Calcimimetics reduce mortality in elderly dialysis patients with protein-energy wasting and inflammation

Calcimimetics reduce mortality in elderly dialysis patients with protein-energy wasting and inflammation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Calcimimetics reduce mortality in elderly dialysis patients with protein-energy wasting and inflammation Kazuyoshi Okada, Manabu Tashiro, Daisuke Hara, Tomoko Inoue, Takahiro Kuragano, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8285601/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Calcimimetics reduce mortality in older patients on dialysis. Because elderly patients are prone to protein-energy wasting (PEW) and inflammation, we investigated whether this effect is independent of these conditions. This retrospective study used propensity score matching to compare 2-year all-cause mortality between calcimimetic users and non-users. Patients were stratified into those without PEW and inflammation (Group 1, n = 240) and those with PEW and/or inflammation (Group 2, n = 498). Survival was assessed using Kaplan–Meier survival curves, censored for calcimimetic use and other covariates. In Group 2, mortality was significantly lower in calcimimetic users than in non-users after matching (hazard ratio [HR] 0.221, 95% confidence interval [CI] 0.073–0.670, P = 0.003, log-rank test), but not in Group 1. The significant difference in Group 2 was no longer observed after Cox proportional hazards regression adjusted for covariates that remained imbalanced following matching (adjusted HR, 0.272, 95% CI 0.073–1.006, P = 0.051). In Group 2, age-stratified analysis (median 69 years) showed significantly lower mortality in calcimimetic users among older patients (HR, 0.206, 95% CI, 0.058–0.728, P = 0.014), but not younger patients. These findings suggest that calcimimetics reduce mortality in elderly patients with PEW and/or inflammation, but not in those without these conditions. Health sciences/Diseases Health sciences/Medical research Health sciences/Nephrology Health sciences/Risk factors calcimimetics mortality protein-energy wasting inflammation CKD-MBD Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Chronic kidney disease-mineral and bone disorder (CKD-MBD) is a systemic condition that increases mortality and morbidity, including cardiovascular disease, in dialysis patients. Protein-energy wasting (PEW) and inflammation are important aggravating factors [ 1 ]. Malnutrition-inflammation-cachexia syndrome has also been linked to mortality in elderly patients, particularly in those with a low body mass index (BMI) [ 2 ]. Secondary hyperparathyroidism (SHPT) has been shown to induce PEW and act as a mediating factor for mortality in dialysis patients [ 3 ]. The Ca-sensing receptor (CaSR) plays an important role in not only Ca homeostasis related disorders but also various other non-Ca-related diseases. CaSR is functionally expressed in almost all components of the cardiovascular system, and abnormalities of CaSR in hematopoietic cells and vascular cells contribute to various conditions, particularly the promotion of inflammation [ 4 ]. Accordingly, parathyroid hormone (PTH)-lowering agents such as calcimimetics may reduce mortality associated with PEW and inflammation. The 2012 clinical practice guidelines of the Japanese Society for Dialysis Therapy (JSDT) set the target intact PTH range for dialysis patients at 60–240 pg/mL [ 5 ]. Prior to the availability of calcimimetics, PTH levels of ≥ 300 pg/mL and < 120 pg/mL were significantly associated with increased mortality in both time-dependent and time-averaged models using the JSDT Renal Data Registry (JRDR) 2006–2009 database [ 6 ]. In contrast, among calcimimetic users, PTH levels of ≥ 326 pg/mL and < 52 pg/mL were not associated with significantly increased mortality relative to the target range in either model using the JRDR 2009–2018 database [ 7 ]. These findings suggest that calcimimetic therapy may allow for a higher upper limit and a decreased lower limit of the PTH target range. In the EVOLVE randomized controlled trial (RCT), a subgroup analysis suggested that calcimimetics may reduce mortality in elderly patients [ 8 ]. Although elderly patients are more susceptible to PEW and inflammation [ 9 ] and the effects of calcimimetics may be influenced by the presence of these conditions, these relationships have not yet been clarified. Therefore, this study aimed to determine whether calcimimetics reduce mortality in elderly dialysis patients, irrespective of the presence of PEW and/or inflammation. Methods Patient selection A total of 944 patients undergoing maintenance dialysis with hemodialysis (HD) or online hemodiafiltration (OHDF) were identified from medical records held in our corporation database as of July 1, 2017. Exclusions were applied according to criteria reported previously (Fig. 1 ) [ 10 ]. After these exclusions, 738 patients remained eligible, including those undergoing HD using membranes with in vitro β 2 -microglobulin (β 2 MG) clearance of ≥ 70 mL/min and those undergoing OHDF. Patients with BMI of ≥ 22 kg/m 2 and high-sensitivity C-reactive protein (hs-CRP) of < 0.3 mg/dL were classified into Group 1 (n = 240; calcimimetic users n = 75 and non-users n = 165), and those with BMI of < 22 kg/m 2 and/or hs-CRP of ≥ 0.3 mg/dL were classified into Group 2 (n = 498; calcimimetic users n = 138 and non-users n = 360) according to previously reported Japanese thresholds for BMI and hs-CRP [ 11 ]. Propensity score matching was then performed between calcimimetic users and non-users within each group. Dialysis and dilution methods were determined by each patient’s physician. Each dialysis session lasted 4 h, with a blood flow rate of 250–350 mL/min. Membrane surface areas were 2.1–2.5 m 2 for HD and 2.0–3.0 m 2 for OHDF. The dialysate flow rate (QD) in HD and total QD (QD plus the substitution volume) in OHDF were both fixed at 500 mL/min. All blood tests were measured centrally (BML, Inc., Japan) and results were extracted from medical records. Some data in this study overlap with those in our previous report [ 12 ]. The previous study, based on the 2012 JSDT CKD-MBD guidelines, analyzed patients with PTH levels within the target range (60–240 pg/mL) and compared mortality between calcimimetic users and non-users. In contrast, the present study included cases across all PTH levels, examined mortality according to PEW and inflammation status, and conducted age-stratified analyses. Thus, the analytical objectives and methods were distinct. Outcomes The primary endpoints were 2-year all-cause mortality, assessed using a propensity score-matched model and an adjusted Cox proportional hazards model, comparing calcimimetic users and non-users in Groups 1 and 2. Secondary endpoints included 2-year all-cause mortality as assessed by a median age-stratified analysis between users and non-users in Groups 1 and 2, and 2-year cumulative survival as assessed by the a median age-stratified Kaplan-Meier analysis, comparing users and non-users within each group. Preparation of Propensity Score-Matched Pairs Propensity scores for Groups 1 and 2 were calculated to match calcimimetic users and non-users. Covariates included age, sex, dialysis vintage, presence or absence of diabetes mellitus (DM), presence or absence of cardiovascular disease (including angina pectoris, myocardial infarction, atrial fibrillation, heart failure, stroke, peripheral artery disease, and limb amputation), administration of active vitamin D analogues (oral and intravenous), administration of phosphate (P) binders, Kt/V, β 2 MG, albumin leakage, normalized protein catabolism rate (nPCR), albumin, systolic blood pressure, hemoglobin, P, corrected Ca, and PTH. The parameters used for grouping (BMI and hs-CRP) were excluded from the covariates. Propensity scores were matched for 75 pairs in Group 1 and 138 pairs in Group 2. Multivariable logistic regression was performed with calcimimetic use (users vs. non-users) as the dependent variable and the covariates listed above as independent variables. After logit transformation, each patient’s propensity score was calculated to 14 decimal places. Patients were then paired at a 1:1 ratio using nearest‑neighbor matching within calipers of 0.318668 for Group 1 and 0.250412 for Group 2 (0.2 × standard deviation of the logit) [ 13 ]. Statistical analysis Survival was determined from medical records, which included information on death and transfer to other hospitals. A daily survival analysis was performed for the two groups using the Kaplan–Meier method, incorporating deaths and transfers. Transfer between Groups 1 and 2, changes between calcimimetic use and non-use, changes in dialysis modality, and transfer of albumin leakage between < 3 g/session and ≥ 3 g/session were confirmed annually. In the Kaplan–Meier analysis, instances of switching between these groups were censored annually. Consequently, only patients with stable conditions during the first year and without censoring events were eligible for further follow-up. The statistical significance of differences between two groups was assessed using the log-rank test. Cox regression analysis was performed to calculate hazard ratios (HRs). All-cause mortality was compared between groups by Cox proportional hazards regression analysis, with adjustment for some covariates that remained significantly different between the groups after propensity score matching. Stratified analyses based on median age were also conducted for all-cause mortality after propensity score were also conducted to compare calcimimetic users and non-users. All analyses were performed using SPSS Statistics for Windows (ver. 26, IBM Corp., Armonk, NY). Two-tailed P values of < 0.05 were considered statistically significant. Ethics This study was approved by the Research Ethics Committee of Kawashima Hospital on October 7, 2025, and registered in the UMIN Clinical Trials Registry (UMIN000059330 registered October 8, 2025 - prospectively registered, https://center6.umin.ac.jp/cgi-bin/ctr/ctr_view_reg.cgi?recptno=R000067867 ). All clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki. The need to obtain informed consent was waived by Research Ethics Committee of Kawashima hospital. The research information was disclosed to patients based on Ethical Guidelines for Life Science and Medical Research Involving Human Subjects before enrollment. Results The mean observation period for calcimimetic users and non-users was 473.9 ± 176.1 and 437.1 ± 156.7 months, respectively, in Group 1, and 502.5 ± 178.5 and 431.0 ± 168.3 months in Group 2. In calcimimetic users and non-users, the numbers of deaths, transfers to other hospitals, and censored cases were respectively 2 and 4, 5 and 3, and 50 (some patients counted more than once: changed to non-users, n = 10; transfer to Group 2, n = 47 [hs-CRP not measured, n = 39]; different dialysis method, n = 14; and change of albumin leakage group, n = 35 [membrane albumin leakage unknown, n = 31]) and 55 (some patients counted more than once: changed to non-users, n = 13; transfer to Group 2, n = 54 [hs-CRP not measured, n = 44]; different dialysis method, n = 19; and change of albumin leakage group, n = 31 [membrane albumin leakage unknown, n = 29]) in Group 1, and 4 and 15, 4 and 6, and 83 (some patients counted more than once: changed to non-users, n = 20; transfer to Group 1, n = 73 [hs-CRP not measured, n = 66]; different dialysis method, n = 19; and change of albumin