Association between organophosphate esters individual and mixed exposure with the risk of hyperlipidemia and serum lipid levels among Chinese adults

Association between organophosphate esters individual and mixed exposure with the risk of hyperlipidemia and serum lipid levels among Chinese 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 Association between organophosphate esters individual and mixed exposure with the risk of hyperlipidemia and serum lipid levels among Chinese adults Qitong Xu, Chang Xie, Sijie Yang, Yaping Li, Mingye Zhang, Zhengce Wan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4127098/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jul, 2024 Read the published version in Environmental Science and Pollution Research → Version 1 posted 6 You are reading this latest preprint version Abstract Toxicologic studies reported that organophosphate esters (OPEs) may disrupt lipid metabolism, thus affecting serum lipid levels. However, epidemiological evidences regarding the association between OPEs and the risk of hyperlipidemia (HPL) as well as serum lipid levels are scarce. In the present study, our aim was to investigate the impact of individual and mixed OPEs exposure on HPL. A total of 1981 Chinese adults were involved based on a cross-sectional design. Overall, we found a positive association between bis(1,3-dichloro-2-propyl) phosphate (BDCIPP) and the risk of HPL. Bis(1-chloro-2-propyl) phosphate (BCIPHIPP) showed a positive association with total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C). BDCIPP, diphenyl phosphate (DPHP), di-ocresyl phosphate and di-p-cresyl phosphate (Docp&Dpcp) as well as 4-hydroxyphenyl-diphenyl phosphate (4-OH-DPHP) exhibited a negative association with high-density lipoprotein cholesterol (HDL-C). In stratified analyses, BDCIPP and BCIPHIPP were significantly correlated with the increase risk of HPL in age ≤45 group. Bis (2-butoxyethyl) phosphate (BBOEP) was in relationship with an elevated risk of HPL in the subgroup of BMI < 24 kg/m 2 . BDCIPP was also positively associated with HPL in men. Quantile-based g computation (qgcomp) and generalized Weighted Quantile Sum Regression (gWQS) models demonstrated a negative association between OPEs mixed exposure and HDL-c in total population, as well as a positive effect of them on HPL in the subgroup of age ≤45 year, which is consistent with the individual analyses. Furthermore, joint effect analyses of revealed that participants with detected BDCIPP urinary levels and unhealthy lifestyle had the highest risk of HPL. Our findings offer evidence supporting the correlation between exposure to OPE and the risk of HPL, necessitating further prospective studies for validation. organophosphate esters hyperlipidemia serum lipids mixed exposure lifestyle Figures Figure 1 Figure 2 Figure 3 1. Introduction Hyperlipidemia, also known as dyslipidemia, refers to the elevation of triglyceride (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-c), or the reduction of high-density lipoprotein cholesterol (HDL-c) (Catapano et al., 2017 ). Numerous studies have recognized hyperlipidemia (HPL) as a significant risk factor for cardiovascular diseases (CVDs) (Michaeli et al., 2023 ). Abnormal serum lipid levels can promote atherosclerosis, thereby increasing the risk of stroke, coronary heart disease, and myocardial infarction (Zhang et al., 2024 ). Futhermore, HPL is also closely related to other metabolic diseases such as diabetes, hypertension, and metabolic syndrome (MetS) (Vesa and Bungau, 2023 ). Statistics reveal alarmingly high overall prevalence of hyperlipidemia among Chinese adults, reaching 40.40%, resulting in severe disease burden (Cong et al., 2021 ). The development of hyperlipidemia is influenced by a combination of genetic factors, dietary habits, lifestyle, and environmental factors (Álvarez Ramírez et al., 2020 ). Organophosphate esters (OPEs) are derivatives of phosphoric acid widely used as plasticizers or flame retardants. The annual consumption of OPEs has significantly increased as substitutes for brominated flame retardants (Ramesh et al., 2020 ). Due to their physicochemical properties, OPEs are prone to leaching and volatilizing from products, thereby entering environmental medias. These compounds have been frequently detected in various environmental and biological samples, such as dust (Wu et al., 2023 ), atmosphere, sediments (Liang et al., 2024 ), water (Ai et al., 2023 ), foods (Chen et al., 2023 ), and human samples (Guo et al., 2022b ). After entering the human body, most OPEs undergo metabolism to form diester or hydroxylated metabolites, which are excreted efficiently through urine (Liang et al., 2024 ). Urinary OPE metabolisms serve as a universal biomarker for the evaluation OPEs exposure, accumulating studies have reported the high frequency and widespread detection of OPE metabolites in human urine (Kang et al., 2019 ; Su et al., 2024b ; Yang et al., 2022b ). In recent years, an increasing number of studies have indicated that OPEs are type of endocrine disrupting chemicals (EDCs) (Rosenmai et al., 2021 ). For instance, a cell experiment found that certain OPEs can disturb steroidogenesis in human adrenocortical carcinoma cells (Zhang et al., 2017 ). Endocrine system plays a crucial role in regulating metabolism, including lipid metabolism. Evidences suggested that exposure to EDCs can disrupt lipid homeostasis and interfere with lipid metabolism (Kopp et al., 2017 ), ultimately leading to dyslipidemia (Cong et al., 2021 ; Wang et al., 2020 ; Wang et al., 2023 ). Toxicological study showed that exposure to OPEs can elevate serum lipid levels for rat (Pelletier et al., 2020 ). Exposure of zebrafish to tris(1-chloro-2-propyl)phosphate caused lipid metabolism dysfunction and lipid accumulation (Yan et al., 2022 ). Jin et al. ( 2024 ) reported that cresyl diphenyl phosphate exposure led to the disruptions in lipid homeostasis in zebrafish embryos. To date, only a few epidemiological studies have investigated the relationship with OPEs exposure and serum lipid levels (Siddique et al., 2020 ; Zhao et al., 2019 ). Study conducted on the association between urinary OPE metabolites and the risk of HPL mainly remains absence. Several research reported the association between OPEs with metabolic disorders, such as diabetes (Zhang et al., 2023a ), obesity (Li et al., 2023 ) and MetS (Luo et al., 2020b ). A cross-sectional investigation reported a positive correlation between high level urinary of OPE metabolites with CVDs, which are closely related to HPL (Guo et al., 2022a ). Therefore, exploring the effect of OPEs on lipid metabolism, especially the relationship OPEs exposure and HPL at the population level is of great significance. In the present study, we aimed to explore the association between individual urinary OPE metabolites and their mixtures with the risk of HPL as well as serum lipid levels among 1981 Chinese adults. The findings from our study contribute essential epidemiological evidence to inform future research on the relationship between exposure to OPEs and HPL. 2. Materials and methods 2.1 Study population The study population was from a cross-sectional study with details were placed in the supplementary material. A total of 2082 eligible participants were initially incorporated into our study. After excluding 96 individuals with missing or insufficient urine samples and 5 individuals with missing serum lipid parameters, a final sample size of 1981 individuals were included in the analysis (Figure S1). All the subjects completed face-to-face questionnaire surveys, as well as physical and biochemical examinations. Urine samples were collected from each subject and stored in a refrigerator at-20 ℃. 2.2 Measurement of OPE metabolites Detailed information can be found in the supplementary material. 2.3 Assessment of HPL We adhered to the updated guidelines for managing dyslipidemia in adults (2016 in China) to identify hyperlipidemia (Zhang et al., 2022 ). Participants were classified as having HPL if they exhibited at least one abnormal serum lipid level. The criteria of dyslipidemia are shown in Table S1. 2.4 Covariates Based on previous research (Cong et al., 2021 ; Siddique et al., 2020 ; Wang et al., 2023 ; Zhao et al., 2019 ) and the collected questionnaire information, socio-demographics data (gender, age, education), as well as lifestyle factors, including smoking, alcohol drinking, physical activity, and diet (red meat intake frequency, vegetable intake frequency), were taken into account as potential confounders of OPE metabolites and hyperlipidemia. Body mass index (BMI), calculated as weight (kg) divided by height (m) squared, was included in the final model as well. The final covariates of our analysis were as follows: gender (male, female), age (≤ 45 year, > 45 year), BMI (underweight/normal weight (< 24 kg/m 2 ), overweight (24–28 kg/m 2 ), obesity (≥ 28 kg/m 2 )), education (middle school or below, high school, college school or above), smoking (never, current/former), alcohol drinking (never, current/former), physical activity (yes, no), vegetable intake (< 7 times/week, 7–14 times/week, ≥ 14 times/week), meat intake (< 3 times/week, 3–7 times/week, ≥ 7 times/week). We further calculated the healthy lifestyle score to assess lifestyle status based on five factors, as defined in previous studies (Guo et al., 2023 ; Tan et al., 2023 ; Yan-Bo et al., 2021 ; Zhang et al., 2023b ), namely smoking, alcohol drinking, physical activity, sleep duration, and dietary diversity. For each factor, subjects who met the criterion of healthy lifestyles were assigned 1 point, or else given 0 point. The total score ranged from 0 to 5. Details were demonstrated in Table S2. Based on the total score, the study population were classified into two categories: healthy lifestyle (scores > 3), and unhealthy lifestyles (scores ≤ 3). 