivariate statistical analysis identified totally 28 differential plasma lipid metabolites
including 16 of them significantly increased and 12 decreased in the AMI group. ②There were 10 lipid metabolites such as diglyceride (DG) and phosphatidyl serine (PS) associated with an increased risk of AMI
while 13 negative predictors
including phosphatidylinositol (PI)
phosphatidylcholine (PC)
phosphatidylserine (PS)
lytic phosphatidylcholine (LPC) and triglyceride (TG)
were associated with a reduced risk of AMI. ③ROC curve identified 2 possible biomarkers of AMI
namely
PS (27∶0) (AUC=0.976) and DG(16∶0/18∶1) (AUC=0.807); ④The disturbed metabolic pathways included metabolism of glycerol phospholipid
sphingomyelin
purine and pyrimidine in the AMI patients
among which the disorder of glycerophospholipid metabolism was the most signficant.
Conclusion
2
The characteristics of lipid metabolism are significantly changed in AMI patients; a total of 28 differential lipid metabolites are identified
among which the disorder of glycerol phospholipid metabolism pathway indicates the most significant. PS and DG may act as the potential biomarkers for the diagnosis of AMI.
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