Temporal Alteration of Serum Bilirubin Levels and its Renoprotective Effects in Diabetic Nephropathy: Exploring the Hormonal Mechanisms

Temporal Alteration of Serum Bilirubin Levels and its Renoprotective Effects in Diabetic Nephropathy: Exploring the Hormonal Mechanisms | 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 Temporal Alteration of Serum Bilirubin Levels and its Renoprotective Effects in Diabetic Nephropathy: Exploring the Hormonal Mechanisms Can Cao, Shuwu Wei, Leijuan He, Xiaonan Wang, Chunyao Li, Yizhen Lu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3057599/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This current study represents a novel endeavor to scrutinize the correlation between the temporal alteration in serum total bilirubin (TBIL) concentrations and the rate of estimated glomerular filtration rate (eGFR). Additionally, this study aims to probe the plausible molecular mechanism underpinning the renoprotective effects of bilirubin concerning its hormonal characteristics. Materials and Methods In this study, a cohort of 103 patients diagnosed with DN and receiving medical care at Dongzhimen Hospital were recruited and monitored over a period of 2–7 years. The progression of DN was ascertained using a threshold of eGFR decline > -5.48%/year. To assess the relationship between the annual change in serum TBIL levels (%/year) and the slope of eGFR, multivariate binary logistic regression analysis was employed. Furthermore, the ROC curve analysis was employed to determine the cut-off value for TBIL levels (%/year). Results The use of multivariate binary logistic regression models revealed that serum TBIL levels (%/year) exhibited a significant correlation with the slope of eGFR. Moreover, the ROC curve analysis indicated a cut-off value of -6.729%/year for TBIL levels (%/year) with a sensitivity of 0.75 and specificity of 0.603, in diagnosing eGFR decline >-5.48%/year. Conclusions The findings of this study suggest that the sustained elevation of serum bilirubin concentration within the physiological range can effectively retard the progression of diabetic nephropathy (DN). Furthermore, the hormonal attributes of bilirubin may underlie its renoprotective effects. serum total bilirubin diabetic kidney disease Figures Figure 1 Figure 2 Introduction Diabetic nephropathy (DN) is a global health concern, accounting for the majority of end-stage renal disease cases [ 1 ]. However, the rate of renal function decline varies among individuals [ 2 ], underscoring the need to identify factors that influence the progression of DN. Previous studies have relied on a 30% reduction in estimated glomerular filtration rate (eGFR) as a surrogate endpoint for renal failure [ 3 – 4 ]. Unfortunately, this endpoint is not applicable to patients with high baseline eGFR or those experiencing a rapid decline in eGFR due to acute medication effects, thereby limiting its utility. The eGFR slope overcomes these limitations, thereby expanding the patient population that can be studied while significantly reducing the sample size and follow-up time [ 5 ]. This metric provides an accurate reflection of disease progression in patients with varying stages of kidney disease. Therefore, this investigation aims to evaluate the effect of bilirubin on DN progression by examining its influence on eGFR slope.In addition, our study used baseline TBIL levels and follow-up TBIL levels to calculate the annual change rate of bilirubin concentration on the basis of the follow-up time, which can better reveal the relationship between TBIL and DN progression over time rather than the bilirubin concentration at a time point or simple numerical difference between the follow-up level and the baseline level. Ahn et al [ 6 ] observed an inverse relationship between serum TBIL levels and DN progression, independent of traditional risk factors. A retrospective longitudinal study demonstrated that individuals with the lowest range of serum TBIL levels had the highest cumulative incidence of CKD stage 3, indicating that serum bilirubin may serve as an early marker of CKD progression in T2DM patients [ 7 ]. Eto et al [ 8 ] further validated the clinical utility of serum TBIL (≤ 0.5 mg/dL) in predicting and identifying high risk of ESRD in DN patients. These findings are rooted in the anti-inflammatory and anti-oxidative stress properties of bilirubin, which were examined in relation to serum bilirubin levels at a certain time point to delay DN progression. Currently, there is a gap in knowledge regarding the correlation between alterations in serum bilirubin levels and the progression of DN. Recent research suggests that bilirubin possesses a novel function as a metabolic hormone that promotes gene transcription in nuclear receptors [ 9 ]. In view of its hormonal properties, elevated levels of bilirubin within the physiological range could exert a positive influence on renal function. To date, no investigation has examined the association between annual change in serum TBIL levels and the eGFR slope, or the specific mechanisms that underlie its renoprotective properties. Hence, this study represents the first attempt to scrutinize these aspects of bilirubin's influence on DN progression. Materials and Methods Subjects and data The medical ethics committee of the Dongzhimen Hospital, Beijing University of Chinese Medicine, granted permission for the conduct of this retrospective study and the study conforms to the provisions of the Declaration of Helsinki. The data were obtained from the inpatient electronic medical records. The inclusion criteria were patients aged between 18 to 80 years, diagnosed with stage III ~ IV DN according to the Chinese guidelines for diagnosis and treatment of DN [ 10 ], and admitted to the Tongzhou Branch of Dongzhimen Hospital from January 2013 to December 2021. Exclusion criteria were patients with incomplete clinical information, history of kidney surgery, severe hepatic failure, infection, malignant tumor, cirrhosis, acute complications, and acid-base disturbances within 1 month. Patients who were admitted to the nephrology department at least twice within the monitoring period of 2 to 7 years were considered eligible for the study. Laboratory Assays Blood samples were collected from the study participants after an overnight fast of at least 8 hours. The biochemical parameters including glycated hemoglobin type A1c (HbA1c, %), fasting plasma glucose (FPG, mmol/L), serum creatinine (Scr, µmol/L), blood urea nitrogen (BUN, mmol/L), uric acid (UA, µmol/L), alanine aminotransferase (ALT, U/L), aspartate aminotransferase (AST, U/L), glutamyl transpeptidase (GGT, U/L), total bilirubin (serum TBIL, µmol/L), direct bilirubin (DBIL, µmol/L), and indirect bilirubin (IBIL, µmol/L) were analyzed using an automatic biochemical analyzer (Beckman-DXC800, American). The eGFR was calculated using the CKD-EPI formula [ 11 ]. Statistical analysis Continuous variables that followed a normal distribution were presented as mean ± standard deviation (SD), and the independent samples t-test was utilized to compare the means between two groups. Skewed data (Kolmogorov–Smirnov test: p < 0.1 for each) were presented as median (interquartile range). The Mann–Whitney U-test was used to compare the differences in the clinical characteristics. The chi-squared test (χ2 test) was applied to analyze categorical variables. Multivariate binary logistic regression was used to evaluate the relationship between the eGFR slope as the dependent variable and demographic, clinical, or laboratory variables as independent ones. The association between the eGFR slope and predictors was estimated using the Spearman correlation analysis. Data analysis was conducted using SPSS software (Statistical Package for the Social Sciences, version 25.0, Chicago). P -5.48% per year. The annual percentage change in eGFR and TBIL were calculated by dividing the absolute change in eGFR or serum TBIL by the baseline value, then by the follow-up time, and finally multiplying by 100. A cut-off value of -5.48% for the eGFR slope was established based on a previous reference[ 12 ]. The study population was categorized into two groups based on their eGFR slopes: Group 1, with an eGFR slope>-5.48%/year, and Group 2, with an eGFR slope ≤ -5.48%/year. Results General Data and Correlation Analysis In this study, a total of 103 individuals with DN were included. These individuals were classified into two groups based on their eGFR slope: group 1, comprising 40 patients with an eGFR slope >-5.48%/year, and group 2, comprising 63 patients with an eGFR slope ≤ -5.48%/year. At baseline, both groups were comparable in terms of their demographic characteristics, including age, gender, diabetes duration, monitoring time, SBP, DBP, and drinking habits, as well as all biochemical parameters. The detailed information is provided in Table 1 . Table 1 Main data on patients enrolled at the baseline of the study. Group1 Annual percentage in eGFR༞-5.48%/year Group2 Annual percentage in eGFR≤-5.48%/year z/t/ χ2 values P value Gender, males/females 25/15 43/20 0.361 0.548 Age, years 58.0(16.5) 59.0(15.5) -1.016 0.310 Monitoring time, years 3.3(1.3) 3.4(1.4) -0.802 0.422 Diabetes duration, years 12.2 ± 8.1 13.0 ± 7.02 -0.527 0.599 SBP, mmHg 133.1 ± 17.9 139.2 ± 20.9 -1.527 0.130 DBP, mmHg 79.5(8.0) 80.0(15.0) -1.506 0.132 Drinking, yes(%) 10(25) 16(25.4) 0.002 0.964 CKD Stage,G1/G2/G3/G4 20/11/9/0 28/15/16/4 2.918 0.404 DBP diastolic blood pressure; SBP systolic blood pressure. Group 1 had a median eGFR slope of 0.33%/year, ranging from – 5.45 to 26.6%/year, while group 2 had a median eGFR slope of -14.8%/year, ranging from − 41.49 to − 5.95. The median annual percentage change in serum TBIL was 0.38%/year, ranging from – 29.81 to 34.43%/year in group 1, and − 8.25%/year, ranging from − 13.81 to 22.81%/year