Lung-function trajectories: relevance and implementation in clinical practice.

Lung development starts in utero and continues during childhood through to adolescence, reaching its peak in early adulthood. This growth is followed by gradual decline due to physiological lung ageing. Lung-function development can be altered by several host and environmental factors during the life course. As a result, a range of lung-function trajectories exist in the population. Below average trajectories are associated with respiratory, cardiovascular, metabolic, and mental health comorbidities, as well as with premature death. This Review presents progressive research into lung-function trajectories and assists the implementation of this knowledge in clinical practice as an innovative approach to detect poor lung health early, monitor respiratory disease progression, and promote lung health. Specifically, we propose that, similar to paediatric height and weight charts used globally to monitor children's growth, lung-function charts could be used for both children and adults to monitor lung health status across the life course. To achieve this proposal, we introduce our free online Lung Function Tracker tool. Finally, we discuss challenges and opportunities for effective implementation of the trajectory concept at population level and outline an agenda for crucial research needed to support such implementation.

Association between polymorphisms on chromosome 17q12-q21 and rhinovirus-induced interferon responses.

BACKGROUND: Single nucleotide polymorphisms (SNPs) in genes on chromosome 17q12-q21 are associated with childhood-onset asthma and rhinovirus-induced wheeze. There are few mechanistic data linking chromosome 17q12-q21 to wheezing illness. OBJECTIVE: We investigated whether 17q12-q21 risk alleles were associated with impaired interferon responses to rhinovirus. METHODS: In a population-based birth cohort of European ancestry, we stimulated peripheral blood mononuclear cells with rhinovirus A1 (RV-A1) and rhinovirus A16 (RV-A16) and measured IFN and IFN-induced C-X-C motif chemokine ligand 10 (aka IP10) responses in supernatants. We investigated associations between virus-induced cytokines and 6 SNPs in 17q12-q21. Bayesian profile regression was applied to identify clusters of individuals with different immune response profiles and genetic variants. RESULTS: Five SNPs (in high linkage disequilibrium, r2 ≥ 0.8) were significantly associated with RV-A1-induced IFN-ß (rs9303277, P = .010; rs11557467, P = .012; rs2290400, P = .006; rs7216389, P = .008; rs8079416, P = .005). A reduction in RV-A1-induced IFN-ß was observed among individuals with asthma risk alleles. There were no significant associations for RV-A1-induced IFN-α or CXCL10, or for any RV-A16-induced IFN/CXCL10. Bayesian profile regression analysis identified 3 clusters that differed in IFN-ß induction to RV-A1 (low, medium, high). The typical genetic profile of the cluster associated with low RV-A1-induced IFN-ß responses was characterized by a very high probability of being homozygous for the asthma risk allele for all SNPs. Children with persistent wheeze were almost 3 times more likely to be in clusters with reduced/average RV-A1-induced IFN-ß responses than in the high immune response cluster. CONCLUSIONS: Polymorphisms on chromosome 17q12-q21 are associated with rhinovirus-induced IFN-ß, suggesting a novel mechanism-impaired IFN-ß induction-links 17q12-q21 risk alleles with asthma/wheeze.

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.

European Respiratory Society Statement on preschool wheezing disorders: updated definitions, knowledge gaps, and proposed future research directions.

Since the publication of the European Respiratory Society (ERS) Task Force reports on the management of preschool wheezing in 2008 and 2014, a large body of evidence has accumulated suggesting the clinical phenotypes that were proposed, episodic (viral) wheezing and multiple-trigger wheezing, do not relate to underlying airway pathology and may not help determine response to treatment. Specifically, using clinical phenotypes alone may no longer be appropriate, and new approaches that can be used to inform clinical care are needed for future research. This ERS Task Force reviewed the literature published after 2008 related to preschool wheezing and has suggested the criteria used to define wheezing disorders in preschool children should include age of diagnosis (0 to <6 years), confirmation of wheezing on at least one occasion, and more than one episode of wheezing ever.Furthermore, diagnosis and management may be improved by identifying treatable traits, including inflammatory biomarkers (blood eosinophils, aeroallergen sensitisation) associated with type-2 immunity and differential response to inhaled corticosteroids, lung function parameters, and airway infection. However, more comprehensive use of biomarkers/treatable traits in predicting the response to treatment requires prospective validation. There is evidence that specific genetic traits may help guide management, but these must be adequately tested. In addition, the Task Force identified an absence of caregiver-reported outcomes, caregiver/self-management options, and features that should prompt specialist referral for this age group. Priorities for future research include a focus on identifying i) mechanisms driving preschool wheezing, ii) biomarkers of treatable traits and efficacy of interventions in those without allergic sensitisation/eosinophilia, iii) the need to include both objective outcomes and caregiver-reported outcomes in clinical trials, iv) the need for a suitable action plan for children with preschool wheezing and v) a definition of severe/difficult-to-treat preschool wheezing.

