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Multicenter Study
. 2025 Jan;12(1):e200347.
doi: 10.1212/NXI.0000000000200347. Epub 2024 Dec 20.

Neurofilament Light Chain as a Discriminator of Disease Activity Status in MOG Antibody-Associated Disease

Ana Beatriz Ayroza Galvão Ribeiro Gomes  1   2   3   4 , Su-Hyun Kim  5 , Roxanne Pretzsch  1   2   3 , Laila Kulsvehagen  1   2   3 , Sabine Schaedelin  6 , Jasmine Lerner  1   2   3 , Nora Sandrine Wetzel  1   2   3 , Pascal Benkert  6 , Aleksandra Maleska Maceski  1   2   3 , Jae-Won Hyun  5 , Anne-Catherine Lecourt  1   2   3 , Patrick Lipps  1   2   3 , Vinicius Andreoli Schoeps  4 , Aline De Moura Brasil Matos  4   7 , Natalia Trombini Mendes  4 , Samira Luisa Apóstolos-Pereira  4 , Matthias Mehling  1   2   3 , Tobias Derfuss  1   2   3 , Ludwig Kappos  3 , Dagoberto Callegaro  4 , Jens Kuhle  1   2   3 , Ho Jin Kim  5 , Anne-Katrin Pröbstel  1   2   3
Affiliations
Multicenter Study

Neurofilament Light Chain as a Discriminator of Disease Activity Status in MOG Antibody-Associated Disease

Ana Beatriz Ayroza Galvão Ribeiro Gomes et al. Neurol Neuroimmunol Neuroinflamm. 2025 Jan.

Abstract

Background and objectives: In patients with myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD), acute disease activity is generally identified through medical history, neurologic examination, and imaging. However, these may be insufficient for detecting disease activity in specific conditions. This study aimed to investigate the dynamics of serum neurofilament light chain (sNfL) and serum glial fibrillary acidic protein (sGFAP) after clinical attacks and to assess their utility in discriminating attacks from remission in patients with MOGAD.

Methods: We conducted a multicenter, retrospective, longitudinal study including 239 sera from 62 MOGAD patients assessed from 1995 to 2023 in a discovery and validation setup. Sera were measured for sNfL and sGFAP with a single-molecule array assay and for MOG-IgG with a live cell-based assay. sNfL and sGFAP Z scores and percentiles adjusted for age, body mass index, and sex (sGFAP) were calculated from a healthy control normative database. Mixed-effects regression models were used to characterize biomarkers' dynamics and to investigate associations between serum biomarkers, clinical variables, and disease activity status.

Results: Among the 62 study participants, 29 (46.8%) were female, with a median age at baseline of 40.0 years (interquartile range [IQR] 29.5-49.8) and a median duration of follow-up of 20.0 months (IQR 3.0-62.8). sNfL and sGFAP Z scores were nonlinearly associated with time from attack onset (p < 0.001 and = 0.002, respectively). During attacks, both biomarkers presented higher median values (sNfL Z score 2.9 [IQR 1.4-3.5], 99.8th; sGFAP Z score 0.4 [IQR -0.5 to 1.5], 65.5th) compared with remission (sNfL Z score 0.9 [IQR -0.1 to 1.6], 81.6th, p < 0.001; sGFAP Z score -0.2 [IQR -0.8 to 0.5], 42.1th; p < 0.001) across all clinical phenotypes. sNfL values consistently discriminated disease activity status in the discovery and validation cohorts, showing a 3.5-fold increase in the odds of attacks per Z score unit (odds ratio 3.5, 95% confidence interval 2.3-5.1; p < 0.001). Logistic models incorporating sNfL Z scores demonstrated favorable performance in discriminating disease activity status across both cohorts.

Discussion: sNfL Z scores may serve as a biomarker for monitoring disease activity in MOGAD.

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Conflict of interest statement

A.B.A.G.R. Gomes has received a research grant from Roche, paid to the University of São Paulo; S.-H. Kim has lectured, consulted, and received honoraria from Bayer Schering Pharma, Biogen, Genzyme, Merck Serono, and UCB; R. Pretzsch has received travel funds from Teva; A.-C. Lecourt was supported by the Horizon 2020 Eurostar program (grant E!113682); A. Maleska Maceski is an investigator in a phase III clinical trial for the treatment of MOGAD sponsored by Hoffmann-LaRoche; S.L. Apóstolos-Pereira has received research grants and honoraria as a speaker and member of advisory boards by AMGEN/Horizon, Alexion, Biogen, Genzyme, Merck, Novartis, Roche; M. Mehling has received research grants from the Swiss National Science Foundation, Roche, and Merck. His institution (University Hospital Basel) has received fees from his participation on the advisory boards of Merck, Roche, Novartis, and Biogen outside the submitted work; T. Derfuss received speaker fees, research support, travel support, and/or served on Advisory Boards or Steering Committees of Actelion, Alexion, Biogen, Celgene, GeNeuro, MedDay, Merck, Mitsubishi Pharma, Novartis, Roche, and Sanofi-Genzyme; he received research support from Alexion, Biogen, Novartis, Roche, Swiss National Research Foundation, University of Basel, and Swiss MS Society; L. Kappos reported having a patent for Neurostatus UHB-AG with royalties paid Payments made to institution (University Hospital Basel); being CEO of RC2NB (employment by University Hospital Basel), part of the MAGNIMS Steering Committee and a board member of the European Charcot Foundation. J. Kuhle has received speaker fees, research support, travel support, and/or served on advisory boards by Swiss MS Society, Swiss National Research Foundation (320030_189140/1), University of Basel, Progressive MS Alliance, Bayer, Biogen, Bristol Myers Squibb, Celgene, Merck, Novartis, Octave Bioscience, Roche, and Sanofi; H.J. Kim received a grant from the National Research Foundation of Korea and research support from Aprilbio and Eisai; received consultancy/speaker fees from Alexion, Aprilbio, Altos Biologics, Biogen, Celltrion, Daewoong, Eisai, GC Pharma, Handok, Horizon Therapeutics, Kaigene, Kolon Life Science, MDimune, Mitsubishi Tanabe Pharma, Merck Serono, Novartis, Roche, Sanofi Genzyme, Teva-Handok, and UCB; is a co-editor for the Multiple Sclerosis Journal and an associated editor for the Journal of Clinical Neurology; A-K. Pröbstel (institution) received financial compensation for participation in advisory boards, and consultations from Biogen, Novartis, Roche, and UCB, all used for research support; All other authors report no potential conflicts of interest related to this study. Go to Neurology.org/NN for full disclosures.

