The role of the sarcopenia index in prognostic assessment of patients with newly initiated peritoneal dialysis: a retrospective cohort study

The role of the sarcopenia index in prognostic assessment of patients with newly initiated peritoneal dialysis: a retrospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The role of the sarcopenia index in prognostic assessment of patients with newly initiated peritoneal dialysis: a retrospective cohort study zhihong Zhang, Man Zhang, Tingting Zhou, Le Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7648129/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The relationship between the sarcopenia index (SI) and poor prognosis in peritoneal dialysis (PD) is not well established. Methods The SI was calculated using fasting morning serum creatinine (Cr, mg/dL) and cystatin C (CysC, mg/L) levels obtained prior to PD catheter insertion. Two formulas were applied: Cr/CysC and Cr×eGFRcys; eGFRcys was estimated using the Chronic Kidney Disease Epidemiology (CKD-EPI) 2021 equation. Associations between SI and the risk of all-cause mortality or technique failure were analyzed using Cox proportional hazards models and competing risk models. Results In total, 752 PD patients (mean age 42.7 ± 13.4 years; 55.6% men) were included. Multivariate Cox regression showed that both Cr/CysC and Cr×eGFRcys were significantly associated with mortality risk (hazard ratio = 0.476, 95% confidence interval: 0.262–0.866, P = 0.015; hazard ratio = 0.985, 95% confidence interval: 0.973–0.997, P = 0.013, respectively). In the competing risk model, both indices remained independent predictors of mortality. Area under the receiver operating characteristic curve values for predicting mortality were 0.614 for Cr/CysC and 0.669 for Cr×eGFRcys (P < 0.001). No significant association was observed between SI and risk of technique failure. Conclusions Both Cr/CysC and Cr×eGFRcys are independent predictors of mortality in PD patients; Cr×eGFRcys shows superior predictive accuracy. peritoneal dialysis mortality technique failure sarcopenia index Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction As renal function declines, patients with end-stage renal disease (ESRD) experience progressive accumulation of uremic toxins. This condition is also characterized by elevated inflammatory markers, reduced dietary protein intake, decreased physical activity, diminished levels of sex hormones and growth hormone, reduced insulin and vitamin D concentrations, fewer stellate cells, metabolic acidosis, protein-energy imbalance, and increased levels of angiotensin II and myostatin. The interplay of these factors often leads to reduced muscle strength, alterations in muscle structure, and pronounced muscle wasting among patients initiating dialysis, a condition referred to as uremic sarcopenia [ 1 , 2 ]. A 2024 meta-analysis of 42,000 patients with chronic kidney disease (CKD) across 25 countries detected a uremic sarcopenia prevalence of 24.5% (95% confidence interval [CI]: 20.9%–28.3%). Among individuals receiving maintenance dialysis, the prevalence of severe sarcopenia was 26.2% (95% CI: 16.6%–37.1%) [ 3 ]. Patients with ESRD and coexisting uremic sarcopenia experience worse quality of life, as well as substantially increased risks of cardiovascular complications and all-cause mortality [ 4 , 5 ]. For patients undergoing home peritoneal dialysis (PD), it is crucial to identify simple, objective, and easily measurable biological markers that can assess uremic sarcopenia and its prognostic implications. The sarcopenia index (SI), calculated from serum creatinine (Cr) and cystatin C (CysC) levels, has demonstrated correlations with muscle mass, strength, physical performance, nutritional status, and survival outcomes in diverse populations, including individuals with cardiovascular disease, cancer, chronic obstructive pulmonary disease, and CKD [ 6 – 9 ]. However, studies evaluating SI in patients on PD remain limited, and its clinical value requires further confirmation. The aim of this study was to investigate the associations of two commonly used SI measures with the risks of all-cause mortality and technique failure in patients with newly initiated PD. Methodology Patients This single-center, retrospective cohort study included adult patients with ESRD who underwent PD catheter placement at our facility between January 2004 and January 2023. A minimum follow-up period of 1 year was required, irrespective of sex. Key exclusion criteria were age ≥ 80 years, presence of malignant tumors, recent severe trauma or infection, acute kidney injury, and limb disabilities. Patients with severe heart failure (New York Heart Association functional class III–IV), severe obesity or underweight status (body mass index [BMI] ≥ 30 kg/m 2 or ≤ 18 kg/m 2 ), or clinically significant edema were also excluded. The study was approved by the Ethics Committee of Jinling Hospital (No. 2023DZKY-003-02). The requirement for written informed consent was waived due to the retrospective nature of the study. Study outcomes Patients were continuously followed until January 2025; routine outpatient visits were supplemented by telephone follow-up. The primary endpoint was all-cause mortality. Secondary endpoints included technique failure (defined as conversion to hemodialysis for at least 3 months), kidney transplantation, and loss to follow-up (defined as inability to establish contact for 3 months or longer). Endpoints were recorded at the time of occurrence of any of these events. Data collection Baseline data were obtained before PD catheter insertion. Collected variables included sex; age; primary renal disease; BMI; and levels of hemoglobin, C-reactive protein, serum albumin, total cholesterol, triglycerides, uric acid, calcium, phosphorus, blood urea nitrogen, Cr, and CysC. Morning fasting venous blood samples were collected, centrifuged, and analyzed within 1 h. Serum Cr was measured using an automated analyzer (Siemens Advia 1800, Siemens Healthcare GmbH, Henkestr, Germany), and CysC was determined by immunonephelometry calibrated against a reference standard. The SI was calculated using serum Cr (mg/dL) and CysC (mg/L), based on the formulas Cr/CysC and Cr×eGFRcys; eGFRcys was estimated using the Chronic Kidney Disease Epidemiology (CKD-EPI) 2021 formula for glomerular filtration rate (eGFR) [ 10 ]. Statistical analysis Continuous variables were expressed as mean ± standard deviation when normally distributed and as median (interquartile range) when they displayed a skewed distribution. Categorical and ordinal variables were expressed as frequencies and percentages. Groupwise comparisons of continuous variables were performed using t-tests or the Mann–Whitney U test, whereas categorical variables were compared using χ² tests or Fisher’s exact test. Associations of Cr/CysC and Cr×eGFRcys with other continuous variables were assessed. Multivariate Cox proportional hazards regression and competing risk models were used to examine the relationships of Cr/CysC and Cr×eGFRcys with all-cause mortality and technique failure. In the competing risks framework, technique failure and kidney transplantation were considered competing events for death; death and kidney transplantation were considered competing events for technique failure. All-cause mortality or technique failure was defined as the outcome event. Receiver operating characteristic (ROC) curves were used to compare the area under the curve (AUC) for various SI measures via DeLong’s test. The optimal cutoff for SI was determined using the Youden index, and patients were stratified according to this cutoff for survival analysis. Variables with P ≤ 0.10 in univariate analysis were included, and P-values < 0.05 were considered statistically significant. Statistical analyses were performed using STATA version 14. Results Patient characteristics In total, 752 patients undergoing PD were included; 418 (55.6%) were men and the mean age was 42.7 ± 13.6 years. The most common cause of renal disease was chronic glomerulonephritis, identified in 587 patients (78.1%). Concurrent diabetes was present in 49 patients (6.5%). The median values of Cr/CysC and Cr×eGFRcys were 1.45 (1.21–1.77) and 69.40 (56.39–87.99), respectively. The median follow-up duration was 47 months (range, 28–83 months). During this