# Levels and determinants of exposure to perfluoroalkyl substances in French pregnant women from the SEPAGES cohort

Nicolas Jovanovic, Anne Lise Brantsæter, Sarah Lyon-Caen, Blandine Duval, Sylvain Carras, Annabelle Bédard, Wassila Ait-Hadad, Blandine de Lauzon-Guillain, Valérie Siroux, Sam Bayat, et al.

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## ► To cite this version:

Nicolas Jovanovic, Anne Lise Brantsæter, Sarah Lyon-Caen, Blandine Duval, Sylvain Carras, et al. "Levels and determinants of exposure to perfluoroalkyl substances in French pregnant women from the SEPAGES cohort." Environment International, 2025, 201, pp.109559. 10.1016/j.envint.2025.109559 hal-05111152

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Full length article

# Levels and determinants of exposure to perfluoroalkyl substances in French pregnant women from the SEPAGES cohort

Nicolas Jovanovic<sup>a, ∗</sup>, Anne Lise Brantsæter<sup>b, c, †</sup>, Sarah Lyon-Caen<sup>a</sup>, Blandine Duval<sup>a</sup>, Sylvain Carras<sup>d</sup>, Annabelle Bédard<sup>e</sup>, Wassila Ait-Hadad<sup>e</sup>, Blandine de Lauzon-Guillain<sup>f</sup>, Valérie Siroux<sup>a, ‡</sup>, Sam Bayat<sup>g</sup>, Inger-Lise Steffensen<sup>b, ‡</sup>, Rémy Slama<sup>a</sup>, Line Småstuen Haug<sup>b, c, ‡</sup>, Claire Philippat<sup>a, †, §</sup>

ARTICLE INFO

Handling Editor: Shoji Nakayama

Keywords:

Eternal pollutants

Food contaminants

Sources of exposure

PFAS

Poly- and perfluoroalkyl substances

Endocrine disruptors

A B S T R A C T

**Background:** Identifying the primary determinants of exposure to contaminants such as poly- and perfluoroalkyl substances (PFAS) is essential for establishing efficient regulations. We aimed to identify determinants of serum PFAS during pregnancy, a sensitive exposure window.

**Methods:** This study relied on 450 pregnant women enrolled in a French cohort from 2014 to 2017. Socio-demographic, diet, water consumption and lifestyle factors were collected through questionnaires. Twenty-six PFAS were measured in maternal serum collected around 19 weeks of gestation (median). Multivariable linear or multinomial regressions were used to study the associations between determinants and individual PFAS, as well as with a three-category variable derived from clustering (categories representing women with the lowest (N = 163), moderate (N = 236) and highest (N = 51) concentration).

**Results:** Seven PFAS were quantified in 83 % or more of the samples, while the remaining PFAS were quantified in less than 20 %. The highest median concentrations were observed for PFOS (3.95 ng/mL), PFOA (1.02 ng/mL) and PFHxS (0.69 ng/mL). Maternal age was positively associated with all PFAS, and parous women had lower levels of PFOA, PFNA, PFHxS, PFOS and PFHpS than nulliparous. Fish and liver consumption were consistently and positively associated with PFUnDA, PFDA and PFNA. Use of hormonal contraception was found to be associated with a higher PFOA concentration, where a high level of education (5 years after high school) was associated with higher PFUnDA and PFDA levels. Multinomial models on clusters of PFAS exposures showed the same associations for maternal characteristics.

**Conclusion:** Among the factors associated with higher PFAS concentrations in our cohort, those related to the consumption of specific food items may offer actionable levers to reduce exposure. Strengthened regulation

**Abbreviations:** EtFOSA, N-ethylperfluorooctane sulphonamide; MeFOSA, N-methylperfluorooctane sulfonamide; PCP, Personal Care Products; PFAS, poly- and perfluoroalkyl substances; PFBA, perfluorobutanoic acid; PFBS, perfluorobutane sulfonic acid; PFCA, perfluoroalkyl carboxylic acids; PFSA, perfluoroalkane sulfonic acids; PFDA, perfluorodecanoic acid; PFDoDA, perfluorododecanoic acid; PFDPA, perfluorodecyl phosphonic acid; PFDS, perfluorodecane sulfonic acid; PFHpA, perfluoroheptanoic acid; PFHpS, perfluoroheptane sulfonic acid; PFHxA, perfluorohexanoic acid; PFHxPA, perfluorohexyl phosphonic acid; PFHxS, perfluotohexane sulfonic acid; PFNA, perfluorononoic acid; PFOA, perfluorooctanoic acid; PFOPA, perfluorooctyl phosphonic acid; PFOS, perfluorooctane sulfonic acid; PFOSA, perfluorooctane sulfonamide; PFPeA, perfluoropentanoic acid; PFTeDA, perfluorotetradecanoic acid; PFTrDA, perfluorotridecanoic acid; PFUnDA, perfluoroundecanoic acid; 6:2diPAP, 6:2 fluorotelomer phosphate diester; 8:2diPAP, 8:2 fluorotelomer phosphate diester; 6:2PAP, 6:2 fluorotelomer phosphate monoester; 8:2PAP, 8:2 fluorotelomer phosphate monoester.

* Corresponding author at: Institut pour l'Avancée des Biosciences, Centre de Recherche UGA / Inserm U 1209 / CNRS-UMR 5309, Equipe d'épidémiologie environnementale appliquée au développement et à la santé respiratoire, Site Santé – Allée des Alpes, 38700 La Tronche, France.

E-mail address: claire.philippat@inserm.fr (C. Philippat).

<footnote><sup>a</sup> University Grenoble Alpes, Inserm U 1209, CNRS UMR 5309, Institute for Advanced Biosciences, 38000 Grenoble, France</footnote>

<footnote>b Norwegian Institute of Public Health, Department of Food Safety, P.O. Box 222 Søyen, N-0213 Oslo, Norway</footnote>

<footnote>c Norwegian Institute of Public Health, Centre for Sustainable Diets, P.O. Box 222 Søyen, N-0213 Oslo, Norway</footnote>

<footnote>Biobank BB-0033-00069, Univ. Grenoble Alpes, Inserm U1209, CNRS UMR5309, Institute for Advanced Biosciences, CHU Grenoble-Alpes, F-38000 Grenoble, France</footnote>

<footnote>Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, Équipe d'Épidémiologie respiratoire intégrative, CESP, Villejuif, France</footnote>

<footnote>f Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), F-75004 Paris, France</footnote>

<footnote>Biobank of Grenoble (BB-0033-00069), STROBE Inserm UA7 Laboratory & Grenoble University Hospital, Department of Pulmonology, Grenoble, France</footnote>
should be prioritized over individual choices, as the latter can be strongly influenced by socio-economic status, potentially limiting the feasibility of individual dietary changes.

# 1. Introduction

The Developmental Origins of Health and Disease (DOHaD) concept states that the risk of adverse health outcomes across the lifespan may be increased by exposure to environmental stressors during the developmental period, which encompasses intrauterine life and the first years after birth (Fleming et al., 2018; Sinclair et al., 2007). An example of such stressors is poly- and perfluoroalkyl substances (PFAS), a class of widespread anthropogenic environmental pollutants. PFAS are used in almost all types of industries and many consumer products because of their chemical properties. They are for example commonly used in coatings on leather, fabrics and paper in contact with food, as stain repellents, in paints or adhesives, in fire-fighting foam, cosmetics and personal care products (PCPs) (Glüge et al., 2020). Due to their long half-lives (up to several years (EFSA CONTAM et al., 2020)), most PFAS are bioaccumulative and widespread in the environment.

