Correlation of the Triglyceride-Glucose-Body Mass Index with All-cause and Cardiovascular Mortality in Patients Undergoing Peritoneal Dialysis: A Retrospective Cohort Study

Correlation of the Triglyceride-Glucose-Body Mass Index with All-cause and Cardiovascular Mortality in Patients Undergoing Peritoneal Dialysis: A 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 Study protocol Correlation of the Triglyceride-Glucose-Body Mass Index with All-cause and Cardiovascular Mortality in Patients Undergoing Peritoneal Dialysis: A Retrospective Cohort Study Jinping Li, Xichao Wang, Wenyu Zhang, Na Sun, Yingying Han, Wenxiu Chang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5011868/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 Triglyceride-glucose-body mass index (TyG-BMI) is a simple indicator of insulin resistance and is linked to an elevated risk of mortality. Nevertheless, limited research has explored the associations between the TyG-BMI and all-cause and cardiovascular mortality in patients undergoing peritoneal dialysis (PD). Methods Patients initiating PD treatment at Tianjin First Central Hospital’s nephrology department from July 2013 to February 2024 had triglycerides, fasting blood glucose, height, and weight measured at baseline and monthly during follow-up. TyG-BMI was calculated, dividing PD patients into high, middle, or low TyG-BMI groups using tri-quantile method. Cox regression analysis assessed hazard ratios (HRs) for all-cause and cardiovascular mortality among these groups. Results A total of 865 patients were included. The mean TyG-BMI value for the entire study population was 212.27 ± 46.64. Patients in the high group had a higher proportion of patients whose primary kidney disease was diabetic nephropathy and the greatest proportion of patients with comorbid diabetes mellitus. During the follow-up, 266 (30.75%) deaths occurred, with CVD being the dominant cause in 110 (41.35%) patients. Univariate and multivariate Cox regression analyses showed that middle group patients had a significantly lower risk of all-cause mortality compared to other groups. For CVD mortality, high group patients had a significantly greater hazard ratio than middle group, while there was no significant difference between low and middle groups. Restricted cubic spline regression revealed U-shaped association between TyG-BMI and all-cause mortality risk, as well as J-shaped association with CVD mortality, inflection points were identified at 209.73 and 206.64 respectively. Conclusion The TyG-BMI shows U-shaped and J-shaped relationships with all-cause and CVD mortality risk, respectively, in PD patients. Additionally, significant sex differences were observed in these associations. Triglyceride glucose-body mass index Peritoneal dialysis All-cause mortality Cardiovascular mortality Insulin resistance Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Peritoneal dialysis (PD) is a primary therapeutic modality for treating end-stage renal disease. The technical simplicity, reduced need for trained staff, lower nurse-to-patient ratios, cost-effectiveness, and other factors have led to the implementation of the PD-First policy over several decades [ 1 – 5 ]. Chronic kidney disease (CKD), type 2 diabetes (T2D) and cardiovascular disease (CVD) are interconnected and mutually influential, with an increasing body of evidence demonstrating their systemic interdependence. This has given rise to the term cardio-metabolic-renal (CMR) disease [ 6 ]. CVD stands as the leading cause of mortality among PD patients [ 7 ], with a higher prevalence in CKD patients than in the general population and is inversely correlated with kidney function [ 8 ]. Insulin resistance (IR) may play a significant role in this context. PD initially improves IR in uremic patients [ 9 ], however, concerns arise regarding potential long-term effects such as systemic hyperglycemia, obesity, and aggravated IR due to cumulative exposure to glucose solutions used in PD (peritoneal glucose exposure, advanced glycation end products, and bioincompatible solutions), which could contribute to increased cardiovascular risk [ 10 , 11 , 12 ]. The triglyceride glucose-body mass index (TyG-BMI), an alternative IR index independent of insulin, was identified as an independent risk factor for diabetic kidney disease (DKD) [ 12 , 13 ]. Recent studies have demonstrated that the TyG-BMI is associated with the risk of a composite outcome including acute myocardial infarction, repeat revascularization, stroke, hypertension, coronary artery disease and all-cause mortality [ 12 , 14 , 15 , 16 , 17 ]. Nevertheless, the associations between the TyG-BMI and all-cause as well as CVD mortality in the PD population of Chinese origin remain to be elucidated. Hence, this study was conducted to explore this relationship within a single-center cohort of the Chinese PD population. Materials and Methods Study Population We retrospectively established a PD cohort between July 2013 and February 2024 at the Department of Nephrology, Tianjin First Central Hospital, China. Patients aged 18–80 years who initiated PD treatment for at least 3 months were enrolled in this study. All of the patients were treated according to the high-quality goal-directed peritoneal dialysis strategy of the International Society for Peritoneal Dialysis Practice Recommendations in our hospital before enrollment and during the observation period. Patients were excluded if they had malignant tumors, were receiving chemotherapy or had insufficient clinical information. Patients who were transferred to another hospital, switched to hemodialysis or received a kidney transplant during the study period were treated as censored and included in the survival analysis. This study has been granted an exemption from requiring written informed consent because it is a retrospective review of medical records. Prior to analysis, the patient records and information were anonymized and de-identified. This study was conducted at the PD Center of the Nephrology Department of our hospital, approved by the institutional review board (IRB) of the hospital (2015009S), and was conducted in accordance with the principles of the Helsinki Declaration. Data Collection and Definitions Data were obtained from medical records. Information was collected on socio-demographic characteristics, medical history, anthropometric measurements and laboratory tests. PD patients in our center underwent monthly hematological and biochemistry examinations during their outpatient follow-up. Baseline demographic and clinical information, including age, sex, original disease, height, weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), diabetes mellitus (DM), hypertension history and body mass index (BMI), was collected at the initiation of PD. SBP and DBP were measured in the morning of the outpatient visits. Diabetes was defined as a self-reported history of diabetes or FBG ≥ 7.0 mmol/L. Hypertension was defined as a self-reported history of hypertension, use of antihypertensive medication, SBP ≥ 140 mmHg, or DBP ≥ 90 mmHg. Hypercholesterolemia was defined as a self-reported history of dyslipidemia, or TC ≥ 5.17 mmol/L. Body mass index (BMI) was calculated as weight (kg)/height (m) 2 . Biochemical and medication data were collected one month after the PD onset as a baseline. Blood parameters included hemoglobin (Hb), albumin (Alb), blood urea nitrogen (BUN), creatinine (Cr), uric acid (UA), sodium (Na), potassium (K), chlorine (Cl), venous carbon dioxide (CO 2 ), calcium (Ca), inorganic phosphorus (P), fasting blood glucose (FBG), triglyceride, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), high-sensitivity C-reactive protein (hsCRP), and intact parathyroid hormone (iPTH). All blood samples were analyzed via commercially available kits and an automated analyzer (Cobas 8000 or e411 for iPTH, Roche Diagnostics Ltd. for others, Germany). The serum albumin concentration was measured via the bromocresol green method. The average 24-hour urinary urea and creatinine clearances were calculated to assess residual renal function (RRF). The total PD treatment volume, total ultrafiltration volume, total effluent dialysate volume, and daily urine volume were recorded daily. The total protein content in the effluent dialysate was measured. The protein equivalent nitrogen appearance (nPNA) was calculated and normalized to the actual weight. In accordance with previous studies [ 18 – 22 ], the TyG index was calculated as Ln [TG (mg/dL) ×FBG (mg/dL)/2]. TyG-BMI = TyG×BMI [ 17 ]. Treatment with angiotensin converting enzyme inhibitors or angiotensin II receptor blockers (both combined as RAAS inhibitors, RAASi) was also recorded. PD types and associated parameters were recorded. PD patients received standard prescriptions of continuous ambulatory peritoneal dialysis (CAPD using Baxter's Low Calcium Peritoneal Dialysis Solution [Lactate-G1.5/2.5%]) or automated peritoneal dialysis (APD using Baxter's Peritoneal Dialysis Solution [Lactate-G1.5/2.5%]) accordingly. Twenty-four-hour urine and dialysate samples were collected one month after PD commencement and every month thereafter to measure the dialysis adequacy, which was defined as the urea clearance index (Kt/V) and creatinine clearance (CCr), via standard methods [ 23 ]. WCCr was calculated by multiplying the 24-hour Ccr by 7. Study Outcomes The primary and secondary endpoints were all-cause mortality and CVD mortality, respectively. The criteria for CVD death were death attributed to congestive heart failure, acute myocardial infarction, atherosclerotic heart disease, cardiac arrest, cardiac arrhythmia, cardiomyopathy, cerebrovascular disorders, anoxic encephalopathy, ischemic brain injury, or peripheral arterial disease [ 24 ]. The cause of death was identified by the comprehensive management team consisting of junior and senior professors at our PD Center. All patients were followed up until death, transferred to hemodialysis therapy, received a kidney transplant, transferred to another center, lost to follow-up, or 29 February 2024. Statistical Analysis Normally distributed continuous variables are presented as the means ± standard deviations (SDs), non-normally distributed continuous variables are presented as medians and interquartile ranges, and categorical variables are presented as frequencies and percentages. All