Open Access

Serum prolidase activity is associated with non-diabetic metabolic syndrome

  • Suzan Tabur1,
  • Elif Oguz2,
  • Mehmet Ali Eren3,
  • Hakan Korkmaz1Email author,
  • Esen Savas4,
  • Nurten Aksoy5 and
  • Tevfik Sabuncu3
Diabetology & Metabolic Syndrome20146:142

https://doi.org/10.1186/1758-5996-6-142

Received: 17 October 2014

Accepted: 11 December 2014

Published: 17 December 2014

Abstract

Objective

The aim of this study was to determine the role of serum prolidase activity and the possible association with oxidative stress parameters in non-diabetic metabolic syndrome.

Methods

30 obese patients without metabolic syndrome (MetS), 34 non-diabetic obese patients with MetS, and 23 volunteer control subjects were enrolled in the study. Fasting plasma glucose (FPG), plasma glucose following 75 g glucose administration, high-density lipoprotein- cholesterol (HDL-C), high-density lipoprotein- cholesterol (LDL-C), total cholesterol, triglyceride (TG), total antioxidant status (TAS), total oxidative status (TOS), oxidative stress index (OSI), and prolidase activities of all subjects were analyzed.

Results

Prolidase levels was significantly higher in MetS group compared to both obese and control groups (p < 0.001 and p < 0.05 respectively). Prolidase was also higher in the obese group than in the control group (p < 0.05). Prolidase was negatively correlated with TAS and HDL-C (r = −0,362, p < 0.001; r = −0.320, p < 0.01, respectively) and positively correlated with BMI, weight, waist-c, SBP, DBP, TG, TC, LDL-C.

Conclusion

Prolidase activity may have a role in the pathogenesis of metabolic syndrome.

Keywords

Metabolic syndrome Non-diabetic Obesity Prolidase

Introduction

Metabolic syndrome (MetS) is defined as the existence of obesity, insulin resistance, glucose intolerance, hypertension, and dyslipidemia [1]. Subjects with MetS may be obese but all obese patients may not have MetS. Both MetS and obesity have been shown to have impacts on cardiovascular mortality and morbidity [2].

Endothelial disfunction causes alterations in the arterial vasculature and leads to micro- and macrovascular complications. The remodelling of the endothelial basal membrane, resulted with erosion and thrombosis, increases the oxidative stress and alters matrix metalloproteinases (MMPs) expression [3].

Prolidase, a member of the MMP family, is a cytosolic imidodipeptidase, which specifically splits imidodipeptides with C-terminal proline or hydroxyproline. The enzyme plays an important role in the recycling of proline from imidodipeptides for resynthesis of collagen and other proline containing proteins [4]. Prolidase enzyme activity has been shown in plasma, erythrocytes, leukocytes, dermal fibroblasts and various organs such as kidney, brain, heart, thymus, uterus, lung, spleen and pancreas [5, 6]. It is demonstrated that the activity of this enzyme may have a role in various disorders such as chronic liver disease, osteoporosis, osteoarthritis, uraemia, and hypertension [711]. To the best of our knowledge, there is no data concerning the serum prolidase activity in metabolic syndrome. Therefore, the aim of this study was to determine the role of serum prolidase activity in non-diabetic metabolic syndrome.

Method

Subjects

Patients who were admitted for the evaluation of obesity were recruited from the Endocrinology and Internal Medicine outpatient clinic. A standard 75 g oral glucose tolerance test (OGTT) was administered to all participants, and patients were randomized to three groups according to their affected glucose metabolism. Groups included 30 obese patients without MetS and glucose intolerance (mean age 33.67 ± 7.9 years, 2M and 28F), 34 non-diabetic obese patients with MetS (mean age 35.18 ± 6.8 years, 3M and 31F), and 23 sex and age- matched healthy control subjects (mean age32.39 ± 4.7 years, 3M and 20F).

