Metabolites · 2026
Targeted Quantitative Metabolomics and Lipidomics Reveal Dysregulated Metabolic Networks and a Serum Candidate Biomarker for Atrial Fibrillation.
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Zhang Yuqing, Xie Yunpeng, Liu Xinyu, Ning Zhen, Xu Guowang, Xia Yunlong, Zhao Xinjie
Аннотация
BACKGROUND: Atrial fibrillation (AF) is the most prevalent clinical arrhythmia with severe cardiovascular complications, yet its metabolic molecular mechanisms remain poorly defined. Omics-based metabolic profiling provides a powerful strategy to systematically decode AF-associated metabolic disorders.
METHODS: In this work, high-coverage targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics and lipidomics were applied to absolutely quantify 746 serum metabolites from AF patients and healthy controls.
RESULTS: We systematically characterized global metabolic perturbations in AF serum, including impaired fatty acid metabolism, suppressed mitochondrial β-oxidation, myocardial lipotoxic lipid accumulation, and systemic depletion of glycerophospholipids. Global multiscale embedded correlation network analysis (MECNA) further identified 11 AF-specific dysregulated metabolic modules and core hub metabolites driving metabolic remodeling. Leveraging binary logistic regression, we constructed and independently validated a two-molecule diagnostic biomarker panel to distinguish AF patients from healthy subjects. The combined biomarkers Phe-Trp and FA 22:5 achieved outstanding diagnostic performance, with area under the curve (AUC) values of 0.964 in the discovery cohort and 0.993 in the validation cohort.
CONCLUSIONS: Collectively, this study adopts high-depth targeted quantitative omics to comprehensively map AF metabolic signatures, dissect disease-relevant metabolic networks, and establish a robust serum biomarker panel with great translational potential for non-invasive AF clinical diagnosis.
METHODS: In this work, high-coverage targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics and lipidomics were applied to absolutely quantify 746 serum metabolites from AF patients and healthy controls.
RESULTS: We systematically characterized global metabolic perturbations in AF serum, including impaired fatty acid metabolism, suppressed mitochondrial β-oxidation, myocardial lipotoxic lipid accumulation, and systemic depletion of glycerophospholipids. Global multiscale embedded correlation network analysis (MECNA) further identified 11 AF-specific dysregulated metabolic modules and core hub metabolites driving metabolic remodeling. Leveraging binary logistic regression, we constructed and independently validated a two-molecule diagnostic biomarker panel to distinguish AF patients from healthy subjects. The combined biomarkers Phe-Trp and FA 22:5 achieved outstanding diagnostic performance, with area under the curve (AUC) values of 0.964 in the discovery cohort and 0.993 in the validation cohort.
CONCLUSIONS: Collectively, this study adopts high-depth targeted quantitative omics to comprehensively map AF metabolic signatures, dissect disease-relevant metabolic networks, and establish a robust serum biomarker panel with great translational potential for non-invasive AF clinical diagnosis.