patients were divided into three groups according to TyG-BMI values via the tri-quantile method: the High group (TyG-BMI>227.15), Middle group (TyG-BMI 189.57–227.15) and Low group (TyG-BMI < 189.57). Comparisons of continuous variables among multiple groups were performed via one-way ANOVA or the Kruskal‒Wallis H test. The chi-square test was employed for categorical variables. Survival analysis was performed via Cox regression to compare the effects on all-cause and cardiovascular mortality among the High, Middle and Low groups. Model 1 was univariate. Model 2 was adjusted for sex, age, original disease, DM, hypertension, SBP. Model 3 was additionally adjusted for Hb, Alb, K, CO 2 , P, total Kt/V urea, nPNA, RRF, iPTH, and RAASi. Survival curves were plotted via the Kaplan–Meier method, and comparisons between groups were performed via the log-rank test. All the statistical analyses were conducted via SPSS version 22 (IBM, Japan). A p-value less than 0.05 was considered statistically significant. Results Clinical Characteristics and Laboratory Data among the three TyG-BMI groups In this study, we excluded patients with missing data (n = 25), patients with malignant disease (n = 2), and patients who received chemotherapy during the follow-up period (n = 1). Therefore, 865 eligible patients were included in the current study (Fig. 1 ). The mean age of the 865 participants was 57.28 ± 15.27 years and 480 (55.5%) were male. The mean TyG-BMI of the entire study population was 212.27 ± 46.64. The clinical characteristics and laboratory data of the three groups according to the TyG-BMI are shown in Table 1 . Patients in the High group had the lowest prevalence of glomerulonephritis as the original disease, the highest original disease prevalence of diabetic nephropathy and hypertensive nephropathy, and the greatest number of people suffering from diabetes. They also had the highest mean SBP, triglyceride, BMI, Hb, and hs-CRP levels, and the lowest mean total cholesterol level. In the Middle group, patients were more likely to be male, and had the lowest prevalence of hypertensive nephropathy as an original disease. These patients also had the lowest mean triglyceride levels. Table 1 Clinical characteristics and laboratory data among the three TyG-BMI groups Characteristic Total (n = 865) High group (n = 288) Middle group (n = 289) Low group (n = 288) p value * Age (y) 57.28 ± 15.27 56.64 ± 14.00 58.26 ± 14.39 56.88 ± 17.27 0.381 Gender (Male, %) 480 (55.5%) 165 (57.3%) 173 (59.8%) 142 (49.3%) 0.031 Original Disease < 0.001 Glomerulonephritis 367 (42.4%) 93 (32.3%) 115 (39.9%) 159 (55.2%) Diabetic Nephropathy 267 (30.9%) 126 (44.0%) 99 (34.2%) 42 (14.6%) Hypertensive nephropathy 87 (10.1%) 34 (11.7%) 24 (8.3%) 29 (10.1%) Others 144 (16.6%) 35 (12.1%) 51 (17.6%) 58 (20.1%) DM (%) 300 (34.7%) 142 (49.3%) 108 (37.5%) 50 (17.4%) < 0.001 Hypertension (%) 430 (49.7%) 142 (49.3%) 155 (53.8%) 133 (46.2%) 0.147 SBP (mmHg) 150.51 ± 20.76 154.30 ± 20.41 150.39 ± 20.31 146.86 ± 20.98 < 0.001 BMI (kg/m 2 ) 20.77 ± 9.22 24.85 ± 10.35 20.43 ± 8.20 17.06 ± 7.19 < 0.001 TyG-BMI 212.27 ± 46.64 263.88 ± 37.17 207.72 ± 10.83 165.52 ± 17.12 < 0.001 Blood Parameters Hb (g/L) 84.21 ± 17.60 87.66 ± 17.48 83.89 ± 17.05 81.11 ± 17.75 < 0.001 Alb (g/L) 34.94 ± 5.87 35.02 ± 5.57 34.76 ± 5.96 35.04 ± 6.09 0.813 BUN (mmol/L) 25.06 ± 11.83 24.62 ± 10.84 24.53 ± 10.88 26.08 ± 13.59 0.214 Cr (µmol/L) 725.40 ± 297.22 717.39 ± 262.17 723.01 ± 289.94 735.94 ± 336.08 0.749 UA (µmol/L) 409.17 ± 153.57 422.77 ± 145.81 406.76 ± 149.86 398.14 ± 164.17 0.154 Na ((mmol/L) 139.16 ± 4.29 139.17 ± 3.96 138.93 ± 4.60 139.40 ± 4.28 0.434 K ((mmol/L) 4.58 ± 0.84 4.56 ± 0.84 4.61 ± 0.87 4.58 ± 0.82 0.763 Cl ((mmol/L) 102.79 ± 4.07 104.95 ± 9.09 101.85 ± 5.14 101.65 ± 7.14 0.433 CO 2 ((mmol/L) 20.72 ± 4.57 20.44 ± 4.53 20.83 ± 4.51 20.87 ± 4.66 0.464 Ca (mmol/L) 1.93 ± 0.34 1.93 ± 0.28 1.95 ± 0.44 1.92 ± 0.2 0.549 P (mmol/L) 1.85 ± 0.62 1.90 ± 0.64 1.81 ± 0.53 1.84 ± 0.68 0.255 FBG (mg/dL) 114.15 ± 48.33 127.99 ± 52.64 111.68 ± 48.09 102.94 ± 40.21 0.099 Triglyceride (mg/dL) 1.85 ± 0.62 1.90 ± 0.64 1.81 ± 0.53 1.84 ± 0.68 0.033 Total cholesterol (mmol/L) 0.92 ± 0.19 0.90 ± 0.18 0.93 ± 0.18 0.94 ± 0.20 0.001 HDL-C (mmol/L) 43.15 ± 14.19 44.71 ± 19.49 45.48 ± 17.10 39.03 ± 14.14 < 0.001 LDL-C (mmol/L) 156.47 ± 100.68 207.13 ± 127.19 148.63 ± 79.71 114.03 ± 61.18 0.244 hs-CRP (mg/L) 4.36 [1.98, 10.70] 4.87 [2.26, 11.55] 4.21 [1.99, 11.30] 3.59 [1.82, 9.49] < 0.001 iPTH (pg/mL) 289.00 [175.25, 451.28] 291.80 [190.70, 442.20] 293.20 [175.50, 450.20] 267.20 [163.30, 459.40] 0.509 Treatment RAASi (%) 532 (61.5%) 172 (59.7%) 176 (60.9%) 184 (63.9%) 0.376 Abbreviations: DM, diabetes mellitus; SBP, systolic blood pressure; BMI, body mass index; TyG-BMI, triglyceride glucosebody mass index; Hb, hemoglobin; Alb, albumin; BUN, blood urea nitrogen; Cr, creatinine; UA, uric acid; Na, sodium; K, potassium; Cl, chlorine; CO 2 , venous carbon dioxide; Ca, calcium; P, phosphorus; FBG, fasting blood glucose; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; iPTH, intact parathyroid hormone; RAASi, RAAS inhibitor. * ANOVA test, Kruskal-Wallis H test (hsCRP, iPTH) or chi-square test as appropriate. Peritoneal dialysis-related parameters among the three TyG-BMI groups As shown in Table 2 , the majority of PD patients in this cohort used the DAPD modality (80.6%) and the median RRF was 2.8 ml/min/1.73m 2 , the urine volume was 800 ml/day, and the nPNA was 0.93 g/kg/d. The mean total Kt/V was 1.96 ± 0.84, and the total WCCr was 70.81 ± 45.20 L/w/1.73m 2 . Patients in the high TyG-BMI group had the highest RRF, urine volume, total WCCr and renal WCCr, and the lowest total Kt/V, renal Kt/V, dialysate Kt/V, and nPNA. Table 2 Peritoneal dialysis-related parameters among the three TyG-BMI groups Characteristics Total (n = 865) High group (n = 288) Middle group (n = 289) Low group (n = 288) p value * PD type 0.386 CAPD (%) 128 (14.8%) 46 (16.0%) 44 (15.3%) 38 (13.2%) APD (%) 40 (4.6%) 18 (6.4%) 12 (4.0%) 10 (3.5%) DAPD (%) 697 (80.6%) 224 (77.7%) 233 (80.7%) 240 (83.3%) RRF (mL/min/1.73m 2 ) 2.80 [0.67, 5.07] 3.85 [1.08, 6.41] 2.47 [0.51, 4.54] 2.58 [0.58, 4.32] 0.001 Urine volume (mL/day) 800.00 [500.00, 1300.00] 1000.00 [500.00, 1500.00] 885.00 [500.00, 1300.00] 715.00 [487.5, 1100.00] < 0.001 Total Kt/V 1.96 ± 0.84 1.80 ± 0.53 1.91 ± 0.61 2.17 ± 1.20 < 0.001 Renal Kt/V 1.06 ± 0.52 1.01 ± 0.43 1.04 ± 0.55 1.14 ± 0.55 0.007 Dialysate Kt/V 0.91 ± 0.84 0.81 ± 0.46 0.89 ± 0.59 1.04 ± 0.86 0.007 Total WCCr (L/w/1.73m 2 ) 70.81 ± 45.20 77.21 ± 29.91 68.52 ± 32.40 66.80 ± 35.29 0.017 Renal WCCr (L/w/1.73m 2 ) 40.70 ± 29.83 48.76 ± 31.52 39.06 ± 29.31 34.33 ± 26.74 < 0.001 Dialysate WCCr (L/w/1.73m 2 ) 30.27 ± 38.72 28.95 ± 15.25 29.42 ± 19.36 32.53 ± 23.45 0.512 nPNA (g/kg/d) 0.93 [0.79, 1.16] 0.86 [0.73, 1.03] 0.89 [0.76, 1.10] 1.02 [0.86, 1.23] < 0.001 Abbreviations: CAPD, continuous ambulatory peritoneal dialysis; APD, automated peritoneal dialysis; DAPD, automated peritoneal dialysis at daytime; RRF, residual renal function; Kt/V, urea clearance index; WCCr, weekly creatinine clearance; nPNA, the normalized protein equivalent of total nitrogen appearance * ANOVA test, Kruskal-Wallis H test (RRF, urine volume, nPNA) or chi-square test, as appropriate. Cox Regression Models for the All-cause and CVD Mortality During the 46.6 (22.4–78.0) months of follow-up, 266 (30.75%) deaths occurred, of which 110 (41.35%) were due to CVD. When analyzed as a continuous variable for Cox regression, TyG-BMI did not affect all-cause mortality. Categorically, patients in the High and Low groups were significantly associated with an increased risk of all-cause mortality compared with those in the Middle group according to univariate and multivariate analyses (Table 3 ). TyG-BMI was significantly associated with CVD mortality, which was analyzed as a continuous variable in all three models. Only patients in the High TyG-BMI group had an increased risk of CVD mortality compared with those in the Middle group. There was no significant difference between the Low group and the Middle group (Table 4 ). Table 3 Cox regression for the effects of the TyG-BMI and TyG-BMI categorical groups on all-cause mortality (n = 865) Parameter Model 1 Model 2 Model 3 HR 95% CI p value HR 95% CI p value HR 95% CI p value TyG-BMI 1.00 1.00 to 1.01 0.100 1.00 1.00 to 1.01 0.859 1.00 1.00 to 1.01 0.318 Categorical groups High group vs. Middle group 1.58 1.17 to 2.14 0.003 1.47 1.08 to 2.00 0.014 1.84 1.03 to 3.27 0.038 Low group vs. Middle group 1.54 1.13 to 2.09 0.006 1.67 1.21 to 2.30 0.002 1.92 1.10 to 3.35 0.022 Abbreviations: TyG-BMI, triglyceride glucosebody mass index Model 1: Univariate. Model 2: Adjusted for sex, age, original disease, DM, hypertension, and SBP. Model 3: Adjusted for sex, age, original disease, DM, hypertension, SBP, Hb, Alb, K, CO 2 , P, total Kt/V urea, nPNA, RRF, iPTH, and RAASi. Table 4 Cox regression for the effects of the TyG-BMI and TyG-BMI categorical groups on cardiovascular mortality (n = 865) Parameter Model 1 Model 2 Model 3 HR 95% CI p value HR 95% CI p value HR 95% CI p value TyG-BMI 1.01 1.00 to 1.01 0.001 1.01 1.00 to 1.01 0.049 1.01 1.00 to 1.02 0.015 Categorical groups High group vs. Middle group 2.04 1.28 to 3.27 0.003 1.89 1.17 to 3.06 0.009 2.94 1.20 to 7.21 0.019 Low group vs. Middle group 1.46 0.88 to 2.41 0.145 1.68 0.99 to 2.85 0.053 2.06 0.74 to 5.70 0.166 Abbreviations: TyG-BMI, triglyceride glucosebody mass index Model 1: Univariate. Model 2: Adjusted for sex, age, original disease, DM, hypertension, SBP. Model 3: Adjusted for sex, age, original disease, DM, hypertension, SBP, Hb, Alb, K, CO 2 , P, total Kt/V urea, nPNA, RRF, iPTH, and RAASi. Kaplan-Meier Analysis and Log-rank Test Kaplan-Meier analysis and the log-rank test were performed among the three groups, and survival curves are plotted in Fig. 2 . For all-cause mortality, the patients in the High and Low groups had a significantly greater risk for death than did the patients in the Middle group did (p = 0.0043) (Fig. 2 A). There was also a statistically significant difference in CVD mortality among the three groups (p = 0.0098) (Fig. 2 B). Restricted cubic spline regression revealed a robust U-shaped association between TyG-BMI and all-cause mortality, with the lowest risk observed at a significant reduction of 209.73 within the lower TyG-BMI