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

Lung function trajectories from school age to adulthood and their relationship with markers of cardiovascular disease risk

RATIONALE

Lung function in early adulthood is associated with subsequent adverse health outcomes.

OBJECTIVES

To ascertain whether stable and reproducible lung function trajectories can be derived in different populations and investigate their association with objective measures of cardiovascular structure and function.

METHODS

Using latent profile modelling, we studied three population-based birth cohorts with repeat spirometry data from childhood into early adulthood to identify trajectories of forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC). We used multinomial logistic regression models to investigate early-life predictors of the derived trajectories. We then ascertained the extent of the association between the derived FEV1/FVC trajectories and blood pressure and echocardiographic markers of increased cardiovascular risk and stroke in ~3200 participants at age 24 years in one of our cohorts.

RESULTS

We identified four FEV1/FVC trajectories with strikingly similar latent profiles across cohorts (pooled N=6377): above average (49.5%); average (38.3%); below average (10.6%); and persistently low (1.7%). Male sex, wheeze, asthma diagnosis/medication and allergic sensitisation were associated with trajectories with diminished lung function in all cohorts. We found evidence of an increase in cardiovascular risk markers ascertained by echocardiography (including left ventricular mass indexed to height and carotid intima-media thickness) with decreasing FEV1/FVC (with p values for the mean crude effects per-trajectory ranging from 0.10 to p<0.001). In this analysis, we considered trajectories as a pseudo-continuous variable; we confirmed the assumption of linearity in all the regression models.

CONCLUSIONS

Childhood lung function trajectories may serve as predictors in the development of not only future lung disease, but also the cardiovascular disease and multimorbidity in adulthood.

Obstructive and restrictive spirometry from school age to adulthood: three birth cohort studies

Background

Spirometric obstruction and restriction are two patterns of impaired lung function which are predictive of poor health. We investigated the development of these phenotypes and their transitions through childhood to early adulthood.

Methods

In this study, we analysed pooled data from three UK population-based birth cohorts established between 1989 and 1995. We applied descriptive statistics, regression modelling and data-driven modelling to data from three population-based birth cohorts with at least three spirometry measures from childhood to adulthood (mid-school: 8-10 years, n = 8404; adolescence: 15-18, n = 5764; and early adulthood: 20-26, n = 4680). Participants were assigned to normal, restrictive, and obstructive spirometry based on adjusted regression residuals. We considered two transitions: from 8-10 to 15-18 and from 15-18 to 20-26 years.

Findings

Obstructive phenotype was observed in ∼10%, and restrictive in ∼9%. A substantial proportion of children with impaired lung function in school age (between one third in obstructive and a half in restricted phenotype) improved and achieved normal and stable lung function to early adulthood. Of those with normal lung function in school-age, <5% declined to adulthood. Underweight restrictive and obese obstructive participants were less likely to transit to normal. Maternal smoking during pregnancy and current asthma diagnosis increased the risk of persistent obstruction and worsening. Significant associate of worsening in restrictive phenotypes was lower BMI at the first lung function assessment. Data-driven methodologies identified similar risk factors for obstructive and restrictive clusters.

Interpretation

The worsening and improvement in obstructive and restrictive spirometry were observed at all ages. Maintaining optimal weight during childhood and reducing maternal smoking during pregnancy may reduce spirometry obstruction and restriction and improve lung function.

Funding

MRC Grant MR/S025340/1.

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.

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.

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.

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.

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.

