Contributors to Serum NfL Levels in People without Neurologic Disease.

Autor: Fitzgerald KC; Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA., Sotirchos ES; Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA., Smith MD; Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA., Lord HN; Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA., DuVal A; Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA., Mowry EM; Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA., Calabresi PA; Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.; Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.
Jazyk: angličtina
Zdroj: Annals of neurology [Ann Neurol] 2022 Oct; Vol. 92 (4), pp. 688-698. Date of Electronic Publication: 2022 Jul 13.
DOI: 10.1002/ana.26446
Abstrakt: 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 2 (R 2 cv ) 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 2 cv  = 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.
(© 2022 American Neurological Association.)
Databáze: MEDLINE
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