Circulating osteoprotegerin and progression of coronary artery calcification in patients with chronic kidney disease: the KoreaN Cohort Study for Outcome in Patients With Chronic Kidney Disease (KNOW-CKD)

OPG and CAC progression in CKD

Article information

Korean J Nephrol. 2024;.j.krcp.24.039

Publication date (electronic) : 2024 November 18

doi : https://doi.org/10.23876/j.krcp.24.039

Sang Heon Suh ¹

, Tae Ryom Oh ¹

, Hong Sang Choi ¹

, Chang Seong Kim ¹

, Eun Hui Bae ¹

, Seong Kwon Ma ¹

, Kook-Hwan Oh ²

, Tae-Hyun Yoo ³

, Soo Wan Kim^,¹

, on behalf of the KoreaN Cohort Study for Outcome in Patients With Chronic Kidney Disease (KNOW-CKD) Investigators

¹Department of Internal Medicine, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Republic of Korea

²Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea

³Department of Internal Medicine and Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, Republic of Korea

Correspondence: Soo Wan Kim Department of Internal Medicine, Chonnam National University Medical School, 42 Jebong-ro, Dong-gu, Gwangju 61469, Republic of Korea. E-mail: skimw@chonnam.ac.kr

Received 2024 February 7; Revised 2024 August 29; Accepted 2024 August 29.

Abstract

Background

Coronary artery calcification (CAC) is a surrogate of cardiovascular events in patients with chronic kidney disease (CKD). To establish the role of circulating osteoprotegerin (OPG) as a cardiovascular biomarker in patients with CKD, we investigated whether an increase in serum OPG levels is associated with the risk of CAC progression.

Methods

A total of 1,130 patients with CKD stage 1 to predialysis 5 were divided into quartiles according to serum OPG levels (Q1 to Q4). The coronary artery calcium score (CACS) was assessed at baseline and at the 4-year follow-up visit. CAC progression was defined as an increase in the CACS of more than 200 Agatston units over 4 years.

Results

Serum OPG levels were positively correlated with the CACS at baseline (R = 0.240, p < 0.001) and at the 4-year follow-up visit (R = 0.280, p < 0.001) as well as with changes in the CACS for 4 years (R = 0.270, p < 0.001) based on scatter plot analysis. Binary logistic regression analysis demonstrated that the risk of CAC progression was significantly increased in Q4 compared with Q1 (adjusted odds ratio, 3.706; 95% confidence interval, 1.154–11.902). Penalized spline curve analysis revealed a linear association between serum OPG levels and the risk of CAC progression.

Conclusion

An increase in circulating OPG levels was associated with the risk of CAC progression in patients with predialysis CKD.

Keywords: Biomarkers; Chronic renal insufficiency; Coronary artery disease; Osteoprotegerin

Introduction

Cardiovascular events are the leading cause of mortality in the general population as well as in patients with chronic kidney disease (CKD) [1–3]. Particularly, together with heart failure, coronary artery disease (CAD) is one of the etiologies of major adverse cardiac events (MACEs) [1,4]. Indeed, guidelines on the management of cardiovascular complications in CKD emphasize the prevention of CAD [5–7]. Accordingly, the early identification of patients with a high risk for coronary events is of critical importance in the management of CKD; however, no reliable biomarkers are currently available for the prediction of future coronary events in patients with CKD.

The gold standard for the evaluation of CAD is coronary angiography [8–10]; the procedure may be both diagnostic and therapeutic if necessary. However, due to its invasiveness and high cost, it is limited mainly to patients with acute coronary syndrome [10]. Therefore, the assessment of coronary artery calcification (CAC) by cardiac computed tomography (CT), which is quantified as the coronary artery calcium score (CACS) in Agatston units (AU), has largely replaced the diagnostic use of coronary angiography [10–13]. The role of the CACS in the prediction of cardiovascular events is well-established for both the general population [14–17] and patients with CKD [18,19]. Nevertheless, issues concerning the radiation dose and cost-effectiveness related to cardiac CT remain unresolved [20–22]. Therefore, the use of a biomarker to predict coronary events in a high-risk population is still necessary even with the availability of noninvasive cardiac imaging techniques.

