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Enhancing the clinical value of serum neurofilament light chain measurement
Peter Kosa, … , Mary Sandford, Bibiana Bielekova
Peter Kosa, … , Mary Sandford, Bibiana Bielekova
Published June 23, 2022
Citation Information: JCI Insight. 2022;7(15):e161415. https://doi.org/10.1172/jci.insight.161415.
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Clinical Research and Public Health Neuroscience

Enhancing the clinical value of serum neurofilament light chain measurement

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Abstract

BACKGROUND Serum neurofilament light chain (sNFL) is becoming an important biomarker of neuro-axonal injury. Though sNFL correlates with CSF NFL (cNFL), 40% to 60% of variance remains unexplained. We aimed to mathematically adjust sNFL to strengthen its clinical value.METHODS We measured NFL in a blinded fashion in 1138 matched CSF and serum samples from 571 patients. Multiple linear regression (MLR) models constructed in the training cohort were validated in an independent cohort.RESULTS An MLR model that included age, blood urea nitrogen, alkaline phosphatase, creatinine, and weight improved correlations of cNFL with sNFL (from R2 = 0.57 to 0.67). Covariate adjustment significantly improved the correlation of sNFL with the number of contrast-enhancing lesions (from R2 = 0.18 to 0.28; 36% improvement) in the validation cohort of patients with multiple sclerosis (MS). Unexpectedly, only sNFL, but not cNFL, weakly but significantly correlated with cross-sectional MS severity outcomes. Investigating 2 nonoverlapping hypotheses, we showed that patients with proportionally higher sNFL to cNFL had higher clinical and radiological evidence of spinal cord (SC) injury and probably released NFL from peripheral axons into blood, bypassing the CSF.CONCLUSION sNFL captures 2 sources of axonal injury, central and peripheral, the latter reflecting SC damage, which primarily drives disability progression in MS.TRIAL REGISTRATION ClinicalTrials.gov NCT00794352.FUNDING Division of Intramural Research, National Institute of Allergy and Infectious Diseases, NIH (AI001242 and AI001243).

Authors

Peter Kosa, Ruturaj Masvekar, Mika Komori, Jonathan Phillips, Vighnesh Ramesh, Mihael Varosanec, Mary Sandford, Bibiana Bielekova

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