FutureMS cohort profile: a Scottish multicentre inception cohort study of relapsing-remitting multiple sclerosis

Patrick K A Kearns, Sarah J Martin, Jessie Chang, Rozanna Meijboom, Elizabeth N York, Yingdi Chen, Christine Weaver, Amy Stenson, Katarzyna Hafezi, Stacey Thomson, Elizabeth Freyer, Lee Murphy, Adil Harroud, Peter Foley, David Hunt, Margaret McLeod, Jonathon O'Riordan, F J Carod-Artal, Niall J J MacDougall, Sergio E Baranzini, Adam D Waldman, Peter Connick, Siddharthan Chandran, Patrick K A Kearns, Sarah J Martin, Jessie Chang, Rozanna Meijboom, Elizabeth N York, Yingdi Chen, Christine Weaver, Amy Stenson, Katarzyna Hafezi, Stacey Thomson, Elizabeth Freyer, Lee Murphy, Adil Harroud, Peter Foley, David Hunt, Margaret McLeod, Jonathon O'Riordan, F J Carod-Artal, Niall J J MacDougall, Sergio E Baranzini, Adam D Waldman, Peter Connick, Siddharthan Chandran

Abstract

Purpose: Multiple sclerosis (MS) is an immune-mediated, neuroinflammatory disease of the central nervous system and in industrialised countries is the most common cause of progressive neurological disability in working age persons. While treatable, there is substantial interindividual heterogeneity in disease activity and response to treatment. Currently, the ability to predict at diagnosis who will have a benign, intermediate or aggressive disease course is very limited. There is, therefore, a need for integrated predictive tools to inform individualised treatment decision making.

Participants: Established with the aim of addressing this need for individualised predictive tools, FutureMS is a nationally representative, prospective observational cohort study of 440 adults with a new diagnosis of relapsing-remitting MS living in Scotland at the time of diagnosis between May 2016 and March 2019.

Findings to date: The study aims to explore the pathobiology and determinants of disease heterogeneity in MS and combines detailed clinical phenotyping with imaging, genetic and biomarker metrics of disease activity and progression. Recruitment, baseline assessment and follow-up at year 1 is complete. Here, we describe the cohort design and present a profile of the participants at baseline and 1 year of follow-up.

Future plans: A third follow-up wave for the cohort has recently begun at 5 years after first visit and a further wave of follow-up is funded for year 10. Longer-term follow-up is anticipated thereafter.

Keywords: epidemiology; internal medicine; multiple sclerosis; neurogenetics; neurology; neuroradiology.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.