Considering biomarkers in asthma disease severity

Amongst patients with asthma, reliance on the type/dose of prescribed medication and symptom control does not adequately capture those at risk of adverse outcomes, and we need biomarkers for risk and treatment stratification which are consistently accurate, readily quantifiable and reproducible. The majority of patients with severe asthma, regardless of age, have predominant type-2 (T2) inflammation mediated disease, making airway/blood eosinophils, FeNO, periostin and/or allergic sensitization potentially important biomarkers for severe disease. In both adult and pediatric asthma, there is scope to improve prediction of severe attacks by using a composite T2 biomarkers of blood eosinophils and FeNO. Technological advances in component-resolved diagnostics (CRD) microarray technologies coupled with the development of interpretation software offer a possibility to use CRD as biomarkers of asthma severity amongst sensitized asthmatics. Genetic predisposition and polygenic risk scores of relevant traits (e.g., lung function, host immune responses, biomarkers of exposure from the indoor and outdoor environment, infection and microbial dysbiosis) may also contribute to prediction algorithms. We challenge the idea that asthma can be accurately defined in an individual patient by a discrete and static "endotype" (e.g., T2-high asthma). As we traverse the new era of molecular endotyping in asthma, we need to understand how relevant mechanisms impact patient outcomes, and in parallel develop new tools and approaches to stratify therapies and define individual patient trajectories.

Impact of early life exposures on respiratory disease

The antecedents of asthma and chronic obstructive pulmonary disease (COPD) lie before school age. Adverse effects are transgenerational, antenatal and in the preschool years. Antenatal adverse effects impair spirometry by causing low birth weight, altered lung structure and immune function, and sensitizing the foetus to later insults. The key stages of normal lung health are lung function at birth, lung growth to a plateau age 20-25 years, and the phase of decline thereafter; contrary to perceived wisdom, accelerated decline is not related to smoking. There are different trajectories of lung function. Lung function usually tracks from preschool to late middle age. Asthma is driven by antenatal and early life influences. The airflow obstruction, emphysema and multi-morbidity of COPD all start early. Failure to reach a normal plateau and accelerated decline in lung function are risk factors for COPD. Airway disease cannot be prevented in adult life; prevention must start early.

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.

Characterization of Asthma Trajectories from Infancy to Young Adulthood

BACKGROUND

Development of asthma is complicated by the multidimensional nature of the disease.

OBJECTIVE

To identify and characterize trajectories of asthma from infancy to young adulthood, and their associations with lung function and inflammatory and respiratory markers in adolescence and young adulthood.

METHODS

A latent class analysis was performed in a population-based cohort (N = 4089). Parental and self-reported symptoms of asthma were used to investigate asthma development. We characterized background factors, allergic comorbidity, and IgE sensitization and investigated associations with asthma markers.

RESULTS

A 4-class solution of asthma trajectories was identified: never/infrequent (n = 3291 [80.4%]), early-onset transient (n = 307 [7.5%]), adolescent-onset (n = 261 [6.4%]), and persistent asthma (n = 230 [5.6%]). Uncontrolled asthma was equally prevalent in the adolescent-onset and persistent asthma trajectory groups, at both age 16 (41.7% vs 42.4%; P = .90) and 24 years (53.7% vs 52.4%; P = .81). The persistent asthma trajectory group had a higher proportion of eosinophil counts greater than or equal to 0.3 (10⁹ cells/L) at age 24 years compared with the adolescent-onset trajectory group (31.0% vs 18.5%; P < .01).

CONCLUSIONS

The adolescent-onset and persistent asthma trajectory groups had equal burdens of asthma control in adolescence and young adulthood. However, the persistent asthma trajectory group showed more signs of type 2 inflammation than the adolescent-onset trajectory group. This unbiased approach highlights the need of identifying patients with adolescent asthma to optimize care, because they suffer the same lack of asthma control as those with persistent asthma.

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.

Longitudinal trajectories of severe wheeze exacerbations from infancy to school age and their association with early-life risk factors and late asthma outcomes

INTRODUCTION

Exacerbation-prone asthma subtype has been reported in studies using data-driven methodologies. However, patterns of severe exacerbations have not been studied.

OBJECTIVE

To investigate longitudinal trajectories of severe wheeze exacerbations from infancy to school age.

METHODS

We applied longitudinal k-means clustering to derive exacerbation trajectories among 887 participants from a population-based birth cohort with severe wheeze exacerbations confirmed in healthcare records. We examined early-life risk factors of the derived trajectories, and their asthma-related outcomes and lung function in adolescence.

