Abstract
Background
To evaluate osteoprotegerin (OPG) levels in relation to cardiovascular (CV) risk factors in patients with chronic kidney disease (CKD) on different regimens of renal replacement therapy.
Methods
A total of 143 patients with CKD and 30 healthy controls were included in this study and divided into five categories, including predialysis patients with chronic renal failure (preD; n = 36), chronic peritoneal dialysis patients (PD; n = 36), hemodialysis patients (HD; n = 35), renal transplant patients (RT; n = 36), and controls (n = 30). Data on demographics, concomitant diseases and CV risk factors, serum OPG levels, and correlates of serum OPG levels were determined.
Results
Serum OPG (pmol/l) levels were significantly higher in HD (P <0.001 for each), PD (P <0.001 for each), and preD (P <0.01 vs. control, P <0.05 vs. RT) groups than RT and control groups. Diabetics than nondiabetics in HD (P = 0.008), PD (P = 0.024), and RT (P = 0.004) groups and males than females in PD group (P = 0.021) had higher OPG levels. Serum OPG levels were associated positively with age in HD (P <0.001), PD (P = 0.001), and in overall population (P <0.001).
Conclusion
Our findings revealed increased serum levels of OPG in dialysis and preD patients compared to RT and controls. In the patient groups receiving two dialysis treatment, the levels were worse, indicating a more pronounced vascular injury. Age, C‐reactive protein (CRP), high‐density lipoprotein cholesterol (HDL‐C), and cystatin C (CysC) in CKD patients, CRP and PTH in the control subjects, and age and BMI in the overall population were the significant correlates of serum OPG levels.
Keywords: chronic kidney disease, renal replacement therapy, osteoprotegerin, cardiovascular risk
Introduction
Chronic kidney disease (CKD) has been consistently reported to be associated with excess cardiovascular (CV) risk, while cardiovascular disease (CVD) has been considered as the leading cause of mortality in patients with CKD who require renal replacement therapy with dialysis or kidney transplantation 1, 2, 3, 4.
Increased risk for CV disease in renal patients has been attributed to the risk factors, such as hypertension (HT), oxidative stress, proteinuria‐albuminuria, anemia, abnormal calcium‐phosphate metabolism, malnutrition, chronic low‐grade inflammation, and dyslipidemia 3, 4, 5, 6. Notably, vascular calcifications have been increasingly recognized among patients with CKD as an important risk factor for uremia‐induced CV disease and an important predictor of all‐cause as well as CV mortality 6, 7. Hence, in addition to traditional risk factors for atherosclerosis such as dyslipidemia, HT, smoking, and age, kidney‐specific mechanisms including uremia, abnormalities in mineral metabolism, increased levels of potentiating molecules, decreased levels of inhibitory molecules, and calcium‐phosphate flux related to dialysis have been suggested in the pathogenesis of vascular pathology 8, 9, 10, 11. In this context being expressed and/or released from these cells, bone matrix proteins including osteoprotegerin (OPG), receptor activator of nuclear factor kappa B ligand (RANKL), and osteopontin (OP) have been considered to be regulating molecules and identified as biological risk factors for arteriosclerosis 12, 13, 14, 15.
OPG, called “bone protector,” is a member of the tumor necrosis factor‐alpha family that inhibits the maturation of osteoclast progenitor. Osteogenic markers including OPG are suggested to play an important role in mineralization of ectopic sites and are linked to vascular calcification. Although OPG is suggested to have an active role in vascular pathophysiology, it is not currently clear whether this role is beneficial or injurious to the vasculature. Anticalcification activity of OP and OPG was suggested to be a part of compensatory vascular defense system upregulated in atherosclerosis 4, 12, 16, 17, 18.
Hence limited data are available on serum OPG levels with respect to the different regimens of renal replacement therapy; the present study was designed to evaluate OPG levels in relation to CV risk factors in patients divided into four categories, including pre‐dialysis (preD) patients with chronic renal failure, peritoneal dialysis (PD), hemodialysis (HD), and renal transplant (RT) patients.
