• Users Online: 1046
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Search Ahead of print Current issue Archives Submit article Instructions Subscribe Contacts Login 


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2016  |  Volume : 28  |  Issue : 3  |  Page : 99-107

Serum chemerin level in chronic kidney disease


1 Department of Internal Medicine, Faculty of Medicine for Girls, Al Azhar University, Cairo, Egypt
2 Department of Clinical and Chemical Pathology, National Research Center, Cairo, Egypt
3 Department of Internal Medicine, General Helwan Hospital, Cairo, Egypt

Date of Submission14-Jul-2016
Date of Acceptance30-Aug-2016
Date of Web Publication27-Feb-2017

Correspondence Address:
Naglaa Abd Elfattah Tawfik
Faculty of Medicine, Assiut University, Assiut
Egypt
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/1110-7782.200964

Rights and Permissions
  Abstract 

Background
Chronic kidney disease (CKD) is a progressive loss in renal function over a period of months or years. In the metabolic association of an elevated circulating chemerin level in the context of uremia demonstrate that high chemerin levels predict a better survival in CKD patients. The aim of the study was to measure serum chemerin and to correlate it with other parameters in CKD patients.
Patients and methods
This study was conducted on 40 patients with CKD, including 20 patients with end-stage renal disease under regular hemodialysis and 20 patients with renal impairment on conservative therapy who have not started hemodialysis, and 22 apparently healthy participants serving as the control group. Human chemerin is determined by sandwich enzyme immunoassay.
Results
There is a highly statistically significant difference in mean serum chemerin and mean serum high-sensitivity C-reactive protein (hs-CRP) in the patient groups in comparison with the control group. In addition, there was a highly statistically significant difference between control group, under hemodialysis group, and renal impairment group as regards serum chemerin and serum hs-CRP. A positive correlation between serum chemerin and hs-CRP studied in the under hemodialysis group, renal impairment group, and in all patients’ group.
Conclusion
A significantly higher chemerin level in patients with impaired kidney function compared with the normal control group, and a high increase in patients under hemodialysis compared with the other two groups.

Keywords: chemerin, chronic kidney disease, high-sensitivity C-reactive protein, insulin


How to cite this article:
Abd Rabo SE, Ghaffar Mohamed NA, Tawfik NE, Hamed MM. Serum chemerin level in chronic kidney disease. Egypt J Intern Med 2016;28:99-107

How to cite this URL:
Abd Rabo SE, Ghaffar Mohamed NA, Tawfik NE, Hamed MM. Serum chemerin level in chronic kidney disease. Egypt J Intern Med [serial online] 2016 [cited 2019 Sep 15];28:99-107. Available from: http://www.esim.eg.net/text.asp?2016/28/3/99/200964


  Introduction Top


Chronic kidney disease (CKD), also known as chronic renal disease, is a progressive loss in renal function over a period of months or years [1]. CKD is defined as kidney damage or glomerular filtration rate (GFR) less than 60 ml/min/l.73 m2 for 3 months or more, regardless of cause [2]. Kidney failure is defined as either (a) GFR less than 15 ml/min/1.73 m2, which is accompanied in most cases by signs and symptoms of uremia, or (b) a need to start kidney replacement therapy (dialysis or transplantation) [3].

The most common causes of CKD are diabetes mellitus, hypertension, and glomerulonephritis. Together, these comprise ∼75% of all adult cases [4]. The major outcomes of CKD, regardless of cause, include progression to kidney failure, complications of decreased kidney function, and cardiovascular disease. Increasing evidence indicates that some of these adverse outcomes can be prevented or delayed by early detection and treatment [5].

Chemerin, also known as tazarotene-induced gene 2 (TIG2) and retinoic acid receptor responder 2 (RARRES2), is a newly discovered adipokine highly expressed by a number of tissues and organs including adipose tissue, liver, pancreas, lung, and skeletal muscles [6].

Several specific functions have been related to chemerin so far, including regulation of specific immune cell migration [7],[8], regulation of adipogenesis [9], and anti-inflammatory effects on macrophages [10]. Chemerin is secreted as an 18-kDa inactive proprotein formed of 143 amino acids, termed as prochemerin [11].

Enzymes that contribute to activation of chemerin and promote the conversion of inactive prochemerin into the active form chemerin include serine proteases of the coagulation, fibrinolytic, and inflammatory cascades, circulating carboxypeptidases, as well as staphopain B, a cysteine protease secreted by Staphylococcus aureus, which is found in some pathological conditions [11].

Prochemerin, the inactive form of chemerin, can be converted to chemerin by either serine proteases or cysteine proteases. Serine proteases result in the production of stimulatory chemerin, whereas cysteine proteases result in the production of inhibitory chemerin, termed as inhibitory peptide chemerin 15 [12].

Chemerin has different roles at the physiological level. The main role of chemerin has been proven to be related to the adipose tissue. Chemerin can enhance insulin sensitivity of adipocytes by stimulating the process of glucose transport through different tissues [13].

