Cortical lesions at diagnosis predict long-term cognitive impairment in multiple sclerosis: A 20-year study.
MRI
cognitive impairment
cortical lesions
multiple sclerosis
predictive marker
Journal
European journal of neurology
ISSN: 1468-1331
Titre abrégé: Eur J Neurol
Pays: England
ID NLM: 9506311
Informations de publication
Date de publication:
05 2023
05 2023
Historique:
revised:
05
12
2022
received:
28
10
2022
accepted:
18
01
2023
medline:
6
4
2023
pubmed:
25
1
2023
entrez:
24
1
2023
Statut:
ppublish
Résumé
Although cognitive impairment (CI) is frequent in multiple sclerosis (MS) patients, few studies (and with conflicting results) have evaluated early predictors of CI in the long term. We aimed at determining associations between early clinical/neuroradiological variables with reference to CI after 20 years of MS. We investigated in 170 MS patients the relationship between clinical/magnetic resonance imaging (MRI) data at diagnosis and cognitive status almost 20 years after MS onset. Among others, number and volume of both white matter lesions (WMLs) and cortical lesions (CLs) were evaluated at diagnosis and after 2 years. All MS patients were followed over time and underwent a comprehensive neuropsychological assessment at the end of study. Advanced statistical methods (unsupervised cluster analysis and random forest model) were conducted. CI patients showed higher focal cortical pathology at diagnosis compared to cognitively normal subjects (p < 0.001). Volumes of both WMLs and CLs emerged as the MRI metrics most associated with long-term CI. Moreover, number of CLs (especially ≥3) was also strongly associated with long-term CI (≥3 CLs: odds ratio [OR] = 3.7, 95% confidence interval = 1.8-7.5, p < 0.001), more than number of WMLs; the optimal cutoff of three CLs (area under the curve = 0.67, specificity = 75%, sensitivity = 55%) was estimated according to the risk of developing CI. These results highlight the impact of considering both white and gray matter focal damage from early MS stages. Given the low predictive value of WML number and the poor clinical applicability of lesion volume estimation in the daily clinical context, the evaluation of number of CLs could represent a reliable prognostic marker of CI.
Sections du résumé
BACKGROUND AND PURPOSE
Although cognitive impairment (CI) is frequent in multiple sclerosis (MS) patients, few studies (and with conflicting results) have evaluated early predictors of CI in the long term. We aimed at determining associations between early clinical/neuroradiological variables with reference to CI after 20 years of MS.
METHODS
We investigated in 170 MS patients the relationship between clinical/magnetic resonance imaging (MRI) data at diagnosis and cognitive status almost 20 years after MS onset. Among others, number and volume of both white matter lesions (WMLs) and cortical lesions (CLs) were evaluated at diagnosis and after 2 years. All MS patients were followed over time and underwent a comprehensive neuropsychological assessment at the end of study. Advanced statistical methods (unsupervised cluster analysis and random forest model) were conducted.
RESULTS
CI patients showed higher focal cortical pathology at diagnosis compared to cognitively normal subjects (p < 0.001). Volumes of both WMLs and CLs emerged as the MRI metrics most associated with long-term CI. Moreover, number of CLs (especially ≥3) was also strongly associated with long-term CI (≥3 CLs: odds ratio [OR] = 3.7, 95% confidence interval = 1.8-7.5, p < 0.001), more than number of WMLs; the optimal cutoff of three CLs (area under the curve = 0.67, specificity = 75%, sensitivity = 55%) was estimated according to the risk of developing CI.
CONCLUSIONS
These results highlight the impact of considering both white and gray matter focal damage from early MS stages. Given the low predictive value of WML number and the poor clinical applicability of lesion volume estimation in the daily clinical context, the evaluation of number of CLs could represent a reliable prognostic marker of CI.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1378-1388Informations de copyright
© 2023 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.
Références
Browne P, Chandraratna D, Angood C, et al. Atlas of multiple sclerosis 2013: a growing global problem with widespread inequity. Neurology. 2014;83:1022-1024.
Thompson AJ, Banwell BL, Barkhof F, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17:162-173.
Calabrese M, De Stefano N, Atzori M, et al. Detection of cortical inflammatory lesions by double inversion recovery magnetic resonance imaging in patients with multiple sclerosis. Arch Neurol. 2007;64:1416-1422.
De Stefano N, Matthews PM, Filippi M, et al. Evidence of early cortical atrophy in MS: relevance to white matter changes and disability. Neurology. 2003;60:1157-1162.