RESULTS

498/887 children (56%) had physician-confirmed wheeze by age 8 years, of whom 160 had at least one severe exacerbation. A two-cluster model provided the optimal solution for severe exacerbation trajectories among these 160 children: "Infrequent exacerbations (IE)" (n = 150, 93.7%) and "Early-onset frequent exacerbations (FE)" (n = 10, 6.3%). Shorter duration of breastfeeding was the strongest early-life risk factor for FE (weeks, median [IQR]: FE, 0 [0-1.75] vs. IE, 6 [0-20], P < .001). Specific airway resistance (sR_aw ) was significantly higher in FE compared with IE trajectory throughout childhood. We then compared children in the two exacerbation trajectories with those who have never wheezed (NW, n = 389) or have wheezed but had no severe exacerbations (WNE, n = 338). At age 8 years, FEV₁ /FVC was significantly lower and FeNO significantly higher among FE children compared with all other groups. By adolescence (age 16), subjects in FE trajectory were significantly more likely to have current asthma (67% FE vs. 30% IE vs. 13% WNE, P < .001) and use inhaled corticosteroids (77% FE vs. 15% IE vs. 18% WNE, P < .001). Lung function was significantly diminished in the FE trajectory (FEV₁ /FVC, mean [95%CI]: 89.9% [89.3-90.5] vs. 88.1% [87.3-88.8] vs. 85.1% [83.4-86.7] vs. 74.7% [61.5-87.8], NW, WNE, IE, FE respectively, P < .001).

CONCLUSION

We have identified two distinct trajectories of severe exacerbations during childhood with different early-life risk factors and asthma-related outcomes in adolescence.

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.

Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv

BACKGROUND

The automation of data analysis in the form of scientific workflows has become a widely adopted practice in many fields of research. Computationally driven data-intensive experiments using workflows enable automation, scaling, adaptation, and provenance support. However, there are still several challenges associated with the effective sharing, publication, and reproducibility of such workflows due to the incomplete capture of provenance and lack of interoperability between different technical (software) platforms.

RESULTS

Based on best-practice recommendations identified from the literature on workflow design, sharing, and publishing, we define a hierarchical provenance framework to achieve uniformity in provenance and support comprehensive and fully re-executable workflows equipped with domain-specific information. To realize this framework, we present CWLProv, a standard-based format to represent any workflow-based computational analysis to produce workflow output artefacts that satisfy the various levels of provenance. We use open source community-driven standards, interoperable workflow definitions in Common Workflow Language (CWL), structured provenance representation using the W3C PROV model, and resource aggregation and sharing as workflow-centric research objects generated along with the final outputs of a given workflow enactment. We demonstrate the utility of this approach through a practical implementation of CWLProv and evaluation using real-life genomic workflows developed by independent groups.

CONCLUSIONS

The underlying principles of the standards utilized by CWLProv enable semantically rich and executable research objects that capture computational workflows with retrospective provenance such that any platform supporting CWL will be able to understand the analysis, reuse the methods for partial reruns, or reproduce the analysis to validate the published findings.

Early-life inhalant allergen exposure, filaggrin genotype, and the development of sensitization from infancy to adolescence

Background

We hypothesized that filaggrin (FLG) loss-of-function mutations modify the effect of allergen exposure on the development of allergic sensitization.

Objective

We sought to determine whether early-life exposure to inhalant allergens increases the risk of specific sensitization and whether FLG mutations modulate these odds.

Methods

In a population-based birth cohort we measured mite, cat, and dog allergen levels in dust samples collected from homes within the first year of life. Sensitization was assessed at 6 time points between infancy and age 16 years. Genotyping was performed for 6 FLG mutations.

