# Maternal exposure to pesticides and gestational diabetes mellitus in the Elfe Cohort

Léa Guesdon, Charline Warembourg, Cécile Chevrier, Blandine de Lauzon-Guillain, Julien Caudeville, Marie-aline Charles, Maela Le Lous, Pauline Blanc-Petitjean, Rémi Béranger

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Léa Guesdon, Charline Warembourg, Cécile Chevrier, Blandine de Lauzon-Guillain, Julien Caudeville, et al.. Maternal exposure to pesticides and gestational diabetes mellitus in the Elfe Cohort. Environmental Research, 2025, 284, pp.122275. 10.1016/j.envres.2025.122275. hal-05162376

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# Maternal exposure to pesticides and gestational diabetes mellitus in the Elfe cohort

Léa Guesdon<sup>a</sup>, Charline Warembourg<sup>a, b</sup>, Cécile Chevrier<sup>a</sup>, Blandine de Lauzon-Guillain<sup>b, c</sup>, Julien Caudeville<sup>c, d</sup>, Marie-Aline Charles<sup>b, d</sup>, Maela Le Lou<sup>e, f</sup>, Pauline Blanc-Petitjean<sup>a, *, 1, e</sup>, Rémi Béranger<sup>a, 1, d</sup>

## ARTICLE INFO

### Keywords:

Gestational diabetes mellitus

Pesticides

Pregnancy

Dietary exposure

Geographic information systems

Toxicogenetics

## ABSTRACT

**Background:** Gestational diabetes mellitus (GDM) increases risks of adverse perinatal outcomes and metabolic disorders in offspring. Some endocrine-disrupting chemicals are presumed to interfere with glucose metabolism. We aimed to assess the association between non-occupational exposures to pesticides and the risk of GDM.

**Methods:** The study was conducted on 11 512 women (791 with GDM) from the French nationwide Elfe cohort. Among 114 suspected pesticides identified using a toxicogenomic approach, 62 with detection frequency ≥10 % were analyzed across three sources of exposure: residential use (self-reported, n = 18), agricultural (geographic information system, n = 14) and dietary (estimated by a food-frequency questionnaire and monitoring data of pesticide residues in foods, n = 30). The association between exposure to pesticides (no or low vs. high) and the occurrence of GDM was tested with single and multi-source adjusted logistic regression models.

**Results:** In the multi-source model, dietary exposure to glyphosate (aOR = 0.6, 95 %CI: 0.5,0.9) or agricultural exposure (aOR = 0.8, 95 %CI:0.6,1.0) were associated with lower odds of GDM. The same effect was identified for two dietary pesticides: epoxiconazole (aOR = 0.6, 95 %CI: 0.5,0.8) and penconazole (aOR = 0.8, 95 %CI: 0.6,1.0). Dietary exposure to cypermethrin (aOR = 1.2, 95 %CI:1.0,1.5) and agricultural exposure to myclobutanil (aOR = 1.4, 95 %CI:1.1,1.9) were associated with higher odds of GDM. No significant association was identified for the residential use of pesticides.

**Conclusion:** This toxico-genomic-based study identifies five pesticides associated with GDM through agricultural or dietary exposure. These findings provide new insights into environmental contributors to GDM. Further research is needed to elucidate the underlying toxicological mechanisms and confirm these associations.

## 1. Introduction

Gestational diabetes mellitus (GDM) is a transient disorder of glucose tolerance that begins during pregnancy and disappears after delivery. It results from an insufficient capacity of the pancreas to balance the insulin resistance caused by the placenta, leading to chronic hyperglycemia of variable severity (Plows et al., 2018). GDM is one of the most frequent pathology of the pregnancy and its prevalence increased over

the recent years. According to the French National Perinatal Surveys, GDM affected 7.2 % of pregnant women in 2010, whereas it affected 16.4 % of them in 2021 (Le Ray et al., 2022).

GDM can lead to fetal macrosomia, and thus obstetrical dystocia, or to increased risk of various gestational disorders, such as pre-eclampsia (Metzger et al., 2008; Billionnet et al., 2017). Beyond the short-term effects, GDM can also increase the risk of the child to develop metabolic disorders in later life, such as type 2 diabetes and obesity (Lowe

<footnote><sup>a</sup> Université de Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000, Rennes, France</footnote>

<footnote><sup>b</sup> Université Paris Cité and Université Sorbonne Paris Nord, INSERM, INRAE, Centre for Research in Epidemiology and Statistics (CRESS), Paris, France</footnote>

<footnote><sup>c</sup> National Institute for Industrial Environment and Risks (INERIS), Verneuil-en-Halatte, France</footnote>

<footnote><sup>d</sup> Ined Inserm EFS Joint Unit Elfe, France</footnote>

<footnote><sup>e</sup> Department of Obstetrics and Gynecology, University Hospital of Rennes, France</footnote>

<footnote><sup>f</sup> LTSI - INSERM UMR 1099, University of Rennes 1, F35000, Rennes, France</footnote>

<footnote>* Corresponding author. Irset, Inserm UMR 1085, Rennes University, 9 Av. du Professeur Léon Bernard, 35000, Rennes, France.
*E-mail address:* Pauline.blanc-petitjean@univ-rennes.fr (P. Blanc-Petitjean).</footnote>

<footnote n="1">¹ Equally contributed.</footnote>
et al., 2019; Blotsky et al., 2019). Women who develop GDM are also 7 times more likely to develop type 2 diabetes compared to others (Bellamy et al., 2009). Interestingly, the strong association between GDM and type 2 diabetes, and the many common risk factors and candidate genes, suggest common etiologies for these two diseases (Ben-Haroush et al., 2004).

Ehrlich et al., (2016) suggested that exposure to endocrine disruptors might impact glucose homeostasis, which highlights the possible role of exogenous factors in the genesis of GDM. Among these, pesticides might be of interest. The European Commission defines pesticides as “something that prevents, destroys, or controls a harmful organism ('pest') or disease, or protects plants or plant products during production, storage and transport”. In Europe, around 350 000 tonnes of pesticides are sold every year for agricultural and non-agricultural use, including road maintenance, veterinary products, human medicine, construction materials, residential use, etc. (Eurostat, 2022). Consequently, pesticides are ubiquitous in all environmental compartments (water, air, soil), and recent studies have found high detection frequencies among pregnant women to several dozen pesticides and pesticide metabolites (Béranger et al., 2020).

In 2016, a meta-analysis of 13 studies suggested an association between exposure to pesticides and type 2 diabetes (OR = 1.61, (95 % CI: 1.37,1.88) (Evangelou et al., 2016). However, to our knowledge, only three studies have investigated the consequences of pesticide exposure on the development of GDM. Based on questionnaires, Saldana et al. observed a higher risk of GDM among women occupationally exposed to pesticides in the Agricultural Health Study (Saldana et al., 2007). The authors specifically identified four herbicides (2,4,5-T; 2,4,5-TD; atrazine; butylate) and three insecticides (diazinon; phorate; carbofuran) associated with a higher risk. Shapiro et al. found no association between GDM and urine concentration of organophosphate and organochlorine pesticide metabolites in pregnant women from the Canadian Maternal-Infant Research on Environmental Chemicals (MIREC, n = 1274) (Shapiro et al., 2016). Similarly, Saunders et al. found no association between serum level of chlordecone (a persistent organochlorine pesticide) and the risk of GDM in 779 women from the Timoun mother-child cohort in the French Indies (Saunders et al., 2014).

Given the high frequency of GDM and pesticide exposures, and the lack of literature on the topic, we aimed to assess the association between non-occupational exposure to pesticides from three environmental sources (residential use, agricultural and dietary) and the occurrence of GDM.

# 2. Materials and methods

## 2.1. Study design and population

Women included were part of the Elfe (Etude Longitudinale Française depuis l'Enfance) nationwide birth cohort (https://doi.org/10.48756/ined-inserm-elfe-8642), the design of which has been described previously (Charles et al., 2020). Briefly, Elfe included 18 042 women aged at least 18, who gave birth in 2011 in mainland France to a single or twin live-born child of at least 33 gestational weeks. Overall, 320 maternity hospitals agreed to participate in the recruitment of children. Inclusion was conducted during four periods, across the whole year (from April 1st to April 4th, from June 27th to July 4th, from September 27th to October 4th and from November 28th to December 5th). The number of participants in Elfe corresponded to around 1/50th of all births in France in 2011. The participation rate at inclusion was 51 %.

We included in our study all women eligible for the Elfe cohort and with available information on GDM status (17 042 of the participating women). We excluded women with pre-gestational type 1 or type 2 diabetes (n = 154) and women living in hyper-urban areas (n = 5378), defined as at least 99 % artificial area in a distance of 500 m from their home. We also excluded farmers (n = 55), who are more likely to be

occupationally and para-occupationally exposed to pesticides (Deziel et al., 2017). We finally focused on 11 512 women, including 791 (6.9 %) diagnosed with GDM (Fig. 1).

### 2.1.1. Ethical approval

Enrolled mothers provided written informed consent to participate in the Elfe cohort. Ethical approvals were obtained for the Elfe study from the Advisory Committee for Treatment of Health Research Information (Comité Consultatif sur le Traitement de l'Information en matière de Recherche dans le domaine de la Santé), the National Data Protection Authority (Commission Nationale Informatique et Libertés), and the National Committee for Statistical Information.

## 2.2. Data collection and management

Women were interviewed face to face by trained midwives and nurses during their maternity stay, and filled self-reported questionnaires at 2 months and 1 year after delivery to collect information about their lifestyle. Medical data, including the diagnosis of GDM, were collected directly from medical records.

