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. 2026 Feb;33(2):e70505.
doi: 10.1111/ene.70505.

Neurofilament Light Chain Concentration in the Prediction of Treatment Response in Multiple Sclerosis

Nahid Moradi  1 , Sifat Sharmin  1   2 , Charles B Malpas  1   2   3 , Jens Kuhle  4 , Pascal Benkert  5 , David Leppert  4 , Eva Kubala Havrdová  6 , Dana Horáková  6 , Pavlína Kleinová  6 , Tomas Uher  6 , Ali Manouchehrinia  7   8 , Jan Hillert  7 , Tomas Olsson  7   8 , Ingrid Kochum  7 , Bruce V Taylor  9 , Michael Barnett  10   11 , Trevor J Kilpatrick  2   12 , Katherine Buzzard  13 , Tomas Kalincik  1   2
Affiliations

Neurofilament Light Chain Concentration in the Prediction of Treatment Response in Multiple Sclerosis

Nahid Moradi et al. Eur J Neurol. 2026 Feb.

Abstract

Introduction: Management of multiple sclerosis (MS) revolves around timely initiation of effective disease-modifying therapy. Here we investigate the additive predictive value of age-adjusted normalised neurofilament light chain (NfL) concentrations when combined with a clinicodemographic model of treatment response.

Methods: Data were obtained from three sources: the University Hospital Basel, the SET cohort in Prague, and EIMS and IMSE cohorts from Sweden. NfL samples were collected within 90 days of baseline, age-adjusted and normalised using a reference population. Principal component analysis reduced the dimensionality of clinicodemographic predictors. Cox proportional hazards models estimated cumulative hazards of relapse, 6-month confirmed disability worsening and 9-month confirmed disability improvement, with and without NfL. Uno's concordance index compared prediction accuracy across pooled and treatment-specific models.

Results: The study included 1716 individuals across three therapies: interferon β (n = 554), fingolimod (n = 307) and natalizumab (n = 369). Clinicodemographic characteristics were associated with relapse and disability outcomes. While NfL showed no association in the pooled cohort, in the natalizumab group, higher NfL predicted lower probability of disability improvement (HR = 0.819, 95% CI: 0.814-0.823). Pooled models predicted outcomes with moderate accuracy (relapse: 63.4%, disability worsening: 56.4%, improvement: 67.7%), with minimal contribution from NfL. In treatment-specific models, NfL-inclusive accuracy ranged from 51.3%-62.2% (relapse), 54.3%-60.3% (worsening) and 65%-67.9% (improvement), closely matching models without NfL.

Conclusion: In well-characterised MS patients treated with interferon β, fingolimod or natalizumab, clinicodemographic information provides modest prognostic value; however, NfL adds minimal incremental utility.

Keywords: multiple sclerosis; neurofilament light; prediction; principal component analysis; treatment response.

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Conflict of interest statement

E.K.H. has received honoraria/research support from Biogen, Merck Serono, Novartis, Roche and Teva and has served as a member of advisory boards for Actelion, Biogen, Celgene, Merck Serono, Novartis and Sanofi Genzyme. D.H. was supported by Cooperation Program in Neuroscience, Charles University; by the project National Institute for Neurological Research (Programme EXCELES, ID Project No. LX22NPO5107) – Funded by the European Union – Next Generation EU, and by General University Hospital in Prague project MH CZ‐RVO‐VFN64165. She also received compensation for travel, speaker honoraria, and consultant fees from Biogen, Novartis, Merck, Bayer, Sanofi Genzyme, Roche and Teva, as well as support for research activities from Biogen Idec. T.U. received financial support for conference travel and honoraria from Biogen, Novartis, Roche, Bristol Myers Squibb and Merck, as well as support for research activities from Biogen and Sanofi. He also received funding from the Czech Ministry of Health project (NU22‐04‐00193), the Charles University Cooperation Program in Neuroscience, and the National Institute for Neurological Research project funded by the European Union – Next Generation EU (Programme EXCELES, ID Project no. LX22NPO5107). T.O. has received advisory board/lecture honoraria as well as unrestricted MS research grants from Biogen, Merck, Novartis and Sanofi, none of which have any relation to the current paper. Academic MS research grants have been received from the Swedish Research Council, the Wallenberg Foundation, the Swedish Brain Foundation and Margaretha af Ugglas Foundation. J.H. declares research grants outside of this study from Biogen, Bristol‐Myers‐Squibb, Janssen, Merck KGaA, Novartis, Roche, and Sanofi‐Genzyme, and speaker's fees or fees for serving on advisory boards for Biogen, Bristol‐Myers‐Squibb, Janssen, Merck KGaA, Novartis, Sandoz, Sanofi‐Genzyme and Teva. I.K. has received lecture honoraria from Merck and a research grant from Pfizer. K.B. Katherine Buzzard has received speaker's fees or honoraria for serving on advisory boards for Biogen, Merck, Roche, Novartis, UCB, Alexion, Argenx, and CSL. T.K. served on scientific advisory boards or as a consultant for MS International Federation and World Health Organisation, Therapeutic Goods Administration, BMS, Roche, Janssen, Genzyme, Novartis, Merck and Biogen, received conference travel support and/or speaker honoraria from WebMD Global, Merck, Sandoz, Novartis, Biogen, Roche, Eisai, Genzyme, Teva and BioCSL, and received research or educational event support from Biogen, Novartis, Genzyme, Roche, Celgene and Merck. The other authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
CONSORT diagram for case inclusion into the study.
FIGURE 2
FIGURE 2
Forest plot for the contributions of patients' clinical and demographic characteristics to the prediction of NfL models of on‐treatment clinical outcomes with and without the background clinical information in the pooled cohort analysis.
FIGURE 3
FIGURE 3
Forest plot for the contributions of NfL z scores to the prediction of on‐treatment clinical outcomes with and without PCs. The results are shown for adjusted multivariable Cox models for treatment‐specific models.
See this image and copyright information in PMC

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