Classification of Pediatric Asthma: From Phenotype Discovery to Clinical Practice

Identification of eczema clusters and their association with filaggrin and atopic comorbidities: analysis of five birth cohorts

BACKGROUND

Longitudinal modelling of the presence/absence of current eczema through childhood has identified similar phenotypes, but their characteristics often differ between studies.

OBJECTIVES

To demonstrate that a more comprehensive description of longitudinal pattern of symptoms may better describe trajectories than binary information on eczema presence.

METHODS

We derived six multidimensional variables of eczema spells from birth to 18 years of age (including duration, temporal sequencing and the extent of persistence/recurrence). Spells were defined as consecutive observations of eczema separated by no eczema across 5 epochs in five birth cohorts: infancy (first year); early childhood (age 2-3 years); preschool/early school age (4-5 years); middle childhood (8-10 years); adolescence (14-18 years). We applied Partitioning Around Medoids clustering on these variables to derive clusters of the temporal patterns of eczema. We then investigated the stability of the clusters, within-cluster homogeneity and associated risk factors, including FLG mutations.

RESULTS

Analysis of 7464 participants with complete data identified five clusters: (i) no eczema (51.0%); (ii) early transient eczema (21.6%); (iii) late-onset eczema (LOE; 8.1%); (iv) intermittent eczema (INT; 7.5%); and (v) persistent eczema (PE; 11.8%). There was very-high agreement between the assignment of individual children into clusters when using complete or imputed (n = 15 848) data (adjusted Rand index = 0.99; i.e. the clusters were very stable). Within-individual symptom patterns across clusters confirmed within-cluster homogeneity, with consistent patterns of symptoms among participants within each cluster and no overlap between the clusters. Clusters were characterized by differences in associations with risk factors (e.g. parental eczema was associated with all clusters apart from LOE; sensitization to inhalant allergens was associated with all clusters, with the highest risk in the PE cluster). All clusters apart from LOE were associated with FLG mutations. Of note, the strongest association was for PE [relative risk ratio (RRR) 2.70, 95% confidence interval (CI) 2.24-3.26; P < 0.001] followed by INT (RRR 2.29, 95% CI 1.82-2.88; P < 0.001).

CONCLUSIONS

Clustering of multidimensional variables identified stable clusters with different genetic architectures. Using multidimensional variables may capture eczema development and derive stable and internally homogeneous clusters. However, deriving homogeneous symptom clusters does not necessarily mean that these are underpinned by completely unique mechanisms.

Characteristics of Allergic, Eosinophilic, and Overlapping Asthma Phenotypes Among Pediatric Patients with Current Asthma: A Cross-Sectional Study from Saudi Arabia

Purpose

Asthma is one of the most common chronic diseases affecting 10%-30% of children in Saudi Arabia. Although data exist on adult asthma phenotyping and endotyping in Saudi Arabia, little is known about asthma phenotypes in Saudi children.

Patients and Methods

This cross-sectional study enrolled pediatric patients diagnosed with bronchial asthma and followed in the pediatric pulmonology clinic of the Abha Maternity and Children Hospital between August 2021 and May 2023.

Results

A total of 321 children (aged 5-14 years) were analyzed. The population was classified into allergic [169 (52.6%)], eosinophilic [144 (44.9%)], and overlapping allergic and eosinophilic asthma [97 (30.2%)] phenotypes. Regarding asthma severity, 35.5%, 50.2%, and 14.3% were classified as mild, moderate, and severe, respectively. Of the 321 patients in the study, 124 (38.6%) had at least one asthma exacerbation that required hospitalization. The number of reported missed school days in the previous year was 1571 days [190 (59.2%) patients reported at least one missed school day]. The factors associated with the likelihood of uncontrolled asthma for all study participants included: emergency room (ER) visit last year (OR = 3.7, 95% CI:0.6-15.9]), overlapping eosinophilic and allergic (OR = 3.2, 95% CI = 1.8-5.9), and allergic phenotype (OR = 2.7, 95% CI = 1.3-5.4). The level of asthma control differed significantly among the three asthma phenotypes (p = 0.037).

