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.

Individual risk assessment tool for school-age asthma prediction in UK birth cohort.

BACKGROUND: Current published asthma predictive tools have moderate positive likelihood ratios (+LR) but high negative likelihood ratios (-LR) based on their recommended cut-offs, which limit their clinical usefulness. OBJECTIVE: To develop a simple clinically applicable asthma prediction tool within a population-based birth cohort. METHOD: Children from the Manchester Asthma and Allergy Study (MAAS) attended follow-up at ages 3, 8 and 11 years. Data on preschool wheeze were extracted from primary-care records. Parents completed validated respiratory questionnaires. Children were skin prick tested (SPT). Asthma at 8/11 years (school-age) was defined as parentally reported (a) physician-diagnosed asthma and wheeze in the previous 12 months or (b) ≥3 wheeze attacks in the previous 12 months. An asthma prediction tool (MAAS APT) was developed using logistic regression of characteristics at age 3 years to predict school-age asthma. RESULTS: Of 336 children with physician-confirmed wheeze by age 3 years, 117(35%) had school-age asthma. Logistic regression selected 5 significant risk factors which formed the basis of the MAAS APT: wheeze after exercise; wheeze causing breathlessness; cough on exertion; current eczema and SPT sensitisation(maximum score 5). A total of 281(84%) children had complete data at age 3 years and were used to test the MAAS APT. Children scoring ≥3 were at high risk of having asthma at school-age (PPV > 75%; +LR 6.3, -LR 0.6), whereas children who had a score of 0 had very low risk(PPV 9.3%; LR 0.2). CONCLUSION: MAAS APT is a simple asthma prediction tool which could easily be applied in clinical and research settings.

Differential associations of allergic disease genetic variants with developmental profiles of eczema, wheeze and rhinitis.

BACKGROUND: Allergic diseases (eczema, wheeze and rhinitis) in children often present as heterogeneous phenotypes. Understanding genetic associations of specific patterns of symptoms might facilitate understanding of the underlying biological mechanisms. OBJECTIVE: To examine associations between allergic disease-related variants identified in a recent genome-wide association study and latent classes of allergic diseases (LCADs) in two population-based birth cohorts. METHODS: Eight previously defined LCADs between birth and 11 years: "No disease," "Atopic march," "Persistent eczema and wheeze," "Persistent eczema with later-onset rhinitis," "Persistent wheeze with later-onset rhinitis," "Transient wheeze," "Eczema only" and "Rhinitis only" were used as the study outcome. Weighted multinomial logistic regression was used to estimate associations between 135 SNPs (and a polygenic risk score, PRS) and LCADs among 6345 individuals from The Avon Longitudinal Study of Parents and Children (ALSPAC). Heterogeneity across LCADs was assessed before and after Bonferroni correction. Results were replicated in Manchester Asthma and Allergy Study (MAAS) (n = 896) and pooled in a meta-analysis. RESULTS: We found strong evidence for differential genetic associations across the LCADs; pooled PRS heterogeneity P-value = 3.3 × 10-14 , excluding "no disease" class. The associations between the PRS and LCADs in MAAS were remarkably similar to ALSPAC. Two SNPs (a protein-truncating variant in FLG and a SNP within an intron of GSDMB) had evidence for differential association (pooled P-values ≤ 0.006). The FLG locus was differentially associated across LCADs that included eczema, with stronger associations for LCADs with comorbid wheeze and rhinitis. The GSDMB locus in contrast was equally associated across LCADs that included wheeze. CONCLUSIONS AND CLINICAL RELEVANCE: We have shown complex, but distinct patterns of genetic associations with LCADs, suggesting that heterogeneous mechanisms underlie individual disease trajectories. Establishing the combination of allergic diseases with which each genetic variant is associated may inform therapeutic development and/or predictive modelling.

Cytokine Responses to Rhinovirus and Development of Asthma, Allergic Sensitization, and Respiratory Infections during Childhood.

