Asthma phenotypes identified by latent class analysis in the ISAAC phase II Spain study

Phenotypes of cough in children: A latent class analysis

INTRODUCTION

Distinguishing phenotypes among children with cough helps understand underlying causes. Using a statistical data-driven approach, we aimed to identify and validate cough phenotypes based on measurable traits, physician diagnoses, and prognosis.

METHODS

We used data from the Swiss Paediatric Airway Cohort and included 531 children aged 5-16 years seen in outpatient clinics since 2017. We included children with any parent-reported cough (i.e. cough without a cold, cough at night, cough more than other children, or cough longer than 4 weeks) without current wheeze. We applied latent class analysis to identify phenotypes using nine symptoms and characteristics and selected the best model using the Akaike information criterion. We assigned children to the most likely phenotype and compared the resulting groups for parental atopy history, comorbidities, spirometry, fractional exhaled nitric oxide (FeNO), skin prick tests and specific IgE, physician diagnoses, and 1-year prognosis.

RESULTS

We identified four cough phenotypes: non-specific cough (26%); non-allergic infectious and night cough with snoring and otitis (4%); chronic allergic dry night cough with snoring (9%); and allergic non-infectious cough with rhino-conjunctivitis (61%). Children with the allergic phenotype often had family or personal history of atopy and asthma diagnosis. FeNO was highest for the allergic phenotype [median 17.9 parts per billion (ppb)] and lowest for the non-allergic infectious phenotype [median 7.0 parts per billion (ppb)]. Positive allergy test results differed across phenotypes (p < .001) and were most common among the allergic (70%) and least common among the non-specific cough (31%) phenotypes. Subsequent wheeze was more common among the allergic than the non-specific phenotype.

CONCLUSION

We identified four clinically relevant cough phenotypes with different prognoses. Although we excluded children with current wheeze, most children with cough belonged to allergy-related phenotypes.

Prevalence and temporal evolution of asthma symptoms in Spain. Global Asthma Network (GAN) study

INTRODUCTION

The temporal evolution of the prevalence of asthma described in the ISAAC (International Study of Asthma and Allergies in Childhood) in 2002 is unknown, or if the geographical or age differences are maintained in Spain.

OBJECTIVE

To describe the prevalence of asthma symptoms in different Spanish geographic areas and compare it with that of those centers that participated in the ISAAC.

METHODS

Cross-sectional study of asthma prevalence, carried out in 2016-2019 with 19,943 adolescents aged 13-14 years and 17,215 schoolchildren aged 6-7 years from 6 Spanish geographical areas (Cartagena, Bilbao, Cantabria, La Coruña, Pamplona and Salamanca). Asthma symptoms were collected using a written questionnaire and video questionnaire according to the Global Asthma Network (GAN) protocol.

RESULTS

The prevalence of recent wheezing (last 12 months) was 15.3% at 13-14 years and 10.4% at 6-7 years, with variations in adolescents, from 19% in Bilbao to 10.2% in Cartagena; and in schoolchildren, from 11.7% in Cartagena to 7% in Pamplona. These prevalences were higher than those of the ISAAC (10.6% in adolescents and 9.9% in schoolchildren). 21.3% of adolescents and 12.4% of schoolchildren reported asthma at some time.

CONCLUSIONS

There is a high prevalence of asthmatic symptoms with an increase in adolescents and a stabilization in Spanish schoolchildren with respect to the ISAAC. Geographic variations in asthma prevalence are not so clearly appreciated, but areas with high prevalences maintain high numbers.

Subtypes of Asthma and Cold Weather-Related Respiratory Symptoms

(1) Poor asthma control increases the occurrence of cold weather-related symptoms among adult asthmatics. We assessed whether the subtype of asthma, taking into account the severity of the asthma, plays a role in these symptoms. (2) We conducted a population-based cross-sectional study of 1995 adult asthmatics (response rate 40.4%) living in northern Finland using a questionnaire that asked about cold weather-related respiratory symptoms including (1) shortness of breath, (2) prolonged cough, (3) wheezing, (4) phlegm production, and (5) chest pain, as well as questions related to the subtype of asthma. For women, the subtypes identified using latent class analysis were: (1) Controlled, mild asthma, (2) Partly controlled, moderate asthma, (3) Uncontrolled, unknown severity, and (4) Uncontrolled, severe asthma, and for men: (1) Controlled, mild asthma, (2) Uncontrolled, unknown severity, and (3) Partly controlled, severe asthma. (3) According to the subtypes of asthma, more severe and more poorly controlled asthma were related to the increased prevalence of cold weather-related respiratory symptoms when compared with those with mild, controlled asthma. This trend was especially clear for wheezing and chest pain. For example, in men, the adjusted prevalence ratio of wheezing was 1.55 (95% CI 1.09–2.19) in uncontrolled asthma with unknown severity and 1.84 (95% CI 1.26–2.71) in partly controlled severe asthma compared with controlled, mild asthma. (4) Our study provides evidence for the influence of subtypes of asthma on experiencing cold weather-related respiratory symptoms. Both women and men reported more cold weather-related symptoms when their asthma was more severe and uncontrolled compared with those who had mild and well-controlled asthma.

