Characterizing wheeze phenotypes to identify endotypes of childhood asthma, and the implications for future management

Understanding progression from pre-school wheezing to school-age asthma: Can modern data approaches help?

Preschool wheezing and childhood asthma create a heavy disease burden which is only exacerbated by the complexity of the conditions. Preschool wheezing exhibits both "curricular" and "aetiological" heterogeneity: that is, heterogeneity across patients both in the time-course of its development and in its underpinning pathological mechanisms. Since these are not fully understood, but clinical presentations across patients may nonetheless be similar, current diagnostic labels are imprecise-not mapping cleanly onto underlying disease mechanisms-and prognoses uncertain. These uncertainties also make a identifying new targets for therapeutic intervention difficult. In the past few decades, carefully designed birth cohort studies have collected "big data" on a large scale, incorporating not only a wealth of longitudinal clinical data, but also detailed information from modalities as varied as imaging, multiomics, and blood biomarkers. The profusion of big data has seen the proliferation of what we term "modern data approaches" (MDAs)-grouping together machine learning, artificial intelligence, and data science-to make sense and make use of this data. In this review, we survey applications of MDAs (with an emphasis on machine learning) in childhood wheeze and asthma, highlighting the extent of their successes in providing tools for prognosis, unpicking the curricular heterogeneity of these conditions, clarifying the limitations of current diagnostic criteria, and indicating directions of research for uncovering the etiology of the diseases underlying these conditions. Specifically, we focus on the trajectories of childhood wheeze phenotypes. Further, we provide an explainer of the nature and potential use of MDAs and emphasize the scope of what we can hope to achieve with them.

Phenotype and endotype based treatment of preschool wheeze

INTRODUCTION

Preschool wheeze (PSW) is a significant public health issue, with a high presentation rate to emergency departments, recurrent symptoms, and severe exacerbations. A heterogenous condition, PSW comprises several phenotypes that may relate to a range of pathobiological mechanisms. However, treating PSW remains largely generalized to inhaled corticosteroids and a short acting beta agonist, guided by symptom-based labels that often do not reflect underlying pathways of disease.

AREAS COVERED

We review the observable features and characteristics used to ascribe phenotypes in children with PSW and available pathobiological evidence to identify possible endotypes. These are considered in the context of treatment options and future research directions. The role of machine learning (ML) and modern analytical techniques to identify patterns of disease that distinguish phenotypes is also explored.

EXPERT OPINION

Distinct clusters (phenotypes) of severe PSW are characterized by different underlying mechanisms, some shared and some unique. ML-based methodologies applied to clinical, biomarker, and environmental data can help design tools to differentiate children with PSW that continues into adulthood, from those in whom wheezing resolves, identifying mechanisms underpinning persistence and resolution. This may help identify novel therapeutic targets, inform mechanistic studies, and serve as a foundation for stratification in future interventional therapeutic trials.

Whispers of change in preschool asthma phenotypes: Findings in the French ELFE cohort

RATIONALE

Early life asthma phenotyping remains an unmet need in pediatric asthma. In France, severe pediatric asthma phenotyping has been done extensively; however, phenotypes in the general population remain underexplored. Based on the course and severity of respiratory/allergic symptoms, we aimed to identify and characterize early life wheeze profiles and asthma phenotypes in the general population.

METHODS

ELFE is a general population based birth cohort; which recruited 18,329 newborns in 2011, from 320 maternity units nationwide. Data was collected using parental responses to modified versions of ISAAC questionnaire on eczema, rhinitis, food allergy, cough, wheezing, dyspnoea and sleep disturbance due to wheezing at 3 time points: post-natal (2 months), infancy (age 1) and pre-school (age 5). We built a supervised trajectory for wheeze profiles and an unsupervised approach was used for asthma phenotypes. Chi squared (χ2) test or fisher's exact test was used as appropriate (p < 0.05).