Figures

Figure 1
Figure 1. Study Design
Flow diagram of study design. MOGAD = MOG antibody-associated disease; sGFAP = serum glial fibrillary acidic protein; sNfL = serum neurofilament light chain. Numbers in parenthesis indicate numbers of serum samples.
Figure 2
Figure 2. sNfL and sGFAP Dynamics
Mixed-effects multivariable linear spline regression models of (A) serum neurofilament light chain (sNfL) Z scores and (B) serum glial fibrillary acidic protein (sGFAP) Z scores during the first 24 weeks following onset of clinical attacks. Marginal effects of time (black lines), 95% confidence intervals (bands), individual biomarker values (black dots), and mean biomarker concentrations in the healthy control reference population (dark blue lines). aZ scores of 0 reflect the mean biomarker concentration in the healthy control population.
Figure 3
Figure 3. sNfL and sGFAP According to Clinical Phenotype and Disease Activity Status
Z scores of (A) serum neurofilament light chain (sNfL) and (B) serum glial fibrillary acidic protein (sGFAP) according to clinical phenotype (orange, cerebral and/or cerebellar; navy-blue, myelitis with/without optic neuritis, cerebral, or cerebellar; mauve, optic neuritis; light-blue, other) and status of disease activity (red dots, attacks; black dots, remission). Adjusted p values from pairwise ANOVA, ns = not significant, *p < 0.05, **p < 0.01, ***p < 0.001. Mixed-effects multivariable linear spline regression models of (C) sNfL and (D) sGFAP Z scores following the onset of clinical attacks. Marginal effects of time (black lines; 95% CIs [bands]), individual biomarker values according to clinical phenotype (orange, cerebral and/or cerebellar; navy-blue, myelitis with/ without optic neuritis, cerebral, or cerebellar; mauve, optic neuritis; light-blue, other), and mean biomarker concentrations in the normative healthy control reference population (dark blue lines). a Z scores of 0 reflect the mean biomarker concentration in the healthy control population.
Figure 4
Figure 4. Associations Between Biomarkers, Clinical Features, and Disease Activity Status
Estimates from univariable mixed-effects logistic regression models for discrimination of status of disease activity (dependent variable) across the discovery (black) and validation (gray) cohorts. Odds ratios (dots), 95% confidence intervals (95% CI; lines), and p values are indicated. The reference levels are untreated for treatment status and female for sex, with age scaled and MOG-IgG displayed in the log2 scale. EDSS = Expanded Disability Status Scale; IgG = immunoglobulin G; MOG = myelin oligodendrocyte protein; sGFAP = serum glial fibrillary acidic protein; sNfL = serum neurofilament light chain.
Figure 5
Figure 5. Discrimination of Disease Activity Status
ROC curves of logistic regression models for discrimination of status of disease activity (dependent variable) across all patients (n = 61, 1 patient excluded because of missing EDSS value). For each patient, samples were averaged according to disease activity status. Green, EDSS and sNfL as independent variables; ochre, sNfL as an independent variable; blue, EDSS as an independent variable. AUC = area under the curve; EDSS = Expanded Disability Status Scale; ROC = receiver operating characteristic; sNfL = serum neurofilament light chain.
See this image and copyright information in PMC

References

    1. Banwell B, Bennett JL, Marignier R, et al. . Diagnosis of myelin oligodendrocyte glycoprotein antibody-associated disease: International MOGAD Panel proposed criteria. Lancet Neurol. 2023;22(3):268-282. doi:10.1016/S1474-4422(22)00431-8 - DOI - PubMed
    1. Jarius S, Ruprecht K, Kleiter I, et al. . MOG-IgG in NMO and related disorders: a multicenter study of 50 patients. Part 2: epidemiology, clinical presentation, radiological and laboratory features, treatment responses, and long-term outcome. J Neuroinflammation. 2016;13(1):280. doi:10.1186/s12974-016-0718-0 - DOI - PMC - PubMed
    1. Syc-Mazurek SB, Chen JJ, Morris P, et al. . Frequency of new or enlarging lesions on MRI outside of clinical attacks in patients with MOG-antibody–associated disease. Neurology. 2022;99(18):795-799. doi:10.1212/WNL.0000000000201263 - DOI - PMC - PubMed
    1. Havla J, Kümpfel T, Schinner R, et al. . Myelin-oligodendrocyte-glycoprotein (MOG) autoantibodies as potential markers of severe optic neuritis and subclinical retinal axonal degeneration. J Neurol. 2017;264(1):139-151. doi:10.1007/s00415-016-8333-7 - DOI - PubMed
    1. Kim H, Lee EJ, Kim S, et al. . Serum biomarkers in myelin oligodendrocyte glycoprotein antibody–associated disease. Neurol Neuroimmunol Neuroinflamm. 2020;7(3):e708. doi:10.1212/NXI.0000000000000708 - DOI - PMC - PubMed

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