Evidence map›Paper›PMID 41741703›Full record

ArticleCommunications medicine2026

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

Tereza Kacerova, Eline Willemse, Johanna Oechtering, Daniel E Radford-Smith, Wenzheng Xiong, Megan Sealey, Luisa Saldana, Aleksandra Maleska Maceski, Tianrong Yeo, Gabriele DeLuca and 5 more

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Article in Communications medicine, 2026. 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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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

15 authors.

Tereza KacerovaChemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-9477-0182
Eline WillemseDepartment of Neurology, University Hospital and University of Basel, Basel, Switzerland.
Johanna OechteringDepartment of Neurology, University Hospital and University of Basel, Basel, Switzerland.
Daniel E Radford-SmithDepartment of Pharmacology, University of Oxford, Oxford, UK.
Wenzheng XiongDepartment of Pharmacology, University of Oxford, Oxford, UK.
Megan SealeyDepartment of Pharmacology, University of Oxford, Oxford, UK.
Luisa SaldanaNuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
Aleksandra Maleska MaceskiDepartment of Neurology, University Hospital and University of Basel, Basel, Switzerland.
Tianrong YeoDepartment of Neurology, National Neuroscience Institute, Singapore, Singapore.ORCID http://orcid.org/0000-0002-3648-0953
Gabriele DeLucaNuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
Jacqueline PalaceNuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
David LeppertDepartment of Neurology, University Hospital and University of Basel, Basel, Switzerland.
Jens KuhleDepartment of Neurology, University Hospital and University of Basel, Basel, Switzerland.ORCID http://orcid.org/0000-0002-6963-8892
Daniel C AnthonyDepartment of Pharmacology, University of Oxford, Oxford, UK. daniel.anthony@pharm.ox.ac.uk.ORCID http://orcid.org/0000-0003-1380-6655
Fay ProbertChemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundReliable 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).

methodsSerum 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.

resultsNMR-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.

conclusionsThe 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.

Identifiers

PMID41741703
PMCPMC13046805

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