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

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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Figure 1

Variance between sNFL and cNFL concentrations.

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Variance between sNFL and cNFL concentrations. (A) Linear regression mod...
(A) Linear regression model between log10-transformed concentration (pg/mL) of sNFL and cNFL in the training cohort of samples where cNFL levels explain 57% of variance of sNFL levels. (B) Remaining 43% of variance shown as NFL residuals generated as differences between measured sNFL concentration and predicted sNFL concentration calculated from measured cNFL using linear regression model. (C) Eleven potential confounders related to distribution volume (BMI = body mass index, Est Blood Vol = estimated blood volume, height, and weight), protein metabolism/clearance (ALT = alanine transaminase, AP = alkaline phosphatase, AST = aspartate transaminase, BUN = blood urea nitrogen, creatinine, and eGFR = estimated glomerular filtration rate), and age were used as explanatory variables in a multiple linear regression model resulting in varied importance represented as a t statistic of each variable in the model (D). Stepwise regression resulted in retention of 5 confounders in the model (E) that showed increased correlation between measured and predicted sNFL levels both in the training (G) and in the validation (I) cohort in comparison with correlations between measured and predicted values using a simple linear regression model in the same training (F) and validation cohort (H). Confounders in color are the ones selected in the multiple linear regression model that underwent stepwise regression. Green line represents linear regression model with gray shading corresponding to 95% confidence interval. ns, number of samples measured; np, number of patients represented by the samples; CCC, concordance correlation coefficient.