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Neurotherapeutics

, Volume 16, Issue 3, pp 797–807 | Cite as

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

  • Lorena LoreficeEmail author
  • Federica Murgia
  • Giuseppe Fenu
  • Jessica Frau
  • Giancarlo Coghe
  • Maria Rita Murru
  • Stefania Tranquilli
  • Andrea Visconti
  • Maria Giovanna Marrosu
  • Luigi Atzori
  • Eleonora Cocco
Original Article

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 “no 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 (R2X = 0.812, R2Y = 0.797, Q2 = 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 (R2X = 0.442, R2Y = 0.768, Q2 = 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.

Key Words

Multiple sclerosis metabolomic analysis biomarkers interferon beta 1a treatment monitoring 

Notes

Acknowledgments

The authors thank Merck Serono for the financial support.

Required Author Forms

Disclosure forms provided by the authors are available with the online version of this article.

Compliance with Ethical Standards

The local institutional Ethics Committee approved the study, and written informed consent was obtained from each participant prior to participation.

Conflict of Interest

L. Lorefice received a speaker fee from Teva and serves on scientific advisory boards for Merck Serono and Biogen.

G. Fenu received honoraria for consultancy from Novartis and for speaking from Merck Serono, Biogen, and Teva.

J. Frau serves on scientific advisory boards for Biogen and Genzyme and received honoraria for speaking from Merck Serono and Teva.

G. Coghe received speaker fees from Teva and Almirall.

A. Visconti is a full time employee to Merck Serono.

L. Atzori, F. Murgia, M. Murru, and S. Tranquilli have nothing to disclose.

M. Marrosu and E. Cocco have received honoraria for consultancy or speaking from Bayer, Biogen, Novartis, Sanofi, Genzyme, Serono, and Teva.

Supplementary material

13311_2019_721_MOESM1_ESM.pdf (14.2 mb)
ESM 1 (PDF 14524 kb)