Gilmore CP, Donaldson I, Bö L, Owens T, Lowe J, Evangelou N. Regional variations in the extent and pattern of grey matter demyelination in multiple sclerosis: a comparison between the cerebral cortex, cerebellar cortex, deep grey matter nuclei and the spinal cord. J Neurol Neurosurg Psychiatry. 2009;80:182-187.
Treaba CA, Herranz E, Barletta VT, et al. The relevance of multiple sclerosis cortical lesions on cortical thinning and their clinical impact as assessed by 7.0-T MRI. J Neurol. 2021;268:2473-2481.
Calabrese M, Poretto V, Favaretto A, et al. Cortical lesion load associates with progression of disability in multiple sclerosis. Brain. 2012;135:2952-2961.
Calabrese M, Romualdi C, Poretto V, et al. The changing clinical course of multiple sclerosis: a matter of gray matter. Ann Neurol. 2013;74:76-83.
Haider L, Prados F, Chung K, et al. Cortical involvement determines impairment 30 years after a clinically isolated syndrome. Brain. 2021;144:1384-1395.
Amato MP, Portaccio E, De Meo E. Understanding the pathophysiology of cognitive changes in MS: a step forward. Mult Scler. 2021;27:4-5.
Chiaravalloti ND, DeLuca J. Cognitive impairment in multiple sclerosis. Lancet Neurol. 2008;7:1139-1151.
Benedict RHB, Amato MP, DeLuca J, Geurts JJG. Cognitive impairment in multiple sclerosis: clinical management, MRI, and therapeutic avenues. Lancet Neurol. 2020;19:860-871.
Sumowski JF, Benedict RHB, Enzinger C, et al. Cognition in multiple sclerosis: state of the field and priorities for the future. Neurology. 2018;90:278-288.
Pitteri M, Magliozzi R, Nicholas R, et al. Cerebrospinal fluid inflammatory profile of cognitive impairment in newly diagnosed multiple sclerosis patients. Mult Scler. 2022;28:768-777.
Di Filippo M, Portaccio E, Mancini A, Calabresi P. Multiple sclerosis and cognition: synaptic failure and network dysfunction. Nat Rev Neurosci. 2018;19:599-609.
Calabrese M, Agosta F, Rinaldi F, et al. Cortical lesions and atrophy associated with cognitive impairment in relapsing-remitting multiple sclerosis. Arch Neurol. 2009;66:1144-1150.
Curti E, Graziuso S, Tsantes E, Crisi G, Granella F. Correlation between cortical lesions and cognitive impairment in multiple sclerosis. Brain Behav. 2018;8:1-8.
Roosendaal SD, Moraal B, Pouwels PJW, et al. Accumulation of cortical lesions in MS: relation with cognitive impairment. Mult Scler. 2009;15:708-714.
Calabrese M, Rinaldi F, Mattisi I, et al. Widespread cortical thinning characterizes patients with MS with mild cognitive impairment. Neurology. 2010;74:321-328.
Tillema JM, Hulst HE, Rocca MA, et al. Regional cortical thinning in multiple sclerosis and its relation with cognitive impairment: a multicenter study. Mult Scler. 2016;22:901-909.
Eijlers AJC, van Geest Q, Dekker I, et al. Predicting cognitive decline in multiple sclerosis: a 5-year follow-up study. Brain. 2018;141:2605-2618.
Filippi M, Preziosa P, Copetti M, et al. Gray matter damage predicts the accumulation of disability 13 years later in MS. Neurology. 2013;81:1759-1767.
Jacobsen C, Zivadinov R, Myhr KM, et al. Brain atrophy and clinical characteristics predicting SDMT performance in multiple sclerosis: a 10-year follow-up study. Mult Scler J Exp Transl Clin. 2021;7:1-10.
Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33:1444-1452.
Amato MP, Portaccio E, Goretti B, et al. The Rao's brief repeatable battery and Stroop test: normative values with age, education and gender corrections in an Italian population. Mult Scler. 2006;12:787-793.
Caffarra P, Vezzadini G, Dieci F, Zonato F, Venneri A. Una versione abbreviata del test di Stroop: dati normativi nella popolazione italiana. Riv Neurol. 2002;12:111-115.
Bottesi G, Ghisi M, Altoè G, Conforti E, Melli G, Sica C. The Italian version of the depression anxiety stress Scales-21; factor structure and psychometric properties on community and clinical samples. Compr Psychiatry. 2015;60:170-181.