Methods
Study Population
A total of 143 patients with CKD and 30 healthy controls were included in this study conducted at two tertiary care centers. Patients were divided into four categories, including preD patients with chronic renal failure (n = 36), PD patients (n = 36), HD patients (n = 35), and RT patients (n = 36). Exclusion criteria included malignancy, clinical signs of acute infection, HIV infection, chronic inflammatory disease, pregnancy, breast feeding, and unwillingness to participate in the study. Thirty individuals with normal physical examination and laboratory results selected from subjects without any history of known chronic disease or drugs were taken as the control group.
Written informed consent was obtained from each subject following a detailed explanation of the objectives and protocol of the study which was conducted in accordance with the ethical principles stated in the “Declaration of Helsinki” and approved by the institutional ethics committee.
Assessments
Data on demographic and clinical characteristics of patients, and comorbidities such as diabetes mellitus (DM), HT, ischemic heart disease (IHD) were retrieved from medical records in patient and control groups. Blood biochemistry findings related to CV risk factors and serum OPG levels were determined in each participant. Correlates of serum OPG levels were determined via linear regression analysis.
Dialysis Therapy
HD patients received dialysis three times weekly for at least 4 hr via native arteriovenous fistula for at least 6 months. Blood flow was standardized to 250–300 ml/min, and dialysate flow to 500 ml/min. PD patients received continuous ambulatory peritoneal dialysis (CAPD, n = 24) and automated peritoneal dialysis (APD, n = 12) treatments. All the patients had tunneled Tenckhoff catheter. Duration of dialysis for both PD and HD were detected in a range of 7 months to 15 years.
Laboratory Analysis
Venous blood collection was performed at the beginning of HD or PD. Serum samples for cystatin C (CysC) and OPG were frozen at −80°C. In routine practice, biochemical parameters including hemoglobin, high‐density lipoprotein cholesterol (HDL‐C), albumin, sodium, calcium (Ca), inorganic phosphorous (iP), parathormone (PTH), C‐reactive protein (CRP), and CysC were measured in the medical biochemistry laboratory (Siemens Healthcare Diagnostics, Terrytown, New York). Low‐density lipoprotein cholesterol (LDL‐C) levels were estimated by using Fridewald formula. Unfortunately, we could not collect blood sample enough for serum CRP measurements of the preD group (n = 2).
When serum containing CysC is mixed with the latex particles coated with rabbit anti‐CysC antibody, agglutination takes place, resulting in an increase in turbidity. According to the kit insert, the intra‐ and interassay precisions coefficients of variation (CV) were 1.0% and 1.8% at a low level of 0.60 mg/l, and 1.8% and 3.1% at a high level of 4.95 mg/l, respectively.
Method of enzyme‐linked immunosorbent assay (ELISA) was used in determination of serum OPG levels (catalog no.: BI‐20402, Biomedica, Germany). Microwells were coated with mouse anti‐OPG, and sandwich technique was used with polyclonal anti‐OPG antibodies sourced from goats. According to the kit insert, the median was found as 1.8 pmol/l (N = 1,134). The intraassay precisions CV were 10% at a low level of 4.59 pmol/l, and 4% at a high level of 10.76 pmol/l. The interassay precisions CV were 7% at a low level of 5.53 pmol/l, and 8% at a high level of 10.1 pmol/l.
Statistical Analysis
Statistical analysis was made using computer software (IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp). Kolmogorov–Smirnov test was used to check the normality of data among groups. ANOVA and post hoc least significant difference (LSD) tests were used for comparison of normally distributed data, while Kruskal–Wallis and post hoc Conover tests were used for nonnormally distributed data. The potential correlations between OPG and biochemical parameters were investigated with univariate Pearson's and Spearman's correlation analysis. To investigate the factors influencing OPG, multiple linear regression analysis was conducted by using the “enter method.” After adjusting for age serum OPG levels between groups were compared via covariance analysis. Data were expressed as “mean (standard deviation; SD)”, minimum–maximum, and percent (%) where appropriate. P < 0.05 was considered statistically significant.
Results
Patient Characteristics in Study Groups
Patient and control groups were similar in terms of gender, while patients in HD, PD, and preD groups were older compared to control and RT groups (P <0.001 for each), and BMI (kg/m2) values in the HD and PD groups were significantly lower than in preD and control groups (P <0.005 for each; Table 1).
Table 1.