Although the MetS group was composed of non-diabetics, all the patients had varying degrees of glucose intolerance or were insulin resistant. The control group had normal OGTT. MetS is defined according to the criteria accepted in the Third Report of the National Cholesterol Education Program (NCEP) [12]. Hypertension and hyperlipidemia were diagnosed for the first time at the initiation of the study, so no participant was using an anti-hypertensive or anti-lipidemic drug before obtaining the blood samples. Subjects having diabetes, heart failure, cirrhosis, infection, renal failure, pregnancy or malignancy; those on antioxidants such as antihypertensive medications, lipid-lowering medications, and vitamin E; and smokers were excluded.

Age, weight, height, body mass index (BMI: body weight (kg)/height (cm)2), and systolic (SBP) and diastolic blood pressures (DBP) of all subjects were recorded. Fasting plasma glucose (FPG), plasma glucose following 75 g glucose administration, high density lipoprotein- cholesterol (HDL-C), Low density lipoprotein-cholesterol (LDL-C), total cholesterol (TC), triglyceride (TG), total antioxidant status (TAS), total oxidative status (TOS), oxidative stress index (OSI), and prolidase activities of all subjects were analyzed. The study was approved by the local ethics committee, and all participants gave signed informed consent.

Blood samples and preparation

Blood samples were drawn after overnight fasting, and serum samples were stored at −80 °C until biochemical determination of TAS, TOS and prolidase activities.

Measurement of total antioxidant status

Serum TAS was determined using a novel automated measurement method developed by Erel [12]. In the method, hydroxyl radical, the most potent biological radical, is produced first. In the assay, reagent 1 containing ferrous ion solution is mixed with reagent 2, which contains hydrogen peroxide. The sequentially produced radicals, such as brown colored dianisidinyl radical cation produced by the hydroxyl radical, are also potent radicals. The anti-oxidative effect of the study sample against the potent-free radical reactions, which are initiated by the produced hydroxyl radical, is measured. The assay has excellent precision values, lower than 3%, and the results are expressed as mmol Trolox Equiv./l.

Measurement of total oxidant status

Serum TOS was determined using a novel automated measurement method developed by Erel [13]. Oxidants present in the study sample oxidize the ferrous ion-o-dianisidine complex to ferric ion. The oxidation is enhanced by glycerol molecules, which are abundantly present in the reaction medium. The ferric ion makes a colored complex with xylenol orange in an acidic medium. The color intensity, which can be measured spectrophotometrically, is related to the total amount of oxidant molecules present in the sample. The assay is calibrated with hydrogen peroxide, and the results are expressed as μmol H2O2 Equiv./l.

Oxidative stress index

Percent ratio of TOS to TAS level was accepted as OSI (OSI (Arbitrary Unit) = TOS (μmol H2O2 Equiv./l)/TAS (mmol Trolox Equiv./l)) [10].

Prolidase measurement

Prolidase activity was determined by a photometric method based on the measurement of the proline levels produced by prolidase [14].

Serum samples (100 μl) were mixed with 100 μl of serum physiological. A total of 25 μl of the mixture was preincubated with 75 ml of the preincubation solution (50 mmol/l Tris HCl buffer pH 7.0 containing 1 mmol/l glutathione, 50 mmol/l MnCl2) at 37°C for 30 min. The reaction mixture, which contained 144 mmol/l gly-pro, pH 7.8 (100 ml), was incubated with 100 ml of preincubated sample at 37°C for 5 min. To stop the incubation reaction, 1 ml glacial acetic acid was added. After adding 300 ml Tris HCl buffer, pH 7.8, and 1 ml ninhydrin solution (3 g/dl ninhydrin was melted in 0.5 mol/l orthophosphoric acid), the mixture was incubated at 90°C for 20 min and cooled with ice. Absorbance was then measured at a 515 nm wavelength to determine proline value.

Intraassay and interassay coefficient of variations (CVs) were lower than 10%. We measured the serum prolidase activity by the method optimized by Gültepe [15], which is a modification of Myara and Chinard's methods [16, 17] based on the spectrophotometric determination of proline levels liberated from glycyl-l-proline by prolidase enzyme.

Plasma TG, total cholesterol, LDL-C, and HDL-C concentrations were measured by automated chemistry analyzer (Aeroset, Abbott) using commercial kits (Abbott).

