Comprehensive Metabolomic Characterization of Atrial Fibrillation

Overview

Journal Front Cardiovasc Med

Specialty Cardiology & Vascular Diseases

Date 2022 Aug 25

PMID 36003904

Authors

Chengcan Lu

Chunyan Liu

Di Mei

Mengjie Yu

Jian Bai

Xue Bao

Min Wang

Kejia Fu

Xin Yi

Weihong Ge

Jizhong Shen

Yuzhu Peng

Wei Xu

Affiliations

Soon will be listed here.

Abstract

Background: Using 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.

Objectives: By investigating disturbed metabolic pathways, we could evaluate the diagnostic value of biomarkers based on metabolomics for different types of AF.

Methods: A 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.

Results: The 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%.

Conclusions: Serum 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.

Citing Articles

Metabolomics in Atrial Fibrillation: Unlocking Novel Biomarkers and Pathways for Diagnosis, Prognosis, and Personalized Treatment.

Rohun J, Dudzik D, Raczak-Gutknecht J, Wabich E, Mlodzinski K, J Markuszewski M J Clin Med. 2025; 14(1.

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

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Limpitikul W, Das S J Cardiovasc Dev Dis. 2023; 10(8).

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Small molecule metabolites: discovery of biomarkers and therapeutic targets.

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