In humans, exposure to certain PFAS, mainly PFOS and PFOA, has been associated with various adverse health effects, such as reduced antibody reaction to vaccination, increased propensity to infection or reduced birth weight (EFSA CONTAM et al., 2020). Furthermore, the International Agency for Research on Cancer has categorized PFOA as a Group 1 “carcinogenic for humans” and PFOS as a Group 2B “possibly carcinogenic for humans”. Identification of key determinants of PFAS exposure is thus crucial for implementing efficient regulations and public health prevention programs.

PFAS can cross the placenta (Gützkow et al., 2012) and are also found in breast milk (Fromme et al., 2010; LaKind et al., 2023). Therefore, in the DOHaD context, it becomes increasingly important to target pregnant women in comprehensive studies of determinants of PFAS body burden. Such studies will also provide insight into the most important covariates for inclusion in epidemiologic investigations of associations between PFAS exposure and health and are essential for better understanding of sources of PFAS exposure.

Diet has been considered the main source of exposure to PFAS in the general population (Poothong et al., 2020; Schecter et al., 2010; Vestergren & Cousins, 2009). However, results have been inconsistent regarding which food groups are the most important PFAS sources. High consumption of red meat, animal fat and snack foods has been associated with higher plasma PFOS and PFOA levels in Danish pregnant women (Halldorsson et al., 2008), whereas seafood consumption (fish and shellfish) was associated with PFOS, PFOA and PFUnDA in Norwegian women (Haug et al., 2010). Only a few previous studies have included a thorough investigation into the contribution of factors other than diet to PFAS blood levels, particularly for PFAS other than PFOS and PFOA.

The objectives of the present study were to describe serum perfluoroalkyl concentrations and to identify their determinants in pregnant women enrolled in the French cohort SEPAGES.

# 2. Materials and methods

## 2.1. Study population

This study is based on data from the French couple-child cohort SEPAGES (Lyon-Caen et al., 2019), for which 484 pregnant women were recruited between July 2014 and July 2017. The inclusion criteria were being older than 18 years of age, less than 19 weeks of amenorrhea, singleton pregnancy, living in the study area (being within 80 km of the center of Grenoble) and planning to deliver in one of the four maternity wards of Grenoble. Most of the participants (90 %) were recruited during obstetrical ultrasound appointments. The current study included a subsample (N = 450) of pregnant women with PFAS assessed in blood

collected during pregnancy (Philippat et al., 2023). The study was approved by the appropriate ethical committees and women signed informed consent prior to inclusion.

## 2.2. Blood sampling

Trained SEPAGES field workers collected non-fasting maternal blood during a study visit at the participants' homes (median gestational age at sampling: 19.4 weeks, 5 - 95th percentiles: 16.1-28.4 gestational weeks). Blood samples were collected in 6 mL dry tubes and transported on ice to the Grenoble Alpes University Hospital biobank where they were processed. After being shaken 10 times manually, they were left for 30 min to allow the blood to coagulate. They were then centrifuged for 10 min, and serum aliquots were made and stored at -80 °C. Serum aliquots were sent on dry ice to the Department of Food Safety at the Norwegian Institute of Public Health for PFAS analyses.

## 2.3. Chemical analyses of PFAS

Nineteen legacy PFAS were measured in 150 µL of serum after sample preparation consisting of protein precipitation by methanol, followed by an analytical method based on column switching liquid chromatography coupled to a triple quadrupole mass spectrometer (Haug et al., 2009). In addition, seven emerging PFAS were analyzed using 50 µL of serum, with a method consisting of rapid protein precipitation by methanol followed by high throughput online solid phase extraction, ultra-high performance liquid chromatography coupled with tandem mass spectrometry and negative electrospray ionization detection (Poothong et al., 2017). Both methods have been validated and are described in detail elsewhere (Haug et al., 2009; Poothong et al., 2017). In total, 26 PFAS with different chemical structures were analyzed in this project; five perfluoroalkane sulfonic acids (PFSA) (including PFOS, PFHxS), 11 perfluoroalkyl carboxylic acids (PFCA) (including PFOA, PFNA), three perfluorooctane sulfonamides (PFOSA), three perfluoroalkyl phosphonic acids (PFPA) and four n:2 Polyfluoroalkyl phosphoric acid esters (PAPs) (Table 1). Notably, the detection frequencies of polyfluoroalkyl substances were too low to be considered in our statistical analysis (see results section for details). Therefore, only perfluoroalkyl substances were included.

## 2.4. Potential determinants of PFAS serum concentrations

Women answered detailed questionnaires (either administered by study field workers or self-completed), including maternal and pregnancy-related characteristics and socioeconomic, lifestyle and dietary factors (Table 2). The potential determinants of PFAS serum concentrations to be investigated in our study were identified from the literature.

**Maternal and pregnancy-related characteristics:** maternal age (continuous); parity (categorized as nulliparous vs. uni-/multiparous), given that higher parity has been associated with PFAS excretion through breastfeeding; body mass index (BMI) at the time of blood sampling (continuous), used as a proxy for maternal blood volume; use of hormonal contraceptives prior to pregnancy (yes/no), as it may reduce the frequency of menstruation and thus influence PFAS elimination; gestational age at blood sampling (continuous); and date of blood sampling (continuous), to account for potential time trends.

**Socioeconomic, lifestyle and dietary factors:** maternal education (categorized as up to 1-2 years after high school, 3-4 years after high school, or ≥ 5 years after high school) a proxy of socio-economic status; active or passive tobacco exposure (yes/no); and use of cosmetics and
personal care products, measured as the total number of personal care products used daily. Dietary variables included the consumption of fish, crustaceans, and eggs (categorized as none/monthly [i.e., once per month or less] vs. weekly [i.e., once or more per week]); liver (no/yes); red meat; poultry or rabbit; and the quantity of tap water consumed daily. These dietary factors were selected based on prior evidence linking them to higher PFAS serum concentrations. (EFSA CONTAM et al., 2020).

Tap water was chosen instead of bottled water, as it showed greater variability in the volume consumed daily in our study. Moreover, only a few women reported bottled water consumption. This choice was also supported by previous national studies that detected the presence of PFAS in tap water (Boiteux et al., 2012). Maternal diet was assessed using a validated Food Frequency Questionnaire (Kadawathagedara et al., 2021). Briefly, consumption of 125 food items were collected, including 12 nonalcoholic and 4 alcoholic beverages, with frequency assessed by a 7-item scale from “never” to “more than once a day.” After the transformation of FFQ frequency categories into daily frequencies, daily intake was assessed, for each food item, by combining frequency of intake and portion size. Energy intakes were then calculated for all subjects by multiplying the daily intake of each food item by the nutritional values in the CALNUT 2020 nutrient composition database. Based on the consumption of each food group, variables were categorized either in a yes/no format (liver), weekly consumption vs none/monthly consumption (eggs, fish, poultry/rabbit) or “Never / <1 per month / ≥1 per month” (crustaceans). Red meat was used as a continuous variable, representing the quantity (grams) of red meat consumed per day.

