Association Between Organophosphorus Flame Retardant Exposure and Chronic Kidney Disease in U.S. Adults: NHANES data from 2011--2016

Association Between Organophosphorus Flame Retardant Exposure and Chronic Kidney Disease in U.S. Adults: NHANES data from 2011--2016 | 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 Organophosphorus Flame Retardant Exposure and Chronic Kidney Disease in U.S. Adults: NHANES data from 2011--2016 Yuebin Yang, Pu Guo, Hongjing Ren, Lingyun Zhuo, Qikai Xiang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6915116/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Feb, 2026 Read the published version in European Journal of Medical Research → Version 1 posted 13 You are reading this latest preprint version Abstract Background As alternatives to brominated flame retardants, organophosphorus flame retardants (OPFRs) have raised concerns regarding their potential nephrotoxicity. However, population-based evidence remains inconsistent. This study aimed to examine the associations between urinary metabolites of OPFRs and chronic kidney disease (CKD), along with renal function markers (estimated glomerular filtration rate [eGFR] and the urinary albumin‒creatinine ratio [ACR]), in the general U.S. population while exploring potential underlying mechanisms. Methods We analyzed data from the National Health and Nutrition Examination Survey (NHANES) 2011–2016, which included 2,156 adults. Five OPFR metabolites (DPHP, BDCPP, BCPP, BCEP, and DBUP) were measured in the urine. Multivariate logistic regression, subgroup analyses, restricted cubic spline (RCS), and weighted quantile sum (WQS) regression were performed, with CKD defined as an eGFR < 60 mL/min/1.73 m² or an ACR ≥ 30 mg/g. Results BCPP was inversely associated with CKD risk (Q4 vs. Q1: OR = 0.42, 95% CI: 0.23–0.77, P = 0.007), particularly in hypertensive, elderly, and high-income populations. DPHP and BDCPP were significantly positively correlated with the eGFR (P for trend < 0.05), although the association weakened after adjustment. DBUP exhibited a U-shaped relationship with the eGFR ( P -nonlinear = 0.048). WQS analysis indicated that OPFR mixtures were associated with higher eGFRs (β = 1.20, 95% CI: 0.38–2.02, P = 0.004), which was driven primarily by DBUP and DPHP. Conclusions Low-level OPFR exposure may be associated with a reduced risk of CKD, potentially through mitochondrial activation. These findings challenge the assumption of linear toxicity, highlight the complexity of dose‒response relationships, and underscore the need for further mechanistic validation and refined risk assessment frameworks. Organophosphorus flame retardants Chronic kidney disease Renal function markers Nonalotonic dose‒response Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Organophosphate flame retardants (OPFRs) have emerged as key alternatives to traditional brominated flame retardants in material science and fire safety applications[ 1 ]. As phosphorus-containing organic compounds, OPFRs thermally decompose to produce phosphoric acid esters, forming a dense carbonized layer that insulates heat and oxygen, thereby effectively inhibiting combustion[ 2 ]. Compared with brominated flame retardants, OPFRs exhibit lower environmental persistence, bioaccumulation potential, and ecotoxicity[ 3 , 4 ], leading to their widespread use in industrial products such as polymers, electronic equipment housings, and building materials[ 5 ]. However, the extensive application of OPFRs has resulted in their pervasive environmental distribution, including in air, water bodies, and indoor dust[ 6 ]. Human exposure occurs primarily through inhalation, dietary intake, and dermal contact[ 7 , 8 ], and their metabolites are frequently detected in population-level urine samples[ 9 ]. Growing evidence suggests that OPFRs may adversely affect human health through multiple mechanisms: endocrine disruption via interference with thyroid hormone signaling pathways[ 10 , 11 ]; neurotoxicity, including neuronal apoptosis linked to cognitive dysfunction in children[ 12 , 13 ]; reproductive impairment via oxidative stress-mediated germ cell damage[ 14 , 15 ]; and nephrotoxicity, particularly renal tubular epithelial cell injury, which may exacerbate chronic kidney disease (CKD)[ 16 ]. Chronic kidney disease (CKD) represents a significant global public health burden, affecting approximately 10% of adults worldwide, with notable geographic disparities in disease prevalence[ 17 ]. While CKD rates have plateaued in developed nations[ 18 ], many developing countries are experiencing rising incidence rates, likely driven by rapid industrialization and lifestyle changes[ 19 ]. Notably, a substantial proportion of CKD cases remain idiopathic, presenting major challenges for disease prevention and management[ 20 ]. Emerging environmental evidence suggests that certain pollutants, including organophosphate flame retardants (OPFRs), may contribute to CKD pathogenesis[ 21 , 22 ]. However, the potential association between OPFR exposure and CKD risk remains insufficiently investigated in population-based studies. To address this evidence gap, we analyzed data from the National Health and Nutrition Examination Survey (NHANES) to (1) characterize OPFR exposure levels in the general U.S. population and (2) systematically evaluate the associations between OPFR metabolites and CKD risk. Our findings provide critical population-based evidence to elucidate the OPFR-CKD relationship and inform potential preventive strategies. Methods Study population This study utilized data from the 2011–2016 National Health and Nutrition Examination Survey (NHANES), a nationally representative survey that combines health interviews, physical examinations, and laboratory testing[ 23 ]. We selected these survey cycles because they employed consistent methodologies for measuring OPFRs metabolites. From the original cohort of 6,699 participants with available urinary OPFR measurements (randomly selected from one-third of NHANES participants), we applied the following exclusion criteria: missing data on renal function markers (estimated glomerular filtration rate [eGFR] or albumin‒creatinine ratio [ACR]), age < 18 years, pregnant women, individuals undergoing renal dialysis, participants reporting urinary incontinence, cancer patients, current use of nephrotoxic medications (including ENALAPRIL, HYDROCHLORHIAZIDE, LOSARTAN, PREDNISONE and FUROSEMIDE), and missing covariate data. After applying these criteria, our final analytical sample comprised 2,156 adults (Fig. 1 ). The NHANES protocol was approved by the National Center for Health Statistics Research Ethics Review Board, and all participants provided written informed consent. OPFR metabolites and creatinine in the urine This study used urinary concentrations of OPFR metabolites as biomarkers of internal exposure, quantified through a validated protocol involving (1) enzymatic hydrolysis of 0.2 mL of urine (0.4 mL in the 2013–2014 cycle), (2) automated solid-phase extraction purification, (3) reversed-phase HPLC separation, and (4) isotope-dilution ESI‒MS/MS analysis[ 24 , 25 ]. The limits of detection (LOD) were 0.10 ng/mL for diphenyl phosphate (DPHP), bis(1,3-dichloro-2-propyl) phos (BDCPP), bis(1-chloro-2-propyl) phosphate (BCPP), bis(2-chloroethyl) phosphate (BCEP), and dibutyl phosphate (DBUP) in the 2011–2012 and 2015–2016 cycles but differed in the 2013–2014 cycles (DPHP = 0.16, BDCPP = 0.11, BCEP = 0.08, DNUP = 0.05 ng/mL; BCPP remained at 0.10 ng/mL). We included only metabolites with ≥ 50% detection rates (excluding dibenzyl phosphate [single detection] and 2,3,4,5-tetrabromobenzoic acid [5.67% detection]), with values below the LOD imputed as LOD/√2. Urinary creatinine was measured via the Roche/Hitachi Modular P Chemistry Analyzer, and all OPFR metabolite concentrations were creatinine-adjusted (ng/g creatinine) to account for urine dilution. Determination of CKD Serum creatinine was measured via DxC800 modular chemistry (Jaffe rate method). Detection of albumin in urine by fluorescence immunoassay. The eGFR and ACR values were used as the basis for determining CKD. The eGFR was calculated via the following CKD-EPI equation[ 26 ]: $$\:{\text{e}\text{G}\text{F}\text{R}}_{\text{C}\text{K}\text{D}-\text{E}\text{P}\text{I}}(\text{m}\text{L}/\text{m}\text{i}\text{n}/1.73{\text{m}}^{2})=141\times\:{\text{m}\text{i}\text{n}(\frac{\text{S}\text{c}\text{r}}{{\kappa\:}},1)}^{{\alpha\:}}\times\:\text{m}\text{a}\text{x}{(\frac{\text{S}\text{c}\text{r}}{{\kappa\:}},1)}^{-1.209}\times\:{0.993}^{\text{A}\text{g}\text{e}}\times\:1.018\left[\text{i}\text{f}\:\text{f}\text{e}\text{m}\text{a}\text{l}\text{e}\right]\times\:1.159\left[\text{i}\text{f}\:\text{b}\text{l}\text{a}\text{c}\text{k}\right]$$ In this equation, Scr represents the serum creatinine level (mg/dL). κ is 0.7 for females and 0.9 for males; α is -0.329 for females and − 0.411 for males; min/max indicates the minimum/maximum of SCR/κ or 1. The ACR was calculated by dividing the urinary albumin concentration (mg/dL) by the urinary creatinine concentration (g/dL), expressed in mg/g. In accordance with current clinical guidelines, participants were classified as having chronic kidney disease (CKD) if they met either of the following criteria: (1) ACR ≥ 30 mg/g or (2) eGFR < 60 mL/min/1.73 m²[ 27 ]. Covariates Covariates included age, sex, race, education, marital status, poverty income ratio (PIR, which is calculated by dividing household income by a specific poverty guideline for the year of the survey), body mass index (BMI), physical activity, smoking, alcohol consumption, hypertension, diabetes, hyperuricemia, and the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR; non-high-density lipoprotein cholesterol is derived by subtracting high-density lipoprotein cholesterol from total cholesterol). The participants belonged to six racial groups: Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, and other races (including multiracial). Education level was grouped as less than 9th grade, 9th–11th grade, high school graduate/GED or equivalent, some college or AA degree, and college graduate or above. Marital status was classified into married/living with a partner, widowed/divorced/separated, and never married. Family PIR was divided into 4 groups: ≤1.3, 1.3–3.5, and > 3.5. Smoking status, drinking status, and physical activity data were obtained from self-report questionnaire data. BMI was divided into 4 groups: <18.5 kg/m 2 , 18.5–24.9 kg/m 2 , 25–29.9 kg/m 2 , and ≥ 30 kg/m 2 . Smoking status is categorized into smokers (those with a serum cotinine detection limit or higher) and nonsmokers (those with a serum cotinine detection limit or lower). Drinking status was dichotomized into drinker (drinking at least 12 alcoholic drinks per year) and nondrinker (drinking fewer than 12 alcoholic drinks per year). Physical activity was categorized as none, moderate, or vigorous. A self-reported diagnosis of hypertension, use of hypertensive medications, and a systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg were considered hypertension. Participants with self-reported congestive heart failure, coronary heart disease, angina pectoris, heart attack or stroke were defined as having CVD. Diabetes mellitus was defined as one of the following: (1) self-reported diagnosis of diabetes mellitus; (2) use of insulin or diabetes medications; (3) glycosylated hemoglobin ≥ 6.5%; (4) fasting blood glucose ≥ 7.0 mmol/L; and (5) or postprandial blood glucose of ≥ 11.1 mmol/L 2 hours after a meal on an oral glucose tolerance test. A uric acid concentration higher than 420 µmol/L in men and 360 µmol/L in women was defined as hyperuricemia. As a new type of comprehensive index, the NHHR has shown significant advantages in the early identification and risk assessment of CKD[ 28 , 29 ]. Statistical analysis Owing to the complex sampling design of the NHAENS, survey weights have been used in both the statistical description and the multiple regression model. Since none of the continuous variables conformed to normality after normality testing, they were expressed as weighted medians and interquartile spacings (Q1–Q3), and the categorical variables were described by frequencies (weighted percentages). The Kruskal‒Wallis test and chi‒square test were used to compare the differences between continuous and categorical variables. The log10-transformed metabolite concentrations were calculated for subsequent statistical analyses, and Spearman correlation coefficients were calculated between metabolite concentrations. Baseline covariates with P values < 0.05 in univariate analysis were included in further multivariate analysis. Multivariate logistic regression was used to assess the associations between urinary OPFR metabolite quartiles and CKD, and odds ratios (ORs) and 95% CIs were calculated. Multivariate linear regression was used to assess the associations between urinary metabolite quartiles of OPFRs and eGFR and ACR. The linear assignment method was used (assigning values of 1–4 to Q1–Q4 and testing continuous trend p values). This study used three models for multiple regression analysis. Model 1 is a one-factor model not adjusted for potential confounders. Model 2 was adjusted for demographic and socioeconomic factors, including age, marital status, and PIR. Model 3 was further adjusted for lifestyle and comorbid factors such as BMI, physical activity, CVD, hypertension, and diabetes on the basis of Model 2. Subgroup analyses were conducted to explore the effects of OPFRs within different subgroups. These subgroups were defined according to age ( 3.5), BMI (< 18.5, 18.5–24.9, 25–29.9, ≥ 30 kg/m2), and the presence