RATIONALE: Immunophenotypes of antiviral responses, and their relationship with asthma, allergy, and lower respiratory tract infections, are poorly understood. OBJECTIVES: We characterized multiple cytokine responses of peripheral blood mononuclear cells to rhinovirus stimulation, and their relationship with clinical outcomes. METHODS: In a population-based birth cohort, we measured 28 cytokines after stimulation with rhinovirus-16 in 307 children aged 11 years. We used machine learning to identify patterns of cytokine responses, and related these patterns to clinical outcomes, using longitudinal models. We also ascertained phytohemagglutinin-induced T-helper cell type 2 (Th2)-cytokine responses (PHA-Th2). MEASUREMENTS AND MAIN RESULTS: We identified six clusters of children based on their rhinovirus-16 responses, which were differentiated by the expression of four cytokine/chemokine groups: interferon-related (IFN), proinflammatory (Inflam), Th2-chemokine (Th2-chem), and regulatory (Reg). Clusters differed in their clinical characteristics. Children with an IFNmodInflamhighestTh2-chemhighestReghighest rhinovirus-16-induced pattern had a PHA-Th2low response, and a very low asthma risk (odds ratio [OR], 0.08; 95% confidence interval [CI], 0.01-0.81; P = 0.03). Two clusters had a high risk of asthma and allergic sensitization, but with different trajectories from infancy to adolescence. The IFNlowestInflamhighTh2-chemlowRegmod cluster exhibited a PHA-Th2lowest response and was associated with early-onset asthma and sensitization, and the highest risk of asthma exacerbations (OR, 1.37; 95% CI, 1.07-1.76; P = 0.014) and lower respiratory tract infection hospitalizations (OR, 2.40; 95% CI, 1.26-4.58; P = 0.008) throughout childhood. In contrast, the IFNhighestInflammodTh2-chemmodReghigh cluster with a rhinovirus-16-cytokine pattern was characterized by a PHA-Th2highest response, and a low prevalence of asthma/sensitization in infancy that increased sharply to become the highest among all clusters by adolescence (but with a low risk of asthma exacerbations). CONCLUSIONS: Early-onset troublesome asthma with early-life sensitization, later-onset milder allergic asthma, and disease protection are each associated with different patterns of rhinovirus-induced immune responses.

Evolution of IgE responses to multiple allergen components throughout childhood.

BACKGROUND: There is a paucity of information about longitudinal patterns of IgE responses to allergenic proteins (components) from multiple sources. OBJECTIVES: This study sought to investigate temporal patterns of component-specific IgE responses from infancy to adolescence, and their relationship with allergic diseases. METHODS: In a population-based birth cohort, we measured IgE to 112 components at 6 follow-ups during childhood. We used a Bayesian method to discover cross-sectional sensitization patterns and their longitudinal trajectories, and we related these patterns to asthma and rhinitis in adolescence. RESULTS: We identified 1 sensitization cluster at age 1, 3 at age 3, 4 at ages 5 and 8, 5 at age 11, and 6 at age 16 years. "Broad" cluster was the only cluster present at every follow-up, comprising components from multiple sources. "Dust mite" cluster formed at age 3 years and remained unchanged to adolescence. At age 3 years, a single-component "Grass" cluster emerged, which at age 5 years absorbed additional grass components and Fel d 1 to form the "Grass/cat" cluster. Two new clusters formed at age 11 years: "Cat" cluster and "PR-10/profilin" (which divided at age 16 years into "PR-10" and "Profilin"). The strongest contemporaneous associate of asthma at age 16 years was sensitization to dust mite cluster (odds ratio: 2.6; 95% CI: 1.2-6.1; P < .05), but the strongest early life predictor of subsequent asthma was sensitization to grass/cat cluster (odds ratio: 3.5; 95% CI: 1.6-7.4; P < .01). CONCLUSIONS: We describe the architecture of the evolution of IgE responses to multiple allergen components throughout childhood, which may facilitate development of better diagnostic and prognostic biomarkers for allergic diseases.