Osteoprotegerin (OPG) was originally identified as a decoy receptor of receptor activator of nuclear factor-κB ligand (RANKL), which can inhibit its binding to receptor activator of nuclear factor-κB (RANK) and ultimately prevent excessive bone resorption [23–25]. Despite its distinct role in bone modeling [26], a large body of evidence has recently highlighted the role of circulating OPG as a biomarker in the prediction of cardiovascular events [27]. An increase in serum OPG levels has been associated with adverse cardiovascular events in patients with established CAD [28–30]. Although the existing evidence on the relationship between circulating OPG levels and the risk of cardiovascular events is not as strong as that for the general population, a recent study with a relatively small number of subjects (n = 291) suggested that high serum OPG levels may be associated with adverse cardiovascular outcomes in patients with CKD [31]. Moreover, although the association between serum OPG levels and the presence of CAC lesions has been consistently reported in cross-sectional analyses [32,33], whether serum OPG levels can predict the progression of CAC either in the general population or in patients with CKD has not been investigated.

Therefore, we aimed to clarify the association between circulating OPG levels and the risk of CAC progression in patients with predialysis CKD by analyzing the changes in the CACS of more than 1,000 subjects for 4 years. We hypothesized that elevated OPG levels may be associated with an increased risk of CAC progression.

Methods

Study population

The study design of the KNOW-CKD (KoreaN Cohort Study for Outcome in Patients With Chronic Kidney Disease) has been described previously [34]. Patients with CKD stage 1 to predialysis 5 who voluntarily gave informed consent were initially enrolled from nine tertiary hospitals in South Korea from 2011 to 2016. The study protocol of the KNOW-CKD was reviewed and approved by the institutional review boards of the participating centers (Seoul National University Hospital [No. 1104–089-359, May 25, 2011], Seoul National University Bundang Hospital [No. B-1106/129–008, August 24, 2011], Severance Hospital [No. 4–2011-0163, June 2, 2011], Kangbuk Samsung Medical Center [No. 2011–01-076, June 16, 2012], The Catholic University of Korea, Seoul St. Mary’s Hospital [No. KC11OIMI0441, June 30, 2011], Gachon University Gil Hospital [No. GIRBA2553, August 8, 2011], Eulji General Hospital [No. 201105–01, June 10, 2011], Chonnam National University Hospital [No. CNUH-2011-092, July 5, 2011], and Inje University Busan Paik Hospital [No. 11–091, July 26, 2011]). Among the initially recruited 2,238 subjects, those without the baseline measurement of serum OPG levels (n = 98), those without the baseline and 4-year follow-up measurement of the CACS (n = 979), and those without data on the follow-up duration (n = 31) were excluded. Finally, a total of 1,130 subjects were included in the analyses. The participants were closely monitored during the follow-up period.

Data collection from the participants

Demographic and anthropometric data and information on medical history were collected at baseline according to the study protocol. Blood and urine sampling was performed after overnight fasting, and the samples were transferred to and analyzed in the central laboratory (Lab Genomics). The present study utilized the estimated glomerular filtration rate (eGFR) calculated by the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation based on serum creatinine levels. The spot urine albumin-to-creatinine ratio (ACR) was preferably measured using second-voided urine samples.

Measurement of serum osteoprotegerin levels

An enzyme-linked immunosorbent assay kit (BioVendor R&D) was used to determine serum OPG levels. Duplicate samples were used to calculate the mean as the reported value. The reported value of samples below the detection range (<1.5 pmol/L) was approximated to be 1.5 pmol/L (n = 3).

Determination of the coronary artery calcium score

Electrocardiography-gated coronary multi-detector CT was conducted following the standard protocol of each participating center at baseline and at the 4-year follow-up visit. The CACS was determined in AU on a digital radiologic workstation.

Quartile and study outcome

Based on serum OPG levels, the participants were divided into quartiles (Q1, Q2, Q3, and Q4) (Figure 1). The study outcome was the progression of CAC, which was defined as an increase in the CACS of more than 200 AU over 4 years, as previously reported [35–37].

Figure 1.

