Serum sEV miRNAs as Biomarkers in Myelin Oligodendrocyte Glycoprotein Antibody-Associated Disease

Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is a distinct CNS demyelinating disorder that differs from multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD). However, diagnosing MOGAD remains challenging due to the need to clinically exclude similar conditions and the variability in assay results. While liquid biomarkers have been extensively studied in MS and NMOSD, research on biomarkers for MOGAD remains limited. This study aims to investigate serum-derived small extracellular vesicle (sEV) miRNAs as potential diagnostic and prognostic biomarkers for MOGAD, distinguishing it from MS, NMOSD, and healthy controls. A comprehensive analysis of miRNAs in serum-derived sEVs was conducted to identify differentially expressed miRNAs among the groups. Correlations between miRNA profiles and clinical parameters, including the expanded disability status scale (EDSS) score and annualized relapse rate (ARR), were examined. The diagnostic potential of miRNAs was evaluated using the area under the curve (AUC) in the receiver operating characteristics (ROC) analyses. Serum samples were obtained from 47 patients (N = 11, MOGAD; N = 12, MS; and N = 12, NMOSD) and 12 healthy controls (HCs). We identified 77 dysregulated miRNAs in MOGAD patients, compared to HCs. Each three-miRNA panel demonstrated the highest AUC values for distinguishing MOGAD from HC (1.000), MOGAD from MS (0.939), and MOGAD from NMOSD (1.000). Additionally, hsa-miR-924 exhibited the strongest correlation with the EDSS score (ρ = -0.67, p < 0.001), while hsa-miR-548i showed the strongest correlation with ARR (ρ = -0.69, p < 0.001) in MOGAD. These miRNAs are involved in various pathways, including neuronal development, immune response, synaptic function, and chromatin remodeling, highlighting their potential roles in the pathophysiology of MOGAD. Serum sEV-derived miRNAs show strong potential as biomarkers for MOGAD, offering high diagnostic accuracy and correlations with clinical parameters. These findings pave the way for improved diagnostic and therapeutic strategies in MOGAD; however, further validation in larger cohorts is necessary.