Evidence map›Paper›PMID 41622995›Full record

ArticleEuropean journal of neurology2026

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

Nahid Moradi, Sifat Sharmin, Charles B Malpas, Jens Kuhle, Pascal Benkert, David Leppert, Eva Kubala Havrdová, Dana Horáková, Pavlína Kleinová, Tomas Uher and 9 more

Abstract read
In one paragraph

Article in European journal of neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Nahid MoradiCore, Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia.ORCID 0000-0001-6253-4487
Sifat SharminCore, Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia.
Charles B MalpasCore, Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia.
Jens KuhleNeurologic Clinic and Policlinic, MS Centre and Research Centre for Clinical Neuroimmunology and Neuroscience Basel, University of Basel, Basel, Switzerland.
Pascal BenkertClinical Trial Unit, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland.
David LeppertNeurologic Clinic and Policlinic, MS Centre and Research Centre for Clinical Neuroimmunology and Neuroscience Basel, University of Basel, Basel, Switzerland.
Eva Kubala HavrdováDepartment of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic.
Dana HorákováDepartment of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic.
Pavlína KleinováDepartment of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic.
Tomas UherDepartment of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic.ORCID 0000-0003-3160-9022
Ali ManouchehriniaDepartment of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Jan HillertDepartment of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Tomas OlssonDepartment of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Ingrid KochumDepartment of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0002-0867-4726
Bruce V TaylorMenzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.
Michael BarnettBrain and Mind Centre, the University of Sydney, Sydney, New South Wales, Australia.
Trevor J KilpatrickNeuroimmunology Centre, Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia.
Katherine BuzzardDepartment of Neurology, Box Hill Hospital, Melbourne, Australia.
Tomas KalincikCore, Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia.

Funding

National Health and Medical Research Council 1157717 Kalincik
6 · The paper itself

Abstract

introductionManagement 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.

methodsData 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.

resultsThe 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.

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

Indexed as

Immunologic FactorsMultiple SclerosisNeurofilament ProteinsAdultCohort StudiesFemaleFingolimod HydrochlorideHumansInterferon-betaMaleMiddle AgedNatalizumabPredictive Value of TestsTreatment Effect HeterogeneityTreatment OutcomeFingolimod HydrochlorideImmunologic FactorsInterferon-betaNatalizumabneurofilament protein LNeurofilament Proteinsmultiple sclerosisneurofilament lightpredictionprincipal component analysistreatment response

Identifiers

PMID41622995
PMCPMC12862443

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.