Novel severe wheezy young children phenotypes: boys atopic multiple-trigger and girls nonatopic uncontrolled wheeze

Childhood asthma: recent developments and update

PURPOSE OF REVIEW

The purpose of this review is to provide an update on childhood asthma specifically related to the underlying genetic background and pathophysiology of asthma and their interaction with environmental stimuli. We will also discuss emerging data in the field of disease phenotyping.

RECENT FINDINGS

The field of genetics is continuously evolving to expand our knowledge on the cause of disease. Childhood onset asthma has been related to atopy and exposure to early-life infections. More recently, phenotypes have been used to classify asthma as transient and persistent, but the association of each phenotype with the genetic origin of asthma is not clearly understood.

SUMMARY

This review covers the topics of genetics, epigenetics, pathophysiology, phenotypes and treatment as they relate to childhood asthma. Overall, it provides a basis for the future of asthma treatment through description of the current research.

Prognostic value of cluster analysis of severe asthma phenotypes

BACKGROUND

Cross-sectional severe asthma cluster analysis identified different phenotypes. We tested the hypothesis that these clusters will follow different courses.

OBJECTIVE

We aimed to identify which asthma outcomes are specific and coherently associated with these different phenotypes in a prospective longitudinal cohort.

METHODS

In a longitudinal cohort of 112 patients with severe asthma, the 5 Severe Asthma Research Program (SARP) clusters were identified by means of algorithm application. Because patients of the present cohort all had severe asthma compared with the SARP cohort, homemade clusters were identified and also tested. At the subsequent visit, we investigated several outcomes related to asthma control at 1 year (6-item Asthma Control Questionnaire [ACQ-6], lung function, and medication requirement) and then recorded the 3-year exacerbations rate and time to first exacerbation.

RESULTS

The SARP algorithm discriminated the 5 clusters at entry for age, asthma duration, lung function, blood eosinophil measurement, ACQ-6 scores, and diabetes comorbidity. Four homemade clusters were mostly segregated by best ever achieved FEV1 values and discriminated the groups by a few clinical characteristics. Nonetheless, all these clusters shared similar asthma outcomes related to asthma control as follows. The ACQ-6 score did not change in any cluster. Exacerbation rate and time to first exacerbation were similar, as were treatment requirements.

CONCLUSION

Severe asthma phenotypes identified by using a previously reported cluster analysis or newly homemade clusters do not behave differently concerning asthma control-related outcomes, which are used to assess the response to innovative therapies. This study demonstrates a potential limitation of the cluster analysis approach in the field of severe asthma.

Determinants of allergic rhinitis in young children with asthma

Background

In the preschool period, allergic rhinitis (AR) is infrequent and thus under-diagnosed. However, recent works have highlighted the occurrence of AR in toddlers although the causes of AR in this young population remain unknown. The objective of this study was to identify determinants of AR in young children with asthma.

Methods

We carried out a case-control study of 227 children with active asthma and enrolled in the Trousseau Asthma Program. AR and other allergic diseases (asthma, food allergy and eczema) were diagnosed by medical doctors using standardized questionnaires. Parental history of AR and asthma, biological markers of atopy (total IgE, blood eosinophilia, allergic sensitization towards food and aeroallergens) and environmental parameters were also collected.

Results

Forty one of the children (18.1%) had AR. By univariate logistic regression analysis, AR was mainly associated with peanut sensitization (OR = 6.75; p = 0.002); food allergy (OR = 4.31; p = 0.026); mold exposure (OR = 3.81 p<0.01) and parental history of AR (OR = 1.42; p = 0.046). Due to the strong link between food allergy and peanut sensitization three models of multivariate logistic regression were performed and confirmed that AR is associated with peanut sensitization but also food allergy and mold exposure. A random forest analysis was also performed to explain AR. The results reinforced the logistic analysis that peanut sensitization and mold exposure were the principal determinants of AR.

Conclusions & Clinical Relevance

These results stress the importance of investigating AR in young children with asthma to potentially diagnose a particularly severe allergic asthmatic phenotype. Moreover, these data evoke the hypothesis that peanut could be an aeroallergen.

Pharmacological approach to wheezing in preschool children

INTRODUCTION

Wheezing disorders are very common during childhood (particularly among preschool children), and represent a significant burden for patients, their families, the healthcare system, and society as a whole. Identifying wheezing phenotypes, and recognizing the risk factors associated with each, may help to predict long-term outcomes, distinguish high-risk children who may benefit from secondary prevention measures, and ensure that the most effective therapy is prescribed for each case.

AREAS COVERED

The main aim of this review is to analyze the characteristics of the drugs currently used to treat wheezing in preschool children, and discuss the results obtained in children with different wheezing phenotypes.

EXPERT OPINION

The continuous or intermittent administration of various oral or inhaled drugs could theoretically be effective in preventing or controlling wheezing in preschool children. However, the optimal management of acute preschool wheezing episodes has not yet been determined mainly because of their phenotypical heterogeneity.

Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data

BACKGROUND

Previous studies have identified asthma phenotypes based on small numbers of clinical, physiologic, or inflammatory characteristics. However, no studies have used a wide range of variables using machine learning approaches.

