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Nabizadeh F, Zafari R, Mohamadi M, Maleki T, Fallahi MS, Rafiei N. MRI features and disability in multiple sclerosis: A systematic review and meta-analysis. J Neuroradiol 2024; 51:24-37. [PMID: 38172026 DOI: 10.1016/j.neurad.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 11/28/2023] [Accepted: 11/28/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND In this systematic review and meta-analysis, we aimed to investigate the correlation between disability in patients with Multiple sclerosis (MS) measured by the Expanded Disability Status Scale (EDSS) and brain Magnetic Resonance Imaging (MRI) features to provide reliable results on which characteristics in the MRI can predict disability and prognosis of the disease. METHODS A systematic literature search was performed using three databases including PubMed, Scopus, and Web of Science. The selected peer-reviewed studies must report a correlation between EDSS scores and MRI features. The correlation coefficients of included studies were converted to the Fisher's z scale, and the results were pooled. RESULTS Overall, 105 studies A total of 16,613 patients with MS entered our study. We found no significant correlation between total brain volume and EDSS assessment (95 % CI: -0.37 to 0.08; z-score: -0.15). We examined the potential correlation between the volume of T1 and T2 lesions and the level of disability. A positive significant correlation was found (95 % CI: 0.19 to 0.43; z-score: 0.31), (95 % CI: 0.17 to 0.33; z-score: 0.25). We observed a significant correlation between white matter volume and EDSS score in patients with MS (95 % CI: -0.37 to -0.03; z-score: -0.21). Moreover, there was a significant negative correlation between gray matter volume and disability (95 % CI: -0.025 to -0.07; z-score: -0.16). CONCLUSION In conclusion, this systematic review and meta-analysis revealed that disability in patients with MS is linked to extensive changes in different brain regions, encompassing gray and white matter, as well as T1 and T2 weighted MRI lesions.
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Affiliation(s)
- Fardin Nabizadeh
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
| | - Rasa Zafari
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mobin Mohamadi
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Tahereh Maleki
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Nazanin Rafiei
- School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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2
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Bose G, Healy BC, Saxena S, Saleh F, Paul A, Barro C, Lokhande HA, Polgar-Turcsanyi M, Anderson M, Glanz BI, Guttmann CRG, Bakshi R, Weiner HL, Chitnis T. Early neurofilament light and glial fibrillary acidic protein levels improve predictive models of multiple sclerosis outcomes. Mult Scler Relat Disord 2023; 74:104695. [PMID: 37060852 DOI: 10.1016/j.msard.2023.104695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 03/08/2023] [Accepted: 03/31/2023] [Indexed: 04/07/2023]
Abstract
BACKGROUND Early risk-stratification in multiple sclerosis (MS) may impact treatment decisions. Current predictive models have identified that clinical and imaging characteristics of aggressive disease are associated with worse long-term outcomes. Serum biomarkers, neurofilament (sNfL) and glial fibrillary acidic protein (sGFAP), reflect subclinical disease activity through separate pathological processes and may contribute to predictive models of clinical and MRI outcomes. METHODS We conducted a retrospective analysis of the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB study), where patients with multiple sclerosis are seen every 6 months and undergo Expanded Disability Status Scale (EDSS) assessment, have annual brain MRI scans where volumetric analysis is conducted to calculate T2-lesion volume (T2LV) and brain parenchymal fraction (BPF), and donate a yearly blood sample for subsequent analysis. We included patients with newly diagnosed relapsing-remitting MS and serum samples obtained at baseline visit and 1-year follow-up (both within 3 years of onset), and were assessed at 10-year follow-up. We measured sNfL and sGFAP by single molecule array at baseline visit and at 1-year follow-up. A predictive clinical model was developed using age, sex, Expanded Disability Status Scale (EDSS), pyramidal