in group 2. The annual percentage change in serum TBIL was significantly lower in group 2 than in group 1 (0.38 (11.3) vs -8.25 (14.7), z= -2.768, P = 0.006). The laboratory results showed that HbA1c, ACR, ALT, AST, serum TBIL, IBIL, and DBIL levels decreased significantly in group 2 after monitoring, while there was no significant change in these parameters in group 1.The detailed information is provided in Table 2 . Table 2 Results of laboratory analyses at the before and the after monitoring of the study for two groups formed according to the annual percentange change in eGFR Group1 Annual percentage in eGFR༞-5.48%/year Group2 Annual percentage in eGFR≤-5.48%/year Before After P value Before After P value Annual percentage change in eGFR,%/year 0.33(5.9) -14.8(11.0) Annual percentage change in serum TBIL,%/year 0.38(11.3) -8.25(14.7) eGFR, ml/min/1.73m 2 89.9(46.0) 96.6(44.4) 0.460 84.4(61.9) 38.1(37.3) < 0.001 BUN, mmol/L 5.2(2.5) 6.4(3.1) 0.008 6.2(2.9) 10.4(9.0) < 0.001 Scr, µmol/L 77.0(31.7) 71.5(30.5) 0.039 79.0(54.0) 147.0(152.0) < 0.001 ACR,A2/A3 24/16 20/20 0.369 16/47 7/56 0.038 GLU, mmol/L 10.0(5.9) 10.2(5.7) 0.587 9.2(4.3) 7.7(4.3) 0.031 HbA1c, % 9.2(1.5) 8.7(3.0) 0.798 9.7(3.3) 8.2(2.5) < 0.001 ALT, U/L 19.5(19.5) 18.5(13) 0.231 17.0(12.0)) 14.0(8.2) < 0.001 AST, U/L 19.0(14.9) 19.5(7.5) 0.214 18.0(7.5) 15.0(7.0) < 0.001 GGT, U/L 28.0(26.5) 26.5(20.5) 0.261 25.0(20.3) 24.0(11.8) 0.084 serum TBIL, µmol/L 12.6(7.8) 13.8(7.0) 0.941 12.2(7.6) 8.7(4.6) < 0.001 DBIL, µmol/L 3.4(2.1) 3.5(1.6) 0.411 2.8(1.9) 2.2(1.4) 0.010 IBIL, µmol/L 9.7(6.5) 9.7(6.4) 0.752 9.1(6.2) 6.4(2.9) < 0.001 ACR, A2:<300mg/g; A3: ≥300mg/g. Given the potential association between several biochemical parameters (ACR, SCR, GLU, ALT, AST, serum TBIL, serum TBIL (%/year), IBIL, and DBIL) and the eGFR slope, we conducted a multivariate binary logistic regression analysis to identify potential protective factors for the annual percentage change in eGFR. Specifically, we included ACR, DBIL, and serum TBIL (%/year) as covariates in our analysis. Our results, as presented in Table 3 , indicate that DBIL was independently associated with a protective effect against changes in eGFR (OR 0.565, 95% CI 0.392–0.814, P = 0.002), along with serum TBIL (%/year) (OR 0.938, 95% CI 0.898–0.979, P = 0.003). In contrast, ACR was identified as an independent risk factor (OR 4.850, 95% CI 1.914–12.290, P = 0.001).The detailed information is provided in Table 3 . Table 3 Variables associated with the annual percentage change in eGFR(Binary logistic regression) Univariate analysis Multivariat analysis OR 95CI% P value OR 95CI% P value GLU, mmol/L 0.975 0.889 ~ 1.070 0.595 ACR, A2/A3 4.339 1.897 ~ 10.201 0.001 4.850 1.914 ~ 12.290 0.001 HbA1c, % 1.129 0.908 ~ 1.404 0.275 BUN 1.153 0.972 ~ 1.369 0.102 SCR 1.010 0.998 ~ 1.023 0.090 GFR 0.997 0.984 ~ 1.009 0.583 ALB 0.989 0.907 ~ 1.078 0.802 ALT, U/L 0.976 0.949 ~ 1.003 0.075 AST, U/L 0.969 0.935 ~ 1.005 0.087 GGT, U/L 0.996 0.988 ~ 1.006 0.448 serum TBIL, µmol/L 0.924 0.857 ~ 0.996 0.038 DBIL,µmol/L 0.680 0.508 ~ 0.909 0.009 0.565 0.392 ~ 0.814 0.002 IBIL, µmol/L 0.929 0.853 ~ 1.012 0.093 serum TBIL,%/year 0.958 0.925 ~ 0.992 0.017 0.938 0.898 ~ 0.979 0.003 Gender 1.290 0.562 ~ 2.962 0.548 Age 0.977 0.940 ~ 1.014 0.977 Diabetes duration 1.015 0.962 ~ 1.071 0.595 SBP 1.016 0.995 ~ 1.038 0.131 DBP 1.025 0.983 ~ 1.070 0.243 Drinking 1.021 0.410 ~ 2.545 0.964 Spearman correlation analysis was performed to assess the association between eGFR(%/year) and various biochemical parameters including serum TBIL,%/year, DBIL, and ACR. The analysis revealed that serum TBIL,%/year and DBIL had a significantly positive correlation with eGFR(%/year) (rs value: 0.352, P < 0.001; rs value: 0.228, P = 0.021, respectively). Conversely, ACR showed a negative correlation with eGFR(%/year) (rs value: -0.338, P < 0.001). These findings are presented in Table 4 . Table 4 Correlation of eGFR(%/year) and predictors Variable rs values P values serum TBIL,%/year 0.352 < 0.001 ACR -0.338 < 0.001 DBIL 0.228 0.021 Receiver operating characteristic (ROC) curves were utilized to analyze the endpoint of -5.48/year for the eGFR slope, as shown in Fig. 1 . Patients were divided into group 1 (marked as 0), with an eGFR slope greater than − 5.48%/year, and group 2 (marked as 1), with an eGFR slope less than or equal to -5.48%/year. ROC curves were utilized to identify a cutoff value for serum TBIL,%/year that distinguished patients with a high risk of an annual percentage change in eGFR greater than − 5.48%/year. The cutoff value with the highest accuracy was found to be -6.729% (sensitivity 75.0%; specificity 60.3%), and patients were subsequently categorized into two groups: Group 3, those with serum TBIL,%/year greater than − 6.729%, and group 4, those with serum TBIL,%/year less than or equal to -6.729%.The detailed information of the two groupsis provided in Table 5 Table 5 Results of laboratory analyses at the before and the after monitoring of the study between low or high serum TBIL Group3 Annual percentage in serum TBIL༞-6.729%/year(n = 55) Group4 Annual percentage in serum TBIL≤-6.729%/year(n = 48) Before After P value Before After P value Annual percentage change in eGFR,%/year -5.6 ± 10.6 -14.4 ± 10.7 ACR,A2/A3 26/29 18/37 0.119 14/34 9/39 0.232 Monitoring time, years 3.5(1.4) 3.0(1.3) eGFR, ml/min/1.73m 2 90.34(51.9) 77.6(53.8) < 0.001 79.8(63.0) 43.1(20.9) < 0.001 BUN, mmol/L 5.5(2.8) 7.2(5.4) < 0.001 5.75(2.7) 9.8(6.6) < 0.001 Scr, µmol/L 77.0(46.0) 86.0(59.0) 0.003 79.0(48.0) 134.5(96.0) < 0.001 GLU, mmol/L 9.1(4.1) 9.0(4.7) 0.804 10.5(6.8) 7.7(5.4) 0.015 HbA1c, % 9.4(3.2) 8.6(2.3) 0.233 9.6(3.1) 8.4(2.8) 0.001 ALT, U/L 18.0(17.1) 15.0(13.1) 0.007 17.5(14.0) 15.0(10.3) 0.012 AST, U/L 17.0(11.5) 17.0(7.0) 0.014 19.0(8.5) 17.0(5.0) 0.010 GGT, U/L 24.0(23.5) 23.0(15.9) 0.047 27.0(17.8) 25.5(18.0) 0.346 Upon comparing the laboratory results, it was observed that the eGFR after monitoring was significantly lower compared to before monitoring. However, in Group 4, a significant decrease was observed in the annual percentage change in eGFR as compared to Group 3, as evidenced by the mean values of -5.6 ± 10.6 and − 14.4 ± 10.7, respectively (t = 4.149, P < 0.001), as presented in Fig. 2 . Discussion This investigation demonstrates a statistically significant association between annual change in serum TBIL levels within the physiological range and the eGFR slope, and highlights the independent protective effect of serum DBIL levels against DN progression. These findings suggest that fluctuations in serum bilirubin concentration over time could be implicated in the progression of DN, and that higher levels of bilirubin within the physiological range may have a beneficial effect in delaying DN progression. Many previous studies have been tried to demonstrate the relationship between serum total bilirubin and DN. Tafese R et al.[26]showed that the serum total bilirubin level was significantly lower in DN patients compared with non-DN patients and was significantly associated with an increased risk of DN patients. A 10-year observational cohort study in Japanese patients with diabetes was to evaluate the association of baseline serum TBIL levels with progression of DN and found that serum TBIL levels were negatively associated with PRD in diabetic nephropathy and its cut-off point was 0.5 mg/dL.[ 8 ].Liu M et al.[27] showed that not only baseline TBIL, but also follow-up changes were significantly associated with DN incidence and progression based on a 5 years' cohort study. Ahn KH et al.[ 6 ]conducted a retrospective observational longitudinal study of patients, and showed that serum bilirubin might be an early clinical marker for predicting the progression of CKD in patients with T2DM and preserved renal function.Our study used baseline TBIL levels and follow-up TBIL levels to calculate the annual change rate of bilirubin concentration on the basis of the follow-up time, which can better reveal the relationship between TBIL and DN progression over time rather than the bilirubin concentration at a time point or simple numerical difference between the follow-up level and the baseline level. Our study defined progression of DN as having an eGFR slope > -5.48% per year rather than the follow-up eGFR 30% lower than that of the baseline, which metric provides an accurate reflection of disease progression in patients with varying stages of kidney disease. In addition, instead of analyzing the protective effect of bilirubin based on its anti-inflammatory and antioxidant properties, we tried to elucidate a completely new mechanism.However, it is worth noting that extremely high serum bilirubin levels are considered pathological and have been associated with increased mortality[ 13 ]. Our investigation reveals that patients with a faster annual decline in renal function exhibit a faster annual decline in serum TBIL levels. Specifically, patients with an eGFR slope of greater than − 5.48%/year had higher serum TBIL (%/year) levels, while patients with an eGFR slope of less than or equal to -5.48%/year had lower serum TBIL (%/year) levels. Furthermore, our long-term follow-up analysis revealed that all bilirubin levels, including TBIL, DBIL, and IBIL, were significantly decreased in the group with a lower eGFR slope. Multivariate binary logistic regression models showed that serum TBIL (%/year) level was significantly associated with eGFR slope, while serum DBIL level was an independent protective factor for eGFR slope, and ACR was an independent risk factor. In addition, we found a significant positive correlation between eGFR (%/year) and serum TBIL (%/year) and DBIL, while ACR showed a significant negative correlation. These findings support the notion that annual change in serum TBIL levels may be associated with diabetic nephropathy progression. Higher levels