4.7 Article

NfL predicts relapse-free progression in a longitudinal multiple sclerosis cohort study

期刊

EBIOMEDICINE
卷 72, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.ebiom.2021.103590

关键词

Multiple sclerosis; Disease progression; Neurofilament light chain; SPMS transition

资金

  1. German Research Council (DFG) [CRC-TR-128]
  2. Else Kroner Fresenius Foundation (Else-Kroner Memorial Stipendium)
  3. Hertie-Stiftung

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This study found that sNfL levels at baseline predict relapse-free disability progression in patients with multiple sclerosis, and further discriminates patients with secondary progressive MS (SPMS) at follow-up.
Background: Easily accessible biomarkers enabling the identification of those patients with multiple sclerosis (MS) who will accumulate irreversible disability in the long term are essential to guide early therapeutic decisions. We here examine the utility of serum neurofilament light chain (sNfL) for forecasting relapse-free disability progression and conversion to secondary progressive MS (SPMS) in the prospective Neurofilament and longterm outcome in MS (NaloMS) cohort. Methods: The predictive ability of sNfL at Baseline and sNfL follow-up (FU)/ Baseline (BL) ratio with regard to disability progression was assessed within a development cohort (NaloMS, n=19 6 patients with relapsing-remitting MS (RRMS) or clinically isolated syndrome) and validated with an external independent cohort (Du euro sseldorf, Essen, n=2 04). Both relapse-free EDSS-progression (RFP: inflamma-tory-independent EDSS-increase 12 months prior to FU) and SPMS-transition (minimum EDSS-score of 3.0) were investigated. Findings: During the study period, 17% (n=34) of NaloMS patients suffered from RFP and 14% (n=27) converted to SPMS at FU (validation cohort RFP n=42, SPMS-conversion n=24). sNfL at BL was increased in patients with RFP (10.8 pg/ml (interquartile range (IQR) 7.7-15.0) vs. 7.2 pg/ml (4.5-12.5), p<0.017). In a multivariable logistic regression model, increased sNfL levels at BL (Odds Ratio (OR) 1.02, 95% confidence interval (CI) 1.01-1.04, p=0.012) remained an independent risk factor for RFP and predicted individual RFP risk with an accuracy of 82% (NaloMS) and 83% (validation cohort) as revealed by support vector machine. In addition, the sNfL FU/BL ratio was increased in SPMS-converters (1.16 (0.89-1.70) vs. 0.96 (0.75-1.23), p=0.011). This was confirmed by a multivariable logistic regression model, as sNfL FU/BL ratio remained in the model (OR 1.476, 95%CI 1.078-2,019, p=0.015) and individual sNfL FU/BL ratios showed a predictive accuracy of 72% in NaloMS (63% in the validation cohort) as revealed by machine learning. Interpretation: sNfL levels at baseline predict relapse-free disability progression in a prospective longitudinal cohort study 6 years later. While prediction was confirmed in an independent cohort, sNfL further discriminates patients with SPMS at follow-up and supports early identification of patients at risk for later SPMS conversion. (C) 2021 The Author(s). Published by Elsevier B.V.

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