period, 82 patients (10.9%) died, 271 (36.0%) experienced technique failure, 172 (22.9%) underwent kidney transplantation, and 55 (7.3%) were lost to follow-up. ROC curve analysis for all-cause mortality as the primary endpoint demonstrated AUCs of 0.614 (95% CI: 0.551–0.678; P < 0.001) for Cr/CysC and 0.669 (95% CI: 0.607–0.730; P < 0.001) for Cr×eGFRcys; the difference between these two AUCs was statistically significant (Fig. 1 ). Based on the Youden index, optimal cutoff values for predicting all-cause mortality were 1.48 for Cr/CysC and 61.67 for Cr×eGFRcys. Baseline clinical characteristics of patients stratified according to the optimal cutoff for Cr×eGFRcys are summarized in Table 1 . Table 1 Baseline characteristics of 752 peritoneal dialysis patients Characteristics All Patients ( n = 752) Cr×eGFRcys ≥ 61.67 ( n = 503) Cr×eGFRcys < 61.67 ( n = 249) P -value Age (year) 42.7 ± 13.6 44.15 ± 12.42 46.50 ± 15.56 < 0.001 Male n(%) 418 (55.6) 334 (61.4) 139 (44.7) < 0.001 Body mass index (kg/m2 ) 21.4 ± 3.4 21.59 ± 3.44 21.10 ± 3.25 0.050 Glomerulonephritis n(%) 587 (78.1) 412 (81.9) 175 (70.3) < 0.001 Diabetes mellitus n(%) 49 (6.5) 23 (4.6) 26 (10.4) 0.004 Hemoglobin (g/L) 110.6 ± 17.4 110.7 ± 17.9 110.3 ± 16.2 0.715 Serum creatinine (mg/dL) 7.54 ± 2.89 8.20 ± 3.05 6.22 ± 1.96 < 0.001 Serum cystatin C (mg/L) 5.00 ± 1.37 4.68 ± 1.11 5.67 ± 1.59 < 0.001 Uric acid (µmol/L) 499.0 ± 115.4 489.9 ± 118.4 517.5 ± 106.8 0.002 Corrected calcium (mmol/L) 2.21 ± 0.21 2.21 ± 0.20 2.20 ± 0.21 0.668 Phosphorus (mmol/L) 1.61 ± 0.50 1.65 ± 0.48 1.52 ± 0.52 0.001 Albumin (g/L) 40.0 ± 4.8 40.2 ± 4.9 39.6 ± 4.7 0.073 Total cholesterol (mmol/L) 4.93 ± 1.40 4.67 ± 1.27 5.46 ± 1.49 0.001 Triglycerides (mmol/L) 1.80 (1.31, 2.47) 1.69 (1.24, 2.35) 2.03 (1.42, 2.66) < 0.001 eGFRcys (ml/min/1.73 m 2 ) 9.80 (8.02, 12.32) 10.76 (8.72, 13.469) 8.46 (6.88, 10.10) < 0.001 Cr/CysC 1.45 (1.21, 1.77) 1.64 (1.44, 1.95) 1.11 (0.99, 1.24) < 0.001 Cr×eGFRcys 69.40 (56.39, 87.99) 80.77 (69.19, 97.90) 51.84 (45.21, 56.38) < 0.001 Cr/CysC, creatinine-to-cystatin C ratio; eGFRcys, estimated glomerular filtration rate based on serum cystatin C. Correlations of Cr/CysC and Cr×eGFRcys with other parameters Table 2 summarizes the correlations of Cr/CysC and Cr×eGFRcys with selected clinical variables. Both indices showed positive correlations with BMI and serum phosphorus; they demonstrated negative correlations with total cholesterol, triglycerides, and uric acid. A strong positive correlation was also observed between Cr/CysC and Cr×eGFRcys (Fig. 2 ). Table 2 Spearman correlation analysis of factors associated with Cr/CysC and Cr×eGFRcys Variables Cr/CysC Cr×eGFRcys r P-value r P-value Age (years) -0.064 0.079 -0.209 < 0.001 BMI (kg/m2 ) 0.106 0.004 0.112 0.002 Hemoglobin (g/L) -0.103 0.005 -0.010 0.791 Albumin (g/L) 0.018 0.618 0.058 0.119 Total cholesterol (mmol/L) -0.267 < 0.001 -0.307 < 0.001 Triglycerides (mmol/L) -0.172 < 0.001 -0.156 < 0.001 Uric acid (µmol/L) -0.138 < 0.001 -0.116 0.001 Phosphorus (mmol/L) 0.305 < 0.001 0.231 < 0.001 Cr/CysC, creatinine-to-cystatin C ratio; eGFRcys, estimated glomerular filtration rate based on serum cystatin C. Associations of Cr/CysC and Cr×eGFRcys with all-cause mortality When analyzed as continuous variables in univariate Cox regression models, both Cr/CysC and Cr×eGFRcys were significantly associated with all-cause mortality (hazard ratio [HR] = 0.532, 95% CI: 0.304–0.933, P = 0.028; HR = 0.981, 95% CI: 0.970–0.992, P < 0.001, respectively). Patients with Cr/CysC < 1.48 had a mortality rate of 14.4% during follow-up; this rate was 7.2% among patients with Cr/CysC ≥ 1.48. Similarly, patients with Cr×eGFRcys < 61.67 had a mortality rate of 18.9%, compared with 7.0% among patients exhibiting Cr×eGFRcys ≥ 61.67. Kaplan–Meier survival curves stratified using these cutoff points demonstrated statistically significant differences in mortality risk (Fig. 3 ). Table 3 indicates that, after adjustments for age, sex, BMI, diabetes, albumin, uric acid, total cholesterol, phosphorus, and hemoglobin, both Cr/CysC and Cr×eGFRcys remained significantly associated with all-cause mortality in Cox regression and competing risk models. Comparable results were observed when data were analyzed using the cutoff values of 1.48 for Cr/CysC and 61.67 for Cr×eGFRcys. Associations of Cr/CysC and Cr×eGFRcys with technique failure Cr/CysC and Cr×eGFRcys were analyzed as continuous variables in univariate Cox regression models. HRs for technique failure were 1.292 (95% CI: 0.994–1.679; P = 0.056) and 1.004 (95% CI: 0.999–1.009; P = 0.122), respectively. After adjustments for age, sex, BMI, diabetes, albumin, uric acid, total cholesterol, phosphorus, and hemoglobin, Cr/CysC was not significantly associated with technique failure (HR = 1.065, 95% CI: 0.789–1.437; P = 0.680). The predictive ability of both indices for technique failure was further evaluated using ROC analysis. The AUCs were 0.514 (95% CI, 0.471–0.557; P = 0.325) for Cr/CysC, and 0.534 (95% CI, 0.491–0.577; P = 0.122) for Cr×eGFRcys. Subgroup analysis of mortality according to age, sex, BMI, and eGFRcys Age, sex, BMI, and residual renal function may influence Cr/CysC and Cr×eGFRcys levels. Patients were stratified by sex (male or female), age (< 60 years or ≥ 60 years), BMI (< 25 kg/m 2 or ≥ 25 kg/m 2 ), and eGFRcys (< 10 mL/min/1.73 m 2 or ≥ 10 mL/min/1.73 m 2 ). Univariate Cox regression analysis was performed to evaluate associations of Cr/CysC and Cr×eGFRcys with all-cause mortality within each subgroup. Significant associations of both indices with mortality risk were observed among patients aged < 60 years, those with BMI < 25 kg/m 2 , those with eGFRcys < 10 mL/min/1.73 m 2 , and across sex-stratified groups (Fig. 4). Discussion This study analyzed a large cohort of patients undergoing PD and yielded three principal findings. First, a significant correlation was observed between Cr/CysC and Cr×eGFRcys among patients with newly initiated PD. Second, both Cr/CysC and Cr×eGFRcys were independent predictors of all-cause mortality; Cr×eGFRcys demonstrated stronger predictive performance. Third, neither Cr/CysC nor Cr×eGFRcys was significantly associated with technique failure in PD patients. The SI is derived from serum Cr and CysC levels. Although various formulas are used for its calculation, the associations between Cr/CysC and Cr×eGFRcys are consistent. Studies conducted in populations with varying kidney function have shown that both indices are promising surrogate markers of muscle mass across diverse groups. For example, in a study of 226 intensive care unit (ICU) patients exhibiting normal renal function, Cr/CysC demonstrated a strong positive correlation with paraspinal skeletal muscle surface area at the level of the fourth lumbar vertebra measured by computed tomography (correlation coefficient = 0.62) [ 11 ]. Similarly, data from the National Health and Nutrition Examination Survey, which included 3,741 participants with normal renal function, indicated that Cr/CysC showed moderate agreement with dual-energy X-ray absorptiometry, the reference standard for diagnosing low muscle mass, with an AUC approaching 0.70 [ 12 ]. In longitudinal analyses, a study of 38 ICU patients on mechanical ventilation, all with initially normal renal function, assessed changes in rectus femoris cross-sectional area using ultrasound on days 1, 3, 5, 7, and 10. The results showed that the cross-sectional area decreased by 2% per day (range, 1%–3%), and the decline closely paralleled changes in Cr/CysC levels (correlation coefficient = 0.61) [ 13 ]. Concerning patients with renal insufficiency, one study evaluated 297 individuals with CKD and an eGFR < 45 mL/min/1.73 m 2 . Skeletal muscle mass was measured using bioimpedance analysis (BIA), and a significant positive correlation was observed between Cr×eGFRcys and total skeletal muscle mass (correlation coefficient = 0.503). The AUCs for Cr×eGFRcys in diagnosing sarcopenia, as defined by the Asian Working Group for Sarcopenia 2019 consensus, were 0.646 in men and 0.754 in women [ 14 ]. Another study of 272 CKD patients, with a median eGFR of 36.5 mL/min/1.73 m 2 , revealed a significant positive association between Cr/CysC and the skeletal muscle index (SMI) (correlation coefficient = 0.306) [ 15 ]. Among patients with ESRD and near-total loss of