References

  1. 1.
    Lucchinetti CF, Brück W, Rodriguez M, et al. Distinct patterns of multiple sclerosis pathology indicate heterogeneity on pathogenesis. Brain Pathol. 1996 Jul;6(3):259–74CrossRefGoogle Scholar
  2. 2.
    Lublin FD, Reingold SC, Cohen JA, et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology 2014 83(3):278–286Google Scholar
  3. 3.
    Hum S, Lapierre Y, Scott SC, et al. Trajectory of MS disease course for men and women over three eras. Mult Scler. 2017 Apr;23(4):534–545.CrossRefGoogle Scholar
  4. 4.
    Hughes SE, Spelman T, Gray OM, et al. Predictors and dynamics of postpartum relapses in women with multiple sclerosis. Mult Scler. 2014 May;20(6):739–46.CrossRefGoogle Scholar
  5. 5.
    Zhang T, Tremlett H, Zhu F, et al. Effects of physical comorbidities on disability progression in multiple sclerosis. Neurology. 2018 Jan 30;90(5):e419-e427.CrossRefGoogle Scholar
  6. 6.
    Lorefice L, Fenu G, Pitzalis R, et al. Autoimmune comorbidities in multiple sclerosis: what is the influence on brain volumes? A case-control MRI study. J Neurol. 2018 May;265(5):1096–1101.CrossRefGoogle Scholar
  7. 7.
    Confavreux C, Vukusic S. Natural history of multiple sclerosis: a unifying concept. Brain. 2006 Mar;129(Pt 3):606–16.CrossRefGoogle Scholar
  8. 8.
    Steyerberg EW, Claggett B. Towards personalized therapy for multiple sclerosis: limitations of observational data. Brain. 2018 May 1;141(5):e38.CrossRefGoogle Scholar
  9. 9.
    Sormani MP. Prognostic factors versus markers of response to treatment versus surrogate endpoints: three different concepts. Mult Scler. 2017 Mar;23(3):378–381.CrossRefGoogle Scholar
  10. 10.
    Cree BA, Gourraud PA, Oksenberg JR, et al. Long-term evolution of multiple sclerosis disability in the treatment era. Ann Neurol. 2016 Oct;80(4):499–510.CrossRefGoogle Scholar
  11. 11.
    Hegen H, Auer M, Deisenhammer F. Predictors of response to multiple sclerosis. Therapeutics in individual patients. Drugs. 2016 Oct;76(15):1421–1445.CrossRefGoogle Scholar
  12. 12.
    Jain KK. Personalized neurology. Per Med. 2005 Mar;2(1):15–21.CrossRefGoogle Scholar
  13. 13.
    Villoslada P, Baranzini S. Data integration and systems biology approaches for biomarker discovery: challenges and opportunities for multiple sclerosis. J Neuroimmunol. 2012 Jul 15;248(1–2):58–65.CrossRefGoogle Scholar
  14. 14.
    Nicholson JK, Lindon JC, Holmes E. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999 Nov;29(11):1181–9.CrossRefGoogle Scholar
  15. 15.
    Bhargava P, Calabresi PA. Metabolomics in multiple sclerosis. Mult Scler. 2016 Apr;22(4):451–60.CrossRefGoogle Scholar
  16. 16.
    Dickens AM, Larkin JR, Griffin JL, et al. A type 2 biomarker separates relapsing-remitting from secondary progressive multiple sclerosis. Neurology. 2014 Oct 21;83(17):1492–9.CrossRefGoogle Scholar
  17. 17.
    Cocco E, Murgia F, Lorefice L, et al. (1)H-NMR analysis provides a metabolomic profile of patients with multiple sclerosis. Neurol Neuroimmunol Neuroinflamm. 2015 Dec 24;3(1):e185.CrossRefGoogle Scholar
  18. 18.
    Poddighe S, Murgia F, Lorefice L, et al. Metabolomic analysis identifies altered metabolic pathways in multiple sclerosis. Int J Biochem Cell Biol. 2017 Dec;93:148–155.CrossRefGoogle Scholar
  19. 19.
    Goodin DS, Reder AT, Traboulsee AL, et al. Predictive validity of NEDA in the 16- and 21-year follow-up from the pivotal trial of interferon beta-1b. Mult Scler. 2018 May 1:1352458518773511.Google Scholar
  20. 20.
    Trojano M, Pellegrini F, Paolicelli D, et al. Real-life impact of early interferon beta therapy in relapsing multiple sclerosis. Ann Neurol. 2009 Oct;66(4):513–20.CrossRefGoogle Scholar
  21. 21.
    Sormani MP, De Stefano N. Defining and scoring response to IFN-β in multiple sclerosis. Nat Rev Neurol. 2013 Sep;9(9):504–12.CrossRefGoogle Scholar
  22. 22.
    Giovannoni G, Tomic D, Bright JR, et al. “No evident disease activity”: the use of combined assessments in the management of patients with multiple sclerosis. Mult Scler. 2017 Aug;23(9):1179–118.CrossRefGoogle Scholar
  23. 23.
    Polman CH, Reingold SC, Banwell B, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol. 2011 Feb;69(2):292–302.CrossRefGoogle Scholar