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.

Distinguishing benign from pathologic TH2 immunity in atopic children.

BACKGROUND: Although most children with asthma and rhinitis are sensitized to aeroallergens, only a minority of sensitized children are symptomatic, implying the underlying operation of efficient anti-inflammatory control mechanisms. OBJECTIVE: We sought to identify endogenous control mechanisms that attenuate expression of IgE-associated responsiveness to aeroallergens in sensitized children. METHODS: In 3 independent population samples we analyzed relationships between aeroallergen-specific IgE and corresponding allergen-specific IgG (sIgG) and associated immunophenotypes in atopic children and susceptibility to asthma and rhinitis, focusing on responses to house dust mite and grass. RESULTS: Among mite-sensitized children across all populations and at different ages, house dust mite-specific IgG/IgE ratios (but not IgG4/IgE ratios) were significantly lower in children with asthma compared with ratios in those without asthma and lowest among the most severely symptomatic. This finding was mirrored by relationships between rhinitis and antibody responses to grass. Depending on age/allergen specificity, 20% to 40% of children with allergen-specific IgE (sIgE) of 0.35 kU/L or greater had negative skin test responses, and these children also expressed the high sIgG/sIgE immunophenotype. sIgG1 from these children inhibited allergen-induced IgE-dependent basophil activation in a dose-dependent fashion. Profiling of aeroallergen-specific CD4(+) TH memory responses revealed positive associations between sIgG/sIgE ratios and IL-10-dependent gene signatures and significantly higher IL-10/TH2 cytokine (protein) ratios among nonsymptomatic children. CONCLUSION: In addition to its role in blocking TH2 effector activation in the late-phase allergic response, IL-10 is a known IgG1 switch factor. We posit that its production during allergen-induced memory responses contributes significantly to attenuation of inflammation through promoting IgG1-mediated damping of the FcεRI-dependent acute-phase reaction. sIgG1/sIgE balance might represent a readily accessible therapeutic target for asthma/rhinitis control.

Age, sex and the association between skin test responses and IgE titres with asthma.

BACKGROUND: Skin prick tests (SPTs) and allergen-specific serum IgE (sIgE) measurements are the main diagnostic tools for confirming atopic sensitization. Results are usually reported as 'positive' or 'negative', using the same arbitrary cut-offs (SPT>3 mm, sIgE>0.35 kUA /l) across different ages and sexes. We investigated the influence of age and sex on the interpretation of allergy test in the context of childhood asthma. METHODS: In a population-based birth cohort (n = 1051), we ascertained the information on asthma/wheeze (validated questionnaires) and performed SPTs and sIgE measurement to inhalant allergens (dust mite, cat, dog) at follow-ups between ages three and 11 years. We investigated the association between quantitative sensitization (sum of SPT mean wheal diameters [MWD] and sIgE titres to the three allergens) and current wheeze and asthma across ages and sexes. RESULTS: We observed a significant association between the SPT MWD and sIgE titres and wheeze/asthma at most ages and for both sexes. However, the strength of this association was age- and sex-dependent. For SPTs, the strength of the association between MWD and asthma increased with increasing age; we observed the opposite pattern for sIgE titre. For any given SPT MWD/sIgE titre, boys were significantly more likely to express clinical symptoms, particularly in early life; this difference between males and females diminished with age and was no longer significant by age 11 years. CONCLUSIONS: Age and sex should be taken into account when interpreting the results of skin tests and sIgE measurement, and age- and sex-specific normative data are needed for these allergy tests.

Patterns of IgE responses to multiple allergen components and clinical symptoms at age 11 years.