range, followed by an increasing trend (p for nonlinearity = 0.002). For TyG-BMI 209.73, the HR increases by 1.36 times for every one standard deviation increase (95% CI [1.24–1.49]) (Fig. 3 A). No similar relationship was evident between the TyG-BMI and cardiovascular mortality; instead, a J-shaped pattern was observed (non-linear p = 0.024). The risk of CVD mortality remains relatively stable until the TyG-BMI reaches 206.64, after which it begins to increase rapidly (non-linear p = 0.024). When TyG-BMI is 206.64, the risk increases by 1.52 times for every one standard deviation increase (95% CI [1.36–1.69]) (Fig. 3 B). Subgroup analyses In the sensitivity analysis, our research findings remained robust, with the exception of sex as a factor. We observed that higher TyG-BMI values were associated with increased all-cause and cardiovascular mortality in men, whereas in women, lower TyG-BMI values were linked to elevated all-cause mortality but showed no difference in cardiovascular mortality risk between the groups (p = 0.0016, p = 0.0046). Therefore, adjustments for comorbidities such as hypertension and diabetes were not necessary (Fig. 4 ). Discussion In this study, we observed an independent association between higher or lower TyG-BMIs and an elevated risk of all-cause mortality among the population undergoing PD. A U-shaped relationship was identified between TyG-BMI and all-cause mortality, with the inflection point at TyG-BMI = 209.73. At this time, patients have the lowest all-cause mortality rate, whereas those above or below this threshold have increased mortality rates. A J-shaped relationship was found between TyG-BMI and CVD mortality, with the inflection point at TyG-BMI = 206.64. The risk of CVD mortality remains relatively stable until the TyG-BMI reaches 206.64, after which it begins to increase rapidly. Sex differences were observed between the groups. Higher TyG-BMI values were associated with increased all-cause and cardiovascular mortality in men, whereas in women, lower TyG-BMI values were linked to elevated all-cause mortality but showed no difference in cardiovascular mortality risk between the groups. However, comorbidities such as hypertension and diabetes did not influence these relationships. These findings support the potential clinical utility of the TyG-BMI as a reference value and predictive marker. It is also essential to consider the modulating impact of gender differences. A number of publications have reported that an elevated TyG-BMI is associated with an increased risk of cardiovascular diseases [ 12 , 14 , 15 , 16 , 17 ]. Our findings also indicate that higher TyG-BMIs are linked to an elevated risk of all-cause and CVD mortality in individuals undergoing PD than are TyG-BMIs ranging from 189.57 to 227.15. This could be attributed to the fact that participants with high TyG-BMIs presented higher SBP, TG, and BMIs, and a higher incidence of diabetes as well as original diseases such as diabetic nephropathy and hypertensive nephropathy, contributing to increased mortality. Importantly, IR was identified as one explanation for this association (25) and is a prominent characteristic in end-stage kidney disease patients [ 6 , 8 , 9 , 26 , 27 ], particularly in PD patients. In PD patients, prolonged exposure to glucose solutions, which are widely used in most countries, may lead to systemic hyperglycemia and obesity while exacerbating IR due to exposure to peritoneal glucose exposure along with advanced glycation end products and bioincompatible solutions [ 10 , 11 , 12 ]. On the basis of this premise, IR can induce an imbalance in glucose metabolism which subsequently triggers inflammation and oxidative stress. Furthermore, IR can stimulate increased production of free radicals and glycosylated products leading to nitric oxide (NO) inactivation [ 28 , 29 ]. Kiran et al. elucidated the U-shaped correlation between BMI and mortality by enrolling 274 Asian PD patients [ 30 ]. Similarly, our findings revealed that a lower TyG-BMI was associated with an increased risk of all-cause mortality compared with the intermediate TyG-BMI, which is potentially linked to poor nutritional status [ 31 , 32 ]. The TyG-BMI exhibited a U-shaped association with all-cause mortality in the population undergoing PD. The level associated with the lowest risk of all-cause mortality ranged from 189.57–227.15. Another study involving 2,689 PD patients demonstrated that the TyG-BMI had a linear association with all-cause and CVD mortality [ 12 ]. Importantly, our results indicated that higher and lower levels of glucose, triglycerides or BMI could lead to poorer prognosis. These findings strongly support the need to establish target ranges for triglycerides, glucose and BMI rather than specific target levels. Numerous studies have demonstrated that the TyG-BMI serves as an independent predictor of adverse cardiovascular events [ 12 , 14 , 15 , 16 , 17 ]. It is also closely associated with IR, which not only contributes to the development of CVD in both the CKD patients and diabetic patients but also predicts the cardiovascular prognosis of CVD patients [ 6 , 29 ]. Previous research has consistently shown a significant correlation between TyG-BMI and future CVD mortality, myocardial infarction, and stroke, indicating that insulin resistance plays a pivotal role in the pathogenesis and prognosis of cardiovascular diseases [ 12 , 15 , 17 ]. Our study similarly revealed a significant association between TyG-BMI and cardiovascular mortality. When the two groups were compared, the high TyG-BMI group clearly presented significantly higher rates of CVD-related death than did the middle group across all three models, however, no significant difference was observed between the low TyG-BMI group and the middle TyG-BMI group. Interestingly, we observed that sex has a modifying effect on the risk of mortality. This phenomenon may be attributed to hormonal disparities (33), insulin resistance, visceral adiposity, endothelial dysfunction, and chronic inflammation. Furthermore, lifestyle variations (e.g., a higher prevalence of risky health behaviors such as smoking, excessive alcohol consumption, and sedentary habits among men) contribute to increased all-cause and cardiovascular disease mortality in men with elevated TyG-BMIs. Naturally, this necessitates thorough examination and validation within a more extensive cohort of peritoneal dialysis patients. Study strengths and limitations In this study, we identified an independent association between the TyG-BMI and all-cause as well as CVD mortality in a single PD center. Furthermore, we observed for the first time that the relationship between the TyG-BMI and all-cause mortality exhibited a U-shaped pattern in the population undergoing PD. However, our study has several limitations. First, the levels of triglycerides and glucose may have been influenced by prescribed medications, which were not reported in our study, potentially introducing bias to the results. Second, data on triglycerides, glucose and BMI were only collected only once at baseline, and it remains unclear whether changes in TyG-BMI over time could impact its association with mortality. Therefore, longitudinal cohort studies are necessary to explore the persistence of the association between TyG-BMI and mortality over time. Third, we did not assess the homeostasis model assessment of insulin resistance or compare it with the TyG-BMI because of insufficient data on insulin levels during follow-up. Finally, potential residual confounding factors should be acknowledged given that this is an observational study. Conclusion The relationship between the TyG-BMI and the risk of all-cause and CVD mortality in patients undergoing PD demonstrated a U-shaped and J-shaped pattern. The inflection points were identified at 209.73 and 206.64, respectively. Additionally, notable sex differences were observed in these associations. Declarations Acknowledgments We thank all the doctors at the division of nephrology in our hospital, Tianjin, China, for their work. Statement of Ethics This study was exempt from requiring written informed consent because it was a retrospective medical chart review. Before analysis, the patient records and information were anonymized and de-identified. This study was conducted at the PD Center of the Nephrology Department in our hospital and was approved by the institutional review board (IRB) of the hospital (2015009S) and was conducted following the principles of the Helsinki Declaration. Disclosure Statement The authors have no conflicts of interest to declare. Funding Statement There was no funding for the present study. Author Contributions Research idea and study design: WXC; data acquisition: JPL; data analysis: WXC; supervision or mentorship: WYZ, XCW; manuscript writing: WXC, JPL; literature retrieval: NS, YYH; interpretation of results: JPL; All authors approved it. Data Availability Statement The data underlying this article will be shared on reasonable request to the corresponding author. References Chaudhary K, Sangha H, Khanna R. Peritoneal dialysis first: rationale.Clin. J Am Soc Nephrol. 2011;6(2):447–56. 10.2215/CJN.07920910 . Epub 2010 Nov 29. Choy AS, Li PK. Sustainability of the Peritoneal Dialysis-First Policy in Hong Kong. Blood Purif. 2015;40(4):320–5. 10.1159/000441580 . Epub 2015 Nov 17. Davidson B, Crombie K, Manning K et al. Outcomes and Challenges of a PD-First Program, a South-African Perspective. Perit Dial Int 2018 May-Jun;38(3):179–86. 10.3747/pdi.2017.00182 Briggs V, Davies S, Wilkie M. International Variations in Peritoneal Dialysis Utilization and Implications for Practice. Am J Kidney Dis. 2019;74(1):101–10. 10.1053/j.ajkd.2018.12.033 . Epub 2019 Feb 22. Blake PG. Integrated end-stage renal disease care: the role of peritoneal dialysis. Nephrol Dial Transplant. 2001;16 Suppl 5:61 – 6. 10.1093/ndt/16.suppl_5.61 Marassi M, Fadini GP. The cardio-renal-metabolic connection: a review of the evidence. Cardiovasc Diabetol. 2023;22(1):195. 10.1186/s12933-023-01937-x . Bello AK, Okpechi IG, Osman MA, et al. Epidemiology of peritoneal dialysis outcomes. Nat Rev Nephrol. 2022;18(12):779–93. 10.1038/s41581-022-00623-7 . Epub 2022 Sep 16. Jankowski J, Floege J, Fliser D, Böhm M, Marx N. Cardiovascular disease in chronic kidney disease: pathophysiological insights and therapeutic options. Circulation. 