Waist-to-Height Ratio and Metabolic, Inflammatory, and Ultrafiltration-Related Correlates in Maintenance Peritoneal Dialysis: A Cross-Sectional Study

Waist-to-Height Ratio and Metabolic, Inflammatory, and Ultrafiltration-Related Correlates in Maintenance Peritoneal Dialysis: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Waist-to-Height Ratio and Metabolic, Inflammatory, and Ultrafiltration-Related Correlates in Maintenance Peritoneal Dialysis: A Cross-Sectional Study Fangyu Yi, Ning Weng, XiaoKe Zhu, ChunXiang Huang, QiaoZhen Wu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9326819/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Objective To examine the associations of waist-to-height ratio (WHtR) with metabolic, inflammatory, ultrafiltration-related, peritoneal transport, and residual kidney function measures in maintenance peritoneal dialysis (PD), and to compare its information yield with that of other anthropometric indices. Methods This single-center cross-sectional study included 303 patients receiving maintenance PD. WHtR was analyzed continuously as the primary exposure and descriptively categorized at 0.5. Continuous outcomes were examined using Spearman correlation and hierarchical regression models; binary outcomes were examined using logistic regression. Exploratory sensitivity analyses included ordinal logistic regression for PET, false discovery rate (FDR) adjustment for multiple testing, and two-part-style analyses for zero-inflated urine volume and residual GFR. Results In the extended Model 3, each 1-SD increment in WHtR was associated with higher log(TG) [beta = 0.206, 95% CI 0.090 to 0.322], lower HDL-C [beta = -0.239, 95% CI -0.358 to -0.119], higher log(hsCRP) [beta = 0.327, 95% CI 0.211 to 0.444], and higher ultrafiltration [beta = 0.145, 95% CI 0.025 to 0.264]; these four associations remained significant after FDR adjustment. No stable independent associations were observed for PET or continuous residual kidney function measures, and ordinal logistic sensitivity analysis for PET was likewise null (Model 3 cumulative OR = 0.89, 95% CI 0.69 to 1.15). WHtR and BRI were almost completely overlapping in this cohort (Pearson r = 0.997). Single-index AUCs were low overall; for WHtR, AUCs were 0.536 (95% CI 0.480 to 0.603) for anuria, 0.542 (0.479 to 0.598) for no residual kidney function, and 0.504 (0.435 to 0.566) for high transport. Conclusions In maintenance PD, WHtR was consistently associated with metabolic, inflammatory, and ultrafiltration-related correlates, whereas its independent associations with PET and residual kidney function were limited. In this cohort, WHtR may be best viewed as a simple phenotypic surrogate with information yield similar to BRI rather than as a mechanistic determinant or stand-alone screening tool. waist-to-height ratio peritoneal dialysis residual kidney function peritoneal equilibration test anthropometric indices Figures Figure 1 Figure 2 Figure 3 1. Background Peritoneal dialysis (PD) offers important clinical advantages, including home-based treatment and relative hemodynamic stability. Nevertheless, chronic glucose exposure, weight gain, visceral fat accumulation, and volume overload are common in this population and may jointly shape metabolic status, inflammation, and clinical outcomes[ 1 – 3 ]. Prior studies suggest that obesity-related phenotypes in PD should not be interpreted simply as excess body weight, because they often coexist with fluid retention, altered fat distribution, and incident dysglycemia[ 4 ]. Anthropometric assessment has traditionally relied on body mass index (BMI), but BMI does not adequately distinguish fat distribution, muscle mass, and volume status, thereby limiting its interpretability in PD. Central adiposity measures may therefore be more informative in this setting. Among them, WHtR is especially attractive because it standardizes waist circumference to body frame and is simpler to apply at the bedside than formula-based indices such as BRI or ABSI[ 5 ]. Recent PD studies have explored visceral fat area, body composition change, and selected anthropometric indices in relation to clinical outcomes[ 6 , 7 ]. However, direct head-to-head data remain sparse, and it is still unclear whether WHtR provides information distinct from simpler waist circumference measures or more complex formula-based indices such as BRI. This gap is particularly relevant because WHtR would only be clinically advantageous if it retained similar information yield while remaining easier to obtain and interpret. Accordingly, the present study evaluated the associations of WHtR with residual kidney function, dialysis adequacy, PET, ultrafiltration, and metabolic-inflammatory markers in maintenance PD, and compared its performance with waist circumference, BMI, BRI, ABSI, and the conicity index. 2. Materials and Methods Study Design and Participants This single-center cross-sectional study was conducted at Hangzhou Hospital of Traditional Chinese Medicine between May 2023 and May 2025. Patients undergoing regular follow-up at the PD center were screened. Inclusion criteria were age ≥ 18 years, receipt of maintenance PD, availability of concurrent anthropometric measurements, and at least one dialysis-related outcome within the predefined time window. Exclusion criteria included active peritonitis or other acute infection, recent hospitalization or major physiologic stress, transfer to another kidney replacement modality before the study index date, and missing key exposure data. The study was approved by the Research Ethics Committee of Hangzhou Hospital of Traditional Chinese Medicine, approval number 2023KLL036. The requirement for written informed consent was waived according to the approved ethics record. Variable Collection and Time Window All variables were extracted from the clinical database and medical records. Waist circumference, height, body weight, and blood pressure were obtained from the outpatient or inpatient assessment closest to the study date. Urine volume, ultrafiltration, Kt/V, creatinine clearance rate (CCR), residual GFR, prescription-related variables, and laboratory indices were collected within the same observation window nearest to anthropometry, defined as within 30 days. PET score was obtained from the most recent standardized PET examination within 90 days so that the exposure and outcome measurements remained clinically proximate. The main outcomes included urine volume, residual GFR, CCR, Kt/V, PET score, ultrafiltration, daily glucose exposure, mean prescription glucose concentration, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), albumin, and high-sensitivity C-reactive protein (hsCRP). PET scores of 1 to 4 represented low, low-average, high-average, and high transport, respectively; high transport was defined as PET score ≥ 3. Anuria was defined as urine volume < 100 mL/day according to clinical convention. PET was analyzed as both an ordered 4-level variable in sensitivity analysis and an approximate continuous variable in the primary models for comparability across outcomes. Anthropometric and Prescription Calculations WHtR was calculated as waist circumference divided by height. BMI was calculated as weight divided by height squared. BRI, ABSI, and the conicity index were derived according to previously published equations or methodologic studies[ 8 – 10 ]. Because a WHtR threshold of 0.5 is easy to interpret clinically, it was used for descriptive grouping; all regression analyses treated WHtR as a continuous variable standardized to 1 SD. Mean prescription glucose concentration was calculated as the glucose-equivalent mean concentration: the total glucose mass from all glucose-containing PD bags was summed and divided by the total prescribed dialysate volume, then converted to a percentage concentration. The denominator included glucose-free icodextrin solution. For example, if the prescription consisted of 1.5% glucose solution 2 L x2 bags, 2.5% glucose solution 2 L x1 bag, and 7.5% icodextrin 2 L x1 bag, the total glucose mass would be 60 g + 50 g = 110 g, the total dialysate volume would be 8,000 mL, and the glucose-equivalent mean concentration would be 1.375%. Statistical Analysis Baseline characteristics were compared between the WHtR < 0.5 and WHtR ≥ 0.5 groups. Normally distributed continuous variables are presented as mean ± standard deviation, skewed variables as median [interquartile range], and categorical variables as number (%). Group comparisons were performed using the t test, Mann-Whitney U test, or chi-square test, as appropriate. Spearman correlation analyses were first performed for continuous outcomes, followed by linear regression models reporting standardized beta coefficients and 95% confidence intervals (CIs). Model 1 adjusted for age, sex, and PD duration; Model 2 additionally adjusted for systolic blood pressure, automated PD, icodextrin use, edema score, and lipid-lowering drug use; Model 3 was specified as an extended exploratory adjustment set that additionally included albumin and log(hsCRP). When log(hsCRP) was the outcome, it was not included as its own covariate. PET was treated as an approximate continuous outcome in the primary models to preserve comparability across multiple endpoints, and ordinal logistic regression was performed as a sensitivity analysis. Binary outcomes were assessed using logistic regression and are reported as odds ratios (ORs) and 95% CIs per 1-SD increase in WHtR. Because urine volume and residual GFR showed marked zero inflation, we used a two-part-style sensitivity framework that combined binary models for anuria or absent residual kidney function with log-linear models among non-anuric participants (urine volume ≥ 100 mL/day) or those with residual GFR > 0. Given the moderate sample size, multiple endpoints, and multivariable models, all adjusted analyses were interpreted as exploratory rather than definitive evidence of independence. Missing data were handled using complete-case analysis. PET was missing in 10 (3.3%) participants; missingness of the other variables was low. Complete-case sample sizes, missingness, and collinearity diagnostics are shown in Supplementary Tables S1A, S1B, and S3. Exploratory FDR sensitivity analyses were additionally performed for the main WHtR associations. A two-sided P value < 0.05 was considered statistically significant. 