Flow diagram of study participant selection.

CACS, coronary artery calcium score; OPG, osteoprotegerin; Q1, 1st quartile; Q2, 2nd quartile; Q3, 3rd quartile; Q4, 4th quartile; SD, standard deviation.

Statistical analysis

The baseline characteristics of the participants (continuous and categorical variables) were analyzed by one-way analysis of variance and chi-square test, respectively. The correlations of serum OPG levels with the CACS at baseline, the CACS at the 4-year follow-up visit, and changes in the CACS for 4 years were visualized by scatter plots and assessed by Spearman’s correlation coefficient. Binary logistic regression models were constructed to elucidate the independent association between serum OPG levels and the risk of CAC progression. The proportion of the missing values in most variables included for the adjustment was far less than 5%. Excluding the participants with any missing data for the models, a total of 856 cases were included for the primary analysis. The results are presented as the odds ratio (OR) with 95% confidence interval (CI). The variables in Model 1 were unadjusted. Model 2 was additionally adjusted for age, sex, medical history (Charlson comorbidity index, primary causes of CKD, smoking status), medications (angiotensin-converting enzyme inhibitors and/or angiotensin receptor blockers, diuretics, statins, and antiplatelets/anticoagulants), and anthropometric data (waist-to-hip ratio [WHR] and systolic blood pressure). Model 3 was additionally adjusted for baseline laboratory findings (hemoglobin, iron, albumin, high-density lipoprotein cholesterol, fasting glucose, high-sensitivity C-reactive protein, eGFR, and spot urine ACR). Model 4 was further adjusted for the CACS at baseline as a categorical variable. A penalized spline curve was used to demonstrate the association between serum OPG levels (continuous variable) and the risk of CAC progression. To confirm our findings, we conducted a series of sensitivity analyses. First, we analyzed the association between log-transformed serum OPG levels (continuous variable) and the risk of CAC progression. Second, we excluded those with eGFR ≥90 mL/min/1.73 m² at baseline (n = 245) for repeated binary logistic regression analysis; these patients were considered to have near normal kidney function and not reflect the pathologic conditions accompanied by CKD. Third, those with eGFR <15 mL/min/1.73 m² at baseline (n = 8) were excluded because they were small in number. Fourth, those with CACS = 0 AU at baseline (n = 606) were excluded because the progression of CAC was relatively rare in this subpopulation. Lastly, we replaced missing values with multiple imputations using a random sampling method to impute 10 independent copies of the data and repeated binary logistic regression analysis. Two-sided p-values <0.05 were considered statistically significant. Statistical analysis was performed using IBM SPSS for Windows version 22.0 (IBM Corp.) and R version 4.1.1 (R Foundation for Statistical Computing).

Results

Baseline characteristics

Baseline characteristics were significantly different according to serum OPG levels (Table 1). Higher serum OPG levels were associated with higher mean age, higher CACS at baseline, higher burden of comorbid conditions, higher frequency of diabetes mellitus as a primary cause of CKD, higher prevalence of current smoking, and more frequent use of diuretics, statins, and antiplatelets/anticoagulants. The WHR and diastolic blood pressure were also the highest in Q4. Hemoglobin, iron, albumin, and eGFR levels were the lowest in Q4, whereas fasting glucose levels were the highest in Q4. Overall, unfavorable medical conditions were associated with higher serum OPG levels at baseline.

Table 1.

Baseline characteristics of study participants according to serum OPG levels

Association between serum osteoprotegerin levels and the risk of coronary artery calcification progression

Scatter plots revealed that log-transformed serum OPG levels were positively correlated with the CACS at baseline (R = 0.377, p < 0.001) (Supplementary Fig. 1, available online) and at the 4-year follow-up visit (R = 0.397, p < 0.001) (Supplementary Fig. 2, available online). Importantly, log-transformed serum OPG levels were weakly, but significantly correlated with changes in the CACS for 4 years (R = 0.355, p < 0.001) (Fig. 2). Binary logistic regression analysis revealed that the risk of CAC progression was significantly increased in Q4 compared with Q1 (adjusted OR, 3.71; 95% CI, 1.15–11.90) (Table 2). Penalized spline curve analysis demonstrated a linear association between log-transformed serum OPG levels and the risk of CAC progression (Fig. 3).

