The value of serum glial fibrillary acidic protein as a biomarker of astrogliosis in different neurological diseases

IF 3.2 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinica Chimica Acta Pub Date : 2025-03-18 DOI:10.1016/j.cca.2025.120248

Luisa Agnello , Caterina Maria Gambino , Anna Maria Ciaccio , Giuseppe Salemi , Filippo Brighina , Paolo Ragonese , Tommaso Piccoli , Valeria Blandino , Vincenzo Di Stefano , Francesco Cacciabaudo , Anna Masucci , Roberta Vassallo , Concetta Scazzone , Fabio Del Ben , Marcello Ciaccio

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引用次数: 0

Abstract

Background

Glial Fibrillary Acidic Protein (GFAP) is a well-established biomarker of astrocytes and astrogliosis, a pathological response observed in various neurological diseases. This study aimed to evaluate the diagnostic performance of serum GFAP in Alzheimer’s disease (AD), multiple sclerosis (MS), and transthyretin amyloidosis (ATTR) polyneuropathy.

Methods

We performed a retrospective observational study, including 498 participants (337 healthy controls and 161 patients with AD, MS, or ATTR amyloidosis). Serum GFAP levels were measured using the Lumipulse G1200 platform, and statistical analyses were performed to compare levels across disease groups and assess their diagnostic accuracy.

Results

GFAP levels were significantly elevated in all neurological disease groups compared to age-matched controls, with the highest levels found in AD (79.4 pg/mL vs. 39.5 pg/mL, p = 2.55 × 10⁻¹²). ROC curve analysis revealed that GFAP had strong diagnostic performance for AD (AUC = 0.86), moderate performance for ATTR amyloidosis (AUC = 0.67), and poor performance for MS (AUC = 0.61).

Conclusions

These findings suggest that GFAP is a promising biomarker for AD, reflecting astrocytic activation and neuroinflammatory processes. Its diagnostic utility in ATTR amyloidosis is moderate, while its role in MS remains limited.

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血清胶质原纤维酸性蛋白在不同神经系统疾病中作为星形胶质变性生物标志物的价值。

背景：胶质纤维酸性蛋白（Glial Fibrillary acid Protein， GFAP）是一种公认的星形胶质细胞和星形胶质细胞形成的生物标志物，是在多种神经系统疾病中观察到的病理反应。本研究旨在评价血清GFAP在阿尔茨海默病（AD）、多发性硬化症（MS）和转甲状腺素淀粉样变性（ATTR）多发性神经病中的诊断价值。方法：我们进行了一项回顾性观察性研究，包括498名参与者（337名健康对照和161名AD、MS或ATTR淀粉样变患者）。使用Lumipulse G1200平台测量血清GFAP水平，并进行统计分析以比较不同疾病组的水平并评估其诊断准确性。结果：与年龄匹配的对照组相比，所有神经系统疾病组GFAP水平均显著升高，AD组GFAP水平最高（79.4 pg/mL vs 39.5 pg/mL, p = 2.55 × 10-12）。ROC曲线分析显示，GFAP对AD的诊断效果较好（AUC = 0.86），对ATTR淀粉样变的诊断效果中等（AUC = 0.67），对MS的诊断效果较差（AUC = 0.61）。结论：这些发现表明GFAP是一种很有希望的AD生物标志物，反映了与疾病病理相关的星形细胞激活和神经炎症过程。它在ATTR淀粉样变性诊断中的应用是中等的，而在多发性硬化症中的作用仍然有限。

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 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.