Inhaled corticosteroids (ICSs) constitute the most commonly
prescribed therapies for asthma. They are effective, but there are up to 24% of
asthma patients who do not achieve significant improvement with them. ICSs
produce treatment responses in six clinical phenotypes: lung function,
bronchodilator response, airway responsiveness, symptoms, need for oral
steroids, and frequency of emergency department visits or need for hospitalization.
For the past 15 years and in an escalating prevalence of asthma, researchers
have considered these phenotypes to be guided by separate mechanisms.
Clemmer et al
propose a move away from the focus on single phenotypes to a more holistic
approach. They suggest that there is a corticosteroid responsiveness
endophenotype that modulates the asthma disease process, is latent in
ICS-untreated patients, and is active in ICS-treated patients. Under this
hypothesis, the corticosteroid responsiveness endophenotype influences the
asthma disease process to produce the treatment effect observed in all the
clinical phenotypes (J Allergy Clin Immunol 2015; 136(2): 274-281).
As such, the authors present a composite phenotype
responsiveness model that combines the six clinical phenotypes and measures the
endophenotype. They used principal component analysis (PCA) to determine the
model in a study population of both ICS-treated and non-ICS-treated patients
with mild to moderately severe asthma. The model was then tested in four
replication populations. Using treatment effect area under the receiver
operating characteristic curve (AUC), they demonstrate that a composite
phenotype measures corticosteroid responsiveness with greater accuracy and
stability across populations than the individual clinical
phenotypes do.
The potential applications of the composite phenotype are
many. It should enable asthma pharmacogenetic studies with more power for a
given sample size or that require a smaller sample to achieve a given power.
Given that it collapses multiple longitudinal clinical observations into a
corticosteroid response metric and that it is easily implemented in a single
computer program, it could allow a clinical practitioner to more accurately
estimate ICS response. Finally, the model could be used to characterize the
many asthma patients who do not respond to ICS treatment with better accuracy.