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ORIGINAL ARTICLE
Year : 2016  |  Volume : 29  |  Issue : 2  |  Page : 324-329

Osteoprotegerin in type 2 diabetic patients with microalbuminuria


1 Department of Clinical Pathology, Faculty of Medicine, Menoufiya University, Menoufiya, Egypt
2 Department of Internal Medicine, Faculty of Medicine, Menoufiya University, Menoufiya, Egypt

Date of Submission07-Jul-2014
Date of Acceptance21-Sep-2014
Date of Web Publication18-Oct-2016

Correspondence Address:
Rawaby A Atta
Department of Clinical Pathology, Faculty of Medicine, Menoufiya University, Menoufiya, 32511
Egypt
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/1110-2098.192424

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  Abstract 

Objectives:
The aim or the study is to study the role of osteoprotegerin (OPG) in type 2 diabetic (T2DM) patients with microalbuminuria as an early marker of atherosclerosis.
Background:
OPG is a bone-related glycopeptide produced by vascular smooth muscle cells because of arterial damage. OPG is a glycoprotein that is part of the tumor necrosis factor receptor superfamily mainly involved in bone metabolism, but it is also found in various other tissues such as vascular smooth muscle cells.
Participants and methods:
The study included 80 individuals divided into three groups: group I included 20 T2DM patients without microalbuminuria, group II included 40 T2DM patients with microalbuminuria, and group III included 20 healthy individuals as controls. For all participants, the following were performed: assessment of history, clinical examination including BMI, systolic blood pressure, and diastolic blood pressure, and assessment of fasting blood glucose, glycated hemoglobin, lipid profile, serum creatinine and urea, microalbumin in urine, serum OPG, and carotid artery duplex for measurement of carotid intima media thickness (CIMT).
Results:
Group II had significantly higher values of fasting blood glucose, glycated hemoglobin, serum creatinine and urea, total cholesterol, triglycerides, LDL-cholesterol and CIMT compared with groups I and III. OPG levels were significantly higher in diabetics than in the controls; the highest level was observed in group II (10.49 ± 3.17), followed by group I (6.24 ± 1.10), and group III (3.66 ± 1.88). OPG levels were significantly correlated to CIMT, urea, creatinine, and lipid profile.
Conclusion:
OPG could be used as an early marker of atherosclerosis in patients with T2DM, especially those with microalbuminuria.

Keywords: atherosclerosis, diabetes, microalbuminuria, osteoprotegerin


How to cite this article:
El-Saeed GK, Ahmed Khamis SS, Khodeer S, Atta RA. Osteoprotegerin in type 2 diabetic patients with microalbuminuria. Menoufia Med J 2016;29:324-9

How to cite this URL:
El-Saeed GK, Ahmed Khamis SS, Khodeer S, Atta RA. Osteoprotegerin in type 2 diabetic patients with microalbuminuria. Menoufia Med J [serial online] 2016 [cited 2019 Dec 16];29:324-9. Available from: http://www.mmj.eg.net/text.asp?2016/29/2/324/192424


  Introduction Top


Diabetes mellitus, especially type 2 (T2DM), represents one of the most important health problems worldwide [1]. Atherosclerosis and coronary artery disease are the most important determinants of the excessive morbidity and mortality in T2DM patients, especially in patients with albuminuria [2]. Osteoprotegerin (OPG) is a glycoprotein that is part of the tumor necrosis factor receptor superfamily mainly involved in bone metabolism, but it is also found in various other tissues such as vascular smooth muscle cells [3]. OPG appears to play a crucial role in vascular homeostasis, acting as a vascular calcification inhibitor and a regulatory molecule in atherosclerosis. In numerous clinical studies, OPG has been reported to be implicated in atherosclerosis and cardiovascular disease (CVD) [4].

This study aimed to assess the level of OPG in patients with T2DM as an early marker for atherosclerosis, especially in patients with microalbuminuria, and its correlation with other risk factors of CVD in diabetic patients.