or absence of a history of a specific disease (e.g., CVD, hypertension, or diabetes). Nonlinear Associations between OPFRs and Renal Function Indicators Analyzed via Restricted Cubic Splines (RCSs). Estimating the combined health effects of multiple pollutants is particularly important given that humans are consistently exposed to complex chemical mixtures. Weighted quantile sum (WQS) regression is a commonly used method for assessing the health effects of a mixture of pollutants, which takes into account all the measured chemicals[ 30 ]. In view of the uncertainty of the direction of the association between OPFRs and CKD, this study carried out two sets of WQS analyses, one of which assumed a positive correlation between OPFRs and CKD, and the other assumed a negative correlation between the two. To improve the reliability of the results, we randomly divided the data into two subsets: the training set (40%) and the validation set (60%), and the number of bootstrap iterations was set at 1000. The weights of the urinary metabolites were calculated for each OPFR in the training dataset, and the WQS scores were calculated in the validation dataset to explore the associations between WQS scores and CKD. All analyses were performed via R Studio (version 4.4.1). The significance level for this study was 0.05. WQS was calculated via the R software “gWQS” (version 3.0.5). The findings of the regression model should be interpreted as exploratory since multiple comparisons may lead to type I errors. Results Baseline characteristics The data in Table 1 are weighted. The prevalence of CKD among the general adult population in the United States is approximately 7.4%. In terms of age, the median age of the CKD population was 52.41 years, which was significantly greater than that of the non-CKD population, which was 40.00 years ( P < 0.001). With respect to marital status, 62.3% were married or living with a partner. In addition, the percentage of individuals with PIRs less than 1.3 was 22.8%. In addition, 32.9% were involved in vigorous physical activity. The prevalence of cardiovascular disease, hypertension and diabetes varied among the study population, with 5.40% of the population suffering from cardiovascular disease, 29.3% from hypertension and 8.70% from diabetes mellitus. Notably, the eGFR was significantly lower in the CKD population than in the non-CKD population ( P = 0.002), and the ACR was significantly greater in the CKD population than in the non-CKD population ( P < 0.001). Table 1 Baseline characteristics of included participants (n = 2156) in the NHANES 2011–2016, weighted. Characteristics Total (n = 2156) Non-CKD (n = 1957) CKD (n = 199) P value Age (year) 41.00 [29.00, 54.00] 40.00 [29.00, 53.00] 52.41 [37.73, 65.00] < 0.001 Gender,n(%) 0.535 Female 888 (39.6) 802 (39.4) 86 (42.4) Male 1268 (60.4) 1155 (60.6) 113 (57.6) Race,n (%) 0.276 Mexican American 291 (8.7) 264 (8.7) 27 (9.4) Other Hispanic 249 (7.0) 228 (7.0) 21 (7.5) Non-Hispanic White 821 (63.7) 747 (63.9) 74 (60.5) Non-Hispanic Black 444 (11.2) 392 (10.9) 52 (15.7) Non-Hispanic Asian 279 (6.1) 260 (6.2) 19 (5.1) Other Race - Including Multi-Racial 72 (3.2) 66 (3.3) 6 (1.8) Education level,n (%) 0.074 Less than 9th grade 160 (4.0) 136 (3.8) 24 (6.7) 9-11th grade 277 (9.8) 244 (9.4) 33 (14.8) High school graduate/GED or equivalent 463 (20.4) 424 (20.8) 39 (16.1) Some college or AA degree 681 (33.3) 618 (33.2) 63 (35.3) College graduate or above 575 (32.4) 535 (32.9) 40 (27.1) Marital status, n (%) 0.019 Married/living with partner 1272 (62.3) 1159 (62.5) 113 (59.7) Widowed/divorced/separated 371 (14.5) 321 (13.8) 50 (22.6) Never married 513 (23.2) 477 (23.7) 36 (17.7) Poverty income ratio, n (%) 0.032 ≤ 1.3 718 (22.8) 631 (21.9) 87 (33.6) 1.31–3.5 785 (34.8) 712 (34.7) 73 (35.5) > 3.5 653 (42.4) 614 (43.4) 39 (30.9) BMI,n (%) 0.029 < 18.5 36 (2.0) 29 (1.8) 7 (4.2) 18.5–24.9 657 (29.9) 610 (30.4) 47 (23.8) 25–29.9 730 (34.0) 670 (34.4) 63 (28.6) ≥ 30 733 (34.1) 648 (33.4) 82 (43.3) Physical activity, n (%) 0.001 Vigorous activity 637 (32.9) 609 (34.3) 28 (16.0) Moderate activity 543 (25.1) 492 (24.8) 51 (29.4) Others 976 (41.9) 856 (40.9) 120 (54.5) Smoking status,n (%) 0.359 <LOD 601 (31.8) 544 (32.1) 57 (28.2) ≥LOD 1555 (68.2) 1413 (67.9) 142 (71.8) Alcohol consumption ,n (%) 0.299 < 12 549 (19.1) 493 (18.8) 56 (22.9) ≧ 12 1607 (80.9) 1464 (81.2) 143 (77.1) CVD,n (%) < 0.001 No 2030 (94.6) 1866 (95.5) 164 (83.5) Yes 126 (5.4) 91 (4.5) 35 (16.5) Hypertension,n (%) < 0.001 No 1425 (70.7) 1345 (72.3) 80 (50.7) Yes 731 (29.3) 612 (27.7) 119 (49.3) Diabetes (%) < 0.001 No 1888 (91.3) 1766 (93.3) 122 (65.9) Yes 268 (8.7) 191 (6.7) 77 (34.1) Hyperuricemia,n (%) 0.051 No 1805 (84.5) 1654 (85.0) 151 (78.4) Yes 351 (15.5) 303 (15.0) 48 (21.6) NHHR 2.74 [1.95, 3.64] 2.74 [1.97, 3.66] 2.81 [1.88, 3.56] 0.647 eGFR 107.34 [96.18, 118.28] 107.66 [96.49, 118.29] 101.37 [81.30, 115.38] 0.002 ACR 6.00 [4.04, 9.85] 5.69 [3.96, 8.70] 53.49 [36.56, 118.90] < 0.001 Continuous variables are presented as weighted medians (first quartile, third quartile), followed by P values. Categorical variables are presented as weighted percentages (%), followed by P values. Abbreviations: CKD: chronic kidney disease; PIR: poverty income ratio; BMI: body mass index; NHHR: non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio. OPFR metabolite concentrations in urine The concentrations of OPFR metabolites in the urine samples are shown in Supplementary Table 1. BDCPP had the highest creatinine-adjusted median concentration (0.87 µg/g creatinine), followed by DPHP (0.72 µg/g creatinine). Notably, most of the biomarkers of OPFRs were correlated with each other, with correlation coefficients ranging from 0.03 to 0.29, among which BCPP and DBUP had the highest correlation (r = 0.29, P < 0.001), as shown in Supplementary Fig. 1. Associations between OPFR metabolites and renal outcomes (CKD, eGFR and ACR) The results of multivariate logistic regression analyses showed (Table 2 ) that only BCPP demonstrated statistical significance in all three models. In Model 1, Q4 of BCPP was associated with a significantly lower risk of CKD (OR = 0.40, 95% CI: 0.23–0.70, P = 0.002), and the overall trend test was significant ( P for trend = 0.005). This association was significant in Model 2 (Q4: OR = 0.43, 95% CI: 0.24–0.76, P = 0.005; P for trend = 0.012), and Model 3 (Q4: OR = 0.42, 95% CI: 0.23–0.77, P = 0.007; P for trend = 0.011) remained consistent. Other metabolites (DPHP, BDCPP, BCEP and DBUP) did not significantly differ among the three models. The results of multivariate linear regression analyses (Table 3 and Supplementary Table 2) revealed significant associations between several OPFRs and the eGFR. In the eGFR analysis, DPHP showed a significant upward trend in unadjusted Model 1 ( P for trend = 0.001), in which the ratios of the Q3 (β = 4.43, 95% CI: 1.94–6.92, P = 0.001) and Q4 (β = 4.56, 95% CI: 1.67–7.45, P = 0.003) ratios were significantly greater than those in Q1. Although the effect sizes diminished in Model 2 (Q3 vs. Q1: β: 1.87, 95% CI: 0.30–3.43, P = 0.036) and Model 3 (Q3 vs. Q1: β: 1.83, 95% CI: 0.23–3.44, P = 0.026), this trend remained statistically significant (Model 2: P for trend = 0.036; Model 3: P for trend = 0.031). BDCPP also showed a significant positive correlation with eGFR in Model 1 ( p for trend < 0.001), especially for Q4 (β = 6.45, 95% CI: 3.16–9.74, P < 0.001), but the association disappeared after adjusting for covariates. In contrast, DBUP showed a negative trend in Model 1 ( P for trend = 0.018), and Q4 was significantly associated with a lower eGFR (β=-2.44, 95% CI: -4.75–0.13, P = 0.039), but this trend was reversed in Model 2 ( P for trend = 0.004) and Model 3 (P for trend = 0.005), where DBUP at the Q4 concentration was associated with an elevated eGFR (Model 2: β = 2.14, 95% CI: 0.65–3.64, P = 0.006; Model 3: β = 2.01, 95% CI: 0.58–3.43, P = 0.007). In the ACR analysis, none of the metabolites showed statistically significant associations after adjustment for covariates. Table 2 Associations between urinary OPFR metabolite concentrations and CKD incidence. Variable Model 1 Model 2 Model 3 OR(95%CI) P P for trend OR(95%CI) P P for trend OR(95%CI) P P for trend DPHP Q1 1.00 1.00 1.00 Q2 0.83 (0.52–1.32) 0.423 0.718 0.89 (0.55–1.46) 0.648 0.898 0.93 (0.55–1.54) 0.760 0.988 Q3 1.19 (0.70–2.03) 0.510 1.40 (0.81–2.43) 0.219 1.37 (0.77–2.45) 0.278 Q4 0.78 (0.42–1.45) 0.423 0.88 (0.48–1.64) 0.687 0.87 (0.45–1.69) 0.673 BDCPP Q1 1.00 1.00 1.00 Q2 0.67 (0.43–1.04) 0.075 0.181 0.77 (0.49–1.21) 0.243 0.850 0.85 (0.52–1.39) 0.515 0.873 Q3 0.88 (0.52–1.49) 0.626 1.09 (0.63–1.90) 0.744 1.08 (0.63–1.85) 0.775 Q4 0.65 (0.40–1.06) 0.083 0.94 (0.59–1.48) 0.774 0.88 (0.50–1.56) 0.662 BCPP Q1 1.00 1.00 1.00 Q2 0.71 (0.46–1.11) 0.127 0.005 0.72 (0.47–1.12) 0.140 0.012 0.73 (0.44–1.23) 0.234 0.011 Q3 0.89 (0.54–1.45) 0.624 0.88 (0.53–1.47) 0.628 0.87 (0.52–1.47) 0.601 Q4 0.40 (0.23–0.70) 0.002 0.43 (0.24–0.76) 0.005 0.42 (0.23–0.77) 0.007 BCEP Q1 1.00 1.00 1.00 Q2 0.88 (0.58–1.31) 0.513 0.511 0.89 (0.57–1.38) 0.590 0.370 0.90 (0.55–1.46) 0.65 0.430 Q3 0.64 (0.37–1.09) 0.096 0.62 (0.35–1.09) 0.095 0.62 (0.34–1.10) 0.099 Q4 0.93 (0.59–1.46) 0.743 0.88 (0.57–1.38) 0.578 0.90 (0.56–1.46) 0.672 DBUP Q1 1.00 1.00 1.00 Q2 0.77 (0.45–1.32) 0.329 0.616 0.75 (0.42–1.34) 0.319 0.878 0.64 (0.38–1.11) 0.107 0.702 Q3 0.86 (0.47–1.55) 0.605 0.82 (0.44–1.53) 0.523 0.72 (0.38–1.36) 0.3 Q4 1.10 (0.68–1.79) 0.679 0.93 (0.52–1.66) 0.806 0.86 (0.49–1.51) 0.59 Model 1: not adjusted. Model 2: adjusted for age, marital status and PIR. Model 3: adjusted for age, marital status, PIR, BMI, physical activity, CVD, hypertension, and diabetes. Table 3 Associations between urinary OPFR metabolite concentrations and eGFR. Variable Model 1 Model 2 Model 3 β(95%CI) P P for trend β(95%CI) P P for trend β(95%CI) P P for trend DPHP Q1 1.00 1.00 1.00 Q2 1.27 (-1.04-3.59) 0.274 0.001 -0.15 (-1.60-1.30) 0.836 0.036 -0.40 (-1.81-1.01) 0.568 0.031 Q3 4.43 (1.94–6.92) 0.001 1.87 (0.30–3.43) 0.020 1.83 (0.23–3.44) 0.026 Q4 4.56 (1.67–7.45) 0.003 1.60 (-0.47-3.68) 0.126 1.56 (-0.47-3.59) 0.127 BDCPP Q1 1.00 1.00 1.00 Q2 3.07 (0.38–5.76) 0.026 < 0.001 0.25 (-1.50-2.01) 0.773 0.696 0.25 (-1.50-2.00) 0.776 0.829 Q3 4.35 (1.43–7.26) 0.004 -0.13 (-1.94-1.67) 0.882 -0.27 (-2.07-1.52) 0.758 Q4 6.45 (3.16–9.74) < 0.001 0.49 (-1.43-2.41) 0.609 0.37 (-1.68-2.42) 0.714 BCPP Q1 1.00 1.00 1.00 Q2 1.38 (-1.50-4.26) 0.339 0.644 0.04 (-1.47-1.55) 0.961 0.291 0.05 (-1.47-1.57) 0.948 0.370 Q3 0.65 (-2.15-3.45) 0.645 1.27 (-0.26-2.80) 0.102 1.09 (-0.50-2.68) 0.174 Q4 0.91 (-1.98-3.80) 0.527 0.56 (-1.19-2.30) 0.523 0.48 (-1.26-2.23) 0.576 BCEP Q1 1.00 1.00 1.00 Q2 1.36 (-0.78-3.50) 0.208 0.765 0.41 (-0.82-1.64) 0.506 0.414 0.37 (-0.96-1.70) 0.576 0.559 Q3 1.60 (-0.40-3.60) 0.114 0.90 (-0.34-2.14) 0.150 1.01 (-0.24-2.26) 0.109 Q4 0.28 (-1.95-2.50) 0.804 -0.84 (-2.35-0.67) 0.270 -0.71 (-2.26-0.85) 0.362 DBUP Q1 1.00 1.00 1.00 Q2 0.22 (-2.17-2.61) 0.855 0.018 1.21 (-0.30-2.73) 0.112 0.004 1.23 (-0.19-2.65) 0.088 0.005 Q3 -1.01 (-3.25-1.23) 0.370 1.19 (-0.20-2.57) 0.091 1.22 (-0.19-2.63) 0.088 Q4 -2.44 (-4.75–0.13) 0.039 2.14 (0.65–3.64) 0.006 2.01 (0.58–3.43) 0.007 Model 1: not adjusted. Model 2: adjusted for age, marital status and PIR. Model 3: adjusted for age, marital status, PIR, BMI, physical activity, CVD, hypertension, and diabetes. Stratification Analyses Subgroup analysis revealed significant heterogeneity in the associations of different OPFRs with CKD and eGFR risk. In the CKD risk analysis, an elevated DPHP concentration was associated with a reduced risk of CKD in hypertensive patients (OR = 0.54, 95% CI: 0.33–0.87; P = 0.017). There was a significant interaction effect between BDCPP and PIR ( P for interaction = 0.034). BDCPP was significantly associated with CKD risk in hypertensive patients (OR = 0.31, 95% CI: 0.15–0.61, P = 0.002), in those ≥ 60 years of age (OR = 0.30, 95% CI: 0.11–0.80, P = 0.023), and in those with PIR > 3.5 (OR = 0.27, 95% CI: 0.10–0.74, P = 0.016), BDCPP was significantly negatively associated with CKD and was significantly negatively associated with hypertension status ( P for interaction = 0.034). in CKD (P for interaction = 0.029). BCEP also had a protective effect in hypertensive patients (OR = 0.59, 95% CI: 0.37–0.94; P = 0.032). The results of all the above analyses are shown in Fig. 2 . In the eGFR analysis, positive associations of DPHP with eGFR were detected in people < 60 years old (β = 3.69, 95% CI: 1.73–5.65, P = 0.001), in the low-income group (β = 3.16, 95% CI: 1.14–5.18, P = 0.004), and in the hypertensive group (β = 3.05, 95% CI: 0.66–5.44, P = 0.017), and these associations remained significant in the nondiabetic population (β = 2.00, 95% CI: 0.43–3.56, P = 0.017). BDCPP was significantly greater in the < 60-year-olds (β = 3.46, 95% CI: 1.29–5.62, P = 0.004), and DBUP was significantly greater in the PIR1.31–3.5-year-olds (β = 2.70, 95% CI. 0.46–4.94, P = 0.024) and in the nondiabetic population (β = 1.66, 95% CI: 0.42–2.90, P = 0.013) were positively associated with the eGFR. The results of all the above analyses are shown in Fig. 3 . Nonlinear relationships between OPFRs, eGFR and ACR The RCS curves revealed that after adjusting for all covariates, there was a significant inflection point in the trend of the eGFR with increasing DBUP concentration (P for nonlinearity = 0.048), which showed an inverted U shape, and at the same time, its inflection point was at a lnDBUP value of -0.918 µg/g creatinine (DBUP = 0.121 µg/g creatinine) (Fig. 4). WQS regression analysis The WQS regression model was used to assess the associations between exposure to mixed OPFRs and CKD, eGFR and