Reaching an evidence-based prognosis for personalized treatment of multiple sclerosis


Personalized treatment is ideal for multiple sclerosis (MS) owing to the heterogeneity of clinical features, but current knowledge gaps, including validation of biomarkers and treatment algorithms, limit practical implementation. The contemporary approach to personalized MS therapy depends on evidence-based prognostication, an initial treatment choice and evaluation of early treatment responses to identify the need to switch therapy. Prognostication is directed by baseline clinical, environmental and demographic factors, MRI measures and biomarkers that correlate with long-term disability measures. The initial treatment choice should be a shared decision between the patient and physician. In addition to prognosis, this choice must account for patient-related factors, including comorbidities, pregnancy planning, preferences of the patients and their comfort with risk, and drug-related factors, including safety, cost and implications for treatment sequencing. Treatment response has traditionally been assessed on the basis of relapse rate, MRI lesions and disability progression. Larger longitudinal data sets have enabled development of composite outcome measures and more stringent standards for disease control. Biomarkers, including neurofilament light chain, have potential as early surrogate markers of prognosis and treatment response but require further validation. Overall, attainment of personalized treatment for MS is complex but will be refined as new data become available.

Key points

  • Personalized treatment of multiple sclerosis (MS) depends on prognostication at baseline, a shared treatment decision between the physician and patient, and early assessment of response to therapy.

  • Prognosis can be evaluated soon after diagnosis on the basis of demographic and environmental factors, clinical features, MRI measures and biomarkers.

  • Individuals with poor prognostic features should be recommended high-efficacy therapies early on; studies are underway to investigate whether most patients with relapsing–remitting MS could benefit from initial aggressive therapy.

  • During the treatment discussion between the neurologist and patient, factors such as comorbidities, pregnancy planning, patient preferences, risk tolerance, safety, cost and treatment sequencing should be considered in addition to prognosis.

  • Early assessment of treatment response is important to identify the need to switch therapy; composite outcome measures that incorporate clinical and MRI data are best for predicting long-term disability.

  • Personalized MS therapy is currently limited by a lack of evidence-based biomarkers; newer biomarkers, such as neurofilament light chain, have potential, but further validation and standardization of assays are required.

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Fig. 1: Predictors of a poor prognosis in multiple sclerosis.
Fig. 2: Factors that influence the initial treatment decision for patients with multiple sclerosis.
Fig. 3: Treatment algorithm for personalized therapy of relapsing–remitting multiple sclerosis.


  1. 1.

    Kalincik, T. et al. Towards personalized therapy for multiple sclerosis: prediction of individual treatment response. Brain 140, 2426–2443 (2017). This study is an important effort to use modelling techniques in a large cohort to predict individual treatment response.

    PubMed  Google Scholar 

  2. 2.

    Gourraud, P. A. et al. Precision medicine in chronic disease management: the multiple sclerosis BioScreen. Ann. Neurol. 76, 633–642 (2014).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Matthews, P. M. Decade in review-multiple sclerosis: new drugs and personalized medicine for multiple sclerosis. Nat. Rev. Neurol. 11, 614–616 (2015).

    CAS  PubMed  Google Scholar 

  4. 4.

    Comabella, M., Sastre-Garriga, J. & Montalban, X. Precision medicine in multiple sclerosis: biomarkers for diagnosis, prognosis, and treatment response. Curr. Opin. Neurol. 29, 254–262 (2016).

    CAS  PubMed  Google Scholar 

  5. 5.

    Collins, F. S. & Varmus, H. A new initiative on precision medicine. N. Engl. J. Med. 372, 793–795 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Ruda, R., Bruno, F. & Soffietti, R. What have we learned from recent clinical studies in low-grade gliomas? Curr. Treat. Opt. Neurol. 20, 33 (2018).

    Google Scholar 

  7. 7.

    Ahmed, S., Sami, A. & Xiang, J. HER2-directed therapy: current treatment options for HER2-positive breast cancer. Breast Cancer 22, 101–116 (2015).

    PubMed  Google Scholar 

  8. 8.

    Sormani, M. P. et al. Will Rogers phenomenon in multiple sclerosis. Ann. Neurol. 64, 428–433 (2008).

    PubMed  Google Scholar 

  9. 9.

    Thompson, A. J. et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 17, 162–173 (2018).

    PubMed  Google Scholar 

  10. 10.

    Arrambide, G. et al. The value of oligoclonal bands in the multiple sclerosis diagnostic criteria. Brain 141, 1075–1084 (2018). This study is one of the largest to have demonstrated the prognostic value of OCBs in addition to MRI findings after CIS.

    PubMed  Google Scholar 

  11. 11.

    Filippini, G. et al. Treatment with disease-modifying drugs for people with a first clinical attack suggestive of multiple sclerosis. Cochrane Database Syst. Rev. 4, CD012200 (2017).

    PubMed  Google Scholar 

  12. 12.

    Rae-Grant, A. et al. Comprehensive systematic review summary: disease-modifying therapies for adults with multiple sclerosis: report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. Neurology 90, 789–800 (2018).

    PubMed  Google Scholar 

  13. 13.

    Confavreux, C., Vukusic, S., Moreau, T. & Adeleine, P. Relapses and progression of disability in multiple sclerosis. N. Engl. J. Med. 343, 1430–1438 (2000). This key study investigates disability accrual in MS across different clinical subtypes.

    CAS  PubMed  Google Scholar 

  14. 14.

    Koch, M., Kingwell, E., Rieckmann, P. & Tremlett, H. The natural history of primary progressive multiple sclerosis. Neurology 73, 1996–2002 (2009).

    PubMed  Google Scholar 

  15. 15.

    Confavreux, C. & Vukusic, S. Natural history of multiple sclerosis: a unifying concept. Brain 129, 606–616 (2006).

    PubMed  Google Scholar 

  16. 16.

    Ebers, G. C. Natural history of primary progressive multiple sclerosis. Mult. Scler. 10 (Suppl. 1), 8–13 (2004).

    Google Scholar 

  17. 17.

    Koch, M. W., Cutter, G., Stys, P. K., Yong, V. W. & Metz, L. M. Treatment trials in progressive MS—current challenges and future directions. Nat. Rev. Neurol. 9, 496–503 (2013).

