ArticleThe World Allergy Organization journal2025
Risk of IgE-mediated food allergy and its impact on child growth: A machine learning approach.
Article in The World Allergy Organization journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Machine Learning-Based Prediction of Food Allergy in Children Aged <2 Years with Atopic Dermatitis.Journal of clinical medicine · 2026Article
- Prevalence of adverse reactions and food allergies in Colombia: Cross-sectional national study.The journal of allergy and clinical immunology. Global · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Food allergy (FA) directly affects children's nutritional status, with a significantly higher risk of growth retardation among affected children. Identifying risk factors for FA and strategies to promote growth catch-up can offer valuable guidance for the treatment and nutritional management of children with FA. Design: We developed machine learning models to predict the occurrence of immunoglobulin E-mediated food allergy (IgE-FA) and the likelihood of post-treatment growth catch-up, using demographic and biological baseline data. Patients: We recruited 130 children aged 0-3 years with IgE-FA as the FA group and 65 healthy children as the control group. Results: Using machine-learning-based bioinformatics analysis, we developed predictive models and identified key factors influencing growth in IgE-FA children. The IgE-FA prediction model achieved an area under the curve (AUC) of 0.78 (95% CI: 0.708-0.848). Greater birthweight, a family history of allergies, and early-life antibiotic exposure were identified as risk factors for IgE-FA. Notably, early antibiotic exposure increased the risk of IgE-FA by 2.77 times and the risk of milk allergy by 2.56 times. Growth analysis, both overall and by subgroup, revealed that pre-treatment weight strongly correlates with post-treatment height, weight, and body mass index (BMI), offering new perspectives for predicting and monitoring outcomes in IgE-FA. Milk allergy mainly impacts weight catch-up, whereas egg allergy affects BMI. Controlled avoidance of allergenic foods supports growth recovery in affected children. Conclusion: Growth in children with IgE-FA is often restricted, and achieving expected growth levels remains challenging even after treatment. Weight is a sensitive and accessible indicator for predicting IgE-FA and plays a key role in post-treatment growth catch-up. Early and personalized nutritional guidance, along with regular weight monitoring, is recommended for all children with food allergy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.