Jessica Tressou

The PERICLES research program: An integrated approach to characterize the combined effects of mixtures of pesticide residues to which the French population is exposed

Due to the broad spectrum of pesticide usages, consumers are exposed to mixtures of residues, which may have combined effects on human health. The PERICLES research program aims to test the potential combined effects of pesticide mixtures, which are likely to occur through dietary exposure. The co-exposure of the French general population to 79 pesticide residues present in the diet was first assessed. A Bayesian nonparametric model was then applied to define the main mixtures to which the French general population is simultaneously and most heavily exposed. Seven mixtures made of two to six pesticides were identified from the exposure assessment. An in vitro approach was used for investigating the toxicological effects of these mixtures and their corresponding individual compounds, using a panel of cellular models, i.e. primary rat and human hepatocytes, liver, intestine, kidney, colon and brain human cell lines. A set of cell functions and corresponding end-points were monitored such as cytotoxicity, real-time cell impedance, genotoxicity, oxidative stress, apoptosis and PXR nuclear receptor transactivation. The mixtures were tested in equimolar concentrations. Among the seven mixtures, two appeared highly cytotoxic, five activated PXR and depending on the assay one or two were genotoxic. In some experiments, the mixture effect was quantitatively different from the effect expected from the addition concept. The PERICLES program shows that, for the most pesticides mixtures to which the French general population is exposed, the toxic effects observed on human cells cannot be easily predicted based on the toxic potential of each compound. Consequently, additional studies should be carried on in order to more accurately define the mixtures of chemicals to which the consumers are exposed, as well as to improve the investigation, prediction and monitoring of their potential human health effects.

Identification of the main pesticide residue mixtures to which the French population is exposed

Owing to the intensive use of pesticides and their potential persistence in the environment, various pesticide residues can be found in the diet. Consumers are therefore exposed to complex pesticide mixtures which may have combined adverse effects on human health. By modelling food exposure to multiple pesticides, this paper aims to determine the main mixtures to which the general population is exposed in France. Dietary exposure of 3337 individuals from the INCA2 French national consumption survey was assessed for 79 pesticide residues, based on results of the 2006 French food monitoring programmes. Individuals were divided into groups with similar patterns of co-exposure using the clustering ability of a Bayesian nonparametric model. In the 5 groups of individuals with the highest exposure, mixtures are formed by pairs of pesticides with correlations above 0.7. Seven mixtures of 2-6 pesticides each were characterised. We identified the commodities that contributed the most to exposure. Pesticide mixtures can either be components of a single plant protection product applied together on the same crop or be from separate products that are consumed together during a meal. Of the 25 pesticides forming the mixtures, two--DDT and Dieldrin--are known persistent organic pollutants. The approach developed is generic and can be applied to all types of substances found in the diet in order to characterise the mixtures that should be studied first because of their adverse effects on health.

Identification of pesticide mixtures and connection between combined exposure and diet.

The identification of the major associations of pesticides to which the population is exposed is the first step for the risk assessment of mixtures. Moreover, the interpretation of the mixtures through the individuals diet and the characterization of potentially high-risk populations constitute a useful tool for risk management. This paper proposes a method based on Non-Negative Matrix Factorization which allows the identification of the major mixtures to which the French population is exposed and the connection between this exposure and the diet. Exposure data of the French population are provided by the Second French Total Diet Study. The NMF is implemented on consumption data to extract consumption systems which are combined with the residue levels to link dietary behavior with exposure to mixtures of pesticides. A clustering of the individuals is achieved in order to highlight clusters of individuals with similar exposure to pesticides/consumption habits. The model provides 6 main consumption systems, 6 associated mixtures of pesticides and the description of the population which is most exposed to each mixture. Two different ways to estimate the matrix providing the mixtures of pesticides to which the population is exposed are suggested. Their advantages in different contexts of risk assessment are discussed.

Statistical analysis of a dynamic model for dietary contaminant exposure

This paper is devoted to the statistical analysis of a stochastic model introduced in [P. Bertail, S. Clémençon, and J. Tressou, A storage model with random release rate for modelling exposure to food contaminants, Math. Biosci. Eng. 35 (1) (2008), pp. 35–60] for describing the phenomenon of exposure to a certain food contaminant. In this modelling, the temporal evolution of the contamination exposure is entirely determined by the accumulation phenomenon due to successive dietary intakes and the pharmacokinetics governing the elimination process inbetween intakes, in such a way that the exposure dynamic through time is described as a piecewise deterministic Markov process. Paths of the contamination exposure process are scarcely observable in practice, therefore intensive computer simulation methods are crucial for estimating the time-dependent or steady-state features of the process. Here we consider simulation estimators based on consumption and contamination data and investigate how to construct accurate bootstrap confidence intervals (CI) for certain quantities of considerable importance from the epidemiology viewpoint. Special attention is also paid to the problem of computing the probability of certain rare events related to the exposure process path arising in dietary risk analysis using multilevel splitting or importance sampling (IS) techniques. Applications of these statistical methods to a collection of data sets related to dietary methyl mercury contamination are discussed thoroughly.