2.5 Statistical analysis Descriptive statistics are utilized to summarize the fundamental characteristics of study participants. For continuous variables, the mean and standard deviation (SD) or median and interquartile range (IQR) are employed, while categorical variables are summarized using numbers or percentages. To compare the characteristics between HPL group and non-HPL group, Student’s t-test and Mann-Whitney U test were used for continuous variables, while the chi-square test was used for categorical variables. In this study, 8 OPE metabolites were detected and detection frequency greater than or equal to 60% of them were analyzed, with their name and abbreviations listed in Table S3. The OPE metabolites levels lower than the LOD were substituted by the value of LOD/ \(\surd 2\) . All the concentrations of urinary metabolites of OPEs were first converted using the natural logarithm (ln) and corrected based on specific gravity before conducting the analysis. The correlation coefficients between these six OPE metabolites were then estimated using Spearman's rank correlation analysis. In the correlation analysis, we initially utilized restricted cubic spline (RCS) models to examine the linear relationships between OPE metabolites and the risk of HPL, as well as serum lipid levels. We also employed the RCS model to investigate the dose-response relationship between individual OPE metabolites and the risk of HPL. Non-linear associations were not found between OPE metabolites and HPL but were found with serum lipid levels (Table S4). Based on these findings, generalized liner models were used to investigate the association of OPE metabolites with the risk of HPL and serum lipid levels. The study population were categorized into tertiles according to their urinary OPE metabolites concentrations, with the lowest tertile serving as the reference group. When conducting trend tests, we treated the midpoint value of the logarithmically transformed data for each tertile as a continuous variable. For HPL, odds ratio (OR) and its 95% confidence interval (CI) were used as effect estimates. For serum lipid component, we used the regression coefficient β as the effect value. Crude model and adjusted models for potential confounders were both analyzed. The adjusted covariates included gender, age, BMI, education, smoking, alcohol drinking, physical activity, red meat intake frequency, and vegetable intake frequency. Missing covariates data were imputed by multiple interpolation method. In addition, stratification analyses of correlation between OPE metabolites and HPL were conducted on several crucial modifying factors, including age (≤ 45 year, > 45 year), BMI (< 24 kg/m 2 , ≥ 24 kg/m 2 ), and gender (male, female). In the subgroup analysis of healthy lifestyle scores, to investigated the joint effects of lifestyle status and OPEs exposure, we selected OPE metabolites that were significantly associated with HPL in single exposure models, and further estimated the additive interactions of these OPE metabolites and unhealthy lifestyles on HPL by calculating the relative excess risk due to interaction (RERI). A multi-pollutant model quantile-based g-computation (qgcomp) was further utilized to explore the impacts of OPEs mixtures exposure on the risk of HPL. The qgcomp model does not impose restrictions on the direction of the effects, enabling the comprehensive estimation of the contribution of each OPE metabolite to the mixed effects, which can reduce bias and enhance the stability of the results. The mixed OPEs exposure on the effects of HPL in total study population and age stratified groups were both analyzed. Furthermore, we also analyzed mixed OPEs exposure on the effect of HPL by using generalized Weighted Quantile Sum Regression (gWQS) to validate the results of qgcomp. Data was randomly divided into 40% training sets and 60% validation sets, with 500 bootstrap iterations performed to determine each OPE metabolites’ effect weights. Finally, we conducted several sensitivity analyses to assess the robustness of the results. Firstly, we excluded participants with obesity (BMI ≥ 28 kg/m 2 ), diabetes mellitus (fasting blood glucose ≥ 7.0 mmol/L), and abnormal urinary creatinine ( 106 µmol/L for men, 97 µmol/L for women ) to analyze the correlation between OPE metabolites and HPL, which may disturb the outcome of HPL (Catapano et al., 2017 ). Additionally, we followed the National Cholesterol Education Program-Adult Treatment Group III Guidelines (NCEP-ATP III guide) (Álvarez Ramírez et al., 2020 ) to identify participants with HPL and calculated the effects of probable outcome misclassification. Specifically, HPL was defined as the presence of high-risk levels of TG, TC, or LDL-C, or a risk level of HDL-C. The software used for statistical analysis is consistent with the previous work of our research group. Statistical significance was defined as a two-sided p-value of less than 0.05. 3. Results 3.1 Baseline characteristics of study population The demographic statistics of participants as well as serum lipid levels are shown in Table 1 . The mean age of participants was 44.62 ± 10.58, and 35.3 percent were women. Out of the total 1981 individuals, 579 (29.2%) were identified as HPL, while 1402 (70.8%) were classified as non-HPL. Significant differences were observed between the two groups in terms of gender, BMI, smoking habits, and alcohol consumption characteristics. Participants with HPL are more inclined to smoke, drink alcohol, have higher BMI, and more likely to be male. In addition, substantial differences were found in healthy lifestyle behaviors and scores Table S5. Table 1 Baseline characteristics of the studied participants. Variables Overall (n = 1981) Non-HPL (1402) HPL (n = 579) P-value a N (%) Age (years) 0.744 ≤ 45 year 983 (49.6%) 699 (49.9%) 284 (49.1%) > 45 year 998 (50.4%) 703 (50.1%) 295 (50.9%) Gender <0.001 Male 1282 (64.7%) 799 (57.0%) 483 (83.4%) Female 699 (35.3%) 603 (43.0%) 96 (16.6%) BMI (kg/m 2 ) <0.001 Underweight/Normal weight 914 (46.1%) 760 (54.2%) 154 (26.6%) Overweight 788 (39.8%) 510 (36.4%) 278 (48.0%) Obesity 275 (13.9%) 129 (9.2%) 146 (25.2%) Missing 4 (0.20%) 3 (0.2%) 1 (0.2%) Education 0.544 Middle school or below 468 (23.6%) 339 (24.2%) 129 (22.3%) High school 364 (18.4%) 251 (17.9%) 113 (19.5%) College school or above 1130 (57.0%) 798 (56.9%) 332 (57.3%) Missing 19 (1.0%) 14 (1.0%) 5 (0.9%) Smoking <0.001 Never 1374 (69.4%) 1049 (74.8%) 325 (56.1%) Current/Former 603 (30.4%) 340 (25.0%) 253 (43.7%) Missing 4 (0.2%) 3 (0.2%) 1 (0.2%) Alcohol drinking <0.001 Never 1348 (68.0%) 1004 (71.6%) 344 (59.4%) Current/Former 625 (31.4%) 394 (28.1%) 231 (39.9%) Missing 8 (0.4%) 4 (0.3%) 4 (0.7%) Physical activity 0.581 No 585 (29.5%) 409 (29.2%) 176 (30.4%) Yes 1390 (70.2%) 989 (70.5%) 401 (69.3%) Missing 6 (0.3%) 4 (0.3%) 2 (0.3%) Vegetable intake 0.658 < 7 times/week 366 (18.5%) 257 (18.3%) 109 (18.8%) 7–14 times/week 496 (25.0%) 344 (24.5%) 152 (26.3%) ≥ 14 times/week 1075 (54.3%) 769 (54.9%) 306 (52.8%) Missing 44 (2.2%) 32 (2.3%) 12 (2.1%) Meat intake 0.026 < 3 times/week 436 (22.0%) 330 (23.5%) 106 (18.3%) 3–7 times/week 551 (27.8%) 375 (26.7%) 176 (30.4%) ≥ 7 times/week 961 (48.5%) 673 (48.1) 288 (49.7%) Missing 33 (1.7%) 24 (1.7%) 9 (1.6%) Mean ± SD Age (years) 44.62 ± 10.58 44.48 ± 10.81 44.98 ± 9.99 0.318 BMI (kg/m 2 ) 24.39 ± 3.30 23.69 ± 3.10 26.08 ± 3.17 <0.001 Lipid variables (mmol/L) TG 1.50 ± 1.46 1.08 ± 0.45 2.52 ± 2.30 <0.001 TC 4.49 ± 0.85 4.39 ± 0.67 4.72 ± 1.13 <0.001 HDL-c 1.27 ± 0.31 1.37 ± 0.27 1.03 ± 0.25 <0.001 LDL-c 2.84 ± 0.75 2.80 ± 0.62 2.95 ± 0.99 0.001 Abbreviations: HPL, hyperlipidemia, BMI, body mass index, TG, triglyceride, TC, total cholesterol, LDL-c, low-density lipoprotein cholesterol, HDL-c, high-density lipoprotein cholesterol. a P value was tested by Student's t-test for continuous variables of normal distribution, Mann-Whitney U test for that of non-normal distribution, and Chi-square test for the categorical variables. The detection frequency and concentration distributions of OPE metabolites are presents in Table S6. Among the 15 OPEs, 8 OPEs detected more than 10% were listed and 6 OPEs were detected more than 60%. The concentration of BBOEP exhibits the highest geometric mean (GM) at 0.47 ng/ml, with BCIPHIPP following closely at 0.35 ng/ml. Subsequently, the concentrations of 4-OH-DPHP, BDCIPP, DPHP, and Docp&Dpcp are 0.15 ng/ml, 0.12 ng/ml, 0.11 ng/ml, and 0.10 ng/ml, respectively. Figure S2 depicts the Spearman correlation coefficients (rs) matrix of OPE metabolites, and the result showed significant positive correlations between urinary OPEs metabolites (r s between 0.047–0.64). Among them, the strongest correlation appeared between BBOEP and Docp&Dpcp (r s = 0.64). 3.2 Association of individual OPE metabolites with HPL and serum lipid levels The preliminary RCS analysis reflects the linear dose-response relationships between HPL risk and urinary OPE metabolite levels. As concentrations of urinary BDCIPP, BCIPHIPP, DoCP&DpCP, and BBOEP increased, so did the risk of HPL (P for non-linearity > 0.05) (Figure S3). Table 2 shows the association between the levels of urinary OPE metabolites and risk of HPL. In crude model, BDCIPP, BCIPHIPP, and BBOEP were positively associated with the odds ratios (ORs) for HPL. After adjustment, only BDCIPP remained significant association with the risk of HPL (P < 0.05). Regarding the BDCIPP category, compared to the lowest tertile group (T1), the highest tertile group (T3) showed a significant increase in the risk of HPL (OR: 1.46, 95% CI: 1.14, 1.87, P for trend = 0.002) (Fig. 1 ). Table 2 Associations of urinary OPE metabolites levels with risk of HPL OPE metabolites Crude model a Adjusted model. b OR (95%CI) OR (95% CI) BDCIPP T1 (0.17) 1.54 (1.22, 1.93) 1.46 (1.14, 1.86) P trend 0.000 0.002 Continuous variables 1.16 (1.06, 1.27) 1.12 (1.01, 1.23) BCIPHIPP T1 (0.5) 1.36 (1.08, 1.73) 1.20 (0.93, 1.55) P trend 0.008 0.155 Continuous variables 1.19 (1.04, 1.35) 1.11 (0.97, 1.27) DPHP T1 (0.15) 0.95 (0.75, 1.20) 1.06 (0.82, 1.36) P trend 0.670 0.783 Continuous variables 0.95 (0.85, 1.05) 0.98 (0.88, 1.09) DocpDpcp T1 (0.13) 0.96 (0.76, 1.21) 0.90 (0.70, 1.16) P trend 0.787 0.467 Continuous variables 1.08 (0.96, 1.22) 1.05 (0.93, 1.20) BBOEP T1 (0.96) 1.34 (1.06, 1.70) 1.12 (0.87, 1.45) P trend 0.016 0.384 Continuous variables 1.13 (1.04, 1.22) 1.07 (0.98, 1.17) 4OHDPHP T1 (0.27) 1.04 (0.83, 1.32) 1.10 (0.86, 1.42) P trend 0.605 0.518 Continuous variables 0.99 (0.93, 1.05) 1.00 (0.93, 1.06) Abbreviations: OPE, organophosphate ester, HPL, hyperlipidemia, OR, odds ratios. T1 to T3 refer to1st to 3th tertile of urinary OPE metabolites levels. a Crude model was unadjusted. b Models were adjusted for age, gender, education, physical activity, body mass index (BMI), smoking, alcohol drinking, vegetable intake, and meat intake. For serum lipid levels (Table S7), we can observe positive relationship between BCIPHIPP and TC, as well as LDL-c (P < 0.05). The level of TC (β = 0.051, 95% CI: 0.004,0.099) and LDL-c (β = 0.053, 95% CI: 0.010,0.095) in T3 group of BCIPHIPP significantly increased with reference to T1 group. Substantial negative association were revealed between HDL-c with BDCIPP and DPHP, as well as the continuous variables of Docp&Dpcp and 4-OH-DPHP. For example, when compared to the reference group of DPHP, the levels of HDL-c decreased 3.54% and 4.21% in T2 and T3 group, respectively (The effect value was calculated using the formula 100%×[exp(β)-1].). Additionally, negative associations were also observed between DPHP with TC and LDL-c, as well as Docp&Dpcp with TC. 3.3 Stratified analyses of OPE metabolites with HPL The results of subgroup analyses are presented in Table S8 and Fig. 1 . In the age subgroup, there were positive association between BDCIPP (OR = 1.25, 95%CI: 1.07,1.45), BCIPHIPP (OR = 1.31, 95%CI: 1.07, 1.60) and the risk of HPL in ≤ 45 years groups. Multiplicative interactions were found between BCIPHIPP, DPHP and age on HPL. The correlation between BCIPHIPP and HPL in ≤ 45 years groups was significantly stronger than that in age > 