Silent progression in disease activity-free relapsing multiple sclerosis

University of California, San Francisco MS-EPIC Team, Bruce A C Cree, Jill A Hollenbach, Riley Bove, Gina Kirkish, Simone Sacco, Eduardo Caverzasi, Antje Bischof, Tristan Gundel, Alyssa H Zhu, Nico Papinutto, William A Stern, Carolyn Bevan, Andrew Romeo, Douglas S Goodin, Jeffrey M Gelfand, Jennifer Graves, Ari J Green, Michael R Wilson, Scott S Zamvil, Chao Zhao, Refujia Gomez, Nicholas R Ragan, Gillian Q Rush, Patrick Barba, Adam Santaniello, Sergio E Baranzini, Jorge R Oksenberg, Roland G Henry, Stephen L Hauser, University of California, San Francisco MS-EPIC Team, Bruce A C Cree, Jill A Hollenbach, Riley Bove, Gina Kirkish, Simone Sacco, Eduardo Caverzasi, Antje Bischof, Tristan Gundel, Alyssa H Zhu, Nico Papinutto, William A Stern, Carolyn Bevan, Andrew Romeo, Douglas S Goodin, Jeffrey M Gelfand, Jennifer Graves, Ari J Green, Michael R Wilson, Scott S Zamvil, Chao Zhao, Refujia Gomez, Nicholas R Ragan, Gillian Q Rush, Patrick Barba, Adam Santaniello, Sergio E Baranzini, Jorge R Oksenberg, Roland G Henry, Stephen L Hauser

Abstract

Objective: Rates of worsening and evolution to secondary progressive multiple sclerosis (MS) may be substantially lower in actively treated patients compared to natural history studies from the pretreatment era. Nonetheless, in our recently reported prospective cohort, more than half of patients with relapsing MS accumulated significant new disability by the 10th year of follow-up. Notably, "no evidence of disease activity" at 2 years did not predict long-term stability. Here, we determined to what extent clinical relapses and radiographic evidence of disease activity contribute to long-term disability accumulation.

Methods: Disability progression was defined as an increase in Expanded Disability Status Scale (EDSS) of 1.5, 1.0, or 0.5 (or greater) from baseline EDSS = 0, 1.0-5.0, and 5.5 or higher, respectively, assessed from baseline to year 5 (±1 year) and sustained to year 10 (±1 year). Longitudinal analysis of relative brain volume loss used a linear mixed model with sex, age, disease duration, and HLA-DRB1*15:01 as covariates.

Results: Relapses were associated with a transient increase in disability over 1-year intervals (p = 0.012) but not with confirmed disability progression (p = 0.551). Relative brain volume declined at a greater rate among individuals with disability progression compared to those who remained stable (p < 0.05).

Interpretation: Long-term worsening is common in relapsing MS patients, is largely independent of relapse activity, and is associated with accelerated brain atrophy. We propose the term silent progression to describe the insidious disability that accrues in many patients who satisfy traditional criteria for relapsing-remitting MS. Ann Neurol 2019;85:653-666.

Conflict of interest statement

Companies that make MS disease‐modifying therapies mentioned in this article include Bayer, Biogen, EMD Serono, Novartis, Pfizer, F. Hoffman La Roche, Sanofi Genzyme, and Teva. The following authors disclosed financial relationships with these companies. B.A.C.C.: consultancy, Biogen, EMD Serono, Novartis. R.B.: consultancy, F. Hoffmann‐La Roche, Novartis, Sanofi Genzyme. J.M.G.: consultancy, Biogen. J.G.: speaker honoraria, Biogen, Sanofi Genzyme. D.S.G.: consultancy, Novartis; speaker honoraria, EMD, Serono, Novartis, Sanofi Genzyme. M.R.W.: grant support, F. Hoffman La Roche, Genentech. A.J.G.: consultancy, Novartis. S.S.Z.: consultancy, Biogen, F. Hoffman La Roche, Novartis, Sanofi Genzyme, Teva; grant support, Biogen, Teva. S.E.B.: speaker honoraria, Bayer, Biogen, EMD Serono, Novartis, Pfizer, F. Hoffman La Roche, Sanofi Genzyme, Teva. R.G.H.: grant support, F. Hoffman La Roche, Sanofi Genzyme; advisory boards, F. Hoffman La Roche, Novartis; educational programs, Sanofi Genzyme, Teva. S.L.H., travel reimbursement and writing assistance, F. Hoffmann‐La Roche for CD20‐related meetings and presentations. The other authors have nothing to report.

© 2019 The Authors. Annals of Neurology published by Wiley Periodicals, Inc. on behalf of American Neurological Association.

