Dose–response associations between dietary antioxidant capacity and prevalent chronic kidney disease: NHANES 2003–2018

Dose–response associations between dietary antioxidant capacity and prevalent chronic kidney disease: NHANES 2003–2018 | 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 Dose–response associations between dietary antioxidant capacity and prevalent chronic kidney disease: NHANES 2003–2018 Huian Tang, Huibing Nie, Zejun Chen, Guangyu Ao, Yi Wu, Jing Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8720931/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Oxidative stress plays an important role in the development and progression of chronic kidney disease (CKD). Dietary antioxidants may help counteract oxidative damage; however, the dose–response associations between overall dietary antioxidant capacity, assessed by the composite dietary antioxidant index (CDAI), and prevalent CKD remain unclear. Methods Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 2003–2018. Adults aged ≥ 45 years were included in this cross-sectional analysis. Dietary intakes of antioxidant nutrients (vitamins A, C, and E, selenium, zinc, carotenoids, and niacin) were assessed using 24-hour dietary recall data, and the CDAI was calculated based on energy-adjusted standardized intakes. Survey-weighted multivariable logistic regression models were applied to examine associations between continuous antioxidant exposures and quartiles of antioxidant intake and prevalent CKD. Restricted cubic spline models were used to explore potential nonlinear dose–response associations. Results Compared with participants without CKD, those with CKD had lower intakes of vitamin C, vitamin E, selenium, zinc, niacin, as well as lower CDAI values. After full adjustment for potential confounders, higher intake categories of vitamin E (OR = 0.50, 95% CI: 0.43–0.80), selenium (OR = 0.58, 95% CI: 0.44–0.78), niacin (OR = 0.64, 95% CI: 0.47–0.88), zinc (OR = 0.60, 95% CI: 0.49–0.84), and CDAI (OR = 0.73, 95% CI: 0.55–0.97) were associated with lower odds of prevalent CKD. Restricted cubic spline analyses suggested nonlinear associations, with lower odds of prevalent CKD observed at higher intake levels, followed by a plateau. Conclusion Higher overall dietary antioxidant capacity was inversely associated with prevalent CKD, with evidence of nonlinear dose–response patterns. These findings suggest that the associations between antioxidant intake and CKD prevalence may be more pronounced at higher intake levels. Given the cross-sectional design, causal inference cannot be established, and prospective studies are warranted to further clarify these relationships. dietary antioxidants composite dietary antioxidant index (CDAI) chronic kidney disease cross-sectional survey NHANES Figures Figure 1 Figure 2 Figure 3 1 Introduction CKD is a major global public health challenge. According to the most recent Global Burden of Disease study, more than 780 million adults worldwide were affected by CKD in 2023, with an age-standardized prevalence exceeding 14%, and CKD has become one of the leading causes of death globally( 1 ). In addition to progressing to end-stage renal disease, CKD substantially increases the risk of cardiovascular disease and all-cause mortality, thereby imposing a sustained medical and socioeconomic burden( 2 , 3 ). Oxidative stress is widely recognized as a central mechanism in the development and progression of CKD. Owing to its high metabolic activity, the kidney is particularly vulnerable to damage from reactive oxygen species (ROS), which can induce lipid peroxidation, activate inflammatory pathways, impair mitochondrial function, and promote renal interstitial fibrosis, ultimately leading to progressive loss of renal function ( 4 – 6 ). Evidence from basic and clinical studies suggests that enhancing systemic antioxidant capacity may mitigate oxidative stress–related renal injury and potentially slow CKD progression ( 7 – 9 ). Diet is a major source of exogenous antioxidants. Dietary antioxidant nutrients, including vitamins A, C, and E, carotenoids, selenium, zinc, and niacin, have been implicated in renoprotection through mechanisms such as free radical scavenging, attenuation of lipid peroxidation, and modulation of inflammatory responses ( 10 – 13 ). While most epidemiological studies have examined individual antioxidant nutrients, evidence regarding the combined intake of multiple antioxidant components and their potential synergistic effects remains limited. The CDAI, which integrates multiple energy-adjusted antioxidant nutrients, provides a comprehensive measure of overall dietary antioxidant capacity and has shown predictive value for cardiovascular disease, metabolic disorders, and biological aging ( 14 – 16 ). However, data examining the association between CDAI and prevalent CKD are scarce, particularly with respect to dose–response patterns, potential nonlinearity, and population heterogeneity. Therefore, using data from the NHANES 2003–2018, this cross-sectional study aimed to examine the associations between dietary antioxidant nutrients, overall dietary antioxidant capacity assessed by the CDAI, and prevalent CKD among adults aged 45 years and older. In addition, potential nonlinear dose–response associations were evaluated to further characterize the relationship between dietary antioxidant intake and CKD prevalence in a nationally representative population. 2 Methods 2.1 Study population This cross-sectional study used data from the NHANES 2003–2018, a nationally representative survey of the non-institutionalized civilian population in the United States. Participants aged ≥ 45 years with complete data on dietary intake, kidney function, and relevant covariates were included in the analysis. Figure 1 shows a complete flowchart of the participant recruitment process. 2.2 Dietary assessment and antioxidant exposure assessment Dietary intake was assessed via 24-hour dietary recall interviews conducted according to the NHANES protocol. The first dietary recall was administered face to face at the Mobile Examination Center, followed by a second 24-hour dietary recall conducted by telephone 3–10 days later. Nutrient intakes, including antioxidants, micronutrients, and total energy intake, were estimated using the United States Department of Agriculture (USDA) Food and Nutrient Database for Dietary Studies (FNDDS). Information on dietary supplement use during the previous 30 days, including frequency, dosage, and duration of use, was collected through standardized questionnaires. However, only antioxidant nutrients obtained from dietary sources were considered in the primary analyses and in the construction of the CDAI. The CDAI was constructed based on six dietary antioxidant nutrients—selenium, zinc, vitamins A, C, and E, and carotenoids—following previously established methods. The intake of each nutrient was energy-adjusted and subsequently standardized by subtracting the mean intake and dividing by the corresponding standard deviation. The CDAI was calculated as the sum of the standardized intakes of these six nutrients, with higher CDAI values indicating greater overall dietary antioxidant capacity. The formula for calculating CDAI is as follows: CDAI was calculated as Σ (individual intake − mean) / SD across six nutrients (i = 1–6). In addition to the CDAI, individual dietary antioxidant nutrients were examined separately. Niacin was included in the individual nutrient analyses and dose–response assessments because of its reported antioxidant properties and potential relevance to kidney health. Niacin was not incorporated into the CDAI, as it is not part of the original index definition used in prior epidemiological studies. 2.3 Assessment of Chronic Kidney Disease Estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Urinary albumin-to-creatinine ratio (UACR) was calculated from spot urine samples. CKD was defined as an eGFR < 60 mL/min/1.73 m² and/or a UACR ≥ 30 mg/g. 2.4 Covariates Covariates were selected a priori based on previous literature and biological relevance and included age, sex, race/ethnicity, education level, smoking status, alcohol consumption, body mass index (BMI), physical activity, hypertension, diabetes, total energy intake, and relevant biochemical markers. 2.5 Statistical Methods All statistical analyses were performed using R software (version 4.5.1; R Foundation for Statistical Computing). NHANES employs a complex, multistage probability sampling design; therefore, sampling weights, strata, and primary sampling units were incorporated into all primary analyses to account for unequal probabilities of selection and to support population-representative inference. Combined survey weights for the 2003–2018 cycles were calculated according to NHANES analytic guidelines. Baseline characteristics were summarized according to CKD status. Continuous variables are presented as survey-weighted means with standard deviations and were compared using design-based analysis of variance, whereas categorical variables are presented as survey-weighted percentages and were compared using the Rao–Scott chi-square test. Dietary antioxidant nutrients (vitamins A, C, and E, selenium, zinc, and carotenoids) and the CDAI were analyzed using both continuous and quartile-based approaches. Continuous exposure models served as the primary analytic framework to estimate overall associations between dietary antioxidant exposures and prevalent CKD, while quartile-based models were conducted as secondary analyses to facilitate interpretation and comparison with prior studies. For quartile-based analyses, antioxidant exposures were categorized into quartiles based on survey-weighted distributions, with the lowest quartile serving as the reference category. Median intake values for each quartile were calculated from survey-weighted intake distributions. P for trend was assessed by modeling quartile categories as an ordinal variable in survey-weighted logistic regression models. Survey-weighted multivariable logistic regression models were used to examine associations between antioxidant exposures and prevalent CKD. Three sequential models were constructed for both quartile-based and continuous analyses: Model 1 was unadjusted; Model 2 was adjusted for age, sex, body mass index, smoking status, and race/ethnicity; and Model 3 was further adjusted for diabetes, hypertension, and fasting blood glucose. Diabetes and hypertension were included to reduce confounding by major CKD-related comorbidities; however, potential overadjustment cannot be excluded, and estimates from fully adjusted models should be interpreted cautiously. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated. Continuous exposure models were specified with antioxidant variables entered as continuous terms, with effect estimates expressed per unit increase or per standard deviation increase, as appropriate. For the CDAI, a one-unit increase approximately corresponds to a one standard deviation increase in the summed standardized antioxidant components. Results from continuous exposure analyses are presented in Supplementary Table S1 . To assess robustness, corresponding unweighted logistic regression models with identical covariate specifications were conducted as sensitivity analyses, with particular focus on niacin intake quartiles. Potential nonlinear associations between continuous antioxidant exposures and prevalent CKD were explored using restricted cubic spline functions. Subgroup analyses were conducted across categories of age, sex, body mass index, smoking status, diabetes, hypertension, race/ethnicity, marital status, and household income. Because reliable estimation of complex survey-weighted spline and stratified models is methodologically challenging and may yield unstable variance estimates, these analyses were conducted without survey weighting and are therefore interpreted as exploratory, aiming to characterize dose–response patterns and potential effect heterogeneity within the analytic sample rather than to estimate population-representative associations. All statistical tests were two-sided, and a P value < 0.05 was considered statistically significant. 3 Results 3.1 Characteristics of the study population A total of 4,649 adults with complete dietary data were included in this analysis. After applying NHANES complex survey weights, the sample represented approximately 23.66 million U.S. residents aged 45 years and older, of whom an estimated 13.3% (approximately 3.16 million) had CKD. The weighted demographic and clinical characteristics of participants with and without CKD are presented in Table 1. Overall, participants with CKD were older, had a lower income-to-poverty ratio, and a higher prevalence of diabetes and hypertension compared with those without CKD (all P < 0.001). Indicators of impaired kidney function, including serum creatinine and urine albumin-to-creatinine ratio, were also significantly higher among participants with CKD (all P < 0.001). In terms of dietary antioxidant intake, participants with CKD tended to have lower intakes of several antioxidants. Specifically, a higher proportion of individuals with CKD were observed in the lowest intake quartile for vitamin C, vitamin E, selenium, and zinc (all P < 0.05), and mean daily niacin intake was lower in the CKD group (P = 0.001). In addition, the mean CDAI score was modestly but significantly lower among participants with CKD compared with those without CKD (− 0.05 vs. 0.06, P = 0.014). No significant differences were observed for vitamin A or carotenoid intake between the two groups (both P > 0.05). Table 1 Demographic and clinical characteristics level Overall CKD Non-CKD p n 23656933.41 3157823.42 20499109.99 Age (mean (SD)) 60.26 (10.28) 64.93 (11.08) 59.54 (9.96) <0.001 Gender (%) F 10484367.4 (44.3) 1362484.9 (43.1) 9121882.5 (44.5) 0.63 M 13172566.0 (55.7) 1795338.5 (56.9) 11377227.5 (55.5) Race (%) American 1097892.9 (4.6) 187831.5 (5.9) 910061.4 (4.4) 0.004 Black 1989204.2 (8.4) 351785.6 (11.1) 1637418.6 (8.0 ) Other 1190580.8 (5.0) 174702.1 (5.5) 1015878.7 (5.0 ) Hispanic 845167.7 (3.6) 125768.1 (4.0) 719399.6 (3.5) White 18534087.8 (78.3) 2317736.1 (73.4) 16216351.7 (79.1) Income (mean (SD)) 3.07 (1.60) 2.53 (1.54) 3.15 (1.60) <0.001 Marital_Status (%) Divorce 3548919.3 (15.0) 488464.2 (15.5) 3060455.1 (14.9) <0.001 Married 14453952.4 (61.1) 1645273.2 (52.1) 12808679.2 (62.5) Unmarried 5654061.7 (23.9) 1024086.0 (32.4) 4629975.7 (22.6) VA (%) Q1 5102600.7 (21.6) 787839.4 (24.9) 4314761.4 (21.0) 0.105 Q2 5832836.9 (24.7) 778461.7 (24.7) 5054375.1 (24.7) Q3 6063860.2 (25.6) 844126.4 (26.7) 5219733.7 (25.5) Q4 6657635.7 (28.1) 747395.9 (23.7) 5910239.8 (28.8) VC (%) Q1 5666838.1 (24.0) 902306.4 (28.6) 4764531.7 (23.2) 0.048 Q2 6096262.7 (25.8) 752695.9 (23.8) 5343566.8 (26.1) Q3 5948078.6 (25.1) 817098.5 (25.9) 5130980.1 (25.0) Q4 5945754.0 (25.1) 685722.6 (21.7) 5260031.3 (25.7) VE (%) Q1 5057093.7 (21.4) 820293.8 (26.0) 4236799.9 (20.7) 0.001 Q2 5715172.6 (24.2) 881939.4 (27.9) 4833233.2 (23.6) Q3 5869664.9 (24.8) 785944.9 (24.9) 5083720.1 (24.8) Q4 7015002.3 (29.7) 669645.4 (21.2) 6345356.9 (31.0) Se (%) Q1 5375344.8 (22.7) 960213.9 (30.4) 4415130.9 (21.5) <0.001 Q2 5815002.2 (24.6) 826671.4 (26.2) 4988330.7 (24.3) Q3 6225436.0 (26.3) 681768.5 (21.6) 5543667.4 (27.0) Q4 6241150.4 (26.4) 689169.5 (21.8) 5551980.9 (27.1) Zn (%) Q1 5463951.2 (23.1) 1024910.6 (32.5) 4439040.6 (21.7) <0.001 Q2 5506397.7 (23.3) 686612.9 (21.7) 4819784.8 (23.5) Q3 5941411.6 (25.1) 668119.9 (21.2) 5273291.6 (25.7) Q4 6745173.0 (28.5) 778180.0 (24.6) 5966992.9 (29.1) Niacin (mean (SD)) 25.43 (15.63) 23.21 (13.62) 25.77 (15.89) 0.001 Carotenoids (%) Q1 5267941.3 (22.3) 800023.8 (25.3) 4467917.5 (21.8) 0.144 Q2 6034306.2 (25.5) 827820.4 (26.2) 5206485.8 (25.4) Q3 5964911.3 (25.2) 816122.4 (25.8) 5148788.9 (25.1) Q4 6389774.6 (27.0) 713856.8 (22.6) 5675917.8 (27.7) BMI (mean (SD)) 28.77 (5.83) 29.72 (6.63) 28.62 (5.68) 0.003 Height (mean (SD)) 169.49 (9.45) 167.79 (9.29) 169.75 (9.45) <0.001 Weight (mean (SD)) 82.79 (18.29) 83.89 (20.13) 82.62 (17.98) 0.209 Waist_Circumference (mean (SD)) 101.94 (14.59) 105.25 (15.82) 101.43 (14.33) <0.001 Creatinine (mean (SD)) 0.92 (0.32) 1.06 (0.67) 0.89 (0.21) <0.001 Urinary_albumin (mean (SD)) 41.71 (224.53) 250.10 (572.08) 9.61 (9.58) <0.001 Urinary_creatinine (mean (SD)) 116.45 (67.90) 111.32 (65.43) 117.23 (68.24) 0.052 Diabetes (%) NO 20340155.6 (86.0) 2190077.4 (69.4) 18150078.1 (88.5) <0.001 YES 3316777.8 (14.0) 967746.0 (30.6) 2349031.9 (11.5) Hypertension (%) NO 12366098.7 (52.3) 1033814.1 (32.7) 11332284.5 (55.3) <0.001 YES 11290834.7 (47.7) 2124009.3 (67.3) 9166825.5 (44.7) Glucose (mean (SD)) 111.79 (33.35) 128.30 (52.34) 109.25 (28.53) <0.001 Smoking (%) NO 16496602.2 (69.7) 2215635.7 (70.2) 14280966.6 (69.7) 0.825 YES 7160331.2 (30.3) 942187.8 (29.8) 6218143.4 (30.3) UACR (mean (SD)) 39.71 (227.47) 243.60 (582.92) 8.30 (5.67) <0.001 eGFR_CKDEPI (mean (SD)) 87.75 (17.86) 79.94 (23.79) 88.96 (16.43) <0.001 CDAI (mean (SD)) 0.05 (1.03) -0.05 (0.92) 0.06 (1.05) 0.014 Abbreviations: VA vitamin A, VC vitamin C, VE vitamin E, Se selenium, Zn zinc, BMI body mass index, eGFR_CKDEPI eGFR calculated by the CKD-EPI equation 3.2 Continuous exposure models (primary analysis) In survey-weighted continuous models, CDAI was analyzed as a continuous variable per 1-unit increase and was not linearly associated with prevalent CKD in fully adjusted analyses. When individual antioxidant nutrients were examined as continuous exposures on their original measurement scales, most nutrients similarly did not demonstrate statistically significant linear associations with prevalent CKD after full adjustment. These findings were consistent across the fully adjusted models and are summarized in Supplementary Table S1. 3.3 Dose–response relationships (restricted cubic splines) Restricted cubic spline analyses were conducted to evaluate potential nonlinear dose–response relationships between continuous dietary antioxidant exposures and the odds of prevalent CKD (Fig. 2). These analyses were prespecified to assess departures from linearity suggested by the continuous exposure models. Carotenoid intake showed a statistically significant overall association with prevalent CKD, with evidence of nonlinearity (P for overall association = 0.017; P for nonlinearity = 0.035). The spline curve indicated a steeper inverse association at lower intake levels, followed by a more gradual change at higher intakes. Vitamin E intake demonstrated a significant overall association with prevalent CKD without evidence of nonlinearity (P for overall association < 0.001; P for nonlinearity = 0.502), suggesting a generally monotonic inverse relationship across the observed intake range. Vitamin C intake showed a marginal overall association (P for overall association = 0.061) and no evidence of nonlinearity (P for nonlinearity = 0.420), with relatively wide confidence intervals across intake levels. Vitamin A intake was significantly associated with prevalent CKD (P for overall association = 0.048), with no strong evidence of nonlinearity (P for nonlinearity = 0.155), and the inverse association appeared to plateau at moderate to higher intake levels. Selenium intake exhibited a statistically significant nonlinear association with prevalent CKD (P for overall association < 0.001; P for nonlinearity = 0.020), with lower odds observed at moderate intake levels and greater uncertainty at the highest levels. Niacin intake also showed significant overall and nonlinear associations (P for overall association < 0.001; P for nonlinearity = 0.007), with inverse associations most apparent at low to moderate intake levels and attenuation at higher intakes. CDAI demonstrated statistically significant overall and nonlinear associations with prevalent CKD (P for overall association = 0.012; P for nonlinearity = 0.031), characterized by a steeper inverse association in the lower to moderate range and a flatter association thereafter. Zinc intake similarly showed significant overall and nonlinear associations with prevalent CKD (P for overall association < 0.001; P for nonlinearity = 0.003), displaying a comparable dose–response pattern. 3.4 Quartile-based analyses (secondary) As secondary analyses, categorical models based on quartiles of dietary antioxidant exposures were conducted to facilitate descriptive interpretation and to complement the primary continuous exposure analyses. In these models, higher overall dietary antioxidant capacity, as reflected by CDAI quartiles, was inversely associated with the prevalence of CKD. In the fully adjusted model (Model III), participants in the highest quartile of CDAI had lower odds of prevalent CKD compared with those in the lowest quartile (OR = 0.731, 95% CI: 0.549–0.973). Similar patterns were observed for several individual antioxidant nutrients. Inverse associations were primarily evident at higher intake categories, with lower odds of prevalent CKD observed among participants in the highest intake quartiles of vitamin E (Q4: OR = 0.500, 95% CI: 0.430–0.804), vitamin A (Q4: OR = 0.600, 95% CI: 0.499–0.969), vitamin C (Q4: OR = 0.700, 95% CI: 0.523–0.994), selenium (Q3–Q4: OR range = 0.575–0.586), and zinc (Q2–Q4: OR range = 0.500–0.600), compared with the lowest intake categories. Niacin intake, although not included in the CDAI, also showed an inverse association with prevalent CKD in quartile-based analyses, with lower odds observed in the highest intake quartile (Q4: OR = 0.638, 95% CI: 0.466–0.875). Higher carotenoid intake was similarly associated with lower odds of prevalent CKD in the highest quartile after full adjustment (Q4: OR = 0.718, 95% CI: 0.534–0.966). Across most nutrients, associations at intermediate intake categories were weak or not statistically significant, suggesting that the observed inverse associations were largely concentrated at higher intake levels rather than across the full exposure distribution, consistent with the nonlinearity observed in spline analyses. Table 2 Quartile-based associations of CDAI and antioxidant nutrients with prevalent CKD (secondary analyses) NHANES Model I (95% CI) P P for trend Model II P P for trend Model III P P for trend ( 95% CI ) ( 95% CI ) CarotenoidsQ1 Ref Ref Ref CarotenoidsQ2 0.88 (0.679-1.160) 0.381 0.87 (0.661-1.144) 0.317 0.89 (0.671-1.171) 0.396 CarotenoidsQ3 0.88 (0.648-1.208) 0.439 0.89 (0.647-1.220) 0.464 0.92 (0.660-1.269) 0.593 CarotenoidsQ4 0.70 (0.531-0.927) 0.013 0.022 0.74 (0.553-0.976) 0.033 0.056 0.72 (0.534-0.966) 0.029 0.047 CDAIQ1 Ref Ref Ref CDAIQ2 0.85 (0.656 - 1.111) 0.237 0.83 (0.632-1.088) 0.176 0.84 (0.639 - 1.112) 0.225 CDAIQ3 0.87 (0.650-1.167) 0.353 0.88 (0.649-1.187) 0.394 0.90 (0.664 - 1.231) 0.521 CDAIQ4 0.70 (0.542-0.929) 0.013 0.022 0.74 (0.562-0.975) 0.032 0.062 0.73 (0.549-0.973) 0.032 0.060 Niacin Q1 Ref Ref Ref NiacinQ2 0.83 (0.626-1.125) 0.239 0.86 (0.647 - 1.182) 0.382 0.87 (0.639-1.193) 0.391 Niacin Q3 0.66 (0.492 - 0.908) 0.01 0.73 (0.533 - 1.002) 0.051 0.74 (0.538 - 1.026) 0.071 NiacinQ4 0.56 (0.415-0.767) <0.001 <0.001 0.63 (0.459-0.865) 0.004 <0.001 0.64 (0.466-0.875) 0.005 <0.001 Se Q1 Ref Ref Ref SeQ2 0.76 (0.549-1.056) 0.102 0.74 (0.535-1.015) 0.062 0.71 (0.510-0.999) 0.049 SeQ3 0.56 (0.412-0.774) <0.001 0.59 (0.421-0.818) 0.001 0.58 (0.410-0.808) 0.001 SeQ4 0.57 (0.431-0.755) <0.001 <0.001 0.63 (0.471-0.835) 0.001 <0.001 0.59 (0.441-0.778) <0.001 <0.001 VA Q1 Ref Ref Ref VAQ2 0.80 (0.621-1.145) 0.273 0.79 (0.580-1.076) 0.133 0.81 (0.59-1.121) 0.204 VAQ3 0.80 (0.66-1.172) 0.392 0.80 (0.583 - 1.10) 0.17 0.80 (0.583-1.134) 0.221 VAQ4 0.60 (0.51-0.935) 0.017 0.029 0.65 (0.474-0.891) 0.007 0.018 0.60 (0.499-0.969) 0.032 0.051 VCQ1 Ref Ref Ref VCQ2 0.70 (0.58-0.950) 0.018 0.75 (0.587-0.959) 0.022 0.70 (0.605-1.008) 0.057 VCQ3 0.80 (0.64-1.104) 0.21 0.80 (0.595-1.066) 0.124 0.80 (0.611-1.097) 0.179 VCQ4 0.60 (0.51-0.920) 0.012 0.033 0.68 (0.498-0.934) 0.017 0.034 0.70 (0.523-0.994) 0.046 0.071 VEQ1 Ref Ref Ref VEQ2 0.90 (0.68-1.297) 0.714 0.91 (0.657-1.272) 0.592 0.90 (0.681-1.333) 0.777 VEQ3 0.70 (0.58-1.084) 0.147 0.80 (0.583-1.101) 0.171 0.80 (0.590-1.116) 0.197 VEQ4 0.50 (0.39-0.745) <0.001 <0.001 0.60 (0.440-0.816) 0.001 <0.001 0.50 (0.430-0.804) 0.001 <0.001 ZnQ1 Ref Ref Ref ZnQ2 0.60 (0.46-0.823) 0.001 0.62 (0.462-0.836) 0.001 0.60 (0.457-0.854) 0.003 ZnQ3 0.50 (0.42-0.701) <0.001 0.59 (0.437-0.712) <0.001 0.50 (0.429-0.699) <0.001 ZnQ4 0.50 (0.44-0.721) <0.001 <0.001 0.62(0.477-0.803) <0.001 <0.001 0.60 (0.491-0.839) 0.001 <0.001 Note：Values are odds ratios (ORs) with 95% confidence intervals (CIs) from survey-weighted logistic regression models. In secondary quartile-based analyses, CDAI and antioxidant nutrient intakes were categorized into quartiles (Q1–Q4; Q1 as reference), with prevalent CKD as the outcome. P-for-trend was calculated by modeling quartile categories as an ordinal variable in survey-weighted logistic regression models. P-for-trend values are reported to 3 decimal places or as <0.001 when P < 0.001. Model 1 included the quartile of a single antioxidant nutrient (or CDAI); Model 2 was adjusted for age, sex, race/ethnicity, smoking status, and body mass index; and Model 3 was further adjusted for diabetes, hypertension, and fasting blood glucose. Quartiles were defined using survey-weighted intake distributions. Median intake values for each quartile (Q1–Q4) were: niacin (11.45, 18.22, 25.08, 37.80 mg/day); CDAI (−0.73, −0.52, −0.12, 0.96); vitamin A (147, 352, 584, 1040 μg/day); vitamin C (9.8, 32.4, 73.3, 158 mg/day); vitamin E (2.8, 5.14, 7.83, 12.9 mg/day); selenium (51.8, 82.9, 115.7, 169 μg/day); zinc (4.83, 7.90, 11.29, 17.7 mg/day); and carotenoids (670, 2961.5, 7429.5, 19,400 μg/day). 3.5 Sensitivity analysis To assess the robustness of the primary findings, sensitivity analyses were conducted using unweighted multivariable logistic regression models (Table 3). These analyses were performed to evaluate whether the observed associations were sensitive to the application of NHANES survey weights, rather than to replace the primary survey-weighted analyses. Overall, the unweighted models yielded association patterns that were broadly consistent in direction with those observed in the primary survey-weighted analyses. In fully adjusted unweighted models, inverse associations with prevalent CKD remained evident for the composite dietary antioxidant index, selenium, niacin, vitamin A, and vitamin E. For zinc and carotenoids, inverse associations were observed at intermediate intake categories, whereas associations at the highest intake levels were attenuated and did not reach statistical significance in unweighted analyses. Taken together, these sensitivity analyses suggest that the main findings were not materially altered by the use of survey weights, although some attenuation of associations—particularly at the highest intake categories for certain nutrients—was observed in unweighted models. Table 3 Sensitivity analysis of the association between CDAI components and CKD NHANES Model I (95% CI) P Model II ( 95% CI ) P Model III ( 95% CI ) P CarotenoidsQ1 Ref Ref Ref CarotenoidsQ2 0.85 (0.690-1.046) 0.127 0.825 (0.666 - 1.022) 0.078 0.83 (0.671-1.043) 0.113 CarotenoidsQ3 0.76 (0.619-0.946) 0.013 0.775 (0.623-0.964) 0.022 0.78 (0.627-0.984) 0.036 CarotenoidsQ4 0.76 (0.615-0.941) 0.011 0.796 (0.640-0.990) 0.040 0.80 (0.641-1.005) 0.056 CDAIQ1 Ref Ref Ref CDAIQ2 0.80 (0.649-0.986) 0.037 0.770 (0.621-0.955) 0.017 0.78 (0.630-0.982) 0.034 CDAIQ3 0.75 (0.613-0.936) 0.010 0.769 (0.618-0.956) 0.018 0.78 (0.628-0.985) 0.037 CDAIQ4 0.76 (0.617-0.941) 0.011 0.792 (0.637-0.984) 0.036 0.80 (0.645-1.010) 0.061 Niacin Q1 Ref Ref Ref NiacinQ2 0.95 (0.776-1.169) 0.645 1.0092 (0.81-1.236) 0.993 0.98 (0.792-1.224) 0.894 Niacin Q3 0.81 (0.660-1.004) 0.055 0.905 (0.727-1.125) 0.369 0.91 (0.730-1.145) 0.439 NiacinQ4 0.63 (0.510-0.793) <0.001 0.739 (0.583-0.934) 0.011 0.75 (0.592-0.962) 0.023 Se Q1 Ref Ref Ref SeQ2 0.87 (0.709-1.067) 0.182 0.871 (0.705-1.075) 0.201 0.83 (0.673-1.040) 0.109 SeQ3 0.70 (0.566-0.866) 0.001 0.751 (0.601-0.937) 0.011 0.73 (0.581-0.917) 0.007 SeQ4 0.66 (0.535-0.822) <0.001 0.766 (0.609-0.962) 0.022 0.73 (0.583-0.934) 0.011 VA Q1 Ref Ref Ref VAQ2 0.86 (0.7-1.064) 0.168 0.815 (0.657 - 1.011) 0.063 0.79 (0.63-0.996) 0.046 VAQ3 0.9 (0.74 - 1.127) 0.407 0.842 (0.679-1.044) 0.117 0.83 (0.67-1.046) 0.118 VAQ4 0.7 (0.56-0.87) 0.001 0.648 (0.517-0.812) <0.001 0.68 (0.54-0.864) 0.001 VCQ1 Ref Ref Ref VCQ2 0.86 (0.7-1.068) 0.180 0.875 (0.705-1.086) 0.227 0.88 (0.70-1.102) 0.271 VCQ3 0.86 (0.6-1.063) 0.166 0.837 (0.673 - 1.04) 0.11 0.84 (0.67-1.062) 0.152 VCQ4 0.79 (0.6-0.985) 0.036 0.801 (0.642-0.999) 0.049 0.87 (0.69-1.095) 0.240 VEQ1 Ref Ref Ref VEQ2 0.92 (0.7-1.133) 0.447 0.931 (0.755-1.148) 0.508 0.97 (0.78 - 1.207) 0.801 VEQ3 0.78 (0.6-0.964) 0.021 0.827 (0.665-1.026) 0.085 0.84 (0.67-1.06) 0.148 VEQ4 0.63 (0.5-0.788) <0.001 0.712 (0.567-0.893) 0.003 0.73 (0.57-0.922) 0.008 ZnQ1 Ref Ref Ref ZnQ2 0.70 (0.5-0.865) <0.001 0.711 (0.574-0.881) 0.001 0.72 (0.58-0.908) 0.005 ZnQ3 0.64 (0.5-0.797) <0.001 0.668 (0.535-0.831) <0.001 0.66 (0.53-0.837) <0.001 ZnQ4 0.69 (0.5-0.858) <0.001 0.788 (0.630-0.984) 0.036 0.83 (0.66-1.051) 0.126 Note: Table 3 presents results from sensitivity analyses using unweighted multivariable logistic regression models. Odds ratios (ORs) and 95% confidence intervals (CIs) are shown for quartiles of the composite dietary antioxidant index (CDAI) and individual antioxidant nutrients, with the lowest quartile (Q1) serving as the reference. Model 1 is unadjusted. Model 2 is adjusted for age (continuous), sex, body mass index (continuous), smoking status, and race/ethnicity. Model 3 is further adjusted for diabetes, hypertension, and fasting blood glucose. These analyses were conducted to assess the robustness of the primary survey-weighted findings and should be interpreted as sensitivity analyses rather than population-representative estimates. 