leakage group, n = 59 [membrane albumin leakage unknown, n = 51]) and 95 (some patients counted more than once: changed to non-users, n = 10; transfer to Group 1, n = 93 [hs-CRP not measured, n = 86]; different dialysis method, n = 40; and change of albumin leakage group, n = 73 [membrane albumin leakage unknown, n = 67]) in Group 2. The annual survival rates in the first and second years were 98.6% and 94.5%, respectively, for calcimimetic users and 94.7% and 94.7% for non-users in Group 1. The rates were 97.8% and 96.0%, respectively, for calcimimetic users and 92.0% and 80.0% for non-users in Group 2. Comparison of 2-year all-cause mortality between calcimimetic users and non-users in Group 1 Table 1 compares variables before and after propensity score matching. BMI and hs‑CRP did not differ significantly between calcimimetic users and non‑users either before or after matching. After matching, users had significantly longer dialysis vintage, greater albumin leakage, and higher PTH, while the prevalence of DM was significantly lower relative to non-users. Table 1 Comparison of variables in calcimimetic users and non-users before and after propensity score matching between groups in patients without protein-energy wasting and inflammation. Item Before matching After matching Users Non-users P value Users Non-users P value N 75 165 75 75 Age, yr 62.8 ± 12.3 68.4 ± 11.4 0.002 62.8 ± 12.3 66.5 ± 12.5 0.099 Sex, male/female, % 72.0/28.0 68.5/31.5 0.651 72.0/28.0 76.0/24.0 0.710 Dialysis vintage, mo 138.5 ± 84.5 91.7 ± 98.2 < 0.001 138.5 ± 84.5 111.2 ± 104.1 0.005 Diabetes mellitus, % with/without 25.3/74.7 52.1/47.9 < 0.001 25.3/74.7 48.0/52.0 0.006 Cardioavscular disease, % with/without 49.3/50.7 41.8/58.2 0.327 49.3/50.7 45.3/54.7 0.744 Active vitamin D analogues, % with/without 80.0/20.0 70.9/29.1 0.156 80.0/20.0 73.3/26.7 0.440 Phosphate binders, % with/without 94.7/5.3 83.6/16.4 0.021 94.7/5.3 92.0/8.0 0.745 Kt/V 1.49 ± 0.27 1.57 ± 0.29 0.056 1.49 ± 0.27 1.51 ± 0.26 0.703 β 2 -microglobulin, mg/L 27.5 ± 5.0 25.6 ± 6.2 0.005 27.5 ± 5.0 26.9 ± 6.3 0.169 Albumin leakage, g/session 5.2 ± 3.0 4.1 ± 2.2 0.011 5.2 ± 3.0 4.1 ± 2.1 0.021 Body mass index, kg/m 2 25.0 ± 2.5 25.2 ± 3.0 0.962 25.0 ± 2.5 25.5 ± 3.4 0.720 Normalized protein catabolism rate, g/kg/day 0.87 ± 0.15 0.83 ± 0.15 0.019 0.87 ± 015 0.85 ± 0.13 0.255 Albumin, g/dL 3.5 ± 0.3 3.5 ± 0.3 0.297 3.5 ± 0.3 3.4 ± 0.3 0.484 High-sensitive C-reactive protein, mg/dL 0.092 ± 0.076 0.102 ± 0.078 0.285 0.092 ± 0.076 0.104 ± 0.077 0.251 Systolic blood pressure, mm Hg, predialysis 143 ± 24 145 ± 24 0.442 143 ± 24 144 ± 25 0.985 Hemoglobin, g/dL 11.2 ± 1.1 11.2 ± 1.3 0.665 11.2 ± 1.1 11.1 ± 1.4 0.732 Phosphorus, mg/dL 5.8 ± 1.7 5.1 ± 1.3 0.001 5.8 ± 1.7 5.4 ± 1.3 0.255 Corrected calcium, mg/dL 9.2 ± 0.8 9.2 ± 0.6 0.733 9.2 ± 0.8 9.2 ± 0.7 0.776 Parathyroid hormone, pg/mL 246.0 ± 185.3 157.0 ± 120.1 < 0.001 246.0 ± 185.3 176.4 ± 141.3 0.022 Data are presented as mean ± standard deviation or %. There was no significant difference in 2-year all-cause mortality between users and non-users (HR 0.465, 95% confidence interval [CI] 0.085–2.548, P = 0.366, log-rank test; Fig. 2 ). Cox regression, adjusted for covariates that remained significant differences after matching, likewise showed no significant difference (Table 2 ). Table 2 All-cause mortality between calcimimetics users and non-users by Cox proportional hazard regression analysis with adjustment in patients without protein-energy wasting and inflammation. Models HR (95% CI) P value Univariate 0.465 (0.085–2.551) 0.377 Adjustment 0.746 (0.120–4.623) 0.753 Reference: non-users Adjusted for dialysis vintage, the prevalence of diabetes mellitus, albumin leakage, and parathyroid hormone. Comparison of 2-year all-cause mortality between calcimimetic users and non-users in Group 2 Table 3 compares variables before and after propensity score matching. BMI and hs‑CRP did not differ significantly between calcimimetic users and non‑users either before or after matching. After matching, calcimimetic users had significantly longer dialysis vintage, greater albumin leakage, higher P, and PTH levels, and were more frequently received active vitamin D analogues and phosphate binders. In addition, users were significantly younger and had a significantly lower prevalence of DM relative to non-users. Table 3 Comparison of variables in calcimimetic users and non-users before and after propensity score matching between groups in patients with protein-energy wasting and/or inflammation Item Before matching After matching Users Non-users P value Users Non-users P value N 138 360 138 138 Age, yr 64.7 ± 11.5 69.8 ± 11.5 < 0.001 64.7 ± 11.5 69.1 ± 11.9 0.002 Sex, male/female, % 62.3/37.7 65.8/34.2 0.465 62.3/37.7 67.4/32.6 0.449 Dialysis vintage, mo 190.8 ± 104.0 111.7 ± 112.7 < 0.001 190.8 ± 104.0 124.9 ± 120.5 < 0.001 Diabetes mellitus, % with/without 16.7/83.3 39.2/60.8 < 0.001 16.7/83.3 34.8/65.2 0.001 Cardioavscular disease, % with/without 44.2/55.8 55.6/44.4 0.027 44.2/55.8 52.9/47.1 0.185 Active vitamin D analogues, % with/without 89.9/10.1 69.7/30.3 < 0.001 89.9/10.1 76.1/23.9 0.004 Phosphate binders, % with/without 92.0/8.0 72.8/27.2 < 0.001 92.0/8.0 76.8/23.2 0.001 Kt/V 1.68 ± 0.31 1.69 ± 0.34 0.630 1.68 ± 0.31 1.69 ± 0.31 0.933 β 2 -microglobulin, mg/L 27.8 ± 5.0 27.1 ± 6.2 0.141 27.8 ± 5.0 27.0 ± 5.9 0.179 Albumin leakage, g/session 4.6 ± 2.7 3.5 ± 2.3 < 0.001 4.6 ± 2.7 3.7 ± 2.4 0.004 Body mass index, kg/m 2 21.0 ± 3.3 21.0 ± 3.6 0.764 21.0 ± 3.3 21.1 ± 3.6 0.421 Normalized protein catabolism rate, g/kg/day 0.88 ± 0.16 0.83 ± 0.18 0.003 0.88 ± 0.16 0.85 ± 0.16 0.069 Albumin, g/dL 3.4 ± 0.3 3.3 ± 0.4 0.012 3.4 ± 0.3 3.3 ± 0.3 0.212 High-sensitive C-reactive protein, mg/dL 0.461 ± 1.221 0.707 ± 1.541 0.018 0.461 ± 1.221 0.682 ± 1.555 0.247 Systolic blood pressure, mm Hg, predialysis 141 ± 20 144 ± 26 0.341 141 ± 20 144 ± 27 0.335 Hemoglobin, g/dL 11.1 ± 1.2 11.0 ± 1.2 0.253 11.1 ± 1.2 11.1 ± 1.2 0.809 Phosphorus, mg/dL 5.6 ± 1.4 5.1 ± 1.5 0.001 5.6 ± 1.4 5.1 ± 1.5 0.002 Corrected calcium, mg/dL 9.3 ± 0.7 9.2 ± 0.7 0.250 9.3 ± 0.7 9.3 ± 0.8 0.801 Parathyroid hormone, pg/mL 209.9 ± 154.3 148.8 ± 278.2 < 0.001 209.9 ± 154.3 127.5 ± 101.3 < 0.001 Data are presented as mean ± standard deviation or %. Two-year all-cause mortality was significantly lower in users relative to non-users (HR 0.221, 95% CI 0.073–0.670, P = 0.003, log-rank test; Fig. 3 ). However, after adjustment for covariates that remained imbalanced following matching, Cox regression analysis showed that this difference was no longer statistically significant (adjusted HR, 0.272, 95% CI 0.073–1.006, P = 0.051; Table 4 ). Table 4 All-cause mortality between calcimimetics users and non-users by Cox proportional hazard regression analysis with adjustment in patients with protein-energy wasting and/or inflammation. Models HR (95% CI) P value Univariate 0.221 (0.073–0.670) 0.008 Adjustment 0.272 (0.073–1.006) 0.051 Reference: non-users Adjusted for age, dialysis vintage, the prevalence of diabetes mellitus, active vitamin D analogues, phosphate binders, albumin leakage, phosphorus, and parathyroid hormone. Comparison of 2-year all-cause mortality by stratified analysis based on median age between calcimimetic users and non-users The median age was 69 years. In Group 1, there were 49 users and 41 non-users aged < 69 years, and 26 users and 34 non-users aged ≥ 69 years. In Group 2, there were 82 users and 66 non-users aged < 69 years, and 56 users and 72 non-users aged ≥ 69 years. In Group 1, mortality rates did not differ significantly between users and non-users in either age group. In Group 2, mortality among patients ≥ 69 years of age was significantly lower in users than in non-users (HR, 0.206, 95% CI, 0.058–0.728, P = 0.014; Fig. 4 ), whereas no significant difference was observed in patients < 69 years of age. Comparison of 2-year cumulative survival by Kaplan-Meier analysis between calcimimetic users and non-users The cumulative survival rates were 94.5% for users and 94.7% for non-users in Group1, and 96.0% for users and 80.6% for non-users in Group 2 (Figs. 1 and 2 ). In Group 1, the cumulative survival rates were 90.3% for users and 95.1% for non-users aged < 69 years, and 100.0% for users and 94.1% for non-users aged ≥ 69 years. In Group 2, the cumulative survival rates were 96.8% for users and 95.2% for non-users aged < 69 years, and 94.6% for users and 62.0% for non-users aged ≥ 69 years. Discussion Among patients without PEW and inflammation, mortality did not differ significantly between calcimimetics users and non-users. In contrast, among patients with PEW and/or inflammation, mortality was significantly lower in calcimimetics users relative to non-users in the propensity score-matched model with some covariates with significant differences remaining. However, this difference was no longer significant after adjustment by Cox regression. Stratified analysis further demonstrated that mortality significantly lower in users only among those aged ≥ 69 years with PEW and/or inflammation. Kaplan–Meier analysis indicated that calcimimetic users aged ≥ 69 years with PEW and/or inflammation achieved cumulative survival comparable to patients without PEW and inflammation. In the EVOLVE RCT, calcimimetic user in dialysis patients significantly reduced the primary composite endpoint (death or first nonfatal cardiovascular event) relative to placebo in a lag-censoring analysis. However, no significant difference was observed in the unadjusted intention-to-treat analysis [ 14 ]. The annualized mortality rates were 15.9% in calcimimetic users and 20.4% in non-users. In the subgroup analysis of the EVOLVE RCT, calcimimetic users aged ≥ 65 had significantly lower rates of the primary composite endpoint (death or first nonfatal cardiovascular event) and all‑cause mortality relative to non-users in patients aged ≥ 65 years, whereas no significant difference was observed among patients aged < 65 years [ 8 ]. Although very high discontinuation rates of calcimimetics reduced the trial’s statistical power well below the anticipated level, lower event rates among younger patients also influenced the results. The prevalence of DM among calcimimetic users and non-users was 29% in patients aged < 65 years, and 45–48% in patients aged ≥ 65 years, suggesting more severe baseline arteriosclerosis in older patients. Additionally, the prevalence of comorbidities, including DM, myocardial infarction, heart failure, atrial fibrillation, stroke, and peripheral vascular disease, was higher in older patients [ 8 ]. Post hoc analysis of the EVOLVE RCT demonstrated that randomization to cinacalcet resulted in a statistically significant reduction in the relative hazard of cardiovascular death [ 15 ]. In a mixed retrospective-prospective cohort, there was no significant reduction in the risk of mortality among cinacalcet users. However, cinacalcet users with hypertension and DM as etiologies had a lower mortality, suggesting that cinacalcet may be superior for reducing mortality in patients with arteriosclerosis [ 16 ]. The mechanism by which mortality improved in calcimimetic users is thought to include several factors. Activation of CaSR suppresses osteogenic transformation of vascular smooth muscle cells by stimulating matrix-Gla protein and attenuating the upregulation of bone morphogenetic protein-2. Additionally, this activation reduces fibroblast growth factor-23 levels, thereby reducing the risk of vascular calcification [ 17 , 18 ]. In this paper, the effect of calcimimetics on mortality was not observed in patients aged < 69 years with PEW and/or inflammation or in patients without PEW and inflammation. By contrast, continued calcimimetic therapy in patients ≥ 69 years with PEW and/or inflammation reduced mortality to a level comparable to that in patients without PEW and inflammation. These findings suggest that sustained calcimimetic therapy may reduce mortality in patients with severe vascular calcification and arteriosclerosis driven by aging, PEW, and inflammation. Another possible reason may be the reduction in fracture incidence, which adversely affects the life prognosis, through administration of calcimimetics in patients with SHPT [ 19 , 20 , 21 ]. Cinacalcet not only improved mortality in patients aged ≥ 65 years [ 14 ], but also significantly reduced the cumulative fracture incidence compared with placebo in this age group [ 19 ]. The annualized mortality rates of the EVOLVE subgroup analysis were 7.1% for calcimimetic users and 7.0% for non-users in patients aged < 65 years (relative HR, 1.01, 95% CI, 0.88–1.16) and 15.9% for calcimimetic users and 20.4% for non-users in patients aged ≥ 65 years (relative HR, 0.73, 95% CI, 0.72–0.86) [ 8 ]. In the present study, the 2-year mortality rates were 9.7% for calcimimetic users and 4.9% for non-users in patients aged < 69 years, and 0.0% for calcimimetic users and 5.9% for non-users aged ≥ 69 years in patients without PEW and inflammation. Additionally, in patients with PEW and/or inflammation, the 2-year mortality rates were 3.2% for calcimimetic users and 4.8% for non-users aged < 69 years, and 5.4% for calcimimetic users and 38.0% for non-users aged ≥ 69 years. Although the mortality rate in Japanese dialysis patients is lower than in other countries [ 22 ], the annual mortality estimated from the 2-year mortality rate of calcimimetics users with PEW and/or inflammation in the present study was clearly lower than that of the calcimimetic users in both the < 65 years and ≥ 65 years age groups in the EVOLVE subgroup analysis [ 8 ]. JRDR 2015 demonstrated that the levels of albumin and nPCR decreased, and the level of CRP increased as age increased. Additionally, among elderly dialysis patients, the levels of albumin and nPCR were lowest and the level of CRP was highest [ 9 ]. Since CRP levels in HD patients in Europe and the United States are higher than in Japanese patients [ 23 , 24 ], the effect of continued administration of calcimimetics on improving the life prognosis of elderly patients is thought to be greater in Europeans and Americans. CRP levels were not listed in the EVOLVE RCT or the subgroup analysis. Although BMI values were not listed in the subgroup analysis, the median (10th to 90th percentile) BMI value for the cinacalcet group in the EVOLVE RCT was 26.3 (20.4–36.4) kg/m 2 . Therefore, the subgroup likely included some patients with PEW and many patients with inflammation [ 8 ]. Furthermore, it has been reported that the intravenous coadministration of calcimimetics and active vitamin D analogue was associated with an improvement in inflammatory status [ 25 ]. SHPT is associated with high-turnover bone disease. On the other hand, the low serum PTH does not always indicate low-turnover bone and adynamic bone, but may instead reflect PEW and inflammation. Additionally, low PTH is associated with not only disease-related low PTH (DM, elderly, and osteoporosis), but also treatment-related iatrogenic low PTH (hypercalcemia due to the administration of active vitamin D analogues and higher Ca dialysate, and calcimimetics) [ 26 , 27 , 28 ]. In the Nutritional and Inflammatory Evaluation in Dialysis Study conducted between 2001 and 2006, HD patients with low PTH levels of < 150 pg/mL had lower mortality despite a higher prevalence of risk factors for death, including elderly age, DM, PEW, and inflammation. After removing the confounding impact of the risk factors via multivariate analyses, the mortality rate of patients with PTH 100–149 pg/mL (close to the lower limit of Japanese target levels) was significantly reduced relative to that in patients with levels of 300–599 pg/mL [ 29 ]. It is therefore important to investigate CKD-MBD with stratification by age, DM, BMI, CRP, and related factors. Furthermore, cinacalcet was launched in the United States in 2004, which may have influenced the final results. Because cinacalcet was launched in 2004 in United States, it remains possible that the medicine affected the final results. A retrospective cohort study performed between 2012 and 2020 reported that low median PTH levels (< 132 pg/mL) inhibited the progression of intracoronary calcification and significantly reduced all-cause mortality and cardiovascular mortality compared with high median PTH levels (≥ 132 pg/mL), but the use of calcimimetics was not listed in the paper [ 30 ]. We have recently reported that, within the Japanese target PTH range (60–240 pg/mL), the mortality rate was significantly lower in dialysis patients using calcimimetics than in non-users, despite having comparable PTH levels [ 12 ]. Mortality was higher in patients whose PTH levels failed to rise above the target range due to factors such as PEW, inflammation, or aging, relative to patients with SHPT, PEW, inflammation, and aging whose PTH levels fell back to within the target range with calcimimetic therapy. Therefore, lower PTH levels, even within the target range, may be considered risk factors for mortality in calcimimetic non-users. This study has some limitations. First, the main limitation of this study was the relatively small number of patients in comparison with nationwide database studies. To address this, we applied a caliper value of 0.2 times the standard deviation of the logit propensity score for all cases, which we consider reasonable despite some residual imbalance. Accordingly, we performed Cox proportional hazards regression with covariate adjustments. Second, we did not determine whether calcimimetic users and non-users actually continued their respective treatments throughout the study period. We extracted data from medical records for the 3 months prior to the start of the study and found that, at baseline, 88.0% of calicimimetic users and 100.0% of non-users in Group 1 and 88.4% of users and 97.8% in non-users in Group 2 continuously had maintained the same treatment status for the past 3 months. The high retention rates of calcimimetic users and non-users during the three months prior to study initiation indicate that the definitions of users and non-users were clear. Furthermore, the reliability of the results for users and non-users was strengthened by annual censoring with switching between users and non-users. Nevertheless, it is difficult to completely eliminate bias from baseline characteristics, and there remains a possibility of residual confounding or bias related to the timing of administration. Accordingly, our findings should be interpreted with appropriate caution. Third, it is difficult to determine the BMI and hs-CRP thresholds in dialysis patients. We selected a BMI threshold of 22 kg/m 2 and an hs-CRP threshold of 0.3 mg/dL, as previously reported in Japanese studies [ 11 ]. Fourth, we did not conduct separate analyses for HD patients, predilution OHDF patients, or postdilution OHDF patients. Mortality did not differ significantly between patients undergoing predilution OHDF and those undergoing postdilution OHDF [ 10 ], nor between patients undergoing HD and those undergoing OHDF when serum albumin and albumin leakage were comparable [ 31 ]. In HD patients, the use of super high-flux membranes with in vitro β 2 MG clearance of ≥ 70 mL/min was associated with significantly lower membrane in comparison to membranes with β 2 MG clearance of < 70 mL/min [ 32 ]. Similarly, mortality with albumin leakage ≥ 3.0 g/session was significantly lower than with albumin leakage < 3.0 g/session [ 33 ]. Because high albumin leakage significantly reduced mortality in dialysis patients with PEW and/or inflammation [ 11 ], albumin leakage was used as a matching variable and cases were censored accordingly. Finally, data on the residual kidney function were not available, although the dialysis vintage for patients receiving HD or OHDF was > 3 months. An RCT is needed to confirm our findings. In conclusion, this study is the first to suggest that mortality in calcimimetic users may be significantly lower than that in non-users in dialysis patients aged ≥ 69 years with PEW and/or inflammation, and not in those aged < 69 years or in patients without PEW and inflammation. Because survival in calcimimetic users aged ≥ 69 years with PEW and/or inflammation was similar to that in patients without PEW and inflammation, the prognostic benefit of calcimimetics in elderly patients that was observed in the subgroup analysis of the EVOLVE RCT may be limited to patients with severe vascular calcification and arteriosclerosis due to aging, PEW, inflammation, and associated factors. This study suggested that age, and the presence or absence of PEW and inflammation should be considered when analyzing mortality in calcimimetic users. Declarations Competing Interests K.O., M.T., T.I., and J.M. received funding for specific clinical research (Japan Registry of Clinical Trials registration number jRCTs032220723) from Nipro Co., Ltd. and specific clinical research (Japan Registry of Clinical Trials with the registration number jRCTs062190020) from Asahi Kasei Medical Co., Ltd.. K.O. reports personal fees from Kyowa Kirin Co., Ltd., Bayer Yakuhin, Ltd., Ono Pharmaceutical Co., Ltd., Kissei Pharmaceutical Co., Ltd., AstraZeneca Plc., Torii Pharmaceutical Co., Ltd, Sanwa Kagaku Kenkyusho Co., Kowa Co., Ltd., Nippon Boehringer Ingelheim Co., Ltd., and Eli Lilly Japan K.K.. M. T. reports personal fees from Nippon Boehringer Ingelheim Co., Ltd., Eli Lilly Japan K.K., Toray Medical Co., Ltd., Novartis Pharm Co. Ldt., Otsuka Pharmaceutical Co., Ltd., Nova Biomedical Co., Ldt., and JMS Co. Ldt. T.I. reports personal fees from Nipro Co., Ltd., Kyowa Kirin Co., Ltd., AstraZeneca Plc., Torii Pharmaceutical Co., Ltd., Otsuka Pharmaceutical Co., Ltd., Terumo Co. Ltd., JMS Co. Ldt., and Fuso Pharmaceutical Industries, Ltd. T.K. reports personal fees from Kyowa Kirin Co., Ltd., personal fees from Astellas Pharma Inc., personal fees from Bayer Yakuhin, Ltd., grants and personal fees from Ono Pharmaceutical Co., Ltd., grants from Kissei Pharmaceutical Co., Ltd., personal fees from Mitsubishi Tanabe Pharma Corporation, personal fees from Vantive Japan, personal fees from AstraZeneca Plc., personal fees from Torii Pharmaceutical Co., Ltd, personal fees from Sanwa Kagaku Kenkyusho Co., Ltd, and personal fees from Kaneka Medical products. J.M. reports personal fees from Kyowa Kirin Co., Ltd., Ono Pharmaceutical Co., Ltd., Kissei Pharmaceutical Co., Ltd., Vantive Japan, AstraZeneca Plc., Torii Pharmaceutical Co., Ltd, Sanwa Kagaku Kenkyusho Co., Nippon Boehringer Ingelheim Co., Ltd., Eli Lilly Japan K.K., Novartis Pharm Co. Ldt., Otsuka Pharmaceutical Co., Ltd., Chugai Parmaceutical Co., Ltd., Terumo Co. Ltd., and JMS Co. Ldt.. D. H. has no Conflict of Interest. Fundings This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. Author Contribution K.O. was responsible for the research idea and study design, and contributed to the drafting of the manuscript. M.T., D. H,. T.I., T.K. and J.M. contributed to the design, interpretation of data and revision of the manuscript. Acknowledgement We are grateful to all of the staff in our medical corporation for providing a similar quality of healthcare management and dialysis conditions across facilities. We are also grateful to Dr. Shigeaki Ohtsuki of Japan Institute of Statistical Technology for performing the statistical analysis. Data Availability The data supporting the findings of this study have been deposited in the Japan Institute of Statistical Technology ( https://www.jiost.com/ ). The data are available from the authors upon reasonable request and with the permission of the Research Ethics Committee of Kawashima Hospital. References de Mutsert, R. et al. Excess mortality due to interaction between protein-energy wasting, inflammation and cardiovascular disease in chronic dialysis patients. Nephrol. Dial Transpl. 