Blood levels of Persistent Organic Pollutants and circulating biomarkers of systemic inflammation in French women: Evidence from the E3N-Generations cohort

Blood levels of Persistent Organic Pollutants and circulating biomarkers of systemic inflammation in French women: Evidence from the E3N-Generations cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Blood levels of Persistent Organic Pollutants and circulating biomarkers of systemic inflammation in French women: Evidence from the E3N-Generations cohort Francesca Romana Mancini, Pauline Frénoy, German Cano-Sancho, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9427652/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Persistent organic pollutants (POPs) comprise a diverse class of chemicals characterized by environmental persistence, bioaccumulation, and potential toxicity to humans. Immune and inflammatory dysregulation has been proposed as a key pathway underlying their adverse health effects. This study aimed to investigate associations between circulating levels of multiple POPs—organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and per- and polyfluoroalkyl substances (PFAS)—and biomarkers of systemic inflammation, while accounting for complex exposure patterns. We analyzed a cross-sectional sample of 468 women from the French E3N-Generations cohort. Concentrations of 45 POPs and four inflammatory markers (CRP, IL-8, MCP-1, TNF-α) were measured in blood collected in 1994–1999. Logistic regression models were first applied to assess associations between individual POPs and dichotomized biomarkers. Principal component analysis (PCA) was then used to derive exposure profiles, which were subsequently examined in regression models. Several OCPs were positively associated with CRP levels, a finding corroborated by PCA-based analyses. After false discovery rate (FDR) correction, cis-heptachlor epoxide remained significantly associated with elevated CRP. In contrast, multiple PFAS were inversely associated with IL-8 concentrations, both individually and as part of a shared exposure component. Among them, PFOS showed the most robust association after FDR correction. Overall, these findings provide further insight into the complex links between mixed POP exposures and inflammatory processes, highlighting the importance of considering both individual compounds and exposure mixtures. persistent organic pollutants systemic inflammation biomarkers mixtures Introduction Persistent organic pollutants (POPs) are a heterogeneous group of compounds that share several key characteristics, including persistence in the environment and in the human body, long-range environmental transport, toxicity to humans and ecosystems, and the ability to bioaccumulate and biomagnify along the food chain. POPs have been widely used in agricultural and industrial applications, leading to their global dispersion in the environment (1). This group of contaminants includes organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and per- and polyfluoroalkyl substances (PFASs). Following their inclusion in the Stockholm Convention in 2001, the production, use, and environmental release of POPs have been progressively restricted (2). Nevertheless, owing to their persistence, human populations worldwide remain substantially exposed. Most POPs, such as OCPs, PCBs, and PBDEs, are lipophilic and tend to accumulate in adipose tissue, whereas PFASs preferentially bind to proteins. Consequently, POPs bioaccumulate along the food chain, and for the general population, diet, in particular consumption of foods of animal origin, represents the main source of exposure (3,4). Growing evidence links exposure to POPs with a range of non-communicable diseases, including cardiometabolic disorders, cancers, and auto-immune diseases (5–7). Among the biological mechanisms implicated in POP-related toxicity, disruption of immune and inflammatory processes is suspected to play a key role (8). Although inflammation plays a crucial role in host defence, excessive or dysregulated inflammatory responses can lead to tissue damage and contribute to disease pathogenesis (9). Chronic low-grade inflammation is involved in the early stages of many disease processes, and elevated levels of inflammatory markers have been associated with an increased risk of developing multiple disorders. Various toxic compounds may induce abnormal inflammatory responses, either by directly activating immune pathways or by disrupting normal cellular homeostasis (10). More in details, there is evidence suggesting that PCBs and OCPs may promote a pro‑inflammatory milieu by altering cytokine expression, skewing macrophage polarization toward pro‑inflammatory phenotypes, and activating transcriptional regulators such as NF‑κB and the aryl hydrocarbon receptor (AhR) (11). Similarly, PFAS exposures are thought to modulate chronic inflammatory responses through activation of innate immune sensors and inflammasomes (e.g., AIM2, NLRP3), regulation of NF‑κB and PPAR signaling, and induction of pro‑inflammatory cytokines (12). PBDEs likely contribute to inflammation by generating oxidative stress and reactive oxygen species and increase pro‑inflammatory cytokine production (12). Despite such evidence suggesting an effect of POPs on the disruption of immune and inflammatory processes, results from observational studies in populations chronically exposed to background levels of POPs are too limited and inconsistent to allow firm conclusions. To address this, we focused on four complementary markers, namely C-reactive protein (CRP), interleukin-8 (IL-8), tumor necrosis factor-alpha (TNF-α), and monocyte chemoattractant protein-1 (MCP-1), capturing distinct aspects of the inflammatory cascade. CRP, an acute-phase protein, reflects systemic inflammation and is linked to cardiovascular and metabolic disease risk. IL-8, a chemokine produced by multiple cell types, mediates neutrophil recruitment and activation and plays a key role in innate immunity. TNF-α orchestrates early immune activation and contributes to both acute and chronic inflammation. MCP-1 regulates monocyte and macrophage trafficking, driving chronic inflammatory processes and atherosclerosis. Together, these markers provide complementary insight into systemic inflammation and immune pathways potentially affected by POPs exposure. The aim of this study was thus to explore the relationship between POPs blood levels, including OCPs, PCBs, PBDEs and PFASs, individually and as profiles of exposure to multiple POPs, and the four circulating biomarkers of systemic inflammation in a cross-sectional study based on a random sample of women from the French E3N-Generations cohort study. Materials and methods Study population E3N-Generations is a family-based, multigenerational prospective cohort derived from the original E3N study (Étude Épidémiologique auprès de femmes de l’Éducation Nationale). The E3N study was launched in 1990 and enrolled 98,995 women aged 40–65 years who were members of a health insurance plan mainly covering workers in the French national education system (Mutuelle Générale de l’Éducation Nationale). The study was later expanded to include relatives of these women, resulting in a three-generation cohort comprising the original women and the fathers of their children (E3N-G1), their children (E3N-G2), and their grandchildren (E3N-G3) ( https://www.e3n-generations.fr/ ). The methodology of the original E3N study, corresponding to the female participants of E3N-G1, has been reported elsewhere (13,14). In summary, participants were followed through self-completed questionnaires sent by mail every two to three years, collecting detailed information on lifestyle characteristics, reproductive factors, medical history, and overall health. From 1994 to 1999, a subset of participants provided blood samples, leading to the establishment of a biobank including samples from approximately 25,000 women. Blood specimens were fractionated into 28 components, including plasma, serum, buffy coat, leukocytes, and erythrocytes, and preserved in plastic straws stored in liquid nitrogen at − 196°C. The present analysis is restricted to women belonging to the first generation of the E3N-Generations cohort. A random subset of 468 women was drawn from participants with adequate remaining biological samples in the biobank for laboratory analyses (specifically, five plasma straws and two serum straws), who had completed the 1993 dietary questionnaire and were not enrolled in any other ongoing cohort studies requiring the use of plasma or serum samples. Measurement of POPs biomarkers Blood levels of 74 POPs were measured in serum (for PFAS) or plasma (for other POPs) by the LABERCA laboratory using accredited methods based on liquid and gas chromatography coupled to mass-spectrometry (ISO 17025), as described in our previous study (15). Biomarkers with more than 75% of non-quantified values, as well as linear PFOS-, were not included in further statistical analyses. After these exclusions, 45 POPs biomarkers with at least 25% of quantified values were retained for this study, namely: 12 PFASs (PFUnDA, PFDA, PFNA, PFHxS, PFHpS, PFOA, PFHpA, PFPeS, PFOSA, PFOS-total, N-MeFOSAA, N-EtFOSAA); 15 OCPs (PeCBz, HCB, mirex, γ-HCH, β-HCH, oxychlordane, cis-heptachlor-epoxide, o,p’-DDT, o,p’-DDE, trans-nonachlor, p,p’-DDE, dieldrin, cis-nonachlor, p,p’-DDD, p,p’-DDT); 14 PCBs (6 non-dioxin-like congeners : PCB-28, PCB-52, PCB-101, PCB-138, PCB-153, PCB-180, and 8 dioxin-like congeners: PCB-105, PCB-114, PCB-118, PCB-123, PCB-156, PCB-157, PCB-167, PCB-189); 4 PBDEs (PBDE-47, PBDE-99, PBDE-100, PBDE-153). Measurement of inflammatory biomarkers Plasma concentrations of CRP, IL-8, MCP-1, and TNF-α were measured at the Nantes-Atlantic Immunomonitoring Center (CIMNA), using a high-throughput continuous-flow fluorometric analyzer utilizing xMAP technology (Luminex MAGPIX), ensuring sensitive and multiplexed detection of inflammatory biomarkers under quality certification ISO15189. Covariates Plasma lipid levels were measured enzymatically (Biolabo, Maizy, France) and calculated as the sum of phospholipids, triglycerides, total cholesterol, and free cholesterol (16). Participants’ body mass index (BMI) was computed from height and weight reported at baseline and at blood collection, allowing estimation of annual BMI change (kg/m² per year) between inclusion in the cohort and blood collection; this variable was included as a covariate because POPs are stored in adipose tissue and may be released during weight loss (17). Information on birth cohort (≤ 1930, 1930–1935, 1935–1940, 1940–1945, > 1945) and age at blood draw (continuous, in years) was derived from participants’ dates of birth. Years of school education ( 14 years) and residential area (urban: commune or group of communes with > 2,000 inhabitants; rural) were collected via the baseline questionnaire in 1990 (18). Smoking status (never, former, current) was obtained from the most recent questionnaire preceding blood collection. Age at menopause was estimated from gynecological history to define menopausal status at blood collection (premenopausal vs. postmenopausal). Physical activity, expressed as metabolic equivalent task-hours per week, was assessed in the 1993 questionnaire. Dietary habits over the prior year were captured as well in 1993 using a validated semi-quantitative food frequency questionnaire (19), providing estimates of usual daily intake of main food groups (meat, fish, fruits and vegetables, starches, dairy, eggs) and alcohol (g/day). Data from the food questionnaire were also used to calculate the inflammatory capacity of the diet, a measure of the diet’s potential to promote or reduce systemic inflammation (20,21). Finally, the Programme National Nutrition Santé (PNNS) adequacy score, a composite dietary index reflecting adherence to French dietary recommendations, was calculated based on the same food frequency questionnaire data (22). Statistical analyses Of the 468 women included in the study, one woman for whom the PFAS could not be measured due to insufficient serum amount was excluded from the analyses. For the 45 POPs included in the study, non-quantified values were imputed using the lower limit of quantification divided by √2, following recommendations for distributions that are not highly skewed (23). POPs concentrations were expressed per volume of plasma or serum For inflammatory biomarkers, handling of non-quantified values depended on their proportion. Biomarkers with fewer than 25% of non-quantified values (i.e. CRP and MCP-1) were imputed using the machine-estimated value, i.e., the value extrapolated by the instrument despite being outside the quantification range. For biomarkers with more than 25% of non-quantified values (i.e. TNF-α and IL-8), no imputation was performed, instead, measurements were classified as quantified or non-quantified. The main characteristics of the study population were described by numbers and percentages for categorical variables, or median and interquartile range for continuous variables. Spearman rank correlation coefficients between levels of POPs and inflammatory biomarkers were estimated after imputation of non-quantified values. Separate logistic regression models were fitted for each POP as independent variable (exposure) and each inflammatory biomarker as independent variable (outcome), with both analyzed as binary variables. POPs were categorized as below versus above the median when more than 75% of values were quantified, otherwise as non-quantified versus