Abstract
Introduction
Despite advancements in hemodialysis (HD) technology, mortality remains unacceptably high in end-stage kidney disease (ESKD) patients, with cardiovascular disease (CVD) as the leading cause. Chronic kidney disease-mineral and bone disorder (CKD-MBD) is a critical contributor to CVD pathogenesis. However, longitudinal evidence on trends in CKD-MBD biomarker (calcium, phosphorus, parathyroid hormone [PTH]) target attainment and its association with clinical outcomes in Chinese HD patients remains limited.
Methods
Utilizing data from the China Dialysis Outcomes and Practice Patterns Study (DOPPS, 2009–2022), this multicenter cohort included 4,650 maintenance HD patients across 45 facilities in Beijing, Shanghai, and Guangzhou. Through multistage cluster sampling, we analyzed decade-long trends in CKD-MBD target achievement (per KDIGO and Chinese guidelines) and evaluated associations with all-cause and cardiovascular mortality using multivariable Cox models.
Results
Over the decade, phosphorus target attainment (3.5–5.5 mg/dL) significantly improved (32.8%→43.2%, Δ + 31.7%), yet achievement of stricter targets (2.5–4.5 mg/dL) remained suboptimal (24.4%). Calcium target rates (8.4–10.2 mg/dL) increased from 66.3% to 70.5%, paralleled by rising hypercalcemia (>10.2 mg/dL: 9.5%→12.7%). Triple target attainment (calcium+phosphorus+PTH) improved from 10.8% to 22.5%. Survival analysis suggested that achieving phosphorus targets was independently associated with lower all-cause mortality (HR = 0.77, 95% CI 0.64–0.93) and cardiovascular mortality (HR = 0.73, 0.58–0.93), while stricter phosphorus targets did not show additional associations with improved outcomes. Concurrent triple target achievement was associated with a 39.6% reduction in cardiovascular mortality risk (HR = 0.60, 0.38–0.96).
Conclusion
While single-parameter CKD-MBD management has progressed in Chinese HD patients, multiparameter synergistic control remains inadequate. Achieving phosphorus targets appears to be strongly associated with survival benefits. Further optimization of guideline implementation, particularly through integrated management of multiple parameters, may contribute to reducing cardiovascular events and mortality.
Keywords: Chronic kidney disease-mineral and bone disorder, Hemodialysis, Cardiovascular mortality, Target attainment, DOPPS study
Introduction
Hemodialysis (HD) serves as a life-sustaining therapy for over 3 million patients with end-stage kidney disease (ESKD) worldwide, a population projected to reach 5.4 million by 2030 [1]. China has witnessed a remarkable growth in its HD population, escalating from 578,000 cases in 2016 to 844,000 in 2023 [2]. Despite technological advancements, mortality rates in HD patients remain disproportionately high, with cardiovascular diseases (CVDs) accounting for approximately 40% of deaths in this population [3].
The pathogenesis of CVD in chronic kidney disease (CKD) involves both traditional atherosclerotic risk factors (e.g., hypertension, diabetes) and CKD-specific mechanisms. Among these, CKD-mineral and bone disorder (CKD-MBD) has emerged as a critical determinant of cardiovascular outcomes. Defined by the KDIGO guidelines as a systemic disorder characterized by abnormalities in calcium (Ca), phosphorus (P), parathyroid hormone (PTH) metabolism, bone remodeling defects, and vascular calcification, CKD-MBD management has become a cornerstone of therapeutic strategies for HD patients.
While global guidelines have evolved to emphasize tighter control of mineral metabolism parameters, evidence from the Chinese HD population remains scarce. Particularly lacking are longitudinal data examining: (1) temporal trends in CKD-MBD target attainment under real-world conditions; (2) the clinical significance of achieving multiple biomarker targets simultaneously; and (3) regional disparities in guideline implementation effectiveness. Addressing these knowledge gaps is crucial given China’s unique healthcare challenges, including geographic heterogeneity in resource allocation and rapid adoption of novel dialysis technologies.
Utilizing data from the China Dialysis Outcomes and Practice Patterns Study (DOPPS) spanning 2011–2022, this investigation pursues three primary objectives: (1) to characterize decade-long trends in CKD-MBD biomarker control within China’s HD population; (2) to quantify the mortality risk reduction associated with single versus combined target achievement; (3) to evaluate the clinical implications of stricter phosphorus control targets (2.5–4.5 mg/dL) advocated by recent guidelines.
Materials and Methods
Study Design and Subjects
The DOPPS study initiated in 1996 is an international, multicenter, prospective cohort study that includes hemodialysis (HD) patients. [4, 5]. China joined the DOPPS study in 2011. This longitudinal analysis utilized data from phases 4 (2011), 5 (2012–2015), and 7 (2018–2022) of the China DOPPS data. For China DOPPS study, Beijing, Shanghai, and Guangzhou – the largest cities in the northern, central, and southern regions, respectively – were selected to ensure representation across different economic and healthcare landscapes [6]. Using a two-stage cluster sampling strategy, we randomly selected approximately 30 patients per facility [4]. To maintain a consistent sample size, departing patients were annually replaced by new patients who had joined the facility since the last sampling. The inclusion criteria were as follows: (1) receiving chronic in-center HD; (2) patients’ age ≥18 years old; (3) expectation that patient will continue to receive care at the dialysis clinic. The exclusion criteria were (1) treated with a home-based dialysis modality; (2) receiving HD for acute kidney injury; (3) expectation that patient will receive dialysis transiently (<6 weeks) at the study site (e.g., anticipated transfer to another dialysis facility); (4) receiving concomitant peritoneal dialysis and hemodialysis (hybrid therapy utilized in Japan only). Demographic information, comorbid conditions, laboratory values, and dialysis prescriptions are extracted from medical records using consistent and standardized data collection tools known as DOPPS-link. Baseline demographic and clinical data were collected at the initiation of DOPPS phase 4, 5, and 7.