  24. 24.
    Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983 Nov;33(11):1444–52.CrossRefGoogle Scholar
  25. 25.
    Banwell B, Giovannoni G, Hawkes C, et al. Editors’ welcome and a working definition for a multiple sclerosis cure. Mult Scler Relat Disord. 2013 Apr;2(2):65–7.CrossRefGoogle Scholar
  26. 26.
    Murgia F, Svegliati S, Poddighe S, et al. Metabolomic profile of systemic sclerosis patients. Sci Rep. 2018 May 16;8(1):7626.CrossRefGoogle Scholar
  27. 27.
    Weljie AM, Newton J, Mercier P, et al. Targeted profiling: quantitative analysis of 1H NMR metabolomics data. Anal Chem. 2006 Jul 1;78(13):4430–42.CrossRefGoogle Scholar
  28. 28.
    Xia J, Sinelnikov IV, Han B, et al. MetaboAnalyst 3.0—making metabolomics more meaningful. Nucleic Acids Res. 2015 Jul 1;43(W1):W251–7.CrossRefGoogle Scholar
  29. 29.
    Monaco F, Fumero S, Mondino A, et al. Plasma and cerebrospinal fluid tryptophan in multiple sclerosis and degenerative diseases. J Neurol Neurosurg Psychiatry. 1979 Jul;42(7):640–1.CrossRefGoogle Scholar
  30. 30.
    Moffett JR, Namboodiri MA. Tryptophan and the immune response. Immunol Cell Biol. 2003 Aug;81(4):247–65.CrossRefGoogle Scholar
  31. 31.
    Aeinehband S, Brenner P, Ståhl S, et al. Cerebrospinal fluid kynurenines in multiple sclerosis; relation to disease course and neurocognitive symptoms. Brain Behav Immun. 2016 Jan;51:47–55.CrossRefGoogle Scholar
  32. 32.
    Rajda C, Majláth Z, Pukoli D, et al. Kynurenines and multiple sclerosis: the dialogue between the immune system and the central nervous system. Int J Mol Sci. 2015 Aug 6;16(8):18270–82.CrossRefGoogle Scholar
  33. 33.
    Lovelace MD, Varney B, Sundaram G, et al. Current evidence for a role of the kynurenine pathway of tryptophan metabolism in multiple sclerosis. Front Immunol. 2016 Aug 4;7:246.CrossRefGoogle Scholar
  34. 34.
    Kwidzinski E, Bechmann I. IDO expression in the brain: a double-edged sword. J Mol Med (Berl). 2007 Dec;85(12):1351–9.CrossRefGoogle Scholar
  35. 35.
    Tavares RG, Tasca CI, Santos CE, et al. Quinolinic acid stimulates synaptosomal glutamate release and inhibits glutamate uptake into astrocytes. Neurochem Int. 2002 Jun;40(7):621–7.CrossRefGoogle Scholar
  36. 36.
    Levite M. Glutamate, T cells and multiple sclerosis. J Neural Transm (Vienna). 2017 Jul;124(7):775–798.CrossRefGoogle Scholar
  37. 37.
    Böhmig GA, Krieger PM, Säemann MD, et al. n-Butyrate downregulates the stimulatory function of peripheral blood-derived antigen-presenting cells: a potential mechanism for modulating T-cell responses by short-chain fatty acids. Immunology. 1997 Oct;92(2):234–43.CrossRefGoogle Scholar
  38. 38.
    Baeuerle PA, Henkel T. Function and activation of NF-kappa B in the immune system. Annu Rev Immunol. 1994;12:141–79.CrossRefGoogle Scholar
  39. 39.
    Steinman L. Multiple sclerosis: a two-stage disease. Nat Immunol. 2001 Sep;2(9):762–4.CrossRefGoogle Scholar
  40. 40.
    Sato S, Parr EB, Devlin BL, et al. Human metabolomics reveal daily variations under nutritional challenges specific to serum and skeletal muscle. Mol Metab. 2018 Oct;16:1–11.CrossRefGoogle Scholar
  41. 41.
    Chitnis T, Giovannoni G, Trojano M. Complexity of MS management in the current treatment era. Neurology. 2018 Apr 24;90(17):761–762.CrossRefGoogle Scholar
  42. 42.
    Giovannoni G. Personalized medicine in multiple sclerosis. Neurodegener Dis Manag. 2017 Nov;7(6s):13–17.CrossRefGoogle Scholar

Copyright information

© The American Society for Experimental NeuroTherapeutics, Inc. 2019

Authors and Affiliations

  • Lorena Lorefice
    • 1
    Email author
  • Federica Murgia
    • 1
  • Giuseppe Fenu
    • 1
  • Jessica Frau
    • 1
  • Giancarlo Coghe
    • 1
  • Maria Rita Murru
    • 1
  • Stefania Tranquilli
    • 1
  • Andrea Visconti
    • 2
  • Maria Giovanna Marrosu
    • 1
  • Luigi Atzori
    • 3
  • Eleonora Cocco
    • 1
  1. 1.Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi HospitalUniversity of CagliariCagliariItaly
  2. 2.Merck Serono S.p.A.RomeItaly
  3. 3.Department of Biomedical SciencesUniversity of CagliariCagliariItaly

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