The Role of Remnant Cholesterol and Its Interaction with Low-Density Lipoprotein Cholesterol in Chronic Kidney Disease

The Role of Remnant Cholesterol and Its Interaction with Low-Density Lipoprotein Cholesterol in Chronic Kidney Disease | 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 Article The Role of Remnant Cholesterol and Its Interaction with Low-Density Lipoprotein Cholesterol in Chronic Kidney Disease Jiang Bai, Zhouyu Dong, Lijuan Zhang, Suhang Li, Rong Chen, Jingkai Di, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4367440/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background The growing prevalence of chronic kidney disease (CKD) presents a substantial public health issue. Furthermore, the continuous advancements in lipid-lowering strategies and medications highlight the ongoing importance of the correlation between remnant cholesterol (RC) and CKD. This study aims to investigate the link between RC and CKD risk, particularly focusing on the interplay between low-density lipoprotein cholesterol (LDL-C) and RC. Methods This cross-sectional study included 7747 participants in wave 2009 of the China Health and Nutrition Survey which has been in progress since 1989. We enrolled 7747 individuals in the present study from the China Health and Nutrition Survey, with exclusion criteria applied to individuals under 18 and pregnant participants. CKD was defined as eGFR < 60 mL/min/1.73 m 2 , following the Kidney Disease: Improving Global Outcomes (KDIGO) 2012 Clinical Practice Guideline. A logistic regression analysis was conducted to assess the associations between discordant/concordant levels of LDL-C and RC with CKD. Subsequently, a mediation analysis was performed to identify potential mediators. Results Within the clinical cohort of 7747 patients, 910 individuals (11.8%) were diagnosed with CKD, with RC levels categorized into quartiles. Logistic analysis revealed significant associations between elevated RC levels and the prevalence of CKD (OR 1.30, 95% CI 1.06–1.60 for Group 2; OR 1.49, 95% CI 1.22–1.83 for Group 3; and OR 1.33, 95% CI 1.08–1.63 for Group 4). The results of restricted cubic splines (RCS) analysis suggested an “inverted U-shaped” association of RC with CKD. The analysis of discordant/concordant grouping showed that participants in Group 2 (high LDL-C/low RC) and Group 3 (low LDL-C/high RC) were associated with an increased risk for CKD. The odds ratios were 2.35 (95% CI 1.83–3.03) for Group 2 and 1.51 (95% CI 1.14–2.01) for Group 3, compared to Group 1 (low LDL-C/low RC). Causal mediation analysis indicated that inflammation partially mediated the association between RC and CKD. Conclusions This study presented evidence of a non-linear relationship between RC and CKD, suggesting that the association was influenced by LDL-C levels and mediated by the pro-inflammatory state. Health sciences/Nephrology Health sciences/Nephrology/Kidney Health sciences/Nephrology/Kidney diseases Remnant cholesterol Low-density lipoprotein cholesterol Chronic kidney disease Casual mediation analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The increasing prevalence of chronic kidney disease (CKD) is a significant public health concern, involving about 10% of the global population[ 1 ], 1 exceeding the number of cases observed in diabetes, chronic obstructive pulmonary disease, and depressive disorders. 2–4 Individuals with a family history of high cholesterol are predisposed to develop CKD, implicating the composition of circulating lipoproteins, particularly the lipid and protein components, in CKD development. 5 Furthermore, lipids and their metabolites are essential constituents of cell and organelle membranes, signaling molecules, and energy generators, influencing structural and functional aspects 6 . Despite substantial attention on the role of kidney parenchymal lipid metabolism in CKD progression, understanding how dyslipidemia related to CKD results in abnormal lipid metabolism remains limited. The varied array of lipid species and their diverse functions across various parenchymal cells pose a challenge in understanding the mechanisms linking kidney parenchymal lipid accumulation to CKD. While low-density lipoprotein cholesterol (LDL-C) undoubtedly holds a significant position within the spectrum of lipoproteins, 6–8 recent studies have revealed that remnant cholesterol (RC) also plays a significant role in the realm of lipoproteins, with its association with cardiovascular disease extending beyond LDL-C. 9,10 RC is associated with triglyceride (TG)-rich lipoproteins (TRLs), including very low-density lipoprotein cholesterol (VLDL-C), intermediate-density lipoprotein cholesterol (IDL-C), and chylomicron remnants. 11 Previous evidence suggests an association between RC and the development of CKD. 12–14 However, inconsistent findings exist concerning the impact of RC on CKD risk 15 and the nature of the relationship between RC and CKD risk, including its linearity 12 or non-linearity. 14 Significantly, different lipoprotein cholesterol types may influence each other, potentially affecting the results. 16 China continues to have a high prevalence of CKD, with approximately 82 million adult CKD patients, where the prevalence of diabetes mellitus and hypertension as critical risk factors for CKD has been increasing. 17 This study aims to assess the association between RC and CKD risk, including determining its linearity or non-linearity, understanding the discordant/concordant relationship between LDL-C and RC, and exploring the potential mediating role of inflammation. The investigation is based on the data from China Health and Nutrition Survey (CHNS). Methods Study Design and Recruitment Criteria This cohort study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. 18 The study utilized data from the China Health and Nutrition Survey (CHNS), a multi-site, interdisciplinary epidemiological study that has been ongoing since 1989. The survey sampled approximately 7,200 households, comprising over 30,000 individuals, from 15 provinces and municipal cities in China. Ethical approval for the survey was granted by the National Institute of Nutrition and Food Safety, China Center for Disease Control and Prevention (Beijing, China), and the University of North Carolina at Chapel Hill (Chapel Hill, NC). Written informed consent was obtained from all participants. A comprehensive description of CHNS has been previously published. 19,20 CHNS collected 9549 fasting blood samples in 2009. Blood drawing and processing were performed by certified technicians at each field center. The specimen collection flow chart details were provided in Supplemental Fig. 1. This study excluded 849 participants under 18 years old and 62 pregnant women. Recruitment was also denied to 31 individuals with incomplete creatinine information for CKD diagnosis, and 860 patients with missing RC information or whose RC was equal to or less than 0 (Supplemental Fig. 2). Consequently, the study population comprised 7747 individuals, with reported patient details encompassing baseline characteristics, healthy habits, and laboratory test results. Definitions The serum creatinine levels were used to calculate the estimated glomerular filtration rates (eGFR) using the chronic kidney disease epidemiology collaboration (CKD-EPI) 2009 creatinine equation. CKD was defined as eGFR < 60 mL/min/1.73 m 2 , following the Kidney Disease: Improving Global Outcomes (KDIGO) 2012 Clinical Practice Guideline for the Evaluation and Management of CKD. 21 The RC level (mmol/L) was derived from the patient’s fasting state standard lipid profile by subtracting the values of LDL-C and HDL-C from the total cholesterol 10 Individuals were classified as having hypertension if their average systolic blood pressure exceeded 140 mmHg and diastolic blood pressure exceeded 90 mmHg. Diabetes mellitus was defined as fasting blood glucose (FBG) ≥ 7.0 mmol/L or glycated hemoglobin (HbA1c) ≥ 6.5%, following the criteria set by the American Diabetes Association. 22 Measures Self-reported health behaviors included alcohol consumption and smoking. Trained and certified technicians collected venous blood from each participant during the 2009 CHNS wave. The serum was subsequently separated, stored in an ultra-low-temperature freezer, and analyzed at a national central laboratory, adhering to strict quality control measures. 23 The white blood cells (WBCs) were measured using a Beckman Coulter LH751 (Beckman Coulter, USA). The levels of serum creatinine, uric acid (UC), LDL-C, HDL-C, TC and FBG were assessed using a Hitachi 7600 device (Randox, UK and Kyowa, Japan). The HbA1c was evaluated using the HLC-723 G7/D10/PDQ A1c (Tosoh, Japan/Bio-Rad, USA/Primus, USA), and the high-sensitivity C-reactive protein (hs-CRP) was detected using the Hitachi 7600 (Denka Seiken, Japan). 23 Covariates In the present study, demographics, anthropometry, education, biochemical markers, BMI, smoking status, alcohol status and medical comorbidities were regarded as the potential covariates, which might have confounding effects on the association between RC and CKD. Four main models were developed to accommodate covariate adjustment. Model 1 remained unadjusted, while Model 2 was adjusted for age, sex, and BMI. Additionally, Model 3 was adjusted for the variables in Model 2, educational level, urbanization, smoking, and drinking. Model 4 further adjusted for the variables in Model 3, diabetes and hypertension. Statistical Analyses RC levels were categorized into quartiles. Missing data were addressed through multiple imputation, with specific information available in Supplemental Fig. 2. Logistic regression models were used to determine the independent association between RC and the presence of CKD. The potential nonlinear relationships between the change in RC and CKD were evaluated using a logistic regression model with restricted cubic splines (RCS). Knots at 3 and 7 were tested, and the RCS model with the lowest Akaike information criterion value was selected. When interpreting the findings of an RCS analysis, the 10th percentile value of the predictor variable was used as the reference point. The association between RC and LDL-C in CKD risk was assessed using the discordant/concordant LDL-C and RC approach. The cutoff value for RC was set at 0.36mmol/L (median), representing a crucial point with potential statistical significance in our study. Additionally, we adopted 2.6 mmol/L (100 mg/dL) as the cutoff points for LDL-C, aligning with the recommended LDL-C target outlined in the 2019 ESC/EAS Guidelines for the management of dyslipidaemias. 