Ning Zhu MMed , Tingting Chen MMed , Lei Wang PhD , Fangyuan Cai MMed , Xiaoying Zhong , Xiaoxiao Fang MMed , Mingxin Chen MMed , Junyi Lin MMed , Huixi Tu , Yimin Zhao , Yihan Hu Bachelor , Weixi Zhang PhD , Jingjing Song PhD
{"title":"ige介导的食物过敏风险及其对儿童生长的影响:一种机器学习方法","authors":"Ning Zhu MMed , Tingting Chen MMed , Lei Wang PhD , Fangyuan Cai MMed , Xiaoying Zhong , Xiaoxiao Fang MMed , Mingxin Chen MMed , Junyi Lin MMed , Huixi Tu , Yimin Zhao , Yihan Hu Bachelor , Weixi Zhang PhD , Jingjing Song PhD","doi":"10.1016/j.waojou.2025.101088","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>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.</div></div><div><h3>Design</h3><div>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.</div></div><div><h3>Patients</h3><div>We recruited 130 children aged 0–3 years with IgE-FA as the FA group and 65 healthy children as the control group.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":54295,"journal":{"name":"World Allergy Organization Journal","volume":"18 8","pages":"Article 101088"},"PeriodicalIF":4.3000,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Risk of IgE-mediated food allergy and its impact on child growth: A machine learning approach\",\"authors\":\"Ning Zhu MMed , Tingting Chen MMed , Lei Wang PhD , Fangyuan Cai MMed , Xiaoying Zhong , Xiaoxiao Fang MMed , Mingxin Chen MMed , Junyi Lin MMed , Huixi Tu , Yimin Zhao , Yihan Hu Bachelor , Weixi Zhang PhD , Jingjing Song PhD\",\"doi\":\"10.1016/j.waojou.2025.101088\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><div>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.</div></div><div><h3>Design</h3><div>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.</div></div><div><h3>Patients</h3><div>We recruited 130 children aged 0–3 years with IgE-FA as the FA group and 65 healthy children as the control group.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>\",\"PeriodicalId\":54295,\"journal\":{\"name\":\"World Allergy Organization Journal\",\"volume\":\"18 8\",\"pages\":\"Article 101088\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"World Allergy Organization Journal\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1939455125000651\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ALLERGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Allergy Organization Journal","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1939455125000651","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ALLERGY","Score":null,"Total":0}
Risk of IgE-mediated food allergy and its impact on child growth: A machine learning approach
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.
期刊介绍:
The official pubication of the World Allergy Organization, the World Allergy Organization Journal (WAOjournal) publishes original mechanistic, translational, and clinical research on the topics of allergy, asthma, anaphylaxis, and clincial immunology, as well as reviews, guidelines, and position papers that contribute to the improvement of patient care. WAOjournal publishes research on the growth of allergy prevalence within the scope of single countries, country comparisons, and practical global issues and regulations, or threats to the allergy specialty. The Journal invites the submissions of all authors interested in publishing on current global problems in allergy, asthma, anaphylaxis, and immunology. Of particular interest are the immunological consequences of climate change and the subsequent systematic transformations in food habits and their consequences for the allergy/immunology discipline.