Results

In the longitudinal multivariable model (age 1-16 years), we observed a significant interaction between FLG and Fel d 1 exposure on cat sensitization, with the effect of exposure being significantly greater among children with FLG mutations compared with those without (odds ratio, 1.36; 95% CI, 1.02-1.80; P = .035). The increase in risk of mite sensitization with increasing Der p 1 exposure was consistently greater among children with FLG mutations, but the interaction did not reach statistical significance. Different associations were observed for dogs: there was a significant interaction between FLG and dog ownership, but the risk of sensitization to any allergen was significantly lower among children with FLG mutations who were exposed to a dog in infancy (odds ratio, 0.16; 95% CI, 0.03-0.86; P = .03).

Conclusions

FLG loss-of-function mutations modify the relationship between allergen exposure and sensitization, but effects differ at different ages and between different allergens.

Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond

The last 6 years have seen sustained investment in health data science in the United Kingdom and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and well-being. However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper, we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency. We believe a step change can be achieved through meaningful stakeholder involvement at every stage of research planning, design, and execution and team-based data science, as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social licence for health data research and ensure transparent and secure data usage for public benefit.

Different definitions of atopic dermatitis: impact on prevalence estimates and associated risk factors

Background

There is no objective test that can unequivocally confirm the diagnosis of atopic dermatitis (AD), and no uniform clinical definition.

Objectives

To investigate to what extent operational definitions of AD cause fluctuation in the prevalence estimates and the associated risk factors.

Methods

We first reviewed the operational definitions of AD used in the literature. We then tested the impact of the choice of the most common definitions of ‘cases’ and ‘controls’ on AD prevalence estimates and associated risk factors (including filaggrin mutations) among children aged 5 years in two population‐based birth cohorts: the Manchester Asthma and Allergy Study (MAAS) and Asthma in Ashford. Model performance was measured by the percentage of children within an area of clinical indecision (defined as having a posterior probability of AD between 25% and 60%).

Results

We identified 59 different definitions of AD across 45 reviewed studies. Of those, we chose four common ‘case’ definitions and two definitions of ‘controls’. The prevalence estimates using different case definitions ranged between 22% and 33% in MAAS, and between 12% and 22% in Ashford. The area of clinical indecision ranged from 32% to 44% in MAAS and from 9% to 29% in Ashford. Depending on the case definition used, the associations with filaggrin mutations varied, with odds ratios (95% confidence intervals) ranging from 1·8 (1·1–2·9) to 2·2 (1·3–3·7) in MAAS and 1·7 (0·8–3·7) to 2·3 (1·2–4·5) in Ashford. Associations with filaggrin mutations also differed when using the same ‘case’ definition but different definitions of ‘controls’.

Conclusions

Use of different definitions of AD results in substantial differences in prevalence estimates, the performance of prediction models and association with risk factors.

What's already known about this topic?

There is no objective test that can unequivocally confirm the diagnosis of atopic dermatitis (AD) and no uniform clinical definition.

This results in different definitions utilized in AD studies, raising concerns on the generalizability of the results and comparability across different studies.

What does this study add?

This study has shown that different definitions of ‘cases’ and ‘controls’ have major impacts upon prevalence estimates and associations with risk factors, including genetics, in two population‐based birth cohorts.

These findings suggest the importance of developing a consensus on AD definitions of both ‘controls’ and ‘cases’ to minimize biases in studies.

https://www.bjdonline.com/article/different-definitions-of-atopic-dermatitis-impact-on-prevalence-estimates-and-associated-risk-factors/

Linked Comment: https://doi.org/10.1111/bjd.18303.

https://doi.org/10.1111/bjd.18571 available online

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.

The Children's Respiratory and Environmental Workgroup (CREW) birth cohort consortium: design, methods, and study population

BACKGROUND

Single birth cohort studies have been the basis for many discoveries about early life risk factors for childhood asthma but are limited in scope by sample size and characteristics of the local environment and population. The Children's Respiratory and Environmental Workgroup (CREW) was established to integrate multiple established asthma birth cohorts and to investigate asthma phenotypes and associated causal pathways (endotypes), focusing on how they are influenced by interactions between genetics, lifestyle, and environmental exposures during the prenatal period and early childhood.