Study Population and Patient Groups
The study population for this analysis included participants in the 3 phases of China DOPPS. In DOPPS 4, a total of 1,379 patients were sampled from the cities of Beijing, Shanghai, and Guangzhou. Moving on to DOPPS 5, the sample size increased to 1,427 patients. Subsequently, in DOPPS 7, 1,844 patients were sampled within the same three cities. Participants were divided into 3 groups according to their study phase. Baseline data regarding patient demographics, comorbidities, and dialysis treatment were obtained at each study entry. Laboratory values, including levels of calcium (corrected for albumin), phosphorus, and intact PTH, were collected at baseline and at 4-month intervals. Since death events and reasons were not recorded in DOPPS4 in China. We mainly used data from DOPPS 5 and DOPPS 7 when distributing the primary causes of death and analyzing the implication between mortality and target achievement of CKD-MBD markers.
CKD-MBD Markers
Target attainment was assessed by the following criteria and for serum phosphorus dual criteria were used: (1) corrected calcium: 8.4–10.2 mg/dL; (2) phosphorus: 3.5–5.5 mg/dL and phosphorus: 2.5–4.5 mg/dL (strict phosphorus control); (3) iPTH: 150–600 pg/mL.
Outcome Ascertainment
The main outcome was all-cause mortality and cardiovascular death (CV mortality). CV mortality was defined by the primary death records in the dataset. The following diagnoses in the primary death records were considered CV mortality: atherosclerotic heart disease, cardiac arrest, cardiac arrhythmia, cardiomyopathy, cerebrovascular accident (including intracranial hemorrhage), CHF, hemorrhage from ruptured vascular aneurysm, ischemic brain damage/anoxic encephalopathy, acute myocardial infarction, pulmonary embolus, stroke, valvular heart disease and hyperkalemia.
Statistics Analysis
Continuous variables were represented as mean ± SD or median (25th, 75th) according to the results of the normality test. Categorical variables were expressed as numbers and percentages. We stratified data by study phase. Differences in the mean and the median among groups were evaluated using analysis of variance (ANOVA) and non-parametric tests, respectively. Categorical data were compared using the chi-squared test. To ensure the results were representative of the overall HD patient population, given the variability in facility size, the data were weighted by the facility sampling fraction. We used Cox proportional hazards models to assess the association between all-cause mortality, CV mortality, and the single and combined achievement of the target response range for corrected Ca, P, and iPTH levels in the study population at baseline. All of the patients in the target response group were defined as the reference group. All Cox models accounted for facility clustering effects by using the robust sandwich covariance estimate. Survival time for Cox models of all-cause mortality was the time from the study entry to the end of the study or to death, whichever occurred first. A similar calculation was made for the CV mortality risk. We produced Cox regression models with five incremental levels of covariate adjustment to show the effects of adjusting for each group of covariates. Model 1: unadjusted, case-mix adjustment; model 2: adjusted for age, sex, body mass index (BMI), dialysis vintage; model 3: model 2 variables plus comorbidities (diabetes, coronary artery disease, other CVD, cerebrovascular disease, hepatitis B and C, cancer (non-skin), peripheral vascular disease, lung disease, hypertension, psychiatric disorder, gastrointestinal bleeding, recurrent cellulitis, fracture, neurologic disease); model 4 (case-mix + laboratory): model 3 plus hemoglobin, albumin, white blood cells, pre-dialysis serum potassium, and pre-dialysis serum creatinine; model 5: model 4 plus intradialytic weight loss, fistula use, primary kidney disease, standardized Kt/V, urine output <200 mL/d, and study phase. Intradialytic weight loss was defined as the value from pre-dialysis weight minus post-dialysis weight for the 2nd dialysis session of the week.
We performed the MI procedure to impute missing data, and continuous and categorical variables were imputed 25 times by fully conditional specification regression and logistic regression, respectively. The imputed datasets were analyzed using the MI Analyze procedure in SAS/STAT 9.4. The percentages of missing data for most variables were <10%. p value <0.05 was considered statistically significant. All statistical analyses were performed with SAS, version 9.4 (SAS Institute, Cary, NC, USA).
Results
Cohort Characteristics
The analytical cohort comprised 4,650 chronic HD patients with median age 59.8 (IQR 50–71) years and dialysis vintage 4.5 (0.9–6.5) years. Male predominance (60.4%) and high comorbidity burden were observed, with 29.2% having diabetes and 87.9% hypertension. Altogether, 576 all-cause deaths (12.4%) and 364 cardiovascular deaths (7.8%) were recorded (Table 1). Table 2 exhibits the trends in CKD-MBD markers and clinical outcomes of Chinese hemodialysis patients in different research phases (DOPPS4∼DOPPS7).
Table 1.
