# Dietary intake of polycyclic aromatic hydrocarbons (PAHs) and breast cancer risk: Evidence from the French E3N-Generations prospective cohort

Amina Amadou, Delphine Praud, Chloé Marques, Hwayoung Noh, Pauline Frenoy, Arnaud Vigneron, Thomas Coudon, Floriane Deygas, Gianluca Severi, Béatrice Fervers, et al.

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Amina Amadou, Delphine Praud, Chloé Marques, Hwayoung Noh, Pauline Frenoy, et al. Dietary intake of polycyclic aromatic hydrocarbons (PAHs) and breast cancer risk: Evidence from the French E3N-Generations prospective cohort. Environment International, 2025, 200, pp.109505-109505. 10.1016/j.envint.2025.109505. hal-05297671

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# Dietary intake of polycyclic aromatic hydrocarbons (PAHs) and breast cancer risk: Evidence from the French E3N-Generations prospective cohort

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Amina Amadou<sup>a,b,*</sup>, Delphine Praud<sup>a</sup>, Chloé Marques<sup>c,†</sup>, Hwayoung Noh<sup>a,‡</sup>, Pauline Frenoy<sup>c,§</sup>, Arnaud Vigneron<sup>d,¶</sup>, Thomas Coudon<sup>a,b</sup>, Floriane Deygas<sup>a,b</sup>, Gianluca Severi<sup>c,e</sup>, Béatrice Fervers<sup>a,b,1</sup>, Francesca Romana Mancini<sup>c,1</sup>

## ARTICLE INFO

Associate editor: Adrian Covaci

**Keywords:**

Polycyclic aromatic hydrocarbons (PAHs)
benzo[a]pyrene (BaP)
Dietary exposure
Breast cancer
Hormone receptor status
Prospective cohort

## ABSTRACT

**Background:** While there is compelling evidence of the association between occupational exposure to polycyclic aromatic hydrocarbons (PAHs) and risk of breast cancer (BC), findings on PAH dietary exposure are less consistent. The present study aims to evaluate the association between PAH dietary intake and BC risk.

**Methods:** The study included 67,879 women who completed a validated semi-quantitative dietary questionnaire (208 food items) from the E3N-Generations cohort study. PAH dietary intake was estimated by combining E3N food consumption data with food contamination levels obtained from the second French total diet study (TDS2). Cox regression was used to estimate adjusted hazard ratios (HRs) and 95 % confidence intervals (CIs) for the association between PAH dietary intake (sum of four PAHs (PAH<sub>4</sub>) namely benzo[a]pyrene (BaP), chrysene (CHR), benzo[a]anthracene (BaA) and benzo[b]fluoranthene (BbF)) and BC risk. Additionally, BaP, a surrogate for total PAHs, was investigated as the second exposure variable.

**Results:** After an average follow-up of 17.6 years, 5,686 incident BC were diagnosed. Overall, the estimated HRs for the associations between each quintile of PAH<sub>4</sub> and BC risk, taking the first quintile as reference, were all greater than 1, but were statistically significant only for the third quintile (HR<sub>Q3</sub> vs Q1 = 1.10; CI: 1.01–1.20). By estrogen (ER) and progesterone (PR) hormone receptor status, we observed a positive association between PAH<sub>4</sub> dietary intake and ER-PR- BC (HR<sub>Q4</sub> vs Q1 = 1.34; CI: 1.01–1.76). Moreover, there was a borderline positive association with BaP, for the second (HR<sub>Q2</sub> vs Q1 = 1.08; CI: 0.99–1.17) and third (HR<sub>Q3</sub> vs Q1 = 1.07; CI: 0.98–1.16) quintiles.

**Conclusions:** This study supports a relationship between PAH<sub>4</sub> dietary intake and BC risk, notably with a non-linear trend. A positive but marginal association was observed between BaP dietary intake and BC risk.

**Abbreviations:** BC, breast cancer; PAHs, polycyclic aromatic hydrocarbons; BaA, benzo[a]anthracene; BaP, benzo[a]pyrene; BbF, benzo[b]fluoranthene; CHR, chrysene; TDS2, second French total diet study; IARC, International Agency for Research on Cancer; EPA, Environmental Protection Agency; CrI, credible intervals; CIs, confidence intervals; ORs, odds ratios; HR, hazard ratios; SD, standard deviation; E3N, Etude Epidémiologique auprès des femmes de la Mutuelle Générale de l'Éducation Nationale; MGEN, Mutuelle Générale de l'Éducation Nationale; ER, estrogen receptor; PR, progesterone receptor; TNM, tumor-node-metastasis; CIQUAL, French Information Centre on Food Quality; LB, lower bound; MB, middle bound; LOD, limit of detection; FHBC, family history of BC history; AFFP, age at first full-term pregnancy; BMI, body mass index; MHT, menopausal hormone replacement therapy; CNIL, Commission for data protection and privacy.

* Corresponding author at: Département Prévention Cancer Environnement, Centre Léon Bérard - Inserm U1296 Radiations : Défense, Santé, Environnement 28 rue Laënnec, 69373 Lyon Cedex 08, France.

E-mail address: amina.amadou@lyon.unicancer.fr (A. Amadou).

¹ These authors contributed equally to this work and share last authorship.

<footnote><sup>a</sup> Department of Prevention, Cancer and Environment, Centre Léon Bérard, Lyon, France</footnote>

<footnote><sup>b</sup> Inserm U1296, "Radiation: Defense, Health and Environment", Lyon, France</footnote>

<footnote><sup>c</sup> Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, Villejuif, France</footnote>

<footnote><sup>d</sup> Centre de Recherche en Cancérologie de Lyon, INSERM U1052-CNRS UMR5286, Centre Léon Bérard, Université de Lyon, Université Claude Bernard Lyon1, 69008</footnote>

<footnote>Lyon, France</footnote>

<footnote><sup>e</sup> Department of Statistics, Computer Science and Applications (DISIA), University of Florence, Florence, Italy</footnote>
# 1. Introduction

Polycyclic aromatic hydrocarbons (PAHs) are a diverse group of organic compounds ubiquitous in the environment, comprising two or more fused aromatic (benzene) rings, and formed through incomplete combustion or high-pressure processes of organic materials (Famiyeh et al., 2021). The primary sources of PAH emission are human activities, including household activities, motor-vehicle exhaust, industrial activities, and agricultural practices (such as burning biomass and waste) (Gupte et al., 2016; Patel et al., 2020). Small natural sources like volcanic eruptions and forest fires also contribute to PAH levels (Gupte et al., 2016; Patel et al., 2020). For the general population, exposure to PAHs occurs primarily through diet, accounting for more than 90 % of total exposure in non-smokers (Polachova et al., 2020). Specific food processing methods such as grilling, smoking, or charring, can generate PAHs in food (Sampaio et al., 2021). The individual exposure levels vary depending on dietary habits. For example, in the general population, the average daily intake ranges between 0.1 to 2.0 µg/day measured as benzo[a]pyrene (BaP) (a marker of PAHs), whereas people consuming large amounts of grilled, smoked, or charred foods may have exposure levels exceeding 5–10 µg/day (Zhao et al., 2023). For smokers, tobacco smoke can contribute to exposure levels comparable to those from food consumption (EFSA, 2008). Additionally, PAH exposure can occur through inhalation of indoor or outdoor air pollution) and dermal contact (Mojiri et al., 2019).

There is emerging evidence linking exposure to PAHs with various adverse health effects, including cancer, and several of these PAHs are classified as probable carcinogens and potential endocrine disruptors (Bolden et al., 2017; Hwang et al., 2022; Mallah et al., 2022). Out of the hundreds of known PAHs, the Environmental Protection Agency (EPA) has identified sixteen PAHs as high priority pollutants due to their potential toxicity in humans and their persistence in the environment (Hussar et al., 2012). Among these, the International Agency for Research on Cancer (IARC) has classified BaP (one of the most well-known PAHs) as carcinogenic to humans (Group 1), while others like chrysene (CHR), benzo[a]anthracene (BaA) or benzo[b]fluoranthene (BbF) are considered possibly carcinogenic to humans (Group 2B) (IARC Working Group on the Evaluation of Carcinogenic Risks to Humans, 2010).

While numerous studies have consistently shown a positive association between occupational exposure to PAHs (often occurring at higher concentrations) and the risk of breast cancer (BC) (Lee et al., 2019; Pedersen and Hansen, 2022; Shen et al., 2017). The role of non-occupational PAH exposures, has, received far less attention.

In the general population, although some studies have investigated associations between PAH exposure from various sources and BC risk, their primary focus has been on vehicular traffic emissions, indoor burning (synthetic firelog) or tobacco smoking (Jiang et al., 2018; Mordukhovich et al., 2016; White et al., 2016). Scientific evidence suggests positive associations between PAH air pollution and BC risk (Mordukhovich et al., 2016; Nie et al., 2007; Stults and Wei, 2018). For example, our prior research in a case-control study nested in the French prospective E3N-Generations cohort study found a significantly increased risk of BC associated with long-term exposure to ambient air BaP, the most common PAHs, used as a surrogate for total PAHs (Amadou et al., 2021). Similarly, studies have linked indoor sources of PAHs, such as burning wood or synthetic logs, and tobacco smoke to an increased risk of BC (White et al., 2016, 2014; White and Sandler, 2017). Findings from a recent meta-analysis investigating the impact of multiple PAH sources on BC risk, revealed a positive association between PAH exposure and BC risk, with a summary OR of 1.21 (95 % confidence intervals (CI): 1.11–1.33) (Gamboa-Loira et al., 2022).