BACKGROUND: The relationship between sensitization to allergens and disease is complex. OBJECTIVE: We sought to identify patterns of response to a broad range of allergen components and investigate associations with asthma, eczema, and hay fever. METHODS: Serum specific IgE levels to 112 allergen components were measured by using a multiplex array (Immuno Solid-phase Allergen Chip) in a population-based birth cohort. Latent variable modeling was used to identify underlying patterns of component-specific IgE responses; these patterns were then related to asthma, eczema, and hay fever. RESULTS: Two hundred twenty-one of 461 children had IgE to 1 or more components. Seventy-one of the 112 components were recognized by 3 or more children. By using latent variable modeling, 61 allergen components clustered into 3 component groups (CG1, CG2, and CG3); protein families within each CG were exclusive to that group. CG1 comprised 27 components from 8 plant protein families. CG2 comprised 7 components of mite allergens from 3 protein families. CG3 included 27 components of plant, animal, and fungal origin from 12 protein families. Each CG included components from different biological sources with structural homology and also nonhomologous proteins arising from the same biological source. Sensitization to CG3 was most strongly associated with asthma (odds ratio [OR], 8.20; 95% CI, 3.49-19.24; P < .001) and lower FEV1 (P < .001). Sensitization to CG1 was associated with hay fever (OR, 12.79; 95% CI, 6.84-23.90; P < .001). Sensitization to CG2 was associated with both asthma (OR, 3.60; 95% CI, 2.05-6.29) and hay fever (OR, 2.52; 95% CI, 1.38-4.61). CONCLUSIONS: Latent variable modeling with a large number of allergen components identified 3 patterns of IgE responses, each including different protein families. In 11-year-old children the pattern of response to components of multiple allergens appeared to be associated with current asthma and hay fever but not eczema.

Evolution pathways of IgE responses to grass and mite allergens throughout childhood.

BACKGROUND: Little is known about longitudinal patterns of the development of IgE to distinct allergen components. OBJECTIVE: We sought to investigate the evolution of IgE responses to allergenic components of timothy grass and dust mite during childhood. METHODS: In a population-based birth cohort (n = 1184) we measured IgE responses to 15 components from timothy grass and dust mite in children with available samples at 3 time points (ages 5, 8, and 11 years; n = 235). We designed a nested, 2-stage latent class analysis to identify cross-sectional sensitization patterns at each follow-up and their longitudinal trajectories. We then ascertained the association of longitudinal trajectories with asthma, rhinitis, eczema, and lung function in children with component data for at least 2 time points (n = 534). RESULTS: Longitudinal latent class analysis revealed 3 grass sensitization trajectories: (1) no/low sensitization; (2) early onset; and (3) late onset. The early-onset trajectory was associated with asthma and diminished lung function, and the late-onset trajectory was associated with rhinitis. Four longitudinal trajectories emerged for mite: (1) no/low sensitization; (2) group 1 allergens; (3) group 2 allergens; and (3) complete mite sensitization. Children in the complete mite sensitization trajectory had the highest odds ratios (ORs) for asthma (OR, 7.15; 95% CI, 3.80-13.44) and were the only group significantly associated with comorbid asthma, rhinitis, and eczema (OR, 5.91; 95% CI, 2.01-17.37). Among children with wheezing, those in the complete mite sensitization trajectory (but not other longitudinal mite trajectories) had significantly higher risk of severe exacerbations (OR, 3.39; 95% CI, 1.62-6.67). CONCLUSIONS: The nature of developmental longitudinal trajectories of IgE responses differed between grass and mite allergen components, with temporal differences (early vs late onset) dominant in grass and diverging patterns of IgE responses (group 1 allergens, group 2 allergens, or both) in mite. Different longitudinal patterns bear different associations with clinical outcomes, which varied by allergen.

Trajectories of lung function during childhood.

