Late Breaking Abstract - Prediction of asthma in early-life: an individual participant data meta-analysis of 135,000 European and Australian children.

<bold>Background:</bold> Predicting in early life which children will develop asthma is difficult. <bold>Objective:</bold> We aimed to develop a prediction model for asthma integrating socio-demographic, growth, lifestyle and dietary factors, clinical conditions, allergy and lung function in critical developmental periods from prepregnancy to adolescence. <bold>Methods:</bold> We used individual participant data of 134,563 children from the EU Child Cohort Network to predict current asthma during childhood (median 7y), and in adolescence (median 13y) by candidate predictors in consecutive periods from prepregnancy to infancy, preschool age, and school age. We performed 1-stage fixed-effect meta-analyses on the DataSHIELD platform using backward logistic regression and area under the receiver operating curve modeling and external leave-one-cohort-out cross-validation. <bold>Results:</bold> Early life factors predicted childhood asthma well (range AUC (95%CI): 0.63 (0.62, 0.65) to 0.74 (0.68, 0.80)). Prominent predictors were parental asthma, eczema or allergy, higher maternal prepregnancy body mass index, child’s sex, lower gestational age and birth weight, and higher infant weight gain (pregnancy to infancy), wheezing and smoke exposure (preschool age), and wheezing and obesity (school age) (p-values <0.025). Findings remained consistent for adolescent asthma and across North and South European countries, maternal education levels and sex. <bold>Conclusions:</bold> This large-scale validated prediction model effectively predicts asthma based on early life factors from prepregnancy onwards across different socio-demographic backgrounds and sex, indicating its potential for guiding personalized early intervention strategies.

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Published
2024-09-14
DOI
https://doi.org/10.1183/13993003.congress-2024.pa1343
Primary Topic
Asthma and respiratory diseases
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article
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article

Late Breaking Abstract - Prediction of asthma in early-life: an individual participant data meta-analysis of 135,000 European and Australian children.

Henrique Barros, David van Klaveren, Angela Pinot de Moira, Judith M. Vonk et al.
Asthma and respiratory diseases
article

Late Breaking Abstract - Prediction of asthma in early-life: an individual participant data meta-analysis of 135,000 European and Australian children.

Henrique Barros, David van Klaveren, Angela Pinot de Moira, Judith M. Vonk, A. Anderson, Vincent W. V. Jaddoe, Ferran Ballester, Tarik Karramass, Liesbeth Duijts, Loreto Santa‐Marina, Muriel Tafflet, Eva Corpeleijn, Mònica Guxens, Marieke Welten, Susana Santos, Rae‐Chi Huang, Demetris Avraam, Marianna Karachaliou, Martine Vrijheid, Maja Beck Popovic, Blandine de Lauzon‐Guillain, Ana Esplugues, Leire Luque, Theano Roumelotaki
article en

Abstract

<bold>Background:</bold> Predicting in early life which children will develop asthma is difficult. <bold>Objective:</bold> We aimed to develop a prediction model for asthma integrating socio-demographic, growth, lifestyle and dietary factors, clinical conditions, allergy and lung function in critical developmental periods from prepregnancy to adolescence. <bold>Methods:</bold> We used individual participant data of 134,563 children from the EU Child Cohort Network to predict current asthma during childhood (median 7y), and in adolescence (median 13y) by candidate predictors in consecutive periods from prepregnancy to infancy, preschool age, and school age. We performed 1-stage fixed-effect meta-analyses on the DataSHIELD platform using backward logistic regression and area under the receiver operating curve modeling and external leave-one-cohort-out cross-validation. <bold>Results:</bold> Early life factors predicted childhood asthma well (range AUC (95%CI): 0.63 (0.62, 0.65) to 0.74 (0.68, 0.80)). Prominent predictors were parental asthma, eczema or allergy, higher maternal prepregnancy body mass index, child’s sex, lower gestational age and birth weight, and higher infant weight gain (pregnancy to infancy), wheezing and smoke exposure (preschool age), and wheezing and obesity (school age) (p-values <0.025). Findings remained consistent for adolescent asthma and across North and South European countries, maternal education levels and sex. <bold>Conclusions:</bold> This large-scale validated prediction model effectively predicts asthma based on early life factors from prepregnancy onwards across different socio-demographic backgrounds and sex, indicating its potential for guiding personalized early intervention strategies.

University of Copenhagen (DK), University Medical Center Groningen (NL), University of Crete (GR), Inserm (FR), Universitat de València (ES), University of the Basque Country (ES), The Kids Research Institute Australia (AU), Université Paris Cité (FR), Royal Perth Hospital (AU), Erasmus MC (NL), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Sorbonne Paris Cité (FR), Department of Medical Sciences (BY), Instituto de Saúde (BR), Barcelona Institute for Global Health (ES), Department of Public Health (US), i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto (PT), Centre de Recherche Épidémiologie et Statistique (FR), Erasmus University Rotterdam (NL)
Gender equality
Openalex Percentile: Top 73%
Asthma and respiratory diseases
DOISourceOpenAlex
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