The elucidation of metabolic perturbations and gender-age-specific metabolic characteristics associated with acute myocardial infarction (AMI) is essential for clinical risk stratification and disease management. A comprehensive cross-comparative metabolomics analysis was performed on the sera from 445 healthy controls, 347 AMI patients without cardiovascular disease (CVD), 79 AMI with CVD (AMICVD) patients including 27 deaths. Machine-learning-based integrated biomarker profiling and global network analysis were used to create a multi-biomarker for distinguishing the different AMI outcomes. The changes of most metabolites were dependent on AMI, but gender and age also give additional contributions to the changes of histidine, malonate, O-acetyl-glycoprotein and trimethylamine N-oxide. The altered metabolic pathways included gut dysbiosis, increased amino acid metabolism, glucose metabolism and ketone metabolism, and inactivation of tricarboxylic acid cycle. Enhanced histidine metabolism and microbiota dysbiosis may be one of the key factors during the developing of AMI into AMICVD. For the differential diagnosis of AMI events, three sets of specific multi-biomarkers provided relatively high accuracy with the areas under the curve more than 0.8 and hazard ratio more than 1 in the discovery set, and the results were reproduced and confirmed by the validation set. First use of cross-comparative metabolomics and machine-learning-based integrated biomarker analysis gives great capability to discriminate the different AMI outcomes. Also, the multi-biomarkers seem to be a valid and accurate auxiliary diagnosis biomarker in addition to standard stratification based on clinical parameters.
Keywords: Acute myocardial infarction; Biomarker; Machine-learning; Metabolomics; Network analysis.
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