Gender Differences in Association Between Serum Indirect Bilirubin and Chronic Kidney Disease Risk: A Prospective Cohort Study in Northwest China

Gender Differences in Association Between Serum Indirect Bilirubin and Chronic Kidney Disease Risk: A Prospective Cohort Study in Northwest China | 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 Gender Differences in Association Between Serum Indirect Bilirubin and Chronic Kidney Disease Risk: A Prospective Cohort Study in Northwest China Jinlong You, Xue Wang, Jing Tang, Xiuqian Li, Rui Wang, Yuanyuan Li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7457824/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2026 Read the published version in BMC Nephrology → Version 1 posted 17 You are reading this latest preprint version Abstract Background Current evidence remains limited regarding associations between serum indirect bilirubin (IBIL) levels and chronic kidney disease (CKD) risk, particularly in gender-stratified analyses. This study investigated the gender-specific relationship between serum IBIL and CKD incidence. Methods Using data from a prospective cohort in northwestern China, we followed 25,684 CKD-free participants. Cox proportional hazards models and restricted cubic spline regression were employed to assess IBIL-CKD associations. The predictive capacity of IBIL was evaluated through ROC curve analysis. Robustness of results was examined via subgroup and sensitivity analyses. Results Over 122,401.17 person-years of follow-up, 1,219 incident CKD cases emerged. Adjusted hazard ratios (95% CIs) for CKD were 0.794 (0.676–0.932) overall and 0.713 (0.589–0.862) among males. Area under the curve (AUC) values were 0.710 (0.704–0.715; p < 0.001) overall, 0.710 (0.703–0.718; p < 0.001) for males, and 0.679 (0.670–0.688; p < 0.001) for females. A linear dose-response pattern was observed exclusively in males. Results remained consistent across subgroup and sensitivity analyses. Conclusions Our findings demonstrate an inverse association between serum IBIL levels and CKD risk, with particular clinical relevance in male populations. These results suggest serum IBIL could function as a valuable biomarker for early CKD detection in males. Jinchang Cohort bilirubin indirect bilirubin chronic kidney disease gender Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Chronic kidney disease (CKD), marked by gradual onset, complex etiology, and lack of early overt symptoms, is a prevalent condition threatening human health [1] . In 2017, the global CKD prevalence was 11.1%, with 843.6 million individuals affected and 12 million dying from CKD. All-age prevalence and mortality rates were 29.3% and 41.5% higher than in 1990, respectively [2–4] . In 2010, CKD was the 18th leading global cause of mortality, up significantly from 27th in 1990 [5] . CKD is projected to be the fifth leading global cause of mortality by 2040 [6] . In 2017, the number of patients with end-stage renal disease needing renal replacement therapy worldwide was 4.902 million to 7.083 million [7] . In 2010, 228.4 million to 708.3 million premature deaths occurred due to inaccessible renal replacement therapy [8] . CKD prevalence in China rose from 10.8% in 2012 [9] to 11.6% in 2018 [10] . China has the most CKD patients worldwide, totaling 132.3 million from 1990 to 2017 [3] . CKD has become one of the most significant diseases threatening public health in China. Recently, strong correlations have been reported between CKD development and both oxidative stress and the inflammatory response [11] . Total bilirubin (TBIL) consists of 80% indirect bilirubin (IBIL) and 20% direct bilirubin (DBIL) [12] . Although bilirubin was long considered a nonfunctional metabolic waste product [13] , studies continue to identify its additional properties, including antioxidant and anti-inflammatory effects [14–16] . studies continue to identify its additional properties, including antioxidant and anti-inflammatory effects [17] . Similarly, another cross-sectional study linked elevated serum TBIL levels to a decreased CKD risk [18] . A prospective cohort study revealed low TBIL levels are a CKD risk factor [19] . Interestingly, a study found no significant correlation between TBIL or IBIL and CKD development [20] , and some studies identified elevated TBIL levels as a renal impairment risk factor [21,22] . In summary, the relationship between serum bilirubin and the risk of CKD remains inconclusive. Most current studies focus on TBIL, are cross-sectional, and are confined to economically developed regions. Most studies have focused on individuals at elevated risk for CKD, with few conducted in the general population. Since IBIL constitutes the majority of TBIL, it likely plays a dominant role in bilirubin's bioregulatory function, as shown in several studies [23,24] . Moreover, serum IBIL levels are typically lower in females than in males in the general population [25,26] . As CKD risk calculation criteria and prevalence differ between males and females [9] , it is crucial to determine whether the correlation between serum IBIL and CKD varies by gender, facilitating more precise CKD prognostication. However, research on the correlation between serum IBIL levels and CKD risk is scarce in China, particularly in the economically underdeveloped northwestern region. Thus, we aimed to analyze the associations between serum IBIL levels and CKD in the total population and by gender via a prospective cohort study in Northwest China. 2 Methods 2.1 Study subjects and design This study is based on the Jinchang Cohort in Northwest China. The basic information of the Jinchang Cohort has been detailed in prior research [27,28] . We identified 30291 participants with a 100% matching rate by pairing employee and health insurance IDs between baseline and the second follow-up. After excluding participants with CKD (n = 982), without IBIL data (n = 263), with jaundice (n = 591), with malignant tumors (n = 196), and without essential medical data on serum uric acid, blood pressure, fasting blood glucose, and serum creatinine (n = 2602) at baseline, 25684 participants were included in this study (Fig. 1 ). Before enrollment, all participants signed a written informed consent form. This study was approved by the Ethics Committees of the College of Public Health, Lanzhou University, and the Workers’ Hospital of the JNMC, and conforms to the ethical principles of the 2008 Declaration of Helsinki (sixth revision). All participants completed epidemiological surveys, physical exams, and biochemical tests. Trained investigators conducted epidemiological surveys using standardized questionnaires for face‒to‒face interviews on demographics (age, sex, education, occupation, income), habits (smoking, alcohol, exercise), diet (high salt, fat, sugar), and disease history (kidney disease, hypertension, diabetes). Qualified medical professionals at enterprise-affiliated tertiary hospitals performed physical exams and biochemical tests. Height and weight were measured using an automated device (SK-X80/TCS-160D-W/H, Sonka, China). Blood pressure was measured using an electronic sphygmomanometer (Omron HEM-7071A, Japan). Blood and urine samples were collected after an overnight fast. Levels of bilirubin, total cholesterol (TC), triglyceride (TG), HDL-C, LDL-C, serum uric acid (SUA), fasting plasma glucose (FPG), and serum creatinine (Scr), along with other common clinical indicators, were measured using an automated analyzer (Hitachi 7600-020, Kyoto, Japan). Serum fasting bilirubin was measured using the vanadate oxidation method. Albuminuria in urine samples was measured using an automated analyzer and categorized into five grades (-, ±, 1+, 2+, and 3+). 2.2 Outcome of interest CKD was the primary outcome of this study. Participants self-reporting CKD were required to provide a diagnosis certificate from hospitals of the second level or above. CKD was defined as an estimated glomerular filtration rate (eGFR) 0.7 mg/dl, eGFR = 144×(Scr/0.7) −1.209 × (0.993) Age . Males: Scr ≤ 0.9 mg/dl, eGFR = 141×(Scr/0.9) −0.411 × (0.993) Age ; Scr > 0.9 mg/dl, eGFR = 141×(Scr/0.9) −1.209 × (0.993) Age . 2.3 Variable definitions Smoking status was categorized as current smoker, former smoker, or never smoker. Current smokers were those who smoked at least one cigarette per day for over 6 months or had quit for less than 6 months. Former smokers had quit for over 6 months. Alcohol consumption was classified as non-drinker, occasional drinker, or regular drinker. Regular drinkers consumed spirits, beer, or wine at least 3 times per week for over 6 months. Occasional drinkers consumed alcohol for over 6 months but less than once a week on average. Physical exercise was categorized as none, occasional, or regular. Occasional exercisers exercised one to three times per week. Regular exercisers exercised at least three times per week. High salt, high fat, and high sugar diets were defined as > 6 g/d salt, > 30 g/d cooking oil, and > 50 g/d sugar. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m 2 ). BMI categories were underweight (BMI < 18.5 kg/m 2 ), normal (18.5–23.9 kg/m 2 ), overweight (24.0-27.9 kg/m 2 ), and obese (BMI ≥ 28.0 kg/m 2 ) [31] . Additionally, diseases were defined as follows: Hypertension was systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg, or a self-reported diagnosis with a certificate from a second-level or higher hospital [32] . Diabetes was FPG ≥ 7.0 mmol/L, or a self-reported diagnosis with a certificate from a second-level or higher hospital [33] . Dyslipidemia was blood lipids exceeding critical levels (TC ≥ 6.2 mmol/L, TG ≥ 2.3 mmol/L, HDL-C 420 µmol/L after 2 days of a normal diet [35] . 2.4 Statistical analysis Data were presented as means ± SDs, medians (lower and upper quartiles), or frequencies (percentages) based on data type. Student’s t-test, Mann–Whitney U test, and Pearson’s chi-square test compared normally distributed continuous variables, unevenly distributed variables, and categorical variables, respectively. Baseline serum IDIL levels were divided into four quartile groups: Q1 (reference), Q2, Q3, and Q4. The cumulative incidence of CKD was compared among quartile groups using Kaplan‒Meier curves and the log-rank test. Four multivariable-adjusted Cox proportional hazard models calculated adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). Model 1 adjusted for gender and age. Model 2 further adjusted for occupation, education, income, smoking, alcohol use, exercise, high-salt diet, high-fat diet, high-sugar diet, and BMI. Model 3 further adjusted for hypertension, diabetes, hyperuricemia, dyslipidemia, and baseline eGFR. Model 4 further adjusted for SUA, Scr, ALT, AST, TC, TG, HDL-C, and LDL-C. P values for trend were calculated using each quartile’s median value. The HR for CKD incidence per-SD increase in IBIL was also calculated. A restricted cubic spline (RCS) regression model analyzed the dose‒response relationship between serum IBIL levels and CKD, with nodes at independent variable percentiles (5%, 35%, 65%, 95%). Additionally, subgroup analysis assessed the adjusted HR and 95% CI for per-SD increase in serum IBIL. A receiver operating characteristic (ROC) curve assessed serum IBIL levels’ ability to predict CKD. Additionally, the area under the curve (AUC), sensitivity, specificity, and Youden indices were calculated. Lastly, a sensitivity analysis was performed. Serum IBIL concentration was converted to a binary variable (≥ 11.4 µmol/L, < 11.4 µmol/L) based on its median. Using this binary variable as the dependent variable and Table 1 baseline characteristics as covariates, propensity score matching (PSM) was conducted. Matching used a 1:1 protocol without replacement and a 0.0005 caliper width. More stringent calipers were also used, but 0.0005 yielded the best matching model. A standardized mean difference (SMD) < 0.1 indicated well-balanced matched covariates [36] . HRs and 95% CIs for serum IBIL and CKD risk post-PSM were evaluated for result stability. All statistical tests were two-sided, with p < 0.05 considered significant. R software (version 4.3.2; R Foundation, Vienna, Austria) and SPSS (version 29.0; IBM SPSS Inc., Chicago, IL, USA) were used for analyses. 