Slowly expanding lesions relate to persisting black-holes and clinical outcomes in relapse-onset multiple sclerosis.


Journal

NeuroImage. Clinical
ISSN: 2213-1582
Titre abrégé: Neuroimage Clin
Pays: Netherlands
ID NLM: 101597070

Informations de publication

Date de publication:
2022
Historique:
received: 31 01 2022
revised: 25 04 2022
accepted: 12 05 2022
pubmed: 23 5 2022
medline: 25 8 2022
entrez: 22 5 2022
Statut: ppublish

Résumé

Slowly expanding lesions (SELs) are MRI markers of chronic active lesions in multiple sclerosis (MS). T1-hypointense black holes, and reductions in magnetization transfer ratio (MTR) are pathologically correlated with myelin and axonal loss. While all associated with progressive MS, the relationship between these lesion's metrics and clinical outcomes in relapse-onset MS has not been widely investigated. To explore the relationship of SELs with T1-hypointense black holes, and longitudinal T1 intensity contrast ratio and MTR, their correlation to brain volume, and their contribution to MS disability in relapse-onset patients. 135 patients with relapsing-remitting MS (RRMS) were studied with clinical assessments and brain MRI (T2/FLAIR and T1-weighted scans at 1.5/3 T) at baseline and two subsequent follow-ups; a subset of 83 patients also had MTR acquisitions. Early-onset patients were defined when the baseline disease duration was ≤ 5 years (n = 85). SELs were identified using deformation field maps from the manually segmented baseline T2 lesions and differentiated from the non-SELs. Persisting black holes (PBHs) were defined as a subset of T2 lesions with a signal below a patient-specific grey matter T1 intensity in a semi-quantitative manner. SELs, PBH counts, and brain volume were computed, and their associations were assessed through Spearman and Pearson correlation. Clusters of patients according to low (up to 2), intermediate (3 to 10), or high (more than 10) SEL counts were determined with a Gaussian generalised mixture model. Mixed-effects and logistic regression models assessed volumes, T1 and MTR within SELs, and their correlation with Expanded Disability Status Scale (EDSS) and confirmed disability progression (CDP). Mean age at study onset was 35.5 years (73% female), disease duration 5.5 years and mean time to last follow-up 6.5 years (range 1 to 12.5); median baseline EDSS 1.5 (range 0 to 5.5) and a mean EDSS change of 0.31 units at final follow-up. Among 4007 T2 lesions, 27% were classified as SELs and 10% as PBHs. Most patients (n = 65) belonged to the cluster with an intermediate SEL count (3 to 10 SELs). The percentage of PBHs was higher in SELs than non-SELs (up to 61% vs 44%, p < 0.001) and within-patient SEL volumes positively correlated with PBH volumes (r = 0.53, p < 0.001). SELs showed a decrease in T1 intensity over time (beta = -0.004, 95%CI -0.005 to -0.003, p < 0.001), accompanied by lower cross-sectional baseline and follow-up MTR. In mixed-effects models, EDSS worsening was predicted by the SEL log-volumes increase over time (beta = 0.11, 95%CI 0.03 to 0.20, p = 0.01), which was confirmed in the sub-cohort of patients with early onset MS (beta = 0.14, 95%CI 0.04 to 0.25, p = 0.008). In logistic regressions, a higher risk for CDP was associated with SEL volumes (OR = 5.15, 95%CI 1.60 to 16.60, p = 0.006). SELs are associated with accumulation of more destructive pathology as indicated by an association with PBH volume, longitudinal reduction in T1 intensity and MTR. Higher SEL volumes are associated with clinical progression, while lower ones are associated with stability in relapse-onset MS.