kidney function, a study of 85 individuals on maintenance hemodialysis showed a median dialysis duration of 4.6 years (range: 2.0–7.6 years). Body composition was assessed using multifrequency BIA performed 30 min after hemodialysis while patients were supine, followed by calculation of the SMI. A significant positive correlation was identified between Cr/CysC and SMI (correlation coefficient = 0.504) [ 16 ]. Given the retrospective design of the present study, dual-energy X-ray absorptiometry and BIA could not be utilized to measure SMI in PD patients. Future studies should validate the relationships of Cr/CysC and Cr×eGFRcys with muscle mass in this population. Previous studies have shown that the SI has considerable predictive value for survival outcomes across a range of disease populations [ 17 – 19 ]. Because all-cause mortality and technique failure are considered key standardized outcomes in patients undergoing PD [ 20 , 21 ], the present study performed correlation analyses to examine associations of SI with these endpoints. In this cohort, Cr/CysC and Cr×eGFRcys levels were significantly correlated with BMI, total cholesterol, triglycerides, uric acid, and phosphorus. These findings suggest that exposure to cardiovascular risk factors influences SI levels, which are also associated with future cardiovascular events and overall mortality. Furthermore, multivariate Cox regression and competing risk analyses demonstrated that both Cr/CysC and Cr×eGFRcys were independent predictors of all-cause mortality in PD patients; Cr×eGFRcys exhibited stronger predictive performance. A possible explanation for this difference is that Cr/CysC is calculated directly from serum Cr and CysC concentrations, whereas the estimation of eGFRcr and eGFRcys incorporates additional factors such as age and sex. This adjustment may enhance the discriminative power of Cr×eGFRcys, making it a more robust predictor than Cr/CysC [ 22 ]. Reports have also demonstrated that in ICU patients receiving mechanical ventilation, eGFR estimated using the CKD-EPI 2012 equation—whether based on cystatin C (eGFRcys) or creatinine (eGFRcr) [ 23 ]—was assessed on days 1, 3, 5, 7, and 10. Simultaneously, actual renal function was measured using iodinated contrast clearance. The findings revealed that both eGFRcys and eGFRcr considerably overestimated renal function, with median values exceeding iodinated contrast clearance by 22 (13–31) mL/min/1.73 m 2 and 59 (49–69) mL/min/1.73 m 2 , respectively. The discrepancy was significantly greater for eGFRcr than for eGFRcys [ 13 ]. In the present study, optimal diagnostic thresholds of Cr/CysC and Cr×eGFRcys for predicting all-cause mortality were determined, independent of factors such as age and sex. The identified cutoffs were 1.48 for Cr/CysC and 61.67 for Cr×eGFRcys. Kaplan–Meier survival curves demonstrated that patients with higher Cr/CysC or Cr×eGFRcys levels had significantly better survival relative to those with lower levels. Technical failure in PD patients may result from complications related to solute and water clearance, catheter malfunction, hernia-associated leaks, infections, psychological or cognitive disorders, and encapsulating sclerosing peritonitis. The present study focused on Cr/CysC and Cr×eGFRcys, derived from baseline cross-sectional data obtained immediately before initiation of PD. These indices do not reflect subsequent changes in peritoneal function during long-term dialysis. Accordingly, baseline values of Cr/CysC and Cr×eGFRcys showed no significant association with the risk of technical failure. Previous studies have shown dissimilar findings. Notably, in anuric PD patients, the rate of technique failure was significantly lower among those with higher Cr/CysC ratios than among those with lower ratios [ 24 ]. Such discrepancies may be explained by differences in patient demographics. Variables including age, sex, renal function, muscle mass, fluid status, medications, and dialysis practices may influence these indices. For example, in a cohort of 1,588 patients receiving continuous renal replacement therapy for acute kidney injury, Cr/CysC ratios ranged from 0.08 to 10.48 [ 25 ]. A decline in the SI has been linked to increased risks of frailty and pneumonia. Data from the China Health and Retirement Longitudinal Study (2011–2015) demonstrated that each 1-unit increase in Cr/CysC was associated with a 17% reduction in the likelihood of frailty (odds ratio = 0.83, 95% CI: 0.74–0.93) [ 26 ]. Similarly, in a study of 312 patients with acute alcohol withdrawal syndrome, after adjustments for age, drinking index, albumin, chronic obstructive pulmonary disease, and diabetes, the risk of pneumonia remained significantly lower in the high SI group (odds ratio = 0.358, 95% CI: 0.132–0.968, P = 0.043) [ 27 ]. In PD patients, the development of frailty or pneumonia is associated with substantially increased risks of adverse outcomes, including technique failure. Thus, the monitoring of fluctuations in Cr/CysC, and Cr×eGFRcys may be needed for early identification of patients with elevated risk of technical failure. The strength of this study lies in its pioneering evaluation of the predictive capacity of Cr/CysC and Cr×eGFRcys for all-cause mortality and technique failure in a relatively large cohort of PD patients. The timing of sample collection and the methods for measuring key indicators, including serum Cr and CysC, were standardized across all participants. Additionally, each patient was followed for more than 1 year, and the dropout rate was minimal. However, several limitations should be acknowledged. First, this was a single-center cross-sectional study without objective measures of muscle mass or strength. The dynamic changes in the SI may be more helpful in predicting adverse outcomes in PD patients. However, due to the characteristics of home treatment in PD, it is challenging to obtain sufficient follow-up data from a large number of patients at the same time point. Second, potential confounding variables, such as dialysis regimens and medication use, were not included in the analysis. Third, in subgroup analyses stratified according to age, sex, BMI, and eGFRcys, the associations of Cr/CysC and Cr×eGFRcys with all-cause mortality varied. It remains unclear whether these inconsistencies were due to selection bias, differences in muscle mass, or hormonal influences across subgroups. Finally, we note that eGFRcys calculations are not suitable for assessing renal function in prevalent PD patients. According to International Society for Peritoneal Dialysis recommendations, renal function in prevalent PD patients should be evaluated using a combination of renal urea clearance and renal Cr clearance [ 28 ]. Conclusion Both Cr/CysC and Cr×eGFRcys independently predict mortality in patients with newly initiated PD. Cr×eGFRcys shows superior predictive ability compared with Cr/CysC. Abbreviations AUC: area under the curve BIA: bioimpedance analysis BMI: body mass index CKD: chronic kidney disease CKD-EPI: Chronic Kidney Disease Epidemiology Cr: creatinine CysC: cystatin C eGFR: estimated glomerular filtration rate ESRD: end-stage renal disease ROC: receiver operating characteristic SI: sarcopenia index Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Jinling Hospital (No. 2023DZKY-003-02) and conducted in accordance with the 1975 Declaration of Helsinki. The requirement for written informed consent was waived due to the retrospective nature of the study. Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors have no conflicts of interest to declare. Funding This work was supported by the Clinical Research and Cultivation Plan Project of Jinling Hospital, Nanjing University School of Medicine (Grant No. 22LCZLXJS25). Authors’ contributions Zhihong Zhang and Man Zhang contributed equally to this work. Zhihong Zhang conceived and designed the study. Zhihong Zhang and Man Zhang analyzed the data and drafted the manuscript. Tingting Zhou and Le Yu collected the data. All authors read and approved the final manuscript. Author details National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210016, Jiangsu, China. References Yang SY, Chen JH, Chiang CK, et al. Pathophysiology and potential treatment of uremic sarcopenia[J]. Kidney Res Clin Pract. 