24 Causal mediation analysis was utilized to assess the impact of the mediator and the proportion of mediated effects on the association between RC and CKD, implementing the Bootstrap method with 500 simulations. This approach was embedded within the counterfactual framework of causal inference and formalizes the implicit assumption in applied research that the treatment inadvertently influences the outcome through the mediator, despite specific statistical models. 25 It permit the estimation of causal mediation effects, considering both continuous and discrete mediators, as well as parametric and nonparametric models, and various types of outcome variables. 25 The general estimation procedures establish causal mediation effects or indirect effects for each unit i using the following approach: 25 $${{\delta }}_{i}\left(t\right)\equiv {Y}_{i}\left(t,{M}_{i}\left(1\right)\right)-{Y}_{i}\left(t,{M}_{i}\left(0\right)\right)$$ In the context of the above formula, Ti represents the binary treatment variable, Mi denotes the mediator, and Yi(t) represents the potential outcome of the treatment state t. The Yi (t, m) represents the potential outcome that would result if the treatment and mediating variables equal t and m , respectively. More details are provided in the Supplementary Methods. Our study utilized SPSS 26.0 (IBM, USA) to conduct descriptive statistics and logistic regression. Additionally, the MSTATA software ( www.mstata.com ) was utilized for Causal Mediation Analysis and RCS analyses. Results Baseline Characteristics Within the clinical cohort of 7,747 individuals, the participants had an average age of 50 years. Of these individuals, 3,701 (48%) were male, and 910 (12%) were diagnosed with chronic kidney disease (CKD). Table 1 presents the baseline characteristics of the 7747 participants in the cohort study, categorized by quartiles of RC levels (Q1:1839, Q2:1952, Q3:1990, Q4:1966). With increasing quartiles of RC, we observed a higher frequency of participants with age, sex, BMI index, alcohol intake, smoking, hypertension and diabetes in Table 1 . Patients in Group Q4 (high RC levels) tended to have lower LDL-C levels compared to those in Group Q1, Q2, and Q3 (Table 1 ). Table 1 Baseline characteristics of enrolled participants among groups of RC quartile. Q1 n = 1839 Q2 n = 1952 Q3 n = 1990 Q4 n = 1966 p-Value a Age, years 49(38,60) 50(39,62) 51(40,61) 52(42,61) < 0.001 Male(sex) 794(43.2%) 877(44.9%) 962(48.3%) 1068(54.3%) < 0.001 BMI, kg/m 2a 22.06(20.26,24.34) 22.45(20.50,24.97) 23.44(21.21,25.86) 24.71(22.54,27.12) < 0.001 Junior high school or above 1016(55.2%) 1103(56.5%) 1151(57.8%) 1120(57.0%) = 0.437 Residence = 0.006 Urban 587(31.9%) 603(30.9%) 689(34.6%) 697(35.5%) Rural 1252(68.1%) 1349(69.1%) 1301(65.4%) 1269(64.5%) Currently smoking 517(28.1%) 586(30.0%) 629(31.6%) 714(36.3%) < 0.001 Alcohol consumption 569(30.9%) 596(30.5%) 659(33.1%) 735(37.4%) < 0.001 CKD 173(9.4%) 232(11.9%) 267(13.4%) 238(12.1%) = 0.002 Hs-CRP, mg/L 1.00(0.00,2.00) 1.00(0.00,2.00) 1.00(1.00,3.00) 2.00(1.00,3.00) < 0.001 TP, g/L 77.20(73.70,80.40) 77.10(73.80,80.60) 77.10(73.80,80.33) 76.80(73.40,80.23) = 0.083 ALB, g/L 46.80(45.00,49.00) 47.15(44.90,49.40) 47.20(45.10,49.40) 47.90(46.00,50.20) < 0.001 ALT, U/L 16.00(12.00,22.00) 17.00(13.00,23.00) 19.00(14.00,27.00) 27.00(17.00,35.00) < 0.001 TC, mmol/L 4.59(4.02,5.21) 4.64(4.02,5.30) 4.74(4.15,5.42) 5.10(4.48,5.82) < 0.001 TG, mmol/L 0.80(0.63,1.04) 1.06(0.84,1.33) 1.51(1.23,1.83) 2.81(2.22,3.82) < 0.001 HDL-C, mmol/L 1.55(1.35,1.79) 1.43(1.24,1.64) 1.32(1.13,1.53) 1.12(0.96,1.30) < 0.001 LDL-C, mmol/L 2.87(2.37,3.48) 2.92(2.33,3.50) 2.93(2.33,3.56) 2.74(2.11,3.40) < 0.001 APO-A, g/L 1.15(1.00,1.34) 1.10(0.95,1.28) 1.07(0.93,1.26) 1.02(0.88,1.21) < 0.001 APO-B, g/L 0.81(0.66,0.97) 0.85(0.69,1.02) 0.90(0.74,1.10) 0.98(0.81,1.17) < 0.001 LPA, mg/L 88.00(45.00,186.00) 82.00(42.00,174.75) 80.00(42.00,163.25) 64.00(32.00,135.25) < 0.001 UA, umol/L 261.00(214.00,319.00) 279.00(229.00,336.00) 303.00(246.00,363.00) 360.50(298.00,433.00) < 0.001 UREA, mmol/L 5.33(4.38,6.37) 5.14(4.27,6.19) 5.16(4.31,6.19) 5.46(4.59,6.48) < 0.001 HGB, g/L 137.00(126.00,151.00) 138.89(127.37,151.75) 141.00(129.00,154.00) 146.00(133.00,159.00) < 0.001 WBC, 10 9 /L 5.75(4.80,6.90) 5.96(5.04,7.10) 6.20(5.20,7.34) 6.40(5.42,7.70) < 0.001 HbA1c, % 5.40(5.20,5.70) 5.50(5.20,5.80) 5.50(5.20,5.90) 5.60(5.30,6.00) < 0.001 Hypertension 396(21.5%) 503(25.8%) 592(29.7%) 737(37.5%) < 0.001 DM 96(5.2%) 148(7.6%) 224(11.3%) 408(20.8%) < 0.001 BMI body mass index, CKD chronic kidney disease, Hs-CRP high-sensitivity C-reactive protein, TP total protein, ALB albumin, ALT alanine aminotransferase, T C total cholesterol, TG triglyceride, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, APO-A apolipoprotein A, APO-B apolipoprotein B, LPA lipoprotein (а), UA Uric acid, UREA urea, HGB hemoglobin, WBC white blood cell, HbA1c glycosylated hemoglobin A1c, DM diabetes mellitus a p-values by Kruskal-Wallis rank test Association Between RC Levels and CKD Logistic regression analysis was utilized to assess the association between CKD and RC levels, stratified based on quartiles of the elevated RC levels. Univariate logistic analysis indicated significant associations between RC levels and the prevalence of CKD. Specific odds ratios and their corresponding confidence intervals within the groups were as follows: OR 1.30, 95% CI 1.06–1.60 for Group 2; OR 1.49, 95% CI 1.22–1.83 for Group 3; and OR 1.33, 95% CI 1.08–1.63 for Group 4 (Fig. 1 ). Four main models were constructed for covariate adjustment. After adjusting for covariates of model 4, the association between the group Q2 and CKD showed no statistical significance (OR 1.21, 95% CI 0.95–1.55, P = 0.123). The Group Q4 had a lower OR value for CKD than the group Q3 (OR:1.30 vs. OR:1.44) (Fig. 1 ). A nonlinear relationship between RC and CKD was a possibility. The Relationship Between RC and CKD by RCS To explore the nonlinear association between the change in RC levels and the risk of CKD, a logistic regression model with RCS was used. The knots between 3 and 7 were tested respectively, and the model with the lowest Akaike information criterion value was selected for RCS. Finally, we used RCS with 3 knots. The RCS analysis suggested an “Inverted U-shaped” association of RC with CKD (P < 0.001) (Fig. 2 ). The formula for calculating RC at a specific TC level revealed an inverse relationship between RC and LDL-C levels. Therefore, LDL-C was adjusted as a covariate in the subsequent analysis. Results from the RCS analysis revealed that the latter segment of the RCS curve displayed a flat pattern compared to the unadjusted RCS curve after adjustment for LDL-C (P = 0.001) (Fig. 2 ). Inter‑relation between Discordant/Concordant LDL-C and RC with CKD Risk The discordant/concordant grouping was utilized to examine the interaction between LDL-C and RC, illustrating specific distribution details in Fig. 3 . The logistic analysis demonstrated that participants in Group 2 (high LDL-C/low RC), Group 3 (low LDL-C/high RC) and Group 4 (high LDL-C/high RC) were associated with 135%, 51%, and 179%, respectively, increased risk for CKD (OR: 2.35, 95% CI 1.83–3.03 for Group 2; OR: 1.51, 95% CI 1.14–2.01 for Group 3; and OR: 2.79, 95% CI 2.17–3.59 for Group 4) relative to Group 1 (low LDL-C/low RC) (Fig. 3 ). Mediator Analysis A causal mediation analysis was performed to investigate the mediating roles of hs-CRP and WBCs in the association between RC levels and CKD. The results revealed that hs-CRP and WBCs were significant mediators in the relationship between RC and CKD. These two indices mediated 6.9% and 10.5% of the correlation, respectively, utilizing the bootstrap method with 500 simulations for modeling, as shown in Fig. 4 (P = 0.004). Sensitivity Analysis In the sensitivity analysis of the RCS, after adjusting for the variables in Model 4, the “inverted U-shaped” association of RC with CKD was also observed. Additionally, upon further adjustment for the variables in Model 4 and LDL-C, the latter segment of the RCS curve displayed a flat pattern (Supplemental Fig. 3). After adjusting for the covariates included in model 4, the odds ratio of Group 2 (high LDL-C/low RC) remained higher than that of Group 3 (low LDL-C/high RC) concerning the association with CKD as opposed to Group 1 (low LDL-C/low RC) (Supplemental Fig. 4). Discussion In this study, we identified a significant association between elevated RC levels and the prevalence of CKD in the general Chinese population. This association remained independent of demographic factors, education, residency, lifestyle, and traditional cardiovascular risk factors. Additionally, a non-linear correlation between RC and the risk of prevalent CKD was observed, and influenced by serum LDL-C levels. In the analysis of discordant/concordant LDL-C and RC groups, it was determined that RC played a smaller role in the cholesterol-related pathogenesis of CKD compared to LDL-C. The findings shed light on the pathophysiology of RC in CKD, indicating a correlation between elevated serum levels of RC and the interaction of LDL-C in the progression of CKD. Specifically, this association was attributed to the pro-inflammatory state serving as a mediator. Several studies have observed inconsistencies in the relationship between CKD and RC. He et al. 14 identified a significant inverse correlation between RC levels and eGFR in a U.S. population. The risk of CKD progressively increased across RC quartiles in a cohort comprising a general population of middle-aged and elderly individuals of Chinese descent. 