    CAS  PubMed  Google Scholar 

  18. 18.

    Montalban, X. et al. Ocrelizumab versus placebo in primary progressive multiple sclerosis. N. Engl. J. Med. 376, 209–220 (2017).

    CAS  PubMed  Google Scholar 

  19. 19.

    Runmarker, B. & Andersen, O. Prognostic factors in a multiple sclerosis incidence cohort with twenty-five years of follow-up. Brain 116, 117–134 (1993). This article presents one of the initial studies to determine clinical factors that are predictive of long-term disability.

    PubMed  Google Scholar 

  20. 20.

    Confavreux, C., Vukusic, S. & Adeleine, P. Early clinical predictors and progression of irreversible disability in multiple sclerosis: an amnesic process. Brain 126, 770–782 (2003).

    PubMed  Google Scholar 

  21. 21.

    Guillemin, F. et al. Older age at multiple sclerosis onset is an independent factor of poor prognosis: a population-based cohort study. Neuroepidemiology 48, 179–187 (2017).

    PubMed  Google Scholar 

  22. 22.

    Tintore, M. et al. Defining high, medium and low impact prognostic factors for developing multiple sclerosis. Brain 138, 1863–1874 (2015). This study is one of the first to incorporate clinical, MRI and CSF data to determine prognosis after CIS.

    PubMed  Google Scholar 

  23. 23.

    Bergamaschi, R. et al. Disability and mortality in a cohort of multiple sclerosis patients: a reappraisal. Neuroepidemiology 25, 15–18 (2005).

    PubMed  Google Scholar 

  24. 24.

    Langer-Gould, A. et al. Clinical and demographic predictors of long-term disability in patients with relapsing-remitting multiple sclerosis: a systematic review. Arch. Neurol. 63, 1686–1691 (2006).

    PubMed  Google Scholar 

  25. 25.

    Cree, B. A. et al. Clinical characteristics of African Americans versus Caucasian Americans with multiple sclerosis. Neurology 63, 2039–2045 (2004).

    CAS  PubMed  Google Scholar 

  26. 26.

    Ventura, R. E., Antezana, A. O., Bacon, T. & Kister, I. Hispanic Americans and African Americans with multiple sclerosis have more severe disease course than Caucasian Americans. Mult. Scler. 23, 1554–1557 (2017).

    PubMed  Google Scholar 

  27. 27.

    Sidhom, Y. et al. Fast multiple sclerosis progression in North Africans: both genetics and environment matter. Neurology 88, 1218–1225 (2017).

    PubMed  Google Scholar 

  28. 28.

    Ascherio, A., Munger, K. L. & Lunemann, J. D. The initiation and prevention of multiple sclerosis. Nat. Rev. Neurol. 8, 602–612 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Mowry, E. M. et al. Body mass index, but not vitamin D status, is associated with brain volume change in MS. Neurology 91, e2256–e2264 (2018).

    CAS  PubMed  Google Scholar 

  30. 30.

    Fitzgerald, K. C. et al. Diet quality is associated with disability and symptom severity in multiple sclerosis. Neurology 90, e1–e11 (2018).

    PubMed  Google Scholar 

  31. 31.

    Kvistad, S. et al. Antibodies to Epstein-Barr virus and MRI disease activity in multiple sclerosis. Mult. Scler. 20, 1833–1840 (2014).

    CAS  PubMed  Google Scholar 

  32. 32.

    Munger, K. L. et al. Vitamin D intake and incidence of multiple sclerosis. Neurology 62, 60–65 (2004).

    CAS  PubMed  Google Scholar 

  33. 33.

    Munger, K. L., Levin, L. I., Hollis, B. W., Howard, N. S. & Ascherio, A. Serum 25-hydroxyvitamin D levels and risk of multiple sclerosis. JAMA 296, 2832–2838 (2006).

    CAS  PubMed  Google Scholar 

  34. 34.

    Simpson, S. Jr. et al. Higher 25-hydroxyvitamin D is associated with lower relapse risk in multiple sclerosis. Ann. Neurol. 68, 193–203 (2010).

    CAS  PubMed  Google Scholar 

  35. 35.

    Mowry, E. M. et al. Vitamin D status predicts new brain magnetic resonance imaging activity in multiple sclerosis. Ann. Neurol. 72, 234–240 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Ascherio, A. et al. Vitamin D as an early predictor of multiple sclerosis activity and progression. JAMA Neurol. 71, 306–314 (2014).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Cortese, M. Vitamin D, smoking, EBV and long-term cognitive performance among CIS patients: 11-year follow-up of BENEFIT. ECTRIMS Online Library (2018).

  38. 38.

    Handel, A. E. et al. Smoking and multiple sclerosis: an updated meta-analysis. PLOS ONE 6, e16149 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Heydarpour, P. et al. Smoking and worsening disability in multiple sclerosis: a meta-analysis. Acta Neurol. Scand. 138, 62–69 (2018).

    CAS  PubMed  Google Scholar 

  40. 40.

    Graetz, C. et al. Association of smoking but not HLA-DRB1*15:01, APOE or body mass index with brain atrophy in early multiple sclerosis. Mult. Scler. (2018).

    Article  PubMed  Google Scholar 

  41. 41.

    Ramanujam, R. et al. Effect of smoking cessation on multiple sclerosis prognosis. JAMA Neurol. 72, 1117–1123 (2015).

    PubMed  Google Scholar 

  42. 42.

    Kowalec, K. et al. Comorbidity increases the risk of relapse in multiple sclerosis: a prospective study. Neurology 89, 2455–2461 (2017).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Tettey, P. et al. Frequency of comorbidities and their association with clinical disability and relapse in multiple sclerosis. Neuroepidemiology 46, 106–113 (2016).

    PubMed  Google Scholar 

  44. 44.