Although dietary PAHs constitute a significant source of exposure, especially in non-smoking and non-occupationally exposed populations, their link to BC risk remains under-explored. While some studies support an association between grilled, barbecued or smoked food intake and an

increased risk of BC (Fu et al., 2011; Niehoff et al., 2017); others such as a study examining intake of benzo(a)pyrene using a food frequency questionnaire (FFQ), reported no consistent association (Steck et al., 2007).

Dietary exposure may interact with other risk factors such as alcohol intake, body mass index (BMI) (Lichtiger et al., 2021; Niehoff et al., 2017), smoking status (Gamboa-Loira et al., 2022), or family history of BC (FHBC) (Gamboa-Loira et al., 2022). Therefore, more research is essential to better understand the health implications of PAH dietary intake, especially the impact on BC risk.

The present study, therefore, aims to address this gap by evaluating the association between PAH dietary intake and risk of BC in the E3N-Generations cohort study.

# 2. Methods

## 2.1. Study population and design

The E3N-Generations prospective study is an ongoing French familial cohort study, established as an extension of the E3N cohort of women (Etude Epidémiologique auprès des femmes de la Mutuelle Générale de l'Education Nationale). It includes the children of E3N women, their fathers, and their grandchildren in the future. The present study focuses only on the E3N cohort that was created in 1990, with the recruitment of 98,995 French women aged 40–65 years at inclusion and insured by the MGEN (Mutuelle Générale de l'Education, a national health insurance covering employees from the French National Education System) to investigate the etiology of cancer and severe chronic diseases in women (Clavel-Chapelon, 2015). Most of these women were employees or worked in affiliated occupations of the French national education. The E3N cohort is still followed up after more than 30 years with regular self-administered questionnaires on socio-demographic characteristics, lifestyle, reproductive factors, anthropometry, past medical history, and familial history of cancer. Complete follow-up questionnaires were sent out to women every 2–3 years thereafter, with an average participation rate above 80 % throughout follow-up. An informed consent was obtained from each participant in agreement with the rules of the French National Commission for Data Protection and Privacy (CNIL), from which the study was approved.

For the present analysis, we included women who completed the dietary questionnaire sent in 1993 (the third questionnaire sent since the initial enrolment) (n = 74,522). Among these, we excluded women with no follow-up after the dietary questionnaire (n = 568), those with prevalent cancer in 1993 (n = 4,709), and those reporting extreme energy intake values (i.e., below the 1st and above the 99th percentiles of the energy intake over energy requirement distribution in the population; n = 1,366). The final study population, after exclusions, involved 67,879 women; of which 5,686 confirmed cases of BC were ascertained during the follow-up (June 1993-November 2014). The flowchart of study participant selection is given in Supplementary Fig. 1.

## 2.2. Ascertainment of breast cancer cases

BC occurrences were primarily self-reported in follow-up questionnaires, with a few additional cases sourced from health insurance records or mortality data, including causes of death obtained from the National Services on Causes of Deaths (CépiDC-Inserm). Self-reported cases were validated through retrieval of medical records from treating physicians and pathological confirmation was obtained for 93 % of incident BC cases. The present study included these confirmed cases, as well as cases whose pathological reports could not be traced, given the low false-positive rate of self-reports in the cohort population (<5%). Details regarding hormone receptor status (estrogen receptor, ER and progesterone receptor, PR), tumor status, tumor-node-metastasis (TNM) stage, BC grade of differentiation as well as histological type were obtained from pathology reports (Clavel-Chapelon, 2015).
## 2.3. Dietary questionnaire and assessment of PAH dietary intake

Dietary data were collected through a semi-quantitative food questionnaire, including 208 food items of 22 food groups. This questionnaire was structured into two parts, the first quantified food group' consumption (frequency and portion sizes), whereas the second provided more details on food items within each food group. Questions were requested for eight consumption occasions from breakfast to after-dinner snacks (i.e., breakfast, morning snack, aperitif before lunch, lunch, afternoon snack, pre-dinner aperitif, dinner and after-dinner snack). The combination of the two parts of the questionnaire allowed to collect both qualitative and quantitative information for all food items. Mean daily nutrient intakes for individuals were assessed using a food composition table derived from the food composition table of the French Information Centre on Food Quality (CIQUAL). As previously described (van Liere et al., 1997), the French food questionnaire demonstrated good reproducibility (correlations of 0.40–0.74 for foods and 0.54–0.75 for nutrients) and moderate relative validity when compared to 9–12 monthly 24-hour recalls (correlations of 0.10–0.71 for foods and 0.29–0.81 for nutrients). It classified participants effectively, with 76 % of food intake and 72 % of nutrient intake being categorized in the same or adjacent quintile, indicating its reliability for estimating dietary intake over a one-year period (van Liere et al., 1997).

PAH contamination levels in food were obtained from the second French total diet study (TDS2) conducted by the Anses, the French Agency for Food, Environmental and Occupational Health & Safety "Avis et rapport relatifs à l'étude de l'alimentation totale française 2 (EAT 2) - Tome 2," 2011). A total of 20,280 food items were purchased from June 2007 to January 2009 in eight major regions of French metropolitan, resulting in 1,352 composite samples prepared "as consumed" in order to estimate the levels of 445 different chemical substances (Sirot et al., 2009). PAHs were estimated in 725 samples, i.e. including known or potential contributors to dietary exposure to PAHs (Veyrand et al., 2013). The present study investigated food contamination levels for 20 PAHs. Two scenarios for dealing with non-quantified values were considered: the lower bound (LB) and the middle bound (MB) ones. In the LB scenario, values below the limit of detection (LOD)

were replaced by zero, whereas in the MB scenario they were substituted by half of the LOD (Marques et al., 2022). The LB scenario was used for the principal analyses, whereas the MB scenario was used in a sensitivity analysis.

To estimate the PAH dietary intake of the E3N participants, the E3N database on food consumption and the Anses database on food contaminants were combined to obtain one database (Mancini et al., 2020). For each participant of the E3N-Generations cohort, the PAH dietary intake (ng/day) was calculated as follows:

$$Y_i = \sum_{eachx} c_x \times x_i$$

where $Y_i$ is the PAH dietary intake of an individual i (ng/day), $c_x$ is the PAH contamination level in the food x (ng/g of food), and $x_i$ is the quantity of food consumed by the individual i (g of food/day). PAH$_{20}$ refers to the sum of the 20 PAHs estimated (Fig. 1). PAH$_{4}$ refers to the sum of the four PAHs: BaP, CHR, BaA, and BbF. Of note, PAH$_{4}$ is one of the best indicators of total PAH contamination levels in food according to EFSA (EFSA, 2008).

## 2.4. Statistical analysis

Characteristics of the study population were described according to quintiles of PAH$_{4}$ dietary intake (ng/day), using mean and standard deviation (SD) for continuous variables and frequency and percentage for categorical variables. Spearman rank correlation coefficients were used to estimate the correlations between the PAH dietary intake variables.

Cox proportional hazard models, with age as the time scale, were used to estimate the hazard ratios (HR) and their 95 % CI of the associations between PAH dietary intake and risk of BC. In the current analysis, PAH$_{4}$ was used as the primary exposure variable. BaP (considered as a surrogate for total PAHs in most epidemiological studies) was used as the second exposure variable. Analyses were conducted by considering PAHs in both continuous (for one SD increase) and categorical (quintiles, using the lowest one as the reference group)

<figure><img src="image_3.png" /><figcaption>Fig. 1. Spearman rank correlation coefficients between PAH dietary intake (ng/day) estimated according to the lower bound scenario in the E3N cohort. PAH20: anthracene (AN), benzo[a]anthracene (BaA), benzo[a]pyrene (BaP), benzo[b]fluoranthene (BbF), benzo[c]fluorine (BcFL), benzo[g,h,i]perylene (BghiP), benzo[j]fluoranthene (BjF), benzo[k]fluoranthene (BkF), chrysene (CHR), cyclopenta(c,d)pyrene (CPP), dibenzo[a,h]anthracene (DBahA), dibenzo[a,e]pyrene (DBaeP), dibenzo[a,h]pyrene (DbahP), dibenzo[a,i]pyrene (DbaiP), dibenzo[a,l]pyrene (DbalP), fluoranthene (FA), indeno[1,2,3-cd]pyrene (IP), 5-methylchrysene (MCH), phenanthrene (PHE) and pyrene (PY) PAH4: BaP, CHR, BaA, BbF.</figcaption></figure>
variables. Quintiles were determined by dividing the population into five equal groups based on intake levels, with Q1 representing the lowest 20 % of PAHs intake and Q5 representing the highest 20 %. Age at entry was the age at responding to the dietary questionnaire and exit time was the age at diagnosis of primary BC or another cancer, at the last completed questionnaire before death or lost to follow-up, or at the last follow-up (until 17/11/2014 at the latest), whichever occurred first. The proportional hazards assumptions were tested using scaled Schoenfeld residuals. When the hypothesis was not satisfied, the baseline hazard function was stratified on this covariate. The nonlinearity between PAH dietary intake variable and BC risk was explored graphically with restricted cubic splines (Durrleman and Simon, 1989), using five knots placed at the 5th, 27.5th, 50th, 72.5th and 95th percentiles, as recommended by Harrell et al. for large datasets (Harrell, 2001).

