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Int J Hyg Environ Health. 2018 Dec 19. pii: S1438-4639(18)30548-0. doi: 10.1016/j.ijheh.2018.12.002. [Epub ahead of print]

Selecting mixtures on the basis of dietary exposure and hazard data: application to pesticide exposure in the European population in relation to steatosis.

Author information

1
ANSES, French Agency for Food, Environmental and Occupational Health and Safety, Risk Assessment Department, Methodology and Studies Unit, 947001, Maisons-Alfort, France. Electronic address: amelie.crepet@anses.fr.
2
ANSES, French Agency for Food, Environmental and Occupational Health and Safety, Risk Assessment Department, Methodology and Studies Unit, 947001, Maisons-Alfort, France.
3
RIVM, National Institute for Public Health and the Environment, The Netherlands, PO Box 1, 3720 BA, Bilthoven, the Netherlands.
4
Wageningen University & Research, Biometris, Droevendaalsesteeg 1, 6708 PB, Wageningen, the Netherlands.
5
National Institute of Public Health, Environmental Health Centre, Trubarjeva 2, Ljubljana, Slovenia.
6
Fera Science Ltd, Sand Hutton, York, YO41 1LZ, United Kingdom.
7
Benaki Phytopathological Institute, Department of Pesticides Control and Phytopharmacy, Laboratory of Pesticide Residues, 8 Stefanou Delta Street, Kifissia, Athens, 14561, Greece.
8
State General Laboratory, Ministry of Nicosia, Cyprus.
9
National Institute of Public Health in Prague, Centre for Health, Nutrition and Food, Brno, Czech Republic.
10
Laboratory of Toxicology and Environmental Health, School of Medicine, IISPV, Universitat Rovira I Virgili, Reus, Catalonia, Spain.
11
Technical University of Denmark, National Food Institute, Division of Risk Assessment and Nutrition, Kemitorvet, Building 201, DK 2800, Lyngby, Denmark.
12
ICPS, International Centre for Pesticides and Health Risk Prevention, ASST Fatebenefratelli Sacco, Ospedale L. sacco via GB Grassi 74, 20157, Milano, Italy.
13
ICPS, International Centre for Pesticides and Health Risk Prevention, ASST Fatebenefratelli Sacco, Ospedale L. sacco via GB Grassi 74, 20157, Milano, Italy; Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, Italy.
14
Ugent University, Coupure Links, 653b, Ghent, Belgium.
15
Ugent, Laboratory for Crop Protection Chemistry, Coupure Links, 653b, Ghent, Belgium.

Abstract

Populations are exposed to mixtures of pesticides through their diet on a daily basis. The question of which substances should be assessed together remains a major challenge due to the complexity of the mixtures. In addition, the associated risk is difficult to characterise. The EuroMix project (European Test and Risk Assessment Strategies for Mixtures) has developed a strategy for mixture risk assessment. In particular, it has proposed a methodology that combines exposures and hazard information to identify relevant mixtures of chemicals belonging to any cumulative assessment group (CAG) to which the European population is exposed via food. For the purposes of this study, food consumption and pesticide residue data in food and drinking water were obtained from national surveys in nine European countries. Mixtures of pesticides were identified by a sparse non-negative matrix underestimation (SNMU) applied to the specific liver steatosis effect in children from 11 to 15 years of age, and in adults from 18 to 64 years of age in nine European countries. Exposures and mixtures of 144 pesticides were evaluated through four different scenarios: (1) chronic exposure with a merged concentration dataset in the adult population, (2) chronic exposure with country-specific concentration datasets in the adult population, (3) acute exposure with a merged concentration dataset in the adult population, and (4) chronic exposure with a merged concentration dataset in the paediatric population. The relative potency factors of each substance were calculated to express their potency relative to flusilazole, which was chosen as the reference compound. The selection of mixtures and the evaluation of exposures for each country were carried out using the Monte Carlo Risk Assessment (MCRA) software. Concerning chronic exposure, one mixture explained the largest proportion of the total variance for each country, while in acute exposure, several mixtures were often involved. The results showed that there were 15 main pesticides in the mixtures, with a high contribution of imazalil and dithiocarbamate. Since the concentrations provided by the different countries were merged in the scenario using merged concentration data, differences between countries result from differences in food consumption behaviours. These results support the approach that using merged concentration data to estimate exposures in Europe seems to be realistic, as foods are traded across European borders. The originality of the proposed approach was to start from a CAG and to integrate information from combined exposures to identify a refined list of mixtures with fewer components. As this approach was sensitive to the input data and required significant resources, efforts should continue regarding data collection and harmonisation among the different aspects within the pesticides regulatory framework, and to develop methods to group substances and mixtures to characterise the risk.

KEYWORDS:

Cumulative assessment group; Dietary exposure and hazard; Mixture prioritisation; Relative potency factors; Sparse non-negative matrix underestimation

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