Enhancing the clinical value of serum neurofilament light chain measurement
Peter Kosa, Ruturaj Masvekar, Mika Komori, Jonathan Phillips, Vighnesh Ramesh, Mihael Varosanec, Mary Sandford, Bibiana Bielekova
Peter Kosa, Ruturaj Masvekar, Mika Komori, Jonathan Phillips, Vighnesh Ramesh, Mihael Varosanec, Mary Sandford, Bibiana Bielekova
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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 2

Adjustment for 5 confounders improves correlation of sNFL with number of MRI CELs and eliminates noise.

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Adjustment for 5 confounders improves correlation of sNFL with number of...
(A) CELs have been used as a surrogate outcome of blood-brain barrier opening and active inflammation in the brains of patients with MS. Logistic regression that predicts probability of CEL presence/absence and linear regression between NFL and total number of CELs have been tested. A binomial regression classifier was generated to predict dichotomous outcome of present/absent CEL. The area under the curve (AUC), sensitivity, and specificity have been calculated for classifiers using measured cNFL (B and E), measured sNFL (C and F), and sNFL-predicted cNFL (D and G) to predict probability of presence of CELs. Dotted line represents the best probability cutoff value determined in the training cohort with corresponding NFL concentration displayed above the line. Horizontal lines represent medians. Two-sided Wilcoxon 2-sample test evaluated the significance of differences between 2 groups of patients. The linear model between number of CELs (y axes, transformed as natural logarithm of [CEL+1]) and NFL (x axes) shows higher predictive power of cNFL in both training (H) and validation (K) cohorts, compared with sNFL in training (I) and validation (L) cohorts. Adjustment of sNFL for 5 confounders (age, weight, AP, BUN, and creatinine) improved the correlation with number of CELs in both training (J) and validation (M) cohorts compared with measured sNFL. Purple line represents linear regression model with gray shading corresponding to 95% confidence interval.