12 In contrast, Rysz-Gorzynska et al. 15 did not observe significant differences in levels of very VLDL-C or IDL-C between individuals with CKD and those with normal kidney function. Additionally, a non-independent relationship was noted between declining kidney function and VLDL-C in a prospective cohort study with CKD. 16 These discrepancies may stem from variations in population demographics, geographical region, sample size, analytical methods, and confounding variables. In our study, we observed an “inverted U-shape” curve in the association between serum RC levels and the risk of CKD. Understanding and recognizing this non-linear trend is paramount in the context of clinical management of CKD, as it suggests a potential threshold effect where the influence of RC on the development or progression of the disease may not be uniform across all levels of cholesterol. After adjusting for LDL-C level as a confounder, the decreased section of the RCS curve became flat. Elevated RC levels were associated with increased TG enrichment of LDL particles, particularly in small, dense LDL particles with an extended half-life in circulation. 26 We hypothesized that LDL-C may exert a dominant effect in the relationship between RC and the risk of CKD. It was further elucidated that serum RC levels have less impact than LDL-C in contributing to the pathogenesis of CKD, according to the LDL-C and RC discordance/concordance group. With new-generation TG and RC lowering medications, such as apoC3 and ANGPTL3 inhibitors, 27 currently under evaluation, transitioning to a novel strategy that separately considers each component may be the most prudent course of action. 26 In clinical practice, it is essential to simultaneously consider the levels of both RC and LDL to ensure a dynamic balance. The influence of RC on the development and progression of CKD involves various complex processes. Historical evidence suggested that RC induced pro-inflammatory activation of the endothelium and low-grade inflammation, both of which contribute to atherosclerosis. 28 Our investigation revealed that the relationship between RC and CKD was partially mediated by WBC count and hs-CRP concentrations, suggesting that RC was frequently associated with a pro-inflammatory state because of its highly atherogenic nature and unique physicochemical properties. The inflammatory state plays a crucial role in the onset and progression of CKD, impacting the quality of life and prognosis of individuals with CKD. 29 The hydrolysis of apolipoprotein C (ApoC), triglycerides, and cholesterol within RC under physiological conditions leads to the production of free fatty acids (FFAs) and glycerol. Zewinger et al. 30 found that elevated levels of ApoC3 in plasma were associated with a notable increase in markers of systemic inflammation, such as hs-CRP, in patients with CKD. Furthermore, in humanized mouse models, ApoC3 activated human monocytes in vivo to promote kidney injury in an NLRP3 and caspase-8-dependent manner 30 (Fig. 5 ). In CKD, the upregulation of fatty acid protein transporters and CD36 results in the excessive accumulation of FFAs in kidney cells and lipid droplets (LDs), causing kidney lipotoxicity. 6 Moreover, the increased activity of fatty acid-binding proteins (FABPs) in CKD patients enhances the transportation of FFAs to the mitochondria for further oxidation. Conversely, the decreased expression of carnitine palmitoyltransferases (CPT1 and CPT2) leads to reduced fatty acid oxidation, ineffective NADH production, decreased electron transport chain (ETC) activity, resulting in mitochondrial dysfunction and subsequent mitochondrial DNA (mtDNA) instability. This instability activates the cGAS-STING pathway, triggering NF-κB activation 6,31 (Fig. 5 ). An important strength of this study lies in our examination of the intermediary role of inflammation in systemic lipid metabolism disorders. This contributes to the selection of anti-inflammatory medications and lipid-lowering drugs for nephrologists to halt the progression of CKD, reduce proteinuria, and mitigate the risk of cardiovascular disease. In most Chinese institutions, the direct measurement of RC using nuclear magnetic resonance or ultracentrifugation is impractical in clinical practice due to its time-consuming, expensive, and radioactive nature. In our study, we assessed the RC value due to its convenience, minimal time requirement, and established reliability, as it has been widely used in large cohort studies. 12,32–34 This assessment may be sufficient for predicting and determining treatment strategies in actual clinical settings. limitations The current study acknowledges several limitations. Firstly, the cross-sectional design precludes the establishment of causal relationships between RC and CKD. It remains uncertain whether dyslipidemia causes kidney problems or if it is a metabolic consequence. Additionally, as the data was solely derived from the CHNS study in China, the generalizability of our findings to individuals of diverse ethnic backgrounds remains uncertain, potentially constraining their applicability. In conclusion, while our comprehension of RC and CKD advances in recent years, we strongly advocate for ongoing research to explore the occurrence and underlying mechanisms of RC in CKD, including systemic lipid levels or cellular lipid metabolism disorders. Conclusions The results of this cohort indicated a notable connection between increased levels of RC and the prevalence of CKD. This relationship was found to be nonlinear and was influenced by LDL-C levels. More specifically, the presence of a pro-inflammatory state was identified as a mediating factor in this association. The diverse nature of lipid species and their various functions within different parenchymal cells presents a challenge in understanding the mechanisms linking lipid levels to CKD. As such, it is imperative in clinical practice to simultaneously consider both RC and LDL levels to maintain a dynamic equilibrium. Declarations Acknowledgments This manuscript does not include any non-author contributors to acknowledge. Author Contributions: Jiang Bai have full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Jiang Bai, Zhouyu Dong. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Jiang Bai, Zhouyu Dong, Lijuan Zhang. Critical review of the manuscript for important intellectual content: All authors. Statistical analysis: Jiang Bai, Zhouyu Dong. Obtained funding: No Administrative, technical, or material support: Yun Zhou. Supervision: Yun Zhou. Funding/Support This work was supported by no grants. Data availability The data sets from this study are available at https://www.cpc.unc.edu/projects/china. The comprehensive data set creation plan and underlying analytic code can be obtained from the first author, Jiang Bai, upon request. Ethics approval and consent to participate Ethical approval was not required for this study in accordance with local/national guidelines. Written informed consent to participate in the study was not required in accordance with local/national guidelines. This study does not involve sensitive personal data, ethical issues, or breaches of policy. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests References Jadoul M, Aoun M, Masimango Imani M. The major global burden of chronic kidney disease. Lancet Glob Health. Mar 2024;12(3):e342-e343.doi:10.1016/s2214-109x(24)00050-0 Collaborators GDaIIaP. 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Jan 4 2024;23(1):2.doi:10.1186/s12944-023-01995-w Rysz-Gorzynska M, Gluba-Brzozka A, Banach M. High-Density Lipoprotein and Low-Density Lipoprotein Subfractions in Patients with Chronic Kidney Disease. Curr Vasc Pharmacol. 2017;15(2):144-151.doi:10.2174/1570161114666161003093032 Rahman M, Yang W, Akkina S, et al. Relation of serum lipids and lipoproteins with progression of CKD: The CRIC study. Clin J Am Soc Nephrol. Jul 2014;9(7):1190-1198.doi:10.2215/cjn.09320913 Wang L, Xu X, Zhang M, et al. Prevalence of Chronic Kidney Disease in China: Results From the Sixth China Chronic Disease and Risk Factor Surveillance. JAMA Intern Med. Apr 1 2023;183(4):298-310.doi:10.1001/jamainternmed.2022.6817 Vandenbroucke JP, von Elm E, Altman DG, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLoS Med. Oct 16 2007;4(10):e297.doi:10.1371/journal.pmed.0040297 He J, Fang A, Yu S, Shen X, Li K. Dietary Nonheme, Heme, and Total Iron Intake and the Risk of Diabetes in Adults: Results From the China Health and Nutrition Survey. Diabetes Care. Apr 2020;43(4):776-784.doi:10.2337/dc19-2202 Tao Z, Qu Q, Li J, Li X. Factors influencing blood pressure variability in postmenopausal women: evidence from the China Health and Nutrition Survey. Clin Exp Hypertens. Dec 31 2023;45(1):2181356.doi:10.1080/10641963.2023.2181356 Andrassy KM. Comments on 'KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease'. Kidney Int. Sep 2013;84(3):622-623.doi:10.1038/ki.2013.243 Diagnosis and classification of diabetes mellitus. Diabetes Care. Jan 2010;33 Suppl 1(Suppl 1):S62-69.doi:10.2337/dc10-S062 Li Y, Zhu B, Song N, Shi Y, Fang Y, Ding X. Alcohol consumption and its association with chronic kidney disease: Evidence from a 12-year China health and Nutrition Survey. Nutr Metab Cardiovasc Dis. Jun 2022;32(6):1392-1401.doi:10.1016/j.numecd.2022.02.012 Mach F, Baigent C, Catapano AL, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. Jan 1 2020;41(1):111-188.doi:10.1093/eurheartj/ehz455 Imai K, Keele L, Tingley D. A general approach to causal