Associations between organophosphate esters exposure and metabolic syndrome: Exploring the mediating role of oxidative stress and inflammation in adults

Associations between organophosphate esters exposure and metabolic syndrome: Exploring the mediating role of oxidative stress and inflammation in adults | 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 Associations between organophosphate esters exposure and metabolic syndrome: Exploring the mediating role of oxidative stress and inflammation in adults Sijie Yang, Yaping Li, Ling Liu, Zhengce Wan, Qitong Xu, Chang Xie, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4160250/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jul, 2024 Read the published version in Exposure and Health → Version 1 posted 5 You are reading this latest preprint version Abstract Epidemiological evidence regarding the relationships of organophosphate esters (OPEs) with metabolic syndrome (MetS) and its underlying mechanism was largely unknown. This study sought to estimate the correlations of individual OPEs and their mixture with MetS risk, while also evaluating the potential mediation of oxidative stress and inflammation biomarkers. We measured urinary OPE metabolites, urinary biomarkers of oxidative stress, and serum biomarkers of inflammation among 694 adults based on a case-control design. Our findings revealed positive correlations between urinary 1-hydroxy-2-propyl bis(1-chloro-2-propyl) phosphate (BCIPHIPP) and bis(2-butoxyethyl) phosphate (BBOEP) and elevated odds of MetS risk. Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS) analyses demonstrated the overall effect of the OPE mixtures on MetS risk, with BBOEP identified as the primary contributor. Mediation analysis further revealed that the association between urinary BCIPHIPP and BBOEP and MetS risk was mediated by urinary 8-hydroxy-2-deoxyguanosine (8-OHdG), with mediation proportions of 23.27% and 8.70%, respectively. In addition, serum C-reactive protein (CRP) concentration mediated the association between BBOEP and MetS risk, and the proportion of mediation was 16.32%. Our results indicated that oxidative stress and inflammation might exert a substantial influence on the correlations between OPE exposure and the risk of MetS. Organophosphate esters Metabolic syndrome Oxidative stress Inflammation Mediation Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Metabolic syndrome (MetS) is identified by a cluster of metabolic risk factors and encompasses central obesity, dyslipidemia, hypertension, and hyperglycemia. MetS not only contributes to cardiovascular diseases and diabetes but also elevates the risk of mortality from other illnesses (Lakka et al., 2002; Wilson et al., 2005). It is estimated that approximately 25% of the global population suffers from MetS, and the prevalence of MetS in China is as high as 32.97%, with an increasing trend year by year (Gu et al., 2005; Li et al., 2016). MetS often arises from the combined influence of genetics, behavior, and environmental factors. An increasing body of research suggests that environmental chemicals, especially with endocrine-disrupting effects, are important risk factors for metabolic impairment (Le Magueresse-Battistoni et al., 2018; Lind and Lind, 2018). Organophosphate esters (OPEs), classified as endocrine disruptors, are extensively employed in a multitude of industrial and consumer commodities, such as electronics, decoration materials, and personal care products (Wei et al., 2015). Toxicological studies revealed that OPEs can cause metabolic impairments, which encompass disturbances in lipid metabolism and energy equilibrium, induction of insulin resistance, facilitation of obesity, and advancement of other metabolic diseases (Hao et al., 2019). In addition, low-dose triphenyl phosphate (TPHP) exposure still induced comprehensive metabolic disturbances in the liver and even systemically in mice (Selmi-Ruby et al., 2020). Epidemiological studies on the correlation between OPE exposure and the risk of MetS remain notably limited. Only one study, based on a cross-sectional survey of 1,157 adults in the National Health and Nutrition Examination Survey (NHANES) 2011–2014, has demonstrated a positive correlation between OPE exposure and risk of MetS in adults (Luo et al., 2020c). Additionally, several other epidemiological studies have indicated that OPE exposure is linked to metabolic diseases such as obesity, lipid disturbances, elevated blood glucose, and increased hypertension (Luo et al., 2020c; Hu et al., 2022; Yang et al., 2022). Considering the escalating burden of MetS in our country, it is imperative to explore the potential correlation between OPE exposure and MetS risk in China. The biological mechanisms underlying the associations between OPE exposure and MetS remain unclear. It has been widely acknowledged that oxidative stress and inflammations play pivotal roles in the development of MetS. Oxidative damage can promote cellular lipid peroxidation so that the structure and function of the cell membrane are damaged (Grandl and Wolfrum, 2018); Inflammatory response can stimulate the inflammatory signaling pathway, inducing pancreatic islet β-cell dysfunction and apoptosis, thus affecting the insulin signaling pathway, further causing insulin resistance (Lee and Pratley, 2005). Both in vitro and in vivo investigations showed that OPE exposure induces decreases in the activity of several antioxidant enzymes in male mice and Leydig cells, leading to an upsurge in free radicals and subsequent oxidative damage harm within the organism (Chen et al., 2015a; Chen et al., 2015b). Meanwhile, toxicological studies revealed that OPEs regulated the expression of inflammatory cytokines and chemokines, thereby activating the mechanism of initiation of intracellular inflammatory responses (Hu et al., 2020). Limited epidemiologic studies also found positive correlations between OPE exposure and oxidative stress and inflammation biomarkers (Ait Bamai et al., 2019; Araki et al., 2020; Yao et al., 2021b). Nevertheless, few researchers examined the potential mediating role of oxidative stress and inflammation biomarkers on the correlation between urinary OPE metabolites and MetS risk. In this study, we recruited 694 participants based on a case-control study to quantify the association of individual OPE metabolites and their mixture with MetS risk and whether oxidative stress and inflammation biomarkers mediated the “OPEs-MetS” associations. 2. Materials And Methods 2.1 Study population Participants were randomly enrolled from August 2018 to November 2018 at the Health Management Center of Wuhan Tongji Hospital. The criteria for participant inclusion were established as follows: 1) residents in the study area; 2) adults ≥18 years old; 3) no history of occupational exposure to OPEs. A sample of 2103 individuals was enrolled, and both urine and blood samples were collected during their clinic visit. 347 individuals who met the diagnostic criteria for MetS were selected as the case group, while 347 participants were randomly selected as the control group by matching the case on gender and age (±5 years). A total of 694 participants (347 pairs) were ultimately enrolled in this study. Of these, 592 participants (294 in the case group and 298 in the control group) were examined for inflammation biomarkers, after excluding 102 individuals who did not have sufficient blood samples. The research received approval from the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology (NO. [2018] IEC (S329)). 2.2 Measurement of urinary OPE metabolites The concentration of urinary OPE metabolites was measured by solvent-induced phase transition extraction coupled with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) following the methods described in previous studies (Hu et al., 2019). In brief, 1 mL of urine was incubated at 37°C for 12h with mixed internal standard, ammonium acetate buffer solution, and β-glucuronidase. Acetonitrile and methyl tert-butyl ether were added and the upper organic phase was isolated and subjected to evaporation until complete dryness. Then, 100 μL of a methanol/water (1:9, v/v) mixture was used for reconstitution of the solutions before UPLC-MS/MS analysis. Quality control was performed by measuring blank samples and quality control (QC) samples (0.2 ng/mL, 2 ng/mL) in each batch of 30 samples. The recoveries of the 16 OPE metabolites ranged from 71.34% to 117.63%, with intra-day relative standard deviations (RSD) less than 18.3% and inter-day RSD less than 22.5%. The limit of detections (LODs) for 16 OPE metabolites varied between 0.012 to 0.121 ng/mL. More comprehensive details on standards and internal standards for the 16 OPE metabolites were described in Supplementary Materials. Concentrations below the LODs are replaced with the value of LOD/. 2.3 Definition of MetS and its components MetS was determined in accordance with the International Diabetes Federation's criteria for Chinese people. Individuals with central obesity (waist circumference ≥90 cm in men and ≥80 cm in women) and two or more of the following criteria were defined as having MetS: (1) Triacylglycerol (TG): fasting serum TG ≥1.7 mmol/L; (2) high-density lipoprotein cholesterol (HDL-C): fasting serum HDL-C <1.04 mmol/L for men, <1.29 mmol/L for women; (3) blood glucose: fasting blood glucose (FBG) ≥5.6 mmol/L or have been diagnosed with type II diabetes; (4) blood pressure (BP): systolic blood pressure (SBP) ≥130 mmHg, diastolic blood pressure (DBP) ≥85 mmHg or are undergoing blood pressure-lowering therapy. Trained physicians carried out physical examinations, comprising height, weight, and waist circumference, and used a calibrated electronic sphygmomanometer to measure the resting BP of each participant three times. Biochemical indexes including HDL-C, FBG, and TG were analyzed by a HITACHI auto-analyzer (Cobas 8000, Roche Diagnostics, Basel, Switzerland). 2.4 Measurements of oxidative stress and inflammation biomarkers Methods for determining urinary levels of 8-iso-prostaglandin F2a (8-isoPGF2a), 8-hydroxy-2-deoxyguanosine (8-OHdG), and 4-hydroxy-2-nonenal-mercapturic acid (HNE-MA) referenced to previous studies (Wang et al., 2019b). In brief, 100 μL of urine samples were subjected to solid phase extraction (SPE) after adding 1.5 mL of deionized water and 50 μL of internal standard. The resulting eluate, upon evaporation to dryness, was re-dissolved in 100 μL of 10% methanol in water. Three oxidative stress biomarkers were measured using LC-MS/MS. Each batch of urine samples consists of 1 blank sample and 2 QC samples. The spiked recoveries of the three oxidative stress biomarkers varied between 85.6% to 110.9%, with intra-day RSD less than 10.5% and inter-day RSD less than 15.6%. The LODs of 8-isoPGF2a, 8-OHdG, and HNE-MA were 0.02 ng/mL, 0.03 ng/mL, and 0.01 ng/mL, respectively. Serum concentrations of inflammation biomarkers, including interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP), were measured using an enzyme-linked immunosorbent assay (ELISA) kit (Link Bio, China). The protocol was executed under the guidelines provided by the manufacturer. To minimize systematic bias and inter-assay variation, all samples were processed identically and analyzed randomly. The LODs for IL-6, TNF-α, and CRP were 0.02 pg/mL, 0.16 pg/mL, and 0.4 mg/dL, respectively. 2.5 Statistical analysis Descriptive statistics were applied to examine the basic characteristics of participants. Differences in demographic characteristics, MetS components, urinary OPE metabolites, and levels of oxidative stress and inflammation biomarkers between MetS and non-MetS groups were assessed using either parametric or nonparametric methods. In this research, solely urinary OPE metabolites with detection rates above 60% were considered. The levels of urinary OPE metabolites, oxidative stress, and inflammation biomarkers below the LOD were identified as the square root of LOD values over 2. In addition, urinary OPE metabolites, oxidative stress, and inflammation biomarker concentrations were ln-transformed due to their skewed distributions. Spearman’s test was assessed to calculate the correlations between urinary OPE metabolites. The correlations of urinary OPE metabolites, oxidative stress, and inflammation biomarkers with the MetS risk were assessed using multivariate logistic regression models with odds ratio (OR) and its 95% confidence interval (CI) as effect estimates. In addition, the correlations between urinary OPE metabolites and oxidative stress and inflammation biomarkers were assessed using multiple linear regression models with the corresponding percentage change (% change) and its 95% CI as the effect estimate and with the following equation: (% change) = 100%×[exp(β)-1], where β is the regression coefficient for urinary OPEs metabolites. Specifically, urinary OPE metabolites, oxidative stress, and inflammation biomarkers were modeled as both continuous and categorical (tertiles) variables in the above two models. Linear trend tests by tertiles of OPE metabolites, oxidative stress, and inflammatory biomarkers were executed by utilizing the median value of each tertile as a continuous variable in the above two models. Restricted cubic splines (RCS) were further applied to estimate the potential nonlinear correlations between the OPE exposure and MetS. Weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) were employed to further assess the relationship between OPE mixtures and MetS risk. The WQS analysis was employed to estimate the collective effect of OPE mixtures on the MetS and to identify the primary pollutants involved (Carrico et al., 2014). The WQS index was computed by utilizing the "gWQS" software package in R, where individual OPE concentrations were combined via a weighted sum. The WQS index, ranging from 0 to 1, indicates the weight of the seven urinary OPE metabolites and their relative importance. BKMR offers a flexible approach to modeling the individual and combined effects of exposure to chemical mixtures (Bobb et al., 2015). This method takes into account the potentially non-linear and non-cumulative dose-response relationships between these chemicals by utilizing kernel functions. To assess the overall joint effect of the OPEs on MetS, we estimated the difference in outcome levels at various percentiles for seven OPEs compared to their 50th percentile values. To fit the BKMR models, we employed a Markov chain Monte Carlo (MCMC) sampler, running for 20,000 iterations. Furthermore, we calculated posterior post-inclusion probabilities (PIPs) to determine the relative importance of each outcome. Mediation analysis was applied to examine the potential mediating mechanisms involving oxidative stress and inflammation biomarkers in the association of urinary OPE metabolite concentrations with MetS (Tingley et al., 2014). The direct effect (DE) indicates the unmediated association between OPE metabolites and MetS, whereas the indirect effect (IE) quantifies the impact of OPE exposure on MetS risk mediated by oxidative stress and inflammation biomarkers. The mediation proportion arising from oxidative stress and inflammation biomarkers was computed as the ratio between the indirect effect and the total effect (TE). Covariates were identified based on a combination of biological and statistical considerations (Yao et al., 2021a). The models incorporated the following covariates: urinary creatinine (continuous), age (continuous), gender (male, female), education (below high school, high school, college, and above), income (<6000, 6000-10000, ≥10000 yuan/month), smoking status (never smoker, former/current smoker), physical activity-metabolic equivalent (MET) (low, moderate, high), red meat intake (≤3 times/week, 4-6 times/week, ≥7 times/week), and vegetable intake (≤6 times/week, 7-12 times/week, ≥13 times/week). Details of physical activity-MET were described in Supplementary Material. We conducted sensitivity analyses to evaluate the robustness of our findings. We re-examined the associations between creatinine-adjusted concentrations of OPE metabolites and the risk of MetS using multivariate logistic regression models, excluding creatinine as a covariate. Statistical analyses were performed using SPSS version 25.0 (SPSS Inc., Chicago, IL, USA) and R version 4.1.1 (R Core Team 2021). P -values < 0.05 were considered to be statistically significant. 3. Results 3.1. Characteristics of the study population Table 1 displayed the basic demographic characteristics of the 694 participants, comprising 347 cases and 347 controls. The mean age of the case and control groups was 50.50±10.30 and 50.48±10.52 years old, respectively. There were comparable proportions of age, education, income, smoking, meat intake, and vegetable intake between the case and control groups ( P > 0.05). The MetS group exhibited higher levels of waist circumference, total triglycerides, systolic blood pressure, diastolic blood pressure, and fasting blood glucose, as well as lower levels of HDL-c comparable to the control group ( P < 0.05). 3.2. Distribution of urinary OPE metabolites, oxidative stress, and inflammation biomarkers The distribution of OPE metabolites, oxidative stress, and inflammation biomarkers of the study population were shown in Table 2. A total of seven OPE metabolites had detection rates >60%. Compared with the non-MetS group, the MetS group demonstrated higher levels of DoCP & DpCP and BBOEP ( P < 0.05). Spearman’s correlation analysis illustrated a significant positive correlation among the OPE metabolites (Fig. S1). The detection rates of urinary 8-OHdG, 8-isoPGF2α, HNE-MA, IL-6, TNF-α, and CRP were 99.71%, 96.36%, 100.00%, 93.10%, 82.59%, and 96.93%, respectively. Participants with MetS had higher concentrations of urinary 8-OHdG and serum CRP ( P < 0.05) compared to the non-MetS group (Table 2). 