Search

Background: Peanut allergy has a rising prevalence in high‐income countries, affecting 0.5%–1.4% of children. This study aimed to better understand peanut anaphylaxis in comparison to anaphylaxis to other food triggers in European children and adolescents. Methods: Data was sourced from the European Anaphylaxis Registry via an online questionnaire, after in‐depth review of food‐induced anaphylaxis cases in a tertiary paediatric allergy centre. Results: 3514 cases of food anaphylaxis were reported between July 2007 ‐ March 2018, 56% in patients younger than 18 years. Peanut anaphylaxis was recorded in 459 children and adolescents (85% of all peanut anaphylaxis cases). Previous reactions (42% vs. 38%; p =.001), asthma comorbidity (47% vs. 35%; p <.001), relevant cofactors (29% vs. 22%; p =.004) and biphasic reactions (10% vs. 4%; p =.001) were more commonly reported in peanut anaphylaxis. Most cases were labelled as severe anaphylaxis (Ring&Messmer grade III 65% vs. 56% and grade IV 1.1% vs. 0.9%; p =.001). Self‐administration of intramuscular adrenaline was low (17% vs. 15%), professional adrenaline administration was higher in non‐peanut food anaphylaxis (34% vs. 26%; p =.003). Hospitalization was higher for peanut anaphylaxis (67% vs. 54%; p =.004). Conclusions: The European Anaphylaxis Registry data confirmed peanut as one of the major causes of severe, potentially life‐threatening allergic reactions in European children, with some characteristic features e.g., presence of asthma comorbidity and increased rate of biphasic reactions. Usage of intramuscular adrenaline as first‐line treatment is low and needs to be improved. The Registry, designed as the largest database on anaphylaxis, allows continuous assessment of this condition. [ABSTRACT FROM AUTHOR]

Background: Peanut allergy is a frequent cause of food allergy and potentially life‐threatening. Within this interdisciplinary research approach, we aim to unravel the complex mechanisms of peanut allergy. As a first step were applied in an exploratory manner the analysis of peanut allergic versus non‐allergic controls. Methods: Biosamples were studied regarding DNA methylation signatures, gut microbiome, adaptive and innate immune cell populations, soluble signaling molecules and allergen‐reactive antibody specificities. We applied a scalable systems medicine computational workflow to the assembled data. Results: We identified combined cellular and soluble biomarker signatures that stratify donors into peanut‐allergic and non‐allergic with high specificity. DNA methylation profiling revealed various genes of interest and stool microbiota differences in bacteria abundances. Conclusion: By extending our findings to a larger set of patients (e.g., children vs. adults), we will establish predictors for food allergy and tolerance and translate these as for example, indicators for interventional studies. [ABSTRACT FROM AUTHOR]