**Home environment characteristics:** presence of controlled mechanical ventilation (no/yes) and vacuum cleaner use frequency (<1 per week, 2 to 3 per week, ≥4 or more per week) were also considered, as dust might be a source of PFAS exposure (Haug et al., 2011; Trudel et al., 2008).

## 2.5. Statistical analyses

### 2.5.1. Exposure variables

We computed for each PFAS the percentage of serum concentrations below the limit of detection (LOD) and of quantification (LOQ) as well as descriptive statistics including 5th, 25th, 50th, 75th and 95th percentiles (Table 1). PFAS quantified in less than 70 % of the participants were excluded from this study. Concentrations of the remaining PFAS (greater than 70 % quantification rate) were ln-transformed and values below the LOQ were singly imputed using the compound’s probability distribution (Helsel, 2012). Spearman correlations were then computed. In addition to individual PFAS concentrations, we also used a three-category variable representing PFAS exposure patterns. This variable was defined in a previous study (Philippat et al., 2023) using a clustering analysis. Briefly, a Variable Selection for Model-Based Clustering of Mixed-Type Data Method (package VarSelLCM, R) enabled the identification of three distinct groups of women characterized by low, moderate and high exposure, based on medians in our study population. (Fig. S1). The first group (lowest exposure) comprised 163 mothers with the lowest PFAS concentrations, with only 5 % to 21 % exceeding the median concentration of the overall population, depending on the specific PFAS. The second group (medium exposure) included 236 mothers with moderate PFAS concentrations, where 62 % to 70 % had levels above the population median. The final group (highest exposure) consisted of 51 mothers with the highest PFAS concentrations, with 76 % to 96 % exceeding the median concentration.

### 2.5.2. Missing data imputation

Missing values for maternal diet, socioeconomic, lifestyle and dietary factors were imputed using Multiple Imputation by Chained Equations (MICE) (R package “mice”) to create 100 imputed datasets. Except for the complete case sensitivity analysis, all models were run on each

<table><caption>**Table 1**
Distribution of 26 PFAS serum concentrations from 450 pregnant women in SEPAGES with blood sampled between 2014 and 2017.</caption><thead><tr><th rowspan="2">Family</th><th rowspan="2">Abbreviation</th><th rowspan="2">LOD/LOQ<br>(ng/ml)</th><th colspan="2">%<br>&lt; LOD</th><th colspan="5">Percentiles (ng/ml)</th></tr><tr><th>%<br>&lt; LOQ</th><th>5th</th><th>25th</th><th>50th</th><th>75th</th><th>95th</th></tr></thead><tbody><tr><td>PFCA</td><td>PFOA<sup>a</sup></td><td>0.02/0.05</td><td>0</td><td>0</td><td>0.44</td><td>0.72</td><td>1.02</td><td>1.46</td><td>2.39</td></tr><tr><td>PFCA</td><td>PFNA<sup>a</sup></td><td>0.02/0.05</td><td>0</td><td>0</td><td>0.26</td><td>0.38</td><td>0.50</td><td>0.63</td><td>0.94</td></tr><tr><td>PFSA</td><td>PFHxS<sup>a</sup></td><td>0.02/0.05</td><td>0</td><td>0</td><td>0.31</td><td>0.49</td><td>0.69</td><td>0.95</td><td>1.73</td></tr><tr><td>PFSA</td><td>PFOS<sup>a</sup></td><td>0.02/0.05</td><td>0</td><td>0</td><td>1.64</td><td>2.7</td><td>3.95</td><td>5.35</td><td>8.93</td></tr><tr><td></td><td>Sum of 4 above<sup>b</sup></td><td></td><td></td><td></td><td>2.84</td><td>4.72</td><td>6.17</td><td>8.42</td><td>13.9</td></tr><tr><td>PFCA</td><td>PFDA<sup>a</sup></td><td>0.02/0.05</td><td>0</td><td>0.2</td><td>0.1</td><td>0.16</td><td>0.22</td><td>0.29</td><td>0.51</td></tr><tr><td>PFCA</td><td>PFUnDA<sup>a</sup></td><td>0.02/0.05</td><td>0</td><td>2</td><td>0.06</td><td>0.11</td><td>0.15</td><td>0.21</td><td>0.33</td></tr><tr><td>PFSA</td><td>PFHpS<sup>a</sup></td><td>0.02/0.05</td><td>2</td><td>17</td><td>&lt;LOQ</td><td>0.06</td><td>0.08</td><td>0.12</td><td>0.19</td></tr><tr><td>PFPA</td><td>PFHxPA<sup>b</sup></td><td>0.02/0.06</td><td>16</td><td>48</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOQ</td><td>0.05</td><td>0.10</td></tr><tr><td>PFCA</td><td>PFTrDa<sup>a</sup></td><td>0.02/0.05</td><td>37</td><td>80</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>0.05</td><td>0.09</td></tr><tr><td>PFCA</td><td>PFHpA<sup>a</sup></td><td>0.02/0.05</td><td>78</td><td>94</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>0.05</td><td>0.09</td></tr><tr><td>PFCA</td><td>PFDoDa<sup>a</sup></td><td>0.02/0.05</td><td>13</td><td>83</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>0.09</td></tr><tr><td>PFSA</td><td>PFBS<sup>a</sup></td><td>0.02/0.05</td><td>65</td><td>91</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>0.05</td><td>0.08</td></tr><tr><td>PFOSA</td><td>6:2 diPAB<sup>b</sup></td><td>0.01/0.03</td><td>78</td><td>94</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>0.03</td><td>0.08</td></tr><tr><td>PAP</td><td>8:2 diPAB<sup>b</sup></td><td>0.01/0.01</td><td>89</td><td>96</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>0.03</td></tr><tr><td>PFOSA</td><td>PFOSA<sup>a</sup></td><td>0.02/0.05</td><td>66</td><td>100</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td></tr><tr><td>PFCA</td><td>PFTeDa<sup>a</sup></td><td>0.03/0.10</td><td>97</td><td>100</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td><td>&lt;LOQ</td></tr><tr><td>PFCA</td><td>PFBA<sup>a</sup></td><td>0.30/1.00</td><td>100</td><td>100</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td></tr><tr><td>PFCA</td><td>PFPeA<sup>a</sup></td><td>0.02/0.05</td><td>100</td><td>100</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td></tr><tr><td>PFCA</td><td>PFHxA<sup>a</sup></td><td>0.02/0.05</td><td>100</td><td>100</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td></tr><tr><td>PFSA</td><td>PFDS<sup>a</sup></td><td>0.07/0.20</td><td>100</td><td>100</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td></tr><tr><td>PFOSA</td><td>MeFOSA<sup>a</sup></td><td>0.02/0.05</td><td>100</td><td>100</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td></tr><tr><td>PFOSA</td><td>EtFOSA<sup>a</sup></td><td>0.02/0.05</td><td>100</td><td>100</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td></tr><tr><td>PAP</td><td>6:2 PAP<sup>b</sup></td><td>0.20/0.60</td><td>100</td><td>100</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td></tr><tr><td>PAP</td><td>8:2 PAP<sup>b</sup></td><td>0.10/0.30</td><td>100</td><td>100</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td></tr><tr><td>PFPA</td><td>PFOPA<sup>b</sup></td><td>0.05/0.15</td><td>100</td><td>100</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td></tr><tr><td>PFPA</td><td>PFDPA<sup>b</sup></td><td>0.05/0.15</td><td>100</td><td>100</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td><td>&lt;LOD</td></tr></tbody></table>

**Abbreviations:** LOD: limit of detection; LOQ: limit of quantification.

<sup>b</sup> Sum of PFOA, PFNA, PFHxS and PFOS concentrations, for evaluation of chronic exposure to these PFAS above the limit of 6.9 ng/mL, which corresponds to a exposure higher than tolerable weekly intake of these four PFAS set to 4.4 ng per kg body weight per week by EFSA CONTAM (2020).

