Neurofilament-light and contactin-1 association with long-term brain atrophy in natalizumab-treated relapsing-remitting multiple sclerosis

Abstract

Background: Despite highly effective treatment strategies for patients with relapsing-remitting multiple sclerosis (RRMS), long-term neurodegeneration and disease progression are often considerable. Accurate blood-based biomarkers that predict long-term neurodegeneration are lacking. Objective: To assess the predictive value of serum neurofilament-light (sNfL) and serum contactin-1 (sCNTN1) for long-term magnetic resonance imaging (MRI)–derived neurodegeneration in natalizumab-treated patients with RRMS. Methods: sNfL and sCNTN1 were measured in an observational cohort of natalizumab-treated patients with RRMS at baseline (first dose) and at 3 months, Year 1, Year 2, and last follow-up (median = 5.2 years) of treatment. Disability progression was quantified using “EDSS-plus” criteria. Neurodegeneration was measured by calculating annualized percentage brain, ventricular, and thalamic volume change (PBVC, VVC, and TVC, respectively). Linear regression analysis was performed to identify longitudinal predictors of neurodegeneration. Results: In total, 88 patients (age = 37 ± 9 years, 75% female) were included, of whom 48% progressed. Year 1 sNfL level (not baseline or 3 months) was associated with PBVC (standardized (std.) β = −0.26, p = 0.013), VVC (standardized β = 0.36, p < 0.001), and TVC (standardized β = −0.24, p = 0.02). For sCNTN1, only 3-month level was associated with VVC (standardized β = −0.31, p = 0.002). Conclusion: Year 1 (but not baseline) sNfL level was predictive for long-term brain atrophy in patients treated with natalizumab. sCNTN1 level did not show a clear predictive value.

Serum glial fibrillary acidic protein in natalizumab-treated relapsing-remitting multiple sclerosis: An alternative to neurofilament light

“…Brain volumes and volume changes were accordingly assessed on the PD/T2-weighted MRI scans, as previously described in this cohort. 10 Longitudinal brain segmentation was performed with sequence adaptive multimodal segmentation (SAMSEG) method, a method validated on T2 as provided in the open-source package FreeSurfer 7.1.1. 24,25 The longitudinal pipeline of the SAMSEG method is designed with the ability to handle MRI data from different sources, such as different scanners and sequences encountered in longitudinal clinical cohorts, by adapting to the different protocols and making use of the shared information across intrapersonal repeated scans.…”

“…26 Brain tissue segmentation was performed, and brain structure volumes were calculated using previously described methods. 10 Normalized whole brain volume, ventricular volume, thalamic volume, and lesion volume were defined as a fraction and divided by intracranial volume. To calculate annualized percentage whole brain volume change rate, subject-wise linear regression was applied to volume measurements performed between year 1 and last follow-up to calculate their slope of change.…”

“…The same procedure was performed for annualized percentage ventricular, thalamic, and lesion volume change rates. 10 Measurements between baseline and year 1 were excluded for the calculation of annualized percentage change rate and linear mixedeffects analyses of volume measurements to rule out the effects of pseudo-atrophy. 27,28 Statistical analyses Statistical analyses and visualizations were performed with R statistical software version 4.0.3.…”

“…[7][8][9] One such biomarker, brain atrophy, could quantify magnetic resonance imaging (MRI)-derived neurodegeneration in treated cohorts. 7,8,10 Blood-based…”

“…7–9 One such biomarker, brain atrophy, could quantify magnetic resonance imaging (MRI)-derived neurodegeneration in treated cohorts. 7,8,10 Blood-based biomarkers have also been proposed as a proxy to imaging or cerebrospinal fluid (CSF)-based biomarkers, offering an easily accessible and relatively non-invasively collected alternative. 11 The identification of blood-based biomarkers in treated patients enabling explanation of the underlying neurodegenerative mechanisms is thus greatly relevant in consideration of explaining, predicting, or monitoring disease progression and possibly brain atrophy.…”