of DBIL in the physiological range appear to be an independent protective factor for DN progression, whereas ACR is an independent risk factor. The pathogenesis of DN mainly involves damage to the glomerulus, with podocyte injury being a key hallmark of this condition. Podocyte injury manifests as a disappearance and fusion of podocyte foot processes, leading to the accumulation of damage over time and ultimately resulting in podocyte apoptosis and shedding, which results in a substantial increase in proteinuria. This process is observed in the early stages of DN, and is a major driver of disease progression. Proression proteinuria is one of the most important prognostic risk factors for DN, which promotes the development of DN to ESRD[ 14 ]. Among the various morphological features, the decreased number of glomerular podocytes was the strongest predictor of DN progression, with fewer podocytes indicating the faster the progression of DN. Dyslipidemia is an independent risk factor for the development of DN[ 15 ]. High glucose can induce lipid droplet deposition, enabling the continuous accumulation of the extracellular matrix and progressive fibrosis in the kidneys with the progression of DN[ 16 – 17 ]. Previous studies have initially shown the underlying molecular mechanisms of this developmental process: High glucose causes intracellular lipid accumulation and phenotype changes in the podocytes, which can cause the podocytes to lose their various morphological structure and function, and damage the integrity of glomerular filtration barrier and the synthesis of extracellular matrix components such as collagen, thus producing proteinuria and promoting DN progression[ 18 ]. Herman et al [ 19 ] reported severe lipid deposition and increased intracellular lipid droplets in renal biopsies of DN patients, while the levels of several genes involved in the fatty acid oxidation pathway were significantly downregulated, including PPAR α. Activated PPAR α significantly reduced triglyceride levels, and moderately reduced total cholesterol levels and low-density lipoprotein cholesterol levels, thus increasing high-density lipoprotein cholesterol levels[ 20 ]. PPAR α is expressed in kidney podocytes, and activated PPAR α is able to reduce albuminuria and improve insulin resistance [ 21 ]. Activation of PPAR α may be a favorable factor for DN progression. Recent studies suggest that bilirubin has a completely new function as a metabolic hormone for transcription of the genes that drive the nuclear receptors [ 9 ]. Higher levels of bilirubin in the physiological range may have beneficial effects on the kidney due to this hormonal characteristics. Bilirubin can bind directly to PPARα and increase transcriptional activity[ 22 ]. Stec et al [ 23 ] showed that global PPAR α knockout mice have a reduced genetic response to bilirubin treatment, particularly hepatic fibroblast growth factor-21, a well-characterized PPAR α target gene. The hormone must bind directly to the target receptor to exert its effects. Although bilirubin may have an unknown role in activating other pathways, PPAR α has been shown to be a direct binding protein for bilirubin as a ligand agonist. Recent studies indicated that bilirubin only selectively bound PPAR α and did not interact with PPAR γ or PPAR δ[ 24 ]. It is therefore speculated that the specific mechanism by which higher levels of bilirubin delay DN progression is related to the hormonal properties of bilirubin. Bilirubin flows through the blood over time and gradually enters the cell through the uptake system, continuously binding to the target receptor PPARα within the cell, resulting in core pressurized protein translated into coactivators to activate transcriptional control of the genes, which can further increase the downstream fat burning, improve insulin resistance and lipid metabolism disorders, regulate blood glucose levels, and thus play a role in delaying the progression of DN. Although the results obtained in this clinical study can not directly reveal the intrinsic mechanism by which bilirubin as a hormone exerts a protective effect on DN, they showed that with the gradual increase of total bilirubin concentration in the physiological range over time, it may delay the progression of DN, which has a positive effect on the above mechanisms. ROC curves were utilized to determine the optimal cutoff value for serum TBIL (%/year) that would identify patients at high risk of annual percentage in eGFR > -5.48%/year. The cutoff value that provided the highest accuracy was found to be -6.729%, with a sensitivity of 75.0% and specificity of 60.3%. Using this threshold, patients were divided into two groups: Group 3 comprised individuals with serum TBIL (%/year) > -6.729%, while group 4 included those with serum TBIL (%/year) ≤ -6.729%. Comparative analysis of laboratory data demonstrated that the eGFR after monitoring was significantly lower than that prior to monitoring, with the eGFR slope in group 4 being considerably lower than that observed in group 3. Furthermore, the results indicated that the serum creatinine and serum urea nitrogen levels were significantly elevated, reaching pathological levels in group 4, thereby validating the association between a more rapid decrease in TBIL levels over time and a more pronounced decline in eGFR slope, as well as greater impairment of renal function. Notably, our retrospective investigation relied solely on the clinical data of the participants, without any molecular information. Therefore, it remains necessary to verify the precise molecular mechanism by which bilirubin operates as a hormone and confers protection to renal function. It is crucial to highlight that this study had a limited sample size and was conducted solely in Dongzhimen Hospital, indicating the need for further research on a larger scale, involving multiple centers and adopting a prospective approach to validate the serum TBIL (%/year) cutoff value obtained from the present findings. The augmentation of serum bilirubin concentration within the physiological range over an extended period is advantageous in retarding the progression of diabetic nephropathy (DN), whereby the hormonal capacity of bilirubin might serve as a prospective molecular mechanism underlying its renoprotective effect. These findings could open up novel avenues for clinical intervention and targets to delay the progression of DN. Declarations Ethical approval and consent to participate This study was approved by ethical committee in Dongzhimen Hospital, The First Affiliated Hospital of Beijing University of Chinese Medicine (2017-JYB-JS-076). All experiments were performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments as revised in 2008. Informed consent was obtained from all subjects and/or their legal guardian(s). Consent for publication Not Applicable Availability of data and material The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Competing interest All the authors declare that they have no conflict of interest. Funding This project was not funded. Authors' contributions Can Cao conceived the study, analyzed the data. Can Cao and Shuwu Wei drafted the manuscript and recruited patients. Leijuan He and Xiaonan Wang recruited patients, collected data, and Chunyao Li and Yizheng Lu participated in patient visits. Wang Yaoxian and Sun Weiwei conceived the study, participated in its design and coordination, and helped draft the manuscript. All authors read and approved the final manuscript. Acknowledgments The authors thank all the participants in the study and colleagues in the nursing group in their department for blood sampling. References Tuttle KR, Bakris GL, Bilous RW, et al. Diabetic kidney disease: a report from an ADA Consensus Conference. Diabetes Care. 2014 Oct;37(10):2864-83. Thomas MC, Brownlee M, Susztak K, et a1. Diabetic kidney disease. Nat Rev Dis Primers. 2015 Jul 30;1:15018. Levey AS, Inker LA, Matsushita K, et a1. GFR decline as an end point for clinical trials in CKD: a scientific workshop sponsored by the National Kidney Foundation and the US Food and Drug Administration. Am J Kidney Dis. 2014 Dec;64(6):821-35. Coresh J, Turin TC, Matsushita K, et al. Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality. JAMA. 2014 Jun 25;311(24):2518-2531. Levey AS, Gansevoort RT, Coresh J, et al. Change in Albuminuria and GFR as End Points for Clinical Trials in Early Stages of CKD: A Scientific Workshop Sponsored by the National Kidney Foundation in Collaboration With the US Food and Drug Administration and European Medicines Agency. Am J Kidney Dis. 2020 Jan;75(1):84-104. Ahn KH, Kim SS, Kim WJ, et al. Low serum bilirubin level predicts the development of chronic kidney disease in patients with type 2 diabetes mellitus. Korean J Intern Med. 2017 Sep;32(5):875-882. Riphagen IJ, Deetman PE, Bakker SJ, et al. Bilirubin and progression of nephropathy in type 2 diabetes: a post hoc analysis of RENAAL with independent replication in IDNT. Diabetes. 2014 Aug;63(8):2845-53. Eto E, Maeda Y, Sonoda N, et al. Association of serum total bilirubin levels with progressive renal decline and end-stage kidney disease: 10-year observational cohort study in Japanese patients with diabetes. PLoS One. 2022 Jul 12;17(7):e0271179. Creeden JF, Gordon DM, Stec DE, et al. Bilirubin as a metabolic hormone: the physiological relevance of low levels. Am J Physiol Endocrinol Metab. 2021 Feb 1;320(2):E191-E207. Expert Group of Chinese Society of Nephrology. Chinese guidelines for diagnosis and treatment of diabetic kidney disease. Chin J Nephrol,37,255-304(2021). Levey A S, Stevens L A, Schmid C H, et a1. A New Equation to Estimate Glomerular Filtration Rate[J]. Ann Intern Med, 2009, 150(9): 604-612. Kovačević M, Mališ S, Pavlović D, Kovačević M, Savić Radojević A, Djukanović L. Plasma activity of the antioxidant enzymes in predicting diabetic nephropathy progression. Int Urol Nephrol. 