Conclusion

Allergic asthma is the most prevalent asthma phenotype in this study, followed by the eosinophilic phenotype. The research has also shown that several factors predict uncontrolled asthma, including a family history of asthma, previous admission to the PICU, and previous hospitalization ever. There is, therefore, a definite need for multicenter cohort studies to better understand the phenotypes and endotypes of childhood asthma, as it could offer therapeutic and prognostic relevance.

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.

Current state and prospects of artificial intelligence in allergy

The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.

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.

Molecular Mapping of Allergen Exposome among Different Atopic Phenotypes

Climate change and exposure to environmental pollutants play a key role in the onset and aggravation of allergic diseases. As different climate-dependent patterns of molecular immunoglobulin E (IgE) reactivity have been regionally described, we sought to investigate the evolving allergen exposome in distinctive allergic phenotypes and subtropical weather conditions through a Precision Allergy Molecular Diagnosis (PAMD@) model. Concurrent sensitization to several house dust mites (HDM) and storage mite molecules were broadly dominant in the investigated cohort, followed by the major cat allergen Fel d 1, and regardless of the basal allergic disease. Although a complex repertoire of allergens was recognized, a steadily increasing number of IgE binding molecules was associated with the complexity of the underlying atopic disease. Besides the highly prevalent IgE responses to major HDM allergens, Der p 21, Der p 5, and Der p 7 also showed up as serodominant molecules, especially in subjects bothered by asthma and atopic dermatitis. The accurate characterization of the external exposome at the molecular level and their putative role as clinically relevant allergens is essential to elucidate the phenotypic diversity of atopic disease in terms of personalized diagnosis and therapy.

A meta-analysis of genome-wide association studies of childhood wheezing phenotypes identifies ANXA1 as a susceptibility locus for persistent wheezing

Background

Many genes associated with asthma explain only a fraction of its heritability. Most genome-wide association studies (GWASs) used a broad definition of 'doctor-diagnosed asthma', thereby diluting genetic signals by not considering asthma heterogeneity. The objective of our study was to identify genetic associates of childhood wheezing phenotypes.

Methods

We conducted a novel multivariate GWAS meta-analysis of wheezing phenotypes jointly derived using unbiased analysis of data collected from birth to 18 years in 9568 individuals from five UK birth cohorts.

Results

Forty-four independent SNPs were associated with early-onset persistent, 25 with pre-school remitting, 33 with mid-childhood remitting, and 32 with late-onset wheeze. We identified a novel locus on chr9q21.13 (close to annexin 1 [ANXA1], p<6.7 × 10-9), associated exclusively with early-onset persistent wheeze. We identified rs75260654 as the most likely causative single nucleotide polymorphism (SNP) using Promoter Capture Hi-C loops, and then showed that the risk allele (T) confers a reduction in ANXA1 expression. Finally, in a murine model of house dust mite (HDM)-induced allergic airway disease, we demonstrated that anxa1 protein expression increased and anxa1 mRNA was significantly induced in lung tissue following HDM exposure. Using anxa1-/- deficient mice, we showed that loss of anxa1 results in heightened airway hyperreactivity and Th2 inflammation upon allergen challenge.

Conclusions

Targeting this pathway in persistent disease may represent an exciting therapeutic prospect.

Funding

UK Medical Research Council Programme Grant MR/S025340/1 and the Wellcome Trust Strategic Award (108818/15/Z) provided most of the funding for this study.

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.