quantified. To assess the impact of covariate adjustment on effect estimates, a series of progressively adjusted models was constructed. The initial model was only adjusted on age at blood draw. A second model was additionally adjusted for birth generation, years of school education, residential area, smoking status, physical activity, menopausal status, alcohol consumption, PNNS adequacy score, and inflammatory capacity of the diet. The final model was further adjusted for annual BMI change and plasma lipid level. Covariates of the fully adjusted model was selected a priori using a directed acyclic graph (DAG) (Supplementary Fig. 1). Principal component analysis (PCA) was applied to the 45 POPs using standardized values (i.e., values divided by their standard deviation). The number of principal components retained was determined based on the proportion of explained variance and the interpretability of the first ten principal components. To improve their interpretability, an orthogonal Varimax rotation was applied to the selected principal components. Logistic regression models were then fitted with the six retained components, treated as continuous exposure variables, and each inflammatory biomarker as a binary outcome (non-quantified vs. quantified or below vs. above the median, depending on the proportion of non-quantified values). Separate models were run for each outcome and adjusted for the same set of covariates presented previously. Covariates with less than 5% missing values were imputed using the median for continuous variables and the modal category for categorical variables, these were years of school education (3%), physical activity (0.6%), annual BMI change (1.5%). When more than 5% of values were missing, a “missing” category was created. This scenario applied only to residential area for which 7.5% of values were missing. A p-value strictly below 0.05 was considered statistically significant. The database was built using Statistical Analysis Systems software, version 9.4 (SAS Institute, Cary, NC, USA), and statistical analyses were conducted using R, version 4.1.2. Sensitivity analyses To assess potential residual confounding by diet, two supplementary models adjusted for dietary factors were tested. The first model was further adjusted for adherence to the PNNS nutritional recommendations (sPNNS-GS2 score), while the second model was further adjusted for consumption of major food groups (meat, fish, fruits and vegetables, starches, and dairy products). The main model was performed with false discovery rate (FDR) correction (Benjamini-Hochberg method) applied to account for multiple testing. Results A total of 467 women were included in this study. At blood draw participants were aged between 45 and 73 years, were mostly post-menopausal (76.7%) and had an average BMI of 23.9 kg/m 2 (Table 1 ). Table 1 Characteristics of the study population: 467 women in the French E3N cohort study. Age at blood draw (years) Mean (standard deviation) or Number (Percent) 56.9 (6.5) Physical activity (MET-hours/week) 47.1 (45.5) Missing 3 [0.6%] BMI variation (kg/m2/year) 0.5 (0.6) Missing 7 [1.5%] BMI (kg/m2) 23.9 (3.6) Lipid plasma level (g/L of plasma) 6.7 (1.1) Alcohol consumption (g of ethanol/day) 11.3 (13.8) Inflammatory capacity of the diet (adapted score by Woudenbergh using Shivappa weights) -0.4 (3.6) Adherence to the French dietary recommendations (sPNNS-GS2 score) 4.0 (2.9) Meat consumption (g/day) 149.8 (69.5) Fish consumption (g/day) 40.3 (27.5) Fruits and vegetables consumption (g/day) 747.3 (279.3) Dairies consumption (g/day) 367.1 (204.4) Starches consumption (g/day) 293.4 (127.0) Eggs consumption (g/day) 25.7 (19.2) Birth generation 1945 132 [28.3%] Years of school education 14 years 175 [37.5%] Missing 14 [3.0%] Residential area Urban 391 [83.7%] Rural 41 [8.8%] Missing 35 [7.5%] Smoking habits Current 51 [10.9%] Former 169 [36.2%] Never 247 [52.9%] Menopausal status Premenopaused 109 [23.3%] Postmenopaused 358 [76.7%] Concentrations of POPs and inflammatory biomarkers, along with the number and proportion of non-quantified values, are summarized in Supplementary Table 1. A heat map displaying Spearman rank correlation coefficients among POPs is provided in Supplementary Fig. 2, with coefficients ranging from − 0.14 to 0.95 (median: 0.23). Additional details on the distribution of POPs biomarker levels have been reported previously (Frenoy et al., 2024). The mean (SD) for CRP, IL-8, MCP-1, and TNF-α was 59.0 µg/mL (130.2), 1.4 pg/mL (9.0), 174.3 pg/mL (65.8), and 4.4 pg/mL (4.9), respectively. For inflammatory biomarkers, correlation coefficients ranged from 0.08 (between CRP and IL-8) to 0.48 (between IL-8 and TNF-α) (Supplementary Fig. 3). Single-pollutant regression CRP Results from the single-pollutant adjusted logistic regression models overall indicate a positive association between OCPs and CRP levels. These associations reached statistical significance for o,p′-DDE (OR 1.52 [95%CI 1.05–2.21], β-HCH (2.14 [1.44–3.20]), trans-nonachlor (1.50 [1.03–2.19]), cis-heptachlor epoxide (2.29 [1.56–3.36]), dieldrin (1.96 [1.35–2.87]), and HCB (1.83 [1.25–2.70]). No consistent pattern was observed between CRP levels and other POP families included in the study, except for PCB-157 (0.67 (0.46–0.98)), which showed a statistically significant inverse association with CRP levels (Table 2 ). These findings were largely consistent across adjusted models (data not shown). Table 2 Logistic regression models estimating adjusted associations between 45 POPs biomarkers and four biomarkers of systemic inflammation (CRP, IL-8, MCP-1, and TNFα). Each POP biomarker is fitted in a separate model, adjusted for age at blood draw, birth generation, years of school education, residential area, smoking status, physical activity, variation of BMI, alcohol consumption, inflammatory capacity of the diet, menopausal status, and lipid plasma level. POPs biomarkers CRP OR [95% CI] CRP p IL-8 OR [95% CI] IL-8 p MCP-1 OR [95% CI] MCP-1 p TNF-α OR [95% CI] TNF-α p p,p’-DDT, Quantified (ref: Non-quantified) 1.05 [0.72; 1.55] 0.79 0.91 [0.61; 1.34] 0.63 1.28 [0.87; 1.89] 0.22 0.92 [0.62; 1.38] 0.70 o,p'-DDT, Quantified (ref: Non-quantified) 0.75 [0.49; 1.14] 0.19 0.62 [0.40; 0.95] 0.03 0.85 [0.55; 1.30] 0.45 0.91 [0.58; 1.40] 0.67 p,p’-DDE, >Median (ref: Median (ref: Median (ref: <=Median) 1.01 [0.69; 1.47] 0.96 0.89 [0.60; 1.30] 0.54 1.00 [0.68; 1.46] 1.00 0.86 [0.58; 1.27] 0.45 γ-HCH, Quantified (ref : Non-quantified) 1.17 [0.79; 1.73] 0.42 0.89 [0.60; 1.33] 0.58 1.04 [0.70; 1.54] 0.84 0.95 [0.63; 1.42] 0.79 β-HCH, >Median (ref: <=Median) 2.14 [1.44; 3.20] Median (ref: Median (ref: Median (ref: Median (ref: <=Median) 2.29 [1.56; 3.36] Median (ref: <=Median) 1.96 [1.35; 2.87] < 0.01 0.85 [0.58; 1.25] 0.41 1.16 [0.79; 1.69] 0.44 1.41 [0.95; 2.08] 0.09 mirex, Quantified (ref: Non-quantified) 1.20 [0.79; 1.81] 0.39 0.68 [0.44; 1.03] 0.07 0.90 [0.59; 1.37] 0.63 0.77 [0.49; 1.18] 0.23 HCB, >Median (ref: <=Median) 1.83 [1.25; 2.70] Median (ref: Median (ref: <=Median) 0.92 [0.63; 1.33] 0.65 0.59 [0.40; 0.87] Median (ref: <=Median) 1.03 [0.71; 1.50] 0.88 0.54 [0.36; 0.79] Median (ref: <=Median) 0.86 [0.59; 1.26] 0.44 0.58 [0.39; 0.86] Median (ref: Median (ref: <=Median) 1.07 [0.74; 1.55] 0.72 1.17 [0.80; 1.71] 0.41 1.81 [1.24; 2.64] Median (ref: <=Median) 0.71 [0.49; 1.05] 0.08 0.47 [0.31; 0.70] Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: Median (ref: <=Median) 0.69 [0.48; 1.00] 0.05 0.72 [0.49; 1.05] 0.09 0.74 [0.51; 1.08] 0.12 1.03 [0.70; 1.51] 0.88 IL-8 Overall, PFAS showed an inverse relationship with IL-8 levels. Indeed, results from the single-pollutant adjusted logistic regression models highlighted a statistically significant inverse association for PFUnDA (0.59 [0.40–0.87]), PFDA (0.54 [0.36–0.79]), PFNA (0.58 [0.39–0.86]), PFOS (0.47 [0.31–0.70]), PFHpS (0.64 [0.42–0.96]), and PFHxS (0.62 [0.42–0.92]). Statistically significant inverse associations were also observed between o.p’-DDT (0.62 [0.40–0.95]), cis-nanochlor (0.61 [0.42–0.90]), PCB-114 (0.62 [0.41–0.93]), and PCB-118 (0.60 [0.40–0.89]) (Table 2 ). Also, associations between POP levels and IL-8 appeared overall consistent across adjusted models (data not shown). MCP-1 The results of the main single-pollutant analyses did not reveal any meaningful association between POP levels and MCP-1 blood concentrations in any of the tested models. The only statistically significant associations were observed for HCB (0.62 [0.42–0.92]) and PFHpA (1.81 [1.24–2.64]) (Table 2 ). TNF-α Similarly, no relevant association between individual POP levels and TNF-α concentrations was observed except for inverse associations for PFNA (0.64 [0.43–0.96]) and PFHxS (0.66 [0.44–0.98]) (Table 2 ). Principal component regression From PCA six principal components were retained, explaining 60% of the total variance. Loading factors for POPs in the 6 retained principal components are presented in Supplementary Table 2. Component 1 – Non-DDT OCPs and dioxin-like PCBs The first component accounted for 14.5% of the variance and was characterized by high positive loadings (> 0.50) for several organochlorine pesticides (β-HCH, oxychlordane, trans-nonachlor, cis-nonachlor, cis-heptachlor epoxide, dieldrin, and HCB), as well as dioxin-like PCBs (PCB-105, PCB-114, PCB-118, PCB-123, and PCB-167). Component 2 – PCBs (dioxin-like and non-dioxin-like) The second component explained 14.3% of the total variance and showed high positive loadings for PCBs, including both dioxin-like (PCB-138, PCB-153, PCB-180) and non-dioxin-like congeners (PCB-114, PCB-156, PCB-157, PCB-167, and PCB-189). Component 3 – PFAS The third component accounted for 10.3% of the variance and was mainly characterized by high positive loadings for most PFAS, including PFUnDA, PFDA, PFNA, PFOA, total PFOS, PFHpS, and PFPeS. Component 4 – PBDEs Very high loadings for the four PBDEs included in this study were observed for the fourth component, which alone explained 7.3% of the total variance. Component 5 – Mixed POP profile The fifth component accounted for 6.8% of the variance and displayed a more heterogeneous loading pattern, with no clear predominance of a single POP family, but rather moderate to high loadings for individual compounds such as γ-HCH, PCB-52, and PCB-101. Component 6 – DDT metabolites The sixth and last retained component accounted for 6.8% of the total variance and was characterized by high positive loadings for DDT metabolites (p,p′-DDT, o,p′-DDT, p,p′-DDE, o,p′-DDE, and p,p′-DDD). When adjusted logistic regression models were fitted including the 6 retained principal components as exposures variables, the results largely were in line with those observed in the mono-pollutant analyses. Indeed, Component 1 – Non-DDT OCP and dioxin-like PCB was positively associated with CRP blood concentration levels (1.71 [1.35–2.18]), whereas Component 3 – PFAS presented an inverse association with IL-8 levels (0.75 [0.61–0.91]). No statistically significant associations were observed between the remaining components and any of the inflammatory biomarkers investigated in this study (Table 3 ). Table 3 Logistic regression model estimating adjusted associations between 6 retained principal components and four biomarkers of systemic inflammation (CRP, IL-8, MCP-1, and TNFα). The model is adjusted for age at blood draw, birth generation, years of school education, residential area, smoking status, physical activity, variation of BMI, alcohol consumption, inflammatory capacity of the diet, menopausal status, and lipid plasma level. Principal components CRP OR [95% CI] CRP p IL-8 OR [95% CI] IL-8 p MCP-1 OR [95% CI] MCP-1 p TNF-α OR [95% CI] TNF-α p Non-DDT OCPs and DL-PCBs 1.71 [1.35; 2.18] < 0.01 0.97 [0.78; 1.20] 0.76 0.89 [0.73; 1.10] 0.29 1.20 [0.97; 1.48] 0.09 PCBs (DL and NDL) 0.90 [0.74; 1.09] 0.29 0.87 [0.72; 1.06] 0.16 0.98 [0.81; 1.18] 0.79 1.10 [0.91; 1.34] 0.33 PFASs 0.91 [0.74; 1.10] 0.31 0.75 [0.61; 0.91] < 0.01 0.95 [0.78; 1.15] 0.59 0.83 [0.67; 1.02] 0.08 PBDEs 0.99 [0.80; 1.19] 0.89 0.83 [0.65; 1.02] 0.10 0.83 [0.63; 1.03] 0.13 0.94 [0.72; 1.14] 0.56 Mixed POP profile 0.96 [0.78; 1.16] 0.65 0.94 [0.77; 1.14] 0.50 0.95 [0.78; 1.14] 0.57 0.97 [0.79; 1.17] 0.73 DDT metabolites 1.01 [0.82; 1.22] 0.94 0.99 [0.82; 1.20] 0.90 1.03 [0.85; 1.25] 0.73 0.99 [0.79; 1.20] 0.91 Sensitivity analyses When further adjusting the main model for adherence to the PNNS nutritional recommendations (sPNNS-GS2 score) or for consumption of major food groups (meat, fish, fruits and vegetables, starches, and dairy products), the results of the mono-pollutant regressions models were virtually unchanged (data not shown). When performing the same sensitivity analyses for PCA, results remained unchanged adjusting for sPNNS-GS2, while a statistically significant inverse association was observed between Component 2 – PCBs (dioxin-like and non-dioxin-like) and IL-8 when additionally adjusting the model for consumption of major food groups (0.78 [0.62–0.99]). After applying FDR correction for multiple testing, the associations between cis-heptachlor-epoxide and CRP (2.29 [1.56–3.36], p = 0.01) and between PFOS and IL-8 (0.47 [0.31–0.70], p = 0.01) remained statistically significant. No other association reached statistical significance (Supplementary table 3). Discussion The results of the present study are generally consistent with previous evidence suggesting that inflammation may represent a key mechanism through which exposure to POPs affects human health. A positive association was observed between several OCPs and CRP and