Figures

Figure 1
Figure 1
Map of FutureMS participants by approximate location of residence at the time of diagnosis. Participant locations are not precise, located at the population centroid of the nearest SIMD intermediate zone (mean population ~4000). FutureMS cases (purple) are displayed alongside intermediate zone of residence of a random selection of 440 individuals from the SMSR (green). All map positions have latitudinal and longitudinal random noise added to prevent personal identifiability. MS, multiple sclerosis; SIMD, Scottish Index of Multiple Deprivation; SMSR, Scottish Multiple Sclerosis Register.
Figure 2
Figure 2
FutureMS cohort design. 9HPT, Nine Hole Peg Test; BMI, body mass index; BP, blood pressure; CRP, C reactive protein; EDSS, Extended Disability Status Scale; FBC, full blood count; HbA1C, glycosylated haemoglobin A1C; LFT, liver function test panel; MS, multiple sclerosis; OCT, optical coherence tomography; PASAT, paced auditory serial addition test 3; PBMC, peripheral blood mononuclear cells; SDMT, symbol digit modality test; T25W, Timed 25foot walk test; U&E, urea and electrolytes and renal function tests.
Figure 3
Figure 3
Density plots stratifying the cohort at baseline visit. (A) Distribution of EDSS (a measure of physical disability) by smoking status. (B) Evidence of greater burden of depression as detected by PHQ9 in those who are unemployed at baseline. EDSS, Expanded Disability Severity Score.
Figure 4
Figure 4
Physical measures of disability across the cohort at baseline and month 12. 9-HPT is the mean between hands of the mean of two attempts at the 9-HPT with each hand and is a measure of upper limb disability measured in seconds (longer time reflects less dexterity). EDSS is an ordinal scale where higher scores reflect greater disability. MSFC is a continuous scale (z-score) where lower values reflect greater disability, participants who are unable to walk are arbitrarily attributed very low Z-scores for the walking component of their test (−13.7) as per published instructions. This gives a long negative tail to the distribution as the −13.7 is chosen to allow for the cohort to progress in disability with time and still capture variance in walking ability. 9HPT, Nine Hole Peg Test; EDSS, Expanded Disability Severity Score; MSFC, Multiple Sclerosis Functional Composite score.
Figure 5
Figure 5
Individual level change in physical disability between the waves. (A) Outlier participants who have worsened or improved over the course of wave one are compared in B&C for their Fatigue Severity Score (FSS) at baseline and age at diagnosis. Circle size reflects size of difference between MSFC measurements between study visits (squared residual from least squares regression line of MSFC at year 1 on MSFC at year two). Outlier groups defined as above the 90th and below the 10th centile for regression residual. MSFC, Multiple Sclerosis Functional Composite score.
Figure 6
Figure 6
Fatigue Severity Score (FSS), PHQ-9 screening tool for depression, Symbol Digit Modality Tool (SDMT), Paced Serial Addition Tool (PASAT-3). Higher scores on Fatigue Severity Scale indicate worse fatigue. Higher scores on PHQ-9 indicate risk of depression. Higher scores on PASAT and SDMT indicate better performance on cognition testing and less impairment.
Figure 7
Figure 7
Correlation between adjusted measures of MS disease severity (MSSS and ARMSS). Size and colour of the points reflects the patient determined diseases steps a patient reported outcome. In these figures, points are study individuals, and the size and colour of the points are scaled using the PDDS (range 0–7, where 7 is most severe). ARMSS, Age-related Multiple Sclerosis Severity Score; MS, multiple sclerosis; MSSS, Multiple Sclerosis Severity Score; PDDS, Patient Determined Disease Steps.
Figure 8
Figure 8
Clinical and radiological measures at baseline visit stratified by HLA-DRB1*15:01 genotype. FSS, Fatigue Severity Scale; EDSS, Expanded Disability Severity Score; PDDS, Patient Determined Disease Steps; SDMT, symbol digit modality test; WMH, white matter hyperintensity.