2021;143:1157–72. 10.1161/CIRCULATIONAHA.120.050686 . Epub 2021 Mar 15. Kobayashi S, Maejima S, Ikeda T, et al. Impact of dialysis therapy on insulin resistance in end-stage renal disease: comparison of haemodialysis and continuous ambulatory peritoneal dialysis. Nephrol Dial Transpl. 2000;15(1):65–70. 10.1093/ndt/15.1.65 . Bernardo AP, Oliveira JC, Santos O, et al. Insulin Resistance in Nondiabetic Peritoneal Dialysis Patients: Associations with Body Composition, Peritoneal Transport, and Peritoneal Glucose Absorption. Clin J Am Soc Nephrol. 2015;10(12):2205–12. 10.2215/CJN.03170315 . Epub 2015 Oct 27. Krediet RT, Balafa O. Cardiovascular risk in the peritoneal dialysis patient. Nat Rev Nephrol. 2010;6(8):451–60. 10.1038/nrneph.2010.68 . Epub 2010 Jun 22. Zhan C, Peng Y, Ye H, et al. Triglyceride glucose-body mass index and cardiovascular mortality in patients undergoing peritoneal dialysis: a retrospective cohort study. Lipids Health Dis. 2023;22(1):143. 10.1186/s12944-023-01892-2 . Mu X, Wu A, Hu H, et al. Correlation between alternative insulin resistance indexes and diabetic kidney disease: a retrospective study. Endocrine. 2024;84(1):136–47. 10.1007/s12020-023-03574-6 . Epub 2023 Oct 31. Huang X, He J, Wu G et al. TyG-BMI and hypertension in Normoglycemia subjects in Japan: A cross-sectional study. Diab Vasc Dis Res 2023 May-Jun;20(3):14791641231173617. 10.1177/14791641231173617 Huo RR, Zhai L, Liao Q, et al. Changes in the triglyceride glucose-body mass index estimate the risk of stroke in middle-aged and older Chinese adults: a nationwide prospective cohort study. Cardiovasc Diabetol. 2023;22(1):254. 10.1186/s12933-023-01983-5 . Yu XR, Du JL, Jiang M et al. Correlation of TyG-BMI and TyG-WC with severity and short-term outcome in new-onset acute ischemic stroke. Front Endocrinol (Lausanne). 2024;15:1327903. 10.3389/fendo.2024.1327903 . eCollection 2024. Cheng Y, Fang Z, Zhang X, et al. Association between triglyceride glucose-body mass index and cardiovascular outcomes in patients undergoing percutaneous coronary intervention: a retrospective study. Cardiovasc Diabetol. 2023;22(1):75. 10.1186/s12933-023-01794-8 . Li H, Zuo Y, Qian F, et al. Triglyceride-glucose index variability and incident cardiovascular disease: a prospective cohort study. Cardiovasc Diabetol. 2022;21(1):105. 10.1186/s12933-022-01541-5 . Dang K, Wang X, Hu J, et al. The association between triglyceride-glucose index and its combination with obesity indicators and cardiovascular disease: NHANES 2003–2018. Cardiovasc Diabetol. 2024;23(1):8. 10.1186/s12933-023-02115-9 . Xiang Q, Xu H, Zhan J, et al. Association between the Triglyceride-Glucose Index and Vitamin D Status in Type 2 Diabetes Mellitus. Nutrients. 2023;15(3):639. 10.3390/nu15030639 . Colladant M, Chabannes M, Crepin T, et al. Triglyceride-Glucose Index and Cardiovascular Events in Kidney Transplant Recipients. Kidney Int Rep. 2023;8(11):2307–14. eCollection 2023 Nov. Nam KW, Kang MK, Jeong HY, et al. Triglyceride-glucose index is associated with early neurological deterioration in single subcortical infarction: Early prognosis in single subcortical infarctions. Int J Stroke. 2021;16(8):944–52. Epub 2021 Jan 10. Nolph KD, Moore HL, Twardowski ZJ et al. Cross-sectional assessment of weekly urea and creatinine clearances in patients on continuous ambulatory peritoneal dialysis. ASAIO J 1992 Jul-Sep;38(3):M139–42. 10.1097/00002480-199207000-00004 Wu H, Xiong L, Xu Q, et al. Higher serum triglyceride to high-density lipoprotein cholesterol ratio was associated with increased cardiovascular mortality in female patients on peritoneal dialysis. Nutr Metab Cardiovasc Dis. 2015;25(8):749–55. 10.1016/j.numecd.2015.05.006 . Epub 2015 May 19. Khan SH, Sobia F, Niazi NK et al. Metabolic clustering of risk factors: evaluation of Triglyceride-glucose index (TyG index) for evaluation of insulin resistance. Diabetol Metab Syndr. 2018;10:74. 10.1186/s13098-018-0376-8 . eCollection 2018. Xie E, Ye Z, Wu Y, et al. The triglyceride-glucose index predicts 1-year major adverse cardiovascular events in end-stage renal disease patients with coronary artery disease. Cardiovasc Diabetol. 2023;22(1):292. 10.1186/s12933-023-02028-7 . Nishimura M, Tsukamoto K, Tamaki N, et al. Risk stratification for cardiac death in hemodialysis patients without obstructive coronary artery disease. Kidney Int. 2011;79(3):363–71. 10.1038/ki.2010.392 . Epub 2010 Oct 13. Das UN. Is There a Role for Bioactive Lipids in the Pathobiology of Diabetes Mellitus? Front Endocrinol (Lausanne). 2017;8:182. 10.3389/fendo.2017.00182 . eCollection 2017. Li H, Jiang Y, Su X, et al. The triglyceride glucose index was U-shape associated with all-cause mortality in population with cardiovascular diseases. Diabetol Metab Syndr. 2023;15(1):181. 10.1186/s13098-023-01153-3 . Kiran VR, Zhu TY, Yip T, et al. Body mass index and mortality risk in asian peritoneal dialysis patients in Hong Kong-impact of diabetes and cardiovascular disease status. Perit Dial Int. 2014;34:390–8. Ladhani M, Craig JC, Irving M, et al. Obesity and the risk of cardiovascular and all-cause mortality in chronic kidney disease: a systematic review and meta-analysis. Nephrol Dial Transpl. 2017;32(3):439–49. 10.1093/ndt/gfw075 . Ahmadi SF, Zahmatkesh G, Streja E et al. Association of Body Mass Index With Mortality in Peritoneal Dialysis Patients: A Systematic Review and Meta-Analysis. Perit Dial Int 2016 May-Jun;36(3):315–25. doi: 10.3747/pdi.2015.00052. Epub 2015 Oct 16. Yu Y, Wang J, Ding L, et al. Sex differences in the nonlinear association of triglyceride glucose index with all-cause and cardiovascular mortality in the general population. Diabetol Metab Syndr. 2023;15(1):136. 10.1186/s13098-023-01117-7 . Additional Declarations No competing interests reported. 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08:08:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5011868/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5011868/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66952354,"identity":"801ff688-d583-4c05-bdc4-d7d95396c526","added_by":"auto","created_at":"2024-10-18 10:35:44","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":378496,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5011868/v1/4a6b9759735f1e70167108cc.jpg"},{"id":66952356,"identity":"c97ad26c-3425-42fd-bdd9-e97a432f1a49","added_by":"auto","created_at":"2024-10-18 10:35:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":393748,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure 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legend\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5011868/v1/624896abb6476456e0425174.jpg"},{"id":76151804,"identity":"a4dbf476-c8dd-4d9a-babe-0bcf4d225957","added_by":"auto","created_at":"2025-02-12 23:16:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2993999,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5011868/v1/d7ec9d87-2941-4d5d-9586-69d9ae1bae70.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation of the Triglyceride-Glucose-Body Mass Index with All-cause and Cardiovascular Mortality in Patients Undergoing Peritoneal Dialysis: A Retrospective Cohort Study","fulltext":[{"header":"Background","content":"\u003cp\u003ePeritoneal dialysis (PD) is a primary therapeutic modality for treating end-stage renal disease. The technical simplicity, reduced need for trained staff, lower nurse-to-patient ratios, cost-effectiveness, and other factors have led to the implementation of the PD-First policy over several decades [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Chronic kidney disease (CKD), type 2 diabetes (T2D) and cardiovascular disease (CVD) are interconnected and mutually influential, with an increasing body of evidence demonstrating their systemic interdependence. This has given rise to the term cardio-metabolic-renal (CMR) disease [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. CVD stands as the leading cause of mortality among PD patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], with a higher prevalence in CKD patients than in the general population and is inversely correlated with kidney function [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Insulin resistance (IR) may play a significant role in this context. PD initially improves IR in uremic patients [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], however, concerns arise regarding potential long-term effects such as systemic hyperglycemia, obesity, and aggravated IR due to cumulative exposure to glucose solutions used in PD (peritoneal glucose exposure, advanced glycation end products, and bioincompatible solutions), which could contribute to increased cardiovascular risk [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe triglyceride glucose-body mass index (TyG-BMI), an alternative IR index independent of insulin, was identified as an independent risk factor for diabetic kidney disease (DKD) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Recent studies have demonstrated that the TyG-BMI is associated with the risk of a composite outcome including acute myocardial infarction, repeat revascularization, stroke, hypertension, coronary artery disease and all-cause mortality [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Nevertheless, the associations between the TyG-BMI and all-cause as well as CVD mortality in the PD population of Chinese origin remain to be elucidated. Hence, this study was conducted to explore this relationship within a single-center cohort of the Chinese PD population.