<sup>a</sup> PFAS analyzed using the method by Haug et al. (2009).

<sup>b</sup> PFAS analyzed using the method by Poothong et al. (2017).
<caption>**Table 2**</caption>

Study population characteristics of 450 pregnant women in SEPAGES recruited between 2014 and 2017.

<table><thead><tr><th>Characteristic</th><th>N = 450<sup>1</sup></th></tr></thead><tbody><tr><td><strong>Year of blood sampling</strong></td><td></td></tr><tr><td>2014</td><td>21 (4.7%)</td></tr><tr><td>2015</td><td>161 (36%)</td></tr><tr><td>2016</td><td>198 (44%)</td></tr><tr><td>2017</td><td>70 (16%)</td></tr><tr><td><strong>Gestational age at blood sampling (weeks)<sup>a</sup></strong></td><td>19.4 ± 3.10</td></tr><tr><td><strong>Maternal age (year)</strong></td><td>32.6 ± 3.9</td></tr><tr><td><strong>BMI at blood sampling (kg/m2)</strong></td><td>24.3 ± 3.9</td></tr><tr><td>Unknown</td><td>12</td></tr><tr><td><strong>Parity</strong></td><td></td></tr><tr><td>Nulliparous</td><td>203 (45%)</td></tr><tr><td>Uni/Multiparous</td><td>247 (55%)</td></tr><tr><td><strong>Hormonal contraception</strong></td><td></td></tr><tr><td>No</td><td>156 (35%)</td></tr><tr><td>Yes</td><td>291 (65%)</td></tr><tr><td>Unknown</td><td>3</td></tr><tr><td><strong>Maternal education</strong></td><td></td></tr><tr><td>Up to 1-2 years after high school</td><td>76 (17%)</td></tr><tr><td>3-4 years after high school</td><td>115 (26%)</td></tr><tr><td>≥5 years after high school</td><td>257 (57%)</td></tr><tr><td>Unknown</td><td>2</td></tr><tr><td><strong>Red meat daily consumption quantity (g/day)</strong></td><td>34 ± 38</td></tr><tr><td>Unknown</td><td>60</td></tr><tr><td><strong>Poultry/Rabbit consumption</strong></td><td></td></tr><tr><td>Monthly consumption</td><td>301 (74%)</td></tr><tr><td>Weekly consumption</td><td>108 (26%)</td></tr><tr><td>Unknown</td><td>41</td></tr><tr><td><strong>Liver consumption</strong></td><td></td></tr><tr><td>No</td><td>312 (76%)</td></tr><tr><td>Yes</td><td>97 (24%)</td></tr><tr><td>Unknown</td><td>41</td></tr><tr><td><strong>Eggs consumption</strong></td><td></td></tr><tr><td>Monthly consumption</td><td>247 (60%)</td></tr><tr><td>Weekly consumption</td><td>162 (40%)</td></tr><tr><td>Unknown</td><td>41</td></tr><tr><td><strong>Fish consumption</strong></td><td></td></tr><tr><td>Monthly consumption</td><td>244 (60%)</td></tr><tr><td>Weekly consumption</td><td>164 (40%)</td></tr><tr><td>Unknown</td><td>42</td></tr><tr><td><strong>Crustacean consumption</strong></td><td></td></tr><tr><td>Never</td><td>167 (41%)</td></tr><tr><td>&lt;1/month</td><td>156 (38%)</td></tr><tr><td>≥1/month</td><td>85 (21%)</td></tr><tr><td>Unknown</td><td>42</td></tr><tr><td><strong>Tap water consumption (L/day)</strong></td><td>2.07 ± 2.05</td></tr><tr><td>Unknown</td><td>46</td></tr><tr><td><strong>Daily caloric intake (kcal)</strong></td><td>1,759 ± 659</td></tr><tr><td>Unknown</td><td>50</td></tr><tr><td><strong>Maternal active smoking</strong></td><td></td></tr><tr><td>No</td><td>380 (93%)</td></tr><tr><td>Yes</td><td>27 (6.6%)</td></tr><tr><td>Unknown</td><td>43</td></tr><tr><td><strong>Maternal passive smoking</strong></td><td></td></tr><tr><td>No</td><td>334 (80%)</td></tr><tr><td>Yes</td><td>84 (20%)</td></tr><tr><td>Unknown</td><td>32</td></tr><tr><td><strong>PCP daily use (number of use/day)</strong></td><td>13.3 ± 5.7</td></tr><tr><td>Unknown</td><td>19</td></tr><tr><td><strong>House CMV</strong></td><td></td></tr><tr><td>No</td><td>130 (31%)</td></tr><tr><td>Yes</td><td>289 (69%)</td></tr><tr><td>Unknown</td><td>31</td></tr><tr><td><strong>Vacuum usage frequency</strong></td><td></td></tr><tr><td>&lt;1 per week</td><td>111 (27%)</td></tr><tr><td>2 to 3 per week</td><td>269 (65%)</td></tr><tr><td>≥ 4 per week</td><td>33 (8.0%)</td></tr><tr><td>Unknown</td><td>37</td></tr></tbody></table>

“Monthly consumption” implies that the food type is consumed once per month or less, where “weekly consumption” indicates once or more per week.

**Abbreviation:** CMV: controlled mechanical ventilation, PCP: Personal Care Products.

imputed dataset and the results pooled following Rubin's method (Rubin, 1987).

### 2.5.3. Associations between potential determinants and PFAS concentrations

The association between potential determinants and each PFAS concentration was estimated by multiple linear regression models adjusted for all the determinants mentioned above along with analytical batch. We used adjusted multinomial regression models to study associations with the cluster variable (three categories representing low, moderate and high exposure, regarding medians in our study population). However, we could not include the vacuum cleaner frequency variable in this model because no women in the “≥ 4 times per week” group fell into the highest exposure cluster.

Collinearity between covariates was evaluated with the Variance Inflation Factor (VIF), which ranged from 1.11 to 1.40), except for the analytical batch variable (3.43) and the date of blood sample collection (2.58), which was expected, as the samples were analyzed for PFAS concentrations in chronological order.

P-values < 0.05 were considered significant. To investigate the possibility of a trend, if the determinant was significantly associated with one PFAS, other associations for this PFAS with a p-value < 0.1 were reported and considered as “suggesting an association”.