References

    1. Compston A, McDonald I, Noseworthy J. McAlpine’s multiple sclerosis. 4th edn. Churchill Livingstone Elsevier, 2006.
    1. Confavreux C, Vukusic S. Natural history of multiple sclerosis: a unifying concept. Brain 2006;129:606–16. 10.1093/brain/awl007
    1. Benedikz J, Stefánsson M, Guomundsson J, et al. . The natural history of untreated multiple sclerosis in Iceland. A total population-based 50 year prospective study. Clin Neurol Neurosurg 2002;104:208–10. 10.1016/S0303-8467(02)00040-9
    1. Kappos L, Li D, Calabresi PA, et al. . Ocrelizumab in relapsing-remitting multiple sclerosis: a phase 2, randomised, placebo-controlled, multicentre trial. Lancet 2011;378:1779–87. 10.1016/S0140-6736(11)61649-8
    1. Hauser SL, Waubant E, Arnold DL, et al. . B-cell depletion with rituximab in relapsing-remitting multiple sclerosis. N Engl J Med 2008;358:676–88. 10.1056/NEJMoa0706383
    1. Comi G, Kappos L, Selmaj KW, et al. . Safety and efficacy of ozanimod versus interferon beta-1a in relapsing multiple sclerosis (SUNBEAM): a multicentre, randomised, minimum 12-month, phase 3 trial. Lancet Neurol 2019;18:1009–20. 10.1016/S1474-4422(19)30239-X
    1. Polman CH, O’Connor PW, Havrdova E, et al. . A randomized, placebo-controlled trial of natalizumab for relapsing multiple sclerosis. N Engl J Med 2006;354:899–910. 10.1056/NEJMoa044397
    1. Khan OA, Tselis AC, Kamholz JA, et al. . A prospective, open-label treatment trial to compare the effect of IFN beta-1a (Avonex), IFNbeta-1b (Betaseron), and glatiramer acetate (Copaxone) on the relapse rate in relapsing-remitting multiple sclerosis. Eur J Neurol 2001;8:141–8. 10.1046/j.1468-1331.2001.00189.x
    1. Giovannoni G, Comi G, Cook S, et al. . A placebo-controlled trial of oral cladribine for relapsing multiple sclerosis. N Engl J Med 2010;362:416–26. 10.1056/NEJMoa0902533
    1. Coles AJ, Twyman CL, Arnold DL, et al. . Alemtuzumab for patients with relapsing multiple sclerosis after disease-modifying therapy: a randomised controlled phase 3 trial. Lancet 2012;380:1829–39. 10.1016/S0140-6736(12)61768-1
    1. Cohen JA, Coles AJ, Arnold DL, et al. . Alemtuzumab versus interferon beta 1A as first-line treatment for patients with relapsing-remitting multiple sclerosis: a randomised controlled phase 3 trial. Lancet 2012;380:1819–28. 10.1016/S0140-6736(12)61769-3
    1. Renoux C. Natural history of multiple sclerosis: long-term prognostic factors. Neurol Clin 2011;29:293–308. 10.1016/j.ncl.2011.01.006
    1. Confavreux C, Vukusic S, Adeleine P. Early clinical predictors and progression of irreversible disability in multiple sclerosis: an amnesic process. Brain 2003;126:770–82. 10.1093/brain/awg081
    1. Tintore M, Rovira Àlex, Río J, et al. . Defining high, medium and low impact prognostic factors for developing multiple sclerosis. Brain 2015;138:1863–74. 10.1093/brain/awv105
    1. Brown FS, Glasmacher SA, Kearns PKA, et al. . Systematic review of prediction models in relapsing remitting multiple sclerosis. PLoS One 2020;15:e0233575. 10.1371/journal.pone.0233575
    1. Hauser SL, Cree BAC. Treatment of multiple sclerosis: a review. Am J Med 2020;133:1380–90. 10.1016/j.amjmed.2020.05.049
    1. Kearns PKA, Paton M, O’Neill M. Regional variation in the incidence rate and sex ratio of multiple sclerosis in Scotland 2010–2017: findings from the Scottish multiple sclerosis register 2019.
    1. Thompson AJ, Banwell BL, Barkhof F, et al. . Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 2018;17:162–73. 10.1016/S1474-4422(17)30470-2
    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. 10.1002/ana.22366
    1. Kremenchutzky M, Rice GPA, Baskerville J, et al. . The natural history of multiple sclerosis: a geographically based study 9: observations on the progressive phase of the disease. Brain 2006;129:584–94. 10.1093/brain/awh721