2025 Feb 21. https://doi.org/10.23876/j.krcp.24.176. Pommer W. Preventive nephrology: the role of obesity in different stages of chronic kidney disease[J]. Kidney Dis (Basel). 2018; 4:199-204. Duarte MP, Almeida LS, Neri S, et al. 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Jung CY, Joo YS, Kim HW, et al. Creatinine-cystatin C ratio and mortality in patients receiving intensive care and continuous kidney replacement therapy: a retrospective cohort study[J]. Am J Kidney Dis. 2021; 77(4):509-16.e1. Song Q, Lin T, Liang R, et al. Creatinine-to-cystatin C ratio and frailty in older adults: a longitudinal cohort study[J]. BMC Geriatr. 2024; 24(1):753. Zhao L, Huang S, Jing F, et al. Pneumonia risk prediction in patients with acute alcohol withdrawal syndrome through evaluation of sarcopenia index as a prognostic factor[J]. BMC Geriatr. 2023; 23(1): 84. Chen CH, Perl J, Teitelbaum I. Prescribing high-quality peritoneal dialysis: the role of preserving residual kidney function[J]. Perit Dial Int. 2020; 40(3):274-81. Table 3 Table 3 is available in the Supplementary Files section. Additional Declarations No competing interests reported. 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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-7648129","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":526622770,"identity":"a3ce18b9-e9b1-4923-8e27-c0771e9ed85b","order_by":0,"name":"zhihong Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIie2RMUvEMBTHUw46RbomqA1+ACGhUATlvoiL4aAu9wE6ZnpdCrfexzg46JyS4Zaqa0ehoItDocsNHUz1RlNxc8hvCbz3fvx5Lwh5PP+enpM4QoH6fVKf3mCb3yVU/UVZ4CaTOz0zOcHuTT30o2FRUXT9GRi5fzHwekTL+NoRJp6zFanBiG3TpISCSapWFqJEqyR1xIkSc1Ir88DJOkQCzGXVBkAw0rJyKtFw1KNV2HvXSzDBflMDHWcUhnFIdDilIE50k13tkITzuRSOw/TmCR7tLuuUKntk0lrlgrt3YeWia/Px1l7s0A3j9JWbwxv9yJexS+Gu+s/lrxTl7nk8Ho/nm0/tXmPtfrB4GgAAAABJRU5ErkJggg==","orcid":"","institution":"Jinling Hospital, Nanjing University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"zhihong","middleName":"","lastName":"Zhang","suffix":""},{"id":526622774,"identity":"1f14ee0e-9461-42a8-809e-c69507a58dee","order_by":1,"name":"Man Zhang","email":"","orcid":"","institution":"Jinling Hospital, Nanjing University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Man","middleName":"","lastName":"Zhang","suffix":""},{"id":526622777,"identity":"b1f6d19a-30ff-44a7-9b6c-1151498f5c89","order_by":2,"name":"Tingting Zhou","email":"","orcid":"","institution":"Jinling Hospital, Nanjing University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Zhou","suffix":""},{"id":526622781,"identity":"8d7049f2-f767-4198-916b-25f7d48cd8eb","order_by":3,"name":"Le Yu","email":"","orcid":"","institution":"Jinling Hospital, Nanjing University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Le","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-09-18 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15:54:35","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87857,"visible":true,"origin":"","legend":"","description":"","filename":"a3ba51fe1e0044b6aec582fd9361e60c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7648129/v1/87b52c0d5b1fcb605e2bb0b8.xml"},{"id":93253642,"identity":"a8a3d19e-3103-4d32-8964-8bf22a460c0e","added_by":"auto","created_at":"2025-10-10 16:10:35","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93841,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7648129/v1/1cd698c61858732968c2993d.html"},{"id":93251198,"identity":"4218cc5f-c191-4ce8-9635-c1cecdf6ccc2","added_by":"auto","created_at":"2025-10-10 15:46:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54090,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves of Cr/CysC and Cr×eGFRcys for predicting all-cause mortality.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7648129/v1/4e990963a6348c2f96d131aa.png"},{"id":93252375,"identity":"92395904-1a0b-4327-b895-46a3afc6886b","added_by":"auto","created_at":"2025-10-10 15:54:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":111190,"visible":true,"origin":"","legend":"\u003cp\u003eDistributions of Cr/CysC and Cr×eGFRcys levels in patients and the correlation between the two indices.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7648129/v1/af5e3138f93163973ce8bf23.png"},{"id":93253937,"identity":"077b16a3-0419-44e1-848e-c208a193eaa8","added_by":"auto","created_at":"2025-10-10 16:18:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113839,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative survival curves stratified using optimal cutoff values of Cr/CysC and Cr×eGFRcys.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7648129/v1/cdbca1b10748bc0661152449.png"},{"id":93251202,"identity":"f3e97cd4-3f44-4b70-a8ae-dbc5cfdb0148","added_by":"auto","created_at":"2025-10-10 15:46:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":312736,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of all-cause mortality according to age, sex, BMI, and eGFRcys.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7648129/v1/293e243fde3a807ce7d2e361.png"},{"id":105371587,"identity":"9636e36c-6cc0-4f1a-9a31-2d6a3e4e3896","added_by":"auto","created_at":"2026-03-25 09:28:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1273333,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7648129/v1/f261995d-88ac-43f5-b8a9-e2bc9daf17d7.pdf"},{"id":93251199,"identity":"6d4b0129-0488-4fd8-8a57-d726c7f6dd74","added_by":"auto","created_at":"2025-10-10 15:46:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16856,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7648129/v1/1966f875833b709a91c762c7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The role of the sarcopenia index in prognostic assessment of patients with newly initiated peritoneal dialysis: a retrospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs renal function declines, patients with end-stage renal disease (ESRD) experience progressive accumulation of uremic toxins. This condition is also characterized by elevated inflammatory markers, reduced dietary protein intake, decreased physical activity, diminished levels of sex hormones and growth hormone, reduced insulin and vitamin D concentrations, fewer stellate cells, metabolic acidosis, protein-energy imbalance, and increased levels of angiotensin II and myostatin. The interplay of these factors often leads to reduced muscle strength, alterations in muscle structure, and pronounced muscle wasting among patients initiating dialysis, a condition referred to as uremic sarcopenia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A 2024 meta-analysis of 42,000 patients with chronic kidney disease (CKD) across 25 countries detected a uremic sarcopenia prevalence of 24.5% (95% confidence interval [CI]: 20.9%\u0026ndash;28.3%). Among individuals receiving maintenance dialysis, the prevalence of severe sarcopenia was 26.2% (95% CI: 16.6%\u0026ndash;37.1%) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Patients with ESRD and coexisting uremic sarcopenia experience worse quality of life, as well as substantially increased risks of cardiovascular complications and all-cause mortality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For patients undergoing home peritoneal dialysis (PD), it is crucial to identify simple, objective, and easily measurable biological markers that can assess uremic sarcopenia and its prognostic implications. The sarcopenia index (SI), calculated from serum creatinine (Cr) and cystatin C (CysC) levels, has demonstrated correlations with muscle mass, strength, physical performance, nutritional status, and survival outcomes in diverse populations, including individuals with cardiovascular disease, cancer, chronic obstructive pulmonary disease, and CKD [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, studies evaluating SI in patients on PD remain limited, and its clinical value requires further confirmation. The aim of this study was to investigate the associations of two commonly used SI measures with the risks of all-cause mortality and technique failure in patients with newly initiated PD.