The University of Queensland

Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba

We are pleased to introduce the collection Frontiers in Allergy – Highlights in Asthma 2021/22. This collection will welcome and showcase a selection of high impact articles authored by leaders in the field. The work presented here highlights the broad diversity of research performed across the Asthma section, and aims to put a spotlight on the main areas of interest. This collection aims to further support Frontiers’ strong community by shining a spotlight on our authors' highly impactful research.

Karolinska University Hospital

National and Kapodistrian University of Athens

University of Groningen

Frontiers in Allergy

Asthma

Highlights in Asthma 2021/22

Introduction: Acute bronchiolitis is one of the most common respiratory infections in infancy. Although most infants with bronchiolitis do not get hospitalized, infants with hospitalized bronchiolitis are more likely to develop wheeze exacerbations during the first years of life. The objective of this prospective cohort study was to develop machine learning models to predict incidence and persistence of wheeze exacerbations following the first hospitalized episode of acute bronchiolitis.Methods: One hundred thirty-one otherwise healthy term infants hospitalized with the first episode of bronchiolitis at a tertiary pediatric hospital in Athens, Greece, and 73 age-matched controls were recruited. All patients/controls were followed up for 3 years with 6-monthly telephone reviews. Through principal component analysis (PCA), a cluster model was used to describe main outcomes. Associations between virus type and the clusters and between virus type and other clinical characteristics and demographic data were identified. Through random forest classification, a prediction model with smallest classification error was identified. Primary outcomes included the incidence and the number of caregiver-reported wheeze exacerbations.Results: PCA identified 2 clusters of the outcome measures (Cluster 1 and Cluster 2) that were significantly associated with the number of recurrent wheeze episodes over 3-years of follow-up (Chi-Squared, p < 0.001). Cluster 1 included infants who presented higher number of wheeze exacerbations over follow-up time. Rhinovirus (RV) detection was more common in Cluster 1 and was more strongly associated with clinical severity on admission (p < 0.01). A prediction model based on virus type and clinical severity could predict Cluster 1 with an overall error 0.1145 (sensitivity 75.56% and specificity 91.86%).Conclusion: A prediction model based on virus type and clinical severity of first hospitalized episode of bronchiolitis could predict sensitively the incidence and persistence of wheeze exacerbations during a 3-year follow-up. Virus type (RV) was the strongest predictor.

Introduction: Acute bronchiolitis is one of the most common respiratory infections in infancy. Although most infants with bronchiolitis do not get hospitalized, infants with hospitalized bronchiolitis are more likely to develop wheeze exacerbations during the first years of life. The objective of this prospective cohort study was to develop machine learning models to predict incidence and persistence of wheeze exacerbations following the first hospitalized episode of acute bronchiolitis.Methods: One hundred thirty-one otherwise healthy term infants hospitalized with the first episode of bronchiolitis at a tertiary pediatric hospital in Athens, Greece, and 73 age-matched controls were recruited. All patients/controls were followed up for 3 years with 6-monthly telephone reviews. Through principal component analysis (PCA), a cluster model was used to describe main outcomes. Associations between virus type and the clusters and between virus type and other clinical characteristics and demographic data were identified. Through random forest classification, a prediction model with smallest classification error was identified. Primary outcomes included the incidence and the number of caregiver-reported wheeze exacerbations.Results: PCA identified 2 clusters of the outcome measures (Cluster 1 and Cluster 2) that were significantly associated with the number of recurrent wheeze episodes over 3-years of follow-up (Chi-Squared, p &lt; 0.001). Cluster 1 included infants who presented higher number of wheeze exacerbations over follow-up time. Rhinovirus (RV) detection was more common in Cluster 1 and was more strongly associated with clinical severity on admission (p &lt; 0.01). A prediction model based on virus type and clinical severity could predict Cluster 1 with an overall error 0.1145 (sensitivity 75.56% and specificity 91.86%).Conclusion: A prediction model based on virus type and clinical severity of first hospitalized episode of bronchiolitis could predict sensitively the incidence and persistence of wheeze exacerbations during a 3-year follow-up. Virus type (RV) was the strongest predictor.