signs, relapse rate, and spinal cord lesions at first visit. The main outcome was odds of developing of secondary progressive (SP)MS at year 10. Secondary outcomes included 10-year EDSS, brain T2LV and BPF. We compared the goodness-of-fit of the predictive clinical model with and without sNfL and sGFAP at baseline and 1-year follow-up, for each outcome by area under the receiver operating characteristic curve (AUC) or R-squared. RESULTS A total 144 patients with median MS onset at age 37.4 years (interquartile range: 29.4-45.4), 64% female, were included. SPMS developed in 25 (17.4%) patients. The AUC for the predictive clinical model without biomarker data was 0.73, which improved to 0.77 when both sNfL and sGFAP were included in the model (P = 0.021). In this model, higher baseline sGFAP associated with developing SPMS (OR=3.3 [95%CI:1.1,10.6], P = 0.04). Adding 1-year follow-up biomarker levels further improved the model fit (AUC = 0.79) but this change was not statistically significant (P = 0.15). Adding baseline biomarker data also improved the R-squared of clinical models for 10-year EDSS from 0.24 to 0.28 (P = 0.032), while additional 1-year follow-up levels did not. Baseline sGFAP was associated with 10-year EDSS (ß=0.58 [95%CI:0.00,1.16], P = 0.05). For MRI outcomes, baseline biomarker levels improved R-squared for T2LV from 0.12 to 0.27 (P<0.001), and BPF from 0.15 to 0.20 (P = 0.042). Adding 1-year follow-up biomarker data further improved T2LV to 0.33 (P = 0.0065) and BPF to 0.23 (P = 0.048). Baseline sNfL was associated with T2LV (ß=0.34 [95%CI:0.21,0.48], P<0.001) and 1-year follow-up sNfL with BPF (ß=-2.53% [95%CI:-4.18,-0.89], P = 0.003). CONCLUSIONS Early biomarker levels modestly improve predictive models containing clinical and MRI variables. Worse clinical outcomes, SPMS and EDSS, are associated with higher sGFAP levels and worse MRI outcomes, T2LV and BPF, are associated with higher sNfL levels. Prospective study implementing these predictive models into clinical practice are needed to determine if early biomarker levels meaningfully impact clinical practice.
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Affiliation(s)
- Gauruv Bose
- Harvard Medical School, 60 Fenwood Road, 9002 K, Boston, MA 02115, USA; Brigham MS Center, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Brian C Healy
- Harvard Medical School, 60 Fenwood Road, 9002 K, Boston, MA 02115, USA; Brigham MS Center, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Shrishti Saxena
- Harvard Medical School, 60 Fenwood Road, 9002 K, Boston, MA 02115, USA; Brigham MS Center, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Fermisk Saleh
- Harvard Medical School, 60 Fenwood Road, 9002 K, Boston, MA 02115, USA; Brigham MS Center, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Anu Paul
- Harvard Medical School, 60 Fenwood Road, 9002 K, Boston, MA 02115, USA; Brigham MS Center, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Christian Barro
- Harvard Medical School, 60 Fenwood Road, 9002 K, Boston, MA 02115, USA; Brigham MS Center, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Hrishikesh A Lokhande
- Harvard Medical School, 60 Fenwood Road, 9002 K, Boston, MA 02115, USA; Brigham MS Center, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Mariann Polgar-Turcsanyi
- Harvard Medical School, 60 Fenwood Road, 9002 K, Boston, MA 02115, USA; Brigham MS Center, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Mark Anderson
- Harvard Medical School, 60 Fenwood Road, 9002 K, Boston, MA 02115, USA; Brigham MS Center, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Bonnie I Glanz
- Harvard Medical School, 60 Fenwood Road, 9002 K, Boston, MA 02115, USA; Brigham MS Center, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Charles R G Guttmann
- Harvard Medical School, 60 Fenwood Road, 9002 K, Boston, MA 02115, USA; Department of Radiology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Rohit Bakshi
- Harvard Medical School, 60 Fenwood Road, 9002 K, Boston, MA 02115, USA; Brigham MS Center, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Howard L Weiner
- Harvard Medical School, 60 Fenwood Road, 9002 K, Boston, MA 02115, USA; Brigham MS Center, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Tanuja Chitnis
- Harvard Medical School, 60 Fenwood Road, 9002 K, Boston, MA 02115, USA; Brigham MS Center, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA.
