Asthma is a heterogenous disease [1] and dichotomisation between childhood/early-onset (EO) and adult/late-onset (LO) disease [2] identified differences in lung function decline and response to anti-inflammatory therapies, including biologics [3]. This suggests distinct inflammatory mechanisms underpin EO and LO asthma. In parallel, a relationship exists between airway neutrophilia and the airway microbiome [4, 5]. We postulate that differences in host–microbial interactions are associated with the age of asthma onset and would be maintained over time. Here, we applied a recently described machine learning framework, sparse canonical correlation analysis (Sparse-CCA) [6], to identify differences in host–microbial interactions in the airways of EO and LO asthma.

哮喘是一种异质性疾病 [1]，儿童/早发型 (EO) 和成人/晚发型 (LO) 疾病 [2] 之间的二分法确定了肺功能下降和对抗炎治疗（包括生物制剂）反应的差异 [3 ]。这表明 EO 和 LO 哮喘有不同的炎症机制。与此同时，气道中性粒细胞增多和气道微生物组之间也存在关系 [4, 5]。我们假设宿主-微生物相互作用的差异与哮喘发病年龄有关，并且会随着时间的推移而维持。在这里，我们应用了最近描述的机器学习框架，稀疏典型相关分析（Sparse-CCA）[6]，来识别 EO 和 LO 哮喘气道中宿主-微生物相互作用的差异。