Statistical analysis

Continuous variables were expressed as mean ± S.D, and non-parametric data were expressed as median and ranges. Categorical data were compared by Chi-square tests. One-way ANOVA was used for multiple comparisons among the groups, and the LSD test was used if any statistical significance was found. Normality of distribution was evaluated with the Kolmogorov–Smirnov test. Pearson correlation test was used to evaluate any relationships between parameters. All statistical tests were two-sided. P <0.05 was regarded as significant for all analysis. All analyses were conducted using SPSS 11.5 (SPSS for Windows 11.5, Chicago, IL, USA).

Results

Mean ages of the three groups were similar. SBP, DBP and TG levels were significantly higher in the MetS group compared to both obese and control groups (all p < 0.001) (Table 1). Both obese and MetS groups had significantly higher BMI levels than the control group (all p < 0.001 and p < 0.001 respectively). The obese group had higher SBP and DBP than the control group (all p < 0.001). MetS group had significantly lower HDL-C levels than the control group (p < 0.001). HDL-C levels was also lower in the obese group than in the control group, but the difference was not significant. In the MetS group 10 patients had only impaired fasting glucose (IFG), 2 impaired glucose tolerance (IGT), 9 had both IFG and IGT and 13 were only insülin resistant. Prolidase levels were significantly higher in MetS group compared to both obese and control groups (p < 0.001 and p < 0.05respectively) and also in the obese group compared to the control group (p < 0.05). TAS was lower in both MetS and obese groups than in the control group (p < 0.001 and p < 0.05 respectively). There wasn’t any significant difference according to BMI levels between MetS and obese groups. OSI was significantly higher in both obese and MetS groups than in the control group (p < 0.001 and p < 0.001 respectively). The clinical and biochemical data are shown in Table 1.
Table 1

Clinical and metabolic parameters of MetS, Obese and control groups

 

MetS (n = 34, 31M and 3F)

Obese (n = 30, 28M and 2F)

Control (n = 23, 20M and 3F)

Age (years)

35.18 ± 6.8

33.67 ± 7.9

32.39 ± 4.7

Body weight (kg)

97.27 ± 12.6b,f

92.07 ± 19.2c

60.30 ± 10.3

BMI (kg/m2)

38.91 ± 5.5b

36.59 ± 5.2c

22.93 ± 3.4

Waist-C (cm)

106.85 ± 10.3b,f

99.73 ± 13.2c

75.52 ± 8.1

SBP (mmHg)

136.77 ± 12.0a,b

118.67 ± 13.1c

105.65 ± 7.7

DBP (mmHg)*

90(70–110)a,b

80(70–100)c

60(50–80)

TC (mg/dl)

194.39 ± 35.6e

181.66 ± 24.2

168.13 ± 24.7

HDL (mg/dl)

44.39 ± 13.3b

42.14 ± 9.9

55.65 ± 5.2

TG (mg/dl)

174.91 ± 72.9a,b

100.07 ± 35.3

73.44 ± 30.6

LDL (mg/dl)

113.68 ± 37.4

117.76 ± 23.4h

97.80 ± 21.9

TOS (mmol H2O2 Equiv./l)

13.94 ± 2.19e,f

12.74 ± 2.1

13.05 ± 2.3

TAS (mmol Trolox Equiv./l)

0.95 ± 0.1a,b

1.10 ± 0.1h

1.16 ± 0.1

OSI (arbitrary unit)

14.67 ± 2.2a,b

11.48 ± 2.3

10.65 ± 2.4

FPG (mg/dl)

102.88 ± 13.3b

97.94 ± 7.4h

90.87 ± 9.8

Prolidase

708.93 ± 10.4b,f

703.17 ± 8.1h

696.69 ± 11.5

BMI, body mass index; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MetS, metabolic syndrome; OSI, oxidative stress index SBP, systolic blood pressure; TAS, total antioxidant status; TC, total cholesterol;TG, triglyceride; TOS, total oxidative status; Waist-C, waist circumference. *Data in which non-parametric tests were used and expressed as median (range). P < 0.001: aMetS versus obese; bMetS versus control; cobese versus control. P < 0.01: dMetS versus obese; eMetS versus control. P < 0.05: fMetS versus obese; gMetS versus control; hobese versus control.

