Early cognitive dysfunction as a marker for the poor course of multiple sclerosis: a prospective 12-year follow-up

Owing to the advent of current methods for the prevention of exacerbations of multiple sclerosis (MS), it has become possible to increase the period to the development of obvious persistent cerebellar and motor disorders that mainly lead to disability. This makes it possible to focus on less evident and latent symptoms, in particular on cognitive impairment (CI) that are recorded since the diagnosis of MS and slowly progress over time.

Objective: to assess the prognostic capabilities of the Paced Auditory Serial Addition Test (PASAT) to identify a group of patients with early disability (10-year risk of reaching 6.5 Expanded Disability Status Scale (EDSS) scores.

Patients and methods. The paper presents the data of a 12-year (2005–2018) follow-up of 36 patients having MS with and without mild CI. The patients' mean age at the time of study inclusion was 31.7 years (confidence interval (CI) 29.2–34.1; α<0.05); the disease duration was 4.69 months (CI 3.31–6.08; α<0.05); the EDSS scores averaged 2.51 (CI 2.23–2.82; α<0.05). Severe disability (6.5 EDSS scores) was observed in 75% of cases in the presence of mild CI and in 25% of cases in the absence of mild CI; it occurred an average of 118.3 (CI 93.1–143.4; α<0.05) and 141.2 (CI 126.0–156.5; α<0.05) months later, respectively.

Results and discussion. The findings suggest that the patients with MS in the presence of impaired information processing speed and decreased attentional function (failure to complete more than 25% of the PASAT tasks) had a significantly greater risk for persistent disability than the patients without CI or with its minimal manifestations. Limitation in walking function (4.5 EDSS scores) occurred an average of 3.5 years earlier, and its significant limitation (6.5 EDSS scores) did 2 years earlier in the severe CI group, which may be important in planning therapy.

Conclusion. The presence of CI in early MS is likely to have a prognostic value in relation to the course of the disease. 

1. Eriksson I, Komen J, Piehl F, et al. The changing multiple sclerosis treatment landscape: impact of new drugs and treatment recommendations. Eur J Clin Pharmacol. 2018 May;74(5):663-670. doi: 10.1007/s00228-018-2429-1. Epub 2018 Feb 10.

2. Saposnik G, Montalban X. Therapeutic Inertia in the New Landscape of Multiple Sclerosis Care. Front Neurol. 2018 Mar 20;9:174. doi: 10.3389/fneur.2018.00174. eCollection 2018.

3. Fiest KM, Greenfield J, Metz LM, et al. Discriminative ability of quality of life measures in multiple sclerosis. Health Qual Life Outcomes. 2017 Dec 21;15(1):246. doi: 10.1186/s12955-017-0828-0.

4. Campbell J, Rashid W, Cercignani M, et al. Cognitive impairment among patients with multiple sclerosis: associations with employment and quality of life. Postgrad Med J. 2017 Mar;93(1097):143-147. doi: 10.1136/postgradmedj-2016-134071. Epub 2016 Aug 10.

5. Pflugshaupt T, Geisseler O, Nyffeler T, et al. Cognitive Impairment in Multiple Sclerosis: Clinical Manifestation, Neuroimaging Correlates, and Treatment. Semin Neurol. 2016 Apr;36(2):203-11. doi: 10.1055/s-0036-1579696. Epub 2016 Apr 26.

6. Grzegorski T, Losy J. Cognitive impairment in multiple sclerosis - a review of current knowledge and recent research. Rev Neurosci. 2017 Nov 27;28(8):845-860. doi: 10.1515/revneuro-2017-0011.

7. Korakas N, Tsolaki M. Cognitive Impairment in Multiple Sclerosis: A Review of Neuropsychological Assessments. Cogn Behav Neurol. 2016 Jun;29(2):55-67. doi: 10.1097/WNN.0000000000000097.

8. Rao SM, Leo GJ, Bernardin L, et al. Cognitive dysfunction in multiple sclerosis: I: frequency, patterns, and prediction. Neurology. 1991 May;41(5):685-91.

9. Benedict RH, Cookfair D, Gavett R, et al. Validity of the Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS). J Int Neuropsychol Soc. 2006 Jul;12(4):549-58.

10. Sokolov AA, Miall RC, Ivry RB. The Cerebellum: Adaptive Prediction for Movement and Cognition. Trends Cogn Sci. 2017 May;21(5):313-332. doi: 10.1016/j.tics.2017.02.005. Epub 2017 Apr 3.