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatients\u003c/h2\u003e\u003cp\u003eThis single-center, retrospective cohort study included adult patients with ESRD who underwent PD catheter placement at our facility between January 2004 and January 2023. A minimum follow-up period of 1 year was required, irrespective of sex. Key exclusion criteria were age\u0026thinsp;\u0026ge;\u0026thinsp;80 years, presence of malignant tumors, recent severe trauma or infection, acute kidney injury, and limb disabilities. Patients with severe heart failure (New York Heart Association functional class III\u0026ndash;IV), severe obesity or underweight status (body mass index [BMI]\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e or \u0026le;\u0026thinsp;18 kg/m\u003csup\u003e2\u003c/sup\u003e), or clinically significant edema were also excluded. The study was approved by the Ethics Committee of Jinling Hospital (No. 2023DZKY-003-02). The requirement for written informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy outcomes\u003c/h3\u003e\n\u003cp\u003ePatients were continuously followed until January 2025; routine outpatient visits were supplemented by telephone follow-up. The primary endpoint was all-cause mortality. Secondary endpoints included technique failure (defined as conversion to hemodialysis for at least 3 months), kidney transplantation, and loss to follow-up (defined as inability to establish contact for 3 months or longer). Endpoints were recorded at the time of occurrence of any of these events.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eBaseline data were obtained before PD catheter insertion. Collected variables included sex; age; primary renal disease; BMI; and levels of hemoglobin, C-reactive protein, serum albumin, total cholesterol, triglycerides, uric acid, calcium, phosphorus, blood urea nitrogen, Cr, and CysC. Morning fasting venous blood samples were collected, centrifuged, and analyzed within 1 h. Serum Cr was measured using an automated analyzer (Siemens Advia 1800, Siemens Healthcare GmbH, Henkestr, Germany), and CysC was determined by immunonephelometry calibrated against a reference standard. The SI was calculated using serum Cr (mg/dL) and CysC (mg/L), based on the formulas Cr/CysC and Cr\u0026times;eGFRcys; eGFRcys was estimated using the Chronic Kidney Disease Epidemiology (CKD-EPI) 2021 formula for glomerular filtration rate (eGFR) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation when normally distributed and as median (interquartile range) when they displayed a skewed distribution. Categorical and ordinal variables were expressed as frequencies and percentages. Groupwise comparisons of continuous variables were performed using t-tests or the Mann\u0026ndash;Whitney U test, whereas categorical variables were compared using χ\u0026sup2; tests or Fisher\u0026rsquo;s exact test. Associations of Cr/CysC and Cr\u0026times;eGFRcys with other continuous variables were assessed. Multivariate Cox proportional hazards regression and competing risk models were used to examine the relationships of Cr/CysC and Cr\u0026times;eGFRcys with all-cause mortality and technique failure. In the competing risks framework, technique failure and kidney transplantation were considered competing events for death; death and kidney transplantation were considered competing events for technique failure. All-cause mortality or technique failure was defined as the outcome event. Receiver operating characteristic (ROC) curves were used to compare the area under the curve (AUC) for various SI measures via DeLong\u0026rsquo;s test. The optimal cutoff for SI was determined using the Youden index, and patients were stratified according to this cutoff for survival analysis. Variables with P\u0026thinsp;\u0026le;\u0026thinsp;0.10 in univariate analysis were included, and P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. Statistical analyses were performed using STATA version 14.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePatient characteristics\u003c/h2\u003e\u003cp\u003eIn total, 752 patients undergoing PD were included; 418 (55.6%) were men and the mean age was 42.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6 years. The most common cause of renal disease was chronic glomerulonephritis, identified in 587 patients (78.1%). Concurrent diabetes was present in 49 patients (6.5%). The median values of Cr/CysC and Cr\u0026times;eGFRcys were 1.45 (1.21\u0026ndash;1.77) and 69.40 (56.39\u0026ndash;87.99), respectively.\u003c/p\u003e\u003cp\u003eThe median follow-up duration was 47 months (range, 28\u0026ndash;83 months). During this period, 82 patients (10.9%) died, 271 (36.0%) experienced technique failure, 172 (22.9%) underwent kidney transplantation, and 55 (7.3%) were lost to follow-up. ROC curve analysis for all-cause mortality as the primary endpoint demonstrated AUCs of 0.614 (95% CI: 0.551\u0026ndash;0.678; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for Cr/CysC and 0.669 (95% CI: 0.607\u0026ndash;0.730; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for Cr\u0026times;eGFRcys; the difference between these two AUCs was statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Based on the Youden index, optimal cutoff values for predicting all-cause mortality were 1.48 for Cr/CysC and 61.67 for Cr\u0026times;eGFRcys. Baseline clinical characteristics of patients stratified according to the optimal cutoff for Cr\u0026times;eGFRcys are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of 752 peritoneal dialysis patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll Patients\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;752)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCr\u0026times;eGFRcys\u0026thinsp;\u0026ge;\u0026thinsp;61.67\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;503)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCr\u0026times;eGFRcys\u0026thinsp;\u0026lt;\u0026thinsp;61.67\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;249)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.15\u0026thinsp;\u0026plusmn;\u0026thinsp;12.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.50\u0026thinsp;\u0026plusmn;\u0026thinsp;15.56\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e418 (55.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e334 (61.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139 (44.7)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody mass index (kg/m2 )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.59\u0026thinsp;\u0026plusmn;\u0026thinsp;3.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.10\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlomerulonephritis n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e587 (78.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e412 (81.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e175 (70.3)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110.6\u0026thinsp;\u0026plusmn;\u0026thinsp;17.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.715\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum creatinine (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.20\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum cystatin C (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric acid (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499.0\u0026thinsp;\u0026plusmn;\u0026thinsp;115.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e489.9\u0026thinsp;\u0026plusmn;\u0026thinsp;118.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e517.5\u0026thinsp;\u0026plusmn;\u0026thinsp;106.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorrected calcium (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.668\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhosphorus (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\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\u003e40.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\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\u003e1.80 (1.31, 2.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.69 (1.24, 2.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.03 (1.42, 2.66)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFRcys (ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.80 (8.02, 12.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.76 (8.72, 13.469)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.46 (6.88, 10.10)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr/CysC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.45 (1.21, 1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.64 (1.44, 1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.11 (0.99, 1.24)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr\u0026times;eGFRcys\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.40 (56.39, 87.