Phenotyping Methodologies

Identification of the heterogeneity and classification of asthma by sample phenotypes may provide a foundation from which to understand disease causality.1

Cluster data in severe asthma1-3

In severe asthma, identification of phenotypes has commonly been approached through:

  • a priori definitions of a phenotype based on clinical characteristics of subjects
  • Pathobiologic differences in sputum or bronchoscopy specimens1,2

Several research groups have developed cluster analyses of phenotypes in severe asthma. While their findings differ in groupings and classification, the clusters support the importance of disease heterogeneity in asthma and suggest differences in pathophysiologic mechanisms that determine cluster assignments.1,2

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Used an unbiased hierarchical method:

  • Purely observational, without a preexisting hypothesis (also known as “unsupervised” study)
  • Used a data set that led to 628 variables; reduced to 34 for the cluster analysis
  • Broad spectrum of routine assessment criteria covered by the cluster analysis variables: demographics, elements of current classification schemes, risk factors, and physiologic measures
  • Using 3 variables (baseline FEV1, maximal FEV1, and age of asthma onset), patients were assigned to clusters ranging from milder asthma (Cluster 1) to more severe disease (Clusters 4 and 5); 80% of patients were correctly assigned, indicating a simple method for phenotyping of asthma subclasses

Moore: Tree Performance by Cluster*

*Individual figure size is proportional to the frequency of a specific cluster. The percentage of subjects from that cluster who are correctly assigned is indicated numerically within the shape.

Reprinted from the American Journal of Respiratory and Critical Care Medicine. Vol 181. Moore WC, Meyers DA, Wenzel SE, et al; Identification of Asthma Phenotypes Using Cluster Analysis in the Severe Asthma Research Program, pp 315-323, 2009, with permission from The American Thoracic Society.

Used the 5 asthma clusters proposed by Moore’s tree analysis to create these sample categories:

CLUSTER 1: Mild allergic asthma—early onset; atopic; normal lung function; ≤2 controller medications; minimal healthcare use; minimal sputum eosinophilia

CLUSTER 2: Mild-moderate allergic asthma—most common cluster; early onset; atopic; borderline FEV1, but reverse to normal; ≤2 controller medications; low healthcare use; infrequent need for oral corticosteroids; minimal sputum eosinophilia

CLUSTER 3: More severe older-onset asthma—older; very late onset; higher BMI (obese); less atopic; slightly decreased FEV1 with some reversibility; frequent need for oral corticosteroids, despite ≥3 controller medications, including high doses of inhaled corticosteroids; sputum eosinophilia

CLUSTER 4: Severe variable allergic asthma—early onset; atopic; severely decreased FEV1, but very reversible to near normal; high frequency of symptoms and albuterol use; “variable” with need for frequent oral corticosteroids; high healthcare use; sputum eosinophilia

CLUSTER 5: Severe fixed-airflow asthma—older; longest duration; less atopic; severely decreased FEV1 with less reversibility (COPD similarities); high frequency of symptoms and albuterol use despite oral corticosteroids; high healthcare use; comorbidities; both sputum eosinophilia and neutrophilia

 

Mild, moderate, and refractory (including severe) asthma:

  • Performed a cluster analysis using a presupposed hypothesis (also known as “supervised” study) to identify phenotypic groups comparing 2 sets of patients:
    • Those managed in primary care with mostly mild to moderate asthma
    • Those managed in secondary care with refractory asthma
  • Proposed 4 clusters of refractory patients in the secondary care group:
    • Early-onset, atopic asthma
    • Obese, noneosinophilic asthma
    • Early symptom predominant asthma
    • Inflammation predominant asthma
 

Reprinted with permission from the American Thoracic Society. 1

References: 1. Moore WC, Meyers DA, Wenzel SE, et al; for the National Heart, Lung, and Blood Institute's Severe Asthma Research Program. Identification of asthma phenotypes using cluster analysis in the Severe Asthma Research Program. Am J Respir Crit Care Med. 2010;181(4):315-323. 2. Holgate ST, Sly PD. Asthma pathogenesis. In: Adkinson Jr NF, Bochner BS, Burks AW, et al, eds. Middleton’s Allergy Principles and Practice. 8th ed. Philadelphia, PA: Elsevier Saunders; 2014:812-841. 3. Haldar P, Pavord ID, Shaw DE, et al. Cluster analysis and clinical asthma phenotypes. Am J Respir Crit Care Med. 2008;178(3):218-224.

825000R0 August 2017