Skip to main content
SpringerLink
Log in

Dietary and lifestyle factors in multiple sclerosis progression: results from a 5-year longitudinal MRI study

  • Original Communication
  • Published:
Journal of Neurology Aims and scope Submit manuscript

Abstract

Background

Evidence regarding the role, if any, of dietary and lifestyle factors in the pathogenesis of multiple sclerosis (MS) is poorly understood.

Objective

To assess the effect of lifestyle-based risk factors linked to cardiovascular disease (CVD) on clinical and MRI-derived MS outcomes.

Methods

The study enrolled 175 MS or clinically isolated syndrome (CIS) patients and 42 age- and sex-matched healthy controls (HCs) who were longitudinally followed for 5.5 years. The 20-year CVD risk was calculated by Healthy Heart Score (HHS) prediction model which includes age, smoking, body mass index, dietary intake, exercise, and alcohol consumption. Baseline and follow-up MRI scans were obtained and cross-sectional and longitudinal changes of T2-lesion volume (LV), whole brain volume (WBV), white matter volume (WMV), gray matter volume (GMV), and lateral ventricular volume (LVV) were calculated.

Results

After correcting for disease duration, the baseline HHS values of the MS group were associated with baseline GMV (rs = − 0.20, p = 0.01), and longitudinal LVV change (rs = 0.19, p = 0.01). The association with LVV remained significant after adjusting for baseline LVV volumes (rs = 0.2, p = 0.008) in MS patients. The diet component of the HHS was associated with the 5-year T2-LV accrual (rs = − 0.191, p = 0.04) in MS. In the HC group, the HHS was associated with LVV (rs = 0.58, p < 0.001), GMV (rs = − 0.57, p < 0.001), WBV (rs = − 0.55, p = 0.001), T2-LV (rs = 0.41, p = 0.027), and WMV (rs = − 0.38, p = 0.042). Additionally, the HC HHS was associated with the 5-year change in LVV (rs = 0.54, p = 0.001) and in WBV (rs = − 0.45, p = 0.011).

Conclusion

Lifestyle risk factors contribute to accelerated central brain atrophy in MS patients, whereas unhealthier diet is associated with MS lesion accrual. Despite the lower overall effect when compared to HCs, lifestyle-based modifications may still provide a beneficial effect on reducing brain atrophy in MS patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Reich DS, Lucchinetti CF, Calabresi PA (2018) Multiple Sclerosis. N Engl J Med 378(2):169–180. https://doi.org/10.1056/NEJMra1401483

    Article  CAS  PubMed  Google Scholar 

  2. Fisher E, Lee JC, Nakamura K, Rudick RA (2008) Gray matter atrophy in multiple sclerosis: a longitudinal study. Ann Neurol 64(3):255–265. https://doi.org/10.1002/ana.21436

    Article  Google Scholar 

  3. Belbasis L, Bellou V, Evangelou E, Ioannidis JP, Tzoulaki I (2015) Environmental risk factors and multiple sclerosis: an umbrella review of systematic reviews and meta-analyses. Lancet Neurol 14(3):263–273. https://doi.org/10.1016/S1474-4422(14)70267-4

    Article  PubMed  Google Scholar 

  4. Trojano M, Pellegrini F, Fuiani A, Paolicelli D, Zipoli V, Zimatore GB, Di Monte E, Portaccio E, Lepore V, Livrea P, Amato MP (2007) New natural history of interferon-beta-treated relapsing multiple sclerosis. Ann Neurol 61(4):300–306. https://doi.org/10.1002/ana.21102

    Article  CAS  PubMed  Google Scholar 

  5. University of California SFMSET, Cree BA, Gourraud PA, Oksenberg JR, Bevan C, Crabtree-Hartman E, Gelfand JM, Goodin DS, Graves J, Green AJ, Mowry E, Okuda DT, Pelletier D, von Budingen HC, Zamvil SS, Agrawal A, Caillier S, Ciocca C, Gomez R, Kanner R, Lincoln R, Lizee A, Qualley P, Santaniello A, Suleiman L, Bucci M, Panara V, Papinutto N, Stern WA, Zhu AH, Cutter GR, Baranzini S, Henry RG, Hauser SL (2016) Long-term evolution of multiple sclerosis disability in the treatment era. Ann Neurol 80(4):499–510. https://doi.org/10.1002/ana.24747

