41 results found
Wang R, Simpson A, Custovic A, et al., 2019, Individual risk assessment tool for school-age asthma prediction in UK birth cohort, Clinical and Experimental Allergy, Vol: 49, Pages: 292-298, ISSN: 0954-7894
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.
Tang HHF, Teo SM, Belgrave DCM, et al., 2018, Trajectories of childhood immune development and respiratory health relevant to asthma and allergy, eLife, Vol: 7, ISSN: 2050-084X
Events in early life contribute to subsequent risk of asthma; however, the causes andtrajectories of childhood wheeze are heterogeneous and do not always result in asthma. Similarly,not all atopic individuals develop wheeze, and vice versa. The reasons for these differences areunclear. Using unsupervised model-based cluster analysis, we identified latent clusters within aprospective birth cohort with deep immunological and respiratory phenotyping. We characterisedeach cluster in terms of immunological profile and disease risk, and replicated our results inexternal cohorts from the UK and USA. We discovered three distinct trajectories, one of which is ahigh-risk ‘atopic’ cluster with increased propensity for allergic diseases throughout childhood.Atopy contributes varyingly to later wheeze depending on cluster membership. Our findingsdemonstrate the utility of unsupervised analysis in elucidating heterogeneity in asthmapathogenesis and provide a foundation for improving management and prevention of childhoodasthma.
Howard R, Belgrave D, Papastamoulis P, et al., 2018, Evolution of IgE responses to multiple allergen components throughout childhood, Journal of Allergy and Clinical Immunology, Vol: 142, Pages: 1322-1330, ISSN: 0091-6749
BACKGROUND: There is a paucity of information about longitudinal patterns of IgE responses to allergenic proteins (components) from multiple sources. OBJECTIVE: 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 related these patterns to asthma and rhinitis in adolescence. RESULTS: We identified one sensitization cluster at age one, 3 at age three, 4 at ages five and eight, 5 at age 11, and six at age 16 years. "Broad" cluster was the only cluster present at every follow-up, comprising of components from multiple sources. "Dust mite" cluster formed at age three and remained unchanged to adolescence. At age three, a single-component "Grass" cluster emerged, which at age five absorbed additional grass components and Fel d 1 to form the "Grass/cat" cluster. Two new clusters formed at age 11: "Cat" cluster and "PR-10/profilin" (which divided at age 16 into "PR-10" and "Profilin"). The strongest contemporaneous associate of asthma at age 16 years was sensitization to "Dust mite" cluster (OR [95% CI]: 2.6 [1.2-6.1], P<0.05), but the strongest early-life predictor of subsequent asthma was sensitization to "Grass/cat" cluster (3.5 [1.6-7.4], P<0.01). 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.
Belgrave DCM, Granell R, Turner SW, et al., 2018, 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, Lancet Respiratory Medicine, Vol: 6, Pages: 526-534, ISSN: 2213-2600
BackgroundMaximal 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.MethodsTo 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.FindingsWe 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.InterpretationReduction of childhood smoke exposure and minimisation of the risk of early-life sensitisation and wheezing exacerbations migh
Custovic A, Belgrave D, Lin L, et al., 2018, Cytokine responses to rhinovirus and development of asthma, allergic sensitization and respiratory infections during childhood, American Journal of Respiratory and Critical Care Medicine, Vol: 197, Pages: 1265-1274, ISSN: 1073-449X
BACKGROUND: Immunophenotypes of anti-viral responses, and their relationship with asthma, allergy and lower respiratory tract infections (LRTIs) are poorly understood. 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 post-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 phytohaemagglutinin-induced TH2-cytokine responses [PHA-TH2]. 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); pro-inflammatory-(Inflam); TH2-chemokine-(TH2-chem); regulatory-(Reg). Clusters differed in their clinical characteristics. Children with IFNmodInflamhighestTH2-chemhighestReghighestrhinovirus-16-induced pattern had PHA-TH2lowresponse, and a very low asthma risk (OR:0.08 [95%CI 0.01-0.81], P=0.03). Two clusters had high risk of asthma and allergic sensitization, but with different trajectories from infancy to adolescence. The IFNlowestInflamhighTH2-chemlowRegmodcluster exhibited PHA-TH2lowestresponse, and was associated with early-onset asthma and sensitization, and the highest risk of asthma exacerbations (1.37 [1.07-1.76], P=0.014) and LRTI hospitalizations (2.40 [1.26-4.58], P=0.008) throughout childhood. In contrast, cluster with IFNhighestInflammodTH2-chemmodReghighrhinovirus-16-cytokine pattern was characterized by PHA-TH2highestresponse, and a low prevalence of asthma/sensitization in infancy which increased sharply to become the highest among all clusters by adolescence (but with low risk of asthma exacerbations). CONCLUSIONS: Early-onset troublesome asthma with early-life sensitization, later-