Risk, Mechanisms and Implications of Asthma-Associated Infectious and Inflammatory Multimorbidities (AIMs) among Individuals With Asthma: a Systematic Review and a Case Study

Our prior work and the work of others have demonstrated that asthma increases the risk of a broad range of both respiratory (e.g., pneumonia and pertussis) and non-respiratory (e.g., zoster and appendicitis) infectious diseases as well as inflammatory diseases (e.g., celiac disease and myocardial infarction [MI]), suggesting the systemic disease nature of asthma and its impact beyond the airways. We call these conditions asthma-associated infectious and inflammatory multimorbidities (AIMs). At present, little is known about why some people with asthma are at high-risk of AIMs, and others are not, to the extent to which controlling asthma reduces the risk of AIMs and which specific therapies mitigate the risk of AIMs. These questions represent a significant knowledge gap in asthma research and unmet needs in asthma care, because there are no guidelines addressing the identification and management of AIMs. This is a systematic review on the association of asthma with the risk of AIMs and a case study to highlight that 1) AIMs are relatively under-recognized conditions, but pose major health threats to people with asthma; 2) AIMs provide insights into immunological and clinical features of asthma as a systemic inflammatory disease beyond a solely chronic airway disease; and 3) it is time to recognize AIMs as a distinctive asthma phenotype in order to advance asthma research and improve asthma care. An improved understanding of AIMs and their underlying mechanisms will bring valuable and new perspectives improving the practice, research, and public health related to asthma.

Air pollution-related asthma profiles among children/adolescents: A multi-group latent class analysis

BACKGROUND

This study aimed to investigate the asthma profile among children/adolescents and the relationship of the prevalence of air pollution profiles using latent class analysis (LCA).

OBJECTIVES

In this cross-sectional study, a case rural community was selected in an industrial area, and two rural control communities were selected in unexposed areas.

METHODS

Hourly concentrations of PM₁₀, SO₂, NO_2, and total volatile organic compounds were obtained from the records of a fixed air quality monitoring station, and the concentrations of benzene, toluene, xylenes styrene were measured during six campaigns. Asthma data was collected using the International Study of Asthma and Allergies in Childhood in 7-18 years old children/adolescents. The modeling was conducted using LCA.

RESULTS

A higher amount of air pollution indices were observed in the case than both control communities. LCA divided the participants into three clusters; "healthy" (92.8%), "moderate" (2.8%), and "severe" (4.4%). A higher probability of severe asthma (6.8%) was observed in the case than control communities (2.6% and 1.8%). Additionally, after adjusting for possible confounders, the odds of asthma was lower in the control communities than the case in both moderate and sever classes (Odds Ratios in the range of 0.135-0.697).

CONCLUSIONS

This study indicates asthma profiles of children/adolescents and the higher prevalence of severe class in the area, explaining the possible effect of air pollution.

Genetics of Asthma and Allergic Diseases

Asthma genes have been identified through a range of approaches, from candidate gene association studies and family-based genome-wide linkage analyses to genome-wide association studies (GWAS). The first GWAS of asthma, reported in 2007, identified multiple markers on chromosome 17q21 as associates of the childhood-onset asthma. This remains the best replicated asthma locus to date. However, notwithstanding undeniable successes, genetic studies have produced relatively heterogeneous results with limited replication, and despite considerable promise, genetics of asthma and allergy has, so far, had limited impact on patient care, our understanding of disease mechanisms, and development of novel therapeutic targets. The paucity of precise replication in genetic studies of asthma is partly explained by the existence of numerous gene-environment interactions. Another important issue which is often overlooked is that of time of the assessment of the primary outcome(s) and the relevant environmental exposures. Most large GWASs use the broadest possible definition of asthma to increase the sample size, but the unwanted consequence of this is increased phenotypic heterogeneity, which dilutes effect sizes. One way of addressing this is to precisely define disease subtypes (e.g. by applying novel mathematical approaches to rich phenotypic data) and use these latent subtypes in genetic studies.

Asthma clustering methods: a literature-informed application to the children's health study data

OBJECTIVE

The heterogeneity of asthma has inspired widespread application of statistical clustering algorithms to a variety of datasets for identification of potentially clinically meaningful phenotypes. There has not been a standardized data analysis approach for asthma clustering, which can affect reproducibility and clinical translation of results. Our objective was to identify common and effective data analysis practices in the asthma clustering literature and apply them to data from a Southern California population-based cohort of schoolchildren with asthma.

METHODS

As of January 1, 2020, we reviewed key statistical elements of 77 asthma clustering studies. Guided by the literature, we used 12 input variables and three clustering methods (hierarchical clustering, k-medoids, and latent class analysis) to identify clusters in 598 schoolchildren with asthma from the Southern California Children's Health Study (CHS).

RESULTS

Clusters of children identified by latent class analysis were characterized by exhaled nitric oxide, FEV₁/FVC, FEV₁ percent predicted, asthma control and allergy score; and were predictive of control at two year follow up. Clusters from the other two methods were less clinically remarkable, primarily differentiated by sex and race/ethnicity and less predictive of asthma control over time.

CONCLUSION

Upon review of the asthma phenotyping literature, common approaches of data clustering emerged. When applying these elements to the Children's Health Study data, latent class analysis clusters-represented by exhaled nitric oxide and spirometry measures-had clinical relevance over time.