mediation analysis. Psychol Methods. Dec 2010;15(4):309-334.doi:10.1037/a0020761 Quispe R, Martin SS, Michos ED, et al. Remnant cholesterol predicts cardiovascular disease beyond LDL and ApoB: a primary prevention study. Eur Heart J. Nov 7 2021;42(42):4324-4332.doi:10.1093/eurheartj/ehab432 Graham MJ, Lee RG, Brandt TA, et al. Cardiovascular and Metabolic Effects of ANGPTL3 Antisense Oligonucleotides. N Engl J Med. Jul 20 2017;377(3):222-232.doi:10.1056/NEJMoa1701329 Varbo A, Benn M, Tybjærg-Hansen A, Nordestgaard BG. Elevated remnant cholesterol causes both low-grade inflammation and ischemic heart disease, whereas elevated low-density lipoprotein cholesterol causes ischemic heart disease without inflammation. Circulation. Sep 17 2013;128(12):1298-1309.doi:10.1161/circulationaha.113.003008 Yuan Q, Tang B, Zhang C. Signaling pathways of chronic kidney diseases, implications for therapeutics. Signal Transduct Target Ther. Jun 9 2022;7(1):182.doi:10.1038/s41392-022-01036-5 Zewinger S, Reiser J, Jankowski V, et al. Apolipoprotein C3 induces inflammation and organ damage by alternative inflammasome activation. Nat Immunol. Jan 2020;21(1):30-41.doi:10.1038/s41590-019-0548-1 Chung KW, Dhillon P, Huang S, et al. Mitochondrial Damage and Activation of the STING Pathway Lead to Renal Inflammation and Fibrosis. Cell Metab. Oct 1 2019;30(4):784-799.e785.doi:10.1016/j.cmet.2019.08.003 Lamprea-Montealegre JA, Staplin N, Herrington WG, et al. Apolipoprotein B, Triglyceride-Rich Lipoproteins, and Risk of Cardiovascular Events in Persons with CKD. Clin J Am Soc Nephrol. Jan 7 2020;15(1):47-60.doi:10.2215/cjn.07320619 Varbo A, Freiberg JJ, Nordestgaard BG. Extreme nonfasting remnant cholesterol vs extreme LDL cholesterol as contributors to cardiovascular disease and all-cause mortality in 90000 individuals from the general population. Clin Chem. Mar 2015;61(3):533-543.doi:10.1373/clinchem.2014.234146 Varbo A, Benn M, Tybjærg-Hansen A, Jørgensen AB, Frikke-Schmidt R, Nordestgaard BG. Remnant cholesterol as a causal risk factor for ischemic heart disease. J Am Coll Cardiol. Jan 29 2013;61(4):427-436.doi:10.1016/j.jacc.2012.08.1026 Additional Declarations No competing interests reported. Supplementary Files SupplementalMaterial.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Apr, 2025 Reviews received at journal 13 Apr, 2025 Reviews received at journal 10 Apr, 2025 Reviewers agreed at journal 10 Apr, 2025 Reviewers agreed at journal 10 Apr, 2025 Reviewers agreed at journal 08 Apr, 2025 Reviewers invited by journal 31 Jul, 2024 Editor assigned by journal 23 Jul, 2024 Editor invited by journal 10 May, 2024 Submission checks completed at journal 10 May, 2024 First submitted to journal 04 May, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4367440","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":304652397,"identity":"b46d5c96-d599-439f-b239-5242ec2cef57","order_by":0,"name":"Jiang Bai","email":"","orcid":"","institution":"Shanxi Provincial People’s Hospital (Fifth Hospital) of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiang","middleName":"","lastName":"Bai","suffix":""},{"id":304652398,"identity":"0d74347c-b3f9-4503-a245-e450946a2dba","order_by":1,"name":"Zhouyu Dong","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Zhouyu","middleName":"","lastName":"Dong","suffix":""},{"id":304652399,"identity":"71a55690-9adc-4128-bdf5-a96861447cc0","order_by":2,"name":"Lijuan Zhang","email":"","orcid":"","institution":"Shanxi Provincial People’s Hospital (Fifth Hospital) of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Zhang","suffix":""},{"id":304652400,"identity":"0c220acc-da61-4a0c-bbbc-8cefe13b2ce4","order_by":3,"name":"Suhang Li","email":"","orcid":"","institution":"Shanxi Provincial People’s Hospital (Fifth Hospital) of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Suhang","middleName":"","lastName":"Li","suffix":""},{"id":304652401,"identity":"80be5069-d972-49e7-b85b-9ed65bf783ba","order_by":4,"name":"Rong Chen","email":"","orcid":"","institution":"Shanxi Provincial People’s Hospital (Fifth Hospital) of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Chen","suffix":""},{"id":304652402,"identity":"cd74a923-625c-48db-91e3-bee9187645c5","order_by":5,"name":"Jingkai Di","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingkai","middleName":"","lastName":"Di","suffix":""},{"id":304652403,"identity":"98fd5579-32c9-4856-93c3-d38dffb9028a","order_by":6,"name":"Wenyu Wang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenyu","middleName":"","lastName":"Wang","suffix":""},{"id":304652404,"identity":"f4656675-fe9c-466b-bd82-6a7858acce0e","order_by":7,"name":"Yawen Wu","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yawen","middleName":"","lastName":"Wu","suffix":""},{"id":304652405,"identity":"5dbda570-2228-439f-a8a8-d12d81e46a5e","order_by":8,"name":"Yun Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYDACCTiL+cCBDz9I08KWeHBmD2laeIwPc7ARoUN+dvMxad62OwzyM3I+HGbgYZDnFzuAXwvjnGNpQC3PGAzOnN1wuMCCwXDm7AT8WpglcsyAWg4zGLD3bjg8g4chweA2AS1sMC3yzTwPDvOwEaGFB6aF4XgPA3FaJCTSki3nnAM67MwxA2AgSxD2i/yM5IM33pQdBjEef/jww0aeX5qAFhBg4mFgqG+A2kpYOQgwEpVMRsEoGAWjYOQCAC4PQKvEnxzKAAAAAElFTkSuQmCC","orcid":"","institution":"The Fifth Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, China","correspondingAuthor":true,"prefix":"","firstName":"Yun","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-05-04 08:21:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4367440/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4367440/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57035589,"identity":"670d0fb3-0697-4806-9b7b-78d18ba5263a","added_by":"auto","created_at":"2024-05-23 18:33:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89098,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of logistic regression analysis, stratified based on quartiles of the elevated RC levels. Model 1 remained unadjusted. Model 2 was adjusted for age, sex, and BMI. Model 3 was adjusted for the variables in Model 2, educational level, urbanization, smoking, and drinking. Model 4 further adjusted for the variables in Model 3, diabetes and hypertension. Figures are created by the Xiantao (https://www.xiantaozi.com)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4367440/v1/4ab625474231a3979df95ca2.png"},{"id":57037046,"identity":"39defded-7937-41ba-a291-8e350720affc","added_by":"auto","created_at":"2024-05-23 18:41:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":106641,"visible":true,"origin":"","legend":"\u003cp\u003eThe nonlinear association between the change in RC levels and the risk of CKD. A. Unadjusted restricted cubic spline logistic regression model. B. Restricted cubic spline logistic regression model adjusted for LDL cholesterol. The knots between 3 and 7 were tested respectively, and the model with lowest Akaike information criterion value was selected for restricted cubic spline.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4367440/v1/cdca7c919192a4ca00edcc22.png"},{"id":57035592,"identity":"f2aef38c-8393-4d3e-aac9-2b2e2adbf6c9","added_by":"auto","created_at":"2024-05-23 18:33:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":107761,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of the discordance/concordance of LDL-C and RC with CKD. The proportion of the discordant/concordant groups in CKD and non-CKD population and the odds ratios (95% CIs) of CKD according to the discordance/concordance of LDL-C and RC. LDL-C low-density lipoprotein cholesterol, RC remnant cholesterol, OR odds ratio, CI confidence interval.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4367440/v1/3de118a4672d98bd7b914bee.png"},{"id":57035590,"identity":"be2eb655-a45e-40cd-a9f9-194ff0ed73bb","added_by":"auto","created_at":"2024-05-23 18:33:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":137924,"visible":true,"origin":"","legend":"\u003cp\u003eCasual mediation analysis of the association between RC and CKD, with the Bootstrap method (500 simulations) used for modeling. RC remnant cholesterol, DM diabetes mellitus, hs-CRP high-sensitivity C-reactive protein, WBC white blood cell.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4367440/v1/584c9c583fc518af5db90af9.png"},{"id":57035588,"identity":"7b49ebc0-06d8-4d9a-a035-1498dd78337a","added_by":"auto","created_at":"2024-05-23 18:33:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":460071,"visible":true,"origin":"","legend":"\u003cp\u003eThe underlying mechanism of RC and the CKD by inflammation. ApoC3 activated human monocytes in vivo to promote kidney injury in an NLRP3 and caspase-8-dependent manner [30]. In CKD, the upregulation of fatty acid protein transporters and CD36 results in the excessive accumulation of FFAs in kidney cells and lipid droplets (LDs), causing kidney lipotoxicity [6], and mitochondrial dysfunction and subsequent mitochondrial DNA (mtDNA) instability. This instability activates the cGAS-STING pathway, triggering NF-κB activation [6,31]. The graph was created by Figdraw.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4367440/v1/9d972eecf559b07a06ab2417.png"},{"id":57037428,"identity":"6a680af0-1ed0-484d-8ced-ae47a90612e9","added_by":"auto","created_at":"2024-05-23 18:49:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1445654,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4367440/v1/0d301252-ba60-4e23-829f-badc37eac467.pdf"},{"id":57035593,"identity":"4c3833e3-e825-440e-bb15-e9ace52db176","added_by":"auto","created_at":"2024-05-23 18:33:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1454428,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4367440/v1/84d3723ec67e811f03a5a995.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Role of Remnant Cholesterol and Its Interaction with Low-Density Lipoprotein Cholesterol in Chronic Kidney Disease \u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe increasing prevalence of chronic kidney disease (CKD) is a significant public health concern, involving about 10% of the global population[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e],\u003csup\u003e1\u003c/sup\u003e exceeding the number of cases observed in diabetes, chronic obstructive pulmonary disease, and depressive disorders.