2022,54(6):1365-1372. Dhawan A, Lawlor MW, Mazariegos GV, et al. Disease burden of Crigler-Najjar syndrome: Systematic review and future perspectives. J Gastroenterol Hepatol. 2020 Apr;35(4):530-543. Denhez B, Geraldes P. Regulation of Nephrin Phosphorylation in Diabetes and Chronic Kidney Injury. Adv Exp Med Biol. 2017;966:149-161. Kuwabara T, Mori K, Mukoyama M, et al. Macrophage-mediated glucolipotoxicity via myeloid-related protein 8/toll-like receptor 4 signaling in diabetic nephropathy. Clin Exp Nephrol. 2014 Aug;18(4):584-92. Sun L, Halaihel N, Zhang W, et al. Role of sterol regulatory element-binding protein 1 in regulation of renal lipid metabolism and glomerulosclerosis in diabetes mellitus. J Biol Chem. 2002 May 24;277(21):18919-27. Wang Z, Jiang T, Li J, et al. Regulation of renal lipid metabolism, lipid accumulation, and glomerulosclerosis in FVBdb/db mice with type 2 diabetes. Diabetes. 2005 Aug;54(8):2328-35. Zhang Y, Ma KL, Liu J, et al. Dysregulation of low-density lipoprotein receptor contributes to podocyte injuries in diabetic nephropathy. Am J Physiol Endocrinol Metab. 2015 Jun 15;308(12):E1140-8. Herman-Edelstein M, Scherzer P, Tobar A, et al. Altered renal lipid metabolism and renal lipid accumulation in human diabetic nephropathy. J Lipid Res. 2014 Mar;55(3):561-72. Yu XH, Zheng XL, Tang CK. Peroxisome Proliferator-Activated Receptor α in Lipid Metabolism and Atherosclerosis. Adv Clin Chem. 2015;71:171-203. Gao J, Gu Z. The Role of Peroxisome Proliferator-Activated Receptors in Kidney Diseases. Front Pharmacol. 2022 Mar 4;13:832732. Stec DE, John K, Trabbic CJ, et al. Bilirubin Binding to PPARα Inhibits Lipid Accumulation. PLoS One. 2016 Apr 12;11(4):e0153427. Gordon DM, Neifer KL, Hamoud AA, et al. Bilirubin remodels murine white adipose tissue by reshaping mitochondrial activity and the coregulator profile of peroxisome proliferator-activated receptor α. J Biol Chem. 2020 Jul 17;295(29):9804-9822. Tafese R, Genet S, Addisu S. Association of Serum Total Bilirubin and Uric Acid with Low Glomerular Filtration Rate Diabetic Kidney Disease in Type 2 Diabetic Patients. Diabetes Metab Syndr Obes. 2022 Dec 21;15:3993-3999. Liu M, Li J, Lv X, et al Bilirubin and its changes were negatively associated with diabetic kidney disease incidence and progression: A five-year's cohort study based on 5323 Chinese male diabetic patients. J Diabetes Complications. 2018 Nov;32(11):1012-1017. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3057599","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":213109926,"identity":"1bdcba13-67df-407a-8e59-7f71c418c16a","order_by":0,"name":"Can Cao","email":"","orcid":"","institution":"Dongzhimen Hospital, Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Can","middleName":"","lastName":"Cao","suffix":""},{"id":213109930,"identity":"4c2d43de-9ee7-400e-9b8f-e255b4ed396c","order_by":1,"name":"Shuwu Wei","email":"","orcid":"","institution":"Dongzhimen Hospital, Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shuwu","middleName":"","lastName":"Wei","suffix":""},{"id":213109935,"identity":"66dc6f07-e729-407e-b961-8130021eb7b3","order_by":2,"name":"Leijuan He","email":"","orcid":"","institution":"Dadushe Community Health Service Center, Majuqiao Town, Tongzhou District, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Leijuan","middleName":"","lastName":"He","suffix":""},{"id":213109940,"identity":"e3d555e4-ea51-4552-aea4-95ef7e2cbda3","order_by":3,"name":"Xiaonan Wang","email":"","orcid":"","institution":"Dongzhimen Hospital, Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaonan","middleName":"","lastName":"Wang","suffix":""},{"id":213109942,"identity":"9b29d6fb-782d-48bc-aa2f-e0446fb00d87","order_by":4,"name":"Chunyao Li","email":"","orcid":"","institution":"Dongzhimen Hospital, Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chunyao","middleName":"","lastName":"Li","suffix":""},{"id":213109945,"identity":"176191ff-a78c-4878-bbc5-59fcd54d8723","order_by":5,"name":"Yizhen Lu","email":"","orcid":"","institution":"Dongzhimen Hospital, Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yizhen","middleName":"","lastName":"Lu","suffix":""},{"id":213109947,"identity":"ae9075ac-b600-4ded-b5f9-93d10f51b010","order_by":6,"name":"Weiwei Sun","email":"","orcid":"","institution":"Dongzhimen Hospital, Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Sun","suffix":""},{"id":213109949,"identity":"1b2823aa-0f1a-40b2-ab8f-e7852674031f","order_by":7,"name":"Yaoxian Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYJCCAx8qbHj42ZsPPmAwIE4H48EZZ9LkJHuOJRsQq4X5MG/LIWODGzlmEkSpB6o0OMzbcCCx4UCCWeWPgjvyDOyHj27AryUt4eDcHXcSGxsOpN3mMXhm2MCTlnYDnxazG8kHDrw98yyxmbHh2G0Gg8OMDRI8ZgS0AJ3E23Y4sY2Zsa3wh8FheyK0JB84CNRizMPGzMbAY3A4kaAW+zPPEsCBLAHUIw3UktxGyC+S7TnGH0BRaX///cePP/4ctu1nP3wMrxYGgQQ0ATa8ykGA/wBBJaNgFIyCUTDSAQD9/Fjs2ZEc7AAAAABJRU5ErkJggg==","orcid":"","institution":"Dongzhimen Hospital, Beijing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yaoxian","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2023-06-13 10:29:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3057599/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3057599/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":39317454,"identity":"a9826211-7860-41ad-8bd8-2ba8810c5fb5","added_by":"auto","created_at":"2023-06-29 17:49:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":106079,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of eGFR(%/year) and serum TBIL(%/year), DBIL\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3057599/v1/60ce0cd9ba022ddb84486f09.png"},{"id":39317453,"identity":"fb58a340-86e0-47e7-9756-ad0a965c5a7a","added_by":"auto","created_at":"2023-06-29 17:49:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43224,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves in predicing annual percentage change in eGFR by serum TBIL(continuous, AUC= 0.662, \u003cem\u003eP\u003c/em\u003e=0.006, CI% 0.557~0.768.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3057599/v1/4f5290c1d8b5bf667cf2e5ba.png"},{"id":53053438,"identity":"febc74ee-07cc-470b-b144-ab9137e5f956","added_by":"auto","created_at":"2024-03-20 05:26:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":506784,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3057599/v1/11a29846-77c0-4a13-87b5-a4489df3fbca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Temporal Alteration of Serum Bilirubin Levels and its Renoprotective Effects in Diabetic Nephropathy: Exploring the Hormonal Mechanisms","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetic nephropathy (DN) is a global health concern, accounting for the majority of end-stage renal disease cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, the rate of renal function decline varies among individuals [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], underscoring the need to identify factors that influence the progression of DN. Previous studies have relied on a 30% reduction in estimated glomerular filtration rate (eGFR) as a surrogate endpoint for renal failure [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Unfortunately, this endpoint is not applicable to patients with high baseline eGFR or those experiencing a rapid decline in eGFR due to acute medication effects, thereby limiting its utility. The eGFR slope overcomes these limitations, thereby expanding the patient population that can be studied while significantly reducing the sample size and follow-up time [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This metric provides an accurate reflection of disease progression in patients with varying stages of kidney disease. Therefore, this investigation aims to evaluate the effect of bilirubin on DN progression by examining its influence on eGFR slope.In addition, our study used baseline TBIL levels and follow-up TBIL levels to calculate the annual change rate of bilirubin concentration on the basis of the follow-up time, which can better reveal the relationship between TBIL and DN progression over time rather than the bilirubin concentration at a time point or simple numerical difference between the follow-up level and the baseline level.\u003c/p\u003e \u003cp\u003eAhn et al [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] observed an inverse relationship between serum TBIL levels and DN progression, independent of traditional risk factors. A retrospective longitudinal study demonstrated that individuals with the lowest range of serum TBIL levels had the highest cumulative incidence of CKD stage 3, indicating that serum bilirubin may serve as an early marker of CKD progression in T2DM patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Eto et al [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] further validated the clinical utility of serum TBIL (\u0026le;\u0026thinsp;0.5 mg/dL) in predicting and identifying high risk of ESRD in DN patients. These findings are rooted in the anti-inflammatory and anti-oxidative stress properties of bilirubin, which were examined in relation to serum bilirubin levels at a certain time point to delay DN progression.\u003c/p\u003e \u003cp\u003eCurrently, there is a gap in knowledge regarding the correlation between alterations in serum bilirubin levels and the progression of DN. Recent research suggests that bilirubin possesses a novel function as a metabolic hormone that promotes gene transcription in nuclear receptors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In view of its hormonal properties, elevated levels of bilirubin within the physiological range could exert a positive influence on renal function. To date, no investigation has examined the association between annual change in serum TBIL levels and the eGFR slope, or the specific mechanisms that underlie its renoprotective properties. Hence, this study represents the first attempt to scrutinize these aspects of bilirubin's influence on DN progression.