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Introduction <p>Serum neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) may synergistically enhance early risk stratification of multiple sclerosis (MS) diagnosis after clinically isolated syndromes (CIS). We investigated the prognostic value of combined NfL and GFAP for McDonald 2024 MS diagnosis after CIS and associations with key genetic and environmental risk factors.</p> Methods <p>CIS participants, within six months after symptom onset, were included in a prospective cohort. We measured baseline serum NfL and GFAP levels and calculated z-scores. We evaluated weighted genetic risk scores for MS susceptibility, HLA-DRB1*15:01 risk and measured Anti-Epstein Barr virus Nuclear Antigen-1 (anti-EBNA1) immunoglobulin G (IgG) antibodies. Associations with MS diagnosis were evaluated using Cox proportional hazards models and time-dependent receiver operating characteristic (ROC) analyses.</p> Results <p>During follow-up, 162/221 CIS participants were diagnosed with McDonald 2024 MS. Separately, high NfL and GFAP associated with earlier MS diagnoses (hazard ratio (HR) 1.36, 95% confidence interval (CI) 1.12–1.66, <i>p</i> = 0.002, HR 1.12, 95% CI 1.02–1.42, <i>p</i> = 0.01, respectively). In combined models, only NfL remained independently predictive (HR 1.30, 95% CI 1.02–1.60, <i>p</i> = 0.01). Time-dependent ROC analyses showed similar results for NfL alone and combined with GFAP. HLA-DRB1*15:01-risk, but not GFAP or anti-EBNA1 IgG, improved predictive value.</p> Conclusion <p>Our study found that serum NfL outperformed GFAP in predicting early MS diagnoses after CIS. Baseline NfL, together with HLA-DRB1*15:01 status, provides robust early risk stratification for MS after CIS, whereas GFAP and anti-EBNA1 titres add limited prognostic value. Additional immunological and imaging markers are essential to further refine predictive models.</p>

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Serum neurofilament light chain and glial fibrillary acidic protein predicting multiple sclerosis after clinically isolated syndrome

  • 期刊论文

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  • 发表日期：

    2026年2月9日

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  • Cato E. A. Corsten,
  • Veerle S. A. Geraedts,
  • Ana M. Marques,
  • Marie-José Melief,
  • Barry Koelewijn - van Vliet,
  • Jeroen van Rooij,
  • Marcello Ciaccio,
  • Luisa Agnello,
  • Jens Kuhle,
  • Andrei N. Tintu,
  • Beatrijs Wokke,
  • Joost Smolders

摘要

Introduction

Serum neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) may synergistically enhance early risk stratification of multiple sclerosis (MS) diagnosis after clinically isolated syndromes (CIS). We investigated the prognostic value of combined NfL and GFAP for McDonald 2024 MS diagnosis after CIS and associations with key genetic and environmental risk factors.

Methods

CIS participants, within six months after symptom onset, were included in a prospective cohort. We measured baseline serum NfL and GFAP levels and calculated z-scores. We evaluated weighted genetic risk scores for MS susceptibility, HLA-DRB1*15:01 risk and measured Anti-Epstein Barr virus Nuclear Antigen-1 (anti-EBNA1) immunoglobulin G (IgG) antibodies. Associations with MS diagnosis were evaluated using Cox proportional hazards models and time-dependent receiver operating characteristic (ROC) analyses.

Results

During follow-up, 162/221 CIS participants were diagnosed with McDonald 2024 MS. Separately, high NfL and GFAP associated with earlier MS diagnoses (hazard ratio (HR) 1.36, 95% confidence interval (CI) 1.12–1.66, p = 0.002, HR 1.12, 95% CI 1.02–1.42, p = 0.01, respectively). In combined models, only NfL remained independently predictive (HR 1.30, 95% CI 1.02–1.60, p = 0.01). Time-dependent ROC analyses showed similar results for NfL alone and combined with GFAP. HLA-DRB1*15:01-risk, but not GFAP or anti-EBNA1 IgG, improved predictive value.

Conclusion

Our study found that serum NfL outperformed GFAP in predicting early MS diagnoses after CIS. Baseline NfL, together with HLA-DRB1*15:01 status, provides robust early risk stratification for MS after CIS, whereas GFAP and anti-EBNA1 titres add limited prognostic value. Additional immunological and imaging markers are essential to further refine predictive models.