Machine learning in asthma research: moving toward a more integrated approach

Introduction: Big data are reshaping the future of medicine. The growing availability and increasing complexity of data have favored the adoption of modern analytical and computational methodologies in every area of medicine. Over the past decades, asthma research has been characterized by a shift in the way studies are conducted and data are analyzed. Motivated by the assumptions that 'data will speak for themselves', hypothesis-driven approaches have been replaced by data-driven hypotheses-generating methods to explore hidden patterns and underlying mechanisms. However, even with all the advancement in technologies and the new important insight that we gained to understand and characterize asthma heterogeneity, very few research findings have been translated into clinically actionable solutions.Areas covered: To investigate some of the fundamental analytical approaches adopted in the current literature and appraise their impact and usefulness in medicine, we conducted a bibliometric analysis of big data analytics in asthma research in the past 50 years.Expert opinion: No single data source or methodology can uncover the complexity of human health and disease. To fully capitalize on the potential of 'big data', we will have to embrace the collaborative science and encourage the creation of integrated cross-disciplinary teams brought together around technological advances.

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity and socioeconomic burden worldwide. Although there are extensive guidelines for the prevention, early diagnosis, and rational treatment of these lifelong diseases, their value in precision medicine is very limited. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD. However, very few methods have significantly contributed to clinical practice. Here, we review four aspects of AI and ML implementation in asthma and COPD to summarize existing knowledge and indicate future steps required for the safe and effective application of AI and ML tools by clinicians.

Trajectories of early-onset rhinitis in the Singapore GUSTO mother-offspring cohort

BACKGROUND

The natural history of childhood rhinitis is not well described.

OBJECTIVE

This study aimed to identify different rhinitis trajectories in early childhood and their predictors and allergic associations.

METHODS

Rhinitis symptoms were ascertained prospectively from birth until 6 years using standardized questionnaires in 772 participants. Rhinitis was defined as one or more episodes of sneezing, runny and/or blocked nose >2 weeks duration. Latent trajectories were identified using group-based modelling, and their predictive risk factors and allergic associations were examined.

RESULTS

Three rhinitis trajectory groups were identified: 7.6% (n = 59) were termed early transient rhinitis, 8.6% (n = 66) late transient rhinitis, and 6.6% (n = 51) persistent rhinitis. The remaining 77.2% (n = 596) were classified as non-rhinitis/reference group. Early transient rhinitis subjects were more likely of Indian ethnicity, had siblings, reported childcare attendance, early wheezing and eczema in the first 3 years of life. Late transient rhinitis was associated with antenatal exposure to smoking, higher maternal education levels, and wheezing at age 36-72 months. Persistent rhinitis was associated with male gender, paternal and maternal history of atopy, eczema, and house dust mite sensitization.

CONCLUSIONS & CLINICAL RELEVANCE

Risk factors for early transient rhinitis involve a combination of genetic and early environmental exposures, whereas late transient rhinitis may relate to maternal factors and early respiratory infections independent of atopy. In contrast, persistent rhinitis is strongly associated with atopic risk and likely represents the typical trajectory associated with allergic disorders. Allergic rhinitis symptoms may commence as early as the first year of life and may inform development of early interventive strategies.

Longitudinal Asthma Patterns in Italian Adult General Population Samples: Host and Environmental Risk Factors

Background: Asthma patterns are not well established in epidemiological studies. Aim: To assess asthma patterns and risk factors in an adult general population sample. Methods: In total, 452 individuals reporting asthma symptoms/diagnosis in previous surveys participated in the AGAVE survey (2011–2014). Latent transition analysis (LTA) was performed to detect baseline and 12-month follow-up asthma phenotypes and longitudinal patterns. Risk factors associated with longitudinal patterns were assessed through multinomial logistic regression. Results: LTA detected four longitudinal patterns: persistent asthma diagnosis with symptoms, 27.2%; persistent asthma diagnosis without symptoms, 4.6%; persistent asthma symptoms without diagnosis, 44.0%; and ex -asthma, 24.1%. The longitudinal patterns were differently associated with asthma comorbidities. Persistent asthma diagnosis with symptoms showed associations with passive smoke (OR 2.64, 95% CI 1.10–6.33) and traffic exposure (OR 1.86, 95% CI 1.02–3.38), while persistent asthma symptoms (without diagnosis) with passive smoke (OR 3.28, 95% CI 1.41–7.66) and active smoke (OR 6.24, 95% CI 2.68–14.51). Conclusions: LTA identified three cross-sectional phenotypes and their four longitudinal patterns in a real-life setting. The results highlight the necessity of a careful monitoring of exposure to active/passive smoke and vehicular traffic, possible determinants of occurrence of asthma symptoms (with or without diagnosis). Such information could help affected patients and physicians in prevention and management strategies.

Understanding the dynamics of asthma symptoms between childhood and adolescence using latent transition analysis

OBJECTIVES

Asthma patterns in childhood are important predictors of unwanted outcomes in adolescence. We aimed to define asthma phenotypes in childhood and adolescence and evaluate the transitions between these phenotypes and factors potentially associated with the transitions.