23 , 2957–2964. https://doi.org/10.1093/ndt/gfn167 (2008). Ou, S. M. et al. Association of estimated glomerular filtration rate with all-cause and cardiovascular mortality: the role of malnutrition–inflammation–cachexia syndrome. J. Cachexia Sarcopenia Muscle . 7 , 144–1451. https://doi.org/10.1002/jcsm.12053 (2016). Komaba, H. et al. 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(2023). https://doi.org/10.1186/s41100-023-00490-3 Additional Declarations Competing interest reported. K.O., M.T., T.I., and J.M. received funding for specific clinical research (Japan Registry of Clinical Trials registration number jRCTs032220723) from Nipro Co., Ltd. and specific clinical research (Japan Registry of Clinical Trials with the registration number jRCTs062190020) from Asahi Kasei Medical Co., Ltd.. K.O. reports personal fees from Kyowa Kirin Co., Ltd., Bayer Yakuhin, Ltd., Ono Pharmaceutical Co., Ltd., Kissei Pharmaceutical Co., Ltd., AstraZeneca Plc., Torii Pharmaceutical Co., Ltd, Sanwa Kagaku Kenkyusho Co., Kowa Co., Ltd., Nippon Boehringer Ingelheim Co., Ltd., and Eli Lilly Japan K.K.. M. T. reports personal fees from Nippon Boehringer Ingelheim Co., Ltd., Eli Lilly Japan K.K., Toray Medical Co., Ltd., Novartis Pharm Co. Ldt., Otsuka Pharmaceutical Co., Ltd., Nova Biomedical Co., Ldt., and JMS Co. Ldt. T.I. reports personal fees from Nipro Co., Ltd., Kyowa Kirin Co., Ltd., AstraZeneca Plc., Torii Pharmaceutical Co., Ltd., Otsuka Pharmaceutical Co., Ltd., Terumo Co. Ltd., JMS Co. Ldt., and Fuso Pharmaceutical Industries, Ltd. T.K. reports personal fees from Kyowa Kirin Co., Ltd., personal fees from Astellas Pharma Inc., personal fees from Bayer Yakuhin, Ltd., grants and personal fees from Ono Pharmaceutical Co., Ltd., grants from Kissei Pharmaceutical Co., Ltd., personal fees from Mitsubishi Tanabe Pharma Corporation, personal fees from Vantive Japan, personal fees from AstraZeneca Plc., personal fees from Torii Pharmaceutical Co., Ltd, personal fees from Sanwa Kagaku Kenkyusho Co., Ltd, and personal fees from Kaneka Medical products. J.M. reports personal fees from Kyowa Kirin Co., Ltd., Ono Pharmaceutical Co., Ltd., Kissei Pharmaceutical Co., Ltd., Vantive Japan, AstraZeneca Plc., Torii Pharmaceutical Co., Ltd, Sanwa Kagaku Kenkyusho Co., Nippon Boehringer Ingelheim Co., Ltd., Eli Lilly Japan K.K., Novartis Pharm Co. Ldt., Otsuka Pharmaceutical Co., Ltd., Chugai Parmaceutical Co., Ltd., Terumo Co. Ltd., and JMS Co. Ldt.. D. H. has no Conflict of Interest. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Jan, 2026 Reviews received at journal 17 Jan, 2026 Reviews received at journal 15 Jan, 2026 Reviewers agreed at journal 26 Dec, 2025 Reviewers agreed at journal 26 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviewers invited by journal 11 Dec, 2025 Editor invited by journal 08 Dec, 2025 Editor assigned by journal 05 Dec, 2025 Submission checks completed at journal 05 Dec, 2025 First submitted to journal 05 Dec, 2025 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-8285601","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":558611693,"identity":"b713d4e4-6053-40ec-8078-05c07c4ebc73","order_by":0,"name":"Kazuyoshi 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07:12:23","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138079,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8285601/v1/cf509dd564324e241d97d5c2.html"},{"id":98377286,"identity":"50194a2b-9953-4f38-a0bc-f7b15cd9c2fb","added_by":"auto","created_at":"2025-12-17 07:12:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":677576,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram showing the patient selection process.\u003c/p\u003e","description":"","filename":"Figure11000dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-8285601/v1/fe615c76f6412c889d9c109e.png"},{"id":98377284,"identity":"5587edd0-edf4-4954-855f-ddc769bc24f5","added_by":"auto","created_at":"2025-12-17 07:12:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":237550,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of two-year all-cause mortality between calcimimetic users and non-users after propensity score matching in patients without protein-energy wasting and inflammation.\u003c/p\u003e\n\u003cp\u003eHR, hazard ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure21000dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-8285601/v1/8f3bfa9ef2664eddf467afef.png"},{"id":98440342,"identity":"cf6b3d81-4d35-4af7-b52a-a5c1256cca2c","added_by":"auto","created_at":"2025-12-17 17:03:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":254810,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of two-year all-cause mortality between calcimimetic users and non-users after propensity score matching in patients with protein-energy wasting and/or inflammation.\u003c/p\u003e\n\u003cp\u003eHR, hazard ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure31000dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-8285601/v1/751bc78c545e08d0fabbeda6.png"},{"id":98377287,"identity":"6e445595-374d-4aac-8242-345af7aa220c","added_by":"auto","created_at":"2025-12-17 07:12:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":113789,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of two-year all-cause mortality by the stratified analyses for based on age between calcimimetic users and non-users.\u003c/p\u003e\n\u003cp\u003e(A) Patients without protein-energy wasting and inflammation.\u003c/p\u003e\n\u003cp\u003e* Because no deaths occurred in calcimimetic users, the hazard ratio estimate is driven toward an extreme boundary value.\u003c/p\u003e\n\u003cp\u003e(B) Patients with protein-energy wasting and/or inflammation.\u003c/p\u003e\n\u003cp\u003eHR, hazard ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure4AB1000dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-8285601/v1/09300688f9c485a19e14f313.png"},{"id":98774917,"identity":"0e244371-4238-4605-8ec3-8ba213ffa131","added_by":"auto","created_at":"2025-12-22 12:16:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2377138,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8285601/v1/81fb9357-e24f-4c4f-b78e-151e36369a6e.pdf"}],"financialInterests":"Competing interest reported. K.O., M.T., T.I., and J.M. received funding for specific clinical research (Japan Registry of Clinical Trials registration number jRCTs032220723) from Nipro Co., Ltd. and specific clinical research (Japan Registry of Clinical Trials with the registration number jRCTs062190020) from Asahi Kasei Medical Co., Ltd.. K.O. reports personal fees from Kyowa Kirin Co., Ltd., Bayer Yakuhin, Ltd., Ono Pharmaceutical Co., Ltd., Kissei Pharmaceutical Co., Ltd., AstraZeneca Plc., Torii Pharmaceutical Co., Ltd, Sanwa Kagaku Kenkyusho Co., Kowa Co., Ltd., Nippon Boehringer Ingelheim Co., Ltd., and Eli Lilly Japan K.K.. M. T. reports personal fees from Nippon Boehringer Ingelheim Co., Ltd., Eli Lilly Japan K.K., Toray Medical Co., Ltd., Novartis Pharm Co. Ldt., Otsuka Pharmaceutical Co., Ltd., Nova Biomedical Co., Ldt., and JMS Co. Ldt. T.I. reports personal fees from Nipro Co., Ltd., Kyowa Kirin Co., Ltd., AstraZeneca Plc., Torii Pharmaceutical Co., Ltd., Otsuka Pharmaceutical Co., Ltd., Terumo Co. Ltd., JMS Co. Ldt., and Fuso Pharmaceutical Industries, Ltd. T.K. reports personal fees from Kyowa Kirin Co., Ltd., personal fees from Astellas Pharma Inc., personal fees from Bayer Yakuhin, Ltd., grants and personal fees from Ono Pharmaceutical Co., Ltd., grants from Kissei Pharmaceutical Co., Ltd., personal fees from Mitsubishi Tanabe Pharma Corporation, personal fees from Vantive Japan, personal fees from AstraZeneca Plc., personal fees from Torii Pharmaceutical Co., Ltd, personal fees from Sanwa Kagaku Kenkyusho Co., Ltd, and personal fees from Kaneka Medical products. J.M. reports personal fees from Kyowa Kirin Co., Ltd., Ono Pharmaceutical Co., Ltd., Kissei Pharmaceutical Co., Ltd., Vantive Japan, AstraZeneca Plc., Torii Pharmaceutical Co., Ltd, Sanwa Kagaku Kenkyusho Co., Nippon Boehringer Ingelheim Co., Ltd., Eli Lilly Japan K.K., Novartis Pharm Co. Ldt., Otsuka Pharmaceutical Co., Ltd., Chugai Parmaceutical Co., Ltd., Terumo Co. Ltd., and JMS Co. Ldt.. D. H. has no Conflict of Interest.","formattedTitle":"Calcimimetics reduce mortality in elderly dialysis patients with protein-energy wasting and inflammation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease-mineral and bone disorder (CKD-MBD) is a systemic condition that increases mortality and morbidity, including cardiovascular disease, in dialysis patients. Protein-energy wasting (PEW) and inflammation are important aggravating factors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Malnutrition-inflammation-cachexia syndrome has also been linked to mortality in elderly patients, particularly in those with a low body mass index (BMI) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecondary hyperparathyroidism (SHPT) has been shown to induce PEW and act as a mediating factor for mortality in dialysis patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The Ca-sensing receptor (CaSR) plays an important role in not only Ca homeostasis related disorders but also various other non-Ca-related diseases. CaSR is functionally expressed in almost all components of the cardiovascular system, and abnormalities of CaSR in hematopoietic cells and vascular cells contribute to various conditions, particularly the promotion of inflammation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Accordingly, parathyroid hormone (PTH)-lowering agents such as calcimimetics may reduce mortality associated with PEW and inflammation.\u003c/p\u003e \u003cp\u003eThe 2012 clinical practice guidelines of the Japanese Society for Dialysis Therapy (JSDT) set the target intact PTH range for dialysis patients at 60\u0026ndash;240 pg/mL [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Prior to the availability of calcimimetics, PTH levels of \u0026ge;\u0026thinsp;300 pg/mL and \u0026lt;\u0026thinsp;120 pg/mL were significantly associated with increased mortality in both time-dependent and time-averaged models using the JSDT Renal Data Registry (JRDR) 2006\u0026ndash;2009 database [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In contrast, among calcimimetic users, PTH levels of \u0026ge;\u0026thinsp;326 pg/mL and \u0026lt;\u0026thinsp;52 pg/mL were not associated with significantly increased mortality relative to the target range in either model using the JRDR 2009\u0026ndash;2018 database [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These findings suggest that calcimimetic therapy may allow for a higher upper limit and a decreased lower limit of the PTH target range.