    Article  Google Scholar 

  6. Marrie RA, Yu BN, Leung S, Elliott L, Caetano P, Warren S, Wolfson C, Patten SB, Svenson LW, Tremlett H, Fisk J, Blanchard JF, Epidemiology, CTi, Impact of Comorbidity on Multiple S (2012) Rising prevalence of vascular comorbidities in multiple sclerosis: validation of administrative definitions for diabetes, hypertension, and hyperlipidemia. Mult Scler 18(9):1310–1319. https://doi.org/10.1177/1352458512437814

    Article  PubMed  Google Scholar 

  7. Marrie RA (2017) Comorbidity in multiple sclerosis: implications for patient care. Nat Rev Neurol 13(6):375–382. https://doi.org/10.1038/nrneurol.2017.33

    Article  PubMed  Google Scholar 

  8. Olsson T, Barcellos LF, Alfredsson L (2017) Interactions between genetic, lifestyle and environmental risk factors for multiple sclerosis. Nat Rev Neurol 13(1):25–36. https://doi.org/10.1038/nrneurol.2016.187

    Article  CAS  PubMed  Google Scholar 

  9. Munger KL, Bentzen J, Laursen B, Stenager E, Koch-Henriksen N, Sorensen TI, Baker JL (2013) Childhood body mass index and multiple sclerosis risk: a long-term cohort study. Mult Scler 19(10):1323–1329. https://doi.org/10.1177/1352458513483889

    Article  PubMed  PubMed Central  Google Scholar 

  10. Jakimovski D, Gandhi S, Paunkoski I, Bergsland N, Hagemeier J, Ramasamy DP, Hojnacki D, Kolb C, Benedict RHB, Weinstock-Guttman B, Zivadinov R (2018) Hypertension and heart disease are associated with development of brain atrophy in multiple sclerosis: a 5-year longitudinal study. Eur J Neurol. https://doi.org/10.1111/ene.13769

    Article  PubMed  Google Scholar 

  11. Zivadinov R, Weinstock-Guttman B, Hashmi K, Abdelrahman N, Stosic M, Dwyer M, Hussein S, Durfee J, Ramanathan M (2009) Smoking is associated with increased lesion volumes and brain atrophy in multiple sclerosis. Neurology 73(7):504–510. https://doi.org/10.1212/WNL.0b013e3181b2a706

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kappus N, Weinstock-Guttman B, Hagemeier J, Kennedy C, Melia R, Carl E, Ramasamy DP, Cherneva M, Durfee J, Bergsland N, Dwyer MG, Kolb C, Hojnacki D, Ramanathan M, Zivadinov R (2016) Cardiovascular risk factors are associated with increased lesion burden and brain atrophy in multiple sclerosis. J Neurol Neurosurg Psychiatry 87(2):181–187. https://doi.org/10.1136/jnnp-2014-310051

    Article  PubMed  Google Scholar 

  13. Farez MF, Fiol MP, Gaitan MI, Quintana FJ, Correale J (2015) Sodium intake is associated with increased disease activity in multiple sclerosis. J Neurol Neurosurg Psychiatry 86(1):26–31. https://doi.org/10.1136/jnnp-2014-307928

    Article  PubMed  Google Scholar 

  14. Marrie RA, Patten SB, Tremlett H, Wolfson C, Warren S, Svenson LW, Jette N, Fisk J, Epidemiology, CTit, Impact of Comorbidity on Multiple S (2016) Sex differences in comorbidity at diagnosis of multiple sclerosis: a population-based study. Neurology. https://doi.org/10.1212/WNL.0000000000002481

