Asthma phenotypes in childhood

Understanding the heterogeneity of childhood allergic sensitization and its relationship with asthma

PURPOSE OF REVIEW

To review the current state of knowledge on the relationship between allergic sensitization and asthma; to lay out a roadmap for the development of IgE biomarkers that differentiate, in individual sensitized patients, whether their sensitization is important for current or future asthma symptoms, or has little or no relevance to the disease.

RECENT FINDINGS

The evidence on the relationship between sensitization and asthma suggests that some subtypes of allergic sensitization are not associated with asthma symptoms, whilst others are pathologic. Interaction patterns between IgE antibodies to individual allergenic molecules on component-resolved diagnostics (CRD) multiplex arrays might be hallmarks by which different sensitization subtypes relevant to asthma can be distinguished. These different subtypes of sensitization are associated amongst sensitized individuals at all ages, with different clinical presentations (no disease, asthma as a single disease, and allergic multimorbidity); amongst sensitized preschool children with and without lower airway symptoms, with different risk of subsequent asthma development; and amongst sensitized patients with asthma, with differing levels of asthma severity.

SUMMARY

The use of machine learning-based methodologies on complex CRD data can help us to design better diagnostic tools to help practising physicians differentiate between benign and clinically important sensitization.

Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets

Variability in case severity and in the range of symptoms experienced has been apparent from the earliest months of the COVID-19 pandemic. From a clinical perspective, symptom variability might indicate various routes/mechanisms by which infection leads to disease, with different routes requiring potentially different treatment approaches. For public health and control of transmission, symptoms in community cases were the prompt upon which action such as PCR testing and isolation was taken. However, interpreting symptoms presents challenges, for instance, in balancing the sensitivity and specificity of individual symptoms with the need to maximise case finding, whilst managing demand for limited resources such as testing. For both clinical and transmission control reasons, we require an approach that allows for the possibility of distinct symptom phenotypes, rather than assuming variability along a single dimension. Here we address this problem by bringing together four large and diverse datasets deriving from routine testing, a population-representative household survey and participatory smartphone surveillance in the United Kingdom. Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVID-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom-based management of other diseases and syndromes.

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.

Severe pediatric asthma endotypes: current limits and future perspectives

INTRODUCTION

Although rare, pediatric severe therapy-resistant asthma (STRA) is a highly heterogeneous, resource-demanding disease that differs significantly from severe adult asthma and whose pathogenesis is still poorly understood.

AREAS COVERED

This review summarizes the latest 10 years of English-written studies defining pediatric STRA endotypes using lung-specific techniques such as bronchoalveolar lavage and endobronchial biopsy. Results of the studies and limits on the field are discussed, together with some future perspectives.

EXPERT OPINION

Over the years, it has become increasingly clear that 'one size does not fit all" in asthma. However, "Does an extremely tailored size fit more than one?'. Only using multicentric, longitudinal pediatric studies, will we be able to answer. Three issues could be particularly critical for future research. First, to provide, if existing, a distinction between prepuberal STRA and puberal STRA endotypes to understand the transition from pediatric to adult STRA and to design effective, tailored therapies in adolescents, usually suffering from poorer asthma control. Second, design early treatments for pediatric airway remodeling to preserve lifelong good lung function. Finally, to better characterize inflammation before and during biological therapies, to provide clues on whether to stop or change treatments.

Differentially expressed microRNAs in peripheral blood cell are associated with downregulated expression of IgE in nonallergic childhood asthma

Childhood asthma is a heterogeneous disease characterized by chronic airway inflammation, leading to a broad range of clinical presentations. Nonallergic asthma is asthma without allergic sensitization. Both clinical manifestations and immunopathological mechanisms of nonallergic childhood asthma were rarely investigated. We aimed to compare the clinical features between nonallergic and allergic childhood asthma and apply microRNA to explore the underlying mechanism of nonallergic childhood asthma. We enrolled 405 asthmatic children (76 nonallergic, 52 allergic with total IgE < 150 IU/mL and 277 allergic with total IgE > 150 IU/mL). Clinical characteristics were compared between groups. Comprehensive miRNA sequencing (RNA-seq) was performed using peripheral blood from 11 nonallergic and 11 allergic patients with elevated IgE, respectively. Differentially expressed miRNA (DEmiRNA) were determined with DESeq2. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis was performed to determine functional pathways involved. Publicly available mRNA expression data was applied to investigate the predicted target mRNA networks via Ingenuity Pathway Analysis (IPA). The average age of nonallergic asthma was significantly younger (5.614 ± 2.743 vs 6.676 ± 3.118 years-old). Higher severity and worse control were more common in nonallergic asthma (two-way ANOVA, P < 0.0001). Long-term severity was higher, and intermittent attacks persisted in nonallergic patients. We identified 140 top DEmiRNAs based on false discovery rate (FDR) q-value < 0.001. Forty predicted target mRNA gene were associated with nonallergic asthma. The enriched pathway based on GO included Wnt signaling pathway. IgE expression was predicted to be downregulated by a network involving simultaneous interaction with IL-4, activation of IL-10 and inhibition of FCER2. Nonallergic childhood asthma were distinct in their younger age, higher long-term severity and more persistent course. Differentially expressed miRNA signatures associate with downregulation of total IgE expression and predicted target mRNA genes related molecular networks contribute to canonical pathways of nonallergic childhood asthma. We demonstrated the negative role of miRNAs involved in regulating IgE expression indicating differences between asthma phenotypes. Identification of biomarkers of miRNAs could contribute to understand the molecular mechanism of endotypes in nonallergic childhood asthma, which can potentially allow delivery of precision medicine to pediatric asthma.