3.3. Associations of urinary OPE metabolites with MetS The correlations of urinary OPE metabolites with MetS risk were shown in Fig 1. In both crude and adjusted models, positive correlations between urinary BCIPHIPP and BBOEP concentrations and MetS risk were found (all P trend < 0.05). After adjusting for covariates, compared to the first tertile, the ORs (95% CI) of MetS in the third tertiles were 1.53 (1.02, 2.29) for urinary BCIPHIPP and 1.96 (1.30, 2.97) for urinary BBOEP concentrations, respectively. RCS was conducted to estimate the potential nonlinear dose-response correlations between OPE metabolites and MetS risk, and linear correlations between urinary BCIPHIPP and BBOEP and risk of MetS (all P for non-linear > 0.05) were found after adjusting for covariates (Fig. S2). The WQS analysis was utilized to estimate the correlation between OPE mixtures and MetS (Fig. 2A). OPE mixtures presented positive associations with MetS (OR=1.45; 95% CI: 1.07-1.96). Urinary BBOEP emerged as the primary contributor to the mixture effect on MetS. Similarly, the BKMR model also exhibited positive associations between OPE mixtures and MetS when OPE mixtures were concurrently at a particular percentile ranging from the 5th to 90th percentile compared to the median (Fig. 2B). The PIPs for OPEs in the BKMR model were summarized in Table S1, and BBOEP was the predominant contributor on MetS (PIP=1.00). 3.4. Associations of oxidative stress and inflammation biomarkers with OPE metabolites and MetS Table 3 presented the relationships of tertiles and continuous OPE metabolite concentrations with oxidative stress and inflammation biomarkers. When adjusting for covariates, we observed that urinary BCIPHIPP was positively correlated with levels of urinary 8-OHdG and urinary HNE-MA, and urinary BBOEP showed positive relationships with concentrations of urinary 8-OHdG and serum CRP. We analyzed the correlations of oxidative stress and inflammation biomarkers with MetS risk (Fig. 3). We found that urinary 8-OHdG and serum CRP were correlated with an elevated risk of MetS, and the ORs (95%CI) were 1.41 (1.19, 1.67) and 1.33 (1.17, 1.53), respectively. Compared to the first tertiles of urinary 8-OHdG and serum CRP, the ORs (95% CI) of MetS in the third tertiles were 1.73 (1.17, 2.56) for urinary 8-OHdG and 2.81 (1.83, 4.35) for serum CRP, respectively. 3.5. Mediation analyses Based on the association between urinary OPE metabolite concentrations, oxidative stress/inflammation biomarkers, and MetS, we further analyzed the mediating effect of urinary 8-OHdG and serum CRP in the correlation of urinary OPE metabolites and risk of MetS (Fig. 4). We identified a notable mediation effect of urinary 8-OHdG linking the relationships of urinary BCIPHIPP and BBOEP with MetS risk (the proportion mediated was 23.27% and 8.70%, respectively). The correlation between urinary BBOEP and risk of MetS was also partially mediated by serum CRP, and the proportion of mediation was 16.32%. 3.6. Secondary and sensitivity analyses No significant interaction was observed between OPEs and the predetermined effect modification factors in the stratified analysis (all P for interaction > 0.05) (Table S2). We also evaluated the correlations between creatinine-adjusted OPE concentrations and MetS risk, and the associations remained robust (Table S3). 4. Discussion This study investigated the "OPEs-MetS" association and estimated the mediated role of oxidative stress and inflammation biomarkers. We found that individual OPEs and their mixtures exhibited correlations with an escalated risk of MetS. The mediation model revealed that urinary 8-OHdG and serum CRP concentration mediated the correlation between urinary OPE metabolites and MetS risk. The distribution of OPE concentrations in different regions were shown in Table S4. Urinary levels of BDCIPP and DPHP were slightly lower in comparison to the reported levels in the general populations of the United States (Ospina et al., 2018), Canada (Yang et al., 2019), Belgium (Bastiaensen et al., 2019), Norway (Xu et al., 2019), and Australia (Van den Eede et al., 2015), but were comparable to those found in our countries (Zhang et al., 2018; Li et al., 2020). Urinary BCIPHIPP concentration was also lower than that reported in the general populations of Australia (Van den Eede et al., 2015), but surpassed the reported levels in the general population of Norway (Xu et al., 2019). In addition, urinary DoCP & DpCP concentration was below the reported levels in the general population of Canada (Kosarac et al., 2016), but slightly higher than that in our country (Zhang et al., 2018). Overall, urinary OPE concentrations vary geographically, which may be closely related to the extent of exposure, health status, economic level, etc. Epidemiologic studies on OPE exposure and MetS are limited. Only one study from NHANES (2011-2014) in the United States observed that urinary OPE metabolites, both individually and as mixtures, exhibited a correlation with increased risk of MetS in adults (Luo et al., 2020c), which is in line with our research. In addition, several studies assessed the correlations between urinary OPE metabolites and MetS components. Wang et al. highlighted increased urinary concentrations of certain OPEs metabolites in overweight/obese populations compared to those with normal weight within the United States (Hao et al., 2019). A recent study based on the NHANES (2013-2014) also revealed that urinary OPE metabolites were linked to increased waist circumference and an escalated risk of overweight/obesity in the US general adult population (Boyle et al., 2019). Only two studies to date have investigated the correlation between OPEs and blood glucose levels. Yan et al. identified positive associations between OPE metabolites with fasting glucose and glycated hemoglobin in a case-control population with type 2 diabetes (Ji et al., 2021). Meanwhile, Luo et al. identified a positive correlation between urinary OPE concentrations in female adolescents and the susceptibility to pre-diabetes, alongside 2-hour blood glucose levels observed during an oral glucose tolerance test (Luo et al., 2020a). However, Luo et al. also observed that urinary dibutyl phosphate (DBP) concentrations in male adolescents were negatively correlated with several indicators of glucose homeostasis (Luo et al., 2020a). Differences in sample sizes, population composition, and concentration level may contribute to the inconsistencies among the findings. Individuals are typically exposed to OPE mixtures rather than single OPEs. Therefore, it is worth exploring whether OPE mixtures are associated with MetS. WQS categorized continuous variables into quartiles to minimize the effect of extreme concentrations and assessed the chemical exposure burden based on the weights of bootstrap sampling, while BKMR allowed for the identification of non-linear and non-cumulative effects resulting from the combined exposure. The results consistently demonstrated that OPE mixtures were positively linked to MetS. Consistent with our findings, one study (Luo et al., 2020b) also found OPE mixtures were associated with MetS in adult men. Additionally, the primary factor contributing to the overall effect was found to be BBOEP. As a commonly used component in paints, flooring, and electronic devices, TBOEP, the precursor of BBOEP (Völkel et al., 2017), is encountered regularly by adults in their daily activities. It ought to be of great concern for policies limiting the usage of specific hazardous compounds. The role of OPEs in modulating oxidative stress and inflammation is of utmost importance. We found that urinary OPE concentrations were correlated with elevated biomarkers of oxidative stress in adults. In vivo studies, OPEs induced cytotoxicity in different cell lines (e.g., HepG2 and A549 cells) at higher concentrations, producing excess ROS (An et al., 2016). Limited epidemiologic research examined the link between OPE exposure and oxidative stress, focusing on pregnant women and occupational populations (Lu et al., 2017; Ait Bamai et al., 2019; Ingle et al., 2020). A study based on pregnant women in Puerto Rico found positive correlations between urinary bis(1-chloro-2-propyl) phosphate (BCIPP) and DPHP levels and 8-OHdG and 8-isoprostane concentrations (Ingle et al., 2020). Lu et al. reported associations between urinary OPE metabolite concentrations and increased 8-OHdG levels in 175 individuals residing in an electronic waste dismantling area (Lu et al., 2017). The findings from this study, along with the above studies, collectively indicate a positive association between OPE metabolites and elevated oxidative stress biomarkers. We also found inverse relationships between urinary BDCIPP concentration and serum IL-6 and TNF-α, and positive correlations between urinary BBOEP concentration and serum CRP. Toxicological studies showed that OPE exposure leads to decreases in the number of macrophages in zebrafish, which in turn secrete the cytokine TNF-α (Yan et al., 2022). Zhang et al. also reported the link between OPEs and diminished IL-6 concentrations in hepatocytes (Zhang et al., 2017). Evidence suggested that oxidative stress and inflammation play important roles in causing metabolic disorders in humans (Rani et al., 2016; Carrier, 2017). Our study revealed a significant association between urinary 8-OHdG concentration and heightened risk of MetS within the general population. Oxidative stress can cause insulin resistance and impaired pancreatic β-cell function, leading to decreased levels of insulin secretion. This disruption in insulin levels can subsequently affect glucose and lipid metabolism in the body, potentially contributing to the onset of MetS (Rani et al., 2016; Carrier, 2017). In alignment with our results, Butkowski et al. observed an association linking increased urinary 8-OHdG concentrations to an escalated risk of MetS in Australian adults (Butkowski et al., 2017). We also revealed a correlation between increased serum CRP concentrations and an escalated risk of MetS. CRP, part of the pentraxin protein family, functions as an acute-phase reactant and is acknowledged as a vital inflammation biomarker in the advancement of metabolic diseases. Earlier studies have consistently demonstrated a correlation between increased serum CRP levels and an elevated risk of MetS (Ridker et al., 2000). Meanwhile, toxicological studies showed that CRP can cause disorders of lipid metabolism, abnormalities in endothelial cell structure and function, and impairment of insulin signaling, leading to atherosclerotic thrombosis and the development of MetS (Devaraj et al., 2009). Urinary 8-OHdG and serum CRP concentrations acted as mediators in the association between OPE exposure and elevated risk of MetS. 8-OHdG serves as a biomarker indicating oxidative DNA damage, suggesting the crucial involvement of oxidative DNA damage in the connections between OPE exposure and metabolic disorders. 8-OHdG can be paired with adenine during DNA replication, leading to the occurrence of the G:C-T: A subversion, causing elevated microsatellite instability, which may lead to aberrant apoptosis and necrosis of adipocytes and endothelial cells, affecting normal metabolic functions of the body (Valavanidis et al., 2009; Urbaniak et al., 2020). A recent epidemiologic study also found that CRP concentrations mediated the correlations between heavy (metalloid) metal exposure and MetS risk (Ma et al., 2020). Considering the limited sample size, further exploration is crucial to delineate the involvement of inflammatory factors in mediating the association between OPE exposure and MetS. There are some strengths of this study. Firstly, our investigation focuses on examining the link of individual OPEs and their mixtures with the risk of MetS based on a case-control design. Secondly, we applied the mediation analyses to explore the potential involvement of oxidative stress and inflammation biomarkers in the correlations between OPEs and MetS risk. The study also had some limitations. First, due to continuous exposure to urinary OPEs, urinary metabolites of OPEs are variable (Wang et al., 2019a), and spot urine samples may lead to bias in exposure estimation. Second, participants were recruited from the general population of health management centers, with some selection bias, which limited the generalization of our results to the broader population. Third, due to the case-control design of this study, determining a causal correlation between OPEs-oxidative stress/inflammation-MetS proves challenging. 5. Conclusions This study revealed associations between individual OPEs and their mixtures with MetS risk based on a case-control design, with BBOEP as the predominated contributor. Urinary 8-OHdG and serum CRP as potential mechanisms underlay the correlations between OPE exposure and MetS risk. Further prospective cohort research is urgently needed to confirm our results in the future. Declarations Acknowledgements We acknowledge the participants in the study. Funding This research was funded by the National Natural Science Foundation of China (No. 42077397). CRediT authorship contribution statement Sijie Yang: Conceptualization, Methodology, Software, Investigation, Writing–original draft; Yaping Li : Validation, Methodology, Data curation, Investigation, Writing–original draft; Ling Liu: Methodology, Investigation, Validation, Data curation; Zhengce Wan : Investigation, Validation; Qitong Xu: Methodology, Investigation; Chang Xie : Validation, Data curation; Lulu Song : Conceptualization, Methodology, Formal analysis; Youjie Wang: Resources, Data curation, Supervision; Hui Chen: Investigation, Resources, Project administration. Surong Mei: Reviewing, Supervision, Project administration, Funding acquisition. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Ethical approval and consent to participate This study was approved by the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology. Consent for publication Not applicable Declaration of Competing Interest The authors declare no competing interests in this paper. References Ait Bamai, Y., Bastiaensen, M., Araki, A., Goudarzi, H., Konno, S., Ito, S., Miyashita, C., Yao, Y., Covaci, A., Kishi, R., 2019. Multiple exposures to organophosphate flame retardants alter urinary oxidative stress biomarkers among children: The Hokkaido Study. Environ Int. 131, 105003. https://doi.org/10.1016/j.envint.2019.105003. An, J., Hu, J., Shang, Y., Zhong, Y., Zhang, X., Yu, Z., 2016. The cytotoxicity of organophosphate flame retardants on HepG2, A549 and Caco-2 cells. Journal of Environmental Science and Health, Part A. 51, 980-988. https://doi.org/10.1080/10934529.2016.1191819. Araki, A., Ait Bamai, Y., Bastiaensen, M., Van den Eede, N., Kawai, T., Tsuboi, T., Miyashita, C., Itoh, S., Goudarzi, H., Konno, S., Covaci, A., Kishi, R., 2020. Combined exposure to phthalate esters and phosphate flame retardants and plasticizers and their associations with wheeze and allergy symptoms among school children. Environ Res. 183, 109212. https://doi.org/10.1016/j.envres.2020.109212. Bastiaensen, M., Malarvannan, G., Been, F., Yin, S., Yao, Y., Huygh, J., Clotman, K., Schepens, T., Jorens, P.G., Covaci, A., 2019. Metabolites of phosphate flame retardants and alternative plasticizers in urine from intensive care patients. Chemosphere. 233, 590-596. https://doi.org/10.1016/j.chemosphere.2019.05.280. Bobb, J.F., Valeri, L., Claus Henn, B., Christiani, D.C., Wright, R.O., Mazumdar, M., Godleski, J.J., Coull, B.A., 2015. Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. Biostatistics. 16, 493-508. https://doi.org/10.1093/biostatistics/kxu058. Boyle, M., Buckley, J.P., Quirós-Alcalá, L., 2019. Associations between urinary organophosphate ester metabolites and measures of adiposity among U.S. children and adults: NHANES 2013–2014. Environment International. 127, 754-763. https://doi.org/10.1016/j.envint.2019.03.055. Butkowski, E.G., Al-Aubaidy, H.A., Jelinek, H.F., 2017. Interaction of homocysteine, glutathione and 8-hydroxy-2'-deoxyguanosine in metabolic syndrome progression. Clin Biochem. 50, 116-120. https://doi.org/10.1016/j.clinbiochem.2016.10.006. Carrico, C., Gennings, C., Wheeler, D.C., Factor-Litvak, P., 2014. Characterization of Weighted Quantile Sum Regression for Highly Correlated Data in a Risk Analysis Setting. Journal of Agricultural, Biological, and Environmental Statistics. 20, 100-120. https://doi.org/10.1007/s13253-014-0180-3. Carrier, A., 2017. Metabolic Syndrome and Oxidative Stress: A Complex Relationship. Antioxid Redox Signal. 26, 429-431. https://doi.org/10.1089/ars.2016.6929. Chen, G., Jin, Y., Wu, Y., Liu, L., Fu, Z., 2015a. Exposure of male mice to two kinds of organophosphate flame retardants (OPFRs) induced oxidative stress and endocrine disruption. Environ Toxicol Pharmacol. 40, 310-318. https://doi.org/10.1016/j.etap.2015.06.021. Chen, G., Zhang, S., Jin, Y., Wu, Y., Liu, L., Qian, H., Fu, Z., 2015b. TPP and TCEP induce oxidative stress and alter steroidogenesis in TM3 Leydig cells. Reprod Toxicol. 57, 100-110. https://doi.org/10.1016/j.reprotox.2015.05.011. Devaraj, S., Singh, U., Jialal, I., 2009. Human C-reactive protein and the metabolic syndrome. Curr Opin Lipidol. 