METHODS AND RESULTS

CREW is funded by the NIH Environmental influences on Child Health Outcomes (ECHO) program, and consists of 12 individual cohorts and three additional scientific centers. The CREW study population is diverse in terms of race, ethnicity, geographical distribution, and year of recruitment. We hypothesize that there are phenotypes in childhood asthma that differ based on clinical characteristics and underlying molecular mechanisms. Furthermore, we propose that asthma endotypes and their defining biomarkers can be identified based on personal and early life environmental risk factors. CREW has three phases: 1) to pool and harmonize existing data from each cohort, 2) to collect new data using standardized procedures, and 3) to enroll new families during the prenatal period to supplement and enrich extant data and enable unified systems approaches for identifying asthma phenotypes and endotypes.

CONCLUSIONS

The overall goal of CREW program is to develop a better understanding of how early life environmental exposures and host factors interact to promote the development of specific asthma endotypes.

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.

Cytokines and Chemokines as Biomarkers of Future Asthma

Antenatal and preschool factors are key in determining the progression to pre-school wheeze and eosinophilic school age asthma. The conventional view of eosinophilic asthma is that airway inflammation is the fundamental underlying abnormality, and airway inflammation and hyper-responsiveness are secondary; in fact, these three are parallel processes. Very early structural changes, independent of inflammation and infection, are associated with early airway hyper-responsiveness and later adverse respiratory outcomes. There is a bidirectional relationship between structural airway wall changes and airway inflammation, with airway contraction per se leading to the release of growth factors, and inflammatory pathways promoting airway remodeling. Early viral infection (and increasingly being appreciated, bacterial infection) is important in wheeze outcomes. There is evidence of abnormal immune function including cytokine release before the onset of viral infections. However, viral infections may also have prolonged effects on the host immune system, and the evidence for beneficial and adverse effects of viral infection is conflicting. In older children and adults, asthmatic epithelial cells show impaired interferon responses to viral infection, but only in the presence of uncontrolled type 2 inflammation, implying these are secondary phenomena. There are also compelling data relating the innate immune system to later asthma and atopy, and animal studies suggest that the effects of a high endotoxin, microbiologically diverse environment may be modulated via the epithelial alarmin IL-33. Whereas, previously only viral infection was thought to be important, early bacterial colonization of the upper airway is coming to the fore, associated with a mixed pattern of TH1/TH2/TH17 cytokine secretion, and adverse long term outcomes. Bacterial colonization is probably a marker of a subtle immune deficiency, rather than directly causal. The airway and gut microbiome critically impacts the development of Type 2 inflammatory responses. However, Type 2 inflammatory cytokines, which are critical both to progression from pre-school wheeze to eosinophilic asthma, and sustaining the eosinophilic asthmatic state, are not implicated in the very early development of the disease. Taken together, the evidence is that the earliest cytokine and chemokine signals will come from the study of bronchial epithelial cell function and their interactions with viruses and the microbiome.

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.

Big data hurdles in precision medicine and precision public health

BACKGROUND

Nowadays, trendy research in biomedical sciences juxtaposes the term 'precision' to medicine and public health with companion words like big data, data science, and deep learning. Technological advancements permit the collection and merging of large heterogeneous datasets from different sources, from genome sequences to social media posts or from electronic health records to wearables. Additionally, complex algorithms supported by high-performance computing allow one to transform these large datasets into knowledge. Despite such progress, many barriers still exist against achieving precision medicine and precision public health interventions for the benefit of the individual and the population.

MAIN BODY

The present work focuses on analyzing both the technical and societal hurdles related to the development of prediction models of health risks, diagnoses and outcomes from integrated biomedical databases. Methodological challenges that need to be addressed include improving semantics of study designs: medical record data are inherently biased, and even the most advanced deep learning's denoising autoencoders cannot overcome the bias if not handled a priori by design. Societal challenges to face include evaluation of ethically actionable risk factors at the individual and population level; for instance, usage of gender, race, or ethnicity as risk modifiers, not as biological variables, could be replaced by modifiable environmental proxies such as lifestyle and dietary habits, household income, or access to educational resources.

