eISSN: 1896-9151
ISSN: 1734-1922
Archives of Medical Science
Current issue Archive Special issues Subscription
SCImago Journal & Country Rank

 
4/2008
vol. 4
 
Share:
Share:
more
 
 

Clinical research
Decreased kidney function as a risk factor for cardiovascular events in subjects with metabolic syndrome – a pilot study

Marcin Barylski
,
Maciej Banach
,
Dimitri P. Mikhailidis
,
Lucjan Pawlicki
,
Jan Kowalski

Arch Med Sci 2008; 4, 4: 417–423
Online publish date: 2009/01/26
Article file
- Decreased.pdf  [0.08 MB]
Get citation
ENW
EndNote
BIB
JabRef, Mendeley
RIS
Papers, Reference Manager, RefWorks, Zotero
AMA
APA
Chicago
Harvard
MLA
Vancouver
 
 

Introduction

Introducing the concept of the metabolic syndrome (MetS) into clinical practice has important practical consequences. Simple, objective criteria [1] allow early identification of patients at risk of developing type 2 diabetes mellitus (T2DM) and cardiovascular disease thus allowing the initiation of effective prevention and treatment. These diseases are major causes of premature death in the European population [2].
Several studies have indicated that chronic kidney disease (CKD), which manifests by increased elimination of albumin in urine or decreased estimated glomerular filtration rate (eGFR), is a strong and independent risk factor for cardiovascular disease. This increased risk is seen in patients with risk factors of atherosclerosis, in those with diagnosed cardiovascular diseases as well as in patients without clinical symptoms [3]. Decreased eGFR increases the rate of development of atherosclerosis and the risk of cardiovascular complications [4].
We conducted a study to estimate eGFR in patients with MetS.


Material and methods


Patient characteristics

Fifty patients [24 women and 26 men, aged
18-75 years (mean 56 ± 12 years)] with MetS diagnosed on the basis of NCEP/ATP III criteria [1] were recruited (Table I). None had been treated for the condition prior to the study. The control group consisted of 25 healthy subjects [12 women and 13 men (mean 54±13 years)]. None of the subjects smoked and none were on any medication including vitamins. The characteristics of the participants are listed in Table I.
All the study participants were volunteers and signed an informed consent form before their inclusion in the study, which was approved by the local Ethics Committee of the Medical University in Lodz (Nr RNN/257/05/KB). The Helsinki Declaration recommendations were observed.
Blood samples (5 ml) were collected from an antecubital vein in the early morning, 12 hours after the previous meal. All the assays were performed within 2 hours of blood collection. The laboratory staff that performed the assays (using established standardized methods) was blind to the group the sample came from.


Methods of glomerular filtration rate (GFR) determination

Glomerular filtration rate was calculated using
2 formulas [5]:

1. Cockcroft-Gault (CG) formula:




2. Modification of diet in renal disease (MDRD) formula:
GFR [ml/min/1.73 m2] = 186.3 × creatinine level [mg/dl]–1,154 ×
× age –0,203 × 0.742 (if female) and
× 1.21 (if African-American)

Classification of chronic kidney disease
Classification of CKD stages was based on National Kidney Foundation guidelines – Kidney Disease Outcomes Quality Initiative (NFK-K/DOQI) [6]. Criteria for CKD classification are listed in Table II.


Statistical analysis

The data were analyzed using Student’s t-test for independent groups. The comparisons between each measurement were performed using the t-test for paired data. Values were expressed as mean ±SD. A P<0.05 was considered significant.