RESULTS

Wheeze profiles and asthma phenotypes were ascertained at age 5. Supervised wheeze trajectory of 9161 children resulted in 4 wheeze profiles: Persistent (0.8%), Transient (12.1%), Incident wheezers at age 5 (13.3%) and Non wheezers (73.9%). While 9517 children in unsupervised clusters displayed 4 distinct asthma phenotypes: Mildly symptomatic (70%), Post-natal bronchiolitis with persistent rhinitis (10.2%), Severe early asthma (16.9%) and Early persistent atopy with late onset severe wheeze (2.9%).

CONCLUSION

We successfully determined early life wheeze profiles and asthma phenotypes in the general population of France.

Modeling Wheezing Spells Identifies Phenotypes with Different Outcomes and Genetic Associates

Rationale: Longitudinal modeling of current wheezing identified similar phenotypes, but their characteristics often differ between studies. Objectives: We propose that a more comprehensive description of wheeze may better describe trajectories than binary information on the presence/absence of wheezing. Methods: We derived six multidimensional variables of wheezing spells from birth to adolescence (including duration, temporal sequencing, and the extent of persistence/recurrence). We applied partition-around-medoids clustering on these variables to derive phenotypes in five birth cohorts. We investigated within- and between-phenotype differences compared with binary latent class analysis models and ascertained associations of these phenotypes with asthma and lung function and with polymorphisms in asthma loci 17q12-21 and CDHR3 (cadherin-related family member 3). Measurements and Main Results: Analysis among 7,719 participants with complete data identified five spell-based wheeze phenotypes with a high degree of certainty: never (54.1%), early-transient (ETW) (23.7%), late-onset (LOW) (6.9%), persistent (PEW) (8.3%), and a novel phenotype, intermittent wheeze (INT) (6.9%). FEV₁/FVC was lower in PEW and INT compared with ETW and LOW and declined from age 8 years to adulthood in INT. 17q12-21 and CDHR3 polymorphisms were associated with higher odds of PEW and INT, but not ETW or LOW. Latent class analysis- and spell-based phenotypes appeared similar, but within-phenotype individual trajectories and phenotype allocation differed substantially. The spell-based approach was much more robust in dealing with missing data, and the derived clusters were more stable and internally homogeneous. Conclusions: Modeling of spell variables identified a novel intermittent wheeze phenotype associated with lung function decline to early adulthood. Using multidimensional spell variables may better capture wheeze development and provide a more robust input for phenotype derivation.

Asthma and Allergy: Unravelling a Tangled Relationship with a Focus on New Biomarkers and Treatment

Asthma is a major driver of health care costs across ages. Despite widely disseminated asthma-treatment guidelines and a growing variety of effective therapeutic options, most patients still experience symptoms and/or refractoriness to standard of care treatments. As a result, most patients undergo a further intensification of therapy to optimize symptom control with a subsequent increased risk of side effects. Raising awareness about the relevance of evaluating aeroallergen sensitizations in asthmatic patients is a key step in better informing clinical practice while new molecular tools, such as the component resolved diagnosis, may be of help in refining the relationship between sensitization and therapeutic recommendations. In addition, patient care should benefit from reliable, easy-to-measure and clinically accessible biomarkers that are able to predict outcome and disease monitoring. To attain a personalized asthma management and to guide adequate treatment decisions, it is of paramount importance to expand clinicians' knowledge about the tangled relationship between asthma and allergy from a molecular perspective. Our review explores the relevance of allergen testing along the asthma patient's journey, with a special focus on recurrent wheezing children. Here, we also discuss the unresolved issues regarding currently available biomarkers and summarize the evidence supporting the eosinophil-derived neurotoxin as promising biomarker.

Wheeze trajectories: Determinants and outcomes in the CHILD Cohort Study

BACKGROUND

Wheezing in early life is associated with asthma in adulthood; however, the determinants of wheezing trajectories and their associations with asthma and lung function in childhood remain poorly understood.

OBJECTIVE

In the CHILD Cohort Study, we aimed to identify wheezing trajectories and examine the associations between these trajectories, risk factors, and clinical outcomes at age 5 years.

METHODS

Wheeze data were collected at 8 time points from 3 months to 5 years of age. We used group-based trajectory models to derive wheeze trajectories among 3154 children. Associations with risk factors and clinical outcomes were analyzed by weighted regression models.