### 2.5.4. Sensitivity analyses

We conducted several sensitivity analyses to test result consistency. A sensitivity analysis was made by excluding participants whose studentized residuals were outside of the (-3; 3) range (1 to 9 individuals depending on the PFAS) to evaluate the potential impact of these influential values. A complete-case analysis restricted to individuals with no missing values for any determinants was also done. In addition, we ran a model adjusted for total energy intake to assess if the associations seen with diet variables could be partially driven by the total amount of food consumed.

Analyses were performed with R 4.3.2. Packages used were “tibble” and “dplyr” for dataframe manipulation, “stringr” for string (chain of characters) manipulation, “lubridate” for date manipulation, “tidymodels” for regression modeling and “mice” for multiple imputations. Snakemake 8.12.0 was used to manage the analysis workflow. Code is available on a public repository: https://gricad-gitlab.univ-grenoble-alpes.fr/iab-env-epi/jovanovicLevels_2024.

### 2.5.5. Ethic approval

Ethical agreements were obtained from the CPP (Comité de Protection des Personnes Sud-Est V) and the Commission Nationale de l'Informatique et des Libertés (CNIL), the French data privacy institution. All participants provided written informed consent before enrollment.

## 3. Results

### 3.1. Characteristics of the participants

Included women had a mean age at enrollment of 32.6 years (± standard deviation: 3.9) and a mean BMI of 24.3 ± 3.9 kg/m² at enrolment; 57% were highly educated (≥5 years after high school). About two-thirds (65%) of participants reported using hormonal contraception before the SEPAGES pregnancy. Weekly consumption of fish and eggs was reported for 40% of the women, whereas fewer women reported liver consumption (24%) (Table 2).

### 3.2. Serum concentrations

Twelve PFAS (PFTeDa, PFBA, PFPeA, PFHxA, PFDS, MeFOSA, EtFOSA, 6:2 PAP, 8:2 PAP, PFOPA, PFDPA and PFOSA) were not quantified in any of the samples, while 7 others were rarely detected or quantifiable (detection/quantification frequencies < 70% for 6:2

<footnote n="1">¹ n (%); Mean ± SD.</footnote>

<footnote>* Median ± Inter-quartile range.</footnote>
diPAP, 8:2 diPAP, PFBS, PFDODA, PFHpA, PFTrDa, PFHxPA). PFHxPA was quantified in 52 % of the samples while the remaining seven PFAS (PFOA, PFNA, PFHxS, PFOS, PFDA, PFUnDA, PFHpS) were quantified in more than 83 % of the samples (Table 1). Among these compounds, the highest median concentrations were observed for PFOS (3.95 ng/mL), followed by PFOA (1.02 ng/mL), PFHxS (0.69 ng/mL) and PFNA (0.50 ng/mL). The median sum of PFOS, PFOA, PFNA and PFHxS was 6.17 ng/ mL.

### 3.3. Correlations between continuous PFAS

The Spearman correlation coefficients are presented in Fig. 1, showing two distinct patterns. The first reflected high correlations between three PFSA, PFOS, PFHxS and PFHpS (r: 0.82–0.99); the second reflected high correlations between four PFCA, PFOA, PFNA, PFDA and PFUnDA (r: 0.59–0.85, except for the correlation between PFOA and PFUnDA (r: 0.28)). Moderate correlation between these two patterns were observed only for PFOS with PFNA, PFDA and PFUnDA (r: 0.34–0.35).

### 3.4. Determinants of PFAS serum concentrations

#### 3.4.1. Maternal and pregnancy-related characteristics

In the adjusted regression analyses, higher parity was associated with a lower PFOA, PFNA, PFHxS, PFOS, PFHpS concentrations (Fig. 2 and Table S1). An association was also suggested with PFDA. Increase in maternal age was associated with increased concentrations of PFNA, PFOS, PFDA, PFUnDA, PFHpS and to some extent with increased PFOA and PFHxS (Fig. 3 and Table S1).

An increase in the blood sampling date was associated with a decreased risk of belonging to the highest PFAS concentration cluster (Odds Ratio (OR) 95 % Confidence Interval (CI): 0.4 [0.33; 0.47] for a one year increase in blood sampling date) and with decreased PFOA concentration (Fig. 4, Table S2). Finally, ln-PFOA concentration was on average 0.12 ng/ml [0.03; 0.22] higher among women reporting use of hormonal contraceptives prior to the SEPAGES pregnancy compared to those using none or another type of contraceptives (Fig. 2, Table S1).

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Spearman correlation plot between PFAS detected in more than 70% of the samples. Abbreviations: PFOA: perfluorooctanoic acid; PFNA: perfluorononanoic acid; PFHxS: perfluotohexane sulfonic acid; PFOS: perfluorooctane sulfonic acid; PFDA: perfluorodecanoic acid; PFUnDA: perfluoroundecanoic acid; PFHpS: perfluoroheptane sulfonic acid.</figcaption></figure>

Such associations were also found in the analysis relying on exposure pattern (i.e., clusters) (Fig. 4).

#### 3.4.2. Socioeconomics, lifestyle and dietary factors

On average, highly educated women (≥5 years after high school) had higher PFUnDA (β [95 %CI]: 0.14 [0.02; 0.27]) and PFDA (β [95 %CI]: 0.12 [-0.01; 0.25]) concentrations compared to those with lower education levels. No associations were observed in the cluster analysis for this variable (Fig. 4, Supplementary Table S2).

Compared to none or monthly consumption, weekly fish consumption was associated with an increase in PFUnDA (β [95 %CI]: 0.22 [0.12; 0.32]), PFDA (β [95 %CI]: 0.11 [0; 0.21]) and PFNA concentrations (β [95 %CI]: 0.1 [0.01; 0.19]). It also showed a suggested positive association with PFOS (β [95 %CI]: 0.1 [-0.01; 0.21]) concentrations. Women reporting liver consumption (n = 97) had on average higher concentrations of PFDA, PFUnDA and PFNA (Fig. 3).

Increased poultry/rabbit, read meat and tap water consumption were not associated with individual PFAS concentrations, nor with the cluster variable. Compared to none/monthly consumption, weekly egg consumption was associated with lower PFUnDA concentrations (β [95 %CI]: -0.15 [-0.25; -0.06]).

#### 3.4.3. Home environment and cleaning habits

The use of a vacuum cleaner 4 or more time per week (n = 33 women) was associated with a decrease in PFUnDA serum concentrations (β [95 %CI]: -0.22 [-0.42; -0.02]) (Fig. 2, Supplementary Table S1), while no association was observed for the other PFAS. No associations were observed with the presence of mechanical ventilation at home.

### 3.5. Sensitivity analysis

#### 3.5.1. Exclusion of outliers

In the models excluding participants whose studentized residuals were outside of the (-3; 3) range, effect estimates remained in the same direction. Several associations were strengthened (Table S3), including the associations between maternal education and PFDA and fish consumption and PFDA.

#### 3.5.2. Complete case model

Restricting the analysis to complete cases (N = 331) resulted in comparable effect estimates as for the main model (Table S4). Several associations were strengthened, including the associations between PFNA and liver consumption, or PFDA and fish consumption. Notably, the association between vacuum cleaner use and PFUnDA disappeared.