3.6 Subgroup analysis Subgroup analyses were conducted using stratified multivariable logistic regression models to explore potential heterogeneity in the associations between dietary antioxidant nutrients, the CDAI, and prevalent CKD. These analyses were exploratory in nature and are presented in Fig. 3. Evidence suggestive of effect heterogeneity was observed for several antioxidant nutrients. The inverse association between zinc intake and prevalent CKD appeared more pronounced among participants without hypertension (P for interaction = 0.017), whereas no statistically significant interaction was observed by smoking status (P for interaction = 0.150). For selenium, stronger inverse associations were observed among participants without hypertension (P for interaction = 0.004), non-smokers (P for interaction = 0.002), and those who were overweight (P for interaction = 0.041). Similarly, the inverse association between niacin intake and prevalent CKD appeared stronger among participants without hypertension (P for interaction = 0.016) and non-smokers (P for interaction = 0.003). For vitamin E, inverse associations were more evident among participants with normal or overweight body mass index, non-smokers, and those with higher household income; however, formal tests for interaction were not consistently statistically significant across these subgroups. In contrast, associations of vitamin A, vitamin C, carotenoids, and the CDAI with prevalent CKD did not materially differ across most subgroups, with no statistically significant interaction effects detected. Given the multiple comparisons and exploratory nature of these analyses, subgroup findings should be interpreted with caution. 4 Discussion Using nationally representative data from the NHANES, this cross-sectional study found that higher overall dietary antioxidant capacity and greater intake of several antioxidant nutrients were associated with lower odds of prevalent chronic kidney disease among U.S. adults aged 45 years and older. While linear associations were generally not observed in continuous exposure models, restricted cubic spline analyses and categorical analyses consistently suggested inverse associations that were concentrated at higher intake levels, supporting the presence of nonlinear or threshold-type relationships. The observed associations are generally consistent with prior NHANES-based studies reporting inverse relationships between dietary antioxidant capacity and adverse renal outcomes (4). The present analyses extend this literature by highlighting substantial heterogeneity in dose–response patterns across individual antioxidant nutrients. Restricted cubic spline analyses indicated predominantly nonlinear associations, characterized by steeper inverse relationships at lower to moderate intake levels followed by plateauing trends at higher intakes. These findings differ from the linear associations reported by Wang et al.(17). but are in line with previous studies suggesting nonlinear relationships in specific subpopulations (18) or for alternative kidney-related outcomes (19). Together, these results suggest that assuming linear dose–response relationships may oversimplify complex diet–kidney associations. Distinct dose–response patterns were observed among individual components of the CDAI. Vitamins A, C, and E demonstrated generally monotonic inverse associations across the observed intake range, whereas associations for selenium and zinc appeared to attenuate at higher intake levels. Such heterogeneity may reflect differences in biological roles, absorption, metabolism, or saturation of antioxidant-related pathways. These findings underscore the importance of examining nutrient-specific associations rather than treating dietary antioxidants as a homogeneous exposure. Carotenoids, a key component of dietary antioxidant capacity and commonly included in composite indices (20-22), demonstrated a more complex association with prevalent CKD in this study. Although categorical analyses did not show statistically significant associations at the highest intake category, spline modeling suggested a nonlinear dose–response pattern. This discrepancy may reflect the heterogeneous nature of carotenoids, which encompass multiple biologically distinct compounds (e.g., β-carotene, lycopene, and lutein), and aggregation of these compounds may obscure divergent associations of individual subtypes. Similar nonlinear patterns reported in recent studies examining CDAI and CKD further support the notion that diet–kidney relationships are complex and may not be adequately captured by linear models alone (23). The inverse associations observed in this study are biologically plausible given the established role of oxidative stress in the pathogenesis and progression of CKD. The kidney is particularly susceptible to oxidative injury due to its high metabolic activity, which may promote lipid peroxidation, mitochondrial dysfunction, inflammatory signaling, and fibrotic remodeling. Dietary antioxidants have been implicated in counteracting these processes through free radical scavenging and modulation of inflammatory pathways, and the observed associations are consistent with these biological mechanisms (24-30). The nonlinear or threshold-like patterns observed for several nutrients may reflect differences in bioavailability, tissue distribution, or functional limits of endogenous antioxidant systems. Exploratory subgroup analyses suggested that inverse associations for zinc, selenium, niacin, and vitamin E were more evident among individuals without hypertension or smoking exposure. Given the cross-sectional design, limited statistical power within subgroups, and multiple testing, these findings should be interpreted cautiously and may serve as hypothesis-generating observations to inform future studies examining potential effect modification. Several limitations warrant consideration. First, the cross-sectional design precludes causal inference, and residual confounding or reverse causation cannot be excluded. Second, CKD classification was based on single measurements of serum creatinine and urine albumin-to-creatinine ratio, which may result in misclassification, although such misclassification would likely bias associations toward the null. Third, dietary intake was assessed using 24-hour dietary recall data, which are subject to recall bias and within-person variability. Finally, although survey weights were applied in the primary analyses to support population-representative inference, restricted cubic spline and subgroup analyses were conducted without survey weights. These secondary analyses were intended to characterize potential dose–response patterns and effect heterogeneity and should therefore be interpreted as exploratory. 5. Conclusion In summary, using nationally representative U.S. data, this study found that higher overall dietary antioxidant capacity and greater intake of specific antioxidant nutrients were associated with lower odds of prevalent CKD among middle-aged and older adults. The observed associations varied across individual antioxidant components and exhibited heterogeneous, predominantly nonlinear dose–response patterns. Although the cross-sectional design precludes causal inference, these findings provide epidemiological evidence suggesting that dietary antioxidant exposure may be relevant to kidney health in the general population. Future prospective cohort studies and randomized intervention trials are needed to confirm these associations, clarify temporal and causal relationships, and elucidate the underlying biological mechanisms. Declarations Ethics approval and consent to participate The National Health and Nutrition Examination Survey (NHANES) is a publicly available database conducted by the Centers for Disease Control and Prevention (CDC). The survey protocols were approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and written informed consent was obtained from all participants. The present study involved secondary analysis of anonymized publicly available data and therefore did not require additional ethical approval. Consent for publication Not applicable. Availability of data and materials The datasets analyzed in this study are publicly available from the National Health and Nutrition Examination Survey (NHANES) conducted by the Centers for Disease Control and Prevention (CDC) and can be accessed at: https://www.cdc.gov/nchs/nhanes/ Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Medical Science and Technology Innovation Research Association of Sichuan Province (Special Research Program; grant number 2025YCZD210) and the General Program of the Sichuan Provincial Administration of Traditional Chinese Medicine (grant number 2024MS610). The funding bodies had no role in the study design; data collection, analysis, or interpretation; or manuscript preparation. Authors’ contributions HT and MC conceived and designed the study. HN, ZC, GA, YW, and JL were responsible for data acquisition and statistical analysis. HT and YC drafted the manuscript. ZC and MC supervised the study and critically revised the manuscript for important intellectual content. All authors reviewed and approved the final manuscript. Acknowledgements The authors thank the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS) for providing access to the NHANES database. References Global. regional, and national burden of chronic kidney disease in adults, 1990–2023, and its attributable risk factors: a systematic analysis for the Global Burden of Disease Study 2023. Lancet (London England). 2025;406(10518):2461–82. Hill NR, Fatoba ST, Oke JL, Hirst JA, O'Callaghan CA, Lasserson DS, et al. Global Prevalence of Chronic Kidney Disease - A Systematic Review and Meta-Analysis. PLoS ONE. 2016;11(7):e0158765. Zhang L, Wang F, Wang L, Wang W, Liu B, Liu J, et al. Prevalence of chronic kidney disease in China: a cross-sectional survey. Lancet (London England). 2012;379(9818):815–22. 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Understanding mechanisms of antioxidant action in health and disease. Nat Rev Mol Cell Biol. 2024;25(1):13–33. Zhong H, Shao Y, Chen X, Wang N, Zhan Y, Gong B, et al. Associations of composite dietary antioxidant index with premature death and all-cause mortality: a cohort study. BMC Public Health. 2025;25(1):796. He H, Chen X, Ding Y, Chen X, He X. Composite dietary antioxidant index associated with delayed biological aging: a population-based study. Aging. 2024;16(1):15–27. Liu C, Lai W, Zhao M, Zhang Y, Hu Y. Association between the Composite Dietary Antioxidant Index and Atherosclerotic Cardiovascular Disease in Postmenopausal Women: A Cross-Sectional Study of NHANES Data, 2013–2018. Antioxidants (Basel, Switzerland). 2023;12(9). Wang M, Huang ZH, Zhu YH, He P, Fan QL. Association between the composite dietary antioxidant index and chronic kidney disease: evidence from NHANES 2011–2018. Food Funct. 2023;14(20):9279–86. Deng X, Ma L, Li P, He M, Jin R, Tao Y, et al. Identification and optimization of relevant factors for chronic kidney disease in abdominal obesity patients by machine learning methods: insights from NHANES 2005–2018. Lipids Health Dis. 2024;23(1):390. Li S, Yang S, Wang Y, Lin Z, Chen F, Gao Q, et al. Association between composite dietary antioxidant index and increased urinary albumin excretion: a population-based study. Front Nutr. 2025;12:1552889. Nadimi E, Jamal Omidi S, Ghasemi M, Hashempur MH, Iraji A. Carotenoids as neuroprotective agents in multiple sclerosis: Pathways, mechanisms, and clinical prospects. Volume 191. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie; 2025. p. 118496. Lotfi A, Abroodi Z, Khazaei M. Biological activities of astaxanthin in the treatment of neurodegenerative diseases. Neurodegenerative disease Manage. 2024;14(6):241–56. Ba W, Xu W, Deng Z, Zhang B, Zheng L, Li H. The Antioxidant and Anti-Inflammatory Effects of the Main Carotenoids from Tomatoes via Nrf2 and NF-κB Signaling Pathways. Nutrients. 2023;15(21). Li W, Chen S, Wan J, Chen L, Xing L, Gao Z, et al. Comprehensive dietary antioxidant index and chronic kidney disease: mediating role of frailty and its impact on mortality outcomes in adults. Front Nutr. 2025;12:1679774. Pehlivan VF, Pehlivan B, Duran E, Taskın A, Koyuncu I, Çakmak Y. Effects of Vitamin E on Redox balance in regulating thiol/disulfide homeostasis in sepsis: An antioxidant therapy perspective. PLoS ONE. 2025;20(11):e0336334. Di Salvo M, Ventre A, Dato E, Casciaro M, Gangemi S. Nutraceuticals Against Oxidative Stress in Allergic Diseases. Biomolecules. 2025;15(9). Demirtas C, Çetin E, Yucel M, Sönmez C, Güler EM, Beyaztaş H, et al. Vitamin E reduces vasospasm in a rat subarachnoid hemorrhage model. Neurosurg Rev. 2025;48(1):722. Chrysikopoulou V, Rampaouni A, Koutsia E, Ofrydopoulou A, Mittas N, Tsoupras A, Anti-Inflammatory. Antithrombotic and Antioxidant Efficacy and Synergy of a High-Dose Vitamin C Supplement Enriched with a Low Dose of Bioflavonoids; In Vitro Assessment and In Vivo Evaluation Through a Clinical Study in Healthy Subjects. Nutrients. 2025;17(16). Zhang Y, Zhen S, Xu H, Sun S, Wang Z, Li M, et al. Vitamin C alleviates rheumatoid arthritis by modulating gut microbiota balance. Biosci Trends. 2024;18(2):187–94. Xu YY, Xu CZ, Liang YF, Jin DQ, Ding J, Sheng Y, et al. Ascorbic acid and hydrocortisone synergistically inhibit septic organ injury via improving oxidative stress and inhibiting inflammation. Immunopharmacol Immunotoxicol. 