DIABETES/METABOLISM RESEARCH AND REVIEWS RESEARCH ARTICLE Diabetes Metab Res Rev 2015; 31: 752–757 Published online 15 July 2015 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/dmrr.2668
Bone remodelling markers in hypertensive patients with and without diabetes mellitus: link between bone and glucose metabolism
Z. Feldbrin1,3 M. Shargorodsky2,3* 1
Department of Diabetic Foot, Wolfson Medical Center, Holon, Israel
2
Department of Endocrinology, Wolfson Medical Center, Holon, Israel
Abstract Objective Growing evidence suggests the presence of a complex interplay between hypertension as well as type 2 diabetes mellitus (DM) and osteoporosis. The present study was designed to investigate a possible effect of type 2 DM on bone remodelling markers such as osteoprotegerin and N-terminal propeptide of type 1 collagen (P1NP) in hypertensive patients.
3
Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel *Correspondence to: M. Shargorodsky, Department of Endocrinology, Wolfson Medical Center, POB 5, Holon 58100, Israel. E-mail: [email protected]. gov.il
Design and Methods The 100 study participants were divided into three groups according to the presence of DM and hypertension: group one included diabetic hypertensive subjects, group 2 included hypertensive subjects without diabetes and group 3 included subjects without hypertension and without DM (controls). Blood sampling for metabolic parameters, including osteoprotegerin, P1NP, adiponectin, fasting glucose, HbA1c, CRP, homeostasis model assessmentinsulin resistance, homeostasis model assessment-beta function was performed. Results Circulating P1NP increased from group 1 to group 3 in a continuous fashion. P1NP was significantly lower in hypertensive subjects with DM (group 1), than in groups 2 and 3 (p < 0.0001). P1NP, was marginally lower in diabetic hypertensive subjects as compared with nondiabetic subjects with hypertension (p = 0.079). Circulating osteoprotegerin did not differ significantly between groups (p = 0.593). Conclusions In the present study, bone formation marker, P1NP, was significantly lower in diabetic hypertensive subjects as compared with nondiabetic subjects with and without hypertension. P1NP was inversely associated with parameters of glucose homeostasis such as fasting glucose, HbA1c and positively with homeostasis model assessment-beta cell function. Type 2 DM was associated with an adverse effect on bone formation independently of age, sex and exposure to anti-diabetic drugs. Copyright © 2015 John Wiley & Sons, Ltd. Keywords bone remodelling; osteoporosis; type 2 diabetes mellitus; hypertension
Introduction Received: 22 February 2015 Revised: 7 May 2015 Accepted: 18 May 2015
Copyright © 2015 John Wiley & Sons, Ltd.
Growing evidence suggests the presence of a complex interplay between hypertension as well as type 2 diabetes mellitus (DM) and osteoporosis [1–5]. Age is a major risk factor for these conditions; however, even after adjustment for
753
Diabetes and Bone Remodelling
age, DM as well as hypertension is associated with reduced bone density and/or bone quality leading to excess osteoporotic fractures at an earlier time of life. In hypertensive patients, excess urinary calcium secretion, differentiation and activation of osteoclasts through the upregulation of the activator of nuclear factor-κB ligand by the final mediators of the renin-angiotensin-aldosterone system cascade, Angiotensin II and anti-hypertensive drugs may accelerate osteoporosis [6,7]. The pathogenesis of osteoporosis in patients with type 2 DM is multifactorial and still not completely understood. Recent meta-analyses have determined that type 2 diabetic patients have an increased risk for hip as well as vertebral fractures compared with nondiabetic controls, despite the higher bone mineral density (BMD) [8,9]. It has also been shown that circulating markers of bone metabolism, including c-telopeptide (CTX), osteocalcin and parathyroid hormone are altered in patients with DM [10,11]. These findings suggest that patients with DM might have poor bone quality that is not apparent in BMD measurements. However, the precise mechanism behind the reduced bone quality in diabetic patients is still unclear. Even less information is available regarding combined effect diabetes and hypertension on bone remodelling. The present study was designed to investigate a possible impact of type 2 diabetes on bone remodelling markers such as osteoprotegerin (OPG) and N-terminal propeptide of type 1 collagen (P1NP) in hypertensive patients.
Materials and methods Subjects The study group consisted of 100 Caucasian subjects (mean age 58 +/ 12 years, 55 postmenopausal women and 45 men) who were recruited from the outpatient metabolic clinic and evaluated for the study. Study participants were classified as diabetic if fasting plasma glucose level was ≥126 mg/dL on at least two blood samples, or if they were treated with antidiabetic medications. Hypertension was defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg and/or pharmacological treatment. The participants were divided into three groups according to the presence of type 2 diabetes and hypertension: group 1 included diabetic hypertensive subjects, group 2 included hypertensive subjects without diabetes and group 3 included subjects without hypertension and DM (controls). Patients included in the study were stabilised on their previous medical treatment in the outpatient clinic for Copyright © 2015 John Wiley & Sons, Ltd.
up to 3 months before entrance to the study, with an effort to minimize treatment change during the study. Patients with history of major disease or surgery within the 6 months preceding entrance to the study were excluded. Patients with unbalanced endocrine disease were excluded, as were patients with plasma creatinine >2.5 mg/dL and elevation of liver enzymes to more than twice the upper normal limit. Patients included in the study were stabilised on their previous medical treatment in the outpatient clinic for up to 3 months before entrance to the study. This study was approved by the local scientific committee, and all participants gave informed consent before entering the study.