Belgrave D, Cassidy R, Custovic A, et al., 2018, Predictive Modelling Strategies to Understand Heterogeneous Manifestations of Asthma in Early Life, 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Publisher: IEEE, Pages: 68-75
Wheezing is common among children and ~50% of those under 6 years of age are thought to experience at least one episode of wheeze. However, due to the heterogeneity of symptoms there are difficulties in treating and diagnosing these children. `Phenotype specific therapy' is one possible avenue of treatment, whereby we use significant pathology and physiology to identify and treat pre-schoolers with wheeze. By performing feature selection algorithms and predictive modelling techniques, this study will attempt to determine if it is possible to robustly distinguish patient diagnostic categories among pre-school children. Univariate feature analysis identified more objective variables and recursive feature elimination a larger number of subjective variables as important in distinguishing between patient categories. Predicative modelling saw a drop in performance when subjective variables were removed from analysis, indicating that these variables are important in distinguishing wheeze classes. We achieved 90%+ performance in AUC, sensitivity, specificity, and accuracy, and 80%+ in kappa statistic, in distinguishing ill from healthy patients. Developed in a synergistic statistical - machine learning approach, our methodologies propose also a novel ROC Cross Evaluation method for model post-processing and evaluation. Our predictive modelling's stability was assessed in computationally intensive Monte Carlo simulations.
Stamate D, Alghamdi W, Stahl D, et al., 2018, Predicting first-episode psychosis associated with cannabis use with artificial neural networks and deep learning, Pages: 691-702, ISSN: 1865-0929
© Springer International Publishing AG, part of Springer Nature 2018. In recent years, a number of researches started to investigate the existence of links between cannabis use and psychotic disorder. More recently, artificial neural networks and in particular deep learning have set a revolutionary wave in pattern recognition and machine learning. This study proposes a novel machine learning approach based on neural network and deep learning algorithms, to developing highly accurate predictive models for the onset of first-episode psychosis. Our approach is based also on a novel methodology of optimising and post-processing the predictive models in a computationally intensive framework. A study of the trade-off between the volume of the data and the extent of uncertainty due to missing values, both of which influencing the predictive performance, enhanced this approach. Furthermore, we extended our approach by proposing and encapsulating a novel post-processing k-fold cross-testing method in order to further optimise, and test these models. The results show that the average accuracy in predicting first-episode psychosis achieved by our models in intensive Monte Carlo simulation, is about 89%.
Wickman M, Lupinek C, Andersson N, et al., 2017, Detection of IgE Reactivity to a Handful of Allergen Molecules in Early Childhood Predicts Respiratory Allergy in Adolescence., EBioMedicine, Vol: 26, Pages: 91-99, ISSN: 2352-3964
BACKGROUND: Sensitization in early childhood may precede respiratory allergy in adolescence. METHODS: IgE reactivity against 132 allergen molecules was evaluated using the MeDALL microarray in sera obtained from a random sample of 786 children at the age of 4, 8 and 16years in a population based birth cohort (BAMSE). Symptoms were analyzed by questionnaire at ages 4, 8 and 16years. Clinically and independent relevant allergen molecules accounting for ≥90% of IgE reactivities in sensitized individuals and at all time-points were identified as risk molecules and used to predict respiratory allergy. The data was replicated in the Manchester Asthma and Allergy Study (MAAS) birth cohort by studying IgE reactivity with the use of a commercial IgE microarray. Sera were obtained from children at the ages of 3, 5, 8 and 11years (N=248) and the outcome was studied at 11years. FINDINGS: In the BAMSE cohort 4 risk molecules could be identified, i.e.: Ara h 1 (peanut), Bet v 1 (birch), Fel d 1 (cat), Phl p 1 (grass). For MAAS the corresponding number of molecules was 5: Der p 1 (dust mite), Der f 2 (dust mite), Phl p 1 (grass), Phl p 5 (grass), Fel d 1 (cat). In BAMSE, early IgE reactivity to ≥3 of 4 allergen molecules at four years predicted incident and persistent asthma and/or rhinitis at 16years (87% and 95%, respectively). The corresponding proportions in the MAAS cohort at 16years were 100% and 100%, respectively, for IgE reactivity to ≥3 of 5 risk molecules. INTERPRETATIONS: IgE reactivity to a few allergen molecules early in life identifies children with a high risk of asthma and/or rhinitis at 16years. These findings will be of importance for developing preventive strategies for asthma and rhinitis in children.