2022;44(5):786–94. Gozzi-Silva SC, Teixeira FME, Duarte A, Sato MN, Oliveira LM. Immunomodulatory Role of Nutrients: How Can Pulmonary Dysfunctions Improve? Front Nutr. 2021;8:674258. Additional Declarations No competing interests reported. Supplementary Files TableS1AssociationsbetweencontinuousantioxidantexposuresandprevalentCKD.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviewers invited by journal 05 Mar, 2026 Submission checks completed at journal 28 Feb, 2026 First submitted to journal 28 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8720931","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601985425,"identity":"8f9f184d-5770-4bbf-84a9-7a267fd2876f","order_by":0,"name":"Huian Tang","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Huian","middleName":"","lastName":"Tang","suffix":""},{"id":601985426,"identity":"6e1528e1-ad4a-4e56-8573-bed3171fcf9c","order_by":1,"name":"Huibing Nie","email":"","orcid":"","institution":"Chengdu First People's Hospital,Chengdu Integrated TCM and Western Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huibing","middleName":"","lastName":"Nie","suffix":""},{"id":601985427,"identity":"6a517dac-0af2-414c-ad9f-cba5d44d2a02","order_by":2,"name":"Zejun Chen","email":"","orcid":"","institution":"Chengdu First People's Hospital,Chengdu Integrated TCM and Western Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zejun","middleName":"","lastName":"Chen","suffix":""},{"id":601985428,"identity":"6fa86371-be00-4353-973d-f1b60d29da26","order_by":3,"name":"Guangyu Ao","email":"","orcid":"","institution":"Chengdu First People's Hospital,Chengdu Integrated TCM and Western Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guangyu","middleName":"","lastName":"Ao","suffix":""},{"id":601985429,"identity":"c0cbe3e1-7761-434a-bd89-ad8b2b0a006c","order_by":4,"name":"Yi Wu","email":"","orcid":"","institution":"Chengdu First People's Hospital,Chengdu Integrated TCM and Western Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Wu","suffix":""},{"id":601985430,"identity":"3afb19aa-6919-4138-945a-610a798ea32b","order_by":5,"name":"Jing Li","email":"","orcid":"","institution":"Chengdu First People's Hospital,Chengdu Integrated TCM and Western Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Li","suffix":""},{"id":601985431,"identity":"a94ac806-253b-4c1f-8c40-8f7e26e12ceb","order_by":6,"name":"Yuan Chen","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Chen","suffix":""},{"id":601985432,"identity":"356a7cbc-fc70-437e-ba08-509eb713d3d7","order_by":7,"name":"Min Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYDACCRBhwFDf2Mx84MCHHyRoYWxub0s8OLOHaC0MDIztPWeMD3OwEaGDf3bzsUc3Cu4w887I+XCYgYdBnl/sAAFL7hxLN84xeMYmOSN3w+ECCwbDmbMT8GsxkMgxk84xOMxjCNIyg4chweA2QS3530BaJOxv5Dw4zMNGlJYcNpAWA8aeMwzEaZG4kQZ2WAJje5sBMJAlCPuFf0byM+mcP0AtzcyPP3z4YSPPL01AC4atpCkfBaNgFIyCUYAdAADeP0ZgaFaUjwAAAABJRU5ErkJggg==","orcid":"","institution":"Chengdu First People's Hospital,Chengdu Integrated TCM and Western Medicine Hospital","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-01-28 12:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8720931/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8720931/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104375000,"identity":"f11a9b66-3cac-49b3-afa7-773dcbd2696e","added_by":"auto","created_at":"2026-03-11 06:11:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86325,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant selection process.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8720931/v1/188e67988b895cbc7e14c04f.png"},{"id":104374997,"identity":"f3770033-df93-4796-9e4b-5782287802ae","added_by":"auto","created_at":"2026-03-11 06:11:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157080,"visible":true,"origin":"","legend":"\u003cp\u003eDose–response associations between dietary antioxidant capacity and prevalent chronic kidney disease.\u003c/p\u003e\n\u003cp\u003eNote: Restricted cubic spline models illustrate the associations between continuous dietary antioxidant exposures and the odds of prevalent chronic kidney disease. Solid lines represent adjusted odds ratios, and shaded areas indicate 95% confidence intervals. The reference value (odds ratio = 1.0) is indicated by the dashed horizontal line. P values for the overall association and for nonlinearity are shown in each panel.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8720931/v1/a4205bd2dd81d699cac16c51.png"},{"id":104374996,"identity":"7292024b-a9ee-42ab-a709-1a89c7db6d2b","added_by":"auto","created_at":"2026-03-11 06:11:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":405699,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the association between CDAI components and CKD\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8720931/v1/5f8d8126a6af342d8bdcefdf.png"},{"id":104405780,"identity":"1ae0f90a-00b6-4bdd-ab41-fbefae496aff","added_by":"auto","created_at":"2026-03-11 12:23:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1684183,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8720931/v1/df0ec300-3001-4602-9809-ce0a8f50abbe.pdf"},{"id":104374998,"identity":"ec9d86c7-ce92-4b53-89c9-95e763ae302b","added_by":"auto","created_at":"2026-03-11 06:11:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19241,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1AssociationsbetweencontinuousantioxidantexposuresandprevalentCKD.docx","url":"https://assets-eu.researchsquare.com/files/rs-8720931/v1/70b6b9bd51978d4306a0cafc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dose–response associations between dietary antioxidant capacity and prevalent chronic kidney disease: NHANES 2003–2018","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eCKD is a major global public health challenge. According to the most recent Global Burden of Disease study, more than 780\u0026nbsp;million adults worldwide were affected by CKD in 2023, with an age-standardized prevalence exceeding 14%, and CKD has become one of the leading causes of death globally(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In addition to progressing to end-stage renal disease, CKD substantially increases the risk of cardiovascular disease and all-cause mortality, thereby imposing a sustained medical and socioeconomic burden(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOxidative stress is widely recognized as a central mechanism in the development and progression of CKD. Owing to its high metabolic activity, the kidney is particularly vulnerable to damage from reactive oxygen species (ROS), which can induce lipid peroxidation, activate inflammatory pathways, impair mitochondrial function, and promote renal interstitial fibrosis, ultimately leading to progressive loss of renal function (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Evidence from basic and clinical studies suggests that enhancing systemic antioxidant capacity may mitigate oxidative stress\u0026ndash;related renal injury and potentially slow CKD progression (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiet is a major source of exogenous antioxidants. Dietary antioxidant nutrients, including vitamins A, C, and E, carotenoids, selenium, zinc, and niacin, have been implicated in renoprotection through mechanisms such as free radical scavenging, attenuation of lipid peroxidation, and modulation of inflammatory responses (\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). While most epidemiological studies have examined individual antioxidant nutrients, evidence regarding the combined intake of multiple antioxidant components and their potential synergistic effects remains limited. The CDAI, which integrates multiple energy-adjusted antioxidant nutrients, provides a comprehensive measure of overall dietary antioxidant capacity and has shown predictive value for cardiovascular disease, metabolic disorders, and biological aging (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, data examining the association between CDAI and prevalent CKD are scarce, particularly with respect to dose\u0026ndash;response patterns, potential nonlinearity, and population heterogeneity.\u003c/p\u003e \u003cp\u003eTherefore, using data from the NHANES 2003\u0026ndash;2018, this cross-sectional study aimed to examine the associations between dietary antioxidant nutrients, overall dietary antioxidant capacity assessed by the CDAI, and prevalent CKD among adults aged 45 years and older. In addition, potential nonlinear dose\u0026ndash;response associations were evaluated to further characterize the relationship between dietary antioxidant intake and CKD prevalence in a nationally representative population.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eThis cross-sectional study used data from the NHANES 2003\u0026ndash;2018, a nationally representative survey of the non-institutionalized civilian population in the United States. Participants aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years with complete data on dietary intake, kidney function, and relevant covariates were included in the analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a complete flowchart of the participant recruitment process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Dietary assessment and antioxidant exposure assessment\u003c/h2\u003e \u003cp\u003eDietary intake was assessed via 24-hour dietary recall interviews conducted according to the NHANES protocol. The first dietary recall was administered face to face at the Mobile Examination Center, followed by a second 24-hour dietary recall conducted by telephone 3\u0026ndash;10 days later. Nutrient intakes, including antioxidants, micronutrients, and total energy intake, were estimated using the United States Department of Agriculture (USDA) Food and Nutrient Database for Dietary Studies (FNDDS).\u003c/p\u003e \u003cp\u003eInformation on dietary supplement use during the previous 30 days, including frequency, dosage, and duration of use, was collected through standardized questionnaires. However, only antioxidant nutrients obtained from dietary sources were considered in the primary analyses and in the construction of the CDAI.\u003c/p\u003e \u003cp\u003eThe CDAI was constructed based on six dietary antioxidant nutrients\u0026mdash;selenium, zinc, vitamins A, C, and E, and carotenoids\u0026mdash;following previously established methods. The intake of each nutrient was energy-adjusted and subsequently standardized by subtracting the mean intake and dividing by the corresponding standard deviation. The CDAI was calculated as the sum of the standardized intakes of these six nutrients, with higher CDAI values indicating greater overall dietary antioxidant capacity. The formula for calculating CDAI is as follows:\u003c/p\u003e \u003cp\u003eCDAI was calculated as Σ (individual intake\u0026thinsp;\u0026minus;\u0026thinsp;mean) / SD across six nutrients (i\u0026thinsp;=\u0026thinsp;1\u0026ndash;6).\u003c/p\u003e \u003cp\u003eIn addition to the CDAI, individual dietary antioxidant nutrients were examined separately. Niacin was included in the individual nutrient analyses and dose\u0026ndash;response assessments because of its reported antioxidant properties and potential relevance to kidney health. Niacin was not incorporated into the CDAI, as it is not part of the original index definition used in prior epidemiological studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Assessment of Chronic Kidney Disease\u003c/h2\u003e \u003cp\u003eEstimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Urinary albumin-to-creatinine ratio (UACR) was calculated from spot urine samples. CKD was defined as an eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2; and/or a UACR\u0026thinsp;\u0026ge;\u0026thinsp;30 mg/g.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Covariates\u003c/h2\u003e \u003cp\u003eCovariates were selected a priori based on previous literature and biological relevance and included age, sex, race/ethnicity, education level, smoking status, alcohol consumption, body mass index (BMI), physical activity, hypertension, diabetes, total energy intake, and relevant biochemical markers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Methods\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.5.1; R Foundation for Statistical Computing). NHANES employs a complex, multistage probability sampling design; therefore, sampling weights, strata, and primary sampling units were incorporated into all primary analyses to account for unequal probabilities of selection and to support population-representative inference. Combined survey weights for the 2003\u0026ndash;2018 cycles were calculated according to NHANES analytic guidelines.\u003c/p\u003e \u003cp\u003eBaseline characteristics were summarized according to CKD status. Continuous variables are presented as survey-weighted means with standard deviations and were compared using design-based analysis of variance, whereas categorical variables are presented as survey-weighted percentages and were compared using the Rao\u0026ndash;Scott chi-square test.\u003c/p\u003e \u003cp\u003eDietary antioxidant nutrients (vitamins A, C, and E, selenium, zinc, and carotenoids) and the CDAI were analyzed using both continuous and quartile-based approaches. Continuous exposure models served as the primary analytic framework to estimate overall associations between dietary antioxidant exposures and prevalent CKD, while quartile-based models were conducted as secondary analyses to facilitate interpretation and comparison with prior studies.\u003c/p\u003e \u003cp\u003eFor quartile-based analyses, antioxidant exposures were categorized into quartiles based on survey-weighted distributions, with the lowest quartile serving as the reference category. Median intake values for each quartile were calculated from survey-weighted intake distributions. P for trend was assessed by modeling quartile categories as an ordinal variable in survey-weighted logistic regression models.\u003c/p\u003e \u003cp\u003eSurvey-weighted multivariable logistic regression models were used to examine associations between antioxidant exposures and prevalent CKD. Three sequential models were constructed for both quartile-based and continuous analyses: Model 1 was unadjusted; Model 2 was adjusted for age, sex, body mass index, smoking status, and race/ethnicity; and Model 3 was further adjusted for diabetes, hypertension, and fasting blood glucose. Diabetes and hypertension were included to reduce confounding by major CKD-related comorbidities; however, potential overadjustment cannot be excluded, and estimates from fully adjusted models should be interpreted cautiously. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated.\u003c/p\u003e \u003cp\u003eContinuous exposure models were specified with antioxidant variables entered as continuous terms, with effect estimates expressed per unit increase or per standard deviation increase, as appropriate. For the CDAI, a one-unit increase approximately corresponds to a one standard deviation increase in the summed standardized antioxidant components. Results from continuous exposure analyses are presented in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTo assess robustness, corresponding unweighted logistic regression models with identical covariate specifications were conducted as sensitivity analyses, with particular focus on niacin intake quartiles.\u003c/p\u003e \u003cp\u003ePotential nonlinear associations between continuous antioxidant exposures and prevalent CKD were explored using restricted cubic spline functions. Subgroup analyses were conducted across categories of age, sex, body mass index, smoking status, diabetes, hypertension, race/ethnicity, marital status, and household income. Because reliable estimation of complex survey-weighted spline and stratified models is methodologically challenging and may yield unstable variance estimates, these analyses were conducted without survey weighting and are therefore interpreted as exploratory, aiming to characterize dose\u0026ndash;response patterns and potential effect heterogeneity within the analytic sample rather than to estimate population-representative associations.\u003c/p\u003e \u003cp\u003eAll statistical tests were two-sided, and a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of the study population\u003c/h2\u003e \u003cp\u003eA total of 4,649 adults with complete dietary data were included in this analysis. After applying NHANES complex survey weights, the sample represented approximately 23.66\u0026nbsp;million U.S. residents aged 45 years and older, of whom an estimated 13.3% (approximately 3.16\u0026nbsp;million) had CKD. The weighted demographic and clinical characteristics of participants with and without CKD are presented in Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eOverall, participants with CKD were older, had a lower income-to-poverty ratio, and a higher prevalence of diabetes and hypertension compared with those without CKD (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Indicators of impaired kidney function, including serum creatinine and urine albumin-to-creatinine ratio, were also significantly higher among participants with CKD (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn terms of dietary antioxidant intake, participants with CKD tended to have lower intakes of several antioxidants. Specifically, a higher proportion of individuals with CKD were observed in the lowest intake quartile for vitamin C, vitamin E, selenium, and zinc (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and mean daily niacin intake was lower in the CKD group (P\u0026thinsp;=\u0026thinsp;0.001). In addition, the mean CDAI score was modestly but significantly lower among participants with CKD compared with those without CKD (\u0026minus;\u0026thinsp;0.05 vs. 0.06, P\u0026thinsp;=\u0026thinsp;0.014). No significant differences were observed for vitamin A or carotenoid intake between the two groups (both P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eTable 1 Demographic and clinical characteristics\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"747\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003elevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eNon-CKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e23656933.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3157823.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e20499109.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eAge (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e60.26 (10.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e64.93 (11.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e59.54 (9.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eGender (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e10484367.4 (44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1362484.9 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e9121882.5 (44.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e13172566.0 (55.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1795338.5 (56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e11377227.5 (55.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eRace (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eAmerican\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1097892.9 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e187831.5 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e910061.4 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1989204.2 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e351785.6 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e1637418.6 (8.0 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1190580.8 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e174702.1 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e1015878.7 (5.0 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e845167.7 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e125768.1 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e719399.6 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e18534087.8 (78.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2317736.1 (73.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e16216351.7 (79.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eIncome (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e3.07 (1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2.53 (1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e3.15 (1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eMarital_Status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eDivorce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e3548919.3 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e488464.2 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e3060455.1 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e14453952.4 (61.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1645273.2 (52.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e12808679.2 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5654061.7 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1024086.0 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e4629975.7 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eVA (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5102600.7 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e787839.4 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e4314761.4 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5832836.9 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e778461.7 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5054375.1 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e6063860.2 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e844126.4 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5219733.7 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e6657635.7 (28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e747395.9 (23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5910239.8 (28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eVC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5666838.1 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e902306.4 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e4764531.7 (23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e6096262.7 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e752695.9 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5343566.8 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5948078.6 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e817098.5 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5130980.1 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5945754.0 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e685722.6 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5260031.3 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eVE (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5057093.7 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e820293.8 (26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e4236799.9 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5715172.6 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e881939.4 (27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e4833233.2 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5869664.9 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e785944.9 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5083720.1 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e7015002.3 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e669645.4 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e6345356.9 (31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eSe (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5375344.8 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e960213.9 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e4415130.9 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5815002.2 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e826671.4 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e4988330.7 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e6225436.0 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e681768.5 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5543667.4 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e6241150.4 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e689169.5 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5551980.9 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eZn (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5463951.2 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1024910.6 (32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e4439040.6 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5506397.7 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e686612.9 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e4819784.8 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5941411.6 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e668119.9 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5273291.6 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e6745173.0 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e778180.0 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5966992.9 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNiacin (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e25.43 (15.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e23.21 (13.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e25.77 (15.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eCarotenoids (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5267941.3 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e800023.8 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e4467917.5 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e6034306.2 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e827820.4 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5206485.8 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5964911.3 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e816122.4 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5148788.9 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e6389774.6 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e713856.8 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e5675917.8 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eBMI (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e28.77 (5.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e29.72 (6.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e28.62 (5.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eHeight (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e169.49 (9.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e167.79 (9.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e169.75 (9.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eWeight (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e82.79 (18.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e83.89 (20.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e82.62 (17.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eWaist_Circumference (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e101.94 (14.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e105.25 (15.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e101.43 (14.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eCreatinine (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e0.92 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1.06 (0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e0.89 (0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eUrinary_albumin (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e41.71 (224.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e250.10 (572.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e9.61 (9.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eUrinary_creatinine (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e116.45 (67.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e111.32 (65.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e117.23 (68.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eDiabetes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e20340155.6 (86.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2190077.4 (69.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e18150078.1 (88.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e3316777.8 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e967746.0 (30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e2349031.9 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e12366098.7 (52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e1033814.1 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e11332284.5 (55.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e11290834.7 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2124009.3 (67.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e9166825.5 (44.