Blood pressure measurement The investigations were performed between 8:00 AM and 10:00 AM, in a quiet, temperature-controlled laboratory. Blood pressure was measured using an automated digital oscillometric device (Omron model HEM 705-CP, Omron Corporation, Tokyo, Japan), and a mean of three readings was taken.
Biochemical parameters Blood sampling for full chemistry and metabolic parameters, including total cholesterol, high-density lipoprotein and low-density lipoprotein cholesterol, triglycerides, fasting glucose, HbA1c, fasting insulin and hs-C-reactive protein, was performed. Blood samples for fasting blood glucose were centrifuged at 1500 g for 10 minutes at room temperature and analysed on the same day. Concentrations of glucose in plasma were measured by Olympus AU2700 analyser, using the manufacture’s kits. Serum OPG levels were determined by enzyme-linked immunosorbent assay (BioVendor). The intra-assay and interassay coefficients of variation for OPG were 2.4–7.0% and 3.4–7.4%, respectively. The bone formation marker P1NP was determined by electrochemiluminescence Immunoassaay (Roche). The intra-assay and inter-assay coefficients of variation were 2.0–2.3% and 1.4–2.0%, respectively. Adiponectin was determined by a commercial sandwich enzyme immunoassay technique, R&D Systems, Minneapolis, USA (catalogue number DRP300) with 2.8% intra-assay and 6.5% inter-assay variability. Homeostasis model assessment-insulin resistance was calculated by the following formula: fasting plasma insulin (mU/mL) × fasting plasma glucose (mg/dL)/405. The homeostasis model assessment (HOMA)-beta cell function was calculated by using the following formula: 20 × fasting insulin (μU/mL)/ fasting glucose (mmol/L) 3.5. Diabetes Metab Res Rev 2015; 31: 752–757 DOI: 10.1002/dmrr
754
Z. Feldbrin and M. Shargorodsky
Statistical analysis The sample size calculation was prospective and with a sample size of 23 subjects per group to detect a true, between-group difference of 10 +/ 11 in P1NP. Analysis of data was carried out using SPSS 11.0 statistical analysis software (SPSS Inc., Chicago, IL, USA). For continuous variables, such as hemodynamic, arterial compliance and chemistry parameters, descriptive statistics were calculated and reported as mean ± standard deviation. Normalcy of distribution of continuous variables was assessed using the Kolmogorov– Smirnov test (cut off at p = 0.01). Continuous variables were compared across groups using one-way analysis of variance. Variables for which across-group differences were detected underwent post hoc pairwise testing using the Bonferroni test. Categorical variables such as comorbidities and prescribed medications were described using frequency distributions and are presented as frequency (%). Categorical variables were compared across groups using the chi-square test (exact as needed). Pearson’s correlation analysis was used to calculate correlation coefficients to describe associations between continuous variables. Pulse wave
velocity and augmentation index were modelled using multiple linear regression analysis with a backward, stepwise approach. For inclusion, the probability of F was set at 0.05, and at 0.10, for exclusion. Variables for inclusion were identified in univariate associations with the outcome of interest. All tests were two-sided and considered significant at p < 0.05.
Results Demographic and clinical characteristics of the study groups are presented in Table 1. As can be seen, controls (group 3) were significantly younger than groups 1 and 2; groups 2 and 3 did not differ in age. Nondiabetic patients (groups 2 and 3) were similar in terms of concomitant cardiovascular risk factors such as hyperlipidemia, obesity and smoking, while subjects with type 2 diabetes (group 1) had a greater number of cardiovascular risk factors. As expected, parameters of glucose homeostasis, including fasting plasma glucose, HbA1c and homeostasis model assessment-insulin resistance, were significantly lower in nondiabetic subjects (group 2 and group 3)
Table 1. Baseline characteristics of the three study groups
Variables Sex (F/M) Age (years) 2 BMI (kg/m ) Cardiovascular risk factors: Dislipidemia (%) Smokers (%) Obesity (%) Concomitant medications: ACE/ARB (%) CCBs (%) B-blockers (%) Statins (%) Anti-diabetic medications (%) Systolic BP (mmHg) Diastolic BP (mmHg) Heart rate (beats/min) Fasting glucose (mg/dL) HbA1c (%) Creatinine (mg/dL) Total cholesterol (mg/dL) LDL cholesterol (mg/dL) HDL cholesterol (mg/dL) Triglycerides (mg/dL) CRP (mg/dL) HOMA-IR HOMA-beta Adiponectin Osteoprotegerin (pmol/l) P1NP (mcg/l)
Group 1 DM + HTN + (N = 33) 18/15 62.7+/ 7.0 31.9+/ 4.4
DM
Group2 HTN + (N = 39) 24/15 59.8+/ 10.8 31.6+/ 6.4
DM
Group 3 HTN (N = 28) 13/15 49.6+/ 13.5 31.1+/ 5.3
p-value 0.471 0.0001 0.866
12 (36%) 1 (3%) 4 (12%)
27 (69%) 4 (10%) 9 (23%)
21 (75%) 7 (25%) 9 (32%)
0.171 0.098 0.079
16 (48%) 9 (27%) 4 (12%) 17 (52%) 28 (85%) 148.2 ± 15.3 78.1 ± 7.5 66.9 ± 12.1 172.7 ± 32.1 8.1 ± 1.4 1.0 ± 0.2 192.9 ± 37.5 119.3 ± 32.3 43.8 ± 11.6 194.2 ± 63.4 0.9 ± 1.3 9.7 ± 10.3 84.1 ± 97.1 6641.5 ± 5177.5 99.7 ± 64.3 21.5 ± 9.3
18 (46%) 13 (33%) 18 (55%) 23 (70%) 0 148.5 ± 17.8 80.4 ± 10.7 64.3 ± 9.8 102.8 ± 25.6 6.7 ± 0.5 1.0 ± 0.2 198.1 ± 40.8 119.2 ± 36.3 49.0 ± 14.8 172.8 ± 95.3 0.7 ± 0.9 4.5 ± 5.9 158.0 ± 129.1 8302.4 ± 5818.2 89.1 ± 37.2 34.6 ± 15.6
0 0 12 (43%) 7 (25%) 0 129.2 ± 14.0 75.1 ± 12.1 66.9 ± 12.1 106.7 ± 32.1 5.9 ± 1.3 0.9 ± 0.2 192.91 ± 37.5 93.0 ± 27.5 43.1 ± 8.5 152.2 ± 63.4 0.5 ± 0.5 5.5 ± 3.8 215.5 ± 162.4 7047.9 ± 4791.0 86.3 ± 29.1 44.2 ± 11.4
0.0001 0.003 0.024 0.019 0.0001 0.0001 0.121 0.116 0.0001 0.0001 0.556 0.008 0.004 0.107 0.498 0.0001 0.011 0.001 0.483 0.593 0.0001
DM, diabetes mellitus; HTN, hypertension; BMI, body mass index; ACE, angiotensin-converting-enzyme; ARB, angiotensin receptor blocker; CCB, calcium channel blocker; BP, blood pressure; LDL, low-density lipoprotein; HDL, high-density lipoprotein; CRP, C-reactive protein; HOMA-IR, homeostasis model assessment-insulin resistance; HOMA-beta, homeostasis model assessment-beta.