RESULTS

We identified 4 trajectories: a never/infrequent trajectory, transient wheeze, intermediate-onset (preschool) wheeze, and persistent wheeze. Higher body mass index was a common risk factor for all wheeze trajectories compared with that in the never/infrequent group. The unique predictors for specific wheeze trajectories included male sex, lower respiratory tract infections, and day care attendance for transient wheeze; paternal history of asthma, atopic sensitization, and child genetic risk score of asthma for intermediate wheeze; and maternal asthma for persistent wheeze. Blood eosinophil counts were higher in children with the intermediate wheeze trajectory than in those children with the other trajectories at the ages of 1 and 5 years. All wheeze trajectories were associated with decreased lung function and increased risk of asthma at age 5 years.

CONCLUSIONS

We identified 4 distinct trajectories in children from 3 months to 5 years of age, reflecting different phenotypes of early childhood wheeze. These trajectories were characterized by different biologic and physiologic traits and risk factors.

Probabilistic Machine Learning for Healthcare

Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial, including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.

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.

Recurrent Severe Preschool Wheeze: From Pre-Specified Diagnostic Labels to Underlying Endotypes

Rationale: Preschool wheezing is heterogeneous, but the underlying mechanisms are poorly understood.

Objectives: To investigate lower airway inflammation and infection in preschool children with different clinical diagnoses undergoing elective bronchoscopy and BAL.

Methods: We recruited 136 children aged 1–5 years (105 with recurrent severe wheeze [RSW]; 31 with nonwheezing respiratory disease [NWRD]). Children with RSW were assigned as having episodic viral wheeze (EVW) or multiple-trigger wheeze (MTW). We compared lower airway inflammation and infection in different clinical diagnoses and undertook data-driven analyses to determine clusters of pathophysiological features, and we investigated their relationships with prespecified diagnostic labels.

Measurements and Main Results: Blood eosinophil counts and percentages and allergic sensitization were significantly higher in children with RSW than in children with a NWRD. Blood neutrophil counts and percentages, BAL eosinophil and neutrophil percentages, and positive bacterial culture and virus detection rates were similar between groups. However, pathogen distribution differed significantly, with higher detection of rhinovirus in children with RSW and higher detection of Moraxella in sensitized children with RSW. Children with EVW and children with MTW did not differ in terms of blood or BAL-sample inflammation, or bacteria or virus detection. The Partition around Medoids algorithm revealed four clusters of pathophysiological features: 1) atopic (17.9%), 2) nonatopic with a low infection rate and high use of inhaled corticosteroids (31.3%), 3) nonatopic with a high infection rate (23.1%), and 4) nonatopic with a low infection rate and no use of inhaled corticosteroids (27.6%). Cluster allocation differed significantly between the RSW and NWRD groups (RSW was evenly distributed across clusters, and 60% of the NWRD group was assigned to cluster 4; P < 0.001). There was no difference in cluster membership between the EVW and MTW groups. Cluster 1 was dominated by Moraxella detection (P = 0.04), and cluster 3 was dominated by Haemophilus or Staphylococcus or Streptococcus detection (P = 0.02).

Conclusions: We identified four clusters of severe preschool wheeze, which were distinguished by using sensitization, peripheral eosinophilia, lower airway neutrophilia, and bacteriology.

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.

New Avenues for Phosphodiesterase Inhibitors in Asthma

Introduction

Phosphodiesterases (PDEs) are isoenzymes ubiquitously expressed in the lungs where they catalyse cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (GMP), which are fundamental second messengers in asthma, thereby regulating the intracellular concentrations of these cyclic nucleotides, their signaling pathways and, consequently, myriad biological responses. The superfamily of PDEs is composed of 11 families with a distinct substrate specificity, molecular structure and subcellular localization. Experimental studies indicate a possible role in asthma mainly for PDE3, PDE4, PDE5 and PDE7. Consequently, drugs that inhibit PDEs may offer novel therapeutic options for the treatment of this disease.

Areas Covered

In this article, we describe the progress made in recent years regarding the possibility of using PDE inhibitors in the treatment of asthma.