Allergen Management in Children with Type 2-High Asthma

Children exposed to various indoor and outdoor allergens are placed at an increased risk of developing asthma in later life, with sensitization in these individuals being a strong predictor of disease morbidity. In addition, aeroallergen exposure influences asthma outcomes through an interplay with adverse determinants of health. The goal of this review is to provide an introductory overview of factors related to aeroallergen exposure in type 2-high childhood asthma. These include the relevance of exposure in asthma exacerbations and severity, and the evidence-base for avoidance and treatment for sensitization to these allergens. This review will focus on both indoor aeroallergens (house dust mite, pet, cockroach, mold, and rodent) and outdoor aeroallergens (pollens and molds). Treatment of aeroallergen sensitization in children with asthma includes avoidance and removal measures, although there is limited evidence of clinical benefit especially with single-strategy approaches. We will also address the interplay of aeroallergens and climate change, adverse social determinants, and the current COVID-19 pandemic, when we have seen a dramatic reduction in asthma exacerbations and emergency department visits among children. While there are many factors that are hypothesized to contribute to this reduction, among them is a reduced exposure to outdoor seasonal aeroallergens.

Childhood CCL18, CXCL10 and CXCL11 levels differentially relate to and predict allergy development

BACKGROUND

Chemokines are important mediators in immune cell recruitment, contributing to allergy development. However, extensive studies of chemokines in the circulation in relation to the presence and development of allergic diseases remain scarce. Our aim was to investigate associations of circulating allergy-related chemokines with the development of asthma and sensitization cross-sectionally and longitudinally in a population-based cohort.

METHODS

The chemokines CCL17, CCL22, CXCL10, CXCL11 and CCL18 were measured in plasma samples from children in the Manchester Asthma and Allergy Study. Samples were available from cord blood at birth (n = 376), age 1 (n = 195) and age 8 (n = 334). Cross-sectional and longitudinal association analyses were performed in relation to asthma and allergic sensitization, as well as allergic phenotype clusters previously derived using machine learning in the same study population.

RESULTS

In children with asthma and/or allergic sensitization, CCL18 levels were consistently elevated at 1 and/or 8 years of ages. In a longitudinal model including information on asthma from 4 time points (5, 8, 11 and 16 years of ages), we observed a significant association between increasing CCL18 levels at age 1 and a higher risk of asthma from early school age to adolescence (OR = 2.9, 95% CI 1.1-7.6, p = .028). We observed similar associations in longitudinal models for allergic sensitization. Asthma later in life was preceded by increased CXCL10 levels after birth and decreased CXCL11 levels at birth.

CONCLUSION

Elevated CCL18 levels throughout childhood precede the development of asthma and allergic sensitization. The Th1-associated chemokines CXCL10 and CXCL11 also associated with the development of both outcomes, with differential temporal effects.

Four subtypes of childhood allergic rhinitis identified by latent class analysis

BACKGROUND

Childhood allergic rhinitis (AR) is clinically heterogenous. We aimed to identify distinct phenotypes among children with AR using data-driven techniques and to ascertain their association with patterns of symptoms, allergic sensitization, and comorbidities.

METHODS

We recruited 510 children with physician-diagnosed AR, of whom 205 (40%) had asthma. Latent class analysis (LCA) was performed to identify latent structure within the data set using 17 variables (allergic conjunctivitis, eczema, asthma, family history of asthma, family history of allergic rhinitis, skin sensitization to 8 common allergens, tonsillectomy, adenoidectomy).

RESULTS

A four-class solution was selected as the optimal model based on statistical fit. We labeled latent classes as: (1) AR with grass mono-sensitization and conjunctivitis (n = 361, 70.8%); (2) AR with house dust mite sensitization and asthma (n = 75, 14.7%); (3) AR with pet and grass polysensitization and conjunctivitis (n = 35, 6.9%); and (4) AR among children with tonsils and adenoids removed (n = 39, 7.6%). Perennial AR was significantly more common among children in Class 2 (OR 5.83, 95% CI 3.42-9.94, p < .001) and Class 3 (OR 2.88, 95% CI 1.36-6.13, p = .006). Mild and intermittent AR symptoms were significantly more common in children in Class 2 compared to those in Class 1. AR was more severe in Class 1 compared to other 3 classes, indicating that upper respiratory symptoms are more severe among children with isolated seasonal rhinitis, than in those with rhinitis and coexisting asthma.

CONCLUSION

We have identified 4 phenotypes in school-age children with AR, which were associated with different patterns of clinical symptoms and comorbidities.