Comparison of Demographic Characteristics and Laboratory Findings in Study Groups
| HD (n = 35) | PD (n = 36) | preD (n = 36) | RT (n = 36) | Control (n = 30) | P‐value | ||
|---|---|---|---|---|---|---|---|
| Age (years) | 52.0 (15.5)*** , +++ | 51.2 (9.8)*** , +++ | 54.6 (9.4)*** , +++ | 40.1 (13.1) | 36.5 (11.4) | <0.001 | |
| Males (%) | 51.4 | 50 | 50 | 36.1 | 33.3 | 0.392 | |
| Smoking (%) | 42.9** , ++ , qq , ww | 25.0 | 25.0 | 0.0** , qq , ww | 13.3 | <0.001 | |
| DM (%) | 31.4 | 30.6 | 47.2 | 25 | 0.220 | ||
| IHD (%) | 22.9 | 22.2 | 13.9 | 5.6 | 0.152 | ||
| HT (%) | 24.4 | 22.7 | 28.6 | 24.4 | 0.159 | ||
| BMI (kg/m2) | 24.1 (5.0)* , + , qq | 25.0 (4.2)* , q | 27.3 (4.9) | 26.5 (5.8) | 27.4 (5.6) | 0.025 | |
| SBP (mmHg) | 122.3 (26.4) qqq | 131.7 (24.5) q | 142.5 (23.5)*** | 128.3 (18.3)qq | 118.3 (9.1) | <0.001 | |
| Duration of therapy (months) | 47.7 (26.4) qqq | 60.3 (37.5)qqq | 4.5 (0.6) | <0.001 | |||
| Hb (g/dl) | 10.8 (1.4)*** , +++ | 11.2 (1.8)*** , +++ | 11.1 (1.7)*** , +++ | 13.0 (1.5) | 13.7 (1.5) | <0.001 | |
| Glukoz (mg/dl) | 1251 (70.9) | 123.1 (63.8) | 115.0 (45.6) | 101.3 (38.9) | 91.7 (7.0) | 0.138 | |
| Alb (g/dl) | 4.0 (0.4)*** , +++ , qqq | 3.7 (0.4)*** , +++ , qqq | 4.1 (0.4)*** , +++ | 4.6 (0.3) | 4.5 (0.2) | <0.001 | |
| HDL‐C (mg/dl) | 34.3 (10.8)*** , +++ , qq | 37.9 (9.7)*** , +++ | 40.7 (9.4)* , +++ | 55.0 (12.7)** | 49.1 (12.1) | <0.001 | |
| Ca × iP | 48.7 (11.3)*** , +++ , qq | 48.8 (16.9)*** , +++ , qqq | 39.9 (8.3)* , +++ | 30.0 (5.6) | 34.1 (6.3) | <0.001 | |
| Na (mmol/l) | 137.0 (3.1)*** , +++ , qqq , www | 139.7 (2.9) | 140.4 (2.1) | 141.8 (2.2)* , q , www | 140.6 (1.8) | <0.001 | |
| CRP (mg/l) | 2.0 (2.7)*** | 1.3 (1.8)* | 1.6 (1.4) | 0.3 (0.1) | 0.002 | ||
| PTH (pg/ml) | 573.9 (798.2)** , ++ , www | 1029.5 (973.8)*** , +++ , qqq | 339.9 (206.1) | 106.2 (77.7) | 65.1 (32.1) | <0.001 | |
| OPG (pmol/l) | Mean(SD) | 14.0 (7.7)*** , +++ , qqq , w | 11.5 (5.7)*** , +++ , qqq | 6.8 (3.4)** , + | 4.5 (2.1) | 2.7 (1.0) | <0.001 |
| Median | 12.7 | 10.4 | 6.6 | 4.0 | 2.8 | ||
| CysC (mg/dl) | 6.2 (1.4)*** , +++ , qqq | 5.9 (1.1)*** , +++ , qqq | 3.4 (0.7)*** , +++ | 1.3 (0.3)** | 0.7 (0.1) | <0.001 |
*P < 0.05.
**P <0.01.
***P <0.001.
Compared to control group.
+P <0.05.
++P <0.01.
+++P <0.001.
Compared to RT group.
qP < 0.05.
qqP < 0.01.
qqqP < 0.001.
