Data-driven research on eczema: systematic characterization of the field and recommendations for the future

Background The past decade has seen a substantial rise in the employment of modern data-driven methods to study atopic dermatitis (AD)/eczema. The objective of this study is to summarise the past and future of data-driven AD research, and identify areas in the field that would benefit from the application of these methods. Methods We retrieved the publications that applied multivariate statistics (MS), artificial intelligence (AI, including machine learning-ML), and Bayesian statistics (BS) to AD and eczema research from the SCOPUS database over the last 50 years. We conducted a bibliometric analysis to highlight the publication trends and conceptual knowledge structure of the field, and applied topic modelling to retrieve the key topics in the literature. Results Five key themes of data-driven research on AD and eczema were identified: (1) allergic co-morbidities, (2) image analysis and classification, (3) disaggregation, (4) quality of life and treatment response, and (5) risk factors and prevalence. ML&AI methods mapped to studies investigating quality of life, prevalence, risk factors, allergic co-morbidities and disaggregation of AD/eczema, but seldom in studies of therapies. MS was employed evenly between the topics, particularly in studies on risk factors and prevalence. BS was focused on three key topics: treatment, risk factors and allergy. The use of AD or eczema terms was not uniform, with studies applying ML&AI methods using the term eczema more often. Within MS, papers using cluster and factor analysis were often only identified with the term AD. In contrast, those using logistic regression and latent class/transition models were “eczema” papers. Conclusions Research areas that could benefit from the application of data-driven methods include the study of the pathogenesis of the condition and related risk factors, its disaggregation into validated subtypes, and personalised severity management and prognosis. We highlight BS as a new and promising approach in AD and eczema research

Differences in both prevalence and titre of specific immunoglobulin E among children with asthma in affluent and poor communities within a large town in Ghana.

Background Reports from several African countries have noted an increasing prevalence of asthma in areas of extensive urbanization. Objective To investigate the relevance of allergen-specific sensitization and body mass index (BMI) to asthma/wheezing and exercise-induced bronchospasm (EIB) among children from affluent and poorer communities within a large town in Ghana. Methods Children with physician-diagnosed asthma and/or current wheezing aged 9-16 years (n=99; cases) from three schools with differing socio-economic backgrounds [urban affluent (UA), urban poor (UP) or suburban/rural (SR)] were recruited from a cross-sectional study (n=1848) in Kumasi, Ghana, and matched according to age, sex and area of residence with non-asthmatic/non-wheezy controls. We assayed sera for IgE antibodies to mite, cat, dog, cockroach, Ascaris and galactose-α-1,3-galactose. Results Children from the UA school had the lowest total serum IgE. However, cases from the UA school had a higher prevalence and mean titre of sIgE to mite (71.4%, 21.2IU/mL) when compared with controls (14.3%, 0.8IU/mL) or cases from UP (30%, 0.8IU/mL) and SR community (47.8%, 1.6IU/mL). While similar findings were observed with EIB in the whole population, among cases there was no difference in IgE antibody prevalence or titre between children with or without EIB. BMI was higher among UA children with and without asthma; in UP and SR communities, children with EIB (n=14) had a significantly higher BMI compared with children with asthma/wheezing without EIB (n=38) (18.2 vs. 16.4, respectively, P<0.01). Conclusions and Clinical Relevance In the relatively affluent school, asthma/wheezing and EIB were associated with high titre IgE antibodies to mite, decreased total IgE, and increased BMI. This contrasted with children in the urban poor school and suggests that changes relevant to a Western model of childhood asthma can occur within a short geographical distance within a large city in Africa. © 2011 Blackwell Publishing Ltd

Will oral food challenges still be part of allergy care in 10 years' time?

Oral food challenges (OFCs) are currently the definitive diagnostic procedure in food allergy. Their design has evolved over the decades to maximize safety, optimize convenience, and address several specific clinical questions. However, they are a resource-intensive investigation that carry a risk for severe allergic reaction in which fatal outcomes, although rare, have been reported. In this review, we explore the many roles that OFC fulfil in the clinical and research settings. We also discuss progress that has been made in developing alternative diagnostic tools and how far these have reached in offering a viable replacement to OFC in clinical practice. Finally, we discuss the ongoing importance of research OFC to improve the future diagnostic capabilities of novel diagnostic tools

Differing associations of BMI and body fat with asthma and lung function in children.