3. Results Baseline Characteristics A total of 303 patients receiving maintenance PD were included, of whom 114 had WHtR < 0.5 and 189 had WHtR ≥ 0.5. Compared with the lower-WHtR group, the higher-WHtR group was older and had greater waist circumference, BMI, BRI, and ABSI. The higher-WHtR group also showed higher TG and hsCRP, lower HDL-C, a higher prevalence of edema, and higher ultrafiltration, whereas CCR was lower. PET score, Kt/V, continuous urine volume, and continuous residual GFR did not differ significantly between the groups (Table 1 ). Table 1 Baseline characteristics overall and by WHtR category Variable Total (n = 303) WHtR < 0.5 (n = 114) WHtR ≥ 0.5 (n = 189) Statistical P value Age, years 55.38 ± 11.80 51.89 ± 12.58 57.49 ± 10.80 t test < 0.001 Male sex, n (%) 150 (49.5) 54 (47.4) 96 (50.8) Chi-square 0.646 PD duration, months 47.00 [25.00, 85.50] 50.00 [24.00, 95.50] 46.00 [25.00, 82.00] Mann-Whitney U 0.509 Waist circumference, cm 85.07 ± 10.46 75.70 ± 6.16 90.72 ± 8.22 t test < 0.001 BMI, kg/m² 22.52 ± 3.36 19.86 ± 1.85 24.13 ± 3.03 t test < 0.001 Body roundness index (BRI) 3.94 ± 1.30 2.74 ± 0.47 4.66 ± 1.09 t test < 0.001 A body shape index (ABSI) 0.084 ± 0.005 0.081 ± 0.004 0.086 ± 0.004 t test < 0.001 Albumin, g/L 33.92 ± 4.23 33.84 ± 4.35 33.97 ± 4.17 t test 0.801 Triglycerides, mmol/L 1.33 [0.98, 1.95] 1.18 [0.93, 1.64] 1.45 [1.05, 2.08] Mann-Whitney U 0.002 HDL-C, mmol/L 1.04 ± 0.24 1.10 ± 0.24 1.00 ± 0.23 t test < 0.001 hsCRP, mg/L 2.20 [0.87, 6.14] 1.25 [0.59, 4.58] 2.74 [1.21, 7.03] Mann-Whitney U < 0.001 Systolic blood pressure, mmHg 134.63 ± 22.43 134.80 ± 23.45 134.54 ± 21.85 t test 0.924 Lipid-lowering drug use, n (%) 229 (75.6) 77 (67.5) 152 (80.4) Chi-square 0.017 Icodextrin use, n (%) 164 (54.1) 54 (47.4) 110 (58.2) Chi-square 0.086 APD, n (%) 23 (7.6) 6 (5.3) 17 (9.0) Chi-square 0.335 Any edema, n (%) 151 (49.8) 42 (36.8) 109 (57.7) Chi-square < 0.001 Ultrafiltration, mL/day 547.50 [257.00, 845.25] 470.00 [251.25, 700.00] 638.00 [268.75, 912.50] Mann-Whitney U 0.004 Urine volume, mL/day 200.00 [0.00, 650.00] 300.00 [0.00, 650.00] 100.00 [0.00, 650.00] Mann-Whitney U 0.332 Residual GFR, mL/min 0.49 [0.00, 2.44] 0.74 [0.00, 2.83] 0.00 [0.00, 2.23] Mann-Whitney U 0.165 CCR, L/week/1.73 m² 55.77 [47.40, 67.44] 59.06 [50.45, 71.02] 53.97 [46.70, 65.22] Mann-Whitney U 0.013 PET score 2.00 [2.00, 3.00] 2.00 [2.00, 3.00] 2.00 [2.00, 3.00] Mann-Whitney U 0.802 Kt/V 2.06 ± 0.43 2.12 ± 0.47 2.03 ± 0.40 t test 0.093 Daily glucose exposure, g/day 90.00 [69.00, 120.00] 90.00 [64.29, 115.53] 90.00 [75.00, 120.00] Mann-Whitney U 0.280 Mean prescription glucose concentration, % 1.44 [1.13, 1.50] 1.50 [1.13, 1.50] 1.39 [1.13, 1.50] Mann-Whitney U 0.746 Continuous variables are shown as mean ± SD or median [IQR], and categorical variables as n (%). Statistical methods are listed for each comparison. ABSI, a body shape index; APD, automated peritoneal dialysis; BRI, body roundness index; CCR, creatinine clearance rate; HDL-C, high-density lipoprotein cholesterol; hsCRP, high-sensitivity C-reactive protein; IQR, interquartile range; PET, peritoneal equilibration test; WHtR, waist-to-height ratio. Associations Between WHtR and Continuous Outcomes The clearest continuous associations of WHtR were with TG, HDL-C, hsCRP, and ultrafiltration. Correlations with PET score, urine volume, and residual GFR were weak or absent, whereas correlations with CCR, Kt/V, and daily glucose exposure attenuated after covariate adjustment (Table 2 ). In the extended Model 3, each 1-SD increment in WHtR was associated with higher log(TG) [0.206 (0.090, 0.322)], lower HDL-C [-0.239 (-0.358, -0.119)], higher log(hsCRP) [0.327 (0.211, 0.444)], and higher ultrafiltration [0.145 (0.025, 0.264)]. In exploratory FDR sensitivity analyses, these four associations remained significant (q = 0.002, q < 0.001, q < 0.001, and q = 0.048, respectively). By contrast, PET was not associated with WHtR in either the primary linear model or ordinal logistic sensitivity analysis (Model 3 cumulative OR = 0.89, 95% CI 0.69 to 1.15; P = 0.374). No stable independent associations were observed for urine volume, residual GFR, mean prescription glucose concentration, or PET score (Table 2 and Fig. 1 ). Table 2 WHtR and continuous PD-related outcomes Outcome Spearman rho P value Model 1 beta (95% CI) Model 1 P Model 2 beta (95% CI) Model 2 P Model 3 beta (95% CI) Model 3 P log(TG) 0.245 < 0.001 0.248 (0.135, 0.361) < 0.001 0.277 (0.159, 0.395) < 0.001 0.206 (0.090, 0.322) < 0.001 HDL-C -0.276 < 0.001 -0.305 (-0.417, -0.194) < 0.001 -0.288 (-0.404, -0.171) < 0.001 -0.239 (-0.358, -0.119) < 0.001 log(hsCRP) 0.318 < 0.001 0.310 (0.200, 0.420) < 0.001 0.312 (0.194, 0.430) < 0.001 0.327 (0.211, 0.444) < 0.001 Ultrafiltration 0.189 < 0.001 0.212 (0.099, 0.326) < 0.001 0.159 (0.046, 0.272) 0.006 0.145 (0.025, 0.264) 0.018 CCR -0.175 0.002 -0.180 (-0.293, -0.067) 0.002 -0.132 (-0.249, -0.015) 0.028 -0.091 (-0.214, 0.031) 0.143 Kt/V -0.075 0.191 -0.175 (-0.277, -0.073) < 0.001 -0.133 (-0.240, -0.026) 0.015 -0.095 (-0.207, 0.018) 0.100 Daily glucose exposure 0.057 0.321 0.139 (0.028, 0.250) 0.015 0.171 (0.055, 0.288) 0.004 0.121 (-0.001, 0.243) 0.051 Mean prescription glucose concentration -0.063 0.272 0.003 (-0.113, 0.120) 0.954 0.087 (-0.018, 0.193) 0.105 0.052 (-0.057, 0.160) 0.350 PET score 0.013 0.831 -0.006 (-0.124, 0.112) 0.925 -0.093 (-0.215, 0.028) 0.130 -0.055 (-0.177, 0.068) 0.380 Urine volume -0.052 0.363 -0.048 (-0.152, 0.057) 0.368 -0.006 (-0.110, 0.098) 0.910 0.044 (-0.064, 0.151) 0.422 Residual GFR -0.082 0.157 -0.087 (-0.197, 0.022) 0.117 -0.026 (-0.135, 0.083) 0.638 -0.005 (-0.117, 0.106) 0.924 Standardized beta coefficients and 95% CIs are shown. Model 1 adjusted for age, sex, and PD duration; Model 2 additionally adjusted for systolic blood pressure, APD, icodextrin use, edema score, and lipid-lowering drug use; Model 3 additionally included albumin and log(hsCRP). When log(hsCRP) was the outcome, it was not entered as a covariate. TG and hsCRP were log-transformed. PET was analyzed as an approximate continuous outcome in the primary models, with ordinal logistic regression as sensitivity analysis. CI, confidence interval; hsCRP, high-sensitivity C-reactive protein; PET, peritoneal equilibration test; TG, triglycerides. Points show standardized beta coefficients and horizontal lines show 95% CIs. Red denotes positive associations and blue denotes negative associations; the dashed vertical line marks beta = 0. CI, confidence interval; PD, peritoneal dialysis; WHtR, waist-to-height ratio. Head-to-Head Comparison with Other Anthropometric Indices Under the uniformly adjusted Model 2, waist circumference, WHtR, and BRI showed similar association patterns across continuous outcomes, with mean absolute beta values of 0.208, 0.196, and 0.196, respectively, and seven statistically significant outcomes each. Because WHtR and BRI were almost collinear in this cohort (Pearson r = 0.997), this comparison should be interpreted mainly from the standpoint of simplicity rather than superiority. In this dataset, WHtR appeared to be a simpler surrogate with information yield similar to BRI ( Table 3 and Fig. 2 ) . Table 3 Continuous outcome comparison across anthropometric indices Outcome WHtR Waist circumference BMI BRI ABSI Conicity index log(TG) 0.277** 0.288** 0.226** 0.282** 0.188** 0.255** HDL-C -0.288** -0.284** -0.255** -0.279** -0.141* -0.228** log(hsCRP) 0.312** 0.311** 0.238** 0.315** 0.218** 0.292** Ultrafiltration 0.159** 0.178** 0.160** 0.151** 0.057 0.118* CCR -0.132* -0.148* -0.168** -0.135* 0.004 -0.061 Kt/V -0.133* -0.159** -0.183** -0.142** 0.015 -0.054 Daily glucose exposure 0.171** 0.204** 0.196** 0.172** 0.031 0.108 PET score -0.093 -0.093 -0.109 -0.096 0.002 -0.041 Mean |beta| 0.196 0.208 0.192 0.196 0.082 0.144 No. of significant outcomes 7 7 7 7 3 4 Standardized beta coefficients from Model 2 are shown. Mean absolute beta and the number of significant outcomes are descriptive summaries only and do not imply formal ranking. ABSI, a body shape index; BMI, body mass index; BRI, body roundness index; PET, peritoneal equilibration test; WHtR, waist-to-height ratio. Points show standardized beta coefficients and horizontal lines show 95% CIs under Model 2. Filled circles denote P < 0.05 and open circles denote P ≥ 0.05. ABSI, a body shape index; BRI, body roundness index; CI, confidence interval; PD, peritoneal dialysis; WHtR, waist-to-height ratio. Sensitivity Analyses for Zero Inflation For anuria, defined as urine volume < 100 mL/day, and for absent residual kidney function, WHtR was associated with higher odds in Model 1 (anuria: OR = 1.37 (1.02, 1.83); no residual kidney function: OR = 1.40 (1.05, 1.88)), but these associations attenuated and were no longer significant in Models 2 and 3. In this dataset, no urine volume values between 1 and 99 mL/day were recorded, so the clinical anuria definition coincided with the recorded 0-mL category. WHtR was not significantly associated with high transport in any model. In exploratory two-part-style sensitivity analyses, WHtR was not associated with log urine volume among non-anuric patients (urine volume ≥ 100 mL/day) or with log residual GFR among patients with residual GFR > 0 ( Supplementary Table S2 ). The discrimination performance of all single anthropometric indices was weak. For WHtR, the AUCs were 0.536 (95% CI 0.480 to 0.603) for anuria, 0.542 (0.479 to 0.598) for no residual kidney function, and 0.504 (0.435 to 0.566) for high transport. Even the numerically highest exploratory AUCs were low, reaching 0.596 (95% CI 0.536 to 0.664) for anuria, 0.599 (0.538 to 0.655) for no residual kidney function, and 0.562 (0.493 to 0.634) for high transport, all with ABSI. These results argue against any strong discriminative advantage of a single anthropometric index (Table 4 and Fig. 3 ). Table 4 Binary outcomes and exploratory discrimination by WHtR Outcome Events/Total Model 1 OR (95% CI) Model 1 P Model 2 OR (95% CI) Model 2 P Model 3 OR (95% CI) Model 3 P WHtR AUC (95% CI) Highest exploratory AUC (index; 95% CI) Anuria (< 100 mL/day) 140/303 1.37 (1.02, 1.83) 0.035 1.22 (0.89, 1.67) 0.214 1.05 (0.75, 1.47) 0.785 0.536 (0.480, 0.603) 0.596 (ABSI; 0.536, 0.664) No residual kidney function 142/303 1.40 (1.05, 1.88) 0.023 1.28 (0.93, 1.76) 0.132 1.11 (0.79, 1.57) 0.534 0.542 (0.479, 0.598) 0.599 (ABSI; 0.538, 0.655) High transport 133/303 0.97 (0.76, 1.23) 0.783 0.83 (0.64, 1.08) 0.175 0.92 (0.69, 1.23) 0.593 0.504 (0.435, 0.566) 0.562 (ABSI; 0.493, 0.634) Odds ratios are reported per 1-SD increase in WHtR. Anuria was defined as urine volume < 100 mL/day; no values between 1 and 99 mL/day were recorded. AUCs with 95% CIs are exploratory single-index discrimination measures, and no formal DeLong comparisons were performed. ABSI, a body shape index; AUC, area under the receiver operating characteristic curve; CI, confidence interval; WHtR, waist-to-height ratio. Bars show single-index AUCs for anuria, absent residual kidney function, and high transport; the dashed vertical line marks AUC = 0.50. ABSI, a body shape index; AUC, area under the receiver operating characteristic curve; BRI, body roundness index; WHtR, waist-to-height ratio. 