#### 3.5.3. Adjusting on caloric intake

When daily caloric intake was added as an adjustment factor, the results remained consistent. The association between PFDA and weekly fish consumption was strengthened (Table S5).

## 4. Discussion

In this study, seven PFAS were quantified in serum from at least 83 % of the pregnant women, with PFOS, PFOA, PFHxS and PFNA showing the highest median values. While serum concentrations were overall lower than those reported in previous studies in European women, more than one third of the SEPAGES women was above the EFSA Tolerable Weekly Intake (TWI) defined for four PFAS based on their effects on the immune system. Regarding exposure determinants, we identified several maternal (maternal age, parity and education) and sample (date of sampling) characteristics, as well as dietary (fish and liver consumption) factors which were associated with changes in PFAS levels.
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Adjusted linear regression coefficients between individual PFAS serum concentrations (In-transformed) and categorical determinants. **Abbreviations:** CMV: controlled mechanical ventilation; PFOA: perfluorooctanoic acid; PFNA: perfluorononanoic acid; PFHxS: perfluotohexane sulfonic acid; PFOS: perfluorooctane sulfonic acid; PFDA: perfluorodecanoic acid; PFUnDA: perfluoroundecanoic acid; PFHpS: perfluoroheptane sulfonic acid; NS: Non-significant **Full list of predictors in the model**: Date of blood sampling (year), maternal age (5 years), BMI at blood sampling, hormonal contraception (yes/no), maternal education, liver consumption, eggs consumption, fish consumption, crustacean consumption, tap water consumption, maternal active smoking, maternal passive smoking, house mechanical ventilation, vacuum usage frequency, red meat daily consumption (5 g/day), poultry/rabbit consumption, pcp daily use (per 5 PCP), parity. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

## 4.1. PFAS concentrations: comparison with older cohorts and time trends

Among the 26 PFAS measured, 19 were not or rarely detected. As method sensitivity was similar across compounds, differences in detection likely reflect variations in toxicokinetics (i.e., absorption, distribution, metabolism and elimination) and in exposure levels. The highest median values were observed for PFOS, PFOA, PFHxS and PFNA, which is in line with previous reports (Fig. 5, Supplementary Material, Table S6). As an example, in the French ELFE pilot study measuring 14 PFAS in breast milk (Antignac et al., 2013), PFOS, PFOA and PFHxS were major contributors to total PFAS body burden, whereas other PFAS were rarely detected. Compared to previous studies, median values for these four PFAS were similar or lower in SEPAGES, except for PFHxS, for which the median value was sometimes higher (study with the 5th highest PFHxS median, out of 11 studies reported) (Fig. 5). Our results showed high correlation between substances within the subgroups PFSA and PFCA, and moderate correlation between PFOS and some PFCA. Correlated PFAS share similar chemical structure and may also indicate concomitant determinants, sources of exposure and toxicokinetic properties.

We observed a negative time trend for PFOA, suggesting that serum concentrations have decreased between 2014 and 2017 (Berg et al., 2015; Chatzi et al., 2017; Dalsager et al., 2021; Grazuleviciene et al., 2009; Heude et al., 2016; Manzano-Salgado et al., 2016; Meng et al., 2018; Starling et al., 2014; Wikström et al., 2020; Wright et al., 2013), summarized in Table S6), while no association was observed for other PFAS nor as the cluster variables. The decrease in PFOA concentrations over the years of sample collection may be related to its classification as a substance of very high concern (SVHC) under REACH (Registration,

Evaluation, Authorisation and Restriction of Chemicals) (ECHA, 2013). This classification led to strengthened regulation by the European Union and likely motivated industry to progressively replace PFOA with alternative substances. The other studied PFAS such as PFHxS, PFNA and PFDA were added to the REACH candidate list later. In addition, PFOA tends to have a slightly shorter half-life than the most of the other PFAS studied; therefore, a decrease in serum levels is expected to occur more rapidly following regulatory measures targeting this compound (Rosato et al., 2024). The SEPAGES recruitment period which spanned only three years might be too short a timeframe to observe the effects of such regulatory measures, limiting the statistical power of the analysis.

## 4.2. Determinants of PFAS concentrations in serum

### 4.2.1. Maternal and pregnancy-related characteristics

Maternal age was associated with increased concentrations of all PFAS, as well as with the odds of being in the group with the highest exposure (Fig. 3 and Fig. 4). This is typical of persistent chemicals reflecting the long half-lives of up to several years for these substances in human bodies (EFSA CONTAM et al., 2020).

Inversely, parity tended to be negatively associated with PFAS concentrations as well as with a decrease in odds of being in either the medium or highest exposure group. This was in line with previous reports and likely resulted from the fact that childbirth and breastfeeding contribute to PFAS excretion (Brantsæter et al., 2013). In addition, PFAS can cross the placental barrier and be transferred from the mother into the fetus' blood stream during pregnancy (Appel et al., 2022). Therefore, multiparous women could have excreted PFAS in previous pregnancies, explaining their lower levels.
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Adjusted linear regression coefficients between individual PFAS serum concentrations (ln-transformed) and potential continuous determinants. Abbreviations: PCP: Personal Care Products, PFOA: perfluorooctanoic acid; PFNA: perfluorononanoic acid; PFHxS: perfluotohexane sulfonic acid; PFOS: perfluorooctane sulfonic acid; PFDA: perfluorodecanoic acid; PFUnDA: perfluoroundecanoic acid; PFHpS: perfluoroheptane sulfonic acid; NS: Non-significant; Effect estimate given in: 1 year for Maternal age, 5 kg/m² for BMI at blood sampling, year for Date of blood sampling, 50g/day for Red meat daily consumption quantity, L/day for tap water consumption and per 5 PCP for PCP daily use. **Full list of predictors in the model:** Date of blood sampling (year), maternal age (5 years), BMI at blood sampling, hormonal contraception (yes/no), maternal education, liver consumption, eggs consumption, fish consumption, crustacean consumption, tap water consumption, maternal active smoking, maternal passive smoking, house mechanical ventilation, vacuum usage frequency, red meat daily consumption (5 g/day), poultry/rabbit consumption, pcp daily use (per 5 PCP/day), parity. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

In line with previous reports (McAdam & Bell, 2023), we did not observe associations between BMI, as a proxy for blood volume expansion, and PFAS concentrations, although some studies have reported associations for specific PFAS (Brantsæter et al., 2013; Fábelová et al., 2023). Null findings may result from the fact that while BMI serves as an indirect indicator of blood volume expansion, it is also influenced by various lifestyle and socio-demographic factors that may better predict serum PFAS concentrations than BMI alone (McAdam & Bell, 2023).

The use of hormonal contraceptives prior to the SEPAGES pregnancy was positively associated with PFOA. Only a few studies have explored such associations (McAdam & Bell, 2023). In line with our results, Rush et al. reported increased concentrations of PFOA, as well as other PFAS, in the 12 months preceding pregnancy. Hormonal contraceptives may decrease menstruation frequency, a physiological mechanism during which PFAS can be eliminated (Rush et al., 2018). Another possible reason for these positive associations may be longer interpregnancy intervals for women using hormonal contraceptives, and therefore longer time periods in which to accumulate PFOA and possibly other PFAS since previous pregnancy (Brantsæter et al., 2013).

