ArticleThe World Allergy Organization journal2026
Multidimensional risk prediction model for infant atopic dermatitis: A prospective cohort study.
Article in The World Allergy Organization journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study aimed to construct a multi-dimensional risk prediction model for atopic dermatitis (AD) by integrating the maternal-fetal immune axis, genetic risk factors, and environmental exposures. Methods: The study prospectively enrolled 503 full-term newborns, with parental allergic history collected via questionnaire, maternal/cord blood biomarkers (IL-4, IL-13, IL-31, IL-33, IgE, TSLP) quantified by ELISA, and dust mite exposure dynamically assessed through quarterly standardized sampling. A 1-year follow-up was conducted to assess AD incidence in the neonatal cohort. Variables were screened via univariate analysis, LASSO regression and multivariable logistic regression to construct a nomogram model, with performance evaluated by ROC curve, calibration curve, Hosmer-Lemeshow test, triple cross-validation (repeated 10-fold, leave-one-out and bootstrap), and decision curve analysis. Results: A total of 456 infants were finally included (106 infants in the AD group and 350 infants in the non-AD group). Through a multi-stage screening process, 6 risk factors were identified, including cord blood IgE and TSLP, maternal blood IL-4 and IL-33, mother with allergic history, and dust mite exposure levels; subsequently, a predictive model was constructed based on these factors. Upon evaluation, the model showed good discriminatory ability, calibration degree, robustness, and clinical applicability. Conclusions: Cord blood IgE and TSLP, maternal blood IL-4 and IL-33, mother with allergic history, and dust mite exposure levels have shown good predictive value for AD. However, multicenter studies will be required to verify the universality of the model.
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