4. Discussion Associations of WHtR with metabolic, inflammatory, and ultrafiltration-related parameters In this study, WHtR showed the most consistent associations with metabolic, inflammatory, and ultrafiltration-related parameters in patients receiving maintenance PD. In the fully adjusted model, each 1-SD increase in WHtR was associated with higher TG, lower HDL-C, higher hsCRP, and greater ultrafiltration, and these associations remained significant after FDR correction. Taken together, these findings suggest that, in PD patients, WHtR may reflect a central adiposity-related phenotype characterized by dyslipidemia, low-grade inflammation, and greater fluid-management burden, rather than serving as a broad marker of all PD-related outcomes. This pattern is clinically plausible. Long-term exposure to glucose-based dialysate is often accompanied by weight gain, visceral fat accumulation, and metabolic disturbance in PD patients[ 11 , 12 ]. Molecular studies have further shown that adipose- and plasma-derived microRNA profiles are linked to obesity, insulin resistance, and new-onset diabetes after PD[ 13 ].These observations support the view that WHtR captures more than body size alone and may instead track a broader metabolic phenotype. The inflammatory and vascular implications are also relevant. In PD populations, greater visceral or abdominal fat accumulation has been associated with arterial stiffness and endothelial dysfunction[ 14 ],while abdominal adiposity has also been linked to inflammation and metabolic syndrome[ 15 ].Accordingly, the associations of higher WHtR with higher hsCRP and TG and lower HDL-C in our cohort are consistent with a state of central adiposity-related cardiometabolic stress rather than an isolated anthropometric finding. The association with ultrafiltration should be interpreted in the same clinical context. Previous studies suggest that PD prescription intensity, body composition, and patient outcomes are closely interrelated[ 16 , 17 ].Evidence from studies on intraperitoneal pressure and segmental bioelectrical impedance also indicates that abdominal body habitus is closely linked to fluid assessment in routine PD practice[ 18 ].Longitudinal data further show that extracellular water imbalance is associated with adverse outcomes[ 19 ].Taken together, these findings support the interpretation that WHtR may be better understood as a marker of treatment complexity and volume-related burden, rather than a direct determinant of ultrafiltration requirement. Limited associations with PET and residual kidney function By contrast, the associations of WHtR with PET and residual kidney function were limited and not robust. PET score was not significantly associated with WHtR in the primary analysis, and this null finding remained unchanged in the ordinal logistic sensitivity analysis. This negative result is clinically informative. PET mainly reflects intrinsic peritoneal membrane transport characteristics, whereas WHtR is more likely to represent a patient-level phenotype related to adiposity, metabolic status, and fluid burden. The absence of a stable association therefore suggests that WHtR should not be regarded as a surrogate marker of peritoneal transport behavior. The findings for residual kidney function also warrant caution. The associations with anuria and absent residual kidney function were attenuated after more comprehensive adjustment, and no graded association was identified in the two-part sensitivity analyses among patients who retained urine output or residual GFR. This differs from findings in non-PD CKD populations, where WHtR and other indices of central obesity have been associated with CKD risk and kidney function decline[ 20 – 22 ].Similar observations have been reported in NHANES and in large Chinese cohorts examining anthropometric indices and kidney outcomes[ 23 ]. One possible explanation is that residual kidney function in established PD is influenced not only by body habitus, but also by dialysate exposure, prescription intensity, and volume status[ 17 ].Once PD is established, the contribution of anthropometric measures to residual kidney function may therefore be diluted by treatment-related and disease-related factors. Comparison with other anthropometric indices and clinical implications From a comparative perspective, WHtR appears to offer practical advantages over other anthropometric indices. WHtR has been proposed as a simple screening measure for cardiometabolic risk because it standardizes waist circumference to height while remaining easy to obtain[ 5 ]. BRI, ABSI, and the conicity index were developed to characterize body shape and central adiposity more explicitly, although these measures overlap to some extent in the information they capture[ 24 , 25 ]. In PD cohorts, previous studies have mainly focused on BRI or selected anthropometric indices in relation to outcomes or sarcopenia, and direct head-to-head comparisons remain limited[ 26 , 27 ]. In our cohort, waist circumference, WHtR, and BRI showed broadly similar association spectra across continuous outcomes, and WHtR and BRI were almost completely overlapping. This pattern is consistent with the close geometric relation between WHtR and BRI[ 24 ], and suggests that the additional computational complexity of BRI may provide little incremental value in this setting. The AUC findings should also be interpreted conservatively. All single-index AUCs were low, which does not support the use of any anthropometric index as a strong stand-alone discrimination tool in PD patients. From a clinical perspective, the value of WHtR may therefore lie more in bedside risk phenotyping than in formal prediction. A higher WHtR may prompt closer attention to TG, HDL-C, inflammatory activity, edema or volume status, dialysate glucose exposure, and prescription intensity. It should not replace PET, direct volume measurements, or comprehensive assessment of residual kidney function, but it may serve as a simple entry point for identifying patients with a less favorable cardiometabolic-volume profile. Overall, our findings suggest that WHtR is best interpreted as a practical marker of central adiposity-related metabolic and volume burden in maintenance PD, rather than as a surrogate for peritoneal membrane transport or residual kidney function. Future prospective multicenter studies with richer confounder data and longitudinal follow-up are needed to determine whether WHtR has value in long-term risk stratification and treatment monitoring in PD. Conclusions Among patients receiving maintenance PD, WHtR was most consistently associated with TG, HDL-C, hsCRP, and ultrafiltration, whereas associations with PET and residual kidney function were limited and not robust after broader adjustment. In this cohort, WHtR may be best viewed as a simple phenotypic surrogate with information yield similar to BRI, rather than as a mechanistic determinant or stand-alone predictive tool. Prospective multicenter studies with richer confounder assessment are needed to define its role in PD risk phenotyping. Abbreviations ABSI a body shape index APD automated peritoneal dialysis AUC area under the receiver operating characteristic curve BRI body roundness index CCR creatinine clearance rate CI confidence interval HDL-C high-density lipoprotein cholesterol hsCRP high-sensitivity C-reactive protein IQR interquartile range PD peritoneal dialysis PET peritoneal equilibration test TG triglycerides WHtR waist-to-height ratio. Declarations Author Contributions: Conceptualization, Ning Weng; methodology, Xiaoke Zhu; formal analysis, Chunxiang Huang; investigation, Fangyu Yi; resources, Qiaozhen Wu; data curation, Dan Liu; writing—original draft preparation, Fangyu Yi; writing—review and editing, Fangyu Yi; visualization, Ning Weng; supervision, Ning Weng; project administration, Ning Weng; funding acquisition, Yuexing Huang. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by The Leading Goose Project of Zhejiang Provincial Department of Science and Technology (Grant Nos. 2025C02191). Data Availability Statement: All data are provided in the manuscript. Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results. Ethics Statement: This study was approved by the Research Ethics Committee of Hangzhou Hospital of Traditional Chinese Medicine (Approval No. 2023KLL036). The requirement for written informed consent was waived according to the approved ethics record. All procedures were conducted in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki. References Ng J, Than W, Szeto C. Obesity, Weight Gain, and Fluid Overload in Peritoneal Dialysis. Front Nephrol. 2022;2:880097. Ikeda M, et al. Effect of Long-term Peritoneal Dialysis on Change in Visceral Fat Area: A Single-Center Experience. Indian J Nephrol. 2020;30(6):398–402. Than W, et al. The change in the prevalence of obesity and new-onset diabetes in Chinese peritoneal dialysis patients over 25 years. Clin Kidney J. 2022;15(1):70–8. Chan G, et al. Adipose and Plasma microRNAs miR-221 and 222 Associate with Obesity, Insulin Resistance, and New Onset Diabetes after Peritoneal Dialysis. Nutrients. 2022;14(22):4889. Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012;13(3):275–86. Xu L, et al. The Impact of Change in Body Roundness Index on the Clinical Outcome of New Peritoneal Dialysis Patients. Kidney Dis (Basel). 2025;11(1):674–83. Do J, Kang S. Comparison of various indices for predicting sarcopenia and its components in patients receiving peritoneal dialysis. Sci Rep. 2022;12(1):14102. Krakauer N, Krakauer J. A new body shape index predicts mortality hazard independently of body mass index. PLoS ONE. 2012;7(7):e39504. Thomas D, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obes (Silver Spring). 2013;21(11):2264–71. Nkwana M, Monyeki K, Lebelo S. Body Roundness Index, A Body Shape Index, Conicity Index, and Their Association with Nutritional Status and Cardiovascular Risk Factors in South African Rural Young Adults. Int J Environ Res Public Health. 2021;18(1):281. Ng JK, Than WH, Szeto CC. Obesity, Weight Gain, and Fluid Overload in Peritoneal Dialysis. Front Nephrol. 2022;2:880097. Than WH, et al. The change in the prevalence of obesity and new-onset diabetes in Chinese peritoneal dialysis patients over 25 years. Clin Kidney J. 2022;15(1):70–8. Chan GCK et al. Adipose and Plasma microRNAs miR-221 and 222 Associate with Obesity, Insulin Resistance, and New Onset Diabetes after Peritoneal Dialysis. Nutrients, 2022. 14(22). Lee MJ, et al. Visceral fat thickness is associated with carotid atherosclerosis in peritoneal dialysis patients. Obes (Silver Spring). 2012;20(6):1301–7. de Mattos AM, et al. Association of body fat with inflammation in peritoneal dialysis. Inflammation. 2013;36(3):689–95. Verger C, et al. Association of Prescription With Body Composition and Patient Outcomes in Incident Peritoneal Dialysis Patients. Front Med (Lausanne). 2021;8:737165. Ng JK et al. The Impact of Volume Overload on the Longitudinal Change of Adipose and Lean Tissue Mass in Incident Chinese Peritoneal Dialysis Patients. Nutrients, 2022. 14(19). Li X, et al. Novel equations for estimating intraperitoneal pressure among peritoneal dialysis patients. Clin Kidney J. 2023;16(9):1447–56. Chao HN, et al. Time-Dependent Changes in Extracellular-to-Intracellular Water Ratios and Risk of All-Cause Mortality in Peritoneal Dialysis Patients: A Single-Center Retrospective Cohort Study. Kidney Med. 2026;8(1):101171. Blaslov K, Bulum T, Duvnjak L. Waist-to-height ratio is independently associated with chronic kidney disease in overweight type 2 diabetic patients. Endocr Res. 2015;40(4):194–8. Liu L, et al. Waist height ratio predicts chronic kidney disease: a systematic review and meta-analysis, 1998–2019. Arch Public Health. 2019;77:55. Memarian E, et al. The risk of chronic kidney disease in relation to anthropometric measures of obesity: A Swedish cohort study. BMC Nephrol. 2021;22(1):330. Zhu F, et al. Association of Obesity-Related Indices with Rapid Kidney Function Decline and Chronic Kidney Disease: A Study from a Large Longitudinal Cohort in China. Obes Facts. 2025;18(5):445–58. Thomas DM, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obes (Silver Spring). 2013;21(11):2264–71. Nkwana MR, Monyeki KD, Lebelo SL. Body Roundness Index, A Body Shape Index, Conicity Index, and Their Association with Nutritional Status and Cardiovascular Risk Factors in South African Rural Young Adults. Int J Environ Res Public Health, 2021. 