Systemic Corticosteroid Responses in Children with Severe Asthma: Phenotypic and Endotypic Features.

BACKGROUND:Severe asthma in children is a heterogeneous disorder associated with variable responses to corticosteroid treatment. Criterion standards for corticosteroid responsiveness assessment in children are lacking. OBJECTIVE:This study sought to characterize systemic corticosteroid responses in children with severe asthma after treatment with intramuscular triamcinolone and to identify phenotypic and molecular predictors of an intramuscular triamcinolone response. METHODS:Asthma-related quality of life, exhaled nitric oxide, blood eosinophils, lung function, and inflammatory cytokine and chemokine mRNA gene expression in peripheral blood mononuclear cells were assessed in 56 children with severe asthma at baseline and 14 days after intramuscular triamcinolone injection. The Asthma Control Questionnaire was used to classify children with severe asthma into corticosteroid response groups. RESULTS:Three groups of children with severe asthma were identified: controlled severe asthma, children who achieved control after triamcinolone, and children who did not achieve control. At baseline, these groups were phenotypically similar. After triamcinolone, discordance between symptoms, lung function, exhaled nitric oxide, and blood eosinophils was noted. Clinical phenotypic predictors were of limited utility in predicting the triamcinolone response, whereas systemic mRNA expression of inflammatory cytokines and chemokines related to IL-2, IL-10, and TNF signaling pathways, namely, AIMP1, CCR2, IL10RB, and IL5, strongly differentiated children who failed to achieve control with triamcinolone administration. CONCLUSIONS:Systemic corticosteroid responsiveness in children with severe asthma is heterogeneous. Alternative prediction models that include molecular endotypic as well as clinical phenotypic features are needed to identify which children derive the most clinical benefit from systemic corticosteroid step-up therapy given the potential side effects.

KEYWORDS: {{ getKeywords(articleDetailText.words) }}