\u003csup\u003e2\u0026ndash;4\u003c/sup\u003e Individuals with a family history of high cholesterol are predisposed to develop CKD, implicating the composition of circulating lipoproteins, particularly the lipid and protein components, in CKD development.\u003csup\u003e5\u003c/sup\u003e Furthermore, lipids and their metabolites are essential constituents of cell and organelle membranes, signaling molecules, and energy generators, influencing structural and functional aspects\u003csup\u003e6\u003c/sup\u003e. Despite substantial attention on the role of kidney parenchymal lipid metabolism in CKD progression, understanding how dyslipidemia related to CKD results in abnormal lipid metabolism remains limited.\u003c/p\u003e \u003cp\u003eThe varied array of lipid species and their diverse functions across various parenchymal cells pose a challenge in understanding the mechanisms linking kidney parenchymal lipid accumulation to CKD. While low-density lipoprotein cholesterol (LDL-C) undoubtedly holds a significant position within the spectrum of lipoproteins,\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e recent studies have revealed that remnant cholesterol (RC) also plays a significant role in the realm of lipoproteins, with its association with cardiovascular disease extending beyond LDL-C.\u003csup\u003e9,10\u003c/sup\u003e RC is associated with triglyceride (TG)-rich lipoproteins (TRLs), including very low-density lipoprotein cholesterol (VLDL-C), intermediate-density lipoprotein cholesterol (IDL-C), and chylomicron remnants.\u003csup\u003e11\u003c/sup\u003e Previous evidence suggests an association between RC and the development of CKD.\u003csup\u003e12\u0026ndash;14\u003c/sup\u003e However, inconsistent findings exist concerning the impact of RC on CKD risk\u003csup\u003e15\u003c/sup\u003e and the nature of the relationship between RC and CKD risk, including its linearity\u003csup\u003e12\u003c/sup\u003e or non-linearity.\u003csup\u003e14\u003c/sup\u003e Significantly, different lipoprotein cholesterol types may influence each other, potentially affecting the results.\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eChina continues to have a high prevalence of CKD, with approximately 82\u0026nbsp;million adult CKD patients, where the prevalence of diabetes mellitus and hypertension as critical risk factors for CKD has been increasing.\u003csup\u003e17\u003c/sup\u003e This study aims to assess the association between RC and CKD risk, including determining its linearity or non-linearity, understanding the discordant/concordant relationship between LDL-C and RC, and exploring the potential mediating role of inflammation. The investigation is based on the data from China Health and Nutrition Survey (CHNS).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Recruitment Criteria\u003c/h2\u003e \u003cp\u003eThis cohort study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.\u003csup\u003e18\u003c/sup\u003e The study utilized data from the China Health and Nutrition Survey (CHNS), a multi-site, interdisciplinary epidemiological study that has been ongoing since 1989. The survey sampled approximately 7,200 households, comprising over 30,000 individuals, from 15 provinces and municipal cities in China. Ethical approval for the survey was granted by the National Institute of Nutrition and Food Safety, China Center for Disease Control and Prevention (Beijing, China), and the University of North Carolina at Chapel Hill (Chapel Hill, NC). Written informed consent was obtained from all participants. A comprehensive description of CHNS has been previously published.\u003csup\u003e19,20\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCHNS collected 9549 fasting blood samples in 2009. Blood drawing and processing were performed by certified technicians at each field center. The specimen collection flow chart details were provided in Supplemental Fig.\u0026nbsp;1. This study excluded 849 participants under 18 years old and 62 pregnant women. Recruitment was also denied to 31 individuals with incomplete creatinine information for CKD diagnosis, and 860 patients with missing RC information or whose RC was equal to or less than 0 (Supplemental Fig.\u0026nbsp;2). Consequently, the study population comprised 7747 individuals, with reported patient details encompassing baseline characteristics, healthy habits, and laboratory test results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDefinitions\u003c/h2\u003e \u003cp\u003eThe serum creatinine levels were used to calculate the estimated glomerular filtration rates (eGFR) using the chronic kidney disease epidemiology collaboration (CKD-EPI) 2009 creatinine equation. CKD was defined as eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, following the Kidney Disease: Improving Global Outcomes (KDIGO) 2012 Clinical Practice Guideline for the Evaluation and Management of CKD.\u003csup\u003e21\u003c/sup\u003e The RC level (mmol/L) was derived from the patient\u0026rsquo;s fasting state standard lipid profile by subtracting the values of LDL-C and HDL-C from the total cholesterol\u003csup\u003e10\u003c/sup\u003e Individuals were classified as having hypertension if their average systolic blood pressure exceeded 140 mmHg and diastolic blood pressure exceeded 90 mmHg. Diabetes mellitus was defined as fasting blood glucose (FBG)\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L or glycated hemoglobin (HbA1c)\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, following the criteria set by the American Diabetes Association.\u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003eSelf-reported health behaviors included alcohol consumption and smoking. Trained and certified technicians collected venous blood from each participant during the 2009 CHNS wave. The serum was subsequently separated, stored in an ultra-low-temperature freezer, and analyzed at a national central laboratory, adhering to strict quality control measures.\u003csup\u003e23\u003c/sup\u003e The white blood cells (WBCs) were measured using a Beckman Coulter LH751 (Beckman Coulter, USA). The levels of serum creatinine, uric acid (UC), LDL-C, HDL-C, TC and FBG were assessed using a Hitachi 7600 device (Randox, UK and Kyowa, Japan). The HbA1c was evaluated using the HLC-723 G7/D10/PDQ A1c (Tosoh, Japan/Bio-Rad, USA/Primus, USA), and the high-sensitivity C-reactive protein (hs-CRP) was detected using the Hitachi 7600 (Denka Seiken, Japan).\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eIn the present study, demographics, anthropometry, education, biochemical markers, BMI, smoking status, alcohol status and medical comorbidities were regarded as the potential covariates, which might have confounding effects on the association\u003c/p\u003e \u003cp\u003ebetween RC and CKD. Four main models were developed to accommodate covariate adjustment. Model 1 remained unadjusted, while Model 2 was adjusted for age, sex, and BMI. Additionally, Model 3 was adjusted for the variables in Model 2, educational level, urbanization, smoking, and drinking. Model 4 further adjusted for the variables in Model 3, diabetes and hypertension.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eRC levels were categorized into quartiles. Missing data were addressed through multiple imputation, with specific information available in Supplemental Fig.\u0026nbsp;2.\u003c/p\u003e \u003cp\u003eLogistic regression models were used to determine the independent association between RC and the presence of CKD. The potential nonlinear relationships between the change in RC and CKD were evaluated using a logistic regression model with restricted cubic splines (RCS). Knots at 3 and 7 were tested, and the RCS model with the lowest Akaike information criterion value was selected. When interpreting the findings of an RCS analysis, the 10th percentile value of the predictor variable was used as the reference point.\u003c/p\u003e \u003cp\u003eThe association between RC and LDL-C in CKD risk was assessed using the discordant/concordant LDL-C and RC approach. The cutoff value for RC was set at 0.36mmol/L (median), representing a crucial point with potential statistical significance in our study. Additionally, we adopted 2.6 mmol/L (100 mg/dL) as the cutoff points for LDL-C, aligning with the recommended LDL-C target outlined in the 2019 ESC/EAS Guidelines for the management of dyslipidaemias.\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCausal mediation analysis was utilized to assess the impact of the mediator and the proportion of mediated effects on the association between RC and CKD, implementing the Bootstrap method with 500 simulations. This approach was embedded within the counterfactual framework of causal inference and formalizes the implicit assumption in applied research that the treatment inadvertently influences the outcome through the mediator, despite specific statistical models.\u003csup\u003e25\u003c/sup\u003e It permit the estimation of causal mediation effects, considering both continuous and discrete mediators, as well as parametric and nonparametric models, and various types of outcome variables.\u003csup\u003e25\u003c/sup\u003e The general estimation procedures establish causal mediation effects or indirect effects for each unit \u003cem\u003ei\u003c/em\u003e using the following approach:\u003csup\u003e25\u003c/sup\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${{\\delta }}_{i}\\left(t\\right)\\equiv {Y}_{i}\\left(t,{M}_{i}\\left(1\\right)\\right)-{Y}_{i}\\left(t,{M}_{i}\\left(0\\right)\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the context of the above formula, \u003cem\u003eTi\u003c/em\u003e represents the binary treatment variable, \u003cem\u003eMi\u003c/em\u003e denotes the mediator, and \u003cem\u003eYi(t)\u003c/em\u003e represents the potential outcome of the treatment state t. The \u003cem\u003eYi (t, m)\u003c/em\u003e represents the potential outcome that would result if the treatment and mediating variables equal \u003cem\u003et\u003c/em\u003e and \u003cem\u003em\u003c/em\u003e, respectively. More details are provided in the Supplementary Methods.