Association of the Serum Osteoprotegerin Level With Target Organ Damage in Patients at High Risk of Coronary Artery Disease

Abstract

Background: There is little data as to whether osteoprotegerin (OPG) is associated with target organ damage (TOD), so we evaluated the association in patients at high risk of coronary artery disease (CAD).

Methods and Results: A total of 349 patients who underwent invasive coronary angiography (ICA) for suspected CAD were prospectively recruited. During the index admission, 6 TOD parameters were collected: extent of CAD, glomerular filtration rate (GFR), left ventricular mass index (LVMI), E/e’, brachial-ankle pulse wave velocity (baPWV), and ankle-brachial index (ABI). Serum OPG levels were measured using enzyme-linked immunosorbent assay. The OPG level was significantly higher in patients with ≥1 TOD parameter than in those without (314±186 vs. 202±74 pg/mL, P<0.001). For each TOD parameter, the serum OPG level was significantly higher in patients with TOD than in those without (P<0.05 for each) except for ABI. In correlation analysis, OPG was significantly associated with GFR, LVMI, E/e’, baPWV and ABI (P<0.05 for each). The OPG concentration increased proportionally with increasing TOD (P<0.001). Higher OPG concentrations (≥198 pg/mL) was significantly associated with the presence of TOD (odds ratio 3.22; 95% confidence interval 1.51–6.85; P=0.002) even after controlling for potential confounders.

Conclusions: Serum OPG level was significantly associated with a variety of TOD in patients undergoing ICA. OPG may be a useful marker for TOD and in the risk stratification of patients at high risk of CAD.

Cardiovascular disease (CVD) is the leading cause of death worldwide. In 2017, it accounted for about 17,800,000 deaths globally, over 850,000 deaths in the USA and over 4,100,000 deaths across European Society of Cardiology (ESC) member countries.^1,2 Therefore, it is important to urgently select high-risk patients and provide individualized management to reduce their CVD risk. As CVD is a lifelong systemic disease that progresses from subclinical target organ damage (TOD),³ early detection of TOD can predict and prevent future cardiovascular events.^3–5 Nowadays, various tools are used to detect TOD and although recent technological advancements have made it possible to detect TOD more accurately at earlier stages, their application for screening purposes is limited because diagnostic equipment such as computed tomography and ultrasonography has become more complex and requires specialized facilities and skills. Considering this, biomarkers would be easier and more useful in risk stratification, especially when applied to a large number of subjects. Indeed, cardiac biomarkers, such as troponin and natriuretic peptide, have shown strong predictive power and are widely used in clinical practice.^6–8 Recognizing the importance and usefulness of biomarkers, research efforts to find new ones have continued.

Osteoprotegerin (OPG), a regulator of bone metabolism, has recently emerged as an important factor in the development of atherosclerosis.⁹ In addition, the serum OPG level has been recently associated with CVD in several studies.^10–16 However, those reports are dedicated to only 1 TOD parameter such as coronary artery disease (CAD) or arterial stiffness. Data on the relationship between OPG and various TOD parameters, including CAD, are scarce. To better use OPG for TOD prediction, it is valuable to know which TOD parameter is more correlated with OPG, and whether the OPG concentration is related to the number of TOD parameters. If we can obtain this information, OPG could be more useful for detecting TOD before clinically overt CVD and assessment of patients’ risk. In this study, we evaluated the association between OPG and various TOD parameters in Korean patients at high CAD risk.

Methods

Study Population

Between May 2013 and November 2015, patients who underwent invasive coronary angiography (ICA) for suspected CAD were prospectively recruited. From the patients who agreed to participate in the registry, arterial blood samples for serum OPG were collected at the time of ICA. There was no clinical limitation to enrollment; however, patients whose serum OPG level was below the measurement limit (n=8) were excluded from analysis. Finally, data from 349 patients were used in this study, which was conducted in accordance with the Declaration of Helsinki, and the study protocol was approved by the Institutional Review Board of Boramae Medical Center (IRB no. 10-2017-21). Written informed consent was given by each study participant.