In correlation analysis, prolidase was negatively correlated with TAS and HDL-C (r = −0,362, p < 0.001; r = −0.320, p < 0.01) and positively correlated with BMI, weight, waist-c, SBP, DBP, TG, TC, LDL-C ( r = 0.330, p = 0.002; r = 0.298 p = 0.006; r = 0.4 p <0.001; r = 0.367, p = 0.001; r = 0.358, p = 0.001; r = 0.293, p = 0.008; r = 0.297, p = 0.006; r = 0.306, p = 0.005, respectively; Table 2). These associations were confirmed in the multiple regression analysis ( R2 = 0,226, p = 0,001). In multivariate logistic regression analysis prolidase activity was found to be an important predictor for MetS (A one unit change in prolidase would make the MetS 1,115 as likely to ocur; R2 = 0,324, p = 0,001).
Table 2

Correlations between prolidase, and other clinical and metabolic parameters

 

BMI

WEIGHT

WAIST-C

SBP

DBP

TG

TC

HDL-C

LDL-C

TAS

PROLIDASE

R

0.330

0.298

0.400

0.367

0.358

0.293

0.297

−0,320

0,306

−0,362

P

0.002

0.006

<0.001

,001

,001

,008

,006

,003

,005

,001

BMI

R

 

0.925

0.892

0.591

0.675

0.423

0.245

−0.304

0.170

−0.437

P

 

<0.001

<0.001

<0.001

<0.001

<0.001

0.023

0.004

0.125

<0.001

WEIGHT

R

  

0.915

0.581

0.669

0.427

0.311

−0.302

0.234

−0.406

P

  

<0.001

<0.001

<0.001

<0.001

0.004

0.005

0.034

<0.001

WAIST-C

R

   

0.594

0.675

0.481

0.322

−0.292

0.220

−0.434

P

   

<0.001

<0.001

<0.001

0.002

0.006

0.046

<0.001

SBP

R

    

0.844

0.436

0.298

−0.197

0.161

−0.494

P

    

<0.001

<0.001

0.005

0.069

0.147

<0.001

DBP

R

     

0.382

0.333

−0.217

0.218

−0.409

P

     

<0.001

0.002

0.044

0.048

<0.001

TG

R

      

0.351

−0.166

−0.033

−0.379

P

      

0.001

0.133

0.767

<0.001

TK

R

       

−0.006

0.849

−0.326

P

       

0.954

<0.001

0.002

HDL-C

R

        

−0.338

0.123

P

        

0.002

0.264

LDL-C

R

         

−0.225

P

         

0.042

BMI, body mass index; DBP, diastolic blood pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MetS, metabolic syndrome; SBP, systolic blood pressure; TG, triglyceride; Waist-C, waist circumference; TAS, total antioxidant status.

Discussion

In this study, we investigated the possible association between serum prolidase activity and non-diabetic metabolic syndrome.We found a significant increase in serum prolidase activity, a member of MMPs, in patients with non-diabetic metabolic syndrome compared to obese or healthy control groups. A significant correlation of serum prolidase activity was also determined both with increased BMI, weight, waist-c, SBP, DPB, TG, TC, LDL-C and decreased TAS and HDL-C levels.

MetS is defined as the existance of obesity and at least other two factors among hypertension, dyslipidemia and diabetes mellitus or glycemia of >100 mg/dl [3]. Endothelial dysfunction in MetS leads cardiovascular risk accompained by high morbidity and mortality. Increased oxidative stress and altered MMPs are shown two of the factors that play in the pathogenesis of MetS. Prolidase, a member of MMPs, plays an important role in collagen metabolism and extracellular matrix remodeling [4, 18]. Prolidase enzyme activity has been investigated in various disorders such as chronic liver disease [7], osteoporosis [8], osteoarthritis [9], uremia [10], diabetic neuropathy [19], hypertension [11], coronary artery disease [20], and ovarian cancer [21]. There are some studies revealing the role of MMPs in MetS. Goncalves et al. reported an increase in pro-MMP-9, MMP-8 and TIMP-1 levels while without any difference in MMP-2, MMP-3 and TIMP-2 levels compared to healthy controls [22]. Additionally, an increase in MMP-8 levels in MetS patients [23] and elevated levels of MMP-2 activity, but not of MMP-9 in non-diabetic MetS [24] was reported. On the other hand, there are some studies in the literature regarding MMPs profile in obesity [2429], diabetes mellitus [3033] hypertension [3436] and dyslipidemia [37, 38], clinical conditions representing diagnostic criteria for the definition of the metabolic syndrome.

