Multifactorial Model of Predictors for the Development of Depressive Disorders Following Conversion of Clinically Isolated Syndrome to Definite Multiple Sclerosis: A Longitudinal Prospective Study

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Abstract

BACKGROUND: The conversion rate of clinically isolated syndrome to multiple sclerosis may reach up to 50%. Irreversible brain damage may occur following a clinically isolated syndrome episode. However, it has not been considered as a risk factor for the development of mental disorders in patients with multiple sclerosis.

AIM: The work was aimed to develop a multifactorial model of predictors for the development of depressive disorders in patients with multiple sclerosis and a history of clinically isolated syndrome, considering sociodemographic, clinical and psychopathological, as well as clinical and functional characteristics.

METHODS: The following assessment tools were used: the Spielberger–Khanin Anxiety Scale, MFI-20 (Multidimensional Fatigue Inventory of 20 items), Beck Depression Inventory, visual analog scale for pain, PASAT (Paced Auditory Serial Addition Test), and EDSS (Expanded Disability Status Scale). Significant stressful life events, clinical course of multiple sclerosis, comorbid conditions, and magnetic resonance imaging findings were also considered. Depression was diagnosed based on the criteria of the 10th revision of the International Classification of Diseases. The analysis of variance and multiple linear regression equations were applied to develop multifactorial models of depression predictors. The study was conducted over a 10-year period.

RESULTS: The main group included 30 patients with multiple sclerosis and a history of clinically isolated syndrome who developed depression. The control group included 30 patients with multiple sclerosis and a history of clinically isolated syndrome without depression. The multifactorial model of predictors for depression demonstrated a high multiple correlation coefficient (r = 0.85). The following predictors had a pronounced impact on the development of depression: asthenia 60.6 ± 1.1 points on the MFI-20 scale, with an annual increase by 1.38 points (Beta = 0.733); an annual increase in the volume of existing brain lesions by 0.74% (Beta = 0.663); and anxiety measured using the Spielberger–Khanin scale (trait anxiety: 42.73 ± 0.43; state anxiety: 41.16 ± 0.41, with an annual increase in state anxiety by 1.43%; Beta = 0.622). Statistically significant but less influential predictors included female sex, secondary education, absence of family, history of major stressful life events, autoimmune diseases, predominant lesion localization in the frontal and temporal lobes of the right hemisphere, history of visual disturbances (optic neuritis), cognitive impairment (with an annual increase in the PASAT score of 2.47%), and elevated body mass index (with an annual increase by 1.67%).

CONCLUSION: A multifactorial model has been developed to support a personalized approach to providing specialized medical care for patients with clinically isolated syndrome converting to multiple sclerosis, based on the prediction of depressive disorder development.

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About the authors

Ksenya V. Gubskaia

Ivanovo State Medical University

Author for correspondence.
Email: dr.gubskaia@ya.ru
ORCID iD: 0009-0007-6952-2367

MD, Cand. Sci. (Med.), Assistant Professor at the Department of Psychiatry, Addiction Medicine and Psychotherapy, Institute of Postgraduate Education

Russian Federation, 8 Sheremetevsky ave, Ivanovo, 153012

Yaroslav V. Malygin

Lomonosov Moscow State University

Email: malygin-y@yandex.ru
ORCID iD: 0000-0003-4633-6872

MD, Dr. Sci. (Med.), Associate Professor at the Department of Multidisciplinary Clinical Training, Faculty of Fundamental Medicine

Russian Federation, 1 Leninskiye gory, Moscow, 119991

Алексей V. Khudyakov

Ivanovo State Medical University

Email: app237110@yandex.ru
ORCID iD: 0000-0002-1933-7936

MD, Dr. Sci. (Med.), Professor, Head of the Department of Psychiatry, Addiction Medicine and Psychotherapy, Institute of Postgraduate Education

Russian Federation, 8 Sheremetevsky ave, Ivanovo, 153012

Evgeniy N. Odnorob

Ivanovo State Medical University

Email: k0ll3k70rw1n5@gmail.com
ORCID iD: 0009-0009-3189-1305

MD, Postgraduate Student at the Department of Psychiatry, Addiction Medicine and Psychotherapy, Institute of Postgraduate Education