confirmed by the principal component regression analysis. After correcting for multiple testing, the positive association persisted for cis-heptachlor-epoxide suggesting that the association between this pesticide and CRP is the most robust. The first identified component was largely characterized by non-DDT OCP and dioxin-like PCBs. These two groups of substances share the main sources of exposure, being largely present in fatty food of animal origin, as well as their ability to bioaccumulate in fat tissue, and their capacity of activate the AhR (24). Consequently, co-exposure is expected, and it is plausible that these compounds may exert an additive effect through AhR activation. AhR signaling can subsequently lead to upregulation of CRP indirectly via cytokine-mediated hepatic pathways, providing a mechanistic explanation for the observed associations (11). Previous studies have observed similar results. In particular, an analysis of 2,847 participants from NHANES observed a positive correlation between OCP biomarkers and CRP levels (25). Similarly, a cross-sectional study conducted among 748 non-diabetic adults highlighted a positive association between OCP blood levels and CRP concentrations. Interestingly, in the same study an inverse association was observed between PCBs and CRP (26). A prospective study of 453 adult women reported that serum β-HCH levels were positively associated with hs-CRP concentrations (27). However, some studies have reported divergent results. For example, a cross-sectional study including 774 elderly men and women from Sweden found no association between OCP concentrations and CRP levels (28). A meta-analysis focusing more widely on the association between exposure to endocrine disrupting chemicals and inflammatory biomarkers concluded that OCP exposure was positively associated with CRP, as well as to IL-1β, IL-2, and IL-10, whereas no association was observed for PCBs (29). More recently, meta-analysis including 15 studies, concluded that pesticide exposure can induce an inflammatory response and that CRP represents a reliable biomarker to investigate this association (30). Moreover, there is compelling evidence that chronic exposure to OCPs increases the risk of cardiometabolic diseases (31,32). It is therefore highly plausible that chronic exposure to AhR ligands, such as OCPs, sustains low-grade inflammatory signaling, contributing to prolonged elevation of CRP levels, which in turn is associated with cardiometabolic diseases and other chronic diseases. The present study also identified an inverse association between several PFAS and IL-8 concentrations. This inverse relationship persisted when PFAS were considered jointly as contributors to the same component identified through PCA. PFOS was the only PFAS for which the association with IL-8 remained statistically significant after FDR correction, suggesting that the overall inverse association may be largely driven by this specific congener. Although the relationship between PFAS exposure and inflammation remains incompletely characterized, growing evidence suggests that PFAS may exert immunomodulatory effects rather than uniformly promoting pro-inflammatory responses (33). IL-8 is a key chemokine involved in innate immune responses, primarily regulating neutrophil recruitment and activation during acute inflammation. Reduced IL-8 levels may reflect altered neutrophil signaling or broader dysregulation of chemokine-mediated immune pathways (34). Previous epidemiological studies investigating the relationship between PFAS exposure and cytokine levels, including IL‑8, have mainly reported inverse or null associations. For instance, a cross-sectional study of 212 non-smoking adults observed that higher concentrations of multiple PFAS were associated with lower IL‑1β levels and, to a lesser extent, with reductions in other cytokines (35). Similarly, a study of 198 Chinese women of childbearing age observed divergent effects of PFAS mixtures on several cytokine levels, while specifically reporting an overall null effect of PFAS mixtures on IL-8 levels (36). A recent literature review summarizing evidence on effects of PFASs on innate immunity in humans, experimental models, and wildlife, highlighted predominantly inverse associations between several PFAS and IL-8 levels from studies in humans, primary human leukocytes, and human cell lines (12). The same review concluded that although results on the impact of PFAS on the innate immunity are still equivocal, these findings collectively indicate that PFAS can perturb the innate immune system (12). It is therefore plausible that PFAS-induced dysregulation of the innate immune system may contribute to the associations reported in epidemiological studies between PFAS exposure and increased susceptibility to infections, altered inflammatory responses, and potentially elevated risk of cardiometabolic and immune-related diseases. Several limitations should be considered when interpreting the results of the present study. The main limitation is the cross-sectional design, which does not allow distinction between short- and long-term effects. Nevertheless, the study design is unlikely to introduce reverse causation bias, as it is improbable that inflammatory biomarker levels would substantially influence POPs exposure. Only a single exposure measurement was available, preventing characterization of exposure trajectories. However, given the long environmental and biological half-lives of POPs, individual exposure levels are expected to change slowly over time. Another limitation is the relatively small sample size, primarily due to budget constraints, which reduced the statistical power of the study and may have limited the ability to detect modest associations. Moreover, single-pollutant models are subject to confounding due to co-exposure to other pollutants; therefore, their results must always be interpreted with caution. In addition, inflammatory biomarker levels can be influenced by numerous factors. Although several potential confounders were adjusted for in the analyses, residual confounding cannot be excluded. Another limitation is that the 4-plex panel captures only a limited portion of the inflammatory cascade and does not include several key cytokines, such as IL-1β and IL-10, which may play important roles in the underlying biological mechanisms. Finally, the E3N cohort includes only women and is not fully representative of the general French population, which may limit the generalizability of the findings. Among the main strengths of this study are the comprehensive measurement of a large number of POPs biomarkers, which allowed identification of the main exposure profiles in the study population of adult women residing in France. Additionally, the availability of multiple inflammatory biomarkers provided complementary information on the potential effects of POPs on the immune and inflammatory systems. Controlling for multiple testing, achieved by applying FDR correction, reduces the likelihood of false-positive findings and increases the likelihood that the observed associations are true. Finally, the extensive data collected in the E3N cohort enabled adjustment for numerous potential confounders and the conduct of sensitivity analyses, strengthening the robustness of the findings. In conclusion, the findings of the present study help clarify the complex relationship between exposure to multiple POPs and the inflammatory response. Further research is warranted to better characterize this association, ideally incorporating repeated exposure measurements and a broader panel of inflammatory biomarkers. Declarations Clinical trial number : not applicable. Ethics approval The E3N Generations study was approved by the French National Commission for Data Protection and Privacy (ClinicalTrials.gov identifier: NCT03285230). The E3N Generations blood collection study was approved by the Bicêtre ethics committee (CPP-IDF-VII, IRB # IORG0001140). Informed consent All participants included in the E3N Generations study gave written informed consent, and informed consent was obtained for novel biomarkers testing. Availability of data and materials The data used in this study are not publicly available. Author contributions Francesca-Romana Mancini: Conceptualization, Methodology, Writing - Original Draft, Supervision, Project administration, Funding acquisition. Pauline Frénoy: Conceptualization, Methodology, Formal analysis, Writing - Review & Editing. German Cano-Sancho: Conceptualization, Methodology, Investigation, Resources, Writing - Review & Editing. Chloé Marques: Writing - Review & Editing. Xuan Ren: Writing-Review & Editing. Claire Perrrin: Writing - Review & Editing. Vittorio Perduca: Writing - Review & Editing. Philippe Marchand: Investigation, Resources. Bruno Le Bizec: Investigation, Resources. Jean-Philippe Antignac: Conceptualization, Methodology, Investigation, Resources, Writing - Review & Editing. Gianluca Severi : Conceptualization, Methodology, Writing - Review & Editing. Competing interests The authors declare that they have no competing interests that could have appeared to influence the work reported in this paper. Funding This work was realised with the data of the E3N Generations cohort of the Inserm and supported by the Mutuelle Générale de l’Education Nationale (MGEN), the Gustave Roussy Institute, and the French League against Cancer for the constitution and maintenance of the cohort. The cohort has benefited from state funding managed by the French National Research Agency (ANR) under the programs “Plan Investissement d’Avenir” and “France2030” (ANR-10-COHO-0006 and ANR-21-ESRE-0022) as well as from an annual subsidy from the Ministry of Higher Education, Research and Innovation for public service charges. This work also has benefited from a grant from the Fondation de France bearing the reference n°00110200. Acknowledgements The authors would like to acknowledge all participants enrolled in the E3N Generations cohort for their continued participation. They are also grateful to all members of the E3N Generations study group. The authors also thank the HBM platform of LABERCA, part of the France-Exposome and EIRENE research infrastructures, for analytical support. We also thank the Centre de Ressources Biologiques of the Fondation Jean Dausset–CEPH for their technical support in the identification, extraction, and use of biological samples from the E3N-Generation cohort, which are stored in their facilities. References Ashraf MA. Persistent organic pollutants (POPs): a global issue, a global challenge. Environ Sci Pollut Res. 1 févr 2017;24(5):4223‑7. Listing of POPs in the Stockholm Convention [Internet]. [cité 4 févr 2026]. Disponible sur: https://www.pops.int/TheConvention/ThePOPs/AllPOPs/tabid/2509/Default.aspx Guo W, Pan B, Sakkiah S, Yavas G, Ge W, Zou W, et al. Persistent Organic Pollutants in Food: Contamination Sources, Health Effects and Detection Methods. Int J Environ Res Public Health. nov 2019;16(22):4361. Sunderland EM, Hu XC, Dassuncao C, Tokranov AK, Wagner CC, Allen JG. A Review of the Pathways of Human Exposure to Poly- and Perfluoroalkyl Substances (PFASs) and Present Understanding of Health Effects. J Expo Sci Environ Epidemiol. mars 2019;29(2):131‑47. Wang Z, Zhou Y, Xiao X, Liu A, Wang S, Preston RJS, et al. Inflammation and cardiometabolic diseases induced by persistent organic pollutants and nutritional interventions: Effects of multi-organ interactions. Environ Pollut. 15 déc 2023;339:122756. Kharrazian D. Exposure to Environmental Toxins and Autoimmune Conditions. Integr Med Clin J. avr 2021;20(2):20‑4. Ennour-Idrissi K, Ayotte P, Diorio C. Persistent Organic Pollutants and Breast Cancer: A Systematic Review and Critical Appraisal of the Literature. Cancers. 27 juill 2019;11(8):1063. Guillotin S, Delcourt N. Studying the Impact of Persistent Organic Pollutants Exposure on Human Health by Proteomic Analysis: A Systematic Review. Int J Mol Sci. 17 nov 2022;23(22):14271. Medzhitov R. Origin and physiological roles of inflammation. Nature. juill 2008;454(7203):428‑35. Furman D, Campisi J, Verdin E, Carrera-Bastos P, Targ S, Franceschi C, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med. déc 2019;25(12):1822‑32. Peinado FM, Artacho-Cordón F, Barrios-Rodríguez R, Arrebola JP. Influence of polychlorinated biphenyls and organochlorine pesticides on the inflammatory milieu. A systematic review of in vitro , in vivo and epidemiological studies. Environ Res. 1 juill 2020;186:109561. Phelps DW, Connors AM, Ferrero G, DeWitt JC, Yoder JA. Per- and polyfluoroalkyl substances alter innate immune function: evidence and data gaps. J Immunotoxicol. déc 2024;21(1):2343362. Clavel-Chapelon F, van Liere MJ, Giubout C, Niravong MY, Goulard H, Le Corre C, et al. E3N, a French cohort study on cancer risk factors. E3N Group. Etude Epidémiologique auprès de femmes de l’Education Nationale. Eur J Cancer Prev Off J Eur Cancer Prev Organ. oct 1997;6(5):473‑8. Clavel-Chapelon F, E3N Study Group. Cohort Profile: The French E3N Cohort Study. Int J Epidemiol. juin 2015;44(3):801‑9. Frénoy P, Cano-Sancho G, Antignac JP, Marchand P, Le Bizec B, Marques C, et al. Associations between blood levels of persistent organic pollutants and oxidative stress biomarkers among women in France in the 90’s. Environ Res. 1 mai 2025;272:121185. Akins JR, Waldrep K, Bernert JT. The estimation of total serum lipids by a completely enzymatic « summation » method. Clin Chim Acta Int J Clin Chem. 16 oct 1989;184(3):219‑26. Jansen A, Lyche JL, Polder A, Aaseth J, Skaug MA. Increased blood levels of persistent organic pollutants (POP) in obese individuals after weight loss-A review. J Toxicol Environ Health B Crit Rev. 2017;20(1):22‑37. Binachon B, Dossus L, Danjou AMN, Clavel-Chapelon F, Fervers B. Life in urban areas and breast cancer risk in the French E3N cohort. Eur J Epidemiol. 