What question did this study set out to answer?

This research aims to evaluate the effectiveness of serum biomarkers and metabolomics in predicting the progression of multiple sclerosis.

February 26, 2026Open Access

Serum GFAP and NfL augment a metabolomics-driven strategy for long-term prediction of multiple sclerosis progression

TKTereza KačerováUniversity of Oxford EWEline WillemseUniversity of Basel JOJohanna OechteringUniversity of Freiburg

Key Points

This research aims to evaluate the effectiveness of serum biomarkers and metabolomics in predicting the progression of multiple sclerosis.
Analyzed serum samples using NMR-based metabolomics.
Measured serum levels of glial fibrillary acidic protein (sGFAP) and neurofilament light chain (sNfL).
Conducted supervised multivariate analysis to differentiate MS phenotypes.
Performed multivariable receiver operating characteristic (ROC) analysis to assess predictive capabilities.
Validated key metabolite findings in an independent cohort.
NMR-based metabolomics differentiated between relapsing-remitting MS and secondary-progressive MS.
Identified predictive metabolites associated with disease progression, independent of relapse activity.
Achieved a strong ROC analysis performance with AUC = 0.81 for predicting progression.
Integration with sGFAP and sNfL improved accuracy, yielding AUCs of 0.91 and 0.87, respectively.

Abstract

Abstract Background Reliable biomarkers for predicting disease progression in multiple sclerosis (MS) are crucial for advancing precision medicine and optimising treatment strategies. 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). Methods Serum samples were analysed using NMR-based metabolomics, along with quantification of sNfL and sGFAP. Supervised multivariate analysis was performed to differentiate MS phenotypes and identify future progressors. Multivariable receiver operating characteristic (ROC) analysis evaluated predictive performance, with key metabolite findings validated in an independent Oxford MS cohort. Results NMR-based metabolomics reliably distinguishes relapsing-remitting MS (RRMS) from secondary-progressive MS (SPMS) and predicts individual transitions. The identified predictive metabolites (lipoproteins, glutamine, alanine, valine, glucose) are also associated with progression independent of relapse activity (PIRA), a clinically relevant marker of sustained disability worsening. This demonstrates that the approach can both stage disease and forecast progression irrespective of stage. ROC analysis shows strong predictive performance (AUC = 0.81, p = 0.001), with external validation confirming robustness. Integration of NMR-metabolomics with sGFAP and sNfL further improves accuracy, yielding AUCs of 0.91 ( p < 0.0001) and 0.87 ( p = 0.0002), respectively, supported by independent validation. Conclusions The integration of metabolic and protein biomarkers enables both accurate staging of RRMS versus SPMS and, critically, early prediction of progression irrespective of stage. This dual capability provides a clinically actionable, serum-based tool that can refine monitoring, improve therapeutic decision-making, and support a shift towards stage-agnostic, progression-focused care in MS.

AI에게 질문

Bookmark

View Full Paper