RATIONALE: Developmental patterns of lung function during childhood may have major implications for our understanding of the pathogenesis of respiratory disease throughout life. OBJECTIVES: To explore longitudinal trajectories of lung function during childhood and factors associated with lung function decline. METHODS: In a population-based birth cohort, specific airway resistance (sRaw) was assessed at age 3 (n = 560), 5 (n = 829), 8 (n = 786), and 11 years (n = 644). Based on prospective data (questionnaires, skin tests, IgE), children were assigned to wheeze phenotypes (no wheezing, transient, late-onset, and persistent) and atopy phenotypes (no atopy, dust mite, non-dust mite, multiple early, and multiple late). We used longitudinal linear mixed models to determine predictors of change in sRaw over time. MEASUREMENTS AND MAIN RESULTS: Contrary to the assumption that sRaw is independent of age and sex, boys had higher sRaw than girls (mean difference, 0.080; 95% confidence interval [CI], 0.049-0.111; P < 0.001) and a higher rate of increase over time. For girls, sRaw increased by 0.017 kPa ⋅ s(-1) per year (95% CI, 0.011-0.023). In boys this increase was significantly greater (P = 0.012; mean between-sex difference, 0.011 kPa ⋅ s(-1); 95% CI, 0.003-0.019). Children with persistent wheeze (but not other wheeze phenotypes) had a significantly greater rate of deterioration in sRaw over time compared with never wheezers (P = 0.009). Similarly, children with multiple early, but not other atopy phenotypes had significantly poorer lung function than those without atopy (mean difference, 0.116 kPa ⋅ s(-1); 95% CI, 0.065-0.168; P < 0.001). sRaw increased progressively with the increasing number of asthma exacerbations. CONCLUSIONS: Children with persistent wheeze, frequent asthma exacerbations, and multiple early atopy have diminished lung function throughout childhood, and are at risk of a progressive loss of lung function from age 3 to 11 years. These effects are more marked in boys.

Peanut allergy: effect of environmental peanut exposure in children with filaggrin loss-of-function mutations.

BACKGROUND: Filaggrin (FLG) loss-of-function mutations lead to an impaired skin barrier associated with peanut allergy. Household peanut consumption is associated with peanut allergy, and peanut allergen in household dust correlates with household peanut consumption. OBJECTIVE: We sought to determine whether environmental peanut exposure increases the odds of peanut allergy and whether FLG mutations modulate these odds. METHODS: Exposure to peanut antigen in dust within the first year of life was measured in a population-based birth cohort. Peanut sensitization and peanut allergy (defined by using oral food challenges or component-resolved diagnostics [CRD]) were assessed at 8 and 11 years. Genotyping was performed for 6 FLG mutations. RESULTS: After adjustment for infantile atopic dermatitis and preceding egg skin prick test (SPT) sensitization, we found a strong and significant interaction between natural log (ln [loge]) peanut dust levels and FLG mutations on peanut sensitization and peanut allergy. Among children with FLG mutations, for each ln unit increase in the house dust peanut protein level, there was a more than 6-fold increased odds of peanut SPT sensitization, CRD sensitization, or both in children at ages 8 years, 11 years, or both and a greater than 3-fold increased odds of peanut allergy compared with odds seen in children with wild-type FLG. There was no significant effect of exposure in children without FLG mutations. In children carrying an FLG mutation, the threshold level for peanut SPT sensitization was 0.92 µg of peanut protein per gram (95% CI, 0.70-1.22 µg/g), that for CRD sensitization was 1.03 µg/g (95% CI, 0.90-1.82 µg/g), and that for peanut allergy was 1.17 µg/g (95% CI, 0.01-163.83 µg/g). CONCLUSION: Early-life environmental peanut exposure is associated with an increased risk of peanut sensitization and allergy in children who carry an FLG mutation. These data support the hypothesis that peanut allergy develops through transcutaneous sensitization in children with an impaired skin barrier.

Developmental profiles of eczema, wheeze, and rhinitis: two population-based birth cohort studies.