In addition to the crude model, two adjusted models were considered to progressively account for potential confounders and examine the robustness of the association, across different levels of adjustment, thereby clarifying the true relationship. Confounding covariates were selected a priori, using a directed acyclic graph (DAG) (Supplementary Fig. 2). This allowed us to systematically identify variables known or hypothesized, based on existing literature, to influence both dietary PAH exposure and BC risk. The first model (crude model 1) was adjusted only for age as the time scale (years, in continuous), providing an unadjusted baseline association. The second model (model 2) was additionally adjusted for key lifestyle, demographic, and reproductive factors, known to influence BC risk: BMI (kg/m², in continuous), physical activity (expressed in metabolic equivalent of task-hour per week (MET-h/wk, in continuous), smoking status (never, current, former), age at first full-term pregnancy (AFFP) combined with parity (0, 0–2 children & AFFP < 30 years, 0–2 children & AFFP ≥ 30 years, and ≥ 3 children), level of education (secondary, 1 to 2-year university degree, and ≥ 3-year university degree) and menopausal status combined with recent use of menopausal hormone treatment (MHT) (premenopausal, post-menopausal with recent use of MHT and postmenopausal without recent use of MHT). Model 3, our primary model including all the variables identified by the DAG (Supplementary Fig. 2) was further adjusted for dietary factors, aiming to control for confounding effects that may arise from diet: alcohol consumption (g/day, in continuous), lipid consumption (g/day, in continuous) and energy intake excluding alcohol and lipids (kcal/day, in continuous). With the exception of AFFP combined with parity, level of education and age at menarche, which were collected at inclusion, all other variables were collected at the time of the dietary questionnaire.

In the present study, all variables have less than 5 % of missing value, simple imputation was thus used as in the previous E3N studies (García-Acosta and Clavel-Chapelon, 1999), with continuous and categorical variables replaced by the median or modal value, respectively (García-Acosta and Clavel-Chapelon, 1999).

To investigate heterogeneity, we performed stratified analyses according to hormone receptor (ER/PR) status, histology (in situ, invasive), stage, and grade of BC at diagnosis. Subgroup analyses according to menopausal status at inclusion were also performed.

The likely effect modification by alcohol intake, BMI (Lichtiger et al., 2021), tobacco smoking status (Braun et al., 2021), and FHBC (Niehoff et al., 2022) were evaluated. The interaction tests were evaluated using the likelihood ratio tests comparing models with and without the interaction term between PAH dietary intake and each potential modifier.

Several sensitivity analyses were performed to explore the robustness of the main findings. Participants with less than five years of follow-up were excluded to control for potential reverse causation. We conducted complete-case analysis, after excluding all women with a missing value for any of the adjusted covariates. In addition, to account for the uncertainty related to missing data in the adjustment variables, we performed multivariate imputation using chained equations (MICE) (Sterne et al., 2009), with 10 iterations and fully conditional

specification of prediction equations. Variables with missing values included physical activity, smoking status, AFFP and parity, level of education, menopausal status at inclusion combined with use of MHT, and BMI. We further evaluated the association of each single PAH included in PAH₄ with risk of BC, in order to check the association trends of the four PAHs, given that they may differ in terms of biological mechanisms related to BC development. A residual energy-adjusted analysis was performed to eliminate variation due to total energy intake (Willett et al., 1997), by regressing PAH dietary intake on total energy intake and then using the residual as the main exposure variable in the analysis model. To address concerns about potential biases due to follow-up duration, we conducted additional analyses stratified by the median follow-up time of cases. Finally, weight-adjusted analysis was conducted exposure variable divided by the body weight (ng/kilogram body weight/day), in order to provide results comparable to standard risk assessments, and to account for individual variation in body weight.

The statistical analyses were performed using STATA version 14 (College Station, Texas, USA). The threshold for statistical significance in two-sided was set at 5 %.

## 3. Results

### 3.1. Study population characteristics

Participant characteristics overall and by quintiles of PAH₄ dietary intake are shown in Table 1. After an average (± SD) follow-up of 17.6 (± 5.5) years, 5,686 (8.4 %) incident BC cases were identified among the 67,879 women. The average age at recruitment (± SD) was 52.8 (± 6.6) years. As compared to women in the lowest quintile (Q1), women in the highest quintile (Q5) of PAH₄ dietary intake had higher levels of energy, alcohol, and lipid intakes, and were more likely to be current smokers, to have used oral contraceptive, and to be premenopausal. By contrast, women in the lowest quintile of PAH₄ dietary intake were more likely to be nulliparous compared with those in the highest quintile.

The baseline characteristics of the study population, by BC cases and non-cases, have been previously described (Frenoy et al., 2022). Briefly, slight differences concerned older age at menarche and a higher prevalence of menopausal women with recent MHT use among cases. Additionally, cases were more likely to be nulliparous and had higher alcohol consumption. No substantial differences were observed for other characteristics, including age, BMI, smoking status, and oral contraceptive use.

The correlation between the four individual PAHs and PAH₄ varied from 0.80 to 0.97, with CHR showing the highest correlation with PAH₄ (Fig. 1). CHR was the main contributor to the PAH₄ intake (45.4 %), followed by BaA (22.5 %) and BbF (20.6 %), whereas BaP contributed only 11.5 %. The main food groups contributing to PAH₄ dietary intake were bread and salty cereal products (22.6 %), followed by fatty foods such as butter, margarine, and oil (15.1 %), and seafood (10.7 %) (Marques et al., 2022).

### 3.2. PAH dietary intake and breast cancer risk

Results of crude and multivariable-adjusted associations between PAH₄ and BaP levels and risk of BC are summarized in Table 2. In the model 1 (crude), adjusted only for age, a one SD increase in PAH₄ dietary intake was associated with an HR of 1.03 (95 % CI: 1.01–1.06) in continuous model. In the analyses by quintiles, women in the highest quintile of PAH₄ intake had a 12 % higher risk of BC compared to those in the lowest quintile (HRQ5 vs Q1 = 1.12; 95 % CI: 1.03–1.21).

Model 2, adjusting for additional confounders (including physical activity, smoking status, reproductive factors, level of education, BMI, menopausal status at inclusion combined with MHT), yielded similar results. The corresponding HR for one SD increment in PAH₄ dietary intake, was 1.03 (95 % CI: 1.01–1.06), with an HRQ5 vs Q1 of 1.12 (95 % CI: 1.03–1.21).
<caption>**Table 1**</caption>

Main characteristics of the study population according to quintiles of PAH₄ dietary intake (ng/day) in the French E3N-Generations cohort study, 1993–2014 (N = 67,879).

