Association of combined low-density lipoprotein cholesterol and residual cholesterol stratification with all- cause and cardiovascular mortality in peritoneal dialysis patients: a multicenter retrospective cohort study

Association of combined low-density lipoprotein cholesterol and residual cholesterol stratification with all- cause and cardiovascular mortality in peritoneal dialysis patients: a multicenter retrospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of combined low-density lipoprotein cholesterol and residual cholesterol stratification with all- cause and cardiovascular mortality in peritoneal dialysis patients: a multicenter retrospective cohort study Fuhua Chen, Chuchu Zeng, Hui Guo, Na Tian, Qingdong Xu, Xiaojiang Zhan, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6590878/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background. Low-density lipoprotein cholesterol (LDL-C) combined with residual cholesterol (RC) can predict mortality in the general population. Studies on the effects of LDL-C combined with RC in peritoneal dialysis(PD) patients are lacking. The aim of this study was to elucidate the linkage of LDL-C and RC stratification with all-cause and cardiovascular mortality in PD patients. Methods. In this retrospective analysis of multicenter data, 3397 patients from China undergoing initial PD spanning January 1, 2005, through May 31, 2023, were involved. The included participants were orderly grouped into four cohorts in view of their baseline RC and LDL-C concentrations. The conjunction between baseline LDL-C levels combined with RC values and the cardiovascular and all-cause mortality risk in PD participants was evaluated using Fine-Grey , s hazard models. Results. Among 3397 recipients aging of 50.5±14.4 years , along with 57.3% male were enrolled. During a period of 17179 person-years of follow-up, 904 deaths were documented, of which 512 were caused by cardiovascular disease (CVD). Those with high LDL-C(≥2.6 mmol/L) and RC(≥0.62 mmol/L) levels exhibited a higher likelihood of all-cause mortality risk (adjusted hazards ratio [HR], 1.47; 95% confidence interval [CI],1.21 to 1.79) and cardiovascular mortality (adjusted HR, 1.55; 95% CI,1.19 to 2.01) in comparison to low levels of RC (<0.62 mmol/L) and LDL-C (<2.6mmol/L). This trend remained robust in PD patients who survived the two-year follow-up period. Conclusions. Higher levels of RC and LDL-C at the initiation of PD had significant linked with more elevated cardiovascular and all-cause mortality in PD patients. all-cause mortality cardiovascular mortality low-density lipoprotein cholesterol peritoneal dialysis residual cholesterol Figures Figure 1 Figure 2 Figure 3 1. Introdution More and more patients suffered end-stage kidney disease (ESKD) face a severe challenge to the global renal replacement therapy system[ 1 , 2 ]. Peritoneal dialysis (PD) is currently one of the main renal replacement therapy modalities. The predominant mortality etiologies for patients receiving PD are cardiovascular complications[ 3 ]. Low-density lipoprotein cholesterol (LDL-C) is deemed as a traditional hazard tissue for cardiovascular disease (CVD) occurrence and fatality. In the developed countries, the LDL-C level of general population is significantly linked to cardiovascular and all-cause fatality rate in a U-shaped curve[ 4 , 5 ]. Recently, U- or J-shaped associations have also been found in Chinese individuals[ 6 ]. In PD patients, U-shaped associations are also found[ 7 ]. Current data reveal that the sophisticated relationship between LDL-C concentrations and lethality risk is demonstrated significantly. It is possible that there are other lipids contributing to residual cardiovascular risk. In recent years, residual cholesterol (RC) has win so much eyes, viewed as a non-traditional hazard factor for CVD. RC is famous as the cholesterol content of triglyceride (TG)-rich lipoproteins, including intermediate-density lipoproteins (IDL) and very low-density lipoproteins (VLDL) in fasting, and VLDL, IDL, and celiac remnants in the unfasted state, and is related to TG[ 8 , 9 ]. Recent reports from a national cohort analyses in the United States suggest that elevated RC is connected with a anabatic risk of long-term all-cause mortality, CVD and CVD mortality for ordinary people[ 10 ]. A study about diabetic patients also found this phenomenon[ 9 ]. Therefore, RC, as an additional biomarker of cardiovascular risk, may be a new therapeutic target to reduce adverse cardiovascular outcomes. LDL-C combined with RC has emerged as a new trend in cardiovascular prognostic studies. A European study found that the combination of RC and LDL-C showed an effect on the development of CVD in person without atherosclerotic cardiovascular disease[ 11 ]. Another study from two National Cohorts revealed the relationship between RC and LDL-C discordance with incident stroke[ 12 ]. The monitoring stratagem of Integrating LDL-C with RC better predicts cardiovascular and all-cause prognosis in different populations. However, the relevance of combining RC and LDL-C with the prognosis of continuous ambulatory peritoneal dialysis (CAPD) remains unknown. Therefore, the intent of this research is to evaluate the association of combined LDL-C and RC with all-cause and CVD mortality in cohort of CAPD patients. 2. Methods 2.1. Investigation scheme and participants information This retrospective cohort study involved participants who viewed CAPD as their first option for renal replacement therapy between 1 January 2005 to 31 May 2023 from a multi-center database (The Ever-green Tree Nephrology Group, ETNG) in China. Patients who were age ≥ 18 years at PD commencement and maintained PD treatment for ≥ 3 months were enrolled. Exclusion criteria consisted of individuals whose age is less than 18 years old, follow-up time < 90 days, pregnancy or lactation, missing baseline lipid data containing high density lipoprotein cholesterol (HDL-C), total cholesterol (TC), as well as LDL-C. The value of RC was expressed as TC minus the sum of HDL-C and LDL-C[ 13 ]. This protocol is in line with the declaration of Helsinki and has been fully approved by the clinical research ethics committees. All data included in the investigation were anonymous, therefore informed consent was waived. In accordance with the European Society of Cardiology (ESC) 2019 guidelines for lipid regulation[ 14 ], our study set 2.6 mmol/L as critical value for LDL-C. A study from the European Heart Journal showed that patients with RC ≥ 24 mg/dL (also expresses as 0.62 mmol/L) presented an elevated risk of CVD[ 11 ], so the critical value of RC was 0.62 mmol/L. Based on the datum line of LDL-C and RC, participants were classed into four groups, including group1 (both low LDL-C and RC group: LDL-C < 2.6 mmol/L and RC < 0.62 mmol/L), group2 (low LDL-C but high RC group༚LDL -C < 2.6mmol/L and RC ≥ 0.62 mmol/L), group3 (high LDL-C but low RC group༚LDL ≥ 2.6 mmol/L and RC < 0.62 mmol/L ), group4 (both high LDL-C and RC group༚LDL-C ≥ 2.6 mmol/L and RC ≥ 0.62 mmol/L). The goal of our study was to examine the differences in CVD mortality and all-cause mortality among the 4 groups of patients undergoing CAPD. 2.2. Data acquisition and assessments At the outset of the research, the medical records of patients stored in each dialysis center were thoroughly scrutinized by professional medical staff. Baseline demographic data consisted of sex, age, body mass index (BMI), the primary etiology of end-stage renal disease, current smoking and drinking history, history of prior diabetes mellitus (DM), history of prior hypertension, history of prior CVD, use history of antiplatelet medications and lipid-lowering medications. Baseline laboratory data included hemoglobin, albumin, estimated glomerular filtration rate (eGFR), TC, TG, HDL-C, LDL-C, serum calcium (Ca), and serum phosphorus (P). The patients were all receiving dialysis for the first time in the hospital, and all data was available within the month prior to dialysis. All fasting blood sample data were measured by the laboratory in each hospital. And all patients received CAPD treatment. 2.3. Follow-up and outcome measures Patients were interviewed face-to-face or by telephone once a month by trained nurses at each center. The each patient’s observation period was designed from the date of study start to the death, transfer to hemodialysis (HD), receipt of a kidney transplant, loss to follow-up, transfer to another dialysis center, or completion of follow-up (31 May 2023). During the final examination, a follow-up examination was conducted on the lost patient. The principal and secondary outcome indicators included CVD mortality, as well as all-cause mortality, respectively. Additionally, the cause of death according to the medical records on admission were determined. If patients suffered out-of-facility mortality, we estimated the reason for death by confirming the death through telephone interviews with family members in conjunction with medical record information from the PD centers. CVD mortality contained deaths relevant with heart failure (not purely volume factor), malignant arrhythmia, hemorrhagic or thromboembolic stroke, acute myocardial ischemic event and sudden cardiac death according to the International Classification of Diseases, Clinical Revision 9th edition. Sudden cardiac death refers to an unexpected, non-traumatic death that occurs within 1 hour after the appearance of new or worsening symptoms (witnessed sudden arrest), or within 24 hours after the last survival if not witnessed. 2.4. Statistical analysis of categorical data Continuous variables presented normally distributed and showed a chi-squared variance which are expressed using the mean added or subtracted the standard deviation, skewed variables were denoted as quartiles. Categorical variables represented as number of patients. Covariates up to 5% missing were supplemented using multiple interpolation, and more than 5% were excluded. We first used the cause-specific hazard models for the analysis and sub-distribution hazard models (the Fine-Gray , s models) for the competitive analysis. Patients who have experienced competitive risk events are still in the risk setting of the sub distribution risk model, but have been removed from the specific cause risk model [ 15 ]. Non-CVD mortality was the competing risk for CVD mortality. The competitive factors for all-cause mortality mainly included switching to HD, renal transplantation, transfer to other dialysis centers, and loss to follow-up. The results were descripted as hazard ratios (HRs) and 95% confidence interval (CI). Subgroup analyses were conducted to assess modification effects refer to the relationship between four groups and lethality, in which subgroups were stratified by sex (male or female), age (< 65 or ≥ 65 years), BMI (< 24 or ≥ 24), hypertension (yes or no), diabetes mellitus (yes or no), lipid-lowering medications (yes or no), and history of CVD (yes or no). All statistical results were examined through SPSS software (IBM Corp, Armonk, NY, USA, version 27), along with the R package 4.4.2 ( https://www.r-project.org/ ), and a P value was less than 0.05 that was taken for statistical discrepancy. 