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects and data\u003c/h2\u003e \u003cp\u003e The medical ethics committee of the Dongzhimen Hospital, Beijing University of Chinese Medicine, granted permission for the conduct of this retrospective study and the study conforms to the provisions of the Declaration of Helsinki. The data were obtained from the inpatient electronic medical records. The inclusion criteria were patients aged between 18 to 80 years, diagnosed with stage III\u0026thinsp;~\u0026thinsp;IV DN according to the Chinese guidelines for diagnosis and treatment of DN [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and admitted to the Tongzhou Branch of Dongzhimen Hospital from January 2013 to December 2021. Exclusion criteria were patients with incomplete clinical information, history of kidney surgery, severe hepatic failure, infection, malignant tumor, cirrhosis, acute complications, and acid-base disturbances within 1 month. Patients who were admitted to the nephrology department at least twice within the monitoring period of 2 to 7 years were considered eligible for the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eLaboratory Assays\u003c/h2\u003e \u003cp\u003eBlood samples were collected from the study participants after an overnight fast of at least 8 hours. The biochemical parameters including glycated hemoglobin type A1c (HbA1c, %), fasting plasma glucose (FPG, mmol/L), serum creatinine (Scr, \u0026micro;mol/L), blood urea nitrogen (BUN, mmol/L), uric acid (UA, \u0026micro;mol/L), alanine aminotransferase (ALT, U/L), aspartate aminotransferase (AST, U/L), glutamyl transpeptidase (GGT, U/L), total bilirubin (serum TBIL, \u0026micro;mol/L), direct bilirubin (DBIL, \u0026micro;mol/L), and indirect bilirubin (IBIL, \u0026micro;mol/L) were analyzed using an automatic biochemical analyzer (Beckman-DXC800, American). The eGFR was calculated using the CKD-EPI formula [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables that followed a normal distribution were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and the independent samples t-test was utilized to compare the means between two groups. Skewed data (Kolmogorov\u0026ndash;Smirnov test: p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 for each) were presented as median (interquartile range). The Mann\u0026ndash;Whitney U-test was used to compare the differences in the clinical characteristics. The chi-squared test (χ2 test) was applied to analyze categorical variables. Multivariate binary logistic regression was used to evaluate the relationship between the eGFR slope as the dependent variable and demographic, clinical, or laboratory variables as independent ones. The association between the eGFR slope and predictors was estimated using the Spearman correlation analysis. Data analysis was conducted using SPSS software (Statistical Package for the Social Sciences, version 25.0, Chicago). P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eThe study defined progression of DN as having an eGFR slope \u0026gt; -5.48% per year. The annual percentage change in eGFR and TBIL were calculated by dividing the absolute change in eGFR or serum TBIL by the baseline value, then by the follow-up time, and finally multiplying by 100. A cut-off value of -5.48% for the eGFR slope was established based on a previous reference[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The study population was categorized into two groups based on their eGFR slopes: Group 1, with an eGFR slope\u0026gt;-5.48%/year, and Group 2, with an eGFR slope \u0026le; -5.48%/year.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGeneral Data and Correlation Analysis\u003c/h2\u003e \u003cp\u003eIn this study, a total of 103 individuals with DN were included. These individuals were classified into two groups based on their eGFR slope: group 1, comprising 40 patients with an eGFR slope \u0026gt;-5.48%/year, and group 2, comprising 63 patients with an eGFR slope \u0026le; -5.48%/year. At baseline, both groups were comparable in terms of their demographic characteristics, including age, gender, diabetes duration, monitoring time, SBP, DBP, and drinking habits, as well as all biochemical parameters. The detailed information is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain data on patients enrolled at the baseline of the study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup1\u003c/p\u003e \u003cp\u003eAnnual percentage in eGFR༞-5.48%/year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup2\u003c/p\u003e \u003cp\u003eAnnual percentage in eGFR\u0026le;-5.48%/year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez/t/ χ2 values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, males/females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25/15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43/20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.0(16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.0(15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonitoring time, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes duration, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139.2\u0026thinsp;\u0026plusmn;\u0026thinsp;20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.5(8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.0(15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, yes(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD Stage,G1/G2/G3/G4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20/11/9/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28/15/16/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDBP diastolic blood pressure; SBP systolic blood pressure.\u003c/p\u003e \u003cp\u003eGroup 1 had a median eGFR slope of 0.33%/year, ranging from \u0026ndash; 5.45 to 26.6%/year, while group 2 had a median eGFR slope of -14.8%/year, ranging from \u0026minus;\u0026thinsp;41.49 to \u0026minus;\u0026thinsp;5.95. The median annual percentage change in serum TBIL was 0.38%/year, ranging from \u0026ndash; 29.81 to 34.43%/year in group 1, and \u0026minus;\u0026thinsp;8.25%/year, ranging from \u0026minus;\u0026thinsp;13.81 to 22.81%/year in group 2. The annual percentage change in serum TBIL was significantly lower in group 2 than in group 1 (0.38 (11.3) vs -8.25 (14.7), z= -2.768, P\u0026thinsp;=\u0026thinsp;0.006). The laboratory results showed that HbA1c, ACR, ALT, AST, serum TBIL, IBIL, and DBIL levels decreased significantly in group 2 after monitoring, while there was no significant change in these parameters in group 1.The detailed information is provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of laboratory analyses at the before and the after monitoring of the study for two groups formed according to the annual percentange change in eGFR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGroup1\u003c/p\u003e \u003cp\u003eAnnual percentage in eGFR༞-5.48%/year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eGroup2\u003c/p\u003e \u003cp\u003eAnnual percentage in eGFR\u0026le;-5.48%/year\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBefore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBefore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAfter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual percentage change in eGFR,%/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33(5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-14.8(11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual percentage change in serum TBIL,%/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-8.25(14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.9(46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.6(44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.4(61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.1(37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2(2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.2(2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.4(9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.0(31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.5(30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.0(54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e147.0(152.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACR,A2/A3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24/16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20/20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16/47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7/56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0(5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.2(5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.2(4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.7(4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.2(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.7(3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.7(3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.2(2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.5(19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5(13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.0(12.0))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.0(8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.0(14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.5(7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.0(7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.0(7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.0(26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.5(20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.0(20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.0(11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eserum TBIL, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.6(7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.8(7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.2(7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.7(4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBIL, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.4(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8(1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBIL, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.7(6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.7(6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.1(6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.4(2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eACR, A2:\u0026lt;300mg/g; A3: \u0026ge;300mg/g.