    McKay, K. A. et al. Psychiatric comorbidity is associated with disability progression in multiple sclerosis. Neurology 90, e1316–e1323 (2018).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Zhang, T. et al. Effects of physical comorbidities on disability progression in multiple sclerosis. Neurology 90, e419–e427 (2018).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Marrie, R. A. et al. Vascular comorbidity is associated with more rapid disability progression in multiple sclerosis. Neurology 74, 1041–1047 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Scalfari, A. et al. The natural history of multiple sclerosis: a geographically based study 10: relapses and long-term disability. Brain 133, 1914–1929 (2010).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Eriksson, M., Andersen, O. & Runmarker, B. Long-term follow up of patients with clinically isolated syndromes, relapsing-remitting and secondary progressive multiple sclerosis. Mult. Scler. 9, 260–274 (2003).

    PubMed  Google Scholar 

  49. 49.

    Jokubaitis, V. G. et al. Predictors of long-term disability accrual in relapse-onset multiple sclerosis. Ann. Neurol. 80, 89–100 (2016). This large, international study investigates predictors of disability at 10 years after treatment initiation.

    PubMed  Google Scholar 

  50. 50.

    Comi, G. et al. Effect of early interferon treatment on conversion to definite multiple sclerosis: a randomised study. Lancet 357, 1576–1582 (2001).

    CAS  PubMed  Google Scholar 

  51. 51.

    Novotna, M. et al. Poor early relapse recovery affects onset of progressive disease course in multiple sclerosis. Neurology 85, 722–729 (2015).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Weinshenker, B. G. et al. The natural history of multiple sclerosis: a geographically based study. 3. Multivariate analysis of predictive factors and models of outcome. Brain 114, 1045–1056 (1991).

    PubMed  Google Scholar 

  53. 53.

    Campbell, J., Rashid, W., Cercignani, M. & Langdon, D. Cognitive impairment among patients with multiple sclerosis: associations with employment and quality of life. Postgrad. Med. J. 93, 143–147 (2017).

    CAS  PubMed  Google Scholar 

  54. 54.

    Chiaravalloti, N. D. & DeLuca, J. Cognitive impairment in multiple sclerosis. Lancet Neurol. 7, 1139–1151 (2008).

    PubMed  Google Scholar 

  55. 55.

    Calabrese, M. et al. Widespread cortical thinning characterizes patients with MS with mild cognitive impairment. Neurology 74, 321–328 (2010).

    CAS  PubMed  Google Scholar 

  56. 56.

    Bergamaschi, R. et al. BREMSO: a simple score to predict early the natural course of multiple sclerosis. Eur. J. Neurol. 22, 981–989 (2015).

    CAS  PubMed  Google Scholar 

  57. 57.

    Galea, I. et al. A web-based tool for personalized prediction of long-term disease course in patients with multiple sclerosis. Eur. J. Neurol. 20, 1107–1109 (2013).

    CAS  PubMed  Google Scholar 

  58. 58.

    Barkhof, F. The clinico-radiological paradox in multiple sclerosis revisited. Curr. Opin. Neurol. 15, 239–245 (2002).

    PubMed  Google Scholar 

  59. 59.

    Swanton, J. K. et al. Early MRI in optic neuritis: the risk for clinically definite multiple sclerosis. Mult. Scler. 16, 156–165 (2010).

    CAS  PubMed  Google Scholar 

  60. 60.

    Optic Neuritis Study Group. Multiple sclerosis risk after optic neuritis: final optic neuritis treatment trial follow-up. Arch. Neurol. 65, 727–732 (2008).

    Google Scholar 

  61. 61.

    Fisniku, L. K. et al. Disability and T2 MRI lesions: a 20-year follow-up of patients with relapse onset of multiple sclerosis. Brain 131, 808–817 (2008). This important study with lengthy follow-up demonstrates the correlation between T2 lesion volume and disability outcome.

    CAS  PubMed  Google Scholar 

  62. 62.

    Kuhle, J. et al. Conversion from clinically isolated syndrome to multiple sclerosis: a large multicentre study. Mult. Scler. 21, 1013–1024 (2015).

    CAS  PubMed  Google Scholar 

  63. 63.

    Filippi, M. et al. Correlations between changes in disability and T2-weighted brain MRI activity in multiple sclerosis: a follow-up study. Neurology 45, 255–260 (1995).

    CAS  PubMed  Google Scholar 

  64. 64.

    Popescu, V. et al. Brain atrophy and lesion load predict long term disability in multiple sclerosis. J. Neurol. Neurosurg. Psychiatry 84, 1082–1091 (2013).

    PubMed  Google Scholar 

  65. 65.

    Brex, P. A. et al. A longitudinal study of abnormalities on MRI and disability from multiple sclerosis. N. Engl. J. Med. 346, 158–164 (2002). This is one of the first studies to show the predictive value of MRI at MS presentation.

    PubMed  Google Scholar 

  66. 66.

    Rovira, A. et al. A single, early magnetic resonance imaging study in the diagnosis of multiple sclerosis. Arch. Neurol. 66, 587–592 (2009).

    PubMed  Google Scholar 

  67. 67.

    Kappos, L. et al. Predictive value of gadolinium-enhanced magnetic resonance imaging for relapse rate and changes in disability or impairment in multiple sclerosis: a meta-analysis. Gadolinium MRI Meta-analysis Group. Lancet 353, 964–969 (1999).

    CAS  PubMed  Google Scholar 

  68. 68.

    Minneboo, A. et al. Infratentorial lesions predict long-term disability in patients with initial findings suggestive of multiple sclerosis. Arch. Neurol. 61, 217–221 (2004).

    PubMed  Google Scholar 

  69. 69.

    Sombekke, M. H. et al. Spinal cord lesions in patients with clinically isolated syndrome: a powerful tool in diagnosis and prognosis. Neurology 80, 69–75 (2013).

    PubMed  Google Scholar 

  70. 70.

    Arrambide, G. et al. Spinal cord lesions: A modest contributor to diagnosis in clinically isolated syndromes but a relevant prognostic factor. Mult. Scler. 24, 301–312 (2018).

    PubMed  Google Scholar 

  71. 71.

    Okuda, D. T. et al. Radiologically isolated syndrome: 5-year risk for an initial clinical event. PLOS ONE 9, e90509 (2014).

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    Lavorgna, L. et al. Clinical and magnetic resonance imaging predictors of disease progression in multiple sclerosis: a nine-year follow-up study. Mult. Scler. 20, 220–226 (2014).

