Ong Mei-Sing, Sordillo Joanne E, Dahlin Amber, McGeachie Michael, Tantisira Kelan, Wang Alberta L, Lasky-Su Jessica, Brilliant Murray, Kitchner Terrie, Roden Dan M, Weiss Scott T, Wu Ann Chen
PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA 02215, USA.
Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
J Pers Med. 2024 Feb 25;14(3):246. doi: 10.3390/jpm14030246.
Although inhaled corticosteroids (ICS) are the first-line therapy for patients with persistent asthma, many patients continue to have exacerbations. We developed machine learning models to predict the ICS response in patients with asthma.
The subjects included asthma patients of European ancestry ( = 1371; 448 children; 916 adults). A genome-wide association study was performed to identify the SNPs associated with ICS response. Using the SNPs identified, two machine learning models were developed to predict ICS response: (1) least absolute shrinkage and selection operator (LASSO) regression and (2) random forest.
The LASSO regression model achieved an AUC of 0.71 (95% CI 0.67-0.76; sensitivity: 0.57; specificity: 0.75) in an independent test cohort, and the random forest model achieved an AUC of 0.74 (95% CI 0.70-0.78; sensitivity: 0.70; specificity: 0.68). The genes contributing to the prediction of ICS response included those associated with ICS responses in asthma (), asthma symptoms and severity ( and ), airway remodeling (), mucin production (), leukotriene synthesis (), allergic asthma (), and others.
An accurate risk prediction of ICS response can be obtained using machine learning methods, with the potential to inform personalized treatment decisions. Further studies are needed to examine if the integration of richer phenotype data could improve risk prediction.
尽管吸入性糖皮质激素（ICS）是持续性哮喘患者的一线治疗药物，但许多患者仍会出现病情加重。我们开发了机器学习模型来预测哮喘患者对ICS的反应。
研究对象包括欧洲血统的哮喘患者（n = 1371；448名儿童；916名成人）。进行了全基因组关联研究以确定与ICS反应相关的单核苷酸多态性（SNP）。利用所确定的SNP，开发了两个机器学习模型来预测ICS反应：（1）最小绝对收缩和选择算子（LASSO）回归模型和（2）随机森林模型。
在一个独立测试队列中，LASSO回归模型的曲线下面积（AUC）为0.71（95%置信区间0.67 - 0.76；灵敏度：0.57；特异度：0.75），随机森林模型的AUC为0.74（95%置信区间0.70 - 0.78；灵敏度：0.70；特异度：0.68）。有助于预测ICS反应的基因包括那些与哮喘中ICS反应（……）、哮喘症状和严重程度（……和……）、气道重塑（……）、粘蛋白产生（……）、白三烯合成（……）、过敏性哮喘（……）等相关的基因。
使用机器学习方法可以获得对ICS反应的准确风险预测，这有可能为个性化治疗决策提供依据。需要进一步研究来检验更丰富的表型数据整合是否能改善风险预测。