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3
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Shams H, Shao X, Santaniello A, Kirkish G, Harroud A, Ma Q, Isobe N, Schaefer CA, McCauley JL, Cree BAC, Didonna A, Baranzini SE, Patsopoulos NA, Hauser SL, Barcellos LF, Henry RG, Oksenberg JR. Polygenic risk score association with multiple sclerosis susceptibility and phenotype in Europeans. Brain 2023; 146:645-656. [PMID: 35253861 PMCID: PMC10169285 DOI: 10.1093/brain/awac092] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/29/2022] [Accepted: 02/15/2022] [Indexed: 11/13/2022] Open
Abstract
Polygenic inheritance plays a pivotal role in driving multiple sclerosis susceptibility, an inflammatory demyelinating disease of the CNS. We developed polygenic risk scores (PRS) of multiple sclerosis and assessed associations with both disease status and severity in cohorts of European descent. The largest genome-wide association dataset for multiple sclerosis to date (n = 41 505) was leveraged to generate PRS scores, serving as an informative susceptibility marker, tested in two independent datasets, UK Biobank [area under the curve (AUC) = 0.73, 95% confidence interval (CI): 0.72-0.74, P = 6.41 × 10-146] and Kaiser Permanente in Northern California (KPNC, AUC = 0.8, 95% CI: 0.76-0.82, P = 1.5 × 10-53). Individuals within the top 10% of PRS were at higher than 5-fold increased risk in UK Biobank (95% CI: 4.7-6, P = 2.8 × 10-45) and 15-fold higher risk in KPNC (95% CI: 10.4-24, P = 3.7 × 10-11), relative to the median decile. The cumulative absolute risk of developing multiple sclerosis from age 20 onwards was significantly higher in genetically predisposed individuals according to PRS. Furthermore, inclusion of PRS in clinical risk models increased the risk discrimination by 13% to 26% over models based only on conventional risk factors in UK Biobank and KPNC, respectively. Stratifying disease risk by gene sets representative of curated cellular signalling cascades, nominated promising genetic candidate programmes for functional characterization. These pathways include inflammatory signalling mediation, response to viral infection, oxidative damage, RNA polymerase transcription, and epigenetic regulation of gene expression to be among significant contributors to multiple sclerosis susceptibility. This study also indicates that PRS is a useful measure for estimating susceptibility within related individuals in multicase families. We show a significant association of genetic predisposition with thalamic atrophy within 10 years of disease progression in the UCSF-EPIC cohort (P < 0.001), consistent with a partial overlap between the genetics of susceptibility and end-organ tissue injury. Mendelian randomization analysis suggested an effect of multiple sclerosis susceptibility on thalamic volume, which was further indicated to be through horizontal pleiotropy rather than a causal effect. In summary, this study indicates important, replicable associations of PRS with enhanced risk assessment and radiographic outcomes of tissue injury, potentially informing targeted screening and prevention strategies.
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Affiliation(s)
- Hengameh Shams
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA.,Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA 94720, USA
| | - Xiaorong Shao
- Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA 94720, USA
| | - Adam Santaniello
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Gina Kirkish
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Adil Harroud
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Qin Ma
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Noriko Isobe
- Department of Neurology, Graduate School of medical Sciences, Kyushu University, Fukuoka, 812-8582, Japan
| | | | | | - Jacob L McCauley
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA.,Dr. John T. Macdonald Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Bruce A C Cree
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Alessandro Didonna
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA.,Department of Anatomy and Cell Biology, East Carolina University, Greenville, NC 27834, USA
| | - Sergio E Baranzini
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Nikolaos A Patsopoulos
- Systems Biology and Computer Science Program, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Boston, 02115 MA, USA.,Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Harvard Medical School, Boston, MA 02115, USA.,Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stephen L Hauser
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Lisa F Barcellos
- Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA 94720, USA
| | - Roland G Henry
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jorge R Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
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4
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Bose G, Healy BC, Barro C, Glanz BI, Lokhande HA, Polgar-Turcsanyi M, Guttmann CR, Bakshi R, Weiner HL, Chitnis T. Younger age at multiple sclerosis onset is associated with worse outcomes at age 50. J Neurol Neurosurg Psychiatry 2022; 93:jnnp-2022-329353. [PMID: 35953266 DOI: 10.1136/jnnp-2022-329353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/26/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE Older age at multiple sclerosis (MS) onset has been associated with worse 10-year outcomes. However, disease duration often exceeds 10 years and age-related comorbidities may also contribute to disability. We investigated patients with>10 years disease duration to determine how age at MS onset is associated with clinical, MRI and occupational outcomes at age 50. METHODS We included patients enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital with