Clinical Parameters and Metabolomic Biomarkers That Predict Inhospital Outcomes in Patients With ST-Segment Elevated Myocardial Infarctions

Jie Liu; Lei Huang; Xinrong Shi; Chungang Gu; Hongmin Xu; Shuye Liu

doi:10.3389/fphys.2021.820240

Clinical Parameters and Metabolomic Biomarkers That Predict Inhospital Outcomes in Patients With ST-Segment Elevated Myocardial Infarctions

Front Physiol. 2022 Feb 8:12:820240. doi: 10.3389/fphys.2021.820240. eCollection 2021.

Authors

Jie Liu^{1

2

3

4}, Lei Huang^{2

3

4

5}, Xinrong Shi¹, Chungang Gu¹, Hongmin Xu¹, Shuye Liu^{1

2

3

4}

Affiliations

¹ Clinical Laboratory Department, The Third Central Hospital of Tianjin, Tianjin, China.
² Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin, China.
³ Artificial Cell Engineering Technology Research Center, Tianjin, China.
⁴ Tianjin Institute of Hepatobiliary Disease, Tianjin, China.
⁵ Heart Center, The Third Central Hospital of Tianjin, Tianjin, China.

Abstract

Background: Postoperative risk stratification is challenging in patients with ST-segment elevation myocardial infarction (STEMI) who undergo percutaneous coronary intervention. This study aimed to characterize the metabolic fingerprints of patients with STEMI with different inhospital outcomes in the early stage of morbidity and to integrate the clinical baseline characteristics to develop a prognostic prediction model.

Methods: Plasma samples were collected retrospectively from two propensity score-matched STEMI cohorts from May 6, 2020 to April 20, 2021. Cohort 1 consisted of 48 survivors and 48 non-survivors. Cohort 2 included 48 patients with unstable angina pectoris, 48 patients with STEMI, and 48 age- and sex-matched healthy controls. Metabolic profiling was generated based on ultra-performance liquid chromatography and a mass spectrometry platform. The comprehensive metabolomic data analysis was performed using MetaboAnalyst version 5.0. The hub metabolite biomarkers integrated into the model were tested using multivariate linear support vector machine (SVM) algorithms and a generalized estimating equation (GEE) model. Their predictive capabilities were evaluated using areas under the curve (AUCs) of receiver operating characteristic curves.

Results: Metabonomic analysis from the two cohorts showed that patients with STEMI with different outcomes had significantly different clusters. Seven differentially expressed metabolites were identified as potential candidates for predicting inhospital outcomes based on the two cohorts, and their joint discriminative capabilities were robust using SVM (AUC = 0.998, 95% CI 0.983-1) and the univariate GEE model (AUC = 0.981, 95% CI 0.969-0.994). After integrating another six clinical variants, the predictive performance of the updated model improved further (AUC = 0.99, 95% CI 0.981-0.998).

Conclusion: A survival prediction model integrating seven metabolites from non-targeted metabonomics and six clinical indicators may generate a powerful early survival prediction model for patients with STEMI. The validation of internal and external cohorts is required.

Keywords: STEMI; lipid metabolites; liquid chromatography-mass spectrometry; mortality predictors; myocardial infarction.