The bead-based epitope assay has been used to identify epitope-specific (es) antibodies and successfully used to diagnose clinical allergy to milk, egg, and peanut.

We sought to identify es-IgE, es-IgG4, and es-IgG1 of wheat proteins and determine the optimal peptides to differentiate wheat-allergic from wheat-tolerant using the bead-based epitope assay.

Children and adolescents who underwent an oral food challenge to confirm their wheat allergy status were enrolled. Seventy-nine peptides from α-/β-gliadin, γ-gliadin, ω-5-gliadin, and high- and low-molecular-weight glutenin were commercially synthesized and coupled to LumAvidin beads (Luminex Corporation, Austin, Tex). Machine learning methods were used to identify diagnostic epitopes, and performance was evaluated using the DeLong test.

The analysis included 122 children (83 wheat-allergic and 39 wheat-tolerant; 57.4% male). Machine learning coupled with simulations identified wheat es-IgE, but not es-IgG4 or es-IgG1, to be the most informative for diagnosing wheat allergy. Higher es-IgE binding intensity correlated with the severity of allergy phenotypes, with wheat anaphylaxis exhibiting the highest es-IgE binding intensity. In contrast, wheat-dependent exercise-induced anaphylaxis showed lower es-IgG1 binding intensity than did all the other groups. A set of 4 informative epitopes from ω-5-gliadin and γ-gliadin were the best predictors of wheat allergy, with an area under the curve of 0.908 (sensitivity, 83.4%; specificity, 88.4%), higher than the performance exhibited by wheat-specific IgE (area under the curve = 0.646; P < .001). The predictive ability of our model was confirmed in an external cohort of 71 patients (29 allergic, 42 nonallergic), with an area under the curve of 0.908 (sensitivity, 75.9%; specificity, 90.5%).

The wheat bead-based epitope assay demonstrated greater diagnostic accuracy compared with existing specific IgE tests for wheat allergy.