18(1). Xu L, et al. The Impact of Change in Body Roundness Index on the Clinical Outcome of New Peritoneal Dialysis Patients. Kidney Dis (Basel). 2025;11(1):674–83. Do JY, Kang SH. Comparison of various indices for predicting sarcopenia and its components in patients receiving peritoneal dialysis. Sci Rep. 2022;12(1):14102. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 19 Apr, 2026 Editor invited by journal 08 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 05 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9326819","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627833558,"identity":"c6ceddc8-6de1-4244-847a-c5b1802c0525","order_by":0,"name":"Fangyu Yi","email":"","orcid":"","institution":"Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fangyu","middleName":"","lastName":"Yi","suffix":""},{"id":627833559,"identity":"0729c1f7-dfa6-4859-8a0d-c6b8fb15eb79","order_by":1,"name":"Ning Weng","email":"","orcid":"","institution":"Hangzhou Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Weng","suffix":""},{"id":627833560,"identity":"8a07e4e1-bf13-4b0b-aa5d-e19c642537ab","order_by":2,"name":"XiaoKe Zhu","email":"","orcid":"","institution":"Hangzhou Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"XiaoKe","middleName":"","lastName":"Zhu","suffix":""},{"id":627833568,"identity":"e9576ece-5bbd-4968-acbb-910c425873d8","order_by":3,"name":"ChunXiang Huang","email":"","orcid":"","institution":"Hangzhou Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"ChunXiang","middleName":"","lastName":"Huang","suffix":""},{"id":627833569,"identity":"3d68863d-ef64-4d88-be89-2fa85dae546e","order_by":4,"name":"QiaoZhen Wu","email":"","orcid":"","institution":"Hangzhou Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"QiaoZhen","middleName":"","lastName":"Wu","suffix":""},{"id":627833583,"identity":"629781aa-1854-45ac-9831-46a1ab68c9a1","order_by":5,"name":"Dan Liu","email":"","orcid":"","institution":"Hangzhou Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Liu","suffix":""},{"id":627833584,"identity":"656f5d9f-8d20-4b44-afe4-7fa0d88c1a41","order_by":6,"name":"Yuexing Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACNv7+hw8+8NjIybM3H3yQUFFDWAufxBlmwxkyacaGPceSDR6cOUZYixxDDps0j83hRIYbOWaSD1uYiXAYw9nDhjNyDicwNiSYVSQ2sDHwt3cn4NfC3Jf44MOZ9Dx2hgNpNxJ3yDBInDm7gYAtB4wNZ/ZYFzM2Nhy7kXiGjcFAIpeQlgQzad5/zIkNhxnbChLbmInRkmMmzcPjnNhwjJmNgTgtEseSDWfwgAKZjVki4cwxHoJ+ke8HxiA4KuXff/z4o6JGjr+9F78WDMBDmvJRMApGwSgYBVgBAF3+TGPR7dtgAAAAAElFTkSuQmCC","orcid":"","institution":"Hangzhou Hospital of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yuexing","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2026-04-05 14:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9326819/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9326819/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107918339,"identity":"4c64c2d0-7e7f-4b8a-8bbe-c2806a7fc4d1","added_by":"auto","created_at":"2026-04-27 14:27:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":242850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWHtR associations with major continuous outcomes in Model 3.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePoints show standardized beta coefficients and horizontal lines show 95% CIs. Red denotes positive associations and blue denotes negative associations; the dashed vertical line marks beta = 0. CI, confidence interval; PD, peritoneal dialysis; WHtR, waist-to-height ratio.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9326819/v1/007025c1c04370e1096b5ba3.png"},{"id":107918341,"identity":"2e18ebfa-7da4-4777-ad6d-e8cfcabe699f","added_by":"auto","created_at":"2026-04-27 14:27:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":438859,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eContinuous outcome comparison across anthropometric indices.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePoints show standardized beta coefficients and horizontal lines show 95% CIs under Model 2. Filled circles denote P\u0026lt;0.05 and open circles denote P≥0.05. ABSI, a body shape index; BRI, body roundness index; CI, confidence interval; PD, peritoneal dialysis; WHtR, waist-to-height ratio.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9326819/v1/c575afd136b95db60d59abb2.png"},{"id":107918373,"identity":"cbf6b7c1-587a-4f8b-88c1-28290ee5a384","added_by":"auto","created_at":"2026-04-27 14:27:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":143659,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExploratory discrimination across anthropometric indices.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBars show single-index AUCs for anuria, absent residual kidney function, and high transport; the dashed vertical line marks AUC = 0.50. ABSI, a body shape index; AUC, area under the receiver operating characteristic curve; BRI, body roundness index; WHtR, waist-to-height ratio.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9326819/v1/2f960eb8a33edc7f476e6257.png"},{"id":108007541,"identity":"8ec9502a-e213-40f3-b396-863711acde12","added_by":"auto","created_at":"2026-04-28 13:00:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1250375,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9326819/v1/933cbe96-90c5-47a2-a2ca-88b8cc70d4c8.pdf"},{"id":107918340,"identity":"f1f08b56-a651-4809-96ae-5429cf2c7e6c","added_by":"auto","created_at":"2026-04-27 14:27:50","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21067,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9326819/v1/6f74301db9c6a867b2adc282.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Waist-to-Height Ratio and Metabolic, Inflammatory, and Ultrafiltration-Related Correlates in Maintenance Peritoneal Dialysis: A Cross-Sectional Study","fulltext":[{"header":"1. Background","content":"\u003cp\u003ePeritoneal dialysis (PD) offers important clinical advantages, including home-based treatment and relative hemodynamic stability. Nevertheless, chronic glucose exposure, weight gain, visceral fat accumulation, and volume overload are common in this population and may jointly shape metabolic status, inflammation, and clinical outcomes[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Prior studies suggest that obesity-related phenotypes in PD should not be interpreted simply as excess body weight, because they often coexist with fluid retention, altered fat distribution, and incident dysglycemia[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnthropometric assessment has traditionally relied on body mass index (BMI), but BMI does not adequately distinguish fat distribution, muscle mass, and volume status, thereby limiting its interpretability in PD. Central adiposity measures may therefore be more informative in this setting. Among them, WHtR is especially attractive because it standardizes waist circumference to body frame and is simpler to apply at the bedside than formula-based indices such as BRI or ABSI[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent PD studies have explored visceral fat area, body composition change, and selected anthropometric indices in relation to clinical outcomes[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, direct head-to-head data remain sparse, and it is still unclear whether WHtR provides information distinct from simpler waist circumference measures or more complex formula-based indices such as BRI. This gap is particularly relevant because WHtR would only be clinically advantageous if it retained similar information yield while remaining easier to obtain and interpret.\u003c/p\u003e \u003cp\u003eAccordingly, the present study evaluated the associations of WHtR with residual kidney function, dialysis adequacy, PET, ultrafiltration, and metabolic-inflammatory markers in maintenance PD, and compared its performance with waist circumference, BMI, BRI, ABSI, and the conicity index.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e \u003cb\u003eStudy Design and Participants\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis single-center cross-sectional study was conducted at Hangzhou Hospital of Traditional Chinese Medicine between May 2023 and May 2025. Patients undergoing regular follow-up at the PD center were screened. Inclusion criteria were age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, receipt of maintenance PD, availability of concurrent anthropometric measurements, and at least one dialysis-related outcome within the predefined time window. Exclusion criteria included active peritonitis or other acute infection, recent hospitalization or major physiologic stress, transfer to another kidney replacement modality before the study index date, and missing key exposure data.\u003c/p\u003e \u003cp\u003e The study was approved by the Research Ethics Committee of Hangzhou Hospital of Traditional Chinese Medicine, approval number 2023KLL036. The requirement for written informed consent was waived according to the approved ethics record.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVariable Collection and Time Window\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll variables were extracted from the clinical database and medical records. Waist circumference, height, body weight, and blood pressure were obtained from the outpatient or inpatient assessment closest to the study date. Urine volume, ultrafiltration, Kt/V, creatinine clearance rate (CCR), residual GFR, prescription-related variables, and laboratory indices were collected within the same observation window nearest to anthropometry, defined as within 30 days. PET score was obtained from the most recent standardized PET examination within 90 days so that the exposure and outcome measurements remained clinically proximate.\u003c/p\u003e \u003cp\u003eThe main outcomes included urine volume, residual GFR, CCR, Kt/V, PET score, ultrafiltration, daily glucose exposure, mean prescription glucose concentration, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), albumin, and high-sensitivity C-reactive protein (hsCRP). PET scores of 1 to 4 represented low, low-average, high-average, and high transport, respectively; high transport was defined as PET score\u0026thinsp;\u0026ge;\u0026thinsp;3. Anuria was defined as urine volume\u0026thinsp;\u0026lt;\u0026thinsp;100 mL/day according to clinical convention. PET was analyzed as both an ordered 4-level variable in sensitivity analysis and an approximate continuous variable in the primary models for comparability across outcomes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnthropometric and Prescription Calculations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWHtR was calculated as waist circumference divided by height. BMI was calculated as weight divided by height squared. BRI, ABSI, and the conicity index were derived according to previously published equations or methodologic studies[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Because a WHtR threshold of 0.5 is easy to interpret clinically, it was used for descriptive grouping; all regression analyses treated WHtR as a continuous variable standardized to 1 SD.