Results

In the patients with MetS, the mean eGFR calculated according to the CG formula was
101±36 ml/min/1.73 m2 and was significantly hig-her in males compared with females (104±38
vs. 97±34 ml/min/1.73 m2, P>0.05). The mean eGFR calculated according to the MDRD formula was 73±18 ml/min/1.73 m2, 74±15 in males and 71±21 ml/min/1.73 m2 in females (P>0.05).
The eGFR characteristics in patients with MetS are shown in Table III.
For healthy subjects, the mean eGFR calculated according to the CG formula was 99±23 ml/min/1.73 m2 and was higher in females compared with males (101±29 vs. 97±16 ml/min/1.73 m2, P>0.05). The mean eGFR calculated according to the MDRD formula was 83±14; 85±18 in females and 81±8 ml/min/1.73 m2 in males (P>0.05). The eGFR characteristics for healthy subjects are listed in Table IV.
Based on their eGFR, subjects were placed into categories of <60, 60-90 and >90 ml/min/1.73 m2, as calculated by the CG or MDRD formula. For the MetS group, the number of patients falling in each category is shown in Table V. Healthy subjects were categorized by eGFR in the same way as the MetS patients (Table VI).
Comparison of patients with MetS and healthy subjects showed that there were no significant differences between eGFR values calculated with the CG formula (P>0.05). However, there was a significantly lower MDRD eGFR (P<0.02) in patients with MetS compared with healthy subjects.
In patients with MetS a significant negative correlation was seen between eGFR values calculated by the MDRD formula and triglyceride (TG) levels (r= –0.362, P=0.0098).