Demographic data and clinical characteristics of HD patients
| Variables | All (n = 4,650) | DOPPS4 (n = 1,379) | DOPPS5 (n = 1,427) | DOPPS7 (n = 1,844) |
|---|---|---|---|---|
| Demographics | ||||
| Age, years | 61 (50, 71) | 59 (49, 71) | 60 (49, 71) | 62 (52, 70) |
| Males, % | 60.445 | 53.411 | 54.947 | 60.445 |
| Vintage (years) | 2.874 (0.939, 6.516) | 3.176 (1.415, 6.491) | 2.539 (0.731, 5.297) | 2.824 (0.772, 7.478) |
| BMI | 21.761 (19.542, 24.314) | 21.473 (19.342, 23.742) | 21.408 (19.378, 23.865) | 21.942 (19.605, 24.578) |
| Urine output >200 mL/day, % | 27.893 | | 27.893 | |
| History of smoking | 39.898 | 40.263 | 38.786 | 40.510 |
| Laboratory tests | ||||
| Hgb, g/dL | 11.200 (10.2, 12.0) | 10.700 (9.278, 11.875) | 10.700 (9.5, 11.8) | 11.200 (10.3, 12.0) |
| Alb, g/dL | 3.910 (3.670, 4.150) | 3.950 (3.670, 4.200) | 3.960 (3.700, 4.200) | 3.900 (3.660, 4.120) |
| White blood cells, 109/L | 5.980 (4.900, 7.310) | 5.800 (4.700, 7.200) | 5.940 (4.830, 7.300) | 6.020 (4.980, 7.350) |
| Creatinine, mg/dL | 10.269 (8.213, 12.486) | 10.236 (8.328, 12.358) | 10.405 (8.256, 12.639) | 10.122 (8.121, 12.305) |
| Potassium | 4.800 (4.300, 5.300) | 5.000 (4.450, 5.580) | 4.990 (4.418, 5.500) | 4.770 (4.270, 5.300) |
| Serum HCO3–, mmol/L | 21.300 (18.900, 24.000) | 21.200 (18.425, 24.000) | 21.200 (18.900, 24.000) | 22.000 (19.600, 24.400) |
| Dialysis prescription | ||||
| spKt/V | 1.351 (1.169, 1.548) | 1.341 (1.153, 1.567) | 1.365 (1.181, 1.544) | 1.350 (1.167, 1.546) |
| >1.2, % | 74.343 | 69.256 | 72.500 | 79.541 |
| Dialysis ≥3 times/week, % | 91.426 | | 81.643 | 94.209 |
| Intradialytic weight loss, % | 3.866 (2.853, 4.938) | 4.048 (2.804, 5.253) | 3.984 (2.913, 5.087) | 3.813 (2.852, 4.847) |
| Fistula use, % | 80.180 | 76.471 | 88.372 | 73.529 |
| Dialytic Ca concentration, % | 1.500 (1.500, 1.500) | 1.500 (1.500, 1.500) | 1.500 (1.500, 1.500) | 1.500 (1.500, 1.500) |
| 1.25 | 15.979 | 13.931 | 19.364 | 12.676 |
| 1.5 | 78.972 | 78.848 | 75.021 | 84.936 |
| 1.75 | 3.151 | 1.969 | 5.120 | 1.225 |
| Comorbidities, % | ||||
| Diabetes | 29.217 | 24.413 | 28.188 | 35.214 |
| Coronary artery disease | 31.727 | 38.171 | 27.459 | 29.596 |
| Other cardiovascular disease | 22.537 | 27.939 | 21.146 | 18.445 |
| Cerebrovascular disease | 15.677 | 17.779 | 14.529 | 14.723 |
| Hypertension | 87.962 | 90.044 | 86.910 | 86.921 |
| Peripheral vascular disease | 10.322 | 10.885 | 9.072 | 11.061 |
| Hepatitis | 13.144 | 18.586 | 12.942 | 7.744 |
| Lung disease | 5.406 | 7.039 | 4.961 | 4.192 |
| Cancer (non-skin) | 4.101 | 4.307 | 3.943 | 4.054 |
| Gastrointestinal bleeding | 2.407 | 2.620 | 2.415 | 2.179 |
| All-cause death | 576 | | 190 | 386 |
| Cardiac/vascular death | 364 | | 120 | 244 |
BMI, body mass index; Hgb, hemoglobin; Alb, albumin; spKt/V, single-pooled Kt/V.
Table 2.
Prevalence of hypocalcemia, hyperphosphatemia, secondary hyperparathyroidism (SHPT), parathyroidectomy, and the achieved target range of CKD-MBD markers
| Variables | All (n = 4,650) | DOPPS4 (n = 1,379) | DOPPS5 (n = 1,427) | DOPPS7 (n = 1,844) |
|---|---|---|---|---|
| Serum calcium, mg/dL | 8.960 (8.400, 9.520) | 9.000 (8.400, 9.600) | 9.000 (8.400, 9.640) | 8.9 2 (8.440, 9.480) |
| Serum calcium categories, % | 100 | 100 | 100 | 100 |
| <8.4 | 22.474 | 24.231 | 23.939 | 16.805 |
| 8.4–10.2 | 65.409 | 66.250 | 63.435 | 70.535 |
| >10.2 | 12.116 | 9.519 | 12.625 | 12.660 |
| Serum phosphorus, mg/dL | 5.544 (4.398, 6.771) | 5.915 (4.615, 7.340) | 5.575 (4.491,6.968) | 5.513 (4.398,6.720) |
| Serum phosphorus categories, % | 100 | 100 | 100 | 100 |
| <3.5 | 9.543 | 9.406 | 9.691 | 9.187 |
| 3.5–5.5 | 37.905 | 32.807 | 37.841 | 43.223 |
| 5.6–7.0 | 28.475 | 27.765 | 27.947 | 30.949 |
| >7.0 | 24.077 | 30.023 | 24.521 | 16.642 |
| 2.5–4.5 | 24.090 | 20.617 | 22.853 | 24.443 |
| Parathyroid hormone, pg/mL | 279.000 (147.000, 491.750) | 252.900 (121.000, 494.550) | 274.550 (135.500, 545.100) | 280.550 (150.800, 484.875) |
| PTH categories, % | 100 | 100 | 100 | 100 |
| <150 | 27.552 | 31.589 | 27.957 | 22.412 |
| 150–300 | 25.546 | 24.929 | 25.564 | 26.116 |
| 301–600 | 25.867 | 22.835 | 24.003 | 33.998 |
| >600 | 21.035 | 20.647 | 22.477 | 17.474 |
| Achieved target range of CKD-MBD markers, % | ||||
| Only 1 indicator (Ca/P/PTH) | ||||
| Ca | 65.409 | 66.250 | 63.435 | 70.535 |
| P (3.5–5.5) | 37.905 | 32.807 | 37.841 | 43.223 |
| P (2.5–4.5) | 24.090 | 20.617 | 22.853 | 24.443 |
| PTH | 51.414 | 47.764 | 49.566 | 60.114 |
| Two indicators | ||||
| Ca, P (3.5–5.5) | 25.182 | 23.329 | 22.725 | 31.985 |
| Ca, P (2.5–4.5) | 17.369 | 14.314 | 15.705 | 17.701 |
| Ca, PTH | 33.155 | 30.012 | 28.806 | 45.087 |
| P (3.5–5.5), PTH | 20.758 | 16.282 | 18.509 | 29.191 |