3. Results 3.1. Baseline properties Totally 3,397 patients were finally enrolled in the present study, we supplemented 251 missing covariates (containing 10 hemoglobin indicators, 17 albumin indicators, 5 triglycerides indicators, 131serun calcium indicators, and 88 serum phosphorus indicators) using multiple interpolation. The specific information about patient recruitment procedure was depicted in Fig. 1 . Table 1 represented the baseline features of patients grouped according to LDL-C and RC. The mean age was 50.5 ± 14.4years old, 57.3% were male, 20.9% were diabetes, and 19.5% had prior CVD. Patients in group 4 (presenting both high LDL-C and RC) were older. There were fewer male patients, fewer smokers, and lower blood phosphorus in the group 4. There was more diabetes, history of CVD, hypertension, higher triglycerides, hemoglobin, and blood calcium in the group4. Table 1 Baseline characteristics stratified by LDL-C and RC Variables Total n = 3397 Group1 n = 1007 Group2 n = 790 Group3 n = 705 Group4 n = 895 P value Age (years) 50.5 ± 14.4 49.1 ± 14.3 51.2 ± 14.4 49.2 ± 14.3 52.4 ± 14.3 < 0.001 Male (n, %) 1947(57.3) 653(64.8) 471(59.6) 390(55.3) 433(48.3) < 0.001 BMI (kg/m 2 ) 22.0(20.0, 24.5) 21.7(19.8, 24.1) 22.2(20.3, 24.6) 22.0(20.0, 24.4) 22.5(20.0, 24.9) < 0.001 Current smoker, n (%) 300(8.8) 115(11.4) 75(9.4) 55(7.8) 55(6.1) < 0.001 Current alcohol, n (%) 106(3.1) 40(3.9) 23(2.9) 20(2.8) 23(2.5) 0.306 Comorbidities DM, n (%) 710(20.9) 166(16.4) 171(21.6) 145(20.5) 228(25.4) < 0.001 Hypertension, n (%) 2458(72.3) 696(69.1) 576(72.9) 517(73.3) 669(74.7) 0.04 Prior CVD, n (%) 664(19.5) 164(16.2) 170(21.5) 113(16.0) 217(24.2) < 0.001 Medication use Antiplatelet drug (n, %) 362(10.6) 107(10.6) 94(11.8) 70(9.7) 91(10.1) 0.594 Lipid-lowering drug (n, %) 579(17.0) 164(16.2) 150(18.9) 97(13.7) 168(18.7) 0.02 TC (mmol/L) 4.40(3.62, 5.30) 3.46(3.05, 3.85) 4.07(3.54, 4.61) 5.40(4.82, 5.93) 5.36(5.00, 6.35) 0.000 TG (mmol/L) 1.37(0.95, 1.96) 0.96(0.73, 1.23) 1.78(1.25, 2.70) 1.20(0.92, 1.56) 2.52(1.91, 3.19) < 0.001 HDL-C (mmol/L) 1.07(0.87, 1.34) 1.08(0.89, 1.35) 0.93(0.74, 1.14) 1.22(1.01, 1.50) 1.07(0.89, 1.33) < 0.001 LDL-C (mmol/L) 2.52(1.94, 3.18) 2.00(1.62, 2.29) 1.96(1.60, 2.26) 3.18(2.85, 3.63) 3.26(2.93, 3.85) 0.000 RC (mmol/L) 0.61(0.38, 1.00) 0.38(0.25, 0.50) 1.00(0.74, 1.47) 0.39(0.24, 0.50) 1.01(0.80, 1.34) 0.000 Hemoglobin (g/L) 94.8 ± 23.1 89.0 ± 22.2 94.9 ± 23.1 96.2 ± 23.7 100.0 ± 23.1 < 0.001 Albumin (g/L) 35.2 ± 5.5 34.8 ± 5.4 35.6 ± 5.5 34.9 ± 5.4 35.5 ± 5.6 < 0.001 eGFR(ml/min/1.73m 2 ) 7.19(5.47, 9.69) 7.01(5.40, 9.40) 7.31(5.48, 9.87) 7.12(5.57, 9.54) 7.38(5.45, 9.92) 0.185 Ca (mmol/L) 2.21 ± 0.29 2.14 ± 0.29 2.21 ± 0.28 2.21 ± 0.28 2.27 ± 0.28 < 0.001 P (mmol/L) 1.69 ± 0.56 1.73 ± 0.56 1.67 ± 0.57 1.71 ± 0.54 1.65 ± 0.56 0.013 LDL-C:Low-density lipoprotein cholesterol; RC:residual cholesterol; Group1: LDL-C < 2.6mmol/L and RC < 0.62 mmol/L;Group2༚LDL-C < 2.6mmol/L and RC ≥ 0.62 mmol/L༛Group3༚LDL-C ≥ 2.6 mmol/L and RC < 0.62mmol/L༛Group4 ༚LDL-C ≥ 2.6mmol/L and RC ≥ 0.62mmol/L༛ BMI:body mass index;DM:diabetes mellitus; CVD:cardiovascular disease; eGFR:aestimated glomerular filtration rate; TC:total cholesterol; TG:triglyceride;HDL-C:high-density lipoprotein cholesterol; Ca:serum calcium; P:serum phosphorus. 3.2. Groups and mortality During the follow-up time of 17179 person-years (median 56 [28, 86] months), 904 (26.6%) patients died, 341 (10.0%) cases transferred to hemodialysis, 153 (4.5%) patients received renal transplantation, 30(0.8%) patients transferred to other dialysis centers, as well as 104 (3.0%) patients had been the loss of follow-up before death. For the 904 deaths, 512 (56.6%) were due to CVD, 122(13.5%) to infectious diseases, 22(2.4%) to malignant tumors, 140 (15.5%) to other causes, and 108 (12%) to unknown causes. 187 (35.4/1000 person-years) all-cause deaths, 103 (19.5/1000 person-years) CVD deaths occurred in group 1. 227 (59.2/1000 person-years) all-cause deaths, 128 (33.3/1000 person-years) CVD deaths occurred in group 2. 172 (46.4/1000 person-years) all-cause deaths, 96 (25.9/1000 person-years) CVD deaths occurred in group3. 318(72.9/1000 person-years) all-cause deaths, 185 (42.4/1000 person-years) CVD deaths occurred in group 4 (Table 2 ). Table 2 Incidence rate of death among the 4 groups according to LDL-C and RC Outcomes Overall Group1 Group2 Group3 Group4 No. of patients 3398 1007 790 705 895 Person-years 17179 5276 3834 3706 4363 All-cause mortality Events 904 187 227 172 318 Events per 1000 person-years 52.6 35.4 59.2 46.4 72.9 CVD mortality Events 512 103 128 96 185 Events per 1000 person-years 29.8 19.5 33.3 25.9 42.4 Group1: LDL-C < 2.6mmol/L and RC < 0.62 mmol/L;Group2༚LDL-C < 2.6mmol/L and RC ≥ 0.62 mmol/L༛Group3༚LDL-C ≥ 2.6 mmol/L and RC < 0.62mmol/L༛Group4 ༚LDL-C ≥ 2.6mmol/L and RC ≥ 0.62mmol/L༛LDL-C:Low-density lipoprotein cholesterol; RC:residual cholesterol; CVD:cardiovascular disease. Cumulative survival was dominantly lower in group 4 (P < 0.001, Fig. 2 ). In the cause-specific hazards model, compared with the group 1, the unadjusted HRs (model 1) of all-cause mortality presented 1.67 (95% CI:1.38, 2.03), 1.30 (95% CI:1.06, 1.61), 2.05 (95% CI:1.71, 2.46) for group 2, group 3, group 4 respectively, the unadjusted HRs (model 1) of CVD mortality showed 1.72 (95% CI:1.32, 2.23), 1.32 (95% CI:1.004, 1.75), 2.17 (95% CI:1.70, 2.76) for group 2, group 3,group 4 respectively (Table 3 ). The comparable results were also illustrated in Model 2 after adjustments for covariables, HRs of all-cause mortality depicted 1.51(95% CI:1.24, 1.83), 1.27 (95% CI:1.03, 1.56), 1.75 (95% CI:1.45, 2.11) for group2, group3, group4 respectively, and HRs of CVD mortality were 1.57 (95% CI:1.21, 2.05), 1.29 (95%:0.97, 1.71), 1.92(95% CI:1.50, 2.47) for group 2, group 3, group 4 respectively (Table 3 ). In the final model (Model 3), HRs of all-cause mortality were 1.37 (95% CI:1.11, 1.69), 1.16 (95% CI:0.94, 1.43), 1.50 (95% CI:1.23, 1.84) for group 2, group 3, group 4 respectively, and HRs of CVD mortality were 1.39 (95% CI:1.05, 1.85), 1.17 (95% CI:0.88, 1.56), 1.60 (95% CI:1.23, 2.09) for group 2, group 3, group 4 respectively (Table 3 ). Table 3 Association between groups and mortality using cause-specific hazard models model1 HR (95% CI) model2 HR (95% CI) model3 HR (95% CI) All-cause mortality Group1 1.0 1.0 1.0 Group2 1.67(1.38, 2.03) 1.51(1.24, 1.83) 1.37(1.11, 1.69) Group3 1.30(1.06, 1.61) 1.27(1.03, 1.56) 1.16(0.94, 1.43) Group4 2.05(1.71, 2.46) 1.75(1.45, 2.11) 1.50(1.23, 1.84) CVD mortality Group1 1.0 1.0 1.0 Group2 1.72(1.32, 2.23) 1.57(1.21, 2.05) 1.39(1.05, 1.85) Group3 1.32(1.004, 1.75) 1.29(0.97, 1.71) 1.17(0.88, 1.56) Group4 2.17(1.70, 2.76) 1.92(1.50, 2.47) 1.60(1.23, 2.09) Group1: LDL-C < 2.6mmol/L and RC < 0.62 mmol/L;Group2༚LDL-C < 2.6mmol/L and RC ≥ 0.62 mmol/L༛Group3༚LDL-C ≥ 2.6 mmol/L and RC < 0.62mmol/L༛Group4 ༚LDL-C ≥ 2.6mmol/L and RC ≥ 0.62mmol/L༛Model 1: unadjusted crude HR. Model 2: adjusted for sex,age, BMI, current smoker,current alcohol use,DM, and hypertension,prior CVD,anti-platelet drug, lipid-lowering drug. Model 3: model 2 plus TG, hemoglobin, albumin, eGFR, Ca, P. BMI, body mass index; DM, diabetes mellitus; CVD,cardiovascular disease; TG, triglyceride; eGFR, estimated glomerular filtration rate; Ca,serum calcium;P,serum phosphorus; LDL-C,Low-density lipoprotein cholesterol; RC,residual cholesterol; HR, hazards ratio; CI, confidence interval. 3.3. Competitive analyses Several factors including switching to HD, renal transplantation, transferring to other dialysis centers, and loss to follow-up were the competitive risks for all-cause mortality, while non-CVD mortality was the competing events for CVD death rate. In the Fine-Grey’s hazard model, compared with the group 1, the relative incidence of all-cause death increased 33% (HR:1.33,95% CI:1.08, 1.64), 15% (HR:1.15,95% CI:0.93, 1.41), 47% (HR:1.47,95% CI:1.21, 1.79) respectively for group 2, group 3, group 4 in model 3 (Table 4 ). The relative incidence of CVD death increased 34% (HR:1.34, 95% CI:1.01, 1.78), 14% (HR:1.14, 95% CI:0.86, 1.51), 55% (HR:1.55, 95% CI:1.19, 2.01) respectively for group 2, group 3, group 4 in model 3 (Table 4 ). Mortality rates never differ significantly in two groups (group 3 and group 1). We further analyzed the differences of all-cause and CVD deaths between low and high RC levels in the population with high LDL-C level. In the Fine-Grey ’ s hazard model, group 4 exhibited a 28% (HR:1.28, 95% CI:1.06, 1.55) and 36% (HR:1.36, 95% CI:1.05, 1.76) elevation of all-cause and cardiovascular deaths, respectively, compared with the group3 (Table 5 ). Table 4 Association between groups and mortality using Fine-Grey , s hazard models model1 HR (95% CI) model2 HR (95% CI) model3 HR (95% CI) All-cause mortality Group1 1.0 1.0 1.0 Group2 1.63(1.34, 1.98) 1.44(1.18, 1.75) 1.33(1.08, 1.64) Group3 1.30(1.06, 1.60) 1.26(1.02, 1.55) 1.15(0.93,1.41) Group4 2.01(1.68, 2.41) 1.70(1.41, 2.05) 1.47(1.21, 1.79) CVD mortality Group1 1.0 1.0 1.0 Group2 1.65 (1.27, 2.14) 1.50 (1.15, 1.96) 1.34 (1.01, 1.78) Group3 1.30 (0.98, 1.72) 1.25 (0.94, 1.66) 1.14 (0.86, 1.51) Group4 2.06 (1.62, 2.62) 1.84 (1.44, 2.36) 1.55 (1.19, 2.01) Group1: LDL-C < 2.6mmol/L and RC < 0.62 mmol/L;Group2༚LDL-C < 2.6mmol/L and RC ≥ 0.62 mmol/L༛Group3༚LDL-C ≥ 2.6 mmol/L and RC < 0.62mmol/L༛Group4 ༚LDL-C ≥ 2.6mmol/L and RC ≥ 0.62mmol/L༛Model 1: unadjusted crude HR. Model 2: adjusted for sex,age, BMI, current smoker,current alcohol use,DM, and hypertension,prior CVD,anti-platelet drug, lipid-lowering drug. Model 3: model 2 plus TG, hemoglobin, albumin, eGFR, Ca, P. BMI, body mass index; DM, diabetes mellitus; CVD,cardiovascular disease; TG, triglyceride; eGFR, estimated glomerular filtration rate; Ca,serum calcium;P,serum phosphorus; LDL-C,Low-density lipoprotein cholesterol; RC,residual cholesterol; HR, hazards ratio; CI, confidence interval. Table 5 Association between groups and mortality using Fine-Grey , s hazard models(compared with the group3) model1 HR (95% CI) model2 HR (95% CI) model3 HR (95% CI) All-cause mortality Group3 1.0 1.0 1.0 Group4 1.55 (1.29, 1.87) 1.35 (1.12, 1.63) 1.28 (1.06, 1.55) CVD mortality Group3 1.0 1.0 1.0 Group4 1.58 (1.23, 2.02) 1.47 (1.15, 1.89) 1.36 (1.05, 1.76) Group1: LDL-C < 2.6mmol/L and RC < 0.62 mmol/L;Group2༚LDL-C < 2.6mmol/L and RC ≥ 0.62 mmol/L༛Group3༚LDL-C ≥ 2.6 mmol/L and RC < 0.62mmol/L༛Group4 ༚LDL-C ≥ 2.6mmol/L and RC ≥ 0.62mmol/L༛Model 1: unadjusted crude HR. Model 2: adjusted for sex,age, BMI, current smoker,current alcohol use,DM, and hypertension,prior CVD,anti-platelet drug, lipid-lowering drug. Model 3: model 2 plus TG, hemoglobin, albumin, eGFR, Ca, P. BMI, body mass index; DM, diabetes mellitus; CVD,cardiovascular disease; TG, triglyceride; eGFR, estimated glomerular filtration rate; Ca,serum calcium;P,serum phosphorus; LDL-C,Low-density lipoprotein cholesterol; RC,residual cholesterol; HR, hazards ratio; CI, confidence interval. 