<table><thead><tr><th rowspan="2">Characteristics</th><th rowspan="2">All</th><th colspan="5">Quintiles of PAH<sub>4</sub> dietary intake (ng/day)</th></tr><tr><th>≤ 46</th><th>> 46–59</th><th>> 59–74</th><th>> 74–95</th><th>> 95</th></tr></thead><tbody><tr><td>Cases/non-cases status</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Incident breast cancer cases</td><td>5,686 (8.4)</td><td>1,035 (7.7)</td><td>1,122 (8.3)</td><td>1,185 (8.7)</td><td>1,178 (8.6)</td><td>1,166 (8.6)</td></tr><tr><td>Non-cases</td><td>62,193 (91.6)</td><td>12,439 (92.3)</td><td>12,439 (91.7)</td><td>12,438 (91.3)</td><td>12,438 (91.4)</td><td>12,439 (91.4)</td></tr><tr><td>Age at baseline (years)</td><td>52.8 (6.6)</td><td>54.5 (6.9)</td><td>53.0 (6.7)</td><td>52.6 (6.6)</td><td>52.1 (6.4)</td><td>51.9 (6.3)</td></tr><tr><td>Energy intake (kcal/day)</td><td>1329.2 (355)</td><td>1059.1 (256)</td><td>1233.4 (276)</td><td>1343.5 (303)</td><td>1440.8 (340)</td><td>1566.4 (363)</td></tr><tr><td>Alcohol intake (g/day)</td><td>11.6 (13.9)</td><td>8.5 (11.9)</td><td>10.1 (12.5)</td><td>11.4 (13.3)</td><td>12.9 (14.5)</td><td>15.0 (16.1)</td></tr><tr><td>Lipid consumption (g/day)</td><td>89.0 (27.0)</td><td>67.2 (17.7)</td><td>80.2 (19.0)</td><td>89.3 (21.4)</td><td>98.4 (24.5)</td><td>109.8 (29.0)</td></tr><tr><td>Body Mass Index (kg/m<sup>2</sup>)</td><td>22.9 (3.3)</td><td>22.7 (3.1)</td><td>22.8 (3.1)</td><td>22.9 (3.3)</td><td>23.1 (3.3)</td><td>23.4 (3.6)</td></tr><tr><td>Physical activity (METs-h/week)</td><td>46.3 (49.3)</td><td>45.2 (54.3)</td><td>45.4 (50.9)</td><td>45.8 (48.5)</td><td>46.1 (43.1)</td><td>48.9 (49.1)</td></tr><tr><td>Birth generation</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>≤ 1930</td><td>6,504 (9.6)</td><td>1,917 (14.3)</td><td>1,356 (10.0)</td><td>1,239 (9.1)</td><td>1,052 (7.7)</td><td>940 (6.9)</td></tr><tr><td>[1930 – 1935]</td><td>9,098 (13.4)</td><td>2,304 (17.1)</td><td>1,956 (14.4)</td><td>1,750 (12.8)</td><td>1,602 (11.8)</td><td>1,486 (10.9)</td></tr><tr><td>[1935 – 1940]</td><td>13,616 (20.1)</td><td>2,774 (20.6)</td><td>2,754 (20.3)</td><td>2,747 (20.2)</td><td>2,702 (19.8)</td><td>2,639 (19.4)</td></tr><tr><td>[1940 – 1945]</td><td>16,767 (24.7)</td><td>3,081 (22.9)</td><td>3,301 (24.3)</td><td>3,382 (24.8)</td><td>3,453 (25.4)</td><td>3,550 (26.1)</td></tr><tr><td>> 1945</td><td>21,894 (32.2)</td><td>3,398 (25.2)</td><td>4,194 (30.9)</td><td>4,505 (33.1)</td><td>4,807 (35.3)</td><td>4,990 (36.7)</td></tr><tr><td>Smoking status</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current smoker</td><td>9,280 (13.7)</td><td>1,710 (12.7)</td><td>1,782 (13.1)</td><td>1,828 (13.4)</td><td>1,910 (14.0)</td><td>2,050 (15.1)</td></tr><tr><td>Former smoker</td><td>22,328 (32.9)</td><td>3,987 (29.6)</td><td>4,375 (32.3)</td><td>4,528 (33.2)</td><td>4,637 (34.1)</td><td>4,801 (35.3)</td></tr><tr><td>Never smoker</td><td>36,271 (53.4)</td><td>7,777 (57.7)</td><td>7,404 (54.6)</td><td>7,267 (53.3)</td><td>7,069 (51.9)</td><td>6,754 (49.6)</td></tr><tr><td>Education</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Secondary</td><td>7,586 (11.2)</td><td>1,787 (13.3)</td><td>1,486 (11.0)</td><td>1,442 (10.6)</td><td>1,460 (10.7)</td><td>1,411 (10.4)</td></tr><tr><td>1- to 2 years university degree</td><td>36,031 (53.1)</td><td>7,180 (53.3)</td><td>7,312 (53.9)</td><td>7,240 (53.1)</td><td>7,243 (53.2)</td><td>7,056 (51.8)</td></tr><tr><td>≥ 3 years university degree</td><td>24,262 (35.7)</td><td>4,507 (33.4)</td><td>4,763 (35.1)</td><td>4,941 (36.3)</td><td>4,913 (36.1)</td><td>5,138 (37.8)</td></tr><tr><td>Age at menarche (years)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt; 12</td><td>13,781 (20.3)</td><td>2,738 (20.3)</td><td>2,625 (19.4)</td><td>2,770 (20.3)</td><td>2,811 (20.6)</td><td>2,837 (20.9)</td></tr><tr><td>12–13</td><td>35,221 (51.9)</td><td>6,933 (51.5)</td><td>7,166 (52.8)</td><td>7,064 (51.9)</td><td>7,086 (52.0)</td><td>6,972 (51.3)</td></tr><tr><td>≥ 14</td><td>18,877 (27.8)</td><td>3,803 (28.2)</td><td>3,770 (27.8)</td><td>3,789 (27.8)</td><td>3,719 (27.3)</td><td>3,796 (27.9)</td></tr><tr><td>Parity and age at first full-term pregnancy (years)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>0</td><td>7,972 (11.7)</td><td>1,838 (13.6)</td><td>1,619 (11.9)</td><td>1,564 (11.5)</td><td>1,414 (10.4)</td><td>1,537 (11.3)</td></tr><tr><td>1-2 and AFFP &lt; 30</td><td>33,442 (49.3)</td><td>6,438 (47.8)</td><td>6,706 (49.5)</td><td>6,611 (48.5)</td><td>6,801 (49.9)</td><td>6,886 (50.6)</td></tr><tr><td>1-2 and AFFP ≥ 30</td><td>6,173 (9.1)</td><td>1,278 (9.5)</td><td>1,252 (9.2)</td><td>1,260 (9.3)</td><td>1,220 (9.0)</td><td>1,163 (8.6)</td></tr><tr><td>≥ 3</td><td>20,292 (29.9)</td><td>3,920 (29.1)</td><td>3,984 (29.4)</td><td>4,188 (30.7)</td><td>4,181 (30.7)</td><td>4,019 (29.5)</td></tr><tr><td>Breastfeeding and duration</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>28,397 (41.8)</td><td>5,978 (44.4)</td><td>5,707 (42.1)</td><td>5,577 (40.9)</td><td>5,501 (40.4)</td><td>5,634 (41.4)</td></tr><tr><td>Yes, &lt; 6 months</td><td>27,020 (39.8)</td><td>4,977 (36.9)</td><td>5,328 (39.3)</td><td>5,545 (40.7)</td><td>5,594 (41.1)</td><td>5,576 (41.0)</td></tr><tr><td>Yes, ≥ 6 months</td><td>12,462 (18.4)</td><td>2,519 (18.7)</td><td>2,526 (18.6)</td><td>2,501 (18.4)</td><td>2,521 (18.5)</td><td>2,395 (17.6)</td></tr><tr><td>Previous use of oral contraceptive</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>26,584 (39.2)</td><td>6,293 (46.7)</td><td>5,586 (41.2)</td><td>5,249 (38.5)</td><td>4,871 (33.8)</td><td>4,585 (33.7)</td></tr><tr><td>Yes</td><td>41,295 (60.8)</td><td>7,181 (53.3)</td><td>7,975 (58.8)</td><td>8,374 (61.5)</td><td>8,745 (64.2)</td><td>9,020 (66.3)</td></tr><tr><td>Menopausal status and use of MHT</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Premenopausal</td><td>30,356 (44.7)</td><td>4,972 (36.9)</td><td>5,949 (43.9)</td><td>6,196 (45.5)</td><td>6,537 (48.0)</td><td>6,702 (49.3)</td></tr><tr><td>Postmenopausal with MHT use</td><td>22,370 (33.0)</td><td>5,384 (40.0)</td><td>4,618 (34.0)</td><td>4,364 (32.0)</td><td>4,058 (29.8)</td><td>3,946 (29.0)</td></tr><tr><td>Postmenopausal with no MHT use</td><td>15,153 (22.3)</td><td>3,118 (23.1)</td><td>2,994 (22.1)</td><td>3,063 (22.5)</td><td>3,021 (22.2)</td><td>2,957 (21.7)</td></tr><tr><td>History of mammography before inclusion</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>7,320 (10.8)</td><td>1,423 (11.6)</td><td>1,423 (10.6)</td><td>1,423 (10.7)</td><td>1,423 (10.6)</td><td>1,423 (10.5)</td></tr><tr><td>Yes</td><td>60,559 (89.2)</td><td>11,907 (88.4)</td><td>12,123 (89.4)</td><td>12,168 (89.3)</td><td>12,179 (89.4)</td><td>12,182 (89.5)</td></tr><tr><td>Family history of breast cancer</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>62,776 (92.5)</td><td>12,469 (92.5)</td><td>12,543 (92.5)</td><td>12,586 (92.4)</td><td>12,641 (92.8)</td><td>12,537 (92.2)</td></tr><tr><td>Yes</td><td>5,103 (7.5)</td><td>1,005 (7.5)</td><td>1,018 (7.5)</td><td>1,037 (7.6)</td><td>975 (7.2)</td><td>1,068 (7.8)</td></tr><tr><td>History of personal benign breast disease</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>48,006 (70.7)</td><td>9,724 (72.2)</td><td>9,705 (71.6)</td><td>9,578 (70.3)</td><td>9,554 (70.2)</td><td>9,445 (69.4)</td></tr><tr><td>Yes</td><td>19,873 (29.3)</td><td>3,750 (27.8)</td><td>3,856 (28.4)</td><td>4,045 (29.7)</td><td>4,062 (29.8)</td><td>4,160 (30.6)</td></tr></tbody></table>

Mean (standard deviation) for continuous variables, n (%) for categorical variables, MET: metabolic equivalent of task, AFFP: age at first full-term pregnancy, MHT: menopausal hormone therapy.

However, in model 3 additionally adjusting for lipid, alcohol and energy intakes, the associations were attenuated. The HR for 1 SD increase was no longer significant (HR = 1.01; 95 % CI: 0.98–1.04), and the positive associations across quintiles decreased. Specifically, a positive association remained for the for the third quintile (HRQ₃ vs Q₁ = 1.10; 95 % CI: 1.01–1.20), while the fourth quintile of PAH₄ showed a borderline positive association (HRQ₄ vs Q₁ = 1.08; 95 % CI: 0.99–1.19), and the highest quintile weakened (HRQ₅ vs Q₁ = 1.06; 95 % CI: 0.96–1.17).

For BaP, borderline positive associations were observed (model 3) for the second (HRQ₂ vs Q₁ = 1.08; 95 % CI: 0.99–1.17) and third (HRQ₃ vs Q₁ = 1.07; 95 % CI: 0.98–1.16) quintiles of BaP.

Consistent with our findings in the quintile analysis, the spline plots graphically suggested that PAH₄ dietary intake and BC association was

not linear in both models 2 and 3 (Figs. 2 and 3).

By menopausal status, in the main adjusted model, marginal positive associations were found in postmenopausal without recent use of MHT, specifically in the second, third and fourth quintiles of PAH₄ compared to the lowest one (HRQ₂ vs Q₁ = 1.14; 95 % CI: 0.98–1.33; HRQ₃ vs Q₁ = 1.13; 95 % CI: 0.96–1.33; and HRQ₄ vs Q₁ = 1.15; 95 % CI: 0.97–1.37). There was no association when limited to either premenopausal or postmenopausal with use of MHT (Supplementary Table 1).

