ArticleFrontiers in neurology
From inflammation to neurodegeneration: an exploratory pilot study of a diagnostic framework for progression in MS.
Article in Frontiers in neurology. 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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Abstract
Background: The pathogenesis of Multiple Sclerosis (MS) involves a dynamic interplay between (auto-)inflammation and neurodegeneration, driving its relapsing and progressive course. While both processes have been shown to be involved throughout disease with different emphasis, relapsing MS (RMS) is characterized by acute inflammatory activity, and secondary progressive MS (SPMS) involves chronic, diffuse neurodegeneration. Identifying biomarkers to predict progression independent of relapse activity (PIRA) is critical for improving diagnostic accuracy and treatment strategies. This study aimed to integrate inflammatory and neurodegenerative biomarkers into a Classification And Regression Tree (CART) model to distinguish RMS from SPMS. Methods: We employed a multimodal approach by combining functional as well as structural retinal assessment via multifocal electroretinography (mfERG) and optical coherence tomography (OCT), serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (GFAP) levels via multiplex technology, and subsequent deep immunophenotyping. A CART model was constructed and trained to classify people with MS (PwMS) into RMS or SPMS categories based on these biomarkers. Results: Our results revealed significant thinning of the retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (GCIPL) in pwMS, with more pronounced reductions in SPMS, while functional mfERG-readouts did not differ between MS subgroups. Neurodegenerative markers sNfL and GFAP were elevated in pwMS compared to healthy controls, with higher levels in SPMS. Immunophenotyping showed increased levels of non-classic and intermediate monocytes in SPMS. The CART and Random Forest models identified sNfL, GCIPL thickness, and frequency of intermediate monocytes as the most accurate predictors, achieving approximately 80% accuracy in distinguishing RMS from SPMS. Discussion: These findings suggest that combining sNfL, GCIPL thickness, and monocyte subsets provides an experimental, but robust diagnostic framework for differentiating RMS from SPMS. This approach could enable earlier identification of disease progression, facilitating tailored therapeutic interventions. Future studies should validate this model in larger cohorts to enhance its clinical applicability.
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