Recurrent Wheeze Exacerbations Following Acute Bronchiolitis—A Machine Learning Approach

Childhood asthma develops in 30–40% of children with severe bronchiolitis but accurate prediction remains challenging. In a severe bronchiolitis cohort, we applied the Asthma Predictive Index (API), the modified Asthma Predictive Index (mAPI), and the Pediatric Asthma Risk Score (PARS) to predict asthma at age 5 years. We applied the API, mAPI, and PARS to the 17-center cohort of infants hospitalized with severe bronchiolitis during 2011–2014 (35th Multicenter Airway Research Collaboration, MARC-35). We used data from the first 3 years of life including parent interviews, chart review, and specific IgE testing to predict asthma at age 5 years, defined as parent report of clinician-diagnosed asthma. Among 875/921 (95%) children with outcome data, parent-reported asthma was 294/875 (34%). In MARC-35, a positive index/score for stringent and loose API, mAPI, and PARS were 24, 68, 6, and 55%, respectively. The prediction tools' AUCs (95%CI) ranged from 0.57 (95%CI 0.54–0.59) to 0.68 (95%CI 0.65–0.71). The positive likelihood ratios were lower in MARC-35 compared to the published results from the original cohorts. In this high-risk population of infants hospitalized with severe bronchiolitis, API, mAPI, and PARS had sub-optimal performance (AUC <0.8). Highly accurate (AUC >0.8) asthma prediction tools are desired in infants hospitalized with severe bronchiolitis.

Childhood asthma develops in 30&#x2013;40% of children with severe bronchiolitis but accurate prediction remains challenging. In a severe bronchiolitis cohort, we applied the Asthma Predictive Index (API), the modified Asthma Predictive Index (mAPI), and the Pediatric Asthma Risk Score (PARS) to predict asthma at age 5 years. We applied the API, mAPI, and PARS to the 17-center cohort of infants hospitalized with severe bronchiolitis during 2011&#x2013;2014 (35th Multicenter Airway Research Collaboration, MARC-35). We used data from the first 3 years of life including parent interviews, chart review, and specific IgE testing to predict asthma at age 5 years, defined as parent report of clinician-diagnosed asthma. Among 875/921 (95%) children with outcome data, parent-reported asthma was 294/875 (34%). In MARC-35, a positive index/score for stringent and loose API, mAPI, and PARS were 24, 68, 6, and 55%, respectively. The prediction tools&apos; AUCs (95%CI) ranged from 0.57 (95%CI 0.54&#x2013;0.59) to 0.68 (95%CI 0.65&#x2013;0.71). The positive likelihood ratios were lower in MARC-35 compared to the published results from the original cohorts. In this high-risk population of infants hospitalized with severe bronchiolitis, API, mAPI, and PARS had sub-optimal performance (AUC &lt;0.8). Highly accurate (AUC &gt;0.8) asthma prediction tools are desired in infants hospitalized with severe bronchiolitis.

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

Bronchiolitis is a virus-associated infection of the lower respiratory tract exhibiting signs and symptoms of airway obstruction. Respiratory Syncytial Virus (RSV) is responsible in most cases; however, different rhinoviruses have also been implicated. Specific viruses and time until the first infection, severity of the respiratory condition, and atopic status have a determinant role in the recurrence of wheezing and asthma development. Genetics, lung function, atopic condition, the role of microbiota and environment, pollution, and obesity are considered in the present review. Emergency room visits and hospitalizations because of severe wheezing and smoking during pregnancy among others were identified as risk factors for significant morbidity in our population. Approaching determinant conditions like genetics, allergy, antiviral immunity, and environmental exposures such as farm vs. urban and viral virulence provides an opportunity to minimize morbidity of viral illness and asthma in children.

Bronchiolitis is a virus-associated infection of the lower respiratory tract exhibiting signs and symptoms of airway obstruction. Respiratory Syncytial Virus (RSV) is responsible in most cases; however, different rhinoviruses have also been implicated. Specific viruses and time until the first infection, severity of the respiratory condition, and atopic status have a determinant role in the recurrence of wheezing and asthma development. Genetics, lung function, atopic condition, the role of microbiota and environment, pollution, and obesity are considered in the present review. Emergency room visits and hospitalizations because of severe wheezing and smoking during pregnancy among others were identified as risk factors for significant morbidity in our population. Approaching determinant conditions like genetics, allergy, antiviral immunity, and environmental exposures such as farm vs. urban and viral virulence provides an opportunity to minimize morbidity of viral illness and asthma in children.