Copyright © 2015 John Wiley & Sons, Ltd.
Diabetes Metab Res Rev 2015; 31: 752–757 DOI: 10.1002/dmrr
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compared with diabetic subjects (group 1). The HOMA-beta cell function was significantly lower in diabetic subjects and marginally lower in hypertensive subjects without DM compared with controls (p < 0.0001 and p = 0.062, respectively). Among bone remodelling markers, circulating OPG did not differ significantly between groups (p = 0.593). As shown in Figure 1 and Table 1, circulating P1NP increased from group 1 to group 3 in a continuous fashion. P1NP was significantly lower in subjects with DM (group 1), than in groups 2 and 3 (p < 0.0001). P1NP was marginally lower in diabetic hypertensive subjects as compared with nondiabetic subjects with hypertension (p = 0.079). A general linear model of P1NP was carried out using multiple linear regression analysis with a backward, stepwise approach. For inclusion, the probability of F was set at 0.05 for entry and at 0.10 for exclusion. Included in the model of P1NP were lactate dehydrogenase, HOMA-beta cell function and anti-diabetic drugs on the basis of their associations in univariate analyses with backward approach. Univariate analysis of variance demonstrated that circulating P1NP was not associated with exposure to anti-diabetic drugs. The model was significant (p = 0.002) and explained 25.7% variability in P1NP. As can be seen in Table 2, P1NP was inversely associated with fasting glucose (r = 0.351, p = 0.007) and HbA1c (r = 0.350, p = 0.015). A positive association
Table 2. Correlations
Age Systolic BP Diastolic BP BMI Fasting glucose HbA1c HDL cholesterol LDL cholesterol Triglycerides CRP HOMA-IR HOMA-beta
r-value p-value r-value p-value r-value p-value r-value p-value r-value p-value r-value p-value r-value p-value r-value p-value r-value p-value r-value p-value r-value p-value r-value p-value
P1NP
OPG
0.230 0.083 0.206 0.123 0.048 0.724 0.082 0.467 0.351 0.007 0.350 0.015 0.188 0.502 0.018 0.904 0.018 0.895 0.213 0.445 0.094 0.486 0.284 0.033
0.142 0.215 0.034 0.772 0.015 0.897 0.372 0.003 0.005 0.966 0.136 0.272 0.303 0.273 0.008 0.949 0.018 0.880 0.325 0.237 0.009 0.941 0.027 0.816
Adiponectin 0.174 0.121 0.102 0.366 0.147 0.190 0.100 0.374 0.311 0.005 0.289 0.015 0.406 0.032 0.034 0.781 0.223 0.049 0.327 0.089 0.317 0.004 0.078 0.491
P1NP, N-terminal propeptide of type 1 collagen; OPG, osteoprotegerin; BP, blood pressure; BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein; CRP, C-reactive protein; HOMA-IR, homeostasis model assessment-insulin resistance; HOMA-beta, homeostasis model assessment-beta.
between circulating P1NP and HOMA-beta cell function, as well as lactate dehydrogenase, was observed (r = 0.530, p = 0.0001 and r = 0.284, p = 0.033, respectively). P1NP
Figure 1. Circulating P1NP and OPG levels by study group. Group 1 includes diabetic hypertensive subjects. Group 2 includes hypertensive subjects without diabetes. Group 3 includes subjects without hypertension and DM (controls). Group 1 versus group 3 in terms of P1NP: p < 0.0001. Group 2 versus group 2 in terms of P1NP: p = 0.079. P1NP, N-terminal propeptide of type 1 collagen; OPG, osteoprotegerin; DM, diabetes mellitus
Copyright © 2015 John Wiley & Sons, Ltd.
Diabetes Metab Res Rev 2015; 31: 752–757 DOI: 10.1002/dmrr
756
was not associated with age, sex, BMI, adiponectin or osteoprotegerin levels.