Discussion

The relationship between impaired renal function and cardiovascular risk has been reported in several studies [7-14]. This relationship was confirmed by a meta-analysis (552 258 patients). A significant increase in cardiovascular risk was apparent even for stages 1 and 2 of CKD [11]. Renal insufficiency is also associated with heart failure [12, 13].
Several factors are associated with an increased risk of developing CKD; for example, obesity (especially abdominal obesity), diabetes mellitus, hypertension and insulin resistance [15, 16]. Epidemiological studies suggest that insulin resistance is a risk factor for CKD. In NHANES III [17] the frequency of a diagnosis of CKD increased progressively with insulin levels.
The risk of heart failure and death in a population of 1 091 201 patients was estimated in NHANES III [18]. The population was divided into 4 groups: 1 – patients without diabetes mellitus and kidney disease, 2 – patients with diabetes mellitus but without kidney disease, 3 – patients with kidney disease but without diabetes mellitus, 4 – patients with kidney disease and diabetes mellitus. The risk of development of chronic renal insufficiency for each group in 2 years was 8.6, 18.5, 30.7, and 52.3 per 100 patient/years, respectively. Similarly, the risk of death was 5.5, 8.1, 17.7, and 19.9 per 100 patient/years, respectively. Patients with CKD and without diabetes mellitus had 2 times higher risk of developing heart failure and death than patients with diabetes mellitus but without CKD.
The biochemical manifestations of insulin resistance – MetS – are a risk factor for CKD [19, 20]. Compared with patients without MetS, the relative risk for MetS patients of having an eGFR <60 ml/min/1.73 m2 or microalbuminuria (after ruling out other causes of kidney disease) was 2.6 and 1.89, respectively. The correlation between each component of MetS and CKD was also estimated. After ruling out other causes of CKD, the relative risk of CKD for patients with blood pressure >130/85 mm Hg (but <140/90 mm Hg) was 2.66, for those with a decreased level of HDL cholesterol (HDL-C), 2.11, and 1.8 for those with TG levels >150 mg/dl.
Almost 20 years of observation of the 2585 participants without kidney disease provided information about risk factors for CKD [21]. Apart from age at the beginning of the study and initial eGFR, independent risk factors were diabetes mellitus (relative risk 2.38), hypertension (relative risk 1.57) and a low level of HDL-C (relative risk 0.80). Experimental studies also indicate that insulin resistance is higher than usual in patients with stage 2 CKD [22].
The relationship between dyslipidemia and development or intensification of CKD has been evaluated. In 223 patients with IgA nephropathy, the level of TG was an independent factor for the progression of nephropathy. Compared with patients without hypertriglyceridemia the relative risk of disease progression was 7.3 [23]. A retrospective analysis of data obtained from 4 years of observation of 4326 inhabitants of Okinawa shows that a high level of TG was a risk factor for the development of albuminuria. Relative risk for TG levels was 1.01 for men and 1.03 for women [24]. In the 14 year prospective PHS (Physicians Health Study), the risk factors for having a level of creatinine >1.5 mg/dl or eGFR <55 ml/min/1.73 m2 were total cholesterol >240 mg/dl (relative risk 1.77), HDL-C <40 mg/dl (relative risk 2.16) and upper quartile of total cholesterol/HDL >6.8 ratio (relative risk 2.16) [25]. The long prospective observation in the Framingham study indicates that a risk factor for CKD development is low levels of HDL-C, in addition to hypertension and diabetes mellitus [26].
We used the CG and MDRD formulas to determine eGFR in patients with MetS and in healthy subjects. Patients with MetS and healthy subjects had almost the same eGFR when calculated by the CG formula. However, there was a significant difference (P<0.02) between the groups in eGFR calculated using the MDRD formula. In the group of patients with MetS we observed a lower eGFR compared with healthy subjects (72±18 vs. 83±14 ml/min/1.73 m2, respectively, P<0.05). An eGFR below 60 ml/min/1.73 m2 was observed in 8% of healthy subjects (according to the CG and MDRD formulas), but in the group of patients with MetS, a value below 60 ml/min/1.73 m2 was observed in 10% using the CG formula and in 30% using the MDRD formula. Therefore, the MDRD formula results in a 3-fold higher assignment of patients to a compensated renal dysfunction group (eGFR <60 ml/min/1.73 m2) compared with the CG formula. The eGFR value calculated using the CG formula may be too high in obese patients [27], and for these patients the MDRD formula (and its modification) may be more representative. The MDRD equation is recommended by the National Kidney Foundation [6].
In the CG formula, weight influences the calculated eGFR; the greater the weight, the higher the eGFR. For this reason a correction was proposed [28] for obese subjects but its use has not been widely accepted. The abbreviated-MDRD equation does not include weight and overestimates the eGFR of underweight patients [29]. The Prevention of Renal and Vascular End-Stage Disease (PREVEND) study reported that the relationship between BMI and GFR showed different directions, depending on the method used to calculate GFR [30]. Because of the interest in CKD and associated cardiovascular risk factors, as well as the CG and MDRD equations not being equivalent, there is a need to reach an eGFR-measurement consensus.
In our study there was a relationship between eGFR value calculated with the MDRD formula and TG level in patients with MetS as predicted from the literature cited above. Furthermore, in a large screened cohort TG concentrations were an independent risk factor for the development of proteinuria in men and women, whereas total cholesterol and low density lipoprotein (LDL) cholesterol (LDL-C) were not. High TG in women and low HDL-C in men also correlated with a decrease in GFR [31] and TG-rich, but not cholesterol-rich, apolipoprotein B-containing lipoproteins are associated with a rapid loss of renal function in CKD [32]. Hypertriglyceridemia was also a significant predictor of poor outcome in patients with IgA nephropathy [33]. In a prospective study of 12,728 subjects, high TG and low HDL-C, but not LDL-C, predicted the risk of increase in serum creatinine [34].
Evidence shows that dyslipidemia per se is a risk factor for progressive renal disease [32-37]. Other researchers reported that the development of focal segmental glomerular sclerosis was correlated with serum TG, but not serum cholesterol level in unilaterally nephrectomized rats [38]. LDL and TG-rich lipoproteins caused proliferation of human mesangial cells [36]. In another study, TG-rich apoB-containing lipoproteins promoted the progression of human renal insufficiency [32]. Oxidized lipoprotein has been found in the glomeruli and interstitial regions [39]. The secretion of interleukin-6, platelet-derived growth factor, transforming growth factor-b and tumour necrosis factor-a by mesangial cells were enhanced when mesangial cells were exposed to lipids [39]. Lipoproteins stimulate production of fibronectin and monocyte chemoattractant protein-1 expression in mesangial cells [40].
Previous studies showed that statins reduced levels of serum cholesterol and TGs [41, 42]. Other effects of statins include suppression of the inflammatory and fibrogenic pathways of glomerular injury in vitro and in vivo [43-47]. Athyros et al. showed that in untreated dyslipidaemic patients with CHD and normal renal function at baseline, creatinine clearance declines over a period of 3 years. Statin treatment prevents this decline and significantly improves renal function, potentially offsetting an additional factor associated with CHD risk [48]. They also reported that among CHD patients, those with MetS benefited more from statin treatment than those without MetS. This benefit could be partially attributed to favourable changes in e-GFR level probably induced by statin treatment [49-52].
The decreased eGFR seen in 30% of patients with MetS may be due to impaired endothelial function and be influenced by the numerous abnormal components of the MetS.
Our study has limitations, mainly the small number of patients with MetS categorized by eGFR. However, this is a pilot study, which is still ongoing to increase patient numbers and evaluate the long-term outcomes.
In conclusion, the effects of statins in lowering TG levels and suppressing the pathways for renal injury may be beneficial for patients with hypertriglyceridemia and renal disease. There is a need for a consensus on how to measure eGFR in patients with MetS.