ACR. After adjusting for all confounders, a mixture of OPFRs was associated with an elevated eGFR (β: 1.20, 95% CI: 0.38–2.02, P = 0.004) in a positive effect model (Table 4), with the greatest contribution from DBUP, followed by DPHP (Supplementary Fig. 4). Table 4 WQS regression of OPFR metabolites with renal outcomes (CKD, eGFR and ACR) Outcome Positive model Negative model β/OR(95%CI) P β/OR(95%CI) P CKD 1.06(0.81–1.31) 0.659 1.18(0.87–1.49) 0.293 eGFR 1.20(0.38–2.02) 0.004 0.53(-0.18-1.23) 0.145 ACR -1.14(-18.48-16.20) 0.897 -13.80(-32.19-4.60) 0.142 The WQS regression model was adjusted for age, marital status, PIR, BMI, physical activity, cardiovascular disease, hypertension, and diabetes. Discussion This study systematically evaluated the associations between urinary metabolites of OPFRs and renal function indices on the basis of NHANES 2011–2016 adult samples. The main findings were that different OPFR metabolites showed heterogeneous or even conflicting effect patterns after adjustment for multiple confounders. Among them, BCPP was significantly associated with a reduced risk of CKD in all three multivariate logistic regression models, and this protective effect was more pronounced in the subgroups of hypertensive patients, those of advanced age (≥ 60 years), and those with high income (PIR > 3.5). In contrast, DPHP and BDCPP were positively associated with eGFR in the unadjusted model, but the associations were attenuated or disappeared after adjustment for socioeconomic and comorbid factors. Notably, DBUP showed a complex dose‒response relationship: it was negatively associated with eGFR in the unadjusted model but was positively associated with eGFR after full adjustment for confounders, and RCS analysis further revealed an inverted U-shaped nonlinear association with eGFR. Further WQS analysis of the OPFR mixtures revealed that the five metabolites of OPFRs were associated with increased eGFRs overall, with DBUP and DPHP contributing the most. Available studies generally support the nephrotoxic effects of OPFRs. In vitro experiments have shown that OPFRs can impair renal function through a variety of mechanisms, e.g., low concentrations of Tri (2,3-dichloropropyl) phosphate induce cell cycle arrest in renal cell lines, whereas high concentrations exhibit cytotoxicity[ 31 ]. Moreover, Tris (2-chloroethyl) phosphate (TCEP) may promote apoptosis in a dose-dependent manner, significantly inhibiting the DNA synthesis ability of renal proximal tubule cells and reducing the number of cells[ 16 ]. Epidemiologic evidence further supports this conclusion. A study based on the NHANES 2013–2014 time cycle suggested that OPFRs may be potential risk factors for CKD[ 32 ]. A 2-year longitudinal study in Taiwan reached a similar conclusion that high-dose exposure to TCEP can lead to deterioration in kidney function[ 33 ]. Moreover, a cross-sectional study conducted in Wuhan specifically indicated that OPFRs were associated with a greater risk of kidney injury in hypertensive and diabetic patients[ 34 ]. Notably, although the study revealed a positive correlation between 1-hydroxy-2-propyl bis(1-chloro-2-propyl) phosphate and the eGFR, the trend did not reach statistical significance, a phenomenon that may need to be verified with a larger sample. Previous studies have shown that the damage caused by OPFRs to renal function is realized mainly through oxidative stress and inflammation. First, in terms of oxidative stress, exposure to OPFRs significantly disrupts the redox balance of the body[ 33 ]. Specifically, these substances are capable of interfering with intracellular antioxidant defense systems; for example, tri-n-butyl phosphate has been shown to significantly reduce the activity of key antioxidant enzymes such as superoxide dismutase and glutathione peroxidase[ 35 ]. By inhibiting these enzymes, the body's ability to scavenge reactive oxygen species is weakened, leading to a steady increase in oxidative stress[ 36 ]. This condition, in turn, damages kidney cells in multiple ways: not only does it cause lipid peroxidation of cell membranes, but it also disrupts mitochondrial function[ 37 ]. As the core organelles of energy metabolism in cells, impaired mitochondrial function directly affects the normal physiological activities of renal cells and ultimately leads to renal dysfunction[ 38 ]. In addition, exposure to OPFRs triggers a significant inflammatory response, resulting in tissue and organ damage[ 39 , 40 ]. A large body of evidence suggests that OPFRs can upregulate the expression of various inflammatory factors, including transcription factors (e.g., NF-κB), proinflammatory cytokines (e.g., IL-1β and IL-6), and inflammatory mediators (e.g., TNF-α), in renal tissue[ 41 , 42 ]. These inflammatory factors then mediate renal injury through a variety of pathways, such as those that cause glomerulonephritis[ 43 ] or tubulointerstitial inflammation[ 44 ]. Although the nephrotoxic effects of OPFRs are commonly reported in the above studies, the present study revealed protective associations between their metabolites and renal function indices, a seemingly paradoxical phenomenon that may be due to the biphasic biological effects of oxidative stress. Recently, it has been suggested that the role of oxidative stress in renal disease may be dose dependent: low levels of oxidative stress may enhance mitochondrial biosynthesis and antioxidant defense through activation of the AMPK/PGC1α pathway, a phenomenon known as mitochondrial hormesis[ 45 , 46 ]. In the present study, the urinary metabolites of OPFRs may mimic mild metabolic stress (similar to the physiological stress produced by exercise or caloric restriction) and exert their protective effects mainly through the following pathways: On the one hand, they induced moderate mitochondrial superoxide production, which activated mitochondrial autophagy and the efficiency of oxidative phosphorylation since AMPK, and on the other hand, they inhibited proinflammatory signaling pathways, such as NF-κB, and attenuated renal fibrosis. This hypothesis is supported by existing animal experiments; for example, AMPK agonists (e.g., metformin) can significantly improve renal function indices[ 47 ]. The strength of this study lies in the systematic assessment of the association between OPFR metabolites and CKD using a large sample size and nationally representative data from the NHANES database combined with standardized biomarker assays. In addition, the present study innovatively proposed the potential explanation mechanism of “mitochondrial low-toxicity excitatory effect”, which provides a new perspective on the role of low-dose OPFRs. This study has several limitations. First, the cross-sectional design could not distinguish the causal direction (e.g., decreased renal function may affect the metabolism of OPFRs), and there was a lack of direct oxidative stress or mitochondrial function indices, which needs to be verified in combination with mechanistic studies in the future. Second, single urine measurements may not reflect long-term exposure to OPFRs, and chronic kidney injury is often associated with cumulative toxicity. In addition, residual confounding (e.g., other undetected environmental exposures or dietary differences) may affect the results despite controlling for multiple confounders. Conclusions The present study suggests that the metabolites of OPFRs may be associated with a reduced risk of CKD, and the underlying mechanism may involve adaptive mitochondrial responses induced by low-dose stress. In the future, longitudinal cohort and experimental studies are needed to verify causal relationships and explore biological pathways to provide a more accurate basis for risk assessment and intervention strategies. Abbreviations OPFRs: Organophosphate flame retardants CKD: Chronic kidney disease NHANES: National Health and Nutrition Examination Survey eGFR: Estimated glomerular filtration rate ACR: Albumin‒creatinine ratio DPHP: diphenyl phosphate BDCPP: Bis(1,3-dichloro-2-propyl) phos BCPP: Bis(1-chloro-2-propyl) phosphate BCEP: Bis(2-chloroethyl) phosphate DBUP: Dibutyl phosphate LOD: Limits of detection PIR: Poverty income ratio BMI: Body mass index NHHR: Non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio WQS: Weighted quantile sum TCEP: Tris (2-chloroethyl) phosphate Declarations Availability of data and materials Data are available from the NHANES (NHANES—National Health and Nutrition Examination Survey Homepage (cdc.gov)) in 2011–2016. Acknowledgements We extend our sincere gratitude to NHANES team and all participating staff for their efforts in data collection and for making these valuable resources publicly available. We are also deeply thankful to all study participants whose contributions made this research possible. Funding This work was supported by the National Science Foundation of China (81872701). Shanxi Huajin Orthopaedic Public Foundation. Technology Research Foundation of Shanxi Province (202303021221126). Shanxi Province Higher Education Billion Project Science and Technology Guidance Project (BYBLD002). Author information Authors and Affiliations Department of Epidemiology, School of Public Health, Shanxi Medical University, Jinzhong 030606, China Yuebin Yang, Pu Guo, Hongjing Ren, Lingyun Zhuo, Qikai Xiang, Xiao-rong Guo, Fu-rong Chen, Xiang-xiang Zhang, Ping Zhang, Lijian Lei MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University, Jinzhong 030606, China Ping Zhang & Lijian Lei Research Center for Epidemiology of Environmental Pollution and Major Chronic Diseases, Shanxi Medical University, Jinzhong 030606, China Ping Zhang & Lijian Lei Contributions Yuebin Yang (Co-first author): Directed the study design, performed statistical analyses, and drafted the manuscript. Pu Guo (Co-first author): Contributed equally to methodology development, data interpretation, and revision of the manuscript. Hongjing Ren: Conducted literature review and assisted in data validation. Lingyun Zhuo: Contributed to data collection and preliminary analysis. Qikai Xiang: Assisted with statistical modeling and visualization. Xiao-rong Guo, Fu-rong Chen, and Xiang-xiang Zhang: Contributed to resource provision, administrative support, and technical review. Ping Zhang (Corresponding author): Conceived the study, supervised the research process, and finalized the manuscript. Lijian Lei (Corresponding author): Oversaw project financing, made critical revisions, and approved the final version. Corresponding author Correspondence to Ping Zhang or Lijian Lei. Ethics declarations Ethics approval and consent to participate The National Health and Nutrition Examination Survey (NHANES) data used in this study were collected by the U.S. Centers for Disease Control and Prevention (CDC) and approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board. All participants provided written informed consent prior to data collection. Consent for publication Not applicable. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6915116","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493622749,"identity":"650a9487-ccee-4115-9071-acc5f87220c6","order_by":0,"name":"Yuebin Yang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuebin","middleName":"","lastName":"Yang","suffix":""},{"id":493622750,"identity":"d3d36f91-d855-4e69-8471-86d3d32cf909","order_by":1,"name":"Pu Guo","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Pu","middleName":"","lastName":"Guo","suffix":""},{"id":493622751,"identity":"9c7efa54-f67e-4dd1-b913-d250834187d6","order_by":2,"name":"Hongjing Ren","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongjing","middleName":"","lastName":"Ren","suffix":""},{"id":493622752,"identity":"16e9b491-f85e-4ad5-b0a7-374db6ba5ed1","order_by":3,"name":"Lingyun Zhuo","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lingyun","middleName":"","lastName":"Zhuo","suffix":""},{"id":493622753,"identity":"2cb069ad-96e8-491a-9250-28f28265afd8","order_by":4,"name":"Qikai Xiang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qikai","middleName":"","lastName":"Xiang","suffix":""},{"id":493622758,"identity":"881abc79-54f7-4ca3-90d4-b95bd937a465","order_by":5,"name":"Xiao-rong Guo","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao-rong","middleName":"","lastName":"Guo","suffix":""},{"id":493622759,"identity":"4a335ab8-4f00-4f6a-84f7-8085a9e517fa","order_by":6,"name":"Fu-rong Chen","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fu-rong","middleName":"","lastName":"Chen","suffix":""},{"id":493622760,"identity":"9de2c3b4-c72c-40cc-bd80-737e9baa1f1e","order_by":7,"name":"Xiang-xiang Zhang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiang-xiang","middleName":"","lastName":"Zhang","suffix":""},{"id":493622761,"identity":"3bc45a0a-507f-4f4e-89ca-9adab0da9a50","order_by":8,"name":"Ping Zhang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Zhang","suffix":""},{"id":493622762,"identity":"b5a1a4a4-4aa7-4233-85d3-e86be6e573a4","order_by":9,"name":"Lijian Lei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYHACwwMJPBIMDOwNUP4BwloMIFp4YEqJ0gKmJBKI1GJwI3nDgQcyFvLmks+fSd1sY5Dju5HA+LkAr5a0ApDDDHfOzjE2zm1jMJa8kcAsPQOPFrPbOWC/MG64ncP4GKglccONBDZmHiK02G+4efzBYaCWeqK1AA1nMATZkmBASIv9/WdgvyRvOAP0S845CcOZZx42S+PTItlzeOPDnz11thuOH38mnVNmI893PPngZ3xawICxB86UAHEbCGkAgh9EqBkFo2AUjIKRCwB59FEtGnxUiAAAAABJRU5ErkJggg==","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lijian","middleName":"","lastName":"Lei","suffix":""}],"badges":[],"createdAt":"2025-06-17 13:53:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6915116/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6915116/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40001-026-03926-8","type":"published","date":"2026-02-04T15:58:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88277233,"identity":"4095ebad-974a-4a5c-815a-0038a8acef1f","added_by":"auto","created_at":"2025-08-04 18:31:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210062,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the study population\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6915116/v1/0dea8c861c98217c89992d99.png"},{"id":88277738,"identity":"b446743a-69f7-484e-a74e-154f29171f0f","added_by":"auto","created_at":"2025-08-04 18:39:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":183600,"visible":true,"origin":"","legend":"\u003cp\u003eStratified logistic regression analysis of OPFR metabolites and CKD\u003c/p\u003e\n\u003cp\u003eA: DPHP-CKD subgroup analysis diagram; B: BDCPP-CKD subgroup analysis diagram; C: BCPP-CKD subgroup analysis diagram; D: BCEP-CKD subgroup analysis diagram; E: DPHP-CKD subgroup analysis diagram.