We have shown previously that MetS and obesity may alter oxidative stress, which contributes to atherosclerosis-related cardiovascular events [39]. In this study we also found a significant increase (p < 0.001) of OSİ levels and a significant decrease (p < 0.001) of TAS levels in metabolic syndrome and non-diabetic Mets group compared to obese and healthy control groups similar to our previous study [39].

We eveluated firstly serum prolidase activity in non-diabetic metabolic syndrome and demonstrated its elevation in this patient group. Additionally, the correlation analysis showed that prolidase activity had a significant positive correlation with BMI, weight, waist-c, SBP, DBP, TG, TC, LDL-C and inversely negative correlation with TAS and HDL-C in our study. Correlation between serum prolidase activity and markers of oxidative stress paremeters in this study suggests the association of collagen turnover and oxidative stress in non-diabetic MetS.

Serum prolidase activity was significantly higher in MetS group compared to the only obese group. This may be resulted from that hypertension, hypertriglyceridemia, low HDL-C levels, IGT, IFG and insülin resistance, are found more frequently in MetS compared to obesity. Logistic regression analysis demonstrated that prolidase activity was an important predictor for MetS as for the last point of this study.

Demirbağ et al. [11] has found a significant correlation between prolidase activity and presence and duration of hypertension supporting our data. Yıldız et al. [20] also showed that serum prolidase activity was positively correlated with presence of hypertension, SBP and inversely correlated with HDL-C levels. Hilali et al. [18] reported that elevated serum prolidase activity and oxidative stress may be associated with increased cardiovascular risk in polycystic ovary syndrome and/or menstrual irregularities associated with this syndrome. Serum prolidase activity was suggested as a marker of osteoporosis in type 2 diabetes mellitus [8].

Consequently we suggest that evaluating prolidase activity in subjects with non-diabetic MetS may be important as an independent predictor of the disease. However, further studies in larger patient groups are needed to explain the role of serum prolidase activity in the pathogenesis of metabolic syndrome.

Abbreviations

MetS: 

Metabolic syndrome

MMPs: 

Matrix metalloproteinases

OGTT: 

Oral glucose tolerance test

NCEP: 

National Cholesterol Education Program

BMI: 

Body mass index

SBP: 

Systolic blood pressures

DBP: 

Diastolic blood pressures

FPG: 

Fasting plasma glucose

TG: 

Triglyceride

TAS: 

Total antioxidant status

TOS: 

Total oxidative status

OSI: 

Oxidative stress index

HDL-C: 

High density lipoprotein- cholesterol

LDL-C: 

Low density lipoprotein-cholesterol

IFG: 

Impaired fasting glucose

IGT: 

Impaired glucose tolerance

TIMP: 

Tissue inhibitors of metalloproteinase.

Declarations

Funding

This research did not receive any specific grant from any funding agency in the public, commercial or not-for-profit sector.

Authors’ Affiliations

(1)
Faculty of Medicine, Department of Internal Medicine, Division of Endocrinology, Gaziantep University
(2)
Faculty of Medicine, Department of Medical Pharmacology, Harran University
(3)
Faculty of Medicine, Department of Internal Medicine, Division of Endocrinology, Harran University
(4)
Faculty of Medicine, Department of Internal Medicine, Gaziantep University
(5)
Faculty of Medicine, Department of Clinical Biochemistry, Harran University