Figures

Figure 1
Figure 1
Factors that contribute to or correlate with relapse occurrence and the subsequent impact of relapses on disability. Check marks indicate significant associations, and x marks indicate that associations were not identified. 9HPT = 9‐Hole Peg Test; EDSS = Expanded Disability Status Scale; MRI = magnetic resonance imaging; PASAT = Paced Auditory Serial Addition Test; SDMT = Symbol Digit Modalities Test; T25FW = Timed 25‐Foot Walk.
Figure 2
Figure 2
Factor analysis for mixed data clustering individuals by shared clinical and genetic attributes (from Table 3) that contribute to relapse frequency. Participants appear to cluster together based on annual relapse frequency. Participants with no relapses cluster separately from participants with more than one relapse. Even participants with a single relapse appear to cluster together as a subset of participants with no relapses.
Figure 3
Figure 3
Relative brain atrophy is attenuated in clinically stable patients. Longitudinal response plots show the impact of relapses and disability on relative brain volume loss. Plots of individual data are depicted in addition to the regression lines that are adjusted for covariates (sex, disease duration, age, and HLA‐DRB1*15:01). CSF = cerebrospinal fluid.

References

    1. Polman CH, Reingold SC, Banwell B, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 2011;69:292–302.
    1. Kappos L, Moeri D, Radue EW, 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 1999;353:964–969.
    1. Lublin FD, Baier M, Cutter G. Effect of relapses on development of residual deficit in multiple sclerosis. Neurology 2003;61:1528–1532.
    1. Confavreux C, Vukusic S, Moreau T, et al. Relapses and progression of disability in multiple sclerosis. N Engl J Med 2000;343:1430–1438.
    1. Confavreux C, Vukusic S, Adeleine P. Early clinical predictors and progression of irreversible disability in multiple sclerosis: an amnesic process. Brain 2003;126(pt 4):770–782.
    1. Tremlett H, Yousefi M, Devonshire V, et al. Impact of multiple sclerosis relapses on progression diminishes with time. Neurology 2009;73:1616–1623.
    1. Leray E, Yaouanq J, Le Page E, et al. Evidence for a two‐stage disability progression in multiple sclerosis. Brain 2010;133:1900–1913.
    1. Weinshenker BG, Bass B, Rice GP, et al. The natural history of multiple sclerosis: a geographically based study. 2. Predictive value of the early clinical course. Brain 1989;112:1419–1428.
    1. Runmarker B, Andersen O. Prognostic factors in a multiple sclerosis incidence cohort with twenty‐five years of follow‐up. Brain 1993;116:117–134.
    1. 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 2003;9:260–274.
    1. Scalfari A, Neuhaus A, Degenhardt A, et al. The natural history of multiple sclerosis: a geographically based study 10: relapses and long‐term disability. Brain 2010;133:1914–1929.
    1. Scott TF, Schramke CJ. Poor recovery after the first two attacks of multiple sclerosis is associated with poor outcome five years later. J Neurol Sci 2010;292:52–56.
    1. Jokubaitis VG, Spelman T, Kalincik T, et al. Predictors of long‐term disability accrual in relapse‐onset multiple sclerosis. Ann Neurol 2016;80:89–100.
    1. Scott TF. Understanding the impact of relapses in the overall course of MS; refinement of the 2 stage natural history model. J Neuroimmunol 2017;305:162–166.
    1. Brex PA, Ciccarelli O, O'Riordan JI, et al. A longitudinal study of abnormalities on MRI and disability from multiple sclerosis. N Eng J Med 2002;346:158–164.
    1. Fisniku LK, Brex PA, Altmann DR, et al. Disability and T2 MRI lesions: a 20‐year follow‐up of patients with relapse onset of multiple sclerosis. Brain 2008;131:808–817.
    1. Barkhof F. MRI in multiple sclerosis: correlation with expanded disability status scale (EDSS). Mult Scler 1999;5:283–286.
    1. Barkhof F. The clinico‐radiological paradox in multiple sclerosis revisited. Curr Opin Neurol 2002;15:239–245.
    1. Ford C, Goodman AD, Johnson K, et al. Continuous long‐term immunomodulatory therapy in relapsing multiple sclerosis: results from the 15‐year analysis of the US prospective open‐label study of glatiramer acetate. Mult Scler 2010;16:342–350.
    1. Bermel RA, Weinstock‐Guttman B, Bourdette D, et al. Intramuscular interferon beta‐1a therapy in patients with relapsing‐remitting multiple sclerosis: a 15‐year follow‐up study. Mult Scler 2010;16:588–596.
    1. Kinkel RP, Dontchev M, Kollman C, et al. Association between immediate initiation of intramuscular interferon beta‐1a at the time of a clinically isolated syndrome and long‐term outcomes: a 10‐year follow‐up of the Controlled High‐Risk Avonex Multiple Sclerosis Prevention Study in Ongoing Neurological Surveillance. Arch Neurol 2012;69:183–190.
    1. Kappos L, Kuhle J, Multanen J, et al. Factors influencing long‐term outcomes in relapsing‐remitting multiple sclerosis: PRISMS‐15. J Neurol Neurosurg Psychiatry 2015;86:1202–1207.