Compared to preD group.
wP < 0.05.
wwP < 0.01.
wwwP < 0.001.
Compared to PD group. Data are shown as mean (SD).
Blood Biochemistry in Study Groups
Significantly lower levels for hemoglobin, albumin, and HDL‐C, and higher levels for Ca × iP values were determined in HD, PD, and preD groups compared with RT group and control subjects (P values ranged from <0.005 to <0.001). Highest level of serum sodium (Na) were noted among patients in RT group (P < 0.001), while highest levels for serum PTH in PD group (P <0.001 for each). CRP (mg/l) levels in HD (P <0.001) and PD (P <0.05) groups, while CysC (mg/dl) in all patient groups were significantly higher than in control subjects (P < 0.001 for HD, PD, and preD groups, P < 0.05 for RT group; Table 1).
OPG Levels in Study Groups With Respect to Comorbid Disorders
Serum OPG (pmol/l) levels were significantly higher in HD (P <0.001 for each), PD (P <0.001 for each), and preD (P <0.01 vs. control, P <0.05 vs. RT) groups (Table 1). Higher OPG levels were detected in patients with DM than nondiabetics in HD (P = 0.008), PD (P = 0.024), and RT (P = 0.004) groups. In patients with coronary heart disease (CHD), OPG levels were significantly higher compared to patients without CHD in HD (P < 0.001) group (Table 2).
Table 2.
Serum Levels for OPG With Respect to Concomitant DM and Coronary Heart Disease in Study Groups
| Serum OPG levels (pmol/l) | ||||||
|---|---|---|---|---|---|---|
| DM (−) | DM (+) | P‐value | CHD (−) | CHD (+) | P‐value | |
| HD | 11.8 (7.0) | 18.9 (7.0) | 0.008 | 11.6 (5.4) | 22.3 (8.7) | 0.0001 |
| PD | 10.1 (5.1) | 14.7 (5.9) | 0.024 | 11.3 (6.0) | 12.1 (5.0) | 0.757 |
| Predialysis | 6.0 (2.4) | 7.6 (4.2) | 0.181 | 6.6 (3.4) | 7.6 (3.8) | 0.569 |
| Renal transplantation | 4.0 (1.9) | 6.2 (1.9) | 0.004 | 4.4 (2.0) | 7.1 (1.1) | 0.079 |
DM, diabetes mellitus; CHD, coronary heart disease.
Correlation of Serum OPG Levels With BMI and Lipid Parameters
OPG levels were correlated positively with age in HD (r = 0.664, P = 0.0001) and PD (r = 0.518, P = 0.001) groups, with HDL‐C in HD (r = 0.335, P = 0.049) and preD (r = 0.369, P = 0.027) groups, while negatively with total cholesterol (r = −0.334, P = 0.047) and LDL‐C (r = −0.352, P = 0.036) in RT group. However, OPG levels were correlated negatively with BMI (P = 0.013) in the overall population.
Multivariate Regression Analysis for Correlates of Serum OPG Levels
Linear regression analysis revealed that serum levels for OPG were likely to increase by 0.29 unit for each 1 year increase in age (P = 0.001) in HD group; by 0.26 unit for each 1 year increase in age (P = 0.001) and by 0.95 unit for each 1 unit increase in CRP (P = 0.030) in PD group; by 0.15 unit for each 1 unit increase in HDL‐C (P = 0.006), by 0.55 unit for each 1 unit increase in sodium (P = 0.029), and by 2.16 unit for each 1 unit increase in CysC (P = 0.007) in preD group. In the control group, serum levels for OPG were likely to increase by 3.21 unit for each 1 unit increase in CRP (P = 0.003) and by 0.01 unit for each 1 unit increase in PTH (P = 0.022). In the overall study population, serum levels for OPG were likely to increase by 0.30 unit for each 1 year increase in age (P <0.001) and to decrease by 0.22 unit for each 1 unit increase in BMI (P = 0.013; Table 3).
Table 3.
