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

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Title: Neurofilament Light Chain Concentration in the Prediction of Treatment Response in Multiple Sclerosis.
Authors: Moradi, Nahid (AUTHOR), Sharmin, Sifat (AUTHOR), Malpas, Charles B. (AUTHOR), Kuhle, Jens (AUTHOR), Benkert, Pascal (AUTHOR), Leppert, David (AUTHOR), Havrdová, Eva Kubala (AUTHOR), Horáková, Dana (AUTHOR), Kleinová, Pavlína (AUTHOR), Uher, Tomas (AUTHOR), Manouchehrinia, Ali (AUTHOR), Hillert, Jan (AUTHOR), Olsson, Tomas (AUTHOR), Kochum, Ingrid (AUTHOR), Taylor, Bruce V. (AUTHOR), Barnett, Michael (AUTHOR), Kilpatrick, Trevor J. (AUTHOR), Buzzard, Katherine (AUTHOR), Kalincik, Tomas (AUTHOR)
Source: European Journal of Neurology. Feb2026, Vol. 33 Issue 2, p1-11. 11p.
Subjects: Biomarkers, Prediction models, Disease progression, Treatment effectiveness, Multiple sclerosis, Disease relapse, Therapeutics
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. [ABSTRACT FROM AUTHOR]
Copyright of European Journal of Neurology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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DbLabel: Psychology and Behavioral Sciences Collection
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PubTypeId: academicJournal
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  Data: Neurofilament Light Chain Concentration in the Prediction of Treatment Response in Multiple Sclerosis.
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  Data: <searchLink fieldCode="AR" term="%22Moradi%2C+Nahid%22">Moradi, Nahid</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sharmin%2C+Sifat%22">Sharmin, Sifat</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Malpas%2C+Charles+B%2E%22">Malpas, Charles B.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kuhle%2C+Jens%22">Kuhle, Jens</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Benkert%2C+Pascal%22">Benkert, Pascal</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Leppert%2C+David%22">Leppert, David</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Havrdová%2C+Eva+Kubala%22">Havrdová, Eva Kubala</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Horáková%2C+Dana%22">Horáková, Dana</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kleinová%2C+Pavlína%22">Kleinová, Pavlína</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Uher%2C+Tomas%22">Uher, Tomas</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Manouchehrinia%2C+Ali%22">Manouchehrinia, Ali</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hillert%2C+Jan%22">Hillert, Jan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Olsson%2C+Tomas%22">Olsson, Tomas</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kochum%2C+Ingrid%22">Kochum, Ingrid</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Taylor%2C+Bruce+V%2E%22">Taylor, Bruce V.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Barnett%2C+Michael%22">Barnett, Michael</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kilpatrick%2C+Trevor+J%2E%22">Kilpatrick, Trevor J.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Buzzard%2C+Katherine%22">Buzzard, Katherine</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kalincik%2C+Tomas%22">Kalincik, Tomas</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Neurology%22">European Journal of Neurology</searchLink>. Feb2026, Vol. 33 Issue 2, p1-11. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Biomarkers%22">Biomarkers</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+progression%22">Disease progression</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+effectiveness%22">Treatment effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+sclerosis%22">Multiple sclerosis</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+relapse%22">Disease relapse</searchLink><br /><searchLink fieldCode="DE" term="%22Therapeutics%22">Therapeutics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of European Journal of Neurology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1111/ene.70505
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        Text: English
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      – SubjectFull: Disease relapse
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              Text: Feb2026
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