    1. Kuhle J, Hardmeier M, Disanto G, et al. A 10‐year follow‐up of the European multicenter trial of interferon β‐1b in secondary‐progressive multiple sclerosis. Mult Scler 2016;22:533–543.
    1. Kappos L, Edan G, Freedman MS, et al. The 11‐year long‐term follow‐up study from the randomized BENEFIT CIS trial. Neurology 2016;87:978–987.
    1. O'Connor P, Comi G, Freedman MS, et al. Long‐term safety and efficacy of teriflunomide: nine‐year follow‐up of the randomized TEMSO study. Neurology 2016;86:920–930.
    1. Cree BA, Gourraud PA, Oksenberg JR, et al. Long‐term evolution of multiple sclerosis disability in the treatment era. Ann Neurol 2016;80:499–510.
    1. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 1983;33:1444–1452.
    1. Barcellos LF, Oksenberg JR, Green AJ, et al. Genetic basis for clinical expression in multiple sclerosis. Brain 2002;125(pt 1):150–158.
    1. Isobe N, Keshavan A, Gourraud PA, et al. Association of HLA genetic risk burden with disease phenotypes in multiple sclerosis. JAMA Neurol 2016;73:795–802.
    1. Schmidt P, Gaser C, Arsic M, et al. An automated tool for detection of FLAIR‐hyperintense white‐matter lesions in multiple sclerosis. Neuroimage 2012;59:3774–3783.
    1. Keshavan A, Datta E, McDonough IM, et al. Mindcontrol: a Web application for brain segmentation quality control. Neuroimage 2018;170:365–372.
    1. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation‐maximization algorithm. IEEE Trans Med Imaging 2001;20:45–57.
    1. Lutkenhoff ES, Rosenberg M, Chiang J, et al. Optimized brain extraction for pathological brains (optiBET). PLoS One 2014;9:e115551.
    1. Lorscheider J, Buzzard K, Jokubaitis V, et al. Defining secondary progressive multiple sclerosis. Brain 2016;139:2395–2405.
    1. De Stefano N, Giorgio A, Battaglini M, et al. Assessing brain atrophy rates in a large population of untreated multiple sclerosis subtypes. Neurology 2010;74:1868–1876.
    1. Tiberio M, Chard DT, Altmann DR, et al. Gray and white matter volume changes in early RRMS: a 2‐year longitudinal study. Neurology 2005;64:1001–1007.
    1. Fisniku LK, Chard DT, Jackson JS, et al. Gray matter atrophy is related to long‐term disability in multiple sclerosis. Ann Neurol 2008;64:247–254.
    1. Filippi M, Rocca MA. MRI evidence for multiple sclerosis as a diffuse disease of the central nervous system. J Neurol 2005;252:Suppl 5:v16–v24.
    1. Filippi M, Rocca MA, Barkhof F, et al. Association between pathological and MRI findings in multiple sclerosis. Lancet Neurol 2012;11:349–360.
    1. Lassmann H. The pathologic substrate of magnetic resonance alterations in multiple sclerosis. Neuroimaging Clin N Am 2008;18:563–576.
    1. Frischer JM, Weigand SD, Guo Y, et al. Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque. Ann Neurol 2015;78:710–721.
    1. Craner MJ, Damarjian TG, Liu S et al. Sodium channels contribute to microglia/macrophage activation and function in EAE and MS. Glia 2005;49:220–229.
    1. Kappos L, De Stefano N, Freedman MS, 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 2016;22:1297–1305.
    1. Kappos L, Butzkueven H, Wiendl H, et al. Greater sensitivity to multiple sclerosis disability worsening and progression events using a roving versus a fixed reference value in a prospective cohort study. Mult Scler 2018;24:963–973.
    1. Kappos L, Wolinsky JS, Giovannoni G, et al. Ocrelizumab reduces disability progression independent of relapse activity in patients with relapsing multiple sclerosis. P654. Paper presented at: ECTRIMS 2017; October 25–28, 2017; Paris, France.
    1. Goodin DS, Traboulsee A, Knappertz V, et al. Relationship between early clinical characteristics and long term disability outcomes: 16 year cohort study (follow‐up) of the pivotal interferon β‐1b trial in multiple sclerosis. J Neurol Neurosurg Psychiatry 2012;83:282–287.
    1. Lublin FD, Cofield SS, Cutter GR, et al. Randomized study combining interferon and glatiramer acetate in multiple sclerosis. Ann Neurol 2013;73:327–340.
    1. Bove R, Chitnis T, Cree BA, et al. SUMMIT (Serially Unified Multicenter Multiple Sclerosis Investigation): creating a repository of deeply phenotyped contemporary multiple sclerosis cohorts. Mult Scler 2018;24:1485–1498.
    1. Schlaeger R, Papinutto N, Panara V, et al. Spinal cord gray matter atrophy correlates with multiple sclerosis disability. Ann Neurol 2014;76:568–580.
    1. Cordano C, Nourbakhsh B, Devereux M, et al. pRNFL as a marker of disability worsening in the medium/long term in patients with MS. Neurol Neuroimmunol Neuroinflamm 2018;6:e533.
    1. Kuhle J, Kropshofer H, Haering DA, et al. Blood neurofilament light chain as a biomarker of MS disease activity and treatment response. Neurology 2019;92:e1007–e1015.

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