Assessing the Metabolomic Profile of Multiple Sclerosis Patients Treated with Interferon Beta 1a by 1H-NMR Spectroscopy

Abstract

Metabolomic research has emerged as a promising approach to identify potential biomarkers in multiple sclerosis (MS). The aim of the present study was to determine the effect of interferon beta (IFN ß) on the metabolome of MS patients to explore possible biomarkers of disease activity and therapeutic response. Twenty-one MS patients starting IFN ß therapy (Rebif® 44 μg; s.c. 3 times per week) were enrolled. Blood samples were obtained at baseline and after 6, 12, and 24 months of IFN ß treatment and were analyzed by high-resolution nuclear magnetic resonance spectroscopy. Changes in metabolites were analyzed. After IFN ß exposure, patients were divided into responders and nonresponders according to the Bno evidence of disease activity^(NEDA-3) definition (absence of relapses, disability progression, and magnetic resonance imaging activity), and samples obtained at baseline were analyzed to evaluate the presence of metabolic differences predictive of IFN ß response. The results of the investigation demonstrated differential distribution of baseline samples compared to those obtained during IFN ß exposure, particularly after 24 months of treatment (R 2 X = 0.812, R 2 Y = 0.797, Q 2 = 0.613, p = 0.003). In addition, differences in the baseline metabolome between responder and nonresponder patients with respect to lactate, acetone, 3-OH-butyrate, tryptophan, citrate, lysine, and glucose levels were found (R 2 X = 0.442, R 2 Y = 0.768, Q 2 = 0.532, p = 0.01). In conclusion, a metabolomic approach appears to be a promising, noninvasive tool that could potentially contribute to predicting the efficacy of MS therapies.

Multi-Platform Characterization of Cerebrospinal Fluid and Serum Metabolome of Patients Affected by Relapsing–Remitting and Primary Progressive Multiple Sclerosis

“…[16] Metabolomics represents an innovative approach able advance discover new potential biomarkers and to explore pathophysiological features in a pathological condition. We believe that the use of this new tools will help us come to a greater understanding of the pathogenesis of MS, improving its diagnosis, its classification, availability of effective treatment and to define the response to therapy, as recent investigations have shown [59]. Despite the weak correlation between the age of the patients and the concentration of the selected metabolites, age could be considered as a confounder factor, and its contribution should not be overlooked.…”

Metabolomic Characterization of Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS)

“…Ten milliliter of blood were collected from each subject and were centrifuged at 2,500 g for 10 min at 4 • C. The obtained serum samples were stored at −80 • C until analysis. Subsequently, samples were thawed and treated with a modified Folch method (Bligh and Dyer, 1959;Lorefice et al, 2019) to extract and separate hydrophilic and lipophilic metabolites. 400 µL of each serum sample were mixed with 600 µL of methanol, 600 µL of chloroform, and 175 µL of Milli-Q water.…”

An emerging potential of metabolomics in multiple sclerosis: a comprehensive overview

“…Overall, this demonstrated that DMFinduced alterations in lipid metabolism are associated with immunological changes, which highlights the potential to use metabolic markers as a means to monitor therapeutic interventions in MS, allowing for more precise patientspecific treatment. Similarly, another recent study utilized high-resolution 1H-NMR spectroscopy to identify markers for disease activity and therapeutic response by assessing the effect of interferon β 1a therapy on the metabolome of 21 MS patients treated for a varying period of time at 0, 6, 12, and 24 months, compared with 16 healthy controls [125]. Although the sample size was small, making it necessary to repeat this study with a larger dataset, PLS-DA and OPLS-DA models showed differential blood-based metabolite profiles between responders (n = 16) and non-responders (n = 5), based on "no evidence of disease activity" (NEDA-3) derived from absence of relapses, disability progression, and imaging activity.…”