Discussion In the present study, bone formation marker, P1NP, was significantly lower in diabetic hypertensive subjects as compared with nondiabetic subjects with and without hypertension. P1NP was inversely associated with parameters of glucose homeostasis such as fasting glucose, HbA1c and positively with HOMA-beta cell function. Type 2 DM was associated with decreased P1NP levels independent of age, sex and exposure to anti-diabetic drugs. Circulating P1NP was marginally lower in diabetic hypertensive subjects as compared with nondiabetic subjects with hypertension. Findings of the present study concur with recently published data that found that the presence of type 2 DM was associated with decreased bone turnover, as indicated by lower levels of β-CTX and P1NP, in postmenopausal women. Bone turnover markers decreased with the increase of fasting plasma glucose in diabetic patients, especially those without appropriate glycemic control [12]. Moreover, it has been shown that in patients with poorly controlled type 2 diabetes, bone-specific alkaline phosphatase decreased, whereas osteocalcin increased after glycemic control for a month [13]. The pathophysiology of diabetes-induced impaired bone quality is not precisely known. It may include impaired mesenchymal stem cell differentiation, which leads to suppressed osteoblast gene expression as well as osteoblast function and enhanced osteoclast differentiation, resulting in exacerbated bone resorption [14,15]. Moreover, hyperglycemia associated with reduced insulin signalling and increased end-product glycation may effect collagen cross-linking, resulting in degenerative changes in bone quality [16,17]. Additionally, it has been shown that fasting plasma glucose is negatively associated with P1NP and β-CTX, and it is suggested that lower levels of P1NP and β-CTX may be associated with decreased bone turnover, poor bone quality and eventually osteoporotic fractures. The finding that low bone formation, indicated by P1NP as well as osteocalcin, was associated with increased fracture risks in diabetic patients supports this hypothesis [10,12]. A recent meta-analysis reported that markers of bone resorption and formation seem to be lower, whereas bone-specific alkaline phosphatase is normal to elevated [18], suggesting that the matrix becomes hypermineralized in diabetes patients. This may explain the paradox of low bone strength and increased BMD. A previously published study showed a significant reduction in serum markers of bone formation such as P1NP and bone alkaline phosphatase, for both women Copyright © 2015 John Wiley & Sons, Ltd.
Z. Feldbrin and M. Shargorodsky
and men type 2 DM treated with rosiglitazone, metformin, and glyburide for 12 months [19]. In the present study, univariate analysis of variance with backward approach demonstrated that circulating P1NP was not associated with exposure to anti-diabetic drugs. The model was significant (p = 0.002) and explained 25.7% variability in P1NP1. Because the present study focused on P1NP and osteoprotegerin assessment, the effect of type 2 DM on additional markers of osteoblastic differentiation, such as osteocalcin as well as bone specific ALP, will need to be evaluated by long-term studies in this population. However, the use of N-terminal propeptide of type 1 collagen, as a specific bone formation marker as well as reference analyte for bone turnover markers in clinical studies, was recommended by the International Osteoporosis Foundation and the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) [20]. Osteoprotegerin, a decoy receptor for nuclear factor-κB (RANK) ligand and one of the major players in the balance between bone formation and bone resorption, exerts osteoprotective effects by inhibiting osteoclast differentiation and activation and promoting osteoclast apoptosis. However, in the clinical setting, the association between OPG, bone density and fragility fractures remains controversial. In men, increased OPG levels have been associated with higher BMD of the lumbar spine [21,22], whereas in other studies, a negative correlation or no correlation was found [23,24]. In women, a significant positive relationship between OPG and BMD at total body has been shown [25]. However, no difference between serum OPG levels in osteoporotic compared with healthy postmenopausal women has been found by other authors [26]. In the present study, we did not observe significance between group differences in terms of circulating OPG. Given these discrepant findings, future studies should clarify the relationship between serum OPG and bone density as well as fragility fractures in diabetic patients. The present study has some limitations. Our study includes a relatively small number of participants, and larger long-term studies are required to establish definitively the impact of type 2 diabetes on additional markers of bone remodelling, bone density and fractures rates. Furthermore, addition of a group of diabetic patients without hypertension, especially patients with comparable anti-diabetic treatment, would have elucidated the pathophysiological mechanism behind the reduced bone quality in diabetic patients. However, we did not have enough subjects for this group. In conclusion, we have demonstrated that type 2 DM was associated with decreased levels of bone formation marker, P1NP, independent of age, sex and exposure to anti-diabetic drugs in hypertensive patients. The findings of the present study justify further controlled, long-term Diabetes Metab Res Rev 2015; 31: 752–757 DOI: 10.1002/dmrr
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studies with measurement of bone markers, bone density and fractures rates to demonstrate the overall clinical effect of diabetes as well as improved glucose control, on bone quality in this population.
Conflicts of interest None declared.
