BackgroundIn a population-based sample of school-age children, we investigated factors associated with rhinitis, and differences between allergic and nonallergic rhinitis. Amongst children with asthma, we explored the association between rhinitis and asthma severity.MethodsChildren participating in a birth cohort study (n = 906) were reviewed at age 8 years. Asthma was defined as at least two of the following three features: physician-diagnosed asthma, currently using asthma medication and current wheeze. We measured lung function (plethysmography and spirometry) and airway hyper-reactivity (AHR; methacholine challenge).ResultsIn the analysis adjusted for the presence of asthma, children with rhinitis had significantly higher AHR (P = 0.001). Maternal smoking and absence of breastfeeding were stronger predictors of nonallergic rhinitis, whereas current wheeze and eczema were stronger predictors of allergic rhinitis. Amongst asthmatics (n = 159), when compared to 76 children without rhinitis, those with rhinitis (n = 83) were 2.89-fold (95% CI 1.41–5.91) more likely to experience frequent attacks of wheezing, 3.44-fold (1.19–9.94) more likely to experience severe attacks of wheezing limiting speech, 10.14-fold (1.27–81.21) more likely to have frequent visits to their doctor because of asthma and nine-fold (1.11–72.83) more likely to miss school. Reported use of intranasal corticosteroids resulted in a numerically small, but consistent reduction in risk, rendering the associations between rhinitis and asthma severity nonsignificant.ConclusionWe observed differences in risk factors and severity between allergic and nonallergic rhinitis. In children with asthma, rhinitis had adverse impact on asthma severity. The use of intranasal corticosteroids resulted in a small, but consistent reduction in the risk.

SummaryBackgroundData‐driven methods such as hierarchical clustering (HC) and principal component analysis (PCA) have been used to identify asthma subtypes, with inconsistent results.ObjectiveTo develop a framework for the discovery of stable and clinically meaningful asthma subtypes.MethodsWe performed HC in a rich data set from 613 asthmatic children, using 45 clinical variables (Model 1), and after PCA dimensionality reduction (Model 2). Clinical experts then identified a set of asthma features/domains which informed clusters in the two analyses. In Model 3, we reclustered the data using these features to ascertain whether this improved the discovery process.ResultsCluster stability was poor in Models 1 and 2. Clinical experts highlighted four asthma features/domains which differentiated the clusters in two models: age of onset, allergic sensitization, severity, and recent exacerbations. In Model 3 (HC using these four features), cluster stability improved substantially. The cluster assignment changed, providing more clinically interpretable results. In a 5‐cluster model, we labelled the clusters as: “Difficult asthma” (n = 132); “Early‐onset mild atopic” (n = 210); “Early‐onset mild non‐atopic: (n = 153); “Late‐onset” (n = 105); and “Exacerbation‐prone asthma” (n = 13). Multinomial regression demonstrated that lung function was significantly diminished among children with “Difficult asthma”; blood eosinophilia was a significant feature of “Difficult,” “Early‐onset mild atopic,” and “Late‐onset asthma.” Children with moderate‐to‐severe asthma were present in each cluster.Conclusions and clinical relevanceAn integrative approach of blending the data with clinical expert domain knowledge identified four features, which may be informative for ascertaining asthma endotypes. These findings suggest that variables which are key determinants of asthma presence, severity, or control may not be the most informative for determining asthma subtypes. Our results indicate that exacerbation‐prone asthma may be a separate asthma endotype and that severe asthma is not a single entity, but an extreme end of the spectrum of several different asthma endotypes.

Asthma is a heterogeneous disease comprising a number of subtypes which may be caused by different pathophysiologic mechanisms (sometimes referred to as endotypes) but may share similar observed characteristics (phenotypes). The use of unsupervised clustering in adult and paediatric populations has identified subtypes of asthma based on observable characteristics such as symptoms, lung function, atopy, eosinophilia, obesity, and age of onset. Here we describe different clustering methods and demonstrate their contributions to our understanding of the spectrum of asthma syndrome. Precise identification of asthma subtypes and their pathophysiological mechanisms may lead to stratification of patients, thus enabling more precise therapeutic and prevention approaches.