ArticlePloS one2026
Identification of a diagnostic metabolomic fingerprint in plasma for eosinophilic granulomatosis with polyangiitis.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveEosinophilic granulomatosis with polyangiitis (EGPA) was a rare systemic vasculitis characterized by eosinophilia, asthma, and necrotizing vasculitis. Metabolic dysregulation had been shown to participate in the pathogenesis of autoimmune diseases, but the plasma metabolic profile of EGPA remained unclear. This work was designed to systematically characterize the plasma metabolomic profiles of EGPA patients, identify differential metabolites that distinguish EGPA from bronchial asthma (BA), and explore their potential as biomarkers for differential diagnosis.
methodsTen patients with EGPA, ten patients with BA, and ten age- and gender-matched healthy controls (HCs) were enrolled. Untargeted metabolomics based on liquid chromatography/mass spectrometry (LC/MS) was performed to analyze the metabolic profiles of the three groups. Differential metabolites were identified using VIP > 1 and P < 0.05. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed on the differential metabolites, with statistical significance defined as P < 0.05.
resultsA total of 971 metabolites were differentially expressed between EGPA patients and HCs. KEGG pathway enrichment analysis identified 59 pathways, five of which were statistically significant (P < 0.05): caffeine metabolism; valine, leucine and isoleucine biosynthesis; alanine, aspartate and glutamate metabolism; arginine and proline metabolism; and glyoxylate and dicarboxylate metabolism. Comparison of EGPA with BA patients revealed 161 altered metabolites and 102 enriched pathways, three of which were significant (P < 0.05): caffeine metabolism, basal cell carcinoma, and Fc gamma R-mediated phagocytosis. Receiver operating characteristic (ROC) curve analysis demonstrated that 21 of the 24 metabolites identified from the five key EGPA-HC pathways exhibited strong diagnostic performance (area under the curve [AUC] > 0.8). Four metabolites (cholesterol, 5-acetylamino-6-formylamino-3-methyluracil, 5-acetylamino-6-amino-3-methyluracil, and 3-methylxanthine) showed high diagnostic potential (AUC > 0.8) for distinguishing EGPA from BA.
conclusionThis study revealed, for the first time, a distinct plasma metabolic profile in EGPA patients, with key pathways and candidate biomarkers identified. The metabolites with high diagnostic efficacy (AUC > 0.8) might serve as candidate diagnostic biomarkers for EGPA and its differentiation from BA. These observations provided novel insights into the metabolic basis of EGPA pathogenesis and might provide valuable references for the clinical management of this rare disease.
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