Metabolomics Tools for Describing Complex Pesticide Exposure in Pregnant Women in Brittany (France)

Abstract

BackgroundThe use of pesticides and the related environmental contaminations can lead to human exposure to various molecules. In early-life, such exposures could be responsible for adverse developmental effects. However, human health risks associated with exposure to complex mixtures are currently under-explored.ObjectiveThis project aims at answering the following questions: What is the influence of exposures to multiple pesticides on the metabolome? What mechanistic pathways could be involved in the metabolic changes observed?MethodsBased on the PELAGIE cohort (Brittany, France), 83 pregnant women who provided a urine sample in early pregnancy, were classified in 3 groups according to the surface of land dedicated to agricultural cereal activities in their town of residence. Nuclear magnetic resonance-based metabolomics analyses were performed on urine samples. Partial Least Squares Regression-Discriminant Analysis (PLS-DA) and polytomous regressions were used to separate the urinary metabolic profiles from the 3 exposure groups after adjusting for potential confounders.ResultsThe 3 groups of exposure were correctly separated with a PLS-DA model after implementing an orthogonal signal correction with pareto standardizations (R2 = 90.7% and Q2 = 0.53). After adjusting for maternal age, parity, body mass index and smoking habits, the most statistically significant changes were observed for glycine, threonine, lactate and glycerophosphocholine (upward trend), and for citrate (downward trend).ConclusionThis work suggests that an exposure to complex pesticide mixtures induces modifications of metabolic fingerprints. It can be hypothesized from identified discriminating metabolites that the pesticide mixtures could increase oxidative stress and disturb energy metabolism.

Integration of Traditional and Metabolomics Biomarkers Identifies Prognostic Metabolites for Predicting Responsiveness to Nutritional Intervention against Oxidative Stress and Inflammation

“…The ROC curve for a two-metabolite set (glycine and PAG) outperformed single markers (glycine, PAG, or adenine), highlighting that this set is a promising biomarker that possesses the greatest discriminatory power to predict individual responses to KBR consumption in sedentary overweight/obese adults. Several other studies have supported an association between urinary glycine or PAG levels and status of oxidative stress [47,48,49,50,51,52] or inflammatory stress [52]. Finally, the predictive accuracy of this two-metabolite set was further validated with the LOOCV procedure, because the ROC curve may lead to an overestimation of the AUC if it is constructed using all samples [53].…”

Deployment-Associated Exposure Surveillance With High-Resolution Metabolomics

“…Considerable data are available to show that environmental chemicals are present in all human blood and urine samples (15, 23, 62, 63, 66, 67). As indicated above, detection of chemicals within the body does not provide information concerning the source of exposure, and relatively little information is available concerning the distribution chemicals within human tissues and rates of elimination.…”

“…Thus, in addition to biomonitoring, measurement of metabolic derangement by HRM can be used to identify biosignatures indicating a history of exposure. Additional studies applying metabolic profiling of human exposure to cadmium (69, 70), pesticides (66), PAH exposure (71), welding fumes (72) and arsenic (73) support the use of retrospective chemical measurement for effect markers and evaluation of whether exposure has occurred at a biologically relevant dose.…”

Impact of maternal obesity on the metabolic profiles of pregnant women and their offspring at birth

“…In addition, one year of inclusion (2004) was selected, to avoid potential variability due to different storage durations of biological samples. A detailed description of this methodology is published elsewhere (Bonvallot et al, 2013). Of the 338 eligible women, those whose BMI was unavailable or below 18.5 were excluded (n = 15), because low body weight can influence metabolic profiles (Norman et al, 2014;SarlioLähteenkorva et al, 2004).…”

“…Each integrated region was divided by the total spectral intensity in order to normalise values. Spectra A c c e p t e d m a n u s c r i p t acquisition, pre-processing step, and metabolite identification have been described previously (Bonvallot et al, 2013).…”