TY - JOUR
T1 - Subtyping relapsing–remitting multiple sclerosis using structural MRI
AU - Zhuo, Zhizheng
AU - Li, Yongmei
AU - Duan, Yunyun
AU - Cao, Guanmei
AU - Zheng, Fenglian
AU - Ding, Jinli
AU - Tian, Decai
AU - Wang, Xinli
AU - Wang, Jinhui
AU - Zhang, Xinghu
AU - Li, Kuncheng
AU - Zhou, Fuqing
AU - Huang, Muhua
AU - Li, Yuxin
AU - Li, Haiqing
AU - Zeng, Chun
AU - Zhang, Ningnannan
AU - Sun, Jie
AU - Yu, Chunshui
AU - Han, Xuemei
AU - Haller, Sven
AU - Barkhof, Frederik
AU - Shi, Fudong
AU - Liu, Yaou
N1 - Funding Information:
Beijing Natural Science fund, Grant/Award Number: 7133244; National Science Foundation of China, Grant/Award Numbers: 81571631, 81870958.
Funding Information:
Frederik Barkhof acts as a consultant for Apitope, Bayer-Schering, Biogen-Idec, GeNeuro, Sanofi-Genzyme, Ixico, Janssen Research, Merck-Serono, Novartis, Roche and TEVA. He has received grants, or grants are pending, from the Amyloid Imaging to Prevent Alzheimer’s Disease (AMYPAD) initiative, the Biomedical Research Centre at University College London Hospitals, the Dutch MS Society, ECTRIMS–MAGNIMS, EU-H2020, the Dutch Research Council (NWO), the UK MS Society, and the National Institute for Health Research, University College London. He has received payments for the development of educational presentations from Ixico and to his institution from Biogen-Idec. He is on the editorial board of Radiology, Brain, European Radiology, Multiple Sclerosis Journal and Neurology. None of the other authors declare a relevant conflict of interest.
Publisher Copyright:
© 2021, Springer-Verlag GmbH, DE part of Springer Nature.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/5
Y1 - 2021/5
N2 - Background and purpose: Subtyping relapsing–remitting multiple sclerosis (RRMS) patients may help predict disease progression and triage patients for treatment. We aimed to subtype RRMS patients by structural MRI and investigate their clinical significances. Methods: 155 relapse-remitting MS (RRMS) and 210 healthy controls (HC) were retrospectively enrolled with structural 3DT1, diffusion tensor imaging (DTI) and resting-state functional MRI. Z scores of cortical and deep gray matter volumes (CGMV and DGMV) and white matter fractional anisotropy (WM-FA) in RRMS patients were calculated based on means and standard deviations of HC. We defined RRMS as “normal” (− 2 < z scores of both GMV and WM-FA), DGM (z scores of DGMV < − 2), and DGM-plus types (z scores of DGMV and [CGMV or WM-FA] < − 2) according to combinations of z scores compared to HC. Expanded disability status scale (EDSS), cognitive and functional MRI measurements, and conversion rate to secondary progressive MS (SPMS) at 5-year follow-up were compared between subtypes. Results: 77 (49.7%) patients were “normal” type, 37 (23.9%) patients were DGM type and 34 (21.9%) patients were DGM-plus type. 7 (4.5%) patients who were not categorized into the above types were excluded. DGM-plus type had the highest EDSS. Both DGM and DGM-plus types had more severe cognitive impairment than “normal” type. Only DGM-plus type showed decreased functional MRI measures compared to HC. A higher conversion ratio to SPMS in DGM-plus type (55%) was identified compared to “normal” type (14%, p < 0.001) and DGM type (20%, p = 0.005). Conclusion: Three MRI-subtypes of RRMS were identified with distinct clinical and imaging features and different prognosis.
AB - Background and purpose: Subtyping relapsing–remitting multiple sclerosis (RRMS) patients may help predict disease progression and triage patients for treatment. We aimed to subtype RRMS patients by structural MRI and investigate their clinical significances. Methods: 155 relapse-remitting MS (RRMS) and 210 healthy controls (HC) were retrospectively enrolled with structural 3DT1, diffusion tensor imaging (DTI) and resting-state functional MRI. Z scores of cortical and deep gray matter volumes (CGMV and DGMV) and white matter fractional anisotropy (WM-FA) in RRMS patients were calculated based on means and standard deviations of HC. We defined RRMS as “normal” (− 2 < z scores of both GMV and WM-FA), DGM (z scores of DGMV < − 2), and DGM-plus types (z scores of DGMV and [CGMV or WM-FA] < − 2) according to combinations of z scores compared to HC. Expanded disability status scale (EDSS), cognitive and functional MRI measurements, and conversion rate to secondary progressive MS (SPMS) at 5-year follow-up were compared between subtypes. Results: 77 (49.7%) patients were “normal” type, 37 (23.9%) patients were DGM type and 34 (21.9%) patients were DGM-plus type. 7 (4.5%) patients who were not categorized into the above types were excluded. DGM-plus type had the highest EDSS. Both DGM and DGM-plus types had more severe cognitive impairment than “normal” type. Only DGM-plus type showed decreased functional MRI measures compared to HC. A higher conversion ratio to SPMS in DGM-plus type (55%) was identified compared to “normal” type (14%, p < 0.001) and DGM type (20%, p = 0.005). Conclusion: Three MRI-subtypes of RRMS were identified with distinct clinical and imaging features and different prognosis.
KW - Diffusion tensor imaging
KW - Fractional anisotropy
KW - Gray matter volume
KW - Magnetic resonance imaging
KW - Relapsing–remitting multiple sclerosis
UR - http://www.scopus.com/inward/record.url?scp=85098528680&partnerID=8YFLogxK
U2 - 10.1007/s00415-020-10376-7
DO - 10.1007/s00415-020-10376-7
M3 - Article
C2 - 33387013
AN - SCOPUS:85098528680
SN - 0340-5354
VL - 268
SP - 1808
EP - 1817
JO - Journal of Neurology
JF - Journal of Neurology
IS - 5
ER -