\u003c/p\u003e \u003cp\u003eOur study utilized SPSS 26.0 (IBM, USA) to conduct descriptive statistics and logistic regression. Additionally, the MSTATA software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.mstata.com\u003c/span\u003e\u003cspan address=\"http://www.mstata.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized for Causal Mediation Analysis and RCS analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eWithin the clinical cohort of 7,747 individuals, the participants had an average age of 50 years. Of these individuals, 3,701 (48%) were male, and 910 (12%) were diagnosed with chronic kidney disease (CKD). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of the 7747 participants in the cohort study, categorized by quartiles of RC levels (Q1:1839, Q2:1952, Q3:1990, Q4:1966). With increasing quartiles of RC, we observed a higher frequency of participants with age, sex, BMI index, alcohol intake, smoking, hypertension and diabetes in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients in Group Q4 (high RC levels) tended to have lower LDL-C levels compared to those in Group Q1, Q2, and Q3 (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\u003eBaseline characteristics of enrolled participants among groups of RC quartile.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\u003eQ1\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1839\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1952\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1966\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-Value \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49(38,60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(39,62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51(40,61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52(42,61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eMale(sex)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e794(43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e877(44.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e962(48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1068(54.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eBMI, kg/m\u003csup\u003e2a\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.06(20.26,24.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.45(20.50,24.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.44(21.21,25.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.71(22.54,27.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eJunior high school or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1016(55.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1103(56.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1151(57.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1120(57.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e =\u0026thinsp;0.437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e=\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e587(31.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e603(30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e689(34.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e697(35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1252(68.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1349(69.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1301(65.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1269(64.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e517(28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e586(30.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e629(31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e714(36.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e569(30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e596(30.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e659(33.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e735(37.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173(9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232(11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e267(13.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e238(12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e=\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHs-CRP, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00(0.00,2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00(0.00,2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00(1.00,3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00(1.00,3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eTP, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.20(73.70,80.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.10(73.80,80.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.10(73.80,80.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.80(73.40,80.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e=\u0026thinsp;0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.80(45.00,49.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.15(44.90,49.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.20(45.10,49.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.90(46.00,50.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003e16.00(12.00,22.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.00(13.00,23.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.00(14.00,27.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.00(17.00,35.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eTC, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.59(4.02,5.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.64(4.02,5.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.74(4.15,5.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.10(4.48,5.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eTG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80(0.63,1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06(0.84,1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51(1.23,1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.81(2.22,3.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eHDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.55(1.35,1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43(1.24,1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32(1.13,1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12(0.96,1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eLDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.87(2.37,3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.92(2.33,3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.93(2.33,3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.74(2.11,3.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eAPO-A, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15(1.00,1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10(0.95,1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07(0.93,1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02(0.88,1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eAPO-B, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81(0.66,0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85(0.69,1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90(0.74,1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98(0.81,1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eLPA, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.00(45.00,186.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.00(42.00,174.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.00(42.00,163.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.00(32.00,135.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eUA, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e261.00(214.00,319.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e279.00(229.00,336.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e303.00(246.00,363.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e360.50(298.00,433.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eUREA, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.33(4.38,6.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.14(4.27,6.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.16(4.31,6.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.46(4.59,6.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eHGB, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.00(126.00,151.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.89(127.37,151.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141.00(129.00,154.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e146.00(133.00,159.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eWBC, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.75(4.80,6.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.96(5.04,7.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.20(5.20,7.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.40(5.42,7.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eHbA1c, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.40(5.20,5.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.50(5.20,5.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.50(5.20,5.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.60(5.30,6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e396(21.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e503(25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e592(29.