3.4. Sensitivity analyses Sensitivity analyses were conducted in individuals without deaths in the first biennium of follow-up. Similar results were found among patients surviving during the primary two-year of follow-up, using the Fine-Grey ’ s hazard model, group2, group3, group4 respectively increased the relative incidence of all-cause death by 32%, 24%, 56%, of CVD death by 47%, 29%, 63% in model 3 (Table 6 ). Table 6 Association between groups and mortality using Fine-Grey , s hazard models(patients without deaths during the first 2 years of follow-up) death/number model1 HR(95% CI) model2 HR(95% CI) model3 HR (95% CI) All-cause mortality Group1 126/806 1.0 1.0 1.0 Group2 161/613 1.74 (1.38, 2.20) 1.54 (1.22, 1.96) 1.32 (1.02, 1.70) Group3 128/573 1.45 (1.14, 1.86) 1.40 (1.09, 1.79) 1.24 (0.97, 1.60) Group4 237/716 2.25 (1.81, 2.79) 1.70 (1.55, 2.42) 1.56 (1.23, 1.98) CVD mortality Group1 69/806 1.0 1.0 1.0 Group2 93/613 1.84 (1.35, 2.51) 1.69 (1.23, 2.32) 1.47 (1.04, 2.08) Group3 74/573 1.50 (1.08, 2.08) 1.25 (1.03, 2.02) 1.29 (0.92, 1.80) Group4 133/716 2.25 (1.68, 3.00) 2.00 (1.48, 2.70) 1.63 (1.18, 2.24) Group1: LDL-C < 2.6mmol/L and RC < 0.62 mmol/L;Group2༚LDL-C < 2.6mmol/L and RC ≥ 0.62 mmol/L༛Group3༚LDL-C ≥ 2.6 mmol/L and RC < 0.62mmol/L༛Group4 ༚LDL-C ≥ 2.6mmol/L and RC ≥ 0.62mmol/L༛Model 1: unadjusted crude HR. Model 2: adjusted for sex,age, BMI, current smoker,current alcohol use,DM, and hypertension,prior CVD,anti-platelet drug, lipid-lowering drug. Model 3: model 2 plus TG, hemoglobin, albumin, eGFR, Ca, P. BMI, body mass index; DM, diabetes mellitus; CVD,cardiovascular disease; TG, triglyceride; eGFR, estimated glomerular filtration rate; Ca,serum calcium;P,serum phosphorus; LDL-C,Low-density lipoprotein cholesterol; RC,residual cholesterol; HR, hazards ratio; CI, confidence interval. 3.5. Subgroup analyses We performed subgroup analyses in patients with different subgroups. Trends of mortality risks were consistent with the overall CAPD population whether in patients with or without CVD, BMI < 24 or ≥ 24 (Fig. 3 ). Patients in group 4 had higher risks if they were less than 65 years old (HR:1.59, 95% CI[1.26, 2.02]; HR:1.62, 95% CI [1.19, 2.20] for all-cause and CVD mortality respectively), were male (HR:1.59,95% CI [1.23, 2.06]; HR:1.77,95% [1.26, 2.48] for all-cause and CVD mortality respectively), were not diabetes (HR:1.58, 95% CI [1.25, 2.00]; HR:1.67, 95% CI [1.22,2.29] for CVD and all-cause death proportion respectively), were not using lipid-lowering drug (HR:1.44, 95% CI[1.16, 1.78]; HR:1.59, 95% CI[1.20, 2.11] for all-cause and CVD death percentage, respectively) (Fig. 3 ). 4. Discussion The prognostic value of the combined stratification of RC and LDL-C was investigated firstly in patients undergoing CAPD in this retrospective cohort analyses. The results illustrated the risks of all-cause and CVD mortalities were remarkably superior in the high RC groups (group 2: LDL-C < 2.6 mmol/L and RC ≥ 0.62 mmol/L; group 4༚LDL-C ≥ 2.6 mmol/L and RC ≥ 0.62 mmol/L) compared to the double-low group (group 1༚LDL-C < 2.6 mmol/L and RC < 0.62 mmol/L), while the threat exposures of all-cause and CVD mortalities in the high-LDL-C group alone (group 3: LDL-C ≥ 2.6 mmol/L and RC < 0.62 mmol/L) did not reach a statistically significant. This finding suggests that elevated RC emerges as a central driver of persistent residual cardiovascular likelihood in patients with CAPD, whereas the traditional method focusing on LDL-C may amplify risk when RC is higher. This study provides a new insight into risk stratification of individualized lipid management in patients with CAPD. Similarly, a study from European Heart Journal , reported cardiovascular outcomes were worse owing to elevated both LDL-C and RC[ 11 ], our study also found worse CVD and all-cause outcomes in the both-high group. The concerted actions of RC and LDL-C on unfavorable outcomes may involve synergistic pathophysiological processes across multiple pathways. RC, derived from triglyceride-rich lipoproteins (e.g., VLDL and chylomicron remnants), which comprises small, dense particles that readily penetrate the vascular endothelium. These particles directly deposit within the arterial wall, further to drive macrophage-to-foam cell transformation, which contributes to lipid core formation[ 16 ]. The LDL-C serves as the primary component of atherosclerotic plaques. Notably, oxidized LDL (ox-LDL) triggers inflammatory cascades and accelerates plaque progression[ 17 ]. RC may further elevate plasma LDL-C levels by inhibiting LDL receptor activity[ 18 ], thereby reducing hepatic clearance of LDL-C. Interestingly, RC and LDL-C collectively increase plaque lipid burden, promoting lipid core expansion and plaque destabilization. RC and LDL-C synergistically exhibited the activation of inflammatory molecular signatures (e.g., NF-κB) and oxidative stress[ 19 , 20 ], accelerating endothelial injury and increasing plaque rupture risk. In endothelial dysfunction, RC reduces nitric oxide (NO) bioavailability resulting in vasodilation inspiration[ 21 ], while LDL-C promotes endothelial oxidative damage and upregulates adhesion molecules[ 22 ]. In summary, the high level of LDL-C superimposed on high RC increases the events of adverse outcomes. We can hypothesize that high RC reflects residual risk in cholesterol metabolism not covered by traditional metrics such as LDL-C, which is further exacerbated by high LDL-C. In the high LDC -C (LDL-C ≥ 2.6 mmol/L) population undergoing CAPD, eminent RC (RC ≥ 0.62 mmol/L) outstandingly raised the relevant risks of CVD and all-cause mortalities (group 4 vs group 3). The mechanisms behind this are related to the superimposed synergistic effect of high RC and high LDL, which will not be discussed here. In a prospective observational cohort investigation of patients with coronary heart disease, elevated RC levels had a close association with increased all-cause and cardiovascular fatalities, even in patients with well-managed LDL [ 23 ]. A large cohort study [ 24 ] similarly demonstrated that elevated RC levels combined with elevated LDL-C levels are connected with adverse cardiovascular outcomes and all-cause mortality in prediabetic and diabetic patients undergoing coronary artery bypass grafting, in which four groups were divided with an LDL-C cutoff value of 2.6 mmol/L and an RC cutoff value of 0.8 mmol/L, and the all-cause mortality risk between the four groups was similar to our study. Without regard to concordantly high LDL-C level (≥ 2.6 mmol/L), patients undergoing CAPD with RC levels greater than 0.62 mmol/L suffered a more possible hazard of all-cause and CVD death. In sensitivity analyses, we analyzed survival patients during the two-year of follow-up, further confirming the consistent association in long-term follow-up. Due to reduced kidney function, uremic environment, inflammatory status, and dialysis treatment, Lipids in peritoneal dialysis patients are featured with significantly increased TG, RC, VLDL levels and decreased HDL-C level in serum [ 25 – 29 ]. Combined with our research findings, RC can be considered another clinical predictor and treatment target for CAPD patients, in addition to LDL-C. Subgroup analyses highlight the consistency of the trends in patient outcomes among the four groups of patients undergoing CAPD and younger than 65-year old, male, with different BMI levels, with or without history of CVD, without diabetes mellitus, and not using lipid-lowering medications. However, our study did not find differences in all-cause or cardiovascular prognosis among elderly patients undergoing CAPD among the four groups. On the one hand, this phenomenon may originate from the fact that there were fewer aging individuals in our study, and on the other hand, elderly dialysis patients have more comorbidities and malnutrition[ 30 , 31 ]. The low LDL-C is usually associated with malnutrition. These phenomena complicate the prognosis of elderly dialysis patients. Similarly, no differences were found in the subgroup who were female among the four groups, it may be due to female dyslipidemia exhibiting distinct pathophysiological characteristics compared to male patterns, with varies across the women’s lifespan [ 32 ]. Differences in all-cause and cardiovascular deaths between the four groups also did not find in the diabetes and use of lipid-lowering medications subgroups. This possibly due to small sample sizes in these two subgroups, and also may result from patients with DM undergoing PD suffered multiple complications, malnutrition, as well as a shorter life expectancy[ 33 , 34 ], as evidenced in geriatric cohort. No significant differences in all-cause and cardiovascular prognostic for the four groups were found in the subgroup treated with dyslipidemia medications, which may benefit from the benefits of lipid-modifying management. Although the prognosis of lipid-lowering therapy in HD patients is controversial[ 35 ], further studies are needed in patients with CAPD. Our investigation's advantages were substantial participant enrollment, rigorous multivariate analyses, and validation of Competing Risk Models. In fact, several limitations exist in our study. At first, it is not necessarily to prove causation between the combined stratification of LDL-C and RC and mortality in this retrospective observational study. Second, residual confounding effects of unmeasured covariates cannot be completely eliminated. Third, we used single measurements of LDL-C and RC at the inception timepoint of peritoneal dialysis, which may underestimate the strength of the association, and the predictive value of the cumulative exposure dose of LDL-C and RC requires further analyses. Lastly, our findings may not be applicable to other races because of all patients from China. In conclusion, Both higher LDL-C and RC levels at the beginning of CAPD were line with a likelihood of cardiovascular and all-cause fatality rate in individuals suffering CAPD. Our study suggests that RC may be another meaningful lipid biomarker in addition to LDL-C for predicting outcomes, especially combining the two for risk stratification of CAPD patients. Abbreviations LDL-C Low-density lipoprotein cholesterol RC Residual cholesterol PD Peritoneal dialysis CVD Cardiovascular disease HR Hazards ratio CI Confidence interval ESKD End-stage kidney disease TG Triglyceride IDL Intermediate-density lipoproteins VLDL Very low-density lipoproteins CAPD Continuous ambulatory peritoneal dialysis HDL-C High density lipoprotein cholesterol TC Total cholesterol ESC European Society of Cardiology BMI Body mass index DM Diabetes mellitus eGFR Estimated glomerular filtration rate Ca Calcium P Phosphorus HD Hemodialysis Declarations Acknowledgements We express our gratitude to all patients who participated in the study Funding This work was supported by the National Natural Science Foundation of China, Grant numbers: 82170745. Authors ’ Contributions X.X.W. and X.F.W. conceived and designed research; H.G., N.T., Q.D.X., X.J.Z., F.F.P., X.Y.W., N.S., X.M.T., Y.Q.W. Resources; X.R.F., Data Curation; F.H.C., C.CZ., X.X.W. analyzed data, interpreted results, prepared figures and drafted the manuscript; X.X.W. and X.F.W. edited and revised manuscript. Data Availability All data generated or analyzed during this study are included in this article. Further enquiries can be directed to the corresponding author. Ethics approval and consent to participate All procedures performed in this study involving human participants was in accordance with the ethical standards of Tongren Hospital of Shanghai Jiao Tong University School of Medicine (approval number K2025-023-01), The requirement for informed consent was waived by the Ethics Committee because of the retrospective nature of the study. The study protocol complied with the Declaration of Helsinki. Consent for publication All the authors gave their consent to publication. Competing interests The authors declare that they have no competing interests. 