    1. Tutuncu M, Tang J, Zeid NA, et al. . Onset of progressive phase is an age-dependent clinical milestone in multiple sclerosis. Mult Scler 2013;19:188–98. 10.1177/1352458512451510
    1. Tremlett H, Yinshan Zhao ­, Devonshire V. Natural history of secondary-progressive multiple sclerosis. Mult Scler 2008;14:314–24. 10.1177/1352458507084264
    1. Meijboom R, Wiseman SJ, York EN, et al. . Rationale and design of the brain magnetic resonance imaging protocol for FutureMS: a longitudinal multi-centre study of newly diagnosed patients with relapsing-remitting multiple sclerosis in Scotland. Wellcome Open Res 2022;7:94. 10.12688/wellcomeopenres.17731.1
    1. Hobart J, Lamping D, Fitzpatrick R, et al. . The multiple sclerosis impact scale (MSIS-29): a new patient-based outcome measure. Brain 2001;124:962–73. 10.1093/brain/124.5.962
    1. Richardson LC, Wingo PA, Zack MM, et al. . Health-related quality of life in cancer survivors between ages 20 and 64 years: population-based estimates from the behavioral risk factor surveillance system. Cancer 2008;112:1380–9. 10.1002/cncr.23291
    1. Learmonth YC, Motl RW, Sandroff BM, et al. . Validation of patient determined disease steps (PDDS) scale scores in persons with multiple sclerosis. BMC Neurol 2013;13:37. 10.1186/1471-2377-13-37
    1. Krupp LB, LaRocca NG, Muir-Nash J, et al. . The fatigue severity scale. application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol 1989;46:1121–3. 10.1001/archneur.1989.00520460115022
    1. Spitzer RL, Kroenke K, Williams JBW, et al. . A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med 2006;166:1092–7. 10.1001/archinte.166.10.1092
    1. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001;16:606–13. 10.1046/j.1525-1497.2001.016009606.x
    1. Baecke JA, Burema J, Frijters JE. A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr 1982;36:936–42. 10.1093/ajcn/36.5.936
    1. Cohen RA, Kessler HR, Fischer M. The extended disability status scale (EDSS) as a predictor of impairments of functional activities of daily living in multiple sclerosis. J Neurol Sci 1993;115:132–5. 10.1016/0022-510x(93)90215-k
    1. Polman CH, Rudick RA. The multiple sclerosis functional composite: a clinically meaningful measure of disability. Neurology 2010;74:S8–15. 10.1212/WNL.0b013e3181dbb571
    1. Hagan KA, Munger KL, Ascherio A, et al. . Epidemiology of Major Neurodegenerative Diseases in Women: Contribution of the Nurses’ Health Study. Am J Public Health 2016;106.
    1. Fagnani C, Neale MC, Nisticò L, et al. . Twin studies in multiple sclerosis: a meta-estimation of heritability and environmentality. Mult Scler 2015;21:1404–13. 10.1177/1352458514564492
    1. International Multiple Sclerosis Genetics Consortium, Wellcome Trust Case Control Consortium 2, Sawcer S, et al. . Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 2011;476:214–9. 10.1038/nature10251
    1. Belbasis L, Bellou V, Evangelou E, et al. . Environmental risk factors and multiple sclerosis: an umbrella review of systematic reviews and meta-analyses. Lancet Neurol 2015;14:263–73. 10.1016/S1474-4422(14)70267-4
    1. O'Gorman C, Lucas R, Taylor B. Environmental risk factors for multiple sclerosis: a review with a focus on molecular mechanisms. Int J Mol Sci 2012;13:11718–52. 10.3390/ijms130911718
    1. Hedström AK, Katsoulis M, Hössjer O, et al. . The interaction between smoking and HLA genes in multiple sclerosis: replication and refinement. Eur J Epidemiol 2017;32:909–19. 10.1007/s10654-017-0250-2
    1. Newland P, Starkweather A, Sorenson M. Central fatigue in multiple sclerosis: a review of the literature. J Spinal Cord Med 2016;39:386–99. 10.1080/10790268.2016.1168587
    1. Manjaly Z-M, Harrison NA, Critchley HD, et al. . Pathophysiological and cognitive mechanisms of fatigue in multiple sclerosis. J Neurol Neurosurg Psychiatry 2019;90:642–51. 10.1136/jnnp-2018-320050
