Measuring the corticosteroid responsiveness endophenotype in asthmatic patients

Abstract

Background: Inhaled corticosteroids are the most commonly used controller therapies for asthma, producing treatment responses in 6 clinical phenotypes: lung function, bronchodilator response, airway responsiveness, symptoms, need for oral steroids and frequency of emergency department visits and hospitalizations. We hypothesize that treatment response in all of these phenotypes is modulated by a single quantitative corticosteroid responsiveness endophenotype.

Objective: We sought to develop a composite phenotype that combines multiple clinical phenotypes to measure corticosteroid responsiveness with high accuracy, stability across populations, and robustness to missing data.

Methods: We used principal component analysis to determine a composite corticosteroid responsiveness phenotype that we tested in 4 replication populations. We evaluated the relative accuracy with which the composite and clinical phenotypes measure the endophenotype using treatment effect area under the receiver operating characteristic curve (AUC).

Results: In the study population the composite phenotype measured the endophenotype with an AUC of 0.74, significantly exceeding the AUCs of the 6 individual clinical phenotypes, which ranged from 0.56 (P < .001) to 0.67 (P = .015). In 4 replication populations with a total of 22 clinical phenotypes available, the composite phenotype AUC ranged from 0.69 to 0.73, significantly exceeded the AUCs of 14 phenotypes, and was not significantly exceeded by any single phenotype.

Conclusion: The composite phenotype measured the endophenotype with higher accuracy, higher stability across populations, and higher robustness to missing data than any clinical phenotype. This should provide the capability to model corticosteroid pharmacologic response and resistance with increased accuracy and reproducibility.

Keywords: Asthma; corticosteroids; drug therapy; endophenotype; pharmacogenetics; pharmacologic response.

Figure 1

Measuring corticosteroid responsiveness. We hypothesize a corticosteroid responsiveness endophenotype that modulates the asthma disease process, is latent in untreated subjects, and active in ICS treated subjects. We propose a composite corticosteroid responsiveness phenotype model that combines six clinical phenotypes into a composite phenotype to measure the endophenotype.

Figure 2

Study design. We selected clinical phenotypes known to exhibit significant treatment response and representative study and replication populations. We used PCA to determine the composite corticosteroid responsiveness phenotype in the study population. We generalized this result to produce the composite phenotype model for testing in replication populations. We evaluated the relative measurement accuracy of all phenotypes.

Figure 3

Clinical treatment response phenotypes for one CAMP study participant. We determined the value of each phenotype by performing linear regression of clinical observations versus time from start of treatment. We constrained the regression lines to pass through the average (target) of pre-treatment values (small targets) at time 0. We interpreted the slopes of these lines as the phenotype values.

Figure 4

Learning the composite phenotype in the study population. A. The first (PC1) and second (PC2) principal components of ICS treatment response. B. The distribution of PC1 in treatment groups. C. PC1 receiver operating characteristic curve (AUC = .74). D. AUCs of PC1, clinical phenotypes, and other components, with 95% confidence intervals (single-sided p-values are shown along the left axis).

Figure 5

Composite phenotype performance in the replication populations. A comparison of the relative accuracy (AUC) with which the corticosteroid responsiveness endophenotype was measured by the composite (COMPOSITE), symptoms (AMSYM), lung function (PREFEV) airway responsiveness (LNPC20) bronchodilator response (BDRABPCT), ED visits/hospitalizations (EDHOS) and oral corticosteroid bursts (BURSTS) phenotypes with 95% confidence interval (single-sided p-values are shown along the left axis).