45 groups (P interaction =0.015). BBOEP showed substantial positive association with HPL in groups of BMI < 24 kg/m 2 (OR: 1.21, 95%CI: 1.04, 1.41), and there were interactions between BBOEP with BMI on HPL (P interaction =0.037). In gender subgroup, significant positive association between BDCIPP and HPL can be observed in men. 3.4 Association of OPEs mixed exposure with the risk of HPL and serum lipid levels According to the results of qgcomp analysis, although there was not a substantial correlation between 6 OPE metabolites mixtures and HPL in the total population, significant negative effects on HDL-c was observed (β=-0.022, 95% CI=-0.040, -0.005) (Table S9). Among the OPE metabolites, BBOEP was the major contributors to the decrease of HDL-c (Figure S4). Additionally, a significant positive association was found between mixture of 6 OPE metabolites and the risk of HPL (OR = 1.52, 95% CI = 1.20, 1.91) (Fig. 2 A) as well as HDL-c (β=-0.034, 95% CI=-0.059, -0.010) in the subgroup of age ≤ 45 years (Table S10), while no significant association was found in the subgroup of age > 45 years. In the subgroup of age ≤ 45 years, BDCIPP, BCIPHIPP and DPHP were identified as the primary contributors to the overall effect on HPL (Fig. 2 B). The positive model in gWQS was selected for analyzing the 6 OPE metabolites mixtures and HPL, as only a positive association was evident in the individual OPE metabolite analyses. The results obtained in the gWQS model were consistent with those of the qgcomp model, with an increased risk of HPL (OR = 1.32, 95% CI = 1.02, 1.69) observed in the subgroup of individuals aged ≤ 45 years (Fig. 2 A). The three metabolites with the highest estimated weights for HPL were BDCIPP, BBOEP, and BCIPHIPP (Fig. 2 C). No significant association was found between 6 OPE metabolites mixtures and serum lipid levels (Table S11, Figure S5). 3.5 Joint effect of OPE metabolites and lifestyle status on the risk of HPL Since lifestyle, such as dietary habits, are closely related to exposure levels to OPEs of humans (Su et al., 2024a ) and lipid metabolism, we further investigated the joint effect of lifestyle status and BDCIPP on the risk of HPL. As shown in Fig. 3 , an increasing trend was observed across four groups, although no significant additive interaction was found. When compared to participants with non-detected level of BDCIPP and healthy lifestyle, those with detected level of BDCIPP and unhealthy lifestyle exhibited the highest OR of HPL (OR = 1.45, 95%CI = 1.06, 2.00). 3.6 Sensitivity analyses After excluding participants who had obesity (n = 275), diabetes mellitus (n = 73), or abnormal urinary creatinine (n = 55), the associations between OPE metabolites and HPL were largely consistent with our previous findings (Table S12). Even when we selected the NCEP-ATP III guidelines for the definition of HPL, the association between OPE metabolites and the risk of HPL remained robust (Table S13). 4. Discussion To our knowledge, our study represents the first epidemiological investigation into the association between urinary OPE metabolite levels and the risk of HPL in Chinese adults. In total population, positive effect of BDCIPP on the risk of HPL was observed. There were also significant associations between OPE metabolites and serum lipid levels. Age, BMI, and gender stratified analyses revealed the correlation between OPE metabolites and increase prevalence of HPL. In mixed exposure analyses, negative association were observed between the mixture of 6 OPE metabolites and HDL-c in total population, as well as positive association between them and the risk of HPL in the group of age ≤ 45 year. Additionally, while no joint effect was found, there were more pronounced association between BDCIPP and the risk of HPL in participants with unhealthy lifestyles. Toxicological studies have demonstrated the adverse effects of OPEs on lipid metabolism. An in vivo study conducted on rats showed that OPEs induced hypertriglyceridemia (Morris et al., 2014 ). In a 28-day toxicity test for rats, significant higher levels of serum TG were observed in tris(2-ethylhexyl) phosphate treatment groups (Pelletier et al., 2020 ). Another toxicological study conducted on zebrafish embryos showed that cresyl diphenyl phosphate had disruptive effects on various lipid classes, including TG and fatty acids (Jin et al., 2024 ). Currently, there is a lack of epidemiological observation about the correlation between OPEs and HPL. Population studies have primarily focused on the impact of OPEs exposure on serum lipid components. A study for general population in Shenzhen (Zhao et al., 2019 ) reported that exposure to aryl organic phosphate triesters led to the increase of levels of TG and TC in human blood. A study in Shandong (Zhang et al., 2023a ) for the investigation of relationship between OPEs and type 2 diabetes found a significantly positive association between triphenyl phosphate and TG. In a study for women in Canada, Siddique et al. ( 2020 ) noted a significant correlation between chloro-alkyl phosphates metabolites and serum lipid levels. They observed that bis(2-chloropropyl) phosphate were significantly and positively associated with cholesterol and LDL-c. Positive association was also observed between BDCIPP and total lipid, though not significantly. Likewise, in our present study, BDCIPP was positively corelated with HPL and BCIPHIPP was positively associated with the rise of TC and LDL-c levels. Besides, in mixed exposure analyses, there was negative association between 6 OPE metabolites and HDL-c. In contrast, a cross-sectional survey conducted on Korean firefighters (Lim et al., 2023 ) reported that the mixture of 11 urinary OPEs metabolites were positively associated HDL-c and negatively associated with TG. These unexpected results may be due to the particular characteristics of the study population. Our results also demonstrated protective effects of OPEs exposure on serum lipid levels. We found that DPHP and Docp&Dpcp were inversely associated with TC. These findings may be attributed to the interaction among different OPE metabolites, such as antagonistic effects (Siddique et al., 2020 ), or unknown mechanism induced by OPEs exposure affecting lipid metabolism. Several studies have explored the possible mechanism underlying lipid metabolism disorder caused by OPEs exposure. OPEs possess a broad affinity for various unclear receptors, allowing them to exert endocrine disrupting effects by activating or antagonizing related hormone receptors (Luo et al., 2020a ). Numerous in vitro studies demonstrated that OPEs exposure could lead to intracellular lipid accumulation by interacting with important regulators of lipid metabolism, such as PPARγ (Hao et al., 2019 ; Wang et al., 2021 ), pregnane X receptor (PXR) (Xiang and Wang, 2021 ), and Farnesoid X Receptor (FXR) (Yang et al., 2022a ). OPEs may also impact gene transcription related to lipid metabolism (Yan et al., 2022 ). Specifically, OPEs exposure has been found to disrupted the regulation of intracellular cholesterol through srebp2 signaling, thereby induced lipid metabolic disruption (Hao et al., 2019 ). Furthermore, the functional status of mitochondria, which is closely linked to lipogenesis and β-oxidation, can be affected by OPEs, leading to indirect interference in lipid metabolism and energy homeostasis (Hao et al., 2019 ; Jin et al., 2024 ). Mennillo et al. ( 2019 ) reported that OPEs exposure increased oxidative stress levels, resulting in mitochondrial damage, lipid peroxidation, and ultimately disruption of lipid metabolism. According to age stratification, we observed a significant positive correlation between mixed OPEs exposure and HPL in the subgroup of age ≤ 45 year, with BDCIPP and BCIPHIPP being the major contributors, which were also found associated with HPL in individual subgroup exposure analyses, suggesting that OPEs exposure may pose a higher risk of HPL in younger population. One possible explanation is that individuals with high levels of exposure to OPEs often tended to be indoor workers, use electronic devices, and heavily rely on cars for transportation (Hu et al., 2024 ; Tsai et al., 2022 ), with younger individuals being particularly susceptible. The age ranges of our findings align with the previous epidemiological surveys of association between OPEs exposure and serum lipid levels. Besides, BBOEP was associated with higher HPL risk in the subgroup of BMI < 24 kg/m 2 and there was a significant interaction between BBOEP and BMI on HPL. This is possibly because that overweight and obesity can also cause dyslipidemia, thereby interfering with the association between OPEs and HPL. Related mechanistic studies suspected that obesity may induce metabolic dysfunction and lipid accumulation (Erion and Corkey, 2017 ). Additionally, our gender stratified analysis showed that BDCIPP was significantly corelated with HPL in men. Similar results were reported in studies on the association between OPEs and metabolic diseases. A study based on the National Health and Nutrition Examination Survey (NHANES) found that the association between OPEs and MetS was more pronounced in men (Luo et al., 2020b ). This gender difference may be due to the distinction of metabolic mechanisms and exposure patterns between men and women (Qiao et al., 2016 ). Men exhibit higher rates of smoking and alcohol consumption, which may exert synergistic effect with OPEs exposure, contributing to the development of HPL. Previous studies showed that adherence to healthy lifestyles can mitigate the detrimental impacts of environmental risk factors on human health (Zhang et al., 2023b ). However, the combined influence of lifestyle status and OPEs on the risk of HPL remains unclear. In our study, the increasing risk of HPL was significant in participants with detected level of BDCIPP and unhealthy lifestyle, indicating that an unhealthy lifestyle may exacerbate the adverse effect of OPE exposure. Previous research suggested that human exposure to OPEs were closely related to the lifestyle, such as dietary habits (Su et al., 2024a ). A study conducted by Aimuzi et al. ( 2023 ) found that participants with low BDCIPP levels and high diet quality had lowest odds of hepatic metabolic disorders compared to those with high BDCIPP levels and low diet quality. Our study has several strengths. First of all, as we know, our research is the first to systematically explore the relationship between urinary OPE metabolites and HPL, as well as the lipid components. Secondly, the inclusion of a large sample size reduces the potential for sampling error, thereby enhancing the reliability of our findings. Thirdly, we employed multiple complementary statistical methods to explore the association between individual and mixed OPE exposure and the risk of HPL. In sensitive analyses, we excluded sensitive population, and adopted different HPL criterion to compare with the original results. The outcomes were relatively consistent, suggesting the robustness of our results. However, there are also limitations in our study. The concentration of spot urinary OPE metabolites cannot precisely estimated participants’ exposure levels due to the variability of urinary OPE metabolites levels. Moreover, as a cross-sectional design, it is difficult for our study to extrapolate any causal relationships between OPE exposure and HPL. Finally, we did not account for other potential confounding factors in our analytical model, such as the use of lipid-lowering drugs, exposure to other environmental pollutants, and history of diseases. This omission may introduce bias into our results. 