Serum metabolites from 19 myasthenia gravis (MG) patients and 15 normal controls were analyzed via untargeted metabolomics, including 6 pre/post-treatment paired MG patients, to assess the value of serum metabolites as biomarkers in monitoring MG.Differential metabolites between MG patients and normal controls were identified through liquid and gas chromatography-mass spectrometry simultaneously. Principal component analysis and orthogonal partial least squares-discriminant analysis were conducted to identify the differential metabolites. Candidate metabolites and pathways associated with MG were selected through a random forest machine learning model.A total of 310 differential metabolites were identified with a threshold of variable projected importance > 1 and P value < 0.05. Among these, 158 metabolites were upregulated and 152 were downregulated. The random forest machine learning model selected 5 metabolites as potential biomarkers associated with MG: lignoceric acid (AUC=0.944), uridine diphosphate-N-acetylglucosamine (AUC=0.951), arachidonic acid (AUC=0.951), beta-glycerophosphoric acid (AUC=0.933), and L-Asparagine (AUC=0.877). Further analysis using 6 paired MG patients pre- and post-immunosuppression treatment revealed 25 upregulated and 6 downregulated metabolites in post-treatment serum, which might be relevant to disease intervention. The significance remains elusive due to the limited number of patients.A subset of differential metabolites was identified in the serum of MG patients, some of which changed with immunosuppressive therapy. Small molecule metabolites may serve as valuable biomarkers for disease monitoring in MG.Copyright © 2024. Published by Elsevier B.V.