    CAS  PubMed  Google Scholar 

  73. 73.

    Perez-Miralles, F. et al. Clinical impact of early brain atrophy in clinically isolated syndromes. Mult. Scler. 19, 1878–1886 (2013).

    CAS  PubMed  Google Scholar 

  74. 74.

    Rojas, J. I., Patrucco, L., Miguez, J., Besada, C. & Cristiano, E. Brain atrophy in radiologically isolated syndromes. J. Neuroimaging 25, 68–71 (2015).

    PubMed  Google Scholar 

  75. 75.

    Calabrese, M. et al. Cortical lesion load associates with progression of disability in multiple sclerosis. Brain 135, 2952–2961 (2012).

    PubMed  Google Scholar 

  76. 76.

    Scalfari, A. et al. The cortical damage, early relapses, and onset of the progressive phase in multiple sclerosis. Neurology 90, e2107–e2118 (2018).

    PubMed  Google Scholar 

  77. 77.

    Wattjes, M. P. et al. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis—establishing disease prognosis and monitoring patients. Nat. Rev. Neurol. 11, 597–606 (2015).

    CAS  PubMed  Google Scholar 

  78. 78.

    Radue, E. W. et al. Correlation between brain volume loss and clinical and MRI outcomes in multiple sclerosis. Neurology 84, 784–793 (2015).

    PubMed  PubMed Central  Google Scholar 

  79. 79.

    De Stefano, N. & Arnold, D. L. Towards a better understanding of pseudoatrophy in the brain of multiple sclerosis patients. Mult. Scler. 21, 675–676 (2015).

    PubMed  Google Scholar 

  80. 80.

    Matute-Blanch, C. et al. Neurofilament light chain and oligoclonal bands are prognostic biomarkers in radiologically isolated syndrome. Brain 141, 1085–1093 (2018).

    PubMed  Google Scholar 

  81. 81.

    Ferreira, D. et al. Multiple sclerosis patients lacking oligoclonal bands in the cerebrospinal fluid have less global and regional brain atrophy. J. Neuroimmunol. 274, 149–154 (2014).

    CAS  PubMed  Google Scholar 

  82. 82.

    Avasarala, J. R., Cross, A. H. & Trotter, J. L. Oligoclonal band number as a marker for prognosis in multiple sclerosis. Arch. Neurol. 58, 2044–2045 (2001).

    CAS  PubMed  Google Scholar 

  83. 83.

    Dalla Costa, G. et al. Clinical significance of the number of oligoclonal bands in patients with clinically isolated syndromes. J. Neuroimmunol. 289, 62–67 (2015).

    CAS  PubMed  Google Scholar 

  84. 84.

    Magraner, M. J. et al. Brain atrophy and lesion load are related to CSF lipid-specific IgM oligoclonal bands in clinically isolated syndromes. Neuroradiology 54, 5–12 (2012).

    PubMed  Google Scholar 

  85. 85.

    Villar, L. et al. Influence of oligoclonal IgM specificity in multiple sclerosis disease course. Mult. Scler. 14, 183–187 (2008).

    CAS  PubMed  Google Scholar 

  86. 86.

    Villar, L. M. et al. Lipid-specific immunoglobulin M bands in cerebrospinal fluid are associated with a reduced risk of developing progressive multifocal leukoencephalopathy during treatment with natalizumab. Ann. Neurol. 77, 447–457 (2015).

    CAS  PubMed  Google Scholar 

  87. 87.

    Lu, C. H. et al. Neurofilament light chain: a prognostic biomarker in amyotrophic lateral sclerosis. Neurology 84, 2247–2257 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Disanto, G. et al. Serum neurofilament light: a biomarker of neuronal damage in multiple sclerosis. Ann. Neurol. 81, 857–870 (2017). This is one of the first large studies to investigate serum NfL levels in MS.

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Arrambide, G. et al. Neurofilament light chain level is a weak risk factor for the development of MS. Neurology 87, 1076–1084 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90.

    Teunissen, C. E. et al. Combination of CSF N-acetylaspartate and neurofilaments in multiple sclerosis. Neurology 72, 1322–1329 (2009).

    CAS  PubMed  Google Scholar 

  91. 91.

    Sellebjerg, F., Royen, L., Soelberg Sorensen, P., Oturai, A. B. & Jensen, P. E. H. Prognostic value of cerebrospinal fluid neurofilament light chain and chitinase-3-like-1 in newly diagnosed patients with multiple sclerosis. Mult. Scler. (2018).

    Article  PubMed  Google Scholar 

  92. 92.

    Siller, N. et al. Serum neurofilament light chain is a biomarker of acute and chronic neuronal damage in early multiple sclerosis. Mult. Scler. (2018).

    Article  PubMed  Google Scholar 

  93. 93.

    Kuhle, J. et al. Serum neurofilament light chain in early relapsing remitting MS is increased and correlates with CSF levels and with MRI measures of disease severity. Mult. Scler. 22, 1550–1559 (2016).

    CAS  PubMed  Google Scholar 

  94. 94.

    Salzer, J., Svenningsson, A. & Sundstrom, P. Neurofilament light as a prognostic marker in multiple sclerosis. Mult. Scler. 16, 287–292 (2010).

    CAS  PubMed  Google Scholar 

  95. 95.

    Novakova, L. et al. Monitoring disease activity in multiple sclerosis using serum neurofilament light protein. Neurology 89, 2230–2237 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Barro, C. et al. Serum neurofilament as a predictor of disease worsening and brain and spinal cord atrophy in multiple sclerosis. Brain 141, 2382–2391 (2018). This study demonstrates the relationship between serum NfL levels and various MRI outcomes.

    PubMed  Google Scholar 

  97. 97.

    Yaldizli, O. Value of serum neurofilament light chain levels as a biomarker of suboptimal treatment response in MS clinical practice. ECTRIMS Online Library (2018).

  98. 98.

    Calabresi, P. A. Serum neurofilament light (NfL) for disease prognosis and treatment monitoring in multiple sclerosis patients: is it ready for implementation into clinical care? ECTRIMS Online Library (2018).

  99. 99.