5. Conclusion In conclusion, this cross-sectional study explored the association between urinary OPE metabolites and the risk of HPL, as well as serum lipid levels. We identified that BDCIPP was correlated with increased risk of HPL in total population, with unhealthy lifestyle intensifying the relationship. Mixed exposure analyses demonstrated a negative association between 6 urinary OPE metabolites mixture and HDL-c in total population, as well as positive association between the mixture and the risk of HPL in age ≤ 45 group. Large-scale prospective studies will be necessary in the future to validate and strengthen our findings. Declarations Acknowledgements We acknowledge the participants in the study. Author contribution Qitong Xu: Conceptualization, Data curation, Investigation, Methodology, Writing-original draft; Chang Xie: Conceptualization, Software, Supervision; Sijie Yang: Data curation, Resources, Supervision; Yaping Li: Formal analysis, Supervision, Validation; Mingye Zhang: Resources, Software, Supervision; Zhengce Wan: Validation, Visualization, Supervision; Lulu Song: Software, Supervision; Yongman Lv: Resources, Supervision; Hui Chen: Methodology, Supervision; Youjie Wang: Software, Validation; Surong Mei: Formal analysis,Funding Acquisition, Project administration, Supervision, Writing-review & editing. Funding This research was funded by the National Natural Science Foundation of China (No. 368 42077397) and the National Key Research and Development Program of China 369 (2019YFC1605100) Availability of data and materials The datasets used and analyzed during the current study are available from the 372 corresponding author on reasonable request. Ethical approval and consent to participate This study was approved by the Ethics Committee of Tongji Medical College, 375 Huazhong University of Science and Technology. Consent for publication Not applicable Declaration of Competing Interest The authors declare that they have no known competing interests in this paper. References Ai, S., Chen, X., Zhou, Y., 2023. Critical review on organophosphate esters in water environment: Occurrence, health hazards and removal technologies. Environmental Pollution, 123218. [DOI: https://doi.org/10.1016/j.envpol.2023.123218] Aimuzi, R., Xie, Z., Qu, Y., Jiang, Y., Luo, K., 2023. 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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 School of Public Health","correspondingAuthor":true,"prefix":"","firstName":"Surong","middleName":"","lastName":"Mei","suffix":""}],"badges":[],"createdAt":"2024-03-19 04:06:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4127098/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4127098/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-024-34411-6","type":"published","date":"2024-07-22T03:52:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55537183,"identity":"e34d7d4b-357f-4ea7-9647-b5fa5881b2b8","added_by":"auto","created_at":"2024-04-29 16:41:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":159716,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between organophosphate ester (OPE) metabolites and hyperlipidemia (HPL) in total and stratified analysis, T1 to T3 refer to1st to 3th tertile of urinary OPE metabolites levels.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4127098/v1/19e765567ad7652cc01d3c8c.png"},{"id":55537181,"identity":"845996ad-4593-459d-bb89-f0830fa997bd","added_by":"auto","created_at":"2024-04-29 16:41:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":275516,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated risk (A) and Weight values (B and C) of organophosphate ester (OPEs) mixed exposure for hyperlipidemia (HPL) by quantile-based g computation (qgcomp) model and generalized Weighted Quantile Sum Regression (gWQS) model. Models were adjusted for age, gender, education, physical activity, BMI, smoking, alcohol drinking, vegetable intake, and meat intake.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4127098/v1/c202cf9ac50c0411f81c52a5.png"},{"id":55537182,"identity":"62bd2f5e-dde4-45a6-abcb-c5be8051352b","added_by":"auto","created_at":"2024-04-29 16:41:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43093,"visible":true,"origin":"","legend":"\u003cp\u003eThe joint effect of lifestyle status and BDCIPP on hyperlipidemia (HPL). Note: Models were adjusted for age, gender, education, physical activity, BMI, smoking, alcohol drinking, vegetable intake, and meat intake.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4127098/v1/8ff9efed6091fe9a170cf5c1.png"},{"id":60867867,"identity":"57afcf7b-84a0-42e3-be13-0523e29208dc","added_by":"auto","created_at":"2024-07-23 03:52:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1311714,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4127098/v1/3f6d8834-1a69-48dd-9f6b-ccaa79c5c88b.pdf"}],"financialInterests":"","formattedTitle":"Association between organophosphate esters individual and mixed exposure with the risk of hyperlipidemia and serum lipid levels among Chinese adults","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHyperlipidemia, also known as dyslipidemia, refers to the elevation of triglyceride (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-c), or the reduction of high-density lipoprotein cholesterol (HDL-c) (Catapano et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Numerous studies have recognized hyperlipidemia (HPL) as a significant risk factor for cardiovascular diseases (CVDs) (Michaeli et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Abnormal serum lipid levels can promote atherosclerosis, thereby increasing the risk of stroke, coronary heart disease, and myocardial infarction (Zhang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Futhermore, HPL is also closely related to other metabolic diseases such as diabetes, hypertension, and metabolic syndrome (MetS) (Vesa and Bungau, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Statistics reveal alarmingly high overall prevalence of hyperlipidemia among Chinese adults, reaching 40.40%, resulting in severe disease burden (Cong et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The development of hyperlipidemia is influenced by a combination of genetic factors, dietary habits, lifestyle, and environmental factors (\u0026Aacute;lvarez Ram\u0026iacute;rez et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOrganophosphate esters (OPEs) are derivatives of phosphoric acid widely used as plasticizers or flame retardants. The annual consumption of OPEs has significantly increased as substitutes for brominated flame retardants (Ramesh et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Due to their physicochemical properties, OPEs are prone to leaching and volatilizing from products, thereby entering environmental medias. These compounds have been frequently detected in various environmental and biological samples, such as dust (Wu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), atmosphere, sediments (Liang et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), water (Ai et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), foods (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and human samples (Guo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). After entering the human body, most OPEs undergo metabolism to form diester or hydroxylated metabolites, which are excreted efficiently through urine (Liang et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Urinary OPE metabolisms serve as a universal biomarker for the evaluation OPEs exposure, accumulating studies have reported the high frequency and widespread detection of OPE metabolites in human urine (Kang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, an increasing number of studies have indicated that OPEs are type of endocrine disrupting chemicals (EDCs) (Rosenmai et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, a cell experiment found that certain OPEs can disturb steroidogenesis in human adrenocortical carcinoma cells (Zhang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Endocrine system plays a crucial role in regulating metabolism, including lipid metabolism. Evidences suggested that exposure to EDCs can disrupt lipid homeostasis and interfere with lipid metabolism (Kopp et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), ultimately leading to dyslipidemia (Cong et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Toxicological study showed that exposure to OPEs can elevate serum lipid levels for rat (Pelletier et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Exposure of zebrafish to tris(1-chloro-2-propyl)phosphate caused lipid metabolism dysfunction and lipid accumulation (Yan et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Jin et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported that cresyl diphenyl phosphate exposure led to the disruptions in lipid homeostasis in zebrafish embryos. To date, only a few epidemiological studies have investigated the relationship with OPEs exposure and serum lipid levels (Siddique et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Study conducted on the association between urinary OPE metabolites and the risk of HPL mainly remains absence. Several research reported the association between OPEs with metabolic disorders, such as diabetes (Zhang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e), obesity (Li et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and MetS (Luo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). A cross-sectional investigation reported a positive correlation between high level urinary of OPE metabolites with CVDs, which are closely related to HPL (Guo et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Therefore, exploring the effect of OPEs on lipid metabolism, especially the relationship OPEs exposure and HPL at the population level is of great significance.\u003c/p\u003e \u003cp\u003eIn the present study, we aimed to explore the association between individual urinary OPE metabolites and their mixtures with the risk of HPL as well as serum lipid levels among 1981 Chinese adults. The findings from our study contribute essential epidemiological evidence to inform future research on the relationship between exposure to OPEs and HPL.