References

  1. Third report of the National Cholesterol Education Program (NCEP), Expert panel on the detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). Final report. II Rationale for intervention. Circulation. 2002, 106: 3143-3421.Google Scholar
  2. Msra A, Khurana L: Obesity and metabolic syndrome in developing countries. J Clin Endocrinol Metab. 2008, 93: 9-30. 10.1210/jc.2008-1595.View ArticleGoogle Scholar
  3. Hopps E, Caimi G: Matrix metalloproteinases in metabolic syndrome. Eur J Intern Med. 2012, 23: 99-104. 10.1016/j.ejim.2011.09.012.View ArticlePubMedGoogle Scholar
  4. Surazynski A, Miltyk W, Palka J, Phang JM: Prolidase-dependent regulation of collagen biosynthesis. Amino Acids. 2008, 35: 731-738. 10.1007/s00726-008-0051-8.View ArticlePubMedGoogle Scholar
  5. Zanaboni G, Dyne KM, Rossi A, Monafo V, Cetta G: Prolidase deficiency: biochemical study of erythrocyte and skin fibroblast prolidase activity in Italian patients. Haematologica. 1994, 79: 13-18.PubMedGoogle Scholar
  6. Liu G, Nakayama K, Awata S, Tang S, Kitaoka N, Manabe M, et al: Prolidase isoenzymes in the rat: their organ distribution, developmental change and specific inhibitors. Pediatr Res. 2007, 62: 54-59. 10.1203/PDR.0b013e3180676d05.View ArticlePubMedGoogle Scholar
  7. Myara I, Myara A, Mangeot M, Fabre M, Charpentier C, Lemonnier A: Plasma prolidase activity: a possible index of collagen catabolism in chronic liver disease. Clin Chem. 1984, 30: 211-215.PubMedGoogle Scholar
  8. Erbagci AB, Araz M, Erbagci A, Tarakcioglu M, Namiduru ES: Serum prolidase activity as a marker of osteoporosis in type 2 diabetes mellitus. Clin Biochem. 2002, 35: 263-268. 10.1016/S0009-9120(02)00305-3.View ArticlePubMedGoogle Scholar
  9. Altindag O, Erel O, Aksoy N, Selek S, Celik H, Karaoglanoglu M: Increased oxidative stress and its relation with collagen metabolism in knee osteoarthritis. Rheumatol Int. 2007, 27: 339-344. 10.1007/s00296-006-0247-8.View ArticlePubMedGoogle Scholar
  10. Gejyo F, Kishore BK, Arakawa M: Prolidase and prolinase activities in the erythrocytes of patients with chronic uremia. Nephron. 1983, 35: 58-61. 10.1159/000183046.View ArticlePubMedGoogle Scholar
  11. Demirbag R, Yildiz A, Gur M, Yilmaz R, Elci K, Aksoy N: Serum prolidase activity in patients with hypertension and its relation with left ventricular hypertrophy. Clin Biochem. 2007, 40: 1020-1025. 10.1016/j.clinbiochem.2007.05.015.View ArticlePubMedGoogle Scholar
  12. Erel O: A novel automated method to measure total antioxidant response against potent free radical reactions. Clin Biochem. 2004, 37: 112-119. 10.1016/j.clinbiochem.2003.10.014.View ArticlePubMedGoogle Scholar
  13. Erel O: A new automated colorimetric method for measuring total oxidant status. Clin Biochem. 2005, 38: 1103-1111. 10.1016/j.clinbiochem.2005.08.008.View ArticlePubMedGoogle Scholar
  14. Ozcan O, Gultepe M, Ipcioglu OM, Bolat B, Kayadibi H: Optimization of the photometric enzyme activity assay for evaluating real activity of prolidase. Turk J Biochem. 2007, 32: 12-16.Google Scholar
  15. Gultepe M, Ozcan O, Bolat B, Kayadibi H, Ipcıoglu OM: Measured prolidase activity versus physiological activity of the enzyme: inhibitory effect of proline. FEBS J. 2006, 273: 75-Google Scholar
  16. Myara I, Charpentier C, Lemonnier A: Optimal conditions for prolidase assay by proline colorimetric determination: application to imminodipeptiduria. Clin Chim Acta. 1982, 125: 193-205. 10.1016/0009-8981(82)90196-6.View ArticlePubMedGoogle Scholar