METHODS

Baseline (1445 children), first round (1363 children/early adolescents) and second round (1206 adolescents) data from the SCAALA Project in Salvador, Brazil, were used. Phenotypes were defined by latent class analysis at three time points. Transitions between phenotypes were described and the effects of factors associated with transition probabilities estimated using latent transition analysis.

RESULTS

The "asymptomatic" and "symptomatic" phenotypes were identified. Approximately 5-6% of asymptomatic children in childhood/later childhood and early adolescence became symptomatic later in time. Maternal common mental disorders were identified as important risk factor for unhealthy states.

CONCLUSIONS

Asthma manifestations are characterized by frequent movements, especially between childhood and adolescence. Our study, by simultaneously defining disease subtypes, and examining the transitions and their potential predictors, highlights the importance of longitudinal studies to advance the understanding of the effects of social, environmental and biological mechanisms underlying asthma trajectories over time.

eNose breath prints as a surrogate biomarker for classifying patients with asthma by atopy

BACKGROUND

Electronic noses (eNoses) are emerging point-of-care tools that may help in the subphenotyping of chronic respiratory diseases such as asthma.

OBJECTIVE

We aimed to investigate whether eNoses can classify atopy in pediatric and adult patients with asthma.

METHODS

Participants with asthma and/or wheezing from 4 independent cohorts were included; BreathCloud participants (n = 429), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes adults (n = 96), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes pediatric participants (n = 100), and Pharmacogenetics of Asthma Medication in Children: Medication with Anti-Inflammatory Effects 2 participants (n = 30). Atopy was defined as a positive skin prick test result (≥3 mm) and/or a positive specific IgE level (≥0.35 kU/L) for common allergens. Exhaled breath profiles were measured by using either an integrated eNose platform or the SpiroNose. Data were divided into 2 training and 2 validation sets according to the technology used. Supervised data analysis involved the use of 3 different machine learning algorithms to classify patients with atopic versus nonatopic asthma with reporting of areas under the receiver operating characteristic curves as a measure of model performance. In addition, an unsupervised approach was performed by using a bayesian network to reveal data-driven relationships between eNose volatile organic compound profiles and asthma characteristics.

RESULTS

Breath profiles of 655 participants (n = 601 adults and school-aged children with asthma and 54 preschool children with wheezing [68.2% of whom were atopic]) were included in this study. Machine learning models utilizing volatile organic compound profiles discriminated between atopic and nonatopic participants with areas under the receiver operating characteristic curves of at least 0.84 and 0.72 in the training and validation sets, respectively. The unsupervised approach revealed that breath profiles classifying atopy are not confounded by other patient characteristics.

CONCLUSION

eNoses accurately detect atopy in individuals with asthma and wheezing in cohorts with different age groups and could be used in asthma phenotyping.

Development of allergic sensitization and its relevance to paediatric asthma

PURPOSE OF REVIEW

The purpose of this review is to summarize the recent evidence on the distinct atopic phenotypes and their relationship with childhood asthma. We start by considering definitions and phenotypic classification of atopy and then review evidence on its association with asthma in children.

RECENT FINDINGS

It is now well recognized that both asthma and atopy are complex entities encompassing various different sub-groups that also differ in the way they interconnect. The lack of gold standards for diagnostic markers of atopy and asthma further adds to the existing complexity over diagnostic accuracy and definitions. Although recent statistical phenotyping studies contributed significantly to our understanding of these heterogeneous disorders, translating these findings into meaningful information and effective therapies requires further work on understanding underpinning biological mechanisms.

SUMMARY

The disaggregation of allergic sensitization may help predict how the allergic disease is likely to progress. One of the important questions is how best to incorporate tests for the assessment of allergic sensitization into diagnostic algorithms for asthma, both in terms of confirming asthma diagnosis, and the assessment of future risk.

Epidemiology of Asthma in Children and Adults

Asthma is a globally significant non-communicable disease with major public health consequences for both children and adults, including high morbidity, and mortality in severe cases. We have summarized the evidence on asthma trends, environmental determinants, and long-term impacts while comparing these epidemiological features across childhood asthma and adult asthma. While asthma incidence and prevalence are higher in children, morbidity, and mortality are higher in adults. Childhood asthma is more common in boys while adult asthma is more common in women, and the reversal of this sex difference in prevalence occurs around puberty suggesting sex hormones may play a role in the etiology of asthma. The global epidemic of asthma that has been observed in both children and adults is still continuing, especially in low to middle income countries, although it has subsided in some developed countries. As a heterogeneous disease, distinct asthma phenotypes, and endotypes need to be adequately characterized to develop more accurate and meaningful definitions for use in research and clinical settings. This may be facilitated by new clustering techniques such as latent class analysis, and computational phenotyping methods are being developed to retrieve information from electronic health records using natural language processing (NLP) algorithms to assist in the early diagnosis of asthma. While some important environmental determinants that trigger asthma are well-established, more work is needed to define the role of environmental exposures in the development of asthma in both children and adults. There is increasing evidence that investigation into possible gene-by-environment and environment-by-environment interactions may help to better uncover the determinants of asthma. Therefore, there is an urgent need to further investigate the interrelationship between environmental and genetic determinants to identify high risk groups and key modifiable exposures. For children, asthma may impair airway development and reduce maximally attained lung function, and these lung function deficits may persist into adulthood without additional progressive loss. Adult asthma may accelerate lung function decline and increase the risk of fixed airflow obstruction, with the effect of early onset asthma being greater than late onset asthma. Therefore, in managing asthma, our focus going forward should be firmly on improving not only short-term symptoms, but also the long-term respiratory and other health outcomes.