References

�1. Sarmiento Méndez LM, Roca-Cusachs Coll A, Arroyo Díaz JA, Benet Gusta` MT, Solé Villa MJ, Franco Peral M. Comparison of the definitions of the metabolic syndrome according to ATP III and IDF [Spanish]. Rev Clin Esp 2008; 208: 333-8.
�2. Tuomilehto J, Lindström J, Eriksson JG Valle TT, et al.; Finnish Diabetes Prevention Study Group. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl
J Med 2001; 344: 1343-50.
�3. Rysz J, Banach M, Stolarek RA, et al. Serum matrix metalloproteinases MMP-2 and MMP-9 and metalloproteinase tissue inhibitors TIMP-1 and TIMP-2 in diabetic nephropathy. J Nephrol 2007; 20: 444-52.
�4. Manjunath G, Tighiouart H, Ibrahim H, et al. Level of kidney function as a risk factor for atherosclerotic cardiovascular outcomes in the community. J Am Coll Cardiol 2003; 41: 47-55.
�5. Poggio ED, Wang X, Greene T, Van Lente F, Hall PM. Performance of the modification of diet in renal disease and Cockcroft-Gault equations in the estimation of GFR in health and in chronic kidney disease. J Am Soc Nephrol 2005; 16: 459-66.
�6. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification and stratification. Am J Kidney Dis 2002; 39 (Suppl. 1): S17-266.
�7. Chien KL, Hsu HC, Lee YT, Chen MF. Renal function and metabolic syndrome components on cardiovascular and all-cause mortality. Atherosclerosis 2008; 197: 860-7.
�8. Fried LP, Kronmal RA, Newman AB, et al. Risk factors for 5-year mortality in older adults: the Cardiovascular Health Study. JAMA 1998; 279: 585-92.
�9. Muntner P, He J, Hamm L, Loria C, Whelton PK. Renal insufficiency and subsequent death resulting from cardiovascular disease in the United States. J Am Soc Nephrol 2002; 13: 745-53.
10. Jurkovitz CT, Abramson JL, Vaccarino LV, Weintraub WS, McClellan WM. Association of high serum creatinine and anemia increases the risk of coronary events: results from the prospective community-based atherosclerosis risk in communities (ARIC) Study. J Am Soc Nephrol 2003; 14: 2919-25.
11. Vanholder R, Massy Z, Argiles A, Spasovski G, Verbeke F, Lameire N; European Uremic Toxin Work Group (EUTox). Chronic kidney disease as cause of cardiovascular morbidity and mortality. Nephrol Dial Transplant 2005; 20: 1048-56.
12. McAlister FA, Ezekowitz J, Tonelli M, Armstrong PW. Renal insufficiency and heart failure. Prognostic and therapeutic implications from a prospective cohort study. Circulation 2004; 109: 1004-9.
13. Komukai K, Ogawa T, Yagi H, et al. Decreased renal function as an independent predictor of re-hospitalization for congestive heart failure. Circ J 2008; 72: 1152-7.
14. Hillege HL, Nitsch D, Pfeffer MA, et al.; Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity (CHARM) Investigators. Renal function as a predictor of outcome in a broad spectrum of patients with heart failure. Circulation 2006; 113: 671-8.
15. Sarafidis PA. Obesity, insulin resistance and kidney disease risk: insights into the relationship. Curr Opin Nephrol Hypertens 2008; 17: 450-6.
16. Després JP, Lemieux I, Bergeron J, et al. Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk. Arterioscler Thromb Vasc Biol 2008; 28: 1039-49.
17. Chen J, Muntner P, Hamm LL, et al. Insulin. resistance and risk of chronic kidney disease in nondiabetic US adults. J Am Soc Nephrol 2003; 14: 469-77.
18. Coresh J, Astor BC, Greene T, Eknoyan G, Levey AS. Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third National Health and Nutrition Examination Survey. Am J Kidney Dis 2003; 41: 1-12.
