Combined Metabolomics and Machine Learning Algorithms to Explore Metabolic Biomarkers for Diagnosis of Acute Myocardial Ischemia

Abstract

Background: Acute myocardial ischemia (AMI) remains the leading cause of death worldwide. In particular, when death occurs within a short time, it is hard to find post-mortem specific structural anomalies of the heart at autopsy with standard methods. Therefore, the post-mortem diagnosis of AMI represents a current challenge for both clinical and forensic pathologists. Metabolomics technology plays an important role in searching for new diagnostic biomarkers. Here, we characterize metabolic profiles of AMI and attempted to interpret the role of metabolic changes in sudden cardiac death (SCD).Methods: The untargeted metabolomics was applied to analyze serum metabolic signatures from AMI experimental group (ligation of left coronary artery at 5mm below the left atrial appendage in rats), along with the control and sham groups (n = 10 per group). The analytical strategy based on ultra performance liquid chromatography combined with high-resolution mass spectrometry. The resulting data was preprocessed to discriminant metabolites, and a set of machine learning algorithms were used to construct predictable models. Seventeen blood samples from autopsy cases were applied to validate the classification model's value in human samples.Results: A total of 28 endogenous metabolites in serum were significantly altered in AMI group relative to control and sham groups. Gradient tree boosting, support vector machines, random forests, logistic regression, and multilayer perceptron models were used to further screen the more valuable metabolites from 28 metabolites to optimize the biomarker panel. The results showed that classification accuracy and performance of multilayer perceptron (MLP) models were better than other algorithms when the metabolites consisting of L-threonic acid, N-acetyl-L-cysteine, CMPF, glycocholic acid, L-tyrosine, cholic acid, and glycoursodeoxycholic acid. In autopsy cases, the MLP model constructed based on rat dataset achieved an accuracy of 88.23, and ROC of 0.89 for predicting AMI-SCD.Conclusions: A panel of 7 molecular biomarkers was identified by assessment the accuracy and efficacy of different metabolite combinations in inferring AMI using machine learning algorithms. The constructed MLP model has a high diagnostic performance for both AMI rats and autopsies-based blood samples. Thus, the combination of metabolomics and machine learning algorithms provides a novel strategy for AMI diagnosis.