3 Results 3.1 Baseline characteristics of the participants Table 1 shows participant characteristics. This study included 15538 males (60.5%) and 10146 females (39.5%) with average ages of 46.62±13.53 and 46.54±11.44 years, respectively. Significant differences were observed between CKD and non-CKD regarding age strata, occupation, education, income, smoking, alcohol use, exercise, hypertension, diabetes, dyslipidemia, hyperuricemia, and BMI (p<0.05) in males. However, these differences were only evident regarding age strata, education, hypertension, diabetes, dyslipidemia, hyperuricemia, and BMI in females. Except for ALT in males and AST in females, other clinical biochemicals differed significantly between CKD and non-CKD groups (p<0.05). Notably, except for HDL-C, other clinical biochemicals were higher in the CKD group than in the non-CKD group in both males and females. Additionally, significant differences were observed between males and females for all variables (p<0.05), regardless of CKD status (supplementary material Table S1). Table 1 Baseline characteristics of new-onset CKD and non-CKD participants stratified by gender Variables Males (N=15 538) p Females (N=10 146) p None CKD (N=14 632) CKD (N=906) None CKD (N=9 833) CKD (N=313) Age, n (%) <0.001 <0.001 <45 7568 (51.7) 358 (39.5) 4904 (49.9) 121 (38.7) 45~59 4150 (28.4) 233 (25.7) 3267 (33.2) 97 (31.0) ≥60 2914 (19.9) 315 (34.8) 1662 (16.9) 95 (30.4) Occupation, n (%) 0.006 0.345 Worker staff 12053 (82.4) 769 (84.9) 7659 (77.9) 248 (79.2) Managerial staff 1559 (10.7) 74 (8.2) 1225 (12.5) 32 (10.2) Technical staff 711 (4.9) 34 (3.8) 301 (3.1) 7 (2.2) Logistics staff 309 (2.1) 29 (3.2) 648 (6.6) 26 (8.3) Education, n (%) <0.001 0.001 Junior middle school or below 5394 (36.9) 456 (50.3) 4259 (43.3) 168 (53.7) Senior middle school or equivalent 4286 (29.3) 253 (27.9) 2564 (26.1) 76 (24.3) College or above 4952 (33.8) 197 (21.7) 3010 (30.6) 69 (22.0) Income, n (%) <0.001 0.130 <¥2000 8017 (54.8) 550 (60.7) 4763 (48.4) 138 (44.1) ≥¥2000 6615 (45.2) 356 (39.3) 5070 (51.6) 175 (55.9) Smoking status, n (%) <0.001 0.487 Never 3852 (26.3) 216 (23.8) 9672 (98.4) 307 (98.1) Still 8844 (60.4) 510 (56.3) 130 (1.3) 6 (1.92) Quit 1936 (13.2) 180 (19.9) 31 (0.3) 0 (0.0) Alcohol consumption, n (%) <0.001 0.624 Never 8815 (60.2) 488 (53.9) 9574 (97.4) 304 (97.1) Occasionally 4819 (32.9) 312 (34.4) 229 (2.3) 9 (2.9) Regular 998 (6.8) 106 (11.7) 30 (0.3) 0 (0.0) Physical exercise, n (%) 0.003 0.836 Never 1305 (8.9) 110 (12.1) 885 (9.0) 30 (9.6) Occasionally 6408 (43.8) 369 (40.7) 4012 (40.8) 131 (41.9) Regular 6919 (47.3) 427 (47.1) 4936 (50.2) 152 (48.6) BMI (kg/m 2 ), n (%) <0.001 <0.001 <18.5 6975 (47.7) 345 (38.1) 6156 (62.6) 151 (48.2) 18.5~23.9 487 (3.3) 25 (2.8) 676 (6.9) 14 (4.5) 24~27.9 5753 (39.3) 385 (42.5) 2343 (23.8) 100 (31.9) ≥28 1417 (9.7) 151 (16.7) 658 (6.7) 48 (15.3) High salt diet, n (%) 3950 (27.0) 231 (25.5) 0.343 1619 (16.5) 52 (16.6) 1.000 High fat diet, n (%) 3328 (22.7) 210 (23.2) 0.794 1402 (14.3) 37 (11.8) 0.257 High sugar diet, n (%) 2900 (19.8) 158 (17.4) 0.088 2043 (20.8) 74 (23.6) 0.247 Hypertension, n (%) 4507 (30.8) 494 (54.5) <0.001 2160 (22.0) 135 (43.1) <0.001 Diabetes, n (%) 1088 (7.4) 217 (24.0) <0.001 494 (5.0) 42 (13.4) <0.001 Hyperuricemia, n (%) 2644 (18.1) 223 (24.6) <0.001 128 (1.3) 12 (3.8) 0.001 Dyslipidemia, n (%) 6254 (42.7) 480 (53.0) <0.001 2465 (25.1) 113 (36.1) <0.001 SUA, μmol/L 353.0 (309.0, 402.0) 366.0 (316.0, 420.0) <0.001 263.0 (229.0, 301.0) 284.0 (242.0, 332.0) <0.001 SCR, μmol/L 75.0 (69.0, 82.0) 76.0 (67.0, 86.0) 0.015 58.0 (53.0, 64.0) 59.0 (53.0, 68.0) 0.006 ALT, U/L 32.0 (23.0, 46.0) 33.0 (23.0, 52.0) 0.077 21.0 (16.0, 29.0) 23.0 (17.0, 33.0) 0.001 AST, U/L 33.0 (28.0, 40.0) 34.0 (28.0, 42.0) <0.001 29.0 (24.0, 35.0) 30.0 (25.0, 36.0) 0.092 TC, mmol/L 4.6 (4.0, 5.2) 4.7 (4.2, 5.3) <0.001 4.7 (4.1, 5.3) 4.8 (4.3, 5.5) 0.006 TG, mmol/L 1.7 (1.2, 2.5) 1.9 (1.3, 2.9) <0.001 1.3 (0.9, 1.9) 1.5 (1.1, 2.2) <0.001 HDL-C, mmol/L 1.3 (1.1, 1.5) 1.2 (1.0, 1.5) 0.027 1.5 (1.3, 1.7) 1.4 (1.2, 1.6) 0.001 LDL-C, mmol/L 3.0 (2.5, 3.5) 3.1 (2.6, 3.6) <0.001 3.1 (2.6, 3.6) 3.2 (2.7, 3.7) 0.044 SBP, mmHg 124.0 (113.0, 136.0) 132.0 (122.0, 147.0) <0.001 116.0 (105.0, 130.0) 128.0 (113.0, 142.0) <0.001 DBP, mmHg 78.0 (71.0, 87.0) 84.0 (76.0, 94.0) <0.001 75.0 (68.0, 83.0) 80.0 (72.0, 92.0) <0.001 FPG, mmol/L 5.1 (4.7, 5.6) 5.5 (4.9, 6.4) <0.001 5.0 (4.6, 5.4) 5.2 (4.7, 5.7) <0.001 ALP, U/L 69.0 (58.0, 82.0) 70.0 (58.0, 81.0) 0.627 59.0 (48.0, 73.0) 61.0 (51.0, 79.0) 0.002 eGFR, mL/min/1.73m 2 105.0 (94.5, 112.0) 101.0 (85.3, 111.0) <0.001 106.0 (95.8, 113.0) 102.0 (87.9, 112.0) <0.001 Abbreviations: CKD, chronic kidney disease; BMI, body mass index; SUA, serum uric acid; Scr, serum creatinine; ALT, alanine aminotransferase; AST, aspartate aminotransferase; TC, total cholesterol; TG, triglyceride; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; ALP, alkaline phosphatase; eGFR, estimated glomerular filtration rate. 3.2 Cumulative incidence of CKD During 122401.17 person-years of follow-up, 1,219 new-onset CKD patients were identified, including 906 males and 313 females. The cumulative incidence of CKD was 4.75%, significantly higher in males (5.83%) than females (3.08%) (p < 0.05). Elevated serum IBIL levels were significantly associated with reduced CKD incidence in males, as the highest serum IBIL quartile had the lowest cumulative incidence of CKD among the four subgroups (log-rank p < 0.05), but not in the total population or females (Figure 2). CKD prevalence was also calculated among participants with varying baseline characteristics (supplementary material Table S2). 3.3 Association between serum IBIL levels and the risk of CKD Table 3 shows serum IBIL levels and within-group medians in different quartiles, along with adjusted HRs and 95% CIs for CKD across serum IBIL quartiles in four models. Among all participants, the HRs for CKD incidence in Q4 were 0.805 (95% CI: 0.688-0.942), 0.807 (95% CI: 0.688-0.947), 0.808 (95% CI: 0.689-0.948), and 0.794 (95% CI: 0.676-0.932) compared to Q1 across the four models. An inverse relationship between elevated serum IBIL and increased CKD risk was also found in males but not females. Interestingly, HRs decreased with increasing serum IBIL quartiles in all and male participants (p for trend < 0.05). Furthermore, a per-SD increase in serum IBIL was associated with a 6.5% and 9.3% reduction in CKD risk in all and male participants, respectively. Figure 3 shows the dose‒response relationship between serum IBIL levels and CKD risk in Model 4. A negative linear dose‒response relationship between serum IBIL levels and CKD risk was found in males (p for overall = 0.024, p for nonlinear = 0.455). However, no dose‒response relationship between serum IBIL and CKD risk was identified in all participants or females (p for overall > 0.05). Table 3 Adjusted HRs and 95% CIs of CKD incidence associated with serum IBIL in total, male and female participants Ranges (within-group median), μmol/L Model 1 Model 2 Model 3 Model 4 HRs (95% CIs) P HRs (95% CIs) P HRs (95% CIs) P HRs (95% CIs) P Total Q1 ≤9.0 (7.7) 1.000 (Reference) 1.000 (Reference) 1.000 (Reference) 1.000 (Reference) Q2 9.0 ~ 11.3 (10.2) 0.844 (0.723-0.986) 0.033 0.851 (0.728-0.995) 0.043 0.861 (0.737-1.007) 0.061 0.854 (0.731-0.999) 0.049 Q3 11.3 ~ 14.1 (12.6) 0.828 (0.708-0.968) 0.018 0.840 (0.718-0.984) 0.031 0.856 (0.731-1.002) 0.053 0.840 (0.716-0.984) 0.031 Q4 >14.1 (16.6) 0.805 (0.688-0.942) 0.007 0.807 (0.688-0.947) 0.008 0.808 (0.689-0.948) 0.009 0.794 (0.676-0.932) 0.005 Per SD 0.946 (0.893-1.001) 0.056 0.945 (0.892-1.002) 0.057 0.942 (0.889-0.998) 0.041 0.935 (0.882-0.991) 0.023 P for trend 0.011 0.014 0.014 0.008 Male Q1 ≤9.0 (7.6) 1.000 (Reference) 1.000 (Reference) 1.000 (Reference) 1.000 (Reference) Q2 9.0 ~ 11.4 (10.3) 0.840 (0.703-1.003) 0.054 0.851 (0.712-1.017) 0.076 0.849 (0.710-1.014) 0.072 0.834 (0.697-0.998) 0.047 Q3 11.4 ~ 14.4 (12.8) 0.790 (0.659-0.946) 0.010 0.796 (0.664-0.955) 0.014 0.800 (0.667-0.960) 0.017 0.775 (0.645-0.931) 0.006 Q4 >14.4 (16.9) 0.750 (0.624-0.902) 0.002 0.754 (0.625-0.909) 0.003 0.743 (0.616-0.897) 0.002 0.713 (0.589-0.862) <0.001 Per SD 0.931 (0.871-0.996) 0.038 0.931 (0.870-0.997) 0.041 0.922 (0.861-0.986) 0.018 0.907 (0.846-0.971) 0.005 P for trend 0.002 0.003 0.018 <0.001 Female Q1 ≤9.0 (7.8) 1.000 (Reference) 1.000 (Reference) 1.000 (Reference) 1.000 (Reference) Q2 9.0 ~ 11.1 (10.1) 0.906 (0.661-1.240) 0.537 0.935 (0.682-1.282) 0.675 0.953 (0.695-1.308) 0.767 0.963 (0.701-1.323) 0.816 Q3 11.1 ~ 13.7 (12.3) 0.876 (0.640-1.199) 0.408 0.928 (0.677-1.272) 0.644 0.952 (0.695-1.306) 0.762 0.959 (0.699-1.317) 0.797 Q4 >13.7 (16.1) 0.996 (0.732-1.355) 0.979 1.043 (0.764-1.424) 0.791 1.048 (0.767-1.431) 0.769 1.059 (0.774-1.450) 0.718 Per SD 0.987 (0.882-1.104) 0.818 1.003 (0.897-1.121) 0.956 1.004 (0.899-1.121) 0.947 1.006 (0.901-1.123) 0.918 P for trend 0.963 0.733 0.723 0.680 Note: Model 1 adjusted for gender and age. Model 2 further adjusted for occupation, education, income, smoking, alcohol use, exercise, high-salt diet, high-fat diet, high-sugar diet, and BMI. Model 3 further adjusted for hypertension, diabetes, hyperuricemia, dyslipidemia, and baseline eGFR. Model 4 further adjusted for SUA, Scr, ALT, AST, TC, TG, HDL-C, and LDL-C. Abbreviations: HRs, hazard ratios; CIs, confidence intervals. 3.4 ROC analysis Figure 4 shows ROC curves of serum IBIL for predicting CKD risk after adjusting for confounders. AUCs were 0.710 (95% CI: 0.704-0.715, p<0.001), 0.710 (95% CI: 0.703-0.718, p<0.001), and 0.679 (95% CI: 0.670-0.688, p<0.001) for all, male, and female participants, respectively. Youden indices were 0.327, 0.328, and 0.272 for the total, male, and female participants, respectively (Table 4). Table 4 AUC, sensitivity, and specificity of serum IBIL for CKD AUC (95% CIs) P Sensitivity (%) Specificity (%) Youden indices Total 0.710 (0.704-0.715) <0.001 62.76 69.95 0.327 Male 0.710 (0.703-0.718) <0.001 65.12 67.65 0.328 Female 0.679 (0.670-0.688) <0.001 57.83 69.34 0.272 3.5 Subgroup analysis The inverse correlation between serum IBIL levels and CKD risk was more pronounced in males, as shown by the analysis. Consequently, we further investigated this relationship in males via subgroup analysis. Figure 5 shows subgroup analysis results. The correlation between serum IBIL and CKD risk remained significant for participants aged ≥60 years, managerial staff, those with a junior middle school education or less, current smokers, regular drinkers, occasional exercisers, those with hypertension, those without diabetes, those with dyslipidemia, and those without hyperuricemia (p < 0.05). A significant interaction was observed between serum IBIL concentration and occupation (p for interaction < 0.05). 3.6 Sensitivity analysis After PSM, 8753 participant pairs were successfully matched, with all covariate SMDs < 0.1 (supplementary material Table S3). Additionally, no significant differences were detected in covariates between groups with serum IBIL levels ≥11.4 μmol/L and <11.4 μmol/L (supplementary material Table S4). In Model 4, HRs for Q4 vs. Q1 were 0.788 (95% CI: 0.650-0.954) and 0.732 (95% CI: 0.585-0.917) in the total and male participants, respectively (supplementary material Table S5). Additionally, CKD risk decreased with increasing serum IBIL (p for trend <0.05) in the total and male participants but not females. Cumulative CKD incidence, dose‒response relationships, and ROC analysis results are shown in the supplementary material (Figure S1, Figure S2, Figure S3, Table S6). 4 Discussion In this study, with a median follow-up of 4.77 (4.14, 5.59) years, CKD cumulative incidence was 5.83% in males and 3.08% in females. CKD cumulative incidence varied significantly across most subgroups defined by baseline characteristics. Both serum IBIL levels and quartiles were significantly and negatively associated with CKD risk in the total sample and male participants, even after adjusting for multiple potential confounders. A negative linear dose‒response relationship was identified between serum IBIL levels and CKD in males. Serum IBIL levels were a predictive factor for CKD risk. The observed association between serum IBIL levels and CKD was consistent across male subgroup analyses. After a sensitivity analysis, the associations between serum IBIL levels and quartiles and CKD risk remained consistent. To date, associations between serum bilirubin and CKD in different populations have been extensively investigated, with inconsistent conclusions. A meta-analysis of seven studies showed a 5% reduction in CKD risk per 1 μmol/L bilirubin increase (RR=0.95, 95% CI: 0.92-0.97) [37] . A study of CKD and hyperuricemia patients showed significantly lower renal replacement therapy or mortality risk in subjects with IBIL > 4.55 μmol/L than < 4.55 μmol/L [38] . In contrast, a survey of 12633 hypertensive patients revealed that elevated levels of TBIL and DBIL did not confer protection against CKD progression in regular smokers, indicating that bilirubin may not exert an independent influence on CKD [39] . Additionally, evidence from several studies suggests elevated serum TBIL levels may be a renal impairment risk factor. For example, a cross-sectional study showed elevated serum TBIL levels were independently associated with lower eGFRs and increased urinary ALB in the U.S. adult population [22] . Another cross-sectional study revealed a negative correlation between serum TBIL levels and eGFRs in diabetic and nondiabetic patients [21] . Thus, the independent protective effect of serum bilirubin levels against CKD remains controversial, requiring further studies for substantiation. This study focused on serum IBIL levels. Few studies have distinguished between TBIL and DBIL, especially IBIL, when exploring serum bilirubin levels' relationship with CKD. Most studies suggest IBIL levels may be a protective factor against CKD development. A 5-year prospective cohort study revealed RRs of eGFR decline of 0.74 (95% CI: 0.57-0.97) and 0.75 (95% CI: 0.57-0.98) for the second and third quartiles vs. the lowest IBIL quartile [40] . A study of 316 Chinese Han CKD patients revealed a positive correlation between serum IBIL levels and eGFR [41] . Another study of Chinese hypertensive patients revealed that, after adjusting for multiple confounders, ORs for CKD were 0.70 (95% CI: 0.59-0.81) and 0.52 (95% CI: 0.44-0.61) for the second and third quartiles vs. the lowest serum IBIL quartile [42] . Our findings support these inferences, further endorsing indirect bilirubin as a novel, straightforward, noninvasive CKD risk biomarker. The precise mechanism linking serum IBIL levels to reduced CKD risk remains unclear. However, IBIL's antioxidant and anti-inflammatory effects may be key in high-normal IBIL protecting against CKD [12] . Increased oxidative stress is a CKD hallmark at all stages of onset and progression [43,44] . A review revealed oxidative stress activates multiple enzyme systems (e.g., NADPH oxidase) to produce excess ROS, directly relating to CKD onset and progression mechanisms and processes [45] . Additionally, the triad of oxidative stress, chronic microinflammation, and endothelial dysfunction is a CKD hallmark [45,46] . Thus, CKD pathogenesis and development mechanisms are closely related to oxidative stress and chronic inflammation. In this study, patients in higher serum IBIL quartiles had a lower CKD risk, suggesting serum IBIL levels may significantly impact oxidative stress and inflammatory response suppression [14-16] . Bilirubin also directly inhibits NADPH oxidase activity and suppresses superoxide generation in vascular endothelial and renal tubular cells [47] . Experimental evidence indicates IBIL exerts intracellular and extracellular antioxidant effects by inhibiting NADPH oxidase via heme oxygenase-1 and biliverdin reductase [48,49] . In conclusion, oxidative stress and inflammatory responses significantly impact CKD development, progression, and serum IBIL bioregulation. Thus, elevated serum IBIL levels may reduce CKD risk by boosting circulating antioxidant and anti-inflammatory capacity and inhibiting oxidative stress accumulation. This study's results indicate a statistically significant correlation between serum IBIL levels and reduced CKD risk in males, not females. These findings suggest sex-specific associations between serum bilirubin levels and reduced CKD risk. However, the precise mechanisms by which sex differences influence the serum bilirubin-CKD relationship remain uncertain. Our findings align with prior studies reporting lower female IBIL levels than males [26,50,51] . The observed sex difference in serum bilirubin levels may explain the sex-based disparity in the serum bilirubin-CKD correlation. First, studies show serum bilirubin level differences between men and women are not significantly associated with genetic factors [52] and typically do not manifest before age 10 [53] . Based on this, a study suggested serum bilirubin metabolism changes may be due to hormonal changes after puberty onset [26] . Park et al. reported serum bilirubin levels were independently and positively associated with testosterone [54] , and Muraca et al. reported testosterone suppressed hepatic bilirubin uridine diphosphate-glucuronosyltransferase activity in orchiectomized rats [55] . Conversely, estradiol and progesterone enhance enzyme activity in gonadectomized female rodents [55] . Under physiological conditions, estrogen exerts antioxidant effects [56] . Thus, it can be postulated that in females, a synergistic antioxidant effect between estrogen and serum bilirubin may reduce serum bilirubin's independent protective effect against CKD. Another study indicated estradiol and estrogen receptor signaling facilitate bilirubin metabolism [57] , resulting in lower female serum bilirubin levels than males. Moreover, research shows androgen can impede serum bilirubin metabolism [58] . Thus, sex hormones may significantly contribute to the observed sex differences in the serum IBIL level-reduced CKD risk association. On the other hand, our findings indicate heightened CKD risk in males, confirmed in several studies. A population-based study revealed kidney function declines more rapidly in males than females [59] . Additionally, animal studies show estrogen has renal antifibrotic and antiapoptotic effects [60] , while testosterone has renal proinflammatory, proapoptotic, and profibrotic effects [61] . Regarding lifestyle habits, males are more prone to adopting unhealthy lifestyles than females, which may also contribute to sex-based CKD onset and development differences [62,63] . A notable interaction between serum IBIL levels and occupation was identified in males via subgroup analysis. This study revealed elevated serum IBIL in managers was associated with a greater CKD risk reduction than in workers, technicians, and logisticians. Among male participants from diverse occupational backgrounds, this study found those in managerial roles had the lowest cumulative CKD incidence, while those in logistics and workers had a higher incidence. Generally, managers are less likely to be exposed to hazardous substances and adverse working conditions than other occupations. In a prospective cohort study of 65,069 participants with an average 6.7-year follow-up, adverse working conditions, including heavy workloads, shift work, occupational secondhand cigarette smoke exposure, and occupational heat exposure, significantly increased CKD risk [64] . Participants were from a large mining group involved in mining, beneficiation, metallurgy, chemical engineering, and deep processing [28] . Compared to managers, participants in other occupations may have a greater prevalence of underlying medical conditions and elevated CKD risk due to exposure to harmful environmental factors and suboptimal working conditions, among other factors. These elements may have somewhat diminished serum IBIL's protective influence against CKD. Undeniably, this study has limitations. First, CKD was defined using a single eGFR and albuminuria measurement, without 3-month follow-up blood tests or urine sediment retesting. Proteinuria was diagnosed only qualitatively with urine protein test strips, without quantitative diagnosis. Additionally, CKD-EPI equation measurement bias for CKD diagnosis may differ between males and females [65] . These factors may affect CKD diagnosis accuracy. Second, analysis was based only on baseline serum IBIL levels, which may not reflect long-term serum IBIL changes related to CKD. Third, this study lacked participant medication use information. Consequently, it was not possible to assess medication effects on CKD or obtain post-medication serum IBIL levels. Finally, as all participants were from a single, large mining group in Northwest China, it is unclear if the findings can be generalized to other regions, ethnic populations, or occupational groups. Further multicenter prospective studies are needed for a deeper understanding of this phenomenon. In conclusion, serum IBIL levels have an independent protective effect on CKD risk and provide a reference for CKD risk in males. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committees of the College of Public Health, Lanzhou University, and the Workers’ Hospital of the JNMC, and conforms to the ethical principles of the 2008 Declaration of Helsinki (sixth revision). Consent for publication Not applicable. Availability of data and material The data supporting the findings of this study are available upon reasonable request from the corresponding authors. Competing interests I declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Funding This study was supported by the Gansu Province Joint Fund Project (24JRRA819). The funders played no role in the design or conduct of the study, collection, management, analysis, or interpretation of the data or in the preparation, review, or approval of the article. Authors' contributions JL-Y conceived the study, designed the methodology, developed the software, conducted formal analyses, and drafted the original manuscript. XW, JT, XQ-L, RW, and YY-L performed the software development and conducted formal analyses. CY collected and curated the experimental data. YN-B conceptualized the research framework, administered the project, and supervised the research team. MZ-W contributed to manuscript writing, provided critical revisions, edited the content, and assisted in methodological design. SZ initiated the research concept, developed the methodology, validated the results, oversaw the writing process with editorial revisions, and supervised the entire research program. All authors reviewed and approved the final version of the manuscript. Acknowledgements We express our sincere gratitude to all participants and researchers of the Jinchang cohort study. References Lv K, Liu Y, Zhang X, et al. Prevalence of chronic kidney disease in a city of Northwestern China: a cross-sectional study. Int Urol Nephrol, 2023, 55(8):2035-2045. Cockwell P, Fisher LA. The global burden of chronic kidney disease. Lancet, 2020, 395(10225):662-664. 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Kao TL, Chen YL, Kuan YP, et al. Estrogen-Estrogen Receptor α Signaling Facilitates Bilirubin Metabolism in Regenerating Liver Through Regulating Cytochrome P450 2A6 Expression. Cell Transplant, 2017, 26(11):1822-1829. Walden CE, Knopp RH, Johnson JL, et al. Effect of estrogen/progestin potency on clinical chemistry measures. The Lipid Research Clinics Program Prevalence Study. Am J Epidemiol, 1986, 123(3):517-531. Neugarten J, Acharya A, Silbiger SR. Effect of gender on the progression of nondiabetic renal disease: a meta-analysis. J Am Soc Nephrol, 2000, 11(2):319-329. Hutchens MP, Fujiyoshi T, Komers R, et al. Estrogen protects renal endothelial barrier function from ischemia‒reperfusion in vitro and in vivo. Am J Physiol Renal Physiol, 2012, 303(3):F377-385. Metcalfe PD, Leslie JA, Campbell MT, et al. Testosterone exacerbates obstructive renal injury by stimulating TNF-alpha production and increasing proapoptotic and profibrotic signaling. Am J Physiol Endocrinol Metab, 2008, 294(2):E435-443. Nitsch D. Is there a difference in metabolic burden between men and women?. Nephrol Dial Transplant, 2014, 29(6):1110-1112. Ellam T, Fotheringham J, Kawar B. Differential scaling of glomerular filtration rate and ingested metabolic burden: implications for gender differences in chronic kidney disease outcomes. Nephrol Dial Transplant, 2014, 29(6):1186-1194. Lan R, Qin Y, Chen X, et al. Risky working conditions and chronic kidney disease. J Occup Med Toxicol, 2023, 18(1):26. Carrero JJ, Hecking M, Chesnaye NC, et al. Sex and gender disparities in the epidemiology and outcomes of chronic kidney disease. Nat Rev Nephrol, 2018, 14(3):151-164. Text box Text box 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.docx Textbox1.docx Cite Share Download PDF Status: Published Journal Publication published 02 Apr, 2026 Read the published version in BMC Nephrology → Version 1 posted Editorial decision: Revision requested 25 Nov, 2025 Reviews received at journal 24 Nov, 2025 Reviews received at journal 18 Nov, 2025 Reviews received at journal 13 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 09 Nov, 2025 Reviews received at journal 07 Nov, 2025 Reviewers agreed at journal 02 Nov, 2025 Reviews received at journal 23 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers invited by journal 10 Sep, 2025 Editor invited by journal 29 Aug, 2025 Editor assigned by journal 26 Aug, 2025 Submission checks completed at journal 26 Aug, 2025 First submitted to journal 25 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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2","display":"","copyAsset":false,"role":"figure","size":186176,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative incidence of CKD by quartile of serum IBIL concentration\u003c/p\u003e\n\u003cp\u003eNote: In this figure, the x-axis shows the follow-up time in months, and the y-axis shows the cumulative incidence of serum IBLI in different quartiles.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7457824/v1/8dcb27792f7a5726b613e7f6.png"},{"id":91654554,"identity":"8fc2d443-d8fc-4f36-822b-1b93faf351ec","added_by":"auto","created_at":"2025-09-18 17:48:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":118221,"visible":true,"origin":"","legend":"\u003cp\u003eDose‒response relationships between serum IBIL levels and the risk of CKD in the total, male and female participants\u003c/p\u003e\n\u003cp\u003eNote: In this figure, solid blue lines indicate adjusted HRs, and blue shaded areas represent 95% CIs for HRs; x-axes show serum IBIL levels, and y-axes show HRs for CKD. Adjusted confounders match those in Cox Model 4.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7457824/v1/bb4fee2ed3ea9f6054bd3e8f.png"},{"id":91653806,"identity":"49455f39-cf2c-4767-91c9-6b5e28dc9981","added_by":"auto","created_at":"2025-09-18 17:40:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":121207,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves between serum IBIL and CKD\u003c/p\u003e\n\u003cp\u003eNote: Adjusted confounders match those in Cox Model 4.