Metabolomic discrimination between patients with stable angina, non‐ST elevation myocardial infarction, and acute myocardial infarct

Abstract

The ischemic cascade starts when atherosclerotic plaques decrease the supply of oxygen and substrates to cells and finalizes with myocardial infarction. These states have been here studied at metabolite level by optimization of a metabolomics profiling approach based on high-accuracy MS. For this purpose, serum samples from patients diagnosed with coronary artery disease and affected by stable angina or myocardial infarction (acute myocardial infarction or non-ST elevation myocardial infarction) were analyzed by LC-QTOF/MS after deproteinization to compare the profile of serum metabolites. The data set, composed by tentative molecular features detected in MS analyses, was filtered with statistical algorithms to remove entities resulting in redundant information. Tentative molecules were identified finding mainly lipids as statistically significant metabolites in the discrimination study due to their change in concentration. Lipids such as bile acid derivatives, phospholipids, and triglycerides were identified as relevant compounds for discrimination of individuals who suffered acute or non-ST elevation myocardial infarction from those suffering stable angina. The results achieved by this research could support the capability of metabolomics to go inside the study of artery diseases, and in addition to other omics disciplines could help to detect the occurrence of these disorders at initial stages or even to prognosticate their appearance.

Metabolomics in the Context of Forensic Omics Biomarkers

“…[38,39,42]. The sensitivity and specificity displayed by each metabolite in the ROC curve clearly establish its usefulness as biomarker.…”

“…For example, in the characterization of the salivary epigenome, the importance of previous work in dealing with the salivary microbiome, proteome, endocrine analytes, genome, and transcriptome was highlighted [37], but the possible role of metabolomics was ignored, despite its crucial importance in unraveling epigenetic behavior [10]. At present, the importance of metabolomics biomarkers increases at a fast rhythm [38,39], even surpassing wellestablished proteomics biomarkers. This is the case with ethanol consumption in which metabolites from this drug are clearly better biomarkers of chronic drinkers than well know indirect biomarkers such as enzymes (e.g., aspartate transferase, alanine transferase, γ-glutamyltransferase), mean corpuscular volume of the erythrocytes or carbohydrate-deficient-transferrin, as can be seen in a section below.…”

“…The search of the authors' group for evaluation of coronary artery disease constitutes a strong support to future developments in the forensic field [39]. The research involved discrimination among patients with stable angina, non-ST elevation myocardial infarction and AMI [38], and monitoring of atherosclerotic patients for myocardial infarction prevention [66] (all them developed by LC-QTOF MS/MS or LC-MS QqQ).…”

LC/MS-based polar metabolite profiling reveals gender differences in serum from patients with myocardial infarction

“…However, few clinical models have been used in metabolomics approaches for MI. Some examples include the targeted profiling of carnitine and acylcarnitine [17], and comparisons made between before and after regarding patients with MI [18] or among stable angina, non-ST elevation MI, and acute MI (AMI) [19]. However, to the best of our knowledge, no metabolomics studies have reported gender differences in response to the development of MI, and correlations between clinical characteristics and metabolites in patients with MI and healthy controls.…”

Hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry method for the simultaneous determination of l -valine, l -l eucine, l -isoleucine, l -phenylalanine, and l -tyrosine in human serum

“…Unfortunately, L‐Leu and L‐Ile were not separated. Yang reported an LC–MS/MS method using two transitions 132.1→43.1 and 132.1→69.2 for the separation of L‐Leu and L‐Ile. But the method was not specific and needed coefficients to calibrate the peak areas of L‐Leu and L‐Ile.…”