2014;29(10):743‑51. van Liere MJ, Lucas F, Clavel F, Slimani N, Villeminot S. Relative validity and reproducibility of a French dietary history questionnaire. Int J Epidemiol. 1997;26 Suppl 1:S128-136. Laouali N, Mancini FR, Hajji-Louati M, El Fatouhi D, Balkau B, Boutron-Ruault MC, et al. Dietary inflammatory index and type 2 diabetes risk in a prospective cohort of 70,991 women followed for 20 years: the mediating role of BMI. Diabetologia. déc 2019;62(12):2222‑32. Hajji-Louati M, Gelot A, Frenoy P, Laouali N, Guénel P, Romana Mancini F. Dietary Inflammatory Index and risk of breast cancer: evidence from a prospective cohort of 67,879 women followed for 20 years in France. Eur J Nutr. août 2023;62(5):1977‑89. Marques C, Frenoy P, Laouali N, Shah S, Severi G, Mancini FR. Adherence to French dietary guidelines is associated with a reduced risk of mortality in the E3N French prospective cohort. Nutr J. 15 mars 2025;24:43. Hornung RW, Reed LD. Estimation of Average Concentration in the Presence of Nondetectable Values. Appl Occup Environ Hyg. 1 janv 1990;5(1):46‑51. AOP-Wiki [Internet]. [cité 4 févr 2026]. Disponible sur: https://aopwiki.org/aops/131 Tan J, Ma M, Shen X, Xia Y, Qin W. Potential lethality of organochlorine pesticides: Inducing fatality through inflammatory responses in the organism. Ecotoxicol Environ Saf. 1 juill 2024;279:116508. Interaction Between Persistent Organic Pollutants and C-reactive Protein in Estimating Insulin Resistance Among Non-diabetic Adults [Internet]. [cité 4 févr 2026]. Disponible sur: https://jpmph.org/journal/view.php?doi=10.3961/jpmph.2012.45.2.62 Warner M, Rauch S, Eskenazi B, Calderon L, Gunier RB, Kogut K, et al. Persistent organochlorine pesticides and cardiometabolic outcomes among middle-aged Latina women in a California agricultural community: The CHAMACOS Maternal Cognition Study. Environ Int. 1 févr 2025;196:109302. Kumar J, Lind PM, Salihovic S, van Bavel B, Ingelsson E, Lind L. Persistent Organic Pollutants and Inflammatory Markers in a Cross-Sectional Study of Elderly Swedish People: The PIVUS Cohort. Environ Health Perspect. sept 2014;122(9):977‑83. Liu Z, Lu Y, Zhong K, Wang C, Xu X. The associations between endocrine disrupting chemicals and markers of inflammation and immune responses: A systematic review and meta-analysis. Ecotoxicol Environ Saf. 1 avr 2022;234:113382. Fierro-Barrientos GN, Casarrubias-González E, Moreno-Godínez ME, Flores-Alfaro E, Atrisco-Morales J, Cisneros-Pano J, et al. Effect of pesticide exposure on systemic inflammatory biomarkers: a meta-analysis, and trial sequential analysis. J Environ Health Sci Eng. déc 2025;23(2):24. Lamat H, Sauvant-Rochat MP, Tauveron I, Bagheri R, Ugbolue UC, Maqdasi S, et al. Metabolic syndrome and pesticides: A systematic review and meta-analysis. Environ Pollut. 15 juill 2022;305:119288. Hydoub YM, Loor-Torres R, Qadeer A, Farhan K, Swaid TK, Yeganeh HST, et al. Organic Pollutants and Risk of Type 2 Diabetes: A Systematic Review and Meta-analysis. Mayo Clin Proc Innov Qual Outcomes. févr 2026;10(1):100677. Arnesdotter E, Stoffels CBA, Alker W, Gutleb AC, Serchi T. Per- and polyfluoroalkyl substances (PFAS): immunotoxicity at the primary sites of exposure. Crit Rev Toxicol. 2025;55(4):484‑504. Matsushima K, Yang D, Oppenheim JJ. Interleukin-8: An evolving chemokine. Cytokine. mai 2022;153:155828. Barton KE, Zell-Baran LM, DeWitt JC, Brindley S, McDonough CA, Higgins CP, et al. Cross-sectional associations between serum PFASs and inflammatory biomarkers in a population exposed to AFFF-contaminated drinking water. Int J Hyg Environ Health. mars 2022;240:113905. Nian M, Zhou W, Feng Y, Wang Y, Chen Q, Zhang J. Emerging and legacy PFAS and cytokine homeostasis in women of childbearing age. Sci Rep. 20 avr 2022;12(1):6517. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMancinietal.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 May, 2026 Reviewers invited by journal 17 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Submission checks completed at journal 16 Apr, 2026 First submitted to journal 15 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9427652","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626253244,"identity":"d154262a-c14a-42e6-a212-a058c92b76fe","order_by":0,"name":"Francesca Romana 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07:56:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":579285,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMancinietal.docx","url":"https://assets-eu.researchsquare.com/files/rs-9427652/v1/44467d8c0e0f8d44ccd02dc4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Blood levels of Persistent Organic Pollutants and circulating biomarkers of systemic inflammation in French women: Evidence from the E3N-Generations cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePersistent organic pollutants (POPs) are a heterogeneous group of compounds that share several key characteristics, including persistence in the environment and in the human body, long-range environmental transport, toxicity to humans and ecosystems, and the ability to bioaccumulate and biomagnify along the food chain. POPs have been widely used in agricultural and industrial applications, leading to their global dispersion in the environment (1). This group of contaminants includes organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and per- and polyfluoroalkyl substances (PFASs).\u003c/p\u003e \u003cp\u003eFollowing their inclusion in the Stockholm Convention in 2001, the production, use, and environmental release of POPs have been progressively restricted (2). Nevertheless, owing to their persistence, human populations worldwide remain substantially exposed. Most POPs, such as OCPs, PCBs, and PBDEs, are lipophilic and tend to accumulate in adipose tissue, whereas PFASs preferentially bind to proteins. Consequently, POPs bioaccumulate along the food chain, and for the general population, diet, in particular consumption of foods of animal origin, represents the main source of exposure (3,4).\u003c/p\u003e \u003cp\u003eGrowing evidence links exposure to POPs with a range of non-communicable diseases, including cardiometabolic disorders, cancers, and auto-immune diseases (5\u0026ndash;7). Among the biological mechanisms implicated in POP-related toxicity, disruption of immune and inflammatory processes is suspected to play a key role (8).\u003c/p\u003e \u003cp\u003eAlthough inflammation plays a crucial role in host defence, excessive or dysregulated inflammatory responses can lead to tissue damage and contribute to disease pathogenesis (9). Chronic low-grade inflammation is involved in the early stages of many disease processes, and elevated levels of inflammatory markers have been associated with an increased risk of developing multiple disorders. Various toxic compounds may induce abnormal inflammatory responses, either by directly activating immune pathways or by disrupting normal cellular homeostasis (10). More in details, there is evidence suggesting that PCBs and OCPs may promote a pro‑inflammatory milieu by altering cytokine expression, skewing macrophage polarization toward pro‑inflammatory phenotypes, and activating transcriptional regulators such as NF‑κB and the aryl hydrocarbon receptor (AhR) (11). Similarly, PFAS exposures are thought to modulate chronic inflammatory responses through activation of innate immune sensors and inflammasomes (e.g., AIM2, NLRP3), regulation of NF‑κB and PPAR signaling, and induction of pro‑inflammatory cytokines (12). PBDEs likely contribute to inflammation by generating oxidative stress and reactive oxygen species and increase pro‑inflammatory cytokine production (12). Despite such evidence suggesting an effect of POPs on the disruption of immune and inflammatory processes, results from observational studies in populations chronically exposed to background levels of POPs are too limited and inconsistent to allow firm conclusions. To address this, we focused on four complementary markers, namely C-reactive protein (CRP), interleukin-8 (IL-8), tumor necrosis factor-alpha (TNF-α), and monocyte chemoattractant protein-1 (MCP-1), capturing distinct aspects of the inflammatory cascade. CRP, an acute-phase protein, reflects systemic inflammation and is linked to cardiovascular and metabolic disease risk. IL-8, a chemokine produced by multiple cell types, mediates neutrophil recruitment and activation and plays a key role in innate immunity. TNF-α orchestrates early immune activation and contributes to both acute and chronic inflammation. MCP-1 regulates monocyte and macrophage trafficking, driving chronic inflammatory processes and atherosclerosis. Together, these markers provide complementary insight into systemic inflammation and immune pathways potentially affected by POPs exposure. The aim of this study was thus to explore the relationship between POPs blood levels, including OCPs, PCBs, PBDEs and PFASs, individually and as profiles of exposure to multiple POPs, and the four circulating biomarkers of systemic inflammation in a cross-sectional study based on a random sample of women from the French E3N-Generations cohort study.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eE3N-Generations is a family-based, multigenerational prospective cohort derived from the original E3N study (\u0026Eacute;tude \u0026Eacute;pid\u0026eacute;miologique aupr\u0026egrave;s de femmes de l\u0026rsquo;\u0026Eacute;ducation Nationale). The E3N study was launched in 1990 and enrolled 98,995 women aged 40\u0026ndash;65 years who were members of a health insurance plan mainly covering workers in the French national education system (Mutuelle G\u0026eacute;n\u0026eacute;rale de l\u0026rsquo;\u0026Eacute;ducation Nationale). The study was later expanded to include relatives of these women, resulting in a three-generation cohort comprising the original women and the fathers of their children (E3N-G1), their children (E3N-G2), and their grandchildren (E3N-G3) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.e3n-generations.fr/\u003c/span\u003e\u003cspan address=\"https://www.e3n-generations.fr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe methodology of the original E3N study, corresponding to the female participants of E3N-G1, has been reported elsewhere (13,14). In summary, participants were followed through self-completed questionnaires sent by mail every two to three years, collecting detailed information on lifestyle characteristics, reproductive factors, medical history, and overall health. From 1994 to 1999, a subset of participants provided blood samples, leading to the establishment of a biobank including samples from approximately 25,000 women. Blood specimens were fractionated into 28 components, including plasma, serum, buffy coat, leukocytes, and erythrocytes, and preserved in plastic straws stored in liquid nitrogen at \u0026minus;\u0026thinsp;196\u0026deg;C. The present analysis is restricted to women belonging to the first generation of the E3N-Generations cohort.\u003c/p\u003e \u003cp\u003eA random subset of 468 women was drawn from participants with adequate remaining biological samples in the biobank for laboratory analyses (specifically, five plasma straws and two serum straws), who had completed the 1993 dietary questionnaire and were not enrolled in any other ongoing cohort studies requiring the use of plasma or serum samples.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement of POPs biomarkers\u003c/h3\u003e\n\u003cp\u003eBlood levels of 74 POPs were measured in serum (for PFAS) or plasma (for other POPs) by the LABERCA laboratory using accredited methods based on liquid and gas chromatography coupled to mass-spectrometry (ISO 17025), as described in our previous study (15). Biomarkers with more than 75% of non-quantified values, as well as linear PFOS-, were not included in further statistical analyses. After these exclusions, 45 POPs biomarkers with at least 25% of quantified values were retained for this study, namely: 12 PFASs (PFUnDA, PFDA, PFNA, PFHxS, PFHpS, PFOA, PFHpA, PFPeS, PFOSA, PFOS-total, N-MeFOSAA, N-EtFOSAA); 15 OCPs (PeCBz, HCB, mirex, γ-HCH, β-HCH, oxychlordane, cis-heptachlor-epoxide, o,p\u0026rsquo;-DDT, o,p\u0026rsquo;-DDE, trans-nonachlor, p,p\u0026rsquo;-DDE, dieldrin, cis-nonachlor, p,p\u0026rsquo;-DDD, p,p\u0026rsquo;-DDT); 14 PCBs (6 non-dioxin-like congeners : PCB-28, PCB-52, PCB-101, PCB-138, PCB-153, PCB-180, and 8 dioxin-like congeners: PCB-105, PCB-114, PCB-118, PCB-123, PCB-156, PCB-157, PCB-167, PCB-189); 4 PBDEs (PBDE-47, PBDE-99, PBDE-100, PBDE-153).\u003c/p\u003e\n\u003ch3\u003eMeasurement of inflammatory biomarkers\u003c/h3\u003e\n\u003cp\u003ePlasma concentrations of CRP, IL-8, MCP-1, and TNF-α were measured at the Nantes-Atlantic Immunomonitoring Center (CIMNA), using a high-throughput continuous-flow fluorometric analyzer utilizing xMAP technology (Luminex MAGPIX), ensuring sensitive and multiplexed detection of inflammatory biomarkers under quality certification ISO15189.