Asthma is a clinical syndrome that is characterized by variability in disease expression and severity (4
). Asthma severity classification in current and previous guidelines is based on four to six “steps” that range from intermittent to severe persistent asthma (1
). These classifications of asthma severity are based on clinical characteristics that include frequency of symptoms, short-acting bronchodilator use, pulmonary function, and medication requirements (1
). If an individual with asthma meets any one criterion in that “step,” he is then assigned to that severity despite potential disease heterogeneity within the level. The major assumption in these classification schemes is that all patients within a specific asthma severity level have similar disease characteristics and risk of future asthma exacerbations that should be managed with the same therapeutic regimen. This traditional approach ignores asthma subtypes within and across these levels of asthma severity. Furthermore, this classification approach assumes that patients with asthma who are classified as intermittent, mild, moderate, and severe respond similarly to specific therapies, although optimal management strategies may not always be achieved, specifically in the patients with more severe or “difficult to treat” asthma (3
). Thus, the purpose of this study is to improve our understanding of the basis for severity classification and to develop an asthma classification algorithm using comprehensive phenotyping approaches that reflect pathophysiologic processes and disease heterogeneity. To accomplish this goal, data from the SARP cohort, which includes all levels of asthma severity, was analyzed using an unsupervised cluster approach to determine asthma subphenotypes.
Identification of asthma subphenotypes has generally been accomplished in two ways: (1
) through a priori
definitions of a phenotype based on clinical characteristics of subjects or (2
) through pathobiologic differences in sputum or bronchoscopy specimens. The most studied clinical phenotypes have been related to age and atopy. Studies that have compared childhood with adult asthma have reported more atopy and preserved lung function in the former group (14
). Other studies have described subsets of patients with adult asthma characterized by age of onset that differ clinically, suggesting different underlying pathophysiologic mechanisms of disease (11
Several studies have demonstrated eosinophilic or noneosinophilic inflammation in asthma (28
) and have led to clinical approaches that use these cellular biomarkers to guide asthma management (30
). Sputum eosinophilia is a biomarker that appears to be useful in guiding corticosteroid therapy (30
), but analysis of induced sputum may not be available in most clinical settings because of the complexity of this technique and difficulty with accurate performance of this analysis. Fe
NO has been used clinically as a noninvasive biomarker to diagnose asthma and evaluate therapeutic responsiveness (31
), but more recent studies suggest limitations of its predictive value (32
). A recent study has shown better diagnostic and prognostic utility using a panel of several noninvasive inflammatory biomarkers (including Fe
NO), suggesting that a multidimensional approach may be more effective than single biomarker monitoring (33
). As investigators continue to explore biomarkers that directly reflect airways inflammation and disease severity or guide therapy, more clinically available phenotyping approaches should be evaluated to assess their ability to characterize severity and provide insight into pathobiologic mechanisms in asthma.
The cluster analysis described in this paper is an unsupervised modeling approach to identify asthma phenotypes within the SARP cohort. This article describes five different groups of subjects with asthma who differ in clinical, physiologic, and inflammatory parameters. Of the 11 most important variables that determine assignment to individual clusters, six are pulmonary function tests, two are related to age (age of onset and duration of asthma), two are composite variables that reflect medication use (corticosteroids, β-agonists), and one is gender.
Pulmonary function is an important determinant of disease severity (17
). In the current cluster analysis, the combination of prebronchodilator and postbronchodilator measurements (baseline and best FEV1
) best differentiates the mildest clusters (Cluster 1 from 2) and the most severe groups (Cluster 4 from 5). It is important to identify the patients with the mildest asthma with the lowest risk, and a prebronchodilator FEV1
≥80% predicted identifies all subjects in Cluster 1. The patients with milder athma who do not meet that benchmark (Cluster 2) would appear to be at higher risk. The patients with the most severe asthma have a low prebronchodilator FEV1
(<68% predicted), but it is the postbronchodilator FEV1
that determines assignment to Clusters 4 and 5. Unfortunately, pulmonary function testing is usually performed without reference to recent bronchodilator use, and in that setting the reported values may represent the spectrum of prebronchodilator to postbronchodilator FEV1
. The difference between those measurements determines phenotype in this cluster analysis, and the importance of having a true baseline FEV1
and a maximal postbronchodilator (four puffs of albuterol) FEV1
requires further evaluation.
