Evidence map›Paper›PMID 40794313›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2025

Blood metabolomics improves prediction of central nervous system damage in multiple sclerosis.

Jessica Rebeaud, Nicholas Edward Phillips, Guillaume Thévoz, Solenne Vigne, Sedreh Nassirnia, Aude Gauthier-Jaques, Pansy Lim-Dubois-Ferriere, Satchidananda Panda, Marie Théaudin, Renaud Du Pasquier and 5 more

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Article in Metabolomics : Official journal of the Metabolomic Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Jessica RebeaudLaboratories of Neuroimmunology, Department of Clinical Neurosciences, Center for Research in Neuroscience and Service of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Nicholas Edward PhillipsLaboratories of Neuroimmunology, Department of Clinical Neurosciences, Center for Research in Neuroscience and Service of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Guillaume ThévozLaboratories of Neuroimmunology, Department of Clinical Neurosciences, Center for Research in Neuroscience and Service of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Solenne VigneLaboratories of Neuroimmunology, Department of Clinical Neurosciences, Center for Research in Neuroscience and Service of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Sedreh NassirniaInstitute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Aude Gauthier-JaquesLaboratories of Neuroimmunology, Department of Clinical Neurosciences, Center for Research in Neuroscience and Service of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Pansy Lim-Dubois-FerriereLaboratories of Neuroimmunology, Department of Clinical Neurosciences, Center for Research in Neuroscience and Service of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Satchidananda PandaSalk Institute for Biological Studies, La Jolla, CA, 92037, USA.
Marie ThéaudinLaboratories of Neuroimmunology, Department of Clinical Neurosciences, Center for Research in Neuroscience and Service of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Renaud Du PasquierLaboratories of Neuroimmunology, Department of Clinical Neurosciences, Center for Research in Neuroscience and Service of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Gilbert GreubInstitute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Claire BertelliInstitute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Jens KuhleDepartments of Biomedicine and Clinical Research, Multiple Sclerosis Centre and Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB), University Hospital and University of Basel, Basel, Switzerland.
Tinh-Hai Collet *Service of Endocrinology and Diabetology, Geneva University Hospitals, Geneva, Switzerland. tinh-hai.collet@hug.ch.
Caroline Pot *Laboratories of Neuroimmunology, Department of Clinical Neurosciences, Center for Research in Neuroscience and Service of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland. caroline.pot-kreis@chuv.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMultiple sclerosis (MS) is an autoimmune disorder with an unpredictable outcome at the time of diagnosis. The measurement of serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP) has introduced new biomarkers for assessing MS disease activity and progression. However, there is a need for additional diagnostic and prognostic tools. In this study, we investigated the predictive abilities of metabolomics, gut microbiota, as well as clinical and lifestyle factors for MS outcome parameters.

objectivesThe aim of this study was to assess the predictive capacity of plasma metabolites, gut microbiota, and clinical/lifestyle factors on MS outcome measures including MS-related fatigue, MS disability, and sNfL and sGFAP concentrations.

methodsA prospective cohort study was conducted with 54 individuals with MS. Anthropometric, biological, and lifestyle parameters were collected. The least absolute shrinkage and selection operator (LASSO) algorithm with ten-fold cross-validation was used to identify predictors of MS disease outcome parameters based on plasma metabolomics, microbiota sequencing, and clinical and lifestyle measurements obtained from questionnaires and anthropometric measurements.

resultsCirculating metabolites were found to be superior predictors for sNfL and sGFAP concentrations, while clinical and lifestyle data were associated with EDSS scores. Both plasma metabolites and clinical data significantly predicted MS-related fatigue. Combining multiple multi-omics data did not consistently improve predictive performance.

conclusionsThis study highlights the value of plasma metabolites as predictors of sNfL, sGFAP, and fatigue in MS. Our findings suggest that prioritizing metabolomics over other methods can lead to more accurate predictions of MS disease outcomes.

Indexed as

Central Nervous SystemMetabolomicsMultiple SclerosisAdultBiomarkersFemaleGastrointestinal MicrobiomeHumansMaleMiddle AgedNeurofilament ProteinsProspective StudiesBiomarkersneurofilament protein LNeurofilament ProteinsBiomarkersGut-microbiotaMetabolomicsMultiple sclerosis

Identifiers

PMID40794313
PMCPMC12343719

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.