\u003c/p\u003e \u003cp\u003eGiven the potential association between several biochemical parameters (ACR, SCR, GLU, ALT, AST, serum TBIL, serum TBIL (%/year), IBIL, and DBIL) and the eGFR slope, we conducted a multivariate binary logistic regression analysis to identify potential protective factors for the annual percentage change in eGFR. Specifically, we included ACR, DBIL, and serum TBIL (%/year) as covariates in our analysis. Our results, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, indicate that DBIL was independently associated with a protective effect against changes in eGFR (OR 0.565, 95% CI 0.392\u0026ndash;0.814, P\u0026thinsp;=\u0026thinsp;0.002), along with serum TBIL (%/year) (OR 0.938, 95% CI 0.898\u0026ndash;0.979, P\u0026thinsp;=\u0026thinsp;0.003). In contrast, ACR was identified as an independent risk factor (OR 4.850, 95% CI 1.914\u0026ndash;12.290, P\u0026thinsp;=\u0026thinsp;0.001).The detailed information is provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables associated with the annual percentage change in eGFR(Binary logistic regression)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariat analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95CI%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95CI%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.889\u0026thinsp;~\u0026thinsp;1.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACR, A2/A3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.897\u0026thinsp;~\u0026thinsp;10.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.914\u0026thinsp;~\u0026thinsp;12.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.908\u0026thinsp;~\u0026thinsp;1.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.972\u0026thinsp;~\u0026thinsp;1.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.998\u0026thinsp;~\u0026thinsp;1.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.984\u0026thinsp;~\u0026thinsp;1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.907\u0026thinsp;~\u0026thinsp;1.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.949\u0026thinsp;~\u0026thinsp;1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.935\u0026thinsp;~\u0026thinsp;1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.988\u0026thinsp;~\u0026thinsp;1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eserum TBIL, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.857\u0026thinsp;~\u0026thinsp;0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBIL,\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.508\u0026thinsp;~\u0026thinsp;0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.392\u0026thinsp;~\u0026thinsp;0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBIL, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.853\u0026thinsp;~\u0026thinsp;1.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eserum TBIL,%/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.925\u0026thinsp;~\u0026thinsp;0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.898\u0026thinsp;~\u0026thinsp;0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.562\u0026thinsp;~\u0026thinsp;2.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.940\u0026thinsp;~\u0026thinsp;1.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.962\u0026thinsp;~\u0026thinsp;1.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.995\u0026thinsp;~\u0026thinsp;1.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.983\u0026thinsp;~\u0026thinsp;1.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.410\u0026thinsp;~\u0026thinsp;2.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSpearman correlation analysis was performed to assess the association between eGFR(%/year) and various biochemical parameters including serum TBIL,%/year, DBIL, and ACR. The analysis revealed that serum TBIL,%/year and DBIL had a significantly positive correlation with eGFR(%/year) (rs value: 0.352, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; rs value: 0.228, P\u0026thinsp;=\u0026thinsp;0.021, respectively). Conversely, ACR showed a negative correlation with eGFR(%/year) (rs value: -0.338, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation of eGFR(%/year) and predictors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eserum TBIL,%/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eReceiver operating characteristic (ROC) curves were utilized to analyze the endpoint of -5.48/year for the eGFR slope, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients were divided into group 1 (marked as 0), with an eGFR slope greater than \u0026minus;\u0026thinsp;5.48%/year, and group 2 (marked as 1), with an eGFR slope less than or equal to -5.48%/year. ROC curves were utilized to identify a cutoff value for serum TBIL,%/year that distinguished patients with a high risk of an annual percentage change in eGFR greater than \u0026minus;\u0026thinsp;5.48%/year. The cutoff value with the highest accuracy was found to be -6.729% (sensitivity 75.0%; specificity 60.3%), and patients were subsequently categorized into two groups: Group 3, those with serum TBIL,%/year greater than \u0026minus;\u0026thinsp;6.729%, and group 4, those with serum TBIL,%/year less than or equal to -6.729%.The detailed information of the two groupsis provided in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of laboratory analyses at the before and the after monitoring of the study between low or high serum TBIL\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGroup3\u003c/p\u003e \u003cp\u003eAnnual percentage in serum TBIL༞-6.729%/year(n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eGroup4\u003c/p\u003e \u003cp\u003eAnnual percentage in serum TBIL\u0026le;-6.729%/year(n\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBefore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBefore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAfter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual percentage change in eGFR,%/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-14.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACR,A2/A3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26/29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18/37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14/34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9/39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonitoring time, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.34(51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.6(53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.8(63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.1(20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.2(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.75(2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.8(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.0(46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.0(59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.0(48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e134.5(96.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.1(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.0(4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.5(6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.7(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.4(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6(2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.6(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.4(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.0(17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.5(14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.0(10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.0(11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.0(7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.0(8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.0(5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.0(23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.0(15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.0(17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.5(18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUpon comparing the laboratory results, it was observed that the eGFR after monitoring was significantly lower compared to before monitoring. However, in Group 4, a significant decrease was observed in the annual percentage change in eGFR as compared to Group 3, as evidenced by the mean values of -5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6 and \u0026minus;\u0026thinsp;14.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7, respectively (t\u0026thinsp;=\u0026thinsp;4.149, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis investigation demonstrates a statistically significant association between annual change in serum TBIL levels within the physiological range and the eGFR slope, and highlights the independent protective effect of serum DBIL levels against DN progression. These findings suggest that fluctuations in serum bilirubin concentration over time could be implicated in the progression of DN, and that higher levels of bilirubin within the physiological range may have a beneficial effect in delaying DN progression. Many previous studies have been tried to demonstrate the relationship between serum total bilirubin and DN. Tafese R et al.