Understanding asthma phenotypes: the World Asthma Phenotypes (WASP) international collaboration

The World Asthma Phenotypes (WASP) study started in 2016 and has been conducted in five centres, in the UK, New Zealand, Brazil, Ecuador and Uganda. The objectives of this study are to combine detailed biomarker and clinical information in order to 1) better understand and characterise asthma phenotypes in high-income countries (HICs) and low and middle-income countries (LMICs), and in high and low prevalence centres; 2) compare phenotype characteristics, including clinical severity; 3) assess the risk factors for each phenotype; and 4) assess how the distribution of phenotypes differs between high prevalence and low prevalence centres. Here we present the rationale and protocol for the WASP study to enable other centres around the world to carry out similar analyses using a standardised protocol. Large collaborative and integrative studies like this are essential to further our understanding of asthma phenotypes. The findings of this study will help elucidate the aetiological mechanisms of asthma and might potentially identify new causes and guide the development of new treatments, thereby enabling better management and prevention of asthma in both HICs and LMICs.

Identifying patients with asthma-chronic obstructive pulmonary disease overlap syndrome using latent class analysis of electronic health record data: a study protocol

Asthma and chronic obstructive pulmonary disease (COPD) are two common different clinical diagnoses with overlapping clinical features. Both conditions have been increasingly studied using electronic health records (EHR). Asthma-COPD overlap syndrome (ACOS) is an emerging concept where clinical features from both conditions co-exist, and for which, however, there is no consensus definition. Nonetheless, we expect EHR data of people with ACOS to be systematically different from those with "asthma only" or "COPD only". We aim to develop a latent class model to understand the overlap between asthma and COPD in EHR data. From the Secure Anonymised Information Linkage (SAIL) databank, we will use routinely collected primary care data recorded in or before 2014 in Wales for people who aged 40 years or more on 1st Jan 2014. Based on this latent class model, we will train a classification algorithm and compare its performance with commonly used objective and self-reported case definitions for asthma and COPD. The resulting classification algorithm is intended to be used to identify people with ACOS, 'asthma only', and 'COPD only' in primary care datasets.

African ancestry is associated with cluster-based childhood asthma subphenotypes

BACKGROUND

Childhood asthma is a syndrome composed of heterogeneous phenotypes; furthermore, intrinsic biologic variation among racial/ethnic populations suggests possible genetic ancestry variation in childhood asthma. The objective of the study is to identify clinically homogeneous asthma subphenotypes in a diverse sample of asthmatic children and to assess subphenotype-specific genetic ancestry in African-American asthmatic children.

METHODS

A total of 1211 asthmatic children including 813 in the Childhood Asthma Management Program and 398 in the Childhood Asthma Research and Education program were studied. Unsupervised cluster analysis on clinical phenotypes was conducted to identify homogeneous subphenotypes. Subphenotype-specific genetic ancestry was estimated for 167 African-American asthmatic children. Genetic ancestry association with subphenotypes/clinical phenotypes were determined.

RESULTS

Three distinct subphenotypes were identified: a moderate atopic dermatitis (AD) group with negative skin prick test (SPT) and preserved lung function; a high AD group with positive SPT and airway hyperresponsiveness; and a low AD group with positive SPT and lower lung function. African ancestry at asthma genome-wide association study (GWAS) SNPs differed between subphenotypes (64, 89, and 94% for the three subphenotypes, respectively) and was inversely correlated with AD; each additional 10% increase in African ancestry was associated with 1.5 fold higher in IgE and 6.3 higher odds of positive SPT (all p-values < 0.0001).

CONCLUSIONS

By conducting phenotype-based cluster analysis and assessing subphenotype-specific genetic ancestry, we were able to identify homogeneous subphenotypes for childhood asthma that showed significant variation in genetic ancestry of African-American asthmatic children. This finding demonstrates the utility of these complementary approaches to understand and refine childhood asthma subphenotypes and enable more targeted therapy.

Classification of Pediatric Asthma: From Phenotype Discovery to Clinical Practice

Advances in big data analytics have created an opportunity for a step change in unraveling mechanisms underlying the development of complex diseases such as asthma, providing valuable insights that drive better diagnostic decision-making in clinical practice, and opening up paths to individualized treatment plans. However, translating findings from data-driven analyses into meaningful insights and actionable solutions requires approaches and tools which move beyond mining and patterning longitudinal data. The purpose of this review is to summarize recent advances in phenotyping of asthma, to discuss key hurdles currently hampering the translation of phenotypic variation into mechanistic insights and clinical setting, and to suggest potential solutions that may address these limitations and accelerate moving discoveries into practice. In order to advance the field of phenotypic discovery, greater focus should be placed on investigating the extent of within-phenotype variation. We advocate a more cautious modeling approach by "supervising" the findings to delineate more precisely the characteristics of the individual trajectories assigned to each phenotype. Furthermore, it is important to employ different methods within a study to compare the stability of derived phenotypes, and to assess the immutability of individual assignments to phenotypes. If we are to make a step change toward precision (stratified or personalized) medicine and capitalize on the available big data assets, we have to develop genuine cross-disciplinary collaborations, wherein data scientists who turn data into information using algorithms and machine learning, team up with medical professionals who provide deep insights on specific subjects from a clinical perspective.

