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. 2025 May 19:16:1543547.
doi: 10.3389/fneur.2025.1543547. eCollection 2025.

Metabolomics insights into Charcot-Marie-Tooth disease: toward biomarker discovery

Signe Setlere  1   2 , Theresa Schiemer  3   4 , Annija Vaska  3   4 , Linda Gailite  5 , Dmitrijs Rots  5   6 , Viktorija Kenina  5   7   8 , Kristaps Klavins  3   4
Affiliations

Metabolomics insights into Charcot-Marie-Tooth disease: toward biomarker discovery

Signe Setlere et al. Front Neurol. .

Abstract

Introduction: Charcot-Marie-Tooth disease (CMT) is a group of rare neuropathies but still the most common hereditary neuromuscular disorder with heterogeneous phenotype and usually slow progression. Currently, there are no approved treatments or validated biomarkers for sensitive monitoring of disease progression.

Objectives: This study aimed to analyse selected plasma metabolite concentrations in a CMT cohort and compare them to healthy controls. For this purpose, 84 patients and 34 controls were enrolled in the study.

Results: We detected a total of 33 metabolites from which acetylcarnitine was found elevated and glycine was found decreased in CMT patients. In addition, the CMTX1 subgroup has decreased valine levels compared to controls. However, further analysis revealed poor disease predictive abilities of the detected metabolites for any CMT group. Furthermore, we found no associations of these metabolites with CMT severity.

Conclusion: Our study data provide information about plasma metabolite levels in CMT patients. However, these findings suggest that the metabolites mentioned above might be unspecific biomarkers of neuropathy and do not reflect disease severity.

Keywords: Charcot–Marie Tooth disease; biomarker; genetic and inherited disorders; metabolome; polyneuropathy.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Visualization of differential metabolite profiles compared to healthy controls of (a) CMT1A and (b) CMTX1 using V-plots. Significance thresholds are indicated with dashed lines (FC > 1.3, p < 0.05). oPLSDA plots of (c) CMT1A and (d) CMTX1. (e) Violin plots of metabolites identified to be significantly chqnged in the volcano plots (**** p < 0.0001, ** p < 0.01, *p < 0.05).
Figure 2
Figure 2
Visualization of differential metabolite profiles in all CMT cases compared to healthy controls using (a) V-plot. Significance thresholds are indicated with dashed lines (FC > 1.3, p < 0.05). (b) OPLS-DA score plot of CMT versus control. (c) Violin plot of glycine and acetylcarnitine as the significant metabolites identified in the V-plot (p < 0.05).
Figure 3
Figure 3
(a) Violin plots of one metabolite and two ratios were used for the CMT1A predictive model. Concentrations were normalized to the average of each sample. ANOVA with Bonferroni correction shows no significant differences. (b) ROC curves of a predictive model based on multiple linear regression and (c) classifications of samples using the constructed model.
See this image and copyright information in PMC

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