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eWe retrospectively established a PD cohort between July 2013 and February 2024 at the Department of Nephrology, Tianjin First Central Hospital, China. Patients aged 18\u0026ndash;80 years who initiated PD treatment for at least 3 months were enrolled in this study. All of the patients were treated according to the high-quality goal-directed peritoneal dialysis strategy of the International Society for Peritoneal Dialysis Practice Recommendations in our hospital before enrollment and during the observation period. Patients were excluded if they had malignant tumors, were receiving chemotherapy or had insufficient clinical information. Patients who were transferred to another hospital, switched to hemodialysis or received a kidney transplant during the study period were treated as censored and included in the survival analysis. This study has been granted an exemption from requiring written informed consent because it is a retrospective review of medical records. Prior to analysis, the patient records and information were anonymized and de-identified. This study was conducted at the PD Center of the Nephrology Department of our hospital, approved by the institutional review board (IRB) of the hospital (2015009S), and was conducted in accordance with the principles of the Helsinki Declaration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData Collection and Definitions\u003c/h2\u003e \u003cp\u003eData were obtained from medical records. Information was collected on socio-demographic characteristics, medical history, anthropometric measurements and laboratory tests. PD patients in our center underwent monthly hematological and biochemistry examinations during their outpatient follow-up.\u003c/p\u003e \u003cp\u003eBaseline demographic and clinical information, including age, sex, original disease, height, weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), diabetes mellitus (DM), hypertension history and body mass index (BMI), was collected at the initiation of PD. SBP and DBP were measured in the morning of the outpatient visits. Diabetes was defined as a self-reported history of diabetes or FBG\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L. Hypertension was defined as a self-reported history of hypertension, use of antihypertensive medication, SBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, or DBP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg. Hypercholesterolemia was defined as a self-reported history of dyslipidemia, or TC\u0026thinsp;\u0026ge;\u0026thinsp;5.17 mmol/L. Body mass index (BMI) was calculated as weight (kg)/height (m)\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBiochemical and medication data were collected one month after the PD onset as a baseline. Blood parameters included hemoglobin (Hb), albumin (Alb), blood urea nitrogen (BUN), creatinine (Cr), uric acid (UA), sodium (Na), potassium (K), chlorine (Cl), venous carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e), calcium (Ca), inorganic phosphorus (P), fasting blood glucose (FBG), triglyceride, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), high-sensitivity C-reactive protein (hsCRP), and intact parathyroid hormone (iPTH). All blood samples were analyzed via commercially available kits and an automated analyzer (Cobas 8000 or e411 for iPTH, Roche Diagnostics Ltd. for others, Germany). The serum albumin concentration was measured via the bromocresol green method. The average 24-hour urinary urea and creatinine clearances were calculated to assess residual renal function (RRF). The total PD treatment volume, total ultrafiltration volume, total effluent dialysate volume, and daily urine volume were recorded daily. The total protein content in the effluent dialysate was measured. The protein equivalent nitrogen appearance (nPNA) was calculated and normalized to the actual weight. In accordance with previous studies [\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], the TyG index was calculated as Ln [TG (mg/dL) \u0026times;FBG (mg/dL)/2]. TyG-BMI\u0026thinsp;=\u0026thinsp;TyG\u0026times;BMI [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Treatment with angiotensin converting enzyme inhibitors or angiotensin II receptor blockers (both combined as RAAS inhibitors, RAASi) was also recorded. PD types and associated parameters were recorded. PD patients received standard prescriptions of continuous ambulatory peritoneal dialysis (CAPD using Baxter's Low Calcium Peritoneal Dialysis Solution [Lactate-G1.5/2.5%]) or automated peritoneal dialysis (APD using Baxter's Peritoneal Dialysis Solution [Lactate-G1.5/2.5%]) accordingly. Twenty-four-hour urine and dialysate samples were collected one month after PD commencement and every month thereafter to measure the dialysis adequacy, which was defined as the urea clearance index (Kt/V) and creatinine clearance (CCr), via standard methods [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. WCCr was calculated by multiplying the 24-hour Ccr by 7.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy Outcomes\u003c/h2\u003e \u003cp\u003eThe primary and secondary endpoints were all-cause mortality and CVD mortality, respectively. The criteria for CVD death were death attributed to congestive heart failure, acute myocardial infarction, atherosclerotic heart disease, cardiac arrest, cardiac arrhythmia, cardiomyopathy, cerebrovascular disorders, anoxic encephalopathy, ischemic brain injury, or peripheral arterial disease [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The cause of death was identified by the comprehensive management team consisting of junior and senior professors at our PD Center.\u003c/p\u003e \u003cp\u003eAll patients were followed up until death, transferred to hemodialysis therapy, received a kidney transplant, transferred to another center, lost to follow-up, or 29 February 2024.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eNormally distributed continuous variables are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs), non-normally distributed continuous variables are presented as medians and interquartile ranges, and categorical variables are presented as frequencies and percentages. All patients were divided into three groups according to TyG-BMI values via the tri-quantile method: the High group (TyG-BMI\u0026gt;227.15), Middle group (TyG-BMI 189.57\u0026ndash;227.15) and Low group (TyG-BMI\u0026thinsp;\u0026lt;\u0026thinsp;189.57). Comparisons of continuous variables among multiple groups were performed via one-way ANOVA or the Kruskal‒Wallis H test. The chi-square test was employed for categorical variables. Survival analysis was performed via Cox regression to compare the effects on all-cause and cardiovascular mortality among the High, Middle and Low groups. Model 1 was univariate. Model 2 was adjusted for sex, age, original disease, DM, hypertension, SBP. Model 3 was additionally adjusted for Hb, Alb, K, CO\u003csub\u003e2\u003c/sub\u003e, P, total Kt/V urea, nPNA, RRF, iPTH, and RAASi. Survival curves were plotted via the Kaplan\u0026ndash;Meier method, and comparisons between groups were performed via the log-rank test. All the statistical analyses were conducted via SPSS version 22 (IBM, Japan). A p-value less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical Characteristics and Laboratory Data among the three TyG-BMI groups\u003c/h2\u003e \u003cp\u003eIn this study, we excluded patients with missing data (n\u0026thinsp;=\u0026thinsp;25), patients with malignant disease (n\u0026thinsp;=\u0026thinsp;2), and patients who received chemotherapy during the follow-up period (n\u0026thinsp;=\u0026thinsp;1). Therefore, 865 eligible patients were included in the current study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age of the 865 participants was 57.28\u0026thinsp;\u0026plusmn;\u0026thinsp;15.27 years and 480 (55.5%) were male. The mean TyG-BMI of the entire study population was 212.27\u0026thinsp;\u0026plusmn;\u0026thinsp;46.64. The clinical characteristics and laboratory data of the three groups according to the TyG-BMI are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients in the High group had the lowest prevalence of glomerulonephritis as the original disease, the highest original disease prevalence of diabetic nephropathy and hypertensive nephropathy, and the greatest number of people suffering from diabetes. They also had the highest mean SBP, triglyceride, BMI, Hb, and hs-CRP levels, and the lowest mean total cholesterol level. In the Middle group, patients were more likely to be male, and had the lowest prevalence of hypertensive nephropathy as an original disease. These patients also had the lowest mean triglyceride levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics and laboratory data among the three TyG-BMI groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;865)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh group (n\u0026thinsp;=\u0026thinsp;288)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiddle group (n\u0026thinsp;=\u0026thinsp;289)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow group (n\u0026thinsp;=\u0026thinsp;288)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.28\u0026thinsp;\u0026plusmn;\u0026thinsp;15.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.64\u0026thinsp;\u0026plusmn;\u0026thinsp;14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.26\u0026thinsp;\u0026plusmn;\u0026thinsp;14.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.88\u0026thinsp;\u0026plusmn;\u0026thinsp;17.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e480 (55.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165 (57.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173 (59.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOriginal Disease\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerulonephritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e367 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (39.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e159 (55.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetic Nephropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e267 (30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (44.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (34.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertensive nephropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58 (20.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300 (34.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e430 (49.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155 (53.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133 (46.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150.51\u0026thinsp;\u0026plusmn;\u0026thinsp;20.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154.30\u0026thinsp;\u0026plusmn;\u0026thinsp;20.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150.39\u0026thinsp;\u0026plusmn;\u0026thinsp;20.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e146.86\u0026thinsp;\u0026plusmn;\u0026thinsp;20.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.77\u0026thinsp;\u0026plusmn;\u0026thinsp;9.