  17. Chinard FP: Photometric estimation of proline and ornithine. J Biol Chem. 1952, 199: 91-95.PubMedGoogle Scholar
  18. Hilali N, Vural M, Camuzcuoglu H, Camuzcuoglu A, Aksoy N: Increased prolidase activity and oxidative stress in PCOS. Clin Endocrinol (Oxf). 2013, 79: 105-110. 10.1111/cen.12110.View ArticleGoogle Scholar
  19. Uzar E, Tamam Y, Evliyaoglu O, Tuzcu A, Beyaz C, Acar A, et al: Serum prolidase activity and oxidative status in patients with diabetic neuropathy. Neurol Sci. 2012, 33: 875-880. 10.1007/s10072-011-0857-0.View ArticlePubMedGoogle Scholar
  20. Yildiz A, Demirbag R, Yilmaz R, Gur M, Altiparmak IH, Akyol S, et al: The association of serum prolidase activity with the presence and severity of coronary artery disease. Coron Artery Dis. 2008, 19: 319-325. 10.1097/MCA.0b013e32830042ba.View ArticlePubMedGoogle Scholar
  21. Camuzcuoglu H, Arioz DT, Toy H, Kurt S, Celik H, Aksoy N: Assessment of preoperative serum prolidase activity in epithelial ovarian cancer. Eur J Obstet Gynecol Reprod Biol. 2009, 147: 97-100. 10.1016/j.ejogrb.2009.07.012.View ArticlePubMedGoogle Scholar
  22. Gonçalves FM, Jacob-Ferreira ALB, Gomes VA, Casella-Filho A, Chagas AC, Marcaccini AM, Gerlach RF, Tanus-Santos JE: Increased circulating levels of matrix metalloproteinase (MMP)-8, MMP-9, and pro-inflammatory markers in patients with metabolic syndrome. Clin Chim Acta. 2009, 403: 173-177. 10.1016/j.cca.2009.02.013.View ArticlePubMedGoogle Scholar
  23. Aquilante CL, Beitelshees AL, Zineh I: Correlates of serum matrix metalloproteinase-8 (MMP-8) concentrations in nondiabetic subjects without cardiovascular disease. Clin Chim Acta. 2007, 379: 48-52. 10.1016/j.cca.2006.12.006.View ArticlePubMedGoogle Scholar
  24. Miksztowicz V, Muzzio ML, Royer M, Prada M, Wikinski R, Schreier L, Berg G: Increased plasma activity of metalloproteinase 2 in women with metabolic syndrome. Metabolism. 2008, 57: 1493-1496. 10.1016/j.metabol.2008.06.001.View ArticlePubMedGoogle Scholar
  25. Scroyen I, Cosemans L, Lijnen HR: Effect of tissue inhibitor of matrix metalloproteinases-1 on in vitro and in vivo adipocyte differentiation. Thromb Res. 2009, 124: 578-583. 10.1016/j.thromres.2009.06.020.View ArticlePubMedGoogle Scholar
  26. Gummesson A, Hagg D, Olson FJ, Hulthe J, Carlsson LM, Fagerberg B: Adipose tissue is not an important source for matrix metalloproteinase-9 in the circulation. Scand J Clin Lab Invest. 2009, 69: 636-642. 10.3109/00365510902912747.View ArticlePubMedGoogle Scholar
  27. Demeulemeester D, Collen D, Lijnen HR: Effect of matrix metalloproteinase inhibition on adipose tissue development. Biochem Biophys Res Commun. 2005, 329: 105-110. 10.1016/j.bbrc.2005.01.103.View ArticlePubMedGoogle Scholar
  28. Belo VA, Souza-Costa DC, Lana CM, Caputo FL, Marcaccini AM, Gerlach RF, Bastos MG, Tanus-Santos JE: Assessment of matrix metalloproteinase (MMP)-2, MMP-8, MMP-9, and their inhibitors, the tissue inhibitors of metalloproteinase (TIMP)-1 and TIMP-2 in obese children and adolescents. Clin Biochem. 2009, 42: 984-990. 10.1016/j.clinbiochem.2009.03.025.View ArticlePubMedGoogle Scholar
  29. Głowińska-Olszewska B, Urban M: Elevated matrix metalloproteinase 9 and tissue inhibitor of metalloproteinase 1 in obese children and adolescents. Metabolism. 2007, 56: 799-805. 10.1016/j.metabol.2007.01.011.View ArticlePubMedGoogle Scholar