    Article  PubMed  PubMed Central  Google Scholar 

  15. Magalhaes S, Pugliatti M, Casetta I, Drulovic J, Granieri E, Holmoy T, Kampman MT, Landtblom AM, Lauer K, Myhr KM, Parpinel M, Pekmezovic T, Riise T, Wolfson D, Zhu B, Wolfson C (2015) The EnvIMS study: design and methodology of an international case-control study of environmental risk factors in multiple sclerosis. Neuroepidemiology 44(3):173–181. https://doi.org/10.1159/000381779

    Article  PubMed  Google Scholar 

  16. Xia Z, White CC, Owen EK, Von Korff A, Clarkson SR, McCabe CA, Cimpean M, Winn PA, Hoesing A, Steele SU, Cortese IC, Chitnis T, Weiner HL, Reich DS, Chibnik LB, De Jager PL (2016) Genes and Environment in Multiple Sclerosis project: a platform to investigate multiple sclerosis risk. Ann Neurol 79(2):178–189. https://doi.org/10.1002/ana.24560

    Article  PubMed  Google Scholar 

  17. Ziliotto N, Bernardi F, Jakimovski D, Baroni M, Marchetti G, Bergsland N, Ramasamy DP, Weinstock-Guttman B, Schweser F, Zamboni P, Ramanathan M, Zivadinov R (2018) Hemostasis biomarkers in multiple sclerosis. Eur J Neurol. https://doi.org/10.1111/ene.13681

    Article  PubMed  Google Scholar 

  18. Dwyer MG, Bergsland N, Ramasamy DP, Jakimovski D, Weinstock-Guttman B, Zivadinov R (2018) Atrophied brain lesion volume: a new imaging biomarker in multiple sclerosis. J Neuroimaging. https://doi.org/10.1111/jon.12527

    Article  PubMed  Google Scholar 

  19. Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, Fujihara K, Havrdova E, Hutchinson M, Kappos L, Lublin FD, Montalban X, O’Connor P, Sandberg-Wollheim M, Thompson AJ, Waubant E, Weinshenker B, Wolinsky JS (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69(2):292–302. https://doi.org/10.1002/ana.22366

    Article  PubMed  PubMed Central  Google Scholar 

  20. Dolic K, Weinstock-Guttman B, Marr K, Valnarov V, Carl E, Hagemeier J, Brooks C, Kilanowski C, Hojnacki D, Ramanathan M, Zivadinov R (2011) Risk factors for chronic cerebrospinal venous insufficiency (CCSVI) in a large cohort of volunteers. PLoS One 6(11):e28062. https://doi.org/10.1371/journal.pone.0028062

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kurtzke JF (1983) Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33(11):1444–1452

    Article  CAS  Google Scholar 

  22. Chiuve SE, Cook NR, Shay CM, Rexrode KM, Albert CM, Manson JE, Willett WC, Rimm EB (2014) Lifestyle-based prediction model for the prevention of CVD: the healthy heart score. J Am Heart Assoc 3(6):e000954. https://doi.org/10.1161/JAHA.114.000954

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB (1998) Prediction of coronary heart disease using risk factor categories. Circulation 97(18):1837–1847

    Article  CAS  Google Scholar 

  24. Mukhopadhyay S, Fellows K, Browne RW, Khare P, Krishnan Radhakrishnan S, Hagemeier J, Weinstock-Guttman B, Zivadinov R, Ramanathan M (2017) Interdependence of oxysterols with cholesterol profiles in multiple sclerosis. Mult Scler 23(6):792–801. https://doi.org/10.1177/1352458516666187

    Article  CAS  PubMed  Google Scholar 

  25. Smith SM, Zhang Y, Jenkinson M, Chen J, Matthews PM, Federico A, De Stefano N (2002) Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 17(1):479–489

    Article  Google Scholar 

  26. Gelineau-Morel R, Tomassini V, Jenkinson M, Johansen-Berg H, Matthews PM, Palace J (2012) The effect of hypointense white matter lesions on automated gray matter segmentation in multiple sclerosis. Hum Brain Mapp 33(12):2802–2814. https://doi.org/10.1002/hbm.21402