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eGlucose (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e111.79 (33.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e128.30 (52.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e109.25 (28.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eSmoking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e16496602.2 (69.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e2215635.7 (70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e14280966.6 (69.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e7160331.2 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e942187.8 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e6218143.4 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eUACR (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e39.71 (227.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e243.60 (582.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e8.30 (5.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eeGFR_CKDEPI (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e87.75 (17.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e79.94 (23.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e88.96 (16.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eCDAI (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e0.05 (1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.05 (0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e0.06 (1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: VA vitamin A, VC vitamin C, VE vitamin E, Se selenium, Zn zinc, BMI body mass index, eGFR_CKDEPI eGFR calculated by the CKD-EPI equation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Continuous exposure models (primary analysis)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn survey-weighted continuous models, CDAI was analyzed as a continuous variable per 1-unit increase and was not linearly associated with prevalent CKD in fully adjusted analyses.\u003c/p\u003e\n\u003cp\u003eWhen individual antioxidant nutrients were examined as continuous exposures on their original measurement scales, most nutrients similarly did not demonstrate statistically significant linear associations with prevalent CKD after full adjustment. These findings were consistent across the fully adjusted models and are summarized in Supplementary Table S1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Dose\u0026ndash;response relationships (restricted cubic splines)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRestricted cubic spline analyses were conducted to evaluate potential nonlinear dose\u0026ndash;response relationships between continuous dietary antioxidant exposures and the odds of prevalent CKD (Fig. 2). These analyses were prespecified to assess departures from linearity suggested by the continuous exposure models.\u003c/p\u003e\n\u003cp\u003eCarotenoid intake showed a statistically significant overall association with prevalent CKD, with evidence of nonlinearity (P for overall association = 0.017; P for nonlinearity = 0.035). The spline curve indicated a steeper inverse association at lower intake levels, followed by a more gradual change at higher intakes.\u003c/p\u003e\n\u003cp\u003eVitamin E intake demonstrated a significant overall association with prevalent CKD without evidence of nonlinearity (P for overall association \u0026lt; 0.001; P for nonlinearity = 0.502), suggesting a generally monotonic inverse relationship across the observed intake range. Vitamin C intake showed a marginal overall association (P for overall association = 0.061) and no evidence of nonlinearity (P for nonlinearity = 0.420), with relatively wide confidence intervals across intake levels. Vitamin A intake was significantly associated with prevalent CKD (P for overall association = 0.048), with no strong evidence of nonlinearity (P for nonlinearity = 0.155), and the inverse association appeared to plateau at moderate to higher intake levels.\u003c/p\u003e\n\u003cp\u003eSelenium intake exhibited a statistically significant nonlinear association with prevalent CKD (P for overall association \u0026lt; 0.001; P for nonlinearity = 0.020), with lower odds observed at moderate intake levels and greater uncertainty at the highest levels. Niacin intake also showed significant overall and nonlinear associations (P for overall association \u0026lt; 0.001; P for nonlinearity = 0.007), with inverse associations most apparent at low to moderate intake levels and attenuation at higher intakes.\u003c/p\u003e\n\u003cp\u003eCDAI demonstrated statistically significant overall and nonlinear associations with prevalent CKD (P for overall association = 0.012; P for nonlinearity = 0.031), characterized by a steeper inverse association in the lower to moderate range and a flatter association thereafter. Zinc intake similarly showed significant overall and nonlinear associations with prevalent CKD (P for overall association \u0026lt; 0.001; P for nonlinearity = 0.003), displaying a comparable dose\u0026ndash;response pattern.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Quartile-based analyses (secondary)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs secondary analyses, categorical models based on quartiles of dietary antioxidant exposures were conducted to facilitate descriptive interpretation and to complement the primary continuous exposure analyses.\u003c/p\u003e\n\u003cp\u003eIn these models, higher overall dietary antioxidant capacity, as reflected by CDAI quartiles, was inversely associated with the prevalence of CKD. In the fully adjusted model (Model III), participants in the highest quartile of CDAI had lower odds of prevalent CKD compared with those in the lowest quartile (OR = 0.731, 95% CI: 0.549\u0026ndash;0.973).\u003c/p\u003e\n\u003cp\u003eSimilar patterns were observed for several individual antioxidant nutrients. Inverse associations were primarily evident at higher intake categories, with lower odds of prevalent CKD observed among participants in the highest intake quartiles of vitamin E (Q4: OR = 0.500, 95% CI: 0.430\u0026ndash;0.804), vitamin A (Q4: OR = 0.600, 95% CI: 0.499\u0026ndash;0.969), vitamin C (Q4: OR = 0.700, 95% CI: 0.523\u0026ndash;0.994), selenium (Q3\u0026ndash;Q4: OR range = 0.575\u0026ndash;0.586), and zinc (Q2\u0026ndash;Q4: OR range = 0.500\u0026ndash;0.600), compared with the lowest intake categories.\u003c/p\u003e\n\u003cp\u003eNiacin intake, although not included in the CDAI, also showed an inverse association with prevalent CKD in quartile-based analyses, with lower odds observed in the highest intake quartile (Q4: OR = 0.638, 95% CI: 0.466\u0026ndash;0.875). Higher carotenoid intake was similarly associated with lower odds of prevalent CKD in the highest quartile after full adjustment (Q4: OR = 0.718, 95% CI: 0.534\u0026ndash;0.966).\u003c/p\u003e\n\u003cp\u003eAcross most nutrients, associations at intermediate intake categories were weak or not statistically significant, suggesting that the observed inverse associations were largely concentrated at higher intake levels rather than across the full exposure distribution, consistent with the nonlinearity observed in spline analyses.\u003c/p\u003e\n\u003cp\u003eTable 2 Quartile-based associations of CDAI and antioxidant nutrients with prevalent CKD (secondary analyses)\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"867\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 140px;\"\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003eModel \u003cstrong\u003eI (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 48px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 74px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eModel \u003cstrong\u003eII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 84px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eModel \u003cstrong\u003eIII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 48px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 148px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e( \u003cstrong\u003e95% CI\u0026nbsp;\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e( \u003cstrong\u003e95% CI\u0026nbsp;\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eCarotenoidsQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eCarotenoidsQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.88 (0.679-1.160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.87 (0.661-1.144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.89 (0.671-1.171)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eCarotenoidsQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.88 (0.648-1.208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.89 (0.647-1.220)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.92 (0.660-1.269)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n 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140px;\"\u003e\n \u003cp\u003eCDAIQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.85 (0.656 - 1.111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.83 (0.632-1.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.84 (0.639 - 1.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eCDAIQ3\u003c/p\u003e\n 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76px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eNiacinQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.83 (0.626-1.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n 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74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.73 (0.533 - 1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.74 (0.538 - 1.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eNiacinQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.56 (0.415-0.767)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n 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74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eSeQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.76 (0.549-1.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.74 (0.535-1.015)\u003c/p\u003e\n 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style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eVAQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.80 (0.621-1.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.79 (0.580-1.076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n 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style=\"width: 48px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eVCQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n 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140px;\"\u003e\n \u003cp\u003eVCQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.80 (0.64-1.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.80 (0.595-1.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.80 (0.611-1.097)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eVCQ4\u003c/p\u003e\n \u003c/td\u003e\n 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style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.80 (0.583-1.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.80 (0.590-1.116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eVEQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.50 (0.39-0.745)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.60 (0.440-0.816)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.50 (0.430-0.804)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eZnQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eZnQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.60 (0.46-0.823)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.62 (0.462-0.836)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.60 (0.457-0.854)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eZnQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.50 (0.42-0.701)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.59 (0.437-0.712)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.50 (0.429-0.699)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eZnQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.50 (0.44-0.721)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.62(0.477-0.803)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.60 (0.491-0.839)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote：Values are odds ratios (ORs) with 95% confidence intervals (CIs) from survey-weighted logistic regression models. In secondary quartile-based analyses, CDAI and antioxidant nutrient intakes were categorized into quartiles (Q1\u0026ndash;Q4; Q1 as reference), with prevalent CKD as the outcome. P-for-trend was calculated by modeling quartile categories as an ordinal variable in survey-weighted logistic regression models. P-for-trend values are reported to 3 decimal places or as \u0026lt;0.001 when P \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003eModel 1 included the quartile of a single antioxidant nutrient (or CDAI); Model 2 was adjusted for age, sex, race/ethnicity, smoking status, and body mass index; and Model 3 was further adjusted for diabetes, hypertension, and fasting blood glucose.\u003c/p\u003e\n\u003cp\u003eQuartiles were defined using survey-weighted intake distributions. Median intake values for each quartile (Q1\u0026ndash;Q4) were: niacin (11.45, 18.22, 25.08, 37.80 mg/day); CDAI (\u0026minus;0.73, \u0026minus;0.52, \u0026minus;0.12, 0.96); vitamin A (147, 352, 584, 1040 \u0026mu;g/day); vitamin C (9.8, 32.4, 73.3, 158 mg/day); vitamin E (2.8, 5.14, 7.83, 12.9 mg/day); selenium (51.8, 82.9, 115.7, 169 \u0026mu;g/day); zinc (4.83, 7.90, 11.29, 17.7 mg/day); and carotenoids (670, 2961.5, 7429.5, 19,400 \u0026mu;g/day).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Sensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the robustness of the primary findings, sensitivity analyses were conducted using unweighted multivariable logistic regression models (Table 3). These analyses were performed to evaluate whether the observed associations were sensitive to the application of NHANES survey weights, rather than to replace the primary survey-weighted analyses.\u003c/p\u003e\n\u003cp\u003eOverall, the unweighted models yielded association patterns that were broadly consistent in direction with those observed in the primary survey-weighted analyses. In fully adjusted unweighted models, inverse associations with prevalent CKD remained evident for the composite dietary antioxidant index, selenium, niacin, vitamin A, and vitamin E.