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.85\u0026thinsp;\u0026plusmn;\u0026thinsp;10.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.43\u0026thinsp;\u0026plusmn;\u0026thinsp;8.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.06\u0026thinsp;\u0026plusmn;\u0026thinsp;7.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212.27\u0026thinsp;\u0026plusmn;\u0026thinsp;46.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e263.88\u0026thinsp;\u0026plusmn;\u0026thinsp;37.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207.72\u0026thinsp;\u0026plusmn;\u0026thinsp;10.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e165.52\u0026thinsp;\u0026plusmn;\u0026thinsp;17.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood Parameters\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\u003eHb (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.21\u0026thinsp;\u0026plusmn;\u0026thinsp;17.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.66\u0026thinsp;\u0026plusmn;\u0026thinsp;17.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.89\u0026thinsp;\u0026plusmn;\u0026thinsp;17.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.11\u0026thinsp;\u0026plusmn;\u0026thinsp;17.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.94\u0026thinsp;\u0026plusmn;\u0026thinsp;5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.02\u0026thinsp;\u0026plusmn;\u0026thinsp;5.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.76\u0026thinsp;\u0026plusmn;\u0026thinsp;5.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.04\u0026thinsp;\u0026plusmn;\u0026thinsp;6.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.06\u0026thinsp;\u0026plusmn;\u0026thinsp;11.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.62\u0026thinsp;\u0026plusmn;\u0026thinsp;10.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.53\u0026thinsp;\u0026plusmn;\u0026thinsp;10.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.08\u0026thinsp;\u0026plusmn;\u0026thinsp;13.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e725.40\u0026thinsp;\u0026plusmn;\u0026thinsp;297.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e717.39\u0026thinsp;\u0026plusmn;\u0026thinsp;262.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e723.01\u0026thinsp;\u0026plusmn;\u0026thinsp;289.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e735.94\u0026thinsp;\u0026plusmn;\u0026thinsp;336.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e409.17\u0026thinsp;\u0026plusmn;\u0026thinsp;153.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e422.77\u0026thinsp;\u0026plusmn;\u0026thinsp;145.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e406.76\u0026thinsp;\u0026plusmn;\u0026thinsp;149.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e398.14\u0026thinsp;\u0026plusmn;\u0026thinsp;164.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa ((mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139.16\u0026thinsp;\u0026plusmn;\u0026thinsp;4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138.93\u0026thinsp;\u0026plusmn;\u0026thinsp;4.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139.40\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK ((mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl ((mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.79\u0026thinsp;\u0026plusmn;\u0026thinsp;4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.95\u0026thinsp;\u0026plusmn;\u0026thinsp;9.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101.85\u0026thinsp;\u0026plusmn;\u0026thinsp;5.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101.65\u0026thinsp;\u0026plusmn;\u0026thinsp;7.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e ((mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.72\u0026thinsp;\u0026plusmn;\u0026thinsp;4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.44\u0026thinsp;\u0026plusmn;\u0026thinsp;4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.83\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.87\u0026thinsp;\u0026plusmn;\u0026thinsp;4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.464\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\u003e1.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.549\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\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.15\u0026thinsp;\u0026plusmn;\u0026thinsp;48.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.99\u0026thinsp;\u0026plusmn;\u0026thinsp;52.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111.68\u0026thinsp;\u0026plusmn;\u0026thinsp;48.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102.94\u0026thinsp;\u0026plusmn;\u0026thinsp;40.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\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\u003e43.15\u0026thinsp;\u0026plusmn;\u0026thinsp;14.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.71\u0026thinsp;\u0026plusmn;\u0026thinsp;19.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.48\u0026thinsp;\u0026plusmn;\u0026thinsp;17.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.03\u0026thinsp;\u0026plusmn;\u0026thinsp;14.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156.47\u0026thinsp;\u0026plusmn;\u0026thinsp;100.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207.13\u0026thinsp;\u0026plusmn;\u0026thinsp;127.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148.63\u0026thinsp;\u0026plusmn;\u0026thinsp;79.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114.03\u0026thinsp;\u0026plusmn;\u0026thinsp;61.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.36 [1.98, 10.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.87 [2.26, 11.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.21 [1.99, 11.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.59 [1.82, 9.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eiPTH (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e289.00\u003c/p\u003e \u003cp\u003e[175.25, 451.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e291.80\u003c/p\u003e \u003cp\u003e[190.70, 442.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e293.20\u003c/p\u003e \u003cp\u003e[175.50, 450.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e267.20\u003c/p\u003e \u003cp\u003e[163.30, 459.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment\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\u003eRAASi (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e532 (61.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172 (59.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e176 (60.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e184 (63.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: DM, diabetes mellitus; SBP, systolic blood pressure; BMI, body mass index; TyG-BMI, triglyceride glucosebody mass index; Hb, hemoglobin; Alb, albumin; BUN, blood urea nitrogen; Cr, creatinine; UA, uric acid; Na, sodium; K, potassium; Cl, chlorine; CO\u003csub\u003e2\u003c/sub\u003e, venous carbon dioxide; Ca, calcium; P, phosphorus; FBG, fasting blood glucose; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; iPTH, intact parathyroid hormone; RAASi, RAAS inhibitor.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e*\u003c/sup\u003e ANOVA test, Kruskal-Wallis H test (hsCRP, iPTH) or chi-square test as appropriate.\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\u003ePeritoneal dialysis-related parameters among the three TyG-BMI groups\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the majority of PD patients in this cohort used the DAPD modality (80.6%) and the median RRF was 2.8 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e, the urine volume was 800 ml/day, and the nPNA was 0.93 g/kg/d. The mean total Kt/V was 1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84, and the total WCCr was 70.81\u0026thinsp;\u0026plusmn;\u0026thinsp;45.20 L/w/1.73m\u003csup\u003e2\u003c/sup\u003e. Patients in the high TyG-BMI group had the highest RRF, urine volume, total WCCr and renal WCCr, and the lowest total Kt/V, renal Kt/V, dialysate Kt/V, and nPNA.\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\u003ePeritoneal dialysis-related parameters among the three TyG-BMI groups\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;865)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh group (n\u0026thinsp;=\u0026thinsp;288)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiddle group (n\u0026thinsp;=\u0026thinsp;289)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow group (n\u0026thinsp;=\u0026thinsp;288)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAPD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAPD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e697 (80.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e224 (77.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e233 (80.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e240 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRRF (mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.80 [0.67, 5.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.85 [1.08, 6.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.47 [0.51, 4.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.58 [0.58, 4.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrine volume (mL/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e800.00\u003c/p\u003e \u003cp\u003e[500.00, 1300.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1000.00\u003c/p\u003e \u003cp\u003e[500.00, 1500.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e885.00\u003c/p\u003e \u003cp\u003e[500.00, 1300.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e715.00\u003c/p\u003e \u003cp\u003e[487.5, 1100.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Kt/V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal Kt/V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDialysate Kt/V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal WCCr (L/w/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.81\u0026thinsp;\u0026plusmn;\u0026thinsp;45.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.21\u0026thinsp;\u0026plusmn;\u0026thinsp;29.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.52\u0026thinsp;\u0026plusmn;\u0026thinsp;32.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.80\u0026thinsp;\u0026plusmn;\u0026thinsp;35.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal WCCr (L/w/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.70\u0026thinsp;\u0026plusmn;\u0026thinsp;29.