Department of Pediatrics, People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture, First Affliated Hospital of Jishou University, Jishou, China.
Immun Inflamm Dis. 2024 Nov;12(11):e70043. doi: 10.1002/iid3.70043.
Severe asthma is a significant health burden because children with severe asthma are vulnerable to medication-related side effects, life-threatening deterioration, and impaired quality of life. However, there is a lack of data to elucidate the role of inflammatory variables in asthma. This study aimed to compare the levels of inflammatory factors in serum and sputum in children with acute and stable asthma to those in healthy children and the ability to predict clinical response to azithromycin therapy.
This study recruited 95 individuals aged 1-3 years old and collected data from January 2018 to 2020. We examined serum and sputum inflammatory factors and constructed the least absolute shrinkage and selection operator (LASSO) model. Predictive models were constructed through multifactor logistic regression and presented in the form of column-line plots. The performance of the column-line diagrams was measured by subject work characteristics (ROC) curves, calibration plots, and decision curve analysis (DCA). Then, filter-paper samples were collected from 45 children with acute asthma who were randomly assigned to receive either azithromycin (10 mg/kg, n = 22) or placebo (n = 23). Pretreatment levels of immune mediators were then analyzed and compared with clinical response to azithromycin therapy.
Of the 95 eligible participants, 21 (22.11%) were healthy controls, 29 (30.53%) had stable asthma, and 45 (47.37%) had acute asthma. The levels of interferon-γ (IFN-γ), tumor necrosis factor-a (TNF-α), chemokine CCL22 (CCL22), interleukin 12 (IL-12), chemokine CCL4 (CCL4), chemokine CCL2 (CCL2), and chemokine CCL13 (CCL13）were significantly higher in the acute asthma group than in the stable asthma group. A logistic regression analysis was performed using CCL22 and IL-1 as independent variables. Additionally, IFN-γ, TNF-α, IL-1, IL-13, and CCL22 were identified in the LASSO model. Finally, we found that CCL22 and IL-1 were more responsive in predicting the response to azithromycin treatment.
Our results show that CCL22 and IL-1 are both representative markers during asthma symptom exacerbations and an immune mediator that can predict response to azithromycin therapy.
重度哮喘是一个严重的健康负担，因为患有重度哮喘的儿童容易出现药物相关的副作用、危及生命的恶化和生活质量受损。然而，目前缺乏数据来阐明炎症变量在哮喘中的作用。本研究旨在比较急性和稳定期哮喘儿童与健康儿童血清和痰中炎症因子的水平，并预测阿奇霉素治疗的临床反应。
本研究纳入了 2018 年 1 月至 2020 年期间年龄在 1-3 岁的 95 名个体，并收集了数据。我们检测了血清和痰中的炎症因子，并构建了最小绝对收缩和选择算子（LASSO）模型。通过多因素逻辑回归构建了预测模型，并以列线图的形式呈现。列线图的性能通过受试者工作特征（ROC）曲线、校准图和决策曲线分析（DCA）进行测量。然后，我们从 45 名急性哮喘患儿中收集滤纸样本，这些患儿被随机分为阿奇霉素（10mg/kg，n=22）或安慰剂（n=23）组。然后分析免疫介质的预处理水平，并与阿奇霉素治疗的临床反应进行比较。
在 95 名符合条件的参与者中，21 名（22.11%）为健康对照组，29 名（30.53%）为稳定期哮喘组，45 名（47.37%）为急性哮喘组。干扰素-γ（IFN-γ）、肿瘤坏死因子-α（TNF-α）、趋化因子 CCL22（CCL22）、白细胞介素 12（IL-12）、趋化因子 CCL4（CCL4）、趋化因子 CCL2（CCL2）和趋化因子 CCL13（CCL13）在急性哮喘组中的水平明显高于稳定期哮喘组。使用 CCL22 和 IL-1 作为独立变量进行逻辑回归分析。此外，在 LASSO 模型中鉴定出 IFN-γ、TNF-α、IL-1、IL-13 和 CCL22。最后，我们发现 CCL22 和 IL-1 对预测阿奇霉素治疗反应的反应性更高。
我们的研究结果表明，CCL22 和 IL-1 都是哮喘症状加重期间的代表性标志物，是预测阿奇霉素治疗反应的免疫介质。