CONCLUSIONS

Data science for precision medicine and public health warrants an informatics-oriented formalization of the study design and interoperability throughout all levels of the knowledge inference process, from the research semantics, to model development, and ultimately to implementation.

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

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

The use of pharmacogenomics, epigenomics, and transcriptomics to improve childhood asthma management: Where do we stand?

Asthma is a complex multifactorial disease and it is the most common chronic disease in children. There is a high variability in response to asthma treatment, even in patients with good adherence to maintenance treatment, and a correct inhalation technique. Distinct underlying disease mechanisms in childhood asthma might be the reason of this heterogeneity. A deeper knowledge of the underlying molecular mechanisms of asthma has led to the recent development of advanced and mechanism-based treatments such as biologicals. However, biologicals are recommended only for patients with specific asthma phenotypes who remain uncontrolled despite high dosages of conventional asthma treatment. One of the main unmet needs in their application is lack of clinically available biomarkers to individualize pediatric asthma management and guide treatment. Pharmacogenomics, epigenomics, and transcriptomics are three omics fields that are rapidly advancing and can provide tools to identify novel asthma mechanisms and biomarkers to guide treatment. Pharmacogenomics focuses on variants in the DNA, epigenomics studies heritable changes that do not involve changes in the DNA sequence but lead to alteration of gene expression, and transcriptomics investigates gene expression by studying the complete set of mRNA transcripts in a cell or a population of cells. Advances in high-throughput technologies and statistical tools together with well-phenotyped patient inclusion and collaborations between different centers will expand our knowledge of underlying molecular mechanisms involved in disease onset and progress. Furthermore, it could help to select and stratify appropriate therapeutic strategies for subgroups of patients and hopefully bring precision medicine to daily practice.

Lung function trajectories from pre-school age to adulthood and their associations with early life factors: a retrospective analysis of three population-based birth cohort studies

BACKGROUND

Maximal lung function in early adulthood is an important determinant of mortality and COPD. We investigated whether distinct trajectories of lung function are present during childhood and whether these extend to adulthood and infancy.

METHODS

To ascertain trajectories of FEV₁, we studied two population-based birth cohorts (MAAS and ALSPAC) with repeat spirometry from childhood into early adulthood (1046 participants from 5-16 years and 1390 participants from 8-24 years). We used a third cohort (PIAF) with repeat lung function measures in infancy (V'_maxFRC) and childhood (FEV₁; 196 participants from 1 month to 18 years of age) to investigate whether these childhood trajectories extend from early life. We identified trajectories using latent profile modelling. We created an allele score to investigate genetic associations of trajectories, and constructed a multivariable model to identify their early-life predictors.

FINDINGS

We identified four childhood FEV₁ trajectories: persistently high, normal, below average, and persistently low. The persistently low trajectory (129 [5%] of 2436 participants) was associated with persistent wheezing and asthma throughout follow-up. In genetic analysis, compared with the normal trajectory, the pooled relative risk ratio per allele was 0·96 (95% CI 0·92-1·01; p=0·13) for persistently high, 1·01 (0·99-1·02; p=0·49) for below average, and 1·05 (0·98-1·13; p=0·13) for persistently low. Most children in the low V'_maxFRC trajectory in infancy did not progress to the low FEV₁ trajectory in childhood. Early-life factors associated with the persistently low trajectory included recurrent wheeze with severe wheezing exacerbations, early allergic sensitisation, and tobacco smoke exposure.

INTERPRETATION

Reduction of childhood smoke exposure and minimisation of the risk of early-life sensitisation and wheezing exacerbations might reduce the risk of diminished lung function in early adulthood.

FUNDING

None.

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.