20, 182-189. https://doi.org/10.1097/MOL.0b013e32832ac03e. Grandl, G., Wolfrum, C., 2018. Hemostasis, endothelial stress, inflammation, and the metabolic syndrome. Semin Immunopathol. 40, 215-224. https://doi.org/10.1007/s00281-017-0666-5. Gu, D., Reynolds, K., Wu, X., Chen, J., Duan, X., Reynolds, R.F., Whelton, P.K., He, J., 2005. Prevalence of the metabolic syndrome and overweight among adults in China. Lancet. 365, 1398-1405. https://doi.org/10.1016/s0140-6736(05)66375-1. Hao, Z., Zhang, Z., Lu, D., Ding, B., Shu, L., Zhang, Q., Wang, C., 2019. Organophosphorus Flame Retardants Impair Intracellular Lipid Metabolic Function in Human Hepatocellular Cells. Chem Res Toxicol. 32, 1250-1258. https://doi.org/10.1021/acs.chemrestox.9b00058. Hu, L., Tao, Y., Luo, D., Feng, J., Wang, L., Yu, M., Li, Y., Covaci, A., Mei, S., 2019. Simultaneous biomonitoring of 15 organophosphate flame retardants metabolites in urine samples by solvent induced phase transition extraction coupled with ultra-performance liquid chromatography-tandem mass spectrometry. Chemosphere. 233, 724-732. https://doi.org/10.1016/j.chemosphere.2019.05.242. Hu, L., Yu, M., Li, Y., Liu, L., Li, X., Song, L., Wang, Y., Mei, S., 2022. Association of exposure to organophosphate esters with increased blood pressure in children and adolescents. Environmental Pollution. 295. https://doi.org/10.1016/j.envpol.2021.118685. Hu, W., Kang, Q., Zhang, C., Ma, H., Xu, C., Wan, Y., Hu, J., 2020. Triphenyl phosphate modulated saturation of phospholipids: Induction of endoplasmic reticulum stress and inflammation. Environ Pollut. 263, 114474. https://doi.org/10.1016/j.envpol.2020.114474. Ingle, M.E., Watkins, D., Rosario, Z., VélezVega, C.M., Calafat, A.M., Ospina, M., Ferguson, K.K., Cordero, J.F., Alshawabkeh, A., Meeker, J.D., 2020. An exploratory analysis of urinary organophosphate ester metabolites and oxidative stress among pregnant women in Puerto Rico. Sci Total Environ. 703, 134798. https://doi.org/10.1016/j.scitotenv.2019.134798. Ji, Y., Yao, Y., Duan, Y., Zhao, H., Hong, Y., Cai, Z., Sun, H., 2021. Association between urinary organophosphate flame retardant diesters and steroid hormones: A metabolomic study on type 2 diabetes mellitus cases and controls. Science of The Total Environment. 756. https://doi.org/10.1016/j.scitotenv.2020.143836. Kosarac, I., Kubwabo, C., Foster, W.G., 2016. Quantitative determination of nine urinary metabolites of organophosphate flame retardants using solid phase extraction and ultra performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS). J Chromatogr B Analyt Technol Biomed Life Sci. 1014, 24-30. https://doi.org/10.1016/j.jchromb.2016.01.035. Lakka, H.M., Laaksonen, D.E., Lakka, T.A., Niskanen, L.K., Kumpusalo, E., Tuomilehto, J., Salonen, J.T., 2002. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. Jama. 288, 2709-2716. https://doi.org/10.1001/jama.288.21.2709. Le Magueresse-Battistoni, B., Vidal, H., Naville, D., 2018. Environmental Pollutants and Metabolic Disorders: The Multi-Exposure Scenario of Life. Front Endocrinol (Lausanne). 9, 582. https://doi.org/10.3389/fendo.2018.00582. Lee, Y.H., Pratley, R.E., 2005. The evolving role of inflammation in obesity and the metabolic syndrome. Curr Diab Rep. 5, 70-75. https://doi.org/10.1007/s11892-005-0071-7. Li, M., Yao, Y., Wang, Y., Bastiaensen, M., Covaci, A., Sun, H., 2020. Organophosphate ester flame retardants and plasticizers in a Chinese population: Significance of hydroxylated metabolites and implication for human exposure. Environ Pollut. 257, 113633. https://doi.org/10.1016/j.envpol.2019.113633. Li, R., Li, W., Lun, Z., Zhang, H., Sun, Z., Kanu, J.S., Qiu, S., Cheng, Y., Liu, Y., 2016. Prevalence of metabolic syndrome in Mainland China: a meta-analysis of published studies. BMC Public Health. 16, 296. https://doi.org/10.1186/s12889-016-2870-y. Lind, P.M., Lind, L., 2018. Endocrine-disrupting chemicals and risk of diabetes: an evidence-based review. Diabetologia. 61, 1495-1502. https://doi.org/10.1007/s00125-018-4621-3. Lu, S.Y., Li, Y.X., Zhang, T., Cai, D., Ruan, J.J., Huang, M.Z., Wang, L., Zhang, J.Q., Qiu, R.L., 2017. Effect of E-waste Recycling on Urinary Metabolites of Organophosphate Flame Retardants and Plasticizers and Their Association with Oxidative Stress. Environ Sci Technol. 51, 2427-2437. https://doi.org/10.1021/acs.est.6b05462. Luo, K., Aimuzi, R., Wang, Y., Nian, M., Zhang, J., 2020a. Urinary organophosphate esters metabolites, glucose homeostasis and prediabetes in adolescents. Environmental Pollution. 267. https://doi.org/10.1016/j.envpol.2020.115607. Luo, K., Zhang, R., Aimuzi, R., Wang, Y., Nian, M., Zhang, J., 2020b. Exposure to Organophosphate esters and metabolic syndrome in adults. Environment International. 143. https://doi.org/10.1016/j.envint.2020.105941. Luo, K., Zhang, R., Aimuzi, R., Wang, Y., Nian, M., Zhang, J., 2020c. Exposure to Organophosphate esters and metabolic syndrome in adults. Environ Int. 143, 105941. https://doi.org/10.1016/j.envint.2020.105941. Ma, J., Zhou, Y., Wang, D., Guo, Y., Wang, B., Xu, Y., Chen, W., 2020. Associations between essential metals exposure and metabolic syndrome (MetS): Exploring the mediating role of systemic inflammation in a general Chinese population. Environ Int. 140, 105802. https://doi.org/10.1016/j.envint.2020.105802. Ospina, M., Jayatilaka, N.K., Wong, L.Y., Restrepo, P., Calafat, A.M., 2018. Exposure to organophosphate flame retardant chemicals in the U.S. general population: Data from the 2013-2014 National Health and Nutrition Examination Survey. Environ Int. 110, 32-41. https://doi.org/10.1016/j.envint.2017.10.001. Rani, V., Deep, G., Singh, R.K., Palle, K., Yadav, U.C., 2016. Oxidative stress and metabolic disorders: Pathogenesis and therapeutic strategies. Life Sci. 148, 183-193. https://doi.org/10.1016/j.lfs.2016.02.002. Ridker, P.M., Hennekens, C.H., Buring, J.E., Rifai, N., 2000. C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med. 342, 836-843. https://doi.org/10.1056/nejm200003233421202. Selmi-Ruby, S., Marín-Sáez, J., Fildier, A., Buleté, A., Abdallah, M., Garcia, J., Deverchère, J., Spinner, L., Giroud, B., Ibanez, S., Granjon, T., Bardel, C., Puisieux, A., Fervers, B., Vulliet, E., Payen, L., Vigneron, A.M., 2020. In Vivo Characterization of the Toxicological Properties of DPhP, One of the Main Degradation Products of Aryl Phosphate Esters. Environ Health Perspect. 128, 127006. https://doi.org/10.1289/ehp6826. Tingley, D., Yamamoto, T., Hirose, K., Keele, L., Imai, K., 2014. mediation: R Package for Causal Mediation Analysis. Journal of Statistical Software. 59. Urbaniak, S.K., Boguszewska, K., Szewczuk, M., Kaźmierczak-Barańska, J., Karwowski, B.T., 2020. 8-Oxo-7,8-Dihydro-2'-Deoxyguanosine (8-oxodG) and 8-Hydroxy-2'-Deoxyguanosine (8-OHdG) as a Potential Biomarker for Gestational Diabetes Mellitus (GDM) Development. Molecules. 25. https://doi.org/10.3390/molecules25010202. Valavanidis, A., Vlachogianni, T., Fiotakis, C., 2009. 8-hydroxy-2' -deoxyguanosine (8-OHdG): A critical biomarker of oxidative stress and carcinogenesis. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev. 27, 120-139. https://doi.org/10.1080/10590500902885684. Van den Eede, N., Heffernan, A.L., Aylward, L.L., Hobson, P., Neels, H., Mueller, J.F., Covaci, A., 2015. Age as a determinant of phosphate flame retardant exposure of the Australian population and identification of novel urinary PFR metabolites. Environ Int. 74, 1-8. https://doi.org/10.1016/j.envint.2014.09.005. Völkel, W., Fuchs, V., Wöckner, M., Fromme, H., 2017. Toxicokinetic of tris(2-butoxyethyl) phosphate (TBOEP) in humans following single oral administration. Archives of Toxicology. 92, 651-660. https://doi.org/10.1007/s00204-017-2078-7. Wang, Y., Li, W., Martínez-Moral, M.P., Sun, H., Kannan, K., 2019a. Metabolites of organophosphate esters in urine from the United States: Concentrations, temporal variability, and exposure assessment. Environ Int. 122, 213-221. https://doi.org/10.1016/j.envint.2018.11.007. Wang, Y.X., Liu, C., Shen, Y., Wang, Q., Pan, A., Yang, P., Chen, Y.J., Deng, Y.L., Lu, Q., Cheng, L.M., Miao, X.P., Xu, S.Q., Lu, W.Q., Zeng, Q., 2019b. Urinary levels of bisphenol A, F and S and markers of oxidative stress among healthy adult men: Variability and association analysis. Environ Int. 123, 301-309. https://doi.org/10.1016/j.envint.2018.11.071. Wei, G.L., Li, D.Q., Zhuo, M.N., Liao, Y.S., Xie, Z.Y., Guo, T.L., Li, J.J., Zhang, S.Y., Liang, Z.Q., 2015. Organophosphorus flame retardants and plasticizers: sources, occurrence, toxicity and human exposure. Environ Pollut. 196, 29-46. https://doi.org/10.1016/j.envpol.2014.09.012. Wilson, P.W., D'Agostino, R.B., Parise, H., Sullivan, L., Meigs, J.B., 2005. Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Circulation. 112, 3066-3072. https://doi.org/10.1161/circulationaha.105.539528. Xu, F., Eulaers, I., Alves, A., Papadopoulou, E., Padilla-Sanchez, J.A., Lai, F.Y., Haug, L.S., Voorspoels, S., Neels, H., Covaci, A., 2019. Human exposure pathways to organophosphate flame retardants: Associations between human biomonitoring and external exposure. Environ Int. 127, 462-472. https://doi.org/10.1016/j.envint.2019.03.053. Yan, J., Zhao, Z., Xia, M., Chen, S., Wan, X., He, A., Daniel Sheng, G., Wang, X., Qian, Q., Wang, H., 2022. Induction of lipid metabolism dysfunction, oxidative stress and inflammation response by tris(1-chloro-2-propyl)phosphate in larval/adult zebrafish. Environ Int. 160, 107081. https://doi.org/10.1016/j.envint.2022.107081. Yang, C., Harris, S.A., Jantunen, L.M., Siddique, S., Kubwabo, C., Tsirlin, D., Latifovic, L., Fraser, B., St-Jean, M., De La Campa, R., You, H., Kulka, R., Diamond, M.L., 2019. Are cell phones an indicator of personal exposure to organophosphate flame retardants and plasticizers? Environ Int. 122, 104-116. https://doi.org/10.1016/j.envint.2018.10.021. Yang, W., Braun, J.M., Vuong, A.M., Percy, Z., Xu, Y., Xie, C., Deka, R., Calafat, A.M., Ospina, M., Werner, E., Yolton, K., Cecil, K.M., Lanphear, B.P., Chen, A., 2022. Maternal urinary OPE metabolite concentrations and blood pressure during pregnancy: The HOME study. Environ Res. 207, 112220. https://doi.org/10.1016/j.envres.2021.112220. Yao, F., Bo, Y., Zhao, L., Li, Y., Ju, L., Fang, H., Piao, W., Yu, D., Lao, X., 2021a. Prevalence and Influencing Factors of Metabolic Syndrome among Adults in China from 2015 to 2017. Nutrients. 13. https://doi.org/10.3390/nu13124475. Yao, Y., Li, M., Pan, L., Duan, Y., Duan, X., Li, Y., Sun, H., 2021b. Exposure to organophosphate ester flame retardants and plasticizers during pregnancy: Thyroid endocrine disruption and mediation role of oxidative stress. Environ Int. 146, 106215. https://doi.org/10.1016/j.envint.2020.106215. Zhang, T., Bai, X.Y., Lu, S.Y., Zhang, B., Xie, L., Zheng, H.C., Jiang, Y.C., Zhou, M.Z., Zhou, Z.Q., Song, S.M., He, Y., Gui, M.W., Ouyang, J.P., Huang, H.B., Kannan, K., 2018. Urinary metabolites of organophosphate flame retardants in China: Health risk from tris(2-chloroethyl) phosphate (TCEP) exposure. Environ Int. 121, 1363-1371. https://doi.org/10.1016/j.envint.2018.11.006. Zhang, W., Zhang, Y., Hou, J., Xu, T., Yin, W., Xiong, W., Lu, W., Zheng, H., Chen, J., Yuan, J., 2017. Tris (2-chloroethyl) phosphate induces senescence-like phenotype of hepatocytes via the p21(Waf1/Cip1)-Rb pathway in a p53-independent manner. Environ Toxicol Pharmacol. 56, 68-75. https://doi.org/10.1016/j.etap.2017.08.028. Tables Table 1 Descriptive statistics of participants in this study. Variables Total (n=694) MetS (n=347) Non-MetS (n=347) P value Mean±SD/Median (IQR) Age (years) 50.49±10.41 50.50±10.30 50.48±10.52 0.979 Waist circumference (cm) 89.77±9.03 84.82±7.73 94.72±7.38 < 0.001 Total triglycerides (mmol/L) 1.78 (1.20, 2.45) 1.44 (1.00, 2.13) 2.06 (1.62, 2.88) < 0.001 HDL-c (mmol/L) 1.10 (0.97, 1.28) 1.18 (1.04, 1.38) 1.03 (0.91, 1.17) < 0.001 Fasting blood glucose (mmol/L) 5.25 (4.90, 5.65) 5.14 (4.84, 5.44) 5.41 (4.99, 6.05) < 0.001 Systolic blood pressure (mm Hg) 131.5±17.31 126.41±16.82 136.59±16.29 < 0.001 Diastolic blood pressure (mm Hg) 82.08±11.62 78.9±11.16 85.26±11.21 < 0.001 Urinary creatinine (g/L) 1.14 (0.66, 1.79) 1.14 (0.64, 1.76) 1.14 (0.66, 1.86) 0.625 N (%) Gender NA Male 528 (76.1) 264 (76.1) 264 (76.1) Female 166 (23.9) 83 (23.9) 83 (23.9) Age (years) 0.931 18–45 183 (26.4) 92 (26.5) 91 (26.2) ＞45 511 (73.6) 255 (73.5) 256 (73.8) Education 0.132 Below high school 114 (16.4) 65 (18.7) 49 (14.1) High school 112 (16.1) 60 (17.3) 52 (15) College and above 468 (67.4) 222 (64) 246 (70.9) Income (yuan per month) 0.431 <6000 196 (28.2) 98 (28.2) 98 (28.2) 6000-10000 186 (26.8) 100 (28.8) 86 (24.8) ≥10000 312 (45.0) 149 (42.9) 163 (47.0) Smoking 0.140 Never 419 (60.4) 219 (63.1) 200 (57.6) Current/Former 275 (39.6) 128 (36.9) 147 (42.4) Physical activity-MET (hour/day) 0.894 Low 244 (35.2) 122 (35.2) 122 (35.2) Moderate 241 (34.7) 123 (35.4) 118 (34) High 209 (30.1) 102 (29.4) 107 (30.8) Meat intake (times/wk) 0.365 ≤3 274 (39.5) 142 (40.9) 132 (38) 4–6 170 (24.5) 77 (22.2) 93 (26.8) ≥7 250 (36) 128 (36.9) 122 (35.2) Vegetable intake (times/wk) 0.826 ≤6 183 (26.4) 88 (25.4) 95 (27.4) 7–12 427 (61.5) 217 (62.5) 210 (60.5) ≥13 84 (12.1) 42 (12.1) 42 (12.1) Abbreviations: BMI, body mass index; HDL-c, high-density lipoprotein cholesterol; MET, metabolic equivalent. NA, unavailable. Table 2 Concentration of urinary OPE metabolites and biomarkers of oxidative stress and inflammation. Analyte (ng/mL) Detection rate (%) Non-MetS (n=347) MetS (n=347) P value Geometric mean P25 P50 P75 Geometric mean P25 P50 P75 Urinary OPEs metabolites (ng/mL) BDCIPP 60.66 0.13 <LOD 0.12 0.29 0.13 <LOD 0.10 0.27 0.692 BCIPHIPP 96.82 0.42 0.25 0.46 0.70 0.46 0.29 0.51 0.78 0.057 4-HO-DPHP 62.34 0.12 <LOD 0.09 0.38 0.14 <LOD 0.10 0.37 0.433 DPHP 80.66 0.09 0.04 0.09 0.15 0.09 0.05 0.09 0.16 0.832 DoCP & DpCP 97.69 0.11 0.07 0.10 0.14 0.10 0.07 0.09 0.13 0.041 BBOEP 99.71 0.28 0.17 0.29 0.51 0.39 0.22 0.39 0.68 < 0.001 Biomarkers of oxidative stress (ng/mL) 8-OHdG 99.71 7.91 4.14 8.47 16.47 10.63 5.48 11.11 20.53 < 0.001 8-isoPGF2α 96.36 4.59 2.31 5.65 10.74 4.17 2.40 5.84 10.67 0.786 HNE-MA 100.00 14.58 7.18 12.81 27.34 14.08 6.92 13.27 25.32 0.885 Inflammation biomarkers a IL-6 (pg/mL) 93.10 3.00 1.11 3.13 15.53 3.54 1.57 3.52 12.19 0.503 TNF-α (pg/mL) 82.59 17.40 6.53 26.67 136.94 18.05 6.78 23.51 125.07 0.983 CRP (mg/dL) 96.93 9.53 4.30 10.90 24.30 15.21 7.91 17.93 35.68 < 0.001 a 592 participants (294 in the MetS case group and 298 in the control group) were examined for inflammation biomarkers, after excluding 102 individuals who did not have sufficient blood samples. Table 3 Association of urinary OPEs metabolites with oxidative stress and inflammation biomarkers. OPEs metabolites (ng/mL) 8-OHdG 8-isoPGF2α HNE-MA IL-6 a TNF-α a CRP a %change（95% CI） %change（95% CI） %change（95% CI） %change（95% CI） %change（95% CI） %change（95% CI） BDCIPP T1 (< 0.05) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) T2 (0.05-0.22) -4.18 (-19.74, 14.40) -12.54 (-32.55, 13.41) 14.22 (-4.83, 37.09) -13.30 (-42.68, 31.15) -25.09 (-57.18, 31.05) -3.09 (-25.92, 26.78) T3 (≥ 0.22) -1.00 (-16.55, 17.44) -4.98 (-26.03, 22.05) 4.79 (-12.11, 24.94) -49.58 (-66.29, -24.59) * -46.01 (-68.68, -6.92) * -0.66 (-23.43, 28.89) P trend 0.892 0.660 0.563 0.002 0.027 0.955 Continuous -0.51 (-6.99, 6.42) -1.05 (-10.36, 9.23) 3.52 (-3.42, 10.97) -20.78 (-32.49, -7.06) * -19.74 (-35.31, -0.43) * 0.62 (-9.22, 11.53) BCIPHIPP T1 (< 0.29) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) T2 (0.29-0.60) 33.28 (11.28, 59.63) * 26.59 (-3.07, 65.33) 34.89 (11.91, 62.60) * -18.39 (-47.01, 25.69) 67.34 (-6.17, 198.42) -3.45 (-26.79, 27.33) T3 (≥ 0.60) 42.37 (19.05, 70.25) * 23.40 (-5.29, 60.78) 19.82 (-0.43, 44.19) -24.77 (-50.95, 15.39) -8.45 (-48.59, 63.04) 7.78 (-18.12, 41.87) P trend < 0.001 0.116 0.052 0.191 0.805 0.605 Continuous 22.13 (11.10, 34.25) * 4.23 (-9.38, 19.88) 14.08 (3.45, 25.81) * -13.24 (-30.82, 8.80) -3.35 (-28.67, 30.96) 5.63 (-8.62, 22.09) 4-HO-DPHP T1 (< 0.03) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) T2 (0.03-0.22) 10.48 (-7.76, 32.34) 39.29 (6.97, 81.38) * 13.92 (-5.26, 36.98) 38.88 (-9.39, 112.86) 37.38 (-22.17, 142.47) 12.81 (-14.19, 48.31) T3 (≥ 0.22) 13.86 (-4.12, 35.21) 30.61 (1.59, 67.93) * 46.99 (23.33, 75.19) * 20.61 (-20.00, 81.82) 3.93 (-40.37, 81.14) -7.58 (-29.00, 20.30) P trend 0.154 0.057 < 0.001 0.420 0.945 0.502 Continuous 4.52 (-0.38, 9.67) 6.79 (-0.48, 14.59) 10.95 (5.62, 16.54) * 10.06 (-1.79, 23.36) 3.55 (-11.28, 20.87) -2.20 (-9.11, 5.23) DPHP T1 (< 0.06) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) T2 (0.06-0.13) -2.27 (-17.97, 16.43) 19.34 (-7.66, 54.22) 3.34 (-13.68, 23.71) 48.62 (-1.82, 124.98) -31.69 (-60.97, 19.55) -0.96 (-24.16, 29.33) T3 (≥ 0.13) -5.89 (-21.41, 12.69) 14.09 (-12.41, 48.60) 21.30 (0.77, 46.01) * 39.06 (-9.06, 112.65) -34.21 (-63.02, 17.07) 7.39 (-18.31, 41.17) P trend 0.521 0.271 0.056 0.096 0.136 0.647 Continuous -1.82 (-9.42, 6.43) 7.97 (-4.08, 21.53) 8.12 (-0.49, 17.49) 18.72 (-1.73, 43.43) -9.83 (-30.36, 16.76) -0.56 (-11.97, 12.34) DoCP & DpCP T1 (< 0.08) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) T2 (0.08-0.12) 15.57 (-2.95, 37.64) 30.49 (0.78, 68.96) * 4.35 (-12.82, 24.89) 40.99 (-7.38, 114.61) 6.37 (-39.80, 87.94) -5.89 (-28.08, 23.14) T3 (≥ 0.12) 44.00 (20.24, 72.46) * 33.07 (1.90, 73.78) * 46.44 (21.62, 76.32) * 3.72 (-32.32, 58.93) 5.78 (-40.94, 89.45) -20.15 (-39.35, 5.13) P trend < 0.001 0.035 < 0.001 0.854 0.851 0.111 Continuous 23.43 (7.86, 41.25) * 20.32 (-1.38, 46.80) 29.89 (13.06, 49.23) * -0.26 (-27.09, 36.44) -7.27 (-39.19, 41.41) -10.04 (-26.57, 