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.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.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.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

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. 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

Diagnosis of asthma in symptomatic children based on measures of lung function: an analysis of data from a population-based birth cohort study.

BACKGROUND: Concerns have been expressed about asthma overdiagnosis. The UK National Institute of Health and Care Excellence (NICE) proposed a new diagnostic algorithm applying four lung function measures sequentially (ratio of forced expiratory volume in 1 s [FEV1] to forced vital capacity [FVC] 20%). We aimed to assess the diagnostic value of three of the tests individually, and then test the proposed algorithm in symptomatic children. METHODS: We used follow-up data at age 13-16 years from the Manchester Asthma and Allergy Study, a prospective, population-based, birth cohort study. We initially present results for the whole population, then by subgroup of disease. To simulate the situation in primary care, we included participants reporting symptoms of wheeze, cough, or breathlessness in the previous 12 months and who were not on regular inhaled corticosteroids. We used an epidemiological definition of current asthma, defined as all three of physician-diagnosed asthma, current wheeze, and current use of asthma treatment, reported by parents in a validated questionnaire. We assigned children with negative answers to all three questions as non-asthmatic controls. We also measured spirometry, bronchodilator reversibility, and FeNO at follow-up; data for peak expiratory flow variability were not available. We calculated the proportion of participants with a current positive lung function test at each step of the algorithm, and recorded the number of participants that met our definition of asthma. FINDINGS: Of 1184 children born into the cohort, 772 attended follow-up at age 13-16 years between July 22, 2011, and Nov 11, 2014. Among 630 children who completed spirometry, FEV1:FVC was less than 70% in ten (2%) children, of whom only two (20%) had current asthma. Bronchodilator reversibility was positive in 54 (9%) of 624 children, of whom only 12 (22%) had current asthma. FeNO was 35 or more parts per billion in 115 (24%) of 485 children, of whom 29 (25%) had current asthma. Only four of 56 children with current asthma had positive results for all three tests (spirometry, bronchodilator reversibility, and FeNO). Conversely, 24 (43%) of the 56 children with current asthma were negative on all three tests. FEV1:fvc (p=0·0075) and FeNO (p<0·0001), but not bronchodilator reversibility (p=0·97), were independently associated with asthma in multivariable logistic regression models. Among children who reported recent symptoms, the diagnostic accuracy of the algorithm was poor. INTERPRETATION: Our findings challenge the proposed cutoff values for spirometry, the order in which the lung function tests are done, and the position of bronchodilator reversibility within the algorithm sequence. Until better evidence is available, the proposed NICE algorithm on asthma diagnosis should not be implemented in children. FUNDING: UK Medical Research Council

Machine learning in asthma research: moving toward a more integrated approach

Introduction: Big data are reshaping the future of medicine. The growing availability and increasing complexity of data have favored the adoption of modern analytical and computational methodologies in every area of medicine. Over the past decades, asthma research has been characterized by a shift in the way studies are conducted and data are analyzed. Motivated by the assumptions that ‘data will speak for themselves’, hypothesis-driven approaches have been replaced by data-driven hypotheses-generating methods to explore hidden patterns and underlying mechanisms. However, even with all the advancement in technologies and the new important insight that we gained to understand and characterize asthma heterogeneity, very few research findings have been translated into clinically actionable solutions. Areas covered: To investigate some of the fundamental analytical approaches adopted in the current literature and appraise their impact and usefulness in medicine, we conducted a bibliometric analysis of big data analytics in asthma research in the past 50 years. Expert opinion: No single data source or methodology can uncover the complexity of human health and disease. To fully capitalize on the potential of ‘big data’, we will have to embrace the collaborative science and encourage the creation of integrated cross-disciplinary teams brought together around technological advances