Age at menarche and lung function: a Mendelian randomization study

A trend towards earlier menarche in women has been associated with childhood factors (e.g. obesity) and hypothesised environmental exposures (e.g. endocrine disruptors present in household products). Observational evidence has shown detrimental effects of early menarche on various health outcomes including adult lung function, but these might represent spurious associations due to confounding. To address this we used Mendelian randomization where genetic variants are used as proxies for age at menarche, since genetic associations are not affected by classical confounding. We estimated the effects of age at menarche on forced vital capacity (FVC), a proxy for restrictive lung impairment, and ratio of forced expiratory volume in one second to FVC (FEV1/FVC), a measure of airway obstruction, in both adulthood and adolescence. We derived SNP-age at menarche association estimates for 122 variants from a published genome-wide meta-analysis (N = 182,416), with SNP-lung function estimates obtained by meta-analysing three studies of adult women (N = 46,944) and two of adolescent girls (N = 3025). We investigated the impact of departures from the assumption of no pleiotropy through sensitivity analyses. In adult women, in line with previous evidence, we found an effect on restrictive lung impairment with a 24.8 mL increase in FVC per year increase in age at menarche (95% CI 1.8-47.9; p = 0.035); evidence was stronger after excluding potential pleiotropic variants (43.6 mL; 17.2-69.9; p = 0.001). In adolescent girls we found an opposite effect (-56.5 mL; -108.3 to -4.7; p = 0.033), suggesting that the detrimental effect in adulthood may be preceded by a short-term post-pubertal benefit. Our secondary analyses showing results in the same direction in men and boys, in whom age at menarche SNPs have also shown association with sexual development, suggest a role for pubertal timing in general rather than menarche specifically. We found no effect on airway obstruction (FEV1/FVC).

Rationale and design of the multiethnic Pharmacogenomics in Childhood Asthma consortium

AIM

International collaboration is needed to enable large-scale pharmacogenomics studies in childhood asthma. Here, we describe the design of the Pharmacogenomics in Childhood Asthma (PiCA) consortium.

MATERIALS & METHODS

Investigators of each study participating in PiCA provided data on the study characteristics by answering an online questionnaire.

RESULTS

A total of 21 studies, including 14,227 children/young persons (58% male), from 12 different countries are currently enrolled in the PiCA consortium. Fifty six percent of the patients are Caucasians. In total, 7619 were inhaled corticosteroid users. Among patients from 13 studies with available data on asthma exacerbations, a third reported exacerbations despite inhaled corticosteroid use. In the future pharmacogenomics studies within the consortium, the pharmacogenomics analyses will be performed separately in each center and the results will be meta-analyzed.

CONCLUSION

PiCA is a valuable platform to perform pharmacogenetics studies within a multiethnic pediatric asthma population.

Reliably estimating prevalences of atopic children: an epidemiological study in an extensive and representative primary care database

Electronic health records stored in primary care databases might be a valuable source to study the epidemiology of atopic disorders and their impact on health-care systems and costs. However, the prevalence of atopic disorders in such databases varies considerably and needs to be addressed. For this study, all children aged 0–18 years listed in a representative primary care database in the period 2002–2014, with sufficient data quality, were selected. The effects of four different strategies on the prevalences of atopic disorders were examined: (1) the first strategy examined the diagnosis as recorded in the electronic health records, whereas the (2) second used additional requirements (i.e., the patient had at least two relevant consultations and at least two relevant prescriptions). Strategies (3) and (4) assumed the atopic disorders to be chronic based on strategy 1 and 2, respectively. When interested in cases with a higher probability of a clinically relevant disorder, strategy 2 yields a realistic estimation of the prevalence of atopic disorders derived from primary care data. Using this strategy, of the 478,076 included children, 28,946 (6.1%) had eczema, 29,182 (6.1%) had asthma, and 28,064 (5.9%) had allergic rhinitis; only 1251 (0.3%) children had all three atopic disorders. Prevalence rates are highly dependent on the clinical atopic definitions used. The strategy using cases with a higher probability of clinically relevant cases, yields realistic prevalences to establish the impact of atopic disorders on health-care systems. However, studies are needed to solve the problem of identifying atopic disorders that are missed or misclassified.