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003ePlasma lipid levels were measured enzymatically (Biolabo, Maizy, France) and calculated as the sum of phospholipids, triglycerides, total cholesterol, and free cholesterol (16). Participants\u0026rsquo; body mass index (BMI) was computed from height and weight reported at baseline and at blood collection, allowing estimation of annual BMI change (kg/m\u0026sup2; per year) between inclusion in the cohort and blood collection; this variable was included as a covariate because POPs are stored in adipose tissue and may be released during weight loss (17). Information on birth cohort (\u0026le;\u0026thinsp;1930, 1930\u0026ndash;1935, 1935\u0026ndash;1940, 1940\u0026ndash;1945, \u0026gt;\u0026thinsp;1945) and age at blood draw (continuous, in years) was derived from participants\u0026rsquo; dates of birth. Years of school education (\u0026lt;\u0026thinsp;12, 12\u0026ndash;14, \u0026gt;\u0026thinsp;14 years) and residential area (urban: commune or group of communes with \u0026gt;\u0026thinsp;2,000 inhabitants; rural) were collected via the baseline questionnaire in 1990 (18). Smoking status (never, former, current) was obtained from the most recent questionnaire preceding blood collection. Age at menopause was estimated from gynecological history to define menopausal status at blood collection (premenopausal vs. postmenopausal). Physical activity, expressed as metabolic equivalent task-hours per week, was assessed in the 1993 questionnaire. Dietary habits over the prior year were captured as well in 1993 using a validated semi-quantitative food frequency questionnaire (19), providing estimates of usual daily intake of main food groups (meat, fish, fruits and vegetables, starches, dairy, eggs) and alcohol (g/day). Data from the food questionnaire were also used to calculate the inflammatory capacity of the diet, a measure of the diet\u0026rsquo;s potential to promote or reduce systemic inflammation (20,21). Finally, the Programme National Nutrition Sant\u0026eacute; (PNNS) adequacy score, a composite dietary index reflecting adherence to French dietary recommendations, was calculated based on the same food frequency questionnaire data (22).\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eOf the 468 women included in the study, one woman for whom the PFAS could not be measured due to insufficient serum amount was excluded from the analyses.\u003c/p\u003e \u003cp\u003eFor the 45 POPs included in the study, non-quantified values were imputed using the lower limit of quantification divided by \u0026radic;2, following recommendations for distributions that are not highly skewed (23). POPs concentrations were expressed per volume of plasma or serum\u003c/p\u003e \u003cp\u003eFor inflammatory biomarkers, handling of non-quantified values depended on their proportion. Biomarkers with fewer than 25% of non-quantified values (i.e. CRP and MCP-1) were imputed using the machine-estimated value, i.e., the value extrapolated by the instrument despite being outside the quantification range. For biomarkers with more than 25% of non-quantified values (i.e. TNF-α and IL-8), no imputation was performed, instead, measurements were classified as quantified or non-quantified.\u003c/p\u003e \u003cp\u003eThe main characteristics of the study population were described by numbers and percentages for categorical variables, or median and interquartile range for continuous variables. Spearman rank correlation coefficients between levels of POPs and inflammatory biomarkers were estimated after imputation of non-quantified values.\u003c/p\u003e \u003cp\u003eSeparate logistic regression models were fitted for each POP as independent variable (exposure) and each inflammatory biomarker as independent variable (outcome), with both analyzed as binary variables. POPs were categorized as below \u003cem\u003eversus\u003c/em\u003e above the median when more than 75% of values were quantified, otherwise as non-quantified \u003cem\u003eversus\u003c/em\u003e quantified. To assess the impact of covariate adjustment on effect estimates, a series of progressively adjusted models was constructed. The initial model was only adjusted on age at blood draw. A second model was additionally adjusted for birth generation, years of school education, residential area, smoking status, physical activity, menopausal status, alcohol consumption, PNNS adequacy score, and inflammatory capacity of the diet. The final model was further adjusted for annual BMI change and plasma lipid level. Covariates of the fully adjusted model was selected a priori using a directed acyclic graph (DAG) (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) was applied to the 45 POPs using standardized values (i.e., values divided by their standard deviation). The number of principal components retained was determined based on the proportion of explained variance and the interpretability of the first ten principal components. To improve their interpretability, an orthogonal Varimax rotation was applied to the selected principal components. Logistic regression models were then fitted with the six retained components, treated as continuous exposure variables, and each inflammatory biomarker as a binary outcome (non-quantified vs. quantified or below vs. above the median, depending on the proportion of non-quantified values). Separate models were run for each outcome and adjusted for the same set of covariates presented previously.\u003c/p\u003e \u003cp\u003eCovariates with less than 5% missing values were imputed using the median for continuous variables and the modal category for categorical variables, these were years of school education (3%), physical activity (0.6%), annual BMI change (1.5%). When more than 5% of values were missing, a \u0026ldquo;missing\u0026rdquo; category was created. This scenario applied only to residential area for which 7.5% of values were missing.\u003c/p\u003e \u003cp\u003eA p-value strictly below 0.05 was considered statistically significant. The database was built using Statistical Analysis Systems software, version 9.4 (SAS Institute, Cary, NC, USA), and statistical analyses were conducted using R, version 4.1.2.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eTo assess potential residual confounding by diet, two supplementary models adjusted for dietary factors were tested. The first model was further adjusted for adherence to the PNNS nutritional recommendations (sPNNS-GS2 score), while the second model was further adjusted for consumption of major food groups (meat, fish, fruits and vegetables, starches, and dairy products).\u003c/p\u003e \u003cp\u003eThe main model was performed with false discovery rate (FDR) correction (Benjamini-Hochberg method) applied to account for multiple testing.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 467 women were included in this study. At blood draw participants were aged between 45 and 73 years, were mostly post-menopausal (76.7%) and had an average BMI of 23.9 kg/m\u003csup\u003e2\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study population: 467 women in the French E3N cohort study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAge at blood draw (years)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (standard deviation) or Number (Percent)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.9 (6.5)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity (MET-hours/week)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.1 (45.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 [0.6%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI variation (kg/m2/year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5 (0.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 [1.5%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.9 (3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLipid plasma level (g/L of plasma)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.7 (1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption (g of ethanol/day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.3 (13.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInflammatory capacity of the diet (adapted score by Woudenbergh using Shivappa weights)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.4 (3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdherence to the French dietary recommendations (sPNNS-GS2 score)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.0 (2.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMeat consumption (g/day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e149.8 (69.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFish consumption (g/day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.3 (27.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFruits and vegetables consumption (g/day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e747.3 (279.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDairies consumption (g/day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e367.1 (204.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStarches consumption (g/day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e293.4 (127.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEggs consumption (g/day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.7 (19.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth generation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;=1930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40 [8.6%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(1930; 1935]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68 [14.6%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(1935; 1940]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109 [23.3%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(1940; 1945]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118 [25.3%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132 [28.3%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears of school education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;12 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43 [9.2%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;14 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e235 [50.3%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;14 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e175 [37.5%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 [3.0%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidential area\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e391 [83.7%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 [8.8%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35 [7.5%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking habits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51 [10.9%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169 [36.2%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e247 [52.9%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMenopausal status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePremenopaused\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109 [23.3%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostmenopaused\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e358 [76.7%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConcentrations of POPs and inflammatory biomarkers, along with the number and proportion of non-quantified values, are summarized in Supplementary Table\u0026nbsp;1. A heat map displaying Spearman rank correlation coefficients among POPs is provided in Supplementary Fig.\u0026nbsp;2, with coefficients ranging from \u0026minus;\u0026thinsp;0.14 to 0.95 (median: 0.23). Additional details on the distribution of POPs biomarker levels have been reported previously (Frenoy et al., 2024). The mean (SD) for CRP, IL-8, MCP-1, and TNF-α was 59.0 \u0026micro;g/mL (130.2), 1.4 pg/mL (9.0), 174.3 pg/mL (65.8), and 4.4 pg/mL (4.9), respectively. For inflammatory biomarkers, correlation coefficients ranged from 0.08 (between CRP and IL-8) to 0.48 (between IL-8 and TNF-α) (Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e\n\u003ch3\u003eSingle-pollutant regression\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCRP\u003c/h2\u003e \u003cp\u003eResults from the single-pollutant adjusted logistic regression models overall indicate a positive association between OCPs and CRP levels. These associations reached statistical significance for o,p\u0026prime;-DDE (OR 1.52 [95%CI 1.05\u0026ndash;2.21], β-HCH (2.14 [1.44\u0026ndash;3.20]), trans-nonachlor (1.50 [1.03\u0026ndash;2.19]), cis-heptachlor epoxide (2.29 [1.56\u0026ndash;3.36]), dieldrin (1.96 [1.35\u0026ndash;2.87]), and HCB (1.83 [1.25\u0026ndash;2.70]). No consistent pattern was observed between CRP levels and other POP families included in the study, except for PCB-157 (0.67 (0.46\u0026ndash;0.98)), which showed a statistically significant inverse association with CRP levels (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings were largely consistent across adjusted models (data not shown).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression models estimating adjusted associations between 45 POPs biomarkers and four biomarkers of systemic inflammation (CRP, IL-8, MCP-1, and TNFα). Each POP biomarker is fitted in a separate model, adjusted for age at blood draw, birth generation, years of school education, residential area, smoking status, physical activity, variation of BMI, alcohol consumption, inflammatory capacity of the diet, menopausal status, and lipid plasma level.