Data Collection

Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m²). Systolic and diastolic blood pressures (SBP and DBP, respectively) were recorded at the time of admission by well-trained nurses using an oscillometric device. History of cardiovascular risk factors, such as hypertension, diabetes mellitus and dyslipidemia, was obtained. Hypertension was defined as SBP ≥140 mmHg and/or DBP ≥90 mmHg, or use of antihypertensive medications at the time of admission. Diabetes mellitus was defined as serum fasting glucose ≥126 mg/dL, or use of oral hypoglycemic agents or insulin at the time of admission. Dyslipidemia was defined as having low-density lipoprotein (LDL) cholesterol ≥130 mg/dL or taking any lipid-lowering medication, or having previously been diagnosed. Patients with a history of smoking within the past 12 months were considered to be smokers. All blood samples were collected after overnight fasting. The levels of white blood cells, hemoglobin, glucose, creatinine, LDL, high-density lipoprotein, triglycerides and C-reactive protein were assessed. Left ventricular ejection fraction was measured using Simpson’s biplane method during transthoracic echocardiography. Information on cardiovascular medications, including renin-angiotensin system blockers, β-blockers, calcium-channel blockers and statins, was obtained.

OPG Measurement

Immediately before ICA, 20 mL of arterial blood was collected from the radial or femoral artery, which had been punctured for ICA while the patient was in the supine position. After collection, the blood sample was immediately cooled and centrifuged at 2,000 g for 15 min, and the serum was stored at −70℃ until assayed. The serum OPG levels were measured by enzyme-linked immunosorbent assay using a commercially available kit (ab189580; Abcam, Cambridge, MA, USA). The minimum detectable concentration was 31.25–2,000 pg/mL. The intra- and inter-assay coefficients of variations for OPG were 6.0% and 9.0%, respectively.

TOD Parameters

The following 6 TOD parameters were assessed according to current practice guidelines and data availability: (1) obstructive CAD, defined as ≥50% stenosis in major coronary arteries or their branches with >2 mm diameter on ICA, and the extent of CAD based on the number of affected coronary arteries; (2) estimated glomerular filtration rate (eGFR) calculated using the 4-component Modification of Diet in Renal Disease equation incorporating age, race, sex and serum creatinine level,¹⁷ with eGFR <60 mL/min/1.73 m² considered to indicate TOD; (3) left ventricular mass index (LVMI) calculated with linear method cube formula, and left ventricular hypertrophy (LVH) defined as LVMI >115 g/m² for men and >95 g/m² for women;¹⁸ (4) left ventricular diastolic dysfunction defined as septal E/e’ >15;¹⁹ (5) increased arterial stiffness defined as brachial-ankle pulse wave velocity (baPWV) >1,600 cm/s;²⁰ and (6) peripheral arterial disease defined as ankle-brachial index (ABI) <0.9.²¹

Clinical Events

The composite of all-cause death, non-fatal myocardial infarction, coronary revascularization, including percutaneous coronary intervention and coronary bypass graft surgery, and ischemic stroke during the follow-up period was assessed. Clinical events were assessed by research coordinators reviewing medical records and telephone interviews with standardized report forms.

Statistical Analysis

Continuous variables are expressed as mean±standard deviation and were analyzed using Student’s t-test. Categorical variables are expressed as percentage and were analyzed using Pearson’s chi-square test. Linear relationship between serum OPG and TOD parameters were assessed using Pearson’s correlation analysis. The mean value of OPG according to the extent of TOD was compared using analysis of variance. Receiver-operating characteristic (ROC) curve analysis was used to determine optimal OPG values associated with TOD. Univariable logistic regression analysis was performed to find significant variables related to the presence of TOD. To determine independent associations between the serum OPG value obtained from ROC curve analysis and the presence of TOD, multivariable logistic regression analysis was performed. In the multivariable logistic regression analysis, traditional risk factors, including age, BMI, diabetes mellitus, hypertension, dyslipidemia, and cigarette smoking, were included as covariates. In the survival analysis, Cox regression analysis was performed to find associations between OPG and clinical events. All analyses were 2-tailed and clinical significance was set at P<0.05. Statistical analyses were performed using statistical software SPSS version 20.0 (IBM Co., Armonk, NY, USA).