Subtypes of asthma based on asthma control and severity: a latent class analysis

BACKGROUND

Asthma subtyping is a complex new field of study. Usually both etiological and outcome factors of asthma have been used simultaneously for subtyping thus making the interpretation of the results difficult. Identification of subtypes of asthma based on questionnaire data only will be useful for both treatment of asthma and for research. Our objective was to identify asthma subtypes that capture both asthma control and severity based on easily accessible variables.

METHODS

We applied latent class analysis for the 1995 adult asthmatics, 692 men and 1303 women, of the Northern Finnish Asthma Study (NoFAS). The classifying variables included use of asthma medication within the last 12 months, St. George's Respiratory Questionnaire score, and asthma-related healthcare use within the last 12 months. Covariates adjusted for included COPD, allergic rhinitis/allergic eczema, BMI, age and sex. All information was based on self-administered questionnaires.

RESULTS

We identified four subtypes for women: Controlled, mild asthma (41% of participants); Partly controlled, moderate asthma (24%); Uncontrolled asthma, unknown severity (26%), and Uncontrolled, severe asthma (9%). For men we identified three subtypes: Controlled, mild asthma (31%); Poorly controlled asthma, unknown severity (53%); and Partly controlled, severe asthma (17%). For almost 96% of the subjects this subtyping was accurate. The covariates fitted in the model were based on clinical judgment and were good predictors of class membership.

CONCLUSIONS

Our results show that it is possible to form meaningful and accurate asthma subtypes based on questionnaire data, and that separate classification should be applied for men and women.

Asthma phenotypes in children and stratified pharmacological treatment regimens

INTRODUCTION

Asthma is the most common inflammatory disease in childhood. The interaction of genetic, environmental and host factors may contribute to the development of childhood asthma and defines its progress, including persistence and severity. Until now, various classifications of childhood asthma phenotypes have been suggested based on patient's age during onset of symptoms, type of inflammatory cells, response to treatment and disease severity. Many efforts have been carried out to identify childhood asthma phenotypes and to clarify which are the risk factors that define asthma prediction and the response to therapy. The identification of asthma phenotypes has not only prognostic but also therapeutic role. However, the classification of asthma phenotypes is complex due to the heterogeneity of the disease. Areas covered: The current childhood asthma phenotypes and the new therapeutic strategies for each phenotype are reviewed. Expert commentary: There are multiple phenotypes in childhood asthma and it is crucial to define them before the initiation of personalized treatment. Both the therapeutic strategy and monitoring should follow the recent guidelines.

Allergic sensitization patterns identified through latent class analysis among children with and without asthma

BACKGROUND

Specific patterns of allergic sensitization to common allergens may provide relevant clinical insight into asthma risk.

OBJECTIVE

To identify patterns of allergic sensitization based on multiple individual allergens and link these to current and persistent asthma using baseline and 3-year follow-up data.

METHODS

Children 7 to 8 years old with (n = 196) and without (n = 136) asthma from the New York City Neighborhood Asthma and Allergy Study were studied. IgE against a panel of 112 antigens was measured using the ISAC multiplex panel array. Latent class analysis (LCA) was used to identify patterns of allergic sensitization among the 26 most common allergens against which children had measurable IgE. The association between patterns of allergic sensitization and risk of asthma and other allergic diseases was examined.

RESULTS

LCA identified 4 patterns of allergic sensitization as follows: low risk of sensitization (prevalence of 53% in children with asthma and 76% in children without asthma), indoor (prevalence of 23% in children with asthma and 15% in children without asthma), pollen and indoor group 1 (prevalence of 16% in children with asthma and 5% in children without asthma), and pollen and indoor group 2 (prevalence of 9% in children with asthma and 4% in children without asthma). Compared with the low risk of sensitization pattern, children belonging to the 3 sensitized patterns had significantly higher risk of asthma at ages 7 to 8 years and 3 years later, with the highest risk for children in the pollen and indoor group 1 pattern.

CONCLUSIONS

LCA facilitates the study of sensitization profiles to a large number of common allergens. Analyzing patterns of allergic sensitization from multiple allergens reveals additional relevant associations with asthma than the study of a single allergen or total IgE.

Identification of Asthma Subtypes Using Clustering Methodologies

Asthma is a heterogeneous disease comprising a number of subtypes which may be caused by different pathophysiologic mechanisms (sometimes referred to as endotypes) but may share similar observed characteristics (phenotypes). The use of unsupervised clustering in adult and paediatric populations has identified subtypes of asthma based on observable characteristics such as symptoms, lung function, atopy, eosinophilia, obesity, and age of onset. Here we describe different clustering methods and demonstrate their contributions to our understanding of the spectrum of asthma syndrome. Precise identification of asthma subtypes and their pathophysiological mechanisms may lead to stratification of patients, thus enabling more precise therapeutic and prevention approaches.