Evaluation of measurement errors in the Patient-Oriented Eczema Measure (POEM) outcome.

BACKGROUND: The Patient-Oriented Eczema Measure (POEM) is the recommended core outcome instrument for atopic dermatitis (AD) symptoms. POEM is reported by recalling the presence/absence of seven symptoms in the last 7 days. OBJECTIVE: To evaluate measurement errors in POEM recordings due to imperfect recall. METHODS: Using data from a clinical trial of 247 AD patients aged 12-65 years, we analysed the reported POEM score (r-POEM) and the POEM derived from the corresponding daily scores for the same seven symptoms without weekly recall (d-POEM). We quantified recall error by comparing the r-POEM and d-POEM for 777 patient-weeks collected from 207 patients, and estimated two components of recall error: (1) recall bias due to systematic errors in measurements and (2) recall noise due to random errors in measurements, using a bespoke statistical model. RESULTS: POEM scores have a relatively low recall bias, but a high recall noise. Recall bias was estimated at 1.2 points lower for the r-POEM on average than the d-POEM, with a recall noise of 5.7 points. For example, a patient with a recall-free POEM of 11 (moderate) could report their POEM score anywhere from 5 to 14 (with 95% probability) because of recall error. Model estimates suggested that patients tend to recall itch and dryness more often than experienced (positive bias of less than 1 day), but less often for the other symptoms (bleeding, cracking, flaking, oozing/weeping and sleep disturbance; negative bias ranging 1-4 days). CONCLUSIONS: In this clinical trial data set, we found that patients tended to slightly underestimate their symptoms when reporting POEM, with significant variation in how well they were able to recall the frequency of their symptoms every time they reported POEM. A large recall noise should be taken into consideration when interpreting POEM scores.

Component-specific clusters for diagnosis and prediction of allergic airway diseases.

BACKGROUND: Previous studies which applied machine learning on multiplex component-resolved diagnostics arrays identified clusters of allergen components which are biologically plausible and reflect the sources of allergenic proteins and their structural homogeneity. Sensitization to different clusters is associated with different clinical outcomes. OBJECTIVE: To investigate whether within different allergen component sensitization clusters, the internal within-cluster sensitization structure, including the number of c-sIgE responses and their distinct patterns, alters the risk of clinical expression of symptoms. METHODS: In a previous analysis in a population-based birth cohort, by clustering component-specific (c-s)IgEs, we derived allergen component clusters from infancy to adolescence. In the current analysis, we defined each subject's within-cluster sensitization structure which captured the total number of c-sIgE responses in each cluster and intra-cluster sensitization patterns. Associations between within-cluster sensitization patterns and clinical outcomes (asthma and rhinitis) in early-school age and adolescence were examined using logistic regression and binomial generalized additive models. RESULTS: Intra-cluster sensitization patterns revealed specific associations with asthma and rhinitis (both contemporaneously and longitudinally) that were previously unseen using binary sensitization to clusters. A more detailed description of the subjects' within-cluster c-sIgE responses in terms of the number of positive c-sIgEs and unique sensitization patterns added new information relevant to allergic diseases, both for diagnostic and prognostic purposes. For example, the increase in the number of within-cluster positive c-sIgEs at age 5 years was correlated with the increase in prevalence of asthma at ages 5 and 16 years, with the correlations being stronger in the prediction context (e.g. for the largest 'Broad' component cluster, contemporaneous: r = .28, p = .012; r = .22, p = .043; longitudinal: r = .36, p = .004; r = .27, p = .04). CONCLUSION: Among sensitized individuals, a more detailed description of within-cluster c-sIgE responses in terms of the number of positive c-sIgE responses and distinct sensitization patterns, adds potentially important information relevant to allergic diseases.