The prevalence of atopic disorders in children can be more reliably calculated by incorporating clinical information with diagnosis data. Researchers in the Netherlands, led by David Pols from the Erasmus University Medical Center Rotterdam, examined the electronic health records of more than 660,000 children, aged 0 to 18, from a Dutch primary care database to determine the number of cases of atopic eczema, asthma, and allergic rhinitis. They looked for diagnosed children who also had at least two relevant clinical consultations and at least two relevant prescriptions. This strategy helps correct for the problem of overestimation, because it doesnot assume that a child, once diagnosed, will have an atopic disorder for life. However, other methods are still needed to identify cases that are missed or misclassified in the health database.

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.

Allergy in severe asthma

It is well recognized that atopic sensitization is an important risk factor for asthma, both in adults and in children. However, the role of allergy in severe asthma is still under debate. The term 'Severe Asthma' encompasses a highly heterogeneous group of patients who require treatment on steps 4-5 of GINA guidelines to prevent their asthma from becoming 'uncontrolled', or whose disease remains 'uncontrolled' despite this therapy. Epidemiological studies on emergency room visits and hospital admissions for asthma suggest the important role of allergy in asthma exacerbations. In addition, allergic asthma in childhood is often associated with severe asthma in adulthood. A strong association exists between asthma exacerbations and respiratory viral infections, and interaction between viruses and allergy further increases the risk of asthma exacerbations. Furthermore, fungal allergy has been shown to play an important role in severe asthma. Other contributing factors include smoking, pollution and work-related exposures. The 'Allergy and Asthma Severity' EAACI Task Force examined the current evidence and produced this position document on the role of allergy in severe asthma.

Asthma Biomarkers: Do They Bring Precision Medicine Closer to the Clinic?

Measurement of biomarkers has been incorporated within clinical research of asthma to characterize the population and to associate the disease with environmental and therapeutic effects. Regrettably, at present, there are no specific biomarkers, none is validated or qualified, and endotype-driven choices overlap. Biomarkers have not yet reached clinical practice and are not included in current asthma guidelines. Last but not least, the choice of the outcome upholding the value of the biomarkers is extremely difficult, since it has to reflect the mechanistic intervention while being relevant to both the disease and the particular person. On the verge of a new age of asthma healthcare standard, we must embrace and adapt to the key drivers of change. Disease endotypes, biomarkers, and precision medicine represent an emerging model of patient care building on large-scale biologic databases, omics and diverse cellular assays, health information technology, and computational tools for analyzing sizable sets of data. A profound transformation of clinical and research pattern from population to individual risk and from investigator-imposed subjective disease clustering (hypothesis driven) to unbiased, data-driven models is facilitated by the endotype/biomarker-driven approach.

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.

A systematic review of early life factors which adversely affect subsequent lung function

It has been known for many years that multiple early life factors can adversely affect lung function and future respiratory health. This is the first systematic review to attempt to analyse all these factors simultaneously. We adhered to strict a priori criteria for inclusion and exclusion of studies. The initial search yielded 29,351 citations of which 208 articles were reviewed in full and 25 were included in the review. This included 6 birth cohorts and 19 longitudinal population studies. The 25 studies reported the effect of 74 childhood factors (on their own or in combinations with other factors) on subsequent lung function reported as percent predicted forced expiration in one second (FEV₁). The childhood factors that were associated with a significant reduction in future FEV₁ could be grouped as: early infection, bronchial hyper-reactivity (BHR) / airway lability, a diagnosis of asthma, wheeze, family history of atopy or asthma, respiratory symptoms and prematurity / low birth weight. A complete mathematical model will only be possible if the raw data from all previous studies is made available. This highlights the need for increased cooperation between researchers and the need for international consensus about the outcome measures for future longitudinal studies.

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.

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.