Performance of Three Asthma Predictive Tools in a Cohort of Infants Hospitalized With Severe Bronchiolitis

Type 2 Inflammation and Asthma in Children: A Narrative Review

Increased understanding of the underlying pathophysiology has highlighted the heterogeneity of asthma and identified that most children with asthma have type 2 inflammation with elevated biomarkers, such as blood eosinophils and/or fractional exhaled nitric oxide. Although in the past most of these children may have been categorized as having allergic asthma, identifying the type 2 inflammatory phenotype provides a mechanism to explain both allergic and non-allergic triggers in pediatric patients with asthma. Most children achieve control with low to medium doses of inhaled corticosteroids. However, in a small but significant proportion of children, asthma remains uncontrolled despite maximum conventional treatment, with an increased risk of severe exacerbations. In this review, we focus on the role of type 2 inflammation and allergic processes in children with asthma, together with evidence of the efficacy of available treatment options for those who experience severe symptoms.

An integrated molecular risk score early in life for subsequent childhood asthma risk

BACKGROUND

Numerous children present with early wheeze symptoms, yet solely a subgroup develops childhood asthma. Early identification of children at risk is key for clinical monitoring, timely patient-tailored treatment, and preventing chronic, severe sequelae. For early prediction of childhood asthma, we aimed to define an integrated risk score combining established risk factors with genome-wide molecular markers at birth, complemented by subsequent clinical symptoms/diagnoses (wheezing, atopic dermatitis, food allergy).

METHODS

Three longitudinal birth cohorts (PAULINA/PAULCHEN, n = 190 + 93 = 283, PASTURE, n = 1133) were used to predict childhood asthma (age 5-11) including epidemiological characteristics and molecular markers: genotype, DNA methylation and mRNA expression (RNASeq/NanoString). Apparent (ap) and optimism-corrected (oc) performance (AUC/R2) was assessed leveraging evidence from independent studies (Naïve-Bayes approach) combined with high-dimensional logistic regression models (LASSO).

RESULTS

Asthma prediction with epidemiological characteristics at birth (maternal asthma, sex, farm environment) yielded an ocAUC = 0.65. Inclusion of molecular markers as predictors resulted in an improvement in apparent prediction performance, however, for optimism-corrected performance only a moderate increase was observed (upto ocAUC = 0.68). The greatest discriminate power was reached by adding the first symptoms/diagnosis (up to ocAUC = 0.76; increase of 0.08, p = .002). Longitudinal analysis of selected mRNA expression in PASTURE (cord blood, 1, 4.5, 6 years) showed that expression at age six had the strongest association with asthma and correlation of genes getting larger over time (r = .59, p < .001, 4.5-6 years).

CONCLUSION

Applying epidemiological predictors alone showed moderate predictive abilities. Molecular markers from birth modestly improved prediction. Allergic symptoms/diagnoses enhanced the power of prediction, which is important for clinical practice and for the design of future studies with molecular markers.

Application of Asthma Prediction Tools in a Cohort of Infants with Severe Bronchiolitis

Background: Severe bronchiolitis is a strong childhood asthma risk factor. Early and accurate asthma prediction is key. We applied the Asthma Predictive Index (API), the modified Asthma Predictive Index (mAPI), and the Pediatric Asthma Risk Score (PARS) in a cohort of high-risk infants to predict asthma at age 6 years. Methods: We conducted a 17-center cohort of infants (age <1 year) hospitalized with severe bronchiolitis during 2011-2014. We used only infancy data to predict asthma at age 6 years. Results: The prevalence of parent-reported asthma at age 6 years was 328/880 (37%). The prevalences of a positive index/score for stringent and loose API, mAPI, and PARS were 21%, 51%, 11%, and 34%, respectively. Area under the receiver operating characteristic curves [95% confidence interval (CI)] ranged from 0.57 (95% CI 0.55-0.60) for mAPI to 0.66 (95% CI 0.63-0.70) for PARS. Conclusions: An asthma prediction tool for high-risk infants is needed to identify those who would benefit most from asthma prevention interventions.

Early Prediction of Asthma

The clinical manifestations of asthma in children are highly variable, are associated with different molecular and cellular mechanisms, and are characterized by common symptoms that may diversify in frequency and intensity throughout life. It is a disease that generally begins in the first five years of life, and it is essential to promptly identify patients at high risk of developing asthma by using different prediction models. The aim of this review regarding the early prediction of asthma is to summarize predictive factors for the course of asthma, including lung function, allergic comorbidity, and relevant data from the patient's medical history, among other factors. This review also highlights the epigenetic factors that are involved, such as DNA methylation and asthma risk, microRNA expression, and histone modification. The different tools that have been developed in recent years for use in asthma prediction, including machine learning approaches, are presented and compared. In this review, emphasis is placed on molecular mechanisms and biomarkers that can be used as predictors of asthma in children.