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Review
. 2020 May:139:111259.
doi: 10.1016/j.fct.2020.111259. Epub 2020 Mar 13.

Updated population minimal eliciting dose distributions for use in risk assessment of 14 priority food allergens

Benjamin C Remington  1 , Joost Westerhout  1 , Marie Y Meima  1 , W Marty Blom  2 , Astrid G Kruizinga  1 , Matthew W Wheeler  3 , Steve L Taylor  4 , Geert F Houben  1 , Joseph L Baumert  4
Affiliations
Review

Updated population minimal eliciting dose distributions for use in risk assessment of 14 priority food allergens

Benjamin C Remington et al. Food Chem Toxicol. 2020 May.

Abstract

Food allergy and allergen management are important global public health issues. In 2011, the first iteration of our allergen threshold database (ATDB) was established based on individual NOAELs and LOAELs from oral food challenge in roughly 1750 allergic individuals. Population minimal eliciting dose (EDp) distributions based on this dataset were published for 11 allergenic foods in 2014. Systematic data collection has continued (2011-2018) and the dataset now contains over 3400 data points. The current study provides new and updated EDp values for 14 allergenic foods and incorporates a newly developed Stacked Model Averaging statistical method for interval-censored data. ED01 and ED05 values, the doses at which 1%, and respectively 5%, of the respective allergic population would be predicted to experience any objective allergic reaction were determined. The 14 allergenic foods were cashew, celery, egg, fish, hazelnut, lupine, milk, mustard, peanut, sesame, shrimp (for crustacean shellfish), soy, walnut, and wheat. Updated ED01 estimates ranged between 0.03 mg for walnut protein and 26.2 mg for shrimp protein. ED05 estimates ranged between 0.4 mg for mustard protein and 280 mg for shrimp protein. The ED01 and ED05 values presented here are valuable in the risk assessment and subsequent risk management of allergenic foods.

Keywords: Allergy; Food; Labeling; Model averaging; Risk assessment; Threshold.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1.
Fig. 1.
Dose distribution modelling for peanut (expressed as discrete dose of mg peanut protein) utilizing Bayesian Stacked Parametric Survival methods with Frailty Components and Interval Censored Failure Times as described (Wheeler et al., 2019). In this Stacked Model Averaging technique, five different parametric distributions are modelled, weighted and combined into a single dose distribution estimate in order to maximize the predictive accuracy of the calculated ED values. The predicted Stacked Model Averaging distribution estimate (red line) is presented with its corresponding 95% posterior predicted failure times (dashed red lines). The Kaplan-Meier curves for each individual study in the dataset are also presented (black lines, darker indicates study with more observations).
See this image and copyright information in PMC

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