Results

Clinical Characteristics and TOD Parameters of the Study Patients

Clinical and laboratory data of the 349 patients are shown in Table 1. Mean age was 64.7±10.7 years and 62.8% were men. The prevalence of hypertension, diabetes mellitus and dyslipidemia was 66.5%, 29.5% and 71.9%, respectively. Most of the laboratory findings, including results of blood tests and echocardiography, were within the normal range. Patients with acute coronary syndrome comprised 42.1% of the study group. The mean value of serum OPG level was 300±180 pg/mL. Almost half of the patients (54.2%) had multivessel disease on ICA. Prescription rates of renin-angiotensin system blockers, β-blockers, calcium-channel blockers and statins were 22.6%, 38.4%, 26.1% and 64.2%, respectively. The parameters for TOD are shown in Table 2. About two-thirds of patients (78.8%) had obstructive CAD. Mean eGFR was 81.4 mL/min/1.73 m², and 16.3% of the study patents had chronic kidney disease ≥stage 3 (eGFR <60 mL/min/1.73 m²). The mean LVMI was 96.7±26.8 g/m², and 29.5% of the study patients had LVH. Mean septal E/e’ was 11.2, and 13.5% of the study patients had diastolic dysfunction (E/e’ ≥15). Mean baPWV was 1,620 cm/s, and 40.1% of the study patients had stiffened arteries (baPWV ≥1,600 cm/s). Mean ABI was 1.10, and 4.0% of the study patients had peripheral artery disease (ABI <0.9).

Table 1. Characteristics of the Study Patients

Characteristic	TOD (+) (n=309)	TOD (−) (n=40)	P value
Age, years	65.8±10.1	56.4±11.9	<0.001
Male, n (%)	198 (64.1)	21 (52.5)	0.154
BMI, kg/m2	24.7±3.4	25.8±3.4	0.054
Systolic blood pressure, mmHg	124±17	118±9	0.007
Diastolic blood pressure, mmHg	73±9	72±8	0.599
Cardiovascular risk factors, n (%)
Hypertension	211 (68.3)	21 (52.5)	0.047
Diabetes mellitus	99 (32.0)	4 (10.0)	0.004
Diabetes mellitus with insulin	22 (7.1)	0 (0)	0.091
Dyslipidemia	171 (55.3)	16 (40.0)	0.067
Smoking	65 (21.0)	12 (30.0)	0.198
Laboratory findings
White blood cell count, per μL	6,922±2,282	6,429±2,339	0.207
Hemoglobin, g/dL	13.4±2.1	13.9±1.4	0.099
Fasting blood glucose, mg/dL	123±47	106±19	<0.001
Hemoglobin A1c, %	6.6±1.3	6.0±0.9	0.066
eGFR, mL/min/1.73 m2	79.9±29.1	93.2±18.9	0.005
Total cholesterol, mg/dL	145±32	160±36	0.004
Low-density lipoprotein cholesterol, mg/dL	77.1±21.0	85.1±27.2	0.037
High-density lipoprotein cholesterol, mg/dL	48.5±13.4	51.8±13.0	0.156
Triglycerides, mg/dL	123±61	107±54	0.122
C-reactive protein, mg/dL	0.68±2.24	0.30±0.76	0.369
Osteoprotegerin, pg/mL	314±186	202±74	<0.001
Left ventricular ejection fraction, %	63.2±10.5	66.4±7.1	0.021
Clinical diagnosis, n (%)			0.693
STEMI	6 (1.9)	1 (2.5)
NSTEMI	23 (7.4)	3 (7.5)
Unstable angina	97 (31.4)	17 (42.5)
Stable angina	125 (40.5)	13 (32.5)
Other	58 (18.8)	6 (15.0)
ICA findings, n (%)			<0.001
Insignificant	34 (11.0)	40 (100)
1-vessel disease	86 (27.8)	0 (0)
2-vessel disease	96 (31.1)	0 (0)
3-vessel disease	93 (30.1)	0 (0)
Cardiovascular medications, n (%)
Antiplatelet drugs	173 (56.0)	11 (27.5)	0.001
Renin-angiotensin system blocker	72 (23.3)	7 (17.5)	0.409
β-blocker	128 (41.4)	6 (15.0)	0.001
Calcium-channel blocker	81 (26.2)	10 (25.0)	0.869
Statin	166 (53.7)	12 (30.0)	0.005