    Article  PubMed  Google Scholar 

  27. Dwyer MG, Bergsland N, Zivadinov R (2014) Improved longitudinal gray and white matter atrophy assessment via application of a 4-dimensional hidden Markov random field model. Neuroimage 90:207–217. https://doi.org/10.1016/j.neuroimage.2013.12.004

    Article  PubMed  Google Scholar 

  28. Sormani MP, Kappos L, Radue EW, Cohen J, Barkhof F, Sprenger T, Piani Meier D, Haring D, Tomic D, De Stefano N (2016) Defining brain volume cutoffs to identify clinically relevant atrophy in RRMS. Mult Scler. https://doi.org/10.1177/1352458516659550

    Article  PubMed  Google Scholar 

  29. Marrie RA, Rudick R, Horwitz R, Cutter G, Tyry T, Campagnolo D, Vollmer T (2010) Vascular comorbidity is associated with more rapid disability progression in multiple sclerosis. Neurology 74(13):1041–1047. https://doi.org/10.1212/WNL.0b013e3181d6b125

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bronnum-Hansen H, Koch-Henriksen N, Stenager E (2004) Trends in survival and cause of death in Danish patients with multiple sclerosis. Brain 127(Pt 4):844–850. https://doi.org/10.1093/brain/awh104

    Article  PubMed  Google Scholar 

  31. Thormann A, Magyari M, Koch-Henriksen N, Laursen B, Sorensen PS (2016) Vascular comorbidities in multiple sclerosis: a nationwide study from Denmark. J Neurol 263(12):2484–2493. https://doi.org/10.1007/s00415-016-8295-9

    Article  PubMed  Google Scholar 

  32. Belov P, Jakimovski D, Krawiecki J, Magnano C, Hagemeier J, Pelizzari L, Weinstock-Guttman B, Zivadinov R (2018) Lower arterial cross-sectional area of carotid and vertebral arteries and higher frequency of secondary neck vessels are associated with multiple sclerosis. Am J Neuroradiol 39(1):123–130. https://doi.org/10.3174/ajnr.A5469

    Article  CAS  PubMed  Google Scholar 

  33. Pelizzari L, Jakimovski D, Lagana MM, Bergsland N, Hagemeier J, Baselli G, Weinstock-Guttman B, Zivadinov R (2018) Five-year longitudinal study of neck vessel cross-sectional area in multiple sclerosis. Am J Neuroradiol 39(9):1703–1709. https://doi.org/10.3174/ajnr.A5738

    Article  CAS  PubMed  Google Scholar 

  34. Widmer RJ, Flammer AJ, Lerman LO, Lerman A (2015) The Mediterranean diet, its components, and cardiovascular disease. Am J Med 128(3):229–238. https://doi.org/10.1016/j.amjmed.2014.10.014

    Article  PubMed  Google Scholar 

  35. Marck CH, Neate SL, Taylor KL, Weiland TJ, Jelinek GA (2016) Prevalence of comorbidities, overweight and obesity in an international sample of people with multiple sclerosis and associations with modifiable lifestyle factors. PLoS One 11(2):e0148573. https://doi.org/10.1371/journal.pone.0148573

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Moon SY, de Souto Barreto P, Rolland Y, Chupin M, Bouyahia A, Fillon L, Mangin JF, Andrieu S, Cesari M, Vellas B, Group MDS (2018) Prospective associations between white matter hyperintensities and lower extremity function. Neurology 90(15):e1291–e1297. https://doi.org/10.1212/WNL.0000000000005289

    Article  PubMed  Google Scholar 

  37. Srinivasa RN, Rossetti HC, Gupta MK, Rosenberg RN, Weiner MF, Peshock RM, McColl RW, Hynan LS, Lucarelli RT, King KS (2016) Cardiovascular risk factors associated with smaller brain volumes in regions identified as early predictors of cognitive decline. Radiology 278(1):198–204. https://doi.org/10.1148/radiol.2015142488

