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

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.