Modelling trajectories of parentally reported and physician‐confirmed atopic dermatitis in a birth cohort study*

Nakamura, Toshinori; Haider, Sadia; Fontanella, Sara; Murray, Clare; Simpson, Angela; Čustović, Adnan

doi:10.1111/bjd.20767

Cited by 14 publications

(18 citation statements)

References 47 publications

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“…30 This gene-environment interaction remained significant after adjusting for infantile eczema, suggesting that the effect of FLG genotype may be independent of eczema. 30 Furthermore, application of machine-learning and data-driven methods to longitudinally collected data in this cohort enabled discoveries of different eczema and wheeze phenotypes, 31,32 different subtypes of allergic sensitization, 33 and developmental trajectories and co-occurrence of eczema, wheeze and rhinitis. 34 Our recent study highlighted potential importance of such data-driven phenotypes in relation to peanut allergy by showing that different longitudinal trajectories of eczema differ in their association with comorbidities, including a 9-to 11-fold increase in the risk of peanut allergy among children in the persistent eczema, but not other eczema classes.…”

Section: Key Messagesmentioning

confidence: 99%

“…34 Our recent study highlighted potential importance of such data-driven phenotypes in relation to peanut allergy by showing that different longitudinal trajectories of eczema differ in their association with comorbidities, including a 9-to 11-fold increase in the risk of peanut allergy among children in the persistent eczema, but not other eczema classes. 31 In the current analysis, we aimed to ascertain early-life predictors of peanut allergy and address a specific hypothesis that earlylife eczema and FLG loss-of-function mutations are independent risk factors. We then proceeded to investigate the association between previously derived longitudinal trajectories of sensitization, 33 wheeze 32 and allergic comorbidities, 34 and peanut allergy.…”

Section: Key Messagesmentioning

confidence: 99%

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Early‐life predictors and risk factors of peanut allergy, and its association with asthma in later‐life: Population‐based birth cohort study

Kotsapas

Nicolaou

Haider

et al. 2022

Clin Experimental Allergy

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Background: Understanding risk factors for peanut allergy (PA) is essential to develop effective preventive measures. Objective:The objective was to ascertain associates and predictors of PA, and the relationship between PA and asthma severity. Methods:In a population-based birth cohort, we investigated the association between objectively confirmed PA with early-life environmental exposures, filaggrin (FLG)-lossof-function mutations and other atopic disease. We then examined the association of PA with longitudinal trajectories of sensitization, wheeze and allergic comorbidities, which were previously derived using machine learning. Finally, we ascertained the relationship between PA and asthma severity.Results: PA was confirmed in 30/959 participants with evaluable data. In the multivariate analysis, eczema in infancy (OR = 4.4, 95% CI 1.5-13.2, p = 0.007), egg sensitization at age 3 years (OR = 9.7, 95% CI 3.3-29.9, p < 0.001) and early-life cat ownership (OR = 3.0, 95% CI 1.1-8.4, p = 0.04) were independent associates of PA.In the stratified analysis among 700 participants with genetic information, in children with early-life eczema there was no difference in FLG mutations between children with and without PA (3/18 [16.7%] vs. 42/220 [19.1%], p = 1.00). In contrast, among children without eczema, those with PA were almost eight times more likely to have FLG mutations (2/6 [33.3%] vs. 27/456 [5.9%], p = 0.049). We observed associations between PA and multiple allergic sensitization profiles derived using machine learning, with ~60-fold increase in risk among individuals assigned to multiple early sensitization. PA was significantly associated with persistent wheeze (but not other wheeze phenotypes), and with trajectories of atopic disease characterized by co-morbid persistent eczema and wheeze (but not with transient phenotypes). Children with PA were more likely to have asthma, but among asthmatics we found no evidence of an association between PA and asthma severity.

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Section: Key Messagesmentioning

confidence: 99%

Section: Key Messagesmentioning

confidence: 99%

Early‐life predictors and risk factors of peanut allergy, and its association with asthma in later‐life: Population‐based birth cohort study

Kotsapas

Nicolaou

Haider

et al. 2022

Clin Experimental Allergy

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“…As the development and employment of machine learning and other data‐driven approaches gain popularity in healthcare, experts and government agencies have increasingly collaborated to develop guidelines to facilitate the growth of the field and delineate principles of best practice 45,46 . We further underline the need and benefit of cross‐disciplinary collaborations for the future of data‐driven research on AD and eczema 12 …”

Section: Discussionmentioning

confidence: 99%

Data‐driven research on eczema: Systematic characterization of the field and recommendations for the future

Duverdier

Čustović

Tanaka

2022

Clinical & Translational All

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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.

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“…In terms of atopic dermatitis, there seems to be inconsistency of findings about the association between FLG-LOF and different atopic dermatitis phenotypes with one study showing an inconsistent association between FLG genotype, a different definitions of atopic dermatitis. 37 Data from a UK and European cohort showed an association between FLG-LOF mutations and all definitions of atopic dermatitis (no atopic dermatitis, early-onset-persistent atopic dermatitis, early-onset-lateresolving atopic dermatitis, early-onset-early-resolving atopic dermatitis, mid-onset-resolving atopic dermatitis, and late-onsetresolving atopic dermatitis) other than mid-childhood onset of atopic dermatitis (UK cohort) and children with early-onset atopic dermatitis who outgrew their atopic dermatitis (EU cohort). 38 In terms of asthma, data from the COPSAC study (Denmark) 39 indicated an increased risk of developing recurrent wheeze, asthma, and asthma exacerbations in children with FLG-LOF mutations in the 1.5 years of life.…”

Section: Discussionmentioning

confidence: 99%

Associations between child filaggrin mutations and maternal diet with the development of allergic diseases in children

Venter

Palumbo

Sauder

et al. 2022

Pediatric Allergy Immunology

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Background: Filaggrin (FLG) loss-of-function mutations in children and maternal diet in pregnancy have been implicated in child allergy outcomes. This paper studies the questions: "do FLG mutations modify the effect of maternal diet on the odds of development of allergic diseases?" and "which factor leads to the highest rate of diagnosis allergic diseases over time, maternal diet, or FLG mutations?". Methods: Exact logistic regressions studied effect modification. Cox proportional hazard models compared the rate of allergic disease development in three groups (N = 624): (1) children with FLG mutation, (2) children without FLG mutation whose mothers did not eat an allergy preventive diet, and (3) children without FLG mutation whose mothers ate an allergy preventive diet. Maternal diet was classified using a validated index.Results: Cox models showed the development of atopic dermatitis, asthma, and wheeze was significantly higher for children in group 1 versus 3 (

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Modelling trajectories of parentally reported and physician‐confirmed atopic dermatitis in a birth cohort study*

Cited by 14 publications

References 47 publications

Early‐life predictors and risk factors of peanut allergy, and its association with asthma in later‐life: Population‐based birth cohort study

Early‐life predictors and risk factors of peanut allergy, and its association with asthma in later‐life: Population‐based birth cohort study

Data‐driven research on eczema: Systematic characterization of the field and recommendations for the future

Associations between child filaggrin mutations and maternal diet with the development of allergic diseases in children

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