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Kalantar-Zadeh K, Fouque D, Kopple JD. Outcome research, nutrition, and reverse epidemiology in maintenance dialysis patients. J Ren Nutr. 2004;14(2):64–71. 10.1053/j.jrn.2004.01.005 . Patel N, Mittal N, Wilkinson MJ, Taub PR. Unique features of dyslipidemia in women across a lifetime and a tailored approach to management. Am J Prev Cardiol. 2024;18:100666. 10.1016/j.ajpc.2024.100666 . Cotovio P, Rocha A, Carvalho MJ, Teixeira L, Mendonça D, Cabrita A, Rodrigues A. Better Outcomes of Peritoneal Dialysis in Diabetic Patients in Spite of Risk of Loss of Autonomy for Home Dialysis. Perit Dial Int. 2014;34(7):775–80. 10.3747/pdi.2012.00111 . Cotovio P, Rocha A, Rodrigues A. Peritoneal Dialysis in Diabetics: There Is Room for More. Int J Nephrol. 2011;2011:1–10. 10.4061/2011/914849 . Abidor E, Achkar M, Al Saidi I, Lather T, Jdaidani J, Agarwal A, El-Sayegh S. Comprehensive Review of Lipid Management in Chronic Kidney Disease and Hemodialysis Patients: Conventional Approaches, and Challenges for Cardiovascular Risk Reduction. J Clin Med. 2025;14(2):643. 10.3390/jcm14020643 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6590878","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467981523,"identity":"ee73a330-3221-47a7-942c-6b17ee188a85","order_by":0,"name":"Fuhua Chen","email":"","orcid":"","institution":"Department of Nephrology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai","correspondingAuthor":false,"prefix":"","firstName":"Fuhua","middleName":"","lastName":"Chen","suffix":""},{"id":467981524,"identity":"9e25737a-8075-439e-827f-163eec7dafe0","order_by":1,"name":"Chuchu 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03:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6590878/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6590878/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84329203,"identity":"b52fa10d-b0aa-4f86-8153-5c43ca4fed56","added_by":"auto","created_at":"2025-06-10 15:39:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":941954,"visible":true,"origin":"","legend":"\u003cp\u003eFlow-chart of eligible and ineligible patients\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-6590878/v1/3933166d9914fe440db3c653.png"},{"id":84329204,"identity":"4d712ff1-6e8b-4cb4-9cd8-74ad4bfa2915","added_by":"auto","created_at":"2025-06-10 15:39:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1219775,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative survival in patients stratified by LDL-C combined RC\u003c/p\u003e\n\u003cp\u003eNote: Cumulative mortality curves for all-cause mortality (a) and cardiovascular mortality (b) betwen the four groups. group1 ( LDL-C\u0026lt;2.6 mmol/L and RC\u0026lt;0.62 mmol/L), group2 ( LDL -C\u0026lt;2.6mmol/L and RC≥0.62 mmol/L), group3 (LDL ≥2.6 mmol/L and RC\u0026lt;0.62 mmol/L ), group4 (LDL-C≥2.6 mmol/L and RC≥0.62 mmol/L).\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-6590878/v1/1dd0d79b03491c18f9a378d5.png"},{"id":84329213,"identity":"329a85d8-f613-4a14-8051-a3732f362851","added_by":"auto","created_at":"2025-06-10 15:39:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5727284,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup correlations of combined LDL-C and RC stratification with all-cause mortality and CVD mortality betwen the four groups\u003c/p\u003e\n\u003cp\u003eNote: Group1 ( LDL-C\u0026lt;2.6 mmol/L and RC\u0026lt;0.62 mmol/L), group2 ( LDL -C\u0026lt;2.6mmol/L and RC≥0.62 mmol/L), group3 (LDL ≥2.6 mmol/L and RC\u0026lt;0.62 mmol/L ), group4 (LDL-C≥2.6 mmol/L and RC≥0.62 mmol/L). Values presented as HRs and 95%CIs adjusted for sex, age, BMI, current smoker, current alcohol use, diabetes, and hypertension, prior CVD, anti-platelet drug, lipid-lowering drug, TG, hemoglobin, albumin, eGFR, Ca, P. BMI, body mass index; CVD, cardiovascular disease; TG, triglyceride; eGFR, estimated glomerular filtration rate; Ca, serum calcium;P, serum phosphorus.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-6590878/v1/e4784353f1ccb8a0c4e3433f.png"},{"id":94064386,"identity":"11630cb9-65cf-447a-850f-7ad0ae009b75","added_by":"auto","created_at":"2025-10-22 07:38:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8078682,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6590878/v1/a0c53eb9-05ee-484d-894e-3c00022b748c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of combined low-density lipoprotein cholesterol and residual cholesterol stratification with all- cause and cardiovascular mortality in peritoneal dialysis patients: a multicenter retrospective cohort study","fulltext":[{"header":"1. Introdution","content":"\u003cp\u003eMore and more patients suffered end-stage kidney disease (ESKD) face a severe challenge to the global renal replacement therapy system[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Peritoneal dialysis (PD) is currently one of the main renal replacement therapy modalities. The predominant mortality etiologies for patients receiving PD are cardiovascular complications[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLow-density lipoprotein cholesterol (LDL-C) is deemed as a traditional hazard tissue for cardiovascular disease (CVD) occurrence and fatality. In the developed countries, the LDL-C level of general population is significantly linked to cardiovascular and all-cause fatality rate in a U-shaped curve[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recently, U- or J-shaped associations have also been found in Chinese individuals[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In PD patients, U-shaped associations are also found[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Current data reveal that the sophisticated relationship between LDL-C concentrations and lethality risk is demonstrated significantly. It is possible that there are other lipids contributing to residual cardiovascular risk.\u003c/p\u003e \u003cp\u003eIn recent years, residual cholesterol (RC) has win so much eyes, viewed as a non-traditional hazard factor for CVD. RC is famous as the cholesterol content of triglyceride (TG)-rich lipoproteins, including intermediate-density lipoproteins (IDL) and very low-density lipoproteins (VLDL) in fasting, and VLDL, IDL, and celiac remnants in the unfasted state, and is related to TG[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Recent reports from a national cohort analyses in the United States suggest that elevated RC is connected with a anabatic risk of long-term all-cause mortality, CVD and CVD mortality for ordinary people[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A study about diabetic patients also found this phenomenon[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, RC, as an additional biomarker of cardiovascular risk, may be a new therapeutic target to reduce adverse cardiovascular outcomes. LDL-C combined with RC has emerged as a new trend in cardiovascular prognostic studies. A European study found that the combination of RC and LDL-C showed an effect on the development of CVD in person without atherosclerotic cardiovascular disease[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Another study from two National Cohorts revealed the relationship between RC and LDL-C discordance with incident stroke[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The monitoring stratagem of Integrating LDL-C with RC better predicts cardiovascular and all-cause prognosis in different populations. However, the relevance of combining RC and LDL-C with the prognosis of continuous ambulatory peritoneal dialysis (CAPD) remains unknown.\u003c/p\u003e \u003cp\u003eTherefore, the intent of this research is to evaluate the association of combined LDL-C and RC with all-cause and CVD mortality in cohort of CAPD patients.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Investigation scheme and participants information\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study involved participants who viewed CAPD as their first option for renal replacement therapy between 1 January 2005 to 31 May 2023 from a multi-center database (The Ever-green Tree Nephrology Group, ETNG) in China. Patients who were age\u0026thinsp;\u0026ge;\u0026thinsp;18 years at PD commencement and maintained PD treatment for \u0026ge;\u0026thinsp;3 months were enrolled. Exclusion criteria consisted of individuals whose age is less than 18 years old, follow-up time\u0026thinsp;\u0026lt;\u0026thinsp;90 days, pregnancy or lactation, missing baseline lipid data containing high density lipoprotein cholesterol (HDL-C), total cholesterol (TC), as well as LDL-C. The value of RC was expressed as TC minus the sum of HDL-C and LDL-C[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e This protocol is in line with the declaration of Helsinki and has been fully approved by the clinical research ethics committees. All data included in the investigation were anonymous, therefore informed consent was waived.