Atopic dermatitis phenotype with early onset and high serum IL-13 is linked to the new development of bronchial hyperresponsiveness in school children

BACKGROUND

Atopic dermatitis (AD) is characterized by a heterogeneous clinical spectrum, and some forms of AD are associated with the initial steps of allergic march. The aims of this study were to determine AD phenotypes in school-age children and investigate their associations with the allergic march in each cluster.

METHODS

We included 242 children (6-8 years) with current AD from the Children's HEalth and Environmental Research study, a 4-year prospective follow-up study with 2-year survey intervals. Latent class analysis was used. Serum IL-13 and thymic stromal lymphopoietin (TSLP) levels at the time of enrollment were measured using ELISA.

RESULTS

We identified four current AD phenotypes in children, characterized as 'early onset with low atopy' (26.4% of the sample; group 1), 'early onset with high atopy and high eosinophil percentages' (48.3%; group 2), 'late onset with low atopy' (9.9%; group 3), and 'late onset with high atopy and normal eosinophils' (15.3%; group 4). Although groups 2 and 4 demonstrated high atopic burden, children in group 2 showed the persistence of AD and eosinophilia associated with a high prevalence of new cases of bronchial hyper-responsiveness and asthma symptoms during follow-up. The serum IL-13 level was significantly increased in the early-onset AD groups, but there was no significant difference in the serum TSLP levels across all four groups.

CONCLUSION

An allergic march-associated AD phenotype exists that is characterized by early onset of AD with its persistence, increased serum IL-13 levels, high atopy, and a persistently increased blood eosinophil percentage.

Change in the manifestations of asthma and asthma-related traits in childhood: a latent transition analysis

It is known that asthma is a heterogeneous entity whose manifestations vary with age. Our objective was to examine changes in the manifestation of asthma and asthma-related traits in childhood by defining empirically derived childhood asthma phenotypes and examining their transitions over time.To define the phenotypes we used data on respiratory symptoms, healthcare utilisation, medications, spirometry, airway hyperresponsiveness (AHR), exhaled nitric oxide concentration and atopy from a birth cohort recruited on the basis of having a first-degree relative with asthma. Data were acquired at ages 1.5-11.5 years and analysed using latent transition analysis.In a study population of 370 participants, we classified subjects into four phenotypes: 1) nonatopic, few symptoms (prevalence range from 1.5 to 5 years: 52-60%), 2) atopic, few symptoms (3-21%), 3) nonatopic, asthma and rhinitis symptoms (13-35%), and 4) atopic, asthma and rhinitis symptoms (2-14%) in early childhood; and 1) nonatopic, no respiratory disease (prevalence range from 8 to 11.5 years: 41-46%), 2) atopic, no respiratory disease (23-33%), 3) nonatopic, asthma symptoms, no AHR or airway inflammation (8-12%) and 4) atopic asthma (19%) in mid-childhood. Transitioning between phenotypes was common in early childhood, but less common in later childhood.This analysis represents the first attempt to incorporate longitudinal patterns of several manifestations of asthma into a single model to simultaneously define phenotypes and examine their transitions over time. It provides quantitative support for the view that asthma is a heterogeneous entity, and that some children with wheeze and other respiratory symptoms in early life progress to asthma in mid-childhood, while others become asymptomatic.

Phenotyping asthma, rhinitis and eczema in MeDALL population-based birth cohorts: an allergic comorbidity cluster

BACKGROUND

Asthma, rhinitis and eczema often co-occur in children, but their interrelationships at the population level have been poorly addressed. We assessed co-occurrence of childhood asthma, rhinitis and eczema using unsupervised statistical techniques.

METHODS

We included 17 209 children at 4 years and 14 585 at 8 years from seven European population-based birth cohorts (MeDALL project). At each age period, children were grouped, using partitioning cluster analysis, according to the distribution of 23 variables covering symptoms 'ever' and 'in the last 12 months', doctor diagnosis, age of onset and treatments of asthma, rhinitis and eczema; immunoglobulin E sensitization; weight; and height. We tested the sensitivity of our estimates to subject and variable selections, and to different statistical approaches, including latent class analysis and self-organizing maps.

RESULTS

Two groups were identified as the optimal way to cluster the data at both age periods and in all sensitivity analyses. The first (reference) group at 4 and 8 years (including 70% and 79% of children, respectively) was characterized by a low prevalence of symptoms and sensitization, whereas the second (symptomatic) group exhibited more frequent symptoms and sensitization. Ninety-nine percentage of children with comorbidities (co-occurrence of asthma, rhinitis and/or eczema) were included in the symptomatic group at both ages. The children's characteristics in both groups were consistent in all sensitivity analyses.

CONCLUSION

At 4 and 8 years, at the population level, asthma, rhinitis and eczema can be classified together as an allergic comorbidity cluster. Future research including time-repeated assessments and biological data will help understanding the interrelationships between these diseases.