Custovic A, Ihuoma H, Belgrave DC, et al., 2017, Cat ownership, cat allergen exposure, and trajectories of sensitization and asthma throughout childhood, Journal of Allergy and Clinical Immunology, Vol: 141, Pages: 820-822.e7, ISSN: 0091-6749
Deliu M, Yavuz TS, Sperrin M, et al., 2017, Features of asthma which provide meaningful insights for understanding the disease heterogeneity., Clinical and Experimental Allergy, Vol: 48, Pages: 39-47, ISSN: 0954-7894
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 de
Belgrave DCM, Granell R, Simpson A, et al., 2017, Latent profile analysis to identify heterogeneous subgroups of lung function for personalised and targeted early intervention, International Conference of the American-Thoracic-Society (ATS), Publisher: American Thoracic Society, ISSN: 1073-449X
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.
Belgrave D, Henderson J, Simpson A, et al., 2016, Disaggregating asthma: big Investigation vs. big data, Journal of Allergy and Clinical Immunology, Vol: 139, Pages: 400-407, ISSN: 1097-6825
We are facing a major challenge in bridging the gap between identifying subtypes of asthma, to understanding 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 healthcare; 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 healthcare data and computational tools for data analysis is that the process of data mining may 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-driven 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' healthcare 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.
Belgrave D, Custovic A, 2016, The importance of being earnest in epidemiology, Acta Paediatrica, Vol: 105, Pages: 1384-1386, ISSN: 0803-5253
Mohammad HR, Belgrave D, Kopec Harding K, et al., 2016, Age, sex, and the association between skin test responses and IgE titres with asthma., Pediatric Allergy and Immunology, Vol: 27, Pages: 313-319, ISSN: 1399-3038
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>3mm, 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 sensitisation (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. This article is protected by copyright. All rights reserved.
Holt PG, Strickland D, Bosco A, et al., 2015, Distinguishing benign from pathologic TH2 immunity in atopic children., Journal of Allergy and Clinical Immunology, Vol: 137, Pages: 379-387, ISSN: 1097-6825
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 FcεR
Belgrave DC, Simpson A, Buchan IE, et al., 2015, Atopic dermatitis and respiratory allergy: what is the link, Current Dermatology Reports, Vol: 4, Pages: 221-227, ISSN: 2162-4933
Understanding the aetiology and progression of atopic dermatitis and respiratory allergy may elucidate early preventative and management strategies aimed towards reducing the global burden of asthma and allergic disease. In this article, we review the current opinion concerning the link between atopic dermatitis and the subsequent progression of respiratory allergies during childhood and into early adolescence. Advances in machine learning and statistical methodology have facilitated the discovery of more refined definitions of phenotypes for identifying biomarkers. Understanding the role of atopic dermatitis in the development of respiratory allergy may ultimately allow us to determine more effective treatment strategies, thus reducing the patient and economic burden associated with these conditions.
Belgrave DCM, Simpson A, Buchan I, et al., Atopic Dermatitis and Respiratory Allergy: What is the Link, Current Dermatology Reports
Guerra S, Halonen M, Vasquez MM, et al., 2015, Relation between circulating CC16 concentrations, lung function, and development of chronic obstructive pulmonary disease across the lifespan: a prospective study, LANCET RESPIRATORY MEDICINE, Vol: 3, Pages: 613-620, ISSN: 2213-2600
Custovic A, Sonntag HJ, Buchan IE, et al., 2015, Evolution pathways of IgE responses to grass and mite allergens throughout childhood, Journal of Allergy and Clinical Immunology, Vol: 136, Pages: 1645-1652.e8, ISSN: 1097-6825
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 allergen
Simpson A, Lazic N, Belgrave DC, et al., 2015, Patterns of IgE responses to multiple allergen components and clinical symptoms at age 11 years, Journal of Allergy and Clinical Immunology, Vol: 136, Pages: 1224-1231, ISSN: 1097-6825
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.