We observed no substantial significant difference in the estimates by hormone receptor status, except for ER-PR- BC. In the main adjusted model, the HR of the association was statistically significant in the third (HRQ₃ vs Q₁ = 1.30; 95 % CI: 1.01–1.70) and fourth (HRQ₄ vs Q₁ = 1.34; 95 % CI: 1.01–1.76) quintiles of PAH₄ (Table 3).

Overall, results of sensitivity analyses remained consistent with those
<caption>**Table 2**</caption>

Associations between PAHs dietary intake (estimated according to the lower bound scenario) and breast cancer risk in the French E3N-Generations cohort study, 1993–2014.

<table><thead><tr><th rowspan="2">PAH dietary intake (ng/day)</th><th rowspan="2">n cases / non- cases</th><th colspan="2">Model <sup>a</sup></th><th colspan="2">Model <sup>b</sup></th><th colspan="2">Model <sup>c</sup></th></tr><tr><th>HR (95 % CI)</th><th>P-value</th><th>HR (95 % CI)</th><th>P-value</th><th>HR (95 % CI)</th><th>P-value</th></tr></thead><tbody><tr><td>PAH<sub>4</sub></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Continuous (for an increment of 35 ng/day)</td><td>5,686 / 62,193</td><td>1.03 (1.01–1.06)</td><td></td><td>1.03 (1.01–1.06)</td><td></td><td>1.01 (0.98–1.04)</td><td></td></tr><tr><td>Quintiles</td><td></td><td></td><td>0.015</td><td></td><td>0.024</td><td></td><td>0.292</td></tr><tr><td>I</td><td>1,035 / 12,439</td><td>1 (ref)</td><td></td><td>1 (ref)</td><td></td><td>1 (ref)</td><td></td></tr><tr><td>II</td><td>1,122 / 12,439</td><td>1.07 (0.99–1.17)</td><td></td><td>1.07 (0.98–1.16)</td><td></td><td>1.06 (0.97–1.15)</td><td></td></tr><tr><td>III</td><td>1,185 / 12,438</td><td>1.13 (1.04–1.23)</td><td></td><td>1.13 (1.04–1.23)</td><td></td><td>1.10 (1.01–1.20)</td><td></td></tr><tr><td>IV</td><td>1,178 / 12,438</td><td>1.13 (1.04–1.23)</td><td></td><td>1.12 (1.03–1.22)</td><td></td><td>1.08 (0.99–1.19)</td><td></td></tr><tr><td>V</td><td>1,166 / 12,439</td><td>1.12 (1.03–1.22)</td><td></td><td>1.12 (1.03–1.21)</td><td></td><td>1.06 (0.96–1.17)</td><td></td></tr><tr><td>BaP</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Continuous (for an increment of 6 ng/day)</td><td>5,686 / 62,193</td><td>1.01 (0.99–1.04)</td><td></td><td>1.01 (0.98–1.04)</td><td></td><td>1.00 (0.97–1.02)</td><td></td></tr><tr><td>Quintiles</td><td></td><td></td><td>0.112</td><td></td><td>0.144</td><td></td><td>0.409</td></tr><tr><td>I</td><td>1,054 / 12,439</td><td>1 (ref)</td><td></td><td>1 (ref)</td><td></td><td>1 (ref)</td><td></td></tr><tr><td>II</td><td>1,166 / 12,438</td><td>1.10 (1.01–1.19)</td><td></td><td>1.09 (1.01–1.19)</td><td></td><td>1.08 (0.99–1.17)</td><td></td></tr><tr><td>III</td><td>1,169 / 12,438</td><td>1.11 (1.02–1.20)</td><td></td><td>1.10 (1.02–1.20)</td><td></td><td>1.07 (0.98–1.16)</td><td></td></tr><tr><td>IV</td><td>1,159 / 12,449</td><td>1.10 (1.01–1.19)</td><td></td><td>1.09 (1.00–1.18)</td><td></td><td>1.04 (0.95–1.14)</td><td></td></tr><tr><td>V</td><td>1,138 / 12,439</td><td>1.08 (1.00–1.18)</td><td></td><td>1.07 (0.98–1.16)</td><td></td><td>1.02 (0.93–1.12)</td><td></td></tr></tbody></table>

HR, hazard ratio; CI, confidence interval.

P-values were derived from likelihood ratio test.

Quintiles cut-off values (ng/day): PAH<sub>4</sub>: 46, 59, 74, 95; BaP: 5, 6, 8, 11.

<sup>a</sup> Model 1 = Crude model stratified by age in 5-year categories.

<sup>b</sup> Model 2 = Model 1 additionally adjusted for physical activity, smoking status, age at first full-term pregnancy and parity, level of education, menopausal status at inclusion combined with use of menopausal hormone replacement therapy, body mass index.

<sup>c</sup> Model 3 = Model 2 additionally adjusted for lipid consumption, alcohol consumption, energy intake excluding alcohol and fat.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Cubic spline modelling of the relationship between PAHs dietary intake (estimated according to the lower bound scenario) and breast cancer risk in the French E3N-Generations cohort study, 1993–2014. Multivariable adjusted hazard ratios (solid line) and 95 % confidence intervals (dotted line) obtained using five-knot restricted cubic splines with the knot1 value used as reference. Model 2: stratified by age in 5-year categories and adjusted for physical activity, smoking status, age at first full-term pregnancy and parity, level of education, menopausal status at inclusion combined with use of menopausal hormone replacement therapy, body mass index.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Cubic spline modelling of the relationship between PAHs dietary intake (estimated according to the lower bound scenario) and breast cancer risk in the French E3N-Generations cohort study, 1993–2014. Multivariable adjusted hazard ratios (solid line) and 95 % confidence intervals (dotted line) obtained using five-knot restricted cubic splines with the knot1 value used as reference. Model 3: stratified by age in 5-year categories and adjusted for physical activity, smoking status, age at first full-term pregnancy and parity, level of education, menopausal status at inclusion combined with use of menopausal hormone replacement therapy, body mass index, lipids, alcohol consumption, energy intake excluding alcohol and fat.</figcaption></figure>

of the main analysis (Supplementary Table 2). Specifically, excluding participants with less than five years of follow-up (HR<sub>Q4 vs Q1</sub> = 1.08; 95 % CI: 0.97–1.20), restricting to the complete-case dataset (HR<sub>Q4 vs Q1</sub> = 1.07; 95 % CI: 0.97–1.17), and performing multiple imputation for missing covariate values (HR<sub>Q4 vs Q1</sub> = 1.07; 95 % CI: 0.97–1.18) all yielded comparable results to those of the main analyses (model 3).

Regarding effect modification, there was some evidence of heterogeneity by FHBC (P interaction < 0.001), with a borderline positive association only between the third quintile of PAH<sub>4</sub> levels and BC risk for women with FHBC (HR<sub>Q3 vs Q1</sub> = 1.30; 95 % CI: 0.99–1.70; model 3) (Supplementary Table 3). The association between PAH<sub>4</sub> dietary intake

and risk of BC was not modified by BMI (P interaction = 0.086), smoking status (P interaction = 0.620), or alcohol intake (P interaction = 0.148).

Additional analyses by histology, stage of disease or grade of differentiation at diagnosis revealed no substantial heterogeneity (Supplementary Table 4). Further analysis investigating individually each PAH of the PAH<sub>4</sub> and its association with BC risk, showed no considerable differences in the magnitude of the associations (Supplementary Table 5). Results using the energy adjustment by residual method remained comparable to the main findings, with an HR<sub>Q4 vs Q1</sub> of 1.10 (95 % CI: 0.94–1.29; model 3) (Supplementary Table 6).
<caption>**Table 3**</caption>

Associations between PAHs dietary intake (estimated according to the lower bound scenario) and breast cancer risk, stratified by estrogen/progesterone hormone receptor status in the French E3N-Generations cohort study, 1993–2014.