Expert Opinion

Many data indicate the potential benefits of PDE inhibitors as an add-on treatment especially in severe asthma due to their bronchodilator and/or anti-inflammatory activity, but no compound has yet reached the market as asthma treatment mainly because of their limited tolerability. Therefore, there is a growing interest in developing new PDE inhibitors with an improved safety profile. In particular, the research is focused on the development of drugs capable of interacting simultaneously with different PDEs, or to be administered by inhalation. CHF 6001 and RPL554 are the only molecules that currently are under clinical development but there are several new agents with interesting pharmacological profiles. It will be stimulating to assess the impact of such agents on individual treatable traits in specially designed studies.

Distinguishing Wheezing Phenotypes from Infancy to Adolescence. A Pooled Analysis of Five Birth Cohorts

Rationale: Pooling data from multiple cohorts and extending the time frame across childhood should minimize study-specific effects, enabling better characterization of childhood wheezing.

Objectives: To analyze wheezing patterns from early childhood to adolescence using combined data from five birth cohorts.

Methods: We used latent class analysis to derive wheeze phenotypes among 7,719 participants from five birth cohorts with complete report of wheeze at five time periods. We tested the associations of derived phenotypes with late asthma outcomes and lung function, and investigated the uncertainty in phenotype assignment.

Results: We identified five phenotypes: never/infrequent wheeze (52.1%), early onset preschool remitting (23.9%), early onset midchildhood remitting (9%), persistent (7.9%), and late-onset wheeze (7.1%). Compared with the never/infrequent wheeze, all phenotypes had higher odds of asthma and lower forced expiratory volume in 1 second and forced expiratory volume in 1 second/forced vital capacity in adolescence. The association with asthma was strongest for persistent wheeze (adjusted odds ratio, 56.54; 95% confidence interval, 43.75–73.06). We observed considerable within-class heterogeneity at the individual level, with 913 (12%) children having low membership probability (<0.60) of any phenotype. Class membership certainty was highest in persistent and never/infrequent, and lowest in late-onset wheeze (with 51% of participants having membership probabilities <0.80). Individual wheezing patterns were particularly heterogeneous in late-onset wheeze, whereas many children assigned to early onset preschool remitting class reported wheezing at later time points.

Conclusions: All wheeze phenotypes had significantly diminished lung function in school-age children, suggesting that the notion that early life episodic wheeze has a benign prognosis may not be true for a proportion of transient wheezers. We observed considerable within-phenotype heterogeneity in individual wheezing patterns.

Preschool Wheezing: Trajectories and Long-Term Treatment

Wheezing is very common in infancy affecting one in three children during the first 3 years of life. Several wheeze phenotypes have been identified and most rely on temporal pattern of symptoms. Assessing the risk of asthma development is difficult. Factors predisposing to onset and persistence of wheezing such as breastfeeding, atopy, indoor allergen exposure, environmental tobacco smoke and viral infections are analyzed. Inhaled corticosteroids are recommended as first choice of controller treatment in all preschool children irrespective of phenotype, but they are particularly beneficial in terms of fewer exacerbations in atopic children. Other therapeutic options include the addition of montelukast or the intermittent use of inhaled corticosteroids. Overuse of inhaled steroids must be avoided. Therefore, adherence to treatment and correct administration of the medications need to be checked at every visit.

Childhood Asthma: Advances Using Machine Learning and Mechanistic Studies

A paradigm shift brought by the recognition that childhood asthma is an aggregated diagnosis that comprises several different endotypes underpinned by different pathophysiology, coupled with advances in understanding potentially important causal mechanisms, offers a real opportunity for a step change to reduce the burden of the disease on individual children, families, and society. Data-driven methodologies facilitate the discovery of "hidden" structures within "big healthcare data" to help generate new hypotheses. These findings can be translated into clinical practice by linking discovered "phenotypes" to specific mechanisms and clinical presentations. Epidemiological studies have provided important clues about mechanistic avenues that should be pursued to identify interventions to prevent the development or alter the natural history of asthma-related diseases. Findings from cohort studies followed by mechanistic studies in humans and in neonatal mouse models provided evidence that environments such as traditional farming may offer protection by modulating innate immune responses and that impaired innate immunity may increase susceptibility. The key question of which component of these exposures can be translated into interventions requires confirmation. Increasing mechanistic evidence is demonstrating that shaping the microbiome in early life may modulate immune function to confer protection. Iterative dialogue and continuous interaction between experts with different but complementary skill sets, including data scientists who generate information about the hidden structures within "big data" assets, and medical professionals, epidemiologists, basic scientists, and geneticists who provide critical clinical and mechanistic insights about the mechanisms underpinning the architecture of the heterogeneity, are keys to delivering mechanism-based stratified treatments and prevention.