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOPs biomarkers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003cp\u003eOR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIL-8\u003c/p\u003e \u003cp\u003eOR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIL-8\u003c/p\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMCP-1\u003c/p\u003e \u003cp\u003eOR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMCP-1\u003c/p\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTNF-α\u003c/p\u003e \u003cp\u003eOR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTNF-α\u003c/p\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep,p\u0026rsquo;-DDT, Quantified (ref: Non-quantified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05 [0.72; 1.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91 [0.61; 1.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.28 [0.87; 1.89]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.92 [0.62; 1.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eo,p'-DDT, Quantified (ref: Non-quantified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75 [0.49; 1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.62 [0.40; 0.95]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85 [0.55; 1.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.91 [0.58; 1.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep,p\u0026rsquo;-DDE, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.40 [0.97; 2.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.11 [0.76; 1.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.83 [0.57; 1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.81 [0.54; 1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eo,p\u0026rsquo;-DDE, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.52 [1.05; 2.21]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80 [0.55; 1.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.92 [0.63; 1.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.77 [0.52; 1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep,p\u0026rsquo;-DDD, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 [0.69; 1.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89 [0.60; 1.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00 [0.68; 1.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.86 [0.58; 1.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eγ-HCH, Quantified (ref : Non-quantified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17 [0.79; 1.73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89 [0.60; 1.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.04 [0.70; 1.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.95 [0.63; 1.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-HCH, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.14 [1.44; 3.20]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77 [0.51; 1.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82 [0.54; 1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.03 [0.68; 1.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eoxychlordane, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.46 [1.00; 2.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94 [0.63; 1.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.77 [0.52; 1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.25 [0.84; 1.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etrans-nanochlor, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.50 [1.03; 2.19]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82 [0.56; 1.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.96 [0.66; 1.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.09 [0.74; 1.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecis-nanochlor, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.38 [0.95; 2.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.61 [0.42; 0.90]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.79 [0.54; 1.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.82 [0.55; 1.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecis-heptachlor-epoxyde, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.29 [1.56; 3.36]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.01 [0.68; 1.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.83 [0.56; 1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.47 [0.99; 2.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edieldrin, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.96 [1.35; 2.87]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85 [0.58; 1.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.16 [0.79; 1.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.41 [0.95; 2.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emirex, Quantified (ref: Non-quantified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.20 [0.79; 1.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68 [0.44; 1.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.90 [0.59; 1.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.77 [0.49; 1.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCB, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.83 [1.25; 2.70]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.76 [0.51; 1.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.62 [0.42; 0.92]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.20 [0.80; 1.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeCBz, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85 [0.59; 1.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.13 [0.77; 1.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72 [0.50; 1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.81 [0.55; 1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFUnDA, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92 [0.63; 1.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.59 [0.40; 0.87]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99 [0.68; 1.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.85 [0.57; 1.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFDA, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 [0.71; 1.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.54 [0.36; 0.79]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.98 [0.67; 1.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.78 [0.53; 1.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFNA, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.86 [0.59; 1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.58 [0.39; 0.86]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.94 [0.64; 1.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.64 [0.43; 0.96]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFOA, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83 [0.57; 1.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80 [0.55; 1.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.19 [0.82; 1.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.83 [0.56; 1.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFHpA, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07 [0.74; 1.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17 [0.80; 1.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.81 [1.24; 2.64]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.19 [0.81; 1.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFOS, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71 [0.49; 1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.47 [0.31; 0.70]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.87 [0.59; 1.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.83 [0.56; 1.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFHpS, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85 [0.57; 1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.64 [0.42; 0.96]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.98 [0.65; 1.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.72 [0.48; 1.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFHxS, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 [0.70; 1.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.62 [0.42; 0.92]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.75 [0.51; 1.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.66 [0.44; 0.98]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFPeS, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79 [0.53; 1.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84 [0.57; 1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.69 [0.47; 1.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.76 [0.50; 1.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFOSA, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87 [0.60; 1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.07 [0.73; 1.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.46 [1.01; 2.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.91 [0.62; 1.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-MeFOSAA, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 [0.71; 1.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96 [0.66; 1.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.90 [0.62; 1.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.87 [0.59; 1.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-EtFOSAA, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82 [0.56; 1.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17 [0.80; 1.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.31 [0.90; 1.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.04 [0.71; 1.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCB-28, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.35 [0.93; 1.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80 [0.54; 1.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01 [0.69; 1.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.87 [0.58; 1.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCB-52, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 [0.68; 1.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86 [0.59; 1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99 [0.68; 1.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.82 [0.55; 1.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCB-101, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 [0.67; 1.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78 [0.53; 1.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.07 [0.73; 1.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99 [0.67; 1.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCB-138, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.25 [0.85; 1.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73 [0.49; 1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.83 [0.56; 1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.02 [0.68; 1.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCB-153, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 [0.68; 1.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73 [0.49; 1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01 [0.69; 1.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.09 [0.73; 1.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCB-180, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77 [0.53; 1.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86 [0.59; 1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.06 [0.73; 1.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.12 [0.76; 1.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCB-105, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.18 [0.80; 1.73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71 [0.47; 1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.09 [0.74; 1.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.04 [0.70; 1.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCB-114, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06 [0.72; 1.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.62 [0.41; 0.93]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01 [0.68; 1.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.10 [0.73; 1.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCB-118, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17 [0.80; 1.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.60 [0.40; 0.89]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86 [0.58; 1.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.95 [0.64; 1.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCB-123, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.28 [0.87; 1.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81 [0.55; 1.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.10 [0.75; 1.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.29 [0.86; 1.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCB-156, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91 [0.62; 1.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.76 [0.51; 1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.04 [0.71; 1.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.11 [0.75; 1.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCB-157, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.67 [0.46; 0.98]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81 [0.55; 1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.11 [0.76; 1.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.32 [0.89; 1.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCB-167, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05 [0.71; 1.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67 [0.44; 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74 [0.50; 1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.20 [0.80; 1.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCB-189, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77 [0.53; 1.