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Background Percutaneous coronary intervention (PCI) is the main treatment for acute coronary syndrome (ACS), but some patients may experience recurrent major cardiovascular events (MACE) after the treatment. Recent studies have shown that lysophospholipid metabolites such as lysophosphatidylcholine (LPC) and lysophosphatidic acid (LPA), which are important components of oxidized low-density lipoprotein and low-density lipoprotein, can promote the formation and rupture of atherosclerotic plaques. However, it remains unclear whether lysophospholipid metabolites can be used to predict the occurrence of MACE following PCI in patients with ACS. Objective To investigate the predictive value of lysophospholipids for MACE following PCI in patients with ACS. Methods The study included patients with ACS who underwent PCI at Beijing Anzhen Hospital, Capital Medical University from June 2017 to September 2019. Baseline data of the patients were collected, and targeted metabolomics was performed to detect phospholipids and lysophospholipids. Patients were followed up at 1, 3, 6, 9, and 12 months post-enrollment, and then every 6 months thereafter, through outpatient visits and telephone consultations to record the occurrence of MACE. Principal component analysis (PCA) score plots were used to analyze the metabolic profiles and inter-group distributions of lysophospholipids between the non-MACE and MACE groups. A partial least squares-discriminant analysis (PLS-DA) with variable importance in projection (VIP) plots was utilized to assess the differential metabolites of lysophospholipids between the groups. The importance of each phospholipid and lysophospholipid metabolite was ranked using the random forest accuracy decrease diagram. Monte Carlo cross-validation was applied to construct the receiver operating characteristic (ROC) curve for the multivariable random forest models composed of different numbers of metabolites, and the area under the curve (AUC) was calculated to select key lysophospholipid metabolites associated with MACE. The accuracy of the predictive models was assessed using permutation tests. Results Participants included 212 cases of patients, with an average follow-up of 3 years. Patients were divided into the MACE group (n=29) and the non-MACE group (n=183) based on whether MACE occurred during the follow-up period. There was no statistically significant difference in baseline data between the two groups (P>0.05). The PCA score plot revealed a distinct distribution of MACE and non-MACE group samples, indicating significant differences in the lysophospholipid metabolite profiles. Integrated analysis identified significant changes in LPA, oxidized lysophosphatidylcholine (LPC-O), LPE, LPI, and LPS. Monte Carlo cross-validation was used to construct a predictive model for MACE using the top 13 ranked lipid metabolites [mainly including 10 lysophospholipid metabolites (LPA 16: 0, LPA 18: 1, LPC-O 16: 0, LPC 16: 0, LPG 18: 2, LPC 18: 0, LPE 20: 3, LPE 22: 6, LPG 18: 1, LPS 20: 4) and 3 phospholipid metabolites (PA 16: 0-18: 0, PA 16: 0-20: 4, PA 16: 0-18: 1) ]. The ROC curve indicated that the model had an AUC of 0.934 (95%CI=0.793-0.998) for predicting MACE. Conclusion Abnormal expression of lysophospholipids metabolites in preoperative serum is closely correlated with the risk of recurrent MACE following PCI in patients with ACS. This exhibits significant predictive efficacy and clinical value for MACE following PCI in patients with ACS.