\u003c/p\u003e \u003cp\u003eMean prescription glucose concentration was calculated as the glucose-equivalent mean concentration: the total glucose mass from all glucose-containing PD bags was summed and divided by the total prescribed dialysate volume, then converted to a percentage concentration. The denominator included glucose-free icodextrin solution. For example, if the prescription consisted of 1.5% glucose solution 2 L x2 bags, 2.5% glucose solution 2 L x1 bag, and 7.5% icodextrin 2 L x1 bag, the total glucose mass would be 60 g\u0026thinsp;+\u0026thinsp;50 g\u0026thinsp;=\u0026thinsp;110 g, the total dialysate volume would be 8,000 mL, and the glucose-equivalent mean concentration would be 1.375%.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBaseline characteristics were compared between the WHtR\u0026thinsp;\u0026lt;\u0026thinsp;0.5 and WHtR\u0026thinsp;\u0026ge;\u0026thinsp;0.5 groups. Normally distributed continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, skewed variables as median [interquartile range], and categorical variables as number (%). Group comparisons were performed using the t test, Mann-Whitney U test, or chi-square test, as appropriate. Spearman correlation analyses were first performed for continuous outcomes, followed by linear regression models reporting standardized beta coefficients and 95% confidence intervals (CIs). Model 1 adjusted for age, sex, and PD duration; Model 2 additionally adjusted for systolic blood pressure, automated PD, icodextrin use, edema score, and lipid-lowering drug use; Model 3 was specified as an extended exploratory adjustment set that additionally included albumin and log(hsCRP). When log(hsCRP) was the outcome, it was not included as its own covariate. PET was treated as an approximate continuous outcome in the primary models to preserve comparability across multiple endpoints, and ordinal logistic regression was performed as a sensitivity analysis. Binary outcomes were assessed using logistic regression and are reported as odds ratios (ORs) and 95% CIs per 1-SD increase in WHtR. Because urine volume and residual GFR showed marked zero inflation, we used a two-part-style sensitivity framework that combined binary models for anuria or absent residual kidney function with log-linear models among non-anuric participants (urine volume\u0026thinsp;\u0026ge;\u0026thinsp;100 mL/day) or those with residual GFR\u0026thinsp;\u0026gt;\u0026thinsp;0. Given the moderate sample size, multiple endpoints, and multivariable models, all adjusted analyses were interpreted as exploratory rather than definitive evidence of independence. Missing data were handled using complete-case analysis. PET was missing in 10 (3.3%) participants; missingness of the other variables was low. Complete-case sample sizes, missingness, and collinearity diagnostics are shown in Supplementary Tables S1A, S1B, and S3. Exploratory FDR sensitivity analyses were additionally performed for the main WHtR associations. A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e \u003cb\u003eBaseline Characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA total of 303 patients receiving maintenance PD were included, of whom 114 had WHtR\u0026thinsp;\u0026lt;\u0026thinsp;0.5 and 189 had WHtR\u0026thinsp;\u0026ge;\u0026thinsp;0.5. Compared with the lower-WHtR group, the higher-WHtR group was older and had greater waist circumference, BMI, BRI, and ABSI. The higher-WHtR group also showed higher TG and hsCRP, lower HDL-C, a higher prevalence of edema, and higher ultrafiltration, whereas CCR was lower. PET score, Kt/V, continuous urine volume, and continuous residual GFR did not differ significantly between the groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics overall and by WHtR category\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;303)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWHtR\u0026thinsp;\u0026lt;\u0026thinsp;0.5 (n\u0026thinsp;=\u0026thinsp;114)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWHtR\u0026thinsp;\u0026ge;\u0026thinsp;0.5 (n\u0026thinsp;=\u0026thinsp;189)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.38\u0026thinsp;\u0026plusmn;\u0026thinsp;11.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.89\u0026thinsp;\u0026plusmn;\u0026thinsp;12.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.49\u0026thinsp;\u0026plusmn;\u0026thinsp;10.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150 (49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD duration, months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.00 [25.00, 85.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.00 [24.00, 95.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.00 [25.00, 82.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMann-Whitney U\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\u003eWaist circumference, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.07\u0026thinsp;\u0026plusmn;\u0026thinsp;10.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.70\u0026thinsp;\u0026plusmn;\u0026thinsp;6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.72\u0026thinsp;\u0026plusmn;\u0026thinsp;8.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et test\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\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.52\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.13\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et test\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\u003eBody roundness index (BRI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et test\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\u003eA body shape index (ABSI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.084\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.081\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.086\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et test\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\u003eAlbumin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.92\u0026thinsp;\u0026plusmn;\u0026thinsp;4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.84\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.97\u0026thinsp;\u0026plusmn;\u0026thinsp;4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33 [0.98, 1.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18 [0.93, 1.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45 [1.05, 2.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMann-Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et test\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\u003ehsCRP, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.20 [0.87, 6.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25 [0.59, 4.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.74 [1.21, 7.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMann-Whitney U\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\u003eSystolic blood pressure, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134.63\u0026thinsp;\u0026plusmn;\u0026thinsp;22.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134.80\u0026thinsp;\u0026plusmn;\u0026thinsp;23.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134.54\u0026thinsp;\u0026plusmn;\u0026thinsp;21.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid-lowering drug use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e229 (75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152 (80.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChi-square\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\u003eIcodextrin use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164 (54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110 (58.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny edema, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151 (49.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChi-square\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\u003eUltrafiltration, mL/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e547.50 [257.00, 845.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e470.00 [251.25, 700.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e638.00 [268.75, 912.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMann-Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e200.00 [0.00, 650.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300.00 [0.00, 650.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.00 [0.00, 650.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMann-Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual GFR, mL/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49 [0.00, 2.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74 [0.00, 2.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 [0.00, 2.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMann-Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCR, L/week/1.73 m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.77 [47.40, 67.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.06 [50.45, 71.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.97 [46.70, 65.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMann-Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePET score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00 [2.00, 3.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00 [2.00, 3.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00 [2.00, 3.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMann-Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKt/V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily glucose exposure, g/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.00 [69.00, 120.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.00 [64.29, 115.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.00 [75.00, 120.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMann-Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean prescription glucose concentration, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44 [1.13, 1.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50 [1.13, 1.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39 [1.13, 1.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMann-Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eContinuous variables are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median [IQR], and categorical variables as n (%). Statistical methods are listed for each comparison. ABSI, a body shape index; APD, automated peritoneal dialysis; BRI, body roundness index; CCR, creatinine clearance rate; HDL-C, high-density lipoprotein cholesterol; hsCRP, high-sensitivity C-reactive protein; IQR, interquartile range; PET, peritoneal equilibration test; WHtR, waist-to-height ratio.