Atrophy Patterns in Patients With Multiple Sclerosis With Cognitive Impairment, Fatigue, and Mood Disorders.

Neurology

From the Department of Radiology and Oncology (C.d.M.R., C.C.L.), Hospital das Clínicas da Faculdade de Medicina da Universidade de Sao Paulo (HCFMUSP); LIM 44 (C.d.M.R., M.P.N., I.B.A., C.C.L.), Laboratory of Medical Investigation in Magnetic Resonance of the University of São Paulo, São Paulo, Brazil; MS Center Amsterdam (C.d.M.R., M.M.S.), Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, location VUmc, the Netherlands; Department of Neurology (S.L.A.-P., D.C.), Hospital das Clínicas da Faculdade de Medicina da Universidade de Sao Paulo (HCFMUSP); Department of Epidemiology and Biostatistics (M.B.W.), Faculdade de Medicina, Universidade Federal do Rio Grande do Sul (UFRGS); Department of Epidemiology and Biostatistics (M.B.W.), School of Medicine, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Brazil; MS Center Amsterdam (F.B.), Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC, location VUmc, the Netherlands; and Queen Square MS Center (F.B.), Department of Neuroinflammation, Institute of Neurology, Faculty of Brain Sciences, and Centre for Medical Image Computing (CMIC) (F.B.), Department of Medical Physics and Biomedical Engineering, University College London (UCL), United Kingdom.

Published: December 2024

AI Article Synopsis

  • - The study investigates how cognitive impairment, fatigue, and mood disorders interact in individuals with multiple sclerosis (MS) by examining brain atrophy patterns across different patient profiles.
  • - Researchers categorized MS patients into four clusters based on their cognitive performance, fatigue, anxiety, and depression, using k-means clustering to identify distinct patterns among them.
  • - The findings revealed that cognitive tests and mental health factors significantly impact disability scores, with specific clusters showing correlations between cognitive impairment and levels of fatigue, anxiety, and depression.

Article Abstract

Background And Objectives: Cognitive impairment, fatigue, and mood disorders are common in multiple sclerosis (MS), but their interplay and neurologic substrates are not well understood. This study aims to identify atrophy patterns in patients with MS with varying levels of cognitive impairment, fatigue, anxiety, and depression.

Methods: A cross-sectional cohort study enrolling patients with relapsing-remitting MS meeting the 2017 McDonald criteria and healthy controls (HCs), with similar age, sex, and education, was conducted. Participants completed cognitive assessments across 5 domains and inventories for fatigue, anxiety, and depression. Disability was quantified using the Expanded Disability Status Scale (EDSS). K-means clustering grouped patients with MS based on cognitive performance, fatigue, anxiety, and depression, accounting for disability. Group surface analysis from FreeSurfer, corrected for multiple comparisons, was used to assess cortical atrophy ( < 0.05). Deep gray matter and infratentorial atrophy were defined by a regional volumetric -score <-1.5, with intergroup comparisons using Bayesian multinomial logistic regression (95% CI).

Results: One hundred and two patients with relapsing-remitting MS (67 women; mean age 37 ± 10 years; mean disease duration 9.8 ± 6.5 years; mean EDSS score 2.2 ± 1.73) and 98 HCs (63 women; mean age 36 ± 12 years) were included. K-means clustering identified 4 MS patient clusters: K1 (n = 26): cognitively preserved (CP) without fatigue, anxiety, and depression; K2 (n = 31): CP with fatigue, anxiety, and depression; K3 (n = 18): cognitively impaired (CI) without fatigue, anxiety, and depression; K4 (n = 27): CI with fatigue, anxiety, and depression. Dimension-1 (cognitive tests) explained 34.7% of the variability; Dimension-2 (fatigue, anxiety, and depression) explained 18.7%. EDSS score correlated more with Dimension-2 (loading = 0.32) and was higher in K2 and K4 ( < 0.05). K2 and K3 had similar cortical atrophy, but K3 showed more unilateral hippocampus (log odds = 2.47, = 0.023) and amygdala (log odds = 1.79, = 0.003) atrophy, compared with K1. K4 had widespread bilateral cortical atrophy, with more bilateral thalamus (log odds = 1.28, = 0.023), amygdala (log odds = 2.57, = 0.003), and basal ganglia (log odds = 1.44, = 0.01) atrophies. Cerebellar atrophy was significant in K2 (log odds = 17.68, < 0.001) and K4 (log odds = 18.05, < 0.001), compared with K1.

Discussion: Cognitive impairment, fatigue, anxiety, and depression in patients with MS can coexist or present independently. Our findings suggest a stronger association between cognitive impairment and deep gray matter atrophy while fatigue, anxiety, and depression are more strongly associated with cerebellar atrophy. The accumulation of cognitive impairment, fatigue, anxiety, and depression is associated with increasing global cortical and deep gray matter atrophy.

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Source
http://dx.doi.org/10.1212/WNL.0000000000210080DOI Listing

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