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fractures in postmenopausal women with type 2 diabetes. J Clin Endocrinol Metab 2012; 97: 1277–1284. Dobnig H, Piswanger-Sölkner JC, Roth M, et al. Type 2 diabetes mellitus in nursing home patients: effects on bone turnover, bone mass, and fracture risk. J Clin Endocrinol Metab 2006; 91: 3355–3363. Jiajue R, Jiang Y, Wang O, et al. Suppressed bone turnover was associated with increased osteoporotic fracture risks in non-obese postmenopausal Chinese women with type 2 diabetes mellitus. Osteoporos Int 2014; 25(8): 1999–2005. Kanazawa I, Yamaguchi T, Yamamoto M, et al. Serum osteocalcin level is associated with glucose metabolism and atherosclerosis parameters in type 2 diabetes mellitus. J Clin Endocrinol Metab 2009; 94(1): 45–49. Botolin S, McCabe LR. Chronic hyperglycemia modulates osteoblast gene expression through osmotic and non-osmotic pathways. J Cell Biochem 2006; 99: 411–424. Catalfamo D, Britten T, Storch D, Calderon N, Sorenson H, Wallet S. Hyperglycemia induced and intrinsic alterations in type 2 diabetes-derived osteoclast function. Oral Dis 2013; 19: 303–312. McCabe LR. Understanding the pathology and mechanisms of type I diabetic bone loss. J Cell Biochem 2007; 102: 1343–1357. de Paula FJ, Horowitz MC, Rosen CJ. Novel insights into the relationship between diabetes and osteoporosis. Diabetes Metab Res Rev 2010; 26: 622–630. Zinmam B, Haffner SM, Herman WH, et al. Effect of rosiglitazone, metformin and glyburide on bone biomarkers in patients with type 2 diabetes. J Clin Endocrinol Metab 2010; 95: 134–142. Starup-Linde J, Vestergaard P. Biochemical bone turnover markers in
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Diabetes Metab Res Rev 2015; 31: 752–757 DOI: 10.1002/dmrr
Bone remodelling markers in hypertensive patients with and without diabetes mellitus: link between bone and glucose metabolism
Z. Feldbrin1,3 M. Shargorodsky2,3* 1
Department of Diabetic Foot, Wolfson Medical Center, Holon, Israel
2
Department of Endocrinology, Wolfson Medical Center, Holon, Israel
Abstract Objective Growing evidence suggests the presence of a complex interplay between hypertension as well as type 2 diabetes mellitus (DM) and osteoporosis. The present study was designed to investigate a possible effect of type 2 DM on bone remodelling markers such as osteoprotegerin and N-terminal propeptide of type 1 collagen (P1NP) in hypertensive patients.
3
Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel *Correspondence to: M. Shargorodsky, Department of Endocrinology, Wolfson Medical Center, POB 5, Holon 58100, Israel. E-mail: [email protected]. gov.il
Design and Methods The 100 study participants were divided into three groups according to the presence of DM and hypertension: group one included diabetic hypertensive subjects, group 2 included hypertensive subjects without diabetes and group 3 included subjects without hypertension and without DM (controls). Blood sampling for metabolic parameters, including osteoprotegerin, P1NP, adiponectin, fasting glucose, HbA1c, CRP, homeostasis model assessmentinsulin resistance, homeostasis model assessment-beta function was performed. Results Circulating P1NP increased from group 1 to group 3 in a continuous fashion. P1NP was significantly lower in hypertensive subjects with DM (group 1), than in groups 2 and 3 (p < 0.0001). P1NP, was marginally lower in diabetic hypertensive subjects as compared with nondiabetic subjects with hypertension (p = 0.079). Circulating osteoprotegerin did not differ significantly between groups (p = 0.593). Conclusions In the present study, bone formation marker, P1NP, was significantly lower in diabetic hypertensive subjects as compared with nondiabetic subjects with and without hypertension. P1NP was inversely associated with parameters of glucose homeostasis such as fasting glucose, HbA1c and positively with homeostasis model assessment-beta cell function. Type 2 DM was associated with an adverse effect on bone formation independently of age, sex and exposure to anti-diabetic drugs. Copyright © 2015 John Wiley & Sons, Ltd. Keywords bone remodelling; osteoporosis; type 2 diabetes mellitus; hypertension
Introduction Received: 22 February 2015 Revised: 7 May 2015 Accepted: 18 May 2015
Copyright © 2015 John Wiley & Sons, Ltd.
Growing evidence suggests the presence of a complex interplay between hypertension as well as type 2 diabetes mellitus (DM) and osteoporosis [1–5]. Age is a major risk factor for these conditions; however, even after adjustment for
753
Diabetes and Bone Remodelling
age, DM as well as hypertension is associated with reduced bone density and/or bone quality leading to excess osteoporotic fractures at an earlier time of life. In hypertensive patients, excess urinary calcium secretion, differentiation and activation of osteoclasts through the upregulation of the activator of nuclear factor-κB ligand by the final mediators of the renin-angiotensin-aldosterone system cascade, Angiotensin II and anti-hypertensive drugs may accelerate osteoporosis [6,7]. The pathogenesis of osteoporosis in patients with type 2 DM is multifactorial and still not completely understood. Recent meta-analyses have determined that type 2 diabetic patients have an increased risk for hip as well as vertebral fractures compared with nondiabetic controls, despite the higher bone mineral density (BMD) [8,9]. It has also been shown that circulating markers of bone metabolism, including c-telopeptide (CTX), osteocalcin and parathyroid hormone are altered in patients with DM [10,11]. These findings suggest that patients with DM might have poor bone quality that is not apparent in BMD measurements. However, the precise mechanism behind the reduced bone quality in diabetic patients is still unclear. Even less information is available regarding combined effect diabetes and hypertension on bone remodelling. The present study was designed to investigate a possible impact of type 2 diabetes on bone remodelling markers such as osteoprotegerin (OPG) and N-terminal propeptide of type 1 collagen (P1NP) in hypertensive patients.