Miller DH, Barkhof F, Berry I, Kappos L, Scotti G, Thompson AJ. Magnetic resonance imaging in monitoring the treatment of multiple sclerosis: concerted action guidelines. J Neurol Neurosurg Psychiatry. 1991;54:683-688.
Tintore M, Rovira A, Rìo J, et al. Defining high, medium and low impact prognostic factors for developing multiple sclerosis. Brain. 2015;138:1863-1874.
Filippi M, Preziosa P, Banwell BL, et al. Assessment of lesions on magnetic resonance imaging in multiple sclerosis: practical guidelines. Brain. 2019;142:1858-1875.
Pham DL, Xu C, Prince JL. Current methods in medical image segmentation. Annu Rev Biomed Eng. 2000;2:315-337.
Geurts JJG, Roosendaal SD, Calabrese M, et al. Consensus recommendations for MS cortical lesion scoring using double inversion recovery MRI. Neurology. 2011;76:418-424.
Seewann A, Vrenken H, Kooi EJ, et al. Imaging the tip of the iceberg: visualization of cortical lesions in multiple sclerosis. Mult Scler. 2011;17:1202-1210.
Bouman PM, Steenwijk MD, Pouwels PJW, et al. Histopathology-validated recommendations for cortical lesion imaging in multiple sclerosis. Brain. 2020;143:2988-2997.
Sethi V, Yousry T, Muhlert N, et al. A longitudinal study of cortical grey matter lesion subtypes in relapse-onset multiple sclerosis. J Neurol Neurosurg Psychiatry. 2016;87:750-753.
Kutzelnigg A, Faber-Rod JC, Lucchinetti CF, et al. Widespread demyelination in the cerebellar cortex in multiple sclerosis. Brain Pathol. 2007;17:38-44.
McDonald WI, Compston A, Edan G, et al. Recommended diagnostic criteria for multiple sclerosis: guidelines from the international panel on the diagnosis of multiple sclerosis. Ann Neurol. 2001;50:121-127.
Rocca MA, Amato MP, De Stefano N, et al. Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis. Lancet Neurol. 2015;14:302-317.
Sperling RA, Guttmann CR, Hohol MJ, et al. Regional magnetic resonance imaging lesion burden and cognitive function in multiple sclerosis: a longitudinal study. Arch Neurol. 2001;58:115-121.
Summers M, Swanton J, Fernando K, et al. Cognitive impairment in multiple sclerosis can be predicted by imaging early in the disease. J Neurol Neurosurg Psychiatry. 2008;79:955-958.
Paul F. Pathology and MRI: exploring cognitive impairment in MS. Acta Neurol Scand. 2016;134:24-33.
Papadopoulou A, Müller-Lenke N, Naegelin Y, et al. Contribution of cortical and white matter lesions to cognitive impairment in multiple sclerosis. Mult Scler. 2013;19:1290-1296.
Engl C, Tiemann L, Grahl S, et al. Cognitive impairment in early MS: contribution of white matter lesions, deep grey matter atrophy, and cortical atrophy. J Neurol. 2020;267:2307-2318.
Brownlee WJ, Altmann DR, Prados F, et al. Early imaging predictors of long-term outcomes in relapse-onset multiple sclerosis. Brain. 2019;142:2276-2287.
Winkelmann A, Engel C, Apel A, Zettl UK. Cognitive impairment in multiple sclerosis. J Neurol. 2008;255:309-310.
Migliore S, Ghazaryan A, Simonelli I, et al. Cognitive impairment in relapsing-remitting multiple sclerosis patients with very mild clinical disability. Behav Neurol. 2017;1:1-10.
Lopez-Soley E, Martinez-Heras E, Andorra M, et al. Dynamics and predictors of cognitive impairment along the disease course in multiple sclerosis. J Pers Med. 2021;11:1-11.
Storelli L, Azzimonti M, Gueye M, et al. A deep learning approach to predicting disease progression in multiple sclerosis using magnetic resonance imaging. Investig Radiol. 2022;57:423-432.
Geurts JJG, Calabrese M, Fisher E, Rudick RA. Measurement and clinical effect of grey matter pathology in multiple sclerosis. Lancet Neurol. 2012;11:1082-1092.
Chen MH, Goverover Y, Genova HM, DeLuca J. Cognitive efficacy of pharmacological treatments in multiple sclerosis: a systematic review. CNS Drugs. 2020;34:599-628.