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
Abstract
BACKGROUNDSerum 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.METHODSWe 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.RESULTSAn 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.CONCLUSIONsNFL captures 2 sources of axonal injury, central and peripheral, the latter reflecting SC damage, which primarily drives disability progression in MS.TRIAL REGISTRATIONClinicalTrials.gov NCT00794352.FUNDINGDivision of Intramural Research, National Institute of Allergy and Infectious Diseases, NIH (AI001242 and AI001243).
Keywords: Multiple sclerosis; Neurodegeneration; Neuroscience.
Figures
References
- Thelin EP, et al. Serial sampling of serum protein biomarkers for monitoring human traumatic brain injury dynamics: a systematic review. Front Neurol. 2017;8:300. doi: 10.3389/fneur.2017.00300.
- Novakova L, et al. Monitoring disease activity in multiple sclerosis using serum neurofilament light protein. Neurology. 2017;89(22):2230–2237. doi: 10.1212/WNL.0000000000004683.
- Masvekar R, et al. Cerebrospinal fluid biomarkers of myeloid and glial cell activation are correlated with multiple sclerosis lesional inflammatory activity. Front Neurosci. 2021;15:649876. doi: 10.3389/fnins.2021.649876.
- Leppert D, et al. Blood neurofilament light in progressive multiple sclerosis: post hoc analysis of 2 randomized controlled trials. Neurology. 2022;98(21):2120–2131. doi: 10.1212/WNL.0000000000200258.
- Bergman J, et al. Neurofilament light in CSF and serum is a sensitive marker for axonal white matter injury in MS. Neurol Neuroimmunol Neuroinflamm. 2016;3(5):e271. doi: 10.1212/NXI.0000000000000271.
- Disanto G, et al. Serum neurofilament light: a biomarker of neuronal damage in multiple sclerosis. Ann Neurol. 2017;81(6):857–870. doi: 10.1002/ana.24954.
- Kuhle J, et al. Serum neurofilament light chain in early relapsing remitting MS is increased and correlates with CSF levels and with MRI measures of disease severity. Mult Scler. 2016;22(12):1550–1559. doi: 10.1177/1352458515623365.
- Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS) Neurology. 1983;33(11):1444–1452. doi: 10.1212/WNL.33.11.1444.
- Weideman AM, et al. New multiple sclerosis disease severity scale predicts future accumulation of disability. Front Neurol. 2017;8:598. doi: 10.3389/fneur.2017.00598.
- Petzold A, et al. The new global multiple sclerosis severity score (MSSS) correlates with axonal but not glial biomarkers. Mult Scler. 2006;12(3):325–328. doi: 10.1191/135248505ms1277oa.
- Manouchehrinia A, et al. Age related multiple sclerosis severity score: disability ranked by age. Mult Scler. 2017;23(14):1938–1946. doi: 10.1177/1352458517690618.
- Kosa P, et al. Novel composite MRI scale correlates highly with disability in multiple sclerosis patients. Mult Scler Relat Disord. 2015;4(6):526–535. doi: 10.1016/j.msard.2015.08.009.
- Kelly E, et al. Machine learning-optimized Combinatorial MRI scale (COMRISv2) correlates highly with cognitive and physical disability scales in Multiple Sclerosis patients [preprint]. Posted on medRxiv March 28, 2021.
- Kosa P, et al. NeurEx: digitalized neurological examination offers a novel high-resolution disability scale. Ann Clin Transl Neurol. 2018;5(10):1241–1249. doi: 10.1002/acn3.640.
- Benkert P, et al. Serum neurofilament light chain for individual prognostication of disease activity in people with multiple sclerosis: a retrospective modelling and validation study. Lancet Neurol. 2022;21(3):246–257. doi: 10.1016/S1474-4422(22)00009-6.
- Akamine S, et al. Renal function is associated with blood neurofilament light chain level in older adults. Sci Rep. 2020;10(1):20350. doi: 10.1038/s41598-020-76990-7.
- Gaiottino J, et al. Increased neurofilament light chain blood levels in neurodegenerative neurological diseases. PLoS One. 2013;8(9):e75091. doi: 10.1371/journal.pone.0075091.
- Shiee N, et al. A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. Neuroimage. 2010;49(2):1524–1535. doi: 10.1016/j.neuroimage.2009.09.005.
- Kosa P, et al. Development of a sensitive outcome for economical drug screening for progressive multiple sclerosis treatment. Front Neurol. 2016;7:131. doi: 10.3389/fneur.2016.00131.
- R Foundation for Statistical Computing. R: A language and environment for statistical computing. Version 4.2.0. 2022.
- Kassambara A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.4.0. 2022. .
- Venables WN, Ripley BD, eds. Modern Applied Statistics with S. Springer; 2002.
- Robin X, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77. doi: 10.1186/1471-2105-12-77.
- Ho DE, et al. MatchIt: nonparametric preprocessing for parametric causal inference. J Stat Softw. 2011;42(8):1–28.
Source: PubMed