Materials and methods Subjects The study group consisted of 100 Caucasian subjects (mean age 58 +/ 12 years, 55 postmenopausal women and 45 men) who were recruited from the outpatient metabolic clinic and evaluated for the study. Study participants were classified as diabetic if fasting plasma glucose level was ≥126 mg/dL on at least two blood samples, or if they were treated with antidiabetic medications. Hypertension was defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg and/or pharmacological treatment. The participants were divided into three groups according to the presence of type 2 diabetes and hypertension: group 1 included diabetic hypertensive subjects, group 2 included hypertensive subjects without diabetes and group 3 included subjects without hypertension and DM (controls). Patients included in the study were stabilised on their previous medical treatment in the outpatient clinic for Copyright © 2015 John Wiley & Sons, Ltd.
up to 3 months before entrance to the study, with an effort to minimize treatment change during the study. Patients with history of major disease or surgery within the 6 months preceding entrance to the study were excluded. Patients with unbalanced endocrine disease were excluded, as were patients with plasma creatinine >2.5 mg/dL and elevation of liver enzymes to more than twice the upper normal limit. Patients included in the study were stabilised on their previous medical treatment in the outpatient clinic for up to 3 months before entrance to the study. This study was approved by the local scientific committee, and all participants gave informed consent before entering the study.
Blood pressure measurement The investigations were performed between 8:00 AM and 10:00 AM, in a quiet, temperature-controlled laboratory. Blood pressure was measured using an automated digital oscillometric device (Omron model HEM 705-CP, Omron Corporation, Tokyo, Japan), and a mean of three readings was taken.
Biochemical parameters Blood sampling for full chemistry and metabolic parameters, including total cholesterol, high-density lipoprotein and low-density lipoprotein cholesterol, triglycerides, fasting glucose, HbA1c, fasting insulin and hs-C-reactive protein, was performed. Blood samples for fasting blood glucose were centrifuged at 1500 g for 10 minutes at room temperature and analysed on the same day. Concentrations of glucose in plasma were measured by Olympus AU2700 analyser, using the manufacture’s kits. Serum OPG levels were determined by enzyme-linked immunosorbent assay (BioVendor). The intra-assay and interassay coefficients of variation for OPG were 2.4–7.0% and 3.4–7.4%, respectively. The bone formation marker P1NP was determined by electrochemiluminescence Immunoassaay (Roche). The intra-assay and inter-assay coefficients of variation were 2.0–2.3% and 1.4–2.0%, respectively. Adiponectin was determined by a commercial sandwich enzyme immunoassay technique, R&D Systems, Minneapolis, USA (catalogue number DRP300) with 2.8% intra-assay and 6.5% inter-assay variability. Homeostasis model assessment-insulin resistance was calculated by the following formula: fasting plasma insulin (mU/mL) × fasting plasma glucose (mg/dL)/405. The homeostasis model assessment (HOMA)-beta cell function was calculated by using the following formula: 20 × fasting insulin (μU/mL)/ fasting glucose (mmol/L) 3.5. Diabetes Metab Res Rev 2015; 31: 752–757 DOI: 10.1002/dmrr
754
Z. Feldbrin and M. Shargorodsky
Statistical analysis The sample size calculation was prospective and with a sample size of 23 subjects per group to detect a true, between-group difference of 10 +/ 11 in P1NP. Analysis of data was carried out using SPSS 11.0 statistical analysis software (SPSS Inc., Chicago, IL, USA). For continuous variables, such as hemodynamic, arterial compliance and chemistry parameters, descriptive statistics were calculated and reported as mean ± standard deviation. Normalcy of distribution of continuous variables was assessed using the Kolmogorov– Smirnov test (cut off at p = 0.01). Continuous variables were compared across groups using one-way analysis of variance. Variables for which across-group differences were detected underwent post hoc pairwise testing using the Bonferroni test. Categorical variables such as comorbidities and prescribed medications were described using frequency distributions and are presented as frequency (%). Categorical variables were compared across groups using the chi-square test (exact as needed). Pearson’s correlation analysis was used to calculate correlation coefficients to describe associations between continuous variables. Pulse wave
velocity and augmentation index were modelled using multiple linear regression analysis with a backward, stepwise approach. For inclusion, the probability of F was set at 0.05, and at 0.10, for exclusion. Variables for inclusion were identified in univariate associations with the outcome of interest. All tests were two-sided and considered significant at p < 0.05.
Results Demographic and clinical characteristics of the study groups are presented in Table 1. As can be seen, controls (group 3) were significantly younger than groups 1 and 2; groups 2 and 3 did not differ in age. Nondiabetic patients (groups 2 and 3) were similar in terms of concomitant cardiovascular risk factors such as hyperlipidemia, obesity and smoking, while subjects with type 2 diabetes (group 1) had a greater number of cardiovascular risk factors. As expected, parameters of glucose homeostasis, including fasting plasma glucose, HbA1c and homeostasis model assessment-insulin resistance, were significantly lower in nondiabetic subjects (group 2 and group 3)