BMI, body mass index; eGFR, estimated glomerular filtration rate; ICA, invasive coronary angiography; (N)STEMI, (non-)ST-segment elevation myocardial infarction; TOD, target organ damage.

Table 2. Parameters of Target Organ Damage (TOD)

Parameter	TOD (+) (n=309)	TOD (−) (n=40)	P value
Obstructive CAD, n (%)	275 (89.0)	0 (0)	<0.001
eGFR, mL/min/1.73 m2	79.9±29.1	93.2±18.9	0.005
<60 mL/min/1.73 m2, n (%)	57 (18.8)	0 (0)	0.003
LVMI, g/m2	98.3±27.4	84.5±17.0	<0.001
LVH (LVMI ≥115 g/m2 for men and ≥95 g/m2 for women), n (%)	103 (35.0)	0 (0)	<0.001
Septal E/e’	11.4±4.2	9.6±2.3	<0.001
≥15, n (%)	47 (16.2)	0 (0)	0.009
Brachial-ankle PWV, cm/s	1,715±389	1,372±137	<0.001
≥1,600 cm/s, n (%)	140 (54.1)	0 (0)	<0.001
Ankle-brachial index	1.10±0.12	1.15±0.71	0.030
<0.9, n (%)	14 (5.4)	0 (0)	0.263

CAD, coronary artery disease; eGFR, estimated glomerular filtration rate; LVH, left ventricular hypertrophy; LVMI, left ventricular mass index; PWV, pulse wave velocity.

Associations Between OPG Level and TOD

The serum OPG levels were significantly higher in patients with TOD than in those without (314±186 vs. 202±74 pg/mL, P<0.001). For most of the TOD parameters, the OPG concentrations were significantly higher in patients with TOD than in those without (P<0.05 for each) (Table 3, Figure 1A). The OPG levels were numerically higher in patients with peripheral artery disease than in those without, although there was no statistical significance as a slight difference (P=0.074). As shown in Figure 1B, the serum OPG concentrations increased gradually with increasing extent of TOD (P for trend <0.001). AS CAD is an established CVD, the mean serum OPG concentration was compared according to the number of TOD parameters other than CAD. As with the result of analyzing all TOD parameters, the serum OPG concentration tended to increase as the number of TOD parameters increased (Supplementary Figure 1). The results were similar in further analysis according to the presence or absence of CAD (Supplementary Figure 2). For each component of TOD, the serum OPG value was significantly higher in the presence of TOD regardless of the presence of CAD, except LVMI in patients without CAD (Supplementary Figure 3). The correlations between the serum OPG level and each component of TOD are shown in Table 4. The OPG concentrations negatively correlated with eGFR (r=−0.307, P<0.001) and ABI (r=−0.153, P=0.010), and positively correlated with LVMI (r=0.132, P=0.017), septal E/e’ (r=0.281, P<0.001), and baPWV (r=0.225, P<0.001). These correlations are represented as scatter plots (Figure 2). In the ROC curve analysis, an OPG value of 198 was the optimal cutoff value associated with TOD (Figure 3). In logistic regression analysis, age, the presence of hypertension, diabetes mellitus, serum OPG, glucose, total cholesterol and LDL-cholesterol level were significantly associated with the presence of TOD in the univariate analysis. In the multivariable logistic regression analysis, higher serum OPG levels (≥198 pg/mL) (odds ratio [OR] 3.22; 95% confidence interval [CI] 1.51–6.85; P=0.002) were an independent predictor for the presence of TOD after adjusting for traditional risk factors, including age, BMI, diabetes mellitus, hypertension, dyslipidemia and cigarette smoking (Table 5).