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90 [0.61; 1.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.38 [0.95; 2.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.29 [0.88; 1.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBDE-47, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.90 [0.62; 1.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82 [0.56; 1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72 [0.49; 1.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.07 [0.73; 1.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBDE-99, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24 [0.86; 1.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99 [0.68; 1.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00 [0.69; 1.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.92 [0.63; 1.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBDE-100, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08 [0.74; 1.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91 [0.62; 1.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85 [0.58; 1.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.04 [0.70; 1.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBDE-153, \u0026gt;Median (ref: \u0026lt;=Median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69 [0.48; 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72 [0.49; 1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74 [0.51; 1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.03 [0.70; 1.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIL-8\u003c/h2\u003e \u003cp\u003eOverall, PFAS showed an inverse relationship with IL-8 levels. Indeed, results from the single-pollutant adjusted logistic regression models highlighted a statistically significant inverse association for PFUnDA (0.59 [0.40\u0026ndash;0.87]), PFDA (0.54 [0.36\u0026ndash;0.79]), PFNA (0.58 [0.39\u0026ndash;0.86]), PFOS (0.47 [0.31\u0026ndash;0.70]), PFHpS (0.64 [0.42\u0026ndash;0.96]), and PFHxS (0.62 [0.42\u0026ndash;0.92]). Statistically significant inverse associations were also observed between o.p\u0026rsquo;-DDT (0.62 [0.40\u0026ndash;0.95]), cis-nanochlor (0.61 [0.42\u0026ndash;0.90]), PCB-114 (0.62 [0.41\u0026ndash;0.93]), and PCB-118 (0.60 [0.40\u0026ndash;0.89]) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Also, associations between POP levels and IL-8 appeared overall consistent across adjusted models (data not shown).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMCP-1\u003c/h2\u003e \u003cp\u003eThe results of the main single-pollutant analyses did not reveal any meaningful association between POP levels and MCP-1 blood concentrations in any of the tested models. The only statistically significant associations were observed for HCB (0.62 [0.42\u0026ndash;0.92]) and PFHpA (1.81 [1.24\u0026ndash;2.64]) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTNF-α\u003c/h2\u003e \u003cp\u003eSimilarly, no relevant association between individual POP levels and TNF-α concentrations was observed except for inverse associations for PFNA (0.64 [0.43\u0026ndash;0.96]) and PFHxS (0.66 [0.44\u0026ndash;0.98]) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal component regression\u003c/h2\u003e \u003cp\u003eFrom PCA six principal components were retained, explaining 60% of the total variance. Loading factors for POPs in the 6 retained principal components are presented in Supplementary Table\u0026nbsp;2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eComponent 1 \u0026ndash; Non-DDT OCPs and dioxin-like PCBs\u003c/h2\u003e \u003cp\u003eThe first component accounted for 14.5% of the variance and was characterized by high positive loadings (\u0026gt;\u0026thinsp;0.50) for several organochlorine pesticides (β-HCH, oxychlordane, trans-nonachlor, cis-nonachlor, cis-heptachlor epoxide, dieldrin, and HCB), as well as dioxin-like PCBs (PCB-105, PCB-114, PCB-118, PCB-123, and PCB-167).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eComponent 2 \u0026ndash; PCBs (dioxin-like and non-dioxin-like)\u003c/h2\u003e \u003cp\u003eThe second component explained 14.3% of the total variance and showed high positive loadings for PCBs, including both dioxin-like (PCB-138, PCB-153, PCB-180) and non-dioxin-like congeners (PCB-114, PCB-156, PCB-157, PCB-167, and PCB-189).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eComponent 3 \u0026ndash; PFAS\u003c/h2\u003e \u003cp\u003eThe third component accounted for 10.3% of the variance and was mainly characterized by high positive loadings for most PFAS, including PFUnDA, PFDA, PFNA, PFOA, total PFOS, PFHpS, and PFPeS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eComponent 4 \u0026ndash; PBDEs\u003c/h2\u003e \u003cp\u003eVery high loadings for the four PBDEs included in this study were observed for the fourth component, which alone explained 7.3% of the total variance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eComponent 5 \u0026ndash; Mixed POP profile\u003c/h2\u003e \u003cp\u003eThe fifth component accounted for 6.8% of the variance and displayed a more heterogeneous loading pattern, with no clear predominance of a single POP family, but rather moderate to high loadings for individual compounds such as γ-HCH, PCB-52, and PCB-101.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eComponent 6 \u0026ndash; DDT metabolites\u003c/h2\u003e \u003cp\u003eThe sixth and last retained component accounted for 6.8% of the total variance and was characterized by high positive loadings for DDT metabolites (p,p\u0026prime;-DDT, o,p\u0026prime;-DDT, p,p\u0026prime;-DDE, o,p\u0026prime;-DDE, and p,p\u0026prime;-DDD).\u003c/p\u003e \u003cp\u003eWhen adjusted logistic regression models were fitted including the 6 retained principal components as exposures variables, the results largely were in line with those observed in the mono-pollutant analyses. Indeed, Component 1 \u0026ndash; Non-DDT OCP and dioxin-like PCB was positively associated with CRP blood concentration levels (1.71 [1.35\u0026ndash;2.18]), whereas Component 3 \u0026ndash; PFAS presented an inverse association with IL-8 levels (0.75 [0.61\u0026ndash;0.91]). No statistically significant associations were observed between the remaining components and any of the inflammatory biomarkers investigated in this study (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression model estimating adjusted associations between 6 retained principal components and four biomarkers of systemic inflammation (CRP, IL-8, MCP-1, and TNFα). The model is adjusted for age at blood draw, birth generation, years of school education, residential area, smoking status, physical activity, variation of BMI, alcohol consumption, inflammatory capacity of the diet, menopausal status, and lipid plasma level.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrincipal components\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003cp\u003eOR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIL-8\u003c/p\u003e \u003cp\u003eOR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIL-8\u003c/p\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMCP-1\u003c/p\u003e \u003cp\u003eOR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMCP-1\u003c/p\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTNF-α\u003c/p\u003e \u003cp\u003eOR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTNF-α\u003c/p\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-DDT OCPs and DL-PCBs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.71 [1.35; 2.18]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97 [0.78; 1.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.89 [0.73; 1.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.20 [0.97; 1.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCBs (DL and NDL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.90 [0.74; 1.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87 [0.72; 1.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.98 [0.81; 1.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.10 [0.91; 1.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFASs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91 [0.74; 1.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.75 [0.61; 0.91]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.95 [0.78; 1.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.83 [0.67; 1.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBDEs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 [0.80; 1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83 [0.65; 1.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.83 [0.63; 1.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.94 [0.72; 1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed POP profile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96 [0.78; 1.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94 [0.77; 1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.95 [0.78; 1.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.97 [0.79; 1.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDDT metabolites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 [0.82; 1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99 [0.82; 1.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.03 [0.85; 1.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99 [0.79; 1.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eWhen further adjusting the main model for adherence to the PNNS nutritional recommendations (sPNNS-GS2 score) or for consumption of major food groups (meat, fish, fruits and vegetables, starches, and dairy products), the results of the mono-pollutant regressions models were virtually unchanged (data not shown). When performing the same sensitivity analyses for PCA, results remained unchanged adjusting for sPNNS-GS2, while a statistically significant inverse association was observed between Component 2 \u0026ndash; PCBs (dioxin-like and non-dioxin-like) and IL-8 when additionally adjusting the model for consumption of major food groups (0.78 [0.62\u0026ndash;0.99]).\u003c/p\u003e \u003cp\u003eAfter applying FDR correction for multiple testing, the associations between cis-heptachlor-epoxide and CRP (2.29 [1.56\u0026ndash;3.36], p\u0026thinsp;=\u0026thinsp;0.01) and between PFOS and IL-8 (0.47 [0.31\u0026ndash;0.70], p\u0026thinsp;=\u0026thinsp;0.01) remained statistically significant. No other association reached statistical significance (Supplementary table 3).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of the present study are generally consistent with previous evidence suggesting that inflammation may represent a key mechanism through which exposure to POPs affects human health.\u003c/p\u003e \u003cp\u003eA positive association was observed between several OCPs and CRP and confirmed by the principal component regression analysis. After correcting for multiple testing, the positive association persisted for cis-heptachlor-epoxide suggesting that the association between this pesticide and CRP is the most robust. The first identified component was largely characterized by non-DDT OCP and dioxin-like PCBs. These two groups of substances share the main sources of exposure, being largely present in fatty food of animal origin, as well as their ability to bioaccumulate in fat tissue, and their capacity of activate the AhR (24). Consequently, co-exposure is expected, and it is plausible that these compounds may exert an additive effect through AhR activation. AhR signaling can subsequently lead to upregulation of CRP indirectly via cytokine-mediated hepatic pathways, providing a mechanistic explanation for the observed associations (11). Previous studies have observed similar results. In particular, an analysis of 2,847 participants from NHANES observed a positive correlation between OCP biomarkers and CRP levels (25). Similarly, a cross-sectional study conducted among 748 non-diabetic adults highlighted a positive association between OCP blood levels and CRP concentrations. Interestingly, in the same study an inverse association was observed between PCBs and CRP (26). A prospective study of 453 adult women reported that serum β-HCH levels were positively associated with hs-CRP concentrations (27). However, some studies have reported divergent results. For example, a cross-sectional study including 774 elderly men and women from Sweden found no association between OCP concentrations and CRP levels (28). A meta-analysis focusing more widely on the association between exposure to endocrine disrupting chemicals and inflammatory biomarkers concluded that OCP exposure was positively associated with CRP, as well as to IL-1β, IL-2, and IL-10, whereas no association was observed for PCBs (29). More recently, meta-analysis including 15 studies, concluded that pesticide exposure can induce an inflammatory response and that CRP represents a reliable biomarker to investigate this association (30). Moreover, there is compelling evidence that chronic exposure to OCPs increases the risk of cardiometabolic diseases (31,32). It is therefore highly plausible that chronic exposure to AhR ligands, such as OCPs, sustains low-grade inflammatory signaling, contributing to prolonged elevation of CRP levels, which in turn is associated with cardiometabolic diseases and other chronic diseases.