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociations Between WHtR and Continuous Outcomes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe clearest continuous associations of WHtR were with TG, HDL-C, hsCRP, and ultrafiltration. Correlations with PET score, urine volume, and residual GFR were weak or absent, whereas correlations with CCR, Kt/V, and daily glucose exposure attenuated after covariate adjustment (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the extended Model 3, each 1-SD increment in WHtR was associated with higher log(TG) [0.206 (0.090, 0.322)], lower HDL-C [-0.239 (-0.358, -0.119)], higher log(hsCRP) [0.327 (0.211, 0.444)], and higher ultrafiltration [0.145 (0.025, 0.264)]. In exploratory FDR sensitivity analyses, these four associations remained significant (q\u0026thinsp;=\u0026thinsp;0.002, q\u0026thinsp;\u0026lt;\u0026thinsp;0.001, q\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and q\u0026thinsp;=\u0026thinsp;0.048, respectively). By contrast, PET was not associated with WHtR in either the primary linear model or ordinal logistic sensitivity analysis (Model 3 cumulative OR\u0026thinsp;=\u0026thinsp;0.89, 95% CI 0.69 to 1.15; P\u0026thinsp;=\u0026thinsp;0.374). No stable independent associations were observed for urine volume, residual GFR, mean prescription glucose concentration, or PET score (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWHtR and continuous PD-related outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman rho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 1 beta\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 1 \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 2 beta\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 2 \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 3 beta\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModel 3 \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog(TG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.248 (0.135, 0.361)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.277 (0.159, 0.395)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.206 (0.090, 0.322)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.305 (-0.417, -0.194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.288 (-0.404, -0.171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.239 (-0.358, -0.119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\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\u003elog(hsCRP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.310 (0.200, 0.420)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.312 (0.194, 0.430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.327 (0.211, 0.444)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\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\u003eUltrafiltration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.212 (0.099, 0.326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.159 (0.046, 0.272)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.145 (0.025, 0.264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.180 (-0.293, -0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.132 (-0.249, -0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.091 (-0.214, 0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKt/V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.175 (-0.277, -0.073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.133 (-0.240, -0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.095 (-0.207, 0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily glucose exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.139 (0.028, 0.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.171 (0.055, 0.288)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.121 (-0.001, 0.243)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean prescription glucose concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003 (-0.113, 0.120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.087 (-0.018, 0.193)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.052 (-0.057, 0.160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePET score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.006 (-0.124, 0.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.093 (-0.215, 0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.055 (-0.177, 0.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrine volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.048 (-0.152, 0.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.006 (-0.110, 0.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.044 (-0.064, 0.151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual GFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.087 (-0.197, 0.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.026 (-0.135, 0.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.005 (-0.117, 0.106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eStandardized beta coefficients and 95% CIs are shown. Model 1 adjusted for age, sex, and PD duration; Model 2 additionally adjusted for systolic blood pressure, APD, icodextrin use, edema score, and lipid-lowering drug use; Model 3 additionally included albumin and log(hsCRP). When log(hsCRP) was the outcome, it was not entered as a covariate. TG and hsCRP were log-transformed. PET was analyzed as an approximate continuous outcome in the primary models, with ordinal logistic regression as sensitivity analysis. CI, confidence interval; hsCRP, high-sensitivity C-reactive protein; PET, peritoneal equilibration test; TG, triglycerides.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ePoints show standardized beta coefficients and horizontal lines show 95% CIs. Red denotes positive associations and blue denotes negative associations; the dashed vertical line marks beta\u0026thinsp;=\u0026thinsp;0. CI, confidence interval; PD, peritoneal dialysis; WHtR, waist-to-height ratio.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHead-to-Head Comparison with Other Anthropometric Indices\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUnder the uniformly adjusted Model 2, waist circumference, WHtR, and BRI showed similar association patterns across continuous outcomes, with mean absolute beta values of 0.208, 0.196, and 0.196, respectively, and seven statistically significant outcomes each. Because WHtR and BRI were almost collinear in this cohort (Pearson r\u0026thinsp;=\u0026thinsp;0.997), this comparison should be interpreted mainly from the standpoint of simplicity rather than superiority. In this dataset, WHtR appeared to be a simpler surrogate with information yield similar to BRI \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\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\u003eContinuous outcome comparison across anthropometric indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWaist circumference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eABSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConicity index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog(TG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.277**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.288**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.226**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.282**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.188**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.255**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.288**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.284**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.255**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.279**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.141*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.228**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog(hsCRP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.312**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.311**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.238**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.315**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.218**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.292**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltrafiltration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.159**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.178**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.160**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.151**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.118*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.132*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.148*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.168**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.135*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKt/V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.133*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.159**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.183**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.142**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily glucose exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.171**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.204**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.172**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePET score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean |beta|\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of significant outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eStandardized beta coefficients from Model 2 are shown. Mean absolute beta and the number of significant outcomes are descriptive summaries only and do not imply formal ranking. ABSI, a body shape index; BMI, body mass index; BRI, body roundness index; PET, peritoneal equilibration test; WHtR, waist-to-height ratio.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ePoints show standardized beta coefficients and horizontal lines show 95% CIs under Model 2. Filled circles denote P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and open circles denote P\u0026thinsp;\u0026ge;\u0026thinsp;0.05. ABSI, a body shape index; BRI, body roundness index; CI, confidence interval; PD, peritoneal dialysis; WHtR, waist-to-height ratio.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSensitivity Analyses for Zero Inflation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor anuria, defined as urine volume\u0026thinsp;\u0026lt;\u0026thinsp;100 mL/day, and for absent residual kidney function, WHtR was associated with higher odds in Model 1 (anuria: OR\u0026thinsp;=\u0026thinsp;1.37 (1.02, 1.83); no residual kidney function: OR\u0026thinsp;=\u0026thinsp;1.40 (1.05, 1.88)), but these associations attenuated and were no longer significant in Models 2 and 3. In this dataset, no urine volume values between 1 and 99 mL/day were recorded, so the clinical anuria definition coincided with the recorded 0-mL category. WHtR was not significantly associated with high transport in any model. In exploratory two-part-style sensitivity analyses, WHtR was not associated with log urine volume among non-anuric patients (urine volume\u0026thinsp;\u0026ge;\u0026thinsp;100 mL/day) or with log residual GFR among patients with residual GFR\u0026thinsp;\u0026gt;\u0026thinsp;0 (\u003cb\u003eSupplementary Table S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe discrimination performance of all single anthropometric indices was weak. For WHtR, the AUCs were 0.536 (95% CI 0.480 to 0.603) for anuria, 0.542 (0.479 to 0.598) for no residual kidney function, and 0.504 (0.435 to 0.566) for high transport. Even the numerically highest exploratory AUCs were low, reaching 0.596 (95% CI 0.536 to 0.664) for anuria, 0.599 (0.538 to 0.655) for no residual kidney function, and 0.562 (0.493 to 0.634) for high transport, all with ABSI. These results argue against any strong discriminative advantage of a single anthropometric index (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinary outcomes and exploratory discrimination by WHtR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvents/Total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1 OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 1 \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2 OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 2 \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 3 OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 3 \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWHtR AUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHighest exploratory AUC (index; 95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnuria (\u0026lt;\u0026thinsp;100 mL/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140/303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37 (1.02, 1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.22 (0.89, 1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.05 (0.75, 1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.536 (0.480, 0.603)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.596 (ABSI; 0.536, 0.664)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo residual kidney function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142/303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40 (1.05, 1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.28 (0.93, 1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.11 (0.79, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.542 (0.479, 0.598)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.599 (ABSI; 0.538, 0.655)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133/303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97 (0.76, 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83 (0.64, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92 (0.69, 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.504 (0.435, 0.566)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.562 (ABSI; 0.493, 0.634)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eOdds ratios are reported per 1-SD increase in WHtR. Anuria was defined as urine volume\u0026thinsp;\u0026lt;\u0026thinsp;100 mL/day; no values between 1 and 99 mL/day were recorded. AUCs with 95% CIs are exploratory single-index discrimination measures, and no formal DeLong comparisons were performed. ABSI, a body shape index; AUC, area under the receiver operating characteristic curve; CI, confidence interval; WHtR, waist-to-height ratio.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eBars show single-index AUCs for anuria, absent residual kidney function, and high transport; the dashed vertical line marks AUC\u0026thinsp;=\u0026thinsp;0.50. ABSI, a body shape index; AUC, area under the receiver operating characteristic curve; BRI, body roundness index; WHtR, waist-to-height ratio.\u003c/em\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cb\u003eAssociations of WHtR with metabolic, inflammatory, and ultrafiltration-related parameters\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this study, WHtR showed the most consistent associations with metabolic, inflammatory, and ultrafiltration-related parameters in patients receiving maintenance PD. In the fully adjusted model, each 1-SD increase in WHtR was associated with higher TG, lower HDL-C, higher hsCRP, and greater ultrafiltration, and these associations remained significant after FDR correction. Taken together, these findings suggest that, in PD patients, WHtR may reflect a central adiposity-related phenotype characterized by dyslipidemia, low-grade inflammation, and greater fluid-management burden, rather than serving as a broad marker of all PD-related outcomes.\u003c/p\u003e \u003cp\u003eThis pattern is clinically plausible. Long-term exposure to glucose-based dialysate is often accompanied by weight gain, visceral fat accumulation, and metabolic disturbance in PD patients[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Molecular studies have further shown that adipose- and plasma-derived microRNA profiles are linked to obesity, insulin resistance, and new-onset diabetes after PD[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].These observations support the view that WHtR captures more than body size alone and may instead track a broader metabolic phenotype. The inflammatory and vascular implications are also relevant. In PD populations, greater visceral or abdominal fat accumulation has been associated with arterial stiffness and endothelial dysfunction[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e],while abdominal adiposity has also been linked to inflammation and metabolic syndrome[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].Accordingly, the associations of higher WHtR with higher hsCRP and TG and lower HDL-C in our cohort are consistent with a state of central adiposity-related cardiometabolic stress rather than an isolated anthropometric finding.\u003c/p\u003e \u003cp\u003eThe association with ultrafiltration should be interpreted in the same clinical context. Previous studies suggest that PD prescription intensity, body composition, and patient outcomes are closely interrelated[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].Evidence from studies on intraperitoneal pressure and segmental bioelectrical impedance also indicates that abdominal body habitus is closely linked to fluid assessment in routine PD practice[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].Longitudinal data further show that extracellular water imbalance is associated with adverse outcomes[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].Taken together, these findings support the interpretation that WHtR may be better understood as a marker of treatment complexity and volume-related burden, rather than a direct determinant of ultrafiltration requirement.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimited associations with PET and residual kidney function\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBy contrast, the associations of WHtR with PET and residual kidney function were limited and not robust. PET score was not significantly associated with WHtR in the primary analysis, and this null finding remained unchanged in the ordinal logistic sensitivity analysis. This negative result is clinically informative. PET mainly reflects intrinsic peritoneal membrane transport characteristics, whereas WHtR is more likely to represent a patient-level phenotype related to adiposity, metabolic status, and fluid burden. The absence of a stable association therefore suggests that WHtR should not be regarded as a surrogate marker of peritoneal transport behavior.\u003c/p\u003e \u003cp\u003eThe findings for residual kidney function also warrant caution. The associations with anuria and absent residual kidney function were attenuated after more comprehensive adjustment, and no graded association was identified in the two-part sensitivity analyses among patients who retained urine output or residual GFR. This differs from findings in non-PD CKD populations, where WHtR and other indices of central obesity have been associated with CKD risk and kidney function decline[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].Similar observations have been reported in NHANES and in large Chinese cohorts examining anthropometric indices and kidney outcomes[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. One possible explanation is that residual kidney function in established PD is influenced not only by body habitus, but also by dialysate exposure, prescription intensity, and volume status[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].Once PD is established, the contribution of anthropometric measures to residual kidney function may therefore be diluted by treatment-related and disease-related factors.\u003c/p\u003e \u003cp\u003e \u003cb\u003eComparison with other anthropometric indices and clinical implications\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFrom a comparative perspective, WHtR appears to offer practical advantages over other anthropometric indices. WHtR has been proposed as a simple screening measure for cardiometabolic risk because it standardizes waist circumference to height while remaining easy to obtain[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. BRI, ABSI, and the conicity index were developed to characterize body shape and central adiposity more explicitly, although these measures overlap to some extent in the information they capture[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In PD cohorts, previous studies have mainly focused on BRI or selected anthropometric indices in relation to outcomes or sarcopenia, and direct head-to-head comparisons remain limited[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our cohort, waist circumference, WHtR, and BRI showed broadly similar association spectra across continuous outcomes, and WHtR and BRI were almost completely overlapping. This pattern is consistent with the close geometric relation between WHtR and BRI[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and suggests that the additional computational complexity of BRI may provide little incremental value in this setting. The AUC findings should also be interpreted conservatively. All single-index AUCs were low, which does not support the use of any anthropometric index as a strong stand-alone discrimination tool in PD patients.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, the value of WHtR may therefore lie more in bedside risk phenotyping than in formal prediction. A higher WHtR may prompt closer attention to TG, HDL-C, inflammatory activity, edema or volume status, dialysate glucose exposure, and prescription intensity. It should not replace PET, direct volume measurements, or comprehensive assessment of residual kidney function, but it may serve as a simple entry point for identifying patients with a less favorable cardiometabolic-volume profile. Overall, our findings suggest that WHtR is best interpreted as a practical marker of central adiposity-related metabolic and volume burden in maintenance PD, rather than as a surrogate for peritoneal membrane transport or residual kidney function. Future prospective multicenter studies with richer confounder data and longitudinal follow-up are needed to determine whether WHtR has value in long-term risk stratification and treatment monitoring in PD.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAmong patients receiving maintenance PD, WHtR was most consistently associated with TG, HDL-C, hsCRP, and ultrafiltration, whereas associations with PET and residual kidney function were limited and not robust after broader adjustment. In this cohort, WHtR may be best viewed as a simple phenotypic surrogate with information yield similar to BRI, rather than as a mechanistic determinant or stand-alone predictive tool. Prospective multicenter studies with richer confounder assessment are needed to define its role in PD risk phenotyping.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eABSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ea body shape index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eautomated peritoneal dialysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the receiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody roundness index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecreatinine clearance rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ehsCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-sensitivity C-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eperitoneal dialysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eperitoneal equilibration test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHtR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ewaist-to-height ratio.