<table><thead><tr><th rowspan="2"></th><th colspan="2">Model <sup>a</sup></th><th colspan="2">Model <sup>b</sup></th><th colspan="2">Model <sup>c</sup></th></tr><tr><th>n cases / non- cases</th><th>HR (95 % CI)</th><th>P-value</th><th>HR (95 % CI)</th><th>P-value</th><th>HR (95 % CI)</th><th>P-value</th></tr></thead><tbody><tr><td>ER-</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Continuous (for an increment of 35 ng/day)</td><td>797 / 65,920</td><td>0.98 (0.90-1.04)</td><td></td><td>0.97 (0.90-1.04)</td><td></td><td>0.95 (0.87-1.04)</td><td></td></tr><tr><td>Quintiles</td><td></td><td></td><td>0.242</td><td></td><td>0.269</td><td></td><td>0.253</td></tr><tr><td>I</td><td>140 / 13,131</td><td>1 (ref)</td><td></td><td>1 (ref)</td><td></td><td>1 (ref)</td><td></td></tr><tr><td>II</td><td>164 / 13,152</td><td>1.14 (0.91-1.43)</td><td></td><td>1.14 (0.91-1.43)</td><td></td><td>1.15 (0.91-1.46)</td><td></td></tr><tr><td>III</td><td>174 / 13,203</td><td>1.20 (0.96-1.50)</td><td></td><td>1.20 (0.96-1.51)</td><td></td><td>1.21 (0.95-1.54)</td><td></td></tr><tr><td>IV</td><td>174 / 13,211</td><td>1.20 (0.96-1.50)</td><td></td><td>1.20 (0.96-1.50)</td><td></td><td>1.21 (0.94-1.56)</td><td></td></tr><tr><td>V</td><td>145 / 13,223</td><td>1.00 (0.79-1.26)</td><td></td><td>1.01 (0.80-1.28)</td><td></td><td>1.01 (0.77-1.33)</td><td></td></tr><tr><td>ER+</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Continuous (for an increment of 35 ng/day)</td><td>3,727 / 62,990</td><td>1.05 (1.01-1.08)</td><td></td><td>1.04 (1.01-1.08)</td><td></td><td>1.01 (0.98-1.05)</td><td></td></tr><tr><td>Quintiles</td><td></td><td></td><td>0.083</td><td></td><td>0.110</td><td></td><td>0.893</td></tr><tr><td>I</td><td>692 / 12,579</td><td>1 (ref)</td><td></td><td>1 (ref)</td><td></td><td>1 (ref)</td><td></td></tr><tr><td>II</td><td>713 / 12,603</td><td>1.02 (0.92-1.13)</td><td></td><td>1.02 (0.92-1.13)</td><td></td><td>0.99 (0.89-1.10)</td><td></td></tr><tr><td>III</td><td>765 / 12,612</td><td>1.09 (0.99-1.21)</td><td></td><td>1.09 (0.98-1.21)</td><td></td><td>1.04 (0.93-1.16)</td><td></td></tr><tr><td>IV</td><td>773 / 12,612</td><td>1.11 (1.00-1.23)</td><td></td><td>1.10 (0.99-1.22)</td><td></td><td>1.03 (0.92-1.16)</td><td></td></tr><tr><td>V</td><td>784 / 12,584</td><td>1.13 (1.02-1.25)</td><td></td><td>1.12 (1.01-1.24)</td><td></td><td>1.02 (0.90-1.15)</td><td></td></tr><tr><td>PR-</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Continuous (for an increment of 35 ng/day)</td><td>1,580 / 65,002</td><td>0.99 (0.94-1.04)</td><td></td><td>0.99 (0.94-1.05)</td><td></td><td>0.97 (0.91-1.03)</td><td></td></tr><tr><td>Quintiles</td><td></td><td></td><td>0.339</td><td></td><td>0.337</td><td></td><td>0.383</td></tr><tr><td>I</td><td>299 / 12,951</td><td>1 (ref)</td><td></td><td>1 (ref)</td><td></td><td>1 (ref)</td><td></td></tr><tr><td>II</td><td>307 / 12,980</td><td>1.01 (0.86-1.18)</td><td></td><td>1.01 (0.86-1.19)</td><td></td><td>1.01 (0.85-1.19)</td><td></td></tr><tr><td>III</td><td>325 / 13,018</td><td>1.06 (0.91-1.24)</td><td></td><td>1.07 (0.91-1.25)</td><td></td><td>1.05 (0.89-1.25)</td><td></td></tr><tr><td>IV</td><td>348 / 13,014</td><td>1.14 (0.97-1.33)</td><td></td><td>1.15 (0.98-1.34)</td><td></td><td>1.12 (0.94-1.33)</td><td></td></tr><tr><td>V</td><td>301 / 13,039</td><td>0.99 (0.84-1.16)</td><td></td><td>1.01 (0.86-1.18)</td><td></td><td>0.96 (0.79-1.16)</td><td></td></tr><tr><td>PR+</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Continuous (for an increment of 35 ng/day)</td><td>2,809 / 63,773</td><td>1.06 (1.02-1.09)</td><td></td><td>1.05 (1.02-1.09)</td><td></td><td>1.02 (0.98-1.07)</td><td></td></tr><tr><td>Quintiles</td><td></td><td></td><td>0.084</td><td></td><td>0.139</td><td></td><td>0.896</td></tr><tr><td>I</td><td>512 / 12,738</td><td>1 (ref)</td><td></td><td>1 (ref)</td><td></td><td>1 (ref)</td><td></td></tr><tr><td>II</td><td>541 / 12,746</td><td>1.05 (0.93-1.18)</td><td></td><td>1.04 (0.92-1.18)</td><td></td><td>1.01 (0.89-1.15)</td><td></td></tr><tr><td>III</td><td>580 / 12,763</td><td>1.12 (0.99-1.26)</td><td></td><td>1.11 (0.99-1.26)</td><td></td><td>1.06 (0.94-1.21)</td><td></td></tr><tr><td>IV</td><td>576 / 12,786</td><td>1.12 (0.99-1.26)</td><td></td><td>1.11 (0.98-1.25)</td><td></td><td>1.03 (0.90-1.18)</td><td></td></tr><tr><td>V</td><td>600 / 12,740</td><td>1.17 (1.04-1.31)</td><td></td><td>1.15 (1.02-1.30)</td><td></td><td>1.05 (0.91-1.21)</td><td></td></tr><tr><td>ER-PR-</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Continuous (for an increment of 35 ng/day)</td><td>662 / 65,920</td><td>0.96 (0.89-1.04)</td><td></td><td>0.97 (0.89-1.05)</td><td></td><td>0.95 (0.86-1.05)</td><td></td></tr><tr><td>Quintiles</td><td></td><td></td><td>0.069</td><td></td><td>0.076</td><td></td><td>0.070</td></tr><tr><td>I</td><td>112 / 13,138</td><td>1 (ref)</td><td></td><td>1 (ref)</td><td></td><td>1 (ref)</td><td></td></tr><tr><td>II</td><td>138 / 13,149</td><td>1.20 (0.94-1.55)</td><td></td><td>1.20 (0.94-1.55)</td><td></td><td>1.22 (0.94-1.58)</td><td></td></tr><tr><td>III</td><td>147 / 13,196</td><td>1.28 (1.00-1.63)</td><td></td><td>1.29 (1.00-1.65)</td><td></td><td>1.30 (1.00-1.70)</td><td></td></tr><tr><td>IV</td><td>150 / 13,212</td><td>1.30 (1.02-1.66)</td><td></td><td>1.31 (1.03-1.68)</td><td></td><td>1.34 (1.01-1.76)</td><td></td></tr><tr><td>V</td><td>115 / 13,225</td><td>1.00 (0.77-1.29)</td><td></td><td>1.02 (0.78-1.32)</td><td></td><td>1.03 (0.76-1.40)</td><td></td></tr><tr><td>ER + PR+</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Continuous (for an increment of 35 ng/day)</td><td>2,681 / 63,901</td><td>1.06 (1.02-1.10)</td><td></td><td>1.06 (1.02-1.09)</td><td></td><td>1.03 (0.98-1.07)</td><td></td></tr><tr><td>Quintiles</td><td></td><td></td><td>0.063</td><td></td><td>0.102</td><td></td><td>0.841</td></tr><tr><td>I</td><td>485 / 12,765</td><td>1 (ref)</td><td></td><td>1 (ref)</td><td></td><td>1 (ref)</td><td></td></tr><tr><td>II</td><td>516 / 12,771</td><td>1.05 (0.93-1.19)</td><td></td><td>1.05 (0.93-1.19)</td><td></td><td>1.02 (0.90-1.16)</td><td></td></tr><tr><td>III</td><td>556 / 12,787</td><td>1.14 (1.01-1.28)</td><td></td><td>1.13 (1.00-1.28)</td><td></td><td>1.08 (0.94-1.23)</td><td></td></tr><tr><td>IV</td><td>553 / 12,809</td><td>1.13 (1.00-1.28)</td><td></td><td>1.12 (0.99-1.27)</td><td></td><td>1.05 (0.91-1.20)</td><td></td></tr><tr><td>V</td><td>571 / 12,769</td><td>1.18 (1.04-1.33)</td><td></td><td>1.16 (1.03-1.31)</td><td></td><td>1.05 (0.91-1.22)</td><td></td></tr></tbody></table>

HR, hazard ratio; CI, confidence interval; ER, estrogen receptor; PR, progesterone receptor.

P-values were derived from likelihood ratio test.

Quintiles cut-off values (ng/day): PAH<sub>4</sub>: 46, 59, 74, 95.

<sup>a</sup> Model 1 = Crude model stratified by age in 5-year categories.

<sup>b</sup> Model 2 = Model 1 additionally adjusted for physical activity, smoking status, age at first full-term pregnancy and parity, level of education, menopausal status at inclusion combined with use of menopausal hormone replacement therapy, body mass index.

<sup>c</sup> Model 3 = Model 2 additionally adjusted for lipids, alcohol consumption, energy intake excluding alcohol and fat.

Further analyses stratified by the median follow-up time of cases revealed minor differences between cases with shorter and longer follow-up durations. However, the overall patterns remained consistent, with no statistically significant differences between groups due to overlapping confidence intervals. Notably, while quintile 3 exhibited similar HRs of 1.10 for both groups, a stronger association was observed in quintile IV of the group with follow-up durations ≥ median (HR<sub>Q4 vs Q1</sub> = 1.12; 95 % CI: 0.98–1.28) (Supplementary Table 7). Further analysis using exposure variables divided by the body weight (ng/ kilogram body weight/day) yielded attenuated HRs in the model 3 (HR<sub>Q5 vs Q1</sub> = 1.01; 95 % CI: 0.92–1.12), while HR from model 2 remained consistent to the primary results (HR<sub>Q5 vs Q1</sub> = 1.10; 95 % CI: 1.00–1.18)

(Supplementary Table 8).