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6915116/v1/29f60f32a6301e4a221a1a66.png"},{"id":88277879,"identity":"88fcae41-1c7f-4124-8ec2-e5a47d4ca41a","added_by":"auto","created_at":"2025-08-04 18:47:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":191506,"visible":true,"origin":"","legend":"\u003cp\u003eStratified linear regression analysis of OPFR metabolites and eGFR\u003c/p\u003e\n\u003cp\u003eA: DPHP-eGFR subgroup analysis diagram; B: BDCPP-eGFR subgroup analysis diagram; C: BCPP-eGFR subgroup analysis diagram; D: BCEP-eGFR subgroup analysis diagram; E: DPHP-eGFR subgroup analysis diagram.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6915116/v1/ac285d557907693d40e8eb63.png"},{"id":88277235,"identity":"8eb6ec3f-a303-40f5-b904-fe003195b7f7","added_by":"auto","created_at":"2025-08-04 18:31:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94897,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline curves for the eGFR\u003c/p\u003e\n\u003cp\u003eA: DPHP-eGFR RCS diagram;B: BDCPP-eGFR RCS C: BCPP-eGFR RCS diagram; D: BCEP-eGFR RCS diagram; E: DPHP-eGFR RCS diagram. Age, marital status, PIR, BMI, physical activity, CVD, hypertension, and diabetes were adjusted for in the analysis.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6915116/v1/37d926a8fdd88e331834487d.png"},{"id":102234296,"identity":"eb93d919-a7a7-45ab-8ea4-867a70debc46","added_by":"auto","created_at":"2026-02-09 16:09:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1890263,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6915116/v1/5b00f916-f907-4d00-94fc-51a25415b0f9.pdf"},{"id":88277737,"identity":"d080b418-01cf-4744-a0df-ff8032650aed","added_by":"auto","created_at":"2025-08-04 18:39:50","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":183567,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6915116/v1/ec262385b59da34a6db5cf7f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Organophosphorus Flame Retardant Exposure and Chronic Kidney Disease in U.S. Adults: NHANES data from 2011--2016","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOrganophosphate flame retardants (OPFRs) have emerged as key alternatives to traditional brominated flame retardants in material science and fire safety applications[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As phosphorus-containing organic compounds, OPFRs thermally decompose to produce phosphoric acid esters, forming a dense carbonized layer that insulates heat and oxygen, thereby effectively inhibiting combustion[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Compared with brominated flame retardants, OPFRs exhibit lower environmental persistence, bioaccumulation potential, and ecotoxicity[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], leading to their widespread use in industrial products such as polymers, electronic equipment housings, and building materials[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the extensive application of OPFRs has resulted in their pervasive environmental distribution, including in air, water bodies, and indoor dust[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Human exposure occurs primarily through inhalation, dietary intake, and dermal contact[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and their metabolites are frequently detected in population-level urine samples[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Growing evidence suggests that OPFRs may adversely affect human health through multiple mechanisms: endocrine disruption via interference with thyroid hormone signaling pathways[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]; neurotoxicity, including neuronal apoptosis linked to cognitive dysfunction in children[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; reproductive impairment via oxidative stress-mediated germ cell damage[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]; and nephrotoxicity, particularly renal tubular epithelial cell injury, which may exacerbate chronic kidney disease (CKD)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eChronic kidney disease (CKD) represents a significant global public health burden, affecting approximately 10% of adults worldwide, with notable geographic disparities in disease prevalence[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. While CKD rates have plateaued in developed nations[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], many developing countries are experiencing rising incidence rates, likely driven by rapid industrialization and lifestyle changes[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Notably, a substantial proportion of CKD cases remain idiopathic, presenting major challenges for disease prevention and management[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Emerging environmental evidence suggests that certain pollutants, including organophosphate flame retardants (OPFRs), may contribute to CKD pathogenesis[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, the potential association between OPFR exposure and CKD risk remains insufficiently investigated in population-based studies.\u003c/p\u003e\u003cp\u003eTo address this evidence gap, we analyzed data from the National Health and Nutrition Examination Survey (NHANES) to (1) characterize OPFR exposure levels in the general U.S. population and (2) systematically evaluate the associations between OPFR metabolites and CKD risk. Our findings provide critical population-based evidence to elucidate the OPFR-CKD relationship and inform potential preventive strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy population\u003c/p\u003e\u003cp\u003eThis study utilized data from the 2011\u0026ndash;2016 National Health and Nutrition Examination Survey (NHANES), a nationally representative survey that combines health interviews, physical examinations, and laboratory testing[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. We selected these survey cycles because they employed consistent methodologies for measuring OPFRs metabolites. From the original cohort of 6,699 participants with available urinary OPFR measurements (randomly selected from one-third of NHANES participants), we applied the following exclusion criteria: missing data on renal function markers (estimated glomerular filtration rate [eGFR] or albumin‒creatinine ratio [ACR]), age\u0026thinsp;\u0026lt;\u0026thinsp;18 years, pregnant women, individuals undergoing renal dialysis, participants reporting urinary incontinence, cancer patients, current use of nephrotoxic medications (including ENALAPRIL, HYDROCHLORHIAZIDE, LOSARTAN, PREDNISONE and FUROSEMIDE), and missing covariate data. After applying these criteria, our final analytical sample comprised 2,156 adults (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The NHANES protocol was approved by the National Center for Health Statistics Research Ethics Review Board, and all participants provided written informed consent.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOPFR metabolites and creatinine in the urine\u003c/p\u003e\u003cp\u003eThis study used urinary concentrations of OPFR metabolites as biomarkers of internal exposure, quantified through a validated protocol involving (1) enzymatic hydrolysis of 0.2 mL of urine (0.4 mL in the 2013\u0026ndash;2014 cycle), (2) automated solid-phase extraction purification, (3) reversed-phase HPLC separation, and (4) isotope-dilution ESI‒MS/MS analysis[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The limits of detection (LOD) were 0.10 ng/mL for diphenyl phosphate (DPHP), bis(1,3-dichloro-2-propyl) phos (BDCPP), bis(1-chloro-2-propyl) phosphate (BCPP), bis(2-chloroethyl) phosphate (BCEP), and dibutyl phosphate (DBUP) in the 2011\u0026ndash;2012 and 2015\u0026ndash;2016 cycles but differed in the 2013\u0026ndash;2014 cycles (DPHP\u0026thinsp;=\u0026thinsp;0.16, BDCPP\u0026thinsp;=\u0026thinsp;0.11, BCEP\u0026thinsp;=\u0026thinsp;0.08, DNUP\u0026thinsp;=\u0026thinsp;0.05 ng/mL; BCPP remained at 0.10 ng/mL). We included only metabolites with \u0026ge;\u0026thinsp;50% detection rates (excluding dibenzyl phosphate [single detection] and 2,3,4,5-tetrabromobenzoic acid [5.67% detection]), with values below the LOD imputed as LOD/\u0026radic;2. Urinary creatinine was measured via the Roche/Hitachi Modular P Chemistry Analyzer, and all OPFR metabolite concentrations were creatinine-adjusted (ng/g creatinine) to account for urine dilution.\u003c/p\u003e\u003cp\u003eDetermination of CKD\u003c/p\u003e\u003cp\u003eSerum creatinine was measured via DxC800 modular chemistry (Jaffe rate method). Detection of albumin in urine by fluorescence immunoassay.\u003c/p\u003e\u003cp\u003eThe eGFR and ACR values were used as the basis for determining CKD. The eGFR was calculated via the following CKD-EPI equation[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\text{e}\\text{G}\\text{F}\\text{R}}_{\\text{C}\\text{K}\\text{D}-\\text{E}\\text{P}\\text{I}}(\\text{m}\\text{L}/\\text{m}\\text{i}\\text{n}/1.73{\\text{m}}^{2})=141\\times\\:{\\text{m}\\text{i}\\text{n}(\\frac{\\text{S}\\text{c}\\text{r}}{{\\kappa\\:}},1)}^{{\\alpha\\:}}\\times\\:\\text{m}\\text{a}\\text{x}{(\\frac{\\text{S}\\text{c}\\text{r}}{{\\kappa\\:}},1)}^{-1.209}\\times\\:{0.993}^{\\text{A}\\text{g}\\text{e}}\\times\\:1.018\\left[\\text{i}\\text{f}\\:\\text{f}\\text{e}\\text{m}\\text{a}\\text{l}\\text{e}\\right]\\times\\:1.159\\left[\\text{i}\\text{f}\\:\\text{b}\\text{l}\\text{a}\\text{c}\\text{k}\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn this equation, Scr represents the serum creatinine level (mg/dL). κ is 0.7 for females and 0.9 for males; α is -0.329 for females and \u0026minus;\u0026thinsp;0.411 for males; min/max indicates the minimum/maximum of SCR/κ or 1.\u003c/p\u003e\u003cp\u003eThe ACR was calculated by dividing the urinary albumin concentration (mg/dL) by the urinary creatinine concentration (g/dL), expressed in mg/g. In accordance with current clinical guidelines, participants were classified as having chronic kidney disease (CKD) if they met either of the following criteria: (1) ACR\u0026thinsp;\u0026ge;\u0026thinsp;30 mg/g or (2) eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCovariates\u003c/p\u003e\u003cp\u003e Covariates included age, sex, race, education, marital status, poverty income ratio (PIR, which is calculated by dividing household income by a specific poverty guideline for the year of the survey), body mass index (BMI), physical activity, smoking, alcohol consumption, hypertension, diabetes, hyperuricemia, and the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR; non-high-density lipoprotein cholesterol is derived by subtracting high-density lipoprotein cholesterol from total cholesterol). The participants belonged to six racial groups: Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, and other races (including multiracial). Education level was grouped as less than 9th grade, 9th\u0026ndash;11th grade, high school graduate/GED or equivalent, some college or AA degree, and college graduate or above. Marital status was classified into married/living with a partner, widowed/divorced/separated, and never married. Family PIR was divided into 4 groups: \u0026le;1.3, 1.3\u0026ndash;3.5, and \u0026gt;\u0026thinsp;3.5. Smoking status, drinking status, and physical activity data were obtained from self-report questionnaire data. BMI was divided into 4 groups: \u0026lt;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e, 18.5\u0026ndash;24.9 kg/m\u003csup\u003e2\u003c/sup\u003e, 25\u0026ndash;29.9 kg/m\u003csup\u003e2\u003c/sup\u003e, and \u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e. Smoking status is categorized into smokers (those with a serum cotinine detection limit or higher) and nonsmokers (those with a serum cotinine detection limit or lower). Drinking status was dichotomized into drinker (drinking at least 12 alcoholic drinks per year) and nondrinker (drinking fewer than 12 alcoholic drinks per year). Physical activity was categorized as none, moderate, or vigorous. A self-reported diagnosis of hypertension, use of hypertensive medications, and a systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg were considered hypertension. Participants with self-reported congestive heart failure, coronary heart disease, angina pectoris, heart attack or stroke were defined as having CVD. Diabetes mellitus was defined as one of the following: (1) self-reported diagnosis of diabetes mellitus; (2) use of insulin or diabetes medications; (3) glycosylated hemoglobin\u0026thinsp;\u0026ge;\u0026thinsp;6.5%; (4) fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L; and (5) or postprandial blood glucose of \u0026ge;\u0026thinsp;11.1 mmol/L 2 hours after a meal on an oral glucose tolerance