Multivariate Linear Regression Analysis for Correlates of Serum OPG Levels
| Group | Variable | Beta | SE | P‐value |
|---|---|---|---|---|
| HD | Constant | −4.59 | 14.67 | 0.756 |
| Age (years) | 0.29 | 0.07 | <0.001 | |
| Albumin (g/dl) | −0.47 | 3.26 | 0.886 | |
| Therapy (month) | 0.02 | 0.02 | 0.408 | |
| HDL‐C (mg/dl) | 0.11 | 0.09 | 0.253 | |
| PD | Constant | 7.17 | 11.07 | 0.521 |
| Age (years) | 0.26 | 0.07 | 0.001 | |
| Albumin (g/dl) | −3.22 | 2.29 | 0.170 | |
| CRP (mg/l) | 0.95 | 0.42 | 0.030 | |
| Therapy (month) | 0.02 | 0.02 | 0.271 | |
| PreD | Constant | −84.11 | 34.83 | 0.021 |
| HDL‐C (mg/dl) | 0.15 | 0.05 | 0.006 | |
| Sodium (mmol/l) | 0.55 | 0.24 | 0.029 | |
| CysC (mg/dl) | 2.16 | 0.75 | 0.007 | |
| Control | Constant | 1.22 | 0.39 | 0.004 |
| CRP (mg/l) | 3.21 | 1.01 | 0.003 | |
| PTH (pg/ml) | 0.01 | 0.001 | 0.022 | |
| Overall | Constant | 35.81 | 29.36 | 0.227 |
| Age (years) | 0.30 | 0.06 | <0.001 | |
| BMI (kg/m2) | −0,22 | 0.08 | 0.013 | |
| Therapy (month) | 0.02 | 0.02 | 0.358 | |
| Hemoglobin (g/dl) | −0.06 | 0.47 | 0.891 | |
| Albumin (g/dl) | −1.62 | 2.29 | 0.481 | |
| HDL‐C (mg/dl) | 0.10 | 0.07 | 0.154 | |
| Ca × iP | −0.01 | 0.05 | 0.727 | |
| Sodium (mmol/l) | −0.31 | 0.21 | 0.150 | |
| CRP (mg/l) | 0.25 | 0.32 | 0.439 | |
| PTH (pg/ml) | −0.001 | 0.001 | 0.513 | |
| CysC (mg/dl) | 1.22 | 0.63 | 0.059 |
There was no valid linear regression model for the renal transplanted group.
Discussion
To our knowledge there is limited data on comparison of different renal replacement therapies with respect to OPG. In some studies with stage 5 CKD patients (prior to renal replacement therapy), higher levels of OPG were associated positively with older age, male gender, high CRP and HbA1c values, protein‐energy wasting, and carotid plaques; and negatively with albumin and hemoglobin levels 19, and coronary artery calcification 20.
Similarly, age, CRP, HDL‐C and CysC in our patients, CRP and PTH in the control group and age and BMI in the overall population were the significant correlates of serum OPG levels in the present study (Table 3). Multivariate analysis revealed a correlation between BMI and OPG in the whole group (P = 0.013). Additionally, consistent with identification of highest levels for OPG in our HD group patients, HD patients were reported to show a higher progression of coronary artery calcification scores associated with age, BMI, OPG, and Framingham risk index 6. Higher OPG levels were reported in pediatric HD than PD patients 21.
Significantly higher values for serum OPG were noted in our dialysis (HD and PD) groups than preD, RT, and control groups consistent with literature in CKD patients 21, 22, 23. Besides, identification of lowest serum levels of OPG in the RT group supports the data on the reduction of previously increased serum OPG levels in uremic patients after the transplantation 7, 23, 24, 25.
After adjusting the effects of other factors by using multivariate regression analysis, a positive correlation between age and OPG was shown in dialysis groups and in the overall study population in the present study, which supports findings of past studies 17, 18, 26, 27, 28. We found age and OPG was correlated in HD, PD groups, and in the whole group (all P values were <0.001). Conflicting results of OPG levels were reported to be associated with smoking 17, 26, 29, 30 and duration of dialysis therapy 5, 7, 24. Neither smoking nor duration of dialysis was among the correlates of OPG levels in our study population, while alcohol consumption could not be analyzed due to very low rates of alcohol use (in three patients overall).