## 4. Discussion

In this large prospective cohort of women, we observed a statistically significant increased risk of BC across quintiles of PAH<sub>4</sub> dietary intake, with a non-linear trend. While we did not find a clear overall association between BC risk and BaP, there were significant indications of a positive link with the second and third quintiles of BaP dietary intake. By menopausal status, marginal positive associations were observed only in postmenopausal women without recent use of MHT. By estrogen and progesterone receptor status, there was a positive association between
PAH₄ dietary and ER-PR- BC. The study supports heterogeneity by FHBC.

Most epidemiological studies have focused on populations exposed to high PAHs levels, such as aluminum production workers, where exposure can reach up to 100 µg/m³ (Archibong et al., 2012; IARC Working Group on the Evaluation of Carcinogenic Risks to Humans, 2010); and accumulating evidence links such occupational PAH exposure to BC risk (Lee et al., 2019; Pedersen and Hansen, 2022). Our findings in a cohort of occupationally non-exposed (or very marginally exposed) women are in agreement with a recent *meta*-analysis of 23 studies showing that both occupational and/or environmental exposures to PAHs were statistically significantly associated with an increased BC risk, regardless of whether exposure was assessed by direct (i.e. PAHs-DNA adducts in blood or urinary levels) or indirect methods (i.e. questionnaires or other validated methods) (Gamboa-Loira et al., 2022).

Moreover, the suggested evidence of an association between PAH₄ and BC observed among women with FHBC in the present study is consistent with the study by Lee et al. reporting an elevated risk of BC associated in women with FHBC exposed to high levels of PAHs in occupational settings (Lee et al., 2019).

It is important to highlight that the overall effect of PAHs on human health depends on various factors, including the duration and route of exposure, the concentrations of PAHs, and their relative toxicity (Mallah et al., 2022). While current evidence supports associations of occupational PAH exposure with BC, the literature on PAH dietary exposure and BC risk is relatively limited, with the overall results being somewhat inconsistent across the studies (Niehoff et al., 2017; Steck et al., 2007). Most of these studies used grilled and smoked foods as main PAH dietary exposure (Parada et al., 2017; White et al., 2016, 2014). In agreement with our study, an increased risk of BC was observed among women with a high intake of grilled or smoked meat (Niehoff et al., 2017). Our results are also congruent with several previous studies investigating the impact of grilled/barbecued and smoked food on BC risk (Di Maso et al., 2013; Fu et al., 2011). In contrast the study by Steck et al. using a food frequency questionnaire to estimate PAH dietary intake, found no evidence of an association (Steck et al., 2007).

The main concern across the previous studies was the complex nature of PAH mixtures, making it challenging to accurately assess their health effects. The present study using a validated dietary questionnaire to estimate PAH dietary intake, provides new insights into the impact of dietary exposure to individual PAHs on the development of BC.

Of note, the attenuation of HRs after further adjustments for lipid, alcohol, and energy intakes suggests that part of the crude associations observed in the initial models was attributable to these correlated dietary factors. However, the persistence of some positive associations even after these adjustments reinforces the potential role of dietary PAHs in BC risk. Our findings further underscore the importance of lipids, alcohol, and energy intake as key dietary factors influencing BC risk. These results highlight the necessity of considering these factors collectively when evaluating the impact of PAH₄ intake, as sensitivity analyses adjusting for them individually did not significantly alter the observed associations.

PAHs are well recognized for their carcinogenic properties, and extensive epidemiological research has investigated their effects on cancer. However, the biological mechanisms linking PAH exposure to BC remain complex and not fully understood. Growing evidence shows that PAHs can activate chronic inflammation, one of the key carcinogenic pathways leading to cancer development (Malik et al., 2018). There is also consistent evidence showing a key role of PAHs in oxidative stress by producing reactive oxygen species through numerous mechanisms including metabolic activation (Rodgers et al., 2018; Smith et al., 2019). Similarly, there is an extensive body of literature describing the endocrine disruptive activity of PAHs (Rocha et al., 2021; Zhang et al., 2016). PAHs have been shown to mimic or interfere directly with the estrogen and androgen receptors, as well as with the actions of endogenous hormones, such as estrogen, which plays a crucial role in the

development of BC (Rocha et al., 2021; Santodonato, 1997). Several PAHs such as BaA, BaP, and CHR have demonstrated the ability to inhibit androgenic activity, as well as to activate aryl hydrocarbon responsive genes, resulting in widespread antiestrogenic effects (Bolden et al., 2017). Beyond the induction of its direct target genes, the activation of the Aryl Hydrocarbon Receptor (AHR) is likely central to these processes due to its role in promoting the degradation of ER and AR through its recently characterized E3 ubiquitin ligase activity (Luecke-Johansson et al., 2017; Ohtake et al., 2009). Additionally, AHR target genes are involved in the transformation of PAHs into reactive intermediates that can bind to cellular DNA to form DNA adducts. These adducts can interfere with normal DNA repair mechanisms, which, in turn, lead to mutations and genomic instability (Kennedy et al., 2005; Rodgers et al., 2018). Additionally, PAH exposure has been associated with alterations in epigenetic patterns, including DNA methylation and histone modifications, thereby influencing gene expression patterns and contributing to BC development and progression (Sahay et al., 2019). AHR is also likely involved in these processes, as it is now recognized that this receptor is intrinsically associated with the control of cell identity (Yamamoto et al., 2019) and directly mediates the epigenetic effects of PAHs in the context of BC development (Miret et al., 2023). Taken together, these various mechanisms (involving endocrine disruption, DNA damage, epigenetic alterations, inflammation and oxidative stress) at least partially based on the disruption of AHR activity can collectively promote the development and progression of BC. Similar to the present study, a non-monotonic response relationship between PAHs exposure at environmental relevant doses and their biological effects have been described in molecular and epidemiological studies, suggesting a dose-dependent transitions in the mechanisms of toxicity (Duca et al., 2018; Hinton et al., 2024; Lim et al., 2009). At low doses, the unmetabolized form of PAHs primarily acts as endocrine disruptors, interfering with nuclear receptors such as ER and/or through direct modulation of AhR, likely to stimulate pathways that increase BC development (Romagnolo et al., 2015; Wang et al., 2019). At higher doses, the capacity of phase II detoxification enzymes may be overwhelmed leading to the accumulation of reactive PAH metabolites that additionally induce genotoxic effects such as oxidative stress and DNA damage (Wang et al., 2019). In ER-/PR- BC subpopulations, the non-monotonic dose-response association suggests that the genotoxic or redox stresses may paradoxically reduce risk compared to intermediate doses. This could be due to the inhibitory effect of these stresses on the proliferation of pre-neoplastic lesions. The observed non-monotonic dose-response may reflect the unique biology of these tumors, including their intrinsic sensitivity to perturbations that disrupt cell identity and their heightened vulnerability to DNA-damaging compounds (Alli et al., 2009). Given the non-linear trend observed between PAH exposure and BC risk, it is possible that exposure to PAHs may become more harmful beyond a certain level. Alternatively, certain subgroups might exhibit heightened sensitivity to lower levels of exposure, potentially due to genetic, hormonal, or environmental factors. These observations highlight the need for further studies to investigate the dose-response relationship in more detail and to explore individual variability in sensitivity to PAH exposure.

From a policy and preventive standpoint, PAHs represent an important group of pollutants that should receive greater attention, particularly in the context of air pollution and food contamination (Sekar and T R, 2024; Venkatraman et al., 2024). However, PAHs are present in indoor air, as well as in food, in the form of complex mixtures, with spatially varying compositions. Experimental evidence regarding metabolism, gene expression and DNA adducts suggests that interactions between PAHs in these mixtures can be complex and highly unpredictable, due to their potential inhibitory, additive and synergistic effects (Agudo et al., 2017). Given the difficulties in implementing guidelines for PAH mixtures, BaP, recognized as the most reliable single indicator compound, has been widely utilized as a proxy for PAH exposure in epidemiological studies and can significantly contribute to
the PAH toxicity (Human health effects of polycyclic aromatic hydrocarbons as ambient air pollutants - Report of the Working Group on Polycyclic Aromatic Hydrocarbons of the Joint Task Force on the Health Aspects of Air Pollution [WWW Document], n.d). Thus, reducing exposure to BaP may not only lower the incidence of several cancers but also mitigate the risk of other adverse health effects associated with PAHs. Nevertheless, future studies should explore specific approaches for investigating the effect of exposure to PAHs as a mixture. Also, given the widespread presence of PAHs in processed, grilled, smoked, and charred foods, our findings suggest that reducing the consumption of such processed foods may be an effective dietary strategy to reduce PAH exposure and associated health risks, including BC. As part of wider public health efforts, we recommend promoting alternative cooking methods, such as baking, steaming, or boiling, which generate significantly lower levels of PAHs. Overall, our findings suggest that public health initiatives could benefit from educating the population about PAH exposure sources and raising awareness of the potential health risks associated with a diet high in PAHs, as well as promoting food processing and dietary choices associated with low PAH exposure, such as increased consumption of fresh, and minimally processed foods to reduce the risk of exposure. Implementing these recommendations could have significant implications for BC prevention, particularly in populations with high dietary exposure.

Our study benefits from several strengths inherent to the design of the large E3N cohort study. Notably, a substantial number of BC cases and high statistical power enabled us to more effectively evaluate the associations and to perform sensitivity analyses underlying the robustness of our findings. The present study is the largest study investigating the potential role of PAH dietary intake on the risk of BC. Due to the extensive data available on individual characteristics of women and subtypes of breast tumors, we were able to consider many potential confounders or effect modifiers, as well as perform analyses across tumors subtypes, stages, and histologies of BC. The minimal percentage of missing data emphasizes the high quality of the data collected. Other strengths include a high participation rate and minimal differences between respondents and non-respondents at inclusion with regard to region of residence and year of birth (Clavel-Chapelon, F., E3N Study Group, 2015). Moreover, all sensitivity analyses conducted in this study, yielded results that were consistent with the main findings, further supporting the robustness of our conclusions.