\u003c/p\u003e \u003cp\u003eIn accordance with the European Society of Cardiology (ESC) 2019 guidelines for lipid regulation[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], our study set 2.6 mmol/L as critical value for LDL-C. A study from the \u003cem\u003eEuropean Heart Journal\u003c/em\u003e showed that patients with RC\u0026thinsp;\u0026ge;\u0026thinsp;24 mg/dL (also expresses as 0.62 mmol/L) presented an elevated risk of CVD[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], so the critical value of RC was 0.62 mmol/L. Based on the datum line of LDL-C and RC, participants were classed into four groups, including group1 (both low LDL-C and RC group: LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;2.6 mmol/L and RC\u0026thinsp;\u0026lt;\u0026thinsp;0.62 mmol/L), group2 (low LDL-C but high RC group༚LDL -C\u0026thinsp;\u0026lt;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62 mmol/L), group3 (high LDL-C but low RC group༚LDL\u0026thinsp;\u0026ge;\u0026thinsp;2.6 mmol/L and RC\u0026thinsp;\u0026lt;\u0026thinsp;0.62 mmol/L ), group4 (both high LDL-C and RC group༚LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;2.6 mmol/L and RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62 mmol/L). The goal of our study was to examine the differences in CVD mortality and all-cause mortality among the 4 groups of patients undergoing CAPD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data acquisition and assessments\u003c/h2\u003e \u003cp\u003eAt the outset of the research, the medical records of patients stored in each dialysis center were thoroughly scrutinized by professional medical staff. Baseline demographic data consisted of sex, age, body mass index (BMI), the primary etiology of end-stage renal disease, current smoking and drinking history, history of prior diabetes mellitus (DM), history of prior hypertension, history of prior CVD, use history of antiplatelet medications and lipid-lowering medications. Baseline laboratory data included hemoglobin, albumin, estimated glomerular filtration rate (eGFR), TC, TG, HDL-C, LDL-C, serum calcium (Ca), and serum phosphorus (P). The patients were all receiving dialysis for the first time in the hospital, and all data was available within the month prior to dialysis. All fasting blood sample data were measured by the laboratory in each hospital. And all patients received CAPD treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Follow-up and outcome measures\u003c/h2\u003e \u003cp\u003ePatients were interviewed face-to-face or by telephone once a month by trained nurses at each center. The each patient\u0026rsquo;s observation period was designed from the date of study start to the death, transfer to hemodialysis (HD), receipt of a kidney transplant, loss to follow-up, transfer to another dialysis center, or completion of follow-up (31 May 2023). During the final examination, a follow-up examination was conducted on the lost patient.\u003c/p\u003e \u003cp\u003eThe principal and secondary outcome indicators included CVD mortality, as well as all-cause mortality, respectively. Additionally, the cause of death according to the medical records on admission were determined. If patients suffered out-of-facility mortality, we estimated the reason for death by confirming the death through telephone interviews with family members in conjunction with medical record information from the PD centers. CVD mortality contained deaths relevant with heart failure (not purely volume factor), malignant arrhythmia, hemorrhagic or thromboembolic stroke, acute myocardial ischemic event and sudden cardiac death according to the International Classification of Diseases, Clinical Revision 9th edition. Sudden cardiac death refers to an unexpected, non-traumatic death that occurs within 1 hour after the appearance of new or worsening symptoms (witnessed sudden arrest), or within 24 hours after the last survival if not witnessed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analysis of categorical data\u003c/h2\u003e \u003cp\u003eContinuous variables presented normally distributed and showed a chi-squared variance which are expressed using the mean added or subtracted the standard deviation, skewed variables were denoted as quartiles. Categorical variables represented as number of patients. Covariates up to 5% missing were supplemented using multiple interpolation, and more than 5% were excluded.\u003c/p\u003e \u003cp\u003eWe first used the cause-specific hazard models for the analysis and sub-distribution hazard models (the Fine-Gray\u003csup\u003e,\u003c/sup\u003es models) for the competitive analysis. Patients who have experienced competitive risk events are still in the risk setting of the sub distribution risk model, but have been removed from the specific cause risk model [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Non-CVD mortality was the competing risk for CVD mortality. The competitive factors for all-cause mortality mainly included switching to HD, renal transplantation, transfer to other dialysis centers, and loss to follow-up. The results were descripted as hazard ratios (HRs) and 95% confidence interval (CI).\u003c/p\u003e \u003cp\u003eSubgroup analyses were conducted to assess modification effects refer to the relationship between four groups and lethality, in which subgroups were stratified by sex (male or female), age (\u0026lt;\u0026thinsp;65 or \u0026ge;\u0026thinsp;65 years), BMI (\u0026lt;\u0026thinsp;24 or \u0026ge;\u0026thinsp;24), hypertension (yes or no), diabetes mellitus (yes or no), lipid-lowering medications (yes or no), and history of CVD (yes or no). All statistical results were examined through SPSS software (IBM Corp, Armonk, NY, USA, version 27), along with the R package 4.4.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and a \u003cem\u003eP\u003c/em\u003e value was less than 0.05 that was taken for statistical discrepancy.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Baseline properties\u003c/h2\u003e \u003cp\u003eTotally 3,397 patients were finally enrolled in the present study, we supplemented 251 missing covariates (containing 10 hemoglobin indicators, 17 albumin indicators, 5 triglycerides indicators, 131serun calcium indicators, and 88 serum phosphorus indicators) using multiple interpolation. The specific information about patient recruitment procedure was depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e represented the baseline features of patients grouped according to LDL-C and RC. The mean age was 50.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4years old, 57.3% were male, 20.9% were diabetes, and 19.5% had prior CVD. Patients in group 4 (presenting both high LDL-C and RC) were older. There were fewer male patients, fewer smokers, and lower blood phosphorus in the group 4. There was more diabetes, history of CVD, hypertension, higher triglycerides, hemoglobin, and blood calcium in the group4.\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 stratified by LDL-C and RC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal \u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;3397\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup1\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1007\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup2\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;790\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup3\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;705\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup4\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;895\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1947(57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e653(64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e471(59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e390(55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e433(48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.0(20.0, 24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.7(19.8, 24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.2(20.3, 24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.0(20.0, 24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.5(20.0, 24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115(11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75(9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55(7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55(6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent alcohol, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23(2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e710(20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166(16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171(21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e145(20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e228(25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2458(72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e696(69.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e576(72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e517(73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e669(74.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior CVD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e664(19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164(16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113(16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e217(24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet drug (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e362(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94(11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70(9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91(10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid-lowering drug (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e579(17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164(16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150(18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97(13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e168(18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\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\u003e4.40(3.62, 5.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.46(3.05, 3.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.07(3.54, 4.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.40(4.82, 5.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.36(5.00, 6.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\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\u003e1.37(0.95, 1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96(0.73, 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.78(1.25, 2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.20(0.92, 1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.52(1.91, 3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07(0.87, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08(0.89, 1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93(0.74, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22(1.01, 1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07(0.89, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.52(1.94, 3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00(1.62, 2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96(1.60, 2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.18(2.85, 3.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.26(2.93, 3.