Network analyses of upper and lower airway transcriptomes identify shared mechanisms among children with recurrent wheezing and school-age asthma

Background

Predicting which preschool children with recurrent wheezing (RW) will develop school-age asthma (SA) is difficult, highlighting the critical need to clarify the pathogenesis of RW and the mechanistic relationship between RW and SA. Despite shared environmental exposures and genetic determinants, RW and SA are usually studied in isolation. Based on network analysis of nasal and tracheal transcriptomes, we aimed to identify convergent transcriptomic mechanisms in RW and SA.

Methods

RNA-sequencing data from nasal and tracheal brushing samples were acquired from the Gene Expression Omnibus. Combined with single-cell transcriptome data, cell deconvolution was used to infer the composition of 18 cellular components within the airway. Consensus weighted gene co-expression network analysis was performed to identify consensus modules closely related to both RW and SA. Shared pathways underlying consensus modules between RW and SA were explored by enrichment analysis. Hub genes between RW and SA were identified using machine learning strategies and validated using external datasets and quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Finally, the potential value of hub genes in defining RW subsets was determined using nasal and tracheal transcriptome data.

Results

Co-expression network analysis revealed similarities in the transcriptional networks of RW and SA in the upper and lower airways. Cell deconvolution analysis revealed an increase in mast cell fraction but decrease in club cell fraction in both RW and SA airways compared to controls. Consensus network analysis identified two consensus modules highly associated with both RW and SA. Enrichment analysis of the two consensus modules indicated that fatty acid metabolism-related pathways were shared key signals between RW and SA. Furthermore, machine learning strategies identified five hub genes, i.e., CST1, CST2, CST4, POSTN, and NRTK2, with the up-regulated hub genes in RW and SA validated using three independent external datasets and qRT-PCR. The gene signatures of the five hub genes could potentially be used to determine type 2 (T2)-high and T2-low subsets in preschoolers with RW.

Conclusions

These findings improve our understanding of the molecular pathogenesis of RW and provide a rationale for future exploration of the mechanistic relationship between RW and SA.

Evolution of Eczema, Wheeze, and Rhinitis from Infancy to Early Adulthood: Four Birth Cohort Studies

Rationale: The relationship between eczema, wheeze or asthma, and rhinitis is complex, and epidemiology and mechanisms of their comorbidities is unclear. Objectives: To investigate within-individual patterns of morbidity of eczema, wheeze, and rhinitis from birth to adolescence/early adulthood. Methods: We investigated onset, progression, and resolution of eczema, wheeze, and rhinitis using descriptive statistics, sequence mining, and latent Markov modeling in four population-based birth cohorts. We used logistic regression to ascertain if early-life eczema or wheeze, or genetic factors (filaggrin [FLG] mutations and 17q21 variants), increase the risk of multimorbidity. Measurements and Main Results: Single conditions, although the most prevalent, were observed significantly less frequently than by chance. There was considerable variation in the timing of onset/remission/persistence/intermittence. Multimorbidity of eczema+wheeze+rhinitis was rare but significantly overrepresented (three to six times more often than by chance). Although infantile eczema was associated with subsequent multimorbidity, most children with eczema (75.4%) did not progress to any multimorbidity pattern. FLG mutations and rs7216389 were not associated with persistence of eczema/wheeze as single conditions, but both increased the risk of multimorbidity (FLG by 2- to 3-fold, rs7216389 risk variant by 1.4- to 1.7-fold). Latent Markov modeling revealed five latent states (no disease/low risk, mainly eczema, mainly wheeze, mainly rhinitis, multimorbidity). The most likely transition to multimorbidity was from eczema state (0.21). However, although this was one of the highest transition probabilities, only one-fifth of those with eczema transitioned to multimorbidity. Conclusions: Atopic diseases fit a multimorbidity framework, with no evidence for sequential atopic march progression. The highest transition to multimorbidity was from eczema, but most children with eczema (more than three-quarters) had no comorbidities.