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e737(37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96(5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148(7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e224(11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e408(20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eBMI\u003c/em\u003e body mass index, \u003cem\u003eCKD\u003c/em\u003e chronic kidney disease, \u003cem\u003eHs-CRP\u003c/em\u003e high-sensitivity C-reactive protein, \u003cem\u003eTP\u003c/em\u003e total protein, \u003cem\u003eALB\u003c/em\u003e albumin, \u003cem\u003eALT\u003c/em\u003e alanine aminotransferase, T\u003cem\u003eC\u003c/em\u003e total cholesterol, \u003cem\u003eTG\u003c/em\u003e triglyceride, \u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol, \u003cem\u003eLDL-C\u003c/em\u003e low-density lipoprotein cholesterol, \u003cem\u003eAPO-A\u003c/em\u003e apolipoprotein A, \u003cem\u003eAPO-B\u003c/em\u003e apolipoprotein B, \u003cem\u003eLPA\u003c/em\u003e lipoprotein (а), \u003cem\u003eUA\u003c/em\u003e Uric acid, \u003cem\u003eUREA\u003c/em\u003e urea, \u003cem\u003eHGB\u003c/em\u003e hemoglobin, \u003cem\u003eWBC\u003c/em\u003e white blood cell, \u003cem\u003eHbA1c\u003c/em\u003e glycosylated hemoglobin A1c, \u003cem\u003eDM\u003c/em\u003e diabetes mellitus\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e p-values by Kruskal-Wallis rank test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAssociation Between RC Levels and CKD\u003c/h2\u003e \u003cp\u003eLogistic regression analysis was utilized to assess the association between CKD and RC levels, stratified based on quartiles of the elevated RC levels. Univariate logistic analysis indicated significant associations between RC levels and the prevalence of CKD. Specific odds ratios and their corresponding confidence intervals within the groups were as follows: OR 1.30, 95% CI 1.06\u0026ndash;1.60 for Group 2; OR 1.49, 95% CI 1.22\u0026ndash;1.83 for Group 3; and OR 1.33, 95% CI 1.08\u0026ndash;1.63 for Group 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Four main models were constructed for covariate adjustment. After adjusting for covariates of model 4, the association between the group Q2 and CKD showed no statistical significance (OR 1.21, 95% CI 0.95\u0026ndash;1.55, P\u0026thinsp;=\u0026thinsp;0.123). The Group Q4 had a lower OR value for CKD than the group Q3 (OR:1.30 vs. OR:1.44) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A nonlinear relationship between RC and CKD was a possibility.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe Relationship Between RC and CKD by RCS\u003c/h2\u003e \u003cp\u003eTo explore the nonlinear association between the change in RC levels and the risk of CKD, a logistic regression model with RCS was used. The knots between 3 and 7 were tested respectively, and the model with the lowest Akaike information criterion value was selected for RCS. Finally, we used RCS with 3 knots. The RCS analysis suggested an \u0026ldquo;Inverted U-shaped\u0026rdquo; association of RC with CKD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The formula for calculating RC at a specific TC level revealed an inverse relationship between RC and LDL-C levels. Therefore, LDL-C was adjusted as a covariate in the subsequent analysis. Results from the RCS analysis revealed that the latter segment of the RCS curve displayed a flat pattern compared to the unadjusted RCS curve after adjustment for LDL-C (P\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInter‑relation between Discordant/Concordant LDL-C and RC with CKD Risk\u003c/h2\u003e \u003cp\u003eThe discordant/concordant grouping was utilized to examine the interaction between LDL-C and RC, illustrating specific distribution details in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The logistic analysis demonstrated that participants in Group 2 (high LDL-C/low RC), Group 3 (low LDL-C/high RC) and Group 4 (high LDL-C/high RC) were associated with 135%, 51%, and 179%, respectively, increased risk for CKD (OR: 2.35, 95% CI 1.83\u0026ndash;3.03 for Group 2; OR: 1.51, 95% CI 1.14\u0026ndash;2.01 for Group 3; and OR: 2.79, 95% CI 2.17\u0026ndash;3.59 for Group 4) relative to Group 1 (low LDL-C/low RC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMediator Analysis\u003c/h2\u003e \u003cp\u003eA causal mediation analysis was performed to investigate the mediating roles of hs-CRP and WBCs in the association between RC levels and CKD. The results revealed that hs-CRP and WBCs were significant mediators in the relationship between RC and CKD. These two indices mediated 6.9% and 10.5% of the correlation, respectively, utilizing the bootstrap method with 500 simulations for modeling, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (P\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analysis\u003c/h2\u003e \u003cp\u003eIn the sensitivity analysis of the RCS, after adjusting for the variables in Model 4, the \u0026ldquo;inverted U-shaped\u0026rdquo; association of RC with CKD was also observed. Additionally, upon further adjustment for the variables in Model 4 and LDL-C, the latter segment of the RCS curve displayed a flat pattern (Supplemental Fig.\u0026nbsp;3). After adjusting for the covariates included in model 4, the odds ratio of Group 2 (high LDL-C/low RC) remained higher than that of Group 3 (low LDL-C/high RC) concerning the association with CKD as opposed to Group 1 (low LDL-C/low RC) (Supplemental Fig.\u0026nbsp;4).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we identified a significant association between elevated RC levels and the prevalence of CKD in the general Chinese population. This association remained independent of demographic factors, education, residency, lifestyle, and traditional cardiovascular risk factors. Additionally, a non-linear correlation between RC and the risk of prevalent CKD was observed, and influenced by serum LDL-C levels. In the analysis of discordant/concordant LDL-C and RC groups, it was determined that RC played a smaller role in the cholesterol-related pathogenesis of CKD compared to LDL-C. The findings shed light on the pathophysiology of RC in CKD, indicating a correlation between elevated serum levels of RC and the interaction of LDL-C in the progression of CKD. Specifically, this association was attributed to the pro-inflammatory state serving as a mediator.\u003c/p\u003e \u003cp\u003eSeveral studies have observed inconsistencies in the relationship between CKD and RC. He et al.\u003csup\u003e14\u003c/sup\u003e identified a significant inverse correlation between RC levels and eGFR in a U.S. population. The risk of CKD progressively increased across RC quartiles in a cohort comprising a general population of middle-aged and elderly individuals of Chinese descent.\u003csup\u003e12\u003c/sup\u003e In contrast, Rysz-Gorzynska et al.\u003csup\u003e15\u003c/sup\u003e did not observe significant differences in levels of very VLDL-C or IDL-C between individuals with CKD and those with normal kidney function. Additionally, a non-independent relationship was noted between declining kidney function and VLDL-C in a prospective cohort study with CKD.\u003csup\u003e16\u003c/sup\u003e These discrepancies may stem from variations in population demographics, geographical region, sample size, analytical methods, and confounding variables.\u003c/p\u003e \u003cp\u003eIn our study, we observed an \u0026ldquo;inverted U-shape\u0026rdquo; curve in the association between serum RC levels and the risk of CKD. Understanding and recognizing this non-linear trend is paramount in the context of clinical management of CKD, as it suggests a potential threshold effect where the influence of RC on the development or progression of the disease may not be uniform across all levels of cholesterol. After adjusting for LDL-C level as a confounder, the decreased section of the RCS curve became flat. Elevated RC levels were associated with increased TG enrichment of LDL particles, particularly in small, dense LDL particles with an extended half-life in circulation.\u003csup\u003e26\u003c/sup\u003e We hypothesized that LDL-C may exert a dominant effect in the relationship between RC and the risk of CKD. It was further elucidated that serum RC levels have less impact than LDL-C in contributing to the pathogenesis of CKD, according to the LDL-C and RC discordance/concordance group. With new-generation TG and RC lowering medications, such as apoC3 and ANGPTL3 inhibitors,\u003csup\u003e27\u003c/sup\u003e currently under evaluation, transitioning to a novel strategy that separately considers each component may be the most prudent course of action.\u003csup\u003e26\u003c/sup\u003e In clinical practice, it is essential to simultaneously consider the levels of both RC and LDL to ensure a dynamic balance.\u003c/p\u003e \u003cp\u003eThe influence of RC on the development and progression of CKD involves various complex processes. Historical evidence suggested that RC induced pro-inflammatory activation of the endothelium and low-grade inflammation, both of which contribute to atherosclerosis.\u003csup\u003e28\u003c/sup\u003e Our investigation revealed that the relationship between RC and CKD was partially mediated by WBC count and hs-CRP concentrations, suggesting that RC was frequently associated with a pro-inflammatory state because of its highly atherogenic nature and unique physicochemical properties. The inflammatory state plays a crucial role in the onset and progression of CKD, impacting the quality of life and prognosis of individuals with CKD.\u003csup\u003e29\u003c/sup\u003e The hydrolysis of apolipoprotein C (ApoC), triglycerides, and cholesterol within RC under physiological conditions leads to the production of free fatty acids (FFAs) and glycerol. Zewinger et al.