#### 2.2.1. GDM definition

According to the latest guidelines from the French National College of Gynaecologists and Obstetricians (CNGOF), GDM screening should be performed during the 2nd trimester (24–28 weeks of amenorrhea) using an oral glucose tolerance test (75 g of glucose, diagnosis if glycemia ≥0,92 g/L – fasting; ≥1,80 g/L – 1 h after; ≥1.53 g/L – 2 h after) for women with at least one specific risk factor (Virally and Laloi-Michelin, 2010). The GDM status was collected in the medical chart as coded by the practitioners in charge of the woman's medical care.

#### 2.2.2. Identification of pesticides of interest

We focused on the pesticides most likely to be involved in the development of GDM using a toxicogenomic approach based on the Comparative Toxicogenomics Database (http://ctdbase.org/) and literature searches. The CTD reports information about relationships between chemicals, genes, and diseases published in human or animal literature. We listed 26 genes (Table A1) associated with GDM or with mechanisms related to glucose metabolism, insulin production, reuptake or sensitivity, and insulin receptors expression (Wu et al., 2016; Lin et al., 2018; Hou et al., 2017; Powe and Kwak, 2020; Hivert et al., 2014; Lowe et al., 2016; Wang and Xiuping, 2020; Guo et al., 2018). Based on the CTD, we identified 125 pesticides susceptible to interact with those genes. Four additional pesticides were identified based on the AHS study (Saldana et al., 2007). These pesticides could be identified in three exposure contexts studied in Elfe, each corresponding to distinct sources and exposure pathways: residential use, agricultural, and dietary. For simplicity, we used the term “source” as a generic descriptor of the overall exposure context, encompassing both the origin of pesticides and the routes through which women were exposed. Among the 129 pesticides of interest, 114 were identified in at least one of the three environmental sources of exposure, corresponding to 78 distinct chemicals (Fig. 2).

#### 2.2.3. Exposure from residential use of pesticides

Information on several types of residential pesticide use in the last 12 months was self-reported by the women (used/not used), and collected during a telephone interview two months after birth (participation rate: 80.3 %). Self-reported types of residential use were considered as a proxy for exposure to specific pesticides. We used comprehensive national reports on population usages to identify for each type of residential use the pesticides most commonly involved (Tessier et al., 2023; ANSES, 2010). We considered the type of residential use for analysis if at least one pesticide of our list of interest was linked to this use. Among the 13 types of residential use collected in the Elfe cohort, 8 types involved pesticides (n = 31) that were part of our list of interest: outdoor
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Flowchart of study population
legend: GDM, gestational diabetes mellitus.</figcaption></figure>

herbicide, insecticide for animal protection, flying insects, crawling in-
sects, indoor plants, outdoor plants, fungicide for outdoor plants, lice
and scabies treatment (Table A2).

2.2.4. Exposure to pesticides drift from agricultural crops

The amount of agricultural pesticide exposure near households was
estimated by combining for each crop type the acreage within 1000m
and the estimated density of pesticides applied. Residential address of
mothers at birth were geocoded by the French National Institute of
Statistics and Economic Studies (INSEE) based on national databases
(Directorate of Taxes, French Post Office, municipalities). Data were
available for 92 % of the women. Using a geographic information system
(GIS), we estimated the acreage of 28 crop types within a 1000m buffer
distance from households. Type and location of crops were obtained
from the European Graphic Parcel Registry (GPR, scale 1:5000), except
for vineyards and orchards that were obtained by combining the vege-
tation layer of the BD Topo® (French National Institute of Geographic
and Forest, IGN, scale 1:5000), and the Corine Land Cover database for
2006 (scale 1:100 000).

The density of pesticides applied to each crop types was estimated
using sales data and data on agricultural practices. The annual amount
of each pesticide active ingredient sold by industrials to farmers was
available for 2014 at the postal code level for mainland France within
the BNVd (French national databank of phytopharmaceutical sales by
distributors) (Le Gall, Chavane, and Chatelier 2013). Based on expert
assessment and considering both local agronomic practices and regula-
tory data, the French National Institute of Agronomic Research (INRAE)
defined the possible correspondence between pesticides and crop types
for agricultural practices in 2014. The French National Institute for In-
dustrial Environment and Risks (INERIS) estimated the average density
(kg/hectare) of pesticides applied to each of the crop types available in
the GPR for the year 2014. Since the amount of pesticides purchased was
attributed to the postal code of the head office of the farmer's company,
which possibly differs from the location of the crops, we averaged
densities at the district level to avoid overestimation of the exposure in
some situation. We used densities estimated from practices in 2014 to
infer those of 2011, assuming that no major changes occurred in farming
practices or pesticide regulations during this period in France. To ensure

the plausibility of estimated exposure, densities for the different crop
types were corrected based on the recommended doses for pesticide
agricultural applications in 2011 (Couteux A and Lejeune V 2010).
Estimated densities less than 10 % of the minimum recommended doses
were converted to zero, considering that probability of use was low.
Estimated densities more than 10 times the maximum recommended
dose were shrunk to this threshold, to correct likely outliers. The
quantities of agricultural pesticide applied within 1000m from house-
holds were estimated by multiplying the acreage of each crop type
within 1000m by the corresponding density of pesticides estimated for
the corresponding district. Among the 89 pesticides identified in the Elfe
cohort, 17 were part of our list of interest (Table A3).

2.2.5. Exposure to pesticide residues in contaminated foods

Women filled a self-administrated food frequency questionnaire at birth collecting their dietary habits over the last 3 months of pregnancy (Kadawathagedara et al., 2021). The questionnaire included 125 items to define the types and quantities of food consumed with 7 frequency levels (never; less than once a month; between 1 and 3 times a month; once a week; between 2 and 5 times a week; about once a day; more than once a day). The method used to assess the daily dietary exposure to pesticides residues is detailed elsewhere (de Gavelle et al., 2016). Briefly, the average level of pesticide residue contamination of different food type was defined based on the second French Total Diet Study (TDS2, 2007–2009), which provide good and representative coverage of the French diet, supplemented when needed by the 2011 national monitoring results of pesticide residues in food and drinking water, and the 2011 infant Total Diet Study (iTDS) (ANSES, 2014; Nougadère et al., 2020, 2012). The individual daily intakes of pesticide residue (EDI) were estimated in µg/kg of body weight at the end of pregnancy/day by combining data on food consumption and food contamination. To avoid overestimation of the dietary exposure, the lower bound scenario (LB) was used in the EDI estimations (i.e., values below the limit of detection (LOD) were turn to zero, and values between the LOD and the limit of quantification (LOQ) were set to the LOD). Among the 317 pesticides and metabolites identified in the Elfe cohort, 66 pesticides were part of our list of interest, and EDIs were available for 77.3 % of women (Table A4).
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Flowchart of the identification of pesticides of interest

Legend: GDM: gestational diabetes mellitus, Elfe: Etude Longitudinale Française depuis l'Enfance, ANSES: French National Agency for Food, Environmental and Occupational Health Safety.</figcaption></figure>

## 2.3. Statistical analyses

For each source, only pesticide exposure variables with a detection frequency greater than 10% of the population were analyzed, in order to maintain a minimal statistical power and to limit the number of tests.

Pesticides were first studied source by source (single source analyses), then in a multi-source approach. The residential use variables were binary by construction (presence or absence of use). The dietary and agricultural exposure variables, which were initially continuous, were dichotomized according to their distribution, in order to differentiate women with no or low levels of exposure (the first two terciles, including null values) from those more exposed (the third tertile).

### 2.3.1. Adjustment covariates

The set of adjustment covariates was defined based on a directed acyclic graph (DAG) approach (Figure A1). An a priori model was built based on the literature and the most parsimonious set of covariates was chosen for adjustment (Textor et al., 2016). Models were adjusted for housing area (rural; urban); employment (yes; no); level of education (higher or lower than high school); alcohol consumption (yes; no); smoking during pregnancy (yes; no); maternal age at birth (continuous) and pre-pregnancy body mass index (BMI, continuous). Models on dietary exposure to pesticide were firstly adjusted on the set of covariates precited (partly-adjusted model). In a secondary step, they were additionally adjusted on the dietary patterns (fully-adjusted model). We

identified five dietary patterns by principal component analysis (PCA) using declared consumption frequencies of foods during the last 3 months of pregnancy: western, balanced, bread and toppings, processed products, milk and breakfast cereals (Table A5). Dietary pattern identified were consistent with those previously described in the Elfe cohort (Kadawathagedara et al., 2021).

### 2.3.2. Single and multi-source analyses

Single and multi-source analyses were performed using logistic regression models including pesticide exposure variables (crude models) and the set of covariates (adjusted models). For the dietary and the agricultural models, we had a high number of potentially correlated pesticides to analyze. One-by-one adjusted regression models (single pollutant) were used to pre-select the pesticides. The single-source models (multi-pollutant) included pesticides associated with GDM with p-values <0.2 in the one-by-one models. The multi-source models included pesticides associated with GDM with p-values <0.05 in the adjusted single-source models.

The linearity of continuous covariates was tested using the Box Tidwell test. The maternal age, as well as the different dietary patterns, were categorized according to quartiles because of non-compliance to the test. In order to ensure the correct estimation of the coefficients, variance inflation factors (VIF) measuring multicollinearity between variables were investigated. All VIFs were below 5, suggesting acceptable level of collinearity. We also assessed the absence of influential
individuals for each model based on the Cook's distance.

### 2.3.3. Missing data

Missing data were present for the covariates representing BMI (1.2 %), smoking (0.8 %) and alcohol consumption (13.4 %) during pregnancy, level of education (0.1 %), employment (2.2 %) and maternal age (0.3 %). Missing data on adjustment covariates were completed by using multiple imputations, generating five sets to account for the randomness of the imputation. The imputation was performed with the MICE package, using the variables related to exposure or outcome and their direct or indirect cause as predictors of missing values. A combination of the results of the five complete sets was performed according to Rubin's rules in order to obtain an averaged estimate.