Table 3. Serum Osteoprotegerin Levels (pg/mL) According to the Presence of TOD

TOD	TOD (+)	TOD (−)	P value
Any TOD	314±186	202±74	<0.001
Obstructive CAD	314±188	251±136	0.007
eGFR <60 mL/min/1.73 m2	457±267	266±122	<0.001
LVH*	341±197	281±171	0.005
Septal E/e’ ≥15	390±185	278±163	<0.001
Brachial-ankle PWV ≥1,600 cm/s	335±207	251±113	<0.001
Ankle-brachial index <0.9	404±222	287±167	0.074

*Defined as left ventricular mass index ≥115 g/m² for men and ≥95 g/m² for women. Abbreviations as in Table 2.

Figure 1.

Serum OPG concentration according to the presence of TOD. (A) Serum OPG concentration for each TOD parameter. (B) Trend of serum OPG concentration according to the number of TOD parameters. ABI, ankle-brachial index; baPWV, brachial-ankle pulse wave velocity; CAD, coronary artery disease; eGFR, estimated glomerular filtration rate; LVH, left ventricular hypertrophy; OPG, osteoprotegerin; TOD, target organ damage.

Table 4. Correlation Between Osteoprotegerin Levels and TOD

TOD parameter	r	P value
eGFR	−0.307	<0.001
LVMI	0.132	0.017
E/e’	0.281	<0.001
Brachial-ankle PWV	0.225	<0.001
Ankle-brachial index	−0.153	0.010

Abbreviations as in Table 2.

Figure 2.

Correlation between serum OPG concentration and TOD parameters. (A) eGFR, (B) left ventricular mass index, (C) septal E/e’, (D) brachial-ankle PWV and (E) ankle-brachial index. eGFR, estimated glomerular filtration rate; OPG, osteoprotegerin; PWV, pulse wave velocity; TOD, target organ damage.

Figure 3.

Receiver-operating characteristic curve analysis for osteoprotegerin values predicting the presence of target organ damage.

Table 5. Factors Associated With Presence of Target Organ Damage

Variable	Adjusted OR	95% CI	P value
Age, per 10 years	1.59	1.13–2.24	0.008
BMI ≥25 kg/m2	0.62	0.29–1.31	0.211
Hypertension	1.13	0.54–2.38	0.748
Diabetes mellitus	3.42	1.11–10.56	0.032
Dyslipidemia	2.09	1.00–4.40	0.051
Smoking	1.24	0.52–2.92	0.626
Serum OPG ≥198 pg/mL	3.22	1.51–6.85	0.002

BMI, body mass index; CI, confidence interval; OPG, osteoprotegerin; OR, odds ratio.

Associations Between Serum OPG and Clinical Events

During a median follow-up period of 1,604 days (interquartile range, 1,131–1,726 days), there were 15 (4.6%) prespecified clinical events, which included 2 cases of all-cause death and 14 cases of coronary revascularization. There was no case of non-fatal myocardial infarction or stroke. In the Kaplan-Meier survival analysis, there was no significant difference in clinical events based on the OPG cutoff value during the follow-up period (Supplementary Figure 4). The baseline OPG level was not different between patients with and without events (P=0.996) (Supplementary Figure 5).

Discussion

Summary of the Main Findings

The main finding of this study is that the serum OPG level was associated with various TOD parameters in patients undergoing ICA. The OPG concentrations negatively correlated with eGFR and ABI, and positively correlated with LVMI, E/e’ and baPWV. Also, the OPG levels were significantly higher in patients with obstructive CAD than in those without. As the number of TODs increased, the OPG concentration increased. The association between higher levels of OPG and TOD remained significant even after controlling for potential confounders. However, the baseline serum OPG level was not associated with cardiovascular events during the clinical follow-up period. To the best of our knowledge, this is the first report showing an association between OPG and various TODs in patients at high risk of CAD.