Developments in the field of allergy in 2013 through the eyes of Clinical and Experimental Allergy

2013 was another exciting year for allergy in general and Clinical and Experimental Allergy in particular. In the field of asthma and rhinitis, there continued to be a focus on heterogeneity and phenotypes with increasing use of biostatistical techniques to determine clusters of similar populations. Obesity- and aspirin-associated disease are intriguing associations with asthma which were explored in a number of papers. We published a number of excellent papers on mechanisms of airway inflammation and how this relates to physiology, pathology, genetics and biomarkers in both human and experimental model systems. In terms of mechanisms, there is less on individual cell types in allergic disease at the moment, but the immunology of allergic disease continued to fascinate our authors. Another area that was popular both in the mechanisms and in the epidemiology sections was early life events and how these lead to allergic disease, with an increasing focus on the role of the microbiome and how this influences immune tolerance. In the clinical allergy section, oral immunotherapy for food allergy is clearly a major topic of interest at the moment as was in vitro testing to distinguish between sensitization and allergic disease. There was less on inhalant allergy this year, but a good representation from the drug allergy community including some interesting work on non-IgE-mediated mechanisms. In the allergen section, important new allergens continue to be discovered, but the major focus as in the last couple of years was on working out how component-resolved approaches can improve diagnosis and management of food and venom allergy.

Distinguishing Asthma Phenotypes Using Machine Learning Approaches

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as 'asthma endotypes'. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies.

Overweight/obesity and respiratory and allergic disease in children: international study of asthma and allergies in childhood (ISAAC) phase two

Background

Childhood obesity and asthma are increasing worldwide. A possible link between the two conditions has been postulated.

Methods

Cross-sectional studies of stratified random samples of 8–12-year-old children (n = 10 652) (16 centres in affluent and 8 centres in non-affluent countries) used the standardized methodology of ISAAC Phase Two. Respiratory and allergic symptoms were ascertained by parental questionnaires. Tests for allergic disease were performed. Height and weight were measured, and overweight and obesity were defined according to international definitions. Prevalence rates and prevalence odds ratios were calculated.

Results

Overweight (odds ratio = 1.14, 95%-confidence interval: 0.98; 1.33) and obesity (odds ratio = 1.67, 95%-confidence interval: 1.25; 2.21) were related to wheeze. The relationship was stronger in affluent than in non-affluent centres. Similar results were found for cough and phlegm, rhinitis and eczema but the associations were mostly driven by children with wheeze. There was a clear association of overweight and obesity with airways obstruction (change in FEV1/FVC, −0.90, 95%-confidence interval: −1.33%; −0.47%, for overweight and −2.46%, 95%-confidence interval: −3.84%; −1.07%, for obesity) whereas the results for the other objective markers, including atopy, were null.

Conclusions

Our data from a large international child population confirm that there is a strong relation of body mass index with wheeze especially in affluent countries. Moreover, body mass index is associated with an objective marker of airways obstruction (FEV1/FVC) but no other objective markers of respiratory and allergic disorders.

Atopic phenotypes identified with latent class analyses at age 2 years

BACKGROUND

Atopic sensitization (ie, atopy) is the most commonly reported risk factor for asthma. Recent studies have begun to suggest that atopy, as conventionally defined, might be an umbrella term that obfuscates more specific allergic disease types.

OBJECTIVE

We sought to determine whether distinct and meaningful atopic phenotypes exist within a racially diverse birth cohort using 10 allergen-specific serum IgE (sIgE) measurements from children aged 2 years.

METHODS

Using the Wayne County Health, Environment, Allergy and Asthma Longitudinal Study (WHEALS) birth cohort (62% black), we analyzed sIgE data on 10 allergens (Dermatophagoides farinae, dog, cat, timothy grass, ragweed, Alternaria alternata, egg, peanut, milk, and German cockroach) obtained from 594 children at age 2 years. Conventional atopy was defined as at least 1 sIgE level of 0.35 IU/mL or greater.

RESULTS

A 4-class solution (latent class model) was the best fit. Class types were labeled "low to no sensitization" (76.9% of sample), "highly sensitized" (2.7%), "milk and egg dominated" (15.3%), and "peanut and inhalant(s)" (5.1%). Almost one third (32.2%) of the low to no sensitization group met the criteria for conventional atopy. The highly sensitized group was significantly associated with a doctor's diagnosis of asthma after age 4 years (odds ratio [OR], 5.3; 95% CI, 1.6-17.4), whereas the milk and egg dominated and peanut and inhalant(s) groups were not (ORs of 1.6 [95% CI, 0.8-3.0] and 1.8 [95% CI, 0.6-4.9], respectively). Children of black race were more likely to be in the 3 multisensitized groups (P = .04).

CONCLUSION

Classification by sIgE patterns defined groups whose membership is more strongly associated with atopic dermatitis, wheeze, and asthma compared with conventional atopy.

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.

Relevance of allergy in adult asthma

Recent studies on asthma have demonstrated multiple phenotypes, based on the clinical characteristics of the disease. With the current interest in personalized medicine, the question arises whether the presence of allergic sensitization has any relevance for these phenotypes and the management of asthma. This review will examine the current knowledge of asthma phenotypes and the impact of atopy on asthma diagnosis and severity in adults. In addition, this review will address whether therapies targeted at the atopic axis help improve asthma outcomes, including lung function indices and exacerbations.

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.