Four childhood atopic dermatitis subtypes identified from trajectory and severity of disease and internally validated in a large UK birth cohort

BACKGROUND

Atopic dermatitis (AD) disease activity and severity is highly variable during childhood. Early attempts to identify subtypes based on disease trajectory have assessed AD presence over time without incorporating severity.

OBJECTIVES

To identify childhood AD subtypes from symptom severity and trajectories, and determine associations with genetic risk factors, comorbidities and demographic and environmental variables.

METHODS

We split data from children in the Avon Longitudinal Study of Parents and Children birth cohort into development and validation sets. To identify subtypes, we ran latent class analyses in the development set on AD symptom reports up to age 14 years. We regressed identified subtypes on nongenetic variables in mutually adjusted, multiply imputed (genetic: unadjusted, complete case) multinomial regression analyses. We repeated analyses in the validation set and report confirmed results.

RESULTS

There were 11 866 children who contributed to analyses. We identified one Unaffected/Rare class (66% of children) and four AD subtypes: Severe-Frequent (4%), Moderate-Frequent (7%), Moderate-Declining (11%) and Mild-Intermittent (12%). Symptom patterns within the first two subtypes appeared more homogeneous than the last two. Filaggrin (FLG) null mutations, an AD polygenic risk score (PRS), being female, parental AD and comorbid asthma were associated with higher risk for some or all subtypes; FLG, AD-PRS and asthma associations were stronger along a subtype gradient arranged by increasing severity and frequency; FLG and AD-PRS further differentiated some phenotypes from each other.

CONCLUSIONS

Considering severity and AD trajectories leads to four well-defined and recognizable subtypes. The differential associations of risk factors among and between subtypes is novel and requires further research.

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.

The Hygiene Hypothesis and New Perspectives-Current Challenges Meeting an Old Postulate

During its 30 years history, the Hygiene Hypothesis has shown itself to be adaptable whenever it has been challenged by new scientific developments and this is a still a continuously ongoing process. In this regard, the mini review aims to discuss some selected new developments in relation to their impact on further fine-tuning and expansion of the Hygiene Hypothesis. This will include the role of recently discovered classes of innate and adaptive immune cells that challenges the old Th1/Th2 paradigm, the applicability of the Hygiene Hypothesis to newly identified allergy/asthma phenotypes with diverse underlying pathomechanistic endotypes, and the increasing knowledge derived from epigenetic studies that leads to better understanding of mechanisms involved in the translation of environmental impacts on biological systems. Further, we discuss in brief the expansion of the Hygiene Hypothesis to other disease areas like psychiatric disorders and cancer and conclude that the continuously developing Hygiene Hypothesis may provide a more generalized explanation for health burden in highly industrialized countries also relation to global changes.

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.

Innate and Adaptive Immunity: ILC2 and Th2 Cells in Upper and Lower Airway Allergic Diseases

Advances in our understanding of the immune system, with the recent discovery of a parallel set of innate T lymphocytes, the innate lymphocytes (ILCs), have led to a reassessment of the pathogenesis of allergic and eosinophilic airway disorders, including allergic rhinitis (AR), asthma, and chronic rhinosinusitis with nasal polyps. We review current understanding of both elements of type-2 inflammatory responses and their relative influence in these common conditions and consider possible impacts of this on treatment selection.

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.

Mite Molecular Profile in the Th2-Polarized Moderate-to-Severe Persistent Asthma Endotype Subjected to High Allergen Exposure

BACKGROUND

The association among the IgE responses to prevailing groups of house dust mite (HDM) allergens in the concurrent asthma phenotypes has not been determined.

OBJECTIVE

The aim of the present study lays on a component-resolved diagnosis (CRD) model to investigate the mite molecular signature in subjects with type-2 inflammation asthma.

METHODS

We selected patients showing a clinically relevant sensitization to HDMs with moderate-to-severe persistent asthma. Skin prick test (SPT) with standardized mite extracts, a broad customized CRD serum sIgE panel including 9 Dermatophagoides pteronyssinus allergens and the related protein allergenic characterization, was investigated in all serum samples.

