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

Area of Science:

• Neurology
• Biomarker Discovery
• Clinical Trials

Background:

• Identifying biomarkers for irreversible disability in multiple sclerosis (MS) is crucial for early treatment decisions.
• Serum neurofilament light chain (sNfL) is investigated as a potential predictor of long-term disability accumulation and secondary progressive MS (SPMS) conversion.

Purpose of the Study:

• To assess the utility of sNfL for forecasting relapse-free disability progression (RFP) and SPMS conversion.
• To validate the predictive capabilities of sNfL in independent cohorts.

Main Methods:

• A prospective longitudinal cohort study (NaloMS) and an external validation cohort were used.
• sNfL levels at baseline and the sNfL follow-up/baseline ratio were analyzed.
• Machine learning models (support vector machine, logistic regression) were employed to assess predictive accuracy for RFP and SPMS transition.

Main Results:

• Increased baseline sNfL levels predicted RFP with 82-83% accuracy.
• The sNfL follow-up/baseline ratio predicted SPMS conversion with 63-72% accuracy.
• sNfL levels independently predicted disability progression and SPMS conversion in multivariable models.

Conclusions:

• Baseline sNfL levels are a reliable predictor of long-term disability progression in MS.
• sNfL aids in the early identification of patients at risk for SPMS, supporting timely therapeutic interventions.