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61(0.38, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38(0.25, 0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00(0.74, 1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39(0.24, 0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01(0.80, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.8\u0026thinsp;\u0026plusmn;\u0026thinsp;23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.0\u0026thinsp;\u0026plusmn;\u0026thinsp;22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.9\u0026thinsp;\u0026plusmn;\u0026thinsp;23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.2\u0026thinsp;\u0026plusmn;\u0026thinsp;23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.0\u0026thinsp;\u0026plusmn;\u0026thinsp;23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR(ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.19(5.47, 9.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.01(5.40, 9.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.31(5.48, 9.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.12(5.57, 9.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.38(5.45, 9.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eLDL-C:Low-density lipoprotein cholesterol; RC:residual cholesterol; Group1: LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026lt;\u0026thinsp;0.62 mmol/L;Group2༚LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62 mmol/L༛Group3༚LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;2.6 mmol/L and RC\u0026thinsp;\u0026lt;\u0026thinsp;0.62mmol/L༛Group4 ༚LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62mmol/L༛ BMI:body mass index;DM:diabetes mellitus; CVD:cardiovascular disease; eGFR:aestimated glomerular filtration rate; TC:total cholesterol; TG:triglyceride;HDL-C:high-density lipoprotein cholesterol; Ca:serum calcium; P:serum phosphorus.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Groups and mortality\u003c/h2\u003e \u003cp\u003eDuring the follow-up time of 17179 person-years (median 56 [28, 86] months), 904 (26.6%) patients died, 341 (10.0%) cases transferred to hemodialysis, 153 (4.5%) patients received renal transplantation, 30(0.8%) patients transferred to other dialysis centers, as well as 104 (3.0%) patients had been the loss of follow-up before death. For the 904 deaths, 512 (56.6%) were due to CVD, 122(13.5%) to infectious diseases, 22(2.4%) to malignant tumors, 140 (15.5%) to other causes, and 108 (12%) to unknown causes. 187 (35.4/1000 person-years) all-cause deaths, 103 (19.5/1000 person-years) CVD deaths occurred in group 1. 227 (59.2/1000 person-years) all-cause deaths, 128 (33.3/1000 person-years) CVD deaths occurred in group 2. 172 (46.4/1000 person-years) all-cause deaths, 96 (25.9/1000 person-years) CVD deaths occurred in group3. 318(72.9/1000 person-years) all-cause deaths, 185 (42.4/1000 person-years) CVD deaths occurred in group 4 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncidence rate of death among the 4 groups according to LDL-C and RC\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerson-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll-cause mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvents per 1000 person-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVD mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvents per 1000 person-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eGroup1: LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026lt;\u0026thinsp;0.62 mmol/L;Group2༚LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62 mmol/L༛Group3༚LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;2.6 mmol/L and RC\u0026thinsp;\u0026lt;\u0026thinsp;0.62mmol/L༛Group4 ༚LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62mmol/L༛LDL-C:Low-density lipoprotein cholesterol; RC:residual cholesterol; CVD:cardiovascular disease.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCumulative survival was dominantly lower in group 4 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the cause-specific hazards model, compared with the group 1, the unadjusted HRs (model 1) of all-cause mortality presented 1.67 (95% CI:1.38, 2.03), 1.30 (95% CI:1.06, 1.61), 2.05 (95% CI:1.71, 2.46) for group 2, group 3, group 4 respectively, the unadjusted HRs (model 1) of CVD mortality showed 1.72 (95% CI:1.32, 2.23), 1.32 (95% CI:1.004, 1.75), 2.17 (95% CI:1.70, 2.76) for group 2, group 3,group 4 respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The comparable results were also illustrated in Model 2 after adjustments for covariables, HRs of all-cause mortality depicted 1.51(95% CI:1.24, 1.83), 1.27 (95% CI:1.03, 1.56), 1.75 (95% CI:1.45, 2.11) for group2, group3, group4 respectively, and HRs of CVD mortality were 1.57 (95% CI:1.21, 2.05), 1.29 (95%:0.97, 1.71), 1.92(95% CI:1.50, 2.47) for group 2, group 3, group 4 respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the final model (Model 3), HRs of all-cause mortality were 1.37 (95% CI:1.11, 1.69), 1.16 (95% CI:0.94, 1.43), 1.50 (95% CI:1.23, 1.84) for group 2, group 3, group 4 respectively, and HRs of CVD mortality were 1.39 (95% CI:1.05, 1.85), 1.17 (95% CI:0.88, 1.56), 1.60 (95% CI:1.23, 2.09) for group 2, group 3, group 4 respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between groups and mortality using cause-specific hazard models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emodel1 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emodel2 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emodel3 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll-cause mortality\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.67(1.38, 2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.51(1.24, 1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37(1.11, 1.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.30(1.06, 1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.27(1.03, 1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16(0.94, 1.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.05(1.71, 2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.75(1.45, 2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.50(1.23, 1.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVD mortality\u003c/b\u003e\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.72(1.32, 2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.57(1.21, 2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.39(1.05, 1.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.32(1.004, 1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.29(0.97, 1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17(0.88, 1.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.17(1.70, 2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.92(1.50, 2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.60(1.23, 2.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eGroup1: LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026lt;\u0026thinsp;0.62 mmol/L;Group2༚LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62 mmol/L༛Group3༚LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;2.6 mmol/L and RC\u0026thinsp;\u0026lt;\u0026thinsp;0.62mmol/L༛Group4 ༚LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62mmol/L༛Model 1: unadjusted crude HR. Model 2: adjusted for sex,age, BMI, current smoker,current alcohol use,DM, and hypertension,prior CVD,anti-platelet drug, lipid-lowering drug. Model 3: model 2 plus TG, hemoglobin, albumin, eGFR, Ca, P. BMI, body mass index; DM, diabetes mellitus; CVD,cardiovascular disease; TG, triglyceride; eGFR, estimated glomerular filtration rate; Ca,serum calcium;P,serum phosphorus; LDL-C,Low-density lipoprotein cholesterol; RC,residual cholesterol; HR, hazards ratio; CI, confidence interval.\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\u003e3.3. Competitive analyses\u003c/h2\u003e \u003cp\u003eSeveral factors including switching to HD, renal transplantation, transferring to other dialysis centers, and loss to follow-up were the competitive risks for all-cause mortality, while non-CVD mortality was the competing events for CVD death rate. In the Fine-Grey\u0026rsquo;s hazard model, compared with the group 1, the relative incidence of all-cause death increased 33% (HR:1.33,95% CI:1.08, 1.64), 15% (HR:1.15,95% CI:0.93, 1.41), 47% (HR:1.47,95% CI:1.21, 1.79) respectively for group 2, group 3, group 4 in model 3 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The relative incidence of CVD death increased 34% (HR:1.34, 95% CI:1.01, 1.78), 14% (HR:1.14, 95% CI:0.86, 1.51), 55% (HR:1.55, 95% CI:1.19, 2.01) respectively for group 2, group 3, group 4 in model 3 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Mortality rates never differ significantly in two groups (group 3 and group 1). We further analyzed the differences of all-cause and CVD deaths between low and high RC levels in the population with high LDL-C level. In the Fine-Grey\u003csup\u003e\u0026rsquo;\u003c/sup\u003es hazard model, group 4 exhibited a 28% (HR:1.28, 95% CI:1.06, 1.55) and 36% (HR:1.36, 95% CI:1.05, 1.76) elevation of all-cause and cardiovascular deaths, respectively, compared with the group3 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between groups and mortality using Fine-Grey\u003csup\u003e,\u003c/sup\u003es hazard models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emodel1 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emodel2 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emodel3 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.63(1.34, 1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.44(1.18, 1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.33(1.08, 1.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.30(1.06, 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26(1.02, 1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.15(0.93,1.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.01(1.68, 2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.70(1.41, 2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.47(1.21, 1.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD mortality\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.65 (1.27, 2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.50 (1.15, 1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.34 (1.01, 1.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.30 (0.98, 1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25 (0.94, 1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14 (0.86, 1.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.06 (1.62, 2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.84 (1.44, 2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.55 (1.19, 2.