disease duration>10 years. Outcomes at age 50 included the Expanded Disability Status Scale (EDSS), development of secondary-progressive multiple sclerosis (SPMS), brain T2-lesion volume (T2LV) and brain parenchymal fraction (BPF), and occupational status. We assessed how onset age was independently associated with each outcome when adjusting for the date of visit closest to age 50, sex, time to first treatment, number of treatments by age 50 and exposure to high-efficacy treatments by age 50. RESULTS We included 661 patients with median onset at 31.4 years. The outcomes at age 50 were worse the younger first symptoms developed: for every 5 years earlier, the EDSS was 0.22 points worse (95% CI: 0.04 to 0.40; p=0.015), odds of SPMS 1.33 times higher (95% CI: 1.08 to 1.64; p=0.008), T2LV 1.86 mL higher (95% CI: 1.02 to 2.70; p<0.001), BPF 0.97% worse (95% CI: 0.52 to 1.42; p<0.001) and odds of unemployment from MS 1.24 times higher (95% CI: 1.01 to 1.53; p=0.037). CONCLUSIONS All outcomes at age 50 were worse in patients with younger age at onset. Decisions to provide high-efficacy treatments should consider younger age at onset, equating to a longer expected disease duration, as a poor prognostic factor.
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Affiliation(s)
- Gauruv Bose
- Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, MA, USA
| | - Brian C Healy
- Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, MA, USA
| | - Christian Barro
- Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, MA, USA
| | - Bonnie I Glanz
- Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Mariann Polgar-Turcsanyi
- Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Rohit Bakshi
- Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, MA, USA
| | - Howard L Weiner
- Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, MA, USA
| | - Tanuja Chitnis
- Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, MA, USA
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5
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MINUTTI-ZANELLA C, BOJALIL-ÁLVAREZ L, GARCÍA-VILLASEÑOR E, LÓPEZ-MARTÍNEZ B, PÉREZ-TURRENT M, MURRIETA-ÁLVAREZ I, RUIZ-DELGADO GJ, ARGÜELLES GJRUIZ. miRNAs in multiple sclerosis: A clinical approach. Mult Scler Relat Disord 2022; 63:103835. [DOI: 10.1016/j.msard.2022.103835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 04/22/2022] [Accepted: 04/27/2022] [Indexed: 11/29/2022]
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6
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Bose G, Healy BC, Lokhande HA, Sotiropoulos MG, Polgar‐Turcsanyi M, Anderson M, Glanz BI, Guttman CRG, Bakshi R, Weiner HL, Chitnis T. Early predictors of clinical and MRI outcomes using LASSO in multiple sclerosis. Ann Neurol 2022; 92:87-96. [DOI: 10.1002/ana.26370] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 03/28/2022] [Accepted: 04/10/2022] [Indexed: 11/09/2022]
Affiliation(s)
- Gauruv Bose
- Harvard Medical School Boston MA US
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Brian C. Healy
- Harvard Medical School Boston MA US
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Hrishikesh A. Lokhande
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Marinos G. Sotiropoulos
- Harvard Medical School Boston MA US
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Mariann Polgar‐Turcsanyi
- Harvard Medical School Boston MA US
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Mark Anderson
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Bonnie I. Glanz
- Harvard Medical School Boston MA US
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Charles R. G. Guttman
- Harvard Medical School Boston MA US
- Center for Neurological Imaging, Department of Radiology, Brigham and Women’s Hospital Boston MA US
| | - Rohit Bakshi
- Harvard Medical School Boston MA US
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Howard L. Weiner
- Harvard Medical School Boston MA US
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
| | - Tanuja Chitnis
- Harvard Medical School Boston MA US
- Brigham Multiple Sclerosis Center & Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital Boston MA US
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7
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Figueira GMA, Soares PV, Silveira RCD, Figueira FFA. "Stable" vs. "silent progressive multiple sclerosis": a real-world retrospective clinical imaging Brazilian study. ARQUIVOS DE NEURO-PSIQUIATRIA 2022; 80:405-409. [PMID: 35195220 DOI: 10.1590/0004-282x-anp-2020-0234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 03/16/2021] [Indexed: 11/21/2022]
Abstract
BACKGROUND Clinical and imaging are required to characterize activity and progression in MS. The parameters for activity are well defined but not those for progression. The ideal aim for long-term treatment is that neither clinical nor imaging signs of disease should be present, and also no brain atrophy. OBJECTIVES To conduct a comparative clinical-imaging study focusing on MRI brain volumetry. METHODS 174 consecutive relapsing-remitting MS patients (McDonald 2001) were studied, focusing on activity and progression. Annual clinical evaluations (relapse rate and EDSS) and MRI data, along with the annualized evolution of the corpus callosum index (CCI), were compared. RESULTS Out of 174 patients, 148 were considered clinically "stable" based on EDSS. However, 33 (22.2%) out of this group showed annualized reductions in CCI of more than 0.5%, which was the cutoff for defining significant brain atrophy. CONCLUSIONS Among apparently "stable" relapsing-remitting MS patients, 1/5 showed significant brain atrophy over a follow-up period of at least 7 years. We consider it reasonable to suggest that MRI volume sequences should be included in follow-up protocols, so as to provide information on the real treatment response status.