Blood Biomarkers of Sensitization and Asthma

Biomarkers are essential to determine different phenotypes of childhood asthma, and for the prediction of response to treatments. In young preschool children with asthma, aeroallergen sensitization, and blood eosinophil count of 300/μL or greater may identify those who can benefit from the daily use of inhaled corticosteroids (ICS). We propose that every preschool child who is considered for ICS treatment should have these two features measured as a minimum before a decision is made on the commencement of long-term preventive treatment. In practice, IgE-mediated sensitization should be considered as a quantifiable variable, i.e., we should use the titer of sIgE antibodies or the size of skin prick test response. A number of other blood biomarkers may prove useful (e.g., allergen-specific IgG/IgE antibody ratios amongst sensitized individuals, component-resolved diagnostics which measures sIgE response to a large number of allergenic molecules, assessment of immune responses to viruses, level of serum CC16, etc.), but it remains unclear whether these can be translated into clinically useful tests. Going forward, a more integrated approach which takes into account multiple domains of asthma, from the pattern of symptoms and blood biomarkers to genetic risk and lung function measures, is needed if we are to move toward a stratified approach to asthma management.

Causes of variability in latent phenotypes of childhood wheeze

BACKGROUND

Latent class analysis (LCA) has been used extensively to identify (latent) phenotypes of childhood wheezing. However, the number and trajectory of discovered phenotypes differed substantially between studies.

OBJECTIVE

We sought to investigate sources of variability affecting the classification of phenotypes, identify key time points for data collection to understand wheeze heterogeneity, and ascertain the association of childhood wheeze phenotypes with asthma and lung function in adulthood.

METHODS

We used LCA to derive wheeze phenotypes among 3167 participants in the ALSPAC cohort who had complete information on current wheeze recorded at 14 time points from birth to age 16½ years. We examined the effects of sample size and data collection age and intervals on the results and identified time points. We examined the associations of derived phenotypes with asthma and lung function at age 23 to 24 years.

RESULTS

A relatively large sample size (>2000) underestimated the number of phenotypes under some conditions (eg, number of time points <11). Increasing the number of data points resulted in an increase in the optimal number of phenotypes, but an identical number of randomly selected follow-up points led to different solutions. A variable selection algorithm identified 8 informative time points (months 18, 42, 57, 81, 91, 140, 157, and 166). The proportion of asthmatic patients at age 23 to 24 years differed between phenotypes, whereas lung function was lower among persistent wheezers.

CONCLUSIONS

Sample size, frequency, and timing of data collection have a major influence on the number and type of wheeze phenotypes identified by using LCA in longitudinal data.

Asthma: An Undermined State of Immunodeficiency

Asthma is a heterogeneous chronic respiratory disease characterized by an increased burden of infections. Respiratory tract infections associated with an increased risk for asthma especially when occurring in the first months of life, also represent the most common cause of asthma exacerbations. The association between asthma and the increased frequency of infections and microbiota dysbiosis might be explained by a common mechanism, such as an underlying immune system defect. Apart from the well-established association between primary immunodeficiencies and asthma, several alterations in the immune response following infection have also been observed in asthmatic patients. An impairment in lung epithelial barrier integrity exists and is associated with both an increased susceptibility to infections and the development of asthma. Asthmatic patients are also found to have a deficient interferon (IFN) response upon infection. Additionally, defects in Toll-like receptor (TLR) signaling are observed in asthma and are correlated with both recurrent infections and asthma development. In this review, we summarize the common pathophysiological background of asthma and infections, highlighting the importance of an underlying immune system defect that predispose individuals to recurrent infections and asthma.