Table 1. Baseline characteristics of the three study groups
Variables Sex (F/M) Age (years) 2 BMI (kg/m ) Cardiovascular risk factors: Dislipidemia (%) Smokers (%) Obesity (%) Concomitant medications: ACE/ARB (%) CCBs (%) B-blockers (%) Statins (%) Anti-diabetic medications (%) Systolic BP (mmHg) Diastolic BP (mmHg) Heart rate (beats/min) Fasting glucose (mg/dL) HbA1c (%) Creatinine (mg/dL) Total cholesterol (mg/dL) LDL cholesterol (mg/dL) HDL cholesterol (mg/dL) Triglycerides (mg/dL) CRP (mg/dL) HOMA-IR HOMA-beta Adiponectin Osteoprotegerin (pmol/l) P1NP (mcg/l)
Group 1 DM + HTN + (N = 33) 18/15 62.7+/ 7.0 31.9+/ 4.4
DM
Group2 HTN + (N = 39) 24/15 59.8+/ 10.8 31.6+/ 6.4
DM
Group 3 HTN (N = 28) 13/15 49.6+/ 13.5 31.1+/ 5.3
p-value 0.471 0.0001 0.866
12 (36%) 1 (3%) 4 (12%)
27 (69%) 4 (10%) 9 (23%)
21 (75%) 7 (25%) 9 (32%)
0.171 0.098 0.079
16 (48%) 9 (27%) 4 (12%) 17 (52%) 28 (85%) 148.2 ± 15.3 78.1 ± 7.5 66.9 ± 12.1 172.7 ± 32.1 8.1 ± 1.4 1.0 ± 0.2 192.9 ± 37.5 119.3 ± 32.3 43.8 ± 11.6 194.2 ± 63.4 0.9 ± 1.3 9.7 ± 10.3 84.1 ± 97.1 6641.5 ± 5177.5 99.7 ± 64.3 21.5 ± 9.3
18 (46%) 13 (33%) 18 (55%) 23 (70%) 0 148.5 ± 17.8 80.4 ± 10.7 64.3 ± 9.8 102.8 ± 25.6 6.7 ± 0.5 1.0 ± 0.2 198.1 ± 40.8 119.2 ± 36.3 49.0 ± 14.8 172.8 ± 95.3 0.7 ± 0.9 4.5 ± 5.9 158.0 ± 129.1 8302.4 ± 5818.2 89.1 ± 37.2 34.6 ± 15.6
0 0 12 (43%) 7 (25%) 0 129.2 ± 14.0 75.1 ± 12.1 66.9 ± 12.1 106.7 ± 32.1 5.9 ± 1.3 0.9 ± 0.2 192.91 ± 37.5 93.0 ± 27.5 43.1 ± 8.5 152.2 ± 63.4 0.5 ± 0.5 5.5 ± 3.8 215.5 ± 162.4 7047.9 ± 4791.0 86.3 ± 29.1 44.2 ± 11.4
0.0001 0.003 0.024 0.019 0.0001 0.0001 0.121 0.116 0.0001 0.0001 0.556 0.008 0.004 0.107 0.498 0.0001 0.011 0.001 0.483 0.593 0.0001
DM, diabetes mellitus; HTN, hypertension; BMI, body mass index; ACE, angiotensin-converting-enzyme; ARB, angiotensin receptor blocker; CCB, calcium channel blocker; BP, blood pressure; LDL, low-density lipoprotein; HDL, high-density lipoprotein; CRP, C-reactive protein; HOMA-IR, homeostasis model assessment-insulin resistance; HOMA-beta, homeostasis model assessment-beta.
Copyright © 2015 John Wiley & Sons, Ltd.
Diabetes Metab Res Rev 2015; 31: 752–757 DOI: 10.1002/dmrr
755
Diabetes and Bone Remodelling
compared with diabetic subjects (group 1). The HOMA-beta cell function was significantly lower in diabetic subjects and marginally lower in hypertensive subjects without DM compared with controls (p < 0.0001 and p = 0.062, respectively). Among bone remodelling markers, circulating OPG did not differ significantly between groups (p = 0.593). As shown in Figure 1 and Table 1, circulating P1NP increased from group 1 to group 3 in a continuous fashion. P1NP was significantly lower in subjects with DM (group 1), than in groups 2 and 3 (p < 0.0001). P1NP was marginally lower in diabetic hypertensive subjects as compared with nondiabetic subjects with hypertension (p = 0.079). A general linear model of P1NP was carried out using multiple linear regression analysis with a backward, stepwise approach. For inclusion, the probability of F was set at 0.05 for entry and at 0.10 for exclusion. Included in the model of P1NP were lactate dehydrogenase, HOMA-beta cell function and anti-diabetic drugs on the basis of their associations in univariate analyses with backward approach. Univariate analysis of variance demonstrated that circulating P1NP was not associated with exposure to anti-diabetic drugs. The model was significant (p = 0.002) and explained 25.7% variability in P1NP. As can be seen in Table 2, P1NP was inversely associated with fasting glucose (r = 0.351, p = 0.007) and HbA1c (r = 0.350, p = 0.015). A positive association
Table 2. Correlations
Age Systolic BP Diastolic BP BMI Fasting glucose HbA1c HDL cholesterol LDL cholesterol Triglycerides CRP HOMA-IR HOMA-beta
r-value p-value r-value p-value r-value p-value r-value p-value r-value p-value r-value p-value r-value p-value r-value p-value r-value p-value r-value p-value r-value p-value r-value p-value
P1NP
OPG
0.230 0.083 0.206 0.123 0.048 0.724 0.082 0.467 0.351 0.007 0.350 0.015 0.188 0.502 0.018 0.904 0.018 0.895 0.213 0.445 0.094 0.486 0.284 0.033
0.142 0.215 0.034 0.772 0.015 0.897 0.372 0.003 0.005 0.966 0.136 0.272 0.303 0.273 0.008 0.949 0.018 0.880 0.325 0.237 0.009 0.941 0.027 0.816
Adiponectin 0.174 0.121 0.102 0.366 0.147 0.190 0.100 0.374 0.311 0.005 0.289 0.015 0.406 0.032 0.034 0.781 0.223 0.049 0.327 0.089 0.317 0.004 0.078 0.491
P1NP, N-terminal propeptide of type 1 collagen; OPG, osteoprotegerin; BP, blood pressure; BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein; CRP, C-reactive protein; HOMA-IR, homeostasis model assessment-insulin resistance; HOMA-beta, homeostasis model assessment-beta.
between circulating P1NP and HOMA-beta cell function, as well as lactate dehydrogenase, was observed (r = 0.530, p = 0.0001 and r = 0.284, p = 0.033, respectively). P1NP
Figure 1. Circulating P1NP and OPG levels by study group. Group 1 includes diabetic hypertensive subjects. Group 2 includes hypertensive subjects without diabetes. Group 3 includes subjects without hypertension and DM (controls). Group 1 versus group 3 in terms of P1NP: p < 0.0001. Group 2 versus group 2 in terms of P1NP: p = 0.079. P1NP, N-terminal propeptide of type 1 collagen; OPG, osteoprotegerin; DM, diabetes mellitus
Copyright © 2015 John Wiley & Sons, Ltd.