  Participants and Methods Top


The present study was carried out at the Clinical Pathology Department in collaboration with the Internal Medicine Department, Faculty of Medicine, Menoufiya University. This study involved 80 individuals divided into three groups: group I included 20 diabetic patients without microalbuminuria between 45 and 61 years of age (13 men and seven women), group II included 40 diabetic patients with microalbuminuria between 45 and 64 years of age (21 men and 19 women), in addition to 20 apparently healthy individuals as controls (group III) between 42 and 60 years of age. Patients with a history of congestive heart failure, renal failure, acute or severe chronic liver disease, hematologic diseases, neoplastic diseases, and pregnancy were excluded from the study. For all participants, the following were performed: assessment of history, clinical examination including BMI, systolic blood pressure (SBP), and diastolic blood pressure (DBP), assessment of fasting blood glucose (FBG), glycated hemoglobin (HbA1c), lipid profile, serum creatinine and urea, microalbumin in urine, serum OPG, and carotid artery duplex for participants' measurement of carotid intima media thickness (CIMT). Written informed consents were provided by all participants and approval was obtained from the ethics committee.

Sampling

Blood samples: Under complete aseptic conditions, 6 ml of venous blood was collected after overnight fasting and divided as follows: 1 ml of whole blood was added to an EDTA-containing sterile tube for the determination of HbA1c and 5 ml of whole blood was allowed to clot at 37°C. Serum was separated by centrifugation and divided into two separate sterile plastic tubes:

Tube 1: serum of 3 ml blood for determination of FBG, urea, creatinine, and lipid profile.

Tube 2: serum of 2 ml blood for assay of OPG.

Urine samples: 10 ml, second voided morning samples, were collected without preservative, centrifuged at 3000 rpm for 10 min, and used for the detection of microalbumin.

Laboratory methods

Biochemical tests were performed for the detection of FBG, urea and creatinine, total cholesterol, triglycerides, HDL-cholesterol (HDL-c), and microalbumin in urine on a Synchron CX9 autoanalyser using the kit supplied by Beckman (Beckman Coulter Life Sciences 5350 Lakeview Parkway S. Drive Indianapolis IN 46268, USA). LDL-cholesterol (LDL-c) was calculated according to the Friedwald equation [5]. Quantitative colorimetric measurement of HbA1c as the percent of total hemoglobin was performed using kits supplied by Stanbio Laboratory (Stanbio Laboratory, 1261 North Main Street, Boerne, TX 78006, USA). Estimation of OPG was performed by an enzyme-linked immunosorbent assay using human OPG enzyme-linked immunosorbent assay kits supplied by BioVendor Research and Diagnostic Products company (CTPark Modrice, Evropska 873, Modrice664 42, Czech Republic) [6].

Statistical analysis

Results were collected, tabulated, and analyzed statistically using an IBM personal computer and the statistical package SPSS version 20 (SPSS Inc., Chicago, IL, USA). Two types of statistics were assessed.

Descriptive: For example, percentage (%), mean, and SD and analytical: One-way analysis of variance (F-test) to indicate the presence of a significant difference between several groups for a normally distributed quantitative variable. The Kruskal–Wallis test was used to determine the presence of a significant difference between several groups for a non-normally distributed quantitative variable. A post-hoc test was used to detect a significant difference between the individual groups; c 2-test was used to compare between two groups or more for one qualitative variable. Pearson's correlation analysis was carried out to show the association between two quantitative variables. An receiver operating characteristic (ROC) curve is a graphical plot of the sensitivity versus the false-positive rate (1-specificity). The significance level was set at 0.05 or less.


