This study evaluates the predictive potential of serum nuclear magnetic resonance (NMR)-based metabolomics, individually and in combination with well-established biomarkers of neuroinflammation (serum glial fibrillary acidic protein, sGFAP) and axonal damage (neurofilament light chain, sNfL), in an extreme-phenotype subset of the Swiss Multiple Sclerosis Cohort (SMSC).<h4>Methods</h4>Serum samples were analysed using NMR-based metabolomics, along with quantification of sNfL and sGFAP. The gene discussed is GFAP; the disease is multiple sclerosis.