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7457824/v1/37c77c95ecefb09541e30d34.png"},{"id":91655390,"identity":"c35e7eea-b917-483a-affb-fbe141cce15c","added_by":"auto","created_at":"2025-09-18 17:56:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":286705,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the association between serum IBIL levels and CKD in males\u003c/p\u003e\n\u003cp\u003eNote: In each subgroup, adjusted confounders match those in Cox Model 4.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7457824/v1/e56f2e4bc07481e4c5d4c626.png"},{"id":106345125,"identity":"b6eb9344-bfc7-4269-aa76-b14f5013c811","added_by":"auto","created_at":"2026-04-07 16:18:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1599086,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7457824/v1/cc746047-8491-42e4-ad29-1cce8168fa15.pdf"},{"id":91654556,"identity":"9c5e4984-70bc-4e89-a15e-3b9cb69aa5d5","added_by":"auto","created_at":"2025-09-18 17:48:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":513665,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7457824/v1/b0ceae48c15b3f2214576757.docx"},{"id":91653802,"identity":"b9ddb2b9-feb0-4b35-85f3-5e0b4389b165","added_by":"auto","created_at":"2025-09-18 17:40:25","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14523,"visible":true,"origin":"","legend":"","description":"","filename":"Textbox1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7457824/v1/974d6ded449ff931af0fdde7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gender Differences in Association Between Serum Indirect Bilirubin and Chronic Kidney Disease Risk: A Prospective Cohort Study in Northwest China","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eChronic kidney disease (CKD), marked by gradual onset, complex etiology, and lack of early overt symptoms, is a prevalent condition threatening human health\u003csup\u003e[1]\u003c/sup\u003e. In 2017, the global CKD prevalence was 11.1%, with 843.6\u0026nbsp;million individuals affected and 12\u0026nbsp;million dying from CKD. All-age prevalence and mortality rates were 29.3% and 41.5% higher than in 1990, respectively\u003csup\u003e[2\u0026ndash;4]\u003c/sup\u003e. In 2010, CKD was the 18th leading global cause of mortality, up significantly from 27th in 1990\u003csup\u003e[5]\u003c/sup\u003e. CKD is projected to be the fifth leading global cause of mortality by 2040\u003csup\u003e[6]\u003c/sup\u003e. In 2017, the number of patients with end-stage renal disease needing renal replacement therapy worldwide was 4.902\u0026nbsp;million to 7.083 million\u003csup\u003e[7]\u003c/sup\u003e. In 2010, 228.4\u0026nbsp;million to 708.3\u0026nbsp;million premature deaths occurred due to inaccessible renal replacement therapy\u003csup\u003e[8]\u003c/sup\u003e. CKD prevalence in China rose from 10.8% in 2012\u003csup\u003e[9]\u003c/sup\u003e to 11.6% in 2018\u003csup\u003e[10]\u003c/sup\u003e. China has the most CKD patients worldwide, totaling 132.3\u0026nbsp;million from 1990 to 2017\u003csup\u003e[3]\u003c/sup\u003e. CKD has become one of the most significant diseases threatening public health in China.\u003c/p\u003e\u003cp\u003eRecently, strong correlations have been reported between CKD development and both oxidative stress and the inflammatory response\u003csup\u003e[11]\u003c/sup\u003e. Total bilirubin (TBIL) consists of 80% indirect bilirubin (IBIL) and 20% direct bilirubin (DBIL)\u003csup\u003e[12]\u003c/sup\u003e. Although bilirubin was long considered a nonfunctional metabolic waste product\u003csup\u003e[13]\u003c/sup\u003e, studies continue to identify its additional properties, including antioxidant and anti-inflammatory effects\u003csup\u003e[14\u0026ndash;16]\u003c/sup\u003e. studies continue to identify its additional properties, including antioxidant and anti-inflammatory effects\u003csup\u003e[17]\u003c/sup\u003e. Similarly, another cross-sectional study linked elevated serum TBIL levels to a decreased CKD risk\u003csup\u003e[18]\u003c/sup\u003e. A prospective cohort study revealed low TBIL levels are a CKD risk factor\u003csup\u003e[19]\u003c/sup\u003e. Interestingly, a study found no significant correlation between TBIL or IBIL and CKD development\u003csup\u003e[20]\u003c/sup\u003e, and some studies identified elevated TBIL levels as a renal impairment risk factor\u003csup\u003e[21,22]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn summary, the relationship between serum bilirubin and the risk of CKD remains inconclusive. Most current studies focus on TBIL, are cross-sectional, and are confined to economically developed regions. Most studies have focused on individuals at elevated risk for CKD, with few conducted in the general population. Since IBIL constitutes the majority of TBIL, it likely plays a dominant role in bilirubin's bioregulatory function, as shown in several studies\u003csup\u003e[23,24]\u003c/sup\u003e. Moreover, serum IBIL levels are typically lower in females than in males in the general population\u003csup\u003e[25,26]\u003c/sup\u003e. As CKD risk calculation criteria and prevalence differ between males and females\u003csup\u003e[9]\u003c/sup\u003e, it is crucial to determine whether the correlation between serum IBIL and CKD varies by gender, facilitating more precise CKD prognostication. However, research on the correlation between serum IBIL levels and CKD risk is scarce in China, particularly in the economically underdeveloped northwestern region. Thus, we aimed to analyze the associations between serum IBIL levels and CKD in the total population and by gender via a prospective cohort study in Northwest China.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study subjects and design\u003c/h2\u003e\u003cp\u003eThis study is based on the Jinchang Cohort in Northwest China. The basic information of the Jinchang Cohort has been detailed in prior research\u003csup\u003e[27,28]\u003c/sup\u003e. We identified 30291 participants with a 100% matching rate by pairing employee and health insurance IDs between baseline and the second follow-up. After excluding participants with CKD (n\u0026thinsp;=\u0026thinsp;982), without IBIL data (n\u0026thinsp;=\u0026thinsp;263), with jaundice (n\u0026thinsp;=\u0026thinsp;591), with malignant tumors (n\u0026thinsp;=\u0026thinsp;196), and without essential medical data on serum uric acid, blood pressure, fasting blood glucose, and serum creatinine (n\u0026thinsp;=\u0026thinsp;2602) at baseline, 25684 participants were included in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Before enrollment, all participants signed a written informed consent form. This study was approved by the Ethics Committees of the College of Public Health, Lanzhou University, and the Workers\u0026rsquo; Hospital of the JNMC, and conforms to the ethical principles of the 2008 Declaration of Helsinki (sixth revision).\u003c/p\u003e\u003cp\u003eAll participants completed epidemiological surveys, physical exams, and biochemical tests. Trained investigators conducted epidemiological surveys using standardized questionnaires for face‒to‒face interviews on demographics (age, sex, education, occupation, income), habits (smoking, alcohol, exercise), diet (high salt, fat, sugar), and disease history (kidney disease, hypertension, diabetes). Qualified medical professionals at enterprise-affiliated tertiary hospitals performed physical exams and biochemical tests. Height and weight were measured using an automated device (SK-X80/TCS-160D-W/H, Sonka, China). Blood pressure was measured using an electronic sphygmomanometer (Omron HEM-7071A, Japan). Blood and urine samples were collected after an overnight fast. Levels of bilirubin, total cholesterol (TC), triglyceride (TG), HDL-C, LDL-C, serum uric acid (SUA), fasting plasma glucose (FPG), and serum creatinine (Scr), along with other common clinical indicators, were measured using an automated analyzer (Hitachi 7600-020, Kyoto, Japan). Serum fasting bilirubin was measured using the vanadate oxidation method. Albuminuria in urine samples was measured using an automated analyzer and categorized into five grades (-, \u0026plusmn;, 1+, 2+, and 3+).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Outcome of interest\u003c/h2\u003e\u003cp\u003eCKD was the primary outcome of this study. Participants self-reporting CKD were required to provide a diagnosis certificate from hospitals of the second level or above. CKD was defined as an estimated glomerular filtration rate (eGFR)\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m2 and/or albuminuria\u0026thinsp;\u0026ge;\u0026thinsp;1+\u003csup\u003e[29]\u003c/sup\u003e. The eGFR was calculated using the CKD-EPI equation based on Scr\u003csup\u003e[30]\u003c/sup\u003e:\u003c/p\u003e\u003cp\u003eFemales: Scr\u0026thinsp;\u0026le;\u0026thinsp;0.7 mg/dl, eGFR\u0026thinsp;=\u0026thinsp;144\u0026times;(Scr/0.7)\u003csup\u003e\u0026minus;0.329\u003c/sup\u003e\u0026times; (0.993) \u003csup\u003eAge\u003c/sup\u003e; Scr\u0026thinsp;\u0026gt;\u0026thinsp;0.7 mg/dl, eGFR\u0026thinsp;=\u0026thinsp;144\u0026times;(Scr/0.7)\u003csup\u003e\u0026minus;1.209\u003c/sup\u003e\u0026times; (0.993) \u003csup\u003eAge\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMales: Scr\u0026thinsp;\u0026le;\u0026thinsp;0.9 mg/dl, eGFR\u0026thinsp;=\u0026thinsp;141\u0026times;(Scr/0.9)\u003csup\u003e\u0026minus;0.411\u003c/sup\u003e\u0026times; (0.993) \u003csup\u003eAge\u003c/sup\u003e; Scr\u0026thinsp;\u0026gt;\u0026thinsp;0.9 mg/dl, eGFR\u0026thinsp;=\u0026thinsp;141\u0026times;(Scr/0.9)\u003csup\u003e\u0026minus;1.209\u003c/sup\u003e\u0026times; (0.993) \u003csup\u003eAge\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Variable definitions\u003c/h2\u003e\u003cp\u003eSmoking status was categorized as current smoker, former smoker, or never smoker. Current smokers were those who smoked at least one cigarette per day for over 6 months or had quit for less than 6 months. Former smokers had quit for over 6 months. Alcohol consumption was classified as non-drinker, occasional drinker, or regular drinker. Regular drinkers consumed spirits, beer, or wine at least 3 times per week for over 6 months. Occasional drinkers consumed alcohol for over 6 months but less than once a week on average. Physical exercise was categorized as none, occasional, or regular. Occasional exercisers exercised one to three times per week. Regular exercisers exercised at least three times per week. High salt, high fat, and high sugar diets were defined as \u0026gt;\u0026thinsp;6 g/d salt, \u0026gt;\u0026thinsp;30 g/d cooking oil, and \u0026gt;\u0026thinsp;50 g/d sugar. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m\u003csup\u003e2\u003c/sup\u003e). BMI categories were underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), normal (18.5\u0026ndash;23.9 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (24.0-27.9 kg/m\u003csup\u003e2\u003c/sup\u003e), and obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;28.0 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003csup\u003e[31]\u003c/sup\u003e. Additionally, diseases were defined as follows: Hypertension was systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or a self-reported diagnosis with a certificate from a second-level or higher hospital\u003csup\u003e[32]\u003c/sup\u003e. Diabetes was FPG\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, or a self-reported diagnosis with a certificate from a second-level or higher hospital\u003csup\u003e[33]\u003c/sup\u003e. Dyslipidemia was blood lipids exceeding critical levels (TC\u0026thinsp;\u0026ge;\u0026thinsp;6.2 mmol/L, TG\u0026thinsp;\u0026ge;\u0026thinsp;2.3 mmol/L, HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.0 mmol/L, LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;4.1 mmol/L)\u003csup\u003e[34]\u003c/sup\u003e. Hyperuricemia was fasting SUA\u0026thinsp;\u0026gt;\u0026thinsp;420 \u0026micro;mol/L after 2 days of a normal diet\u003csup\u003e[35]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e\u003cp\u003eData were presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs, medians (lower and upper quartiles), or frequencies (percentages) based on data type. Student\u0026rsquo;s t-test, Mann\u0026ndash;Whitney U test, and Pearson\u0026rsquo;s chi-square test compared normally distributed continuous variables, unevenly distributed variables, and categorical variables, respectively. Baseline serum IDIL levels were divided into four quartile groups: Q1 (reference), Q2, Q3, and Q4. The cumulative incidence of CKD was compared among quartile groups using Kaplan‒Meier curves and the log-rank test. Four multivariable-adjusted Cox proportional hazard models calculated adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). Model 1 adjusted for gender and age. Model 2 further adjusted for occupation, education, income, smoking, alcohol use, exercise, high-salt diet, high-fat diet, high-sugar diet, and BMI. Model 3 further adjusted for hypertension, diabetes, hyperuricemia, dyslipidemia, and baseline eGFR. Model 4 further adjusted for SUA, Scr, ALT, AST, TC, TG, HDL-C, and LDL-C. P values for trend were calculated using each quartile\u0026rsquo;s median value. The HR for CKD incidence per-SD increase in IBIL was also calculated. A restricted cubic spline (RCS) regression model analyzed the dose‒response relationship between serum IBIL levels and CKD, with nodes at independent variable percentiles (5%, 35%, 65%, 95%). Additionally, subgroup analysis assessed