    1. Manouchehrinia A, Westerlind H, Kingwell E, et al. . Age related multiple sclerosis severity score: disability ranked by age. Mult Scler 2017;23:1938–46. 10.1177/1352458517690618
    1. Weideman AM, Barbour C, Tapia-Maltos MA, et al. . New multiple sclerosis disease severity scale predicts future accumulation of disability. Front Neurol 2017;8:598. 10.3389/fneur.2017.00598
    1. Petzold A, de Boer JF, Schippling S, et al. . Optical coherence tomography in multiple sclerosis: a systematic review and meta-analysis. Lancet Neurol 2010;9:921–32. 10.1016/S1474-4422(10)70168-X
    1. Dyment DA, Yee IML, Ebers GC, et al. . Multiple sclerosis in stepsiblings: recurrence risk and ascertainment. J Neurol Neurosurg Psychiatry 2006;77:258–9. 10.1136/jnnp.2005.063008
    1. de Bakker PIW, McVean G, Sabeti PC, et al. . A high-resolution HLA and SNP haplotype map for disease association studies in the extended human MHC. Nat Genet 2006;38:1166–72. 10.1038/ng1885
    1. Baranzini SE, Oksenberg JR. The genetics of multiple sclerosis: from 0 to 200 in 50 years. Trends Genet 2017;33:960–70. 10.1016/j.tig.2017.09.004
    1. Cabre P, Signate A, Olindo S, et al. . Role of return migration in the emergence of multiple sclerosis in the French West Indies. Brain 2005;128:2899–910. 10.1093/brain/awh624
    1. Munk Nielsen N, Corn G, Frisch M, et al. . Multiple sclerosis among first- and second-generation immigrants in Denmark: a population-based cohort study. Brain 2019;142:1587–97. 10.1093/brain/awz088
    1. Bramwell B. The relative frequency of disseminated sclerosis in this country (Scotland and the North of England) and in America. Rev Neurol Psychiatry R Coll Physicians Edinburgh 1903;1.
    1. Dean G, Goodall J, Downie A. The prevalence of multiple sclerosis in the outer Hebrides compared with north-east Scotland and the Orkney and Shetland Islands. J Epidemiol Community Health 1981;35:110–3. 10.1136/jech.35.2.110
    1. Rodríguez Cruz PM, Matthews L, Boggild M, et al. . Time- and region-specific season of birth effects in multiple sclerosis in the United Kingdom. JAMA Neurol 2016;73:954–60. 10.1001/jamaneurol.2016.1463
    1. Sutherland JM. Observations on the prevalence of multiple sclerosis in Northern Scotland. Brain 1956;79:635–54. 10.1093/brain/79.4.635
    1. Ebers G. Month of birth and multiple sclerosis risk in Scotland. Eur Neurol 2010;63:41–2. 10.1159/000268164
    1. Poser CM. The dissemination of multiple sclerosis: a viking SAGA? A historical essay. Ann Neurol 1994;36:S231–43. 10.1002/ana.410360810
    1. Donnan PT, Parratt JDE, Wilson SV, et al. . Multiple sclerosis in Tayside, Scotland: detection of clusters using a spatial scan statistic. Mult Scler 2005;11:403–8. 10.1191/1352458505ms1191oa
    1. Taylor R, Illsley R, Poskanzer DC. Multiple sclerosis in the Orkney and Shetland Islands. VI: the effects of migration and social structure. J Epidemiol Comm Health 1980;34:271–6. 10.1136/jech.34.4.271
    1. Mackenzie IS, Morant SV, Bloomfield GA, et al. . Incidence and prevalence of multiple sclerosis in the UK 1990-2010: a descriptive study in the general practice research database. J Neurol Neurosurg Psychiatry 2014;85:76–84. 10.1136/jnnp-2013-305450
    1. Phadke JG, Downie AW. Epidemiology of multiple sclerosis in the north-east (Grampian region) of Scotland-an update. J Epidemiol Community Health 1987;41:5–13. 10.1136/jech.41.1.5
    1. Handel AE, Jarvis L, McLaughlin R, et al. . The epidemiology of multiple sclerosis in Scotland: inferences from hospital admissions. PLoS One 2011;6:e14606. 10.1371/journal.pone.0014606
    1. Visser EM, Wilde K, Wilson JF, et al. . A new prevalence study of multiple sclerosis in Orkney, Shetland and Aberdeen City. J Neurol Neurosurg Psychiatry 2012;83:719–24. 10.1136/jnnp-2011-301546