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.77 (69.19, 97.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.84 (45.21, 56.38)\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\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCr/CysC, creatinine-to-cystatin C ratio; eGFRcys, estimated glomerular filtration rate based on serum cystatin C.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCorrelations of Cr/CysC and Cr×eGFRcys with other parameters\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the correlations of Cr/CysC and Cr\u0026times;eGFRcys with selected clinical variables. Both indices showed positive correlations with BMI and serum phosphorus; they demonstrated negative correlations with total cholesterol, triglycerides, and uric acid. A strong positive correlation was also observed between Cr/CysC and Cr\u0026times;eGFRcys (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpearman correlation analysis of factors associated with Cr/CysC and Cr\u0026times;eGFRcys\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCr/CysC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eCr\u0026times;eGFRcys\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003er\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003er\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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/m2 )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.791\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\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003eTriglycerides (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003eUric acid (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhosphorus (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCr/CysC, creatinine-to-cystatin C ratio; eGFRcys, estimated glomerular filtration rate based on serum cystatin C.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eAssociations of Cr/CysC and Cr×eGFRcys with all-cause mortality\u003c/h3\u003e\n\u003cp\u003eWhen analyzed as continuous variables in univariate Cox regression models, both Cr/CysC and Cr\u0026times;eGFRcys were significantly associated with all-cause mortality (hazard ratio [HR]\u0026thinsp;=\u0026thinsp;0.532, 95% CI: 0.304\u0026ndash;0.933, P\u0026thinsp;=\u0026thinsp;0.028; HR\u0026thinsp;=\u0026thinsp;0.981, 95% CI: 0.970\u0026ndash;0.992, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). Patients with Cr/CysC\u0026thinsp;\u0026lt;\u0026thinsp;1.48 had a mortality rate of 14.4% during follow-up; this rate was 7.2% among patients with Cr/CysC\u0026thinsp;\u0026ge;\u0026thinsp;1.48. Similarly, patients with Cr\u0026times;eGFRcys\u0026thinsp;\u0026lt;\u0026thinsp;61.67 had a mortality rate of 18.9%, compared with 7.0% among patients exhibiting Cr\u0026times;eGFRcys\u0026thinsp;\u0026ge;\u0026thinsp;61.67. Kaplan\u0026ndash;Meier survival curves stratified using these cutoff points demonstrated statistically significant differences in mortality risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicates that, after adjustments for age, sex, BMI, diabetes, albumin, uric acid, total cholesterol, phosphorus, and hemoglobin, both Cr/CysC and Cr\u0026times;eGFRcys remained significantly associated with all-cause mortality in Cox regression and competing risk models. Comparable results were observed when data were analyzed using the cutoff values of 1.48 for Cr/CysC and 61.67 for Cr\u0026times;eGFRcys.\u003c/p\u003e\u003cp\u003eAssociations of Cr/CysC and Cr\u0026times;eGFRcys with technique failure\u003c/p\u003e\n\u003cp\u003eCr/CysC and Cr\u0026times;eGFRcys were analyzed as continuous variables in univariate Cox regression models. HRs for technique failure were 1.292 (95% CI: 0.994\u0026ndash;1.679; P\u0026thinsp;=\u0026thinsp;0.056) and 1.004 (95% CI: 0.999\u0026ndash;1.009; P\u0026thinsp;=\u0026thinsp;0.122), respectively. After adjustments for age, sex, BMI, diabetes, albumin, uric acid, total cholesterol, phosphorus, and hemoglobin, Cr/CysC was not significantly associated with technique failure (HR\u0026thinsp;=\u0026thinsp;1.065, 95% CI: 0.789\u0026ndash;1.437; P\u0026thinsp;=\u0026thinsp;0.680). The predictive ability of both indices for technique failure was further evaluated using ROC analysis. The AUCs were 0.514 (95% CI, 0.471\u0026ndash;0.557; P\u0026thinsp;=\u0026thinsp;0.325) for Cr/CysC, and 0.534 (95% CI, 0.491\u0026ndash;0.577; P\u0026thinsp;=\u0026thinsp;0.122) for Cr\u0026times;eGFRcys.\u003c/p\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eSubgroup analysis of mortality according to age, sex, BMI, and eGFRcys\u003c/h2\u003e\n \u003cp\u003eAge, sex, BMI, and residual renal function may influence Cr/CysC and Cr\u0026times;eGFRcys levels. Patients were stratified by sex (male or female), age (\u0026lt;\u0026thinsp;60 years or \u0026ge;\u0026thinsp;60 years), BMI (\u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e or \u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e), and eGFRcys (\u0026lt;\u0026thinsp;10 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e or \u0026ge;\u0026thinsp;10 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e). Univariate Cox regression analysis was performed to evaluate associations of Cr/CysC and Cr\u0026times;eGFRcys with all-cause mortality within each subgroup. Significant associations of both indices with mortality risk were observed among patients aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years, those with BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e, those with eGFRcys\u0026thinsp;\u0026lt;\u0026thinsp;10 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, and across sex-stratified groups (Fig. 4).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study analyzed a large cohort of patients undergoing PD and yielded three principal findings. First, a significant correlation was observed between Cr/CysC and Cr\u0026times;eGFRcys among patients with newly initiated PD. Second, both Cr/CysC and Cr\u0026times;eGFRcys were independent predictors of all-cause mortality; Cr\u0026times;eGFRcys demonstrated stronger predictive performance. Third, neither Cr/CysC nor Cr\u0026times;eGFRcys was significantly associated with technique failure in PD patients.\u003c/p\u003e\u003cp\u003eThe SI is derived from serum Cr and CysC levels. Although various formulas are used for its calculation, the associations between Cr/CysC and Cr\u0026times;eGFRcys are consistent. Studies conducted in populations with varying kidney function have shown that both indices are promising surrogate markers of muscle mass across diverse groups. For example, in a study of 226 intensive care unit (ICU) patients exhibiting normal renal function, Cr/CysC demonstrated a strong positive correlation with paraspinal skeletal muscle surface area at the level of the fourth lumbar vertebra measured by computed tomography (correlation coefficient\u0026thinsp;=\u0026thinsp;0.62) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Similarly, data from the National Health and Nutrition Examination Survey, which included 3,741 participants with normal renal function, indicated that Cr/CysC showed moderate agreement with dual-energy X-ray absorptiometry, the reference standard for diagnosing low muscle mass, with an AUC approaching 0.70 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In longitudinal analyses, a study of 38 ICU patients on mechanical ventilation, all with initially normal renal function, assessed changes in rectus femoris cross-sectional area using ultrasound on days 1, 3, 5, 7, and 10. The results showed that the cross-sectional area decreased by 2% per day (range, 1%\u0026ndash;3%), and the decline closely paralleled changes in Cr/CysC levels (correlation coefficient\u0026thinsp;=\u0026thinsp;0.61) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Concerning patients with renal insufficiency, one study evaluated 297 individuals with CKD and an eGFR\u0026thinsp;\u0026lt;\u0026thinsp;45 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e. Skeletal muscle mass was measured using bioimpedance analysis (BIA), and a significant positive correlation was observed between Cr\u0026times;eGFRcys and total skeletal muscle mass (correlation