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, Ning Weng; methodology, Xiaoke Zhu; formal analysis, Chunxiang Huang; investigation, Fangyu Yi; resources, Qiaozhen Wu; data curation, Dan Liu; writing\u0026mdash;original draft preparation, Fangyu Yi; writing\u0026mdash;review and editing, Fangyu Yi; visualization, Ning Weng; supervision, Ning Weng; project administration, Ning Weng; funding acquisition, Yuexing Huang. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by The Leading Goose Project of Zhejiang Provincial Department of Science and Technology (Grant Nos. 2025C02191).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e All data are provided in the manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics Statement: \u003c/strong\u003eThis study was approved by the Research Ethics Committee of Hangzhou Hospital of Traditional Chinese Medicine (Approval No. 2023KLL036). The requirement for written informed consent was waived according to the approved ethics record. All procedures were conducted in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki.\u003c/p\u003e\n\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNg J, Than W, Szeto C. Obesity, Weight Gain, and Fluid Overload in Peritoneal Dialysis. Front Nephrol. 2022;2:880097.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIkeda M, et al. Effect of Long-term Peritoneal Dialysis on Change in Visceral Fat Area: A Single-Center Experience. Indian J Nephrol. 2020;30(6):398\u0026ndash;402.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThan W, et al. The change in the prevalence of obesity and new-onset diabetes in Chinese peritoneal dialysis patients over 25 years. Clin Kidney J. 2022;15(1):70\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan G, et al. Adipose and Plasma microRNAs miR-221 and 222 Associate with Obesity, Insulin Resistance, and New Onset Diabetes after Peritoneal Dialysis. Nutrients. 2022;14(22):4889.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012;13(3):275\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu L, et al. The Impact of Change in Body Roundness Index on the Clinical Outcome of New Peritoneal Dialysis Patients. Kidney Dis (Basel). 2025;11(1):674\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDo J, Kang S. Comparison of various indices for predicting sarcopenia and its components in patients receiving peritoneal dialysis. Sci Rep. 2022;12(1):14102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrakauer N, Krakauer J. A new body shape index predicts mortality hazard independently of body mass index. PLoS ONE. 2012;7(7):e39504.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas D, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obes (Silver Spring). 2013;21(11):2264\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNkwana M, Monyeki K, Lebelo S. Body Roundness Index, A Body Shape Index, Conicity Index, and Their Association with Nutritional Status and Cardiovascular Risk Factors in South African Rural Young Adults. Int J Environ Res Public Health. 2021;18(1):281.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg JK, Than WH, Szeto CC. Obesity, Weight Gain, and Fluid Overload in Peritoneal Dialysis. Front Nephrol. 2022;2:880097.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThan WH, et al. The change in the prevalence of obesity and new-onset diabetes in Chinese peritoneal dialysis patients over 25 years. Clin Kidney J. 2022;15(1):70\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan GCK et al. Adipose and Plasma microRNAs miR-221 and 222 Associate with Obesity, Insulin Resistance, and New Onset Diabetes after Peritoneal Dialysis. Nutrients, 2022. 14(22).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee MJ, et al. Visceral fat thickness is associated with carotid atherosclerosis in peritoneal dialysis patients. Obes (Silver Spring). 2012;20(6):1301\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Mattos AM, et al. Association of body fat with inflammation in peritoneal dialysis. Inflammation. 2013;36(3):689\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerger C, et al. Association of Prescription With Body Composition and Patient Outcomes in Incident Peritoneal Dialysis Patients. Front Med (Lausanne). 2021;8:737165.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg JK et al. \u003cem\u003eThe Impact of Volume Overload on the Longitudinal Change of Adipose and Lean Tissue Mass in Incident Chinese Peritoneal Dialysis Patients.\u003c/em\u003e Nutrients, 2022. 14(19).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, et al. Novel equations for estimating intraperitoneal pressure among peritoneal dialysis patients. Clin Kidney J. 2023;16(9):1447\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChao HN, et al. Time-Dependent Changes in Extracellular-to-Intracellular Water Ratios and Risk of All-Cause Mortality in Peritoneal Dialysis Patients: A Single-Center Retrospective Cohort Study. Kidney Med. 2026;8(1):101171.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlaslov K, Bulum T, Duvnjak L. Waist-to-height ratio is independently associated with chronic kidney disease in overweight type 2 diabetic patients. Endocr Res. 2015;40(4):194\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu L, et al. Waist height ratio predicts chronic kidney disease: a systematic review and meta-analysis, 1998\u0026ndash;2019. Arch Public Health. 2019;77:55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMemarian E, et al. The risk of chronic kidney disease in relation to anthropometric measures of obesity: A Swedish cohort study. BMC Nephrol. 2021;22(1):330.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu F, et al. Association of Obesity-Related Indices with Rapid Kidney Function Decline and Chronic Kidney Disease: A Study from a Large Longitudinal Cohort in China. Obes Facts. 2025;18(5):445\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas DM, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obes (Silver Spring). 2013;21(11):2264\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNkwana MR, Monyeki KD, Lebelo SL. Body Roundness Index, A Body Shape Index, Conicity Index, and Their Association with Nutritional Status and Cardiovascular Risk Factors in South African Rural Young Adults. Int J Environ Res Public Health, 2021. 18(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu L, et al. The Impact of Change in Body Roundness Index on the Clinical Outcome of New Peritoneal Dialysis Patients. Kidney Dis (Basel). 2025;11(1):674\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDo JY, Kang SH. Comparison of various indices for predicting sarcopenia and its components in patients receiving peritoneal dialysis. Sci Rep. 2022;12(1):14102.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"waist-to-height ratio, peritoneal dialysis, residual kidney function, peritoneal equilibration test, anthropometric indices","lastPublishedDoi":"10.21203/rs.3.rs-9326819/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9326819/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo examine the associations of waist-to-height ratio (WHtR) with metabolic, inflammatory, ultrafiltration-related, peritoneal transport, and residual kidney function measures in maintenance peritoneal dialysis (PD), and to compare its information yield with that of other anthropometric indices.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis single-center cross-sectional study included 303 patients receiving maintenance PD. WHtR was analyzed continuously as the primary exposure and descriptively categorized at 0.5. Continuous outcomes were examined using Spearman correlation and hierarchical regression models; binary outcomes were examined using logistic regression. Exploratory sensitivity analyses included ordinal logistic regression for PET, false discovery rate (FDR) adjustment for multiple testing, and two-part-style analyses for zero-inflated urine volume and residual GFR.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the extended Model 3, each 1-SD increment in WHtR was associated with higher log(TG) [beta\u0026thinsp;=\u0026thinsp;0.206, 95% CI 0.090 to 0.322], lower HDL-C [beta = -0.239, 95% CI -0.358 to -0.119], higher log(hsCRP) [beta\u0026thinsp;=\u0026thinsp;0.327, 95% CI 0.211 to 0.444], and higher ultrafiltration [beta\u0026thinsp;=\u0026thinsp;0.145, 95% CI 0.025 to 0.264]; these four associations remained significant after FDR adjustment. No stable independent associations were observed for PET or continuous residual kidney function measures, and ordinal logistic sensitivity analysis for PET was likewise null (Model 3 cumulative OR\u0026thinsp;=\u0026thinsp;0.89, 95% CI 0.69 to 1.15). WHtR and BRI were almost completely overlapping in this cohort (Pearson r\u0026thinsp;=\u0026thinsp;0.997). Single-index AUCs were low overall; for WHtR, AUCs were 0.536 (95% CI 0.480 to 0.603) for anuria, 0.542 (0.479 to 0.598) for no residual kidney function, and 0.504 (0.435 to 0.566) for high transport.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn maintenance PD, WHtR was consistently associated with metabolic, inflammatory, and ultrafiltration-related correlates, whereas its independent associations with PET and residual kidney function were limited. In this cohort, WHtR may be best viewed as a simple phenotypic surrogate with information yield similar to BRI rather than as a mechanistic determinant or stand-alone screening tool.\u003c/p\u003e","manuscriptTitle":"Waist-to-Height Ratio and Metabolic, Inflammatory, and Ultrafiltration-Related Correlates in Maintenance Peritoneal Dialysis: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-27 14:27:39","doi":"10.21203/rs.3.rs-9326819/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-07T01:32:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202469890614670422792107800917830007480","date":"2026-05-06T02:48:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64239917021427927557694034709632797848","date":"2026-05-04T03:30:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264376461077719933723227857469114776178","date":"2026-04-22T14:23:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T02:15:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-08T06:50:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T12:06:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-07T12:05:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2026-04-05T14:17:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"59170343-a39f-4a93-a7d1-b90ce740274f","owner":[],"postedDate":"April 27th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-07T01:32:32+00:00","index":242,"fulltext":""},{"type":"reviewerAgreed","content":"202469890614670422792107800917830007480","date":"2026-05-06T02:48:19+00:00","index":237,"fulltext":""},{"type":"reviewerAgreed","content":"64239917021427927557694034709632797848","date":"2026-05-04T03:30:20+00:00","index":223,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T14:27:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-27 14:27:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9326819","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9326819","identity":"rs-9326819","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}