Previous Investigations of OPG as a Risk Factor for TOD

OPG, a substance involved in bone metabolism that was first discovered in 1997, inhibits osteoclast differentiation and activity.²² The cytokine network of OPG/receptor activator of the nuclear factor kappa-B (RANK)/receptor activator of nuclear factor kappa-B ligand (RNKL) plays the most important role in bone homeostasis and remodeling, controlling the balance between bone resorption and bone formation by regulating the differentiation and activity of osteoclasts. When RANKL expressed on mesenchymal cells, T cells and osteoblasts, binds to the RANK receptor on osteoclasts, the osteoclasts are activated and bone resorption occurs. OPG, a member of the tumor necrosis factor receptor superfamily, acts as a soluble decoy receptor for RANKL, which inhibits RANK-RANKL binding, thereby inhibiting osteoclast differentiation and activity.²³ Interestingly, in a previous animal study, marked calcification of the aorta and renal arteries, as well as severe osteoporosis, were found in OPG knock-out mice.²⁴ After that first study, interest began to emerge in the role of OPG in vascular biology. Several studies have reported the association of the serum OPG level with arterial stiffness in patients on hemodialysis.^10,12 Other studies have shown that the OPG level is significantly associated with arterial stiffness in postmenopausal women,¹¹ hypertensive subjects¹³ and patients with CAD.²⁵ In terms of CAD, the OPG level was significantly associated with the severity of CAD.^16,26 However, most of the previous studies on the association between serum OPG and the cardiovascular system focused on just one TOD parameter: vascular stiffness or CAD.^14–16

Until now, no systematic studies have been conducted on which TOD parameter is more affected by OPG. TOD refers to abnormalities in the function and morphology of organs manifested by various risk factors at subclinical stages before actual cardiovascular events occur. Because TOD is closely related to future cardiovascular events, early detection is important.^3–5 From this point of view, our results are valuable and deserve clinical attentions because they suggest the possibility that OPG can be used as a marker of TOD and in risk stratification. Our study found for the first time that OPG not only affects vascular calcification, but also risk factors for TOD in patients who undergoing ICA. An accurate and rapid risk assessment is important to improve patient prognosis in this high-risk group. Because CAD is a progressive systemic disease, early detection of other TOD factors and active treatment can predict and prevent future cardiovascular events.^3–5 Biomarker assay can be useful for risk stratification if given an appropriate cutoff value.^27,28 In this study, we presented an OPG value for predicting TOD; however, the OPG value at index hospitalization could not predict future cardiovascular events in this study. The appropriate OPG cutoff value for predicting TOD and CVD is still unclear, and further studies with larger numbers of patients and serial follow-up of OPG values may answer this question.

Study Limitations

First, unlike in previous studies, in our study the baseline OPG level at the time of admission was not associated with future clinical events. This result might be caused by the relatively small number of patients analyzed and the low incidence rates of clinical events during the follow-up period. In previous studies, about 35.0% of hemodialysis patients suffered from clinical events, and in another study, 26.5% died of cardiovascular causes.^10,12 However, in our study, only 4.6% of the patient experienced clinical events. A plausible reason for this may be that our high-risk patients (78.8% of patients with proven obstructive CAD and 42.1% with acute coronary syndrome) had a lower frequency of future cardiovascular events than patients on hemodialysis. Another possible explanation is that our patients had already been diagnosed with CAD and received intensive treatment, such as high-dose statins, which led to a more favorable prognosis. Therefore, a larger patient population is needed to confirm whether the baseline OPG level is associated with future cardiovascular events. Second, this study had a cross-sectional design, which does not allow a causal relationship between biological markers and TOD. Lastly, to accurately measure the OPG level, blood sample was immediately centrifuged, and the serum was stored at −70℃ until assayed. Therefore, it is difficult to use in an outpatient setting.

Conclusions

Our study showed that the serum OPG level was significantly associated with various TOD parameters in patients undergoing ICA. The results suggested that serum OPG could be used as a marker of TOD in high-risk patients.

Conflict of Interest / Funding Source

None.

IRB Information

This study was approved by Institutional Review Board (IRB) of Boramae Medical Center (Seoul, Korea) (IRB no. 10-2017-21).

Supplementary Files

Please find supplementary file(s);

http://dx.doi.org/10.1253/circj.CJ-20-0675

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