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.76\u0026thinsp;\u0026plusmn;\u0026thinsp;31.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.06\u0026thinsp;\u0026plusmn;\u0026thinsp;29.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.33\u0026thinsp;\u0026plusmn;\u0026thinsp;26.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDialysate WCCr (L/w/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.27\u0026thinsp;\u0026plusmn;\u0026thinsp;38.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.95\u0026thinsp;\u0026plusmn;\u0026thinsp;15.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.42\u0026thinsp;\u0026plusmn;\u0026thinsp;19.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.53\u0026thinsp;\u0026plusmn;\u0026thinsp;23.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enPNA (g/kg/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93 [0.79, 1.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86 [0.73, 1.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89 [0.76, 1.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.02 [0.86, 1.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: CAPD, continuous ambulatory peritoneal dialysis; APD, automated peritoneal dialysis; DAPD, automated peritoneal dialysis at daytime; RRF, residual renal function; Kt/V, urea clearance index; WCCr, weekly creatinine clearance; nPNA, the normalized protein equivalent of total nitrogen appearance\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e*\u003c/sup\u003e ANOVA test, Kruskal-Wallis H test (RRF, urine volume, nPNA) or chi-square test, as appropriate.\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\u003eCox Regression Models for the All-cause and CVD Mortality\u003c/h2\u003e \u003cp\u003eDuring the 46.6 (22.4\u0026ndash;78.0) months of follow-up, 266 (30.75%) deaths occurred, of which 110 (41.35%) were due to CVD. When analyzed as a continuous variable for Cox regression, TyG-BMI did not affect all-cause mortality. Categorically, patients in the High and Low groups were significantly associated with an increased risk of all-cause mortality compared with those in the Middle group according to univariate and multivariate analyses (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). TyG-BMI was significantly associated with CVD mortality, which was analyzed as a continuous variable in all three models. Only patients in the High TyG-BMI group had an increased risk of CVD mortality compared with those in the Middle group. There was no significant difference between the Low group and the Middle group (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox regression for the effects of the TyG-BMI and TyG-BMI categorical groups on all-cause mortality (n\u0026thinsp;=\u0026thinsp;865)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 to 1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 to 1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 to 1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategorical groups\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=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh group vs. Middle group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 to 2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.08 to 2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.03 to 3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow group vs. Middle group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13 to 2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.21 to 2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.10 to 3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eAbbreviations: TyG-BMI, triglyceride glucosebody mass index\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel 1: Univariate.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel 2: Adjusted for sex, age, original disease, DM, hypertension, and SBP.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel 3: Adjusted for sex, age, original disease, DM, hypertension, SBP, Hb, Alb, K, CO\u003csub\u003e2\u003c/sub\u003e, P, total Kt/V urea, nPNA, RRF, iPTH, and RAASi.\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=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox regression for the effects of the TyG-BMI and TyG-BMI categorical groups on cardiovascular mortality (n\u0026thinsp;=\u0026thinsp;865)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 to 1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 to 1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00 to 1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategorical groups\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=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh group vs. Middle group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 to 3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17 to 3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.20 to 7.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow group vs. Middle group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88 to 2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99 to 2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.74 to 5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eAbbreviations: TyG-BMI, triglyceride glucosebody mass index\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel 1: Univariate.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel 2: Adjusted for sex, age, original disease, DM, hypertension, SBP.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel 3: Adjusted for sex, age, original disease, DM, hypertension, SBP, Hb, Alb, K, CO\u003csub\u003e2\u003c/sub\u003e, P, total Kt/V urea, nPNA, RRF, iPTH, and RAASi.\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\u003eKaplan-Meier Analysis and Log-rank Test\u003c/h2\u003e \u003cp\u003eKaplan-Meier analysis and the log-rank test were performed among the three groups, and survival curves are plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For all-cause mortality, the patients in the High and Low groups had a significantly greater risk for death than did the patients in the Middle group did (p\u0026thinsp;=\u0026thinsp;0.0043) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). There was also a statistically significant difference in CVD mortality among the three groups (p\u0026thinsp;=\u0026thinsp;0.0098) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRestricted cubic spline regression revealed a robust U-shaped association between TyG-BMI and all-cause mortality, with the lowest risk observed at a significant reduction of 209.73 within the lower TyG-BMI range, followed by an increasing trend (p for nonlinearity\u0026thinsp;=\u0026thinsp;0.002). For TyG-BMI\u0026thinsp;\u0026lt;\u0026thinsp;209.73, the HR increases by 0.66 times for every one standard deviation decrease (95% CI [0.55\u0026ndash;0.80]), whereas for TyG-BMI\u0026thinsp;\u0026gt;\u0026thinsp;209.73, the HR increases by 1.36 times for every one standard deviation increase (95% CI [1.24\u0026ndash;1.49]) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). No similar relationship was evident between the TyG-BMI and cardiovascular mortality; instead, a J-shaped pattern was observed (non-linear p\u0026thinsp;=\u0026thinsp;0.024). The risk of CVD mortality remains relatively stable until the TyG-BMI reaches 206.64, after which it begins to increase rapidly (non-linear p\u0026thinsp;=\u0026thinsp;0.024). When TyG-BMI is \u0026lt;\u0026thinsp;206.64, there is no statistically significant difference in HR for every one standard deviation reduction (95% CI [0.69\u0026ndash;1.38]); when TyG-BMI is \u0026gt;\u0026thinsp;206.64, the risk increases by 1.52 times for every one standard deviation increase (95% CI [1.36\u0026ndash;1.69]) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analyses\u003c/h2\u003e \u003cp\u003eIn the sensitivity analysis, our research findings remained robust, with the exception of sex as a factor. We observed that higher TyG-BMI values were associated with increased all-cause and cardiovascular mortality in men, whereas in women, lower TyG-BMI values were linked to elevated all-cause mortality but showed no difference in cardiovascular mortality risk between the groups (p\u0026thinsp;=\u0026thinsp;0.0016, p\u0026thinsp;=\u0026thinsp;0.0046). Therefore, adjustments for comorbidities such as hypertension and diabetes were not necessary (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we observed an independent association between higher or lower TyG-BMIs and an elevated risk of all-cause mortality among the population undergoing PD. A U-shaped relationship was identified between TyG-BMI and all-cause mortality, with the inflection point at TyG-BMI\u0026thinsp;=\u0026thinsp;209.73. At this time, patients have the lowest all-cause mortality rate, whereas those above or below this threshold have increased mortality rates. A J-shaped relationship was found between TyG-BMI and CVD mortality, with the inflection point at TyG-BMI\u0026thinsp;=\u0026thinsp;206.64. The risk of CVD mortality remains relatively stable until the TyG-BMI reaches 206.64, after which it begins to increase rapidly. Sex differences were observed between the groups. Higher TyG-BMI values were associated with increased all-cause and cardiovascular mortality in men, whereas in women, lower TyG-BMI values were linked to elevated all-cause mortality but showed no difference in cardiovascular mortality risk between the groups. However, comorbidities such as hypertension and diabetes did not influence these relationships. These findings support the potential clinical utility of the TyG-BMI as a reference value and predictive marker. It is also essential to consider the modulating impact of gender differences.