Human Plasma Metabolomics Identify 9-cis-retinoic Acid and Dehydrophytosphingosine Levels as Novel biomarkers for Early Ventricular Fibrillation after ST-elevated Myocardial Infarction.

Jieying Luo, Junaid Ahmed Shaikh, Lei Huang, Lei Zhang, Shahid Iqbal, Yu Wang, Bojiang Liu, Quan Zhou, Aisha Ajmal, Maryam Rizvi, Maryam Ajmal, Yingwu Liu

Journal: Bioengineered 2022;13(2):3334-3350

PMID: 35094641

Abstract

The relevant metabolite biomarkers for risk prediction of early onset of ventricular fibrillation (VF) after ST-segment elevation myocardial infarction (STEMI) remain unstudied. Here, we aimed to identify these imetabolites and the important metabolic pathways involved, and explore whether these metabolites could be used as predictors for the phenotype. Plasma samples were obtained retrospectively from a propensity-score matched cohort including 42 STEMI patients (21 consecutive VF and 21 non-VF). Ultra-performance liquid chromatography and mass spectrometry in combination with a comprehensive analysis of metabolomic data using Metaboanalyst 5.0 version were performed. As a result, the retinal metabolism pathway proved to be the most discriminative for the VF phenotype. Furthermore, 9-cis-Retinoic acid (9cRA) and dehydrophytosphingosine proved to be the most discriminative biomarkers. Biomarker analysis through receiver operating characteristic (ROC) curve showed the 2-metabolite biomarker panel yielding an area under the curve (AUC) of 0.836. The model based on Monte Carlo cross-validation found that 9cRA had the greatest probability of appearing in the predictive panel of biomarkers in the model. Validation of model efficiency based on an ROC curve showed that the combination model constructed by 9cRA and dehydrophytosphingosine had a good predictive value for early-onset VF after STEMI, and the AUC was 0.884 (95% CI 0.714-1). Conclusively, the retinol metabolism pathway was the most powerful pathway for differentiating the post-STEMI VF phenotype. 9cRA was the most important predictive biomarker of VF, and a plasma biomarker panel made up of two metabolites, may help to build a potent predictive model for VF.

Address: Department of Heart Center, The Third Central Hospital of Tianjin, Tianjin, China.; Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin, China.; Artificial Cell Engineering Technology Research Center, Tianjin, China.; Faculty of Life Science and Medicine, Tianjin Institute of Hepatobiliary Disease, Tianjin, China.; GKT School of Medical Education, Faculty of Life Science and Medicine, King's College London, London SE1 IUL, UK.; Department of Clinical Laboratory, Tianjin Third Central Hospital, Tianjin, China.; St George's Hospital Medical School, St. George's, University of London, Cranmer Terrace, London, SW17 0RE UK.
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