Several recent reports have indicated that CysC may be a better predictor of adverse CV events and all‐cause mortality than either serum creatinine or creatinine‐based estimating equations 4, 31 in CKD. In our study population, CysC levels were among the significant correlates of serum OPG levels in preD group and showed tendency for a significant relation in the overall study population. Elevated OPG levels were associated with long‐term renal dysfunction in older women in a study using serum creatinine and CysC‐based estimated glomerular filtration rate 32.
Data on the association between OPG and CRP levels revealed a strong correlation between two parameters 17, 19, 28, 30. CRP levels were found higher than controls both in HD and PD patients 33, 34. Although preD, PD, and HD patients had higher OPG, and dialysis patients had higher CRP levels in our research, a correlation with CRP and OPG was observed in PD group (P = 0.030), and in control group (P = 0.003) by multivariate analysis. This may be related to several confounding factors likely to affect CRP and OPG levels. Higher levels for serum CRP in HD compared with PD group in our study population seems to emphasize previously suggested role of extracorporal circulation of blood during HD, which acts as a repeated stimulus for an inflammatory response and the positive association of higher plasma urea levels and CRP 35.With respect to higher OPG levels in HD patients than PD patients, this difference disappeared after adjusting with age, but remained still higher than controls and RT patients (Fig. 1). Klejna et al. reported that OPG is not removed through the polysulfon membranes 36; and the monomers are not eliminated through glomerular membrane filtration either. Although the exact mechanism is not clear, this may be due to the kidneys’ role in OPG metabolism and clearance 37. Increased levels of OPG in dialysis patients may be related to uremic stimulation and the dialysis procedure itself 36.
Figure 1.
Dot plot showing the distribution of serum OPG levels among groups after adjusted with age. In the plot, dots represent the adjusted values of serum OPG levels and the horizontal lines represent the median of OPG levels for each group.
Although higher OPG levels were detected in patients with DM than nondiabetics in our HD, PD, and RT groups, there was no significant difference in the PreD group (Table 2). Elevated OPG levels were associated with the endothelial dysfunction determined by a decreased flow‐mediated dilatation of the brachial artery in diabetics 38. On the other hand, in end stage renal disease, higher levels of OPG were associated with significantly greater risk of all‐cause and CV mortality among nondiabetics, independent of inflammation. Diabetes is a strong independent stimulus for vascular calcification and is associated with poor outcomes among patients receiving dialysis. It is possible that among a population with a very high prevalence of vascular calcification and poor outcomes, higher levels of calcification biomarkers may be less informative 39.
In our study group, the critical value for Ca × iP (37.535) by using receiver operating characteristics was very similar to the median value of the whole group (37.730), although it showed weak diagnostic performance area under curve (AUC = 0.678) 40. The recommended target of <55 mg2 /dl2 41 should be revised to predict the extraskeletal calcification threatening the lifespan of the patient with CKD. Serum levels of OPG was reported to be independent from concomitant DM, serum levels of PTH, Ca, iP, cholesterol, triglyceride, or use of phosphate binders or vitamin D, and not correlated to Ca, iP, Ca × iP product and PTH 7.
As our groups are small and heterogeneous, some limitations to this study should be considered. The first limitation is the cross‐sectional design making impossible to establish any cause and effect relationships in such a short period of time. Second, neither the etiology of renal disease in the patient groups, nor the metabolic bone diseases which may have influenced serum OPG levels were documented. Third, we may have underestimated the true prevalence of vascular disease as our diagnoses were based on the clinical history; our research does not include any evaluation of vascular pathology along with assessment of life‐style characteristics, oxidative stress markers, bone turnover markers, and endothelial or CV status. Although we could not demonstrate the clinical outcomes according to OPG levels in different groups either, this is a preliminary study and we are gathering information of our patients to see the association of this biomarker with CV progress and/or death.
Conclusion
In the present study, we found that serum OPG levels were increased in CKD patients with respect to different replacement therapies. In the patients receiving two dialysis treatments and preD patients, who were also elder, the levels were worse, which may result in more pronounced vascular outcomes. Lower OPG levels in RT group (being slightly higher than control subjects but significantly lower than PD, HD, and preD patient groups) suggested us, along with other advantages, transplantation is the best renal replacement therapy by changing most of the injurious stimuli including OPG, and milieu in CKD patients.
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