Parent's perception of respiratory syncytial virus and subsequent wheezing burden: A multi-country cross-sectional survey.

BACKGROUND: Respiratory Syncytial Virus (RSV) is the leading cause of hospitalization in infants. RSV bronchiolitis is associated with an increased risk of subsequent wheezing. We aimed to document the parents' perception of the link between RSV infection and subsequent wheezing, wheezing-related healthcare and family resources use, and its impact on family daily life. METHODS: This cross-sectional online survey enrolled 1200 parents with at least one child ≤6y living in the United States, United Kingdom, Spain, and Italy. Children diagnosed with RSV bronchiolitis before age of 2 years were included in the RSV group, and those never diagnosed with RSV bronchiolitis in the Reference group. RESULTS: The odds of wheezing were 4.5-fold (95%CI 3.5-5.9) higher in the RSV than in the Reference group. The odds increased to 7.7-fold (95%CI 5.4-11.1) among children who were hospitalized, and 9-fold (95%CI 5.1-16.6) among those admitted to pediatric intensive care with RSV bronchiolitis. Similar trends were observed across all countries. In total, 57% of parents reported their child's wheezing to have moderate to severe impact on their emotional well-being, and 53% on their daily life activities and/or social life. 64% of parents reported moderate-severe impact of wheezing on child's quality of sleep and 49% and 46% reported a moderate-severe impact on their children's emotional well-being and physical activities. CONCLUSIONS: This survey suggests an association between RSV infection and subsequent wheezing in children across different countries. Wheezing, especially in association with RSV infection, was associated with increased healthcare utilization and costs, and significantly impacted parents' and children daily life.

Recommendations for asthma monitoring in children: A PeARL document endorsed by APAPARI, EAACI, INTERASMA, REG, and WAO.

Monitoring is a major component of asthma management in children. Regular monitoring allows for diagnosis confirmation, treatment optimization, and natural history review. Numerous factors that may affect disease activity and patient well-being need to be monitored: response and adherence to treatment, disease control, disease progression, comorbidities, quality of life, medication side-effects, allergen and irritant exposures, diet and more. However, the prioritization of such factors and the selection of relevant assessment tools is an unmet need. Furthermore, rapidly developing technologies promise new opportunities for closer, or even "real-time," monitoring between visits. Following an approach that included needs assessment, evidence appraisal, and Delphi consensus, the PeARL Think Tank, in collaboration with major international professional and patient organizations, has developed a set of 24 recommendations on pediatric asthma monitoring, to support healthcare professionals in decision-making and care pathway design.

Circulating CC16 and Asthma: A Population-based, Multicohort Study from Early Childhood through Adult Life.

