Trends over a decade in the prevalence and eliciting dose of peanut and tree nut allergies in Japan
Background: The prevalence of tree nuts allergy is rapidly increasing in Japan, yet the eliciting dose (ED) for tree nuts, particularly in Asian populations, has not been sufficiently investigated. Objective: This study aimed to quantify EDs for peanut, cashew nut, and walnut in Japanese patients with food allergies and to investigate longitudinal changes in these values. Methods: We retrospectively analyzed 1,275 oral food challenge results for patients with diagnosed peanut, cashew nut, or walnut allergy, conducted between November 2013 and December 2023 at a single allergy center. EDs were calculated using the Weibull, log-normal, and log-logistic models, and bivariate survival analysis was used to examine the relationship between allergen-specific IgE levels and ED causing reaction in 5% of the allergic population (ED05). Results: The ED05 values were determined as 4.88 mg for peanut, 0.53 mg for cashew nut, and 4.37 mg for walnut. When the component-specific IgE was set at 50.0 kUA/L, ED05 decreased to 3.20 mg, 0.55 mg, and 1.92 mg, respectively. Notably, a marked decline in walnut ED was observed over time. Conclusions: EDs in Japanese tree nut-allergic populations are decreasingly aligned with those reported in Western countries, reflecting shifts in dietary habits and allergen exposure. The findings of the dynamic nature of threshold dose distributions over time, even within the same region, emphasize the necessity for periodic reassessment of allergen risk thresholds to ensure optimal patient safety.
https://doi.org/10.1016/j.jacig.2025.100582
HLA variation associated with peanut allergy and anaphylaxis among non-Hispanic Black individuals
Background: Peanut is a major cause of food allergy. HLA genes have been consistently associated with peanut allergy in association studies. To date, however, there have been very few genetic studies of peanut allergy in non-Hispanic Black (Black) individuals, a group disproportionately affected by food allergy. Objective: Our aim was to study the relationship between HLA alleles and peanut allergy among Black individuals. Methods: The analysis consisted of Black participants from the Study of Asthma-Related Phenotypes and Pharmacogenomic Interactions by Race-Ethnicity (SAPPHIRE), a large cohort of individuals from metropolitan Detroit. At the time of enrollment, participants provided detailed food allergy histories, including symptoms. Four-digit resolution HLA allele calls were made using whole genome sequence data. Results: The cases consisted of 119 individuals with reported peanut allergy and 59 individuals with peanut-related anaphylaxis; the comparison group consisted of 2640 individuals without reported food allergy. HLA-DRB1∗13:02 was the most significant allele associated with peanut allergy (adjusted odds ratio = 1.94 [95% CI = 1.28-2.93]), and HLA-DQA1∗01:02 was the top association with peanut-related anaphylaxis (aOR = 1.67 [95% CI = 1.14-2.44]). Amino acid polymorphisms at position 71 in the binding groove of HLA-DRB1 were associated with peanut allergy and estimated to affect peanut allergen binding affinity. Conclusions: In a cohort of Black individuals, this study independently identified the same associations of peanut allergy and HLA that were previously observed in non-Hispanic White individuals. Our findings suggest that specific HLA binding groove amino acid polymorphisms may confer similar peanut allergy risk across population groups and HLA alleles.
https://doi.org/10.1016/j.jacig.2025.100485
Prediction of pediatric peanut oral food challenge outcomes using machine learning
Background: Clinical testing, including food-specific skin and serum IgE level tests, provides limited accuracy to predict food allergy. Confirmatory oral food challenges (OFCs) are often required, but the associated risks, cost, and logistic difficulties comprise a barrier to proper diagnosis. Objective: We sought to utilize advanced machine learning methodologies to integrate clinical variables associated with peanut allergy to create a predictive model for OFCs to improve predictive performance over that of purely statistical methods. Methods: Machine learning was applied to the Learning Early about Peanut Allergy (LEAP) study of 463 peanut OFCs and associated clinical variables. Patient-wise cross-validation was used to create ensemble models that were evaluated on holdout test sets. These models were further evaluated by using 2 additional peanut allergy OFC cohorts: the IMPACT study cohort and a local University of Michigan cohort. Results: In the LEAP data set, the ensemble models achieved a maximum mean area under the curve of 0.997, with a sensitivity and specificity of 0.994 and 1.00, respectively. In the combined validation data sets, the top ensemble model achieved a maximum area under the curve of 0.871, with a sensitivity and specificity of 0.763 and 0.980, respectively. Conclusions: Machine learning models for predicting peanut OFC results have the potential to accurately predict OFC outcomes, potentially minimizing the need for OFCs while increasing confidence in food allergy diagnoses.
https://doi.org/10.1016/j.jacig.2024.100252
Pediatric oral food challenges in the outpatient setting: A single-center experience
Background: Oral food challenge (OFC) is the criterion standard for diagnosing food allergy (FA). It is important to have parameters to aid in selecting ideal OFC candidates. Objective: We sought to characterize outcomes and predictors of OFCs for common food allergens. Methods: We completed a retrospective chart review of all OFCs for IgE-mediated FA performed at Duke University pediatric allergy clinics from June 2017 through May 2022. Patients were deemed eligible for milk, egg, and nut OFC if testing revealed a specific IgE level not exceeding 2 kU/L and a skin prick test (SPT) resulting in a wheal size not exceeding 5 mm. Different parameters were followed for selecting candidates for baked challenge. Results: A total of 663 OFCs were conducted on 510 patients (59% male). The most common foods challenged were peanut (26%), plain egg (23%), baked egg (8%), and milk (8%), with pass rates of 84%, 88%, 62%, and 84%, respectively. Of the patients who failed OFC, 84% had objective symptoms, 23% had multisystemic reactions, and 15% required epinephrine. Although the presence of a personal or family history of atopy or prior failed OFC was not associated with outcomes, a history of anaphylaxis (regardless of the trigger) was associated with increased risk of failure. Conclusion: Although there are no established consensus guidelines, our study provides a benchmark illustrating that cutoffs of a specific IgE level not exceeding 2 kU/L and SPT finding not exceeding 5 mm result in a failure rate of approximately 13% for nonbaked milk, nonbaked egg, and nuts. The high rate of failed baked egg OFCs is likely related to selection bias, but our results illustrate the low negative predictive value of ovomucoid.
https://doi.org/10.1016/j.jacig.2023.100187