19. Park YW, Zhu S, Palaniappan L, Heshka S, Carnethon MR, Heymsfield SB. The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988-1994. Arch Intern Med 2003; 163: 427-36.
20. Ryu S, Chang Y, Woo HY, et al. Time-dependent association between metabolic syndrome and risk of CKD in Korean men without hypertension or diabetes. Am J Kidney Dis 2008 Epub ahead of print.
21. Retnakaran R, Cull CA, Thorne KI, Adler AI, Holman RR; UKPDS Study Group. Risk factors for renal dysfunction in type 2 diabetes: U.K. Prospective Diabetes Study 74. Diabetes 2006; 55: 1832-9.
22. Rasić-Milutinović Z, Perunicić-Peković G. Clinical importance and pathogenic mechanisms of insulin resistance in chronic renal insufficiency (part I): insulin resistance in patients with chronic renal insufficiency [Croatian]. Med Pregl 2000; 53: 45-50.
23. Syrjänen J, Mustonen J, Pasternack A. Hypertriglyceridaemia and hyperuricaemia are risk factors for progression of IgA nephropathy. Nephrol Dial Transplant 2000; 15: 34-42.
24. Iseki K, Ikemiya Y, Fukiyama K. Serum cholesterol and risk of end-stage renal disease in a cohort of mass screening. Clin Exp Nephrol 1998; 2: 18-24.
25. Schaeffner ES, Kurth T, Curhan GC, et al. Cholesterol and the risk of renal dysfunction in apparently healthy men. J Am Soc Nephrol 2003; 14: 2084-91.
26. Fox CS, Larson MG, Leip EP, Culleton B, Wilson PW, Levy D. Predictors of new-onset kidney disease in a community-based population. JAMA 2004; 291: 844-50.
27. Verhave JC, Gansevoort RT, Hillege HL, De Zeeuw D, Curhan GC, De Jong PE. Drawbacks of the use of indirect estimates of renal function to evaluate the effect of risk factors on renal function. J Am Soc Nephrol 2004; 15: 1316-22.
28. Saracino A, Morrone LF, Suriano V, et al. A simple method for correcting overestimated glomerular filtration rate in obese subjects evaluated by the Cockroft and Gault formula: a comparison with 51Cr EDTA clearance. Clin Nephrol 2004; 62: 97-103.
29. Froissart M, Rossert J, Jacquot C, Paillard M, Houillier P. Predictive performance of the modification of diet in renal disease and Cockroft-Gault equations for estimating renal function. J Am Soc Nephrol 2005; 16: 763-73.
30. Verhave JC, Gansevoort RT, Hillege HL, de Zeeuw D, Curhan GC, de Jong PE; PREVEND study group. Drawbacks of the use of indirect estimates of renal function to evaluate the effect of risk factors on renal function. J Am Soc Nephrol 2004; 15: 1316-22.
31. Tozawa M, Iseki K, Iseki C, Oshiro S, Ikemiya Y, Takishita S. Triglyceride, but not total cholesterol or low-density lipoprotein cholesterol levels, predict development of proteinuria. Kidney Int 2002; 62: 1743-9.
32. Samuelsson O, Attman PO, Knight-Gibson C, et al. Complex apolipoprotein B-containing lipoprotein particles are associated with a higher rate of progression of human chronic renal insufficiency. J Am Soc Nephrol 1998; 9: 1482-8.
33. Syrjänen J, Mustonen J, Pasternack A. Hypertriglyceridaemia and hyperuricaemia are risk factors for progression of IgA nephropathy. Nephrol Dial Transplant 2000; 15: 34-42.
34. Muntner P, Coresh J, Smith JC, Eckfeldt J, Klag MJ. Plasma lipids and risk of developing renal dysfunction: the atherosclerosis risk in communities study. Kidney Int 2000; 58: 293-301.
35. Joles JA, van Goor H, van Der Horst ML, van Tol A, Elema JD, Koomans HA. High lipid levels in very low density lipoprotein and intermediate density lipoprotein may cause proteinuria and glomerulosclerosis in aging female analbuminemic rats. Lab Invest 1995; 73: 912-21.