the adjusted HR and 95% CI for per-SD increase in serum IBIL. A receiver operating characteristic (ROC) curve assessed serum IBIL levels\u0026rsquo; ability to predict CKD. Additionally, the area under the curve (AUC), sensitivity, specificity, and Youden indices were calculated. Lastly, a sensitivity analysis was performed. Serum IBIL concentration was converted to a binary variable (\u0026ge;\u0026thinsp;11.4 \u0026micro;mol/L, \u0026lt;\u0026thinsp;11.4 \u0026micro;mol/L) based on its median. Using this binary variable as the dependent variable and Table\u0026nbsp;1 baseline characteristics as covariates, propensity score matching (PSM) was conducted. Matching used a 1:1 protocol without replacement and a 0.0005 caliper width. More stringent calipers were also used, but 0.0005 yielded the best matching model. A standardized mean difference (SMD)\u0026thinsp;\u0026lt;\u0026thinsp;0.1 indicated well-balanced matched covariates\u003csup\u003e[36]\u003c/sup\u003e. HRs and 95% CIs for serum IBIL and CKD risk post-PSM were evaluated for result stability. All statistical tests were two-sided, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant. R software (version 4.3.2; R Foundation, Vienna, Austria) and SPSS (version 29.0; IBM SPSS Inc., Chicago, IL, USA) were used for analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Baseline characteristics of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eparticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 shows participant characteristics. This study included 15538 males (60.5%) and 10146 females (39.5%) with average ages of 46.62\u0026plusmn;13.53 and 46.54\u0026plusmn;11.44 years, respectively. Significant differences were observed between CKD and non-CKD regarding age strata, occupation, education, income, smoking, alcohol use, exercise, hypertension, diabetes, dyslipidemia, hyperuricemia, and BMI (p\u0026lt;0.05) in males. However, these differences were only evident regarding age strata, education, hypertension, diabetes, dyslipidemia, hyperuricemia, and BMI in females. Except for ALT in males and AST in females, other clinical biochemicals differed significantly between CKD and non-CKD groups (p\u0026lt;0.05). Notably, except for HDL-C, other clinical biochemicals were higher in the CKD group than in the non-CKD group in both males and females. Additionally, significant differences were observed between males and females for all variables (p\u0026lt;0.05), regardless of CKD status (supplementary material Table S1).\u003c/p\u003e\n\u003cp\u003eTable 1 Baseline characteristics of new-onset CKD and non-CKD participants stratified by gender\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 27px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 24px;\"\u003e\n \u003cp\u003eMales (N=15 538)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 24px;\"\u003e\n \u003cp\u003eFemales (N=10 146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eNone CKD\u003c/p\u003e\n \u003cp\u003e(N=14\u0026nbsp;632)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003cp\u003e(N=906)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eNone CKD\u003c/p\u003e\n \u003cp\u003e(N=9 833)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003cp\u003e(N=313)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eAge, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\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: 27px;\"\u003e\n \u003cp\u003e\u0026lt;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e7568 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e358 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4904 (49.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e121 (38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e45~59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4150 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e233 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3267 (33.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e97 (31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2914 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e315 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1662 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e95 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eOccupation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eWorker staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e12053 (82.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e769 (84.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e7659 (77.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e248 (79.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eManagerial staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1559 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e74 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1225 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e32 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eTechnical staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e711 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e34 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e301 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e7 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eLogistics staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e309 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e29 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e648 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e26 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eEducation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\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: 27px;\"\u003e\n \u003cp\u003eJunior middle school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e5394 (36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e456 (50.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4259 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e168 (53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\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: 27px;\"\u003e\n \u003cp\u003eSenior middle school or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4286 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e253 (27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2564 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e76 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\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: 27px;\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4952 (33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e197 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3010 (30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e69 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eIncome, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026lt;\u0026yen;2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e8017 (54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e550 (60.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4763 (48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e138 (44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026ge;\u0026yen;2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e6615 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e356 (39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e5070 (51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e175 (55.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eSmoking status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3852 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e216 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e9672 (98.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e307 (98.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eStill\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e8844 (60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e510 (56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e130 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e6 (1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eQuit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1936 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e180 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e31 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eAlcohol consumption, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e8815 (60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e488 (53.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e9574 (97.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e304 (97.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eOccasionally\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4819 (32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e312 (34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e229 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e9 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eRegular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e998 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e106 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e30 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003ePhysical exercise, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1305 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e110 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e885 (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e30 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eOccasionally\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e6408 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e369 (40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4012 (40.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e131 (41.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eRegular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e6919 (47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e427 (47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4936 (50.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e152 (48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\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: 27px;\"\u003e\n \u003cp\u003e\u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e6975 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e345 (38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e6156 (62.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e151 (48.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e18.5~23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e487 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e25 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e676 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e14 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e24~27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e5753 (39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e385 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2343 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e100 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026ge;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1417 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e151 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e658 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e48 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eHigh salt diet, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3950 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e231 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1619 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e52 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eHigh fat diet, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3328 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e210 (23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1402 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e37 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eHigh sugar diet, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2900 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e158 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2043 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e74 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4507 (30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e494 (54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2160 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e135 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\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: 27px;\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1088 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e217 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e494 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e42 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\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: 27px;\"\u003e\n \u003cp\u003eHyperuricemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2644 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e223 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e128 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e12 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e6254 (42.