Contributors to Serum NfL Levels in People Without Neurologic Disease
Overview
Authors
Affiliations
Objective: To assess the effects of demographics, lifestyle factors, and comorbidities on serum neurofilament light chain (sNfL) levels in people without neurologic disease and establish demographic-specific reference ranges of sNfL.
Methods: The National Health and Nutrition Examination Survey (NHANES) is a representative sample of the US population in which detailed information on demographic, lifestyle, routine laboratory tests, and overall health status are systematically collected. From stored serum samples, we measured sNfL levels using a novel high-throughput immunoassay (Siemens Healthineers). We evaluated the predictive capacity of 52 demographic, lifestyle, comorbidity, anthropometric, or laboratory characteristics in explaining variability in sNfL levels. Predictive performance was assessed using cross-validated R (R ) and forward selection was used to obtain a set of best predictors of sNfL levels. Adjusted reference ranges were derived incorporating characteristics using generalized additive models for location, scale, and shape.
Results: We included 1,706 NHANES participants (average age: 43.6 ± 14.8 y; 50.6% male, 35% non-white) without neurological disorders. In univariate models, age explained the most variability in sNfL (R = 26.8%). Multivariable prediction models for sNfL contained three covariates (in order of their selection): age, creatinine, and glycosylated hemoglobin (HbA1c) (standardized β-age: 0.46, 95% confidence interval [CI]: 0.43, 0.50; creatinine: 0.18, 95% CI: 0.13, 0.22; HbA1c: 0.09, 95% CI: 0.06, 0.11). Adjusted centile curves were derived incorporating identified predictors. We provide an interactive R Shiny application to translate our findings and allow other investigators to use the derived centile curves.
Interpretation: Results will help to guide interpretation of sNfL levels as they relate to neurologic conditions. ANN NEUROL 2022;92:688-698.
Alzheimer's disease neuropathology and its estimation with fluid and imaging biomarkers.
Thal D, Poesen K, Vandenberghe R, De Meyer S Mol Neurodegener. 2025; 20(1):33.
PMID: 40087672 DOI: 10.1186/s13024-025-00819-y.
Sheth U, Oijerstedt L, Heckman M, White L, Heuer H, Lario Lago A Mol Neurodegener. 2025; 20(1):30.
PMID: 40075459 PMC: 11905702. DOI: 10.1186/s13024-025-00821-4.
Wang T, Yan L, Ma T, Gao X PLoS One. 2025; 20(2):e0306315.
PMID: 39992894 PMC: 11849891. DOI: 10.1371/journal.pone.0306315.
Koerbel K, Yalachkov Y, Rotter T, Schaller-Paule M, Schaefer J, Friedauer L Int J Mol Sci. 2025; 26(2).
PMID: 39859463 PMC: 11765624. DOI: 10.3390/ijms26020748.
Di Muro G, Tessarolo C, Cagnotti G, Favole A, Ferrini S, Ala U Vet Res. 2025; 56(1):6.
PMID: 39794836 PMC: 11724550. DOI: 10.1186/s13567-024-01441-4.