BACKGROUND: The term "atopic march" has been used to imply a natural progression of a cascade of symptoms from eczema to asthma and rhinitis through childhood. We hypothesize that this expression does not adequately describe the natural history of eczema, wheeze, and rhinitis during childhood. We propose that this paradigm arose from cross-sectional analyses of longitudinal studies, and may reflect a population pattern that may not predominate at the individual level. METHODS AND FINDINGS: Data from 9,801 children in two population-based birth cohorts were used to determine individual profiles of eczema, wheeze, and rhinitis and whether the manifestations of these symptoms followed an atopic march pattern. Children were assessed at ages 1, 3, 5, 8, and 11 y. We used Bayesian machine learning methods to identify distinct latent classes based on individual profiles of eczema, wheeze, and rhinitis. This approach allowed us to identify groups of children with similar patterns of eczema, wheeze, and rhinitis over time. Using a latent disease profile model, the data were best described by eight latent classes: no disease (51.3%), atopic march (3.1%), persistent eczema and wheeze (2.7%), persistent eczema with later-onset rhinitis (4.7%), persistent wheeze with later-onset rhinitis (5.7%), transient wheeze (7.7%), eczema only (15.3%), and rhinitis only (9.6%). When latent variable modelling was carried out separately for the two cohorts, similar results were obtained. Highly concordant patterns of sensitisation were associated with different profiles of eczema, rhinitis, and wheeze. The main limitation of this study was the difference in wording of the questions used to ascertain the presence of eczema, wheeze, and rhinitis in the two cohorts. CONCLUSIONS: The developmental profiles of eczema, wheeze, and rhinitis are heterogeneous; only a small proportion of children (∼ 7% of those with symptoms) follow trajectory profiles resembling the atopic march. Please see later in the article for the Editors' Summary.

Challenges in interpreting allergen microarrays in relation to clinical symptoms: a machine learning approach.

BACKGROUND: Identifying different patterns of allergens and understanding their predictive ability in relation to asthma and other allergic diseases is crucial for the design of personalized diagnostic tools. METHODS: Allergen-IgE screening using ImmunoCAP ISAC(®) assay was performed at age 11 yrs in children participating a population-based birth cohort. Logistic regression (LR) and nonlinear statistical learning models, including random forests (RF) and Bayesian networks (BN), coupled with feature selection approaches, were used to identify patterns of allergen responses associated with asthma, rhino-conjunctivitis, wheeze, eczema and airway hyper-reactivity (AHR, positive methacholine challenge). Sensitivity/specificity and area under the receiver operating characteristic (AUROC) were used to assess model performance via repeated validation. RESULTS: Serum sample for IgE measurement was obtained from 461 of 822 (56.1%) participants. Two hundred and thirty-eight of 461 (51.6%) children had at least one of 112 allergen components IgE > 0 ISU. The binary threshold >0.3 ISU performed less well than using continuous IgE values, discretizing data or using other data transformations, but not significantly (p = 0.1). With the exception of eczema (AUROC~0.5), LR, RF and BN achieved comparable AUROC, ranging from 0.76 to 0.82. Dust mite, pollens and pet allergens were highly associated with asthma, whilst pollens and dust mite with rhino-conjunctivitis. Egg/bovine allergens were associated with eczema. CONCLUSIONS: After validation, LR, RF and BN demonstrated reasonable discrimination ability for asthma, rhino-conjunctivitis, wheeze and AHR, but not for eczema. However, further improvements in threshold ascertainment and/or value transformation for different components, and better interpretation algorithms are needed to fully capitalize on the potential of the technology.

Genetic variants in endotoxin signalling pathway, domestic endotoxin exposure and asthma exacerbations.