Maternal education, used as a proxy of socio-economic status, was positively associated with PFUnDA and PFDA. This is in line with several European study showing higher PFAs concentration in women with the highest education (Fábelová et al., 2023; Montazeri et al., 2019). Individuals with higher educational attainment often have greater economic means, which can influence their dietary patterns and product usage. This increased access to a broader range of consumer goods and foods – particularly imported or processed items – may inadvertently lead to greater exposure to PFAS compounds.

The use of a vacuum cleaner four or more times per week was associated with lower serum concentrations of PFUnDA. While PFAS are known to be present in indoor dust, this association observed for PFUnDA – but not for other PFAS – was unexpected, as previous studies have generally reported lower concentrations of PFUnDA in dust compared to other PFAS such as PFHxA, PFOS, or PFOA (de la Torre

et al., 2019; Weiss et al., 2021). Perfluoroundecanoic acid (PFUnDA) is generally detected at lower levels in indoor environments, particularly in dust, due to its chemical properties and usage patterns. In addition, the relatively small number of individuals in the highest vacuum-use category and the fact that this association was attenuated in some sensitivity analyses warrant a cautious interpretation of these findings.

Regarding dietary factors, in our study, higher consumption of fish, without distinction towards fish species or type, was positively associated with PFUnDA, PFNA, PFDA and to some extend PFOS. This is in line with previous findings in pregnant women (Fábelová et al., 2023) and other subpopulations (Haug et al., 2010; Rylander et al., 2010; Thépaut et al., 2021) also reporting higher PFAS concentrations among consumers of fish and/or shellfish. A limitation of our food questionnaire was its limited detail regarding specific food items; some, but not all, fish species were listed, and certain questions grouped multiple types (e.g. canned fish). This reduced our ability to assess differences in PFAS exposure by species, fat or protein content, or trophic level – factors that could provide insights into PFAS biomagnification and support more targeted public health recommendations on fish consumption. However, such detailed analyses would likely have required a larger sample size than was available in this study.

Women reporting liver consumption (n = 97) had on average higher concentrations of PFDA, PFUnDA and to some extend PFNA than non-consumers. This was in accordance with these chemicals' structure, since associations with animal-based foods are expected for longer-chained PFAS (PFNA, PFDA and PFUnDA), due to their higher bio-accumulation potential as compared to shorter-chained PFAS (PFHxS and PFHpS) (Buck et al., 2011). PFAS binds to proteins and may particularly accumulate in protein-rich tissues, such as the liver, which synthesizes most blood proteins, including albumin (Lu et al., 2021).

### 4.3. Strengths and limitations

Understanding PFAS exposure during pregnancy is particularly
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Adjusted multinomial regression coefficients between PFAS cluster variable and potential determinants. **Abbreviations:** PCP: personal care products; CMV: controlled mechanical ventilation; NS: Non-significant; **Effect estimate given in:** 1 year for Maternal age, 5 kg/m2 for BMI at blood sampling, year for Date of blood sampling, 50 g/day for Red meat daily consumption quantity, L/day for tap water consumption and per 5 PCP for PCP daily use. **Reference category** was No for hormonal contraception, nulli-parous for parity, Up to 1–2 years after high school for maternal education, Monthly consumption Poultry/Rabbit consumption, No for Liver consumption, Monthly consumption for eggs consumption, monthly consumption for fish consumption, Never for Crustaceans consumption, No for Maternal active smoking, No for Maternal passive smoking, No for House CMV. **Full list of predictors in the model:** Date of blood sampling (year), maternal age (5 years), BMI at blood sampling, hormonal contraception (yes/no), maternal education, liver consumption, eggs consumption, fish consumption, crustacean consumption, tap water consumption, maternal active smoking, maternal passive smoking, house mechanical ventilation, meat daily consumption (5 g/day), poultry/rabbit consumption, pcp daily use (per 5 PCP/day), parity. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

important, as pregnancy represents a vulnerable period for both women and their future children. Exposure during these critical windows of vulnerability may lead to adverse health outcomes years or even decades later. We analyzed more PFAS than what has been considered in similar existing studies, including emerging substitute PFAS, though some of the frequencies of detection were null (11 PFAS) or too low (8 PFAS) to examine potential exposure determinants. Given the large number of determinants tested, the risk of chance findings cannot be ruled out, although it is mitigated by the fact that the factors included in our models were selected a priori based on the literature (e.g., Brantsæter et al., 2013; Rush et al., 2018; Sagiv et al., 2015).

While information on a broad range of potential determinants was available in SEPAGES, some factors were missing, such as breastfeeding duration prior to the SEPAGES pregnancy (Brantsæter et al., 2013), the type of carpets or floor polish used in the home, recent renovation or decoration, use of specific food contact materials such as frying pans or wearing of waterproof clothing. Collecting such information through questionnaires is difficult, and doing so in SEPAGES likely would have resulted in a lengthy questionnaire that might have diminished the participation rate. Regarding maternal characteristics, studies investigating associations with physiological variables such as albumin, total protein and glomerular filtration rate would also be useful to understand inter-individual variations in PFAS serum levels (Sagiv et al., 2015).

For diet, we relied on a Food Frequency Questionnaire (FFQ), a validated tool employed in large-scale epidemiological studies (Kadawathagedara et al., 2021). Nonetheless, potential sources of error

include recall bias, as participants may have difficulty accurately recalling their dietary habits, and imprecise estimation of portion sizes. These errors are likely non-differential, introducing random noise that may have reduced our statistical power to detect associations for FFQ-derived variables. However, given that we were still able to identify several associations with dietary factors, we believe such measurement error was rather limited. In addition to studying each PFAS individually, we applied a clustering method to identify exposure profiles and account for PFAS co-exposures. This approach, which aims to capture the determinants of the PFAS mixture, is of interest but may have had limited statistical power due to the relatively small number of individuals in the highly exposed group (n = 51). Nonetheless, the results were consistent with the main models regarding maternal characteristics, which strengthens the credibility of our findings. Finally, the SEPAGES cohort was established from a limited geographical area around Grenoble (France) and included women with relatively high levels of education, which limits results extrapolation to women under different life conditions, as well as to men.

### 4.4. Impact for public health

The median sum of PFOS, PFOA, PFHxS and PFNA in the current study population was 6.2 ng/mL, with 41 % of the pregnant women exceeding the limit of 6.9 ng/mL, which is the serum concentration equivalent to the TWI of 4.4 ng/kg body weight per week in adults. This limit was established by EFSA based on immune system effects, in
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Median maternal blood concentrations of PFOS, PFOA, PFNA and PFHxS in European cohorts. Abbreviations: DNBC: Danish National Birth Cohort (Meng et al., 2018); EDEN: Study on the pre- and postnatal determinants of child health and development (France) (Heude et al., 2016); MoBa: Norwegian Mother and Child Cohort Study (Starling et al., 2014); INMA: Environment and Childhood Project (Spain) (Manzano-Salgada et al., 2016); KANC: Kaunas Cohort (Lithuania) (Grazuleviciene et al., 2009); RHEA: The Mother-Child Cohort in Crete (Greece) (Chatzi et al., 2017); MISA: Northern Norway Mother-and-Child contaminant Cohort Study (Berg et al., 2015); SELMA: Swedish Environmental, Longitudinal, Mother and child, Asthma and allergy study (Wickström et al., 2020); BIB: Born in Bradford (UK) (Wright et al., 2013); OCC: Odense Child Cohort (Denmark) (Dalsager et al., 2021); SEPAGES: SEPAGES mother and child cohort (France) (Jovanovic et al., 2024), the present publication). Some of the information regarding the study of measured PFAS was taken from Haug et al. (2018).</figcaption></figure>

particular reduced vaccine response in children (EFSA CONTAM et al., 2020). PFAS concentrations exceeding this TWI have been reported for other European populations (EFSA CNTAM et al., 2020), which calls for actions to be taken to reduce these exposures.

