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Classification of multiple sclerosis based on patterns of CNS regional atrophy covariance

There is evidence that multiple sclerosis (MS) pathology leads to distinct patterns of volume loss over time (VLOT) in different central nervous system (CNS) structures. We aimed to use such patterns to identify patient subgroups. MS patients of all classical disease phenotypes underwent annual clin...

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Autores principales: Tsagkas, Charidimos, Parmar, Katrin, Pezold, Simon, Barro, Christian, Chakravarty, Mallar M., Gaetano, Laura, Naegelin, Yvonne, Amann, Michael, Papadopoulou, Athina, Wuerfel, Jens, Kappos, Ludwig, Kuhle, Jens, Sprenger, Till, Granziera, Cristina, Magon, Stefano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8090784/
https://www.ncbi.nlm.nih.gov/pubmed/33624390
http://dx.doi.org/10.1002/hbm.25375
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author Tsagkas, Charidimos
Parmar, Katrin
Pezold, Simon
Barro, Christian
Chakravarty, Mallar M.
Gaetano, Laura
Naegelin, Yvonne
Amann, Michael
Papadopoulou, Athina
Wuerfel, Jens
Kappos, Ludwig
Kuhle, Jens
Sprenger, Till
Granziera, Cristina
Magon, Stefano
author_facet Tsagkas, Charidimos
Parmar, Katrin
Pezold, Simon
Barro, Christian
Chakravarty, Mallar M.
Gaetano, Laura
Naegelin, Yvonne
Amann, Michael
Papadopoulou, Athina
Wuerfel, Jens
Kappos, Ludwig
Kuhle, Jens
Sprenger, Till
Granziera, Cristina
Magon, Stefano
author_sort Tsagkas, Charidimos
collection PubMed
description There is evidence that multiple sclerosis (MS) pathology leads to distinct patterns of volume loss over time (VLOT) in different central nervous system (CNS) structures. We aimed to use such patterns to identify patient subgroups. MS patients of all classical disease phenotypes underwent annual clinical, blood, and MRI examinations over 6 years. Spinal, striatal, pallidal, thalamic, cortical, white matter, and T2‐weighted lesion volumes as well as serum neurofilament light chain (sNfL) were quantified. CNS VLOT patterns were identified using principal component analysis and patients were classified using hierarchical cluster analysis. 225 MS patients were classified into four distinct Groups A, B, C, and D including 14, 59, 141, and 11 patients, respectively). These groups did not differ in baseline demographics, disease duration, disease phenotype distribution, and lesion‐load expansion. Interestingly, Group A showed pronounced spinothalamic VLOT, Group B marked pallidal VLOT, Group C small between‐structure VLOT differences, and Group D myelocortical volume increase and pronounced white matter VLOT. Neurologic deficits were more severe and progressed faster in Group A that also had higher mean sNfL levels than all other groups. Group B experienced more frequent relapses than Group C. In conclusion, there are distinct patterns of VLOT across the CNS in MS patients, which do not overlap with clinical MS subtypes and are independent of disease duration and lesion‐load but are partially associated to sNfL levels, relapse rates, and clinical worsening. Our findings support the need for a more biologic classification of MS subtypes including volumetric and body‐fluid markers.
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spelling pubmed-80907842021-05-10 Classification of multiple sclerosis based on patterns of CNS regional atrophy covariance Tsagkas, Charidimos Parmar, Katrin Pezold, Simon Barro, Christian Chakravarty, Mallar M. Gaetano, Laura Naegelin, Yvonne Amann, Michael Papadopoulou, Athina Wuerfel, Jens Kappos, Ludwig Kuhle, Jens Sprenger, Till Granziera, Cristina Magon, Stefano Hum Brain Mapp Research Articles There is evidence that multiple sclerosis (MS) pathology leads to distinct patterns of volume loss over time (VLOT) in different central nervous system (CNS) structures. We aimed to use such patterns to identify patient subgroups. MS patients of all classical disease phenotypes underwent annual clinical, blood, and MRI examinations over 6 years. Spinal, striatal, pallidal, thalamic, cortical, white matter, and T2‐weighted lesion volumes as well as serum neurofilament light chain (sNfL) were quantified. CNS VLOT patterns were identified using principal component analysis and patients were classified using hierarchical cluster analysis. 225 MS patients were classified into four distinct Groups A, B, C, and D including 14, 59, 141, and 11 patients, respectively). These groups did not differ in baseline demographics, disease duration, disease phenotype distribution, and lesion‐load expansion. Interestingly, Group A showed pronounced spinothalamic VLOT, Group B marked pallidal VLOT, Group C small between‐structure VLOT differences, and Group D myelocortical volume increase and pronounced white matter VLOT. Neurologic deficits were more severe and progressed faster in Group A that also had higher mean sNfL levels than all other groups. Group B experienced more frequent relapses than Group C. In conclusion, there are distinct patterns of VLOT across the CNS in MS patients, which do not overlap with clinical MS subtypes and are independent of disease duration and lesion‐load but are partially associated to sNfL levels, relapse rates, and clinical worsening. Our findings support the need for a more biologic classification of MS subtypes including volumetric and body‐fluid markers. John Wiley & Sons, Inc. 2021-02-24 /pmc/articles/PMC8090784/ /pubmed/33624390 http://dx.doi.org/10.1002/hbm.25375 Text en © 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Tsagkas, Charidimos
Parmar, Katrin
Pezold, Simon
Barro, Christian
Chakravarty, Mallar M.
Gaetano, Laura
Naegelin, Yvonne
Amann, Michael
Papadopoulou, Athina
Wuerfel, Jens
Kappos, Ludwig
Kuhle, Jens
Sprenger, Till
Granziera, Cristina
Magon, Stefano
Classification of multiple sclerosis based on patterns of CNS regional atrophy covariance
title Classification of multiple sclerosis based on patterns of CNS regional atrophy covariance
title_full Classification of multiple sclerosis based on patterns of CNS regional atrophy covariance
title_fullStr Classification of multiple sclerosis based on patterns of CNS regional atrophy covariance
title_full_unstemmed Classification of multiple sclerosis based on patterns of CNS regional atrophy covariance
title_short Classification of multiple sclerosis based on patterns of CNS regional atrophy covariance
title_sort classification of multiple sclerosis based on patterns of cns regional atrophy covariance
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8090784/
https://www.ncbi.nlm.nih.gov/pubmed/33624390
http://dx.doi.org/10.1002/hbm.25375
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