Enigma variations: The multi-faceted problems of pre-school wheeze

Numerous publications on wheezing disorders in children younger than 6 years have appeared in the medical literature over the last decades with the aim of shedding light on the mechanistic pathways (endotypes) and treatment. Nevertheless, there is yet no consensus as to the appropriate way to manage preschool wheeze mainly because of the lack of a clear definition of "preschool asthma" and the paucity of scientific evidence concerning its underlying endotypes. A symptom-based approach is inadequate since the human airway can respond to external stimuli with a limited range of symptoms and signs, including cough and wheeze, and these manifestations represent the final expression of many clinical entities with potentially different pathophysiologies requiring different individualized treatments. Hence, new studies challenge the symptom-based approach and promote the importance of managing the wheezy child based on the "airway phenotype." This will enable the clinician to identify not only the child with a serious underlying pathology (e.g., a structural airway disorder or immunodeficiency) who is in need of prompt and specific treatment but also increase the specificity of treatment for the child with symptoms suggestive of an "asthma" syndrome. In the latter case, focus should be given to the identification of treatable traits. This review summarizes the current understanding in management of preschool wheezing and highlights the unmet need for further research.

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.

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.

Childhood wheeze trajectory-specific risk factors: A systematic review and meta-analysis

BACKGROUND

There is growing interest in the use of latent trajectory methodology to identify wheeze patterns in heterogeneous populations of children. This study systematically reviewed and meta-analyzed childhood wheeze trajectory studies to identify childhood wheeze trajectory group-specific risk factors among children from birth through to adolescence.

METHODS

We included studies that used latent trajectory methodology to identify wheeze trajectories and associated risk factors. We searched PubMed, EMBASE, and Google Scholar from 2000 through September 30, 2019, for relevant studies. The study was conducted according to the PRISMA recommendations.

RESULTS

Thirteen cohort studies conducted in eleven high-income countries were included in our meta-analysis with the length of follow-up ranging from 3 to 18 years. Five distinct latent wheeze trajectory groups were identified: Never/Infrequent, Early-Transient, Early-Persistent, Intermediate-Onset, and Late-Onset. We found moderate-to-strong evidence that family history of asthma predicted persistent childhood wheezing among male children but with lower risk among first-born children. There was weak-to-moderate evidence for childhood atopy, male sex, short duration of breastfeeding, tobacco exposure, daycare attendance, and having siblings as risk factors for Early-Transient wheezing; except for breastfeeding, these factors were also associated with intermediate and Late-Onset wheezing with varying effect sizes in high-risk vs general population cohorts.

CONCLUSIONS

Our findings confirm the consistency of wheeze trajectory groups defined by onset, peak prevalence, and duration; we also suggest a common nomenclature for future trajectory studies. With the exception of the relationship between a family history of asthma and persistent childhood wheezing, commonly suspected wheeze risk factors (childhood atopy, male sex, short duration of breastfeeding, tobacco exposure, daycare attendance, and having siblings) are not trajectory-specific and have varying effects in high-risk vs general population cohorts. Delineation of time-varying risk factor effects may be critical to the specificity of wheeze trajectory group prediction to better inform prognosis and targeted early preventive intervention among at-risk children.

Effects of low exposure to traffic related air pollution on childhood asthma onset by age 10 years

Although NO₂, a major traffic related air pollutant, has been associated with onset of childhood asthma, young children may be more susceptible to traffic related air pollution exposure compared to other individuals. We linked data from National Longitudinal Survey of Children and Youths Cycle 1-5 (1994-2003) and the National Air Pollution Surveillance Program to determine the association between NO₂ exposure and either early or late onset childhood asthma phenotypes. Children diagnosed with asthma from age 0-3 were defined as having early onset asthma. Children diagnosed with asthma from age 4-9 were defined as having late onset asthma. Mean NO₂ exposure for each quartile was 6.31 ppb, 9.45 ppb, 11.83 ppb, and 17.9 ppb. Higher levels of NO₂ exposure were more strongly associated with early childhood asthma (Quartile 3 OR: 2.11, 95% CI: 1.29, 3.44, Quartile 4 OR: 2.16, 95% CI: 1.27, 3.68) compared to the lowest level of NO₂ exposure (Quartile 1). No such association was observed with risk of late childhood asthma onset. Asthma susceptibility to NO₂ exposure may vary with the childhood developmental stage, and young children may be susceptible to NO₂ exposure at levels well below national and international guidelines. Our study emphasizes the importance of considering the timing of childhood asthma onset in future studies and confirms the increased risk of early onset of childhood asthma associated even with relatively low NO₂ exposure levels.