\u003c/p\u003e\n\u003cp\u003eFor zinc and carotenoids, inverse associations were observed at intermediate intake categories, whereas associations at the highest intake levels were attenuated and did not reach statistical significance in unweighted analyses.\u003c/p\u003e\n\u003cp\u003eTaken together, these sensitivity analyses suggest that the main findings were not materially altered by the use of survey weights, although some attenuation of associations\u0026mdash;particularly at the highest intake categories for certain nutrients\u0026mdash;was observed in unweighted models.\u003c/p\u003e\n\u003cp\u003eTable 3 Sensitivity analysis of the association between CDAI components and CKD\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"690\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eModel \u003cstrong\u003eI (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eModel \u003cstrong\u003eII\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e( \u003cstrong\u003e95% CI\u0026nbsp;\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eModel \u003cstrong\u003eIII\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e( \u003cstrong\u003e95% CI\u0026nbsp;\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eCarotenoidsQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eCarotenoidsQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.85 (0.690-1.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.825 (0.666 - 1.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.83 (0.671-1.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eCarotenoidsQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.76 (0.619-0.946)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.775 (0.623-0.964)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.78 (0.627-0.984)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eCarotenoidsQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.76 (0.615-0.941)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.796 (0.640-0.990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.80 (0.641-1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eCDAIQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eCDAIQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.80 (0.649-0.986)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.770 (0.621-0.955)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.78 (0.630-0.982)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eCDAIQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.75 (0.613-0.936)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.769 (0.618-0.956)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.78 (0.628-0.985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eCDAIQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.76 (0.617-0.941)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.792 (0.637-0.984)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.80 (0.645-1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNiacin Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNiacinQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.95 (0.776-1.169)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e1.0092 (0.81-1.236)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.98 (0.792-1.224)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNiacin Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.81 (0.660-1.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.905 (0.727-1.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.91 (0.730-1.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNiacinQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.63 (0.510-0.793)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.739 (0.583-0.934)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.75 (0.592-0.962)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eSe Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eSeQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.87 (0.709-1.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.871 (0.705-1.075)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.83 (0.673-1.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eSeQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.70 (0.566-0.866)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.751 (0.601-0.937)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.73 (0.581-0.917)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eSeQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.66 (0.535-0.822)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.766 (0.609-0.962)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.73 (0.583-0.934)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eVA Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eVAQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.86 (0.7-1.064)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.815 (0.657 - 1.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.79 (0.63-0.996)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eVAQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.9 (0.74 - 1.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.842 (0.679-1.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.83 (0.67-1.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eVAQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.7 (0.56-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.648 (0.517-0.812)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.68 (0.54-0.864)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eVCQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eVCQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.86 (0.7-1.068)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.875 (0.705-1.086)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.88 (0.70-1.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eVCQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.86 (0.6-1.063)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.837 (0.673 - 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.84 (0.67-1.062)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eVCQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.79 (0.6-0.985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.801 (0.642-0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.87 (0.69-1.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eVEQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eVEQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.92 (0.7-1.133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.931 (0.755-1.148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.97 (0.78 - 1.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eVEQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.78 (0.6-0.964)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.827 (0.665-1.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.84 (0.67-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eVEQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.63 (0.5-0.788)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.712 (0.567-0.893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.73 (0.57-0.922)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eZnQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eZnQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.70 (0.5-0.865)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.711 (0.574-0.881)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.72 (0.58-0.908)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eZnQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.64 (0.5-0.797)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.668 (0.535-0.831)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.66 (0.53-0.837)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eZnQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.69 (0.5-0.858)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e0.788 (0.630-0.984)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.83 (0.66-1.051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Table 3 presents results from sensitivity analyses using unweighted multivariable logistic regression models. Odds ratios (ORs) and 95% confidence intervals (CIs) are shown for quartiles of the composite dietary antioxidant index (CDAI) and individual antioxidant nutrients, with the lowest quartile (Q1) serving as the reference. Model 1 is unadjusted. Model 2 is adjusted for age (continuous), sex, body mass index (continuous), smoking status, and race/ethnicity. Model 3 is further adjusted for diabetes, hypertension, and fasting blood glucose. These analyses were conducted to assess the robustness of the primary survey-weighted findings and should be interpreted as sensitivity analyses rather than population-representative estimates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Subgroup analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubgroup analyses were conducted using stratified multivariable logistic regression models to explore potential heterogeneity in the associations between dietary antioxidant nutrients, the CDAI, and prevalent CKD. These analyses were exploratory in nature and are presented in Fig. 3.\u003c/p\u003e\n\u003cp\u003eEvidence suggestive of effect heterogeneity was observed for several antioxidant nutrients. The inverse association between zinc intake and prevalent CKD appeared more pronounced among participants without hypertension (P for interaction = 0.017), whereas no statistically significant interaction was observed by smoking status (P for interaction = 0.150). For selenium, stronger inverse associations were observed among participants without hypertension (P for interaction = 0.004), non-smokers (P for interaction = 0.002), and those who were overweight (P for interaction = 0.041). Similarly, the inverse association between niacin intake and prevalent CKD appeared stronger among participants without hypertension (P for interaction = 0.016) and non-smokers (P for interaction = 0.003).\u003c/p\u003e\n\u003cp\u003eFor vitamin E, inverse associations were more evident among participants with normal or overweight body mass index, non-smokers, and those with higher household income; however, formal tests for interaction were not consistently statistically significant across these subgroups.\u003c/p\u003e\n\u003cp\u003eIn contrast, associations of vitamin A, vitamin C, carotenoids, and the CDAI with prevalent CKD did not materially differ across most subgroups, with no statistically significant interaction effects detected. Given the multiple comparisons and exploratory nature of these analyses, subgroup findings should be interpreted with caution.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eUsing nationally representative data from the NHANES, this cross-sectional study found that higher overall dietary antioxidant capacity and greater intake of several antioxidant nutrients were associated with lower odds of prevalent chronic kidney disease among U.S. adults aged 45 years and older. While linear associations were generally not observed in continuous exposure models, restricted cubic spline analyses and categorical analyses consistently suggested inverse associations that were concentrated at higher intake levels, supporting the presence of nonlinear or threshold-type relationships.\u003c/p\u003e\n\u003cp\u003eThe observed associations are generally consistent with prior NHANES-based studies reporting inverse relationships between dietary antioxidant capacity and adverse renal outcomes (4). The present analyses extend this literature by highlighting substantial heterogeneity in dose\u0026ndash;response patterns across individual antioxidant nutrients. Restricted cubic spline analyses indicated predominantly nonlinear associations, characterized by steeper inverse relationships at lower to moderate intake levels followed by plateauing trends at higher intakes. These findings differ from the linear associations reported by Wang et al.(17). but are in line with previous studies suggesting nonlinear relationships in specific subpopulations (18) or for alternative kidney-related outcomes (19). Together, these results suggest that assuming linear dose\u0026ndash;response relationships may oversimplify complex diet\u0026ndash;kidney associations.\u003c/p\u003e\n\u003cp\u003eDistinct dose\u0026ndash;response patterns were observed among individual components of the CDAI. Vitamins A, C, and E demonstrated generally monotonic inverse associations across the observed intake range, whereas associations for selenium and zinc appeared to attenuate at higher intake levels. Such heterogeneity may reflect differences in biological roles, absorption, metabolism, or saturation of antioxidant-related pathways. These findings underscore the importance of examining nutrient-specific associations rather than treating dietary antioxidants as a homogeneous exposure.\u003c/p\u003e\n\u003cp\u003eCarotenoids, a key component of dietary antioxidant capacity and commonly included in composite indices (20-22), demonstrated a more complex association with prevalent CKD in this study. Although categorical analyses did not show statistically significant associations at the highest intake category, spline modeling suggested a nonlinear dose\u0026ndash;response pattern. This discrepancy may reflect the heterogeneous nature of carotenoids, which encompass multiple biologically distinct compounds (e.g., \u0026beta;-carotene, lycopene, and lutein), and aggregation of these compounds may obscure divergent associations of individual subtypes. Similar nonlinear patterns reported in recent studies examining CDAI and CKD further support the notion that diet\u0026ndash;kidney relationships are complex and may not be adequately captured by linear models alone (23).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe inverse associations observed in this study are biologically plausible given the established role of oxidative stress in the pathogenesis and progression of CKD. The kidney is particularly susceptible to oxidative injury due to its high metabolic activity, which may promote lipid peroxidation, mitochondrial dysfunction, inflammatory signaling, and fibrotic remodeling. Dietary antioxidants have been implicated in counteracting these processes through free radical scavenging and modulation of inflammatory pathways, and the observed associations are consistent with these biological mechanisms (24-30). The nonlinear or threshold-like patterns observed for several nutrients may reflect differences in bioavailability, tissue distribution, or functional limits of endogenous antioxidant systems.