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was not associated with age, sex, BMI, adiponectin or osteoprotegerin levels.
Discussion In the present study, bone formation marker, P1NP, was significantly lower in diabetic hypertensive subjects as compared with nondiabetic subjects with and without hypertension. P1NP was inversely associated with parameters of glucose homeostasis such as fasting glucose, HbA1c and positively with HOMA-beta cell function. Type 2 DM was associated with decreased P1NP levels independent of age, sex and exposure to anti-diabetic drugs. Circulating P1NP was marginally lower in diabetic hypertensive subjects as compared with nondiabetic subjects with hypertension. Findings of the present study concur with recently published data that found that the presence of type 2 DM was associated with decreased bone turnover, as indicated by lower levels of β-CTX and P1NP, in postmenopausal women. Bone turnover markers decreased with the increase of fasting plasma glucose in diabetic patients, especially those without appropriate glycemic control [12]. Moreover, it has been shown that in patients with poorly controlled type 2 diabetes, bone-specific alkaline phosphatase decreased, whereas osteocalcin increased after glycemic control for a month [13]. The pathophysiology of diabetes-induced impaired bone quality is not precisely known. It may include impaired mesenchymal stem cell differentiation, which leads to suppressed osteoblast gene expression as well as osteoblast function and enhanced osteoclast differentiation, resulting in exacerbated bone resorption [14,15]. Moreover, hyperglycemia associated with reduced insulin signalling and increased end-product glycation may effect collagen cross-linking, resulting in degenerative changes in bone quality [16,17]. Additionally, it has been shown that fasting plasma glucose is negatively associated with P1NP and β-CTX, and it is suggested that lower levels of P1NP and β-CTX may be associated with decreased bone turnover, poor bone quality and eventually osteoporotic fractures. The finding that low bone formation, indicated by P1NP as well as osteocalcin, was associated with increased fracture risks in diabetic patients supports this hypothesis [10,12]. A recent meta-analysis reported that markers of bone resorption and formation seem to be lower, whereas bone-specific alkaline phosphatase is normal to elevated [18], suggesting that the matrix becomes hypermineralized in diabetes patients. This may explain the paradox of low bone strength and increased BMD. A previously published study showed a significant reduction in serum markers of bone formation such as P1NP and bone alkaline phosphatase, for both women Copyright © 2015 John Wiley & Sons, Ltd.
Z. Feldbrin and M. Shargorodsky
and men type 2 DM treated with rosiglitazone, metformin, and glyburide for 12 months [19]. In the present study, univariate analysis of variance with backward approach demonstrated that circulating P1NP was not associated with exposure to anti-diabetic drugs. The model was significant (p = 0.002) and explained 25.7% variability in P1NP1. Because the present study focused on P1NP and osteoprotegerin assessment, the effect of type 2 DM on additional markers of osteoblastic differentiation, such as osteocalcin as well as bone specific ALP, will need to be evaluated by long-term studies in this population. However, the use of N-terminal propeptide of type 1 collagen, as a specific bone formation marker as well as reference analyte for bone turnover markers in clinical studies, was recommended by the International Osteoporosis Foundation and the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) [20]. Osteoprotegerin, a decoy receptor for nuclear factor-κB (RANK) ligand and one of the major players in the balance between bone formation and bone resorption, exerts osteoprotective effects by inhibiting osteoclast differentiation and activation and promoting osteoclast apoptosis. However, in the clinical setting, the association between OPG, bone density and fragility fractures remains controversial. In men, increased OPG levels have been associated with higher BMD of the lumbar spine [21,22], whereas in other studies, a negative correlation or no correlation was found [23,24]. In women, a significant positive relationship between OPG and BMD at total body has been shown [25]. However, no difference between serum OPG levels in osteoporotic compared with healthy postmenopausal women has been found by other authors [26]. In the present study, we did not observe significance between group differences in terms of circulating OPG. Given these discrepant findings, future studies should clarify the relationship between serum OPG and bone density as well as fragility fractures in diabetic patients. The present study has some limitations. Our study includes a relatively small number of participants, and larger long-term studies are required to establish definitively the impact of type 2 diabetes on additional markers of bone remodelling, bone density and fractures rates. Furthermore, addition of a group of diabetic patients without hypertension, especially patients with comparable anti-diabetic treatment, would have elucidated the pathophysiological mechanism behind the reduced bone quality in diabetic patients. However, we did not have enough subjects for this group. In conclusion, we have demonstrated that type 2 DM was associated with decreased levels of bone formation marker, P1NP, independent of age, sex and exposure to anti-diabetic drugs in hypertensive patients. The findings of the present study justify further controlled, long-term Diabetes Metab Res Rev 2015; 31: 752–757 DOI: 10.1002/dmrr
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Diabetes and Bone Remodelling
studies with measurement of bone markers, bone density and fractures rates to demonstrate the overall clinical effect of diabetes as well as improved glucose control, on bone quality in this population.
Conflicts of interest None declared.
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Diabetes Metab Res Rev 2015; 31: 752–757 DOI: 10.1002/dmrr