11. Megna R, Alfano B, Lanzillo R, et al. Brain tissue volumes and relaxation rates in multiple sclerosis: implications for cognitive impairment. J Neurol. 2019 Feb;266(2):361-368. doi: 10.1007/s00415-018-9139-6. Epub 2018 Nov 29.

12. Forslin Y, Bergendal Å, Hashim F, et al. Detection of Leukocortical Lesions in Multiple Sclerosis and Their Association with Physical and Cognitive Impairment: A Comparison of Conventional and Synthetic Phase-Sensitive Inversion Recovery MRI. AJNR Am J Neuroradiol. 2018 Nov;39(11):1995-2000. doi: 10.3174/ajnr.A5815. Epub 2018 Sep 27.

13. Sumowski JF, Benedict R, Enzinger C, et al. Cognition in multiple sclerosis. Neurology. 2018 Feb 6;90(6):278-288. doi: 10.1212/WNL. 0000000000004977. Epub 2018 Jan 17.

14. Вenedict RH, DeLuca J, Phillips G. Validity of the Symbol Digit Modalities Test as a cognitive performance outcome measure for multiple sclerosis. Mult Scler. 2017 Apr;23(5):721-733. doi: 10.1177/1352458517690821. Epub 2017 Feb 16.

15. Kasatkin DS, Spirin NN. Possible mechanisms of fatigue syndrome formation in multiple sclerosis clinic. Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova. 2006;(3):87-91. (In Russ.).

16. Fuchs TA, Wojcik C, Wilding GE, et al. Trait Conscientiousness predicts rate of longitudinal SDMT decline in multiple sclerosis. Mult Scler. 2019 Jan 7:1352458518820272. doi: 10.1177/1352458518820272. [Epub ahead of print]

17. Meijer KA, Muhlert N, Cercignani M, et al. White matter tract abnormalities are associated with cognitive dysfunction in secondary progressive multiple sclerosis. Mult Scler. 2016 Oct; 22(11):1429-1437. Epub 2016 Jan 5.

18. Bergsland N, Horakova D, Dwyer MG, et al. Gray matter atrophy patterns in multiple sclerosis: A 10-year source-based morphometry study. Neuroimage Clin. 2017 Nov 5;17:444-451. doi: 10.1016/j.nicl.2017.11.002. eCollection 2018.

19. Giovannoni G, Turner B, Gnanapavan S, et al. Is it time to target no evident disease activity (NEDA) in multiple sclerosis? Mult Scler Relat Disord. 2015 Jul;4(4):329-33. doi: 10.1016/j.msard.2015.04.006. Epub 2015 May 8.

20. Conroy RM, Pyö rä lä K, Fitzgerald AP, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003 Jun;24(11):987-1003.

21. 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 Sep;22(10):1297-305. doi: 10.1177/1352458515616701. Epub 2015 Nov 19.

22. Leung KK, Malone IM, Ourselin S. Effects of changing from non-accelerated to accelerated MRI for follow-up in brain atrophy measurement. Neuroimage. 2015 Feb 15;107:46-53. doi: 10.1016/j.neuroimage.2014.11.049. Epub 2014 Dec 4.

23. Coghe G, Fenu G, Lorefice L, et al. Association between brain atrophy and cognitive motor interference in multiple sclerosis. Mult Scler Relat Disord. 2018 Oct;25:208-211. doi: 10.1016/j.msard.2018.07.045. Epub 2018 Jul 31.

24. Cruz-Gomez AJ, Aguirre N, SanchisSegura C, et al. Subcortical grey matter structures in multiple sclerosis: what is their role in cognition? Neuroreport. 2018 May 2;29(7): 547-552. doi: 10.1097/WNR.0000000000000976.

25. Dekker I, Eijlers AJC, Popescu V, et al. Predicting clinical progression in multiple sclerosis after six and twelve years. Eur J Neurol. 2019 Jun;26(6):893-902. doi: 10.1111/ene.13904. Epub 2019 Feb 2.

26. Benedict RH, Duquin JA, Jurgensen S, et al. Repeated assessment of neuropsychological deficits in multiple sclerosis using the Symbol Digit Modalities Test and the MS Neuropsychological Screening Questionnaire. Mult Scler. 2008 Aug;14(7):940-6. doi: 10.1177/1352458508090923. Epub 2008 Jun 23.