Development of lipid-based biomarkers for the activity and progression of Multiple Sclerosis

This study investigates the potential of plasma lipidomic profiles to support the diagnosis of Multiple Sclerosis (MS) and to distinguish between clinical disease courses. Plasma samples from 60 patients with MS, classified as relapsing–remitting (RRMS) or chronic progressive MS (CPMS), and 60 age-matched controls were analyzed using direct-infusion quantitative shotgun lipidomics. Lipid data were adjusted for age and body mass index to reduce confounding effects. Multivariate analyses, including principal component analysis, orthogonal partial least squares discriminant analysis, and random forest modeling, were applied, complemented by a class composition visualization to assess lipid class–level changes. A total of 670 lipid species across 16 classes were identified. Patients with MS showed increased diacylglycerol levels, with DAG 16:0;0_18:1;0 demonstrating the highest predictive value for MS. Alterations in phosphatidylethanolamines were mainly associated with RRMS, whereas ether-linked phosphatidylethanolamines characterized CPMS. Additionally, cholesteryl esters were reduced in CPMS, while triacylglycerols were decreased in RRMS. Overall, distinct plasma lipidomic signatures were identified that correlate with specific MS disease courses, highlighting their potential utility for improved disease stratification and personalized therapeutic approaches.