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eThe study population was from a cross-sectional study with details were placed in the supplementary material. A total of 2082 eligible participants were initially incorporated into our study. After excluding 96 individuals with missing or insufficient urine samples and 5 individuals with missing serum lipid parameters, a final sample size of 1981 individuals were included in the analysis (Figure S1). All the subjects completed face-to-face questionnaire surveys, as well as physical and biochemical examinations. Urine samples were collected from each subject and stored in a refrigerator at-20 ℃.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measurement of OPE metabolites\u003c/h2\u003e \u003cp\u003eDetailed information can be found in the supplementary material.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Assessment of HPL\u003c/h2\u003e \u003cp\u003eWe adhered to the updated guidelines for managing dyslipidemia in adults (2016 in China) to identify hyperlipidemia (Zhang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Participants were classified as having HPL if they exhibited at least one abnormal serum lipid level. The criteria of dyslipidemia are shown in Table S1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Covariates\u003c/h2\u003e \u003cp\u003eBased on previous research (Cong et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Siddique et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the collected questionnaire information, socio-demographics data (gender, age, education), as well as lifestyle factors, including smoking, alcohol drinking, physical activity, and diet (red meat intake frequency, vegetable intake frequency), were taken into account as potential confounders of OPE metabolites and hyperlipidemia. Body mass index (BMI), calculated as weight (kg) divided by height (m) squared, was included in the final model as well. The final covariates of our analysis were as follows: gender (male, female), age (\u0026le;\u0026thinsp;45 year, \u0026gt;\u0026thinsp;45 year), BMI (underweight/normal weight (\u0026lt;\u0026thinsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (24\u0026ndash;28 kg/m\u003csup\u003e2\u003c/sup\u003e), obesity (\u0026ge;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e)), education (middle school or below, high school, college school or above), smoking (never, current/former), alcohol drinking (never, current/former), physical activity (yes, no), vegetable intake (\u0026lt;\u0026thinsp;7 times/week, 7\u0026ndash;14 times/week, \u0026ge;\u0026thinsp;14 times/week), meat intake (\u0026lt;\u0026thinsp;3 times/week, 3\u0026ndash;7 times/week, \u0026ge;\u0026thinsp;7 times/week).\u003c/p\u003e \u003cp\u003eWe further calculated the healthy lifestyle score to assess lifestyle status based on five factors, as defined in previous studies (Guo et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tan et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yan-Bo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e), namely smoking, alcohol drinking, physical activity, sleep duration, and dietary diversity. For each factor, subjects who met the criterion of healthy lifestyles were assigned 1 point, or else given 0 point. The total score ranged from 0 to 5. Details were demonstrated in Table S2. Based on the total score, the study population were classified into two categories: healthy lifestyle (scores\u0026thinsp;\u0026gt;\u0026thinsp;3), and unhealthy lifestyles (scores\u0026thinsp;\u0026le;\u0026thinsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics are utilized to summarize the fundamental characteristics of study participants. For continuous variables, the mean and standard deviation (SD) or median and interquartile range (IQR) are employed, while categorical variables are summarized using numbers or percentages. To compare the characteristics between HPL group and non-HPL group, Student\u0026rsquo;s t-test and Mann-Whitney U test were used for continuous variables, while the chi-square test was used for categorical variables. In this study, 8 OPE metabolites were detected and detection frequency greater than or equal to 60% of them were analyzed, with their name and abbreviations listed in Table S3. The OPE metabolites levels lower than the LOD were substituted by the value of LOD/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\surd 2\\)\u003c/span\u003e\u003c/span\u003e. All the concentrations of urinary metabolites of OPEs were first converted using the natural logarithm (ln) and corrected based on specific gravity before conducting the analysis. The correlation coefficients between these six OPE metabolites were then estimated using Spearman's rank correlation analysis.\u003c/p\u003e \u003cp\u003eIn the correlation analysis, we initially utilized restricted cubic spline (RCS) models to examine the linear relationships between OPE metabolites and the risk of HPL, as well as serum lipid levels. We also employed the RCS model to investigate the dose-response relationship between individual OPE metabolites and the risk of HPL. Non-linear associations were not found between OPE metabolites and HPL but were found with serum lipid levels (Table S4). Based on these findings, generalized liner models were used to investigate the association of OPE metabolites with the risk of HPL and serum lipid levels. The study population were categorized into tertiles according to their urinary OPE metabolites concentrations, with the lowest tertile serving as the reference group. When conducting trend tests, we treated the midpoint value of the logarithmically transformed data for each tertile as a continuous variable. For HPL, odds ratio (OR) and its 95% confidence interval (CI) were used as effect estimates. For serum lipid component, we used the regression coefficient β as the effect value. Crude model and adjusted models for potential confounders were both analyzed. The adjusted covariates included gender, age, BMI, education, smoking, alcohol drinking, physical activity, red meat intake frequency, and vegetable intake frequency. Missing covariates data were imputed by multiple interpolation method. In addition, stratification analyses of correlation between OPE metabolites and HPL were conducted on several crucial modifying factors, including age (\u0026le;\u0026thinsp;45 year, \u0026gt;\u0026thinsp;45 year), BMI (\u0026lt;\u0026thinsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e, \u0026ge; 24 kg/m\u003csup\u003e2\u003c/sup\u003e), and gender (male, female). In the subgroup analysis of healthy lifestyle scores, to investigated the joint effects of lifestyle status and OPEs exposure, we selected OPE metabolites that were significantly associated with HPL in single exposure models, and further estimated the additive interactions of these OPE metabolites and unhealthy lifestyles on HPL by calculating the relative excess risk due to interaction (RERI).\u003c/p\u003e \u003cp\u003eA multi-pollutant model quantile-based g-computation (qgcomp) was further utilized to explore the impacts of OPEs mixtures exposure on the risk of HPL. The qgcomp model does not impose restrictions on the direction of the effects, enabling the comprehensive estimation of the contribution of each OPE metabolite to the mixed effects, which can reduce bias and enhance the stability of the results. The mixed OPEs exposure on the effects of HPL in total study population and age stratified groups were both analyzed. Furthermore, we also analyzed mixed OPEs exposure on the effect of HPL by using generalized Weighted Quantile Sum Regression (gWQS) to validate the results of qgcomp. Data was randomly divided into 40% training sets and 60% validation sets, with 500 bootstrap iterations performed to determine each OPE metabolites\u0026rsquo; effect weights.\u003c/p\u003e \u003cp\u003eFinally, we conducted several sensitivity analyses to assess the robustness of the results. Firstly, we excluded participants with obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e), diabetes mellitus (fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L), and abnormal urinary creatinine (\u0026lt;\u0026thinsp;53 or \u0026gt;\u0026thinsp;106 \u003cem\u003e\u0026micro;mol/L for men, \u0026lt;\u0026thinsp;44 or \u0026gt;\u0026thinsp;97 \u0026micro;mol/L for women\u003c/em\u003e) to analyze the correlation between OPE metabolites and HPL, which may disturb the outcome of HPL (Catapano et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, we followed the National Cholesterol Education Program-Adult Treatment Group III Guidelines (NCEP-ATP III guide) (\u0026Aacute;lvarez Ram\u0026iacute;rez et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to identify participants with HPL and calculated the effects of probable outcome misclassification. Specifically, HPL was defined as the presence of high-risk levels of TG, TC, or LDL-C, or a risk level of HDL-C. The software used for statistical analysis is consistent with the previous work of our research group. Statistical significance was defined as a two-sided p-value of less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics of study population\u003c/h2\u003e \u003cp\u003eThe demographic statistics of participants as well as serum lipid levels are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of participants was 44.62\u0026thinsp;\u0026plusmn;\u0026thinsp;10.58, and 35.3 percent were women. Out of the total 1981 individuals, 579 (29.2%) were identified as HPL, while 1402 (70.8%) were classified as non-HPL. Significant differences were observed between the two groups in terms of gender, BMI, smoking habits, and alcohol consumption characteristics. Participants with HPL are more inclined to smoke, drink alcohol, have higher BMI, and more likely to be male. In addition, substantial differences were found in healthy lifestyle behaviors and scores Table S5.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the studied participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;1981)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-HPL (1402)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHPL (n\u0026thinsp;=\u0026thinsp;579)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;45 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e983 (49.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e699 (49.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e284 (49.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;45 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e998 (50.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e703 (50.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e295 (50.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1282 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e799 (57.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e483 (83.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e699 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e603 (43.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight/Normal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e914 (46.