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Affiliation(s)
| | - Paula Vallegas Soares
- Hospital São Francisco na Providência de Deus, Departamento de Neurologia, Rio de Janeiro RJ, Brazil
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8
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Cairns J, Vavasour IM, Traboulsee A, Carruthers R, Kolind SH, Li DKB, Moore GRW, Laule C. Diffusely abnormal white matter in multiple sclerosis. J Neuroimaging 2021; 32:5-16. [PMID: 34752664 DOI: 10.1111/jon.12945] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 10/17/2021] [Accepted: 10/18/2021] [Indexed: 02/06/2023] Open
Abstract
MRI enables detailed in vivo depiction of multiple sclerosis (MS) pathology. Localized areas of MS damage, commonly referred to as lesions, or plaques, have been a focus of clinical and research MRI studies for over four decades. A nonplaque MRI abnormality which is present in at least 25% of MS patients but has received far less attention is diffusely abnormal white matter (DAWM). DAWM has poorly defined boundaries and a signal intensity that is between normal-appearing white matter and classic lesions on proton density and T2 -weighted images. All clinical phenotypes of MS demonstrate DAWM, including clinically isolated syndrome, where DAWM is associated with higher lesion volume, reduced brain volume, and earlier conversion to MS. Advanced MRI metric abnormalities in DAWM tend to be greater than those in NAWM, but not as severe as focal lesions, with myelin, axons, and water-related changes commonly reported. Histological studies demonstrate a primary lipid abnormality in DAWM, with some axonal damage and lesser involvement of myelin proteins. This review provides an overview of DAWM identification, summarizes in vivo and postmortem observations, and comments on potential pathophysiological mechanisms, which may underlie DAWM in MS. Given the prevalence and potential clinical impact of DAWM, the number of imaging studies focusing on DAWM is insufficient. Characterization of DAWM significance and microstructure would benefit from larger longitudinal and additional quantitative imaging efforts. Revisiting data from previous studies that included proton density and T2 imaging would enable retrospective DAWM identification and analysis.