We considered that the statistical significance was reached at $p < 0.05$. All analyses were performed with R version 4.0.2.

### 2.3.4. Sensitivity analysis

Several sensitivity analyses were conducted to assess the robustness of the results and the variable selection strategy. We first performed analyses on the complete-case dataset to assess the influence of the imputation on our results. Secondly, we focused on women having their two first trimesters of pregnancy corresponding to the main period of pesticide application on crops (spring/summer). Thirdly, we performed the analyses on dietary exposure by focusing on women reporting consuming non-organic foods only (assuming a higher exposure to dietary pesticide residues). The same set of adjustment covariates was used for the main analyses and the sensitivity analyses.

Fourthly, we sought to confirm our source-by-source pesticide pre-selection procedure by using a ridge regression model (Hoerl and Kenward, 1970a, 1970b) that allows highly correlated exposure variables to be modelled by penalizing the size of regression coefficients (Huang et al., 2017). The penalization parameter ($\lambda$) was defined using 10-fold cross-validation to identify the value that minimized the model's mean square error ($\lambda_{min}$) (Huang et al., 2017), implemented with the *glmnet* package for R (Friedman et al., 2010). The models included the previously identified potential confounders. To improve the stability of the $\lambda$ selection, cross-validation was repeated 100 times, and the resulting 100 $\lambda_{min}$ values were averaged to derive a single, robust optimal $\lambda$. This optimal lambda was then used in ridge regression models, fitted using the *ridge* package for R, including pesticide exposure variables and the set of covariates (Cule and Iorio, 2013). The p-values were computed by applying the significance test of Cule et al. (Cule et al., 2011).

## 3. Results

### 3.1. Population characteristics

Our study population comprised 11 512 women, 791 of whom had GDM (6.9 %) (Table 1). The average age of the mothers at the time of delivery was 30.5 years, almost 60 % of them were parous. Most of them (58.7 %) had higher levels of education than the baccalaureate, and 79.6 % were employed at the time of their delivery. Sixty-four percent (64.6 %) had a normal BMI. Alcohol and tobacco consumption during pregnancy concerned 18.1 % and 21.6 % of the women respectively. On average, children were born at 39.2 gestational weeks with a weight of 3.324 kg.

Women with GDM had a higher pre-pregnancy BMI, compared to others: 25.1 % were overweight and 25.1 % obese compared to 17.2 and 9.2 respectively in women without GDM. As expected, women with history of GDM were more likely to have GDM for the present pregnancy (28.8 % versus 0.9 %). The other characteristics were rather similar for the two groups of women.

### 3.2. Preselection of pesticides

Among the 114 pesticides of interest identified, 62 pesticides with

<table><caption>**Table 1**

Characteristics of study population.</caption><thead><tr><th></th><th>Study population<br>n = 11 512</th><th>Without GDM<br>n = 10 721</th><th>With GDM<br>n = 791</th></tr></thead><tbody><tr><td>Maternal age (years):<br>mean ± SD</td><td>30.5 ± 5.0</td><td>30.4 ± 4.9</td><td>31.8 ± 5.5</td></tr><tr><td>NA: n (%)</td><td>36 (0.3)</td><td>35 (0.3)</td><td>1 (0.1)</td></tr><tr><td>BMI category: n (%)</td><td></td><td></td><td></td></tr><tr><td>Thinness (&lt;18.5 kg/m²)</td><td>835 (7.3)</td><td>809 (7.6)</td><td>26 (3.3)</td></tr><tr><td>Normal (18.5–29.9 kg/m²)</td><td>7350 (64.6)</td><td>6986 (65.9)</td><td>364 (46.4)</td></tr><tr><td>Overweight (25.0–29.9 kg/m²)</td><td>2023 (17.8)</td><td>1826 (17.2)</td><td>197 (25.1)</td></tr><tr><td>Obesity (&gt;30.0 kg/m²)</td><td>1170 (10.3)</td><td>973 (9.2)</td><td>197 (25.1)</td></tr><tr><td>NA</td><td>134 (1.2)</td><td>127 (1.2)</td><td>7 (0.9)</td></tr><tr><td>Smoking during pregnancy: n (%)</td><td></td><td></td><td></td></tr><tr><td>No</td><td>6300 (55.2)</td><td>5838 (54.9)</td><td>462 (58.7)</td></tr><tr><td>Only before pregnancy</td><td>2661 (23.3)</td><td>2492 (23.4)</td><td>169 (21.5)</td></tr><tr><td>Before and during<br>pregnancy</td><td>2462 (21.6)</td><td>2306 (21.7)</td><td>156 (19.8)</td></tr><tr><td>NA</td><td>89 (0.8)</td><td>85 (0.8)</td><td>4 (0.5)</td></tr><tr><td>Alcohol consumption during pregnancy: n (%)</td><td></td><td></td><td></td></tr><tr><td>No</td><td>8165 (81.9)</td><td>7598 (81.6)</td><td>567 (85.4)</td></tr><tr><td>Yes</td><td>1806 (18.1)</td><td>1709 (18.4)</td><td>97 (14.6)</td></tr><tr><td>NA</td><td>1541 (13.4)</td><td>1414 (13.2)</td><td>127 (16.1)</td></tr><tr><td>Twin pregnancy: n (%)</td><td></td><td></td><td></td></tr><tr><td>No</td><td>11337 (98.5)</td><td>10566 (98.6)</td><td>771 (97.5)</td></tr><tr><td>Yes</td><td>175 (1.5)</td><td>155 (1.4)</td><td>20 (2.5)</td></tr><tr><td>Previous pregnancy: n (%)</td><td></td><td></td><td></td></tr><tr><td>No</td><td>4092 (40.0)</td><td>3807 (40.0)</td><td>285 (40.0)</td></tr><tr><td>Yes</td><td>6147 (60.0)</td><td>5719 (60.0)</td><td>428 (60.0)</td></tr><tr><td>NA</td><td>1273 (11.1)</td><td>1195 (11.1)</td><td>78 (9.9)</td></tr><tr><td>History of gestational diabetes: n (%)</td><td></td><td></td><td></td></tr><tr><td>No</td><td>11019 (97.2)</td><td>10468 (99.1)</td><td>551 (71.2)</td></tr><tr><td>Yes</td><td>315 (2.8)</td><td>92 (0.9)</td><td>223 (28.8)</td></tr><tr><td>NA</td><td>178 (1.5)</td><td>161 (1.5)</td><td>17 (2.1)</td></tr><tr><td>Gestational diabetes treatment: n (%)</td><td></td><td></td><td></td></tr><tr><td>No</td><td>10721 (93.3)</td><td>10721 (100.0)</td><td>/</td></tr><tr><td>Insulin</td><td>177 (1.5)</td><td>/</td><td>177 (23.6)</td></tr><tr><td>Diet</td><td>572 (5.0)</td><td>/</td><td>572 (76.4)</td></tr><tr><td>NA</td><td>42 (0.4)</td><td>/</td><td>42 (5.3)</td></tr><tr><td>Educational level: n (%)</td><td></td><td></td><td></td></tr><tr><td>Secondary school</td><td>2224 (19.3)</td><td>2057 (19.2)</td><td>167 (21.1)</td></tr><tr><td>Higher than secondary<br>school</td><td>2549 (22.2)</td><td>2368 (22.1)</td><td>181 (22.9)</td></tr><tr><td>Higher than high school</td><td>6733 (58.5)</td><td>6290 (58.7)</td><td>443 (56.0)</td></tr><tr><td>NA</td><td>6 (0.1)</td><td>6 (0.1)</td><td>/</td></tr><tr><td>Employment: n (%)</td><td></td><td></td><td></td></tr><tr><td>No</td><td>2300 (20.4)</td><td>2118 (20.2)</td><td>182 (23.4)</td></tr><tr><td>Yes</td><td>8960 (79.6)</td><td>8365 (79.8)</td><td>595 (76.6)</td></tr><tr><td>NA</td><td>252 (2.2)</td><td>238 (2.2)</td><td>14 (1.8)</td></tr><tr><td>Urban/rural housing: n (%)</td><td></td><td></td><td></td></tr><tr><td>Rural (≤30 % of<br>artificial area)</td><td>2969 (27.7)</td><td>2786 (27.9)</td><td>183 (24.9)</td></tr><tr><td>Suburban (30–50 %)</td><td>4044 (37.7)</td><td>3768 (37.7)</td><td>276 (37.6)</td></tr><tr><td>Urban (&gt;50 %)</td><td>3713 (34.6)</td><td>3438 (34.4)</td><td>275 (37.5)</td></tr><tr><td>NA</td><td>786 (6.8)</td><td>729 (6.8)</td><td>57 (7.2)</td></tr><tr><td>Study inclusion period: n (%)</td><td></td><td></td><td></td></tr><tr><td>1st - 4th April</td><td>1768 (15.4)</td><td>1659 (15.5)</td><td>109 (13.8)</td></tr><tr><td>27 June - 4 July</td><td>2912 (25.3)</td><td>2730 (25.5)</td><td>182 (23.0)</td></tr><tr><td>27 September - 4<br>October</td><td>3256 (28.3)</td><td>3034 (28.3)</td><td>222 (28.1)</td></tr><tr><td>28 November - 5<br>December</td><td>3576 (31.1)</td><td>3298 (30.8)</td><td>278 (35.1)</td></tr><tr><td>Birth weight (g): mean ±<br>SD</td><td>3324.5 ± 486.6</td><td>3324.0 ±<br>485.4</td><td>3332.1 ±<br>503.4</td></tr><tr><td>NA: n (%)</td><td>119 (1.0)</td><td>107 (1.0)</td><td>12 (1.5)</td></tr><tr><td>Gestational age (WG):<br>mean ± SD</td><td>39.2 ± 1.5</td><td>39.2 ± 1.5</td><td>38.8 ± 1.5</td></tr><tr><td>NA: n (%)</td><td>61 (0.5)</td><td>57 (0.5)</td><td>4 (0.5)</td></tr><tr><td>Consumption of organic food: n(%)</td><td></td><td></td><td></td></tr><tr><td>No</td><td>9550 (91.8)</td><td>8893 (91.7)</td><td>657 (93.2)</td></tr><tr><td>Yes (systematically or<br>almost every day)</td><td>848 (8.2)</td><td>800 (8.3)</td><td>48 (6.8)</td></tr><tr><td>NA</td><td>1114 (9.7)</td><td>1028 (9.6)</td><td>86 (10.9)</td></tr></tbody></table>