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e760 (54.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e154 (26.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e788 (39.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e510 (36.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e278 (48.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e275 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146 (25.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (0.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e468 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e339 (24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e129 (22.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e364 (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e251 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113 (19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege school or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1130 (57.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e798 (56.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e332 (57.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1374 (69.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1049 (74.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e325 (56.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent/Former\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e603 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e340 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e253 (43.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1348 (68.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1004 (71.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e344 (59.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent/Former\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e625 (31.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e394 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e231 (39.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e585 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e409 (29.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e176 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1390 (70.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e989 (70.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e401 (69.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetable intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;7 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e366 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e257 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e109 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u0026ndash;14 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e496 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e344 (24.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e152 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;14 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1075 (54.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e769 (54.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e306 (52.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeat intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e436 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e330 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e106 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;7 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e551 (27.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e375 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e176 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;7 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e961 (48.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e673 (48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e288 (49.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.62\u0026thinsp;\u0026plusmn;\u0026thinsp;10.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.48\u0026thinsp;\u0026plusmn;\u0026thinsp;10.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.98\u0026thinsp;\u0026plusmn;\u0026thinsp;9.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.39\u0026thinsp;\u0026plusmn;\u0026thinsp;3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.69\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.08\u0026thinsp;\u0026plusmn;\u0026thinsp;3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid variables (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: HPL, hyperlipidemia, BMI, body mass index, TG, triglyceride, TC, total cholesterol, LDL-c, low-density lipoprotein cholesterol, HDL-c, high-density lipoprotein cholesterol.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e P value was tested by Student's t-test for continuous variables of normal distribution, Mann-Whitney U test for that of non-normal distribution, and Chi-square test for the categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe detection frequency and concentration distributions of OPE metabolites are presents in Table S6. Among the 15 OPEs, 8 OPEs detected more than 10% were listed and 6 OPEs were detected more than 60%. The concentration of BBOEP exhibits the highest geometric mean (GM) at 0.47 ng/ml, with BCIPHIPP following closely at 0.35 ng/ml. Subsequently, the concentrations of 4-OH-DPHP, BDCIPP, DPHP, and Docp\u0026amp;Dpcp are 0.15 ng/ml, 0.12 ng/ml, 0.11 ng/ml, and 0.10 ng/ml, respectively. Figure S2 depicts the Spearman correlation coefficients (rs) matrix of OPE metabolites, and the result showed significant positive correlations between urinary OPEs metabolites (r\u003csub\u003es\u003c/sub\u003e between 0.047\u0026ndash;0.64). Among them, the strongest correlation appeared between BBOEP and Docp\u0026amp;Dpcp (r\u003csub\u003es\u003c/sub\u003e= 0.64).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association of individual OPE metabolites with HPL and serum lipid levels\u003c/h2\u003e \u003cp\u003eThe preliminary RCS analysis reflects the linear dose-response relationships between HPL risk and urinary OPE metabolite levels. As concentrations of urinary BDCIPP, BCIPHIPP, DoCP\u0026amp;DpCP, and BBOEP increased, so did the risk of HPL (P for non-linearity\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Figure S3). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the association between the levels of urinary OPE metabolites and risk of HPL. In crude model, BDCIPP, BCIPHIPP, and BBOEP were positively associated with the odds ratios (ORs) for HPL. After adjustment, only BDCIPP remained significant association with the risk of HPL (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Regarding the BDCIPP category, compared to the lowest tertile group (T1), the highest tertile group (T3) showed a significant increase in the risk of HPL (OR: 1.46, 95% CI: 1.14, 1.87, P for trend\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of urinary OPE metabolites levels with risk of HPL\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOPE metabolites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude model \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted model.\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBDCIPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 (\u0026lt;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2 (0.05\u0026ndash;0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.80, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.79 1.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3 (\u0026gt;0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.54 (1.22, 1.93)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.46 (1.14, 1.86)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.16 (1.06, 1.27)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.12 (1.01, 1.23)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCIPHIPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 (\u0026lt;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2 (0.27\u0026ndash;0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.82, 1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.75, 1.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3 (\u0026gt;0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.36 (1.08, 1.73)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20 (0.93, 1.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.19 (1.04, 1.35)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11 (0.97, 1.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 (\u0026lt;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2 (0.07\u0026ndash;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.72, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.72, 1.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3 (\u0026gt;0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.75, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06 (0.82, 1.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.85, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.88, 1.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDocpDpcp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 (\u0026lt;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2 (0.07\u0026ndash;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.69, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82 (0.64, 1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3 (\u0026gt;0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96 (0.76, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90 (0.70, 1.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (0.96, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 (0.93, 1.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBBOEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 (\u0026lt;0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2 (0.31\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11 (0.87, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 (0.81, 1.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3 (\u0026gt;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.34 (1.06, 1.70)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12 (0.87, 1.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.13 (1.04, 1.22)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.98, 1.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4OHDPHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 (\u0026lt;0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2 (0.03\u0026ndash;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.72, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97 (0.75, 1.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3 (\u0026gt;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.83, 1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10 (0.86, 1.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.93, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.93, 1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviations: OPE, organophosphate ester, HPL, hyperlipidemia, OR, odds ratios.