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Affiliation(s)
- James Cairns
- Department of Medicine (Neurology), University of British Columbia, British Columbia, Vancouver, Canada.,Department of Radiology, University of British Columbia, British Columbia, Vancouver, Canada
| | - Irene M Vavasour
- Department of Radiology, University of British Columbia, British Columbia, Vancouver, Canada.,International Collaboration on Repair Discoveries, Blusson Spinal Cord Centre, University of British Columbia, British Columbia, Vancouver, Canada
| | - Anthony Traboulsee
- Department of Medicine (Neurology), University of British Columbia, British Columbia, Vancouver, Canada
| | - Robert Carruthers
- Department of Medicine (Neurology), University of British Columbia, British Columbia, Vancouver, Canada
| | - Shannon H Kolind
- Department of Medicine (Neurology), University of British Columbia, British Columbia, Vancouver, Canada.,Department of Radiology, University of British Columbia, British Columbia, Vancouver, Canada.,International Collaboration on Repair Discoveries, Blusson Spinal Cord Centre, University of British Columbia, British Columbia, Vancouver, Canada.,Department of Physics & Astronomy, University of British Columbia, British Columbia, Vancouver, Canada
| | - David K B Li
- Department of Medicine (Neurology), University of British Columbia, British Columbia, Vancouver, Canada.,Department of Radiology, University of British Columbia, British Columbia, Vancouver, Canada
| | - G R Wayne Moore
- Department of Medicine (Neurology), University of British Columbia, British Columbia, Vancouver, Canada.,International Collaboration on Repair Discoveries, Blusson Spinal Cord Centre, University of British Columbia, British Columbia, Vancouver, Canada.,Department of Pathology & Laboratory Medicine, University of British Columbia, British Columbia, Vancouver, Canada
| | - Cornelia Laule
- Department of Radiology, University of British Columbia, British Columbia, Vancouver, Canada.,International Collaboration on Repair Discoveries, Blusson Spinal Cord Centre, University of British Columbia, British Columbia, Vancouver, Canada.,Department of Physics & Astronomy, University of British Columbia, British Columbia, Vancouver, Canada.,Department of Pathology & Laboratory Medicine, University of British Columbia, British Columbia, Vancouver, Canada
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9
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Tommasin S, Cocozza S, Taloni A, Giannì C, Petsas N, Pontillo G, Petracca M, Ruggieri S, De Giglio L, Pozzilli C, Brunetti A, Pantano P. Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis. J Neurol 2021; 268:4834-4845. [PMID: 33970338 PMCID: PMC8563671 DOI: 10.1007/s00415-021-10605-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 05/05/2021] [Accepted: 05/05/2021] [Indexed: 01/22/2023]
Abstract
Objectives To evaluate the accuracy of a data-driven approach, such as machine learning classification, in predicting disability progression in MS. Methods We analyzed structural brain images of 163 subjects diagnosed with MS acquired at two different sites. Participants were followed up for 2–6 years, with disability progression defined according to the expanded disability status scale (EDSS) increment at follow-up. T2-weighted lesion load (T2LL), thalamic and cerebellar gray matter (GM) volumes, fractional anisotropy of the normal appearing white matter were calculated at baseline and included in supervised machine learning classifiers. Age, sex, phenotype, EDSS at baseline, therapy and time to follow-up period were also included. Classes were labeled as stable or progressed disability. Participants were randomly chosen from both sites to build a sample including 50% patients showing disability progression and 50% patients being stable. One-thousand machine learning classifiers were applied to the resulting sample, and after testing for overfitting, classifier confusion matrix, relative metrics and feature importance were evaluated. Results At follow-up, 36% of participants showed disability progression. The classifier with the highest resulting metrics had accuracy of 0.79, area under the true positive versus false positive rates curve of 0.81, sensitivity of 0.90 and specificity of 0.71. T2LL, thalamic volume, disability at baseline and administered therapy were identified as important features in predicting disability progression. Classifiers built on radiological features had higher accuracy than those built on clinical features. Conclusions Disability progression in MS may be predicted via machine learning classifiers, mostly evaluating neuroradiological features. Supplementary Information The online version contains supplementary material available at 10.1007/s00415-021-10605-7.
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Affiliation(s)
- Silvia Tommasin
- Department of Human Neurosciences, Sapienza University of Rome, Viale dell'Università, 30, 00185, Rome, Italy.