\u003c/p\u003e \u003cp\u003eIn the EVOLVE randomized controlled trial (RCT), a subgroup analysis suggested that calcimimetics may reduce mortality in elderly patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although elderly patients are more susceptible to PEW and inflammation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and the effects of calcimimetics may be influenced by the presence of these conditions, these relationships have not yet been clarified. Therefore, this study aimed to determine whether calcimimetics reduce mortality in elderly dialysis patients, irrespective of the presence of PEW and/or inflammation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient selection\u003c/h2\u003e \u003cp\u003eA total of 944 patients undergoing maintenance dialysis with hemodialysis (HD) or online hemodiafiltration (OHDF) were identified from medical records held in our corporation database as of July 1, 2017. Exclusions were applied according to criteria reported previously (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. After these exclusions, 738 patients remained eligible, including those undergoing HD using membranes with \u003cem\u003ein vitro\u003c/em\u003e β\u003csub\u003e2\u003c/sub\u003e-microglobulin (β\u003csub\u003e2\u003c/sub\u003eMG) clearance of \u0026ge;\u0026thinsp;70 mL/min and those undergoing OHDF. Patients with BMI of \u0026ge;\u0026thinsp;22 kg/m\u003csup\u003e2\u003c/sup\u003e and high-sensitivity C-reactive protein (hs-CRP) of \u0026lt;\u0026thinsp;0.3 mg/dL were classified into Group 1 (n\u0026thinsp;=\u0026thinsp;240; calcimimetic users n\u0026thinsp;=\u0026thinsp;75 and non-users n\u0026thinsp;=\u0026thinsp;165), and those with BMI of \u0026lt;\u0026thinsp;22 kg/m\u003csup\u003e2\u003c/sup\u003e and/or hs-CRP of \u0026ge;\u0026thinsp;0.3 mg/dL were classified into Group 2 (n\u0026thinsp;=\u0026thinsp;498; calcimimetic users n\u0026thinsp;=\u0026thinsp;138 and non-users n\u0026thinsp;=\u0026thinsp;360) according to previously reported Japanese thresholds for BMI and hs-CRP [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Propensity score matching was then performed between calcimimetic users and non-users within each group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDialysis and dilution methods were determined by each patient\u0026rsquo;s physician. Each dialysis session lasted 4 h, with a blood flow rate of 250\u0026ndash;350 mL/min. Membrane surface areas were 2.1\u0026ndash;2.5 m\u003csup\u003e2\u003c/sup\u003e for HD and 2.0\u0026ndash;3.0 m\u003csup\u003e2\u003c/sup\u003e for OHDF. The dialysate flow rate (QD) in HD and total QD (QD plus the substitution volume) in OHDF were both fixed at 500 mL/min. All blood tests were measured centrally (BML, Inc., Japan) and results were extracted from medical records.\u003c/p\u003e \u003cp\u003eSome data in this study overlap with those in our previous report [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The previous study, based on the 2012 JSDT CKD-MBD guidelines, analyzed patients with PTH levels within the target range (60\u0026ndash;240 pg/mL) and compared mortality between calcimimetic users and non-users. In contrast, the present study included cases across all PTH levels, examined mortality according to PEW and inflammation status, and conducted age-stratified analyses. Thus, the analytical objectives and methods were distinct.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary endpoints were 2-year all-cause mortality, assessed using a propensity score-matched model and an adjusted Cox proportional hazards model, comparing calcimimetic users and non-users in Groups 1 and 2. Secondary endpoints included 2-year all-cause mortality as assessed by a median age-stratified analysis between users and non-users in Groups 1 and 2, and 2-year cumulative survival as assessed by the a median age-stratified Kaplan-Meier analysis, comparing users and non-users within each group.\u003c/p\u003e\n\u003ch3\u003ePreparation of Propensity Score-Matched Pairs\u003c/h3\u003e\n\u003cp\u003ePropensity scores for Groups 1 and 2 were calculated to match calcimimetic users and non-users. Covariates included age, sex, dialysis vintage, presence or absence of diabetes mellitus (DM), presence or absence of cardiovascular disease (including angina pectoris, myocardial infarction, atrial fibrillation, heart failure, stroke, peripheral artery disease, and limb amputation), administration of active vitamin D analogues (oral and intravenous), administration of phosphate (P) binders, Kt/V, β\u003csub\u003e2\u003c/sub\u003eMG, albumin leakage, normalized protein catabolism rate (nPCR), albumin, systolic blood pressure, hemoglobin, P, corrected Ca, and PTH. The parameters used for grouping (BMI and hs-CRP) were excluded from the covariates.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePropensity scores were matched for 75 pairs in Group 1 and 138 pairs in Group 2. Multivariable logistic regression was performed with calcimimetic use (users vs. non-users) as the dependent variable and the covariates listed above as independent variables. After logit transformation, each patient\u0026rsquo;s propensity score was calculated to 14 decimal places. Patients were then paired at a 1:1 ratio using nearest‑neighbor matching within calipers of 0.318668 for Group 1 and 0.250412 for Group 2 (0.2 \u0026times; standard deviation of the logit) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSurvival was determined from medical records, which included information on death and transfer to other hospitals. A daily survival analysis was performed for the two groups using the Kaplan\u0026ndash;Meier method, incorporating deaths and transfers. Transfer between Groups 1 and 2, changes between calcimimetic use and non-use, changes in dialysis modality, and transfer of albumin leakage between \u0026lt;\u0026thinsp;3 g/session and \u0026ge;\u0026thinsp;3 g/session were confirmed annually. In the Kaplan\u0026ndash;Meier analysis, instances of switching between these groups were censored annually. Consequently, only patients with stable conditions during the first year and without censoring events were eligible for further follow-up.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe statistical significance of differences between two groups was assessed using the log-rank test. Cox regression analysis was performed to calculate hazard ratios (HRs). All-cause mortality was compared between groups by Cox proportional hazards regression analysis, with adjustment for some covariates that remained significantly different between the groups after propensity score matching. Stratified analyses based on median age were also conducted for all-cause mortality after propensity score were also conducted to compare calcimimetic users and non-users.\u003c/p\u003e \u003cp\u003eAll analyses were performed using SPSS Statistics for Windows (ver. 26, IBM Corp., Armonk, NY). Two-tailed P values of \u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthics\u003c/h3\u003e\n\u003cp\u003eThis study was approved by the Research Ethics Committee of Kawashima Hospital on October 7, 2025, and registered in the UMIN Clinical Trials Registry (UMIN000059330 registered October 8, 2025 - prospectively registered, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://center6.umin.ac.jp/cgi-bin/ctr/ctr_view_reg.cgi?recptno=R000067867\u003c/span\u003e\u003cspan address=\"https://center6.umin.ac.jp/cgi-bin/ctr/ctr_view_reg.cgi?recptno=R000067867\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki. The need to obtain informed consent was waived by Research Ethics Committee of Kawashima hospital. The research information was disclosed to patients based on Ethical Guidelines for Life Science and Medical Research Involving Human Subjects before enrollment.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe mean observation period for calcimimetic users and non-users was 473.9\u0026thinsp;\u0026plusmn;\u0026thinsp;176.1 and 437.1\u0026thinsp;\u0026plusmn;\u0026thinsp;156.7 months, respectively, in Group 1, and 502.5\u0026thinsp;\u0026plusmn;\u0026thinsp;178.5 and 431.0\u0026thinsp;\u0026plusmn;\u0026thinsp;168.3 months in Group 2. In calcimimetic users and non-users, the numbers of deaths, transfers to other hospitals, and censored cases were respectively 2 and 4, 5 and 3, and 50 (some patients counted more than once: changed to non-users, n\u0026thinsp;=\u0026thinsp;10; transfer to Group 2, n\u0026thinsp;=\u0026thinsp;47 [hs-CRP not measured, n\u0026thinsp;=\u0026thinsp;39]; different dialysis method, n\u0026thinsp;=\u0026thinsp;14; and change of albumin leakage group, n\u0026thinsp;=\u0026thinsp;35 [membrane albumin leakage unknown, n\u0026thinsp;=\u0026thinsp;31]) and 55 (some patients counted more than once: changed to non-users, n\u0026thinsp;=\u0026thinsp;13; transfer to Group 2, n\u0026thinsp;=\u0026thinsp;54 [hs-CRP not measured, n\u0026thinsp;=\u0026thinsp;44]; different dialysis method, n\u0026thinsp;=\u0026thinsp;19; and change of albumin leakage group, n\u0026thinsp;=\u0026thinsp;31 [membrane albumin leakage unknown, n\u0026thinsp;=\u0026thinsp;29]) in Group 1, and 4 and 15, 4 and 6, and 83 (some patients counted more than once: changed to non-users, n\u0026thinsp;=\u0026thinsp;20; transfer to Group 1, n\u0026thinsp;=\u0026thinsp;73 [hs-CRP not measured, n\u0026thinsp;=\u0026thinsp;66]; different dialysis method, n\u0026thinsp;=\u0026thinsp;19; and change of albumin leakage group, n\u0026thinsp;=\u0026thinsp;59 [membrane albumin leakage unknown, n\u0026thinsp;=\u0026thinsp;51]) and 95 (some patients counted more than once: changed to non-users, n\u0026thinsp;=\u0026thinsp;10; transfer to Group 1, n\u0026thinsp;=\u0026thinsp;93 [hs-CRP not measured, n\u0026thinsp;=\u0026thinsp;86]; different dialysis method, n\u0026thinsp;=\u0026thinsp;40; and change of albumin leakage group, n\u0026thinsp;=\u0026thinsp;73 [membrane albumin leakage unknown, n\u0026thinsp;=\u0026thinsp;67]) in Group 2. The annual survival rates in the first and second years were 98.6% and 94.5%, respectively, for calcimimetic users and 94.7% and 94.7% for non-users in Group 1. The rates were 97.8% and 96.0%, respectively, for calcimimetic users and 92.0% and 80.0% for non-users in Group 2.\u003c/p\u003e\n\u003ch3\u003eComparison of 2-year all-cause mortality between calcimimetic users and non-users in Group 1\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e compares variables before and after propensity score matching. BMI and hs‑CRP did not differ significantly between calcimimetic users and non‑users either before or after matching. After matching, users had significantly longer dialysis vintage, greater albumin leakage, and higher PTH, while the prevalence of DM was significantly lower relative to non-users.\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\u003eComparison of variables in calcimimetic users and non-users before and after propensity score matching between groups in patients without protein-energy wasting and inflammation.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBefore matching\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAfter matching\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-users\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUsers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon-users\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male/female, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.0/28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.5/31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.0/28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.0/24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDialysis vintage, mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138.5\u0026thinsp;\u0026plusmn;\u0026thinsp;84.