\u003c/p\u003e \u003cp\u003eA number of publications have reported that an elevated TyG-BMI is associated with an increased risk of cardiovascular diseases [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our findings also indicate that higher TyG-BMIs are linked to an elevated risk of all-cause and CVD mortality in individuals undergoing PD than are TyG-BMIs ranging from 189.57 to 227.15. This could be attributed to the fact that participants with high TyG-BMIs presented higher SBP, TG, and BMIs, and a higher incidence of diabetes as well as original diseases such as diabetic nephropathy and hypertensive nephropathy, contributing to increased mortality. Importantly, IR was identified as one explanation for this association (25) and is a prominent characteristic in end-stage kidney disease patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], particularly in PD patients. In PD patients, prolonged exposure to glucose solutions, which are widely used in most countries, may lead to systemic hyperglycemia and obesity while exacerbating IR due to exposure to peritoneal glucose exposure along with advanced glycation end products and bioincompatible solutions [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. On the basis of this premise, IR can induce an imbalance in glucose metabolism which subsequently triggers inflammation and oxidative stress. Furthermore, IR can stimulate increased production of free radicals and glycosylated products leading to nitric oxide (NO) inactivation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKiran et al. elucidated the U-shaped correlation between BMI and mortality by enrolling 274 Asian PD patients [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Similarly, our findings revealed that a lower TyG-BMI was associated with an increased risk of all-cause mortality compared with the intermediate TyG-BMI, which is potentially linked to poor nutritional status [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The TyG-BMI exhibited a U-shaped association with all-cause mortality in the population undergoing PD. The level associated with the lowest risk of all-cause mortality ranged from 189.57\u0026ndash;227.15. Another study involving 2,689 PD patients demonstrated that the TyG-BMI had a linear association with all-cause and CVD mortality [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Importantly, our results indicated that higher and lower levels of glucose, triglycerides or BMI could lead to poorer prognosis. These findings strongly support the need to establish target ranges for triglycerides, glucose and BMI rather than specific target levels.\u003c/p\u003e \u003cp\u003eNumerous studies have demonstrated that the TyG-BMI serves as an independent predictor of adverse cardiovascular events [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It is also closely associated with IR, which not only contributes to the development of CVD in both the CKD patients and diabetic patients but also predicts the cardiovascular prognosis of CVD patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Previous research has consistently shown a significant correlation between TyG-BMI and future CVD mortality, myocardial infarction, and stroke, indicating that insulin resistance plays a pivotal role in the pathogenesis and prognosis of cardiovascular diseases [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our study similarly revealed a significant association between TyG-BMI and cardiovascular mortality. When the two groups were compared, the high TyG-BMI group clearly presented significantly higher rates of CVD-related death than did the middle group across all three models, however, no significant difference was observed between the low TyG-BMI group and the middle TyG-BMI group.\u003c/p\u003e \u003cp\u003eInterestingly, we observed that sex has a modifying effect on the risk of mortality. This phenomenon may be attributed to hormonal disparities (33), insulin resistance, visceral adiposity, endothelial dysfunction, and chronic inflammation. Furthermore, lifestyle variations (e.g., a higher prevalence of risky health behaviors such as smoking, excessive alcohol consumption, and sedentary habits among men) contribute to increased all-cause and cardiovascular disease mortality in men with elevated TyG-BMIs. Naturally, this necessitates thorough examination and validation within a more extensive cohort of peritoneal dialysis patients.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStudy strengths and limitations\u003c/h2\u003e \u003cp\u003eIn this study, we identified an independent association between the TyG-BMI and all-cause as well as CVD mortality in a single PD center. Furthermore, we observed for the first time that the relationship between the TyG-BMI and all-cause mortality exhibited a U-shaped pattern in the population undergoing PD.\u003c/p\u003e \u003cp\u003eHowever, our study has several limitations. First, the levels of triglycerides and glucose may have been influenced by prescribed medications, which were not reported in our study, potentially introducing bias to the results. Second, data on triglycerides, glucose and BMI were only collected only once at baseline, and it remains unclear whether changes in TyG-BMI over time could impact its association with mortality. Therefore, longitudinal cohort studies are necessary to explore the persistence of the association between TyG-BMI and mortality over time. Third, we did not assess the homeostasis model assessment of insulin resistance or compare it with the TyG-BMI because of insufficient data on insulin levels during follow-up. Finally, potential residual confounding factors should be acknowledged given that this is an observational study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe relationship between the TyG-BMI and the risk of all-cause and CVD mortality in patients undergoing PD demonstrated a U-shaped and J-shaped pattern. The inflection points were identified at 209.73 and 206.64, respectively. Additionally, notable sex differences were observed in these associations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the doctors at the division of\u0026nbsp;nephrology in our hospital, Tianjin, China, for their work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study\u0026nbsp;was exempt\u0026nbsp;from requiring written informed consent because it was a retrospective medical chart review. Before analysis, the patient records and information were anonymized and de-identified. This study was conducted at the PD Center of\u0026nbsp;the\u0026nbsp;Nephrology Department in our hospital and was approved by the institutional review board (IRB) of the hospital (2015009S) and was conducted following the principles of the Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no funding\u0026nbsp;for\u0026nbsp;the present study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch idea and study design: WXC; data acquisition: JPL; data analysis: WXC; supervision or mentorship: WYZ, XCW; manuscript writing: WXC, JPL; literature retrieval: NS, YYH; interpretation of results: JPL; All authors approved it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article will be shared on reasonable request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChaudhary K, Sangha H, Khanna R. 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Epub 2015 Oct 16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Y, Wang J, Ding L, et al. Sex differences in the nonlinear association of triglyceride glucose index with all-cause and cardiovascular mortality in the general population. Diabetol Metab Syndr. 2023;15(1):136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13098-023-01117-7\u003c/span\u003e\u003cspan address=\"10.1186/s13098-023-01117-7\" 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":"Triglyceride glucose-body mass index, Peritoneal dialysis, All-cause mortality, Cardiovascular mortality, Insulin resistance","lastPublishedDoi":"10.21203/rs.3.rs-5011868/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5011868/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTriglyceride-glucose-body mass index (TyG-BMI) is a simple indicator of insulin resistance and is linked to an elevated risk of mortality. Nevertheless, limited research has explored the associations between the TyG-BMI and all-cause and cardiovascular mortality in patients undergoing peritoneal dialysis (PD).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePatients initiating PD treatment at Tianjin First Central Hospital\u0026rsquo;s nephrology department from July 2013 to February 2024 had triglycerides, fasting blood glucose, height, and weight measured at baseline and monthly during follow-up. TyG-BMI was calculated, dividing PD patients into high, middle, or low TyG-BMI groups using tri-quantile method. Cox regression analysis assessed hazard ratios (HRs) for all-cause and cardiovascular mortality among these groups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 865 patients were included. The mean TyG-BMI value for the entire study population was 212.27\u0026thinsp;\u0026plusmn;\u0026thinsp;46.64. Patients in the high group had a higher proportion of patients whose primary kidney disease was diabetic nephropathy and the greatest proportion of patients with comorbid diabetes mellitus. During the follow-up, 266 (30.75%) deaths occurred, with CVD being the dominant cause in 110 (41.35%) patients. Univariate and multivariate Cox regression analyses showed that middle group patients had a significantly lower risk of all-cause mortality compared to other groups. For CVD mortality, high group patients had a significantly greater hazard ratio than middle group, while there was no significant difference between low and middle groups. Restricted cubic spline regression revealed U-shaped association between TyG-BMI and all-cause mortality risk, as well as J-shaped association with CVD mortality, inflection points were identified at 209.73 and 206.64 respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe TyG-BMI shows U-shaped and J-shaped relationships with all-cause and CVD mortality risk, respectively, in PD patients. Additionally, significant sex differences were observed in these associations.\u003c/p\u003e","manuscriptTitle":"Correlation of the Triglyceride-Glucose-Body Mass Index with All-cause and Cardiovascular Mortality in Patients Undergoing Peritoneal Dialysis: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 10:35:39","doi":"10.21203/rs.3.rs-5011868/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":"cc348bbe-d476-41a4-82da-acb834c38365","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-12T23:08:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-18 10:35:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5011868","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5011868","identity":"rs-5011868","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}