001     164974
005     20230915090603.0
024 7 _ |a pmc:PMC9762941
|2 pmc
024 7 _ |a 10.1093/brain/awac253
|2 doi
024 7 _ |a pmid:35903017
|2 pmid
024 7 _ |a 0006-8950
|2 ISSN
024 7 _ |a 1460-2156
|2 ISSN
024 7 _ |a altmetric:133268374
|2 altmetric
037 _ _ |a DZNE-2022-01378
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Chelban, Viorica
|0 0000-0002-7797-0756
|b 0
245 _ _ |a Neurofilament light levels predict clinical progression and death in multiple system atrophy.
260 _ _ |a Oxford
|c 2022
|b Oxford Univ. Press
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1671620123_26522
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Disease-modifying treatments are currently being trialed in multiple system atrophy (MSA). Approaches based solely on clinical measures are challenged by heterogeneity of phenotype and pathogenic complexity. Neurofilament light chain protein has been explored as a reliable biomarker in several neurodegenerative disorders but data in multiple system atrophy have been limited. Therefore, neurofilament light chain is not yet routinely used as an outcome measure in MSA. We aimed to comprehensively investigate the role and dynamics of neurofilament light chain in multiple system atrophy combined with cross-sectional and longitudinal clinical and imaging scales and for subject trial selection. In this cohort study we recruited cross-sectional and longitudinal cases in multicentre European set-up. Plasma and cerebrospinal fluid neurofilament light chain concentrations were measured at baseline from 212 multiple system atrophy cases, annually for a mean period of 2 years in 44 multiple system atrophy patients in conjunction with clinical, neuropsychological and MRI brain assessments. Baseline neurofilament light chain characteristics were compared between groups. Cox regression was used to assess survival; ROC analysis to assess the ability of neurofilament light chain to distinguish between multiple system atrophy patients and healthy controls. Multivariate linear mixed effects models were used to analyse longitudinal neurofilament light chain changes and correlated with clinical and imaging parameters. Polynomial models were used to determine the differential trajectories of neurofilament light chain in multiple system atrophy. We estimated sample sizes for trials aiming to decrease NfL levels. We show that in multiple system atrophy, baseline plasma neurofilament light chain levels were better predictors of clinical progression, survival, and degree of brain atrophy than the NfL rate of change. Comparative analysis of multiple system atrophy progression over the course of disease, using plasma neurofilament light chain and clinical rating scales, indicated that neurofilament light chain levels rise as the motor symptoms progress, followed by deceleration in advanced stages. Sample size prediction suggested that significantly lower trial participant numbers would be needed to demonstrate treatment effects when incorporating plasma neurofilament light chain values into multiple system atrophy clinical trials in comparison to clinical measures alone. In conclusion, neurofilament light chain correlates with clinical disease severity, progression, and prognosis in multiple system atrophy. Combined with clinical and imaging analysis, neurofilament light chain can inform patient stratification and serve as a reliable biomarker of treatment response in future multiple system atrophy trials of putative disease-modifying agents.
536 _ _ |a 353 - Clinical and Health Care Research (POF4-353)
|0 G:(DE-HGF)POF4-353
|c POF4-353
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: pub.dzne.de
650 _ 7 |a MSA
|2 Other
650 _ 7 |a NfL
|2 Other
650 _ 7 |a multiple system atrophy
|2 Other
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Cohort Studies
|2 MeSH
650 _ 2 |a Multiple System Atrophy
|2 MeSH
650 _ 2 |a Cross-Sectional Studies
|2 MeSH
650 _ 2 |a Intermediate Filaments
|2 MeSH
650 _ 2 |a Neurofilament Proteins
|2 MeSH
650 _ 2 |a Biomarkers
|2 MeSH
650 _ 2 |a Disease Progression
|2 MeSH
