Relationship between brain atrophy and disability in a multi-site multiple sclerosis registry
Status PubMed-not-MEDLINE Language English Country England, Great Britain Media electronic-ecollection
Document type Journal Article
PubMed
40734995
PubMed Central
PMC12306358
DOI
10.1136/bmjno-2025-001126
PII: bmjno-2025-001126
Knihovny.cz E-resources
- Keywords
- MRI, MULTIPLE SCLEROSIS,
- Publication type
- Journal Article MeSH
BACKGROUND: In a retrospective multicentre cohort study, we explored the association between brain atrophy and multiple sclerosis (MS) disability using different MRI scanners and protocols at multiple sites. METHODS: Relapse-onset MS patients were included if they had two clinical MRIs 12 months apart and ≥2 Expanded Disability Status Scale (EDSS) scores. Percentage brain volume change (PBVC), percentage grey matter change (PGMC), fluid-attenuated inversion recovery (FLAIR) lesion volume change, whole brain volume (BV), grey matter volume (GMV), FLAIR lesion volume and T1 hypointense lesion volume were assessed by icobrain. Disability was measured by EDSS scores and 6-month confirmed disability progression (CDP). RESULTS: Of the 260 relapse-onset MS patients included, 204 (78%) MRI pairs were performed in the same scanner and 56 (22%) pairs were from different scanners. 93% of patients were on treatment and mean PBVC was -0.26% (±0.52). During the median follow-up of 2.8 years from the second MRI, median EDSS change was 0.0 and 12% patients experienced 6-month CDP. Cross-sectional BV and GMV at the later MRI showed a trend for association with CDP (HR 0.99; 95% CI 0.98 to 1.00; p=0.06). Only BV at the later MRI was associated with EDSS score (β -0.03, SE 0.01, p<0.001) and the rate of EDSS change over time (β -0.001, SE 0.0003, p=0.02). There was no association between longitudinal PBVC or PGMC and CDP or EDSS (p>0.05). CONCLUSION: In this highly treated MS cohort with low disability accrual, only cross-sectional BV showed an association with future EDSS scores, while no MRI metric predicted 6-month CDP. These findings highlight the limitations of current clinical MRI measures in predicting disability worsening in real-world settings.
AI Supported Modelling in Clinical Sciences Vrije Universiteit Brussel Brussels Belgium
Brain and Mind Centre The University of Sydney Sydney New South Wales Australia
Centro de Esclerosis Múltiple de Buenos Aires Buenos Aires Argentina
CORe Department of Medicine The University of Melbourne Melbourne Victoria Australia
Department of Health Sciences Biostatistics Unit University of Genoa Genoa Italy
Department of Medical Imaging Royal Melbourne Hospital Melbourne Victoria Australia
Department of Medicine Surgery and Neuroscience University of Siena Siena Italy
Department of Neurology Ghent University Hospital Gent Belgium
Department of Neurology John Hunter Hospital New Lambton Heights New South Wales Australia
Department of Neurology Universite catholique de Louvain Louvain la Neuve Belgium
Department of Radiology Box Hill Hospital Box Hill Victoria Australia
Department of Radiology University of Melbourne Melbourne Victoria Australia
National MS Center Melsbroek Melsbroek Belgium
Neuroimmunology Centre The Royal Melbourne Hospital Melbourne Victoria Australia
Neurology State University of New York at Buffalo Buffalo New York USA
See more in PubMed
GBD 2016 Multiple Sclerosis Collaborators Global, regional, and national burden of multiple sclerosis 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18:269–85. doi: 10.1016/S1474-4422(18)30443-5. PubMed DOI PMC
Brown JWL, Coles A, Horakova D, et al. Association of Initial Disease-Modifying Therapy With Later Conversion to Secondary Progressive Multiple Sclerosis. JAMA. 2019;321:175–87. doi: 10.1001/jama.2018.20588. PubMed DOI PMC