| | - Sirio Cocozza
- Dipartimento di Scienze Biomediche Avanzate, Università degli Studi di Napoli Federico II, Naples, Italy
| | - Alessandro Taloni
- Institute for Complex Systems, Italian National Research Council, Rome, Italy
| | - Costanza Giannì
- Department of Human Neurosciences, Sapienza University of Rome, Viale dell'Università, 30, 00185, Rome, Italy
| | | | - Giuseppe Pontillo
- Dipartimento di Scienze Biomediche Avanzate, Università degli Studi di Napoli Federico II, Naples, Italy.,Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, Naples, Italy
| | - Maria Petracca
- Department of Human Neurosciences, Sapienza University of Rome, Viale dell'Università, 30, 00185, Rome, Italy.,Dipartimento di Neuroscienze, Scienze Riproduttive e Odontostomatologiche, Università degli Studi di Napoli Federico II, Naples, Italy
| | - Serena Ruggieri
- Department of Human Neurosciences, Sapienza University of Rome, Viale dell'Università, 30, 00185, Rome, Italy.,Neuroimmunology Unit, IRCSS Fondazione Santa Lucia, Rome, Italy
| | - Laura De Giglio
- Department of Human Neurosciences, Sapienza University of Rome, Viale dell'Università, 30, 00185, Rome, Italy.,Neurology Unit, Medicine Department, San Filippo Neri Hospital, Rome, Italy
| | - Carlo Pozzilli
- Department of Human Neurosciences, Sapienza University of Rome, Viale dell'Università, 30, 00185, Rome, Italy
| | - Arturo Brunetti
- Dipartimento di Scienze Biomediche Avanzate, Università degli Studi di Napoli Federico II, Naples, Italy
| | - Patrizia Pantano
- Department of Human Neurosciences, Sapienza University of Rome, Viale dell'Università, 30, 00185, Rome, Italy.,Department of Radiology, IRCCS NEUROMED, Pozzilli, Italy
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10
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Uher T, Bergsland N, Krasensky J, Dwyer MG, Andelova M, Sobisek L, Havrdova EK, Horakova D, Zivadinov R, Vaneckova M. Interpretation of Brain Volume Increase in Multiple Sclerosis. J Neuroimaging 2020; 31:401-407. [PMID: 33314460 DOI: 10.1111/jon.12816] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 11/10/2020] [Accepted: 11/11/2020] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE A high variability of brain MRI volume change measurement renders challenging its interpretation in multiple sclerosis (MS). Occurrence and clinical relevance of observed apparent brain volume increase (BVI) in MS patients have not been investigated yet. The objective was to quantify the prevalence and factors associated with BVI. METHODS We examined 366 MS patients (2,317 scans) and 44 controls (132 scans). Volumetric analysis of brain volume changes was performed by SIENA and ScanView. BVI was defined as brain volume change >0%. We compared characteristics of patients with and without BVI. RESULTS BVI was found in 26.3% (from 1,951) longitudinal scans (SIENA). If BVI occurred, a probability that BVI will be repeated consecutively more than or equal to two times was 15.9%. The repeated BVI was associated with clinical disease activity in 50% of cases. BVI was associated with shorter time and lower T2 lesion volume increase between two MRI scans, and higher normalized brain volume (all P < .0001). A proportion of scans with BVI was higher when analyzed by ScanView (35.3%) and in controls (36.4% by SIENA). CONCLUSIONS BVI occurs in a great proportion of MR scans over short-term follow-up and is not associated with disease stabilization. Although BVI can be caused by several factors, the results indicate that measurement error may contribute to BVI in the majority of cases. Clinicians should be aware of the frequent occurrence of apparent BVI, interpret brain volume changes in MS patients with great caution, and use methods with precise quantification of brain volume changes.
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Affiliation(s)
- Tomas Uher
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, University Hospital in Prague, Prague, Czech Republic
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY.,IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Jan Krasensky
- Department of Radiology, First Faculty of Medicine, Charles University and General, University Hospital in Prague, Prague, Czech Republic
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY.,Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, Buffalo, NY
| | - Michaela Andelova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, University Hospital in Prague, Prague, Czech Republic
| | - Lukas Sobisek
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, University Hospital in Prague, Prague, Czech Republic
| | - Eva Kubala Havrdova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, University Hospital in Prague, Prague, Czech Republic
| | - Dana Horakova
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, University Hospital in Prague, Prague, Czech Republic
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY.,Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, Buffalo, NY
