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. 2025 Aug 12;21(5):114.
doi: 10.1007/s11306-025-02315-2.

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

Jessica Rebeaud  1 , Nicholas Edward Phillips  1   2 , Guillaume Thévoz  1 , Solenne Vigne  1 , Sedreh Nassirnia  3 , Aude Gauthier-Jaques  1 , Pansy Lim-Dubois-Ferriere  1 , Satchidananda Panda  4 , Marie Théaudin  1 , Renaud Du Pasquier  1 , Gilbert Greub  3 , Claire Bertelli  3 , Jens Kuhle  5   6 , Tinh-Hai Collet #  7   8 , Caroline Pot #  9
Affiliations

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

Jessica Rebeaud et al. Metabolomics. .

Abstract

Introduction: Multiple 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.

Objectives: The 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.

Methods: A 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.

Results: Circulating 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.

Conclusions: This 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.

Keywords: Biomarkers; Gut-microbiota; Metabolomics; Multiple sclerosis.

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Conflict of interest statement

Declarations. Conflict of interest: All authors declare that they have no conflict of interest. Ethical approval: Each participant signed a written informed consent form approved by the local Ethics Committee (CER-VD 2018–01862) before inclusion. All procedures involving human participants were conducted following the ethical standards of the institutional and/or national research committee and relevant ethical guidelines.

Figures

Fig. 1
Fig. 1
Metabolomics and clinical data are the best predictors for MS-related parameters. A-B Evaluation of model performance using Pearson correlation for (A) each model individually, or (B) by combining the different datasets. Non-significant models are displayed with transparency
Fig. 2
Fig. 2
Metabolomics as a predictor of sNfL concentration, sGFAP concentration, and MS-related fatigue score. A-C Summary of the 10 most important LASSO coefficients and their univariate Spearman’s coefficient correlation for (A) sNfL concentration, B sGFAP concentration, and (C) MS-related fatigue (EMIF-SEP). Non-significant Spearman’s correlations are left blank
Fig. 3
Fig. 3
Clinical data can significantly predict sGFAP concentration, EDSS, and, MS-related fatigue score. A-C Summary of the 10 most important LASSO coefficients and their univariate Spearman’s coefficient correlation for (A) sGFAP concentration, B EDSS, and C MS-related fatigue (EMIF-SEP). Non-significant Spearman’s correlations are left blank. “Abs.” denotes the absolute value function
Fig. 4
Fig. 4
Most variables highlighted by the LASSO models are specific to one MS-related parameter. A Spearman’s correlation matrix of the variables selected by at least one of the LASSO models. B Venn Diagram showing variables shared among the four different MS-linked variables studied. Non-significant Spearman’s correlations are left blank
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