BACKGROUND: We investigated the interaction between genetic variants in endotoxin signalling pathway and domestic endotoxin exposure in relation to asthma presence, and amongst children with asthma, we explored the association of these genetic variants and endotoxin exposure with hospital admissions due to asthma exacerbations. METHODS: In a case-control study, we analysed data from 824 children (417 asthmatics, 407 controls; age 5-18 yr). Amongst asthmatics, we extracted data on hospitalization for asthma exacerbation from medical records. Endotoxin exposure was measured in dust samples collected from homes. We included 26 single-nucleotide polymorphisms (SNPs) in the final analysis (5 CD14, 7LY96 and 14 TLR4). RESULTS: Two variants remained significantly associated with hospital admissions with asthma exacerbations after correction for multiple testing: for CD14 SNP rs5744455, carriers of T allele had decreased risk of repeated hospital admissions compared with homozygotes for C allele [OR (95% CI), 0.42 (0.25-0.88), p = 0.01, False Discovery Rate (FDR) p = 0.02]; for LY96 SNP rs17226566, C-allele carriers were at a lower risk of hospital admissions compared with T-allele homozygotes [0.59 (0.38-0.90), p = 0.01, FDR p = 0.04]. We observed two interactions between SNPs in CD14 and LY96 with environmental endotoxin exposure in relation to hospital admissions due to asthma exacerbation which remained significant after correction for multiple testing (CD14 SNPs rs2915863 and LY96 SNP rs17226566). CONCLUSION: Amongst children with asthma, genetic variants in CD14 and LY96 may increase the risk of hospital admissions with acute exacerbations. Polymorphisms in endotoxin pathway interact with domestic endotoxin exposure in further modification of the risk of hospitalization.

Challenges in identifying asthma subgroups using unsupervised statistical learning techniques.

RATIONALE: Unsupervised statistical learning techniques, such as exploratory factor analysis (EFA) and hierarchical clustering (HC), have been used to identify asthma phenotypes, with partly consistent results. Some of the inconsistency is caused by the variable selection and demographic and clinical differences among study populations. OBJECTIVES: To investigate the effects of the choice of statistical method and different preparations of data on the clustering results; and to relate these to disease severity. METHODS: Several variants of EFA and HC were applied and compared using various sets of variables and different encodings and transformations within a dataset of 383 children with asthma. Variables included lung function, inflammatory and allergy markers, family history, environmental exposures, and medications. Clusters and original variables were related to asthma severity (logistic regression and Bayesian network analysis). MEASUREMENTS AND MAIN RESULTS: EFA identified five components (eigenvalues ≥ 1) explaining 35% of the overall variance. Variations of the HC (as linkage-distance functions) did not affect the cluster inference; however, using different variable encodings and transformations did. The derived clusters predicted asthma severity less than the original variables. Prognostic factors of severity were medication usage, current symptoms, lung function, paternal asthma, body mass index, and age of asthma onset. Bayesian networks indicated conditional dependence among variables. CONCLUSIONS: The use of different unsupervised statistical learning methods and different variable sets and encodings can lead to multiple and inconsistent subgroupings of asthma, not necessarily correlated with severity. The search for asthma phenotypes needs more careful selection of markers, consistent across different study populations, and more cautious interpretation of results from unsupervised learning.

Joint modeling of parentally reported and physician-confirmed wheeze identifies children with persistent troublesome wheezing.

BACKGROUND: Previous studies have suggested the presence of different childhood wheeze phenotypes through statistical modeling based on parentally reported wheezing. OBJECTIVE: We sought to investigate whether joint modeling of observations from both medical records and parental reports helps to more accurately define wheezing disorders during childhood and whether incorporating information from medical records better characterizes severity. METHODS: In a population-based birth cohort (n = 1184), we analyzed data from 2 sources (parentally reported current wheeze at 4 follow-ups and physician-confirmed wheeze from medical records in each year from birth to age 8 years) to determine classes of children who differ in wheeze trajectories. We tested the validity of these classes by examining their relationships with objective outcomes (lung function, airway hyperreactivity, and atopy), asthma medication, and severe exacerbations. RESULTS: Longitudinal latent class modeling identified a 5-class model that best described the data. We assigned classes as follows: no wheezing (53.3%), transient early wheeze (13.7%), late-onset wheeze (16.7%), persistent controlled wheeze (13.1%), and persistent troublesome wheeze (PTW; 3.2%). Longitudinal trajectories of atopy and lung function differed significantly between classes. Patients in the PTW class had diminished lung function and more hyperreactive airways compared with all other classes. We observed striking differences in exacerbations, hospitalizations, and unscheduled visits, all of which were markedly higher in patients in the PTW class compared with those in the other classes. For example, the risk of exacerbation was much higher in patients in the PTW class compared with patients with persistent controlled wheeze (odds ratio [OR], 3.58; 95% CI, 1.27-10.09), late-onset wheeze (OR, 15.92; 95% CI, 5.61-45.15), and transient early wheeze (OR, 12.24; 95% CI, 4.28-35.03). CONCLUSION: We identified a novel group of children with persistent troublesome wheezing, who have markedly different outcomes compared with persistent wheezers with controlled disease.