Several clusters (Clusters 1, 2, and 4) consist of more atopic subjects with early or childhood onset of disease, which is consistent with the presence of an allergic phenotype in 76% of patients. Late-onset asthma (after the age of 12 years) and less atopy are more characteristic of the older subjects in Clusters 3 and 5, suggesting additional nonallergic disease mechanisms. Regardless of age of onset, the subjects with the longest duration of disease have the most severe asthma and lowest lung function (Clusters 4 and 5). These results suggest that patients with long-standing asthma are at risk for developing chronic airflow obstruction whether they have an allergic or nonallergic phenotype. Previous studies support this observation, with some groups reporting severe chronic airflow obstruction in patients with persistent airway eosinophilia and subjects with less atopy and late-onset asthma (27
Understanding the basis for persistent symptoms and reduced quality of life in Clusters 3 and 5 is confounded by a higher frequency of obesity in these older subjects, suggesting that impairment may be caused both by asthma and obesity. The interaction of asthma and obesity is complex because obesity may worsen asthma or represent a coexistent condition that increases respiratory symptoms (36
). Obesity can be associated with reductions in FEV1
and FVC with a relatively preserved FEV1
/FVC ratio, and recent studies have suggested dynamic hyperinflation as a possible etiology for dyspnea in these patients (39
). Subjects in Cluster 3 show evidence of mild airways obstruction with symptoms somewhat out of proportion to their pulmonary impairment. All subjects in Cluster 3 had bronchial hyperresponsiveness to methacholine, which is consistent with their asthma diagnosis. Thus, Cluster 3 represents a difficult-to–manage, late-onset group of mostly older obese women with frequent exacerbations requiring oral corticosteroid therapies.
The frequency and intensity of HCU is greatest in the clusters with the lowest lung function (Clusters 4 and 5) despite therapy with high doses of inhaled and oral corticosteroids. It is possible that reduced lung function may predispose to severe exacerbations and frequent hospitalizations. The increased frequency of pneumonia in these groups, especially Cluster 5, may be related to higher exposure to corticosteroids and is similar to the more frequent history of pneumonia observed in patients with COPD who were treated with high doses of inhaled corticosteroids (40
Biomarkers are not included in the cluster analysis because only a subset of subjects had these assessments. A post hoc
analysis of this subset of subjects within the clusters provides potential insight into pathobiologic mechanisms that may be related to the different phenotypes observed, especially in Clusters 3, 4, and 5. Although eosinophils are present in the sputum of subjects in all three of these clusters, subjects in Cluster 4 are characterized by elevated clinical measures of atopy (skin testing, serum IgE), suggesting that allergic, IgE-mediated eosinophilic airways inflammation is important in this group. In contrast, sputum neutrophils are also elevated in Cluster 5, which contains subjects who are clinically less atopic with frequent sinopulmonary infections, suggesting complex mechanisms that may reflect allergic inflammation and other pathobiologic factors, including the systemic effects of obesity (37
). Persistent airway eosinophilia while receiving high doses of inhaled or oral corticosteroids in Clusters 3, 4, and 5 suggests the possibility of relative steroid insensitivity.
Other groups have reported statistical modeling approaches to investigate novel asthma phenotypes (5
). The overall purpose and methodology (factor or cluster), the size and demographics of the cohorts, and the number and type of variables used in these analyses differ. The cluster analysis reported by Haldar and colleagues has similarities to the current study but was performed in three smaller asthma cohorts (n = 187 in the largest cohort) and used fewer clinical variables to generate the disease clusters (5
). Although some variables are the same as those used in this study (age of onset, BMI, sex, atopy, symptom scores), variables related to pulmonary function and bronchodilator reversibility were limited (only peak flow variability). Sputum eosinophil counts were used, however, which was not possible in the larger SARP multicenter network.
Although the clusters described by Haldar show overlap with the clusters described in this paper, there are important differences. Both cluster analyses identify a group of older obese patients (mostly women) with adult-onset asthma and less atopy (Cluster 3) that comprise approximately 10% of patients with severe asthma. Both analyses report a group of patients with severe asthma with late-onset asthma, less atopy, and decreased lung function, but the patients in Cluster 5 in this study are characterized by elevated sputum neutrophils and significant pulmonary function impairments. The Haldar analysis also describes two severe asthma atopic clusters that are differentiated by level of sputum eosinophilia and symptoms. The current analysis reveals three atopic clusters (Clusters 1, 2, and 4) that differ in baseline lung function, response to bronchodilators, medication requirements, HCU, and asthma symptoms. Clusters 1, 2, and 4 represent a continuum of allergic phenotype across three levels of disease severity, with the most severe patients assigned to Cluster 4. The ability to identify this severe subset of atopic asthma without assessment of sputum eosinophilia is a significant finding in the current analysis.
In conclusion, the five asthma clusters support the importance of disease heterogeneity in asthma and suggest differences in pathophysiologic mechanisms that determine cluster assignments. In retrospective and prospective population samples, the tree or algorithm can be used to evaluate the therapeutic implications of these clusters. The apparent divergent phenotypic characteristics observed, especially in Clusters 3, 4, and 5, suggest different pathophysiologic processes that may determine therapeutic responses and thus affect asthma control.
An important question is how well this cluster approach can be applied to clinical settings. Algorithms have been used successfully for the differential diagnoses of asthma in research studies (45
) but have not been applied to different levels of asthma severity. In the current study, we developed an algorithm to assign subjects to asthma severity clusters using readily available clinical testing: the pre- and postbronchodilator FEV1
and an assessment of age of onset. This algorithm was successful in 80% of subjects. Future studies are needed to evaluate our ability to use this cluster analysis in a prospective manner to classify disease severity and improve asthma control by personalizing asthma management and identifying individuals at risk for adverse outcomes.