Metabolomics Study Revealing the Potential Risk and Predictive Value of Fragmented QRS for Acute Myocardial Infarction

Abstract

Patients with nonobstructive coronary artery disease (NOCAD) have high risk associated with acute myocardial infarction (AMI), and fragmented QRS (fQRS) has a predictive value of AMI after percutaneous coronary intervention (PCI). A cohort of 254 participants were recruited including 136 NOCAD and 118 AMI patients from Xi’an No. 1 Hospital. Comprehensive metabolomics was performed by UPLC-Q/TOF-MS with multivariate statistical analyses. Hazard ratios were measured to discriminate the prognostic in AMI after PCI between differential metabolites and fQRS. OPLS-DA separated metabolites from NOCAD and AMI in serum. A total of 23 differential metabolites were identified between NOCAD and AMI. In addition, four differential metabolites, namely, acetylglycine, threoninyl-glycine, glutarylglycine, and nonanoylcarnitine, were identified between fQRS and non-fQRS in AMI. The hazard ratios demonstrate that the metabolites were associated with the risk of cardiac death, recurrent angina, readmissions, and major adverse cardiovascular events, which may clarify the mechanism of fQRS as a predictor in the prognostic of AMI after PCI. This study identified novel differential metabolites to distinguish the difference from NOCAD to AMI and clarify the mechanism of fQRS in prognostic of AMI after PCI, which may provide novel insights into potential risks and prognostic of AMI.

Metabolomics: A Scoping Review of Its Role as a Tool for Disease Biomarker Discovery in Selected Non-Communicable Diseases

“…These medical disorders are Parkinson’s disease (PD), diabetic retinopathy, Alzheimer’s disease (AD)/dementia, cardiovascular disease, and inborn error or metabolism (IEM). Out of the 26 publications that were included in this review, 3 were adjudged to be relevant to our research interest with respect to PD [ 41 , 42 , 43 ], 5 with respect to diabetic retinopathy [ 30 , 44 , 45 , 46 , 47 ], 2 for AD [ 16 , 46 ], 14 for cardiovascular disease [ 18 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ], and 2 for IEM [ 59 , 60 ]. Of the 3 most often utilised platforms for metabolic profiling, MS spectroscopy (coupled with other separation techniques) ranks first, being employed for biomarker discovery in 17 out of the 26 studies identified, and accounting for 65.4% of all studies.…”

“…This was followed by NMR spectroscopy, which accounted for just 19.2% (5 studies) of cases. In terms of statistical analysis, multivariate analysis involving PCA and/or PLS-DA was utilised in 6 studies [ 41 , 46 , 47 , 49 , 54 , 61 ], followed by the use of receiver operating characteristic (ROC) curve or area under the curve (AUC) analysis [ 30 , 42 , 50 , 56 , 58 ]. Details of the study characteristics, the analytical tools employed, and the respective biomarkers identified are summarised in Table 1 and Table 2 , respectively.…”

“…These metabolite biomarkers include C16-sphingosine, N -methyl arachidonic amide, phosphatidylserine, N -(2-methoxyethyl) arachidonic amide, linoleamidoglycerophosphate choline, lysophosphatidylcholine (C18:2), lysophosphatidylcholine (C16:0), lysophosphatidylcholine (C18:1), arachidonic, and linoleic acid, as well as perturbations in energy, fatty acid, and phospholipid metabolism. Aside from its potential in discriminating between persons with myocardial infarction and healthy controls, metabolomics has equally been shown to have the ability to predict the risk of developing acute myocardial infarction with fragmented QRS in patients who undergo percutaneous coronary intervention (PCI) [ 50 ]. In a recent hospital-based cohort study, Li et al [ 50 ] performed a global metabolic profiling using UPLS–Q/TOF–MS on a cohort of 136 non-coronary artery disease and 118 acute myocardial infarction (AMI) patients undergoing PCI and identified four metabolites as important biomarkers.…”

“…Aside from its potential in discriminating between persons with myocardial infarction and healthy controls, metabolomics has equally been shown to have the ability to predict the risk of developing acute myocardial infarction with fragmented QRS in patients who undergo percutaneous coronary intervention (PCI) [ 50 ]. In a recent hospital-based cohort study, Li et al [ 50 ] performed a global metabolic profiling using UPLS–Q/TOF–MS on a cohort of 136 non-coronary artery disease and 118 acute myocardial infarction (AMI) patients undergoing PCI and identified four metabolites as important biomarkers. These metabolites (acetylglycine, threoninyl-glycine, glutarylglycine, and nonanoylcarnitine) were reported to significantly discriminate between AMI with fragmented QRS and that without it.…”

Improved Discrimination of Disease States Using Proteomics Data with the Updated Aristotle Classifier

“…In fact, only if the disease related difference is the largest contribution to data variability will the disease group and healthy group separate on a PCA plot. To do a better job at classifying omics data, many researchers have turned to supervised methods, such as partial least-squares discriminant analysis (PLS-DA) and variants thereof. − This approach, originally developed around 1975, explicitly uses the disease status of a sample to eagerly identify the key differences in the data, which can be capitalized on to better separate the samples into a diseased group and a healthy one. PLS-DA, like all supervised machine learning methods, obtains improved classification performance specifically because the class labels are used in the model.…”