Neurofilament light chain – Can it be measured in urine?

IF 2.9 3区 医学 Q2 MEDICAL LABORATORY TECHNOLOGY Clinica Chimica Acta Pub Date : 2025-03-01 Epub Date: 2025-01-25 DOI:10.1016/j.cca.2025.120163
Lea Tybirk , Cindy Soendersoe Knudsen , Tina Parkner
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Abstract

Objective

This exploratory study investigates if neurofilament light chain (NfL) is excreted in the urine and whether this depends on plasma NfL (pNfL) levels and kidney function in terms of eGFR and urine albumin-creatinine ratio (uACR).

Methods

Using a computer algorithm, we identified excess urine and plasma from routine testing of uACR and eGFR in patients 45–50 years old. Up to 17 paired urine-plasma samples in each of six categories of kidney function defined by uACR and eGFR were analysed for NfL, and the urinary NfL-creatinine ratio (uNCR) was calculated to correct for urine dilution.

Results

In the 35 subjects with normal eGFR (>90 ml/min/1.73 m2) and varying degrees of albuminuria, uNfL was only above the lower limit of quantification in one subject with microalbuminuria (uACR 30–300 mg/g), and in none of the subjects with uACR < 30 mg/g. In the 47 subjects with impaired eGFR (15–60 ml/min/1.73 m2), the percentage of subjects with detectable uNfL and the average level of uNCR increased with increasing albuminuria. However, multiple regression analysis revealed that uNCR only significantly correlated with pNfL, not eGFR and uACR. pNfL correlated inversely with eGFR, but not uACR.

Conclusions

Our results show that the urinary NfL excretion in subjects with normal kidney function and normal pNfL levels is very low. Thus, the increased pNfL often observed in patients with low eGFR seems not to be explained by impaired urinary NfL excretion, and urine is generally not a suitable matrix for NfL measurements.
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神经丝轻链-能在尿液中测量吗?
目的:本探索性研究探讨神经丝轻链(neurofilament light chain, NfL)是否随尿液排出,以及这是否依赖于血浆NfL (pNfL)水平和肾功能的eGFR和u -白蛋白-肌酐比值(uACR)。方法:使用计算机算法,我们从45-50岁 患者的常规uACR和eGFR检测中识别出过量的尿液和血浆。在uACR和eGFR定义的六种肾功能中,每一种最多17对尿血浆样本进行了NfL分析,并计算了尿NfL-肌酐比(uNCR)以校正尿稀释。结果:35例eGFR正常(bbb90 ml/min/1.73 m2)、不同程度蛋白尿的受试者中,只有1例微量蛋白尿受试者(uACR 30 ~ 300 mg/g)的unl高于定量下限,而uACR < 30 mg/g的受试者均未高于定量下限。在47例eGFR受损的受试者中(15-60 ml/min/1.73 m2),可检测到unl的受试者百分比和uNCR的平均水平随着蛋白尿的增加而增加。然而,多元回归分析显示uNCR仅与pNfL显著相关,而与eGFR和uACR无关。pNfL与eGFR呈负相关,但与uACR无关。结论:我们的研究结果表明,在肾功能正常且pNfL水平正常的受试者中,尿NfL排泄量非常低。因此,在低eGFR患者中经常观察到的pNfL增加似乎不能用尿中NfL排泄受损来解释,并且尿液通常不是测量NfL的合适基质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Clinica Chimica Acta
Clinica Chimica Acta 医学-医学实验技术
CiteScore
10.10
自引率
2.00%
发文量
1268
审稿时长
23 days
期刊介绍: The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.
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