    Shahim, P., Zetterberg, H., Tegner, Y. & Blennow, K. Serum neurofilament light as a biomarker for mild traumatic brain injury in contact sports. Neurology 88, 1788–1794 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Modvig, S. et al. Cerebrospinal fluid levels of chitinase 3-like 1 and neurofilament light chain predict multiple sclerosis development and disability after optic neuritis. Mult. Scler. 21, 1761–1770 (2015).

    CAS  PubMed  Google Scholar 

  101. 101.

    Canto, E. et al. Chitinase 3-like 1: prognostic biomarker in clinically isolated syndromes. Brain 138, 918–931 (2015).

    PubMed  Google Scholar 

  102. 102.

    Comabella, M. et al. Cerebrospinal fluid chitinase 3-like 1 levels are associated with conversion to multiple sclerosis. Brain 133, 1082–1093 (2010).

    PubMed  Google Scholar 

  103. 103.

    Lambe, J., Murphy, O. C. & Saidha, S. Can optical coherence tomography be used to guide treatment decisions in adult or pediatric multiple sclerosis? Curr. Treat. Opt. Neurol. 20, 9 (2018).

    Google Scholar 

  104. 104.

    Martinez-Lapiscina, E. H. et al. Retinal thickness measured with optical coherence tomography and risk of disability worsening in multiple sclerosis: a cohort study. Lancet Neurol. 15, 574–584 (2016).

    PubMed  Google Scholar 

  105. 105.

    Sepulcre, J. et al. Diagnostic accuracy of retinal abnormalities in predicting disease activity in MS. Neurology 68, 1488–1494 (2007).

    PubMed  Google Scholar 

  106. 106.

    Toledo, J. et al. Retinal nerve fiber layer atrophy is associated with physical and cognitive disability in multiple sclerosis. Mult. Scler. 14, 906–912 (2008).

    CAS  PubMed  Google Scholar 

  107. 107.

    Pisa, M. et al. No evidence of disease activity is associated with reduced rate of axonal retinal atrophy in MS. Neurology 89, 2469–2475 (2017).

    PubMed  Google Scholar 

  108. 108.

    Gelfand, J. M. et al. Retinal axonal loss begins early in the course of multiple sclerosis and is similar between progressive phenotypes. PLOS ONE 7, e36847 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. 109.

    Bates, D. Treatment effects of immunomodulatory therapies at different stages of multiple sclerosis in short-term trials. Neurology 76, S14–S25 (2011).

    CAS  PubMed  Google Scholar 

  110. 110.

    Trojano, M. et al. Real-life impact of early interferon beta therapy in relapsing multiple sclerosis. Ann. Neurol. 66, 513–520 (2009).

    CAS  PubMed  Google Scholar 

  111. 111.

    Cocco, E. et al. Influence of treatments in multiple sclerosis disability: a cohort study. Mult. Scler. 21, 433–441 (2015).

    PubMed  Google Scholar 

  112. 112.

    Montalban, X. et al. ECTRIMS/EAN guideline on the pharmacological treatment of people with multiple sclerosis. Mult. Scler. 24, 96–120 (2018).

    PubMed  Google Scholar 

  113. 113.

    Rae-Grant, A. et al. Practice guideline recommendations summary: disease-modifying therapies for adults with multiple sclerosis: report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. Neurology 90, 777–788 (2018).

    PubMed  Google Scholar 

  114. 114.

    Comi, G., Radaelli, M. & Soelberg Sorensen, P. Evolving concepts in the treatment of relapsing multiple sclerosis. Lancet 389, 1347–1356 (2017).

    PubMed  Google Scholar 

  115. 115.

    Corboy, J. R., Weinshenker, B. G. & Wingerchuk, D. M. Comment on 2018 American Academy of Neurology guidelines on disease-modifying therapies in MS. Neurology 90, 1106–1112 (2018). This article is a concise summary of current controversies in MS treatment decision-making.

    PubMed  Google Scholar 

  116. 116.

    Merkel, B., Butzkueven, H., Traboulsee, A. L., Havrdova, E. & Kalincik, T. Timing of high-efficacy therapy in relapsing-remitting multiple sclerosis: a systematic review. Autoimmun. Rev. 16, 658–665 (2017).

    PubMed  Google Scholar 

  117. 117.

    Patient-Centered Outcomes Research Institute. Examining whether early aggressive therapy can prevent or delay disability in people with multiple sclerosis: the TREAT-MS study. PCORI (2018).

  118. 118.

    Biogen Canada. Tysabri (natalizumab) product monograph. (2016).

  119. 119.

    Singer, B. A. Initiating oral fingolimod treatment in patients with multiple sclerosis. Ther. Adv. Neurol. Disord. 6, 269–275 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. 120.

    Patten, S. B. et al. The relationship between depression and interferon beta-1a therapy in patients with multiple sclerosis. Mult. Scler. 11, 175–181 (2005).

    CAS  PubMed  Google Scholar 

  121. 121.

    Caraccio, N. et al. Long-term follow-up of 106 multiple sclerosis patients undergoing interferon-beta 1a or 1b therapy: predictive factors of thyroid disease development and duration. J. Clin. Endocrinol. Metab. 90, 4133–4137 (2005).

    CAS  PubMed  Google Scholar 

  122. 122.

    Lebrun, C. & Rocher, F. Cancer risk in patients with multiple sclerosis: potential impact of disease-modifying drugs. CNS Drugs 32, 939–949 (2018).

    CAS  PubMed  Google Scholar 

  123. 123.

    Hedstrom, A. K. et al. Smoking and risk of treatment-induced neutralizing antibodies to interferon beta-1a. Mult. Scler. 20, 445–450 (2014).

    PubMed  Google Scholar 

  124. 124.

    Hedstrom, A. K. et al. Smokers run increased risk of developing anti-natalizumab antibodies. Mult. Scler. 20, 1081–1085 (2014).

    CAS  PubMed  Google Scholar 

  125. 125.

    Zhang, T. et al. Examining the effects of comorbidities on disease-modifying therapy use in multiple sclerosis. Neurology 86, 1287–1295 (2016).

    PubMed  PubMed Central  Google Scholar 

  126. 126.