RESULTS

Ninety out of 93 (96.77%) patients with a positive SPT to HDM showed a concordant sIgE (≥0.35 kUA/L) to the crude extract of D. pteronyssinus. Major allergens (Der p 2, Der p 23, and Der p 1) were present in >70% of all subjects, with mid-tier allergens (Der p 5, Der p 7, and Der p 21) reaching up to 51% in the present cohort. A complex pleomorphic repertoire of HDM molecules recognized by IgE was depicted, including 38 distinct profiles.

CONCLUSIONS AND CLINICAL RELEVANCE

The proposed CRD panel approach, containing the most prevalent HDM allergens, appeared to be sufficient to obtain a precise D. pteronyssinus molecular diagnosis in asthmatics with a climate-dependent high-mite allergen exposure and coexisting sensitization. A dominant role of both major and mid-tier allergens has been confirmed in moderate and severe persistent asthmatics with the preponderant Th2-high endotype.

Quantitative and semiquantitative estimates of mold exposure in infancy and childhood respiratory health

BACKGROUND

Previous epidemiologic studies of dampness and mold relied on metrics that did not fully assess exposure-response relationships. Our objective was to examine quantitative metrics of dampness and mold during infancy and respiratory health outcomes during childhood.

METHODS

In-home visits were conducted before age 1 for children in the Cincinnati Childhood Allergy and Air Pollution Study. Respiratory outcomes included age 3 wheeze and age 7 asthma and wheeze. The associations between home exposure and respiratory outcomes were evaluated for 779 children using logistic regression adjusting for household income, neighborhood socioeconomic status, and the presence of pests.

RESULTS

Children residing in homes with ≥0.29 m2 of moisture damage were significantly more likely to have wheezing at age 3 and persistent wheeze through age 7 (adjusted odds ratio [aOR] = 2.2; 95% confidence interval [CI] = 1.0, 4.3 and aOR = 3.2; CI = 1.3, 7.5, respectively). Additionally, homes having ≥0.19 m2 of mold damage were associated with wheezing at age 3 and early transient wheeze assessed at age 7 (aOR = 2.9; CI = 1.3, 6.4 and aOR = 3.5; CI = 1.5, 8.2, respectively). Mold damage <0.19 m2 and moisture damage <0.29 m2 were not associated with health outcomes. Mold and moisture damage were also not associated with asthma.

CONCLUSION

Our data indicate that only the highest categories analyzed for mold (≥0.19 m2) and moisture damage (≥0.29 m2) in homes at age 1 were significantly associated with wheeze at ages 3 and 7; however, data below these levels were too sparse to assess the shape of the relationship or explore potential health-relevant thresholds.

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.

The Syndrome We Agreed to Call Bronchiolitis

We are ignoring evidence suggesting that the diagnosis of bronchiolitis encompasses several diseases with distinct underlying mechanisms, considerable heterogeneity in treatment responses, and ultimately different therapeutic targets. Understanding this heterogeneity may be the only way to deliver appropriate, stratified treatments.

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.

Unsupervised Learning Techniques for the Investigation of Chronic Rhinosinusitis

OBJECTIVES

This article reviews the principles of unsupervised learning, a novel technique which has increasingly been reported as a tool for the investigation of chronic rhinosinusitis (CRS). It represents a paradigm shift from the traditional approach to investigating CRS based upon the clinically recognized phenotypes of "with polyps" and "without polyps" and instead relies upon the application of complex mathematical models to derive subgroups which can then be further examined. This review article reports on the principles which underlie this investigative technique and some of the published examples in CRS.

METHODS

This review summarizes the different types of unsupervised learning techniques which have been described and briefly expounds upon their useful applications. A literature review of studies which have unsupervised learning is then presented to provide a practical guide to its uses and some of the new directions of investigations suggested by their findings.