700 1 _ |a Nikram, Elham
|b 1
700 1 _ |a Perez-Soriano, Alexandra
|b 2
700 1 _ |a Wilke, Carlo
|0 P:(DE-2719)2814101
|b 3
|u dzne
700 1 _ |a Foubert-Samier, Alexandra
|b 4
700 1 _ |a Vijiaratnam, Nirosen
|0 0000-0002-9671-0212
|b 5
700 1 _ |a Guo, Tong
|b 6
700 1 _ |a Jabbari, Edwin
|0 0000-0001-6844-882X
|b 7
700 1 _ |a Olufodun, Simisola
|b 8
700 1 _ |a Gonzalez, Mariel
|b 9
700 1 _ |a Senkevich, Konstantin
|b 10
700 1 _ |a Laurens, Brice
|b 11
700 1 _ |a Péran, Patrice
|0 0000-0001-7200-0139
|b 12
700 1 _ |a Rascol, Olivier
|b 13
700 1 _ |a Le Traon, Anne Pavy
|b 14
700 1 _ |a Todd, Emily G
|0 0000-0003-1551-5691
|b 15
700 1 _ |a Costantini, Alyssa A
|b 16
700 1 _ |a Alikhwan, Sondos
|b 17
700 1 _ |a Tariq, Ambreen
|b 18
700 1 _ |a Lin Ng, Bai
|b 19
700 1 _ |a Muñoz, Esteban
|b 20
700 1 _ |a Painous, Celia
|b 21
700 1 _ |a Compta, Yaroslau
|b 22
700 1 _ |a Junque, Carme
|b 23
700 1 _ |a Segura, Barbara
|b 24
700 1 _ |a Zhelcheska, Kristina
|b 25
700 1 _ |a Wellington, Henny
|b 26
700 1 _ |a Schöls, Ludger
|0 P:(DE-2719)2810795
|b 27
|u dzne
700 1 _ |a Jaunmuktane, Zane
|b 28
700 1 _ |a Kobylecki, Christopher
|b 29
700 1 _ |a Church, Alistair
|b 30
700 1 _ |a Hu, Michele T M
|b 31
700 1 _ |a Rowe, James B
|b 32
700 1 _ |a Leigh, P Nigel
|b 33
700 1 _ |a Massey, Luke
|b 34
700 1 _ |a Burn, David J
|b 35
700 1 _ |a Pavese, Nicola
|0 0000-0002-6801-6194
|b 36
700 1 _ |a Foltynie, Tom
|0 0000-0003-0752-1813
|b 37
700 1 _ |a Pchelina, Sofya
|b 38
700 1 _ |a Wood, Nicholas
|b 39
700 1 _ |a Heslegrave, Amanda J
|b 40
700 1 _ |a Zetterberg, Henrik
|b 41
700 1 _ |a Bocchetta, Martina
|0 0000-0003-1814-5024
|b 42
700 1 _ |a Rohrer, Jonathan D
|b 43
700 1 _ |a Marti, Maria J
|b 44
700 1 _ |a Synofzik, Matthis
|0 P:(DE-2719)2811275
|b 45
|u dzne
700 1 _ |a Morris, Huw R
|0 0000-0002-5473-3774
|b 46
700 1 _ |a Meissner, Wassilios G
|b 47
700 1 _ |a Houlden, Henry
|0 0000-0002-2866-7777
|b 48
773 _ _ |a 10.1093/brain/awac253
|g p. awac253
|0 PERI:(DE-600)1474117-9
|n 12
|p 4398-4408
|t Brain
|v 145
|y 2022
|x 0006-8950
856 4 _ |u https://pub.dzne.de/record/164974/files/DZNE-2022-01378.pdf
|y OpenAccess
856 4 _ |u https://pub.dzne.de/record/164974/files/DZNE-2022-01378.pdf?subformat=pdfa
|x pdfa
|y OpenAccess
909 C O |o oai:pub.dzne.de:164974
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Deutsches Zentrum für Neurodegenerative Erkrankungen
|0 I:(DE-588)1065079516
|k DZNE
|b 3
|6 P:(DE-2719)2814101
910 1 _ |a Deutsches Zentrum für Neurodegenerative Erkrankungen
|0 I:(DE-588)1065079516
|k DZNE
|b 27
|6 P:(DE-2719)2810795
910 1 _ |a Deutsches Zentrum für Neurodegenerative Erkrankungen
|0 I:(DE-588)1065079516
|k DZNE
|b 45
|6 P:(DE-2719)2811275
913 1 _ |a DE-HGF
|b Gesundheit
|l Neurodegenerative Diseases
|1 G:(DE-HGF)POF4-350
|0 G:(DE-HGF)POF4-353
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Clinical and Health Care Research
|x 0
914 1 _ |y 2022
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-01-29
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2021-01-29
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-01-29
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2022-11-09
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2022-11-09
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2022-11-09
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2022-11-09
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2022-11-09
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2022-11-09
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2022-11-09
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2022-11-09
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b BRAIN : 2021
|d 2022-11-09
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2022-11-09
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2022-11-09
915 _ _ |a IF >= 15
|0 StatID:(DE-HGF)9915
|2 StatID
|b BRAIN : 2021
|d 2022-11-09
920 1 _ |0 I:(DE-2719)1210000
|k AG Gasser 1
|l Parkinson Genetics
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-2719)1210000
980 _ _ |a UNRESTRICTED
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21