    Thone, J., Thiel, S., Gold, R. & Hellwig, K. Treatment of multiple sclerosis during pregnancy — safety considerations. Expert Opin. Drug Saf. 16, 523–534 (2017). This paper is a thorough review of considerations regarding MS therapy in pregnancy and breastfeeding.

    PubMed  Google Scholar 

  127. 127.

    Thiel, S. et al. Interferon-beta exposure during first trimester is safe in women with multiple sclerosis — a prospective cohort study from the German Multiple Sclerosis and Pregnancy Registry. Mult. Scler. 22, 801–809 (2016).

    CAS  PubMed  Google Scholar 

  128. 128.

    Herbstritt, S. et al. Glatiramer acetate during early pregnancy: a prospective cohort study. Mult. Scler. 22, 810–816 (2016).

    CAS  PubMed  Google Scholar 

  129. 129.

    Ebrahimi, N. et al. Pregnancy and fetal outcomes following natalizumab exposure in pregnancy. A prospective, controlled observational study. Mult. Scler. 21, 198–205 (2015).

    CAS  PubMed  Google Scholar 

  130. 130.

    Haghikia, A. et al. Natalizumab use during the third trimester of pregnancy. JAMA Neurol. 71, 891–895 (2014).

    PubMed  Google Scholar 

  131. 131.

    Karlsson, G. et al. Pregnancy outcomes in the clinical development program of fingolimod in multiple sclerosis. Neurology 82, 674–680 (2014).

    PubMed  PubMed Central  Google Scholar 

  132. 132.

    Sanofi Genzyme Canada. Lemtrada (alemtuzumab) product monograph. (2017).

  133. 133.

    EMD Serono Canada. Mavenclad (cladribine tablets) product mongraph. (2017).

  134. 134.

    Langer-Gould, A. et al. Exclusive breastfeeding and the risk of postpartum relapses in women with multiple sclerosis. Arch. Neurol. 66, 958–963 (2009).

    PubMed  Google Scholar 

  135. 135.

    Poulos, C. et al. A discrete-choice experiment to determine patient preferences for injectable multiple sclerosis treatments in Germany. Ther. Adv. Neurol. Disord. 9, 95–104 (2016).

    PubMed  PubMed Central  Google Scholar 

  136. 136.

    Devonshire, V. et al. The Global Adherence Project (GAP): a multicenter observational study on adherence to disease-modifying therapies in patients with relapsing-remitting multiple sclerosis. Eur. J. Neurol. 18, 69–77 (2011).

    CAS  PubMed  Google Scholar 

  137. 137.

    Giovannoni, G., Southam, E. & Waubant, E. Systematic review of disease-modifying therapies to assess unmet needs in multiple sclerosis: tolerability and adherence. Mult. Scler. 18, 932–946 (2012). This study is an important attempt to identify barriers to DMT use through a systematic review of studies.

    CAS  PubMed  Google Scholar 

  138. 138.

    Fernandez, O. et al. Treatment satisfaction with injectable disease-modifying therapies in patients with relapsing-remitting multiple sclerosis (the STICK study). PLOS ONE 12, e0185766 (2017).

    PubMed  PubMed Central  Google Scholar 

  139. 139.

    Longbrake, E. E., Cross, A. H. & Salter, A. Efficacy and tolerability of oral versus injectable disease-modifying therapies for multiple sclerosis in clinical practice. Mult. Scler. J. Exp. Transl Clin. (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  140. 140.

    Vollmer, B. et al. Discontinuation and comparative effectiveness of dimethyl fumarate and fingolimod in 2 centers. Neurol. Clin. Pract. 8, 292–301 (2018).

    PubMed  PubMed Central  Google Scholar 

  141. 141.

    Hersh, C. M. et al. Comparative efficacy and discontinuation of dimethyl fumarate and fingolimod in clinical practice at 24-month follow-up. Mult. Scler. J. Exp. Transl Clin. (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  142. 142.

    Noussair, C. N., Trautmann, S. T. & Van de Kuilen, G. Higher order risk attitudes, demographics, and financial deicisions. Rev. Econom. Studies 81, 325–355 (2014).

    Google Scholar 

  143. 143.

    Williams, T. & Chataway, J. Immune-mediated encephalitis with daclizumab: the final nail. Mult. Scler. (2018).

    Article  PubMed  Google Scholar 

  144. 144.

    Weideman, A. M., Tapia-Maltos, M. A., Johnson, K., Greenwood, M. & Bielekova, B. Meta-analysis of the age-dependent efficacy of multiple sclerosis treatments. Front. Neurol. 8, 577 (2017).

    PubMed  PubMed Central  Google Scholar 

  145. 145.

    Matell, H. et al. Age-dependent effects on the treatment response of natalizumab in MS patients. Mult. Scler. 21, 48–56 (2015).

    CAS  PubMed  Google Scholar 

  146. 146.

    Hua, L. H., Fan, T. H., Conway, D., Thompson, N. & Kinzy, T. G. Discontinuation of disease-modifying therapy in patients with multiple sclerosis over age 60. Mult. Scler. (2018).

    Article  PubMed  Google Scholar 

  147. 147.

    Corboy, J. R. Disease modifying therapy in the aging multiple sclerosis patient. ECTRIMS Online Library (2017).

  148. 148.

    Ho, P. R. et al. Risk of natalizumab-associated progressive multifocal leukoencephalopathy in patients with multiple sclerosis: a retrospective analysis of data from four clinical studies. Lancet Neurol. 16, 925–933 (2017). This study uses a large data set to update risk stratification for PML in patients with MS on natalizumab.

    CAS  PubMed  Google Scholar 

  149. 149.

    Bloomgren, G. et al. Risk of natalizumab-associated progressive multifocal leukoencephalopathy. N. Engl. J. Med. 366, 1870–1880 (2012).

    CAS  PubMed  Google Scholar 

  150. 150.

    Schwab, N. et al. PML risk stratification using anti-JCV antibody index and L-selectin. Mult. Scler. 22, 1048–1060 (2016).

    CAS  PubMed  Google Scholar 

  151. 151.