Figure 2.

Scatter plot of log-transformed serum OPG levels with changes in the CACS for 4 years.

The correlation of serum OPG levels with changes in the CACS for 4 years was assessed by Spearman’s correlation coefficient (R).

AU, Agatston units; CACS, coronary artery calcium score; OPG, osteoprotegerin.

Table 2.

ORs for CAC progression according to serum OPG levels

Figure 3.

Penalized spline curve of the association between log-transformed serum OPG levels and CAC progression.

Adjusted ORs of the association between serum OPG levels (continuous variable) and CAC progression are shown. The model was adjusted for age, sex, Charlson comorbidity index, primary causes of chronic kidney disease, smoking status, medications (angiotensin-converting enzyme inhibitors and/or angiotensin receptor blockers, diuretics, statins, and antiplatelets/anticoagulants), waist-to-hip ratio, systolic blood pressure, laboratory findings (hemoglobin, iron, albumin, high-density lipoprotein cholesterol, fasting glucose, high-sensitivity C-reactive protein, estimated glomerular filtration rate, and spot urine albumin-to-creatinine ratio), and coronary artery calcium score at baseline.

CAC, coronary artery calcification; OR, odds ratio; OPG, osteoprotegerin.

Sensitivity analysis

An increase in log-transformed serum OPG levels (continuous variable) was significantly associated with the risk of CAC progression (adjusted OR, 2.80; 95% CI, 1.09–7.19) (Supplementary Table 1, available online). After excluding subjects with eGFR ≥90 mL/min/1.73 m² (adjusted OR, 6.31; 95% CI, 1.39–28.62) (Supplementary Table 2, available online) or with eGFR <15 mL/min/1.73 m² (adjusted OR, 3.68; 95% CI, 1.14–11.82) (Supplementary Table 3, available online) at baseline, the risk of CAC progression was still significantly increased in Q4 compared with Q1. After excluding those with CACS = 0 AU at baseline, high serum OPG levels were strongly associated with the risk of CAC progression (adjusted OR in Q4 vs. Q1, 3.56; 95% CI, 1.10–11.53) (Supplementary Table 4, available online). Finally, even after replacing missing values by multiple imputation, the risk of CAC progression was still significantly increased in Q4 compared with Q1 (adjusted OR, 3.23; 95% CI, 1.04–10.05) (Supplementary Table 5, available online).

Discussion

In the present study, we found that an increase in circulating OPG levels was associated with the risk of CAC progression in patients with predialysis CKD. Serum OPG levels were positively correlated with not only the CACS at baseline but also changes in the CACS for 4 years. Therefore, the present study demonstrated the novel role of serum OPG levels in the prediction of CAC progression in patients with CKD.

Calcification is a typical feature of advanced plaque lesions [38]; thus, early noncalcified atheroma lesions may be more sensitively detected by coronary CT angiography rather than CACS assessment [39]. However, the risk of contrast-induced acute kidney injury should be considered particularly in patients with advanced CKD [40]. Moreover, vascular calcification can be attributed to two distinct mechanisms [41]; one is the atherosclerotic process arising from the intimal layer of the artery, and the other is Monckeberg’s arteriosclerosis primarily involving the medial layer of the arterial vessels. The latter is known to predominate in CAC in patients with CKD [41]. In addition, it has been reported that nonobstructive arteriosclerotic coronary lesions may be also associated with a decrease in the coronary flow reserve [42,43], leading to myocardial fibrosis. Therefore, assessing the CACS may be a useful approach for predicting overall cardiovascular outcomes in patients with CKD.

The precise mechanism underlying the association of circulating OPG levels with CAC progression remains elusive. It is well-known that OPG is expressed by various extra-osseous tissues, including an inflamed vascular bed, and functions as a decoy receptor of RANKL [25,44]. As the binding of RANKL to RANK promotes the calcification of vascular smooth muscle cells via the activation of inflammatory pathways [45], the overall function of OPG in vascular beds may be most likely protective. Indeed, the whole-body deletion of an OPG-encoding gene in an experimental murine model resulted in accelerated vascular calcification [46,47]. The findings collectively suggest that elevated circulating OPG levels may reflect the compensatory upregulation of OPG in inflamed vascular beds and that OPG may be more likely to be a biomarker rather than a mediator of vascular calcification.