| P (2.5–4.5), PTH | 11.913 | 10.261 | 9.368 | 12.393 |
| All 3 indicators | ||||
| P (3.5–5.5) | 14.098 | 10.814 | 11.407 | 22.543 |
| P (2.5–4.5) | 8.589 | 6.197 | 5.903 | 9.030 |
| Parathyroidectomy, % | 3.794 | 2.120 | 1.940 | 7.472 |
| History of fracture, % | 2.231 | 2.544 | 2.403 | 1.727 |
CKD-MBD marker targets used: PTH, 150–600 pg/mL; Ca, 8.4–10.2 mg/dL; P, 3.5–5.5 mg/dL. P, 2.5–4.5 mg/dL.
Decadal Trends in CKD-MBD Control
Serum Calcium
Mean corrected calcium levels demonstrated a progressive rise from 8.99 mg/dL (95% CI 8.87–9.11) in DOPPS4 to 9.20 mg/dL (9.08–9.32) in DOPPS7. Correspondingly, KDIGO target attainment (8.4–10.2 mg/dL) improved from 66.3% to 70.5%, while hypercalcemia (>10.2 mg/dL) prevalence significantly increased from 9.5% to 12.7% (Table 2).
Serum Phosphorus
Mean serum phosphorus declined from 6.11 mg/dL (5.98–6.24) to 5.50 mg/dL (5.38–5.62), with three notable patterns (Table 2). Target achievement of phosphorus (3.5–5.5 mg/dL) improved from 32.8% (DOPPS4) to 43.2% (DOPPS7), while hyperphosphatemia (>7.0 mg/dL) prevalence decreased from 30.0% to 16.6%. Target achievement of phosphorus (2.5–4.5 mg/dL) improved from 20.6% (DOPPS4) to 24.4% (DOPPS7).
Parathyroid Hormone
Mean levels of PTH peaked at 461.2 pg/mL in DOPPS5 but declined to 377.8 pg/mL in DOPPS7. Target achievement of PTH (150–600 pg/mL) increased from 47.8% (DOPPS4) to 60.1% (DOPPS7), though 17.5% of patients still exhibited severe secondary hyperparathyroidism (PTH >600 pg/mL) in DOPPS7.
Multiparameter Target Achievement
The proportion of target achievement of both serum calcium and phosphorus improved from 23.3% (DOPPS4) to 32.0% (DOPPS7). Calcium + PTH increased from 30.0% (DOPPS4) to 45.1% (DOPPS7). Phosphorus + PTH increased from 16.2% (DOPPS4) to 29.1% (DOPPS7), while the target achievement of all three-parameter targets improved from 10.8% (DOPPS4) to 22.5% (DOPPS7). If using stricter phosphorus control target (2.5–4.5 mg/dL), then only 9.0% achieved all three targets in DOPPS7.
Clinical Interventions and Outcomes
Parathyroidectomy rates increased significantly from 2.1% (DOPPS4) to 7.5% (DOPPS7), reflecting intensified management of refractory SHPT, while the fracture history of patients declined from 2.5% (DOPPS4) to 1.7% (DOPPS7) (p < 0.001), suggesting improved bone health management.
Table 3 shows the distribution of primary causes of death in hemodialysis patients. During the follow-up period from DOPPS5 to DOPPS7, 560 patients died. Main causes of death included cardiovascular disease (364, 65.0%) and infection (103, 18.4%). Other causes included malignant disease (22, 3.9%) and unknown (16, 2.8%). No significant temporal trend was observed between DOPPS5 and DOPPS7 (p > 0.05).
Table 3.
The distribution of primary causes of death
| Variables | All (n = 560) | DOPPS5 (n = 189) | DOPPS7 (n = 371) | p value |
|---|---|---|---|---|
| Cardiac/vascular | 364 | 124 | 240 | 0.903 |
| Infection | 103 | 35 | 68 | 1.000 |
| Malignancy | 22 | 10 | 12 | 0.340 |
| Other | 55 | 17 | 38 | 0.750 |
| Unknown | 16 | 3 | 13 | 0.308 |
Table 4 and Figures 1 and 2 present adjusted hazard ratios (HRs) for all-cause and cardiovascular (CV) mortality based on the achievement of CKD-MBD targets (calcium [Ca], phosphorus [P], parathyroid hormone [PTH]) as defined by KDIGO guidelines. The reference group included patients who did not achieve any targets simultaneously for all three CKD-MBD markers. In single-parameter target group, patients who only achieved target P levels had a lower risk for all-cause mortality and CV mortality compared to those who only achieved target Ca or iPTH levels. Achieving phosphorus targets was independently associated with reduced risks of both all-cause (model 5: HR = 0.769 [95% CI: 0.635–0.930]) and CV mortality (model 5: HR = 0.732 [95% CI: 0.576–0.928]). In dual-parameter targets group, the risk for mortality associated with simultaneously achieving two target CKD-MBD marker levels revealed that achieving target P and Ca (P + Ca) levels (model 5: HR = 0.755 [95% CI: 0.601–0.948]) was associated with a lower HR for all-cause mortality and demonstrated a clinically relevant trend toward lower cardiovascular mortality, although this association did not achieve statistical significance (model 5: HR = 0.759 [95% CI: 0.571–1.008]). In patients who achieved targets for all three CKD-MBD markers, a 39.6% reduction was shown in CV mortality risk (model 5: HR = 0.604 [95% CI: 0.379–0.963]), and a non-significant trend was suggested toward reduced all-cause mortality (model 5: HR = 0.833 [95% CI: 0.593–1.169]). When using stricter phosphorus control target (2.5–4.5 mg/dL), no statistically significant additional association with reduced mortality was observed.