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eGroup1: LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026lt;\u0026thinsp;0.62 mmol/L;Group2༚LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62 mmol/L༛Group3༚LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;2.6 mmol/L and RC\u0026thinsp;\u0026lt;\u0026thinsp;0.62mmol/L༛Group4 ༚LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62mmol/L༛Model 1: unadjusted crude HR. Model 2: adjusted for sex,age, BMI, current smoker,current alcohol use,DM, and hypertension,prior CVD,anti-platelet drug, lipid-lowering drug. Model 3: model 2 plus TG, hemoglobin, albumin, eGFR, Ca, P. BMI, body mass index; DM, diabetes mellitus; CVD,cardiovascular disease; TG, triglyceride; eGFR, estimated glomerular filtration rate; Ca,serum calcium;P,serum phosphorus; LDL-C,Low-density lipoprotein cholesterol; RC,residual cholesterol; HR, hazards ratio; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between groups and mortality using Fine-Grey\u003csup\u003e,\u003c/sup\u003es hazard models(compared with the group3)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emodel1 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emodel2 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emodel3 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.55 (1.29, 1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.35 (1.12, 1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.28 (1.06, 1.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD mortality\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.58 (1.23, 2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.47 (1.15, 1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.36 (1.05, 1.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eGroup1: LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026lt;\u0026thinsp;0.62 mmol/L;Group2༚LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62 mmol/L༛Group3༚LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;2.6 mmol/L and RC\u0026thinsp;\u0026lt;\u0026thinsp;0.62mmol/L༛Group4 ༚LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62mmol/L༛Model 1: unadjusted crude HR. Model 2: adjusted for sex,age, BMI, current smoker,current alcohol use,DM, and hypertension,prior CVD,anti-platelet drug, lipid-lowering drug. Model 3: model 2 plus TG, hemoglobin, albumin, eGFR, Ca, P. BMI, body mass index; DM, diabetes mellitus; CVD,cardiovascular disease; TG, triglyceride; eGFR, estimated glomerular filtration rate; Ca,serum calcium;P,serum phosphorus; LDL-C,Low-density lipoprotein cholesterol; RC,residual cholesterol; HR, hazards ratio; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Sensitivity analyses\u003c/h2\u003e \u003cp\u003eSensitivity analyses were conducted in individuals without deaths in the first biennium of follow-up. Similar results were found among patients surviving during the primary two-year of follow-up, using the Fine-Grey\u003csup\u003e\u0026rsquo;\u003c/sup\u003es hazard model, group2, group3, group4 respectively increased the relative incidence of all-cause death by 32%, 24%, 56%, of CVD death by 47%, 29%, 63% in model 3 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between groups and mortality using Fine-Grey\u003csup\u003e,\u003c/sup\u003es hazard models(patients without deaths during the first 2 years of follow-up)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edeath/number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emodel1 HR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emodel2 HR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emodel3 HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126/806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161/613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.74 (1.38, 2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.54 (1.22, 1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.32 (1.02, 1.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128/573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.45 (1.14, 1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.40 (1.09, 1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.24 (0.97, 1.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e237/716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.25 (1.81, 2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.70 (1.55, 2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.56 (1.23, 1.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD mortality\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69/806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93/613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.84 (1.35, 2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.69 (1.23, 2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.47 (1.04, 2.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74/573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.50 (1.08, 2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25 (1.03, 2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.29 (0.92, 1.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133/716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.25 (1.68, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00 (1.48, 2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.63 (1.18, 2.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eGroup1: LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026lt;\u0026thinsp;0.62 mmol/L;Group2༚LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62 mmol/L༛Group3༚LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;2.6 mmol/L and RC\u0026thinsp;\u0026lt;\u0026thinsp;0.62mmol/L༛Group4 ༚LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;2.6mmol/L and RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62mmol/L༛Model 1: unadjusted crude HR. Model 2: adjusted for sex,age, BMI, current smoker,current alcohol use,DM, and hypertension,prior CVD,anti-platelet drug, lipid-lowering drug. Model 3: model 2 plus TG, hemoglobin, albumin, eGFR, Ca, P. BMI, body mass index; DM, diabetes mellitus; CVD,cardiovascular disease; TG, triglyceride; eGFR, estimated glomerular filtration rate; Ca,serum calcium;P,serum phosphorus; LDL-C,Low-density lipoprotein cholesterol; RC,residual cholesterol; HR, hazards ratio; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Subgroup analyses\u003c/h2\u003e \u003cp\u003eWe performed subgroup analyses in patients with different subgroups. Trends of mortality risks were consistent with the overall CAPD population whether in patients with or without CVD, BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 or \u0026ge;\u0026thinsp;24 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Patients in group 4 had higher risks if they were less than 65 years old (HR:1.59, 95% CI[1.26, 2.02]; HR:1.62, 95% CI [1.19, 2.20] for all-cause and CVD mortality respectively), were male (HR:1.59,95% CI [1.23, 2.06]; HR:1.77,95% [1.26, 2.48] for all-cause and CVD mortality respectively), were not diabetes (HR:1.58, 95% CI [1.25, 2.00]; HR:1.67, 95% CI [1.22,2.29] for CVD and all-cause death proportion respectively), were not using lipid-lowering drug (HR:1.44, 95% CI[1.16, 1.78]; HR:1.59, 95% CI[1.20, 2.11] for all-cause and CVD death percentage, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe prognostic value of the combined stratification of RC and LDL-C was investigated firstly in patients undergoing CAPD in this retrospective cohort analyses. The results illustrated the risks of all-cause and CVD mortalities were remarkably superior in the high RC groups (group 2: LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;2.6 mmol/L and RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62 mmol/L; group 4༚LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;2.6 mmol/L and RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62 mmol/L) compared to the double-low group (group 1༚LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;2.6 mmol/L and RC\u0026thinsp;\u0026lt;\u0026thinsp;0.62 mmol/L), while the threat exposures of all-cause and CVD mortalities in the high-LDL-C group alone (group 3: LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;2.6 mmol/L and RC\u0026thinsp;\u0026lt;\u0026thinsp;0.62 mmol/L) did not reach a statistically significant. This finding suggests that elevated RC emerges as a central driver of persistent residual cardiovascular likelihood in patients with CAPD, whereas the traditional method focusing on LDL-C may amplify risk when RC is higher. This study provides a new insight into risk stratification of individualized lipid management in patients with CAPD.\u003c/p\u003e \u003cp\u003eSimilarly, a study from \u003cem\u003eEuropean Heart Journal\u003c/em\u003e, reported cardiovascular outcomes were worse owing to elevated both LDL-C and RC[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], our study also found worse CVD and all-cause outcomes in the both-high group. The concerted actions of RC and LDL-C on unfavorable outcomes may involve synergistic pathophysiological processes across multiple pathways. RC, derived from triglyceride-rich lipoproteins (e.g., VLDL and chylomicron remnants), which comprises small, dense particles that readily penetrate the vascular endothelium. These particles directly deposit within the arterial wall, further to drive macrophage-to-foam cell transformation, which contributes to lipid core formation[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The LDL-C serves as the primary component of atherosclerotic plaques. Notably, oxidized LDL (ox-LDL) triggers inflammatory cascades and accelerates plaque progression[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. RC may further elevate plasma LDL-C levels by inhibiting LDL receptor activity[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], thereby reducing hepatic clearance of LDL-C. Interestingly, RC and LDL-C collectively increase plaque lipid burden, promoting lipid core expansion and plaque destabilization. RC and LDL-C synergistically exhibited the activation of inflammatory molecular signatures (e.g., NF-κB) and oxidative stress[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], accelerating endothelial injury and increasing plaque rupture risk. In endothelial dysfunction, RC reduces nitric oxide (NO) bioavailability resulting in vasodilation inspiration[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], while LDL-C promotes endothelial oxidative damage and upregulates adhesion molecules[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In summary, the high level of LDL-C superimposed on high RC increases the events of adverse outcomes. We can hypothesize that high RC reflects residual risk in cholesterol metabolism not covered by traditional metrics such as LDL-C, which is further exacerbated by high LDL-C.