test. A uric acid concentration higher than 420 \u0026micro;mol/L in men and 360 \u0026micro;mol/L in women was defined as hyperuricemia. As a new type of comprehensive index, the NHHR has shown significant advantages in the early identification and risk assessment of CKD[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eOwing to the complex sampling design of the NHAENS, survey weights have been used in both the statistical description and the multiple regression model. Since none of the continuous variables conformed to normality after normality testing, they were expressed as weighted medians and interquartile spacings (Q1\u0026ndash;Q3), and the categorical variables were described by frequencies (weighted percentages). The Kruskal‒Wallis test and chi‒square test were used to compare the differences between continuous and categorical variables. The log10-transformed metabolite concentrations were calculated for subsequent statistical analyses, and Spearman correlation coefficients were calculated between metabolite concentrations. Baseline covariates with \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis were included in further multivariate analysis. Multivariate logistic regression was used to assess the associations between urinary OPFR metabolite quartiles and CKD, and odds ratios (ORs) and 95% CIs were calculated. Multivariate linear regression was used to assess the associations between urinary metabolite quartiles of OPFRs and eGFR and ACR. The linear assignment method was used (assigning values of 1\u0026ndash;4 to Q1\u0026ndash;Q4 and testing continuous trend p values). This study used three models for multiple regression analysis. Model 1 is a one-factor model not adjusted for potential confounders. Model 2 was adjusted for demographic and socioeconomic factors, including age, marital status, and PIR. Model 3 was further adjusted for lifestyle and comorbid factors such as BMI, physical activity, CVD, hypertension, and diabetes on the basis of Model 2. Subgroup analyses were conducted to explore the effects of OPFRs within different subgroups. These subgroups were defined according to age (\u0026lt;\u0026thinsp;60 or \u0026ge;\u0026thinsp;60 years), PIR (\u0026le;\u0026thinsp;1.3, 1.31\u0026ndash;3.5, \u0026gt;\u0026thinsp;3.5), BMI (\u0026lt;\u0026thinsp;18.5, 18.5\u0026ndash;24.9, 25\u0026ndash;29.9, \u0026ge;\u0026thinsp;30 kg/m2), and the presence or absence of a history of a specific disease (e.g., CVD, hypertension, or diabetes). Nonlinear Associations between OPFRs and Renal Function Indicators Analyzed via Restricted Cubic Splines (RCSs).\u003c/p\u003e\u003cp\u003eEstimating the combined health effects of multiple pollutants is particularly important given that humans are consistently exposed to complex chemical mixtures. Weighted quantile sum (WQS) regression is a commonly used method for assessing the health effects of a mixture of pollutants, which takes into account all the measured chemicals[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In view of the uncertainty of the direction of the association between OPFRs and CKD, this study carried out two sets of WQS analyses, one of which assumed a positive correlation between OPFRs and CKD, and the other assumed a negative correlation between the two. To improve the reliability of the results, we randomly divided the data into two subsets: the training set (40%) and the validation set (60%), and the number of bootstrap iterations was set at 1000. The weights of the urinary metabolites were calculated for each OPFR in the training dataset, and the WQS scores were calculated in the validation dataset to explore the associations between WQS scores and CKD. All analyses were performed via R Studio (version 4.4.1). The significance level for this study was 0.05. WQS was calculated via the R software \u0026ldquo;gWQS\u0026rdquo; (version 3.0.5). The findings of the regression model should be interpreted as exploratory since multiple comparisons may lead to type I errors.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline characteristics\u003c/p\u003e\u003cp\u003eThe data in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e are weighted. The prevalence of CKD among the general adult population in the United States is approximately 7.4%. In terms of age, the median age of the CKD population was 52.41 years, which was significantly greater than that of the non-CKD population, which was 40.00 years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). With respect to marital status, 62.3% were married or living with a partner. In addition, the percentage of individuals with PIRs less than 1.3 was 22.8%. In addition, 32.9% were involved in vigorous physical activity. The prevalence of cardiovascular disease, hypertension and diabetes varied among the study population, with 5.40% of the population suffering from cardiovascular disease, 29.3% from hypertension and 8.70% from diabetes mellitus. Notably, the eGFR was significantly lower in the CKD population than in the non-CKD population (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and the ACR was significantly greater in the CKD population than in the non-CKD population (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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 included participants (n\u0026thinsp;=\u0026thinsp;2156) in the NHANES 2011\u0026ndash;2016, weighted.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2156)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-CKD (n\u0026thinsp;=\u0026thinsp;1957)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCKD\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;199)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41.00 [29.00, 54.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.00 [29.00, 53.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.41 [37.73, 65.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender,n(%)\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\u003e0.535\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e888 (39.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e802 (39.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e86 (42.4)\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\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1268 (60.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1155 (60.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e113 (57.6)\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\u003eRace,n (%)\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\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e291 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e264 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27 (9.4)\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\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e249 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e228 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21 (7.5)\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\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e821 (63.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e747 (63.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e74 (60.5)\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\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e444 (11.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e392 (10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52 (15.7)\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\u003eNon-Hispanic Asian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e279 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e260 (6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19 (5.1)\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\u003eOther Race - Including Multi-Racial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (1.8)\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\u003eEducation level,n (%)\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\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 9th grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e160 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e136 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24 (6.7)\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\u003e9-11th grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e277 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e244 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33 (14.8)\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\u003eHigh school graduate/GED or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e463 (20.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e424 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39 (16.1)\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\u003eSome college or AA degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e681 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e618 (33.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63 (35.3)\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\u003eCollege graduate or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e575 (32.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e535 (32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40 (27.1)\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\u003eMarital status, n (%)\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\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried/living with partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1272 (62.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1159 (62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e113 (59.7)\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\u003eWidowed/divorced/separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e371 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e321 (13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50 (22.6)\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\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e513 (23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e477 (23.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36 (17.7)\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\u003ePoverty income ratio, n (%)\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\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e718 (22.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e631 (21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87 (33.6)\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\u003e1.31\u0026ndash;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e785 (34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e712 (34.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73 (35.5)\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\u003e\u0026gt;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e653 (42.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e614 (43.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39 (30.9)\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\u003eBMI,n (%)\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\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (4.2)\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\u003e18.5\u0026ndash;24.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e657 (29.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e610 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47 (23.8)\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\u003e25\u0026ndash;29.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e730 (34.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e670 (34.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63 (28.6)\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\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e733 (34.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e648 (33.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e82 (43.3)\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\u003ePhysical activity, n (%)\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\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVigorous activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e637 (32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e609 (34.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28 (16.0)\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\u003eModerate activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e543 (25.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e492 (24.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51 (29.4)\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\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e976 (41.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e856 (40.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e120 (54.5)\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\u003eSmoking status,n (%)\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\u003e0.359\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;LOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e601 (31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e544 (32.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57 (28.2)\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\u003e\u0026ge;LOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1555 (68.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1413 (67.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e142 (71.8)\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\u003eAlcohol consumption ,n (%)\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\u003e0.299\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e549 (19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e493 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56 (22.9)\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\u003e≧\u0026thinsp;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1607 (80.