Table 4.
Hazard ratio of all-cause and CV mortality by achievement of KDIGO clinical guideline
| HR (95% CI) | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 |
|---|---|---|---|---|---|
| All-cause mortality | |||||
| Only 1 indicator (Ca/P/PTH) | |||||
| Ca | 0.999 (0.846, 1.180) | 0.953 (0.807, 1.126) | 0.929 (0.781, 1.105) | 0.977 (0.818, 1.165) | 1.142 (0.954, 1.367) |
| P (3.5–5.5) | 0.939 (0.786, 1.122) | 0.857 (0.715, 1.027) | 0.838 (0.696, 1.009) | 0.786 (0.649, 0.952) | 0.769 (0.635, 0.930) |
| P (2.5–4.5) | 1.009 (0.831, 1.225) | 0.900 (0.737, 1.098) | 0.841 (0.684, 1.033) | 0.797 (0.644, 0.985) | 0.773 (0.626, 0.955) |
| PTH | 1.092 (0.907, 1.315) | 1.045 (0.869, 1.265) | 1.065 (0.879, 1.290) | 1.067 (0.879, 1.295) | 1.135 (0.937, 1.377) |
| Two indicators | |||||
| Ca, P | 0.814 (0.657, 1.009) | 0.725 (0.583, 0.902) | 0.704 (0.562, 0.881) | 0.699 (0.556, 0.878) | 0.755 (0.601, 0.948) |
| Ca, P (2.5–4.5) | 0.889 (0.703, 1.124) | 0.772 (0.607, 0.982) | 0.719 (0.559, 0.925) | 0.744 (0.576, 0.961) | 0.782 (0.606, 1.008) |
| Ca, PTH | 0.920 (0.733, 1.155) | 0.875 (0.697, 1.100) | 0.902 (0.715, 1.138) | 0.926 (0.730, 1.174) | 1.094 (0.860, 1.392) |
| P, PTH | 0.931 (0.717, 1.209) | 0.843 (0.648, 1.097) | 0.868 (0.664, 1.133) | 0.810 (0.617, 1.064) | 0.817 (0.622, 1.074) |
| P (2.5–4.5), PTH | 0.917 (0.666, 1.262) | 0.806 (0.584, 1.113) | 0.825 (0.594, 1.146) | 0.767 (0.550, 1.070) | 0.736 (0.528, 1.027) |
| All 3 indicators | |||||
| Ca, P (3.5–5.5), PTH | 0.798 (0.578, 1.102) | 0.712 (0.515, 0.984) | 0.755 (0.543, 1.049) | 0.728 (0.520, 1.018) | 0.833 (0.593, 1.169) |
| Ca, P (2.5–4.5), PTH | 0.738 (0.494, 1.102) | 0.662 (0.443, 0.990) | 0.686 (0.453, 1.038) | 0.671 (0.442, 1.019) | 0.730 (0.480, 1.111) |
| CV mortality | |||||
| Only 1 indicator (Ca/P/PTH) | |||||
| Ca | 1.030 (0.839, 1.263) | 0.999 (0.814, 1.227) | 0.992 (0.801, 1.227) | 1.019 (0.821, 1.265) | 1.184 (0.951, 1.474) |
| P | 0.827 (0.661, 1.034) | 0.780 (0.621, 0.979) | 0.789 (0.626, 0.995) | 0.739 (0.582, 0.939) | 0.732 (0.576, 0.928) |
| P (2.5–4.5) | 1.072 (0.848, 1.357) | 0.992 (0.779, 1.264) | 0.964 (0.753, 1.235) | 0.914 (0.708, 1.181) | 0.898 (0.696, 1.160) |
| PTH | 1.137 (0.907, 1.425) | 1.102 (0.878, 1.384) | 1.132 (0.896, 1.429) | 1.133 (0.895, 1.434) | 1.204 (0.953, 1.521) |
| Two indicators | |||||
| Ca, P | 0.756 (0.576, 0.990) | 0.699 (0.532, 0.920) | 0.723 (0.547, 0.956) | 0.702 (0.528, 0.932) | 0.759 (0.571, 1.008) |
| Ca, P (2.5–4.5) | 0.952 (0.717, 1.263) | 0.863 (0.646, 1.153) | 0.847 (0.627, 1.142) | 0.847 (0.625, 1.149) | 0.881 (0.650, 1.194) |
| Ca, PTH | 0.858 (0.645, 1.141) | 0.832 (0.625, 1.107) | 0.850 (0.635, 1.138) | 0.852 (0.633, 1.146) | 0.999 (0.739, 1.351) |
| P, PTH | 0.781 (0.555, 1.099) | 0.720 (0.510, 1.016) | 0.740 (0.522, 1.050) | 0.697 (0.489, 0.994) | 0.708 (0.496, 1.011) |
| P(2.5–4.5), PTH | 0.874 (0.587, 1.303) | 0.775 (0.518, 1.160) | 0.772 (0.512, 1.164) | 0.720 (0.474, 1.092) | 0.711 (0.469, 1.077) |
| All 3 indicators | |||||
| Ca, P (3.5–5.5), PTH | 0.595 (0.381, 0.931) | 0.545 (0.348, 0.854) | 0.570 (0.361, 0.898) | 0.541 (0.341, 0.859) | 0.604 (0.379, 0.963) |
| Ca, P (2.5–4.5), PTH | 0.643 (0.382, 1.084) | 0.586 (0.347, 0.989) | 0.576 (0.336, 0.986) | 0.552 (0.321, 0.950) | 0.595 (0.345, 1.026) |
Model 1: unadjusted.