In contrast, in vitro treatment with exogenous OPG in endothelial cells could induce endothelial dysfunction and oxidative stress [48]. In addition, direct exposure of endothelial cells to inflammatory cytokines in vitro could upregulate OPG expression, leading to the increased surface expression of ICAM-1 and VCAM-1, which are adhesion molecules involved in the initial process of vascular inflammation [49]. As the currently available evidence cannot exclude the possibility that circulating OPG may directly accelerate vascular inflammation, elevated circulating OPG levels per se may be an independent indicator of future cardiovascular events.

Serum OPG has been strongly proposed as a biomarker of cardiovascular events. In a meta-analysis involving 26,442 participants, elevated serum OPG levels were associated with an increased risk of incident cardiovascular disease [50]. In addition, serum OPG levels were associated with an increased incidence of all-cause and cardiovascular mortality in patients with established CAD, where the mean eGFR of 3,766 participants was 78 mL/min/1.73 m² [29]. Further, recent studies on CKD patients also reported a significant association between serum OPG levels and the risk of cardiovascular mortality [51] or the risk of the risk of three-point MACE [52]. The findings showing the association of serum OPG levels with the risk of CAC progression further support the role of serum OPG as a cardiovascular biomarker in patients with CKD [31]. Although CAD is an important risk factor for cardiovascular events, the prevalence of myocardial dysfunction and arrhythmia is relatively higher among patients with CKD [1]. As the findings of the current study could not exclude the possibility that serum OPG levels may also be significantly associated with the risk of heart failure or symptomatic arrhythmia, further studies are needed to define the role of serum OPG as a cardiovascular biomarker.

There are several limitations in the present study. First, due to the observational study design, we could not confirm the causal relationship between serum OPG levels and the risk of CAC progression in patients with predialysis CKD. Nevertheless, it should be noted that the main focus of the current study is not therapeutic intervention, and randomized controlled trials mainly focusing on the role of a biomarker are rarely conducted. Second, all variables, including serum OPG levels, were measured once at baseline. However, the follow-up duration to assess the progression of CAC was not considerably long, which was fixed to 4 years. Moreover, the association between serum OPG levels and the risk of cardiovascular events has been previously reported [31], which showed a similar result based on the baseline measurement of serum OPG levels. Lastly, as the KNOW-CKD recruited only ethnically Korean patients with predialysis CKD who live in South Korea, the generalization of the results in the present study to other populations should be made with caution. Nevertheless, a cross-sectional association between serum OPG levels and the presence of CAC lesions has been reported in studies from other countries [32,33].

In conclusion, we found that an increase in circulating OPG levels was associated with the risk of CAC progression in patients with predialysis CKD. Therefore, the measurement of serum OPG levels may facilitate the early identification of patients with a high risk for CAC progression. The adoption of serum OPG measurement for clinical purposes should be affirmatively considered in future perspectives.

Supplementary Materials

Supplementary data are available at Kidney Research and Clinical Practice online (https://doi.org/10.23876/j.krcp.24.039).

j-krcp-24-039-Supplementary-Table-1.pdf

j-krcp-24-039-Supplementary-Table-2.pdf

j-krcp-24-039-Supplementary-Table-3.pdf

j-krcp-24-039-Supplementary-Table-4.pdf

j-krcp-24-039-Supplementary-Table-5.pdf

j-krcp-24-039-Supplementary-Fig-1.pdf

j-krcp-24-039-Supplementary-Fig-2.pdf

Notes

Conflicts of interest

All authors have no conflicts of interest to declare.

Funding

This work was supported by the Research Program funded by the Korea Disease Control and Prevention Agency (2011E3300300, 2012E3301100, 2013E3301600, 2013E3301601, 2013E3301602, 2016E3300200, 2016E3300201, 2016E3300202, 2019E320100, 2019E320101, 2019E320102, and 2022-11-007) and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2023-00217317 and RS-2023-00278258).