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1464 (81.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e143 (77.1)\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\u003eCVD,n (%)\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\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2030 (94.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1866 (95.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e164 (83.5)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e126 (5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35 (16.5)\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\u003eHypertension,n (%)\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\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1425 (70.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1345 (72.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80 (50.7)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e731 (29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e612 (27.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e119 (49.3)\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\u003eDiabetes (%)\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\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1888 (91.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1766 (93.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e122 (65.9)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e268 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e191 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77 (34.1)\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\u003eHyperuricemia,n (%)\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\u003e0.051\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1805 (84.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1654 (85.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e151 (78.4)\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e351 (15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e303 (15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48 (21.6)\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\u003eNHHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.74 [1.95, 3.64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.74 [1.97, 3.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.81 [1.88, 3.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.647\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e107.34 [96.18, 118.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107.66 [96.49, 118.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e101.37 [81.30, 115.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.00 [4.04, 9.85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.69 [3.96, 8.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.49 [36.56, 118.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eContinuous variables are presented as weighted medians (first quartile, third quartile), followed by P values. Categorical variables are presented as weighted percentages (%), followed by P values. Abbreviations: CKD: chronic kidney disease; PIR: poverty income ratio; BMI: body mass index; NHHR: non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio.\u003c/p\u003e\u003cp\u003eOPFR metabolite concentrations in urine\u003c/p\u003e\u003cp\u003eThe concentrations of OPFR metabolites in the urine samples are shown in Supplementary Table\u0026nbsp;1. BDCPP had the highest creatinine-adjusted median concentration (0.87 \u0026micro;g/g creatinine), followed by DPHP (0.72 \u0026micro;g/g creatinine). Notably, most of the biomarkers of OPFRs were correlated with each other, with correlation coefficients ranging from 0.03 to 0.29, among which BCPP and DBUP had the highest correlation (r\u0026thinsp;=\u0026thinsp;0.29, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as shown in Supplementary Fig.\u0026nbsp;1.\u003c/p\u003e\u003cp\u003eAssociations between OPFR metabolites and renal outcomes (CKD, eGFR and ACR)\u003c/p\u003e\u003cp\u003eThe results of multivariate logistic regression analyses showed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) that only BCPP demonstrated statistical significance in all three models. In Model 1, Q4 of BCPP was associated with a significantly lower risk of CKD (OR\u0026thinsp;=\u0026thinsp;0.40, 95% CI: 0.23\u0026ndash;0.70, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and the overall trend test was significant (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.005). This association was significant in Model 2 (Q4: OR\u0026thinsp;=\u0026thinsp;0.43, 95% CI: 0.24\u0026ndash;0.76, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; \u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.012), and Model 3 (Q4: OR\u0026thinsp;=\u0026thinsp;0.42, 95% CI: 0.23\u0026ndash;0.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007; \u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.011) remained consistent. Other metabolites (DPHP, BDCPP, BCEP and DBUP) did not significantly differ among the three models.\u003c/p\u003e\u003cp\u003eThe results of multivariate linear regression analyses (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Table\u0026nbsp;2) revealed significant associations between several OPFRs and the eGFR. In the eGFR analysis, DPHP showed a significant upward trend in unadjusted Model 1 (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.001), in which the ratios of the Q3 (β\u0026thinsp;=\u0026thinsp;4.43, 95% CI: 1.94\u0026ndash;6.92, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and Q4 (β\u0026thinsp;=\u0026thinsp;4.56, 95% CI: 1.67\u0026ndash;7.45, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) ratios were significantly greater than those in Q1. Although the effect sizes diminished in Model 2 (Q3 vs. Q1: β: 1.87, 95% CI: 0.30\u0026ndash;3.43, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036) and Model 3 (Q3 vs. Q1: β: 1.83, 95% CI: 0.23\u0026ndash;3.44, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026), this trend remained statistically significant (Model 2: \u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.036; Model 3: \u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.031). BDCPP also showed a significant positive correlation with eGFR in Model 1 (\u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001), especially for Q4 (β\u0026thinsp;=\u0026thinsp;6.45, 95% CI: 3.16\u0026ndash;9.74, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but the association disappeared after adjusting for covariates. In contrast, DBUP showed a negative trend in Model 1 (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.018), and Q4 was significantly associated with a lower eGFR (β=-2.44, 95% CI: -4.75\u0026ndash;0.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039), but this trend was reversed in Model 2 (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.004) and Model 3 (P for trend\u0026thinsp;=\u0026thinsp;0.005), where DBUP at the Q4 concentration was associated with an elevated eGFR (Model 2: β\u0026thinsp;=\u0026thinsp;2.14, 95% CI: 0.65\u0026ndash;3.64, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006; Model 3: β\u0026thinsp;=\u0026thinsp;2.01, 95% CI: 0.58\u0026ndash;3.43, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). In the ACR analysis, none of the metabolites showed statistically significant associations after adjustment for covariates.\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 between urinary OPFR metabolite concentrations and CKD incidence.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eModel 3\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\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\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\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.83 (0.52\u0026ndash;1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.89 (0.55\u0026ndash;1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.93 (0.55\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.19 (0.70\u0026ndash;2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.40 (0.81\u0026ndash;2.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.37 (0.77\u0026ndash;2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.78 (0.42\u0026ndash;1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.88 (0.48\u0026ndash;1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.87 (0.45\u0026ndash;1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBDCPP\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\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\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.67 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colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88 (0.52\u0026ndash;1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.09 (0.63\u0026ndash;1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.08 (0.63\u0026ndash;1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.65 (0.40\u0026ndash;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.94 (0.59\u0026ndash;1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.88 (0.50\u0026ndash;1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCPP\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\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\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.71 (0.46\u0026ndash;1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.72 (0.47\u0026ndash;1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.73 (0.44\u0026ndash;1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89 (0.54\u0026ndash;1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.88 (0.53\u0026ndash;1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.87 (0.52\u0026ndash;1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.40 (0.23\u0026ndash;0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.43 (0.24\u0026ndash;0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.42 (0.23\u0026ndash;0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCEP\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\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\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88 (0.58\u0026ndash;1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.89 (0.57\u0026ndash;1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.90 (0.55\u0026ndash;1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.430\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.64 (0.37\u0026ndash;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.62 (0.35\u0026ndash;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.62 (0.34\u0026ndash;1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.93 (0.59\u0026ndash;1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.88 (0.57\u0026ndash;1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.90 (0.56\u0026ndash;1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBUP\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\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\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.77 (0.45\u0026ndash;1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.75 (0.42\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.64 (0.38\u0026ndash;1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.702\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.86 (0.47\u0026ndash;1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.82 (0.44\u0026ndash;1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.72 (0.38\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.10 (0.68\u0026ndash;1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.93 (0.52\u0026ndash;1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.86 (0.49\u0026ndash;1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel 1: not adjusted. Model 2: adjusted for age, marital status and PIR. Model 3: adjusted for age, marital status, PIR, BMI, physical activity, CVD, hypertension, and diabetes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations between urinary OPFR metabolite concentrations and eGFR.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eβ(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eβ(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\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\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.27 (-1.04-3.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.15 (-1.60-1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.40 (-1.81-1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.43 (1.94\u0026ndash;6.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.87 (0.30\u0026ndash;3.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.83 (0.23\u0026ndash;3.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.56 (1.67\u0026ndash;7.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.60 (-0.47-3.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.56 (-0.47-3.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBDCPP\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\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\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.07 (0.38\u0026ndash;5.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.25 (-1.50-2.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.25 (-1.50-2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.829\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.35 (1.43\u0026ndash;7.