Model 2: adjusted for age, gender, BMI, dialysis vintage.
Model 3: model 2 variables plus comorbidities (diabetes, coronary artery disease, congestive heart failure, other cardiovascular disease, cerebrovascular disease, hepatitis B and C, cancer (non-skin), peripheral vascular disease, lung disease, hypertension, psychiatric disorder, GI Bleeding, recurrent cellulitis, fracture, neurologic disease).
Model 4: model 3 plus hemoglobin, albumin, white blood cells, potassium, and serum creatinine.
Model 5: model 4 plus Intradialytic weight loss, fistula use, primary kidney disease, sp kt/v, urine output>200 mL/d, DOPPS study phase.
Fig. 1.
Hazard ratio and 95% confidence intervals for risk of all-cause mortality associated with the proportion of patients achieving different CKD-MBD biomarkers achievement in model 5.
Fig. 2.
Hazard ratio and 95% confidence intervals for risk of CV mortality associated with the proportion of patients achieving different CKD-MBD biomarkers achievement in model 5.
Discussion
This study provides detailed information for the distribution and trends of mineral metabolism markers in a large cohort of HD patients during a 10-year period. Our results suggest a significant association between the attainment of target levels for CKD-MBD markers and lower mortality risk. Cardiovascular disease (65.0%) was the leading cause of death, consistent with previous research findings. It is important to note that the definition of cardiovascular disease in this study incorporates hyperkalemia, reflecting its established clinical role as a trigger for fatal arrhythmias in patients with end-stage kidney disease. Consequently, the proportion of deaths attributed to cardiovascular disease is higher than that reported in previous studies on the distribution of causes of mortality [3].
Our research revealed a significant increase in the proportion of patients achieving target ranges for CKD-MBD markers, including calcium (8.4–10.2 mg/dL: 66.2% in DOPPS4 vs. 70.5% in DOPPS7), phosphorus (3.5–5.5 mg/dL: 32.8% in DOPPS4 vs. 43.2% in DOPPS7) and PTH (150–600 pg/mL: 47.8% vs. 60.1%). Moreover, the pre-existing guidelines for the management of chronic kidney disease in China recommend reducing the elevated serum phosphorus levels in CKD G3a-G5D patients to a level close to the normal range (2.5–4.5 mg/dL). Based on this range, the proportion of patients achieving target raised from 20.6% in DOPPS4 to 24.4% in DOPPS7. This finding represents the first report on the new target range for the Chinese population. These improvements likely reflect broader adoption of KDIGO guidelines in China, particularly the emphasis on phosphate binder use and vitamin D receptor activators (VDRAs). Notably, the decline in severe hyperphosphatemia (>7.0 mg/dL: 30.0% in DOPPS 4–16.6% in DOPPS 7) aligns with global trends observed in other DOPPS regions (e.g., Japan and Europe), where stricter phosphorus control has been prioritized. However, the prevalence of hypercalcemia has shown a concurrent increase (>10.2 mg/dL: 9.5% in DOPPS 4–12.7% in DOPPS 7). This trend may be attributed to the consistently high utilization rate of calcium-based phosphate binders and the limited adoption of non-calcium-based therapeutic approaches. Additionally, other contributing factors might include the calcium concentration in dialysate, the usage of calcium and active vitamin D supplements, patients’ nutritional status. Further investigation is warranted to elucidate these associations.
Despite improvements in CKD-MBD Markers control, the percentage of patients achieving concurrent targets for calcium, phosphorus, and PTH (22.5% in DOPPS7) remains significantly lower than that observed in Japan and Taiwan, China [7, 8]. This discrepancy could be ascribed to variations in dialysis practices (e.g., dialysate calcium concentration), dietary phosphate restrictions, or the delayed implementation of novel therapeutic options such as etelcalcetide. Furthermore, the rising incidence of parathyroidectomy (from 2.1% to 7.5%) underscores the inadequate pharmacological management of severe secondary hyperparathyroidism (SHPT). These deficiencies indicate that several challenges and gaps in the management of CKD-MBD need to be resolved to enhance patient outcomes. Increasing the frequency of testing is crucial for enabling precise diagnoses and timely treatment plan adjustments. Adherence to KDIGO guidelines is vital in this regard. What’s more, there is a need to enhance treatment adherence. Patients often struggle to adhere to complex treatment regimens, which may involve multiple medications and dietary restrictions. Educational initiatives are also necessary to boost understanding of the condition and its treatment. The management of CKD-MBD typically requires a multidisciplinary approach, involving nephrologists, endocrinologists, nutritionists, and other specialists. Nevertheless, fragmented care can result in disjointed management plans. To address these unmet needs, a multifaceted approach is required, including improving access to care, enhancing education, developing new treatment options, ensuring patients have the necessary support to effectively manage their condition, and conducting further research.