coefficient\u0026thinsp;=\u0026thinsp;0.503). The AUCs for Cr\u0026times;eGFRcys in diagnosing sarcopenia, as defined by the Asian Working Group for Sarcopenia 2019 consensus, were 0.646 in men and 0.754 in women [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Another study of 272 CKD patients, with a median eGFR of 36.5 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, revealed a significant positive association between Cr/CysC and the skeletal muscle index (SMI) (correlation coefficient\u0026thinsp;=\u0026thinsp;0.306) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Among patients with ESRD and near-total loss of kidney function, a study of 85 individuals on maintenance hemodialysis showed a median dialysis duration of 4.6 years (range: 2.0\u0026ndash;7.6 years). Body composition was assessed using multifrequency BIA performed 30 min after hemodialysis while patients were supine, followed by calculation of the SMI. A significant positive correlation was identified between Cr/CysC and SMI (correlation coefficient\u0026thinsp;=\u0026thinsp;0.504) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Given the retrospective design of the present study, dual-energy X-ray absorptiometry and BIA could not be utilized to measure SMI in PD patients. Future studies should validate the relationships of Cr/CysC and Cr\u0026times;eGFRcys with muscle mass in this population.\u003c/p\u003e\u003cp\u003ePrevious studies have shown that the SI has considerable predictive value for survival outcomes across a range of disease populations [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Because all-cause mortality and technique failure are considered key standardized outcomes in patients undergoing PD [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the present study performed correlation analyses to examine associations of SI with these endpoints. In this cohort, Cr/CysC and Cr\u0026times;eGFRcys levels were significantly correlated with BMI, total cholesterol, triglycerides, uric acid, and phosphorus. These findings suggest that exposure to cardiovascular risk factors influences SI levels, which are also associated with future cardiovascular events and overall mortality. Furthermore, multivariate Cox regression and competing risk analyses demonstrated that both Cr/CysC and Cr\u0026times;eGFRcys were independent predictors of all-cause mortality in PD patients; Cr\u0026times;eGFRcys exhibited stronger predictive performance. A possible explanation for this difference is that Cr/CysC is calculated directly from serum Cr and CysC concentrations, whereas the estimation of eGFRcr and eGFRcys incorporates additional factors such as age and sex. This adjustment may enhance the discriminative power of Cr\u0026times;eGFRcys, making it a more robust predictor than Cr/CysC [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Reports have also demonstrated that in ICU patients receiving mechanical ventilation, eGFR estimated using the CKD-EPI 2012 equation\u0026mdash;whether based on cystatin C (eGFRcys) or creatinine (eGFRcr) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u0026mdash;was assessed on days 1, 3, 5, 7, and 10. Simultaneously, actual renal function was measured using iodinated contrast clearance. The findings revealed that both eGFRcys and eGFRcr considerably overestimated renal function, with median values exceeding iodinated contrast clearance by 22 (13\u0026ndash;31) mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e and 59 (49\u0026ndash;69) mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, respectively. The discrepancy was significantly greater for eGFRcr than for eGFRcys [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In the present study, optimal diagnostic thresholds of Cr/CysC and Cr\u0026times;eGFRcys for predicting all-cause mortality were determined, independent of factors such as age and sex. The identified cutoffs were 1.48 for Cr/CysC and 61.67 for Cr\u0026times;eGFRcys. Kaplan\u0026ndash;Meier survival curves demonstrated that patients with higher Cr/CysC or Cr\u0026times;eGFRcys levels had significantly better survival relative to those with lower levels.\u003c/p\u003e\u003cp\u003eTechnical failure in PD patients may result from complications related to solute and water clearance, catheter malfunction, hernia-associated leaks, infections, psychological or cognitive disorders, and encapsulating sclerosing peritonitis. The present study focused on Cr/CysC and Cr\u0026times;eGFRcys, derived from baseline cross-sectional data obtained immediately before initiation of PD. These indices do not reflect subsequent changes in peritoneal function during long-term dialysis. Accordingly, baseline values of Cr/CysC and Cr\u0026times;eGFRcys showed no significant association with the risk of technical failure. Previous studies have shown dissimilar findings. Notably, in anuric PD patients, the rate of technique failure was significantly lower among those with higher Cr/CysC ratios than among those with lower ratios [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Such discrepancies may be explained by differences in patient demographics. Variables including age, sex, renal function, muscle mass, fluid status, medications, and dialysis practices may influence these indices. For example, in a cohort of 1,588 patients receiving continuous renal replacement therapy for acute kidney injury, Cr/CysC ratios ranged from 0.08 to 10.48 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A decline in the SI has been linked to increased risks of frailty and pneumonia. Data from the China Health and Retirement Longitudinal Study (2011\u0026ndash;2015) demonstrated that each 1-unit increase in Cr/CysC was associated with a 17% reduction in the likelihood of frailty (odds ratio\u0026thinsp;=\u0026thinsp;0.83, 95% CI: 0.74\u0026ndash;0.93) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Similarly, in a study of 312 patients with acute alcohol withdrawal syndrome, after adjustments for age, drinking index, albumin, chronic obstructive pulmonary disease, and diabetes, the risk of pneumonia remained significantly lower in the high SI group (odds ratio\u0026thinsp;=\u0026thinsp;0.358, 95% CI: 0.132\u0026ndash;0.968, P\u0026thinsp;=\u0026thinsp;0.043) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In PD patients, the development of frailty or pneumonia is associated with substantially increased risks of adverse outcomes, including technique failure. Thus, the monitoring of fluctuations in Cr/CysC, and Cr\u0026times;eGFRcys may be needed for early identification of patients with elevated risk of technical failure.\u003c/p\u003e\u003cp\u003eThe strength of this study lies in its pioneering evaluation of the predictive capacity of Cr/CysC and Cr\u0026times;eGFRcys for all-cause mortality and technique failure in a relatively large cohort of PD patients. The timing of sample collection and the methods for measuring key indicators, including serum Cr and CysC, were standardized across all participants. Additionally, each patient was followed for more than 1 year, and the dropout rate was minimal. However, several limitations should be acknowledged. First, this was a single-center cross-sectional study without objective measures of muscle mass or strength. The dynamic changes in the SI may be more helpful in predicting adverse outcomes in PD patients. However, due to the characteristics of home treatment in PD, it is challenging to obtain sufficient follow-up data from a large number of patients at the same time point. Second, potential confounding variables, such as dialysis regimens and medication use, were not included in the analysis. Third, in subgroup analyses stratified according to age, sex, BMI, and eGFRcys, the associations of Cr/CysC and Cr\u0026times;eGFRcys with all-cause mortality varied. It remains unclear whether these inconsistencies were due to selection bias, differences in muscle mass, or hormonal influences across subgroups. Finally, we note that eGFRcys calculations are not suitable for assessing renal function in prevalent PD patients. According to International Society for Peritoneal Dialysis recommendations, renal function in prevalent PD patients should be evaluated using a combination of renal urea clearance and renal Cr clearance [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBoth Cr/CysC and Cr\u0026times;eGFRcys independently predict mortality in patients with newly initiated PD. Cr\u0026times;eGFRcys shows superior predictive ability compared with Cr/CysC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC: area under the curve\u003c/p\u003e\n\u003cp\u003eBIA: bioimpedance analysis\u003c/p\u003e\n\u003cp\u003eBMI: body mass index\u003c/p\u003e\n\u003cp\u003eCKD: chronic kidney disease\u003c/p\u003e\n\u003cp\u003eCKD-EPI: Chronic Kidney Disease Epidemiology\u003c/p\u003e\n\u003cp\u003eCr: creatinine\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCysC: cystatin C\u003c/p\u003e\n\u003cp\u003eeGFR: estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eESRD: end-stage renal disease\u003c/p\u003e\n\u003cp\u003eROC: receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eSI: sarcopenia index\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Jinling Hospital (No. 2023DZKY-003-02) and conducted in accordance with the 1975 Declaration of Helsinki. The requirement for written informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Clinical Research and Cultivation Plan Project of Jinling Hospital, Nanjing University School of Medicine (Grant No. 22LCZLXJS25).