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.13 (-1.94-1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.27 (-2.07-1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.45 (3.16\u0026ndash;9.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.49 (-1.43-2.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.37 (-1.68-2.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCPP\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\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\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.38 (-1.50-4.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04 (-1.47-1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.05 (-1.47-1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.370\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.65 (-2.15-3.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.27 (-0.26-2.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.09 (-0.50-2.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.91 (-1.98-3.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.56 (-1.19-2.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.48 (-1.26-2.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCEP\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\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\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.36 (-0.78-3.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.41 (-0.82-1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.37 (-0.96-1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.60 (-0.40-3.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90 (-0.34-2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.01 (-0.24-2.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.28 (-1.95-2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.84 (-2.35-0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.71 (-2.26-0.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBUP\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\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\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.22 (-2.17-2.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.21 (-0.30-2.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.23 (-0.19-2.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.01 (-3.25-1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.19 (-0.20-2.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.22 (-0.19-2.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.44 (-4.75\u0026ndash;0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.14 (0.65\u0026ndash;3.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.01 (0.58\u0026ndash;3.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel 1: not adjusted. Model 2: adjusted for age, marital status and PIR. Model 3: adjusted for age, marital status, PIR, BMI, physical activity, CVD, hypertension, and diabetes.\u003c/p\u003e\u003cp\u003eStratification Analyses\u003c/p\u003e\u003cp\u003eSubgroup analysis revealed significant heterogeneity in the associations of different OPFRs with CKD and eGFR risk. In the CKD risk analysis, an elevated DPHP concentration was associated with a reduced risk of CKD in hypertensive patients (OR\u0026thinsp;=\u0026thinsp;0.54, 95% CI: 0.33\u0026ndash;0.87; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017). There was a significant interaction effect between BDCPP and PIR (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.034). BDCPP was significantly associated with CKD risk in hypertensive patients (OR\u0026thinsp;=\u0026thinsp;0.31, 95% CI: 0.15\u0026ndash;0.61, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), in those\u0026thinsp;\u0026ge;\u0026thinsp;60 years of age (OR\u0026thinsp;=\u0026thinsp;0.30, 95% CI: 0.11\u0026ndash;0.80, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023), and in those with PIR\u0026thinsp;\u0026gt;\u0026thinsp;3.5 (OR\u0026thinsp;=\u0026thinsp;0.27, 95% CI: 0.10\u0026ndash;0.74, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), BDCPP was significantly negatively associated with CKD and was significantly negatively associated with hypertension status (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.034). in CKD (P for interaction\u0026thinsp;=\u0026thinsp;0.029). BCEP also had a protective effect in hypertensive patients (OR\u0026thinsp;=\u0026thinsp;0.59, 95% CI: 0.37\u0026ndash;0.94; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032). The results of all the above analyses are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eIn the eGFR analysis, positive associations of DPHP with eGFR were detected in people\u0026thinsp;\u0026lt;\u0026thinsp;60 years old (β\u0026thinsp;=\u0026thinsp;3.69, 95% CI: 1.73\u0026ndash;5.65, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), in the low-income group (β\u0026thinsp;=\u0026thinsp;3.16, 95% CI: 1.14\u0026ndash;5.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), and in the hypertensive group (β\u0026thinsp;=\u0026thinsp;3.05, 95% CI: 0.66\u0026ndash;5.44, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017), and these associations remained significant in the nondiabetic population (β\u0026thinsp;=\u0026thinsp;2.00, 95% CI: 0.43\u0026ndash;3.56, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017). BDCPP was significantly greater in the \u0026lt;\u0026thinsp;60-year-olds (β\u0026thinsp;=\u0026thinsp;3.46, 95% CI: 1.29\u0026ndash;5.62, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), and DBUP was significantly greater in the PIR1.31\u0026ndash;3.5-year-olds (β\u0026thinsp;=\u0026thinsp;2.70, 95% CI. 0.46\u0026ndash;4.94, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) and in the nondiabetic population (β\u0026thinsp;=\u0026thinsp;1.66, 95% CI: 0.42\u0026ndash;2.90, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) were positively associated with the eGFR. The results of all the above analyses are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eNonlinear relationships between OPFRs, eGFR and ACR\u003c/p\u003e\n\u003cp\u003eThe RCS curves revealed that after adjusting for all covariates, there was a significant inflection point in the trend of the eGFR with increasing DBUP concentration (P for nonlinearity\u0026thinsp;=\u0026thinsp;0.048), which showed an inverted U shape, and at the same time, its inflection point was at a lnDBUP value of -0.918 \u0026micro;g/g creatinine (DBUP\u0026thinsp;=\u0026thinsp;0.121 \u0026micro;g/g creatinine) (Fig. 4).\u003c/p\u003e\n\u003cp\u003eWQS regression analysis\u003c/p\u003e\n\u003cp\u003eThe WQS regression model was used to assess the associations between exposure to mixed OPFRs and CKD, eGFR and ACR. After adjusting for all confounders, a mixture of OPFRs was associated with an elevated eGFR (\u0026beta;: 1.20, 95% CI: 0.38\u0026ndash;2.02, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) in a positive effect model (Table 4), with the greatest contribution from DBUP, followed by DPHP (Supplementary Fig. 4).\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eWQS regression of OPFR metabolites with renal outcomes (CKD, eGFR and ACR)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePositive model\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNegative model\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;/OR(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;/OR(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.06(0.81\u0026ndash;1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.18(0.87\u0026ndash;1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.20(0.38\u0026ndash;2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53(-0.18-1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.14(-18.48-16.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-13.80(-32.19-4.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cp\u003eThe WQS regression model was adjusted for age, marital status, PIR, BMI, physical activity, cardiovascular disease, hypertension, and diabetes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically evaluated the associations between urinary metabolites of OPFRs and renal function indices on the basis of NHANES 2011\u0026ndash;2016 adult samples. The main findings were that different OPFR metabolites showed heterogeneous or even conflicting effect patterns after adjustment for multiple confounders. Among them, BCPP was significantly associated with a reduced risk of CKD in all three multivariate logistic regression models, and this protective effect was more pronounced in the subgroups of hypertensive patients, those of advanced age (\u0026ge;\u0026thinsp;60 years), and those with high income (PIR\u0026thinsp;\u0026gt;\u0026thinsp;3.5). In contrast, DPHP and BDCPP were positively associated with eGFR in the unadjusted model, but the associations were attenuated or disappeared after adjustment for socioeconomic and comorbid factors. Notably, DBUP showed a complex dose‒response relationship: it was negatively associated with eGFR in the unadjusted model but was positively associated with eGFR after full adjustment for confounders, and RCS analysis further revealed an inverted U-shaped nonlinear association with eGFR. Further WQS analysis of the OPFR mixtures revealed that the five metabolites of OPFRs were associated with increased eGFRs overall, with DBUP and DPHP contributing the most.\u003c/p\u003e\u003cp\u003eAvailable studies generally support the nephrotoxic effects of OPFRs. In vitro experiments have shown that OPFRs can impair renal function through a variety of mechanisms, e.g., low concentrations of Tri (2,3-dichloropropyl) phosphate induce cell cycle arrest in renal cell lines, whereas high concentrations exhibit cytotoxicity[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Moreover, Tris (2-chloroethyl) phosphate (TCEP) may promote apoptosis in a dose-dependent manner, significantly inhibiting the DNA synthesis ability of renal proximal tubule cells and reducing the number of cells[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Epidemiologic evidence further supports this conclusion. A study based on the NHANES 2013\u0026ndash;2014 time cycle suggested that OPFRs may be potential risk factors for CKD[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A 2-year longitudinal study in Taiwan reached a similar conclusion that high-dose exposure to TCEP can lead to deterioration in kidney function[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Moreover, a cross-sectional study conducted in Wuhan specifically indicated that OPFRs were associated with a greater risk of kidney injury in hypertensive and diabetic patients[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Notably, although the study revealed a positive correlation between 1-hydroxy-2-propyl bis(1-chloro-2-propyl) phosphate and the eGFR, the trend did not reach statistical significance, a phenomenon that may need to be verified with a larger sample.\u003c/p\u003e\u003cp\u003ePrevious studies have shown that the damage caused by OPFRs to renal function is realized mainly through oxidative stress and inflammation. First, in terms of oxidative stress, exposure to OPFRs significantly disrupts the redox balance of the body[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Specifically, these substances are capable of interfering with intracellular antioxidant defense systems; for example, tri-n-butyl phosphate has been shown to significantly reduce the activity of key antioxidant enzymes such as superoxide dismutase and glutathione peroxidase[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. By inhibiting these enzymes, the body's ability to scavenge reactive oxygen species is weakened, leading to a steady increase in oxidative stress[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This condition, in turn, damages kidney cells in multiple ways: not only does it cause lipid peroxidation of cell membranes, but it also disrupts mitochondrial function[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. As the core organelles of energy metabolism in cells, impaired mitochondrial function directly affects the normal physiological activities of renal cells and ultimately leads to renal dysfunction[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In addition, exposure to OPFRs triggers a significant inflammatory response, resulting in tissue and organ damage[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. A large body of evidence suggests that OPFRs can upregulate the expression of various inflammatory factors, including transcription factors (e.g., NF-κB), proinflammatory cytokines (e.g., IL-1β and IL-6), and inflammatory mediators (e.g., TNF-α), in renal tissue[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These inflammatory factors then mediate renal injury through a variety of pathways, such as those that cause glomerulonephritis[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] or tubulointerstitial inflammation[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough the nephrotoxic effects of OPFRs are commonly reported in the above studies, the present study revealed protective associations between their metabolites and renal function indices, a seemingly paradoxical phenomenon that may be due to the biphasic biological effects of oxidative stress. Recently, it has been suggested that the role of oxidative stress in renal disease may be dose dependent: low levels of oxidative stress may enhance mitochondrial biosynthesis and antioxidant defense through activation of the AMPK/PGC1α pathway, a phenomenon known as mitochondrial hormesis[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In the present study, the urinary metabolites of OPFRs may mimic mild metabolic stress (similar to the physiological stress produced by exercise or caloric restriction) and exert their protective effects mainly through the following pathways: On the one hand, they induced moderate mitochondrial superoxide production, which activated mitochondrial autophagy and the efficiency of oxidative phosphorylation since AMPK, and on the other hand, they inhibited proinflammatory signaling pathways, such as NF-κB, and attenuated renal fibrosis. This hypothesis is supported by existing animal experiments; for example, AMPK agonists (e.g., metformin) can significantly improve renal function indices[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe strength of this study lies in the systematic assessment of the association between OPFR metabolites and CKD using a large sample size and nationally representative data from the NHANES database combined with standardized biomarker assays. In addition, the present study innovatively proposed the potential explanation mechanism of \u0026ldquo;mitochondrial low-toxicity excitatory effect\u0026rdquo;, which provides a new perspective on the role of low-dose OPFRs. This study has several limitations. First, the cross-sectional design could not distinguish the causal direction (e.g., decreased renal function may affect the metabolism of OPFRs), and there was a lack of direct oxidative stress or mitochondrial function indices, which needs to be verified in combination with mechanistic studies in the future. Second, single urine measurements may not reflect long-term exposure to OPFRs, and chronic kidney injury is often associated with cumulative toxicity. In addition, residual confounding (e.g., other undetected environmental exposures or dietary differences) may affect the results despite controlling for multiple confounders.