Huang Hung-Ling, Huang Ying-Jhen, Chu Yi-Chi, Chen Chia-Wei, Yang Hsin-Chou, Hwang Jing-Shiang, Chen Chun-Houh, Chan Ta-Chien
Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung, Taiwan.
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
J Asthma Allergy. 2023 Jan 21;16:135-147. doi: 10.2147/JAA.S397067. eCollection 2023.
Uncontrolled asthma in adults leads to poor clinical outcome, while the clinical heterogeneity of phenotypes interferes the applicable genetic determinants. This study aimed to identify phenotypes and genetic impact on poorly-controlled asthma to optimize individualized treatment strategies.
This propensity score-matched case-control study included 340 and 1020 asthmatics with poorly-controlled asthma and well-controlled asthma, respectively. Data were obtained from the 2008-2015 Taiwan Biobank Database and linked to the National Health Insurance Research Database. All asthmatics were aged ≥30 years, without cancer history, and each completed a questionnaire, physical examination, and genome-wide single nucleotide polymorphisms (SNPs). Multivariate adjusted odds ratios (ORs) for genetic risk scores were calculated using conditional logistic regression, stratified by age and sex. A model integrating obesity- and asthma-associated phenotypes and genotypes was applied for poorly-controlled asthma risk prediction.
General obesity with body mass index (BMI) ≥27 kg/m (OR:1.49, 95% confidence interval (CI) 1.09-2.03), central obesity with waist-to-height ratio (WHtR) ≥0.5 (OR:1.62, 95% CI 1.22-2.15), and parental history of asthma (OR:1.65, and 1.68; for BMI model and WHtR model, respectively) were significantly associated with poorly-controlled asthma in adults, and the combination effect of both obesity phenotypes was 1.66 (95% CI 1.17-2.35). A total of 16 obesity-associated SNPs and 9 asthma-associated SNPs were converted into genetic scores, and the aforementioned phenotypes were incorporated into the risk prediction model for poorly-controlled asthma, with an area under curve 0.72 in the receiver operating characteristic curve. The potential biological functions of genes are involved in immunity pathways.
The prediction model integrating obesity-asthma phenotypes and genotypes for poorly-controlled asthma can facilitate the prediction of high-risk asthma and provide potential targets for novel treatment.
成人哮喘控制不佳会导致不良临床结局，而表型的临床异质性会干扰适用的遗传决定因素。本研究旨在确定对控制不佳哮喘的表型和遗传影响，以优化个体化治疗策略。
这项倾向评分匹配的病例对照研究分别纳入了340例和1020例控制不佳哮喘和控制良好哮喘的哮喘患者。数据来自2008 - 2015年台湾生物银行数据库，并与国民健康保险研究数据库相关联。所有哮喘患者年龄≥30岁，无癌症病史，且均完成了问卷调查、体格检查和全基因组单核苷酸多态性（SNP）检测。使用条件逻辑回归计算遗传风险评分的多变量调整比值比（OR），按年龄和性别分层。将肥胖和哮喘相关的表型及基因型整合到一个模型中，用于预测控制不佳哮喘的风险。
体重指数（BMI）≥27 kg/m²的一般肥胖（OR：1.49，95%置信区间（CI）1.09 - 2.03）、腰高比（WHtR）≥0.5的中心性肥胖（OR：1.62，95% CI 1.22 - 2.15）以及哮喘家族史（分别针对BMI模型和WHtR模型，OR为1.65和1.68）与成人控制不佳哮喘显著相关，两种肥胖表型的联合效应为1.66（95% CI 1.17 - 2.35）。总共16个肥胖相关SNP和9个哮喘相关SNP被转化为遗传评分，上述表型被纳入控制不佳哮喘风险预测模型，在受试者工作特征曲线下面积为0.72。基因的潜在生物学功能涉及免疫途径。
整合肥胖 - 哮喘表型和基因型的控制不佳哮喘预测模型有助于预测高危哮喘，并为新的治疗提供潜在靶点。