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eT1 to T3 refer to1st to 3th tertile of urinary OPE metabolites levels.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003ea\u003c/sup\u003e Crude model was unadjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003eb\u003c/sup\u003e Models were adjusted for age, gender, education, physical activity, body mass index (BMI), smoking, alcohol drinking, vegetable intake, and meat intake.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor serum lipid levels (Table S7), we can observe positive relationship between BCIPHIPP and TC, as well as LDL-c (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The level of TC (β\u0026thinsp;=\u0026thinsp;0.051, 95% CI: 0.004,0.099) and LDL-c (β\u0026thinsp;=\u0026thinsp;0.053, 95% CI: 0.010,0.095) in T3 group of BCIPHIPP significantly increased with reference to T1 group. Substantial negative association were revealed between HDL-c with BDCIPP and DPHP, as well as the continuous variables of Docp\u0026amp;Dpcp and 4-OH-DPHP. For example, when compared to the reference group of DPHP, the levels of HDL-c decreased 3.54% and 4.21% in T2 and T3 group, respectively (The effect value was calculated using the formula 100%\u0026times;[exp(β)-1].). Additionally, negative associations were also observed between DPHP with TC and LDL-c, as well as Docp\u0026amp;Dpcp with TC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Stratified analyses of OPE metabolites with HPL\u003c/h2\u003e \u003cp\u003eThe results of subgroup analyses are presented in Table S8 and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the age subgroup, there were positive association between BDCIPP (OR\u0026thinsp;=\u0026thinsp;1.25, 95%CI: 1.07,1.45), BCIPHIPP (OR\u0026thinsp;=\u0026thinsp;1.31, 95%CI: 1.07, 1.60) and the risk of HPL in \u0026le;\u0026thinsp;45 years groups. Multiplicative interactions were found between BCIPHIPP, DPHP and age on HPL. The correlation between BCIPHIPP and HPL in \u0026le;\u0026thinsp;45 years groups was significantly stronger than that in age\u0026thinsp;\u0026gt;\u0026thinsp;45 groups (P \u003csub\u003einteraction\u003c/sub\u003e=0.015). BBOEP showed substantial positive association with HPL in groups of BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e (OR: 1.21, 95%CI: 1.04, 1.41), and there were interactions between BBOEP with BMI on HPL (P \u003csub\u003einteraction\u003c/sub\u003e=0.037). In gender subgroup, significant positive association between BDCIPP and HPL can be observed in men.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Association of OPEs mixed exposure with the risk of HPL and serum lipid levels\u003c/h2\u003e \u003cp\u003eAccording to the results of qgcomp analysis, although there was not a substantial correlation between 6 OPE metabolites mixtures and HPL in the total population, significant negative effects on HDL-c was observed (β=-0.022, 95% CI=-0.040, -0.005) (Table S9). Among the OPE metabolites, BBOEP was the major contributors to the decrease of HDL-c (Figure S4). Additionally, a significant positive association was found between mixture of 6 OPE metabolites and the risk of HPL (OR\u0026thinsp;=\u0026thinsp;1.52, 95% CI\u0026thinsp;=\u0026thinsp;1.20, 1.91) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) as well as HDL-c (β=-0.034, 95% CI=-0.059, -0.010) in the subgroup of age\u0026thinsp;\u0026le;\u0026thinsp;45 years (Table S10), while no significant association was found in the subgroup of age\u0026thinsp;\u0026gt;\u0026thinsp;45 years. In the subgroup of age\u0026thinsp;\u0026le;\u0026thinsp;45 years, BDCIPP, BCIPHIPP and DPHP were identified as the primary contributors to the overall effect on HPL (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The positive model in gWQS was selected for analyzing the 6 OPE metabolites mixtures and HPL, as only a positive association was evident in the individual OPE metabolite analyses. The results obtained in the gWQS model were consistent with those of the qgcomp model, with an increased risk of HPL (OR\u0026thinsp;=\u0026thinsp;1.32, 95% CI\u0026thinsp;=\u0026thinsp;1.02, 1.69) observed in the subgroup of individuals aged\u0026thinsp;\u0026le;\u0026thinsp;45 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The three metabolites with the highest estimated weights for HPL were BDCIPP, BBOEP, and BCIPHIPP (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). No significant association was found between 6 OPE metabolites mixtures and serum lipid levels (Table S11, Figure S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Joint effect of OPE metabolites and lifestyle status on the risk of HPL\u003c/h2\u003e \u003cp\u003eSince lifestyle, such as dietary habits, are closely related to exposure levels to OPEs of humans (Su et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e) and lipid metabolism, we further investigated the joint effect of lifestyle status and BDCIPP on the risk of HPL. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, an increasing trend was observed across four groups, although no significant additive interaction was found. When compared to participants with non-detected level of BDCIPP and healthy lifestyle, those with detected level of BDCIPP and unhealthy lifestyle exhibited the highest OR of HPL (OR\u0026thinsp;=\u0026thinsp;1.45, 95%CI\u0026thinsp;=\u0026thinsp;1.06, 2.00).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Sensitivity analyses\u003c/h2\u003e \u003cp\u003eAfter excluding participants who had obesity (n\u0026thinsp;=\u0026thinsp;275), diabetes mellitus (n\u0026thinsp;=\u0026thinsp;73), or abnormal urinary creatinine (n\u0026thinsp;=\u0026thinsp;55), the associations between OPE metabolites and HPL were largely consistent with our previous findings (Table S12). Even when we selected the NCEP-ATP III guidelines for the definition of HPL, the association between OPE metabolites and the risk of HPL remained robust (Table S13).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo our knowledge, our study represents the first epidemiological investigation into the association between urinary OPE metabolite levels and the risk of HPL in Chinese adults. In total population, positive effect of BDCIPP on the risk of HPL was observed. There were also significant associations between OPE metabolites and serum lipid levels. Age, BMI, and gender stratified analyses revealed the correlation between OPE metabolites and increase prevalence of HPL. In mixed exposure analyses, negative association were observed between the mixture of 6 OPE metabolites and HDL-c in total population, as well as positive association between them and the risk of HPL in the group of age\u0026thinsp;\u0026le;\u0026thinsp;45 year. Additionally, while no joint effect was found, there were more pronounced association between BDCIPP and the risk of HPL in participants with unhealthy lifestyles.\u003c/p\u003e \u003cp\u003eToxicological studies have demonstrated the adverse effects of OPEs on lipid metabolism. An in vivo study conducted on rats showed that OPEs induced hypertriglyceridemia (Morris et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In a 28-day toxicity test for rats, significant higher levels of serum TG were observed in tris(2-ethylhexyl) phosphate treatment groups (Pelletier et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Another toxicological study conducted on zebrafish embryos showed that cresyl diphenyl phosphate had disruptive effects on various lipid classes, including TG and fatty acids (Jin et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Currently, there is a lack of epidemiological observation about the correlation between OPEs and HPL. Population studies have primarily focused on the impact of OPEs exposure on serum lipid components. A study for general population in Shenzhen (Zhao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported that exposure to aryl organic phosphate triesters led to the increase of levels of TG and TC in human blood. A study in Shandong (Zhang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e) for the investigation of relationship between OPEs and type 2 diabetes found a significantly positive association between triphenyl phosphate and TG. In a study for women in Canada, Siddique et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) noted a significant correlation between chloro-alkyl phosphates metabolites and serum lipid levels. They observed that bis(2-chloropropyl) phosphate were significantly and positively associated with cholesterol and LDL-c. Positive association was also observed between BDCIPP and total lipid, though not significantly. Likewise, in our present study, BDCIPP was positively corelated with HPL and BCIPHIPP was positively associated with the rise of TC and LDL-c levels. Besides, in mixed exposure analyses, there was negative association between 6 OPE metabolites and HDL-c. In contrast, a cross-sectional survey conducted on Korean firefighters (Lim et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that the mixture of 11 urinary OPEs metabolites were positively associated HDL-c and negatively associated with TG. These unexpected results may be due to the particular characteristics of the study population. Our results also demonstrated protective effects of OPEs exposure on serum lipid levels. We found that DPHP and Docp\u0026amp;Dpcp were inversely associated with TC. These findings may be attributed to the interaction among different OPE metabolites, such as antagonistic effects (Siddique et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), or unknown mechanism induced by OPEs exposure affecting lipid metabolism.\u003c/p\u003e \u003cp\u003eSeveral studies have explored the possible mechanism underlying lipid metabolism disorder caused by OPEs exposure. OPEs possess a broad affinity for various unclear receptors, allowing them to exert endocrine disrupting effects by activating or antagonizing related hormone receptors (Luo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). Numerous in vitro studies demonstrated that OPEs exposure could lead to intracellular lipid accumulation by interacting with important regulators of lipid metabolism, such as PPARγ (Hao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), pregnane X receptor (PXR) (Xiang and Wang, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Farnesoid X Receptor (FXR) (Yang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). OPEs may also impact gene transcription related to lipid metabolism (Yan et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Specifically, OPEs exposure has been found to disrupted the regulation of intracellular cholesterol through srebp2 signaling, thereby induced lipid metabolic disruption (Hao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, the functional status of mitochondria, which is closely linked to lipogenesis and β-oxidation, can be affected by OPEs, leading to indirect interference in lipid metabolism and energy homeostasis (Hao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jin et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Mennillo et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported that OPEs exposure increased oxidative stress levels, resulting in mitochondrial damage, lipid peroxidation, and ultimately disruption of lipid metabolism.