  Results Top


In the present study, there were no statistically significant differences between the groups studied in age and sex (P = 0.613 and 0.574, respectively) ([Table 1]). There were statistically significant differences between the groups studied (P<0.001) in BMI, SBP, and DBP; meanwhile, there were no statistically significant differences in smoking between the groups studied (P>0.05) ([Table 2]). For biochemical markers (FBG, HbA1c, urea, total cholesterol, triglycerides, LDL-c, HDL-c), the mean differences between the groups studied were highly significant (P<0.001). However, for serum creatinine, there were no statistically significant differences between all the groups studied (P > 0.05) ([Table 3]). There were highly significant differences between the groups studied in CIMT (P<0.001) ([Table 4]). For the OPG level, there were highly significant differences between the groups studied (P<0.001); the highest level was observed in group II (10.49 ± 3.17), followed by group I (6.24 ± 1.10), and group III (3.66 ± 1.88) ([Table 4]). There were significant positive correlations between OPG and each of urea, creatinine, total cholesterol, triglycerides, LDL-c, and CIMT, and a significant negative correlation between OPG and HDL-c. However, there were no significant correlations between OPG and age, BMI, SBP and DBP, FBG, and HbA1c ([Table 5]). The ROC curve showed that the cutoff point was 6.25 ng/ml, the sensitivity was 90%, the specificity was 87%, the positive predictive value was 92%, and the negative predictive value was 84% ([Figure 1] and [Figure 2]).
Table 1: Distribution of the patients studied in age and sex

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Table 2: Distribution of the groups in blood pressure, BMI, and smoking

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Table 3: Distribution of the groups in biochemical parameters

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Table 4: Distribution of the groups in carotid intima media thickness and osteoprotegerin

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Table 5: Correlation between osteoprotegerin and other parameters among the groups studied

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Figure 1: Distribution of the groups in terms of OPG. OPG, osteoprotegerin

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Figure 2: ROC curve analysis of OPG with CIMT (ROC curve showed that the area under the curve was 96.8%, the cutoff point was 6.25 ng/ml, the sensitivity was 90%, the specificity was 87%, the positive predictive value was 92%, and the negative predictive value was 84%). CIMT, carotid intima media thickness; OPG, osteoprotegerin; ROC, receiver operating characteristic

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  Discussion Top


In the present study, there were no statistically significant differences between the groups studied in age and sex. These results are in agreement with those reported by Yilmaz et al. [7], who observed no significant differences among the groups studied in age and sex.

For SBP and DBP, there were statistically significant differences between groups I and II (diabetic patients) and group III (controls), whereas no statistically significant difference was found between groups I and II. These results were in agreement with those of Safiullah et al. [8], who found that diabetic patients had higher SBP and DBP than the controls.

There were statistically significant differences between the groups studied in BMI. These results were in agreement with those of Chen et al. [9], who showed that diabetic patients had higher BMI than controls. However, Mila et al. [0] showed that there were no significant differences in BMI between diabetic patients and controls.

For smoking, there were no statistically significant differences between the groups studied. These results are in agreement with those of Guzel et al. [1].

The results of the present study showed statistically significant differences in the mean of FBG and HbA1c between the three groups studied. These results were in agreement with those of Yishak et al. [2]

For urea level, there were statistically significant differences in group II compared with groups I and III, whereas no significant difference was found between groups I and III. These results were in agreement with those of Ayman et al. [3], who found statistically significant differences in urea levels between the groups studied, with a higher level in T2DM patients.

For the creatinine level, there was a statistically significant difference between groups I and II. However, no significant differences were found between groups I and II (diabetic patients) compared with group III (controls). These results were in agreement with those of Nikzamir et al. [4], who showed that creatinine levels were significantly higher in diabetics with microalbuminuria than normoalbuminuric patients.

For lipid profile, there were statistically significant differences in groups I and II (diabetic patients) compared with group III (controls), whereas no significant difference was found between groups I and II. These results are in agreement with those of Chang et al. [5], who found no significant differences in total cholesterol, HDL-c, and LDL-c between the diabetic patient groups, except for triglycerides. However, Xiang et al. [6] showed that lipid profiles were significantly higher in the microalbuminuric diabetic group compared with the normoalbuminuric group.

In the current study, there were statistically significant increases in the mean CIMT between the groups studied in favor of diabetics, especially group II. These results were in agreement with those of Cadirni et al. [7], who showed significant differences in CIMT between diabetic patients and controls, with higher levels in diabetics. Hyperglycemia accelerates atherosclerosis and changes in the vascular endothelium in diabetics may account for the multiple vascular complications [8].