OBJECTIVES

We sought to identify subphenotypes of asthma by using blood, bronchoscopic, exhaled nitric oxide, and clinical data from the Severe Asthma Research Program with unsupervised clustering and then characterize them by using supervised learning approaches.

METHODS

Unsupervised clustering approaches were applied to 112 clinical, physiologic, and inflammatory variables from 378 subjects. Variable selection and supervised learning techniques were used to select relevant and nonredundant variables and address their predictive values, as well as the predictive value of the full variable set.

RESULTS

Ten variable clusters and 6 subject clusters were identified, which differed and overlapped with previous clusters. Patients with traditionally defined severe asthma were distributed through subject clusters 3 to 6. Cluster 4 identified patients with early-onset allergic asthma with low lung function and eosinophilic inflammation. Patients with later-onset, mostly severe asthma with nasal polyps and eosinophilia characterized cluster 5. Cluster 6 asthmatic patients manifested persistent inflammation in blood and bronchoalveolar lavage fluid and exacerbations despite high systemic corticosteroid use and side effects. Age of asthma onset, quality of life, symptoms, medications, and health care use were some of the 51 nonredundant variables distinguishing subject clusters. These 51 variables classified test cases with 88% accuracy compared with 93% accuracy with all 112 variables.

CONCLUSION

The unsupervised machine learning approaches used here provide unique insights into disease, confirming other approaches while revealing novel additional phenotypes.

How variability in clinical phenotypes should guide research into disease mechanisms in asthma

Asthma is increasingly being considered as a collection of different phenotypes that present with intermittent wheezing. Unbiased approaches to classifying asthma have led to the identification of distinct phenotypes based on age of onset of disease, atopic state, disease severity or activity, degree of chronic airflow obstruction, and sputum eosinophilia. Linking phenotypes to known disease mechanism is likely to be more fruitful in determining the potential targets necessary for successful therapies of specific endotypes. A "Th2-high expression" signature from the epithelium of patients with asthma identifies a subset of patients with high eosinophilia and good therapeutic responsiveness to corticosteroids. Other characteristic traits of asthma include noneosinophilic asthma, corticosteroid insensitivity, obesity-associated, and exacerbation-prone. Further progress into asthma mechanisms will be driven by unbiased data integration of multiscale data sets from omics technologies with those phenotypic characteristics and by using mathematical modeling. This will lead to the discovery of new pathways and their integration into endotypes and also set up further hypothesis-driven research. Continued iteration through experimentation or modeling will be needed to refine the phenotypes that relate to outcomes and also delineate specific treatments for specific phenotypes.

Ten-year follow-up of cluster-based asthma phenotypes in adults. A pooled analysis of three cohorts

RATIONALE

The temporal stability of adult asthma phenotypes identified using clustering methods has never been addressed. Longitudinal cluster-based methods may provide novel insights in the study of the natural history of asthma.

OBJECTIVES

To compare the stability of cluster-based asthma phenotype structures a decade apart in adults and to address the individuals' phenotypic transition across these asthma phenotypes.

METHODS

The latent transition analysis was applied on longitudinal data (twice, 10 yr apart) from 3,320 adults with asthma who took part in the European Community Respiratory Health Survey, the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults, or the Epidemiological Study on Genetics and Environment of Asthma. Nine variables covering personal and phenotypic characteristics measured twice, 10 years apart, were simultaneously considered.

MEASUREMENTS AND MAIN RESULTS

Latent transition analysis identifies seven asthma phenotypes (prevalence range, 8.4-20.8%), mainly characterized by the level of asthma symptoms (low, moderate, high), the allergic status, and pulmonary function. Phenotypes observed 10 years apart showed strong similarities. The probability of membership in the same asthma phenotype at both times varied across phenotypes from 54 to 88%. Different transition patterns were observed across phenotypes. Transitions toward increased asthma symptoms were more frequently observed among nonallergic phenotypes as compared with allergic phenotypes. Results showed a strong stability of the allergic status over time.

CONCLUSIONS

Adult asthma phenotypes identified by a clustering approach, 10 years apart, were highly consistent. This study is the first to model the probabilities of transitioning over time between comprehensive asthma phenotypes.

Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes

Background

Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets.

Results

A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student’s t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups.

Conclusions

The multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease.

Comparison of phenotypes of childhood wheeze and cough in 2 independent cohorts

BACKGROUND

Among children with wheeze and recurrent cough there is great variation in clinical presentation and time course of the disease. We previously distinguished 5 phenotypes of wheeze and cough in early childhood by applying latent class analysis to longitudinal data from a population-based cohort (original cohort).

OBJECTIVE

To validate previously identified phenotypes of childhood cough and wheeze in an independent cohort.

METHODS

We included 903 children reporting wheeze or recurrent cough from an independent population-based cohort (validation cohort). As in the original cohort, we used latent class analysis to identify phenotypes on the basis of symptoms of wheeze and cough at 2 time points (preschool and school age) and objective measurements of atopy, lung function, and airway responsiveness (school age). Prognostic outcomes (wheeze, bronchodilator use, cough apart from colds) 5 years later were compared across phenotypes.