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.

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.

Features of asthma which provide meaningful insights for understanding the disease heterogeneity

BACKGROUND

Data-driven methods such as hierarchical clustering (HC) and principal component analysis (PCA) have been used to identify asthma subtypes, with inconsistent results.

OBJECTIVE

To develop a framework for the discovery of stable and clinically meaningful asthma subtypes.

METHODS

We performed HC in a rich data set from 613 asthmatic children, using 45 clinical variables (Model 1), and after PCA dimensionality reduction (Model 2). Clinical experts then identified a set of asthma features/domains which informed clusters in the two analyses. In Model 3, we reclustered the data using these features to ascertain whether this improved the discovery process.

RESULTS

Cluster stability was poor in Models 1 and 2. Clinical experts highlighted four asthma features/domains which differentiated the clusters in two models: age of onset, allergic sensitization, severity, and recent exacerbations. In Model 3 (HC using these four features), cluster stability improved substantially. The cluster assignment changed, providing more clinically interpretable results. In a 5-cluster model, we labelled the clusters as: "Difficult asthma" (n = 132); "Early-onset mild atopic" (n = 210); "Early-onset mild non-atopic: (n = 153); "Late-onset" (n = 105); and "Exacerbation-prone asthma" (n = 13). Multinomial regression demonstrated that lung function was significantly diminished among children with "Difficult asthma"; blood eosinophilia was a significant feature of "Difficult," "Early-onset mild atopic," and "Late-onset asthma." Children with moderate-to-severe asthma were present in each cluster.

CONCLUSIONS AND CLINICAL RELEVANCE

An integrative approach of blending the data with clinical expert domain knowledge identified four features, which may be informative for ascertaining asthma endotypes. These findings suggest that variables which are key determinants of asthma presence, severity, or control may not be the most informative for determining asthma subtypes. Our results indicate that exacerbation-prone asthma may be a separate asthma endotype and that severe asthma is not a single entity, but an extreme end of the spectrum of several different asthma endotypes.

Disaggregating asthma: Big investigation versus big data

We are facing a major challenge in bridging the gap between identifying subtypes of asthma to understand causal mechanisms and translating this knowledge into personalized prevention and management strategies. In recent years, "big data" has been sold as a panacea for generating hypotheses and driving new frontiers of health care; the idea that the data must and will speak for themselves is fast becoming a new dogma. One of the dangers of ready accessibility of health care data and computational tools for data analysis is that the process of data mining can become uncoupled from the scientific process of clinical interpretation, understanding the provenance of the data, and external validation. Although advances in computational methods can be valuable for using unexpected structure in data to generate hypotheses, there remains a need for testing hypotheses and interpreting results with scientific rigor. We argue for combining data- and hypothesis-driven methods in a careful synergy, and the importance of carefully characterized birth and patient cohorts with genetic, phenotypic, biological, and molecular data in this process cannot be overemphasized. The main challenge on the road ahead is to harness bigger health care data in ways that produce meaningful clinical interpretation and to translate this into better diagnoses and properly personalized prevention and treatment plans. There is a pressing need for cross-disciplinary research with an integrative approach to data science, whereby basic scientists, clinicians, data analysts, and epidemiologists work together to understand the heterogeneity of asthma.

Asthma phenotypes in childhood

INTRODUCTION

Asthma is no longer thought of as a single disease, but rather a collection of varying symptoms expressing different disease patterns. One of the ongoing challenges is understanding the underlying pathophysiological mechanisms that may be responsible for the varying responses to treatment. Areas Covered: This review provides an overview of our current understanding of the asthma phenotype concept in childhood and describes key findings from both conventional and data-driven methods. Expert Commentary: With the vast amounts of data generated from cohorts, there is hope that we can elucidate distinct pathophysiological mechanisms, or endotypes. In return, this would lead to better patient stratification and disease management, thereby providing true personalised medicine.

