ObjectiveTo assess the value of annual serum neurofilament light (NfL) measures in predicting 10‐year clinical and MRI outcomes in multiple sclerosis (MS).MethodsWe identified patients in our center's Comprehensive Longitudinal Investigations in MS at Brigham and Women's Hospital (CLIMB) study enrolled within 5 years of disease onset, and with annual blood samples up to 10 years (n = 122). Serum NfL was measured using a single molecule array (SIMOA) assay. An automated pipeline quantified brain T2 hyperintense lesion volume (T2LV) and brain parenchymal fraction (BPF) from year 10 high‐resolution 3T MRI scans. Correlations between averaged annual NfL and 10‐year clinical/MRI outcomes were assessed using Spearman's correlation, univariate, and multivariate linear regression models.ResultsAveraged annual NfL values were negatively associated with year 10 BPF, which included averaged year 1–5 NfL values (unadjusted P < 0.01; adjusted analysis P < 0.01), and averaged values through year 10. Linear regression analyses of averaged annual NfL values showed multiple associations with T2LV, specifically averaged year 1–5 NfL (unadjusted P < 0.01; adjusted analysis P < 0.01). Approximately 15–20% of the BPF variance and T2LV could be predicted from early averaged annual NfL levels. Also, averaged annual NfL levels with fatigue score worsening between years 1 and 10 showed statistically significant associations. However, averaged NfL measurements were not associated with year 10 EDSS, SDMT or T25FW in this cohort.InterpretationSerum NfL measured during the first few years after the clinical onset of MS contributed to the prediction of 10‐year MRI brain lesion load and atrophy.

ObjectiveMultiple sclerosis (MS) is an autoimmune demyelinating disorder, which is characterized by relapses and remissions. Serum neurofilament light chain (sNfL) is an emerging biomarker of disease activity but its clinical use is still limited. In this study, we aim to characterize the temporal association between sNfL and new clinical relapses and new gadolinium‐enhancing (Gd+) lesions.MethodsAnnual sNfL levels were measured with a single‐molecule array (SIMOA) assay in 94 patients with MS enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women’s Hospital (CLIMB) study. We used a multivariable linear mixed‐effects model to test the temporal association of sNfL with clinical relapses and/or new Gd+ lesions. We adjusted this model for age, disease duration, sex, and disease‐modifying therapies (DMTs) use.ResultsIn the 3 months after a Gd+ lesion, we observed an average 35% elevation in sNfL (P < 0.0001) compared to remission samples. We also observed an average 32.3% elevation in sNfL at the time of or prior to a Gd+ lesion (P = 0.002) compared to remission. We observed a significant elevation in sNfL after a clinical relapse only when associated with a Gd+ lesion.InterpretationOur findings support sNfL as a marker of clinical relapses and Gd+ lesions. sNfL peaks in a 3‐month window around Gd+ lesions. sNfL shows promise as a biomarker of neurological inflammation and possibly of simultaneous Gd+ lesions during a clinical relapse.

Technological advances in passive digital phenotyping present the opportunity to quantify neurological diseases using new approaches that may complement clinical assessments. Here, we studied multiple sclerosis (MS) as a model neurological disease for investigating physiometric and environmental signals. The objective of this study was to assess the feasibility and correlation of wearable biosensors with traditional clinical measures of disability both in clinic and in free-living in MS patients. This is a single site observational cohort study conducted at an academic neurological center specializing in MS. A cohort of 25 MS patients with varying disability scores were recruited. Patients were monitored in clinic while wearing biosensors at nine body locations at three separate visits. Biosensor-derived features including aspects of gait (stance time, turn angle, mean turn velocity) and balance were collected, along with standardized disability scores assessed by a neurologist. Participants also wore up to three sensors on the wrist, ankle, and sternum for 8 weeks as they went about their daily lives. The primary outcomes were feasibility, adherence, as well as correlation of biosensor-derived metrics with traditional neurologist-assessed clinical measures of disability. We used machine-learning algorithms to extract multiple features of motion and dexterity and correlated these measures with more traditional measures of neurological disability, including the expanded disability status scale (EDSS) and the MS functional composite-4 (MSFC-4). In free-living, sleep measures were additionally collected. Twenty-three subjects completed the first two of three in-clinic study visits and the 8-week free-living biosensor period. Several biosensor-derived features significantly correlated with EDSS and MSFC-4 scores derived at visit two, including mobility stance time with MSFC-4 z-score (Spearman correlation −0.546; p = 0.0070), several aspects of turning including turn angle (0.437; p = 0.0372), and maximum angular velocity (0.653; p = 0.0007). Similar correlations were observed at subsequent clinic visits, and in the free-living setting. We also found other passively collected signals, including measures of sleep, that correlated with disease severity. These findings demonstrate the feasibility of applying passive biosensor measurement techniques to monitor disability in MS patients both in clinic and in the free-living setting.

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