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e480 (53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2465 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e113 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\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: 27px;\"\u003e\n \u003cp\u003eSUA, \u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e353.0 (309.0, 402.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e366.0 (316.0, 420.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e263.0 (229.0, 301.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e284.0 (242.0, 332.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\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: 27px;\"\u003e\n \u003cp\u003eSCR, \u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e75.0 (69.0, 82.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e76.0 (67.0, 86.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e58.0 (53.0, 64.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e59.0 (53.0, 68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eALT, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e32.0 (23.0, 46.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e33.0 (23.0, 52.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e21.0 (16.0, 29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e23.0 (17.0, 33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eAST, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e33.0 (28.0, 40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e34.0 (28.0, 42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e29.0 (24.0, 35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e30.0 (25.0, 36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eTC, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4.6 (4.0, 5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4.7 (4.2, 5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4.7 (4.1, 5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4.8 (4.3, 5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eTG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.7 (1.2, 2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.9 (1.3, 2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.3 (0.9, 1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.5 (1.1, 2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\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: 27px;\"\u003e\n \u003cp\u003eHDL-C, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.3 (1.1, 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.2 (1.0, 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.5 (1.3, 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.4 (1.2, 1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eLDL-C, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3.0 (2.5, 3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3.1 (2.6, 3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3.1 (2.6, 3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e3.2 (2.7, 3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eSBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e124.0 (113.0, 136.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e132.0 (122.0, 147.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e116.0 (105.0, 130.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e128.0 (113.0, 142.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\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: 27px;\"\u003e\n \u003cp\u003eDBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e78.0 (71.0, 87.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e84.0 (76.0, 94.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e75.0 (68.0, 83.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e80.0 (72.0, 92.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\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: 27px;\"\u003e\n \u003cp\u003eFPG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e5.1 (4.7, 5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e5.5 (4.9, 6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e5.0 (4.6, 5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e5.2 (4.7, 5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\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: 27px;\"\u003e\n \u003cp\u003eALP, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e69.0 (58.0, 82.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e70.0 (58.0, 81.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e59.0 (48.0, 73.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e61.0 (51.0, 79.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eeGFR, mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e105.0 (94.5, 112.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e101.0 (85.3, 111.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e106.0 (95.8, 113.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e102.0 (87.9, 112.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\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\u003eAbbreviations: CKD, chronic kidney disease; BMI, body mass index; SUA, serum uric acid; Scr, serum creatinine; ALT, alanine aminotransferase; AST, aspartate aminotransferase; TC, total cholesterol; TG, triglyceride; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; ALP, alkaline phosphatase; eGFR, estimated glomerular filtration rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Cumulative incidence of CKD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring 122401.17 person-years of follow-up, 1,219 new-onset CKD patients were identified, including 906 males and 313 females. The cumulative incidence of CKD was 4.75%, significantly higher in males (5.83%) than females (3.08%) (p \u0026lt; 0.05). Elevated serum IBIL levels were significantly associated with reduced CKD incidence in males, as the highest serum IBIL quartile had the lowest cumulative incidence of CKD among the four subgroups (log-rank p \u0026lt; 0.05), but not in the total population or females (Figure 2). CKD prevalence was also calculated among participants with varying baseline characteristics (supplementary material Table S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Association between\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eserum IBIL\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;levels and the risk of CKD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 shows serum IBIL levels and within-group medians in different quartiles, along with adjusted HRs and 95% CIs for CKD across serum IBIL quartiles in four models. Among all participants, the HRs for CKD incidence in Q4 were 0.805 (95% CI: 0.688-0.942), 0.807 (95% CI: 0.688-0.947), 0.808 (95% CI: 0.689-0.948), and 0.794 (95% CI: 0.676-0.932) compared to Q1 across the four models. An inverse relationship between elevated serum IBIL and increased CKD risk was also found in males but not females. Interestingly, HRs decreased with increasing serum IBIL quartiles in all and male participants (p for trend \u0026lt; 0.05). Furthermore, a per-SD increase in serum IBIL was associated with a 6.5% and 9.3% reduction in CKD risk in all and male participants, respectively. Figure 3 shows the dose‒response relationship between serum IBIL levels and CKD risk in Model 4. A negative linear dose‒response relationship between serum IBIL levels and CKD risk was found in males (p for overall = 0.024, p for nonlinear = 0.455). However, no dose‒response relationship between serum IBIL and CKD risk was identified in all participants or females (p for overall \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 3 Adjusted HRs and 95% CIs of CKD incidence associated with serum IBIL in total, male and female participants\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"101%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRanges (within-group median), \u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eHRs (95% CIs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eHRs (95% CIs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eHRs (95% CIs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eHRs (95% CIs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19px;\"\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: 8px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026le;9.0 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.000 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.000 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.000 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.000 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\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: 8px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e9.0 ~ 11.3 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.844 (0.723-0.986)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.851 (0.728-0.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.861 (0.737-1.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.854 (0.731-0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e11.3 ~ 14.1 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.828 (0.708-0.968)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.840 (0.718-0.984)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.856 (0.731-1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.840 (0.716-0.984)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026gt;14.1 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.805 (0.688-0.942)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.807 (0.688-0.947)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.808 (0.689-0.948)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.794 (0.676-0.932)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ePer SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.946 (0.893-1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.945 (0.892-1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.942 (0.889-0.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.935 (0.882-0.991)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\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: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19px;\"\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: 8px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026le;9.0 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.000 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.000 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.000 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.000 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\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: 8px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e9.0 ~ 11.4 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.840 (0.703-1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.851 (0.712-1.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.849 (0.710-1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.834 (0.697-0.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e11.4 ~ 14.4 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.790 (0.659-0.946)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.796 (0.664-0.955)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.800 (0.667-0.960)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.775 (0.645-0.931)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026gt;14.4 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.750 (0.624-0.902)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.754 (0.625-0.909)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.743 (0.616-0.897)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.713 (0.589-0.862)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ePer SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.931 (0.871-0.996)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.931 (0.870-0.997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.922 (0.861-0.986)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.907 (0.846-0.971)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\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: 8px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026le;9.0 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.000\u0026nbsp;(Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.000 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.000 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.000 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\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: 8px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e9.0 ~ 11.1 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.906 (0.661-1.240)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.935 (0.682-1.282)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.953 (0.695-1.308)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.963 (0.701-1.323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e11.1 ~ 13.7 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.876 (0.640-1.199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.928 (0.677-1.272)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.952 (0.695-1.306)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.959 (0.699-1.317)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026gt;13.7 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.996 (0.732-1.355)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.043 (0.764-1.424)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.048 (0.767-1.431)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.059 (0.774-1.450)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ePer SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.987 (0.882-1.