Relationship between cytokine expression patterns and clinical outcomes: two population-based birth cohorts

BACKGROUND

Models that incorporate patterns of multiple cytokine responses to allergens, rather than individual cytokine production, may better predict sensitization and asthma.

OBJECTIVE

To characterize the patterns of peripheral blood mononuclear cells' (PBMCs) cytokine responses to house dust mite (HDM) allergens among children from two population-based birth cohorts using machine learning techniques.

METHODS

PBMCs collected at 8 years of age from the UK Manchester Asthma and Allergy Study (n = 268) and at 14 years of age from the Australian Raine Study (n = 1374) were cultured with HDM extract (10 μg/ml). Cytokine expression (IL-13, IL-5, IFN-γ, and IL10) was measured in the supernatant. Cytokine patterns were identified using a Gaussian mixture model clustering, and classification stability was assessed by bootstrapping.

RESULTS

A six-class model indicated complex latent structure of cytokine expression. Based on the characteristics of each class, we designated them as follows: 'Nonresponders' (n = 905, 55%); 'IL-10 responders' (n = 49, 3%); 'IFN-γ and IL-13 medium responders' (n = 56, 3.4%); 'IL-13 medium responders' (n = 351, 21.4%); 'IL-5 and IL-13 medium responders' (n = 77, 4.7%); and 'IL-13 and IL-5 high responders' (n = 204, 12.4%). 'IL-13 and IL-5 high responders' were at much higher risk of HDM sensitization and asthma compared to all other classes, with 88% of children assigned to this class being sensitized and 28.5% having asthma.

CONCLUSION

Using model-based clustering, we identified several distinct patterns of cytokine response to HDM and observed interplay between cytokine expression level, cytokine patterns (especially IL-13 and IL-5), and clinical outcomes. 'IL-13 and IL-5 high responders' class was strongly associated with HDM sensitization. However, among HDM-sensitized children, one-third showed no PBMC response to HDM, and the majority of HDM-sensitized children did not have asthma or wheeze. Our findings suggest that positive HDM 'allergy tests' and asthma are associated with a broad range of immunophenotypes, which may have important implications for the use of cytokine-targeted treatment approaches.

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 and Respiratory Allergy: What is the Link

Understanding the aetiology and progression of atopic dermatitis and respiratory allergy may elucidate early preventative and management strategies aimed towards reducing the global burden of asthma and allergic disease. In this article, we review the current opinion concerning the link between atopic dermatitis and the subsequent progression of respiratory allergies during childhood and into early adolescence. Advances in machine learning and statistical methodology have facilitated the discovery of more refined definitions of phenotypes for identifying biomarkers. Understanding the role of atopic dermatitis in the development of respiratory allergy may ultimately allow us to determine more effective treatment strategies, thus reducing the patient and economic burden associated with these conditions.

To what extent is allergen exposure a risk factor for the development of allergic disease?

The role of allergen exposure in the development of allergic disease has been a matter of considerable debate, and our understanding of the importance of allergens has evolved over the last 25 years. Several observational and primary prevention studies have investigated these relationships, and different studies reported inconsistent, and sometimes opposite findings. It has to be emphasized that a clear understanding of how aeroallergen exposure occurs, and accurate and reproducible measurement of exposure are essential prerequisites for understanding the role of exposure. However, our current understanding of how we get exposed to allergens is not based on solid evidence, but on a number of assumptions, and we urgently need to develop better proxy measures (or indices) of exposure. In addition, the relative importance of the timing of exposure (e.g. early compared to exposure in later life) is unknown. It is also unclear which route of exposure is the most relevant (e.g. inhaled vs. oral vs. transcutaneous). Available data suggest that the dose-response relationship between allergen exposure and allergic disease may differ between different allergens, dose ranges and exposure patterns, and these relationships may further differ between different populations and geographical areas. It is increasingly clear that childhood asthma and atopy are not single phenotypes, and it is likely that allergen exposure has different effect on distinct subgroups under the umbrella terms of 'sensitization' and 'asthma'. Susceptibility to allergen exposure, other environmental exposures and their interactions may also differ between individuals with different genetic predispositions. However, the precise nature of these complex relationships is unclear. We need a holistic approach offered by systems biology, with integration of information on the standardized and reliable measures of exposures (including allergens and other relevant exposures) with genetic and biological data to fully understand the role of allergens in the development of allergic disease.

