神经丝轻链作为鉴别MOG抗体相关疾病疾病活动状态的指标

Neurofilament Light Chain as a Discriminator of Disease Activity Status in MOG Antibody-Associated Disease.

作者信息

Gomes Ana Beatriz Ayroza Galvão Ribeiro, Kim Su-Hyun, Pretzsch Roxanne, Kulsvehagen Laila, Schaedelin Sabine, Lerner Jasmine, Wetzel Nora Sandrine, Benkert Pascal, Maleska Maceski Aleksandra, Hyun Jae-Won, Lecourt Anne-Catherine, Lipps Patrick, Schoeps Vinicius Andreoli, Matos Aline De Moura Brasil, Mendes Natalia Trombini, Apóstolos-Pereira Samira Luisa, Mehling Matthias, Derfuss Tobias, Kappos Ludwig, Callegaro Dagoberto, Kuhle Jens, Kim Ho Jin, Pröbstel Anne-Katrin

机构信息

Department of Neurology, University Hospital Basel and University of Basel, Basel, Switzerland.

Departments of Biomedicine and Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland.

出版信息

Neurol Neuroimmunol Neuroinflamm. 2025 Jan;12(1):e200347. doi: 10.1212/NXI.0000000000200347. Epub 2024 Dec 20.

DOI:10.1212/NXI.0000000000200347

PMID:39705633

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11666271/

Abstract

BACKGROUND AND OBJECTIVES

In patients with myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD), acute disease activity is generally identified through medical history, neurologic examination, and imaging. However, these may be insufficient for detecting disease activity in specific conditions. This study aimed to investigate the dynamics of serum neurofilament light chain (sNfL) and serum glial fibrillary acidic protein (sGFAP) after clinical attacks and to assess their utility in discriminating attacks from remission in patients with MOGAD.

METHODS

We conducted a multicenter, retrospective, longitudinal study including 239 sera from 62 MOGAD patients assessed from 1995 to 2023 in a discovery and validation setup. Sera were measured for sNfL and sGFAP with a single-molecule array assay and for MOG-IgG with a live cell-based assay. sNfL and sGFAP Z scores and percentiles adjusted for age, body mass index, and sex (sGFAP) were calculated from a healthy control normative database. Mixed-effects regression models were used to characterize biomarkers' dynamics and to investigate associations between serum biomarkers, clinical variables, and disease activity status.

RESULTS

Among the 62 study participants, 29 (46.8%) were female, with a median age at baseline of 40.0 years (interquartile range [IQR] 29.5-49.8) and a median duration of follow-up of 20.0 months (IQR 3.0-62.8). sNfL and sGFAP Z scores were nonlinearly associated with time from attack onset ( < 0.001 and = 0.002, respectively). During attacks, both biomarkers presented higher median values (sNfL Z score 2.9 [IQR 1.4-3.5], 99.8th; sGFAP Z score 0.4 [IQR -0.5 to 1.5], 65.5th) compared with remission (sNfL Z score 0.9 [IQR -0.1 to 1.6], 81.6th, < 0.001; sGFAP Z score -0.2 [IQR -0.8 to 0.5], 42.1th; < 0.001) across all clinical phenotypes. sNfL values consistently discriminated disease activity status in the discovery and validation cohorts, showing a 3.5-fold increase in the odds of attacks per Z score unit (odds ratio 3.5, 95% confidence interval 2.3-5.1; < 0.001). Logistic models incorporating sNfL Z scores demonstrated favorable performance in discriminating disease activity status across both cohorts.

DISCUSSION

sNfL Z scores may serve as a biomarker for monitoring disease activity in MOGAD.

摘要

背景与目的

在髓鞘少突胶质细胞糖蛋白（MOG）抗体相关疾病（MOGAD）患者中，急性疾病活动通常通过病史、神经系统检查和影像学来确定。然而，在特定情况下，这些方法可能不足以检测疾病活动。本研究旨在调查临床发作后血清神经丝轻链（sNfL）和血清胶质纤维酸性蛋白（sGFAP）的动态变化，并评估它们在区分MOGAD患者发作与缓解方面的效用。

方法

我们进行了一项多中心、回顾性、纵向研究，在发现和验证设置中纳入了1995年至2023年评估的62例MOGAD患者的239份血清。使用单分子阵列测定法测量血清中的sNfL和sGFAP，使用基于活细胞的测定法测量MOG-IgG。根据健康对照规范数据库计算调整年龄、体重指数和性别后的sNfL和sGFAP Z分数及百分位数（sGFAP）。使用混合效应回归模型来描述生物标志物的动态变化，并研究血清生物标志物、临床变量和疾病活动状态之间的关联。

结果

在62名研究参与者中，29名（46.8%）为女性，基线时的中位年龄为40.0岁（四分位间距[IQR]29.5 - 49.8），中位随访时间为20.0个月（IQR 3.0 - 62.8）。sNfL和sGFAP Z分数与发作开始后的时间呈非线性相关（分别为<0.001和 = 0.002）。在发作期间，与所有临床表型的缓解期相比，两种生物标志物的中位值均更高（sNfL Z分数2.9[IQR 1.4 - 3.5]，第99.8百分位数；sGFAP Z分数0.4[IQR -0.5至1.5]，第65.5百分位数）（缓解期：sNfL Z分数0.9[IQR -0.1至1.6]，第81.6百分位数，<0.001；sGFAP Z分数 -0.2[IQR -0.8至0.5]，第42.1百分位数；<0.001）。sNfL值在发现队列和验证队列中始终能够区分疾病活动状态，每Z分数单位发作的几率增加3.5倍（优势比3.5，95%置信区间2.3 - 5.1；<0.001）。纳入sNfL Z分数的逻辑模型在区分两个队列的疾病活动状态方面表现良好。