Rationale: Club cell secretory protein (CC16) is an antiinflammatory protein highly expressed in the airways. CC16 deficiency has been associated with lung function deficits, but its role in asthma has not been established conclusively. Objectives: To determine 1) the longitudinal association of circulating CC16 with the presence of active asthma from early childhood through adult life and 2) whether CC16 in early childhood predicts the clinical course of childhood asthma into adult life. Methods: We assessed the association of circulating CC16 and asthma in three population-based birth cohorts: the Tucson Children's Respiratory Study (years 6-36; total participants, 814; total observations, 3,042), the Swedish Barn/Children, Allergy, Milieu, Stockholm, Epidemiological survey (years 8-24; total participants, 2,547; total observations, 3,438), and the UK Manchester Asthma and Allergy Study (years 5-18; total participants, 745; total observations, 1,626). Among 233 children who had asthma at the first survey in any of the cohorts, baseline CC16 was also tested for association with persistence of symptoms. Measurements and Main Results: After adjusting for covariates, CC16 deficits were associated with increased risk for the presence of asthma in all cohorts (meta-analyzed adjusted odds ratio per 1-SD CC16 decrease, 1.20; 95% confidence interval [CI], 1.12-1.28; P < 0.0001). The association was particularly strong for asthma with frequent symptoms (meta-analyzed adjusted relative risk ratio, 1.40; 95% CI, 1.24-1.57; P < 0.0001), was confirmed for both atopic and nonatopic asthma, and was independent of lung function impairment. After adjustment for known predictors of persistent asthma, children with asthma in the lowest CC16 tertile had a nearly fourfold increased risk for having frequent symptoms persisting into adult life compared with children with asthma in the other two CC16 tertiles (meta-analyzed adjusted odds ratio, 3.72; 95% CI, 1.78-7.76; P < 0.0001). Conclusions: Circulating CC16 deficits are associated with the presence of asthma with frequent symptoms from childhood through midadult life and predict the persistence of asthma symptoms into adulthood. These findings support a possible protective role of CC16 in asthma and its potential use for risk stratification.

Dog ownership in infancy is protective for persistent wheeze in 17q21 asthma-risk carriers.

BACKGROUND: Asthma-associated single nucleotide polymorphisms from large genome-wide association studies only explain a fraction of genetic heritability. Likely causes of the missing heritability include broad phenotype definitions and gene-environment interactions (GxE). The mechanisms underlying GxE in asthma are poorly understood. Previous GxE studies on pet ownership showed discordant results. OBJECTIVES: We sought to study the GxE between the 17q12-21 locus and pet ownership in infancy in relation to wheeze. METHODS: Wheezing classes derived from 5 UK-based birth cohorts (latent class analysis) were used to study GxE between the 17q12-21 asthma-risk variant rs2305480 and dog and cat ownership in infancy, using multinomial logistic regression. A total of 9149 children had both pet ownership and genotype data available. Summary statistics from individual analyses were meta-analyzed. RESULTS: rs2305480 G allele was associated with increased risk of persistent wheeze (additive model odds ratio, 1.37; 95% CI, 1.25-1.51). There was no evidence of an association between dog or cat ownership and wheeze. We found significant evidence of a GxE interaction between rs2305480 and dog ownership (P = 8.3 × 10-4) on persistent wheeze; among dog owners, the G allele was no longer associated with an increased risk of persistent wheeze (additive model odds ratio, 0.95; 95% CI, 0.73-1.24). For those without pets, G allele was associated with increased risk of persistent wheeze (odds ratio, 1.61; 95% CI, 1.40-1.86). Among cat owners, no such dampening of the genetic effect was observed. CONCLUSIONS: Among dog owners, rs2305480 G was no longer associated with an increased risk of persistent wheeze (or asthma). Early-life environmental exposures may therefore attenuate likelihood of asthma in those carrying 17q12-21 risk alleles.

Progenitor cell-derived basophils: A novel barcoded passive degranulation assay in allergic diseases.

BACKGROUND: Effector cells assays provide an overall measure of responsiveness to allergen, but the lack of reliable and high-throughput assays limits the clinical utility. We aimed to develop a high-throughput basophil activation test based on human progenitor cell-derived basophils (PCB) and investigate the role of PCB activation test (PCBAT) in allergic diseases. METHODS: Progenitor cell-derived basophils were differentiated from CD34+ progenitor cells and sensitized with sera from subjects sensitized to cat, peanut or atopic controls. Sensitized PCBs were stimulated with increasing concentrations of the corresponding allergens in vitro. Degranulation was assessed by measuring CD63 expression using flow cytometry. The correlations between PCBAT and clinical allergy were assessed. RESULTS: Following passive sensitization of the mature PCBs with serum and allergen stimulation, an allergen specific dose-dependent increase in CD63 expression was observed. Sera from subjects sensitized to cat (n = 35, of which 17 subjects had clinical reactivity quantified using inhaled allergen challenge), peanut allergic (n = 30, of which 15 subjects had clinical reactivity validated using double blind, placebo controlled food challenges [DBPCFC]), peanut-sensitized but tolerant subjects (n = 5) were used to sensitize PCBs. PCBAT area under the curve (AUC) correlated with sIgE (r2  = .49, p = .001) in subjects sensitized to cat (sIgE ≥ 0.35KU/L). The provocation concentration of inhaled cat allergen (PC20 ) correlated with PCBAT AUC (r2  = .33, p = .016). In subjects sensitized to peanut, PCBAT AUC was highly correlated with sIgE to Ara h 2 (r2  = .59, p < .0001). Peanut threshold cumulative dose during DBPCFC was negatively correlated with PCBAT AUC (r2  = .57, p = .001) and IgE to Ara h1 (r2  = .55, p = .007), but not with sIgE to whole peanut or Ara h2. All peanut-sensitized but tolerant subjects showed no reaction to peanut on PCBAT. CONCLUSION: Progenitor cell-derived basophils activation test is a high-throughput assay, which correlates with clinical allergy and may confer a powerful alternative tool in allergy testing.