Fatty acid dietary intake in the general French population: are the French Agency for Food, Environmental and Occupational Health & Safety (ANSES) national recommendations met?

Quantity and quality of fatty acids (FA) in diet influence CVD risk. Consequently, health authorities promote recommended dietary intakes for FA, looking for optimal intakes in a primary prevention of CVD perspective. In parallel, a few data are available detailing intakes in national populations. The objective of the present study was to perform a large analysis combining the data of the French National Survey INCA 2 on food consumption performed in 2006 and 2007, and the nutritional content of food consumed in France updated in 2013 by the French Information Centre on Food Quality, to explore in details the FA intakes in French adults using the most recent available data. To compare the discrepancies in the observed intake levels with the French recommended levels, a weighted fat adherence score was built combining intakes of the different FA. Individual scores were computed in relation to official recommendations, and potential explanatory factors were identified. These data show that SFA intakes are persistently higher than national recommendations, combined with low intakes of MUFA and PUFA, particularly long-chain n-3 FA. Only 14·6 % of the French population met DHA intake recommendation, 7·8 % for EPA and 21·6 % for SFA. This situation remains unfavourable in terms of primary prevention of CVD. Consuming fish and other sources of n-3 FA, living in the south of France, being female, having a higher education level, and low alcohol consumption were associated with a healthier fat adherence score.

Population effects and variability

Chemical risk assessment for human health requires a multidisciplinary approach through four steps: hazard identification and characterization, exposure assessment, and risk characterization. Hazard identification and characterization aim to identify the metabolism and elimination of the chemical (toxicokinetics) and the toxicological dose-response (toxicodynamics) and to derive a health-based guidance value for safe levels of exposure. Exposure assessment estimates human exposure as the product of the amount of the chemical in the matrix consumed and the consumption itself. Finally, risk characterization evaluates the risk of the exposure to human health by comparing the latter to with the health-based guidance value. Recently, many research efforts in computational toxicology have been put together to characterize population variability and uncertainty in each of the steps of risk assessment to move towards more quantitative and transparent risk assessment. This chapter focuses specifically on modeling population variability and effects for each step of risk assessment in order to provide an overview of the statistical and computational tools available to toxicologists and risk assessors. Three examples are given to illustrate the applicability of those tools: derivation of pathway-related uncertainty factors based on population variability, exposure to dioxins, dose-response modeling of cadmium.

Bootstrapping Robust Statistics for Markovian Data Applications to Regenerative R‐Statistics and L‐Statistics

This article is devoted to extending the notion of robustness in the context of Markovian data, based on their (pseudo‐)regenerative properties and by studying its impact on the regenerative block‐bootstrap (RBB). Precisely, it is shown how to possibly define the ‘influence function’ in this framework, so as to measure the impact of (pseudo‐)regeneration data blocks on the statistic of interest. We also define the concept of regeneration‐based signed linear rank statistic and L‐statistic, as specific functionals of the regeneration blocks, which can be made robust against outliers in this sense. The asymptotic validity of the approximate RBB (ARBB), is established here, when applied to such statistics. For illustration purpose, we compare (A)RBB confidence intervals for the mean, the median and some L‐statistics related to the (supposedly existing) stationary probability distribution μ(dx) of the chain observed and for their robustified versions as well.

Reporting and Modeling of Results Below the Limit of Detection

Conducting dietary exposure assessment (E) consists in combining deterministically or probabilistically food consumption figures (Q) with concentrations (C) of a given chemical substance in a number of foods or food categories. Occurrence data can be obtained either from control and monitoring programs or from a total diet Study (TDS). In both cases, data reported to be below the limit of detection (LOD), called “non-detects” or “left-censored data”, are likely to have a critical influence on the results of the assessment. Both the LOD and the limit of quantification (LOQ) are of special importance for exposure estimations in risk assessments as they determine the minimum value that can be detected and quantified, respectively.