Rationale: Unsupervised statistical learning techniques, such as exploratory factor analysis (EFA) and hierarchical clustering (HC), have been used to identify asthma phenotypes, with partly consistent results. Some of the inconsistency is caused by the variable selection and demographic and clinical differences among study populations. Objectives: To investigate the effects of the choice of statistical method and different preparations of data on the clustering results; and to relate these to disease severity. Methods: Several variants of EFA and HC were applied and compared using various sets of variables and different encodings and transformations within a dataset of 383 children with asthma. Variables included lung function, inflammatory and allergy markers, family history, environmental exposures, and medications. Clusters and original variables were related to asthma severity (logistic regression and Bayesian network analysis). Measurements and Main Results: EFA identified five components (eigenvalues > 1) explaining 35% of the overall variance. Variations of the HC (as linkage-distance functions) did not affect the cluster inference; however, using different variable encodings and transformations did. The derived clusters predicted asthma severity less than the original variables. Prognostic factors of severity were medication usage, current symptoms, lung function, paternal asthma, body mass index, and age of asthma onset. Bayesian networks indicated conditional dependence among variables. Conclusions: The use of different unsupervised statistical learning methods and different variable sets and encodings can lead to multiple and inconsistent subgroupings of asthma, not necessarily correlated with severity. The search for asthma phenotypes needs more careful selection of markers, consistent across different study populations, and more cautious interpretation of results from unsupervised learning.

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.

Background: Thymic stromal lymphopoietin (TSLP) is expressed by airway epithelial cells and plays a key role in immunological events in asthma. Data on the genetic variants of TSLP and its association with asthma and allergic rhinitis are scarce. We aimed to investigate the effects of the genetic variants of TSLP in children with asthma and allergic rhinitis. Methods: The genetic variants of the TSLP gene were determined by sequencing 25 asthmatic and 25 healthy children. In an association study, a population of 506 asthmatics and 157 healthy controls was screened for the following single-nucleotide polymorphisms (SNPs): rs3806933 and rs2289276 in the promoter region; rs11466741, rs11466742, and rs2289278 in intron 2; rs10073816, rs11466749, and rs11466750 in exon 4, and rs11466754 in 3′-UTR. Results: In Multifactor Dimensionality Reduction analysis, presence of the rs11466749 AA genotype with atopy was significantly associated with a diagnosis of asthma (testing set accuracy: 0.720 and cross validation: 9/10). Two functional SNPs showed a gender-specific association with allergy, i.e. the rs3806933 CC genotype with asthma in boys (p = 0.032, nonsignificant after multiple testing) and the rs2289276 CC genotype with higher eosinophil numbers in asthmatic girls (p = 0.003). The presence of allergic rhinitis in asthmatic children strengthened the association of the rs11466749 GG genotype with asthma (p = 0.001), and rs2289276 was significantly associated with lower FEV₁ levels in asthmatics without allergic rhinitis (p = 0.003). Conclusion: Variants in the gene encoding the TSLP protein may have differential effects on asthma phenotypes depending on gender, atopy, and the presence of allergic rhinitis.