Prediction of adult asthma risk in early childhood using novel adult asthma predictive risk scores.

BACKGROUND: Numerous risk scores have been developed to predict childhood asthma. However, they may not predict asthma beyond childhood. We aim to create childhood risk scores that predict development and persistence of asthma up to young adult life. METHODS: The Isle of Wight Birth Cohort (n = 1456) was prospectively assessed up to 26 years of age. Asthma predictive scores were developed based on factors during the first 4 years, using logistic regression and tested for sensitivity, specificity and area under the curve (AUC) for prediction of asthma at (i) 18 and (ii) 26 years, and persistent asthma (PA) (iii) at 10 and 18 years, and (iv) at 10, 18 and 26 years. Models were internally and externally validated. RESULTS: Four models were generated for prediction of each asthma outcome. ASthma PredIctive Risk scorE (ASPIRE)-1: a 2-factor model (recurrent wheeze [RW] and positive skin prick test [+SPT] at 4 years) for asthma at 18 years (sensitivity: 0.49, specificity: 0.80, AUC: 0.65). ASPIRE-2: a 3-factor model (RW, +SPT and maternal rhinitis) for asthma at 26 years (sensitivity: 0.60, specificity: 0.79, AUC: 0.73). ASPIRE-3: a 3-factor model (RW, +SPT and eczema at 4 years) for PA-18 (sensitivity: 0.63, specificity: 0.87, AUC: 0.77). ASPIRE-4: a 3-factor model (RW, +SPT at 4 years and recurrent chest infection at 2 years) for PA-26 (sensitivity: 0.68, specificity: 0.87, AUC: 0.80). ASPIRE-1 and ASPIRE-3 scores were replicated externally. Further assessments indicated that ASPIRE-1 can be used in place of ASPIRE-2-4 with same predictive accuracy. CONCLUSION: ASPIRE predicts persistent asthma up to young adult life.

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.

EAACI guidelines on environmental science in allergic diseases and asthma - Leveraging artificial intelligence and machine learning to develop a causality model in exposomics.

Allergic diseases and asthma are intrinsically linked to the environment we live in and to patterns of exposure. The integrated approach to understanding the effects of exposures on the immune system includes the ongoing collection of large-scale and complex data. This requires sophisticated methods to take full advantage of what this data can offer. Here we discuss the progress and further promise of applying artificial intelligence and machine-learning approaches to help unlock the power of complex environmental data sets toward providing causality models of exposure and intervention. We discuss a range of relevant machine-learning paradigms and models including the way such models are trained and validated together with examples of machine learning applied to allergic disease in the context of specific environmental exposures as well as attempts to tie these environmental data streams to the full representative exposome. We also discuss the promise of artificial intelligence in personalized medicine and the methodological approaches to healthcare with the final AI to improve public health.

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.

Environmental influences on childhood asthma: Allergens.