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.003 (0.897-1.121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.004 (0.899-1.121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.006 (0.901-1.123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.680\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: Model 1 adjusted for gender and age. Model 2 further adjusted for occupation, education, income, smoking, alcohol use, exercise, high-salt diet, high-fat diet, high-sugar diet, and BMI. Model 3 further adjusted for hypertension, diabetes, hyperuricemia, dyslipidemia, and baseline eGFR. Model 4 further adjusted for SUA, Scr, ALT, AST, TC, TG, HDL-C, and LDL-C. Abbreviations: HRs, hazard ratios; CIs, confidence intervals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 ROC analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 4 shows ROC curves of serum IBIL for predicting CKD risk after adjusting for confounders. AUCs were 0.710 (95% CI: 0.704-0.715, p\u0026lt;0.001), 0.710 (95% CI: 0.703-0.718, p\u0026lt;0.001), and 0.679 (95% CI: 0.670-0.688, p\u0026lt;0.001) for all, male, and female participants, respectively. Youden indices were 0.327, 0.328, and 0.272 for the total, male, and female participants, respectively (Table 4).\u003c/p\u003e\n\u003cp\u003eTable 4 AUC, sensitivity, and specificity of serum IBIL for CKD\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eAUC (95% CIs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eYouden indices\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.710 (0.704-0.715)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e62.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e69.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.710 (0.703-0.718)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e65.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e67.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e0.679 (0.670-0.688)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e57.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e69.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Subgroup analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inverse correlation between serum IBIL levels and CKD risk was more pronounced in males, as shown by the analysis. Consequently, we further investigated this relationship in males via subgroup analysis. Figure 5 shows subgroup analysis results. The correlation between serum IBIL and CKD risk remained significant for participants aged \u0026ge;60 years, managerial staff, those with a junior middle school education or less, current smokers, regular drinkers, occasional exercisers, those with hypertension, those without diabetes, those with dyslipidemia, and those without hyperuricemia (p \u0026lt; 0.05). A significant interaction was observed between serum IBIL concentration and occupation (p for interaction \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Sensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter PSM, 8753 participant pairs were successfully matched, with all covariate SMDs \u0026lt; 0.1 (supplementary material Table S3). Additionally, no significant differences were detected in covariates between groups with serum IBIL levels \u0026ge;11.4 \u0026mu;mol/L and \u0026lt;11.4 \u0026mu;mol/L (supplementary material Table S4). In Model 4, HRs for Q4 vs. Q1 were 0.788 (95% CI: 0.650-0.954) and 0.732 (95% CI: 0.585-0.917) in the total and male participants, respectively (supplementary material Table S5). Additionally, CKD risk decreased with increasing serum IBIL (p for trend \u0026lt;0.05) in the total and male participants but not females. Cumulative CKD incidence, dose‒response relationships, and ROC analysis results are shown in the supplementary material (Figure S1, Figure S2, Figure S3, Table S6).\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this study, with a median follow-up of 4.77 (4.14, 5.59) years, CKD cumulative incidence was 5.83% in males and 3.08% in females. CKD cumulative incidence varied significantly across most subgroups defined by baseline characteristics. Both serum IBIL levels and quartiles were significantly and negatively associated with CKD risk in the total sample and male participants, even after adjusting for multiple potential confounders. A negative linear dose‒response relationship was identified between serum IBIL levels and CKD in males. Serum IBIL levels were a predictive factor for CKD risk. The observed association between serum IBIL levels and CKD was consistent across male subgroup analyses. After a sensitivity analysis, the associations between serum IBIL levels and quartiles and CKD risk remained consistent.\u003c/p\u003e\n\u003cp\u003eTo date, associations between serum bilirubin and CKD in different populations have been extensively investigated, with inconsistent conclusions. A meta-analysis of seven studies showed a 5% reduction in CKD risk per 1 \u0026mu;mol/L bilirubin increase (RR=0.95, 95% CI: 0.92-0.97)\u003csup\u003e[37]\u003c/sup\u003e.\u0026nbsp;A study of CKD and hyperuricemia patients showed significantly lower renal replacement therapy or mortality risk in subjects with IBIL \u0026gt; 4.55 \u0026mu;mol/L than \u0026lt; 4.55 \u0026mu;mol/L\u003csup\u003e[38]\u003c/sup\u003e. In contrast, a survey of 12633 hypertensive patients revealed that elevated levels of TBIL and DBIL did not confer protection against CKD progression in regular smokers, indicating that bilirubin may not exert an independent influence on CKD\u003csup\u003e[39]\u003c/sup\u003e.\u0026nbsp;Additionally, evidence from several studies suggests elevated serum TBIL levels may be a renal impairment risk factor. For example, a cross-sectional study showed elevated serum TBIL levels were independently associated with lower eGFRs and increased urinary ALB in the U.S. adult population\u003csup\u003e[22]\u003c/sup\u003e.\u0026nbsp;Another cross-sectional study revealed a negative correlation between serum TBIL levels and eGFRs in diabetic and nondiabetic patients\u003csup\u003e[21]\u003c/sup\u003e.\u0026nbsp;Thus, the independent protective effect of serum bilirubin levels against CKD remains controversial, requiring further studies for substantiation.\u003c/p\u003e\n\u003cp\u003eThis study focused on serum IBIL levels. Few studies have distinguished between TBIL and DBIL, especially IBIL, when exploring serum bilirubin levels\u0026apos; relationship with CKD. Most studies suggest IBIL levels may be a protective factor against CKD development. A 5-year prospective cohort study revealed RRs of eGFR decline of 0.74 (95% CI: 0.57-0.97) and 0.75 (95% CI: 0.57-0.98) for the second and third quartiles vs. the lowest IBIL quartile\u003csup\u003e[40]\u003c/sup\u003e.\u0026nbsp;A study of 316 Chinese Han CKD patients revealed a positive correlation between serum IBIL levels and eGFR\u0026nbsp;\u003csup\u003e[41]\u003c/sup\u003e.\u0026nbsp;Another study of Chinese hypertensive patients revealed that, after adjusting for multiple confounders, ORs for CKD were 0.70 (95% CI: 0.59-0.81) and 0.52 (95% CI: 0.44-0.61) for the second and third quartiles vs. the lowest serum IBIL quartile\u003csup\u003e[42]\u003c/sup\u003e.\u0026nbsp;Our findings support these inferences, further endorsing indirect bilirubin as a novel, straightforward, noninvasive CKD risk biomarker.\u003c/p\u003e\n\u003cp\u003eThe precise mechanism linking serum IBIL levels to reduced CKD risk remains unclear. However, IBIL\u0026apos;s antioxidant and anti-inflammatory effects may be key in high-normal IBIL protecting against CKD\u003csup\u003e[12]\u003c/sup\u003e.\u0026nbsp;Increased oxidative stress is a CKD hallmark at all stages of onset and progression\u003csup\u003e[43,44]\u003c/sup\u003e.\u0026nbsp;A review revealed oxidative stress activates multiple enzyme systems (e.g., NADPH oxidase) to produce excess ROS, directly relating to CKD onset and progression mechanisms and processes\u003csup\u003e[45]\u003c/sup\u003e.\u0026nbsp;Additionally, the triad of oxidative stress, chronic microinflammation, and endothelial dysfunction is a CKD hallmark\u003csup\u003e[45,46]\u003c/sup\u003e.\u0026nbsp;Thus, CKD pathogenesis and development mechanisms are closely related to oxidative stress and chronic inflammation. In this study, patients in higher serum IBIL quartiles had a lower CKD risk, suggesting serum IBIL levels may significantly impact oxidative stress and inflammatory response suppression\u003csup\u003e[14-16]\u003c/sup\u003e.\u0026nbsp;Bilirubin also directly inhibits NADPH oxidase activity and suppresses superoxide generation in vascular endothelial and renal tubular cells\u003csup\u003e[47]\u003c/sup\u003e.\u0026nbsp;Experimental evidence indicates IBIL exerts intracellular and extracellular antioxidant effects by inhibiting NADPH oxidase via heme oxygenase-1 and biliverdin reductase\u003csup\u003e[48,49]\u003c/sup\u003e.\u0026nbsp;In conclusion, oxidative stress and inflammatory responses significantly impact CKD development, progression, and serum IBIL bioregulation. Thus, elevated serum IBIL levels may reduce CKD risk by boosting circulating antioxidant and anti-inflammatory capacity and inhibiting oxidative stress accumulation.\u003c/p\u003e\n\u003cp\u003eThis study\u0026apos;s results indicate a statistically significant correlation between serum IBIL levels and reduced CKD risk in males, not females. These findings suggest sex-specific associations between serum bilirubin levels and reduced CKD risk. However, the precise mechanisms by which sex differences influence the serum bilirubin-CKD relationship remain uncertain. Our findings align with prior studies reporting lower female IBIL levels than males\u003csup\u003e[26,50,51]\u003c/sup\u003e.\u0026nbsp;The observed sex difference in serum bilirubin levels may explain the sex-based disparity in the serum bilirubin-CKD correlation. First, studies show serum bilirubin level differences between men and women are not significantly associated with genetic factors\u003csup\u003e[52]\u003c/sup\u003e and typically do not manifest before age 10\u003csup\u003e[53]\u003c/sup\u003e.\u0026nbsp;Based on this, a study suggested serum bilirubin metabolism changes may be due to hormonal changes after puberty onset\u003csup\u003e[26]\u003c/sup\u003e.\u0026nbsp;Park et al. reported serum bilirubin levels were independently and positively associated with testosterone\u003csup\u003e[54]\u003c/sup\u003e,\u0026nbsp;and Muraca et al. reported testosterone suppressed hepatic bilirubin uridine diphosphate-glucuronosyltransferase activity in orchiectomized rats\u003csup\u003e[55]\u003c/sup\u003e.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eConversely, estradiol and progesterone enhance enzyme activity in gonadectomized female rodents\u003csup\u003e[55]\u003c/sup\u003e.\u0026nbsp;Under physiological conditions, estrogen exerts antioxidant effects\u003csup\u003e[56]\u003c/sup\u003e.\u0026nbsp;Thus, it can be postulated that in females, a synergistic antioxidant effect between estrogen and serum bilirubin may reduce serum bilirubin\u0026apos;s independent protective effect against CKD. Another study indicated estradiol and estrogen receptor signaling facilitate bilirubin metabolism\u003csup\u003e[57]\u003c/sup\u003e,\u0026nbsp;resulting in lower female serum bilirubin levels than males. Moreover, research shows androgen can impede serum bilirubin metabolism\u003csup\u003e[58]\u003c/sup\u003e.\u0026nbsp;Thus, sex hormones may significantly contribute to the observed sex differences in the serum IBIL level-reduced CKD risk association. On the other hand, our findings indicate heightened CKD risk in males, confirmed in several studies. A population-based study revealed kidney function declines more rapidly in males than females\u003csup\u003e[59]\u003c/sup\u003e.\u0026nbsp;Additionally, animal studies show estrogen has renal antifibrotic and antiapoptotic effects\u003csup\u003e[60]\u003c/sup\u003e,\u0026nbsp;while testosterone has renal proinflammatory, proapoptotic, and profibrotic effects\u003csup\u003e[61]\u003c/sup\u003e.\u0026nbsp;Regarding lifestyle habits, males are more prone to adopting unhealthy lifestyles than females, which may also contribute to sex-based CKD onset and development differences\u003csup\u003e[62,63]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA notable interaction between serum IBIL levels and occupation was identified in males via subgroup analysis. This study revealed elevated serum IBIL in managers was associated with a greater CKD risk reduction than in workers, technicians, and logisticians. Among male participants from diverse occupational backgrounds, this study found those in managerial roles had the lowest cumulative CKD incidence, while those in logistics and workers had a higher incidence. Generally, managers are less likely to be exposed to hazardous substances and adverse working conditions than other occupations. In a prospective cohort study of 65,069 participants with an average 6.7-year follow-up, adverse working conditions, including heavy workloads, shift work, occupational secondhand cigarette smoke exposure, and occupational heat exposure, significantly increased CKD risk\u003csup\u003e[64]\u003c/sup\u003e.