Serum neurofilament light chain levels associations with gray matter pathology: a 5-year longitudinal study.


Journal

Annals of clinical and translational neurology
ISSN: 2328-9503
Titre abrégé: Ann Clin Transl Neurol
Pays: United States
ID NLM: 101623278

Informations de publication

Date de publication:
09 2019
Historique:
received: 16 04 2019
revised: 05 07 2019
accepted: 29 07 2019
pubmed: 23 8 2019
medline: 19 5 2020
entrez: 23 8 2019
Statut: ppublish

Résumé

Gray matter (GM) pathology is closely associated with physical and cognitive impairment in persons with multiple sclerosis (PwMS). Similarly, serum neurofilament light chain (sNfL) levels are related to MS disease activity and progression. To assess the cross-sectional and longitudinal associations between sNfL and MRI-derived lesion and brain volume outcomes in PwMS and age-matched healthy controls (HCs). Forty-seven HCs and 120 PwMS were followed over 5 years. All subjects underwent baseline and follow-up 3T MRI and sNfL examinations. Lesion volumes (LV) and global, tissue-specific and regional brain volumes were assessed. sNfL levels were analyzed using single molecule array (Simoa) assay and quantified in pg/mL. The associations between sNfL levels and MRI outcomes were investigated using regression analyses adjusted for age, sex, baseline disease modifying treatment (DMT) use and change in DMT over the follow-up. False discovery rate (FDR)-adjusted q-values <0.05 were considered significant. In PwMS, baseline sNfL was associated with baseline T Higher sNfL levels were associated with baseline LVs and greater development of GM atrophy in PwMS.

Sections du résumé

BACKGROUND
Gray matter (GM) pathology is closely associated with physical and cognitive impairment in persons with multiple sclerosis (PwMS). Similarly, serum neurofilament light chain (sNfL) levels are related to MS disease activity and progression.
OBJECTIVES
To assess the cross-sectional and longitudinal associations between sNfL and MRI-derived lesion and brain volume outcomes in PwMS and age-matched healthy controls (HCs).
MATERIALS AND METHODS
Forty-seven HCs and 120 PwMS were followed over 5 years. All subjects underwent baseline and follow-up 3T MRI and sNfL examinations. Lesion volumes (LV) and global, tissue-specific and regional brain volumes were assessed. sNfL levels were analyzed using single molecule array (Simoa) assay and quantified in pg/mL. The associations between sNfL levels and MRI outcomes were investigated using regression analyses adjusted for age, sex, baseline disease modifying treatment (DMT) use and change in DMT over the follow-up. False discovery rate (FDR)-adjusted q-values <0.05 were considered significant.
RESULTS
In PwMS, baseline sNfL was associated with baseline T
CONCLUSION
Higher sNfL levels were associated with baseline LVs and greater development of GM atrophy in PwMS.

Identifiants

pubmed: 31437387
doi: 10.1002/acn3.50872
pmc: PMC6764487
doi:

Substances chimiques

Neurofilament Proteins 0

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

1757-1770

Subventions

Organisme : Novartis Pharma AG, Basel, Switzerland
Pays : International
Organisme : Swiss National Research Foundation
ID : 320030_160221
Pays : International

Commentaires et corrections

Type : CommentIn
Type : ErratumIn

Informations de copyright

© 2019 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals, Inc on behalf of American Neurological Association.

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Auteurs

Dejan Jakimovski (D)

Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York.

Jens Kuhle (J)

Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland.

Murali Ramanathan (M)

Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, New York.

Christian Barro (C)

Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland.

Davorka Tomic (D)

Novartis Pharma AG, Basel, Switzerland.

Jesper Hagemeier (J)

Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York.

Harald Kropshofer (H)

Novartis Pharma AG, Basel, Switzerland.

Niels Bergsland (N)

Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York.

David Leppert (D)

Novartis Pharma AG, Basel, Switzerland.

Michael G Dwyer (MG)

Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York.
Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, New York.

Zuzanna Michalak (Z)

Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine and Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland.

Ralph H B Benedict (RHB)

Jacobs MS Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York.

Bianca Weinstock-Guttman (B)

Jacobs MS Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York.

Robert Zivadinov (R)

Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York.
Center for Biomedical Imaging at Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, New York.

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Classifications MeSH