SaBio. Instituto De Investigación En Recursos Cinegéticos IREC-CSIC-UCLM-JCCM , Ciudad Real, Spain.
Department of Veterinary Pathobiology, Center for Veterinary Health Sciences, Oklahoma State University , Stillwater OK, USA.
Expert Rev Mol Diagn. 2020 Sep;20(9):905-911. doi: 10.1080/14737159.2020.1792781. Epub 2020 Jul 14.
The α-Gal syndrome (AGS) is a type of allergy characterized by an IgE antibody response against the carbohydrate Galα1-3Galβ1-4GlcNAc-R (α-Gal). Tick bites are recognized as the most important cause of anti-α-Gal IgE antibody increase in humans. Several risk factors have been associated with the development of AGS, but their integration into a standardized disease diagnosis has proven challenging.
Herein we discuss the current AGS diagnosis based on anti-α-Gal IgE titers and propose an algorithm that considers all co-factors in the clinical history of α-Gal-sensitized patients to be incorporated into the AGS diagnosis. The need for identification of host-derived gene markers and tick-derived proteins for the diagnosis of the AGS is also discussed.
The current AGS diagnosis based on anti-α-Gal IgE titers has limitations because not all patients sensitized to α-Gal and with anti-α-Gal IgE antibodies higher than the cutoff (0.35 IU/ml) develop anaphylaxis to mammalian meat and AGS. The basophil activation test proposed to differentiate between patients with AGS and asymptomatic α-Gal sensitization cannot be easily implemented as a generalized clinical test. In coming years, the algorithm proposed here could be used in a mobile application for easier AGS diagnosis in the clinical practice.
α-半乳糖综合征（AGS）是一种过敏症，其特征是针对碳水化合物 Galα1-3Galβ1-4GlcNAc-R（α-半乳糖）的 IgE 抗体应答。蜱叮咬被认为是人类抗-α-半乳糖 IgE 抗体增加的最重要原因。已经发现了几个与 AGS 发展相关的风险因素，但将它们整合到标准化疾病诊断中一直具有挑战性。
本文讨论了目前基于抗-α-半乳糖 IgE 滴度的 AGS 诊断，并提出了一种算法，该算法考虑了α-半乳糖致敏患者临床病史中的所有共同因素，以便纳入 AGS 诊断。还讨论了鉴定宿主来源的基因标记和蜱来源的蛋白质用于诊断 AGS 的必要性。
目前基于抗-α-半乳糖 IgE 滴度的 AGS 诊断存在局限性，因为并非所有对 α-半乳糖敏感且抗-α-半乳糖 IgE 抗体滴度高于临界值（0.35IU/ml）的患者都会对哺乳动物肉类和 AGS 产生过敏反应。为了区分 AGS 患者和无症状的 α-半乳糖致敏患者而提出的嗜碱性粒细胞激活试验不能作为一种普遍的临床检测方法来轻易实施。在未来几年，这里提出的算法可以用于移动应用程序中，以便在临床实践中更轻松地进行 AGS 诊断。