10.21) BBOEP T1 (< 0.22) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) 0 (Reference) T2 (0.22-0.42) 26.55 (5.21, 52.21) * 15.71 (-11.93, 52.04) 23.66 (2.12, 49.74) * -0.21 (-35.69, 54.83) 196.70 (66.12, 429.90) * 41.42 (7.03, 86.86) * T3 (≥ 0.42) 34.57 (12.36, 61.17) * 8.72 (-16.74, 41.98) 16.92 (-3.02, 40.97) 19.28 (-22.12, 82.69) 83.56 (3.91, 224.26) * 55.18 (18.29, 103.58) * P trend 0.002 0.602 0.143 0.385 0.081 0.003 Continuous 13.30 (4.85, 22.43) * 0.04 (-10.80, 12.20) 5.29 (-2.86, 14.12) 2.93 (-14.47, 23.86) 22.39 (-4.45, 56.78) 27.59 (13.43, 43.51) * a 592 participants (294 in the MetS case group and 298 in the control group) were examined for inflammation biomarkers, after excluding 102 individuals who did not have sufficient blood samples. % changes were transformed by the natural logarithm and back-transformed. Adjusted for urinary creatinine, age, gender, education level, income, smoking status, physical activity-MET, vegetable intake, and meat intake. * P ＜0.05 Supplementary Files supplementarymaterials.docx Cite Share Download PDF Status: Published Journal Publication published 07 Jul, 2024 Read the published version in Exposure and Health → Version 1 posted Reviewers agreed at journal 02 Apr, 2024 Reviewers invited by journal 29 Mar, 2024 Editor invited by journal 28 Mar, 2024 Editor assigned by journal 27 Mar, 2024 First submitted to journal 26 Mar, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4160250","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":285412313,"identity":"c0dd47c4-26b8-49d6-98bc-e0956d075b8b","order_by":0,"name":"Sijie Yang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Sijie","middleName":"","lastName":"Yang","suffix":""},{"id":285412314,"identity":"381cd660-28d2-45d0-ac36-ab31c8f4ffbb","order_by":1,"name":"Yaping Li","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yaping","middleName":"","lastName":"Li","suffix":""},{"id":285412315,"identity":"102415f7-6613-41c4-949f-bc7c411258aa","order_by":2,"name":"Ling Liu","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Liu","suffix":""},{"id":285412316,"identity":"1835844f-bc72-4a22-81e7-08595f1b48de","order_by":3,"name":"Zhengce Wan","email":"","orcid":"","institution":"Tongji Medical College of HUST: Huazhong University of Science and Technology Tongji Medical College Tongji Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhengce","middleName":"","lastName":"Wan","suffix":""},{"id":285412317,"identity":"3ed9a872-ce85-43c2-b5ae-ce8bf864e668","order_by":4,"name":"Qitong Xu","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Qitong","middleName":"","lastName":"Xu","suffix":""},{"id":285412318,"identity":"b93fa548-bb69-4ff7-9a72-818dd2a4db93","order_by":5,"name":"Chang Xie","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Xie","suffix":""},{"id":285412319,"identity":"3a107085-3398-4a54-8b6b-5cb953edf422","order_by":6,"name":"Lulu Song","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Lulu","middleName":"","lastName":"Song","suffix":""},{"id":285412320,"identity":"e5558f9a-80cd-4d7b-a78d-a17cca68f4ce","order_by":7,"name":"Youjie Wang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Youjie","middleName":"","lastName":"Wang","suffix":""},{"id":285412321,"identity":"490f790f-86af-4713-957e-e4418c556dfe","order_by":8,"name":"Hui Chen","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Chen","suffix":""},{"id":285412322,"identity":"fa429b85-7d7f-491f-9939-1e09b6d12dd2","order_by":9,"name":"Surong Mei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIie2OMQrCMBRAUwpxSTsnBOwVWgJd9DARoS5VegMFoS7irLdwctZFlxwgkkkENyeXKh1MURzTugnmDZ//4T94AFgsvwxszd/btrGChJ78GwXgtKESLIenc/bYtH1y3ZN7Adq+5M4tMyihHDC2WigG6SihiANGJHfp0qTgBFJvrno5TWOqw3pryaGLjGEvZZwTEZOCg3GtAqRWUKE4xCjGOoyHdUooLi7zJirKUco6KMHRSpym1Bg2S5wzKlUQzER0LLrdwD/0dzdjWIWTf1ZcnZM6QVM2+LFYLJb/5QleaURFLRbB5AAAAABJRU5ErkJggg==","orcid":"","institution":"Huazhong University of Science and Technology Tongji Medical College","correspondingAuthor":true,"prefix":"","firstName":"Surong","middleName":"","lastName":"Mei","suffix":""}],"badges":[],"createdAt":"2024-03-25 02:52:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4160250/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4160250/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12403-024-00653-5","type":"published","date":"2024-07-07T10:49:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54039523,"identity":"1d814c1f-bbf5-48fb-b3ee-e8ed9d3d15c5","added_by":"auto","created_at":"2024-04-03 17:22:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":463704,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of OPE metabolites with risk of metabolic syndrome (MetS) (n = 694). Abbreviations: OR, odds ratio; CI, confidence interval. Model 1 was unadjusted. Model 2 was adjusted for urinary creatinine, age, gender, education level, income, smoking status, physical activity-MET, vegetable intake, and meat intake. T1 to T3 refers to the 1st to 3rd tertiles of OPE metabolite concentrations. \u003csup\u003ea\u003c/sup\u003e Concentrations of continuous OPEs were In-transformed. *\u003cem\u003eP\u003c/em\u003e＜0.05\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4160250/v1/2fa872089454da05afc0c934.png"},{"id":54039520,"identity":"7897eee6-8f44-4929-8a49-efa153c24f42","added_by":"auto","created_at":"2024-04-03 17:22:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67878,"visible":true,"origin":"","legend":"\u003cp\u003eCombined effects of OPE metabolites on MetS risk were estimated by weighted quantile sum (WQS) models (A) and Bayesian Kernel Machine Regression (BKMR) models (B). Abbreviations: OR, odds ratio; CI, confidence interval. Both models were adjusted for urinary creatinine, age, gender, education level, income, smoking status, physical activity-MET, vegetable intake, and meat intake.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4160250/v1/82c363f68e2bae2f1ef9b32b.png"},{"id":54040118,"identity":"b7b44ff4-60da-4de8-9063-779a716bea5d","added_by":"auto","created_at":"2024-04-03 17:30:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":174340,"visible":true,"origin":"","legend":"\u003cp\u003eThe associations of oxidative stress and inflammation biomarkers with risk of metabolic syndrome (MetS). Abbreviations: OR, odds ratio; CI, confidence interval. Models were adjusted for urinary creatinine, age, gender, education level, income, smoking status, physical activity-MET, vegetable intake, and meat intake. \u003csup\u003ea\u003c/sup\u003e Concentrations of continuous OPEs were In-transformed. *\u003cem\u003eP\u003c/em\u003e＜0.05\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4160250/v1/c81feef280c28160b6331149.png"},{"id":54039524,"identity":"f8b6f9ba-1e66-4170-841b-e28fe7b923fe","added_by":"auto","created_at":"2024-04-03 17:22:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":164868,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated proportion of the association between urinary OPE metabolites and metabolic syndrome (MetS) mediated by urinary 8-OHdG and serum CRP. The figure presents 8-OHdG and CRP as mediators, the estimate of the indirect effect (IE), the estimate of the total effect (TE) and the proportion of mediation (IE/TE). Abbreviations: CI, confidence interval. The mediation model was adjusted for urine creatinine, age, gender, education level, income, smoking status, physical activity-MET, vegetable intake, and meat intake.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4160250/v1/e7fca13fc1e79373074a48ed.png"},{"id":59797665,"identity":"fd88c320-5eb6-492c-b9e9-88afc3e46c92","added_by":"auto","created_at":"2024-07-07 10:50:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1784504,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4160250/v1/4b2388d9-36ca-4a3f-a7ab-70e240592e70.pdf"},{"id":54039521,"identity":"f3dec22e-08df-43bc-9d1c-bd1dd6394de9","added_by":"auto","created_at":"2024-04-03 17:22:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1185331,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4160250/v1/88363e05d6484eb856a58b1e.docx"}],"financialInterests":"","formattedTitle":"Associations between organophosphate esters exposure and metabolic syndrome: Exploring the mediating role of oxidative stress and inflammation in adults","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMetabolic syndrome (MetS) is identified by a cluster of metabolic risk factors and encompasses central obesity, dyslipidemia, hypertension, and hyperglycemia.\u0026nbsp;MetS not only contributes to cardiovascular diseases and diabetes but also elevates the risk of mortality from other illnesses (Lakka et al., 2002; Wilson et al., 2005).\u0026nbsp;It is estimated that\u0026nbsp;approximately 25% of the global population suffers from MetS, and the prevalence of MetS in China is as high as 32.97%,\u0026nbsp;with an increasing trend year by year (Gu et al., 2005; Li et al., 2016).\u0026nbsp;MetS often arises from the combined influence of genetics, behavior, and environmental factors. An increasing body of research suggests that\u0026nbsp;environmental chemicals, especially with endocrine-disrupting effects, are important risk factors for metabolic impairment (Le Magueresse-Battistoni et al., 2018; Lind and Lind, 2018).\u003c/p\u003e\n\u003cp\u003eOrganophosphate esters (OPEs), classified as endocrine disruptors, are extensively employed in a multitude of industrial and consumer commodities, such as electronics, decoration materials, and personal care products (Wei et al., 2015).\u0026nbsp;Toxicological studies revealed that OPEs can cause metabolic impairments, which encompass disturbances in lipid metabolism and energy equilibrium, induction of insulin resistance, facilitation of obesity, and advancement of other metabolic diseases (Hao et al., 2019).\u0026nbsp;In addition, low-dose triphenyl phosphate (TPHP) exposure still induced comprehensive metabolic disturbances in the liver and even systemically in mice (Selmi-Ruby et al., 2020).\u0026nbsp;Epidemiological studies on the correlation between OPE exposure and the risk of MetS remain notably limited.\u0026nbsp;Only one study, based on a cross-sectional survey of 1,157\u003cem\u003e\u0026nbsp;\u003c/em\u003eadults in the National Health and Nutrition Examination Survey (NHANES) 2011\u0026ndash;2014, has demonstrated a positive correlation between OPE exposure and risk of MetS in adults (Luo et al., 2020c). Additionally, several other epidemiological studies have indicated that OPE exposure is linked to metabolic diseases such as obesity, lipid disturbances, elevated blood glucose, and increased hypertension (Luo et al., 2020c; Hu et al., 2022; Yang et al., 2022). Considering the escalating burden of MetS in our country, it is imperative to explore the potential correlation between OPE exposure and MetS risk in China.\u003c/p\u003e\n\u003cp\u003eThe biological mechanisms underlying the associations between OPE exposure and MetS remain unclear. It has been widely acknowledged that oxidative stress and inflammations play pivotal roles in the development of MetS. Oxidative damage can promote cellular lipid peroxidation so that the structure and function of the cell membrane are damaged (Grandl and Wolfrum, 2018); Inflammatory response can stimulate the inflammatory signaling pathway, inducing pancreatic islet \u0026beta;-cell dysfunction and apoptosis, thus affecting the insulin signaling pathway, further causing insulin resistance (Lee and Pratley, 2005). Both in vitro and in vivo investigations showed that OPE exposure induces decreases in the activity of several antioxidant enzymes in male mice and Leydig cells, leading to an upsurge in free radicals and subsequent oxidative damage harm within the organism (Chen et al., 2015a; Chen et al., 2015b).\u0026nbsp;Meanwhile, toxicological studies revealed that OPEs regulated the expression of inflammatory cytokines and chemokines, thereby activating the mechanism of initiation of intracellular inflammatory responses (Hu et al., 2020).\u0026nbsp;Limited epidemiologic studies also found positive correlations between OPE exposure and oxidative stress and inflammation biomarkers (Ait Bamai et al., 2019; Araki et al., 2020; Yao et al., 2021b). Nevertheless, few researchers examined the potential mediating role of oxidative stress and inflammation biomarkers on the correlation between urinary OPE metabolites and MetS risk.\u003c/p\u003e\n\u003cp\u003eIn this study, we recruited 694 participants based on a case-control study to quantify the association of individual OPE metabolites and their mixture with MetS risk and whether oxidative stress and inflammation biomarkers mediated the \u0026ldquo;OPEs-MetS\u0026rdquo; associations.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cp\u003e2.1 Study population\u003c/p\u003e\n\u003cp\u003eParticipants were randomly enrolled from August 2018 to November 2018 at the Health Management Center of Wuhan Tongji Hospital. The criteria for participant inclusion were established as follows: 1) residents in the study area; 2) adults \u0026ge;18 years old; 3) no history of occupational exposure to OPEs. A sample of 2103 individuals was enrolled, and both urine and blood samples were collected during their clinic visit. 347 individuals who met the diagnostic criteria for MetS were selected as the case group, while 347 participants were randomly selected as the control group by matching the case on gender and age (\u0026plusmn;5 years).\u0026nbsp;A total of 694 participants (347 pairs) were\u0026nbsp;ultimately\u0026nbsp;enrolled in this study. Of these, 592 participants (294 in the case group and 298 in the control group) were examined for inflammation biomarkers, after excluding 102 individuals who did not have sufficient blood samples.\u0026nbsp;The research received approval from the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology (NO. [2018] IEC (S329)).\u003c/p\u003e\n\u003cp\u003e2.2 Measurement of urinary OPE metabolites\u003c/p\u003e\n\u003cp\u003eThe concentration of urinary OPE metabolites was measured by solvent-induced phase transition extraction coupled with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) following the methods described in previous studies (Hu et al., 2019). In brief, 1 mL of urine was incubated at 37\u0026deg;C for 12h with mixed internal standard, ammonium acetate buffer solution, and \u0026beta;-glucuronidase. Acetonitrile and methyl tert-butyl ether were added and the upper organic phase was isolated and subjected to evaporation until complete dryness. Then, 100 \u0026mu;L of a methanol/water (1:9, v/v) mixture was used for reconstitution of the solutions before UPLC-MS/MS analysis. Quality control was performed by measuring blank samples and quality control (QC) samples (0.2 ng/mL, 2 ng/mL) in each batch of 30 samples. The recoveries of the 16 OPE metabolites ranged from 71.34% to 117.63%, with intra-day relative standard deviations (RSD) less than 18.3% and inter-day RSD less than 22.5%. The limit of detections (LODs) for 16 OPE metabolites varied between 0.012 to 0.121 ng/mL. More comprehensive details on standards and internal standards for the 16 OPE metabolites were described in Supplementary Materials. Concentrations below the LODs are replaced with the value of LOD/.\u003c/p\u003e\n\u003cp\u003e2.3 Definition of MetS and its components\u003c/p\u003e\n\u003cp\u003eMetS was determined in accordance with the International Diabetes Federation\u0026apos;s criteria for Chinese people. Individuals with central obesity (waist circumference \u0026ge;90 cm in men and \u0026ge;80 cm in women) and two or more of the following criteria were defined as having MetS: (1) Triacylglycerol (TG): fasting serum TG \u0026ge;1.7 mmol/L; (2) high-density lipoprotein cholesterol (HDL-C): fasting serum HDL-C \u0026lt;1.04 mmol/L for men, \u0026lt;1.29 mmol/L for women; (3) blood glucose: fasting blood glucose (FBG) \u0026ge;5.6 mmol/L or have been diagnosed with type II diabetes; (4) blood pressure (BP): systolic blood pressure (SBP) \u0026ge;130 mmHg, diastolic blood pressure (DBP) \u0026ge;85 mmHg or are undergoing blood pressure-lowering therapy.\u0026nbsp;Trained\u0026nbsp;physicians carried out physical examinations, comprising height, weight, and waist circumference, and used a calibrated electronic sphygmomanometer to measure the resting BP of each participant three times. Biochemical indexes including HDL-C, FBG, and TG were analyzed by a HITACHI auto-analyzer (Cobas 8000, Roche Diagnostics, Basel, Switzerland).\u003c/p\u003e\n\u003cp\u003e2.4 Measurements of oxidative stress and inflammation biomarkers\u003c/p\u003e\n\u003cp\u003eMethods for determining urinary levels of 8-iso-prostaglandin F2a (8-isoPGF2a), 8-hydroxy-2-deoxyguanosine (8-OHdG), and 4-hydroxy-2-nonenal-mercapturic acid (HNE-MA) referenced to previous studies (Wang et al., 2019b). In brief, 100 \u0026mu;L of urine samples were subjected to solid phase extraction (SPE) after adding 1.5 mL of deionized water and 50 \u0026mu;L of internal standard. The resulting eluate, upon evaporation to dryness, was re-dissolved in 100 \u0026mu;L of 10% methanol in water.\u0026nbsp;Three oxidative stress biomarkers were measured using LC-MS/MS.\u0026nbsp;Each batch of urine samples consists of 1 blank sample and 2 QC samples.\u0026nbsp;The spiked recoveries of the three oxidative stress biomarkers varied between 85.6% to 110.9%, with intra-day RSD less than 10.5% and inter-day RSD less than 15.6%. The LODs of 8-isoPGF2a, 8-OHdG, and HNE-MA were 0.02 ng/mL, 0.03 ng/mL, and 0.01 ng/mL, respectively.