Sormani MP, Arnold DL, De Stefano N. Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis. Ann Neurol . 2014;75:43–9. doi: 10.1002/ana.24018. PubMed DOI
Fisher E, Rudick RA, Simon JH, et al. Eight-year follow-up study of brain atrophy in patients with MS. Neurology (ECronicon) 2002;59:1412–20. doi: 10.1212/01.WNL.0000036271.49066.06. PubMed DOI
Rojas JI, Patrucco L, Besada C, et al. Brain atrophy at onset and physical disability in multiple sclerosis. Arq Neuropsiquiatr. 2012;70:765–8. doi: 10.1590/s0004-282x2012001000003. PubMed DOI
Pérez-Miralles FC, Sastre-Garriga J, Vidal-Jordana A, et al. Predictive value of early brain atrophy on response in patients treated with interferon β. Neurol Neuroimmunol Neuroinflamm . 2015;2:e132. doi: 10.1212/NXI.0000000000000132. PubMed DOI PMC
Bakshi R, Healy BC, Dupuy SL, et al. Brain MRI Predicts Worsening Multiple Sclerosis Disability over 5 Years in the SUMMIT Study. J Neuroimaging. 2020;30:212–8. doi: 10.1111/jon.12688. PubMed DOI PMC
Popescu V, Agosta F, Hulst HE, et al. Brain atrophy and lesion load predict long term disability in multiple sclerosis. J Neurol Neurosurg Psychiatry . 2013;84:1082–91. doi: 10.1136/jnnp-2012-304094. PubMed DOI
Dekker I, Eijlers AJC, Popescu V, et al. Predicting clinical progression in multiple sclerosis after 6 and 12 years. Eur J Neurol. 2019;26:893–902. doi: 10.1111/ene.13904. PubMed DOI PMC
Geurts JJG, Calabrese M, Fisher E, et al. Measurement and clinical effect of grey matter pathology in multiple sclerosis. Lancet Neurol. 2012;11:1082–92. doi: 10.1016/S1474-4422(12)70230-2. PubMed DOI
Rudick RA, Lee JC, Nakamura K, et al. Gray matter atrophy correlates with MS disability progression measured with MSFC but not EDSS. J Neurol Sci . 2009;282:106–11. doi: 10.1016/j.jns.2008.11.018. PubMed DOI PMC
Fisniku LK, Chard DT, Jackson JS, et al. Gray matter atrophy is related to long-term disability in multiple sclerosis. Ann Neurol . 2008;64:247–54. doi: 10.1002/ana.21423. PubMed DOI
Roosendaal SD, Bendfeldt K, Vrenken H, et al. Grey matter volume in a large cohort of MS patients: relation to MRI parameters and disability. Mult Scler. 2011;17:1098–106. doi: 10.1177/1352458511404916. PubMed DOI
Filippi M, Preziosa P, Copetti M, et al. Gray matter damage predicts the accumulation of disability 13 years later in MS. Neurology (ECronicon) 2013;81:1759–67. doi: 10.1212/01.wnl.0000435551.90824.d0. PubMed DOI
Uher T, Vaneckova M, Sobisek L, et al. Combining clinical and magnetic resonance imaging markers enhances prediction of 12-year disability in multiple sclerosis. Mult Scler. 2017;23:51–61. doi: 10.1177/1352458516642314. PubMed DOI
Sormani M, Rio J, Tintorè M, et al. Scoring treatment response in patients with relapsing multiple sclerosis. Mult Scler . 2013;19:605–12. doi: 10.1177/1352458512460605. PubMed DOI
Uher T, Horakova D, Kalincik T, et al. Early magnetic resonance imaging predictors of clinical progression after 48 months in clinically isolated syndrome patients treated with intramuscular interferon β-1a. Eur J Neurol . 2015;22:1113–23. doi: 10.1111/ene.12716. PubMed DOI
Prosperini L, De Angelis F, De Angelis R, et al. Sustained disability improvement is associated with T1 lesion volume shrinkage in natalizumab-treated patients with multiple sclerosis. J Neurol Neurosurg Psychiatry . 2015;86:236–8. doi: 10.1136/jnnp-2014-307786. PubMed DOI
Galassi S, Prosperini L, Logoteta A, et al. A lesion topography-based approach to predict the outcomes of patients with multiple sclerosis treated with Interferon Beta. Mult Scler Relat Disord. 2016;8:99–106. doi: 10.1016/j.msard.2016.05.012. PubMed DOI
Dekker I, Sombekke MH, Balk LJ, et al. Infratentorial and spinal cord lesions: Cumulative predictors of long-term disability? Mult Scler. 2020;26:1381–91. doi: 10.1177/1352458519864933. PubMed DOI PMC