Russian Federation, 8 Sheremetevsky ave, Ivanovo, 153012

References

  1. Barkhof F, Rocca M, Francis G, et al. Validation of diagnostic magnetic resonance imaging criteria for multiple sclerosis and response to interferon beta1a. Ann Neurol. 2003;53(6):718–724. doi: 10.1002/ana.10551
  2. Freedman MS. 'Time is brain' also in multiple sclerosis. Mult Scler. 2009;15(10):1133–1134. doi: 10.1177/1352458509345920
  3. Confavreux C, Vukusic S. Natural history of multiple sclerosis: a unifying concept. Brain. 2006;129(Pt 3):606–616. doi: 10.1093/brain/awl007
  4. Daumer M, Neuhaus A, Morrissey S, et al. MRI as an outcome in multiple sclerosis clinical trials. Neurology. 2009;72(8):705–711. doi: 10.1212/01.wnl.0000336916.38629.43
  5. Lövblad KO, Anzalone N, Dörfler A, et al. MR imaging in multiple sclerosis: review and recommendations for current practice. AJNR Am J Neuroradiol. 2010;31(6):983–989. doi: 10.3174/ajnr.A1906
  6. A new era in the study of multiple sclerosis: views on therapeutic approaches. St. Petersburg: Sweetgroup Press; 2012. 94 р. (In Russ.)
  7. 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(Pt 3):808–817. doi: 10.1093/brain/awm329
  8. Schmidt TE, Yakhno NN. Multiple sclerosis: a guide for doctors. Moscow: MEDpress-inform; 2021. 280 р. (In Russ.)
  9. 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(23):1868–1876. doi: 10.1212/WNL.0b013e3181e24136
  10. Kuhlmann T, Lingfeld G, Bitsch A, et al. Acute axonal damage in multiple sclerosis is most extensive in early disease stages and decreases over time. Brain. 2002;125(Pt 10):2202–2212. doi: 10.1093/brain/awf235
  11. Palladino R, Chataway J, Majeed A, Marrie RA. Interface of multiple sclerosis, depression, vascular disease, and mortality: a population-based matched cohort study. Neurology. 2021;97(13):e1322–e1333. doi: 10.1212/WNL.0000000000012610
  12. Boeschoten RE, Braamse AMJ, Beekman ATF, et al. Prevalence of depression and anxiety in multiple sclerosis: a systematic review and meta-analysis. J Neurol Sci. 2017;372:331–341. doi: 10.1016/j.jns.2016.11.067
  13. Binzer S, McKay KA, Brenner P, et al. Disability worsening among persons with multiple sclerosis and depression: a Swedish cohort study. Neurology. 2019;93(24):e2216–e2223. doi: 10.1212/WNL.0000000000008617
  14. Marrie RA, Reingold S, Cohen J, et al. The incidence and prevalence of psychiatric disorders in multiple sclerosis: a systematic review. Multiple Sclerosis. 2015;21(3):305–317. doi: 10.1177/1352458514564487
  15. Malygin VL, Boyko AN, Konovalova OE, et al. Anxiety and depressive psychopathological characteristics of patients with multiple sclerosis at different stages of disease. S.S. Korsakov Journal of Neurology and Psychiatry. 2019;119(2-2):58–63. doi: 10.17116/jnevro20191192258 EDN: UOFCVE
  16. Gubskaia KV, Malygin YaV, Aleksandrova AYu. Multifactorial model of predictors of the development of depressive disorders in multiple sclerosis: a prospective longitudinal study. Neurology, Neuropsychiatry, Psychosomatics. 2024;16(Suppl. 2):11–17. doi: 10.14412/2074-2711-2024-2S-11-17 EDN: BSTPUH
  17. Hadgkiss EJ, Jelinek GA, Weiland TJ, et al. Methodology of an international study of people with multiple sclerosis recruited through Web 2.0 platforms: demographics, lifestyle, and disease characteristics. Neurol Res Int. 2013;2013:580596. doi: 10.1155/2013/580596
  18. Weiland TJ, De Livera AM, Brown CR, et al. Health outcomes and lifestyle in a sample of people with multiple sclerosis (holism): longitudinal and validation cohorts. Front Neurol. 2018;9:1074. doi: 10.3389/fneur.2018.01074.
  19. Mantero V, Abate L, Balgera R, et al. Clinical application of 2017 McDonald diagnostic criteria for multiple sclerosis. J Clin Neurol. 2018;14(3):387–392. doi: 10.3988/jcn.2018.14.3.387
  20. Riemer F, Skorve E, Pasternak O, et al. Microstructural changes precede depression in patients with relapsing-remitting multiple sclerosis. Commun Med (Lond). 2023;3(1):90. doi: 10.1038/s43856-023-00319-4
  21. Zabad RK, Patten SB, Metz LM. The association of depression with disease course in multiple sclerosis. Neurology. 2005;64(2):359–360. doi: 10.1212/01.WNL.0000149760.64921.AA
  22. Wood B, van der Mei IA, Ponsonby AL, et al. Prevalence and concurrence of anxiety, depression and fatigue over time in multiple sclerosis. Mult Scler. 2013;19(2):217–224. doi: 10.1177/1352458512450351
  23. Martynikhin IA. Epidemiology, features and specific risk factors for depressive spectrum disorders in women. In: Neznanov NG, editor. Women's Mental Health — from Hysteria to a Gender-sensitive approach. St. Petersburg: Alef-Press; 2018. Р. 208–222. (In Russ.)
  24. Williams RM, Turner AP, Hatzakis M Jr, et al. Prevalence and correlates of depression among veterans with multiple sclerosis. Neurology. 2005;64(1):75–80. doi: 10.1212/01.WNL.0000148480.31424.2A
  25. Capuron L, Lasselin J, Castanon N. Role of adiposity-driven inflammation in depressive morbidity. Neuropsychopharmacology. 2017;42(1):115–128. doi: 10.1038/npp.2016.123
  26. Greeke EE, Chua AS, Healy BC, et al. Depression and fatigue in patients with multiple sclerosis. J Neurol Sci. 2017;380:236–241. doi: 10.1016/j.jns.2017.07.047
  27. Simpson S Jr, Tan H, Otahal P, et al. Anxiety, depression and fatigue at 5-year review following CNS demyelination. Acta Neurol Scand. 2016;134(6):403–413. doi: 10.1111/ane.12554

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