| | - Manuela Vaneckova
- Department of Radiology, First Faculty of Medicine, Charles University and General, University Hospital in Prague, Prague, Czech Republic
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11
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Ghione E, Bergsland N, Dwyer MG, Hagemeier J, Jakimovski D, Ramasamy DP, Hojnacki D, Lizarraga AA, Kolb C, Eckert S, Weinstock-Guttman B, Zivadinov R. Disability Improvement Is Associated with Less Brain Atrophy Development in Multiple Sclerosis. AJNR Am J Neuroradiol 2020; 41:1577-1583. [PMID: 32763899 DOI: 10.3174/ajnr.a6684] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Accepted: 06/01/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND AND PURPOSE It is unknown whether deceleration of brain atrophy is associated with disability improvement in patients with MS. Our aim was to investigate whether patients with MS with disability improvement develop less brain atrophy compared with those who progress in disability or remain stable. MATERIALS AND METHODS We followed 980 patients with MS for a mean of 4.8 ± 2.4 years. Subjects were divided into 3 groups: progress in disability (n = 241, 24.6%), disability improvement (n = 101, 10.3%), and stable (n = 638, 65.1%) at follow-up. Disability improvement and progress in disability were defined on the basis of the Expanded Disability Status Scale score change using standardized guidelines. Stable was defined as nonoccurrence of progress in disability or disability improvement. Normalized whole-brain volume was calculated using SIENAX on 3D T1WI, whereas the lateral ventricle was measured using NeuroSTREAM on 2D-T2-FLAIR images. The percentage brain volume change and percentage lateral ventricle volume change were calculated using SIENA and NeuroSTREAM, respectively. Differences among groups were investigated using ANCOVA, adjusted for age at first MR imaging, race, T2 lesion volume, and corresponding baseline structural volume and the Expanded Disability Status Scale. RESULTS At first MR imaging, there were no differences among progress in disability, disability improvement, and the stable groups in whole-brain volume (P = .71) or lateral ventricle volume (P = .74). During follow-up, patients with disability improvement had the lowest annualized percentage lateral ventricle volume change (1.6% ± 2.7%) followed by patients who were stable (2.1% ± 3.7%) and had progress in disability (4.1% ± 5.5%), respectively (P < .001). The annualized percentage brain volume change values were -0.7% ± 0.7% for disability improvement, -0.8% ± 0.7% for stable, and -1.1% ± 1.1% for progress in disability (P = .001). CONCLUSIONS Patients with MS who improve in their clinical disability develop less brain atrophy across time compared with those who progress.
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Affiliation(s)
- E Ghione
- From the Department of Neurology (E.G., N.B., M.G.D., J.H., D.J., D.P.R., R.Z.), Buffalo Neuroimaging Analysis Center
| | - N Bergsland
- From the Department of Neurology (E.G., N.B., M.G.D., J.H., D.J., D.P.R., R.Z.), Buffalo Neuroimaging Analysis Center
- IRCCS (N.B.), Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - M G Dwyer
- From the Department of Neurology (E.G., N.B., M.G.D., J.H., D.J., D.P.R., R.Z.), Buffalo Neuroimaging Analysis Center
- Center for Biomedical Imaging at the Clinical Translational Science Institute (M.G.D., R.Z.),University at Buffalo, State University of New York, Buffalo, New York
| | - J Hagemeier
- From the Department of Neurology (E.G., N.B., M.G.D., J.H., D.J., D.P.R., R.Z.), Buffalo Neuroimaging Analysis Center
| | - D Jakimovski
- From the Department of Neurology (E.G., N.B., M.G.D., J.H., D.J., D.P.R., R.Z.), Buffalo Neuroimaging Analysis Center
| | - D P Ramasamy
- From the Department of Neurology (E.G., N.B., M.G.D., J.H., D.J., D.P.R., R.Z.), Buffalo Neuroimaging Analysis Center
| | - D Hojnacki
- Department of Neurology (D.H., A.A.L., C.K., S.E., B.W.-G.), Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences
| | - A A Lizarraga
- Department of Neurology (D.H., A.A.L., C.K., S.E., B.W.-G.), Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences
| | - C Kolb
- Department of Neurology (D.H., A.A.L., C.K., S.E., B.W.-G.), Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences
| | - S Eckert
- Department of Neurology (D.H., A.A.L., C.K., S.E., B.W.-G.), Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences
| | - B Weinstock-Guttman
- Department of Neurology (D.H., A.A.L., C.K., S.E., B.W.-G.), Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences
| | - R Zivadinov
- From the Department of Neurology (E.G., N.B., M.G.D., J.H., D.J., D.P.R., R.Z.), Buffalo Neuroimaging Analysis Center
- Center for Biomedical Imaging at the Clinical Translational Science Institute (M.G.D., R.Z.),University at Buffalo, State University of New York, Buffalo, New York
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