Allergen exposure is associated with the development of allergen-specific sensitization, but their relationship is influenced by other contemporaneous exposures (such as microbial exposure) and the genetic predisposition of the host. Clinical outcomes of the primary prevention studies that tested the effectiveness of allergen avoidance in pregnancy and early life on the subsequent development of sensitization and asthma published to date are inconsistent. Therefore, we cannot provide any evidence-based advice on the use of allergen avoidance for the primary prevention of these conditions. The evidence about the impact of allergen exposure among and among sensitized children with asthma is more consistent, and the combination of sensitization and high exposure to sensitizing allergen increases airway inflammation, triggers symptoms, adversely impacts upon disease control, and is associated with poorer lung function in preschool age. However, there are differing opinions about the role of inhalant allergen avoidance in asthma management, and recommendations differ in different guidelines. Evidence from more recent high-quality trials suggests that mite allergen-impermeable bed encasings reduce hospital attendance with asthma attacks and that multifaceted targeted environmental control improves asthma control in children. We therefore suggest a pragmatic approach to allergen avoidance in the management of childhood asthma for clinical practice, including the recommendations to: (1) tailor the intervention to the patient's sensitization and exposure status by using titer of allergen-specific IgE antibodies and/or the size of the skin test as indicators of potential response; (2) use a multifaceted allergen control regime to reduce exposure as much as possible; and (3) start intervention as early as possible upon diagnosis.

Component-resolved diagnosis in childhood and prediction of asthma in early adolescence: A birth cohort study.

INTRODUCTION: Component-resolved diagnosis (CRD) has been decisive in exploring the mechanisms of IgE sensitization, but the predictive ability to detect asthma has not been addressed. We aim to develop and evaluate the performance of a personalized predictive algorithm for asthma that integrates information on allergic sensitization using CRD. METHODS: One thousand one hundred one twenty-five children from the Generation XXI birth cohort were randomly selected to perform a screening test for allergic sensitization and a subsample was characterized using CRD against 112 allergen components. Allergen components were analyzed using volcano plots and partial least squares (PLS) analysis. Logistic regression was performed to assess the associations between the obtained latent components (LC) and allergic outcomes (asthma, rhinitis, eczema) including other potential predictors used in previous asthma risk scores. The accuracy of the model in predicting asthma was assessed using Receiver Operating Characteristic (ROC) curve statistics. RESULTS: In the PLS, the first LC was positively associated with asthma, rhinitis, and eczema. This LC was mainly driven by positive weights for Der p 1/2/23, Der f 1/2, and Fel d 1. The main components in the second LC were pollen and food allergens. History of early wheezing and parental allergy were included in the predictive model and the area under the curve improved to 0.82. CONCLUSIONS: This is the first approach to improve the clinical applicability of CRD by combining CRD and clinical data to predict asthma at 13 years. Sensitization to distinct allergen molecules seems relevant to improve the accuracy of asthma prediction models.

EAACI Molecular Allergology User's Guide 2.0.

Since the discovery of immunoglobulin E (IgE) as a mediator of allergic diseases in 1967, our knowledge about the immunological mechanisms of IgE-mediated allergies has remarkably increased. In addition to understanding the immune response and clinical symptoms, allergy diagnosis and management depend strongly on the precise identification of the elicitors of the IgE-mediated allergic reaction. In the past four decades, innovations in bioscience and technology have facilitated the identification and production of well-defined, highly pure molecules for component-resolved diagnosis (CRD), allowing a personalized diagnosis and management of the allergic disease for individual patients. The first edition of the "EAACI Molecular Allergology User's Guide" (MAUG) in 2016 rapidly became a key reference for clinicians, scientists, and interested readers with a background in allergology, immunology, biology, and medicine. Nevertheless, the field of molecular allergology is moving fast, and after 6 years, a new EAACI Taskforce was established to provide an updated document. The Molecular Allergology User's Guide 2.0 summarizes state-of-the-art information on allergen molecules, their clinical relevance, and their application in diagnostic algorithms for clinical practice. It is designed for both, clinicians and scientists, guiding health care professionals through the overwhelming list of different allergen molecules available for testing. Further, it provides diagnostic algorithms on the clinical relevance of allergenic molecules and gives an overview of their biology, the basic mechanisms of test formats, and the application of tests to measure allergen exposure.

WITHDRAWN: Update in Asthma 2021.

Ahead of Print article withdrawn by publisher.