脑靶向自身免疫与 Long COVID 及其慢性疲劳综合征以及情感症状密切相关

IF 11.4 1区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Journal of Advanced Research Pub Date : 2024-11-09 DOI:10.1016/j.jare.2024.11.011

Abbas F. Almulla, Michael Maes, Bo Zhou, Hussein K. Al-Hakeim, Aristo Vojdani

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引用次数: 0

摘要

简介：自身免疫反应是长COVID、情感症状和肌痛性脑脊髓炎/慢性疲劳综合征（ME/CFS）的病理生理学因素之一。目的研究长COVID及其伴随的情感症状和慢性疲劳综合征是否与针对神经元蛋白（包括髓鞘碱性蛋白（MBP））的免疫球蛋白（Ig）A/IgM/IgG有关、髓鞘少突胶质细胞糖蛋白（MOG）、突触素、α + β-微管蛋白、神经丝蛋白（NFP）、小脑蛋白-2（CP2）以及血脑屏障脑损伤（BBD）蛋白 claudin-5 和 S100B。方法对 90 名长 COVID 患者和 90 名健康对照者进行了上述神经元蛋白、人类疱疹病毒-6（HHV-6）和严重急性呼吸系统综合征冠状病毒 2（SARS-CoV-2）的 IgA/IgM/IgG 检测，并对其中的一个亚组进行了与情感和 CFS 评级相关的 C 反应蛋白（CRP）和高级氧化蛋白产物（AOPP）的额外评估。结果长期慢性阻塞性肺气肿与针对小管蛋白（IgG-tubulin）、MBP、MOG 和突触素的 IgG；IgM-MBP、MOG、CP2、突触素和 BBD；以及 IgA-CP2 和突触素的显著增加有关。IgM-SARS-CoV-2 和 IgM-HHV-6 抗体滴度与 IgA/IgG/IgM-tubulin 和 -CP2、IgG/IgM-BBD、IgM-MOG、IgA/IgM-NFP 和 IgG/IgM-synapsin 显著相关。二元逻辑回归分析表明，IgM-MBP 和 IgG-MBP 是长 COVID 的最佳预测因子。多元回归分析表明，IgG-MOG、CRP 和 AOPP 可共同解释 CFS 严重程度变异的 41.7%。神经网络分析表明，IgM-synapsin、IgA-MBP、IgG-MOG、IgA-synapsin、IgA-CP2、IgG-MBP 和 CRP 是预测 Long COVID 引起的情感症状的最重要指标，预测准确率为 r = 0.801。

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Brain-targeted autoimmunity is strongly associated with Long COVID and its chronic fatigue syndrome as well as its affective symptoms

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Brain-targeted autoimmunity is strongly associated with Long COVID and its chronic fatigue syndrome as well as its affective symptoms

Introduction

Autoimmune responses contribute to the pathophysiology of Long COVID, affective symptoms and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS).

Objectives

To examine whether Long COVID, and its accompanying affective symptoms and CFS are associated with immunoglobulin (Ig)A/IgM/IgG directed at neuronal proteins including myelin basic protein (MBP), myelin oligodendrocyte glycoprotein (MOG), synapsin, α + β-tubulin, neurofilament protein (NFP), cerebellar protein-2 (CP2), and the blood–brain-barrier-brain-damage (BBD) proteins claudin-5 and S100B.

Methods

IgA/IgM/IgG to the above neuronal proteins, human herpes virus-6 (HHV-6) and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) were measured in 90 Long COVID patients and 90 healthy controls, while C-reactive protein (CRP), and advanced oxidation protein products (AOPP) in association with affective and CFS ratings were additionally assessed in a subgroup thereof.

Results

Long COVID is associated with significant increases in IgG directed at tubulin (IgG-tubulin), MBP, MOG and synapsin; IgM-MBP, MOG, CP2, synapsin and BBD; and IgA-CP2 and synapsin. IgM-SARS-CoV-2 and IgM-HHV-6 antibody titers were significantly correlated with IgA/IgG/IgM-tubulin and -CP2, IgG/IgM-BBD, IgM-MOG, IgA/IgM-NFP, and IgG/IgM-synapsin. Binary logistic regression analysis shows that IgM-MBP and IgG-MBP are the best predictors of Long COVID. Multiple regression analysis shows that IgG-MOG, CRP and AOPP explain together 41.7 % of the variance in the severity of CFS. Neural network analysis shows that IgM-synapsin, IgA-MBP, IgG-MOG, IgA-synapsin, IgA-CP2, IgG-MBP and CRP are the most important predictors of affective symptoms due to Long COVID with a predictive accuracy of r = 0.801.

Conclusion

Brain-targeted autoimmunity contributes significantly to the pathogenesis of Long COVID and the severity of its physio-affective phenome.

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来源期刊

Journal of Advanced Research Multidisciplinary-Multidisciplinary

CiteScore

21.60

自引率

0.90%

发文量

280

审稿时长

12 weeks

期刊介绍： Journal of Advanced Research (J. Adv. Res.) is an applied/natural sciences, peer-reviewed journal that focuses on interdisciplinary research. The journal aims to contribute to applied research and knowledge worldwide through the publication of original and high-quality research articles in the fields of Medicine, Pharmaceutical Sciences, Dentistry, Physical Therapy, Veterinary Medicine, and Basic and Biological Sciences. The following abstracting and indexing services cover the Journal of Advanced Research: PubMed/Medline, Essential Science Indicators, Web of Science, Scopus, PubMed Central, PubMed, Science Citation Index Expanded, Directory of Open Access Journals (DOAJ), and INSPEC.