SE = Standard deviation, NA = not available, WG = weeks of gestation.
detection frequency ≥10% were considered for analyses: 18 pesticides involved in one of the 4 types of residential use (outdoor herbicide, insecticide for animal protection, flying and crawling insects), 14 pesticides for the agricultural exposure and 30 pesticides for the dietary exposure (Fig. 2). Some of them were identified in two (n = 12) or three (n = 3) different sources of exposure, resulting in 44 distinct chemicals considered for the analyses. Using one-by-one single-pollutant models, four agricultural pesticides and 14 dietary pesticides were preselected for inclusion in the single-source models (Table A6 and A7).

### 3.3. Single-source exposure analyses

Focusing on the four identified residential types of use (outdoor herbicides, protection of animals against fleas and ticks, insecticides against flying or insecticides against crawling insects), no clear association was observed with GDM in the single-source model (Table 2).

Regarding exposure to pesticide drifts from agricultural crops, women exposed to myclobutanil were more likely to have GDM (adjusted odds ratio (aOR) = 1.36, (1.01,1.82)), compared to non-exposed women (Table 3). In contrast, women exposed to glyphosate were less likely to have GDM (aOR = 0.78, (0.61,1.00)). No clear associations were observed for epoxiconazole and propiconazole.

Regarding exposure to pesticide residues in contaminated foods, two dietary pesticides (fenvalerate and cyromazine) were perfectly collinear, and only fenvalerate was retained for the statistical models, as a proxy of exposure to both pesticides. In the partly-adjusted model, women exposed to cypermethrin through diet were more likely to have GDM (aOR = 1.50 (1.26,1.79)). Conversely, women exposed to epoxiconazole and glyphosate were less likely to have GDM (aOR = 0.48 (0.38,0.61) and 0.56 (0.44,0.72), respectively (Table 4)). When additionally adjusting on dietary patterns (fully-adjusted model), the associations were attenuated but consistent with the partly-adjusted model for cypermethrin, epoxiconazole and glyphosate. In addition, an association close to the statistical significance was found for women exposed to penconazole who had a lower likelihood of GDM (aOR = 0.80 (0.63,1.01)).

### 3.4. Multi-source exposure analysis

The results of the multi-source analysis including dietary and agricultural pesticides are described in Fig. 3. Women exposed to glyphosate either through dietary exposure (fully-adjusted model, aOR = 0.64 (0.46,0.85)) or agricultural exposure (partly-adjusted model, aOR =

<caption>**Table 2**</caption>

Association between exposure from residential use of pesticides and gestational diabetes mellitus (GDM) with a single-source model.

<table><thead><tr><th>Residential use of pesticides</th><th>GDM (n = 611) n (%)</th><th>No GDM (n = 8634) n(%)</th><th>Crude OR<sup>a</sup> (95 % CI)</th><th>Adjusted OR<sup>b</sup> (95 % CI)</th></tr></thead><tbody><tr><td>Outdoor herbicides</td><td>80 (13.4)</td><td>1211 (14.2)</td><td>0.94 (0.74; 1.21)</td><td>0.96 (0.74; 1.24)</td></tr><tr><td>Animal protection</td><td>188 (32.6)</td><td>2709 (32.3)</td><td>1.00 (0.83; 1.21)</td><td>1.03 (0.85; 1.24)</td></tr><tr><td>Insecticide for flying insects</td><td>172 (28.2)</td><td>2255 (26.1)</td><td>1.14 (0.94; 1.39)</td><td>1.07 (0.88; 1.31)</td></tr><tr><td>Insecticide for crawling insects</td><td>91 (14.9)</td><td>1272 (14.7)</td><td>1.01 (0.79; 1.29)</td><td>1.02 (0.79; 1.30)</td></tr></tbody></table>

<caption>**Table 3**</caption>

Association between agricultural exposure to pesticides and gestational diabetes mellitus (GDM) with a single-source model.

<table><thead><tr><th>Agricultural exposure to pesticides</th><th>GDM (n = 726) N(%)</th><th>No GDM (n = 9872) N(%)</th><th>Crude OR<sup>a</sup> (95 % CI)</th><th>Adjusted OR<sup>b</sup> (95 % CI)</th></tr></thead><tbody><tr><td>Glyphosate</td><td>184 (25.3)</td><td>2913 (29.5)</td><td>0.76 (0.60; 0.96)</td><td>0.78 (0.61; 1.00)</td></tr><tr><td>Myclobutanil</td><td>73 (10.1)</td><td>909 (9.2)</td><td>1.28 (0.95; 1.70)</td><td>1.36 (1.01; 1.82)</td></tr><tr><td>Epoxiconazole</td><td>164 (22.6)</td><td>2522 (25.5)</td><td>0.93 (0.70; 1.25)</td><td>0.94 (0.71; 1.28)</td></tr><tr><td>Propiconazole</td><td>154 (21.2)</td><td>2255 (22.8)</td><td>1.12 (0.85; 1.47)</td><td>1.04 (0.79; 1.38)</td></tr></tbody></table>

GDM: gestational diabetes mellitus.

a Logistic regression fitted on the agricultural pesticides in the table, (no or low [Ref] versus high levels of exposure).

b Logistic regression adjusted for covariates (maternal age, alcohol use, smoking, employment, urban/rural residence, education, body mass index) and pesticides in the table.

0.79 (0.64,0.99)) were less likely to have GDM. Similar findings were observed for dietary exposures to epoxiconazole and penconazole (fully-adjusted model: aOR = 0.64 (0.50,0.83) and 0.78 (0.63,0.96), respectively). Dietary exposure to cypermethrin was close to the statistical significance when additionally adjusted to dietary patterns (partly-adjusted model, aOR = 1.52 (1.27,1.82); fully-adjusted model, aOR = 1.20 (0.99,1.46)). Women exposed to myclobutanil through the agricultural source were more likely to have GDM (partly-adjusted model, aOR = 1.44 (1.07; 1.94)).

### 3.5. Sensitivity analyses

The sensitivity analyses showed coherent results for both direction and strength of association for each source (Table A8 to A11). Regarding the pre-selection of agricultural pesticides applied near crops, the ridge approach and the one-by-one single-pollutant regression approach of pesticides led to a very similar selection of variables. Concerning dietary pesticides, some compounds not retained in our one-by-one approach were selected by the ridge approach (procymidone, pirimicarb, DDT, dieldrin). However, a large VIF (4.7e⁶) between DDT and dieldrin suggested a possible inflation of the coefficient due to a collinearity problem. All the pesticides associated with GDM in the multi-source model were in the set of pesticides pre-selected by the ridge approach, with the exception of myclobutanil from agricultural source.

## 4. Discussion

### 4.1. Principal results

Among 114 pesticides of interest identified using a toxico-genomic approach and identified in the Elfe cohort database through residential use, dietary or agricultural exposure, we founded one fungicide (myclobutanil) and one insecticide (cypermethrin) associated with a higher likelihood of GDM. Conversely, one herbicide (glyphosate) and two fungicides (epoxiconazole and penconazole) were associated with a lower likelihood of GDM. Associations remained consistent in the multi-source model and in sensitivity analyses.

### 4.2. Interpretation

In the Saldana et al. study, seven agricultural pesticides were associated with GDM (Saldana et al., 2007). Some of these compounds were

<footnote>a Logistic regression fitted on the types of residential use of pesticides in the table (not used [Ref] vs. used)</footnote>