[26]showed that the serum total bilirubin level was significantly lower in DN patients compared with non-DN patients and was significantly associated with an increased risk of DN patients. A 10-year observational cohort study in Japanese patients with diabetes was to evaluate the association of baseline serum TBIL levels with progression of DN and found that serum TBIL levels were negatively associated with PRD in diabetic nephropathy and its cut-off point was 0.5 mg/dL.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].Liu M et al.[27] showed that not only baseline TBIL, but also follow-up changes were significantly associated with DN incidence and progression based on a 5 years' cohort study. Ahn KH et al.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]conducted a retrospective observational longitudinal study of patients, and showed that serum bilirubin might be an early clinical marker for predicting the progression of CKD in patients with T2DM and preserved renal function.Our study used baseline TBIL levels and follow-up TBIL levels to calculate the annual change rate of bilirubin concentration on the basis of the follow-up time, which can better reveal the relationship between TBIL and DN progression over time rather than the bilirubin concentration at a time point or simple numerical difference between the follow-up level and the baseline level. Our study defined progression of DN as having an eGFR slope \u0026gt; -5.48% per year rather than the follow-up eGFR 30% lower than that of the baseline, which metric provides an accurate reflection of disease progression in patients with varying stages of kidney disease. In addition, instead of analyzing the protective effect of bilirubin based on its anti-inflammatory and antioxidant properties, we tried to elucidate a completely new mechanism.However, it is worth noting that extremely high serum bilirubin levels are considered pathological and have been associated with increased mortality[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur investigation reveals that patients with a faster annual decline in renal function exhibit a faster annual decline in serum TBIL levels. Specifically, patients with an eGFR slope of greater than \u0026minus;\u0026thinsp;5.48%/year had higher serum TBIL (%/year) levels, while patients with an eGFR slope of less than or equal to -5.48%/year had lower serum TBIL (%/year) levels. Furthermore, our long-term follow-up analysis revealed that all bilirubin levels, including TBIL, DBIL, and IBIL, were significantly decreased in the group with a lower eGFR slope. Multivariate binary logistic regression models showed that serum TBIL (%/year) level was significantly associated with eGFR slope, while serum DBIL level was an independent protective factor for eGFR slope, and ACR was an independent risk factor. In addition, we found a significant positive correlation between eGFR (%/year) and serum TBIL (%/year) and DBIL, while ACR showed a significant negative correlation. These findings support the notion that annual change in serum TBIL levels may be associated with diabetic nephropathy progression. Higher levels of DBIL in the physiological range appear to be an independent protective factor for DN progression, whereas ACR is an independent risk factor.\u003c/p\u003e \u003cp\u003eThe pathogenesis of DN mainly involves damage to the glomerulus, with podocyte injury being a key hallmark of this condition. Podocyte injury manifests as a disappearance and fusion of podocyte foot processes, leading to the accumulation of damage over time and ultimately resulting in podocyte apoptosis and shedding, which results in a substantial increase in proteinuria. This process is observed in the early stages of DN, and is a major driver of disease progression. Proression proteinuria is one of the most important prognostic risk factors for DN, which promotes the development of DN to ESRD[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Among the various morphological features, the decreased number of glomerular podocytes was the strongest predictor of DN progression, with fewer podocytes indicating the faster the progression of DN. Dyslipidemia is an independent risk factor for the development of DN[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. High glucose can induce lipid droplet deposition, enabling the continuous accumulation of the extracellular matrix and progressive fibrosis in the kidneys with the progression of DN[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Previous studies have initially shown the underlying molecular mechanisms of this developmental process: High glucose causes intracellular lipid accumulation and phenotype changes in the podocytes, which can cause the podocytes to lose their various morphological structure and function, and damage the integrity of glomerular filtration barrier and the synthesis of extracellular matrix components such as collagen, thus producing proteinuria and promoting DN progression[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Herman et al [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] reported severe lipid deposition and increased intracellular lipid droplets in renal biopsies of DN patients, while the levels of several genes involved in the fatty acid oxidation pathway were significantly downregulated, including PPAR α. Activated PPAR α significantly reduced triglyceride levels, and moderately reduced total cholesterol levels and low-density lipoprotein cholesterol levels, thus increasing high-density lipoprotein cholesterol levels[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. PPAR α is expressed in kidney podocytes, and activated PPAR α is able to reduce albuminuria and improve insulin resistance [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Activation of PPAR α may be a favorable factor for DN progression. Recent studies suggest that bilirubin has a completely new function as a metabolic hormone for transcription of the genes that drive the nuclear receptors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Higher levels of bilirubin in the physiological range may have beneficial effects on the kidney due to this hormonal characteristics. Bilirubin can bind directly to PPARα and increase transcriptional activity[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Stec et al [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] showed that global PPAR α knockout mice have a reduced genetic response to bilirubin treatment, particularly hepatic fibroblast growth factor-21, a well-characterized PPAR α target gene. The hormone must bind directly to the target receptor to exert its effects. Although bilirubin may have an unknown role in activating other pathways, PPAR α has been shown to be a direct binding protein for bilirubin as a ligand agonist. Recent studies indicated that bilirubin only selectively bound PPAR α and did not interact with PPAR γ or PPAR δ[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. It is therefore speculated that the specific mechanism by which higher levels of bilirubin delay DN progression is related to the hormonal properties of bilirubin. Bilirubin flows through the blood over time and gradually enters the cell through the uptake system, continuously binding to the target receptor PPARα within the cell, resulting in core pressurized protein translated into coactivators to activate transcriptional control of the genes, which can further increase the downstream fat burning, improve insulin resistance and lipid metabolism disorders, regulate blood glucose levels, and thus play a role in delaying the progression of DN. Although the results obtained in this clinical study can not directly reveal the intrinsic mechanism by which bilirubin as a hormone exerts a protective effect on DN, they showed that with the gradual increase of total bilirubin concentration in the physiological range over time, it may delay the progression of DN, which has a positive effect on the above mechanisms.\u003c/p\u003e \u003cp\u003eROC curves were utilized to determine the optimal cutoff value for serum TBIL (%/year) that would identify patients at high risk of annual percentage in eGFR \u0026gt; -5.48%/year. The cutoff value that provided the highest accuracy was found to be -6.729%, with a sensitivity of 75.0% and specificity of 60.3%. Using this threshold, patients were divided into two groups: Group 3 comprised individuals with serum TBIL (%/year) \u0026gt; -6.729%, while group 4 included those with serum TBIL (%/year) \u0026le; -6.729%. Comparative analysis of laboratory data demonstrated that the eGFR after monitoring was significantly lower than that prior to monitoring, with the eGFR slope in group 4 being considerably lower than that observed in group 3. Furthermore, the results indicated that the serum creatinine and serum urea nitrogen levels were significantly elevated, reaching pathological levels in group 4, thereby validating the association between a more rapid decrease in TBIL levels over time and a more pronounced decline in eGFR slope, as well as greater impairment of renal function. Notably, our retrospective investigation relied solely on the clinical data of the participants, without any molecular information. Therefore, it remains necessary to verify the precise molecular mechanism by which bilirubin operates as a hormone and confers protection to renal function. It is crucial to highlight that this study had a limited sample size and was conducted solely in Dongzhimen Hospital, indicating the need for further research on a larger scale, involving multiple centers and adopting a prospective approach to validate the serum TBIL (%/year) cutoff value obtained from the present findings.