\u003csup\u003e30\u003c/sup\u003e found that elevated levels of ApoC3 in plasma were associated with a notable increase in markers of systemic inflammation, such as hs-CRP, in patients with CKD. Furthermore, in humanized mouse models, ApoC3 activated human monocytes in vivo to promote kidney injury in an NLRP3 and caspase-8-dependent manner\u003csup\u003e30\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn CKD, the upregulation of fatty acid protein transporters and CD36 results in the excessive accumulation of FFAs in kidney cells and lipid droplets (LDs), causing kidney lipotoxicity.\u003csup\u003e6\u003c/sup\u003e Moreover, the increased activity of fatty acid-binding proteins (FABPs) in CKD patients enhances the transportation of FFAs to the mitochondria for further oxidation. Conversely, the decreased expression of carnitine palmitoyltransferases (CPT1 and CPT2) leads to reduced fatty acid oxidation, ineffective NADH production, decreased electron transport chain (ETC) activity, resulting in mitochondrial dysfunction and subsequent mitochondrial DNA (mtDNA) instability. This instability activates the cGAS-STING pathway, triggering NF-κB activation\u003csup\u003e6,31\u003c/sup\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). An important strength of this study lies in our examination of the intermediary role of inflammation in systemic lipid metabolism disorders. This contributes to the selection of anti-inflammatory medications and lipid-lowering drugs for nephrologists to halt the progression of CKD, reduce proteinuria, and mitigate the risk of cardiovascular disease.\u003c/p\u003e \u003cp\u003eIn most Chinese institutions, the direct measurement of RC using nuclear magnetic resonance or ultracentrifugation is impractical in clinical practice due to its time-consuming, expensive, and radioactive nature. In our study, we assessed the RC value due to its convenience, minimal time requirement, and established reliability, as it has been widely used in large cohort studies.\u003csup\u003e12,32\u0026ndash;34\u003c/sup\u003e This assessment may be sufficient for predicting and determining treatment strategies in actual clinical settings.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003elimitations\u003c/h2\u003e \u003cp\u003eThe current study acknowledges several limitations. Firstly, the cross-sectional design precludes the establishment of causal relationships between RC and CKD. It remains uncertain whether dyslipidemia causes kidney problems or if it is a metabolic consequence. Additionally, as the data was solely derived from the CHNS study in China, the generalizability of our findings to individuals of diverse ethnic backgrounds remains uncertain, potentially constraining their applicability. In conclusion, while our comprehension of RC and CKD advances in recent years, we strongly advocate for ongoing research to explore the occurrence and underlying mechanisms of RC in CKD, including systemic lipid levels or cellular lipid metabolism disorders.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe results of this cohort indicated a notable connection between increased levels of RC and the prevalence of CKD. This relationship was found to be nonlinear and was influenced by LDL-C levels. More specifically, the presence of a pro-inflammatory state was identified as a mediating factor in this association. The diverse nature of lipid species and their various functions within different parenchymal cells presents a challenge in understanding the mechanisms linking lipid levels to CKD. As such, it is imperative in clinical practice to simultaneously consider both RC and LDL levels to maintain a dynamic equilibrium.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript does not include any non-author contributors to acknowledge.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eJiang Bai have full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003eConcept and design: Jiang Bai, Zhouyu Dong.\u003c/p\u003e\n\u003cp\u003eAcquisition, analysis, or interpretation of data: All authors.\u003c/p\u003e\n\u003cp\u003eDrafting of the manuscript: Jiang Bai, Zhouyu Dong, Lijuan Zhang.\u003c/p\u003e\n\u003cp\u003eCritical review of the manuscript for important intellectual content: All authors.\u003c/p\u003e\n\u003cp\u003eStatistical analysis: Jiang Bai, Zhouyu Dong.\u003c/p\u003e\n\u003cp\u003eObtained funding: No\u003c/p\u003e\n\u003cp\u003eAdministrative, technical, or material support: Yun Zhou.\u003c/p\u003e\n\u003cp\u003eSupervision: Yun Zhou.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding/Support\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by no grants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets from this study are available at https://www.cpc.unc.edu/projects/china. The comprehensive data set creation plan and underlying analytic code can be obtained from the first author, Jiang Bai, upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not required for this study in accordance with local/national guidelines. Written informed consent to participate in the study was not required in accordance with local/national guidelines. This study does not involve sensitive personal data, ethical issues, or breaches of policy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJadoul M, Aoun M, Masimango Imani M. 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Remnant cholesterol as a causal risk factor for ischemic heart disease. \u003cem\u003eJ Am Coll Cardiol. \u003c/em\u003eJan 29 2013;61(4):427-436.doi:10.1016/j.jacc.2012.08.1026\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Remnant cholesterol, Low-density lipoprotein cholesterol, Chronic kidney disease, Casual mediation analysis","lastPublishedDoi":"10.21203/rs.3.rs-4367440/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4367440/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe growing prevalence of chronic kidney disease (CKD) presents a substantial public health issue. Furthermore, the continuous advancements in lipid-lowering strategies and medications highlight the ongoing importance of the correlation between remnant cholesterol (RC) and CKD. This study aims to investigate the link between RC and CKD risk, particularly focusing on the interplay between low-density lipoprotein cholesterol (LDL-C) and RC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e This cross-sectional study included 7747 participants in wave 2009 of the China Health and Nutrition Survey which has been in progress since 1989. We enrolled 7747 individuals in the present study from the China Health and Nutrition Survey, with exclusion criteria applied to individuals under 18 and pregnant participants. CKD was defined as eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, following the Kidney Disease: Improving Global Outcomes (KDIGO) 2012 Clinical Practice Guideline. A logistic regression analysis was conducted to assess the associations between discordant/concordant levels of LDL-C and RC with CKD. Subsequently, a mediation analysis was performed to identify potential mediators.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWithin the clinical cohort of 7747 patients, 910 individuals (11.8%) were diagnosed with CKD, with RC levels categorized into quartiles. Logistic analysis revealed significant associations between elevated RC levels and the prevalence of CKD (OR 1.30, 95% CI 1.06\u0026ndash;1.60 for Group 2; OR 1.49, 95% CI 1.22\u0026ndash;1.83 for Group 3; and OR 1.33, 95% CI 1.08\u0026ndash;1.63 for Group 4). The results of restricted cubic splines (RCS) analysis suggested an \u0026ldquo;inverted U-shaped\u0026rdquo; association of RC with CKD. The analysis of discordant/concordant grouping showed that participants in Group 2 (high LDL-C/low RC) and Group 3 (low LDL-C/high RC) were associated with an increased risk for CKD. The odds ratios were 2.35 (95% CI 1.83\u0026ndash;3.03) for Group 2 and 1.51 (95% CI 1.14\u0026ndash;2.01) for Group 3, compared to Group 1 (low LDL-C/low RC). Causal mediation analysis indicated that inflammation partially mediated the association between RC and CKD.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study presented evidence of a non-linear relationship between RC and CKD, suggesting that the association was influenced by LDL-C levels and mediated by the pro-inflammatory state.\u003c/p\u003e","manuscriptTitle":"The Role of Remnant Cholesterol and Its Interaction with Low-Density Lipoprotein Cholesterol in Chronic Kidney Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-23 18:33:51","doi":"10.21203/rs.3.rs-4367440/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-15T08:00:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-13T20:26:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-10T06:09:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313338506875446896099090026948772638041","date":"2025-04-10T05:58:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193302273390112964646266955741926040883","date":"2025-04-10T05:55:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140857616168663773089066175549261675642","date":"2025-04-08T08:12:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-31T09:59:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-23T05:39:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-10T20:41:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-10T15:35:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-04T08:18:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c565f9fe-bcd7-401b-8a73-82dc028c55c6","owner":[],"postedDate":"May 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":32207369,"name":"Health sciences/Nephrology"},{"id":32207370,"name":"Health sciences/Nephrology/Kidney"},{"id":32207371,"name":"Health sciences/Nephrology/Kidney diseases"}],"tags":[],"updatedAt":"2025-08-28T03:53:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-23 18:33:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4367440","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4367440","identity":"rs-4367440","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}