\u003c/p\u003e \u003cp\u003eAccording to age stratification, we observed a significant positive correlation between mixed OPEs exposure and HPL in the subgroup of age\u0026thinsp;\u0026le;\u0026thinsp;45 year, with BDCIPP and BCIPHIPP being the major contributors, which were also found associated with HPL in individual subgroup exposure analyses, suggesting that OPEs exposure may pose a higher risk of HPL in younger population. One possible explanation is that individuals with high levels of exposure to OPEs often tended to be indoor workers, use electronic devices, and heavily rely on cars for transportation (Hu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tsai et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with younger individuals being particularly susceptible. The age ranges of our findings align with the previous epidemiological surveys of association between OPEs exposure and serum lipid levels. Besides, BBOEP was associated with higher HPL risk in the subgroup of BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e and there was a significant interaction between BBOEP and BMI on HPL. This is possibly because that overweight and obesity can also cause dyslipidemia, thereby interfering with the association between OPEs and HPL. Related mechanistic studies suspected that obesity may induce metabolic dysfunction and lipid accumulation (Erion and Corkey, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, our gender stratified analysis showed that BDCIPP was significantly corelated with HPL in men. Similar results were reported in studies on the association between OPEs and metabolic diseases. A study based on the National Health and Nutrition Examination Survey (NHANES) found that the association between OPEs and MetS was more pronounced in men (Luo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). This gender difference may be due to the distinction of metabolic mechanisms and exposure patterns between men and women (Qiao et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Men exhibit higher rates of smoking and alcohol consumption, which may exert synergistic effect with OPEs exposure, contributing to the development of HPL.\u003c/p\u003e \u003cp\u003ePrevious studies showed that adherence to healthy lifestyles can mitigate the detrimental impacts of environmental risk factors on human health (Zhang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). However, the combined influence of lifestyle status and OPEs on the risk of HPL remains unclear. In our study, the increasing risk of HPL was significant in participants with detected level of BDCIPP and unhealthy lifestyle, indicating that an unhealthy lifestyle may exacerbate the adverse effect of OPE exposure. Previous research suggested that human exposure to OPEs were closely related to the lifestyle, such as dietary habits (Su et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). A study conducted by Aimuzi et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that participants with low BDCIPP levels and high diet quality had lowest odds of hepatic metabolic disorders compared to those with high BDCIPP levels and low diet quality.\u003c/p\u003e \u003cp\u003eOur study has several strengths. First of all, as we know, our research is the first to systematically explore the relationship between urinary OPE metabolites and HPL, as well as the lipid components. Secondly, the inclusion of a large sample size reduces the potential for sampling error, thereby enhancing the reliability of our findings. Thirdly, we employed multiple complementary statistical methods to explore the association between individual and mixed OPE exposure and the risk of HPL. In sensitive analyses, we excluded sensitive population, and adopted different HPL criterion to compare with the original results. The outcomes were relatively consistent, suggesting the robustness of our results. However, there are also limitations in our study. The concentration of spot urinary OPE metabolites cannot precisely estimated participants\u0026rsquo; exposure levels due to the variability of urinary OPE metabolites levels. Moreover, as a cross-sectional design, it is difficult for our study to extrapolate any causal relationships between OPE exposure and HPL. Finally, we did not account for other potential confounding factors in our analytical model, such as the use of lipid-lowering drugs, exposure to other environmental pollutants, and history of diseases. This omission may introduce bias into our results.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this cross-sectional study explored the association between urinary OPE metabolites and the risk of HPL, as well as serum lipid levels. We identified that BDCIPP was correlated with increased risk of HPL in total population, with unhealthy lifestyle intensifying the relationship. Mixed exposure analyses demonstrated a negative association between 6 urinary OPE metabolites mixture and HDL-c in total population, as well as positive association between the mixture and the risk of HPL in age\u0026thinsp;\u0026le;\u0026thinsp;45 group. Large-scale prospective studies will be necessary in the future to validate and strengthen our findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e We acknowledge the participants in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQitong Xu:\u0026nbsp;\u003c/strong\u003eConceptualization,\u0026nbsp;Data curation, Investigation, Methodology, Writing-original draft; \u003cstrong\u003eChang Xie:\u003c/strong\u003e Conceptualization, Software, Supervision; \u003cstrong\u003eSijie Yang:\u003c/strong\u003e Data curation, Resources, Supervision; \u003cstrong\u003eYaping Li:\u003c/strong\u003e\u0026nbsp; Formal analysis, Supervision, Validation; \u003cstrong\u003eMingye Zhang:\u0026nbsp;\u003c/strong\u003eResources, Software, Supervision; \u003cstrong\u003eZhengce Wan:\u003c/strong\u003e Validation, Visualization, Supervision;\u0026nbsp;\u003cstrong\u003eLulu Song:\u003c/strong\u003e Software, Supervision;\u0026nbsp;\u003cstrong\u003eYongman Lv:\u003c/strong\u003e Resources, Supervision;\u0026nbsp;\u003cstrong\u003eHui Chen:\u003c/strong\u003e Methodology, Supervision; \u003cstrong\u003eYoujie Wang:\u0026nbsp;\u003c/strong\u003eSoftware, Validation;\u0026nbsp;\u003cstrong\u003eSurong Mei:\u003c/strong\u003e Formal analysis,Funding Acquisition, Project administration, Supervision, Writing-review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Natural Science Foundation of China (No. 368 42077397) and the National Key Research and Development Program of China 369 (2019YFC1605100)\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 372 corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Tongji Medical College, 375 Huazhong University of Science and Technology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing interests in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAi, S., Chen, X., Zhou, Y., 2023. 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[DOI: http://10.1021/acs.estlett.9b00417]\u003c/li\u003e\n\u003c/ol\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"organophosphate esters, hyperlipidemia, serum lipids, mixed exposure, lifestyle","lastPublishedDoi":"10.21203/rs.3.rs-4127098/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4127098/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u0026nbsp;\u0026nbsp;Toxicologic studies reported that organophosphate esters (OPEs) may disrupt lipid metabolism, thus affecting serum lipid levels. However, epidemiological evidences regarding the association between OPEs and the risk of hyperlipidemia (HPL) as well as serum lipid levels are scarce. In the present study, our aim was to investigate the impact of individual and mixed OPEs exposure on HPL. A total of 1981 Chinese adults were involved based on a cross-sectional design. Overall, we found a positive association between bis(1,3-dichloro-2-propyl) phosphate (BDCIPP) and the risk of HPL. Bis(1-chloro-2-propyl) phosphate (BCIPHIPP) showed a positive association with total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C). BDCIPP, diphenyl phosphate (DPHP), di-ocresyl phosphate and di-p-cresyl phosphate (Docp\u0026amp;Dpcp) as well as 4-hydroxyphenyl-diphenyl phosphate (4-OH-DPHP) exhibited a negative association with high-density lipoprotein cholesterol (HDL-C). In stratified analyses, BDCIPP and BCIPHIPP were significantly correlated with the increase risk of HPL in age ≤45 group. Bis (2-butoxyethyl) phosphate (BBOEP) was in relationship with an elevated risk of HPL in the subgroup of BMI \u0026lt; 24 kg/m\u003csup\u003e2\u003c/sup\u003e. BDCIPP was also positively associated with HPL in men. Quantile-based g computation (qgcomp) and generalized Weighted Quantile Sum Regression (gWQS) models demonstrated a negative association between OPEs mixed exposure and HDL-c in total population, as well as a positive effect of them on HPL in the subgroup of age ≤45 year, which is consistent with the individual analyses. Furthermore, joint effect analyses of revealed that participants with detected BDCIPP urinary levels and unhealthy lifestyle had the highest risk of HPL. Our findings offer evidence supporting the correlation between exposure to OPE and the risk of HPL, necessitating further prospective studies for validation.\u003c/p\u003e","manuscriptTitle":"Association between organophosphate esters individual and mixed exposure with the risk of hyperlipidemia and serum lipid levels among Chinese adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-29 16:41:15","doi":"10.21203/rs.3.rs-4127098/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2024-06-04T11:55:48+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-05-17T00:40:37+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-22T20:34:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2024-04-03T14:08:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-22T05:26:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2024-03-20T03:45:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cffbe38c-60da-45e4-9220-97f9bed96a07","owner":[],"postedDate":"April 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-23T03:52:13+00:00","versionOfRecord":{"articleIdentity":"rs-4127098","link":"https://doi.org/10.1007/s11356-024-34411-6","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2024-07-22 03:52:13","publishedOnDateReadable":"July 22nd, 2024"},"versionCreatedAt":"2024-04-29 16:41:15","video":"","vorDoi":"10.1007/s11356-024-34411-6","vorDoiUrl":"https://doi.org/10.1007/s11356-024-34411-6","workflowStages":[]},"version":"v1","identity":"rs-4127098","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4127098","identity":"rs-4127098","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}