Despite numerous strengths, our study had some limitations. One notable limitation is that the assessment of PAHs dietary intake relied on the currently available single self-reported food consumption dataset collected in 1993 during the cohort follow-up, which may not fully capture changes in dietary pattern over time. While we acknowledge that dietary patterns may have changed among the study participants during the 20-year follow-up period, it has been shown that main dietary patterns tend to be relatively stable over time in middle-age adults (Thorpe et al., 2019). Also, any potential exposure misclassification resulting from change in dietary pattern is likely non-differential and would attenuate the observed associations. Additional analysis stratified by median follow-up duration of cases, did not show statistically significant differences of risk estimates. Moreover, assessment of exposure was based on food contamination data measured in 2007. The temporal discrepancy could potentially lead to non-differential exposure misclassification. The TDS2 offers the most accurate, recent, and comprehensive database on French food contamination levels. Also, PAHs are persistent organic pollutants, and their environmental contamination levels tend to decrease slowly over time, thus minimizing major variations in contamination (Alegbeleye et al., 2017). Thus, any error in estimating dietary PAH exposure due to these limitations is likely to be non-differential and consistent across different food groups, allowing reliable classification into the five exposure quintiles. Furthermore, the lack of dietary exposure data prior to 1993 hinders investigating the potential effects of PAH exposure during critical windows of exposure such as birth, puberty, and pregnancy. Studies

assessing the potential variability of these changes may be of interest for a better understanding of the differences in carcinogenicity across these windows. Moreover, while all food samples were prepared "as consumed" prior to analysis, we lacked specific information on individual cooking habits among study participants, which could influence the variability in PAH food contamination levels. According to the French Total Diet Study (TDS2), no significant inter-regional differences in PAH contamination were observed ("Total Diet Study (TDS 2)", 2014), suggesting a limited impact of regional variability on dietary exposure estimates. While simple imputation methods are commonly used in such situations, we acknowledge that they can introduce bias, particularly if the missing values are not missing completely at random (MCAR). To assess the robustness of our findings, we conducted sensitivity analyses using multiple imputation. These analyses yielded results that did not substantially differ from those obtained using simple imputation, suggesting that the impact of missing data on our findings was minimal. In addition, we used a common Cox model to assess the association between the sum of four PAHs and the risk of BC. However, this approach does not allow to consider possible non-additive effects between substances. Future studies using more flexible statistical methods could provide valuable insights. The lack of representativeness of the study sample is acknowledged, as women were predominately teachers and mostly postmenopausal at diagnosis, consequently, extrapolation of these findings to the general population must be carried out with caution. Finally, although we adjusted for key confounders, including education level as a proxy for socioeconomic status, other potential risk factors such as occupational exposures and environmental pollution (e.g., air quality) may still influence BC risk. However, given the occupational homogeneity of the cohort, we assume that exposure to PAHs from occupational sources was limited and relatively consistent across participants. Future studies should explore these additional environmental and occupational factors to provide a more comprehensive understanding. Despite these efforts, residual confounding from unmeasured pollutants cannot be entirely ruled out.

## 5. Conclusions

This is the largest epidemiological study that investigated the association between PAH dietary intake and BC risk, through a self-reported food questionnaire data combined with a food contaminant database at the national level. The findings support a relationship between PAH dietary intake and risk of BC. A positive but marginal association was observed between BaP dietary intake and risk of BC. This research underlines the need for further studies to confirm these novel findings and delve into details of the impact of PAH dietary exposure, while considering PAHs as a complex mixture. Overall, although single-point dietary assessments can serve as reasonable proxies for long-term intake, future studies would benefit from multiple dietary assessments over time to improve accuracy and strengthen the exploration of links between PAH intake and cancer outcomes.

## CRediT authorship contribution statement

**Amina Amadou:** Writing – review & editing, Writing – original draft, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Delphine Fraud:** Writing – review & editing, Validation, Methodology, Data curation, Conceptualization. **Chloé Marques:** Writing – review & editing, Validation, Methodology, Data curation, Conceptualization. **Hwayoung Noh:** Writing – review & editing, Validation, Methodology, Conceptualization. **Pauline Frenoy:** Writing – review & editing, Validation, Data curation, Conceptualization. **Arnaud Vigneron:** Writing – review & editing, Writing – original draft, Validation, Investigation. **Thomas Coudon:** Writing – review & editing, Validation, Conceptualization. **Floriane Deygas:** Writing – review & editing, Validation, Software, Data curation. **Gianluca Severi:** Writing – review & editing, Validation,
Resources, Project administration. **Béatrice Fervers:** Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Conceptualization. **Francesca Romana Mancini:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Conceptualization.

## 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

The research was carried out using data from the Inserm (French National Institute for Health and Medical Research) E3N-Generations cohort, which was established and maintained with the support of the Mutuelle Générale de l'Éducation Nationale (MGEN), Gustave Roussy, and the French League against Cancer (LNCC). The E3N-Generations cohort is also supported by the French National Research Agency (ANR) under the Investment for the future Program (PIA; ANR-10-COHO-0006) and by the French Ministry of Higher Education, Research and Innovation (subsidy for public service charges No. 2102 918823, 2103236497, and 2103586016). The work was also supported by the grant EQ9-C17014LS from the Cancer ITMO of the French National Alliance for Life and Health Sciences (AVIESAN), in collaboration with the Institut National du Cancer (French National Cancer Institute) and Inserm. Amina Amadou is supported by the Regional Committee of the French League against Cancer of the Savoie Region and ADEME. Amina Amadou is supported by ADEME (21ESD0020) and the Regional Committee of the French League against Cancer of the Savoie Region (18-383-C). The funding body had no role in the conception, design, planning, or writing of the study.

## Ethics approval and consent to participate.

Our research is based on the existing French national cohort E3N. Informed consent was obtained from all participants and the study was approved by the French National Commission for Data Protection and Privacy (CNIL). The datasets generated and/or analyzed during the current study are not publicly available for ethical reasons and in accordance with the specific consent signed by the participants. Data are available from the E3N-Generations team through the corresponding author on reasonable request.

The authors are grateful to the study participants for their continued participation and to medical practitioners for providing pathology reports.

## Consent for publication.

All authors have read the manuscript and have agreed to the submission.

## Authors' contributions

All authors contributed to the study conceptualization, design, and methodology. AA performed the statistical analysis, interpreted the results and drafted the first manuscript. All authors helped with the results interpretation, reviewed and edited previous versions of the manuscript. All authors read and approved the final manuscript.

# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envint.2025.109505.

## Data availability

Data will be made available on request.

## References

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Further reading

Polycyclic Aromatic Hydrocarbons in Food [1] - Scientific Opinion of the Panel on Contaminants in the Food Chain | EFSA [WWW Document], 2008. URL https://www.efsa.europa.eu/en/efsajournal/pub/724 (accessed 4.24.24).

Sekar, M., T R, P., 2024. Critical review on the formations and exposure of polycyclic aromatic hydrocarbons (PAHs) in the conventional hydrocarbon-based fuels: prevention and control strategies. Chemosphere 350, 141005. https://doi.org/10.1016/j.chemosphere.2023.141005.

Sirot, V., Volatier, J.L., Calamassi-Tran, G., Dubuisson, C., Menard, C., Dufour, A., Leblanc, J.C., 2009. Core food of the French food supply: second Total Diet Study. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 26, 623-639. https://doi.org/10.1080/02652030802695506Agudo, A., Peluso, M., Munnia, A., Luján-Barroso, L., Barricarte, A., Amiano, P., Navarro, C., Sánchez, M.-J., Quirós, J. R., Ardanaz, E., Larrañaga, N., Tormo, M.-J., Chirlaque, M.-D., Rodríguez-Barranco, M., Sánchez-Cantalejo, E., Cellai, F., Bonet, C., Sala, N., González, C.A., 2017. Aromatic DNA adducts and breast cancer risk: a case-cohort study within the EPIC-Spain. Carcinogenesis 38, 691-698. https://doi.org/10.1093/carcin/bgx047.

Avis et rapport relatifs à l'étude de l'alimentation totale française 2 (EAT 2) - Tome 2 : Résidus de pesticides, additifs, acrylamide,hydrocarbures aromatiques polycycliques [WWW Document], 2011. Anses - Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail. URL https://www.anses.fr/fr/content/avis-et-rapport-relatifs-%C3%A0-l%E2%80%99%C3%A9tude-de-l%E2%80%99alimentation-totale-fran%C3%A7aise-2-eat-2-tome-2-r%C3%A9sidus (accessed 4.8.24).

Human health effects of polycyclic aromatic hydrocarbons as ambient air pollutants - Report of the Working Group on Polycyclic Aromatic Hydrocarbons of the Joint Task Force on the Health Aspects of Air Pollution [WWW Document], n.d. URL https://www.who.int/europe/publications/i/item/9789289056533 (accessed 10.5.23).

Sirot, V., Volatier, J.L., Calamassi-Tran, G., Dubuisson, C., Menard, C., Dufour, A., Leblanc, J.C., 2009. Core food of the French food supply: second Total Diet Study. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 26, 623-639. https://doi.org/10.1080/02652030802695506.