BackgroundFood allergy is an increasing public health issue and the most common cause of life-threatening anaphylactic reactions. Conventional allergy tests assess for the presence of allergen-specific IgE, significantly overestimating the rate of true clinical allergy and resulting in overdiagnosis and adverse effect on health-related quality of life.ObjectiveTo undertake initial validation and assessment of a novel diagnostic tool, we used the mast cell activation test (MAT).MethodsPrimary human blood-derived mast cells (MCs) were generated from peripheral blood precursors, sensitized with patients' sera, and then incubated with allergen. MC degranulation was assessed by means of flow cytometry and mediator release. We compared the diagnostic performance of MATs with that of existing diagnostic tools to assess in a cohort of peanut-sensitized subjects undergoing double-blind, placebo-controlled challenge.ResultsHuman blood-derived MCs sensitized with sera from patients with peanut, grass pollen, and Hymenoptera (wasp venom) allergy demonstrated allergen-specific and dose-dependent degranulation, as determined based on both expression of surface activation markers (CD63 and CD107a) and functional assays (prostaglandin D2 and β-hexosaminidase release). In this cohort of peanut-sensitized subjects, the MAT was found to have superior discrimination performance compared with other testing modalities, including component-resolved diagnostics and basophil activation tests. Using functional principle component analysis, we identified 5 clusters or patterns of reactivity in the resulting dose-response curves, which at preliminary analysis corresponded to the reaction phenotypes seen at challenge.ConclusionThe MAT is a robust tool that can confer superior diagnostic performance compared with existing allergy diagnostics and might be useful to explore differences in effector cell function between basophils and MCs during allergic reactions.

Background: Peanut allergy causes severe and fatal reactions. Current food allergen labeling does not address these risks adequately against the burden of restricting food choice for allergic patients because of limited data on thresholds of reactivity and the influence of everyday factors. Objective: We estimated peanut threshold doses for a United Kingdom population with peanut allergy and examined the effect of sleep deprivation and exercise. Methods: In a crossover study, after blind challenge, participants with peanut allergy underwent 3 open peanut challenges in random order: with exercise after each dose, with sleep deprivation preceding challenge, and with no intervention. Primary outcome was the threshold dose triggering symptoms (in milligrams of protein). Primary analysis estimated the difference between the nonintervention challenge and each intervention in log threshold (as percentage change). Dose distributions were modeled, deriving eliciting doses in the population with peanut allergy. Results: Baseline challenges were performed in 126 participants, 100 were randomized, and 81 (mean age, 25 years) completed at least 1 further challenge. The mean threshold was 214 mg (SD, 330 mg) for nonintervention challenges, and this was reduced by 45% (95% CI, 21% to 61%; P 5 .001) and 45% (95% CI, 22% to 62%; P 5 .001) for exercise and sleep deprivation, respectively. Mean estimated eliciting doses for 1% of the population were 1.5 mg (95% CI, 0.8-2.5 mg) during nonintervention challenge (n 5 81), 0.5 mg (95% CI, 0.2-0.8 mg) after sleep, and 0.3 mg (95% CI, 0.1-0.6 mg) after exercise. Conclusion: Exercise and sleep deprivation each significantly reduce the threshold of reactivity in patients with peanut allergy, putting them at greater risk of a reaction. Adjusting reference doses using these data will improve allergen risk management and labeling to optimize protection of consumers with peanut allergy.

Food anaphylaxis may be pathophysiologically different than anaphylaxis caused by nonfood triggers. Currently, there are no robust, clinically useful predictors of severity in food allergy. It is likely that patient-specific reaction phenotypes exist in food allergy, which may affect the risk of severe anaphylaxis. Allergen immunotherapy may modulate these phenotypes. Machine-based learning may help with endotype discovery in anaphylaxis.

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The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. Disclosure of potential conflict of interest: M. H. Shamji has received grants from ALK-Abell o, Regeneron, Merck, ASIT Biotech.sa, and the Immune Tolerance Network and has received personal fees from ASIT Biotech.sa, ALK-Abell o, Allergopharma, and UCB. E. N.