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe present study suggests that the metabolites of OPFRs may be associated with a reduced risk of CKD, and the underlying mechanism may involve adaptive mitochondrial responses induced by low-dose stress. In the future, longitudinal cohort and experimental studies are needed to verify causal relationships and explore biological pathways to provide a more accurate basis for risk assessment and intervention strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eOPFRs: Organophosphate flame retardants\u003c/p\u003e\n\u003cp\u003eCKD: Chronic kidney disease\u003c/p\u003e\n\u003cp\u003eNHANES: National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003eeGFR: Estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eACR: Albumin‒creatinine ratio\u003c/p\u003e\n\u003cp\u003eDPHP: diphenyl phosphate\u003c/p\u003e\n\u003cp\u003eBDCPP: Bis(1,3-dichloro-2-propyl) phos\u003c/p\u003e\n\u003cp\u003eBCPP: Bis(1-chloro-2-propyl) phosphate\u003c/p\u003e\n\u003cp\u003eBCEP: Bis(2-chloroethyl) phosphate\u003c/p\u003e\n\u003cp\u003eDBUP: Dibutyl phosphate\u003c/p\u003e\n\u003cp\u003eLOD: Limits of detection\u003c/p\u003e\n\u003cp\u003ePIR: Poverty income ratio\u003c/p\u003e\n\u003cp\u003eBMI: Body mass index\u003c/p\u003e\n\u003cp\u003eNHHR: Non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio\u003c/p\u003e\n\u003cp\u003eWQS: Weighted quantile sum\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTCEP: Tris (2-chloroethyl) phosphate\u003c/p\u003e"},{"header":"Declarations","content":"\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available from the NHANES (NHANES\u0026mdash;National Health and Nutrition Examination Survey Homepage (cdc.gov)) in 2011\u0026ndash;2016.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our sincere gratitude to NHANES team and all participating staff for their efforts in data collection and for making these valuable resources publicly available. We are also deeply thankful to all study participants whose contributions made this research possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Science Foundation of China (81872701). Shanxi Huajin Orthopaedic Public Foundation. Technology Research Foundation of Shanxi Province (202303021221126). Shanxi Province Higher Education Billion Project Science and Technology Guidance Project (BYBLD002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eDepartment of Epidemiology, School of Public Health, Shanxi Medical University, Jinzhong 030606, China\u003c/p\u003e\n\u003cp\u003eYuebin Yang, Pu Guo, Hongjing Ren, Lingyun Zhuo, Qikai Xiang, Xiao-rong Guo, Fu-rong Chen, Xiang-xiang Zhang, Ping Zhang, Lijian Lei\u003c/p\u003e\n\u003cp\u003eMOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University, Jinzhong 030606, China\u003c/p\u003e\n\u003cp\u003ePing Zhang \u0026amp; Lijian Lei\u003c/p\u003e\n\u003cp\u003eResearch Center for Epidemiology of Environmental Pollution and Major Chronic Diseases, Shanxi Medical University, Jinzhong 030606, China\u003c/p\u003e\n\u003cp\u003ePing Zhang \u0026amp; Lijian Lei\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYuebin Yang (Co-first author): Directed the study design, performed statistical analyses, and drafted the manuscript. Pu Guo (Co-first author): Contributed equally to methodology development, data interpretation, and revision of the manuscript. Hongjing Ren: Conducted literature review and assisted in data validation. Lingyun Zhuo: Contributed to data collection and preliminary analysis. Qikai Xiang: Assisted with statistical modeling and visualization. Xiao-rong Guo, Fu-rong Chen, and Xiang-xiang Zhang: Contributed to resource provision, administrative support, and technical review. Ping Zhang (Corresponding author): Conceived the study, supervised the research process, and finalized the manuscript. Lijian Lei (Corresponding author): Oversaw project financing, made critical revisions, and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Ping Zhang or Lijian Lei.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) data used in this study were collected by the U.S. Centers for Disease Control and Prevention (CDC) and approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board. All participants provided written informed consent prior to data collection.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBevington C, Williams AJ, Guider C, Baker NC, Meyer B, Babich MA, et al. Development of a Flame Retardant and an Organohalogen Flame Retardant Chemical Inventory. Sci Data [Internet]. 2022 [cited 2025 Jan 12];9:295. Available from: https://www.nature.com/articles/s41597-022-01351-0\u003c/li\u003e\n\u003cli\u003eLi K, Gao Y, Li X, Zhang Y, Zhu B, Zhang Q. Fragmentation Pathway of Organophosphorus Flame Retardants by Liquid Chromatography-Orbitrap-Based High-Resolution Mass Spectrometry. Molecules. 2024;29:680. \u003c/li\u003e\n\u003cli\u003eMa H, Wang C, Suo H, Huang Y, Huo Y, Yang G, et al. 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Exposure to phthalates and environmental phenols in association with chronic kidney disease (CKD) among the general US population participating in multi-cycle NHANES (2005-2016). Sci Total Environ. 2021;791:148343. \u003c/li\u003e\n\u003cli\u003eCDC. National Health and Nutrition Examination Survey [Internet]. National Health and Nutrition Examination Survey. 2024 [cited 2025 Jan 13]. Available from: https://www.cdc.gov/nchs/nhanes/index.html\u003c/li\u003e\n\u003cli\u003eLaw RJ, Allchin CR, de Boer J, Covaci A, Herzke D, Lepom P, et al. Levels and trends of brominated flame retardants in the European environment. Chemosphere. 2006;64:187\u0026ndash;208. \u003c/li\u003e\n\u003cli\u003eJayatilaka NK, Restrepo P, Davis Z, Vidal M, Calafat AM, Ospina M. Quantification of 16 urinary biomarkers of exposure to flame retardants, plasticizers, and organophosphate insecticides for biomonitoring studies. 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Biomolecules. 2021;11:1144. \u003c/li\u003e\n\u003cli\u003eSrivastava A, Tomar B, Sharma D, Rath SK. Mitochondrial dysfunction and oxidative stress: Role in chronic kidney disease. Life Sci. 2023;319:121432. \u003c/li\u003e\n\u003cli\u003eChen Z, Li F, Fu L, Xia Y, Luo Y, Guo A, et al. Role of inflammatory lipid and fatty acid metabolic abnormalities induced by plastic additives exposure in childhood asthma. J Environ Sci (China). 2024;137:172\u0026ndash;80. \u003c/li\u003e\n\u003cli\u003eTan Y, Fu Y, Yao H, Li H, Wu X, Guo Z, et al. The relationship of organophosphate flame retardants with hyperuricemia and gout via the inflammatory response: An integrated approach. Sci Total Environ. 2024;908:168169. \u003c/li\u003e\n\u003cli\u003eHsu C, Zeng J-H, Chen L, Chen L-J, Li X-W, Yang J-Z, et al. 2-Ethylhexyl diphenyl phosphate aggravates colitis-induced neuroinflammation and behavioral abnormalities by inhibiting the PI3K-AKT-NF-\u0026kappa;B and Wnt/GSK3\u0026beta; signaling pathways. 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Diabetes. 2015;64:663\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eChen X-C, Wu D, Wu H-L, Li H-Y, Yang C, Su H-Y, et al. Metformin improves renal injury of MRL/lpr lupus-prone mice via the AMPK/STAT3 pathway. Lupus Sci Med. 2022;9:e000611. \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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Organophosphorus flame retardants, Chronic kidney disease, Renal function markers, Nonalotonic dose‒response","lastPublishedDoi":"10.21203/rs.3.rs-6915116/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6915116/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs alternatives to brominated flame retardants, organophosphorus flame retardants (OPFRs) have raised concerns regarding their potential nephrotoxicity. However, population-based evidence remains inconsistent. This study aimed to examine the associations between urinary metabolites of OPFRs and chronic kidney disease (CKD), along with renal function markers (estimated glomerular filtration rate [eGFR] and the urinary albumin‒creatinine ratio [ACR]), in the general U.S. population while exploring potential underlying mechanisms.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe analyzed data from the National Health and Nutrition Examination Survey (NHANES) 2011\u0026ndash;2016, which included 2,156 adults. Five OPFR metabolites (DPHP, BDCPP, BCPP, BCEP, and DBUP) were measured in the urine. Multivariate logistic regression, subgroup analyses, restricted cubic spline (RCS), and weighted quantile sum (WQS) regression were performed, with CKD defined as an eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2; or an ACR\u0026thinsp;\u0026ge;\u0026thinsp;30 mg/g.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBCPP was inversely associated with CKD risk (Q4 vs. Q1: OR\u0026thinsp;=\u0026thinsp;0.42, 95% CI: 0.23\u0026ndash;0.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), particularly in hypertensive, elderly, and high-income populations. DPHP and BDCPP were significantly positively correlated with the eGFR \u003cem\u003e(P\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.05), although the association weakened after adjustment. DBUP exhibited a U-shaped relationship with the eGFR (\u003cem\u003eP\u003c/em\u003e-nonlinear\u0026thinsp;=\u0026thinsp;0.048). WQS analysis indicated that OPFR mixtures were associated with higher eGFRs (β\u0026thinsp;=\u0026thinsp;1.20, 95% CI: 0.38\u0026ndash;2.02, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), which was driven primarily by DBUP and DPHP.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLow-level OPFR exposure may be associated with a reduced risk of CKD, potentially through mitochondrial activation. These findings challenge the assumption of linear toxicity, highlight the complexity of dose‒response relationships, and underscore the need for further mechanistic validation and refined risk assessment frameworks.\u003c/p\u003e","manuscriptTitle":"Association Between Organophosphorus Flame Retardant Exposure and Chronic Kidney Disease in U.S. Adults: NHANES data from 2011--2016","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 18:31:45","doi":"10.21203/rs.3.rs-6915116/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-29T04:01:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-05T18:35:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T16:02:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T09:09:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-20T09:36:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335295081422749949432678827647924035769","date":"2025-08-18T05:53:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243914074478187826302353721316973738686","date":"2025-08-14T09:19:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129003830543787309093772035437676798588","date":"2025-08-13T09:59:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69790346750212589487175986353322034218","date":"2025-08-12T13:37:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-30T12:54:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-18T05:56:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-18T02:36:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2025-06-17T13:48:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"da6494c9-066c-405f-a7c0-2f3b013ca2c8","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:05:47+00:00","versionOfRecord":{"articleIdentity":"rs-6915116","link":"https://doi.org/10.1186/s40001-026-03926-8","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2026-02-04 15:58:59","publishedOnDateReadable":"February 4th, 2026"},"versionCreatedAt":"2025-08-04 18:31:45","video":"","vorDoi":"10.1186/s40001-026-03926-8","vorDoiUrl":"https://doi.org/10.1186/s40001-026-03926-8","workflowStages":[]},"version":"v1","identity":"rs-6915116","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6915116","identity":"rs-6915116","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}