    Article  PubMed  Google Scholar 

  38. Jochemsen HM, Muller M, Visseren FL, Scheltens P, Vincken KL, Mali WP, van der Graaf Y, Geerlings MI, Group SS (2013) Blood pressure and progression of brain atrophy: the SMART-MR Study. JAMA Neurol 70(8):1046–1053. https://doi.org/10.1001/jamaneurol.2013.217

    Article  PubMed  Google Scholar 

  39. Bancks MP, Allen NB, Dubey P, Launer LJ, Lloyd-Jones DM, Reis JP, Sidney S, Yano Y, Schreiner PJ (2017) Cardiovascular health in young adulthood and structural brain MRI in midlife: The CARDIA study. Neurology 89(7):680–686. https://doi.org/10.1212/WNL.0000000000004222

    Article  PubMed  PubMed Central  Google Scholar 

  40. Jacobsen C, Hagemeier J, Myhr KM, Nyland H, Lode K, Bergsland N, Ramasamy DP, Dalaker TO, Larsen JP, Farbu E, Zivadinov R (2014) Brain atrophy and disability progression in multiple sclerosis patients: a 10-year follow-up study. J Neurol Neurosurg Psychiatry 85(10):1109–1115. https://doi.org/10.1136/jnnp-2013-306906

    Article  PubMed  Google Scholar 

  41. Bergsland N, Horakova D, Dwyer MG, Uher T, Vaneckova M, Tyblova M, Seidl Z, Krasensky J, Havrdova E, Zivadinov R (2018) Gray matter atrophy patterns in multiple sclerosis: a 10-year source-based morphometry study. Neuroimage Clin 17:444–451. https://doi.org/10.1016/j.nicl.2017.11.002

    Article  PubMed  Google Scholar 

  42. Moccia M, Lanzillo R, Palladino R, Maniscalco GT, De Rosa A, Russo C, Massarelli M, Carotenuto A, Postiglione E, Caporale O, Triassi M, Brescia Morra V (2015) The Framingham cardiovascular risk score in multiple sclerosis. Eur J Neurol 22(8):1176–1183. https://doi.org/10.1111/ene.12720

    Article  CAS  PubMed  Google Scholar 

  43. Zivadinov R, Jakimovski D, Gandhi S, Ahmed R, Dwyer MG, Horakova D, Weinstock-Guttman B, Benedict RR, Vaneckova M, Barnett M, Bergsland N (2016) Clinical relevance of brain atrophy assessment in multiple sclerosis. Implications for its use in a clinical routine. Expert Rev Neurother 16(7):777–793. https://doi.org/10.1080/14737175.2016.1181543

    Article  CAS  PubMed  Google Scholar 

  44. Dwyer MG, Silva D, Bergsland N, Horakova D, Ramasamy D, Durfee J, Vaneckova M, Havrdova E, Zivadinov R (2017) Neurological software tool for reliable atrophy measurement (NeuroSTREAM) of the lateral ventricles on clinical-quality T2-FLAIR MRI scans in multiple sclerosis. Neuroimage Clin 15:769–779. https://doi.org/10.1016/j.nicl.2017.06.022

    Article  PubMed  PubMed Central  Google Scholar 

  45. Zivadinov R, Bergsland N, Korn JR, Dwyer MG, Khan N, Medin J, Price JC, Weinstock-Guttman B, Silva D, Group M-MS (2018) Feasibility of brain atrophy measurement in clinical routine without prior standardization of the MRI protocol: results from MS-MRIUS, a longitudinal observational, multicenter real-world outcome study in patients with relapsing-remitting MS. Am J Neuroradiol 39(2):289–295. https://doi.org/10.3174/ajnr.A5442

    Article  CAS  PubMed  Google Scholar 

  46. Eshaghi A, Prados F, Brownlee WJ, Altmann DR, Tur C, Cardoso MJ, De Angelis F, van de Pavert SH, Cawley N, De Stefano N, Stromillo ML, Battaglini M, Ruggieri S, Gasperini C, Filippi M, Rocca MA, Rovira A, Sastre-Garriga J, Vrenken H, Leurs CE, Killestein J, Pirpamer L, Enzinger C, Ourselin S, Wheeler-Kingshott C, Chard D, Thompson AJ, Alexander DC, Barkhof F, Ciccarelli O, group Ms (2018) Deep gray matter volume loss drives disability worsening in multiple sclerosis. Ann Neurol 83(2):210–222. https://doi.org/10.1002/ana.25145

