Evidence map›Paper›PMID 42871075›Full record

ArticleFrontiers in immunology2026

Comprehensive metabolomic characterization of patients with myasthenia gravis.

Hui Deng, Yuexuan Wang, Xiaowen Yang, Jing Qin, Jing Liu, Tianlong Liu

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Article in Frontiers in immunology, 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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5 · Who and what money

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6 authors.

Hui Deng *Department of Neurology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
Yuexuan Wang *College of Pharmacy, Inner Mongolia Medical University, Hohhot, China.
Xiaowen YangDepartment of Pharmacy, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
Jing QinDepartment of Endocrinology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
Jing LiuDepartment of Pharmacy, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
Tianlong LiuDepartment of Pharmacy, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Myasthenia gravis (MG) is mediated by abnormal antibody generation and is characterized by muscle weakness and pathological fatigue. However, the accuracy of anti-acetylcholine receptor (AChR) antibody testing for the diagnosis of MG needs improvement. Objective: This study was aimed to systematically characterize the plasma metabolome of patients with MG and identify novel biomarkers with significant diagnostic value. Methods: A cohort of 130 subjects was enrolled and divided into discovery and validation sets, and their plasma metabolomic profiles were determined by liquid chromatography-tandem mass spectrometry. Metabolomic variables were analyzed along with clinical information to identify relevant diagnostic biomarkers. The diagnostic efficacy of the screened metabolites and biomarker panel was assessed via receiver operating characteristic (ROC) curve analysis. Results: In positive and negative ion modes, plasma metabolomic profiling revealed 326 and 398 differentially abundant metabolites, respectively, in the discovery and validation sets. The metabolomic profile distinguished patients with MG from healthy controls across the two cohorts evaluated in this study. Furthermore, 890 metabolites were detected in both the discovery and validation sets. Metabolite set enrichment analysis of these 890 metabolites revealed that steroid hormone biosynthesis pathway was significantly enriched, implying that altered metabolism may serve as a potential pathological hallmark of MG. On the basis of the differences between groups, compound level, the value of the variable importance in projection, the area under the ROC curve (AUC) and precision-recall curve analyses, 3 differentially abundant metabolites, namely, LysoPE(P-18:1/0:0), PE(O-18:1/0:0) and PE(P-16:0/0:0), were selected as a biomarker panel to distinguish between patients with MG and healthy controls. ROC curve (AUC) analysis revealed that the biomarker panel and anti-AChR antibody testing had equivalent MG diagnostic ability (AUC: 0.883 vs. 0.825; Conclusion: Our screened biomarker panel discriminated patients with established MG from healthy healthy controls, however, further evaluation in independent cohorts including patients with suspected MG and other relevant autoimmune and neuromuscular junction disease healthy controls is warranted.

Indexed as

MetabolomeMetabolomicsMyasthenia GravisAdultAgedBiomarkersFemaleHumansMaleMiddle AgedROC CurveTandem Mass SpectrometryBiomarkersbiomarkersdiagnostic valuemetabolic alterationmyasthenia gravisplasma metabolomic characterization

Identifiers

PMID42871075
PMCPMC13657992

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.