Generative models improve fairness of medical classifiers under distribution shifts.

Domain generalization is a ubiquitous challenge for machine learning in healthcare. Model performance in real-world conditions might be lower than expected because of discrepancies between the data encountered during deployment and development. Underrepresentation of some groups or conditions during model development is a common cause of this phenomenon. This challenge is often not readily addressed by targeted data acquisition and 'labeling' by expert clinicians, which can be prohibitively expensive or practically impossible because of the rarity of conditions or the available clinical expertise. We hypothesize that advances in generative artificial intelligence can help mitigate this unmet need in a steerable fashion, enriching our training dataset with synthetic examples that address shortfalls of underrepresented conditions or subgroups. We show that diffusion models can automatically learn realistic augmentations from data in a label-efficient manner. We demonstrate that learned augmentations make models more robust and statistically fair in-distribution and out of distribution. To evaluate the generality of our approach, we studied three distinct medical imaging contexts of varying difficulty: (1) histopathology, (2) chest X-ray and (3) dermatology images. Complementing real samples with synthetic ones improved the robustness of models in all three medical tasks and increased fairness by improving the accuracy of clinical diagnosis within underrepresented groups, especially out of distribution.

Genetic variation in vascular endothelial growth factor-a and lung function.

RATIONALE: Given the role of vascular endothelial growth factor (VEGF) in lung development, we hypothesized that polymorphisms in VEGF-A may be associated with lung function. OBJECTIVES: The current study was designed to assess the role of genetic variants in VEGF-A as determinants of airway function from infancy through early adulthood. METHODS: Association between five single-nucleotide polymorphisms (SNPs) in VEGF-A and lung function were assessed longitudinally in two unselected birth cohorts and cross-sectionally among infants. Replication with two SNPs was conducted in adults and children with asthma. We investigated the functionality of the SNP most consistently associated with lung function (rs3025028) using Western blotting to measure the ratio of plasma VEGF-A(165b)/panVEGF-A(165) among homozygotes. MEASUREMENTS AND MAIN RESULTS: In two populations in infancy, C-allele homozygotes of rs3025028 had significantly higher VmaxFRC, forced expiratory flow(50), and forced expiratory flow(25-75) compared with other genotype groups. Among preschool children (age 3 yr), C allele of rs3025028 was associated with significantly higher specific airway conductance, with similar findings observed for lung function in school-age children. For FEV(1)/FVC ratio similar findings were observed among adolescents and young adults (birth cohort), and then replicated in adults and schoolchildren with asthma (cross-sectional studies). For rs3025038, plasma VEGF-A(165b)/panVEGF-A(165) was significantly higher among CC versus GG homozygotes (P ≤ 0.02) at birth, in school-age children, and in adults. CONCLUSIONS: We report significant associations between VEGF-A SNP rs3025028 and parameters of airway function measured throughout childhood, with the effect persisting into adulthood. We propose that the mechanism may be mediated through the ratios of active and inhibitory isoforms of VEGF-A(165), which may be determined by alternative splicing.