RESUMEN
BACKGROUND: Currently, there is no laboratory test that can accurately identify children at risk of developing peanut allergy. Utilizing a subset of children randomized to the peanut avoidance arm of the LEAP trial, we monitored the development of epitope-specific (ses-)IgE and ses-IgG4 from 4-11 months to 5 years of age. OBJECTIVE: The aim of the study was to evaluate the prognostic ability of epitope-specific antibodies to predict the result of an oral food challenge (OFC) at 5 years. METHODS: A Bead-Based Epitope Assay was used to quantitate IgE and IgG4 to 64 sequential (linear) epitopes from Ara h 1-3 proteins at 4-11 months, 1 and 2.5 years of age in 74 subjects (38 of them with a positive OFC at 5 years). Specific IgE (sIgE) to peanut and component proteins was measured using ImmunoCAP. Machine learning methods were used to identify the earliest time point to predict 5-year outcome, developing prognostic algorithms based only on 4-11 month samples, 1-year or 2.5-year, and a combination of them. Data from 74 children were iteratively split 3:1 into training and validation sets, and machine learning models were developed to predict the 5-year outcome. A test set (n = 90) from an independent cohort was used for final evaluation. RESULTS: Elastic-Net algorithm combining ses-IgE and IgE to Ara h 1, 2, 3, and 9 proteins could predict the 5-year peanut allergy status of LEAP participants with an average validation accuracy of 64% at baseline. Samples taken at 1 year accurately predicted a 5-year OFC outcome with 83% accuracy. This performance remained consistent when evaluated on an independent CoFAR2 cohort with an accuracy of 78% for the 1-year model. CONCLUSION: IgE antibody profiles at 1 year of age are predictive of peanut OFC at 5 years in children avoiding peanuts. If further confirmed, this model may enable early identification of infants who may benefit from early immunotherapeutic interventions.