<footnote>b Logistic regression adjusted for covariates (maternal age, alcohol use, smoking, employment, urban/rural residence, education, BMI) and the types of residential use of pesticides in the table.</footnote>
<table><caption>**Table 4**
Association between dietary exposure to pesticides and gestational diabetes mellitus (GDM) with a single-source model.</caption><thead><tr><th>Dietary exposure to pesticides</th><th>GDM (n = 628) N(%)</th><th>No GDM (n = 8913) N(%)</th><th>Crude OR<sup>a</sup> (95 % CI)</th><th>Partly-adjusted OR<sup>b</sup> (95 % CI)</th><th>Fully-adjusted OR<sup>c</sup> (95 % CI)</th></tr></thead><tbody><tr><td>Epoxiconazole</td><td>91 (14.5)</td><td>2529 (28.4)</td><td>0.46 (0.36; 0.57)</td><td>0.48 (0.38; 0.61)</td><td>0.65 (0.51; 0.83)</td></tr><tr><td>Glyphosate</td><td>81 (12.9)</td><td>2230 (25.0)</td><td>0.46 (0.36; 0.58)</td><td>0.56 (0.44; 0.72)</td><td>0.66 (0.51; 0.86)</td></tr><tr><td>Propamocarb</td><td>16 (2.5)</td><td>324 (3.6)</td><td>0.74 (0.44; 1.25)</td><td>0.77 (0.45; 1.31)</td><td>0.71 (0.41; 1.22)</td></tr><tr><td>Fenvalerate</td><td>116 (18.5)</td><td>1565 (17.6)</td><td>1.08 (0.79; 1.47)</td><td>1.09 (0.80; 1.49)</td><td>0.97 (0.70; 1.35)</td></tr><tr><td>Carbofuran</td><td>52 (8.3)</td><td>512 (5.7)</td><td>1.20 (0.82; 1.74)</td><td>1.33 (0.91; 1.94)</td><td>1.24 (0.83; 1.86)</td></tr><tr><td>Permethrin</td><td>43 (6.8)</td><td>422 (4.7)</td><td>1.12 (0.75; 1.67)</td><td>1.07 (0.71; 1.62)</td><td>1.07 (0.69; 1.67)</td></tr><tr><td>Cypermethrin</td><td>223 (35.5)</td><td>2392 (26.8)</td><td>1.52 (1.28; 1.80)</td><td>1.50 (1.26; 1.79)</td><td>1.20 (0.99; 1.45)</td></tr><tr><td>Bifenthrin</td><td>214 (34.1)</td><td>2828 (31.7)</td><td>1.08 (0.85; 1.37)</td><td>1.15 (0.90; 1.46)</td><td>0.91 (0.70; 1.17)</td></tr><tr><td>Dichlorvos</td><td>56 (8.9)</td><td>1038 (11.6)</td><td>0.95 (0.67; 1.36)</td><td>0.94 (0.66; 1.35)</td><td>0.93 (0.64; 1.35)</td></tr><tr><td>Malathion</td><td>74 (11.8)</td><td>1238 (13.9)</td><td>0.92 (0.71; 1.19)</td><td>1.03 (0.79; 1.35)</td><td>0.90 (0.74; 1.10)</td></tr><tr><td>Endosulfan</td><td>184 (29.3)</td><td>2780 (31.2)</td><td>0.85 (0.68; 1.07)</td><td>0.89 (0.70; 1.12)</td><td>0.88 (0.69; 1.13)</td></tr><tr><td>Myclobutanil</td><td>160 (25.5)</td><td>2439 (27.4)</td><td>1.13 (0.90; 1.42)</td><td>1.10 (0.87; 1.39)</td><td>1.02 (0.81; 1.31)</td></tr><tr><td>Penconazole</td><td>141 (22.5)</td><td>2363 (26.5)</td><td>0.90 (0.72; 1.13)</td><td>0.90 (0.72; 1.14)</td><td>0.80 (0.63; 1.01)</td></tr></tbody></table>

non-detected in the Elfé study (phorate) and banned in France in 2011 (2,4,5-T, 2,4,5-TP, butylate). Among those studied in common, we similarly observed no association between malathion exposure and risk of GDM. We also founded inconsistent results with no association observed with GDM for atrazine, diazinon and carbofuran. This might be explained by difference in exposure sources and intensity, since women included in the AHS worked as preparers, pesticide applicators or repairers of pesticide-related equipment, which could lead to higher levels of exposure than in our study. At the opposite, we founded a new association with GDM for glyphosate. Saldana et al. may have lacked power to show the association we observed, since they studied only 238 women agriculturally exposed to glyphosate, of which 19 developed a GDM.

In line with previous studies, we did not observe any association between prenatal exposure to organochlorine or organophosphorus pesticides and the development of GDM. One studied organochlorine and organophosphorus pesticides (Shapiro et al., 2016) and the other studied the effect of chlordecone (an organochlorine) in particular (Saunders et al., 2014). Both studies were fairly similar in their

methodology and used biomarkers to measure pesticide exposure. It should be noted that biomonitoring studies offer advantages in terms of quantification accuracy and reproducibility; however, they are limited in measuring substances with short life spans (e.g. organophosphorus) or concentrations below the detection limit, and they are unable to identify sources of exposure.

In comparison with epidemiological studies, we can note that most of the pesticides studied in Elfé were new molecules not previously studied. Mechanistic hypotheses of interaction with genes associated with GDM might be suggested. Tiwary et al. suggested that cypermethrin would increase the expression of CDKN2B (cyclin dependent kinase inhibitor 2B) gene, which encodes tumour suppressor proteins (p15INK4b and p16INK4a) that regulates pancreatic beta cell replication (Tiwari et al., 2012). Increased production of p15INK4b has been shown in mice to be associated with pancreatic islet hypoplasia and impaired glucose-induced insulin secretion (Hribal et al., 2011). Overexpression of CDKN2B could therefore lead to a state of hyperglycemia induced by reduced insulin secretion, consistent with our findings.

Some evidence in mice suggests that triazole fungicides exposure induces transcription of genes involved in xenobiotoc metabolism, oxidative stress response genes, and inflammation (Martin et al., 2007; Stellavato et al., 2016). More specifically, myclobutanil may decrease the activity of Farnesoid X receptor (FXR), an important xenobiotic nuclear receptor that regulates and maintains lipid, glucose and energy metabolism (Taylor et al., 2023). This may explain the positive association founded between myclobutanil and risk of GDM. On the contrary, and in a surprising way, women dietary exposed to epoxiconazole and penconazole were less likely to have GDM. Dietary exposure was estimated using a food frequency questionnaire covering the last 3 months of pregnancy whereas the diagnosis of GDM is usually made during the second trimester of pregnancy. Given that affected women are typically advised to reduce foods containing slow or fast sugars in favor of those with a low glycemic index, the exposure of these women in early pregnancy to pesticides associated with these types of foods may have been differentially underestimated (for example, epoxiconazole and glyphosate were specifically found in sorbets, ice creams, and cereals in our study, less consumed by women affected by GDM in late pregnancy). This raises the possibility of a reverse causation bias, whereby the GDM diagnose influenced dietary exposure rather than the other way around. The dietary patterns used as adjustment covariates did not take this bias into account, as they were also based on patterns during the last 3 months of pregnancy. We cannot exclude explanations for this inverse association other than reverse causality, such as random finding, unidentified confounding factor, or unknown mechanism. Lichtensteiger et al. tested the effects of a cocktail of anti-androgenic molecules, including epoxiconazole (Lichtensteiger et al., 2015). Exposure to this mixture led to overproduction of BACE2 (beta secretase 2), mainly located in pancreatic β-cells. Casas et al. suggest that BACE2 expression was correlated to the insulin gene expression, which might be in line with our results (Casas et al., 2010). Conversely, penconazole appears to significantly reduce glycogen, α-glucose and β-glucose in the liver by interfering with gluconeogenesis and glycolysis pathways, which is more likely to induce hyperglycemia than hypoglycemia (Meng et al., 2020).

An inverse association was also found for glyphosate, both for dietary and agricultural exposure, the last one not influenced by the GDM status. A similar inverse association was reported by Montgomery et al. in their study on glyphosate and type 2 diabetes (OR 0.85, 0.74; 0.98) (Montgomery et al., 2008). Glyphosate is associated with changes in the activity of numerous genes, some of which are related to diabetes (LEP, LEPR, HKDC1, CDKN2A, IRS1). Uren et al. studied the transcriptomic profile induced by cellular compensation to oxidative stress resulting from glyphosate exposure (Uren Webster and Santos, 2015). This study revealed overproduction of substrates at the IRS2 and IRS4 insulin receptors and leptin, which have a synergistic effect in controlling insulin sensitivity and glucose homeostasis. Absence of IRS2 and IRS4 leads to

<footnote><sup>a</sup> Logistic regression fitted on the dietary pesticides in the table, (no or low [Ref] versus high levels of exposure).</footnote>

<footnote><sup>b</sup> Logistic regression adjusted for covariates (maternal age, alcohol use, smoking, employment, urban/rural residence, education, BMI) and dietary exposure to pesticides in the table.</footnote>

<footnote><sup>c</sup> Logistic regression fitted on dietary patterns in addition to the variables listed in <sup>b</sup>.</footnote>
<figure><img src="image_5.png" /><figcaption>Fig. 3. Association between exposure to agricultural and dietary pesticides and gestational diabetes mellitus (GDM) with a multi-source model</figcaption></figure>

insulin resistance and chronic hyperglycemia, whereas leptin induces a hypoglycemic action via hypothalamic GABAergic neurons (Fujikawa et al., 2013; Sadagurski et al., 2014). By inducing an overproduction of leptin, glyphosate might therefore compensate – at least in part – the insulin resistance induced by the pregnancy, and thus limit the development of GDM. It is also interesting to note that leptin resistance plays a role in lipid storage in adipocytes, promoting weight gain and obesity (Obradovic et al., 2021).

Finally, although few studies have specifically examined the relationship between pesticide exposure and the risk of gestational diabetes, a growing body of epidemiological evidence supports an association with type 2 diabetes. In particular, a recent meta-analysis reported that individuals with the highest pesticide exposure had a significantly increased risk of type 2 diabetes (OR = 1.58) (Evangelou et al., 2016). Given the likely similarities in the pathophysiology of type 2 diabetes and GDM, such findings support the plausibility of an association with GDM. Considering the limited increase in risk, it is likely that pesticide exposure does not play a determining role in the overall prevalence of the disease, especially when compared to lifestyle or genetic factors. However, exposure to pesticides may increase the burden of disease in women with GDM or represent a particular risk for women already predisposed to GDM. Given the ubiquitous nature of pesticide exposure, these findings may therefore support policy decisions regarding the usage of certain substances and the development of prevention strategies regarding domestic habits.

