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  • Title: Longitudinal modelling of the exposure of young UK patients with PKU to acesulfame K and sucralose.
    Author: O'Sullivan AJ, Pigat S, O'Mahony C, Gibney MJ, McKevitt AI.
    Journal: Food Addit Contam Part A Chem Anal Control Expo Risk Assess; 2017 Nov; 34(11):1863-1874. PubMed ID: 28782989.
    Abstract:
    Artificial sweeteners are used in protein substitutes intended for the dietary management of inborn errors of metabolism (phenylketonuria, PKU) to improve the variety of medical foods available to patients and ensure dietary adherence to the prescribed course of dietary management. These patients can be exposed to artificial sweeteners from the combination of free and prescribed foods. Young children have a higher risk of exceeding acceptable daily intakes (ADI) for additives than adults, due to higher food intakes per kg body weight. Young patients with PKU aged 1-3 years can be exposed to higher levels of artificial sweeteners from these dual sources than normal healthy children and are at a higher risk of exceeding the ADI. Standard intake assessment methods are not adequate to assess the additive exposure of young patients with PKU. The aim of this study was to estimate the combination effect on the intake of artificial sweeteners and the impact of the introduction of new provisions for an artificial sweetener (sucralose, E955) on exposure of PKU patients using a validated probabilistic model. Food consumption data were derived from the food consumption survey data of healthy young children in the United Kingdom from the National Diet and Nutrition Survey (NDNS, 1992-2012). Specially formulated protein substitutes as foods for special medical purposes (FSMPs) were included in the exposure model to replace restricted foods. Inclusion of these protein substitutes is based on recommendations to ensure adequate protein intake in these patients. Exposure assessment results indicated the availability of sucralose for use in FSMPs for PKU leads to changes in intakes in young patients. These data further support the viability of probabilistic modelling as a means to estimate food additive exposure in patients consuming medical nutrition products.
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