Wheezing-Related Relevant Factors and the Role of Viral Bronchiolitis

Asthma is a complex and heterogenous disease characterized by variability in disease expression and severity. Multiple extrapulmonary comorbidities and treatable traits are common in people with asthma, and there is an increasing appreciation of how these may complicate asthma management. This review will discuss the prevalence and impact of extrapulmonary comorbidities/risk factors or “traits,” which have been found to co-exist in asthma (obesity, symptoms of depression and/or anxiety and physical inactivity), the impact these traits have on future outcomes (including exacerbation risk and quality of life) and asthma management, and how we should target treatment in asthma when these extrapulmonary traits are present.

Asthma is a complex and heterogenous disease characterized by variability in disease expression and severity. Multiple extrapulmonary comorbidities and treatable traits are common in people with asthma, and there is an increasing appreciation of how these may complicate asthma management. This review will discuss the prevalence and impact of extrapulmonary comorbidities/risk factors or &#x201c;traits,&#x201d; which have been found to co-exist in asthma (obesity, symptoms of depression and/or anxiety and physical inactivity), the impact these traits have on future outcomes (including exacerbation risk and quality of life) and asthma management, and how we should target treatment in asthma when these extrapulmonary traits are present.

The Management of Extrapulmonary Comorbidities and Treatable Traits; Obesity, Physical Inactivity, Anxiety, and Depression, in Adults With Asthma

G protein-coupled receptor kinase 2 (GRK2) is an adapter protein that modulates G protein-coupled receptor (GPCR) signaling. It also regulates the functions and activity of other intracellular proteins in many cell types. Accordingly, GRK2 is thought to contribute to disease progression by a variety of mechanisms related to its multifunctional roles. Indeed, GRK2 levels are enhanced in patient samples as well as in preclinical models of several diseases. We have previously shown that GRK2 regulates mast cell functions, and thereby contributes to exacerbated inflammation during allergic reactions. In the current study, we observed that GRK2 levels are enhanced in the lungs of human asthma patients and in mice sensitized to house dust mite extract (HDME) allergen. Consistent with these findings, interleukin (IL)-4 and IL-13 levels were reduced in the lungs of GRK2+/− mice in a HMDE mouse model of asthma. Because Th2 cells are the major source of these cytokines during asthma, we determined the role of GRK2 in regulating T cell-specific responses in our HMDE mouse model. We observed a significant reduction of airway hyperresponsiveness (AHR), lung eosinophil and lymphocyte counts, serum IgE, Th2 cytokines (IL-4 and IL-13), goblet cell hyperplasia and mucus production in mice that had reduced GRK2 expression specifically in T cells. Collectively, our studies reveal an important role for GRK2 in regulating T cell response during asthma pathogenesis and further elucidation of the mechanisms through which GRK2 modulates airway inflammation will lead to the development of new therapeutic strategies for asthma.

G protein-coupled receptor kinase 2 (GRK2) is an adapter protein that modulates G protein-coupled receptor (GPCR) signaling. It also regulates the functions and activity of other intracellular proteins in many cell types. Accordingly, GRK2 is thought to contribute to disease progression by a variety of mechanisms related to its multifunctional roles. Indeed, GRK2 levels are enhanced in patient samples as well as in preclinical models of several diseases. We have previously shown that GRK2 regulates mast cell functions, and thereby contributes to exacerbated inflammation during allergic reactions. In the current study, we observed that GRK2 levels are enhanced in the lungs of human asthma patients and in mice sensitized to house dust mite extract (HDME) allergen. Consistent with these findings, interleukin (IL)-4 and IL-13 levels were reduced in the lungs of GRK2+/− mice in a HMDE mouse model of asthma. Because Th2 cells are the major source of these cytokines during asthma, we determined the role of GRK2 in regulating T cell-specific responses in our HMDE mouse model. We observed a significant reduction of airway hyperresponsiveness (AHR), lung eosinophil and lymphocyte counts, serum IgE, Th2 cytokines (IL-4 and IL-13), goblet cell hyperplasia and mucus production in mice that had reduced GRK2 expression specifically in T cells. Collectively, our studies reveal an important role for GRK2 in regulating T cell response during asthma pathogenesis and further elucidation of the mechanisms through which GRK2 modulates airway inflammation will lead to the development of new therapeutic strategies for asthma.