Sahiner UM, Semic-Jusufagic A, Curtin JA, et al., 2014, Polymorphisms of endotoxin pathway and endotoxin exposure: in vitro IgE synthesis and replication in a birth cohort, ALLERGY, Vol: 69, Pages: 1648-1658, ISSN: 0105-4538
Deliu M, Belgrave D, Simpson A, et al., 2014, Impact of rhinitis on asthma severity in school-age children, ALLERGY, Vol: 69, Pages: 1515-1521, ISSN: 0105-4538
Belgrave DC, Granell R, Simpson A, et al., 2014, Developmental Profiles of Eczema, Wheeze, and Rhinitis: Two Population-Based Birth Cohort Studies, PLOS Medicine, Vol: 11, ISSN: 1549-1277
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
Wang R, Custovic A, Simpson A, et al., 2014, Differing associations of BMI and body fat with asthma and lung function in children., Pediatric Pulmonology, Vol: 49, Pages: 1049-1057, ISSN: 8755-6863
BACKGROUND: Current evidence suggests that in children there is a significant, albeit weak, association between asthma and obesity. Studies generally use body mass index (BMI) in evaluating body adiposity, but there are limitations to its use. METHOD: Children from a population-based study attending follow-up (age 11 years) were weighed, measured and had percent body (PBF) and truncal (PTF) fat assessed using bioelectrical impedance. They were skin prick tested and completed spirometry. Parents completed a validated respiratory questionnaire. Children were defined as normal or overweight according to BMI and PBF cut-offs. We tested the association between these adiposity markers with wheeze, asthma, atopy, and lung-function. RESULTS: Six hundred forty-six children (339 male) completed follow-up. BMI z-score, PBF, and PTF were all positively associated with current wheeze (odds ratio [95% CI]: 1.27 [1.03, 1.57], P = 0.03; 1.05 [1.00, 1.09], P = 0.03; 1.04 [1.00, 1.08], P = 0.04, respectively). Similar trends were seen with asthma. However, when examining girls and boys separately, significant positive associations were found with PBF and PTF and asthma but only in girls (gender interaction P = 0.06 and 0.04, respectively). Associations between being overweight and wheezing and asthma were stronger when overweight was defined by PBF (P = 0.007, 0.03) than BMI (P > 0.05). Higher BMI was significantly associated with an increase in FEV(1) and FVC, but only in girls. Conversely, increasing body fat (PBF and PTF) was associated with reduced FEV(1) and FVC, but only in boys. No associations between adiposity and atopy were found. CONCLUSION: All adiposity measures were associated with wheeze, asthma, and lung function. However, BMI and PBF did not have the same effects and girls and boys appear to be affected differently.
Brough HA, Simpson A, Makinson K, et al., 2014, Peanut allergy: Effect of environmental peanut exposure in children with filaggrin loss-of-function mutations, JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY, Vol: 134, Pages: 867-U472, ISSN: 0091-6749
Kljaic-Bukvic B, Blekic M, Aberle N, et al., 2014, Genetic variants in endotoxin signalling pathway, domestic endotoxin exposure and asthma exacerbations, PEDIATRIC ALLERGY AND IMMUNOLOGY, Vol: 25, Pages: 552-557, ISSN: 0905-6157
Ic-Jusufagic AS, Belgrave D, Pickles A, et al., 2014, Assessing the association of early life antibiotic prescription with asthma exacerbations, impaired antiviral immunity, and genetic variants in 17q21: a population-based birth cohort study, LANCET RESPIRATORY MEDICINE, Vol: 2, Pages: 621-630, ISSN: 2213-2600
Prosperi MCF, Belgrave D, Buchan I, et al., 2014, Challenges in interpreting allergen microarrays in relation to clinical symptoms: A machine learning approach, PEDIATRIC ALLERGY AND IMMUNOLOGY, Vol: 25, Pages: 71-79, ISSN: 0905-6157
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.