Studies like ours are useful for etiological investigations, as a clear understanding of exposure predictors helps ensure that potential confounding factors are not overlooked when examining associations with health outcomes. Regarding exposure reduction, although maternal characteristics cannot be modified, changes in dietary habits (i.e. reducing the quantity of fish consumed) can be targets for intervention studies and may result in a decrease in PFAS serum concentrations. However, such interventions should be carefully considered, as fish also provides important health benefits, and a significant reduction in its consumption could have unintended negative consequences. While our study may be informative for individuals, actions at a broader level through stronger regulations and bans will be much more effective than modification at the individual level to decrease population exposure to PFAS and improve public health.

## 5. Conclusions

Seven of 26 PFAS were quantified in most pregnant women, with PFOS, PFOA, PFHxS and PFNA having the highest median values. More than 1/3 of the studied population had a PFAS sum concentration exceeding the EFSA tolerable weekly intake. Certain dietary factors

identified in our study may offer opportunities to reduce PFAS exposure. However, at the population level, reductions will be more effectively achieved through regulatory actions and policy interventions than through individual behavior changes.

## Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used ChatGPT in order to improve the readability and language of the manuscript. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

## CRediT authorship contribution statement

**Nicolas Jovanovic:** Writing – review & editing, Writing – original draft, Methodology, Formal analysis. **Anne Lise Brantsæter:** Writing – original draft, Methodology. **Sarah Lyon-Caen:** Resources, Methodology, Funding acquisition, Conceptualization. **Blandine Duval:** Formal analysis. **Sylvain Carras:** Methodology, Investigation. **Annabelle Bédard:** Methodology, Formal analysis, Data curation. **Wassila Ait-Hadad:** Methodology, Formal analysis, Data curation. **Blandine de Lauzon-Guillain:** Methodology, Data curation. **Valérie Siroux:** Resources, Project administration, Methodology, Conceptualization. **Sam Bayat:** Investigation, Data curation, Conceptualization. **Inger-Lise
**Steffensen:** Writing – original draft, Methodology, Conceptualization. **Rémy Slama:** Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Line Småstuen Haug:** Writing – original draft, Methodology, Conceptualization. **Claire Philippat:** Writing – review & editing, Writing – original draft, Validation, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization.

## Funding

The chemical analyses of PFAS in serum were funded by ANR GUMME (project N° ANR-18-CE36-005) and the European Union's Horizon 2020 research and innovation program (N° 825712 OBERON Project). Nicolas Jovanovic was supported by a doctoral grant from University Grenoble Alpes. The processing of diet related data was financed by PENDALIRE (ANSES n°EST-22-169).

The SEPAGES cohort was supported by the European Research Council (N° 311765-E-DOHaD), the European Community's Seventh Framework Programme (FP7/2007-206 – N° 308333-892 HELIX), the European Union's Horizon 2020 research and innovation program (N° 874583 ATHLETE Project, N° 825712 OBERON Project), ANR (PAPER project N° ANR-12-PDOC-0029-01, SHALCOH project N° ANR-14-CE21-0007, N° ANR-15-IDEX-02 and N° ANR-15-IDEX5, GUMME project N° ANR-18-CE36-005, ETAPE project ANR – EDEn project N° ANR-19-CE36-0003-01), ANSES (CNAP project N° EST-2016-121, HyPAxE project N° EST-2019/1/039), the Plan Cancer (Canc'Air project), the French Cancer Research Foundation Association de Recherche sur le Cancer (ARC), the French Endowment Fund AGIR for chronic diseases (APMC, projects PRENAPAR and LCI-FOT), the French Endowment Fund for Respiratory Health, the French Fund – Fondation de France (CLIMATHES – 00081169, SEPAGES 5–0009903).

Other costs of this work are mainly the working hours of the authors, which were covered by the respective institutions, NIPH and INSERM.

## 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.

## Acknowledgements

SEPAGES biospecimens are stored at Grenoble University Hospital (CHU-GA) biobank (bb-0033-00069); we would like to thank the whole CRB team, led by Pr. P. Mossuz and Mr. P. Lorimier, and in particular the technicians for the huge work of biospecimens processing and pooling: Mrs. W. Jayar, Mrs. L. Than, Mrs J.Lafond, Mrs C.Chalazon, Mr T.Caffaratti, Mrs N.Francini and as well as Mr. G. Schummer.

We thank the Sepages study group : A. Licinia (Groupe Hospitalier Mutualiste, Grenoble), S. Bayat, P. Hoffmann, E. Hullo (Grenoble Alpes University Hospital, La Tronche), X. Morin (Clinique Belledonne, Saint-Martin d'Hères), J. Lepeleu, S. Lyon-Caen, M.Ouidir, C. Philippat, I. Pin, J. Quentin, V. Siroux, R. Slama (Grenoble Alpes University, Inserm, CNRS, IAB).

Many thanks to Dr I.Pin who was the Sepages PI from 2012 until 2022. We thank also Mr. Y. Gloria, Mrs. A. Guerrache-Siebert, clinical research assistants; Mrs. M. Marceau, nurses; Mrs. E. Charvet, midwives; Mrs. M. Graca, Mrs. K.Gridel, Mrs. C. Pelini, Mrs M.Barbagallo field-workers; Mrs. A.Bossant, K. Guichardet, J-T Iltis, C.Martel, E. Quinteiro, neuropsychologists; Mrs K.Supernant, A.Boudier the Sepages data managers; the staff from Grenoble Center for Clinical Investigation (CIC): Prof. J.-L. Cracowski, Dr. C. Cracowski, Dr. E. Hodaj, Mrs. D. Abry, Mr. N. Gonnet and Mrs. A. Tournier. A warm thank you also Dr. M. Althuser, Dr. F. Camus-Chauvet, Mr. P. Dusonchet, Mrs. S. Dusonchet, Mrs. A. Royannais, Dr. D. Tournadre, Mr. P. Viossat, and clinicians from Grenoble University Hospital for their support in the recruitment of the

study volunteers. We also thank Mrs. SF. Caraby, Mrs. J. Dujourdil, and all midwives from the four maternity wards of Grenoble urban areas. SEPAGES data are stored thanks to Inserm RE-CO-NAI platform funded by Commissariat Général à l'Investissement, with the implication of Sophie Visme (Inserm DSI). Many thanks to Dr. M.A. Charles, RE-CO-NAI coordinator, for her support. We acknowledge O. Coiffier, for data management Finally, and importantly, we would like to express our sincere thanks to participants of the Sepages study.

## Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envint.2025.109559.

## Data availability

Data will be made available on request.

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