In this study of 154 urban, largely minority children and adolescents from Washington, DC with established asthma, we successfully employed cluster analysis to reduce the cohort’s heterogeneity. This permitted detection of associations between ETS exposure and asthma characteristics within specific clusters. Historically, asthma has proven difficult to characterize because of the complex nature of the disease (1
). Thus, developing tools capable of identifying more homogeneous subgroups is crucial to understanding the variable expression patterns of the disease and to tailoring therapies to subgroups of asthma (3
Recently, Halder, et al. (8
) and Moore, et al. (9
) used cluster analysis to identify subgroups within several independent asthma cohorts. However, our study is the first to extend this observation by demonstrating that associations not found in the overall population exist within these clusters. To accomplish this, we first used hierarchical and k-means clustering of principal component variables to identify more phenotypically homogeneous subgroups within the AsthMaP cohort. Of the resulting four clusters, one consisted of only six individuals and therefore lacked power to be useful in association analyses. Interestingly, these six individuals displayed very mild asthma, suggesting that they did not cluster with the other subjects due to lack of disease expression. The remaining three clusters showed distinct and familiar asthma phenotypes commonly seen in clinical practice.
Cluster 1 was predominantly male (81%) with a relative abundance of neutrophils in their nasal washes. Mucosal neutrophilia is a frequent finding in asthma that may or may not be observed in conjunction with eosinophilic inflammation (20
). Wenzel, et al. (21
) showed that neutrophil-predominant asthma is a distinct inflammatory subgroup of severe asthma, with increased neutrophil levels found in refractory patients. The exact cause of this phenotype is unknown but it has been thought to be exacerbated by environmental exposures such as bacterial endotoxins, air pollution, cigarette smoke, or viral infections (22
Cluster 2 was notable for its female predominance (70%). In addition, subjects in this cluster had high mean BMI percentile and a mean age of asthma-onset of 7.5 years of age, much older than for the other two clusters. These characteristics are often seen in clinical practice, as there appears to be a biological link between asthma and obesity (23
). Several studies have observed that this association is stronger in women (24
). Specifically, a high BMI coupled with early onset of puberty have been reported as risk factors for first developing asthma during adolescence in females (24
Cluster 3 had a nearly balanced gender distribution and exhibited the more classical atopic/allergic asthma phenotype with an increase in eosinophils in their nasal washes. Eosinophil activation and subsequent inflammation in the lungs are established hallmarks of asthma pathology and are highly associated with increased symptoms and frequency of exacerbations, along with worse disease control (1
). This poor asthma control is evident in Cluster 3 as shown by relatively lower ACT scores and FEV1
measurements. The individuals in this cluster also had a high mean BMI percentile similar to what was observed in Cluster 2. The characteristics that make up Cluster 3 have been shown to be associated with obesity. Particularly, high BMI is associated with decreased symptom control and higher prevalence of atopy (25
). Additionally, hierarchical clustering revealed several smaller sub-clusters, suggesting that there is remaining heterogeneity in this cluster.
It is important to note that NAEPP severity levels among the clusters were not significantly different, given that this classification system is frequently used in the diagnosis and management of childhood asthma (14
). Although improved in 2007 with regard to heterogeneity within classification levels due to age (14
), our data show that it remains limited with regard to other sources of heterogeneity. Thus, we propose that the alternative clustering method described be evaluated as a complementary means of effectively grouping individuals based on phenotype.
Intrinsic to the concept of heterogeneity in asthma is the idea that response to environmental stimuli will differ among asthma subgroups (3
). Therefore, we used regression analysis in the overall AsthMaP population and within the identified clusters to explore associations between ETS exposure (i.e. urine cotinine) and asthma characteristics. Urine cotinine levels were not significantly different among the three clusters. However, within-cluster analysis revealed several significant associations that were not found in the overall cohort, supporting our hypothesis that analyzing more homogeneous subgroups would prove useful in identifying new associations.
In particular, we found that within Cluster 1 an increase in ETS exposure was associated with a significant increase in neutrophils in nasal washes and a significant decrease in ACT score. Because it has been shown that asthma symptoms are exacerbated by ETS exposure (31
), it is reasonable that there is a significant association between ETS exposure and decreased asthma control in this cluster.
Within Cluster 3, we found that an increase in ETS exposure was associated with a significant reduction in the bronchodilator-induced % change in FEV1
. This is a potentially novel finding for this asthma phenotype given that ETS exposure typically increases bronchodilator response in children with asthma (33
). It is possible that the eosinophilic inflammation displayed in this cluster, and the subsequent chronic inflammation, makes these individuals less responsive to bronchodilators.
It is notable that no significant associations with ETS exposure were detected within Cluster 2. We suspect that this is due in part to the relatively small sample size. Alternatively, it is possible that the phenotypes in this cluster are not as responsive to ETS exposure. While it has been previously shown that ETS exposure increases the incidence of asthma in overweight individuals (34
), there is no evidence that it leads to greater disease severity.
This study has important limitations. First, our sample size of only 154 participants restricted the number of clusters we were able to detect in our cohort. As mentioned regarding Cluster 3, there are undoubtedly more subgroups of asthma that could be identified in each cluster given a larger sample size. However, the goal of this study was not delineation of clusters but rather defining associations between ETS exposure and asthma characteristics within clusters. Second, the selection of variables is subjective. We aimed to select a wide range of variables representative of disease expression. However, we recognize the likelihood that other variables not included could also have an impact on this analysis. Third, because of the cross-sectional nature of the AsthMaP study, this analysis does not address cluster-stability over time. Given the dynamic nature of asthma disease expression, it is possible that individuals move among clusters with time. Fourth, the AsthMaP cohort is comprised of largely urban AA youth, making it more difficult to extend our findings into other childhood asthma populations. However, our study provides insight into AA children and adolescents with asthma as one of the highest-risk asthma populations. Finally, using k-means cluster analysis as the principal clustering tool required us to pre-specify the number of expected clusters. We took action to eliminate bias, including (1) using hierarchical clustering as a first step to estimate the number of probable clusters and (2) repeating the k-means cluster analysis while specifying one more or less cluster than the estimate to ensure the selection of the most representative model.