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.
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.
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).
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.
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.