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.7\u0026thinsp;\u0026plusmn;\u0026thinsp;98.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138.5\u0026thinsp;\u0026plusmn;\u0026thinsp;84.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111.2\u0026thinsp;\u0026plusmn;\u0026thinsp;104.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, % with/without\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.3/74.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.1/47.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.3/74.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.0/52.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardioavscular disease, % with/without\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.3/50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.8/58.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.3/50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.3/54.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive vitamin D analogues, % with/without\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.0/20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.9/29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.0/20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.3/26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphate binders, % with/without\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.7/5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.6/16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.7/5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.0/8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.745\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\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003csub\u003e2\u003c/sub\u003e-microglobulin, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin leakage, g/session\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormalized protein catabolism rate, g/kg/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-sensitive C-reactive protein, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.092\u0026thinsp;\u0026plusmn;\u0026thinsp;0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.102\u0026thinsp;\u0026plusmn;\u0026thinsp;0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.092\u0026thinsp;\u0026plusmn;\u0026thinsp;0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.104\u0026thinsp;\u0026plusmn;\u0026thinsp;0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure, mm Hg, predialysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143\u0026thinsp;\u0026plusmn;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145\u0026thinsp;\u0026plusmn;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143\u0026thinsp;\u0026plusmn;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144\u0026thinsp;\u0026plusmn;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrected calcium, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParathyroid hormone, pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e246.0\u0026thinsp;\u0026plusmn;\u0026thinsp;185.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157.0\u0026thinsp;\u0026plusmn;\u0026thinsp;120.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246.0\u0026thinsp;\u0026plusmn;\u0026thinsp;185.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e176.4\u0026thinsp;\u0026plusmn;\u0026thinsp;141.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or %.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere was no significant difference in 2-year all-cause mortality between users and non-users (HR 0.465, 95% confidence interval [CI] 0.085\u0026ndash;2.548, P\u0026thinsp;=\u0026thinsp;0.366, log-rank test; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Cox regression, adjusted for covariates that remained significant differences after matching, likewise showed no significant difference (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\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\u003eAll-cause mortality between calcimimetics users and non-users by Cox proportional hazard regression analysis with adjustment in patients without protein-energy wasting and inflammation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.465 (0.085\u0026ndash;2.551)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjustment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.746 (0.120\u0026ndash;4.623)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eReference: non-users\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAdjusted for dialysis vintage, the prevalence of diabetes mellitus, albumin leakage, and parathyroid hormone.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eComparison of 2-year all-cause mortality between calcimimetic users and non-users in Group 2\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e compares variables before and after propensity score matching. BMI and hs‑CRP did not differ significantly between calcimimetic users and non‑users either before or after matching. After matching, calcimimetic users had significantly longer dialysis vintage, greater albumin leakage, higher P, and PTH levels, and were more frequently received active vitamin D analogues and phosphate binders. In addition, users were significantly younger and had a significantly lower prevalence of DM relative to non-users.\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\u003eComparison of variables in calcimimetic users and non-users before and after propensity score matching between groups in patients with protein-energy wasting and/or inflammation\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBefore matching\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAfter matching\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-users\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUsers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon-users\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male/female, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.3/37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.8/34.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.3/37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.4/32.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDialysis vintage, mo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190.8\u0026thinsp;\u0026plusmn;\u0026thinsp;104.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111.7\u0026thinsp;\u0026plusmn;\u0026thinsp;112.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e190.8\u0026thinsp;\u0026plusmn;\u0026thinsp;104.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e124.9\u0026thinsp;\u0026plusmn;\u0026thinsp;120.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, % with/without\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.7/83.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.2/60.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.7/83.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.8/65.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardioavscular disease, % with/without\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.2/55.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.6/44.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.2/55.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.9/47.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive vitamin D analogues, % with/without\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.9/10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.7/30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.9/10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.1/23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphate binders, % with/without\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.0/8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.8/27.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.0/8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.8/23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\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\u003e1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003csub\u003e2\u003c/sub\u003e-microglobulin, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin leakage, g/session\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormalized protein catabolism rate, g/kg/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-sensitive C-reactive protein, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.461\u0026thinsp;\u0026plusmn;\u0026thinsp;1.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.707\u0026thinsp;\u0026plusmn;\u0026thinsp;1.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.461\u0026thinsp;\u0026plusmn;\u0026thinsp;1.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.682\u0026thinsp;\u0026plusmn;\u0026thinsp;1.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure, mm Hg, predialysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144\u0026thinsp;\u0026plusmn;\u0026thinsp;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144\u0026thinsp;\u0026plusmn;\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrected calcium, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParathyroid hormone, pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209.9\u0026thinsp;\u0026plusmn;\u0026thinsp;154.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148.8\u0026thinsp;\u0026plusmn;\u0026thinsp;278.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e209.9\u0026thinsp;\u0026plusmn;\u0026thinsp;154.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e127.5\u0026thinsp;\u0026plusmn;\u0026thinsp;101.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or %.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTwo-year all-cause mortality was significantly lower in users relative to non-users (HR 0.221, 95% CI 0.073\u0026ndash;0.670, P\u0026thinsp;=\u0026thinsp;0.003, log-rank test; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, after adjustment for covariates that remained imbalanced following matching, Cox regression analysis showed that this difference was no longer statistically significant (adjusted HR, 0.272, 95% CI 0.073\u0026ndash;1.006, P\u0026thinsp;=\u0026thinsp;0.051; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\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\u003eAll-cause mortality between calcimimetics users and non-users by Cox proportional hazard regression analysis with adjustment in patients with protein-energy wasting and/or inflammation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.221 (0.073\u0026ndash;0.670)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjustment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.272 (0.073\u0026ndash;1.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eReference: non-users\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAdjusted for age, dialysis vintage, the prevalence of diabetes mellitus, active vitamin D analogues, phosphate binders, albumin leakage, phosphorus, and parathyroid hormone.