Data sharing statement

The data presented in this study are available from the corresponding author upon reasonable request.

Authors’ contributions

Conceptualization: SHS

Formal analysis: SHS, KHO, THY

Investigation: KHO, THY

Methodology: SHS, TRO, HSC, CSK, EHB, SKM

Supervision, Funding acquisition: KHO, SWK

Writing–Original draft: SHS

Writing–review & editing: All authors

All authors read and approved the final manuscript.

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Table 1.

Baseline characteristics of study participants according to serum OPG levels

Characteristic	Serum OPG levels				p-value
Characteristic	Q1	Q2	Q3	Q4	p-value
Follow-up duration (yr)	8.709 ± 1.326	8.544 ± 1.549	8.441 ± 1.661	8.303 ± 1.801	0.02
Age (yr)	43.511 ± 11.086	49.518 ± 9.973	55.484 ± 9.696	61.214 ± 8.681	<0.001
Male sex	182 (65.0)	155 (54.6)	164 (57.5)	161 (57.3)	0.07
CACS					<0.001
0	211 (75.4)	172 (60.6)	143 (50.2)	80 (28.5)
>0, ≤400	65 (23.2)	99 (34.9)	122 (42.8)	159 (56.6)
>400, ≤1,000	3 (1.1)	9 (3.2)	14 (4.9)	25 (8.9)
>1,000	1 (0.4)	4 (1.4)	6 (2.1)	17 (6.0)
Charlson comorbidity index					<0.001
0–3	264 (94.3)	260 (91.5)	229 (80.4)	175 (62.3)
4–5	16 (5.7)	23 (8.1)	52 (18.2)	103 (36.7)
≥6	0 (0)	1 (0.4)	4 (1.4)	3 (1.1)
Primary causes of CKD					<0.001
Diabetes mellitus	11 (3.9)	19 (6.7)	50 (17.5)	85 (30.2)
Hypertension	39 (13.9)	41 (14.4)	59 (20.7)	69 (24.6)
Glomerulonephritis	143 (51.1)	145 (51.1)	99 (34.7)	72 (25.6)
PKD	71 (25.4)	65 (22.9)	61 (21.4)	33 (11.7)
Others	16 (5.7)	14 (4.9)	16 (5.6)	22 (7.8)
Smoking status					0.04
Non-smoker	148 (52.9)	164 (57.7)	159 (55.8)	152 (54.1)
Ex-smoker	55 (19.6)	50 (17.6)	36 (12.6)	34 (12.1)
Current smoker	77 (27.5)	70 (24.6)	90 (31.6)	95 (33.8)
Medications
ACEis/ARBs	241 (86.1)	250 (88.0)	253 (88.8)	231 (82.2)	0.10
Diuretics	54 (19.3)	58 (20.4)	84 (29.5)	89 (31.7)	0.001
Statins	105 (37.5)	150 (52.8)	163 (57.2)	154 (54.8)	<0.001
Antiplatelets/anticoagulants	42 (15.0)	65 (22.9)	86 (30.2)	99 (35.2)	<0.001
Body mass index (kg/m²)	24.611 ± 3.763	24.678 ± 3.269	24.661 ± 3.222	24.484 ± 3.157	0.89
Waist-to-hip ratio	0.885 ± 0.068	0.889 ± 0.063	0.897 ± 0.064	0.907 ± 0.067	0.001
SBP (mmHg)	123.946 ± 14.640	125.810 ± 12.609	126.568 ± 14.330	127.332 ± 16.627	0.06
DBP (mmHg)	77.043 ± 10.828	78.525 ± 9.446	76.877 ± 10.141	75.079 ± 11.024	0.001
Laboratory findings
Hemoglobin (g/dL)	13.998 ± 1.768	13.500 ± 1.798	13.301 ± 1.730	12.648 ± 1.809	<0.001
Iron (μg/dL)	102.470 ± 38.298	95.710 ± 36.450	95.846 ± 34.029	92.313 ± 35.361	0.01
TSAT (%)	33.568 ± 12.940	31.877 ± 12.623	31.715 ± 11.645	31.211 ± 12.294	0.15
Ferritin (ng/mL)	127.846 ± 110.400	122.879 ± 109.892	121.019 ± 110.647	148.322 ± 172.300	0.15
Albumin (g/dL)	4.320 ± 0.342	4.277 ± 0.344	4.246 ± 0.332	4.210 ± 0.346	0.002
Total cholesterol (mg/dL)	176.197 ± 31.870	175.431 ± 34.437	175.852 ± 38.655	172.441 ± 38.278	0.61
HDL-C (mg/dL)	100.527 ± 27.728	97.350 ± 30.660	97.384 ± 31.779	94.002 ± 29.968	0.07
LDL-C (mg/dL)	50.569 ± 14.421	51.897 ± 15.692	50.744 ± 14.906	50.460 ± 15.381	0.68
Triglycerides (mg/dL)	154.338 ± 100.333	146.246 ± 84.929	156.188 ± 104.001	152.710 ± 92.487	0.60
Fasting glucose (mg/dL)	101.667 ± 23.374	102.725 ± 21.458	107.232 ± 32.765	115.039 ± 39.121	<0.001
hs-CRP (mg/dL)	0.440 (0.150–1.400)	0.670 (0.300–1.550)	0.700 (0.252–1.600)	0.600 (0.200–2.100)	0.07
Spot urine ACR (mg/g)	158.102 (26.810–503.180)	287.744 (45.999–724.851)	258.299 (70.486–602.448)	255.895 (71.818–710.652)	0.006
Creatinine (mg/dL)	1.196 ± 0.601	1.340 ± 0.641	1.429 ± 0.675	1.639 ± 0.599	<0.001
eGFR (mL/min/1.73 m²)	74.167 ± 32.275	62.124 ± 28.419	56.234 ± 25.478	45.022 ± 21.366	<0.001
CKD stage					<0.001
Stage 1	108 (38.6)	69 (24.3)	47 (16.5)	21 (7.5)
Stage 2	88 (31.4)	86 (30.3)	69 (24.2)	40 (14.2)
Stage 3a	45 (16.1)	49 (17.3)	75 (26.3)	50 (17.8)
Stage 3b	29 (10.4)	53 (18.7)	64 (22.5)	106 (37.7)
Stage 4	8 (2.9)	25 (8.8)	26 (9.1)	64 (22.8)
Stage 5	2 (0.7)	2 (0.7)	4 (1.4)	0 (0)