\u003c/p\u003e \u003cp\u003eIn the high LDC -C (LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;2.6 mmol/L) population undergoing CAPD, eminent RC (RC\u0026thinsp;\u0026ge;\u0026thinsp;0.62 mmol/L) outstandingly raised the relevant risks of CVD and all-cause mortalities (group 4 vs group 3). The mechanisms behind this are related to the superimposed synergistic effect of high RC and high LDL, which will not be discussed here. In a prospective observational cohort investigation of patients with coronary heart disease, elevated RC levels had a close association with increased all-cause and cardiovascular fatalities, even in patients with well-managed LDL [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A large cohort study [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] similarly demonstrated that elevated RC levels combined with elevated LDL-C levels are connected with adverse cardiovascular outcomes and all-cause mortality in prediabetic and diabetic patients undergoing coronary artery bypass grafting, in which four groups were divided with an LDL-C cutoff value of 2.6 mmol/L and an RC cutoff value of 0.8 mmol/L, and the all-cause mortality risk between the four groups was similar to our study. Without regard to concordantly high LDL-C level (\u0026ge;\u0026thinsp;2.6 mmol/L), patients undergoing CAPD with RC levels greater than 0.62 mmol/L suffered a more possible hazard of all-cause and CVD death. In sensitivity analyses, we analyzed survival patients during the two-year of follow-up, further confirming the consistent association in long-term follow-up. Due to reduced kidney function, uremic environment, inflammatory status, and dialysis treatment, Lipids in peritoneal dialysis patients are featured with significantly increased TG, RC, VLDL levels and decreased HDL-C level in serum [\u003cspan additionalcitationids=\"CR26 CR27 CR28\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Combined with our research findings, RC can be considered another clinical predictor and treatment target for CAPD patients, in addition to LDL-C.\u003c/p\u003e \u003cp\u003eSubgroup analyses highlight the consistency of the trends in patient outcomes among the four groups of patients undergoing CAPD and younger than 65-year old, male, with different BMI levels, with or without history of CVD, without diabetes mellitus, and not using lipid-lowering medications. However, our study did not find differences in all-cause or cardiovascular prognosis among elderly patients undergoing CAPD among the four groups. On the one hand, this phenomenon may originate from the fact that there were fewer aging individuals in our study, and on the other hand, elderly dialysis patients have more comorbidities and malnutrition[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The low LDL-C is usually associated with malnutrition. These phenomena complicate the prognosis of elderly dialysis patients. Similarly, no differences were found in the subgroup who were female among the four groups, it may be due to female dyslipidemia exhibiting distinct pathophysiological characteristics compared to male patterns, with varies across the women\u0026rsquo;s lifespan [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Differences in all-cause and cardiovascular deaths between the four groups also did not find in the diabetes and use of lipid-lowering medications subgroups. This possibly due to small sample sizes in these two subgroups, and also may result from patients with DM undergoing PD suffered multiple complications, malnutrition, as well as a shorter life expectancy[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], as evidenced in geriatric cohort. No significant differences in all-cause and cardiovascular prognostic for the four groups were found in the subgroup treated with dyslipidemia medications, which may benefit from the benefits of lipid-modifying management. Although the prognosis of lipid-lowering therapy in HD patients is controversial[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], further studies are needed in patients with CAPD.\u003c/p\u003e \u003cp\u003e Our investigation's advantages were substantial participant enrollment, rigorous multivariate analyses, and validation of Competing Risk Models. In fact, several limitations exist in our study. At first, it is not necessarily to prove causation between the combined stratification of LDL-C and RC and mortality in this retrospective observational study. Second, residual confounding effects of unmeasured covariates cannot be completely eliminated. Third, we used single measurements of LDL-C and RC at the inception timepoint of peritoneal dialysis, which may underestimate the strength of the association, and the predictive value of the cumulative exposure dose of LDL-C and RC requires further analyses. Lastly, our findings may not be applicable to other races because of all patients from China.\u003c/p\u003e \u003cp\u003eIn conclusion, Both higher LDL-C and RC levels at the beginning of CAPD were line with a likelihood of cardiovascular and all-cause fatality rate in individuals suffering CAPD. Our study suggests that RC may be another meaningful lipid biomarker in addition to LDL-C for predicting outcomes, especially combining the two for risk stratification of CAPD patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLDL-C \u0026nbsp; Low-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eRC \u0026nbsp; \u0026nbsp; \u0026nbsp;Residual cholesterol\u003c/p\u003e\n\u003cp\u003ePD \u0026nbsp; \u0026nbsp; \u0026nbsp;Peritoneal dialysis\u003c/p\u003e\n\u003cp\u003eCVD \u0026nbsp; \u0026nbsp;Cardiovascular disease\u003c/p\u003e\n\u003cp\u003eHR \u0026nbsp; \u0026nbsp; Hazards ratio\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp;Confidence interval\u003c/p\u003e\n\u003cp\u003eESKD \u0026nbsp; End-stage kidney disease\u003c/p\u003e\n\u003cp\u003eTG \u0026nbsp; \u0026nbsp; \u0026nbsp;Triglyceride\u003c/p\u003e\n\u003cp\u003eIDL \u0026nbsp; \u0026nbsp; Intermediate-density lipoproteins\u003c/p\u003e\n\u003cp\u003eVLDL \u0026nbsp; Very low-density lipoproteins\u003c/p\u003e\n\u003cp\u003eCAPD \u0026nbsp; Continuous ambulatory peritoneal dialysis\u003c/p\u003e\n\u003cp\u003eHDL-C \u0026nbsp;High density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eTC \u0026nbsp; \u0026nbsp; Total cholesterol\u003c/p\u003e\n\u003cp\u003eESC \u0026nbsp; \u0026nbsp;European Society of Cardiology\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; Body mass index\u003c/p\u003e\n\u003cp\u003eDM \u0026nbsp; \u0026nbsp;Diabetes mellitus\u003c/p\u003e\n\u003cp\u003eeGFR \u0026nbsp;Estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eCa \u0026nbsp; \u0026nbsp; Calcium\u003c/p\u003e\n\u003cp\u003eP \u0026nbsp; \u0026nbsp; \u0026nbsp;Phosphorus\u003c/p\u003e\n\u003cp\u003eHD \u0026nbsp; \u0026nbsp;Hemodialysis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to all patients who participated in the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China, Grant numbers: 82170745.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.X.W. and X.F.W. conceived and designed research; H.G., N.T., Q.D.X., X.J.Z., F.F.P., X.Y.W., N.S., X.M.T., Y.Q.W. Resources; X.R.F., Data Curation; F.H.C., C.CZ., X.X.W. analyzed data, interpreted results, prepared figures and drafted the manuscript; X.X.W. and X.F.W. edited and revised manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article. Further enquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in this study involving human participants was in accordance with the ethical standards of Tongren Hospital of Shanghai Jiao Tong University School of Medicine (approval number K2025-023-01), The requirement for informed consent was waived by the Ethics Committee because of the retrospective nature of the study. The study protocol complied with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors gave their consent to publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\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\u003cli\u003e\u003cspan\u003eLiyanage T, Ninomiya T, Jha V, Neal B, Patrice HM, Okpechi I, Zhao MH, Lv J, Garg AX, Knight J, et al. Worldwide access to treatment for end-stage kidney disease: a systematic review. 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J Clin Med. 2025;14(2):643. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm14020643\u003c/span\u003e\u003cspan address=\"10.3390/jcm14020643\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"all-cause mortality, cardiovascular mortality, low-density lipoprotein cholesterol, peritoneal dialysis, residual cholesterol","lastPublishedDoi":"10.21203/rs.3.rs-6590878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6590878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground. \u003c/strong\u003eLow-density lipoprotein cholesterol (LDL-C) combined with residual cholesterol (RC) can predict mortality in the general population. Studies on the effects of LDL-C combined with RC in peritoneal dialysis(PD) patients are lacking.\u003cstrong\u003e \u003c/strong\u003eThe aim of this study was to elucidate the linkage of LDL-C and RC stratification with all-cause and cardiovascular mortality in PD patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods.\u003c/strong\u003e In this retrospective analysis of multicenter data, 3397 patients from China undergoing initial PD spanning January 1, 2005, through May 31, 2023, were involved. The included participants were orderly grouped into four cohorts in view of their baseline RC and LDL-C concentrations. The conjunction between baseline LDL-C levels combined with RC values and the cardiovascular and all-cause mortality risk in PD participants was evaluated using Fine-Grey\u003csup\u003e,\u003c/sup\u003es hazard models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults. \u003c/strong\u003eAmong 3397 recipients aging of 50.5±14.4 years , along with 57.3% male were enrolled. During a period of 17179 person-years of follow-up, 904 deaths were documented, of which 512 were caused by cardiovascular disease (CVD). Those with high LDL-C(≥2.6 mmol/L) and RC(≥0.62 mmol/L) levels exhibited a higher likelihood of all-cause mortality risk (adjusted hazards ratio [HR], 1.47; 95% confidence interval [CI],1.21 to 1.79) and cardiovascular mortality (adjusted HR, 1.55; 95% CI,1.19 to 2.01) in comparison to low levels of RC (\u0026lt;0.62 mmol/L) and LDL-C (\u0026lt;2.6mmol/L). This trend remained robust in PD patients who survived the two-year follow-up period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions.\u003c/strong\u003e Higher levels of RC and LDL-C at the initiation of PD had significant linked with more elevated cardiovascular and all-cause mortality in PD patients.\u003c/p\u003e","manuscriptTitle":"Association of combined low-density lipoprotein cholesterol and residual cholesterol stratification with all- cause and cardiovascular mortality in peritoneal dialysis patients: a multicenter retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 15:39:25","doi":"10.21203/rs.3.rs-6590878/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c06d0f55-ad1b-499c-990c-d24b78947177","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T07:38:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 15:39:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6590878","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6590878","identity":"rs-6590878","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}