Enhancing the prediction of childhood asthma remission: Integrating clinical factors with microRNAs

Previously, in the Childhood Asthma Management Program (CAMP), we used clinical variables to predict the remission of childhood asthma by early adulthood with high accuracy (area under the receiver-operating characteristic curve [AUROC], 0.81) and found baseline FEV1 to forced vital capacity (FVC) ratio (FEV1/FVC) to be the greatest predictor of asthma remission.2 In addition, we previously used microRNA (miR, miRNA) networks to predict the remission of airway hyperresponsiveness in CAMP.3 miRNAs are small noncoding single-stranded RNAs with excellent noninvasive biomarker potential. The variables associated with loss to follow-up of the asthma remission outcome (ie, age, race, clinic site, income, exacerbations after exercise, exposure to tobacco smoke) and the differential acquisition of baseline miRNA data (ie, race, treatment group, clinic site) were controlled for in the subsequent multivariable logistic regression model.2 The clinical and miRNA variables statistically significant on univariable analyses were entered into a stepwise logistic regression model. The final model controlled for age, sex, race, treatment group, clinic, income, exposure to tobacco smoke, exercise-induced bronchospasm, pet ownership, batch effect, miR-96-5p, miR-199b-5p, miR-1307-3p, and miR-148-5p. Overall, integrating baseline clinical factors with miRNAs resulted in improved model fit (AIC, 194.1) and prediction performance (AUROC, 0.90; P = .001) of childhood asthma remission compared with using clinical variables alone (AIC, 752.1; AUROC, 0.81).2 Less severe baseline airway obstruction (ie, higher FEV1/FVC) and lower baseline miR-221-5p expression were independent long-term predictors of asthma remission by early adulthood.