\u003c/p\u003e\n\u003cp\u003eExploratory subgroup analyses suggested that inverse associations for zinc, selenium, niacin, and vitamin E were more evident among individuals without hypertension or smoking exposure. Given the cross-sectional design, limited statistical power within subgroups, and multiple testing, these findings should be interpreted cautiously and may serve as hypothesis-generating observations to inform future studies examining potential effect modification.\u003c/p\u003e\n\u003cp\u003eSeveral limitations warrant consideration. First, the cross-sectional design precludes causal inference, and residual confounding or reverse causation cannot be excluded. Second, CKD classification was based on single measurements of serum creatinine and urine albumin-to-creatinine ratio, which may result in misclassification, although such misclassification would likely bias associations toward the null. Third, dietary intake was assessed using 24-hour dietary recall data, which are subject to recall bias and within-person variability. Finally, although survey weights were applied in the primary analyses to support population-representative inference, restricted cubic spline and subgroup analyses were conducted without survey weights. These secondary analyses were intended to characterize potential dose\u0026ndash;response patterns and effect heterogeneity and should therefore be interpreted as exploratory.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, using nationally representative U.S. data, this study found that higher overall dietary antioxidant capacity and greater intake of specific antioxidant nutrients were associated with lower odds of prevalent CKD among middle-aged and older adults. The observed associations varied across individual antioxidant components and exhibited heterogeneous, predominantly nonlinear dose\u0026ndash;response patterns.\u003c/p\u003e\n\u003cp\u003eAlthough the cross-sectional design precludes causal inference, these findings provide epidemiological evidence suggesting that dietary antioxidant exposure may be relevant to kidney health in the general population. Future prospective cohort studies and randomized intervention trials are needed to confirm these associations, clarify temporal and causal relationships, and elucidate the underlying biological mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is a publicly available database conducted by the Centers for Disease Control and Prevention (CDC). The survey protocols were approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and written informed consent was obtained from all participants. The present study involved secondary analysis of anonymized publicly available data and therefore did not require additional ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in this study are publicly available from the National Health and Nutrition Examination Survey (NHANES) conducted by the Centers for Disease Control and Prevention (CDC) and can be accessed at:\u003c/p\u003e\n\u003cp\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Medical Science and Technology Innovation Research Association of Sichuan Province (Special Research Program; grant number 2025YCZD210) and the General Program of the Sichuan Provincial Administration of Traditional Chinese Medicine (grant number 2024MS610). The funding bodies had no role in the study design; data collection, analysis, or interpretation; or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHT and MC conceived and designed the study.\u003c/p\u003e\n\u003cp\u003eHN, ZC, GA, YW, and JL were responsible for data acquisition and statistical analysis.\u003c/p\u003e\n\u003cp\u003eHT and YC drafted the manuscript.\u003c/p\u003e\n\u003cp\u003eZC and MC supervised the study and critically revised the manuscript for important intellectual content.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS) for providing access to the NHANES database.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal. regional, and national burden of chronic kidney disease in adults, 1990\u0026ndash;2023, and its attributable risk factors: a systematic analysis for the Global Burden of Disease Study 2023. Lancet (London England). 2025;406(10518):2461\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHill NR, Fatoba ST, Oke JL, Hirst JA, O'Callaghan CA, Lasserson DS, et al. Global Prevalence of Chronic Kidney Disease - A Systematic Review and Meta-Analysis. PLoS ONE. 2016;11(7):e0158765.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Wang F, Wang L, Wang W, Liu B, Liu J, et al. Prevalence of chronic kidney disease in China: a cross-sectional survey. Lancet (London England). 2012;379(9818):815\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng J, He Y, Zhang B, Liao R, Su B. Relationship between different diet indices and frailty and mortality in population with CKD. Front Nutr. 2025;12:1602587.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKandel R, Roy P, Singh KP. 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Atorvastatin ameliorates diabetic nephropathy through inhibiting oxidative stress and ferroptosis signaling. Eur J Pharmacol. 2024;976:176699.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou MS, Schuman IH, Jaimes EA, Raij L. Renoprotection by statins is linked to a decrease in renal oxidative stress, TGF-beta, and fibronectin with concomitant increase in nitric oxide bioavailability. Am J Physiol Ren Physiol. 2008;295(1):F53\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu X, Pang Y, Fan X. Mitochondria in oxidative stress, inflammation and aging: from mechanisms to therapeutic advances. Signal Transduct Target therapy. 2025;10(1):190.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlivares-Vicente M, Herranz-L\u0026oacute;pez M. The Interplay Between Oxidative Stress and Lipid Composition in Obesity-Induced Inflammation: Antioxidants as Therapeutic Agents in Metabolic Diseases. Int J Mol Sci. 2025;26(17).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKushkestani M, Moghadassi M, Sidossis L. Mediterranean Lifestyle: More Than a Diet, A Way of Living (and Thriving). Endocrine, metabolic \u0026amp; immune disorders drug targets. 2024;24(15):1785\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalliwell B. Understanding mechanisms of antioxidant action in health and disease. Nat Rev Mol Cell Biol. 2024;25(1):13\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong H, Shao Y, Chen X, Wang N, Zhan Y, Gong B, et al. Associations of composite dietary antioxidant index with premature death and all-cause mortality: a cohort study. BMC Public Health. 2025;25(1):796.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe H, Chen X, Ding Y, Chen X, He X. Composite dietary antioxidant index associated with delayed biological aging: a population-based study. 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Lipids Health Dis. 2024;23(1):390.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi S, Yang S, Wang Y, Lin Z, Chen F, Gao Q, et al. Association between composite dietary antioxidant index and increased urinary albumin excretion: a population-based study. Front Nutr. 2025;12:1552889.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNadimi E, Jamal Omidi S, Ghasemi M, Hashempur MH, Iraji A. Carotenoids as neuroprotective agents in multiple sclerosis: Pathways, mechanisms, and clinical prospects. Volume 191. Biomedicine \u0026amp; pharmacotherapy\u0026thinsp;=\u0026thinsp;Biomedecine \u0026amp; pharmacotherapie; 2025. p. 118496.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLotfi A, Abroodi Z, Khazaei M. Biological activities of astaxanthin in the treatment of neurodegenerative diseases. Neurodegenerative disease Manage. 2024;14(6):241\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBa W, Xu W, Deng Z, Zhang B, Zheng L, Li H. The Antioxidant and Anti-Inflammatory Effects of the Main Carotenoids from Tomatoes via Nrf2 and NF-κB Signaling Pathways. Nutrients. 2023;15(21).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi W, Chen S, Wan J, Chen L, Xing L, Gao Z, et al. Comprehensive dietary antioxidant index and chronic kidney disease: mediating role of frailty and its impact on mortality outcomes in adults. Front Nutr. 2025;12:1679774.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePehlivan VF, Pehlivan B, Duran E, Taskın A, Koyuncu I, \u0026Ccedil;akmak Y. Effects of Vitamin E on Redox balance in regulating thiol/disulfide homeostasis in sepsis: An antioxidant therapy perspective. PLoS ONE. 2025;20(11):e0336334.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Salvo M, Ventre A, Dato E, Casciaro M, Gangemi S. Nutraceuticals Against Oxidative Stress in Allergic Diseases. Biomolecules. 2025;15(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemirtas C, \u0026Ccedil;etin E, Yucel M, S\u0026ouml;nmez C, G\u0026uuml;ler EM, Beyaztaş H, et al. Vitamin E reduces vasospasm in a rat subarachnoid hemorrhage model. Neurosurg Rev. 2025;48(1):722.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChrysikopoulou V, Rampaouni A, Koutsia E, Ofrydopoulou A, Mittas N, Tsoupras A, Anti-Inflammatory. Antithrombotic and Antioxidant Efficacy and Synergy of a High-Dose Vitamin C Supplement Enriched with a Low Dose of Bioflavonoids; In Vitro Assessment and In Vivo Evaluation Through a Clinical Study in Healthy Subjects. Nutrients. 2025;17(16).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Zhen S, Xu H, Sun S, Wang Z, Li M, et al. Vitamin C alleviates rheumatoid arthritis by modulating gut microbiota balance. Biosci Trends. 2024;18(2):187\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu YY, Xu CZ, Liang YF, Jin DQ, Ding J, Sheng Y, et al. Ascorbic acid and hydrocortisone synergistically inhibit septic organ injury via improving oxidative stress and inhibiting inflammation. Immunopharmacol Immunotoxicol. 2022;44(5):786\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGozzi-Silva SC, Teixeira FME, Duarte A, Sato MN, Oliveira LM. Immunomodulatory Role of Nutrients: How Can Pulmonary Dysfunctions Improve? Front Nutr. 2021;8:674258.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"dietary antioxidants, composite dietary antioxidant index (CDAI), chronic kidney disease, cross-sectional survey, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-8720931/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8720931/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOxidative stress plays an important role in the development and progression of chronic kidney disease (CKD). Dietary antioxidants may help counteract oxidative damage; however, the dose\u0026ndash;response associations between overall dietary antioxidant capacity, assessed by the composite dietary antioxidant index (CDAI), and prevalent CKD remain unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were obtained from the National Health and Nutrition Examination Survey (NHANES) 2003\u0026ndash;2018. Adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years were included in this cross-sectional analysis. Dietary intakes of antioxidant nutrients (vitamins A, C, and E, selenium, zinc, carotenoids, and niacin) were assessed using 24-hour dietary recall data, and the CDAI was calculated based on energy-adjusted standardized intakes. Survey-weighted multivariable logistic regression models were applied to examine associations between continuous antioxidant exposures and quartiles of antioxidant intake and prevalent CKD. Restricted cubic spline models were used to explore potential nonlinear dose\u0026ndash;response associations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared with participants without CKD, those with CKD had lower intakes of vitamin C, vitamin E, selenium, zinc, niacin, as well as lower CDAI values. After full adjustment for potential confounders, higher intake categories of vitamin E (OR\u0026thinsp;=\u0026thinsp;0.50, 95% CI: 0.43\u0026ndash;0.80), selenium (OR\u0026thinsp;=\u0026thinsp;0.58, 95% CI: 0.44\u0026ndash;0.78), niacin (OR\u0026thinsp;=\u0026thinsp;0.64, 95% CI: 0.47\u0026ndash;0.88), zinc (OR\u0026thinsp;=\u0026thinsp;0.60, 95% CI: 0.49\u0026ndash;0.84), and CDAI (OR\u0026thinsp;=\u0026thinsp;0.73, 95% CI: 0.55\u0026ndash;0.97) were associated with lower odds of prevalent CKD. Restricted cubic spline analyses suggested nonlinear associations, with lower odds of prevalent CKD observed at higher intake levels, followed by a plateau.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHigher overall dietary antioxidant capacity was inversely associated with prevalent CKD, with evidence of nonlinear dose\u0026ndash;response patterns. These findings suggest that the associations between antioxidant intake and CKD prevalence may be more pronounced at higher intake levels. Given the cross-sectional design, causal inference cannot be established, and prospective studies are warranted to further clarify these relationships.\u003c/p\u003e","manuscriptTitle":"Dose–response associations between dietary antioxidant capacity and prevalent chronic kidney disease: NHANES 2003–2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 06:11:31","doi":"10.21203/rs.3.rs-8720931/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"168461158768878038375216603244083518212","date":"2026-05-12T12:23:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25889702692327330590599278028383817129","date":"2026-05-11T02:25:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319970987606100815004959972385431985686","date":"2026-05-10T13:11:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-05T12:18:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-28T07:36:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2026-02-28T07:14:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0f9877c5-f5df-4271-9aa9-d68cc8ecc6dd","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"168461158768878038375216603244083518212","date":"2026-05-12T12:23:39+00:00","index":122,"fulltext":""},{"type":"reviewerAgreed","content":"25889702692327330590599278028383817129","date":"2026-05-11T02:25:47+00:00","index":119,"fulltext":""},{"type":"reviewerAgreed","content":"319970987606100815004959972385431985686","date":"2026-05-10T13:11:23+00:00","index":118,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T06:11:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 06:11:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8720931","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8720931","identity":"rs-8720931","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}