### 4.3. Strengths and limitations

Indirect methods for identifying pesticide exposure may induce measurement errors. The information available from the Elfé cohort did not enable us to identify specific molecules or levels of exposure for residential use of pesticides. The effects of a specific pesticide may have been masked by being grouped with other pesticides with opposite or no effect. The use of GIS may also induce imprecision in exposure assessment, since it is only based on crops acreage within a 1000m radius and estimated pesticide densities per hectare. In addition, participant address tended to be less precise in rural areas, where exposures a supposed to be the highest (Béranger et al., 2014). At last, as mentioned above, since the food frequency questionnaire assessed dietary patterns at the 3rd trimester of pregnancy, it is likely that women had already modified their diets following the diagnosis of GDM. As previously discussed, this represents a limitation due to potential underestimation of the exposure and reverse causation bias. These analyses should therefore be replicated in a study in which the food frequency questionnaire would be given at the start of pregnancy, before the diagnosis of GDM. However, the results of several studies confirm the reliability of the indirect methods to assess pesticide exposures. Gunier et al. identified a positive correlation between the quantities identified by GIS and the pesticides present in household carpet dust (Gunier et al., 2011). In the PELAGIE and the Elfé birth cohorts, similar results were found between GIS estimation and urinary metabolite levels of pyrethroids (Glorennec et al., 2017; Dereumeaux et al., 2018). Kadawathagedara et al. showed that using the questionnaire was a reasonable way of identifying women's dietary intakes, and Chiu et al. suggested that the

<footnote><sup>a</sup>Logistic regression fitted on the pesticides in the figure, (no or low [Ref] versus high levels of exposure)</footnote>

<footnote><sup>b</sup>Logistic regression adjusted for covariates (maternal age, alcohol use, smoking, employment, urban/rural residence, education, BMI) and pesticides in the figure</footnote>

<footnote><sup>c</sup>Logistic regression fitted on the same variables as b and dietary patterns. Fully-adjusted OR for agricultural pesticides not presented because the value adjusted for dietary patterns should not be interpreted.</footnote>
approache used to assess dietary intake of pesticide residues was posi-
tively correlated with the level of urinary metabolites of pesticides
(Kadawathagedara et al., 2021; Chiu et al., 2018).

Among the strengths of this study, the Elfé cohort enabled us to
analyze a large number of women, representative of all socio-economic
groups, while accounting for multiple confounding factors. To our
knowledge, this is the largest study ever carried out on GDM and pes-
ticides. We observed an incidence rate of GDM in the Elfé cohort (7.4 %)
that is consistent with national estimates from the 2010 National Peri-
natal Survey (7.2 %) (Blondel et al., 2012). Given the relatively low
prevalence of gestational diabetes in our study population, the odds
ratios reported can be considered as reasonably close approximations of
relative risks. To ensure a homogeneous study population in terms of
lifestyle, type and intensity of environmental exposure, we excluded
women with high occupational pesticides exposure (e.g. farmers) and
those with no exposure to agricultural pesticides (e.g. women living in
hyper-urban area). This approach allowed for a consistent multi-source
analysis by ensuring population homogeneity across sources and
strengthened the internal validity of our results by reducing variability
related to extreme exposure profiles. We are confident in the selection of
pesticides made for the analyses, particularly for dietary and agricul-
tural exposures, as the ridge approach and the one-by-one pre-selection
of pesticides has led to a similar variable selection. Finally, the tox-
icogenomic approach has limited the risk of false positives linked to
alpha risk inflation, without locking in existing hypotheses. Combining
the toxicogenomic approach with the use of a DAG based on literature
research allows us to assume the plausibility of the associations
observed.

5. Conclusion

This study demonstrates the relevance of a toxicogenomic-driven
approach to screen environmental chemicals potentially involved in
the development of gestational diabetes. Among the 114 pesticides of
interest identified as candidates, 62 were analyzed and five distinct
chemicals were associated with GDM in our study, through agricultural
or dietary exposure. Myclobutanil and cypermethrin were linked to
increased risk, while glyphosate, epoxiconazole, and penconazole were
associated with reduced risk. While these findings offer new hypotheses
about environmental contributors to GDM, mechanistic studies are
needed to better understand how these specific pesticides may influence
glucose metabolism or endocrine pathways during pregnancy. Confir-
matory studies are also required to strengthen the evidence base and
support targeted public health strategies that aimed at reducing prenatal
exposure to harmful pesticides.

CRediT authorship contribution statement

**Léa Guesdon:** Writing – original draft, Methodology, Formal analysis, Conceptualization. **Charline Warethombro:** Writing – review & editing, Methodology. **Cécile Chevrier:** Writing – review & editing, Methodology, Data curation. **Blandine de Lauzon-Guillain:** Writing – review & editing, Methodology, Data curation. **Julien Caudeville:** Writing – review & editing, Methodology, Data curation. **Marie-Aline Charles:** Writing – review & editing, Resources, Methodology. **Maela Le Lous:** Writing – review & editing, Methodology. **Pauline Blanc-Petit-jean:** Writing – original draft, Methodology, Conceptualization. **Rémi Béranger:** Writing – original draft, Supervision, Methodology, Funding acquisition, Conceptualization.

Funding

The EXPEDIA project was funded by the Fondation de France, France (grant N°00099893), a French non-profit organization; and was based on exposure data process funded by the French Agency for Food, Environmental and Occupational Health & Safety, France (grant PNR EST-

13-138) and by the French Ministry of Agriculture, France, with the
financial support of the French Agency for Biodiversity, based on
funding from taxes on diffuse pollution attributed to the Ecophyto plan.

Declaration of competing interest

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.

Acknowledgments

The Elfé (Étude Longitudinale Française depuis l'Enfance) survey is a
joint project between the French Institute for Demographic Studies
(INED) and the National Institute of Health and Medical Research
(INSERM), in partnership with the French blood transfusion service
(Etablissement français du sang, EFS), Santé publique France, the Na-
tional Institute for Statistics and Economic Studies (INSEE), the Direc-
tion générale de la santé (DGS, part of the Ministry of Health and Social
Affairs), the Direction générale de la prévention des risques (DGPR,
Ministry for the Environment), the Direction de la recherche, des études,
de l'évaluation et des statistiques (DREES, Ministry of Health and Social
Affairs), the Département des études, de la prospective et des statistiques
(DEPS, Ministry of Culture), and the Caisse nationale des allocations
familiales (CNAF), with the support of the Ministry of Higher Education
and Research and the Institut national de la jeunesse et de l'éducation
populaire (INJEP). Via the RECONAI platform, it receives a government
grant managed by the National Research Agency under the "Investisse-
ments d'avenir" programme (ANR-11-EQPX-0038et ANR-19-COHO-
0001).

**Appendix A. Supplementary data**

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.envres.2025.122275.

Data availability

The authors do not have permission to share data.

References

ANSES, 2010. "Exposition de La Population Générale Aux Résidus de Pesticides En France. Synthèse et Recommandations Du Comité d'orientation et de Prospective Scientifique de l'observatoire Des Résidus de Pesticides (ORP) Rapport Scientifique."

ANSES, 2014. Avis relatif à l'actualisation des indicateurs de risque alimentaire lié aux résidus de pesticides. ANSES. April 2, 2014. https://www.anses.fr/fr/content/avis-de-l%E2%80%99anses-relatif-%C3%A0-l%E2%80%99actualisation-des-indicateurs-de-risque-alimentaire-li%C3%A9-aux.

Bellamy, Leanne, Casas, Juan-Pablo, Hingorani, Aroon D., Williams, David, 2009. Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis. Lancet 373 (9677), 1773-1779. https://doi.org/10.1016/S0140-6736(09)60731-5.

Ben-Haroush, A., Yogev, Y., Hod, M., 2004. Epidemiology of gestational diabetes mellitus and its association with type 2 diabetes. Diabet. Med.; J. British Diabet. Assoc. 21 (2), 103-113. https://doi.org/10.1046/j.1464-5491.2003.00985.x.

Béranger, Rémi, Blain, Jeffrey, Baudinet, Cédric, Faure, Elodie, Fléchon, Aude, Boyle, Helen, Chases, Virginie, Charbotel, Barbara, Schüz, Joachim, Fervers, Béatrice, 2014. Testicular germ cell tumours and early exposures to pesticides: the TESTEPERA pilot study. Bulletin Du Cancer 101 (3), 225-235. https://doi.org/10.1684/bdc.2014.1901.

Béranger, Rémi, Hardy, Emilie M., Binter, Anne-Claire, Charles, Marie-Aline, Zaros, Cécile, Appenzeller, Brice M.R., Chevrier, Cécile, 2020. Multiple pesticides in mothers' hair samples and children's measurements at birth: results from the French national birth cohort (ELFE). Int. J. Hyg Environ. Health 223 (1), 22-33. https://doi.org/10.1016/j.ijheh.2019.10.010.

Billionnet, Cécile, Mitanchez, Delphine, Weill, Alain, Nizard, Jacky, Alla, François, Hartemann, Agnès, Jacqueminet, Sophie, 2017. Gestational diabetes and adverse perinatal outcomes from 716,152 births in France in 2012. Diabetologia 60 (4), 636-644. https://doi.org/10.1007/s00125-017-4206-6.

Blondel, B., Lelong, N., Kermarrec, M., Goffinet, F., National Coordination Group of the National Perinatal Surveys, 2012. Trends in perinatal health in France from 1995 to 2010. Results from the French national perinatal surveys. Journal De Gynecologie,
Obstetrique Et Biologie De La Reproduction 41 (4), e1-e15. https://doi.org/10.1016/j.gjyn.2012.04.014.