We investigated the association between achieving KDIGO determined target levels for all three CKD-MBD markers and the risks of all-cause and cardiovascular (CV) mortality. Our results indicated that patients who attained target serum phosphorus (P) levels exhibited a lower risk of all-cause mortality and CV mortality compared to those who achieved target serum calcium (Ca) or intact parathyroid hormone (PTH) levels. Furthermore, when evaluating the relationship between simultaneously achieving target levels for two CKD-MBD markers and mortality risk, we observed that patients achieving target P + Ca levels had a lower risk of all-cause and CV mortality than those achieving either Ca + PTH or P + PTH targets, as determined by fully adjusted model analyses. Based on this finding, we suggest that achieving target serum P level or simultaneously achieving P + Ca levels should be prioritized in the management of CKD-MBD in prevalent HD patients. Compared with Ca and iPTH, serum phosphorus levels are more significantly influenced by the dietary habits of Chinese patients. The traditional Chinese diet (high in grains, legumes, meat, and processed foods) is rich in phosphorus, and intake has increased with rising living standards. Controlling phosphorus thus remains a persistent challenge in chronic kidney disease management. In contrast, calcium levels can be reliably managed through dialysate adjustments and oral supplements, making them easier to stabilize. Therefore, systematic dietary education is crucial, helping patients effectively control phosphorus intake while ensuring adequate protein intake and nutritional quality [9, 10]. Additionally, Ca-based phosphate binders are still widely used due to their low cost, which comes with the risk of hypercalcemia. Achieving target phosphorus levels not only improves metabolic control but may also lower calcium load by decreasing the dosage of calcium-based phosphate binders, achieving a “relatively low calcium load state,” potentially enhancing survival.
In this study, achieving the target phosphorus level was significantly associated with lower mortality. This aligns with most prior studies. A 20-year survival analysis in the European DOPPS population showed that better blood phosphorus control improved survival rates in hemodialysis patients [11]. Previous research indicates a U-shaped relationship between serum phosphate levels and all-cause mortality. Low phosphate levels are clinically relevant as they indicate malnutrition, a known mortality predictor [12]. In contrast, hyperphosphatemia is more common among non-compliant hemodialysis patients. The mechanisms linking high phosphorus levels to increased mortality and morbidity remain hypothetical. Studies show that serum phosphorus can stimulate vascular smooth muscle cells to transform into osteoblasts, promoting mineralization [13, 14]. This leads to vascular calcification, which correlates with arterial stiffness, elevated pulse wave velocity, left ventricular enlargement, and higher mortality. Additionally, myocardial calcification increases the risk of arrhythmias and sudden cardiac death, major causes of cardiovascular mortality in this population [15]. In our analysis, we did not observe a statistically significant additional mortality risk reduction associated with achieving the stricter phosphorus target (2.5–4.5 mg/dL), though this finding may be influenced by the low proportion of patients attaining this target (24.1%) and the resulting limited statistical power. This phenomenon may be attributable to the distinct characteristics of the Chinese HD population. As shown in Table 1, the median BMI of our study cohort was 21.76 kg/m2, substantially lower than that reported in multiple Western DOPPS studies [11], indicating a potentially higher risk of malnutrition among Chinese HD patients. Achieving stringent phosphorus control typically necessitates a very low-phosphorus diet (implying marked reduction in protein intake) and/or high-dose phosphate binder therapy, both of which may induce gastrointestinal adverse effects and impair appetite. Consequently, the underlying risk of malnutrition in this population may limit the feasibility of achieving strict phosphorus targets. Consequently, the optimal serum phosphorus target range for this population may require more refined definition through integration with nutritional status, which awaits further validation in future prospective studies.
Our research did not find a significant association between achieving calcium target alone and lower mortality. Chronic kidney disease (CKD) patients often experience hypocalcemia due to insufficient calcium intake, low active vitamin D levels, and PTH resistance. Conversely, hypercalcemia is observed in some CKD patients due to the extensive use of calcium-based phosphate binders, active vitamin D, and its analogs. Previous studies consistently show that both hypo- and hypercalcemia are strongly associated with higher all-cause and cardiovascular mortality [16, 17]. In this study, achieving calcium target alone was not statistically significant, potentially reflecting the complexity of calcium management. Achieving normocalcemia alone can mask underlying issues. Serum calcium assays only reflect the free calcium fraction and are unable to evaluate the total calcium burden. To raise serum calcium levels in hypocalcemic patients, calcium supplements or active vitamin D are often used. These interventions may increase phosphorus levels (via enhanced intestinal absorption) or overly suppress PTH, leading to low-turnover bone disease and potentially negating the benefits of reaching target calcium levels. Additionally, normocalcemia may be attributed to the prolonged use of calcium-based phosphate binders or high-calcium dialysate prior to the onset of hypercalcemia. Ca-based phosphate binders significantly increase the calcium load while reducing serum phosphorus levels [18]. Previous studies also suggest a positive relation between dialysate calcium concentration and vascular stiffening in HD patients [19]. This latent “calcium overload” can exacerbate vascular calcification and elevate cardiovascular risk. Furthermore, achieving the target for both calcium and phosphorus may be associated with excessive suppression of PTH, resulting in a decrease in bone turnover, which in turn facilitates vascular calcification.