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eComparison of 2-year all-cause mortality by stratified analysis based on median age between calcimimetic users and non-users\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe median age was 69 years. In Group 1, there were 49 users and 41 non-users aged\u0026thinsp;\u0026lt;\u0026thinsp;69 years, and 26 users and 34 non-users aged\u0026thinsp;\u0026ge;\u0026thinsp;69 years. In Group 2, there were 82 users and 66 non-users aged\u0026thinsp;\u0026lt;\u0026thinsp;69 years, and 56 users and 72 non-users aged\u0026thinsp;\u0026ge;\u0026thinsp;69 years. In Group 1, mortality rates did not differ significantly between users and non-users in either age group. In Group 2, mortality among patients\u0026thinsp;\u0026ge;\u0026thinsp;69 years of age was significantly lower in users than in non-users (HR, 0.206, 95% CI, 0.058\u0026ndash;0.728, P\u0026thinsp;=\u0026thinsp;0.014; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), whereas no significant difference was observed in patients\u0026thinsp;\u0026lt;\u0026thinsp;69 years of age.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison of 2-year cumulative survival by Kaplan-Meier analysis between calcimimetic users and non-users\u003c/h2\u003e \u003cp\u003eThe cumulative survival rates were 94.5% for users and 94.7% for non-users in Group1, and 96.0% for users and 80.6% for non-users in Group 2 (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In Group 1, the cumulative survival rates were 90.3% for users and 95.1% for non-users aged\u0026thinsp;\u0026lt;\u0026thinsp;69 years, and 100.0% for users and 94.1% for non-users aged\u0026thinsp;\u0026ge;\u0026thinsp;69 years. In Group 2, the cumulative survival rates were 96.8% for users and 95.2% for non-users aged\u0026thinsp;\u0026lt;\u0026thinsp;69 years, and 94.6% for users and 62.0% for non-users aged\u0026thinsp;\u0026ge;\u0026thinsp;69 years.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAmong patients without PEW and inflammation, mortality did not differ significantly between calcimimetics users and non-users. In contrast, among patients with PEW and/or inflammation, mortality was significantly lower in calcimimetics users relative to non-users in the propensity score-matched model with some covariates with significant differences remaining. However, this difference was no longer significant after adjustment by Cox regression. Stratified analysis further demonstrated that mortality significantly lower in users only among those aged\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;69 years with PEW and/or inflammation. Kaplan\u0026ndash;Meier analysis indicated that calcimimetic users aged\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;69 years with PEW and/or inflammation achieved cumulative survival comparable to patients without PEW and inflammation.\u003c/p\u003e \u003cp\u003eIn the EVOLVE RCT, calcimimetic user in dialysis patients significantly reduced the primary composite endpoint (death or first nonfatal cardiovascular event) relative to placebo in a lag-censoring analysis. However, no significant difference was observed in the unadjusted intention-to-treat analysis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The annualized mortality rates were 15.9% in calcimimetic users and 20.4% in non-users. In the subgroup analysis of the EVOLVE RCT, calcimimetic users aged\u0026thinsp;\u0026ge;\u0026thinsp;65 had significantly lower rates of the primary composite endpoint (death or first nonfatal cardiovascular event) and all‑cause mortality relative to non-users in patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, whereas no significant difference was observed among patients aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although very high discontinuation rates of calcimimetics reduced the trial\u0026rsquo;s statistical power well below the anticipated level, lower event rates among younger patients also influenced the results. The prevalence of DM among calcimimetic users and non-users was 29% in patients aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years, and 45\u0026ndash;48% in patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, suggesting more severe baseline arteriosclerosis in older patients. Additionally, the prevalence of comorbidities, including DM, myocardial infarction, heart failure, atrial fibrillation, stroke, and peripheral vascular disease, was higher in older patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Post hoc analysis of the EVOLVE RCT demonstrated that randomization to cinacalcet resulted in a statistically significant reduction in the relative hazard of cardiovascular death [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In a mixed retrospective-prospective cohort, there was no significant reduction in the risk of mortality among cinacalcet users. However, cinacalcet users with hypertension and DM as etiologies had a lower mortality, suggesting that cinacalcet may be superior for reducing mortality in patients with arteriosclerosis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe mechanism by which mortality improved in calcimimetic users is thought to include several factors. Activation of CaSR suppresses osteogenic transformation of vascular smooth muscle cells by stimulating matrix-Gla protein and attenuating the upregulation of bone morphogenetic protein-2. Additionally, this activation reduces fibroblast growth factor-23 levels, thereby reducing the risk of vascular calcification [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this paper, the effect of calcimimetics on mortality was not observed in patients aged\u0026thinsp;\u0026lt;\u0026thinsp;69 years with PEW and/or inflammation or in patients without PEW and inflammation. By contrast, continued calcimimetic therapy in patients\u0026thinsp;\u0026ge;\u0026thinsp;69 years with PEW and/or inflammation reduced mortality to a level comparable to that in patients without PEW and inflammation. These findings suggest that sustained calcimimetic therapy may reduce mortality in patients with severe vascular calcification and arteriosclerosis driven by aging, PEW, and inflammation. Another possible reason may be the reduction in fracture incidence, which adversely affects the life prognosis, through administration of calcimimetics in patients with SHPT [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Cinacalcet not only improved mortality in patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], but also significantly reduced the cumulative fracture incidence compared with placebo in this age group [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe annualized mortality rates of the EVOLVE subgroup analysis were 7.1% for calcimimetic users and 7.0% for non-users in patients aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years (relative HR, 1.01, 95% CI, 0.88\u0026ndash;1.16) and 15.9% for calcimimetic users and 20.4% for non-users in patients aged\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;65 years (relative HR, 0.73, 95% CI, 0.72\u0026ndash;0.86) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In the present study, the 2-year mortality rates were 9.7% for calcimimetic users and 4.9% for non-users in patients aged\u0026thinsp;\u0026lt;\u0026thinsp;69 years, and 0.0% for calcimimetic users and 5.9% for non-users aged\u0026thinsp;\u0026ge;\u0026thinsp;69 years in patients without PEW and inflammation. Additionally, in patients with PEW and/or inflammation, the 2-year mortality rates were 3.2% for calcimimetic users and 4.8% for non-users aged\u0026thinsp;\u0026lt;\u0026thinsp;69 years, and 5.4% for calcimimetic users and 38.0% for non-users aged\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;69 years. Although the mortality rate in Japanese dialysis patients is lower than in other countries [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], the annual mortality estimated from the 2-year mortality rate of calcimimetics users with PEW and/or inflammation in the present study was clearly lower than that of the calcimimetic users in both the \u0026lt;\u0026thinsp;65 years and \u0026ge;\u0026thinsp;65 years age groups in the EVOLVE subgroup analysis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. JRDR 2015 demonstrated that the levels of albumin and nPCR decreased, and the level of CRP increased as age increased. Additionally, among elderly dialysis patients, the levels of albumin and nPCR were lowest and the level of CRP was highest [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Since CRP levels in HD patients in Europe and the United States are higher than in Japanese patients [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], the effect of continued administration of calcimimetics on improving the life prognosis of elderly patients is thought to be greater in Europeans and Americans. CRP levels were not listed in the EVOLVE RCT or the subgroup analysis. Although BMI values were not listed in the subgroup analysis, the median (10th to 90th percentile) BMI value for the cinacalcet group in the EVOLVE RCT was 26.3 (20.4\u0026ndash;36.4) kg/m\u003csup\u003e2\u003c/sup\u003e. Therefore, the subgroup likely included some patients with PEW and many patients with inflammation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, it has been reported that the intravenous coadministration of calcimimetics and active vitamin D analogue was associated with an improvement in inflammatory status [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSHPT is associated with high-turnover bone disease. On the other hand, the low serum PTH does not always indicate low-turnover bone and adynamic bone, but may instead reflect PEW and inflammation. Additionally, low PTH is associated with not only disease-related low PTH (DM, elderly, and osteoporosis), but also treatment-related iatrogenic low PTH (hypercalcemia due to the administration of active vitamin D analogues and higher Ca dialysate, and calcimimetics) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the Nutritional and Inflammatory Evaluation in Dialysis Study conducted between 2001 and 2006, HD patients with low PTH levels of \u0026lt;\u0026thinsp;150 pg/mL had lower mortality despite a higher prevalence of risk factors for death, including elderly age, DM, PEW, and inflammation. After removing the confounding impact of the risk factors via multivariate analyses, the mortality rate of patients with PTH 100\u0026ndash;149 pg/mL (close to the lower limit of Japanese target levels) was significantly reduced relative to that in patients with levels of 300\u0026ndash;599 pg/mL [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. It is therefore important to investigate CKD-MBD with stratification by age, DM, BMI, CRP, and related factors. Furthermore, cinacalcet was launched in the United States in 2004, which may have influenced the final results. Because cinacalcet was launched in 2004 in United States, it remains possible that the medicine affected the final results. A retrospective cohort study performed between 2012 and 2020 reported that low median PTH levels (\u0026lt;\u0026thinsp;132 pg/mL) inhibited the progression of intracoronary calcification and significantly reduced all-cause mortality and cardiovascular mortality compared with high median PTH levels (\u0026ge;\u0026thinsp;132 pg/mL), but the use of calcimimetics was not listed in the paper [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We have recently reported that, within the Japanese target PTH range (60\u0026ndash;240 pg/mL), the mortality rate was significantly lower in dialysis patients using calcimimetics than in non-users, despite having comparable PTH levels [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Mortality was higher in patients whose PTH levels failed to rise above the target range due to factors such as PEW, inflammation, or aging, relative to patients with SHPT, PEW, inflammation, and aging whose PTH levels fell back to within the target range with calcimimetic therapy. Therefore, lower PTH levels, even within the target range, may be considered risk factors for mortality in calcimimetic non-users.