Comprehensive metabolomic characterization of atrial fibrillation

Abstract

BackgroundUsing human humoral metabolomic profiling, we can discover the diagnostic biomarkers and pathogenesis of disease. The specific characterization of atrial fibrillation (AF) subtypes with metabolomics may facilitate effective and targeted treatment, especially in early stages.ObjectivesBy investigating disturbed metabolic pathways, we could evaluate the diagnostic value of biomarkers based on metabolomics for different types of AF.MethodsA cohort of 363 patients was enrolled and divided into a discovery and validation set. Patients underwent an electrocardiogram (ECG) for suspected AF. Groups were divided as follows: healthy individuals (Control), suspected AF (Sus-AF), first diagnosed AF (Fir-AF), paroxysmal AF (Par-AF), persistent AF (Per-AF), and AF causing a cardiogenic ischemic stroke (Car-AF). Serum metabolomic profiles were determined by gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Metabolomic variables were analyzed with clinical information to identify relevant diagnostic biomarkers.ResultsThe metabolic disorders were characterized by 16 cross-comparisons. We focused on comparing all of the types of AF (All-AFs) plus Car-AF vs. Control, All-AFs vs. Car-AF, Par-AF vs. Control, and Par-AF vs. Per-AF. Then, 117 and 94 metabolites were identified by GC/MS and LC-QTOF-MS, respectively. The essential altered metabolic pathways during AF progression included D-glutamine and D-glutamate metabolism, glycerophospholipid metabolism, etc. For differential diagnosis, the area under the curve (AUC) of specific metabolomic biomarkers ranged from 0.8237 to 0.9890 during the discovery phase, and the predictive values in the validation cohort were 78.8–90.2%.ConclusionsSerum metabolomics is a powerful way to identify metabolic disturbances. Differences in small–molecule metabolites may serve as biomarkers for AF onset, progression, and differential diagnosis.

Genetics, transcriptomics, metagenomics, and metabolomics in the pathogenesis and prediction of atrial fibrillation

“…In addition to the polyamine metabolites, the results with carnitines have been most consistent, suggesting a protective role against the onset of AF. 67 , 69–71 Carnitines are an essential part of the cardiomyocyte energy metabolism, transporting fatty acids for use in mitochondrial energy production, while preventing ROS formation. 72 There is also evidence for their cardioprotective abilities, blood pressure benefit, and reduction in left ventricle dilation after an acute myocardial infarction.…”

“…In addition, several lysophosphatidylcholines (LysoPC) and cholesterol esters have been linked to AF in many recent studies. 69 , 77 , 78 , 80 , 81 LysoPC species are antiatherogenic, have anti-inflammatory responses, and reduce metabolic syndrome progression, 82 and the concentrations of these metabolites have been lower in AF patients than in controls. 81 , 82 There is also evidence on the connection between other inflammatory biomarkers and AF.…”

Small molecule metabolites: discovery of biomarkers and therapeutic targets

“…A study has focused on characterizing the specific and precise metabolic features of atrial fibrillation subtypes, indicated that small-molecule metabolites may facilitate effective treatment. 603 Additionally with the use of untargeted metabolomics, a study demonstrated serum biomarkers of progression of diabetic retinopathy in Asians, and there were 171 metabolic features including glutamine, N-acetyl-l-glutamate, glutamate, aspartate, N-acetyl-l-aspartate, docosahexaenoic, icosapentaenoic, and dihomo-gamma-linolenate distinguished proliferative diabetic retinopathy patients from T2DM patients. 604 Enrichment pathway analyses for major metabolite biomarkers indicated arginine biosynthesis metabolism, d-glutamine and d-glutamate metabolism were dysregulated in advanced stages of diabetic retinopathy.…”

Obesity-Related Atrial Fibrillation: Cardiac Manifestation of a Systemic Disease

“…Alternatively, biomarkers obtained non-invasively from serum or other secretions, such as saliva or urine, can prove to be an invaluable tool to predict incident AF and/or prognosticate AF progression. There has been a tremendous effort to identify various biomarkers specific to AF, including serum microRNAs, other non-coding RNAs [ 112 , 113 ], proteins/hormones [ 114 , 115 ], and metabolites [ 116 , 117 ]. However, the accuracy of these biomarkers is not yet sufficient for day-to-day clinical usage.…”