\u0026nbsp;Participants were from a large mining group involved in mining, beneficiation, metallurgy, chemical engineering, and deep processing\u003csup\u003e[28]\u003c/sup\u003e.\u0026nbsp;Compared to managers, participants in other occupations may have a greater prevalence of underlying medical conditions and elevated CKD risk due to exposure to harmful environmental factors and suboptimal working conditions, among other factors. These elements may have somewhat diminished serum IBIL\u0026apos;s protective influence against CKD.\u003c/p\u003e\n\u003cp\u003eUndeniably, this study has limitations. First, CKD was defined using a single eGFR and albuminuria measurement, without 3-month follow-up blood tests or urine sediment retesting. Proteinuria was diagnosed only qualitatively with urine protein test strips, without quantitative diagnosis. Additionally, CKD-EPI equation measurement bias for CKD diagnosis may differ between males and females\u003csup\u003e[65]\u003c/sup\u003e.\u0026nbsp;These factors may affect CKD diagnosis accuracy. Second, analysis was based only on baseline serum IBIL levels, which may not reflect long-term serum IBIL changes related to CKD. Third, this study lacked participant medication use information. Consequently, it was not possible to assess medication effects on CKD or obtain post-medication serum IBIL levels. Finally, as all participants were from a single, large mining group in Northwest China, it is unclear if the findings can be generalized to other regions, ethnic populations, or occupational groups. Further multicenter prospective studies are needed for a deeper understanding of this phenomenon.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, serum IBIL levels have an independent protective effect on CKD risk and provide a reference for CKD risk in males.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committees of the College of Public Health, Lanzhou University, and the Workers\u0026rsquo; Hospital of the JNMC, and conforms to the ethical principles of the 2008 Declaration of Helsinki (sixth revision).\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and material\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available upon reasonable request from the corresponding authors.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eI declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Gansu Province Joint Fund Project (24JRRA819). The funders played no role in the design or conduct of the study, collection, management, analysis, or interpretation of the data or in the preparation, review, or approval of the article.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eJL-Y conceived the study, designed the methodology, developed the software, conducted formal analyses, and drafted the original manuscript. XW, JT, XQ-L, RW, and YY-L performed the software development and conducted formal analyses. CY collected and curated the experimental data. YN-B conceptualized the research framework, administered the project, and supervised the research team. MZ-W contributed to manuscript writing, provided critical revisions, edited the content, and assisted in methodological design. SZ initiated the research concept, developed the methodology, validated the results, oversaw the writing process with editorial revisions, and supervised the entire research program. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe express our sincere gratitude to all participants and researchers of the Jinchang cohort study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLv K, Liu Y, Zhang X, et al. Prevalence of chronic kidney disease in a city of Northwestern China: a cross-sectional study. Int Urol Nephrol, 2023, 55(8):2035-2045.\u003c/li\u003e\n\u003cli\u003eCockwell P, Fisher LA. The global burden of chronic kidney disease. Lancet, 2020, 395(10225):662-664.\u003c/li\u003e\n\u003cli\u003eGlobal, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet, 2020, 395(10225):709-733.\u003c/li\u003e\n\u003cli\u003eJager KJ, Kovesdy C, Langham R, et al. 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J Diabetes Complications, 2016, 30(7):1255-1260.\u003c/li\u003e\n\u003cli\u003eLiu Y, Li M, Song Y, et al. Association of serum bilirubin with renal outcomes in Han Chinese patients with chronic kidney disease. Clin Chim Acta, 2018, 480:9-16.\u003c/li\u003e\n\u003cli\u003eZhao P, Xu H, Shi Y, et al. Association between bilirubin and chronic kidney disease in hypertensive patients: The China hypertension registry study. J Clin Hypertens (Greenwich), 2023, 25(12):1185-1192.\u003c/li\u003e\n\u003cli\u003eDuni A, Liakopoulos V, Rapsomanikis KP, et al. Chronic Kidney Disease and Disproportionally Increased Cardiovascular Damage: Does Oxidative Stress Explain the Burden?. Oxid Med Cell Longev, 2017, 2017:9036450.\u003c/li\u003e\n\u003cli\u003eXu H, Watanabe M, Qureshi AR, et al. Oxidative DNA damage and mortality in hemodialysis and peritoneal dialysis patients. Perit Dial Int, 2015, 35(2):206-215.\u003c/li\u003e\n\u003cli\u003eDuni A, Liakopoulos V, Roumeliotis S, et al. Oxidative Stress in the Pathogenesis and Evolution of Chronic Kidney Disease: Untangling Ariadne\u0026apos;s Thread. Int J Mol Sci, 2019, 20(15).\u003c/li\u003e\n\u003cli\u003eCachofeiro V, Goicochea M, de Vinuesa SG, et al. Oxidative stress and inflammation, a link between chronic kidney disease and cardiovascular disease. Kidney Int Suppl, 2008, (111):S4-9.\u003c/li\u003e\n\u003cli\u003eRatliff BB, Abdulmahdi W, Pawar R, et al. Oxidant Mechanisms in Renal Injury and Disease. Antioxid Redox Signal, 2016, 25(3):119-146.\u003c/li\u003e\n\u003cli\u003eJiang F, Roberts SJ, Datla, Sr., et al. NO modulates NADPH oxidase function via heme oxygenase-1 in human endothelial cells. Hypertension, 2006, 48(5):950-957.\u003c/li\u003e\n\u003cli\u003eFujii M, Inoguchi T, Sasaki S, et al. Bilirubin and biliverdin protect rodents against diabetic nephropathy by downregulating NAD(P)H oxidase. Kidney Int, 2010, 78(9):905-919.\u003c/li\u003e\n\u003cli\u003eOwens D, Evans J. 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Influence of sex and sex steroids on bilirubin uridine diphosphate-glucuronosyltransferase activity of rat liver. Gastroenterology, 1984, 87(2):308-313.\u003c/li\u003e\n\u003cli\u003eHe S, Li Y, Li T, et al. Sex differences between serum total bilirubin levels and cognition in patients with schizophrenia. BMC Psychiatry, 2021, 21(1):396.\u003c/li\u003e\n\u003cli\u003eKao TL, Chen YL, Kuan YP, et al. Estrogen-Estrogen Receptor \u0026alpha; Signaling Facilitates Bilirubin Metabolism in Regenerating Liver Through Regulating Cytochrome P450 2A6 Expression. Cell Transplant, 2017, 26(11):1822-1829.\u003c/li\u003e\n\u003cli\u003eWalden CE, Knopp RH, Johnson JL, et al. Effect of estrogen/progestin potency on clinical chemistry measures. The Lipid Research Clinics Program Prevalence Study. Am J Epidemiol, 1986, 123(3):517-531.\u003c/li\u003e\n\u003cli\u003eNeugarten J, Acharya A, Silbiger SR. Effect of gender on the progression of nondiabetic renal disease: a meta-analysis. J Am Soc Nephrol, 2000, 11(2):319-329.\u003c/li\u003e\n\u003cli\u003eHutchens MP, Fujiyoshi T, Komers R, et al. Estrogen protects renal endothelial barrier function from ischemia‒reperfusion in vitro and in vivo. Am J Physiol Renal Physiol, 2012, 303(3):F377-385.\u003c/li\u003e\n\u003cli\u003eMetcalfe PD, Leslie JA, Campbell MT, et al. Testosterone exacerbates obstructive renal injury by stimulating TNF-alpha production and increasing proapoptotic and profibrotic signaling. Am J Physiol Endocrinol Metab, 2008, 294(2):E435-443.\u003c/li\u003e\n\u003cli\u003eNitsch D. Is there a difference in metabolic burden between men and women?. Nephrol Dial Transplant, 2014, 29(6):1110-1112.\u003c/li\u003e\n\u003cli\u003eEllam T, Fotheringham J, Kawar B. Differential scaling of glomerular filtration rate and ingested metabolic burden: implications for gender differences in chronic kidney disease outcomes. Nephrol Dial Transplant, 2014, 29(6):1186-1194.\u003c/li\u003e\n\u003cli\u003eLan R, Qin Y, Chen X, et al. Risky working conditions and chronic kidney disease. J Occup Med Toxicol, 2023, 18(1):26.\u003c/li\u003e\n\u003cli\u003eCarrero JJ, Hecking M, Chesnaye NC, et al. Sex and gender disparities in the epidemiology and outcomes of chronic kidney disease. Nat Rev Nephrol, 2018, 14(3):151-164.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Text box ","content":"\u003cp\u003eText box 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Jinchang Cohort, bilirubin, indirect bilirubin, chronic kidney disease, gender","lastPublishedDoi":"10.21203/rs.3.rs-7457824/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7457824/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCurrent evidence remains limited regarding associations between serum indirect bilirubin (IBIL) levels and chronic kidney disease (CKD) risk, particularly in gender-stratified analyses. This study investigated the gender-specific relationship between serum IBIL and CKD incidence.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUsing data from a prospective cohort in northwestern China, we followed 25,684 CKD-free participants. Cox proportional hazards models and restricted cubic spline regression were employed to assess IBIL-CKD associations. The predictive capacity of IBIL was evaluated through ROC curve analysis. Robustness of results was examined via subgroup and sensitivity analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOver 122,401.17 person-years of follow-up, 1,219 incident CKD cases emerged. Adjusted hazard ratios (95% CIs) for CKD were 0.794 (0.676\u0026ndash;0.932) overall and 0.713 (0.589\u0026ndash;0.862) among males. Area under the curve (AUC) values were 0.710 (0.704\u0026ndash;0.715; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) overall, 0.710 (0.703\u0026ndash;0.718; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for males, and 0.679 (0.670\u0026ndash;0.688; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for females. A linear dose-response pattern was observed exclusively in males. Results remained consistent across subgroup and sensitivity analyses.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur findings demonstrate an inverse association between serum IBIL levels and CKD risk, with particular clinical relevance in male populations. These results suggest serum IBIL could function as a valuable biomarker for early CKD detection in males.\u003c/p\u003e","manuscriptTitle":"Gender Differences in Association Between Serum Indirect Bilirubin and Chronic Kidney Disease Risk: A Prospective Cohort Study in Northwest China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-18 17:40:20","doi":"10.21203/rs.3.rs-7457824/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-25T07:17:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T21:09:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-18T06:51:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-13T13:46:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59082346462848528388534012630062729143","date":"2025-11-12T21:07:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331764888689965275547240105961732784099","date":"2025-11-11T08:32:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60227310867171280676257605866227524869","date":"2025-11-10T05:31:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187561679355199135861723123377069842648","date":"2025-11-10T01:44:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T13:43:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133589401493474890374705739195341237319","date":"2025-11-02T11:22:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T03:16:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193302273390112964646266955741926040883","date":"2025-09-11T02:48:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-11T02:27:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-29T10:04:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-26T10:52:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-26T10:51:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2025-08-26T02:09:35+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":"2226e927-029e-43b2-a5c4-c3e2cea7f83c","owner":[],"postedDate":"September 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:17:11+00:00","versionOfRecord":{"articleIdentity":"rs-7457824","link":"https://doi.org/10.1186/s12882-026-04929-7","journal":{"identity":"bmc-nephrology","isVorOnly":false,"title":"BMC Nephrology"},"publishedOn":"2026-04-02 16:00:01","publishedOnDateReadable":"April 2nd, 2026"},"versionCreatedAt":"2025-09-18 17:40:20","video":"","vorDoi":"10.1186/s12882-026-04929-7","vorDoiUrl":"https://doi.org/10.1186/s12882-026-04929-7","workflowStages":[]},"version":"v1","identity":"rs-7457824","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7457824","identity":"rs-7457824","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}