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, the main limitation of this study was the relatively small number of patients in comparison with nationwide database studies. To address this, we applied a caliper value of 0.2 times the standard deviation of the logit propensity score for all cases, which we consider reasonable despite some residual imbalance. Accordingly, we performed Cox proportional hazards regression with covariate adjustments. Second, we did not determine whether calcimimetic users and non-users actually continued their respective treatments throughout the study period. We extracted data from medical records for the 3 months prior to the start of the study and found that, at baseline, 88.0% of calicimimetic users and 100.0% of non-users in Group 1 and 88.4% of users and 97.8% in non-users in Group 2 continuously had maintained the same treatment status for the past 3 months. The high retention rates of calcimimetic users and non-users during the three months prior to study initiation indicate that the definitions of users and non-users were clear. Furthermore, the reliability of the results for users and non-users was strengthened by annual censoring with switching between users and non-users. Nevertheless, it is difficult to completely eliminate bias from baseline characteristics, and there remains a possibility of residual confounding or bias related to the timing of administration. Accordingly, our findings should be interpreted with appropriate caution. Third, it is difficult to determine the BMI and hs-CRP thresholds in dialysis patients. We selected a BMI threshold of 22 kg/m\u003csup\u003e2\u003c/sup\u003e and an hs-CRP threshold of 0.3 mg/dL, as previously reported in Japanese studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Fourth, we did not conduct separate analyses for HD patients, predilution OHDF patients, or postdilution OHDF patients. Mortality did not differ significantly between patients undergoing predilution OHDF and those undergoing postdilution OHDF [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], nor between patients undergoing HD and those undergoing OHDF when serum albumin and albumin leakage were comparable [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In HD patients, the use of super high-flux membranes with \u003cem\u003ein vitro\u003c/em\u003e β\u003csub\u003e2\u003c/sub\u003eMG clearance of \u0026ge;\u0026thinsp;70 mL/min was associated with significantly lower membrane in comparison to membranes with β\u003csub\u003e2\u003c/sub\u003eMG clearance of \u0026lt;\u0026thinsp;70 mL/min [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Similarly, mortality with albumin leakage\u0026thinsp;\u0026ge;\u0026thinsp;3.0 g/session was significantly lower than with albumin leakage\u0026thinsp;\u0026lt;\u0026thinsp;3.0 g/session [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Because high albumin leakage significantly reduced mortality in dialysis patients with PEW and/or inflammation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], albumin leakage was used as a matching variable and cases were censored accordingly. Finally, data on the residual kidney function were not available, although the dialysis vintage for patients receiving HD or OHDF was \u0026gt;\u0026thinsp;3 months. An RCT is needed to confirm our findings.\u003c/p\u003e \u003cp\u003eIn conclusion, this study is the first to suggest that mortality in calcimimetic users may be significantly lower than that in non-users in dialysis patients aged\u0026thinsp;\u0026ge;\u0026thinsp;69 years with PEW and/or inflammation, and not in those aged\u0026thinsp;\u0026lt;\u0026thinsp;69 years or in patients without PEW and inflammation. Because survival in calcimimetic users aged\u0026thinsp;\u0026ge;\u0026thinsp;69 years with PEW and/or inflammation was similar to that in patients without PEW and inflammation, the prognostic benefit of calcimimetics in elderly patients that was observed in the subgroup analysis of the EVOLVE RCT may be limited to patients with severe vascular calcification and arteriosclerosis due to aging, PEW, inflammation, and associated factors. This study suggested that age, and the presence or absence of PEW and inflammation should be considered when analyzing mortality in calcimimetic users.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eK.O., M.T., T.I., and J.M. received funding for specific clinical research (Japan Registry of Clinical Trials registration number jRCTs032220723) from Nipro Co., Ltd. and specific clinical research (Japan Registry of Clinical Trials with the registration number jRCTs062190020) from Asahi Kasei Medical Co., Ltd.. K.O. reports personal fees from Kyowa Kirin Co., Ltd., Bayer Yakuhin, Ltd., Ono Pharmaceutical Co., Ltd., Kissei Pharmaceutical Co., Ltd., AstraZeneca Plc., Torii Pharmaceutical Co., Ltd, Sanwa Kagaku Kenkyusho Co., Kowa Co., Ltd., Nippon Boehringer Ingelheim Co., Ltd., and Eli Lilly Japan K.K.. M. T. reports personal fees from Nippon Boehringer Ingelheim Co., Ltd., Eli Lilly Japan K.K., Toray Medical Co., Ltd., Novartis Pharm Co. Ldt., Otsuka Pharmaceutical Co., Ltd., Nova Biomedical Co., Ldt., and JMS Co. Ldt. T.I. reports personal fees from Nipro Co., Ltd., Kyowa Kirin Co., Ltd., AstraZeneca Plc., Torii Pharmaceutical Co., Ltd., Otsuka Pharmaceutical Co., Ltd., Terumo Co. Ltd., JMS Co. Ldt., and Fuso Pharmaceutical Industries, Ltd. T.K. reports personal fees from Kyowa Kirin Co., Ltd., personal fees from Astellas Pharma Inc., personal fees from Bayer Yakuhin, Ltd., grants and personal fees from Ono Pharmaceutical Co., Ltd., grants from Kissei Pharmaceutical Co., Ltd., personal fees from Mitsubishi Tanabe Pharma Corporation, personal fees from Vantive Japan, personal fees from AstraZeneca Plc., personal fees from Torii Pharmaceutical Co., Ltd, personal fees from Sanwa Kagaku Kenkyusho Co., Ltd, and personal fees from Kaneka Medical products. J.M. reports personal fees from Kyowa Kirin Co., Ltd., Ono Pharmaceutical Co., Ltd., Kissei Pharmaceutical Co., Ltd., Vantive Japan, AstraZeneca Plc., Torii Pharmaceutical Co., Ltd, Sanwa Kagaku Kenkyusho Co., Nippon Boehringer Ingelheim Co., Ltd., Eli Lilly Japan K.K., Novartis Pharm Co. Ldt., Otsuka Pharmaceutical Co., Ltd., Chugai Parmaceutical Co., Ltd., Terumo Co. Ltd., and JMS Co. Ldt.. D. H. has no Conflict of Interest.\u003c/p\u003e\u003ch2\u003eFundings\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.O. was responsible for the research idea and study design, and contributed to the drafting of the manuscript. M.T., D. H,. T.I., T.K. and J.M. contributed to the design, interpretation of data and revision of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are grateful to all of the staff in our medical corporation for providing a similar quality of healthcare management and dialysis conditions across facilities. We are also grateful to Dr. Shigeaki Ohtsuki of Japan Institute of Statistical Technology for performing the statistical analysis.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study have been deposited in the Japan Institute of Statistical Technology ( https://www.jiost.com/ ). The data are available from the authors upon reasonable request and with the permission of the Research Ethics Committee of Kawashima Hospital.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ede Mutsert, R. et al. 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(2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s41100-023-00490-3\u003c/span\u003e\u003cspan address=\"10.1186/s41100-023-00490-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"calcimimetics, mortality, protein-energy wasting, inflammation, CKD-MBD","lastPublishedDoi":"10.21203/rs.3.rs-8285601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8285601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCalcimimetics reduce mortality in older patients on dialysis. Because elderly patients are prone to protein-energy wasting (PEW) and inflammation, we investigated whether this effect is independent of these conditions. This retrospective study used propensity score matching to compare 2-year all-cause mortality between calcimimetic users and non-users. Patients were stratified into those without PEW and inflammation (Group 1, n\u0026thinsp;=\u0026thinsp;240) and those with PEW and/or inflammation (Group 2, n\u0026thinsp;=\u0026thinsp;498). Survival was assessed using Kaplan\u0026ndash;Meier survival curves, censored for calcimimetic use and other covariates. In Group 2, mortality was significantly lower in calcimimetic users than in non-users after matching (hazard ratio [HR] 0.221, 95% confidence interval [CI] 0.073\u0026ndash;0.670, P\u0026thinsp;=\u0026thinsp;0.003, log-rank test), but not in Group 1. The significant difference in Group 2 was no longer observed after Cox proportional hazards regression adjusted for covariates that remained imbalanced following matching (adjusted HR, 0.272, 95% CI 0.073\u0026ndash;1.006, P\u0026thinsp;=\u0026thinsp;0.051). In Group 2, age-stratified analysis (median 69 years) showed significantly lower mortality in calcimimetic users among older patients (HR, 0.206, 95% CI, 0.058\u0026ndash;0.728, P\u0026thinsp;=\u0026thinsp;0.014), but not younger patients. These findings suggest that calcimimetics reduce mortality in elderly patients with PEW and/or inflammation, but not in those without these conditions.\u003c/p\u003e","manuscriptTitle":"Calcimimetics reduce mortality in elderly dialysis patients with protein-energy wasting and inflammation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 07:12:14","doi":"10.21203/rs.3.rs-8285601/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-20T10:15:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-17T07:47:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-15T05:33:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134850350152115935588324200010160923509","date":"2025-12-27T02:56:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"288087405554399762128115670118107005522","date":"2025-12-27T01:56:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87141577115631112175253092557422789517","date":"2025-12-16T06:04:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-11T06:01:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-08T11:42:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-05T14:22:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-05T14:20:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-05T08:29:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a1d62311-41fd-4ce1-b450-204f6dd6997d","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59463788,"name":"Health sciences/Diseases"},{"id":59463789,"name":"Health sciences/Medical research"},{"id":59463790,"name":"Health sciences/Nephrology"},{"id":59463791,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-03-09T12:10:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 07:12:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8285601","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8285601","identity":"rs-8285601","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}