    Pignolet, B. et al. CD62L test at 2 years of natalizumab predicts progressive multifocal leukoencephalopathy. Neurology 87, 2491–2494 (2016).

    PubMed  Google Scholar 

  152. 152.

    McGuigan, C. et al. Stratification and monitoring of natalizumab-associated progressive multifocal leukoencephalopathy risk: recommendations from an expert group. J. Neurol. Neurosurg. Psychiatry 87, 117–125 (2016).

    CAS  PubMed  Google Scholar 

  153. 153.

    Oshima, Y., Tanimoto, T., Yuji, K. & Tojo, A. Drug-associated progressive multifocal leukoencephalopathy in multiple sclerosis patients. Mult. Scler. (2018).

    Article  PubMed  Google Scholar 

  154. 154.

    Winkelmann, A., Loebermann, M., Reisinger, E. C., Hartung, H. P. & Zettl, U. K. Disease-modifying therapies and infectious risks in multiple sclerosis. Nat. Rev. Neurol. 12, 217–233 (2016).

    CAS  PubMed  Google Scholar 

  155. 155.

    Cohen, J. A. et al. Oral fingolimod or intramuscular interferon for relapsing multiple sclerosis. N. Engl. J. Med. 362, 402–415 (2010).

    CAS  PubMed  Google Scholar 

  156. 156.

    Achtnichts, L., Obreja, O., Conen, A., Fux, C. A. & Nedeltchev, K. Cryptococcal meningoencephalitis in a patient with multiple sclerosis treated with fingolimod. JAMA Neurol. 72, 1203–1205 (2015).

    PubMed  Google Scholar 

  157. 157.

    Rau, D. et al. Listeria meningitis complicating alemtuzumab treatment in multiple sclerosis — report of two cases. Int. J. Mol. Sci. 16, 14669–14676 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  158. 158.

    Kowalec, K., Carleton, B. & Tremlett, H. The potential role of pharmacogenomics in the prevention of serious adverse drug reactions in multiple sclerosis. Mult. Scler. Relat. Disord. 2, 183–192 (2013).

    PubMed  Google Scholar 

  159. 159.

    Cossburn, M. et al. Autoimmune disease after alemtuzumab treatment for multiple sclerosis in a multicenter cohort. Neurology 77, 573–579 (2011).

    CAS  PubMed  Google Scholar 

  160. 160.

    Havrdova, E., Cohen, J. A., Horakova, D., Kovarova, I. & Meluzinova, E. Understanding the positive benefit:risk profile of alemtuzumab in relapsing multiple sclerosis: perspectives from the Alemtuzumab Clinical Development Program. Ther. Clin. Risk Manag. 13, 1423–1437 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  161. 161.

    Wingerchuk, D. M. & Weinshenker, B. G. Disease modifying therapies for relapsing multiple sclerosis. BMJ 354, i3518 (2016).

    PubMed  Google Scholar 

  162. 162.

    Roche Canada. Ocrevus [ocrelizumab] product monograph. Roche Canada (2018).

  163. 163.

    Gitto, L. in Multiple Sclerosis: Perspectives in Treatment and Pathogenesis (eds Zagon, I. S. & McLaughlin, P. J.) (Codon Publications, 2017).

  164. 164.

    Hartung, D. M., Bourdette, D. N., Ahmed, S. M. & Whitham, R. H. The cost of multiple sclerosis drugs in the US and the pharmaceutical industry: too big to fail? Neurology 84, 2185–2192 (2015).

    PubMed  PubMed Central  Google Scholar 

  165. 165.

    Fox, R. J. et al. Characterizing absolute lymphocyte count profiles in dimethyl fumarate-treated patients with MS: Patient management considerations. Neurol. Clin. Pract. 6, 220–229 (2016).

    PubMed  PubMed Central  Google Scholar 

  166. 166.

    Nagy, S. Lymphocyte recovery in real life clinical practice after discontinuation of fingolimod in patients with multiple sclerosis. ECTRIMS Online Library (2017).

  167. 167.

    Chan, A., de Seze, J. & Comabella, M. Teriflunomide in patients with relapsing-remitting forms of multiple sclerosis. CNS Drugs 30, 41–51 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  168. 168.

    West, T. W. & Cree, B. A. Natalizumab dosage suspension: are we helping or hurting? Ann. Neurol. 68, 395–399 (2010).

    PubMed  Google Scholar 

  169. 169.

    Hatcher, S. E., Waubant, E., Nourbakhsh, B., Crabtree-Hartman, E. & Graves, J. S. Rebound syndrome in patients with multiple sclerosis after cessation of fingolimod treatment. JAMA Neurol. 73, 790–794 (2016).

    PubMed  Google Scholar 

  170. 170.

    Vollmer, B. et al. The impact of very short transition times on switching from natalizumab to fingolimod on imaging and clinical effectiveness outcomes in multiple sclerosis. J. Neurol. Sci. 390, 89–93 (2018).

    CAS  PubMed  Google Scholar 

  171. 171.

    Freedman, M. S., Selchen, D., Prat, A. & Giacomini, P. S. Managing multiple sclerosis: treatment initiation, modification, and sequencing. Can. J. Neurol. Sci. 45, 489–503 (2018). This review offers insight into treatment sequencing strategies.

    PubMed  Google Scholar 

  172. 172.

    Christou, E. A. A., Giardino, G., Worth, A. & Ladomenou, F. Risk factors predisposing to the development of hypogammaglobulinemia and infections post-rituximab. Int. Rev. Immunol. 36, 352–359 (2017).

    CAS  PubMed  Google Scholar 

  173. 173.

    Tur, C. et al. Assessing treatment outcomes in multiple sclerosis trials and in the clinical setting. Nat. Rev. Neurol. 14, 75–93 (2018).

    PubMed  Google Scholar 

  174. 174.

    Bermel, R. A. et al. Predictors of long-term outcome in multiple sclerosis patients treated with interferon beta. Ann. Neurol. 73, 95–103 (2013).

    CAS  PubMed  Google Scholar 

  175. 175.

    Rio, J. et al. Relationship between MRI lesion activity and response to IFN-beta in relapsing-remitting multiple sclerosis patients. Mult. Scler. 14, 479–484 (2008). This paper presents one of the first attempts to devise a score combining early clinical and MRI activity to predict future disability outcomes on MS therapy.