  30. Papazafiropoulou A, Perrea D, Moyssakis I, Kokkinos A, Katsilambros N, Tentolouris N: Plasma levels of MMP-2, MMP-9 and TIMP-1 are not associated with arterial stiffness in subjects with type 2 diabetes mellitus. J Diabetes Complications. 2010, 24: 20-27. 10.1016/j.jdiacomp.2008.10.004.View ArticlePubMedGoogle Scholar
  31. Death AK, Fisher EJ, McGrath KC, Yue DK: High glucose alters matrix metalloproteinase expression in two key vascular cells: potential impact on atherosclerosis in diabetes. Atherosclerosis. 2003, 168: 263-269. 10.1016/S0021-9150(03)00140-0.View ArticlePubMedGoogle Scholar
  32. Ho FM, Liu SH, Lin WW, Liau CSJ: Opposite effects of high glucose on MMP-2 and TIMP-2 in human endothelial cells. Cell Biochem. 2007, 101: 442-450. 10.1002/jcb.21192.View ArticleGoogle Scholar
  33. Derosa G, D'Angelo A, Scalise F, Avanzini MA, Tinelli C, Peros E, Fogari E, Cicero AF: Comparison between metalloproteinases-2 and −9 in healthy subjects, diabetics, and subjects with acute coronary syndrome. Heart Vessels. 2007, 22: 361-370. 10.1007/s00380-007-0989-6.View ArticlePubMedGoogle Scholar
  34. Derosa G, D'Angelo A, Ciccarelli L, Piccinni MN, Pricolo F, Salvadeo S, Montagna L, Gravina A, Ferrari I, Galli S, Paniga S, Tinelli C, Cicero AF: Matrix metalloproteinase-2, −9, and tissue inhibitor of metalloproteinase-1 in patients with hypertension. Endothelium. 2006, 13: 227-231. 10.1080/10623320600780942.View ArticlePubMedGoogle Scholar
  35. Fontana V, Silva PS, Belo VA, Antonio RC, Ceron CS, Biagi C, Gerlach RF, Tanus-Santos JE: Consistent alterations of circulating matrix metalloproteinases levels in untreated hypertensives and in spontaneously hypertensive rats: a relevant pharmacological target. Basic Clin Pharmacol Toxicol. 2011, 109: 130-137. 10.1111/j.1742-7843.2011.00698.x.View ArticlePubMedGoogle Scholar
  36. Franz M, Berndt A, Altendorf-Hofmann A, Fiedler N, Richter P, Schumm J, Fritzenwanger M, Figulla HR, Brehm BR: Serum levels of large tenascin-C variants, matrix metalloproteinase-9, and tissueinhibitors of matrix metalloproteinases in concentric versus eccentric left ventricular hypertrophy. Eur J Heart Fail. 2009, 11: 1057-1162. 10.1093/eurjhf/hfp128.View ArticlePubMedGoogle Scholar
  37. Derosa G, Maffioli P, D'Angelo A, Salvadeo SA, Ferrari I, Fogari E, Gravina A, Mereu R, Palumbo I, Randazzo S, Cicero AF: Evaluation of metalloproteinase 2 and 9 levels and their inhibitors in combined dyslipidemia. Clin Invest Med. 2009, 32: 124-132.Google Scholar
  38. Beaudeux JL, Giral P, Bruckert E, Bernard M, Foglietti MJ, Chapman MJ: Serum matrix metalloproteinase-3 and tissue inhibitor of metalloproteinases-1 as potential markers of carotid atherosclerosis in infraclinical hyperlipidemia. Atherosclerosis. 2003, 169: 139-146. 10.1016/S0021-9150(03)00149-7.View ArticlePubMedGoogle Scholar
  39. Tabur S, Torun AN, Sabuncu T, Turan MN, Celik H, Ocak AR: Non-diabetic metabolic syndrome and obesity do not affect serum paraoxonase and arylesterase activities but do affect oxidative stress and inflammation oxidative stress and inflammation. Eur J Endocrinol. 2010, 162: 535-554. 10.1530/EJE-09-0732.View ArticlePubMedGoogle Scholar

Copyright

© Tabur et al.; licensee BioMed Central. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Advertisement