\u003c/p\u003e\n\u003cp\u003eSerum concentrations of inflammation biomarkers, including interleukin-6 (IL-6), tumor necrosis factor-\u0026alpha; (TNF-\u0026alpha;), and C-reactive protein (CRP), were measured using an enzyme-linked immunosorbent assay (ELISA) kit (Link Bio, China). The protocol was executed under the guidelines provided by the manufacturer. To minimize systematic bias and inter-assay variation, all samples were processed identically and analyzed randomly. The LODs for IL-6, TNF-\u0026alpha;, and CRP were 0.02 pg/mL, 0.16 pg/mL, and 0.4 mg/dL, respectively.\u003c/p\u003e\n\u003cp\u003e2.5 Statistical analysis\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were applied to examine the basic characteristics of participants. Differences in demographic characteristics, MetS components, urinary OPE metabolites, and levels of oxidative stress and inflammation biomarkers between MetS and non-MetS groups were assessed using either parametric or nonparametric methods. In this research, solely urinary OPE metabolites with detection rates above 60% were considered. The levels of urinary OPE metabolites, oxidative stress, and inflammation biomarkers below the LOD were identified as the square root of LOD values over 2. In addition, urinary OPE metabolites, oxidative stress, and inflammation biomarker concentrations were ln-transformed due to their skewed distributions.\u0026nbsp;Spearman\u0026rsquo;s test was assessed to calculate the correlations between urinary OPE metabolites.\u003c/p\u003e\n\u003cp\u003eThe correlations of urinary OPE metabolites, oxidative stress, and inflammation biomarkers with the MetS risk were assessed using multivariate logistic regression models with odds ratio (OR) and its 95% confidence interval (CI) as effect estimates. In addition, the correlations between urinary OPE metabolites and oxidative stress and inflammation biomarkers were assessed using multiple linear regression models with the corresponding percentage change (% change) and its 95% CI as the effect estimate and with the following equation: (% change) = 100%\u0026times;[exp(\u0026beta;)-1], where \u0026beta; is the regression coefficient for urinary OPEs metabolites. Specifically, urinary OPE metabolites, oxidative stress, and inflammation biomarkers were modeled as both continuous and categorical (tertiles) variables in the above two models. Linear trend tests by tertiles of OPE metabolites, oxidative stress, and inflammatory biomarkers were executed by utilizing the median value of each tertile as a continuous variable in the above two models.\u0026nbsp;Restricted cubic splines (RCS) were further applied to estimate the potential nonlinear correlations between the OPE exposure and MetS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWeighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) were employed to further assess the relationship between OPE mixtures and MetS risk.\u0026nbsp;The WQS analysis was employed to estimate the collective effect of OPE mixtures on the MetS and to identify the primary pollutants involved (Carrico et al., 2014). The WQS index was computed by utilizing the \u0026quot;gWQS\u0026quot; software package in R, where individual OPE concentrations were combined via a weighted sum. The WQS index, ranging from 0 to 1, indicates the weight of the seven urinary OPE metabolites and their relative importance. BKMR offers a flexible approach to modeling the individual and combined effects of exposure to chemical mixtures (Bobb et al., 2015). This method takes into account the potentially non-linear and non-cumulative dose-response relationships between these chemicals by utilizing kernel functions. To assess the overall joint effect of the OPEs on MetS, we estimated the difference in outcome levels at various percentiles for seven OPEs compared to their 50th percentile values.\u0026nbsp;To fit the BKMR models, we employed a Markov chain Monte Carlo (MCMC) sampler, running for 20,000 iterations. Furthermore, we calculated posterior post-inclusion probabilities (PIPs) to determine the relative importance of each outcome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMediation analysis was applied to examine the potential mediating mechanisms involving oxidative stress and inflammation biomarkers in the association of urinary OPE metabolite concentrations with MetS (Tingley et al., 2014). The direct effect (DE) indicates the unmediated association between OPE metabolites and MetS, whereas the indirect effect (IE) quantifies the impact of OPE exposure on MetS risk mediated by oxidative stress and inflammation biomarkers. The mediation proportion arising from oxidative stress and inflammation biomarkers was computed as the ratio between the indirect effect and the total effect (TE).\u003c/p\u003e\n\u003cp\u003eCovariates were identified based on a combination of biological and statistical\u0026nbsp;considerations (Yao et al., 2021a).\u0026nbsp;The models incorporated the following covariates: urinary creatinine (continuous), age (continuous), gender (male, female), education (below high school, high school, college, and above), income (\u0026lt;6000, 6000-10000, \u0026ge;10000 yuan/month), smoking status (never smoker, former/current smoker),\u0026nbsp;physical activity-metabolic equivalent (MET) (low, moderate, high), red meat intake (\u0026le;3 times/week, 4-6 times/week, \u0026ge;7 times/week), and vegetable intake (\u0026le;6 times/week, 7-12 times/week, \u0026ge;13 times/week). Details of physical activity-MET were described in Supplementary Material.\u003c/p\u003e\n\u003cp\u003eWe conducted sensitivity analyses to evaluate the robustness of our findings. We re-examined the associations between creatinine-adjusted concentrations of OPE metabolites and the risk of MetS using multivariate logistic regression models, excluding creatinine as a covariate.\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using SPSS version 25.0 (SPSS Inc., Chicago, IL, USA) and R version 4.1.1 (R Core Team 2021). \u003cem\u003eP\u003c/em\u003e-values \u0026lt; 0.05 were considered to be statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1. Characteristics of the study population\u003c/p\u003e\n\u003cp\u003eTable 1 displayed the basic demographic characteristics of the 694 participants, comprising 347 cases and 347 controls. The mean age of the case and control groups was 50.50\u0026plusmn;10.30 and 50.48\u0026plusmn;10.52 years old, respectively. There were comparable proportions of age, education, income, smoking, meat intake, and vegetable intake between the case and control groups (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05). The MetS group exhibited higher levels of waist circumference, total triglycerides, systolic blood pressure, diastolic blood pressure, and fasting blood glucose, as well as lower levels of HDL-c comparable to the control group (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e3.2. Distribution of urinary OPE metabolites, oxidative stress, and inflammation biomarkers\u003c/p\u003e\n\u003cp\u003eThe distribution of OPE metabolites, oxidative stress, and inflammation biomarkers of the study population were shown in Table 2. A total of seven OPE metabolites had detection rates \u0026gt;60%. Compared with the non-MetS group, the MetS group demonstrated higher levels of DoCP \u0026amp; DpCP and BBOEP (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05). Spearman\u0026rsquo;s correlation analysis illustrated a significant positive correlation among the OPE metabolites (Fig. S1). The detection rates of urinary 8-OHdG, 8-isoPGF2\u0026alpha;, HNE-MA, IL-6, TNF-\u0026alpha;, and CRP were 99.71%, 96.36%, 100.00%, 93.10%, 82.59%, and 96.93%, respectively. Participants with MetS had higher concentrations of urinary 8-OHdG and serum\u0026nbsp;CRP (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) compared to the non-MetS group (Table 2).\u003c/p\u003e\n\u003cp\u003e3.3. Associations of urinary OPE metabolites with MetS\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe correlations of urinary OPE metabolites with MetS risk were shown in\u0026nbsp;Fig 1. In both crude and adjusted models, positive correlations between urinary BCIPHIPP and BBOEP concentrations and MetS risk were found (all \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e \u0026lt; 0.05). After adjusting for covariates, compared to the first tertile, the ORs (95% CI) of MetS in the third tertiles were 1.53 (1.02, 2.29) for urinary BCIPHIPP and 1.96 (1.30, 2.97) for urinary BBOEP concentrations, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRCS was conducted to estimate the potential nonlinear dose-response correlations between OPE metabolites and MetS risk, and linear correlations between urinary BCIPHIPP and BBOEP and risk of MetS (all \u003cem\u003eP\u003c/em\u003e for non-linear \u0026gt; 0.05) were found after adjusting for covariates (Fig. S2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe WQS analysis was utilized to estimate the correlation between OPE mixtures and MetS (Fig. 2A). OPE mixtures presented positive associations with MetS (OR=1.45; 95% CI: 1.07-1.96). Urinary BBOEP emerged as the primary contributor to the mixture effect on MetS. Similarly, the BKMR model also exhibited positive associations between OPE mixtures and MetS when OPE mixtures were concurrently at a particular percentile ranging from the 5th to 90th percentile compared to the median (Fig. 2B). The PIPs for OPEs in the BKMR model were summarized in Table S1, and BBOEP was the predominant contributor on MetS (PIP=1.00).\u003c/p\u003e\n\u003cp\u003e3.4. Associations of oxidative stress and inflammation biomarkers with OPE metabolites and MetS\u003c/p\u003e\n\u003cp\u003eTable 3 presented the relationships of tertiles and continuous OPE metabolite concentrations with oxidative stress and inflammation biomarkers. When adjusting for covariates, we observed that urinary BCIPHIPP was positively correlated with levels of urinary 8-OHdG and urinary HNE-MA, and urinary BBOEP showed positive relationships with concentrations of urinary 8-OHdG and serum CRP.\u003c/p\u003e\n\u003cp\u003eWe analyzed the correlations of oxidative stress and inflammation biomarkers with MetS risk (Fig. 3). We found that urinary 8-OHdG and serum CRP were correlated with an elevated risk of MetS, and the ORs (95%CI) were 1.41 (1.19, 1.67) and 1.33 (1.17, 1.53), respectively. Compared to the first tertiles of urinary 8-OHdG and serum CRP, the ORs (95% CI) of MetS in the third tertiles were 1.73 (1.17, 2.56) for urinary 8-OHdG and 2.81 (1.83, 4.35) for serum CRP, respectively.\u003c/p\u003e\n\u003cp\u003e3.5. Mediation analyses\u003c/p\u003e\n\u003cp\u003eBased on the association between urinary OPE metabolite concentrations, oxidative stress/inflammation biomarkers, and MetS, we further analyzed the mediating effect of urinary 8-OHdG and serum CRP in the correlation of urinary OPE metabolites and risk of MetS (Fig. 4). We identified a notable mediation effect of urinary 8-OHdG linking the relationships of urinary BCIPHIPP and BBOEP with MetS risk (the proportion mediated was 23.27% and 8.70%, respectively). The correlation between urinary BBOEP and risk of MetS was also partially mediated by serum CRP, and the proportion of mediation was 16.32%.\u003c/p\u003e\n\u003cp\u003e3.6. Secondary and sensitivity analyses\u003c/p\u003e\n\u003cp\u003eNo significant interaction was observed between OPEs and the predetermined effect modification factors in the stratified analysis (all \u003cem\u003eP\u003c/em\u003e for interaction \u0026gt; 0.05) (Table S2). We also evaluated the correlations between creatinine-adjusted OPE concentrations and MetS risk, and the associations remained robust (Table S3).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study investigated the \u0026quot;OPEs-MetS\u0026quot; association and estimated the mediated role of oxidative stress and inflammation biomarkers. We found that individual OPEs and their mixtures exhibited correlations with an escalated risk of MetS. The mediation model revealed that urinary 8-OHdG and serum CRP concentration mediated the correlation between urinary OPE metabolites and MetS risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe distribution of OPE concentrations in different regions were shown in Table S4. Urinary levels of BDCIPP and DPHP were slightly lower in comparison to the reported levels in the general populations of the United States (Ospina et al., 2018), Canada (Yang et al., 2019), Belgium (Bastiaensen et al., 2019), Norway (Xu et al., 2019), and Australia (Van den Eede et al., 2015), but were comparable to those found in our countries (Zhang et al., 2018; Li et al., 2020). Urinary BCIPHIPP concentration was also lower than that reported in the general populations of Australia (Van den Eede et al., 2015), but surpassed the reported levels in the general population of Norway (Xu et al., 2019). In addition, urinary DoCP \u0026amp; DpCP concentration was below the reported levels in the general population of Canada (Kosarac et al., 2016), but slightly higher than that in our country (Zhang et al., 2018). Overall, urinary OPE concentrations vary geographically, which may be closely related to the extent of exposure, health status, economic level, etc.\u003c/p\u003e\n\u003cp\u003eEpidemiologic studies on OPE exposure and MetS are limited. Only one study from NHANES (2011-2014) in the United States observed that urinary OPE metabolites, both individually and as mixtures, exhibited a correlation with increased risk of MetS in adults (Luo et al., 2020c), which is in line with our research. In addition, several studies assessed the correlations between urinary OPE metabolites and MetS components. Wang et al. highlighted increased urinary concentrations of certain OPEs metabolites in overweight/obese populations compared to those with normal weight within the United States (Hao et al., 2019). A recent study based on the NHANES (2013-2014) also revealed that urinary OPE metabolites were linked to increased waist circumference and an escalated risk of overweight/obesity in the US general adult population (Boyle et al., 2019). Only two studies to date have investigated the correlation between OPEs and blood glucose levels. Yan et al. identified positive associations between OPE metabolites with fasting glucose and glycated hemoglobin in a case-control population with type 2 diabetes (Ji et al., 2021). Meanwhile, Luo et al. identified a positive correlation between urinary OPE concentrations in female adolescents and the susceptibility to pre-diabetes, alongside 2-hour blood glucose levels observed during an oral glucose tolerance test (Luo et al., 2020a). However, Luo et al. also observed that urinary dibutyl phosphate (DBP) concentrations in male adolescents were negatively correlated with several indicators of glucose homeostasis (Luo et al., 2020a). Differences in sample sizes, population composition, and concentration level may contribute to the inconsistencies among the findings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIndividuals are typically exposed to OPE mixtures rather than single OPEs. Therefore, it is worth exploring whether OPE mixtures are associated with MetS. WQS categorized continuous variables into quartiles to minimize the effect of extreme concentrations and assessed the chemical exposure burden based on the weights of bootstrap sampling, while BKMR allowed for the identification of non-linear and non-cumulative effects resulting from the combined exposure. The results consistently demonstrated that OPE mixtures were positively linked to MetS. Consistent with our findings, one study (Luo et al., 2020b) also found OPE mixtures were associated with MetS in adult men. Additionally, the primary factor contributing to the overall effect was found to be BBOEP. As a commonly used component in paints, flooring, and electronic devices, TBOEP, the precursor of BBOEP (V\u0026ouml;lkel et al., 2017), is encountered regularly by adults in their daily activities. It ought to be of great concern for policies limiting the usage of specific hazardous compounds.\u003c/p\u003e\n\u003cp\u003eThe role of OPEs in modulating oxidative stress and inflammation is of utmost importance. We found that urinary OPE concentrations were correlated with elevated biomarkers of oxidative stress in adults. In vivo studies, OPEs induced cytotoxicity in different cell lines (e.g., HepG2 and A549 cells) at higher concentrations, producing excess ROS (An et al., 2016). Limited epidemiologic research examined the link between OPE exposure and oxidative stress, focusing on pregnant women and occupational populations (Lu et al., 2017; Ait Bamai et al., 2019; Ingle et al., 2020). A study based on pregnant women in Puerto Rico found positive correlations between urinary bis(1-chloro-2-propyl) phosphate (BCIPP) and DPHP levels and 8-OHdG and 8-isoprostane concentrations (Ingle et al., 2020). Lu et al. reported associations between urinary OPE metabolite concentrations and increased 8-OHdG levels in 175 individuals residing in an electronic waste dismantling area (Lu et al., 2017).