Premium
A new Luminex‐based peptide assay to identify reactivity to baked, fermented, and whole milk
Author(s) -
Sackesen Cansin,
SuárezFariñas Mayte,
Silva Ronaldo,
Lin Jing,
Schmidt Stephanie,
Getts Robert,
Gimenez Gustavo,
Yilmaz Ebru A.,
Cavkaytar Ozlem,
Buyuktiryaki Betul,
Soyer Ozge,
Grishina Galina,
Sampson Hugh A.
Publication year - 2019
Publication title -
allergy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.363
H-Index - 173
eISSN - 1398-9995
pISSN - 0105-4538
DOI - 10.1111/all.13581
Subject(s) - epitope , milk allergy , immunoglobulin e , oral food challenge , medicine , food science , fermented milk products , allergy , immunology , fermentation , cross reactivity , food allergy , antibody , chemistry , biology , cross reactions , lactic acid , genetics , bacteria
Background The majority of children with cow's milk allergy ( CMA ) tolerate baked milk. However, reactivity to fermented milk products such as yogurt/cheese has not been previously evaluated. We sought to determine whether children with CMA could tolerate yogurt/cheese and whether a patient's IgE and IgG4‐binding pattern to milk protein epitopes could distinguish clinical reactivity. Methods Four groups of reactivity were identified by Oral food challenge: baked milk reactive, fermented milk reactive, whole milk reactive, and outgrown. sIgE and sIgG 4 binding to milk protein epitopes were assessed with a novel Luminex‐based peptide assay ( LPA ). Using machine learning techniques, a model was developed to predict different degrees of CMA. Results The baked milk reactive patients demonstrated the highest degree of IgE epitope binding, which was followed sequentially by fermented milk reactive, whole milk reactive, and outgrown. Data were randomly divided into two groups with 75% of the data utilized for model development (n = 68) and 25% for testing (n = 21). All 68 children used for training were correctly classified with models using IgE and IgG4 epitopes. The average cross‐validation accuracy was much higher for models using IgE plus IgG4 epitopes by LPA (84.8%), twice the performance of the serum component proteins assayed by Uni CAP (41.9%). The performance of the model on “unseen data” was tested using the 21 withheld patients, and the accuracy of IgE was 86% ( AUC = 0.89) while of IgE+IgG4 model was 81% ( AUC = 0.94). Conclusion Using a novel high‐throughput LPA , we were able to distinguish the diversity of IgE/IgG4 binding to epitopes in the varying CMA phenotypes. LPA is a promising tool to predict correctly different degrees of CMA .