Data are expressed as number only, mean ± standard deviation, number (%), or median (interquartile range).

ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; ACR, albumin-to-creatinine ratio; CACS, coronary artery calcium score; CKD, chronic kidney disease; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; LDL-C, low-density lipoprotein cholesterol; OPG, osteoprotegerin; PKD, polycystic kidney disease; Q, 1st quartile; Q2, 2nd quartile; Q3, 3rd quartile; Q4, 4th quartile; SBP, systolic blood pressure; TSAT, transferrin saturation.

Serum OPG levels (pmol/L)	Events, n (%)	Model 1^a		Model 2^b		Model 3^c		Model 4^d
Serum OPG levels (pmol/L)	Events, n (%)	OR (95% CI)	p-value	OR (95% CI)	p-value	OR (95% CI)	p-value	OR (95% CI)	p-value
Q1, 1.5–4.5	10 (2.8)	Reference		Reference		Reference		Reference
Q2, 4.5–6.0	30 (9.0)	3.55 (1.40–8.99)	0.008	3.22 (1.17–8.83)	0.02	3.50 (1.26–9.72)	0.02	2.70 (0.84–8.64)	0.09
Q3, 6.0–8.2	33 (12.1)	5.12 (2.08–12.60)	<0.001	2.15 (0.79–5.82)	0.13	2.36 (0.86–6.52)	0.096	2.46 (0.78–7.74)	0.12
Q4, 8.2–33.4	53 (31.4)	13.36 (5.65–31.56)	<0.001	3.88 (1.43–5.82)	0.008	4.32 (1.54–12.08)	0.005	3.71 (1.15–11.90)	0.03