Blotsky, Andrea L., Rahme, Elham, Dahhou, Mourad, Nakhla, Meranda, Dasgupta, Kaberi, 2019. Gestational diabetes associated with incident diabetes in childhood and youth: a retrospective cohort study. CMAJ: Can. Med. Assoc. J. = Journal de l'Association Medicale Canadienne 191 (15), E410-E417. https://doi.org/10.1503/cmaj.1810018.

Casas, Silvia, Casini, Paola, Piquer, Sandra, Altirriba, Jordi, Soty, Maud, Cadavez, Lisa, Gomis, Ramon, Novials, Anna, 2010. BACE2 plays a role in the insulin receptor trafficking in pancreatic SS-Cells. Am. J. Physiol. Endocrinol. Metab. 299 (6), E1087-E1095. https://doi.org/10.1152/ajpendo.00420.2010.

Charles, Marie Aline, Thierry, Xavier, Marie, Jean-Louis, Bois, Corinne, Dufourg, Marie-Noelle, Popa, Ruxandra, Cheminat, Marie, Zaros, Cécile, Bertrand, Geay, 2020. Cohort profile: the French national cohort of children (ELFE): birth to 5 years. Int. J. Epidemiol. 49 (2), 368-369. https://doi.org/10.1093/ije/dyz227.

Chiu, Yu-Han, Williams, Paige L., Míguez-Alarcón, Lidia, Gillman, Matthew, Qi, Sun, Ospina, Maria, Calafat, Antonia M., Hauser, Russ, Chavarro, Jorge E., 2018. Comparison of questionnaire-based estimation of pesticide residue intake from fruits and vegetables with urinary concentrations of pesticide biomarkers. J. Expo. Sci. Environ. Epidemiol. 28 (1), 31-39. https://doi.org/10.1038/jes.2017.22.

Couteux, A., Lejeune, V., 2010. Index Phytosanitaire ACTA 2011. Acta, Paris.

Cule, Erika, Iorio, Maria De, 2013. Ridge regression in prediction problems: automatic choice of the ridge parameter. Genet. Epidemiol. 37 (7), 704-714. https://doi.org/10.1002/gepi.21750.

Cule, Erika, Vineis, Paolo, De Iorio, Maria, 2011. Significance testing in ridge regression for genetic data. BMC Bioinf. 12 (September), 372. https://doi.org/10.1186/1471-2105-12-372.

Dereumeaux, Clémentine, Saoudi, Abdesattar, Goria, Sarah, Wagner, Vérène, De Crouy-Chanel, Perrine, Pecheux, Marie, Berat, Bénédicte, Zaros, Cécile, Guldner, Laurence, 2018. Urinary levels of pyrethroid pesticides and determinants in pregnant French women from the elfe cohort. Environ. Int. 119 (October), 89-99. https://doi.org/10.1016/j.envint.2018.04.042.

Deziel, Nicole C., Beane Freeman, Laura E., Graubard, Barry I., Jones, Rena R., Hoppin, Jane A., Thomas, Kent, Hines, Cynthia J., et al., 2017. Relative contributions of agricultural drift, para-occupational, and residential use exposure pathways to house dust pesticide concentrations: meta-regression of published data. Environ. Health Perspect. 125 (3), 296-305. https://doi.org/10.1289/EHP426.

Ehrlich, Shelley, Lambers, Donna, Baccarelli, Andrea, Khoury, Jane, Macaluso, Maurizio, Ho, Shuk-Mei, 2016. Endocrine disruptors: a potential risk factor for gestational diabetes mellitus. Am. J. Perinatol. 33 (13), 1313-1318. https://doi.org/10.1055/s-0036-1586500.

Eurostat, 2022. Agri-Environmental Indicator - Consumption of Pesticides, 2022. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Agri-environmentalIndicator_consumption_of_pesticides.

Evangelou, Evangelos, Ntritsoi, Georgios, Chondrogiorgi, Maria, Kavvoura, Fotini K., Hernández, Antonio F., Ntzani, Evangelia E., Tzoulaki, Ioanna, 2016. Exposure to pesticides and diabetes: a systematic review and meta-analysis. Environ. Int. 91 (May), 60-68. https://doi.org/10.1016/j.envint.2016.02.013.

Friedman, Jerome H., Hastie, Trevor, Tibshirani, Rob, 2010. Regularization paths for generalized linear models via coordinate descent. J. Stat. Software 33 (February), 1-22. https://doi.org/10.18637/jss.v033.i01.

Fujikawa, Teppei, Berglund, Eric D., Patel, Vishal R., Ramadori, Giorgio, Vianna, Claudia R., Vong, Linh, Thorel, Fabrizio, et al., 2013. Leptin engages a hypothalamic neurocircuitry to permit survival in the absence of insulin. Cell Metab. 18 (3), 431-444. https://doi.org/10.1016/j.cmet.2013.08.004.

Gall, Le, A-C, Chavane, L., Chatelier, J.-Y., 2013. Analyse Des Données de La BNV-D Sur La Période 2008-2011 - Rapport d'études Interne. INERIS. DRC-12-126848-13383A, 93pp.

Gavelle, Erwan de, de Lauzon-Guillain, Blandine, Charles, Marie-Aline, Chevrier, Cécile, Hulin, Marion, Sirot, Véronique, Merlo, Mathilde, Nougadère, Alexandre, 2016. Chronic dietary exposure to pesticide residues and associated risk in the French ELFEnorme cohort of pregnant women. Environ. Int. 92-93 (July), 533-542. https://doi.org/10.1016/j.envint.2016.04.007.

Glorennec, Philippe, Serrano, Tania, Fravallo, Morgane, Warenbourg, Charline, Monfort, Christine, Cordier, Sylvaine, Viel, Jean-François, Le Gléau, Florent, Bot, Barbara Le, Chevrier, Cécile, 2017. Determinants of children's exposure to pyrethroid insecticides in Western France. Environ. Int. 104 (July), 76-82. https://doi.org/10.1016/j.envint.2017.04.007.

Guner, Robert B., Ward, Mary H., Airola, Matthew, Bell, Erin M., Colt, Joanne, Nishioka, Marcia, Buffler, Patricia A., et al., 2011. Determinants of agricultural pesticide concentrations in carpet dust. Environ. Health Perspect. 119 (7), 970-976. https://doi.org/10.1289/ehp.1002532.

Guo, Fang, Long, Wei, Zhou, Wenbai, Zhang, Bin, Liu, Jianbing, Yu, Bin, 2018. FTO, GCKR, CDKAL1 and CDKN2A/B gene polymorphisms and the risk of gestational diabetes mellitus: a meta-analysis. Arch. Gynecol. Obstet. 298 (4), 705-715. https://doi.org/10.1007/s00404-018-4857-7.

Hivert, Marie-France, Vassy, Jason L., Meigs, James B., 2014. Susceptibility to type 2 diabetes Mellitus-from genes to prevention. Nat. Rev. Endocrinol. 10 (4), 198-205. https://doi.org/10.1038/nrendo.2014.11.

Hoerl, Arthur E., Kennard, Robert W., 1970a. Ridge regression: applications to nonorthogonal problems. Technometrics 12 (1), 69-82. https://doi.org/10.1080/00401706.1970.10488635.

Hoerl, Arthur E., Kennard, Robert W., 1970b. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12 (1), 55-67. https://doi.org/10.2307/1267351.

Hou, Zhi, Li, Ming, Cao, Yanmin, 2017. TCF7L2, CAPN10 polymorphisms are associated with gestational diabetes mellitus (GDM) risks: a meta-analysis. Gynecol. Endocrinol.: Offic. J. Int. Soc. Gynecol. Environ. 33 (5), 399-404. https://doi.org/10.1080/09513590.2017.1290066.

Hribal, M.L., Presta, I., Procopio, T., Marini, M.A., Stančáková, A., Kuusisto, J., Andreozzi, F., et al., 2011. Glucose tolerance, insulin sensitivity and insulin release in European non-diabetic carriers of a polymorphism upstream of CDKN2A and CDKN2B. Diabetologia 54 (4), 795-802. https://doi.org/10.1007/s00125-010-2038-8.

Huang, Yu-Fang, Pan, Wen-Chi, Tsai, Yen-An, Chang, Chia-Huang, Chen, Pei-Jung, Shao, Yi-shuan, Tsai, Ming-Song, Hou, Jia-Woei, Lu, Chensheng Alex, Chen, Mei-Lien, 2017. Concurrent exposures to nonylphenol, bisphenol A, phthalates, and organophosphate pesticides on birth outcomes: a cohort study in Taipei, Taiwan. Sci. Total Environ. 607-608 (December), 1126-1135. https://doi.org/10.1016/j.scitotenv.2017.07.092.

Kadawathagedara, Manik, Ahluwalia, Namanjeet, Dufourg, Marie-Noelle, Forhan, Anne, Charles, Marie Aline, Lioret, Sandrine, Lauzon-Guillain, Blandine de, 2021. Diet during pregnancy: influence of social characteristics and migration in the ELFEnorme cohort. Matern. Child Nutr. 17 (3), e13410. https://doi.org/10.1111/mcn.13410.

Le Ray, Camille, Lelong, Nathalie, Cinelli, Hélène, Blondel, Béatrice, Collaborators - Members of the ENP2021 Study Group, 2022. Results of the 2021 French national perinatal survey and trends in perinatal health in metropolitan France since 1995. J. Gynecol. Obstet. Human Reproduction 51 (10), 102509. https://doi.org/10.1016/j.jgoh.2022.102509.

Lichtensteiger, Walter, Bassetti-Gaille, Catherine, Faass, Oliver, Axelstad, Marta, Boberg, Julie, Christiansen, Sofie, Rehrauer, Hubert, et al., 2015. Differential gene expression patterns in developing sexually dimorphic rat brain regions exposed to antiandrogenic, estrogenic, or complex endocrine disruptor mixtures: glutamatergic synapses as target. Endocrinology 156 (4), 1477-1493. https://doi.org/10.1210/en.2014-1504.