\u0026nbsp;The findings from this study, along with the above studies, collectively indicate a positive association between OPE metabolites and elevated oxidative stress biomarkers. We also found inverse relationships between urinary BDCIPP concentration and serum IL-6 and TNF-\u0026alpha;, and positive correlations between urinary BBOEP concentration and serum CRP. Toxicological studies showed that OPE exposure leads to decreases in the number of macrophages in zebrafish, which in turn secrete the cytokine TNF-\u0026alpha; (Yan et al., 2022). Zhang et al. also reported the link between OPEs and diminished IL-6 concentrations in hepatocytes (Zhang et al., 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEvidence suggested that oxidative stress and inflammation play important roles in causing metabolic disorders in humans (Rani et al., 2016; Carrier, 2017). Our study revealed a significant association between urinary 8-OHdG concentration and heightened risk of MetS within the general population. Oxidative stress can cause insulin resistance and impaired pancreatic \u0026beta;-cell function, leading to decreased levels of insulin secretion. This disruption in insulin levels can subsequently affect glucose and lipid metabolism in the body, potentially contributing to the onset of MetS (Rani et al., 2016; Carrier, 2017). In alignment with our results, Butkowski et al. observed an association linking increased urinary 8-OHdG concentrations to an escalated risk of MetS in Australian adults (Butkowski et al., 2017). We also revealed a correlation between increased serum CRP concentrations and an escalated risk of MetS. CRP, part of the pentraxin protein family, functions as an acute-phase reactant and is acknowledged as a vital inflammation biomarker in the advancement of metabolic diseases. Earlier studies have consistently demonstrated a correlation between increased serum CRP levels and an elevated risk of MetS (Ridker et al., 2000). Meanwhile, toxicological studies showed that CRP can cause disorders of lipid metabolism, abnormalities in endothelial cell structure and function, and impairment of insulin signaling, leading to atherosclerotic thrombosis and the development of MetS (Devaraj et al., 2009).\u003c/p\u003e\n\u003cp\u003eUrinary 8-OHdG and serum CRP concentrations acted as mediators in the association between OPE exposure and elevated risk of MetS. 8-OHdG serves as a biomarker indicating oxidative DNA damage, suggesting the crucial involvement of oxidative DNA damage in the connections between OPE exposure and metabolic disorders. 8-OHdG can be paired with adenine during DNA replication, leading to the occurrence of the G:C-T: A subversion, causing elevated microsatellite instability, which may lead to aberrant apoptosis and necrosis of adipocytes and endothelial cells, affecting normal metabolic functions of the body (Valavanidis et al., 2009; Urbaniak et al., 2020). A recent epidemiologic study also found that CRP concentrations mediated the correlations between heavy (metalloid) metal exposure and MetS risk (Ma et al., 2020).\u0026nbsp;Considering the limited sample size, further exploration is crucial to delineate the involvement of inflammatory factors in mediating the association between OPE exposure and MetS.\u003c/p\u003e\n\u003cp\u003eThere are some strengths of this study. Firstly, our investigation focuses on examining the link of individual OPEs and their mixtures with the risk of MetS based on a case-control design. Secondly, we applied the mediation analyses to explore the potential involvement of oxidative stress and inflammation biomarkers in the correlations between OPEs and MetS risk. The study also had some limitations. First, due to continuous exposure to urinary OPEs, urinary metabolites of OPEs are variable (Wang et al., 2019a), and spot urine samples may lead to bias in exposure estimation. Second, participants were recruited from the general population of health management centers, with some selection bias, which limited the generalization of our results to the broader population. Third, due to the case-control design of this study, determining a causal correlation between OPEs-oxidative stress/inflammation-MetS proves challenging.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study revealed associations between individual OPEs and their mixtures with MetS risk based on a case-control design, with BBOEP as the predominated contributor. Urinary 8-OHdG and serum CRP as potential mechanisms underlay the correlations between OPE exposure and MetS risk. Further prospective cohort research is urgently needed to confirm our results in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe acknowledge the participants in the study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Natural Science Foundation of China (No. 42077397).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSijie Yang:\u003c/strong\u003e Conceptualization, Methodology, Software, Investigation,\u0026nbsp;Writing\u0026ndash;original draft;\u003cstrong\u003e\u0026nbsp;Yaping Li\u003c/strong\u003e: Validation, Methodology, Data curation, Investigation,\u0026nbsp;Writing\u0026ndash;original draft; \u003cstrong\u003eLing Liu:\u0026nbsp;\u003c/strong\u003eMethodology, Investigation, Validation, Data curation; \u003cstrong\u003eZhengce Wan\u003c/strong\u003e:\u0026nbsp;Investigation, Validation; \u003cstrong\u003eQitong Xu:\u0026nbsp;\u003c/strong\u003eMethodology, Investigation; \u003cstrong\u003eChang Xie\u003c/strong\u003e: Validation, Data curation; \u003cstrong\u003eLulu Song\u003c/strong\u003e: Conceptualization, Methodology, Formal analysis; \u003cstrong\u003eYoujie Wang:\u003c/strong\u003e Resources, Data curation, Supervision; \u003cstrong\u003eHui Chen:\u003c/strong\u003e Investigation, Resources, Project administration. \u003cstrong\u003eSurong Mei:\u003c/strong\u003e Reviewing, Supervision, Project administration, Funding acquisition.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAit Bamai, Y., Bastiaensen, M., Araki, A., Goudarzi, H., Konno, S., Ito, S., Miyashita, C., Yao, Y., Covaci, A., Kishi, R., 2019. Multiple exposures to organophosphate flame retardants alter urinary oxidative stress biomarkers among children: The Hokkaido Study. Environ Int. 131, 105003. https://doi.org/10.1016/j.envint.2019.105003.\u003c/li\u003e\n\u003cli\u003eAn, J., Hu, J., Shang, Y., Zhong, Y., Zhang, X., Yu, Z., 2016. The cytotoxicity of organophosphate flame retardants on HepG2, A549 and Caco-2 cells. Journal of Environmental Science and Health, Part A. 51, 980-988. https://doi.org/10.1080/10934529.2016.1191819.\u003c/li\u003e\n\u003cli\u003eAraki, A., Ait Bamai, Y., Bastiaensen, M., Van den Eede, N., Kawai, T., Tsuboi, T., Miyashita, C., Itoh, S., Goudarzi, H., Konno, S., Covaci, A., Kishi, R., 2020. Combined exposure to phthalate esters and phosphate flame retardants and plasticizers and their associations with wheeze and allergy symptoms among school children. Environ Res. 183, 109212. https://doi.org/10.1016/j.envres.2020.109212.\u003c/li\u003e\n\u003cli\u003eBastiaensen, M., Malarvannan, G., Been, F., Yin, S., Yao, Y., Huygh, J., Clotman, K., Schepens, T., Jorens, P.G., Covaci, A., 2019. Metabolites of phosphate flame retardants and alternative plasticizers in urine from intensive care patients. Chemosphere. 233, 590-596. https://doi.org/10.1016/j.chemosphere.2019.05.280.\u003c/li\u003e\n\u003cli\u003eBobb, J.F., Valeri, L., Claus Henn, B., Christiani, D.C., Wright, R.O., Mazumdar, M., Godleski, J.J., Coull, B.A., 2015. Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. Biostatistics. 16, 493-508. https://doi.org/10.1093/biostatistics/kxu058.\u003c/li\u003e\n\u003cli\u003eBoyle, M., Buckley, J.P., Quir\u0026oacute;s-Alcal\u0026aacute;, L., 2019. Associations between urinary organophosphate ester metabolites and measures of adiposity among U.S. children and adults: NHANES 2013\u0026ndash;2014. Environment International. 127, 754-763. https://doi.org/10.1016/j.envint.2019.03.055.\u003c/li\u003e\n\u003cli\u003eButkowski, E.G., Al-Aubaidy, H.A., Jelinek, H.F., 2017. Interaction of homocysteine, glutathione and 8-hydroxy-2\u0026apos;-deoxyguanosine in metabolic syndrome progression. Clin Biochem. 50, 116-120. https://doi.org/10.1016/j.clinbiochem.2016.10.006.\u003c/li\u003e\n\u003cli\u003eCarrico, C., Gennings, C., Wheeler, D.C., Factor-Litvak, P., 2014. Characterization of Weighted Quantile Sum Regression for Highly Correlated Data in a Risk Analysis Setting. Journal of Agricultural, Biological, and Environmental Statistics. 20, 100-120. https://doi.org/10.1007/s13253-014-0180-3.\u003c/li\u003e\n\u003cli\u003eCarrier, A., 2017. Metabolic Syndrome and Oxidative Stress: A Complex Relationship. Antioxid Redox Signal. 26, 429-431. https://doi.org/10.1089/ars.2016.6929.\u003c/li\u003e\n\u003cli\u003eChen, G., Jin, Y., Wu, Y., Liu, L., Fu, Z., 2015a. Exposure of male mice to two kinds of organophosphate flame retardants (OPFRs) induced oxidative stress and endocrine disruption. Environ Toxicol Pharmacol. 40, 310-318. https://doi.org/10.1016/j.etap.2015.06.021.\u003c/li\u003e\n\u003cli\u003eChen, G., Zhang, S., Jin, Y., Wu, Y., Liu, L., Qian, H., Fu, Z., 2015b. TPP and TCEP induce oxidative stress and alter steroidogenesis in TM3 Leydig cells. Reprod Toxicol. 57, 100-110. https://doi.org/10.1016/j.reprotox.2015.05.011.\u003c/li\u003e\n\u003cli\u003eDevaraj, S., Singh, U., Jialal, I., 2009. Human C-reactive protein and the metabolic syndrome. Curr Opin Lipidol. 20, 182-189. https://doi.org/10.1097/MOL.0b013e32832ac03e.\u003c/li\u003e\n\u003cli\u003eGrandl, G., Wolfrum, C., 2018. Hemostasis, endothelial stress, inflammation, and the metabolic syndrome. Semin Immunopathol. 40, 215-224. https://doi.org/10.1007/s00281-017-0666-5.\u003c/li\u003e\n\u003cli\u003eGu, D., Reynolds, K., Wu, X., Chen, J., Duan, X., Reynolds, R.F., Whelton, P.K., He, J., 2005. Prevalence of the metabolic syndrome and overweight among adults in China. Lancet. 365, 1398-1405. https://doi.org/10.1016/s0140-6736(05)66375-1.\u003c/li\u003e\n\u003cli\u003eHao, Z., Zhang, Z., Lu, D., Ding, B., Shu, L., Zhang, Q., Wang, C., 2019. Organophosphorus Flame Retardants Impair Intracellular Lipid Metabolic Function in Human Hepatocellular Cells. Chem Res Toxicol. 32, 1250-1258. https://doi.org/10.1021/acs.chemrestox.9b00058.\u003c/li\u003e\n\u003cli\u003eHu, L., Tao, Y., Luo, D., Feng, J., Wang, L., Yu, M., Li, Y., Covaci, A., Mei, S., 2019. Simultaneous biomonitoring of 15 organophosphate flame retardants metabolites in urine samples by solvent induced phase transition extraction coupled with ultra-performance liquid chromatography-tandem mass spectrometry. Chemosphere. 233, 724-732. https://doi.org/10.1016/j.chemosphere.2019.05.242.\u003c/li\u003e\n\u003cli\u003eHu, L., Yu, M., Li, Y., Liu, L., Li, X., Song, L., Wang, Y., Mei, S., 2022. Association of exposure to organophosphate esters with increased blood pressure in children and adolescents. Environmental Pollution. 295. https://doi.org/10.1016/j.envpol.2021.118685.\u003c/li\u003e\n\u003cli\u003eHu, W., Kang, Q., Zhang, C., Ma, H., Xu, C., Wan, Y., Hu, J., 2020. Triphenyl phosphate modulated saturation of phospholipids: Induction of endoplasmic reticulum stress and inflammation. Environ Pollut. 263, 114474. https://doi.org/10.1016/j.envpol.2020.114474.\u003c/li\u003e\n\u003cli\u003eIngle, M.E., Watkins, D., Rosario, Z., V\u0026eacute;lezVega, C.M., Calafat, A.M., Ospina, M., Ferguson, K.K., Cordero, J.F., Alshawabkeh, A., Meeker, J.D., 2020. An exploratory analysis of urinary organophosphate ester metabolites and oxidative stress among pregnant women in Puerto Rico. Sci Total Environ. 703, 134798. https://doi.org/10.1016/j.scitotenv.2019.134798.\u003c/li\u003e\n\u003cli\u003eJi, Y., Yao, Y., Duan, Y., Zhao, H., Hong, Y., Cai, Z., Sun, H., 2021. Association between urinary organophosphate flame retardant diesters and steroid hormones: A metabolomic study on type 2 diabetes mellitus cases and controls. Science of The Total Environment. 756. https://doi.org/10.1016/j.scitotenv.2020.143836.\u003c/li\u003e\n\u003cli\u003eKosarac, I., Kubwabo, C., Foster, W.G., 2016. Quantitative determination of nine urinary metabolites of organophosphate flame retardants using solid phase extraction and ultra performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS). J Chromatogr B Analyt Technol Biomed Life Sci. 1014, 24-30. https://doi.org/10.1016/j.jchromb.2016.01.035.\u003c/li\u003e\n\u003cli\u003eLakka, H.M., Laaksonen, D.E., Lakka, T.A., Niskanen, L.K., Kumpusalo, E., Tuomilehto, J., Salonen, J.T., 2002. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. Jama. 288, 2709-2716. https://doi.org/10.1001/jama.288.21.2709.\u003c/li\u003e\n\u003cli\u003eLe Magueresse-Battistoni, B., Vidal, H., Naville, D., 2018. Environmental Pollutants and Metabolic Disorders: The Multi-Exposure Scenario of Life. Front Endocrinol (Lausanne). 9, 582. https://doi.org/10.3389/fendo.2018.00582.\u003c/li\u003e\n\u003cli\u003eLee, Y.H., Pratley, R.E., 2005. The evolving role of inflammation in obesity and the metabolic syndrome. Curr Diab Rep. 5, 70-75. https://doi.org/10.1007/s11892-005-0071-7.\u003c/li\u003e\n\u003cli\u003eLi, M., Yao, Y., Wang, Y., Bastiaensen, M., Covaci, A., Sun, H., 2020. Organophosphate ester flame retardants and plasticizers in a Chinese population: Significance of hydroxylated metabolites and implication for human exposure. Environ Pollut. 257, 113633. https://doi.org/10.1016/j.envpol.2019.113633.\u003c/li\u003e\n\u003cli\u003eLi, R., Li, W., Lun, Z., Zhang, H., Sun, Z., Kanu, J.S., Qiu, S., Cheng, Y., Liu, Y., 2016. Prevalence of metabolic syndrome in Mainland China: a meta-analysis of published studies. BMC Public Health. 16, 296. https://doi.org/10.1186/s12889-016-2870-y.\u003c/li\u003e\n\u003cli\u003eLind, P.M., Lind, L., 2018. Endocrine-disrupting chemicals and risk of diabetes: an evidence-based review. Diabetologia. 61, 1495-1502. https://doi.org/10.1007/s00125-018-4621-3.\u003c/li\u003e\n\u003cli\u003eLu, S.Y., Li, Y.X., Zhang, T., Cai, D., Ruan, J.J., Huang, M.Z., Wang, L., Zhang, J.Q., Qiu, R.L., 2017. Effect of E-waste Recycling on Urinary Metabolites of Organophosphate Flame Retardants and Plasticizers and Their Association with Oxidative Stress. Environ Sci Technol. 51, 2427-2437. https://doi.org/10.1021/acs.est.6b05462.\u003c/li\u003e\n\u003cli\u003eLuo, K., Aimuzi, R., Wang, Y., Nian, M., Zhang, J., 2020a. Urinary organophosphate esters metabolites, glucose homeostasis and prediabetes in adolescents. Environmental Pollution. 267. https://doi.org/10.1016/j.envpol.2020.115607.\u003c/li\u003e\n\u003cli\u003eLuo, K., Zhang, R., Aimuzi, R., Wang, Y., Nian, M., Zhang, J., 2020b. Exposure to Organophosphate esters and metabolic syndrome in adults. Environment International. 143. https://doi.org/10.1016/j.envint.2020.105941.\u003c/li\u003e\n\u003cli\u003eLuo, K., Zhang, R., Aimuzi, R., Wang, Y., Nian, M., Zhang, J., 2020c. Exposure to Organophosphate esters and metabolic syndrome in adults. Environ Int. 143, 105941. https://doi.org/10.1016/j.envint.2020.105941.\u003c/li\u003e\n\u003cli\u003eMa, J., Zhou, Y., Wang, D., Guo, Y., Wang, B., Xu, Y., Chen, W., 2020. Associations between essential metals exposure and metabolic syndrome (MetS): Exploring the mediating role of systemic inflammation in a general Chinese population. Environ Int. 140, 105802. https://doi.org/10.1016/j.envint.2020.105802.\u003c/li\u003e\n\u003cli\u003eOspina, M., Jayatilaka, N.K., Wong, L.Y., Restrepo, P., Calafat, A.M., 2018. Exposure to organophosphate flame retardant chemicals in the U.S. general population: Data from the 2013-2014 National Health and Nutrition Examination Survey. Environ Int. 110, 32-41. https://doi.org/10.1016/j.envint.2017.10.001.\u003c/li\u003e\n\u003cli\u003eRani, V., Deep, G., Singh, R.K., Palle, K., Yadav, U.C., 2016. Oxidative stress and metabolic disorders: Pathogenesis and therapeutic strategies. Life Sci. 148, 183-193. https://doi.org/10.1016/j.lfs.2016.02.002.\u003c/li\u003e\n\u003cli\u003eRidker, P.M., Hennekens, C.H., Buring, J.E., Rifai, N., 2000. C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med. 342, 836-843. https://doi.org/10.1056/nejm200003233421202.\u003c/li\u003e\n\u003cli\u003eSelmi-Ruby, S., Mar\u0026iacute;n-S\u0026aacute;ez, J., Fildier, A., Bulet\u0026eacute;, A., Abdallah, M., Garcia, J., Deverch\u0026egrave;re, J., Spinner, L., Giroud, B., Ibanez, S., Granjon, T., Bardel, C., Puisieux, A., Fervers, B., Vulliet, E., Payen, L., Vigneron, A.M., 2020. In Vivo Characterization of the Toxicological Properties of DPhP, One of the Main Degradation Products of Aryl Phosphate Esters. Environ Health Perspect. 128, 127006. https://doi.org/10.1289/ehp6826.\u003c/li\u003e\n\u003cli\u003eTingley, D., Yamamoto, T., Hirose, K., Keele, L., Imai, K., 2014. mediation: R Package for Causal Mediation Analysis. Journal of Statistical Software. 59.\u003c/li\u003e\n\u003cli\u003eUrbaniak, S.K., Boguszewska, K., Szewczuk, M., Kaźmierczak-Barańska, J., Karwowski, B.T., 2020. 8-Oxo-7,8-Dihydro-2\u0026apos;-Deoxyguanosine (8-oxodG) and 8-Hydroxy-2\u0026apos;-Deoxyguanosine (8-OHdG) as a Potential Biomarker for Gestational Diabetes Mellitus (GDM) Development. Molecules. 25. https://doi.org/10.3390/molecules25010202.\u003c/li\u003e\n\u003cli\u003eValavanidis, A., Vlachogianni, T., Fiotakis, C., 2009. 8-hydroxy-2\u0026apos; -deoxyguanosine (8-OHdG): A critical biomarker of oxidative stress and carcinogenesis. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev. 27, 120-139. https://doi.org/10.1080/10590500902885684.\u003c/li\u003e\n\u003cli\u003eVan den Eede, N., Heffernan, A.L., Aylward, L.L., Hobson, P., Neels, H., Mueller, J.F., Covaci, A., 2015. Age as a determinant of phosphate flame retardant exposure of the Australian population and identification of novel urinary PFR metabolites. Environ Int. 74, 1-8. https://doi.org/10.1016/j.envint.2014.09.005.\u003c/li\u003e\n\u003cli\u003eV\u0026ouml;lkel, W., Fuchs, V., W\u0026ouml;ckner, M., Fromme, H., 2017. Toxicokinetic of tris(2-butoxyethyl) phosphate (TBOEP) in humans following single oral administration. Archives of Toxicology. 