Using decomposed household food acquisitions as inputs of a Kinetic Dietary 				Exposure Model

Pyrrolizidine alkaloids (PAs) are a large group of botanical toxins of concern, as they are considered genotoxic carcinogens, with long-term dietary exposure presenting an elevated risk of liver cancer. PAs can contaminate honey through honeybees visiting the flowers of PA-containing plant species. A program of monitoring New Zealand honey has been undertaken over several years to build a comprehensive dataset on the concentration, regional and seasonal distribution, and botanical origin of 18 PAs and PA N-oxides. A bespoke probabilistic exposure model has then been used to assess the averaged lifetime dietary risk to honey consumers, with exposures at each percentile of the model characterized for risk using a margin of exposure from the Joint World Health Organization and United Nations Food and Agriculture Organization Expert Committee on Food Additives (JECFA) Benchmark Dose. Survey findings identify the typical PA types for New Zealand honey as lycopsamine, echimidine, retrorsine and senecionine. Regional and seasonal variation is evident in the types and levels of total PAs, linked to the ranges and flowering times of certain plants. Over a lifetime basis, the average exposure an individual will receive through honey consumption is considered within tolerable levels, although there are uncertainties over high and brand-loyal consumers, and other dietary contributors. An average lifetime risk to the general population from PAs in honey is not expected. However, given the uncertainties in the assessment, risk management approaches to limit or reduce exposures through honey are still of value.

Download Full-text

BIKE: Dietary Exposure Model for Foodborne Microbiological and Chemical Hazards

Foods ◽

10.3390/foods10112520 ◽

2021 ◽

Vol 10 (11) ◽

pp. 2520

Author(s):

Jukka Ranta ◽

Antti Mikkelä ◽

Johanna Suomi ◽

Pirkko Tuominen

Keyword(s):

User Interface ◽

Synthetic Data ◽

Dietary Exposure ◽

Assessment Model ◽

Exposure Model ◽

Bayesian Hierarchical ◽

Chemical Hazards ◽

Consumption Data ◽

Shiny App ◽

Model Structures

BIKE is a Bayesian dietary exposure assessment model for microbiological and chemical hazards. A graphical user interface was developed for running the model and inspecting the results. It is based on connected Bayesian hierarchical models, utilizing OpenBUGS and R in tandem. According to occurrence and consumption data given as inputs, a specific BUGS code is automatically written for running the Bayesian model in the background. The user interface is based on shiny app. Chronic and acute exposures are estimated for chemical and microbiological hazards, respectively. Uncertainty and variability in exposures are visualized, and a few optional model structures can be used. Simulated synthetic data are provided with BIKE for an example, resembling real occurrence and consumption data. BIKE is open source and available from github.

Download Full-text

Research on exposure of residents to pesticides in The Netherlands: Study Protocol (Preprint)

10.2196/preprints.27883 ◽

2021 ◽

Author(s):

Daniel M Figueiredo ◽

Esmeralda JM Krop ◽

Jan Duyzer ◽

Rianda M Gerritsen-Ebben ◽

Yvonne M Gooijer ◽

...

Keyword(s):

The Netherlands ◽

Study Protocol ◽

Environmental Samples ◽

Pesticide Exposure ◽

Dietary Exposure ◽

Environmental Data ◽

Exposure Model ◽

Agricultural Fields ◽

Modelling Framework ◽

Lessons Learnt

BACKGROUND Application of pesticides in the vicinity of homes has caused concern regarding possible health effects in residents living close by. However, the high spatiotemporal variation of pesticide levels and lack of knowledge regarding contribution of exposure routes greatly complicates exposure assessment approaches. OBJECTIVE To describe the study protocol of a large exposure survey in The Netherlands assessing pesticide exposure of residents living close (< 250 meters) to agricultural fields, to better understand possible routes of exposure, to develop an integrative exposure model for residential exposure, and to describe lessons learnt. METHODS We performed an observational study involving residents living in the vicinity of agricultural fields and residents living more than 500 meters away from any agricultural fields (controls). Residential exposures were measured both during pesticide use period (UP) after a specific application, and non-use period (NP), during 7 and 2 days, respectively. We collected environmental samples, outdoor and indoor air, dust, soil (garden, field), and personal samples (urine, hand wipes). We also collected data on spraying applications, as well as on home characteristics, participant’s demographics and food habits via questionnaires and diaries. Environmental samples were analyzed for 46 prioritized pesticides. Urine samples were measured for biomarkers of a subset of five pesticides. Alongside the field study, and by taking spray events and environmental data into account, we developed a modelling framework to estimate environmental exposure of residents to pesticides. RESULTS Our study was conducted between 2016 and 2019. We assessed 96 homes and 192 participants, including seven farmers and 28 controls. We followed 14 applications, applying 20 active ingredients. We collected ~5000 samples: 1018 air, 445 dust (224 vacuumed floor, 221 doormat), 265 soil (238 garden, 27 fields), 2485 urines samples, 112 handwipes, 91 tank mixtures. CONCLUSIONS To our knowledge, this is the first study on resident’s exposure to pesticides addressing all major non-dietary exposure sources and routes (air, soil, dust). Our protocol provides insights on used sampling techniques, the wealth of data collected, developed methods, modelling framework and lessons learnt. Resources and data are open for future collaborations on this important topic.

Download Full-text

Comprehensive European dietary exposure model (CEDEM) for food additives

Food Additives & Contaminants Part A ◽

10.1080/19440049.2016.1166898 ◽

2016 ◽

Vol 33 (5) ◽

pp. 772-781 ◽

Cited By ~ 8

Author(s):

David R. Tennant

Keyword(s):

Food Additives ◽

Dietary Exposure ◽

Exposure Model

Download Full-text

Improving Dietary Exposure Models by Imputing Biomonitoring Data through ABC Methods

The International Journal of Biostatistics ◽

10.1515/ijb-2013-0062 ◽

2014 ◽

Vol 10 (2) ◽

Cited By ~ 1

Author(s):

Camille Béchaux ◽

Amélie Crépet ◽

Stéphan Clémençon

Keyword(s):

Risk Assessment ◽

Body Burden ◽

Dietary Exposure ◽

Human Biomonitoring ◽

The Body ◽

Exposure Model ◽

Dietary Intakes ◽

Burden Distribution ◽

Contamination Levels ◽

Approximate Bayesian

AbstractNew data are available in the field of risk assessment: the biomonitoring data which is measurement of the chemical dose in a human tissue (e.g. blood or urine). These data are original because they represent direct measurements of the dose of chemical substances really taken up from the environment, whereas exposure is usually assessed from contamination levels of the different exposure media (e.g. food, air, water, etc.) and statistical models. However, considered alone, these data provide little help from the perspective of Public Health guidance. The objective of this paper is to propose a method to exploit the information provided by human biomonitoring in order to improve the modeling of exposure. This method is based on the Kinetic Dietary Exposure Model which takes into account the pharmacokinetic elimination and the accumulation phenomenon inside the human body. This model is corrected to account for any possible temporal evolution in exposure by adding a scaling function which describes this evolution. Approximate Bayesian Computation is used to fit this exposure model from the biomonitoring data available. Specific summary statistics and appropriate distances between simulated and observed statistical distributions are proposed and discussed in the light of risk assessment. The promoted method is then applied to measurements of blood concentration of dioxins in a group of French fishermen families. The outputs of the model are an estimation of the body burden distribution from observed dietary intakes and the evolution of dietary exposure to dioxins in France between 1930 and today. This model successfully fit to dioxins data can also be used with other biomonitoring data to improve the risk assessment to many other contaminants.

Download Full-text