When both serum calcium and phosphorus levels are simultaneously within the target range, the survival benefit is enhanced compared to the scenario where only serum calcium reaches the target (hazard ratio [HR]: 0.755 vs. 1.142). The calcium-phosphorus product (Ca × P) has been conventionally utilized as an indicator to reflect the equilibrium of calcium and phosphorus metabolism within the body. In CKD-MBD patients, deviations in the calcium-phosphorus product are closely associated with disease progression. Excessively high values of Ca × P may precipitate vascular and soft tissue calcification, thereby augmenting the risk of cardiovascular events. While past KDOQI guidelines recommended using Ca × P to evaluate secondary hyperparathyroidism (SHPT) [20], research shows blood phosphorus levels in Ca × P vary widely and may lack clinical utility [21]. As a result, the 2009 KDIGO guidelines have ceased to recommend the use of the calcium-phosphorus product for guiding clinical practice, advising instead to analyze blood calcium and phosphorus levels independently [22]. The findings of this study support the notion that achieving target serum phosphorus levels is associated with significantly greater survival benefits compared to achieving only target serum calcium levels. Consequently, similar Ca × P product values may indicate different clinical outcomes depending on variations in Serum Phosphorus concentrations.
Although parathyroid hormone (PTH) has not demonstrated a significant impact on reducing mortality when it meets the target alone or in combination with either calcium or phosphorus, when blood calcium, blood phosphorus, and PTH all concurrently reach the target, the survival benefit of CV mortality is notably greater than when only blood calcium and blood phosphorus are within the target range while PTH remains outside it (HR: 0.595 vs. 0.755). This finding is in accordance with the conclusions of the majority of prior studies [23, 24] and shows the importance of dynamic PTH monitoring. It is generally recognized that patients presenting with low PTH levels in conjunction with elevated serum calcium (Ca) and phosphorus (P) levels are at a heightened risk of mortality [17]. This situation may be indicative of a state of low bone turnover, where the skeletal system is unable to adequately buffer excessive amounts of phosphorus and calcium. As a result, calcium-phosphorus complexes may deposit in extraosseous tissues, including the vascular system, thereby escalating the risk of cardiovascular mortality. Furthermore, a study from Japan has shown a robust association between persistently elevated PTH levels (mean >600 pg/mL) and all-cause mortality. This relationship may be attributed to the induction of vascular calcification and disrupted bone metabolism by chronic hyperparathyroidism [25]. In summary, the comprehensive management of the equilibrium among blood calcium, blood phosphorus, and PTH is pivotal for improving the prognosis of patients with CKD-MBD.
The study has several limitations. First, data were collected only from three major cities, which may not be representative of the entire HD population in China. Patients in smaller cities and rural areas may have different medical conditions and treatment outcomes. Second, as an observational cohort study, residual confounding (e.g., unmeasured nutritional status or dialysis adequacy) cannot be fully excluded, and the findings indicate associations rather than causal relationships. Third, due to the limitations in data collection, there may be selection bias that could affect the generalizability of the study results. What’s more, no mortality events were documented during the DOPPS4 phase, and the principal analysis was grounded in the data of DOPPS5 and DOPPS7. A relatively short follow-up duration may be insufficient to capture long-term treatment outcomes, such as the cumulative risk of vascular calcification.
Conclusion
Although the management of CKD-MBD in Chinese HD patients has shown improvement in certain indicators (e.g., serum phosphorus control and PTH attainment), overall progress remains limited, and the concurrent achievement of multiple CKD-MBD targets is still suboptimal. Patients who achieved the KDIGO-recommended target range for serum phosphorus had a lower associated mortality risk compared to those achieving targets for calcium or iPTH alone. Furthermore, patients who achieved the triple target demonstrated a significantly lower risk for CV mortality. Further optimization of guideline implementation is required, particularly through integrated management of multiple parameters, may help in reducing cardiovascular events and mortality in clinical practice.
Acknowledgments
The Dialysis Outcomes and Practice Patterns Study (DOPPS) Program in China is supported by Vifor Fresenius Renal Pharma, Sanofi Renal, Nipro Trading (Shanghai) Co., Ltd., 3SBio Inc., B. Braun, CEMMA MEDICAL, and Kyowa Kirin China Pharmaceutical Co., Ltd.
Statement of Ethics
The study was approved by the Ethics Committee of Peking University People’s Hospital (Ethical Approval No. 2018PHB028-01). Other participating sub-centers also obtain ethics committee approval documents prior to the start of clinical trials. All patients provided written informed consent. The authors confirm that all the methods used in this study comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration.
Conflict of Interest Statement
No potential conflict of interest was reported by the authors. Fan Fan Hou was a member of the journal’s Editorial Board at the time of submission.
Funding Sources
This study was supported by the Beijing Natural Science Foundation (No. 7222201) and Research Project of Blood Purification Center Branch of Chinese Hospital Association (No. CHABP2021-11); the grant recipients are Li Zuo and Xinju Zhao, respectively.
Author Contributions
Xinju Zhao and Li Zuo contributed to conception and design of research and interpreted results of experiments; Xinju Zhao, Chengqian Ma, Yifei Xiao, and Zeyuan Pei analyzed data; Xinju Zhao and Chengqian Ma prepared figures and drafted manuscript; Liangying Gan, Fan Fan Hou, Xinling Liang, Zhaohui Ni, Xiaonong Chen, Yuqing Chen, and Li Zuo edited and revised manuscript; and Li Zuo approved final version of manuscript.
Funding Statement
This study was supported by the Beijing Natural Science Foundation (No. 7222201) and Research Project of Blood Purification Center Branch of Chinese Hospital Association (No. CHABP2021-11); the grant recipients are Li Zuo and Xinju Zhao, respectively.
Data Availability Statement
The data that support the findings of this study are not publicly available due to privacy reasons but are available from the corresponding author on reasonable request. All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the DOPPS project; see https://www.dopps.org/PartnerwithUs.aspx for more information.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are not publicly available due to privacy reasons but are available from the corresponding author on reasonable request. All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the DOPPS project; see https://www.dopps.org/PartnerwithUs.aspx for more information.