Lin, Ziqi, Wang, Yue, Zhang, Bao, Jin, Zhen, 2018. Association of type 2 diabetes susceptible genes GCKR, SLC30A8, and FTO polymorphisms with gestational diabetes mellitus risk: a meta-analysis. Endocrine 62 (1), 34-45. https://doi.org/10.1007/s12020-018-1651-z.

Lowe, William L., Scholtens, Denise M., Sandler, Victoria, Geoffrey Hayes, M., 2016. Genetics of gestational diabetes mellitus and maternal metabolism. Curr. Diabetes Rep. 16 (2), 15. https://doi.org/10.1007/s11892-015-0790-z.

Lowe, William L., Lowe, Lynn P., Kuang, Alan, Catalano, Patrick M., Nodzenski, Michael, Talbot, Octavious, Tam, Wing-Hung, et al., 2019. Maternal glucose levels during pregnancy and childhood adiposity in the hyperglycemia and adverse pregnancy outcome Follow-up study. Diabetologia 62 (4), 598-610. https://doi.org/10.1007/s00125-018-4809-6.

Martin, Matthew T., Brennan, Richard J., Hu, Wenyue, Ayanoglu, Eser, Lau, Christopher, Ren, Hongzuo, Wood, Carmen R., Christopher Corton, J., Kavlock, Robert J., Dix, David J., 2007. Toxicogenomic study of triazole fungicides and perfluoroalkyl acids in rat livers predicts toxicity and categorizes chemicals based on mechanisms of toxicity. Toxicol. J. Offic. J. Soc. Toxicol. 97 (2), 595-613. https://doi.org/10.1093/toxsci/kfm065.

Meng, Zhiyuan, Liu, Li, Xi, Yexun, Jia, Ming, Yan, Sen, Tian, Sinuo, Sun, Wei, Zhu, Wentao, Li, Xuefeng, Zhou, Zhiqiang, 2020. Different effects of exposure to penconazole and its enantiomers on hepatic glycolipid metabolism of Male mice. Environ. Pollut. 257 (February), 113555. https://doi.org/10.1016/j.envpol.2019.113555.

HAPO Study Cooperative Research Group, Metzger, Boyd E., Lowe, Lynn P., Dyer, Alan R., Trimble, Elisabeth R., Chaovarindr, Udom, Coustan, Donald R., 2008. Hyperglycemia and adverse pregnancy outcomes, et al. N. Engl. J. Med. 358 (19), 1991-2002. https://doi.org/10.1056/NEJMo0707943.

Montgomery, M.P., Kamel, F., Saldana, T.M., Alavanja, M.C.R., Sandler, D.P., 2008. Incident diabetes and pesticide exposure among licensed pesticide applicators: agricultural health study, 1993-2003. Am. J. Epidemiol. 167 (10), 1235-1246. https://doi.org/10.1093/aje/kwn028.

Nougadère, Alexandre, Sirot, Véronique, Ali, Kadar, Fastier, Antony, Truchot, Eric, Vergnet, Claude, Hommet, Frédéric, Baylé, Joëlle, Gros, Philippe, Leblanc, Jean-Charles, 2012. Total diet study on pesticide residues in France: levels in food as consumed and chronic dietary risk to consumers. Environ. Int. 45 (September), 135-150. https://doi.org/10.1016/j.envint.2012.02.001.

Nougadère, Alexandre, Sirot, Véronique, Cravedi, Jean-Pierre, Vasseur, Paule, Feidt, Cyril, Fussell, Richard J., Hu, Renwei, et al., 2020. Dietary exposure to pesticide residues and associated health risks in infants and young children - results of the French infant total diet study. Environ. Int. 137 (April), 105529. https://doi.org/10.1016/j.envint.2020.105529.

Obradovic, Milan, Sudar-Milovanovic, Emina, Soskic, Sanja, Essack, Magbubah, Arya, Swati, Stewart, Alan J., Gojobori, Takashi, Isenovic, Esma R., 2021. Leptin and obesity: role and clinical implication. Front. Endocrinol. 12, 585887. https://doi.org/10.3389/fendo.2021.585887.

Plows, Jasmine F., Stanley, Joanna L., Baker, Philip N., Reynolds, Clare M., Vickers, Mark H., 2018. The pathophysiology of gestational diabetes mellitus. Int. J. Mol. Sci. 19 (11), 3342. https://doi.org/10.3390/ijms19113342.

Powe, Camille E., Kwak, Soo Heon, 2020. Genetic studies of gestational diabetes and glucose metabolism in pregnancy. Curr. Diabetes Rep. 20 (12), 69. https://doi.org/10.1007/s11892-020-01355-3.

Sadagurski, Marianna, Charlie Dong, X., Myers, Martin G., White, Morris F., 2014. IRS2 and IRS4 synergize in Non-LepRb neurons to control energy balance and glucose homeostasis. Mol. Metabol. 3 (1), 55-63. https://doi.org/10.1016/j.molmet.2013.10.004.
Saldana, T.M., Basso, O., Hoppin, J.A., Baird, D.D., Knott, C., Blair, A., Alavanja, M.C.R., Sandler, D.P., 2007. Pesticide exposure and self-reported gestational diabetes mellitus in the agricultural health study. Diabetes Care 30 (3), 529–534. https://doi.org/10.2337/dc06-1832.

Saunders, Lauren, Kadhel, Philippe, Costet, Nathalie, Rouget, Florence, Monfort, Christine, Thomé, Jean-Pierre, Guldner, Laurence, Cordier, Sylvaine, Luc, Multigner, 2014. Hypertensive disorders of pregnancy and gestational diabetes mellitus among French Caribbean women chronically exposed to chlordecone. Environ. Int. 68 (July), 171–176. https://doi.org/10.1016/j.envint.2014.03.024.

Shapiro, Gabriel D., Dodds, Linda, Arbuckle, Tye E., Ashley-Martin, Jillian, Ettinger, Adrienne S., Fisher, Mandy, Taback, Shayne, et al., 2016. Exposure to organophosphorus and organochlorine pesticides, perfluoroalkyl substances, and polychlorinated biphenyls in pregnancy and the association with impaired glucose tolerance and gestational diabetes mellitus: the MIREC study. Environ. Res. 147 (May), 71–81. https://doi.org/10.1016/j.envres.2016.01.040.

Stellavato, Antonietta, Lamberti, Monica, Pirozzi, Anna Virginia Adriana, Novellis, Francesca de, Schiraldi, Chiara, 2016. Myclobutanil worsens nonalcoholic fatty liver disease: an in vitro study of toxicity and apoptosis on HepG2 cells. Toxicol. Lett. 262 (November), 100–104. https://doi.org/10.1016/j.toxlet.2016.09.013.

Taylor, Rulaiha, Armstrong, Laura, Bhattacharya, Anisha, Henry, Zakiyah, Brinker, Anita, Buckley, Brian, Kong, Bo, Guo, Grace, 2023. Myclobutanil-mediated alteration of liver-gut FXR signaling in mice. Toxicol. Sci.: Offic. J. Soc. Toxicol. 191 (2), 387–399. https://doi.org/10.1093/toxsci/kfac129.

Tessier, Natacha, Boissonnot, Romain, Desvignes, Virginie, Fröchen, Marie, Merlo, Mathilde, Blanchard, Olivier, Chevrier, Cécile, et al., 2023. Use and storage of pesticides at home in France (the pesti’home survey 2014). Environ. Res. 216 (Pt 2), 114452. https://doi.org/10.1016/j.envres.2022.114452.

Textor, Johannes, van der Zander, Benito, Gilthorpe, Mark S., Liśkiewicz, Maciej, Ellison, George TH., 2016. Robust causal inference using directed acyclic graphs: the R package ‘dagitty. Int. J. Epidemiol. 45 (6), 1887–1894. https://doi.org/10.1093/ije/dyw341.

Tiwari, Manindra Nath, Singh, Anand Kumar, Agrawal, Sonal, Gupta, Satya Prakash, Jyoti, Anurag, Shanker, Rishi, Prakash, Om, Singh, Mahendra Pratap, 2012. Cypermethrin alters the expression profile of mRNAs in the adult rat striatum: a putative mechanism of postnatal pre-exposure followed by adulthood re-exposure-enhanced neurodegeneration. Neurotox. Res. 22 (4), 321–334. https://doi.org/10.1007/s12640-012-9317-8.

Uren Webster, Tamsyn M., Santos, Eduarda M., 2015. Global transcriptomic profiling demonstrates induction of oxidative stress and of compensatory cellular stress responses in brown trout exposed to glyphosate and roundup. BMC Genom. 16 (1), 32. https://doi.org/10.1186/s12864-015-1254-5.

Virally, M., Laloi-Michelin, M., 2010. Methods of screening of gestational diabetes between 24 and 28 weeks’ gestation. Journal De Gynecologie, Obstetrique Et Biologie De La Reproduction 39 (8 Suppl. 2), S220–S238. https://doi.org/10.1016/S0368-2315(10)70049-1.

Wang, Bi, Xiuping, Xue, 2020. Investigations of associations between seven gene polymorphisms and gestational diabetes mellitus: evidence from a meta-analysis. Gynecol. Obstet. Invest. 85 (3), 229–236. https://doi.org/10.1159/000505453.

Wu, Ling, Cui, Long, Hung Tam, Wing, Ma, Ronald C.W., Wang, Chi Chiu, 2016. Genetic variants associated with gestational diabetes mellitus: a meta-analysis and subgroup analysis. Sci. Rep. 6 (1), 30539. https://doi.org/10.1038/srep30539.