    CAS  PubMed  Google Scholar 

  176. 176.

    Sormani, M. P. et al. Assessing response to interferon-beta in a multicenter dataset of patients with MS. Neurology 87, 134–140 (2016).

    CAS  PubMed  Google Scholar 

  177. 177.

    Rio, J. et al. Measures in the first year of therapy predict the response to interferon beta in MS. Mult. Scler. 15, 848–853 (2009).

    CAS  PubMed  Google Scholar 

  178. 178.

    Sormani, M. P. et al. Scoring treatment response in patients with relapsing multiple sclerosis. Mult. Scler. 19, 605–612 (2013). This study uses modelling to develop a score to predict later disability outcomes on therapy.

    CAS  PubMed  Google Scholar 

  179. 179.

    Rio, J. et al. Disability progression markers over 6–12 years in interferon-beta-treated multiple sclerosis patients. Mult. Scler. 24, 322–330 (2018).

    PubMed  Google Scholar 

  180. 180.

    Rotstein, D. L., Healy, B. C., Malik, M. T., Chitnis, T. & Weiner, H. L. Evaluation of no evidence of disease activity in a 7-year longitudinal multiple sclerosis cohort. JAMA Neurol. 72, 152–158 (2015). This cohort study investigates the predictive value and sustainability of NEDA.

    PubMed  Google Scholar 

  181. 181.

    Kappos, L. et al. Inclusion of brain volume loss in a revised measure of ‘no evidence of disease activity’ (NEDA-4) in relapsing-remitting multiple sclerosis. Mult. Scler. 22, 1297–1305 (2016). This article presents a revision to the NEDA definition incorporating brain volume change.

    PubMed  Google Scholar 

  182. 182.

    Jacobs, B. M., Giovannoni, G. & Schmierer, K. No evident disease activity — more than a risky ambition? JAMA Neurol. 75, 781–782 (2018).

    PubMed  Google Scholar 

  183. 183.

    University of California, San Francisco MS-EPIC Team. Long-term evolution of multiple sclerosis disability in the treatment era. Ann. Neurol. 80, 499–510 (2016).

    Google Scholar 

  184. 184.

    Freedman, M. S. et al. Treatment optimization in MS: Canadian MS Working Group updated recommendations. Can. J. Neurol. Sci. 40, 307–323. (2013).

    Google Scholar 

  185. 185.

    Gunnarsson, M. et al. Axonal damage in relapsing multiple sclerosis is markedly reduced by natalizumab. Ann. Neurol. 69, 83–89 (2011).

    CAS  PubMed  Google Scholar 

  186. 186.

    Kuhle, J. et al. Fingolimod and CSF neurofilament light chain levels in relapsing-remitting multiple sclerosis. Neurology 84, 1639–1643 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  187. 187.

    Bhan, A. et al. Neurofilaments and 10-year follow-up in multiple sclerosis. Mult. Scler. 24, 1301–1307 (2018).

    PubMed  Google Scholar 

  188. 188.

    Varhaug, K. N. et al. Neurofilament light chain predicts disease activity in relapsing-remitting MS. Neurol. Neuroimmunol. Neuroinflamm. 5, e422 (2018).

    PubMed  Google Scholar 

  189. 189.

    Sormani, M. P. Including blood neurofilament light chain in the NEDA concept in relapsing–remitting multiple sclerosis trials. Neurology 90 (Suppl. 15), S24.007 (2018).

    Google Scholar 

  190. 190.

    Kuhle, J. et al. Blood neurofilament light chain as a biomarker of MS disease activity and treatment response. Neurology (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  191. 191.

    Romme Christensen, J. et al. CSF inflammatory biomarkers responsive to treatment in progressive multiple sclerosis capture residual inflammation associated with axonal damage. Mult. Scler. (2018).

    Article  PubMed  Google Scholar 

  192. 192.

    Kappos, L. Neurofilament light levels in the blood of patients with secondary progressive MS are higher than in primary progressive MS and may predict brain atrophy in both MS subtypes. ECTRIMS Online Library (2018).

  193. 193.

    Ratchford, J. N. et al. Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning. Neurology 80, 47–54 (2013).

    PubMed  PubMed Central  Google Scholar 

  194. 194.

    Suhs, K. W., Hein, K., Pehlke, J. R., Kasmann-Kellner, B. & Diem, R. Retinal nerve fibre layer thinning in patients with clinically isolated optic neuritis and early treatment with interferon-beta. PLOS ONE 7, e51645 (2012).

    PubMed  PubMed Central  Google Scholar 

  195. 195.

    Nolan, R., Gelfand, J. M. & Green, A. J. Fingolimod treatment in multiple sclerosis leads to increased macular volume. Neurology 80, 139–144 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  196. 196.

    Button, J. et al. Disease-modifying therapies modulate retinal atrophy in multiple sclerosis: a retrospective study. Neurology 88, 525–532 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

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The authors acknowledge P. Mulero for her assistance with the literature review.

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Nature Reviews Neurology thanks R. Bergamaschi, V. Martinelli and P. Vermersch for their contribution to the peer review of this work.

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D.R. wrote the manuscript. D.R. and X.M. contributed equally to the conception of this work, literature review and revisions to the manuscript.

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Correspondence to Xavier Montalban.

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D.R. has served as a speaker or consultant for Biogen, EMD Serono, Novartis, Roche and Sanofi-Aventis. She has received research support from the Multiple Sclerosis Society of Canada and the Consortium of Multiple Sclerosis Centers (CMSC). X.M. has received speaking honoraria and travel expenses for scientific meetings and has been a steering committee member of clinical trials or participated in advisory boards of clinical trials in the past 3 years with Actelion, Biogen, Celgene, EXCEMED, Genentech, Genzyme, Merck Serono, the Multiple Sclerosis International Federation, the National Multiple Sclerosis Society, Novartis, Roche, Sanofi-Aventis and Teva.

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Rotstein, D., Montalban, X. Reaching an evidence-based prognosis for personalized treatment of multiple sclerosis. Nat Rev Neurol 15, 287–300 (2019).

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