Evaluation of the Metabolomics Profile in Charcot–Marie–Tooth (CMT) Patients: Novel Potential Biomarkers

• Published in: Metabolites Vol. 15; no. 8; p. 520
• Main Authors: Murgia, Federica, Cadeddu, Martina, Frau, Jessica, Coghe, Giancarlo, Lorena, Lorefice, Vannelli, Alessandro, Murru, Maria Rita, Spada, Martina, Noto, Antonio, Atzori, Luigi, Cocco, Eleonora
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.08.2025; MDPI
• Subjects: Biological markers; Biomarkers; Charcot-Marie-Tooth disease; Data analysis; Development and progression; Diabetic neuropathy; Disease; Genetic aspects; Genetic diversity; Glutamate; Hereditary diseases; Isoleucine; Lysine; Magnetic resonance spectroscopy; Medical research; Medicine, Experimental; Metabolic pathways; Metabolism; Metabolites; Metabolomics; Myelination; Nervous system; Nervous system diseases; Neurodegenerative diseases; Neurophysiology; NMR; Nuclear magnetic resonance; Nuclear magnetic resonance spectroscopy; Patients; Peripheral nerves; Proteins; Software; Statistical analysis; Variables; Italy; United Kingdom; Taiwan; United Kingdom > UK; United States > US; Charcot–Marie–Tooth disease; metabolomics; nuclear magnetic resonance; biomarkers; metabolic pathways
• ISSN: 2218-1989; 2218-1989
• DOI: 10.3390/metabo15080520

Background: Charcot–Marie–Tooth (CMT) is a group of inherited diseases impairing the peripheral nervous system. CMT originates from genetic variants that affect proteins fundamental for the myelination of peripheral nerves and survival. Moreover, environmental and humoral factors can impact disease development and evolution. Currently, no therapy is available. Metabolomics is an emerging field of biomedical research that enables the development of novel biomarkers for neurodegenerative diseases by targeting metabolic pathways or metabolites. This study aimed to evaluate the metabolomics profile of CMT disease by comparing patients with healthy individuals. Methods: A total of 22 CMT patients (CMT) were included in this study and were demographically matched with 26 healthy individuals (C). Serum samples were analyzed through Nuclear Magnetic Resonance spectroscopy, and multivariate and univariate statistical analyses were subsequently applied. Results: A supervised model showed a clear separation (R2X = 0.3; R2Y = 0.7; Q2 = 0.4; p-value = 0.0004) between the two classes of subjects, and nine metabolites were found to be significantly different (2-hydroxybutyrate, 3-hydroxybutyrate, 3-methyl-2-oxovalerate, choline, citrate, glutamate, isoleucine, lysine, and methyl succinate). The combined ROC curve showed an AUC of 0.94 (CI: 0.9–1). Additional altered metabolic pathways were also identified within the disease context. Conclusion: This study represents a promising starting point, demonstrating the efficacy of metabolomics in evaluating CMT patients and identifying novel potential disease biomarkers.