36. Nishida Y, Oda H, Yorioka N. Effect of lipoproteins on mesangial cell proliferation. Kidney Int Suppl 1999; 71: S51-3.
37. Massy ZA, Khoa TN, Lacour B, Descamps-Latscha B, Man NK, Jungers P. Dyslipidaemia and the progression of renal disease in chronic renal failure patients. Nephrol Dial Transplant 1999; 14: 2392-7.
38. Olbricht CJ, Wanner C, Thiery J, et al. Simvastatin in nephrotic syndrome. Simvastatin in Nephrotic Syndrome Study Group. Kidney Int Suppl 1999; 71: S113-6.
39. Miyata J, Takebayashi S. Effect of hyperlipidemia on glomerular sclerosis in unilateral nephrectomized rats. Acta Pathol Jpn 1987; 37: 1433-9.�
40. Keane WF, O'Donnell MP, Kasiske BL, et al. Oxidative modification of low-density lipoproteins by mesangial cells. J Am Soc Nephrol 1993; 4: 187-94.
41. Rovin BH, Tan LC. LDL stimulates mesangial fibronectin production and chemoattractant expression. Kidney Int 1993; 43: 218-25.
42. Gould AL, Rossouw JE, Santanello NC, Heyse JF, Furberg CD. Cholesterol reduction yields clinical benefit: impact of statin trials. Circulation 1998; 97: 946-52.
43. Stein EA, Lane M, Laskarzewski P. Comparison of statins in hypertriglyceridemia. Am J Cardiol 1998; 81: 66B-9B.
44. Oda H, Keane WF. Recent advances in statins and the kidney. Kidney Int Suppl 1999; 71: S2-S5.
45. Kasiske BL, O'Donnell MP, Kim Y, Atluru D, Keane WF. Cholesterol synthesis inhibitors inhibit more than cholesterol synthesis. Kidney Int Suppl 1994; 45: S51-3.
46. Kim SY, Guijarro C, O'Donnell MP, Kasiske BL, Kim Y, Keane WF. Human mesangial cell production of monocyte chemoattractant protein-1: Modulation by lovastatin. Kidney Int 1995; 48: 363-71.�
47. Park YS, Guijarro C, Kim Y, et al. Lovastatin reduces glomerular macrophage influx and expression of monocyte chemoattractant protein-1 mRNA in nephrotic rats. Am J Kidney Dis 1998; 31: 190-4.
48. Athyros VG, Mikhailidis DP, Papageorgiou AA, et al. The effect of statins versus untreated dyslipidaemia on renal function in patients with coronary heart disease. A subgroup analysis of the Greek atorvastatin and coronary heart disease evaluation (GREACE) study. J Clin Pathol 2004; 57: 728-34.
49. Athyros VG, Mikhailidis DP, Liberopoulos EN, et al. Effect of statin treatment on renal function and serum uric acid levels and their relation to vascular events in patients with coronary heart disease and metabolic syndrome: a subgroup analysis of the GREek Atorvastatin and Coronary heart disease Evaluation (GREACE) Study. Nephrol Dial Transplant 2007; 22: 118-27.
50. Rysz J, Błaszczak R, Banach M, et al. Evaluation of selected parameters of the antioxidative system in patients with type 2 diabetes in different periods of metabolic compensation. Arch Immunol Ther Exp 2007; 55: 335-40.
51. Barylski M, Kowalczyk E, Banach M, Ciećwierz J, Pawlicki L, Kowalski J. Plasma total antioxidant activity in comparison with plasma NO and VEGF levels in patients with metabolic syndrome. Angiology 2008; 59: (in press).
52. Filippatos TD, Tsimihodimos V, Kostapanos M, et al. Small dense LDL cholesterol and apolipoproteins C-II and C-III in non-diabetic obese subjects with metabolic syndrome. Arch Med Sci 2008; 4: 263-9.
Copyright: © 2009 Termedia & Banach. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License (http://creativecommons.org/licenses/by-nc-sa/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material, provided the original work is properly cited and states its license.
Quick links
© 2021 Termedia Sp. z o.o. All rights reserved.
Developed by Bentus.