Sections du résumé

BACKGROUND
Slowly expanding lesions (SELs) are MRI markers of chronic active lesions in multiple sclerosis (MS). T1-hypointense black holes, and reductions in magnetization transfer ratio (MTR) are pathologically correlated with myelin and axonal loss. While all associated with progressive MS, the relationship between these lesion's metrics and clinical outcomes in relapse-onset MS has not been widely investigated.
OBJECTIVES
To explore the relationship of SELs with T1-hypointense black holes, and longitudinal T1 intensity contrast ratio and MTR, their correlation to brain volume, and their contribution to MS disability in relapse-onset patients.
METHODS
135 patients with relapsing-remitting MS (RRMS) were studied with clinical assessments and brain MRI (T2/FLAIR and T1-weighted scans at 1.5/3 T) at baseline and two subsequent follow-ups; a subset of 83 patients also had MTR acquisitions. Early-onset patients were defined when the baseline disease duration was ≤ 5 years (n = 85). SELs were identified using deformation field maps from the manually segmented baseline T2 lesions and differentiated from the non-SELs. Persisting black holes (PBHs) were defined as a subset of T2 lesions with a signal below a patient-specific grey matter T1 intensity in a semi-quantitative manner. SELs, PBH counts, and brain volume were computed, and their associations were assessed through Spearman and Pearson correlation. Clusters of patients according to low (up to 2), intermediate (3 to 10), or high (more than 10) SEL counts were determined with a Gaussian generalised mixture model. Mixed-effects and logistic regression models assessed volumes, T1 and MTR within SELs, and their correlation with Expanded Disability Status Scale (EDSS) and confirmed disability progression (CDP).
RESULTS
Mean age at study onset was 35.5 years (73% female), disease duration 5.5 years and mean time to last follow-up 6.5 years (range 1 to 12.5); median baseline EDSS 1.5 (range 0 to 5.5) and a mean EDSS change of 0.31 units at final follow-up. Among 4007 T2 lesions, 27% were classified as SELs and 10% as PBHs. Most patients (n = 65) belonged to the cluster with an intermediate SEL count (3 to 10 SELs). The percentage of PBHs was higher in SELs than non-SELs (up to 61% vs 44%, p < 0.001) and within-patient SEL volumes positively correlated with PBH volumes (r = 0.53, p < 0.001). SELs showed a decrease in T1 intensity over time (beta = -0.004, 95%CI -0.005 to -0.003, p < 0.001), accompanied by lower cross-sectional baseline and follow-up MTR. In mixed-effects models, EDSS worsening was predicted by the SEL log-volumes increase over time (beta = 0.11, 95%CI 0.03 to 0.20, p = 0.01), which was confirmed in the sub-cohort of patients with early onset MS (beta = 0.14, 95%CI 0.04 to 0.25, p = 0.008). In logistic regressions, a higher risk for CDP was associated with SEL volumes (OR = 5.15, 95%CI 1.60 to 16.60, p = 0.006).
CONCLUSIONS
SELs are associated with accumulation of more destructive pathology as indicated by an association with PBH volume, longitudinal reduction in T1 intensity and MTR. Higher SEL volumes are associated with clinical progression, while lower ones are associated with stability in relapse-onset MS.

Identifiants

pubmed: 35598462
pii: S2213-1582(22)00113-9
doi: 10.1016/j.nicl.2022.103048
pmc: PMC9130104
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

103048

Subventions

Organisme : Department of Health
Pays : United Kingdom

Informations de copyright

Copyright © 2022. Published by Elsevier Inc.

Auteurs

Alberto Calvi (A)

Queen Square MS Centre, Department of Neuroinflammation, Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), United Kingdom. Electronic address: a.calvi@ucl.ac.uk.

Carmen Tur (C)

Queen Square MS Centre, Department of Neuroinflammation, Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), United Kingdom; Neurology-Neuroimmunology Department, Multiple Sclerosis Centre of Catalonia (Cemcat), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.

Declan Chard (D)

Queen Square MS Centre, Department of Neuroinflammation, Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), United Kingdom.

Jonathan Stutters (J)

Queen Square MS Centre, Department of Neuroinflammation, Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), United Kingdom.

Olga Ciccarelli (O)

Queen Square MS Centre, Department of Neuroinflammation, Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), United Kingdom.

Rosa Cortese (R)

Dep. of Medicine, Surgery and Neuroscience, University of Siena, Italy.

Marco Battaglini (M)

Dep. of Medicine, Surgery and Neuroscience, University of Siena, Italy.

Anna Pietroboni (A)

Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Italy; Department of Biomedical, Surgical and Dental Sciences, University of Milan, Centro Dino Ferrari, Milan, Italy.

Milena De Riz (M)

Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Italy; Department of Biomedical, Surgical and Dental Sciences, University of Milan, Centro Dino Ferrari, Milan, Italy.

Daniela Galimberti (D)

Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Italy; Department of Biomedical, Surgical and Dental Sciences, University of Milan, Centro Dino Ferrari, Milan, Italy.

Elio Scarpini (E)

Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Italy; Department of Biomedical, Surgical and Dental Sciences, University of Milan, Centro Dino Ferrari, Milan, Italy.

Nicola De Stefano (N)

Dep. of Medicine, Surgery and Neuroscience, University of Siena, Italy.

Ferran Prados (F)

Queen Square MS Centre, Department of Neuroinflammation, Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), United Kingdom; Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; e-Health Centre, Universitat Oberta de Catalunya, Barcelona, Spain.

Frederik Barkhof (F)

Queen Square MS Centre, Department of Neuroinflammation, Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), United Kingdom; Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; Radiology & Nuclear Medicine, VU University Medical Centre, Amsterdam, The Netherlands. Electronic address: f.barkhof@ucl.ac.uk.

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