For OPG levels, there were highly statistically significant differences in the mean of all studied groups (P<0.001). The highest level was observed in group II (10.49 ± 3.17 pmol/l), followed by group I (6.24 ± 1.10 pmol/l), and group III (3.66 ± 1.88 pmol/l). This means that OPG increased in the microalbuminuric diabetic group compared with the normoalbuminuric diabetic group and increased in the normoalbuminuric group compared with the control group. These results were in agreement with those of Park and colleagues, who found a higher level of OPG in T2DM patients compared with the controls [8]. Also, Ashley [9] observed that OPG levels were significantly higher in T2DM patients compared with the controls. These results are in agreement with those of Reinhard et al. [0], who showed that the OPG level is associated positively with T2DM and elevated in patients with microalbuminuria more than normoalbuminuria. Consistent with several reports, Ayman et al. [3] reported that OPG levels were significantly higher in T2DM patients with microalbuminuria than those with normoalbuminuria and compared with controls.

Progression of diabetic nephropathy, including microalbuminuria, is a risk factor for atherosclerosis in T2DM patients who often have vascular calcification [1].

ROC curve analysis of OPG with CIMT showed that the cutoff point was 6.25 ng/ml, the sensitivity was 90%, the specificity was 87%, positive predictive value was 92%, and negative predictive value was 84%. These results correlate OPG significantly with CIMT. These results are in agreement with those of Poulsen et al. [2], who observed that increased plasma OPG concentration was associated with carotid and peripheral arterial disease in T2DM patients.

Arterial calcification is part of the atherosclerotic process leading to clinical CVD. OPG is reported to be present in atherosclerotic plaques and studies have shown that OPG colocalize with the area of calcification [3].

There were significant positive correlations between OPG and each of urea and creatinine in groups I and II (diabetic patients). These results were in agreement with those of Uemura et al. [4], who found positive correlations between OPG and urea and creatinine in T2DM patients. However, Reinhard et al. [5] showed that OPG was not correlated with serum creatinine in T2DM.

Also, there were significant correlations between OPG and lipid profile groups I and II (diabetic patients). These results were in agreement with those of Ashley [9], who reported that OPG levels were correlated significantly with total cholesterol and LDL-c. In contrast, Aoki et al. [6] found no correlation of OPG with the lipid profile.

Meanwhile, there were no significant correlations between OPG and age, sex, BMI, SBP, and DBP in the groups studied. These results were in agreement with those of Mila et al. [0]. However, Mogelvang et al. [7] observed that OPG is associated strongly with age in T2DM patients.

In the current study, there were no significant correlations between OPG and FBG and HbA1c in the groups studied. These results are in agreement with those of Reinhard et al. [5]. However, Xiang et al. [6] showed a correlation between OPG and FBG and HbA1c.

There were significant correlations between OPG and CIMT in the groups studied. The Lundby-Christensen et al. [8] studies reported that increased CIMT and the presence of carotid plaques are correlated with plasma OPG levels in T2DM patients.

At first glance, it may seem contradictory that OPG accumulates in the arterial system in diabetes and that high levels of OPG are risk markers for cardiovascular death. However, the accumulation may be a result of a compensatory vascular response toward calcification and thus associated with endothelial dysfunction. As the inflammatory response is a natural sequence of a plaque rupture, OPG may be upregulated to prevent further damage, rather than being responsible for the initial damage [29],[30].


  Conclusion Top


The present study has clearly shown that T2DM patients, especially those with microalbuminuria, showed increased serum OPG level and CIMT measures; thus, it is could be related to atherosclerosis.

As cardiovascular morbidity is high in diabetic patients, it is essential to set up noninvasive methods for monitoring vascular changes such as CIMT and biochemical markers of increased risk for CVD events such as OPG. These markers could be used in the early diagnosis of subclinical atherosclerosis, which would enable strategies to be designed to minimize the cardiovascular event rate in those patients.

However, additional prospective studies are required to determine whether increased OPG levels and/or CIMT in diabetic patients can actually predict later development of endothelial dysfunction and vascular complications.

Conflicts of interest

There are no conflicts of interest.

 
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    Figures

  [Figure 1], [Figure 2]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

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