Hou Xiangqing, Luo Wenting, Wu Liting, Chen Yuemin, Li Guoping, Zhang Rongfang, Zhang Hong, Wu Jing, Sun Yun, Xu Lina, Xu Peiru, Yu Yongmei, Huang Dongming, Hao Chuangli, Sun Baoqing
Faculty of Health Sciences, University of Macau 999078, Macau, China.
Department of Allergy and Clinical Immunology, State Key Laboratory of Respiratory Disease, National Clinical Research Center of Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou 510120, Guangdong, China.
EClinicalMedicine. 2022 Mar 21;46:101349. doi: 10.1016/j.eclinm.2022.101349. eCollection 2022 Apr.
Because of the significant regional differences in the distribution of allergens, the relationship between anaphylaxis and allergic sensitization is complex in China. Using this large-scale epidemiologic survey, we explore the potential patterns of sensitization to common allergens in mainland China and investigate their relationship with various clinical symptoms.
The participants were recruited from 13 medical centers in mainland China from October 2019 to June 2021. Skin prick test (SPT) results that cover 18 common allergens were utilized to diagnose atopic sensitization. The demographic characteristics and clinical information were collected through questionnaires during routine medical follow-up. Latent class analysis (LCA) was conducted to determine the optimal sensitization patterns. The logistic regression was used to assess the associations of different sensitization patterns with allergy symptoms.
A total of 1089 patients who had a positive SPT to at least one of 18 allergens were included for formal analysis. An optimal LCA model with 4 classes was obtained in this study, and the corresponding labels were as follows: Class1, house dust mite sensitization; Class2, low pollen sensitization; Class3, middle pollen sensitization; Class4, high pollen sensitization. The prevalence of different classes varied widely in geographical distribution, which was characterized by Class1 being very common in south and east as well as Class2 in north and west of China. Compared with patients in Class1, those in middle and high pollen sensitization clusters had the higher odds ratios (ORs) of allergic rhinitis and allergic conjunctivitis when controlling for other confounders. However, there was no significant difference between low pollen sensitization and house dust mite sensitization groups in the risks for various clinical performances except dermatitis. Additionally, the adjusted ORs (95% confidence interval) of allergic conjunctivitis and dermatitis for participants in pollen sensitization clusters (Class2, 3 and 4) were 1.56 (1.18, 2.06) and 1.43 (1.09, 1.88) respectively compared with those in Class1.
In this study, we identified four sensitization clusters with specific risks of various clinical symptoms using common allergens by adopting LCA. Our findings may contribute to improved diagnosis and potential immunotherapy approaches to allergy in mainland China.
This study was supported by the National Natural Science Foundation of China (81802076 and 81871736), the Guangzhou Science and Technology Foundation (202102010327), the Foundation of SKLRD (MS-2019-06 and Z-2022-09), and the Foundation of GYYY (ZH201904) and ZNSA-2020012.
由于过敏原分布存在显著的地区差异,在中国,过敏反应与过敏致敏之间的关系较为复杂。通过这项大规模流行病学调查,我们探索中国大陆常见过敏原致敏的潜在模式,并研究它们与各种临床症状的关系。
2019年10月至2021年6月期间,从中国大陆13个医疗中心招募参与者。利用涵盖18种常见过敏原的皮肤点刺试验(SPT)结果来诊断特应性致敏。在常规医学随访期间,通过问卷收集人口统计学特征和临床信息。进行潜在类别分析(LCA)以确定最佳致敏模式。采用逻辑回归评估不同致敏模式与过敏症状之间的关联。
共有1089名对18种过敏原中至少一种SPT呈阳性的患者纳入正式分析。本研究获得了一个具有4个类别的最佳LCA模型,相应标签如下:类别1,屋尘螨致敏;类别2,低花粉致敏;类别3,中花粉致敏;类别4,高花粉致敏。不同类别的患病率在地理分布上差异很大,其特点是类别1在南部和东部非常常见,类别2在中国北部和西部常见。在控制其他混杂因素后,与类别1的患者相比,中高花粉致敏组的患者患过敏性鼻炎和过敏性结膜炎的比值比(OR)更高。然而,除皮炎外,低花粉致敏组和屋尘螨致敏组在各种临床表现的风险方面没有显著差异。此外,与类别1的参与者相比,花粉致敏组(类别2、3和4)的参与者患过敏性结膜炎和皮炎的调整后OR(95%置信区间)分别为1.56(1.18,2.06)和1.43(1.09,1.88)。
在本研究中,我们通过LCA使用常见过敏原确定了四个具有各种临床症状特定风险的致敏组。我们的研究结果可能有助于改善中国大陆过敏的诊断和潜在的免疫治疗方法。
本研究得到了中国国家自然科学基金(81802076和81871736)、广州市科技基金(202102010327)、皮肤与免疫疾病国家重点实验室基金(MS - 2019 - 06和Z - 2022 - 09)以及广东省医学科学技术研究基金(ZH201904)和中山市卫生健康局科研项目(ZNSA - 2020012)的支持。