Atopic phenotypes and their implication in the atopic march

INTRODUCTION

Eczema, allergic rhinitis, and asthma are traditionally considered atopic (or allergic) diseases. They are complex, multifactorial, and are caused by a variety of different mechanisms, which result in multiple heterogeneous clinical phenotypes. Atopic march is usually interpreted as the sequential development of symptoms from eczema in infancy, to asthma, and then allergic rhinitis. Areas covered: The authors reviewed the evidence on the multimorbidity of eczema, asthma, and rhinitis, and the implication of results of data-driven analyses on the concept framework of atopic march. A literature search was conducted in the PubMed and Web of Science for peer-reviewed articles published until July 2020. Application of Bayesian machine learning framework to rich phenotypic data from birth cohorts demonstrated that the postulated linear progression of symptoms (atopic march) does not capture the heterogeneity of allergic phenotypes. Expert opinion: Eczema, wheeze, and rhinitis co-exist more often than would be expected by chance, but their relationship can be best understood in a multimorbidity framework, rather than through atopic march sequence. The observation of their co-occurrence does not imply any specific relationship between them, and certainly not a progressive or causal one. It is unlikely that a sngle mechanism such as allergic sensitization underpins different multimorbidity manifestations.

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.

Genome‑wide analysis of DNA methylation and gene expression changes in an ovalbumin‑induced asthma mouse model

The aim of the present study was to establish an integrated network of DNA methylation and RNA expression in an ovalbumin (OVA)-induced asthma model, and to investigate the epigenetically-regulated genes involved in asthma development. Genome-wide CpG-DNA methylation profiling was conducted through the use of a methylated DNA immunoprecipitation microarray and RNA sequencing was performed using three lung samples from mice with OVA-induced asthma. A total of 35,401 differentially methylated regions (DMRs) were identified between mice with OVA-induced asthma and control mice. Of these, 3,060 were located in promoter regions and 370 of the genes containing these DMRs demonstrated an inverse correlation between methylation and gene expression. Kyoto Encyclopedia of Genes and Genomes pathway analysis identified that 368 genes were upregulated or downregulated in OVA-induced asthma samples, including genes involved in ‘chemokine signalling pathway’, ‘focal adhesion’, ‘leukocyte transendothelial migration’ and ‘vascular smooth muscle contraction signaling’ pathways. Integrated network analysis identified four hub genes, consisting of three upregulated genes [forkhead box O1 (FOXO1), SP1 transcription factor (SP1) and amyloid β precursor protein (APP)], and one downregulated gene [RUNX family transcription factor 1 (RUNX1)], all of which demonstrated an association between DNA methylation and gene expression. These genes were highly interconnected nodes in the Ingenuity Pathway Analysis module and were functionally significant. A total of four interconnected hub genes, FOXO1, RUNX1, SP1 and APP, were identified from the integrated DNA methylation and gene expression networks involved in asthma development. These results suggested that modulating these four genes could effectively control the development of asthma.

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.

A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data

‘Asthma’ is a complex disease that encapsulates a heterogeneous group of phenotypes and endotypes. Research to understand these phenotypes has previously been based on longitudinal wheeze patterns or hypothesis-driven observational criteria. The aim of this study was to use data-driven machine learning to identify asthma and wheeze phenotypes in children based on symptom and symptom history data, and, to further characterize these phenotypes. The study population included an asthma-rich population of twins in Sweden aged 9–15 years (n = 752). Latent class analysis using current and historical clinical symptom data generated asthma and wheeze phenotypes. Characterization was then performed with regression analyses using diagnostic data: lung function and immunological biomarkers, parent-reported medication use and risk-factors. The latent class analysis identified four asthma/wheeze phenotypes: early transient wheeze (15%); current wheeze/asthma (5%); mild asthma (9%), moderate asthma (10%) and a healthy phenotype (61%). All wheeze and asthma phenotypes were associated with reduced lung function and risk of hayfever compared to healthy. Children with mild and moderate asthma phenotypes were also more likely to have eczema, allergic sensitization and a family history of asthma. Furthermore, those with moderate asthma phenotype had a higher eosinophil concentration (β 0.21, 95%CI 0.12, 0.30) compared to healthy and used short-term relievers at a higher rate than children with mild asthma phenotype (RR 2.4, 95%CI 1.2–4.9). In conclusion, using a data driven approach we identified four wheeze/asthma phenotypes which were validated with further characterization as unique from one another and which can be adapted for use by the clinician or researcher.