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Azevedo CJ, Cen SY, Khadka S, Liu S, Kornak J, Shi Y, Zheng L, Hauser SL, Pelletier D (2018) Thalamic atrophy in multiple sclerosis: a magnetic resonance imaging marker of neurodegeneration throughout disease. Ann Neurol 83(2):223–234. https://doi.org/10.1002/ana.25150

    Article  PubMed  PubMed Central  Google Scholar 

  48. Russell RD, Lucas RM, Brennan V, Sherriff JL, Begley A, Ausimmune Investigator G, Black LJ (2018) Reported changes in dietary behavior following a first clinical diagnosis of central nervous system demyelination. Front Neurol 9:161. https://doi.org/10.3389/fneur.2018.00161

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We wish to acknowledge all study participants and investigators that were involved and contributed to the CEG-MS study over the period of 8 years.

Author information

Authors and Affiliations

  1. Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High Street, Buffalo, NY, 142013, USA

    Dejan Jakimovski, Sirin Gandhi, Yi Guan, Jesper Hagemeier, Deepa P. Ramasamy, Tom A. Fuchs, Niels Bergsland, Michael G. Dwyer & Robert Zivadinov

  2. Department of Neurology, Jacobs Multiple Sclerosis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA

    Bianca Weinstock-Guttman

  3. Department of Biotechnical and Clinical Laboratory Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA

    Richard W. Browne

  4. Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA

    Michael G. Dwyer & Robert Zivadinov

  5. Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA

    Murali Ramanathan

Authors
  1. Dejan Jakimovski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bianca Weinstock-Guttman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sirin Gandhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yi Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jesper Hagemeier
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Deepa P. Ramasamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tom A. Fuchs
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Richard W. Browne
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Niels Bergsland
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Michael G. Dwyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Murali Ramanathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Robert Zivadinov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Robert Zivadinov.

Ethics declarations

Conflicts of interest

This study was funded in part by The Annette Funicello Research Fund for Neurological Diseases and internal resources of the Buffalo Neuroimaging Analysis Center. In addition, we received support from the Jacquemin Family Foundation. Research reported in this publication was also funded in part by the National Center for Advancing Translational Sciences of the National Institutes of Health under award Number UL1TR001412. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Dejan Jakimovski, Sirin Gandhi,Yi Guan, Jesper Hagemeier, Deepa P. Ramasamy, Tom Fuchs, Richard Browne, Niels Bergsland, Michael Dwyer have nothing to disclose. Bianca Weinstock- Guttman received honoraria as a speaker and as a consultant for Biogen Idec, Teva Pharmaceuticals, EMD Serono, Genzyme&Sanofi, Novartis and Acorda. Dr Weinstock-Guttman received research funds from Biogen Idec, Teva Pharmaceuticals,, EMD Serono, Genzyme&Sanofi, Novartis, Acorda. Murali Ramanathan received research funding the National Multiple Sclerosis Society, the National Institutes of Health and Otsuka Pharmaceutical and Development. These are unrelated to the research presented in this report. Robert Zivadinov received personal compensation from EMD Serono, Genzyme-Sanofi, Claret Medical, Celgene and Novartis for speaking and consultant fees. He received financial support for research activities from Genenetech, Genzyme-Sanofi, Novartis, and Quintiles/IMS.

Ethical approval for research involving human participants and/or animals

The study was approved by the University at Buffalo Institutional Review Board (IRB) and all participants signed written informed consent.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jakimovski, D., Weinstock-Guttman, B., Gandhi, S. et al. Dietary and lifestyle factors in multiple sclerosis progression: results from a 5-year longitudinal MRI study. J Neurol 266, 866–875 (2019). https://doi.org/10.1007/s00415-019-09208-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00415-019-09208-0

Keywords

Search

Navigation