D’hooghe MB, Gielen J, Van Remoortel A, et al. Single MRI-Based Volumetric Assessment in Clinical Practice Is Associated With MS-Related Disability. J Magn Reson Imaging. 2019;49:1312–21. doi: 10.1002/jmri.26303. PubMed DOI
Huppertz HJ, Kröll-Seger J, Klöppel S, et al. Intra- and interscanner variability of automated voxel-based volumetry based on a 3D probabilistic atlas of human cerebral structures. Neuroimage . 2010;49:2216–24. doi: 10.1016/j.neuroimage.2009.10.066. PubMed DOI
Nguyen A-L, Sormani MP, Horakova D, et al. Utility of icobrain for brain volumetry in multiple sclerosis clinical practice. Mult Scler Relat Disord. 2024;92:106148. doi: 10.1016/j.msard.2024.106148. PubMed DOI
Jain S, Sima DM, Ribbens A, et al. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images. Neuroimage Clin. 2015;8:367–75. doi: 10.1016/j.nicl.2015.05.003. PubMed DOI PMC
Butzkueven H, Chapman J, Cristiano E, et al. MSBase: an international, online registry and platform for collaborative outcomes research in multiple sclerosis. Mult Scler . 2006;12:769–74. doi: 10.1177/1352458506070775. PubMed DOI
Smeets D, Ribbens A, Sima DM, et al. Reliable measurements of brain atrophy in individual patients with multiple sclerosis. Brain Behav . 2016;6:e00518. doi: 10.1002/brb3.518. PubMed DOI PMC
Jain S, Ribbens A, Sima DM, et al. Two Time Point MS Lesion Segmentation in Brain MRI: An Expectation-Maximization Framework. Front Neurosci. 2016;10:576. doi: 10.3389/fnins.2016.00576. PubMed DOI PMC
Roxburgh RHSR, Seaman SR, Masterman T, et al. Multiple Sclerosis Severity Score: using disability and disease duration to rate disease severity. Neurology (ECronicon) 2005;64:1144–51. doi: 10.1212/01.WNL.0000156155.19270.F8. PubMed DOI
Zhou Y, Claflin SB, Stankovich J, et al. Redefining the Multiple Sclerosis Severity Score (MSSS): The effect of sex and onset phenotype. Mult Scler . 2020;26:1765–74. doi: 10.1177/1352458519881994. PubMed DOI
Manouchehrinia A, Westerlind H, Kingwell E, et al. Age Related Multiple Sclerosis Severity Score: Disability ranked by age. Mult Scler . 2017;23:1938–46. doi: 10.1177/1352458517690618. PubMed DOI PMC
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;22:1297–305. doi: 10.1177/1352458515616701. PubMed DOI PMC
Banwell B, Giovannoni G, Hawkes C, et al. Editors’ welcome and a working definition for a multiple sclerosis cure. Mult Scler Relat Disord. 2013;2:65–7. doi: 10.1016/j.msard.2012.12.001. PubMed DOI
De Stefano N, Stromillo ML, Giorgio A, et al. Establishing pathological cut-offs of brain atrophy rates in multiple sclerosis. J Neurol Neurosurg Psychiatry . 2016;87:93–9. doi: 10.1136/jnnp-2014-309903. PubMed DOI PMC
Opfer R, Ostwaldt A-C, Sormani MP, et al. Estimates of age-dependent cutoffs for pathological brain volume loss using SIENA/FSL-a longitudinal brain volumetry study in healthy adults. Neurobiol Aging. 2018;65:1–6. doi: 10.1016/j.neurobiolaging.2017.12.024. PubMed DOI
Fragoso YD, Wille PR, Abreu M, et al. Correlation of clinical findings and brain volume data in multiple sclerosis. J Clin Neurosci. 2017;44:155–7. doi: 10.1016/j.jocn.2017.06.006. PubMed DOI
Andelova M, Uher T, Krasensky J, et al. Additive Effect of Spinal Cord Volume, Diffuse and Focal Cord Pathology on Disability in Multiple Sclerosis. Front Neurol. 2019;10:820. doi: 10.3389/fneur.2019.00820. PubMed DOI PMC
Kalincik T, Sormani MP, Tur C. Has the Time Come to Revisit Our Standard Measures of Disability Progression in Multiple Sclerosis? Neurology (ECronicon) 2021;96:12–3. doi: 10.1212/WNL.0000000000011120. PubMed DOI
Uher T, Vaneckova M, Sormani MP, et al. Identification of multiple sclerosis patients at highest risk of cognitive impairment using an integrated brain magnetic resonance imaging assessment approach. Eur J Neurol. 2017;24:292–301. doi: 10.1111/ene.13200. PubMed DOI