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.

The Study Team for Early Life Asthma Research (STELAR) consortium 'Asthma e-lab': team science bringing data, methods and investigators together

We created Asthma e-Lab, a secure web-based research environment to support consistent recording, description and sharing of data, computational/statistical methods and emerging findings across the five UK birth cohorts. The e-Lab serves as a data repository for our unified dataset and provides the computational resources and a scientific social network to support collaborative research. All activities are transparent, and emerging findings are shared via the e-Lab, linked to explanations of analytical methods, thus enabling knowledge transfer. eLab facilitates the iterative interdisciplinary dialogue between clinicians, statisticians, computer scientists, mathematicians, geneticists and basic scientists, capturing collective thought behind the interpretations of findings.

Impact of rhinitis on asthma severity in school-age children

BACKGROUND

In a population-based sample of school-age children, we investigated factors associated with rhinitis, and differences between allergic and nonallergic rhinitis. Amongst children with asthma, we explored the association between rhinitis and asthma severity.

METHODS

Children participating in a birth cohort study (n = 906) were reviewed at age 8 years. Asthma was defined as at least two of the following three features: physician-diagnosed asthma, currently using asthma medication and current wheeze. We measured lung function (plethysmography and spirometry) and airway hyper-reactivity (AHR; methacholine challenge).

RESULTS

In the analysis adjusted for the presence of asthma, children with rhinitis had significantly higher AHR (P = 0.001). Maternal smoking and absence of breastfeeding were stronger predictors of nonallergic rhinitis, whereas current wheeze and eczema were stronger predictors of allergic rhinitis. Amongst asthmatics (n = 159), when compared to 76 children without rhinitis, those with rhinitis (n = 83) were 2.89-fold (95% CI 1.41-5.91) more likely to experience frequent attacks of wheezing, 3.44-fold (1.19-9.94) more likely to experience severe attacks of wheezing limiting speech, 10.14-fold (1.27-81.21) more likely to have frequent visits to their doctor because of asthma and nine-fold (1.11-72.83) more likely to miss school. Reported use of intranasal corticosteroids resulted in a numerically small, but consistent reduction in risk, rendering the associations between rhinitis and asthma severity nonsignificant.

CONCLUSION

We observed differences in risk factors and severity between allergic and nonallergic rhinitis. In children with asthma, rhinitis had adverse impact on asthma severity. The use of intranasal corticosteroids resulted in a small, but consistent reduction in the risk.

Unmet needs in respiratory diseases : "You can't know where you are going until you know where you have been"--Anonymous

The care of patients with respiratory diseases has improved vastly in the past 50 years. In spite of that, there are still massive challenges that have not been resolved. Although the incidence of tuberculosis has decreased in the developed world, it is still a significant public health problem in the rest of the world. There are still over 2 million deaths annually from tuberculosis, with most of these occurring in the developing world. Even with the development of new pharmaceuticals to treat tuberculosis, there is no indication that the disease will be eradicated. Respiratory syncytial virus, severe acute respiratory syndrome, and pertussis are other respiratory infectious diseases with special problems of their own, from vaccine development to vaccine coverage. Asthma, one of the most common chronic diseases in children, still accounts for significant mortality and morbidity, as well as high health care costs worldwide. Even in developed countries such as the USA, there are over 4,000 deaths per year. Severe asthma presents a special problem, but the question is whether there can be one treatment pathway for all patients with severe asthma. Severe asthma is a heterogeneous disease with many phenotypes and endotypes. The gene for cystic fibrosis was discovered over 24 years ago. The promise of gene therapy as a cure for the disease has fizzled out, and while new antimicrobials and other pharmaceuticals promise improved longevity and better quality of life, the average life span of a patient with cystic fibrosis is still at about 35 years. What are the prospects for gene therapy in the twenty-first century? Autoimmune diseases of the lung pose a different set of challenges, including the development of biomarkers to diagnose and monitor the disease and biological modulators to treat the disease.