92, 651-660. https://doi.org/10.1007/s00204-017-2078-7.\u003c/li\u003e\n\u003cli\u003eWang, Y., Li, W., Mart\u0026iacute;nez-Moral, M.P., Sun, H., Kannan, K., 2019a. Metabolites of organophosphate esters in urine from the United States: Concentrations, temporal variability, and exposure assessment. Environ Int. 122, 213-221. https://doi.org/10.1016/j.envint.2018.11.007.\u003c/li\u003e\n\u003cli\u003eWang, Y.X., Liu, C., Shen, Y., Wang, Q., Pan, A., Yang, P., Chen, Y.J., Deng, Y.L., Lu, Q., Cheng, L.M., Miao, X.P., Xu, S.Q., Lu, W.Q., Zeng, Q., 2019b. Urinary levels of bisphenol A, F and S and markers of oxidative stress among healthy adult men: Variability and association analysis. Environ Int. 123, 301-309. https://doi.org/10.1016/j.envint.2018.11.071.\u003c/li\u003e\n\u003cli\u003eWei, G.L., Li, D.Q., Zhuo, M.N., Liao, Y.S., Xie, Z.Y., Guo, T.L., Li, J.J., Zhang, S.Y., Liang, Z.Q., 2015. Organophosphorus flame retardants and plasticizers: sources, occurrence, toxicity and human exposure. Environ Pollut. 196, 29-46. https://doi.org/10.1016/j.envpol.2014.09.012.\u003c/li\u003e\n\u003cli\u003eWilson, P.W., D\u0026apos;Agostino, R.B., Parise, H., Sullivan, L., Meigs, J.B., 2005. Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Circulation. 112, 3066-3072. https://doi.org/10.1161/circulationaha.105.539528.\u003c/li\u003e\n\u003cli\u003eXu, F., Eulaers, I., Alves, A., Papadopoulou, E., Padilla-Sanchez, J.A., Lai, F.Y., Haug, L.S., Voorspoels, S., Neels, H., Covaci, A., 2019. Human exposure pathways to organophosphate flame retardants: Associations between human biomonitoring and external exposure. Environ Int. 127, 462-472. https://doi.org/10.1016/j.envint.2019.03.053.\u003c/li\u003e\n\u003cli\u003eYan, J., Zhao, Z., Xia, M., Chen, S., Wan, X., He, A., Daniel Sheng, G., Wang, X., Qian, Q., Wang, H., 2022. Induction of lipid metabolism dysfunction, oxidative stress and inflammation response by tris(1-chloro-2-propyl)phosphate in larval/adult zebrafish. Environ Int. 160, 107081. https://doi.org/10.1016/j.envint.2022.107081.\u003c/li\u003e\n\u003cli\u003eYang, C., Harris, S.A., Jantunen, L.M., Siddique, S., Kubwabo, C., Tsirlin, D., Latifovic, L., Fraser, B., St-Jean, M., De La Campa, R., You, H., Kulka, R., Diamond, M.L., 2019. Are cell phones an indicator of personal exposure to organophosphate flame retardants and plasticizers? Environ Int. 122, 104-116. https://doi.org/10.1016/j.envint.2018.10.021.\u003c/li\u003e\n\u003cli\u003eYang, W., Braun, J.M., Vuong, A.M., Percy, Z., Xu, Y., Xie, C., Deka, R., Calafat, A.M., Ospina, M., Werner, E., Yolton, K., Cecil, K.M., Lanphear, B.P., Chen, A., 2022. Maternal urinary OPE metabolite concentrations and blood pressure during pregnancy: The HOME study. Environ Res. 207, 112220. https://doi.org/10.1016/j.envres.2021.112220.\u003c/li\u003e\n\u003cli\u003eYao, F., Bo, Y., Zhao, L., Li, Y., Ju, L., Fang, H., Piao, W., Yu, D., Lao, X., 2021a. Prevalence and Influencing Factors of Metabolic Syndrome among Adults in China from 2015 to 2017. Nutrients. 13. https://doi.org/10.3390/nu13124475.\u003c/li\u003e\n\u003cli\u003eYao, Y., Li, M., Pan, L., Duan, Y., Duan, X., Li, Y., Sun, H., 2021b. Exposure to organophosphate ester flame retardants and plasticizers during pregnancy: Thyroid endocrine disruption and mediation role of oxidative stress. Environ Int. 146, 106215. https://doi.org/10.1016/j.envint.2020.106215.\u003c/li\u003e\n\u003cli\u003eZhang, T., Bai, X.Y., Lu, S.Y., Zhang, B., Xie, L., Zheng, H.C., Jiang, Y.C., Zhou, M.Z., Zhou, Z.Q., Song, S.M., He, Y., Gui, M.W., Ouyang, J.P., Huang, H.B., Kannan, K., 2018. Urinary metabolites of organophosphate flame retardants in China: Health risk from tris(2-chloroethyl) phosphate (TCEP) exposure. Environ Int. 121, 1363-1371. https://doi.org/10.1016/j.envint.2018.11.006.\u003c/li\u003e\n\u003cli\u003eZhang, W., Zhang, Y., Hou, J., Xu, T., Yin, W., Xiong, W., Lu, W., Zheng, H., Chen, J., Yuan, J., 2017. Tris (2-chloroethyl) phosphate induces senescence-like phenotype of hepatocytes via the p21(Waf1/Cip1)-Rb pathway in a p53-independent manner. Environ Toxicol Pharmacol. 56, 68-75. https://doi.org/10.1016/j.etap.2017.08.028.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eDescriptive statistics of participants in this study.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"628\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\" valign=\"top\"\u003e\n \u003cp\u003eTotal (n=694)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\" valign=\"top\"\u003e\n \u003cp\u003eMetS (n=347)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\" valign=\"top\"\u003e\n \u003cp\u003eNon-MetS (n=347)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD/Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e50.49\u0026plusmn;10.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e50.50\u0026plusmn;10.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e50.48\u0026plusmn;10.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eWaist circumference (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e89.77\u0026plusmn;9.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e84.82\u0026plusmn;7.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e94.72\u0026plusmn;7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eTotal triglycerides (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e1.78 (1.20, 2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e1.44 (1.00, 2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e2.06 (1.62, 2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eHDL-c (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e1.10 (0.97, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e1.18 (1.04, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e1.03 (0.91, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eFasting blood glucose (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e5.25 (4.90, 5.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e5.14 (4.84, 5.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e5.41 (4.99, 6.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eSystolic blood pressure (mm Hg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e131.5\u0026plusmn;17.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e126.41\u0026plusmn;16.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e136.59\u0026plusmn;16.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eDiastolic blood pressure (mm Hg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e82.08\u0026plusmn;11.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e78.9\u0026plusmn;11.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e85.26\u0026plusmn;11.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eUrinary creatinine (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e1.14 (0.66, 1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e1.14 (0.64, 1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e1.14 (0.66, 1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e528 (76.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e264 (76.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e264 (76.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e166 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e83 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e83 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003e18\u0026ndash;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e183 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e92 (26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e91 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003e＞45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e511 (73.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e255 (73.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e256 (73.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eBelow high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e114 (16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e65 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e49 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e112 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e60 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e52 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eCollege and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e468 (67.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e222 (64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e246 (70.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eIncome (yuan per month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003e\u0026lt;6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e196 (28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e98 (28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e98 (28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;6000-10000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e186 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e100 (28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e86 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003e\u0026ge;10000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e312 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e149 (42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e163 (47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e419 (60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e219 (63.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e200 (57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eCurrent/Former\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e275 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e128 (36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e147 (42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003ePhysical activity-MET (hour/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e244 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e122 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e122 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e241 (34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e123 (35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e118 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e209 (30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e102 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e107 (30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eMeat intake (times/wk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003e\u0026le;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e274 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e142 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e132 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003e4\u0026ndash;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e170 (24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e77 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e93 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003e\u0026ge;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e250 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e128 (36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e122 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003eVegetable intake (times/wk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003e\u0026le;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e183 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e88 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e95 (27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003e7\u0026ndash;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e427 (61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e217 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e210 (60.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.59872611464968%\"\u003e\n \u003cp\u003e\u0026ge;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15286624203822%\"\u003e\n \u003cp\u003e84 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.993630573248407%\"\u003e\n \u003cp\u003e42 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.745222929936304%\"\u003e\n \u003cp\u003e42 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.509554140127388%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: BMI, body mass index; HDL-c, high-density lipoprotein cholesterol; MET, metabolic equivalent. NA, unavailable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eConcentration of urinary OPE metabolites and biomarkers of oxidative stress and inflammation.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.193548387096774%\" rowspan=\"2\"\u003e\n \u003cp\u003eAnalyte (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.56989247311828%\" rowspan=\"2\"\u003e\n \u003cp\u003eDetection rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"4\"\u003e\n \u003cp\u003eNon-MetS (n=347)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8279569892473118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.903225806451616%\" colspan=\"4\"\u003e\n \u003cp\u003eMetS (n=347)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.172043010752688%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.070866141732283%\"\u003e\n \u003cp\u003eGeometric mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.968503937007874%\"\u003e\n \u003cp\u003eP25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.968503937007874%\"\u003e\n \u003cp\u003eP50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.968503937007874%\"\u003e\n \u003cp\u003eP75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.677165354330709%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.440944881889763%\"\u003e\n \u003cp\u003eGeometric mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.968503937007874%\"\u003e\n \u003cp\u003eP25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.968503937007874%\"\u003e\n \u003cp\u003eP50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.968503937007874%\"\u003e\n \u003cp\u003eP75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"12\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrinary OPEs metabolites (ng/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.163090128755364%\"\u003e\n \u003cp\u003eBDCIPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.549356223175966%\"\u003e\n \u003cp\u003e60.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.905579399141631%\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e\u0026lt;LOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.12\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8240343347639485%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e\u0026lt;LOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.163090128755364%\"\u003e\n \u003cp\u003eBCIPHIPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.549356223175966%\"\u003e\n \u003cp\u003e96.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.905579399141631%\"\u003e\n \u003cp\u003e0.42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.25\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.70\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8240343347639485%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\"\u003e\n \u003cp\u003e0.46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.163090128755364%\"\u003e\n \u003cp\u003e4-HO-DPHP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.549356223175966%\"\u003e\n \u003cp\u003e62.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.905579399141631%\"\u003e\n \u003cp\u003e0.12\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e\u0026lt;LOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.38\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8240343347639485%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\"\u003e\n \u003cp\u003e0.14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e\u0026lt;LOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.37\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.163090128755364%\"\u003e\n \u003cp\u003eDPHP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.549356223175966%\"\u003e\n \u003cp\u003e80.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.905579399141631%\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8240343347639485%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.163090128755364%\"\u003e\n \u003cp\u003eDoCP \u0026amp; DpCP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.549356223175966%\"\u003e\n \u003cp\u003e97.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.905579399141631%\"\u003e\n \u003cp\u003e0.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8240343347639485%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\"\u003e\n \u003cp\u003e0.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.163090128755364%\"\u003e\n \u003cp\u003eBBOEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.549356223175966%\"\u003e\n \u003cp\u003e99.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.905579399141631%\"\u003e\n \u003cp\u003e0.28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8240343347639485%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\"\u003e\n \u003cp\u003e0.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.68\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"12\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarkers of oxidative stress (ng/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.163090128755364%\"\u003e\n \u003cp\u003e8-OHdG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.549356223175966%\"\u003e\n \u003cp\u003e99.