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhihong Zhang and Man Zhang contributed equally to this work. Zhihong Zhang conceived and designed the study. Zhihong Zhang and Man Zhang analyzed the data and drafted the manuscript. Tingting Zhou and Le Yu collected the data. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210016, Jiangsu, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYang SY, Chen JH, Chiang CK, et al. Pathophysiology and potential treatment of uremic sarcopenia[J]. Kidney Res Clin Pract. 2025 Feb 21. https://doi.org/10.23876/j.krcp.24.176.\u003c/li\u003e\n\u003cli\u003ePommer W. Preventive nephrology: the role of obesity in different stages of chronic kidney disease[J]. Kidney Dis (Basel). 2018; 4:199-204.\u003c/li\u003e\n\u003cli\u003eDuarte MP, Almeida LS, Neri S, et al. 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Diagnostic and prognostic value of the creatinine/cystatin C ratio for low muscle mass evaluation among US adults[J]. Front Nutr. 2022; 9: 897774.\u003c/li\u003e\n\u003cli\u003eHaines RW, Fowler AJ, Liang K, et al. Comparison of cystatin C and creatinine in the assessment of Measured kidney function during critical illness[J]. Clin J Am Soc Nephrol. 2023; 18(8): 997-1005.\u003c/li\u003e\n\u003cli\u003eLin YL, Wang CH, Chang IC, et al. A novel application of serum creatinine and cystatin C to predict sarcopenia in advanced CKD[J]. Front Nutr. 2022; 9: 828880.\u003c/li\u003e\n\u003cli\u003eLin YL, Chen SY, Lai YH, et al. Serum creatinine to cystatin C ratio predicts skeletal muscle mass and strength in patients with non-dialysis chronic kidney disease[J]. Clin Nutr. 2020; 39(8): 2435-41.\u003c/li\u003e\n\u003cli\u003eYajima T, Yajima K. Serum creatinine-to-cystatin C ratio as an indicator of sarcopenia in hemodialysis patients[J]. Clin Nutr ESPEN. 2023; 56: 200-6.\u003c/li\u003e\n\u003cli\u003eBruce DG, Davis WA, Chubb SAP, et al. The relationship between shrunken pore syndrome and all-cause mortality in people with type 2 diabetes and normal renal function: the Fremantle Diabetes Study Phase II[J]. Diabetologia. 2025; 68(7): 1440-51.\u003c/li\u003e\n\u003cli\u003eLi M, Liang Y, Wu B, et al. Sex-specific prognostic utility of the sarcopenia index in all-cause mortality risk for patients with heart failure[J]. Front Nutr. 2025; 12:1472596.\u003c/li\u003e\n\u003cli\u003eJi H, Liu B, Jin P, et al. Creatinine-to-cystatin C ratio and body composition predict response to PD-1 inhibitors-based combination treatment in metastatic gastric cancer[J]. Front Immunol. 2024; 15: 1364728.\u003c/li\u003e\n\u003cli\u003eManera KE, Johnson DW, Craig JC, et al. Establishing a core outcome set for peritoneal dialysis: report of the SONG-PD (Standardized Outcomes in Nephrology-Peritoneal Dialysis) Consensus Workshop[J]. Am J Kidney Dis. 2020; 75(3):404-12.\u003c/li\u003e\n\u003cli\u003eBello Ak, Okpechi IG, Osman MA, et al. Epidemiology of peritoneal dialysis outcomes[J]. Nat Rev Nephrol. 2022; 18(12): 779-93.\u003c/li\u003e\n\u003cli\u003eLien YH. Looking for sarcopenia biomarkers[J]. Am J Med. 2017; 130(5): 502-3.\u003c/li\u003e\n\u003cli\u003eInker LA, Schmid CH, Tighiouart H, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C[J]. N Engl J Med. 2012; 367(1): 20-9.\u003c/li\u003e\n\u003cli\u003eZhang ZH, Zhou TT, Zhang M, et al. Predictive value of creatinine-cystatin C ratio for mortality and technique failure in anuric peritoneal dialysis patients[J]. Ren Fail. 2025; 47(1):2444389.\u003c/li\u003e\n\u003cli\u003eJung CY, Joo YS, Kim HW, et al. Creatinine-cystatin C ratio and mortality in patients receiving intensive care and continuous kidney replacement therapy: a retrospective cohort study[J]. Am J Kidney Dis. 2021; 77(4):509-16.e1.\u003c/li\u003e\n\u003cli\u003eSong Q, Lin T, Liang R, et al. Creatinine-to-cystatin C ratio and frailty in older adults: a longitudinal cohort study[J]. BMC Geriatr. 2024; 24(1):753.\u003c/li\u003e\n\u003cli\u003eZhao L, Huang S, Jing F, et al. Pneumonia risk prediction in patients with acute alcohol withdrawal syndrome through evaluation of sarcopenia index as a prognostic factor[J]. BMC Geriatr. 2023; 23(1): 84.\u003c/li\u003e\n\u003cli\u003eChen CH, Perl J, Teitelbaum I. Prescribing high-quality peritoneal dialysis: the role of preserving residual kidney function[J]. Perit Dial Int. 2020; 40(3):274-81.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 3","content":"\u003cp\u003eTable 3 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"peritoneal dialysis, mortality, technique failure, sarcopenia index","lastPublishedDoi":"10.21203/rs.3.rs-7648129/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7648129/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe relationship between the sarcopenia index (SI) and poor prognosis in peritoneal dialysis (PD) is not well established.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe SI was calculated using fasting morning serum creatinine (Cr, mg/dL) and cystatin C (CysC, mg/L) levels obtained prior to PD catheter insertion. Two formulas were applied: Cr/CysC and Cr\u0026times;eGFRcys; eGFRcys was estimated using the Chronic Kidney Disease Epidemiology (CKD-EPI) 2021 equation. Associations between SI and the risk of all-cause mortality or technique failure were analyzed using Cox proportional hazards models and competing risk models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn total, 752 PD patients (mean age 42.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4 years; 55.6% men) were included. Multivariate Cox regression showed that both Cr/CysC and Cr\u0026times;eGFRcys were significantly associated with mortality risk (hazard ratio\u0026thinsp;=\u0026thinsp;0.476, 95% confidence interval: 0.262\u0026ndash;0.866, P\u0026thinsp;=\u0026thinsp;0.015; hazard ratio\u0026thinsp;=\u0026thinsp;0.985, 95% confidence interval: 0.973\u0026ndash;0.997, P\u0026thinsp;=\u0026thinsp;0.013, respectively). In the competing risk model, both indices remained independent predictors of mortality. Area under the receiver operating characteristic curve values for predicting mortality were 0.614 for Cr/CysC and 0.669 for Cr\u0026times;eGFRcys (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant association was observed between SI and risk of technique failure.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eBoth Cr/CysC and Cr\u0026times;eGFRcys are independent predictors of mortality in PD patients; Cr\u0026times;eGFRcys shows superior predictive accuracy.\u003c/p\u003e","manuscriptTitle":"The role of the sarcopenia index in prognostic assessment of patients with newly initiated peritoneal dialysis: a retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 15:46:30","doi":"10.21203/rs.3.rs-7648129/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a29174b2-7c06-49bf-a71c-6e59f684e678","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T09:28:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 15:46:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7648129","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7648129","identity":"rs-7648129","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}