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.

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.

Validation of childhood asthma predictive tools: A systematic review

BACKGROUND

There is uncertainty about the clinical usefulness of currently available asthma predictive tools. Validation of predictive tools in different populations and clinical settings is an essential requirement for the assessment of their predictive performance, reproducibility and generalizability. We aimed to critically appraise asthma predictive tools which have been validated in external studies.

METHODS

We searched MEDLINE and EMBASE (1946-2017) for all available childhood asthma prediction models and focused on externally validated predictive tools alongside the studies in which they were originally developed. We excluded non-English and non-original studies. PROSPERO registration number is CRD42016035727.

RESULTS

From 946 screened papers, eight were included in the review. Statistical approaches for creation of prediction tools included chi-square tests, logistic regression models and the least absolute shrinkage and selection operator. Predictive models were developed and validated in general and high-risk populations. Only three prediction tools were externally validated: the Asthma Predictive Index, the PIAMA and the Leicester asthma prediction tool. A variety of predictors has been tested, but no studies examined the same combination. There was heterogeneity in definition of the primary outcome among development and validation studies, and no objective measurements were used for asthma diagnosis. The performance of tools varied at different ages of outcome assessment. We observed a discrepancy between the development and validation studies in the tools' predictive performance in terms of sensitivity and positive predictive values.

CONCLUSIONS

Validated asthma predictive tools, reviewed in this paper, provided poor predictive accuracy with performance variation in sensitivity and positive predictive value.

Does early onset asthma increase childhood obesity risk? A pooled analysis of 16 European cohorts

The parallel epidemics of childhood asthma and obesity over the past few decades have spurred research into obesity as a risk factor for asthma. However, little is known regarding the role of asthma in obesity incidence. We examined whether early-onset asthma and related phenotypes are associated with the risk of developing obesity in childhood.This study includes 21 130 children born from 1990 to 2008 in Denmark, France, Germany, Greece, Italy, The Netherlands, Spain, Sweden and the UK. We followed non-obese children at 3-4 years of age for incident obesity up to 8 years of age. Physician-diagnosed asthma, wheezing and allergic rhinitis were assessed up to 3-4 years of age.Children with physician-diagnosed asthma had a higher risk for incident obesity than those without asthma (adjusted hazard ratio (aHR) 1.66, 95% CI 1.18-2.33). Children with active asthma (wheeze in the last 12 months and physician-diagnosed asthma) exhibited a higher risk for obesity (aHR 1.98, 95% CI 1.31-3.00) than those without wheeze and asthma. Persistent wheezing was associated with increased risk for incident obesity compared to never wheezers (aHR 1.51, 95% CI 1.08-2.09).Early-onset asthma and wheezing may contribute to an increased risk of developing obesity in later childhood.

Controversies and opportunities in severe asthma

PURPOSE OF REVIEW

Despite currently available treatments, many asthma patients remain inadequately controlled, but identifying distinct patient populations (phenotypes/endotypes) may optimize their management. This review discusses some of the controversies and opportunities for improved disease control in severe asthma.

RECENT FINDINGS

Currently approved anti-immunoglobulin E and anti-interleukin 5 biologics, which target specific pathways instead of using a 'one size fits all' strategy, are efficacious and well tolerated therapies for severe asthma. The appropriate use of these biologics, and of those in development (e.g., benralizumab and dupilumab), should be aided by further understanding of asthma phenotypes and endotypes, utilizing appropriate biomarkers.Oral corticosteroids are often added as maintenance therapy for patients with severe uncontrolled asthma, but their use is associated with significant adverse effects and should be considered a last option. The true cost of this therapy, including the cost of morbidities associated with its use, remains to be determined.Severe asthma in pediatrics poses a unique opportunity for possible prevention strategies and the potential for primary prevention. Although several avenues for primary prevention are being explored and are out of the scope of this review, we focus our discussion on the use of omalizumab, which has been recently explored in clinical trials.

SUMMARY

Appropriate use of biologics in severe asthma should be supported by further understanding of biomarkers predicting response to targeted therapy. Because of their association with significant adverse effects, add-on oral corticosteroids should be considered a last treatment option for patients with uncontrolled severe asthma. Finally, severe asthma in pediatrics poses a unique opportunity for potential prevention strategies.

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.