RESULTS

When using a 5-phenotype model, the analysis distinguished 3 phenotypes of wheeze and 2 of cough as in the original cohort. Two phenotypes were closely similar in both cohorts: Atopic persistent wheeze (persistent multiple trigger wheeze and chronic cough, atopy and reduced lung function, poor prognosis) and transient viral wheeze (early-onset transient wheeze with viral triggers, favorable prognosis). The other phenotypes differed more between cohorts. These differences might be explained by differences in age at measurements.

CONCLUSIONS

Applying the same method to 2 different cohorts, we consistently identified 2 phenotypes of wheeze (atopic persistent wheeze, transient viral wheeze), suggesting that these represent distinct disease processes. Differences found in other phenotypes suggest that the age when features are assessed is critical and should be considered carefully when defining phenotypes.

Wheeze phenotypes in young children have different courses during the preschool period

BACKGROUND

Rules for predicting the course of asthma in wheezy infants have low specificity.

OBJECTIVE

To determine if the novel phenotypes-mild early viral wheeze (EVW), atopic multiple-trigger wheeze (MTW), and nonatopic uncontrolled wheeze (NAUW)-have different courses during the preschool period.

METHODS

Part of the prospectively followed Trousseau Asthma Program cohort was phenotyped using cluster analysis with 12 parameters (sex, asthma severity and control with inhaled corticosteroid [ICS], parental asthma, allergic rhinitis, eczema, food allergy, EVW or MTW, and allergen exposure trigger). Wheezing trajectories were assessed by crossing the original phenotypes with the phenotypes obtained at 5 years.

RESULTS

Four clusters were identified at 5 years of age: asymptomatic children (n = 47) with no wheezing (98%), children with mild EVW (n = 40, 87% with EVW, 50% with EVW controlled with low-dose ICS), those with atopic MTW (n = 30, 100% with MTW, only 17% with MTW controlled with low-dose ICS, more significant for pollen asthmatic trigger), and those with atopic severe UW (n = 33, 63% with UW uncontrolled despite high doses of ICS, more significant for allergic rhinitis and dust as asthmatic trigger). Those with mild EVW became asymptomatic or remained with mild EVW. Those with atopic MTW remained with atopic MTW and those with NAUW developed severe UW in most cases.

CONCLUSION

These results show that remission is most frequently observed in mild EVW and that no remission is observed in atopic MTW.

Atopy is important in the management of asthma

Allergic diseases are a public health problem in industrialized countries due to the increasing number of allergies related to Western lifestyle. The multiplicity of organs affected by allergy (skin (atopic dermatitis), airway epithelium (respiratory allergy), and digestive tract (food allergy)) worsens the prognosis of each of the allergic diseases and specially asthma. The importance of allergy in management of asthma is also underlined by the risk of fatal or near fatal acute asthma attacks due to multiple allergic sensitizations. Preliminary studies suggest that specific treatment of allergy could change the asthma course.

Advances in pediatric asthma in 2012: moving toward asthma prevention

Last year's "Advances in pediatric asthma: moving forward" concluded the following: "Now is also the time to utilize information recorded in electronic medical records to develop innovative disease management plans that will track asthma over time and enable timely decisions on interventions in order to maintain control that can lead to disease remission and prevention." This year's summary will focus on recent advances in pediatric asthma on modifying disease activity, preventing asthma exacerbations, managing severe asthma, and risk factors for predicting and managing early asthma, as indicated in Journal of Allergy and Clinical Immunology publications in 2012. Recent reports continue to shed light on methods to improve asthma management through steps to assess disease activity, tools to standardize outcome measures in asthma, genetic markers that predict risk for asthma and appropriate treatment, and interventions that alter the early presentation of asthma to prevent progression. We are well on our way to creating a pathway around wellness in asthma care and also to use new tools to predict the risk for asthma and take steps to not only prevent asthma exacerbations but also to prevent the early manifestations of the disease and thus prevent its evolution to severe asthma.

The Saudi initiative for asthma - 2012 update: Guidelines for the diagnosis and management of asthma in adults and children

This an updated guidelines for the diagnosis and management of asthma, developed by the Saudi Initiative for Asthma (SINA) group, a subsidiary of the Saudi Thoracic Society. The main objective of SINA is to have updated guidelines, which are simple to understand and easy to use by non-asthma specialists, including primary care and general practice physicians. This new version includes updates of acute and chronic asthma management, with more emphasis on the use of Asthma Control Test in the management of asthma, and a new section on "difficult-to-treat asthma." Further, the section on asthma in children was re-written to cover different aspects in this age group. The SINA panel is a group of Saudi experts with well-respected academic backgrounds and experience in the field of asthma. The guidelines are formatted based on the available evidence, local literature, and the current situation in Saudi Arabia. There was an emphasis on patient-doctor partnership in the management that also includes a self-management plan. The approach adopted by the SINA group is mainly based on disease control as it is the ultimate goal of treatment.