\u003c/p\u003e \u003cp\u003eThe augmentation of serum bilirubin concentration within the physiological range over an extended period is advantageous in retarding the progression of diabetic nephropathy (DN), whereby the hormonal capacity of bilirubin might serve as a prospective molecular mechanism underlying its renoprotective effect. These findings could open up novel avenues for clinical intervention and targets to delay the progression of DN.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthical approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by ethical committee in Dongzhimen Hospital, The First Affiliated Hospital of Beijing University of Chinese Medicine (2017-JYB-JS-076). All experiments were performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments as revised in 2008. Informed consent was obtained from all subjects and/or their legal guardian(s).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and material\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interest\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis project was not funded.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCan Cao conceived the study, analyzed the data. Can Cao and Shuwu Wei drafted the manuscript and recruited patients. Leijuan He and Xiaonan Wang recruited patients, collected data, and Chunyao Li and Yizheng Lu participated in patient visits. Wang Yaoxian and Sun Weiwei conceived the study, participated in its design and coordination, and helped draft the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the participants in the study and colleagues in the nursing group in their department for blood sampling.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTuttle KR, Bakris GL, Bilous RW, et al. Diabetic kidney disease: a report from an ADA Consensus Conference. Diabetes Care. 2014 Oct;37(10):2864-83.\u003c/li\u003e\n\u003cli\u003eThomas MC, Brownlee M, Susztak K, et a1. Diabetic kidney disease. Nat Rev Dis Primers. 2015 Jul 30;1:15018.\u003c/li\u003e\n\u003cli\u003eLevey AS, Inker LA, Matsushita K, et a1. GFR decline as an end point for clinical trials in CKD: a scientific workshop sponsored by the National Kidney Foundation and the US Food and Drug Administration. Am J Kidney Dis. 2014 Dec;64(6):821-35. \u003c/li\u003e\n\u003cli\u003eCoresh J, Turin TC, Matsushita K, et al. Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality. JAMA. 2014 Jun 25;311(24):2518-2531.\u003c/li\u003e\n\u003cli\u003eLevey AS, Gansevoort RT, Coresh J, et al. Change in Albuminuria and GFR as End Points for Clinical Trials in Early Stages of CKD: A Scientific Workshop Sponsored by the National Kidney Foundation in Collaboration With the US Food and Drug Administration and European Medicines Agency. Am J Kidney Dis. 2020 Jan;75(1):84-104.\u003c/li\u003e\n\u003cli\u003eAhn KH, Kim SS, Kim WJ, et al. Low serum bilirubin level predicts the development of chronic kidney disease in patients with type 2 diabetes mellitus. Korean J Intern Med. 2017 Sep;32(5):875-882.\u003c/li\u003e\n\u003cli\u003eRiphagen IJ, Deetman PE, Bakker SJ, et al. Bilirubin and progression of nephropathy in type 2 diabetes: a post hoc analysis of RENAAL with independent replication in IDNT. Diabetes. 2014 Aug;63(8):2845-53.\u003c/li\u003e\n\u003cli\u003eEto E, Maeda Y, Sonoda N, et al. Association of serum total bilirubin levels with progressive renal decline and end-stage kidney disease: 10-year observational cohort study in Japanese patients with diabetes. PLoS One. 2022 Jul 12;17(7):e0271179.\u003c/li\u003e\n\u003cli\u003eCreeden JF, Gordon DM, Stec DE, et al. Bilirubin as a metabolic hormone: the physiological relevance of low levels. Am J Physiol Endocrinol Metab. 2021 Feb 1;320(2):E191-E207.\u003c/li\u003e\n\u003cli\u003eExpert Group of Chinese Society of Nephrology. Chinese guidelines for diagnosis and treatment of diabetic kidney disease. Chin J Nephrol,37,255-304(2021).\u003c/li\u003e\n\u003cli\u003eLevey A S, Stevens L A, Schmid C H, et a1. A New Equation to Estimate Glomerular Filtration Rate[J]. Ann Intern Med, 2009, 150(9): 604-612.\u003c/li\u003e\n\u003cli\u003eKovačević M, Mali\u0026scaron; S, Pavlović D, Kovačević M, Savić Radojević A, Djukanović L. Plasma activity of the antioxidant enzymes in predicting diabetic nephropathy progression. Int Urol Nephrol. 2022,54(6):1365-1372.\u003c/li\u003e\n\u003cli\u003eDhawan A, Lawlor MW, Mazariegos GV, et al. Disease burden of Crigler-Najjar syndrome: Systematic review and future perspectives. J Gastroenterol Hepatol. 2020 Apr;35(4):530-543.\u003c/li\u003e\n\u003cli\u003eDenhez B, Geraldes P. Regulation of Nephrin Phosphorylation in Diabetes and Chronic Kidney Injury. Adv Exp Med Biol. 2017;966:149-161.\u003c/li\u003e\n\u003cli\u003eKuwabara T, Mori K, Mukoyama M, et al. Macrophage-mediated glucolipotoxicity via myeloid-related protein 8/toll-like receptor 4 signaling in diabetic nephropathy. Clin Exp Nephrol. 2014 Aug;18(4):584-92.\u003c/li\u003e\n\u003cli\u003eSun L, Halaihel N, Zhang W, et al. Role of sterol regulatory element-binding protein 1 in regulation of renal lipid metabolism and glomerulosclerosis in diabetes mellitus. J Biol Chem. 2002 May 24;277(21):18919-27.\u003c/li\u003e\n\u003cli\u003eWang Z, Jiang T, Li J, et al. Regulation of renal lipid metabolism, lipid accumulation, and glomerulosclerosis in FVBdb/db mice with type 2 diabetes. Diabetes. 2005 Aug;54(8):2328-35.\u003c/li\u003e\n\u003cli\u003eZhang Y, Ma KL, Liu J, et al. Dysregulation of low-density lipoprotein receptor contributes to podocyte injuries in diabetic nephropathy. Am J Physiol Endocrinol Metab. 2015 Jun 15;308(12):E1140-8.\u003c/li\u003e\n\u003cli\u003eHerman-Edelstein M, Scherzer P, Tobar A, et al. Altered renal lipid metabolism and renal lipid accumulation in human diabetic nephropathy. J Lipid Res. 2014 Mar;55(3):561-72.\u003c/li\u003e\n\u003cli\u003eYu XH, Zheng XL, Tang CK. Peroxisome Proliferator-Activated Receptor \u0026alpha; in Lipid Metabolism and Atherosclerosis. Adv Clin Chem. 2015;71:171-203.\u003c/li\u003e\n\u003cli\u003eGao J, Gu Z. The Role of Peroxisome Proliferator-Activated Receptors in Kidney Diseases. Front Pharmacol. 2022 Mar 4;13:832732.\u003c/li\u003e\n\u003cli\u003eStec DE, John K, Trabbic CJ, et al. Bilirubin Binding to PPAR\u0026alpha; Inhibits Lipid Accumulation. PLoS One. 2016 Apr 12;11(4):e0153427.\u003c/li\u003e\n\u003cli\u003eGordon DM, Neifer KL, Hamoud AA, et al. Bilirubin remodels murine white adipose tissue by reshaping mitochondrial activity and the coregulator profile of peroxisome proliferator-activated receptor \u0026alpha;. J Biol Chem. 2020 Jul 17;295(29):9804-9822.\u003c/li\u003e\n\u003cli\u003eTafese R, Genet S, Addisu S. Association of Serum Total Bilirubin and Uric Acid with Low Glomerular Filtration Rate Diabetic Kidney Disease in Type 2 Diabetic Patients. Diabetes Metab Syndr Obes. 2022 Dec 21;15:3993-3999.\u003c/li\u003e\n\u003cli\u003eLiu M, Li J, Lv X, et al Bilirubin and its changes were negatively associated with diabetic kidney disease incidence and progression: A five-year\u0026apos;s cohort study based on 5323 Chinese male diabetic patients. J Diabetes Complications. 2018 Nov;32(11):1012-1017.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"serum total bilirubin, diabetic kidney disease","lastPublishedDoi":"10.21203/rs.3.rs-3057599/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3057599/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis current study represents a novel endeavor to scrutinize the correlation between the temporal alteration in serum total bilirubin (TBIL) concentrations and the rate of estimated glomerular filtration rate (eGFR). Additionally, this study aims to probe the plausible molecular mechanism underpinning the renoprotective effects of bilirubin concerning its hormonal characteristics.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eIn this study, a cohort of 103 patients diagnosed with DN and receiving medical care at Dongzhimen Hospital were recruited and monitored over a period of 2\u0026ndash;7 years. The progression of DN was ascertained using a threshold of eGFR decline \u0026gt; -5.48%/year. To assess the relationship between the annual change in serum TBIL levels (%/year) and the slope of eGFR, multivariate binary logistic regression analysis was employed. Furthermore, the ROC curve analysis was employed to determine the cut-off value for TBIL levels (%/year).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe use of multivariate binary logistic regression models revealed that serum TBIL levels (%/year) exhibited a significant correlation with the slope of eGFR. Moreover, the ROC curve analysis indicated a cut-off value of -6.729%/year for TBIL levels (%/year) with a sensitivity of 0.75 and specificity of 0.603, in diagnosing eGFR decline \u0026gt;-5.48%/year.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings of this study suggest that the sustained elevation of serum bilirubin concentration within the physiological range can effectively retard the progression of diabetic nephropathy (DN). Furthermore, the hormonal attributes of bilirubin may underlie its renoprotective effects.\u003c/p\u003e","manuscriptTitle":"Temporal Alteration of Serum Bilirubin Levels and its Renoprotective Effects in Diabetic Nephropathy: Exploring the Hormonal Mechanisms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-29 17:49:48","doi":"10.21203/rs.3.rs-3057599/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8eba6d9b-6666-4b97-bb67-93f18a92d035","owner":[],"postedDate":"June 29th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-20T05:17:56+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-29 17:49:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3057599","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3057599","identity":"rs-3057599","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}