[Pubmed] Lipidomic Profiling Reveals Distinct Molecular Signatures Across Clinical Subtypes of Myasthenia Gravis

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Metabolites. 2026 Aug 19;16(8):589. doi: 10.3390/metabo16080589.

ABSTRACT

BACKGROUND/OBJECTIVES: Myasthenia gravis (MG) is an immune-mediated neuromuscular disorder for which antibody-based assays have limited sensitivity, particularly in double-seronegative MG (dsNMG), highlighting the need for complementary biomarkers. Given their roles in immune regulation, membrane integrity, and metabolic stress responses, lipids represent promising candidates for biomarker discovery.

METHODS: We designed a prospective case-control study and systematically stratified 68 patients with myasthenia gravis (MG) according to clinical classification and autoantibody status. Using LC-MS/MS, we quantified 824 lipids in 136 serum samples collected from these patients and 68 healthy controls. The analyzed subtypes included ocular MG (OMG), generalized MG (GMG), acetylcholine receptor antibody-positive MG (AChR-MG), and dsNMG. Differential lipid analysis, correlation network construction, KEGG pathway enrichment, and multivariable logistic regression were performed. Diagnostic and subtype prediction models were developed using LASSO with 10 × 10 repeated cross-validation and interpreted using Shapley Additive exPlanations (SHAP) analysis. A longitudinal follow-up analysis was conducted to assess dynamic associations between lipid signatures and disease activity.

RESULTS: In total, 240 lipids were significantly altered in MG compared with controls. Lipids distinguishing GMG from OMG were enriched in ether lipid metabolism, necroptosis, and sphingolipid signaling pathways. AChR-MG and dsNMG shared lipid networks related to membrane remodeling and signaling regulation, whereas dsNMG exhibited marked elevations in acylcarnitines and bile acid-related metabolites, potentially reflecting a distinct phenotype characterized by altered energy metabolism. The lipid-based model achieved an AUC of 0.917 for distinguishing MG from controls, and AUCs of 0.77 and 0.71 for differentiating AChR-MG from dsNMG and GMG from OMG, respectively. Longitudinal analyses showed that SM(d18:1/23:0) and Cer(d24:1/18:0(2OH)) displayed dynamic changes consistent with disease activity.

CONCLUSIONS: Serum lipidomics revealed subtype-specific metabolic features of MG, with stable disease-associated remodeling and dynamic sphingolipid changes potentially reflecting disease activity. By integrating systematic clinical and antibody-based subtype stratification with longitudinal follow-up, this study supports lipidomics as a complementary tool for precision diagnosis and disease stratification, particularly in antibody-negative dsNMG.

PMID:42646325 | PMC:PMC13515152 | DOI:10.3390/metabo16080589


Source: https://pubmed.ncbi.nlm.nih.gov/4264632 ... 8&v=2.20.1