\u003c/p\u003e \u003cp\u003eThe present study also identified an inverse association between several PFAS and IL-8 concentrations. This inverse relationship persisted when PFAS were considered jointly as contributors to the same component identified through PCA. PFOS was the only PFAS for which the association with IL-8 remained statistically significant after FDR correction, suggesting that the overall inverse association may be largely driven by this specific congener. Although the relationship between PFAS exposure and inflammation remains incompletely characterized, growing evidence suggests that PFAS may exert immunomodulatory effects rather than uniformly promoting pro-inflammatory responses (33). IL-8 is a key chemokine involved in innate immune responses, primarily regulating neutrophil recruitment and activation during acute inflammation. Reduced IL-8 levels may reflect altered neutrophil signaling or broader dysregulation of chemokine-mediated immune pathways (34). Previous epidemiological studies investigating the relationship between PFAS exposure and cytokine levels, including IL‑8, have mainly reported inverse or null associations. For instance, a cross-sectional study of 212 non-smoking adults observed that higher concentrations of multiple PFAS were associated with lower IL‑1β levels and, to a lesser extent, with reductions in other cytokines (35). Similarly, a study of 198 Chinese women of childbearing age observed divergent effects of PFAS mixtures on several cytokine levels, while specifically reporting an overall null effect of PFAS mixtures on IL-8 levels (36). A recent literature review summarizing evidence on effects of PFASs on innate immunity in humans, experimental models, and wildlife, highlighted predominantly inverse associations between several PFAS and IL-8 levels from studies in humans, primary human leukocytes, and human cell lines (12). The same review concluded that although results on the impact of PFAS on the innate immunity are still equivocal, these findings collectively indicate that PFAS can perturb the innate immune system (12). It is therefore plausible that PFAS-induced dysregulation of the innate immune system may contribute to the associations reported in epidemiological studies between PFAS exposure and increased susceptibility to infections, altered inflammatory responses, and potentially elevated risk of cardiometabolic and immune-related diseases.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered when interpreting the results of the present study. The main limitation is the cross-sectional design, which does not allow distinction between short- and long-term effects. Nevertheless, the study design is unlikely to introduce reverse causation bias, as it is improbable that inflammatory biomarker levels would substantially influence POPs exposure. Only a single exposure measurement was available, preventing characterization of exposure trajectories. However, given the long environmental and biological half-lives of POPs, individual exposure levels are expected to change slowly over time. Another limitation is the relatively small sample size, primarily due to budget constraints, which reduced the statistical power of the study and may have limited the ability to detect modest associations. Moreover, single-pollutant models are subject to confounding due to co-exposure to other pollutants; therefore, their results must always be interpreted with caution. In addition, inflammatory biomarker levels can be influenced by numerous factors. Although several potential confounders were adjusted for in the analyses, residual confounding cannot be excluded. Another limitation is that the 4-plex panel captures only a limited portion of the inflammatory cascade and does not include several key cytokines, such as IL-1β and IL-10, which may play important roles in the underlying biological mechanisms. Finally, the E3N cohort includes only women and is not fully representative of the general French population, which may limit the generalizability of the findings.\u003c/p\u003e \u003cp\u003eAmong the main strengths of this study are the comprehensive measurement of a large number of POPs biomarkers, which allowed identification of the main exposure profiles in the study population of adult women residing in France. Additionally, the availability of multiple inflammatory biomarkers provided complementary information on the potential effects of POPs on the immune and inflammatory systems. Controlling for multiple testing, achieved by applying FDR correction, reduces the likelihood of false-positive findings and increases the likelihood that the observed associations are true. Finally, the extensive data collected in the E3N cohort enabled adjustment for numerous potential confounders and the conduct of sensitivity analyses, strengthening the robustness of the findings.\u003c/p\u003e \u003cp\u003eIn conclusion, the findings of the present study help clarify the complex relationship between exposure to multiple POPs and the inflammatory response. Further research is warranted to better characterize this association, ideally incorporating repeated exposure measurements and a broader panel of inflammatory biomarkers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eClinical trial number\u003c/u\u003e: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eEthics approval\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe E3N Generations study was approved by the French National Commission for Data Protection and Privacy (ClinicalTrials.gov identifier: NCT03285230). The E3N Generations blood collection study was approved by the Bic\u0026ecirc;tre ethics committee (CPP-IDF-VII, IRB # IORG0001140).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eInformed consent\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAll participants included in the E3N Generations study gave written informed consent, and informed consent was obtained for novel biomarkers testing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAvailability of data and materials\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are not publicly available.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthor contributions\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eFrancesca-Romana Mancini: Conceptualization, Methodology, Writing - Original Draft, Supervision, Project administration, Funding acquisition. Pauline Fr\u0026eacute;noy: Conceptualization, Methodology, Formal analysis, Writing - Review \u0026amp; Editing. German Cano-Sancho: Conceptualization, Methodology, Investigation, Resources, Writing - Review \u0026amp; Editing. Chlo\u0026eacute; Marques: Writing - Review \u0026amp; Editing. Xuan Ren: Writing-Review \u0026amp; Editing. Claire Perrrin: Writing - Review \u0026amp; Editing. Vittorio Perduca: Writing - Review \u0026amp; Editing. Philippe Marchand: Investigation, Resources. Bruno Le Bizec: Investigation, Resources. Jean-Philippe Antignac: Conceptualization, Methodology, Investigation, Resources, Writing - Review \u0026amp; Editing. Gianluca Severi : Conceptualization, Methodology, Writing - Review \u0026amp; Editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThis work was realised with the data of the E3N Generations cohort of the Inserm and supported by the Mutuelle G\u0026eacute;n\u0026eacute;rale de l\u0026rsquo;Education Nationale (MGEN), the Gustave Roussy Institute, and the French League against Cancer for the constitution and maintenance of the cohort. The cohort has benefited from state funding managed by the French National Research Agency (ANR) under the programs \u0026ldquo;Plan Investissement d\u0026rsquo;Avenir\u0026rdquo; and \u0026ldquo;France2030\u0026rdquo; (ANR-10-COHO-0006 and ANR-21-ESRE-0022) as well as from an annual subsidy from the Ministry of Higher Education, Research and Innovation for public service charges. This work also has benefited from a grant from the Fondation de France bearing the reference n\u0026deg;00110200.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge all participants enrolled in the E3N Generations cohort for their continued participation. They are also grateful to all members of the E3N Generations study group. The authors also thank the HBM platform of LABERCA, part of the France-Exposome and EIRENE research infrastructures, for analytical support. We also thank the \u003cem\u003eCentre de Ressources Biologiques\u0026nbsp;\u003c/em\u003eof the Fondation Jean Dausset\u0026ndash;CEPH for their technical support in the identification, extraction, and use of biological samples from the E3N-Generation cohort, which are stored in their facilities.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAshraf MA. Persistent organic pollutants (POPs): a global issue, a global challenge. Environ Sci Pollut Res. 1 f\u0026eacute;vr 2017;24(5):4223‑7.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eListing of POPs in the Stockholm Convention [Internet]. [cit\u0026eacute; 4 f\u0026eacute;vr 2026]. Disponible sur: https://www.pops.int/TheConvention/ThePOPs/AllPOPs/tabid/2509/Default.aspx\u003c/li\u003e\n \u003cli\u003eGuo W, Pan B, Sakkiah S, Yavas G, Ge W, Zou W, et al. Persistent Organic Pollutants in Food: Contamination Sources, Health Effects and Detection Methods. 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Ecotoxicol Environ Saf. 1 juill 2024;279:116508.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eInteraction Between Persistent Organic Pollutants and C-reactive Protein in Estimating Insulin Resistance Among Non-diabetic Adults [Internet]. [cit\u0026eacute; 4 f\u0026eacute;vr 2026]. Disponible sur: https://jpmph.org/journal/view.php?doi=10.3961/jpmph.2012.45.2.62\u003c/li\u003e\n \u003cli\u003eWarner M, Rauch S, Eskenazi B, Calderon L, Gunier RB, Kogut K, et al. Persistent organochlorine pesticides and cardiometabolic outcomes among middle-aged Latina women in a California agricultural community: The CHAMACOS Maternal Cognition Study. Environ Int. 1 f\u0026eacute;vr 2025;196:109302.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKumar J, Lind PM, Salihovic S, van\u0026nbsp;Bavel B, Ingelsson E, Lind L. Persistent Organic Pollutants and Inflammatory Markers in a Cross-Sectional Study of Elderly Swedish People: The PIVUS Cohort. 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Int J Hyg Environ Health. mars 2022;240:113905.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eNian M, Zhou W, Feng Y, Wang Y, Chen Q, Zhang J. Emerging and legacy PFAS and cytokine homeostasis in women of childbearing age. Sci Rep. 20 avr 2022;12(1):6517. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"persistent organic pollutants, systemic inflammation, biomarkers, mixtures","lastPublishedDoi":"10.21203/rs.3.rs-9427652/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9427652/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePersistent organic pollutants (POPs) comprise a diverse class of chemicals characterized by environmental persistence, bioaccumulation, and potential toxicity to humans. Immune and inflammatory dysregulation has been proposed as a key pathway underlying their adverse health effects. This study aimed to investigate associations between circulating levels of multiple POPs\u0026mdash;organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and per- and polyfluoroalkyl substances (PFAS)\u0026mdash;and biomarkers of systemic inflammation, while accounting for complex exposure patterns.\u003c/p\u003e \u003cp\u003eWe analyzed a cross-sectional sample of 468 women from the French E3N-Generations cohort. Concentrations of 45 POPs and four inflammatory markers (CRP, IL-8, MCP-1, TNF-α) were measured in blood collected in 1994\u0026ndash;1999. Logistic regression models were first applied to assess associations between individual POPs and dichotomized biomarkers. Principal component analysis (PCA) was then used to derive exposure profiles, which were subsequently examined in regression models.\u003c/p\u003e \u003cp\u003eSeveral OCPs were positively associated with CRP levels, a finding corroborated by PCA-based analyses. After false discovery rate (FDR) correction, cis-heptachlor epoxide remained significantly associated with elevated CRP. In contrast, multiple PFAS were inversely associated with IL-8 concentrations, both individually and as part of a shared exposure component. Among them, PFOS showed the most robust association after FDR correction.\u003c/p\u003e \u003cp\u003eOverall, these findings provide further insight into the complex links between mixed POP exposures and inflammatory processes, highlighting the importance of considering both individual compounds and exposure mixtures.\u003c/p\u003e","manuscriptTitle":"Blood levels of Persistent Organic Pollutants and circulating biomarkers of systemic inflammation in French women: Evidence from the E3N-Generations cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 07:54:14","doi":"10.21203/rs.3.rs-9427652/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"128133892487143390854031679400091734989","date":"2026-05-08T11:22:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-17T13:14:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-17T00:39:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T00:39:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Health","date":"2026-04-15T13:05:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"65b35cd0-dd48-45c3-a289-dafc90328ce1","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"128133892487143390854031679400091734989","date":"2026-05-08T11:22:41+00:00","index":23,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T07:54:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 07:54:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9427652","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9427652","identity":"rs-9427652","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}