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.905579399141631%\"\u003e\n \u003cp\u003e7.91\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e4.14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e8.47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e16.47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8240343347639485%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\"\u003e\n \u003cp\u003e10.63\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e5.48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e11.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e20.53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.163090128755364%\"\u003e\n \u003cp\u003e8-isoPGF2\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.549356223175966%\"\u003e\n \u003cp\u003e96.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.905579399141631%\"\u003e\n \u003cp\u003e4.59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e2.31\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e5.65\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e10.74\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8240343347639485%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\"\u003e\n \u003cp\u003e4.17\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e2.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e5.84\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e10.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.163090128755364%\"\u003e\n \u003cp\u003eHNE-MA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.549356223175966%\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.905579399141631%\"\u003e\n \u003cp\u003e14.58\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e7.18\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e12.81\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e27.34\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8240343347639485%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\"\u003e\n \u003cp\u003e14.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e6.92\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e13.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e25.32\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"12\"\u003e\n \u003cp\u003e\u003cstrong\u003eInflammation biomarkers\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.163090128755364%\" valign=\"top\"\u003e\n \u003cp\u003eIL-6 (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.549356223175966%\"\u003e\n \u003cp\u003e93.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.905579399141631%\"\u003e\n \u003cp\u003e3.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e1.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e3.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e15.53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8240343347639485%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\"\u003e\n \u003cp\u003e3.54\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e1.57\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e3.52\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e12.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.163090128755364%\" valign=\"top\"\u003e\n \u003cp\u003eTNF-\u0026alpha; (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.549356223175966%\"\u003e\n \u003cp\u003e82.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.905579399141631%\"\u003e\n \u003cp\u003e17.40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e6.53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e26.67\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e136.94\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8240343347639485%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\"\u003e\n \u003cp\u003e18.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e6.78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e23.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e125.07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.163090128755364%\" valign=\"top\"\u003e\n \u003cp\u003eCRP (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.549356223175966%\"\u003e\n \u003cp\u003e96.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.905579399141631%\"\u003e\n \u003cp\u003e9.53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e4.30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e10.90\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e24.30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.8240343347639485%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.476394849785407%\"\u003e\n \u003cp\u003e15.21\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e7.91\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e17.93\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e35.68\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.15450643776824%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003e592 participants (294 in the MetS case group and 298 in the control group) were examined for inflammation biomarkers, after excluding 102 individuals who did not have sufficient blood samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Association of urinary OPEs metabolites with oxidative stress and inflammation biomarkers.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1002\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\" rowspan=\"2\"\u003e\n \u003cp\u003eOPEs metabolites\u003c/p\u003e\n \u003cp\u003e(ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e8-OHdG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e8-isoPGF2\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003eHNE-MA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003eIL-6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003eTNF-\u0026alpha;\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003eCRP\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.1547212741752%\"\u003e\n \u003cp\u003e%change（95% CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1547212741752%\"\u003e\n \u003cp\u003e%change（95% CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.1547212741752%\"\u003e\n \u003cp\u003e%change（95% CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.178612059158134%\"\u003e\n \u003cp\u003e%change（95% CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.178612059158134%\"\u003e\n \u003cp\u003e%change（95% CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.178612059158134%\"\u003e\n \u003cp\u003e%change（95% CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eBDCIPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eT1 (\u0026lt; 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eT2 (0.05-0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e-4.18 (-19.74, 14.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e-12.54 (-32.55, 13.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e14.22 (-4.83, 37.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e-13.30 (-42.68, 31.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e-25.09 (-57.18, 31.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e-3.09 (-25.92, 26.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eT3 (\u0026ge; 0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e-1.00 (-16.55, 17.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e-4.98 (-26.03, 22.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e4.79 (-12.11, 24.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-49.58 (-66.29, -24.59) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-46.01 (-68.68, -6.92) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e-0.66 (-23.43, 28.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e-0.51 (-6.99, 6.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e-1.05 (-10.36, 9.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e3.52 (-3.42, 10.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-20.78 (-32.49, -7.06) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-19.74 (-35.31, -0.43) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0.62 (-9.22, 11.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eBCIPHIPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eT1 (\u0026lt; 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0 (Reference)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0 (Reference)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eT2 (0.29-0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e33.28 (11.28, 59.63) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e26.59 (-3.07, 65.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e34.89 (11.91, 62.60) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e-18.39 (-47.01, 25.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e67.34 (-6.17, 198.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e-3.45 (-26.79, 27.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eT3 (\u0026ge; 0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e42.37 (19.05, 70.25) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e23.40 (-5.29, 60.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e19.82 (-0.43, 44.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e-24.77 (-50.95, 15.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e-8.45 (-48.59, 63.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e7.78 (-18.12, 41.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e22.13 (11.10, 34.25) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e4.23 (-9.38, 19.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.08 (3.45, 25.81) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e-13.24 (-30.82, 8.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e-3.35 (-28.67, 30.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e5.63 (-8.62, 22.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003e4-HO-DPHP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eT1 (\u0026lt; 0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eT2 (0.03-0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e10.48 (-7.76, 32.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e39.29 (6.97, 81.38) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n 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width=\"15.069860279441118%\"\u003e\n \u003cp\u003e20.61 (-20.00, 81.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e3.93 (-40.37, 81.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e-7.58 (-29.00, 20.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e4.52 (-0.38, 9.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e6.79 (-0.48, 14.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.95 (5.62, 16.54) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e10.06 (-1.79, 23.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e3.55 (-11.28, 20.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n 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width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eT2 (0.06-0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e-2.27 (-17.97, 16.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e19.34 (-7.66, 54.22)\u003c/p\u003e\n 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width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e-1.82 (-9.42, 6.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e7.97 (-4.08, 21.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e8.12 (-0.49, 17.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e18.72 (-1.73, 43.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e-9.83 (-30.36, 16.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e-0.56 (-11.97, 12.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eDoCP \u0026amp; DpCP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eT1 (\u0026lt; 0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eT2 (0.08-0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e15.57 (-2.95, 37.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e30.49 (0.78, 68.96) 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\u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e46.44 (21.62, 76.32) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e3.72 (-32.32, 58.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e5.78 (-40.94, 89.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e-20.15 (-39.35, 5.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035\u003c/strong\u003e\u003c/p\u003e\n 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width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eT2 (0.22-0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e26.55 (5.21, 52.21) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e15.71 (-11.93, 52.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e23.66 (2.12, 49.74) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e-0.21 (-35.69, 54.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u003cstrong\u003e196.70 (66.12, 429.90) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n 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103.58) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.275449101796408%\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.30 (4.85, 22.43) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e0.04 (-10.80, 12.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171656686626747%\"\u003e\n \u003cp\u003e5.29 (-2.86, 14.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e2.93 (-14.47, 23.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e22.39 (-4.45, 56.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.069860279441118%\"\u003e\n \u003cp\u003e\u003cstrong\u003e27.59 (13.43, 43.51) *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003e592 participants (294 in the MetS case group and 298 in the control group) were examined for inflammation biomarkers, after excluding 102 individuals who did not have sufficient blood samples. % changes were transformed by the natural logarithm and back-transformed. Adjusted for urinary creatinine, age, gender, education level, income, smoking status,\u0026nbsp;physical activity-MET, vegetable intake, and meat intake. *\u003cem\u003eP\u003c/em\u003e＜0.05\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"exposure-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wqeh","sideBox":"Learn more about [Exposure and Health](https://www.springer.com/journal/12403)","snPcode":"12403","submissionUrl":"https://submission.nature.com/new-submission/12403/3","title":"Exposure and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Organophosphate esters, Metabolic syndrome, Oxidative stress, Inflammation, Mediation","lastPublishedDoi":"10.21203/rs.3.rs-4160250/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4160250/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Epidemiological evidence regarding the relationships of organophosphate esters (OPEs) with metabolic syndrome (MetS) and its underlying mechanism was largely unknown. This study sought to estimate the correlations of individual OPEs and their mixture with MetS risk, while also evaluating the potential mediation of oxidative stress and inflammation biomarkers. We measured urinary OPE metabolites, urinary biomarkers of oxidative stress, and serum biomarkers of inflammation among 694 adults based on a case-control design. Our findings revealed positive correlations between urinary 1-hydroxy-2-propyl bis(1-chloro-2-propyl) phosphate (BCIPHIPP) and bis(2-butoxyethyl) phosphate (BBOEP) and elevated odds of MetS risk. Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS) analyses demonstrated the overall effect of the OPE mixtures on MetS risk, with BBOEP identified as the primary contributor. Mediation analysis further revealed that the association between urinary BCIPHIPP and BBOEP and MetS risk was mediated by urinary 8-hydroxy-2-deoxyguanosine (8-OHdG), with mediation proportions of 23.27% and 8.70%, respectively. In addition, serum C-reactive protein (CRP) concentration mediated the association between BBOEP and MetS risk, and the proportion of mediation was 16.32%. Our results indicated that oxidative stress and inflammation might exert a substantial influence on the correlations between OPE exposure and the risk of MetS.","manuscriptTitle":"Associations between organophosphate esters exposure and metabolic syndrome: Exploring the mediating role of oxidative stress and inflammation in adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-03 17:22:19","doi":"10.21203/rs.3.rs-4160250/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-04-02T10:23:51+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-29T14:28:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Exposure and Health","date":"2024-03-28T13:35:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-28T01:32:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Exposure and Health","date":"2024-03-26T06:52:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"exposure-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wqeh","sideBox":"Learn more about [Exposure and Health](https://www.springer.com/journal/12403)","snPcode":"12403","submissionUrl":"https://submission.nature.com/new-submission/12403/3","title":"Exposure and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cffbe38c-60da-45e4-9220-97f9bed96a07","owner":[],"postedDate":"April 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-07T10:49:53+00:00","versionOfRecord":{"articleIdentity":"rs-4160250","link":"https://doi.org/10.1007/s12403-024-00653-5","journal":{"identity":"exposure-and-health","isVorOnly":false,"title":"Exposure and Health"},"publishedOn":"2024-07-07 10:49:53","publishedOnDateReadable":"July 7th, 2024"},"versionCreatedAt":"2024-04-03 17:22:19","video":"","vorDoi":"10.1007/s12403-024-00653-5","vorDoiUrl":"https://doi.org/10.1007/s12403-024-00653-5","workflowStages":[]},"version":"v1","identity":"rs-4160250","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4160250","identity":"rs-4160250","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}