Development Of An Algorithm To Better Predict Clinical Responsiveness To Peanut

Children at risk of peanut allergy often avoid peanut, yet a subgroup shows sensitization to peanut on skin prick tests (SPT) and serum specific IgE. Frequently, they must undergo an oral challenge to determine if they are truly allergic. This study examines the relationship between SPT, peanut specific serum IgE, and cytokine production by peripheral blood mononuclear cells (PBMC) to generate an algorithm to better predict peanut allergy. Patient groups: 1) True positives: History of peanut allergy and a positive peanut SPT. 2) False positives: Tolerate peanut but have a positive peanut SPT. 3) Unknown reactivity: Positive peanut SPT but no previous peanut ingestion 4) Non-atopic controls. PBMCs were isolated and cultured in the presence and absence of peanut. Cytokines were measured at baseline and in the presence of peanut. Statistical analysis was done using Classification And Regression Trees (CART). Peanut specific IgE levels ranged from 0 to >100 kU/L in peanut allergic patients and patients with unknown reactivity. False positive individuals had IgE levels from 0 to 27 kU/L. Non-atopic controls had undetectable peanut specific IgE. Of 12 variables examined, 4 variables identified as important for predicting peanut reactivity by CART analysis were wheal size, patient age, total IgE, and IFN-Gamma. In peanut allergic individuals, there is significant variability in peanut specific IgE. CART analysis identified 4 variables as important for predicting peanut reactivity. The next phase involves validation of the algorithm through oral peanut challenge in patients with unknown reactivity.