RESULTS

The commonest unsupervised learning technique applied to rhinology research is cluster analysis, which can be further subdivided into hierarchical and non-hierarchical approaches. The mathematical principles which underpin these approaches are explained within this article. Studies which have used these techniques can be broadly divided into those which have used clinical data only and that which includes biomarkers. Studies which include biomarkers adhere closely to the established canon of CRS disease phenotypes, while those that use clinical data may diverge from the typical "polyp versus non-polyp" phenotypes and reflect subgroups of patients who share common symptom modifiers.

SUMMARY

Artificial intelligence is increasingly influential in health care research and machine learning techniques have been reported in the investigation of CRS, promising several interesting new avenues for research. However, when critically appraising studies which use this technique, the reader needs to be au fait with the limitations and appropriate uses of its application.

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.

Does understanding endotypes translate to better asthma management options for all?

Despite the development of novel treatments, improvement in the design of delivery devices, and new technologies for monitoring and improving adherence, the burden of asthma is not decreasing. Predicting an individual patient's response to asthma drugs remains challenging, and the provision of personalized treatment remains elusive. Although biomarkers, such as allergic sensitization and blood eosinophilia, might be important predictors of response to inhaled corticosteroids in preschool children, these relatively cheap and available investigations are seldom used in clinical practice to select patients for corticosteroid prescription. However, for the majority of patients, response to different treatments cannot be accurately predicted. One of the key factors preventing further advances is the reductionist view of asthma as a single disease, which is forcing patients with different asthma subtypes into a single group for empiric treatment. This inevitably results in treatment failures and, for some, an unacceptable risk/benefit ratio. The approach to asthma today is an example of the traditional symptom (diagnosis)-based, one-size-fits-all approach rather than a stratified approach, and our guidelines-driven management based on a unitary diagnosis might not be the optimal way to deliver care. The only way to deliver stratified medicine and find a cure is through the understanding of asthma endotypes. We propose that the way to discover endotypes, biomarkers, and personalized treatments is through the iterative process based on interpretation of big data analytics from birth and patient cohorts, responses to treatments in randomized controlled trials, and in vitro mechanistic studies using human samples and experimental animal models, with technological and methodological advances at its core.

Asthma phenotypes and associated comorbidities in a large cohort of adolescents in Israel

Objectives: Asthma is a multifactorial, heterogeneous, complex and common chronic respiratory disease driven by diverse mechanisms. Although asthma presents various clinical forms with different levels of severity, it is unclear whether asthma severities are a consequence of disease management or varied etiologies. We sought to investigate this question.Methods: This article presents a cross-sectional study of 113,671 Israeli adolescents. Univariate and multivariable logistic regression models were performed to analyze the independent associations between mild asthma and moderate-to-severe asthma phenotypes and coexistent medical conditions within each gender separately. Hierarchical clustering of the odds ratios of the diverse statistically significant medical conditions associated with asthma severity-gender groups was also performed. We focused on the allergic and neurological-cognitive-mental disorders.Results: Among males, two associations were common to both asthma groups (atopic dermatitis and allergic rhinitis), five unique to mild asthma (urticaria/angioedema, Hymenoptera/bee allergies, allergic conjunctivitis, epilepsy and migraine) and two unique to moderate-to-severe asthma (learning disabilities and ADD/ADHD (Attention-deficit disorder/Attention-deficit/hyperactivity disorder)). Among females, two associations were common to both clinical asthma groups (allergic rhinitis and urticaria/angioedema), and five unique to moderate-to-severe asthma (atopic dermatitis, learning disabilities, ADD/ADHD, anxiety/mood disorders and migraine). Allergic rhinitis was the only condition to be associated with all four groups. Learning disabilities and ADD/ADHD were only associated with moderate-to-severe asthma (but not with mild asthma), in both males and females. Hierarchical clustering analysis uncovered two prominent clusters, separating mild from moderate-to-severe asthma.Conclusions: The differences between mild and moderate-to-severe asthma enhance asthma phenotype characterization, with respect to comorbidities, and indicate varied etiologies.