    1. Shepherd DI, Downie AW. Prevalence of multiple sclerosis in north-east Scotland. Br Med J 1978;2:314–6. 10.1136/bmj.2.6133.314
    1. Rothwell PM, Charlton D. High incidence and prevalence of multiple sclerosis in South East Scotland: evidence of a genetic predisposition. J Neurol Neurosurg Psychiatry 1998;64:730–5. 10.1136/jnnp.64.6.730
    1. Poskanzer DC, Prenney LB, Sheridan JL, et al. . Multiple sclerosis in the Orkney and Shetland Islands. I: epidemiology, clinical factors, and methodology. J Epidemiol Comm Health 1980;34:229–39. 10.1136/jech.34.4.229
    1. International Multiple Sclerosis Genetics Consortium, Hafler DA, Compston A, et al. . Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med 2007;357:851–62. 10.1056/NEJMoa073493
    1. Alcina A, Abad-Grau MDM, Fedetz M, et al. . Multiple sclerosis risk variant HLA-DRB1*1501 associates with high expression of DRB1 gene in different human populations. PLoS One 2012;7:e29819. 10.1371/journal.pone.0029819
    1. Benešová Y, Vašků A, Stourač P, et al. . Association of HLA-DRB1*1501 tagging rs3135388 gene polymorphism with multiple sclerosis. J Neuroimmunol 2013;255:92–6. 10.1016/j.jneuroim.2012.10.014
    1. George MF, Briggs FBS, Shao X, et al. . Multiple sclerosis risk loci and disease severity in 7,125 individuals from 10 studies. Neurol Genet 2016;2:e87. 10.1212/NXG.0000000000000087
    1. Schmidt H, Williamson D, Ashley-Koch A. Human genome epidemiology (huge) review HLA-DR15 haplotype and multiple sclerosis: a huge review. 10.1093/aje/kwk118
    1. Roberts DF, Roberts MJ, Poskanzer DC. Genetic analysis of multiple sclerosis in Shetland. J Epidemiol Community Health 1983;37:281–5. 10.1136/jech.37.4.281
    1. Bihrmann K, Nielsen NM, Magyari M, et al. . Small-scale geographical variation in multiple sclerosis: a case-control study using Danish register data 1971-2013. Mult Scler Relat Disord 2018;23:40–5. 10.1016/j.msard.2018.04.021
    1. Kurtzke JF. Multiple sclerosis in time and space-geographic clues to cause. J Neurovirol 2000;6:S134–40.
    1. Wallin MT, Culpepper WJ, Coffman P, et al. . The Gulf War era multiple sclerosis cohort: age and incidence rates by race, sex and service. Brain 2012;135:1778–85. 10.1093/brain/aws099
    1. van der Mei IA, Ponsonby AL, Blizzard L, et al. . Regional variation in multiple sclerosis prevalence in Australia and its association with ambient ultraviolet radiation. Neuroepidemiology 2001;20:168–74. 10.1159/000054783
    1. Beck CA, Metz LM, Svenson LW, et al. . Regional variation of multiple sclerosis prevalence in Canada. Mult Scler 2005;11:516–9. 10.1191/1352458505ms1192oa
    1. Murray S, Bashir K, Penrice G, et al. . Epidemiology of multiple sclerosis in Glasgow. Scott Med J 2004;49:100–4. 10.1177/003693300404900310
    1. Kearns PKA, Paton M, O'Neill M, et al. . Regional variation in the incidence rate and sex ratio of multiple sclerosis in Scotland 2010-2017: findings from the Scottish multiple sclerosis register. J Neurol 2019;266:2376–86. 10.1007/s00415-019-09413-x
    1. Ascherio A, Munger KL. Environmental risk factors for multiple sclerosis. Part II: noninfectious factors. Ann Neurol 2007;61:504–13. 10.1002/ana.21141
    1. Tod E, Bromley C, Millard AD, et al. . Obesity in Scotland: a persistent inequality. Int J Equity Health 2017;16:135. 10.1186/s12939-017-0599-6
    1. NHS Health Scotland, ISD Scotland . An atlas of Tobaco smoking in Scotland: a report presenting estimated smoking prevalence and smoking-attributable deaths within Scotland, 2007. Available: [Accessed 11 Jan 2019].
    1. Purdon G, Comrie F, Rutherford L. Vitamin D status of Scottish adults: results from the 2010 & 2011 Scottish health surveys 2013.
    1. Hedström AK. Smoking and disability progression in multiple sclerosis. Expert Rev Neurother 2020;20:739–41. 10.1080/14737175.2020.1743176

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