The metabolic associations of an elevated circulating chemerin level in the context of uremia demonstrate that high chemerin levels predict a better survival in CKD patients. Furthermore, associations between circulating chemerin levels and GFR, insulin resistance, blood lipids and inflammatory markers, but not with body fat, have been reported [14]. A study has reported circulating chemerin in a CKD population, finding increased levels in hemodialysis patients, as well as an inverse correlation with residual renal function [15]. However, as links between circulating chemerin and inflammation, body composition, and metabolism were not investigated, they explored these associations, as well as the possibility that chemerin predicted 5-year mortality in an observational cohort study of incident dialysis patients.

The aim of the study was to find the role of serum chemerin in CKD patients.


  Patients and methods Top


This study was conducted on 40 patients with CKD and 22 apparently healthy participants serving as the control group. All patients were selected from Internal Medicine Department, Al Zahraa University Hospital, and Nephrology Department Medical Insurance Hospital, Helwan in the period between December 2013 and March 2014.

Group I included 22 healthy participants as the control group (11 male and 11 female); their ages ranged between 22 and 65 years, with a mean age of 43±13.11 years.

Group II included 40 CKD patients (19 male and 21 female); their ages ranged between 18 and 62 years, with a mean age of 43±12.17 years. This group was further divided into two groups:

Group IIa included 20 patients with end-stage renal disease under regular hemodialysis (10 male and 10 female); their ages ranged between 18 and 60 years, with a mean age of 41.35±11.95 years.

Group IIb included 20 patients with renal impairment on conservative therapy who have not started hemodialysis yet (nine male and 11 female); their ages ranged between 19 and 62 years, with a mean age of 41.75±12.70 years.

Exclusion criteria

Patients with acute or chronic known infection, cardiovascular disease, hypertension, diabetes mellitus, chronic liver disease (hepatitis B or C), or HIV infection were excluded.

After taking a written consent from all participants participating in this study and approval of ethical committee of Faculty of Medicine, Al-Azhar University, they were subjected to the following tests:

  1. Full history and full clinical examination.
  2. Complete blood picture (CBC).
  3. Fasting blood glucose.
  4. Kidney function tests (serum urea and serum creatinine).
  5. Serum glutamic oxaloacetic transaminase (SGOT) and AGPT.
  6. Lipid profile [total cholesterol, triglyceride (TG), high-density lipoprotein (HDL) and low-density lipoprotein (LDL)].
  7. Serum insulin.
  8. Serum high-sensitivity C-reactive protein (hs-CRP).
  9. Serum chemerin.


Five milliliters of fasting (12–16 h) venous blood samples were taken from each subject in the study and divided into two parts: the first part was 2 ml of blood and was put in a tube containing EDTA for CBC determination on Coulter Counter T890 (Coulter Counter, Harpenden, UK). The second part was 3 ml of blood and was left to clot and the serum was separated by centrifugation for 15 min at 3000g. Samples should be assayed immediately after collection or they should be stored at −20°C for determination of fasting blood glucose (which is determined immediately on Hitachi 912 autoanalyzer using colorimetric techniques) and kidney function tests. SGOT, serum glutamic pyruvic transaminase (SGPT), lipid profile, insulin, and chemerin were also determined.

  1. The determination of serum urea, creatinine, SGOT, SGPT, total cholesterol, and TG was performed on Hitachi 912 auto analyzer (Roche Diagnostics GmbH, D-68298 Mannheim, USA) by colorimetric techniques. For determination of HDL-cholesterol, phosphotungestic acid and magnesium ions are used for precipitating all lipoproteins, except HDL fraction, which was present in the supernatant and measured by the autoanalyzer. LDL-cholesterol was measured by Friedwald formula [16].
  2. Fasting serum insulin was determined using radioimmunoassay [17]. Insulin resistance was calculated as HOMA-IR using the following equation [18]:

  3. Determination of hs-CRP was done by a solid-phase immunosorbent assay (enzyme-linked immunosorbent assay) [19], and the kit was supplied by DRG International Inc. (Springfield, New Jersey, USA).
  4. Human chemerin is determined by sandwich enzyme immunoassay [20], and the kit was supplied from bio Vendor (Bio Vendor GmbH, Heidelberg, Germany).


Statistical analysis

Data were collected, coded, revised, and entered to the Statistical Package for Social Science version 20 (IBM SPSS version 20, USA). The qualitative data were presented as number and percentages and as mean, SD, and ranges with the quantitative data. Comparison between groups with qualitative data was done by using χ2-test, whereas the comparison between two groups with quantitative data was done by independent t-test; more than two groups were compared using one-way analysis of variance followed by post-hoc least significant difference test when the comparison showed significant difference. Spearman’s correlation coefficients were used to assess the relation between two quantitative parameters in the same group. Receiver operating characteristic curve was used to assess the best cutoff point with its sensitivity, specificity, positive predictive value, and negative predictive value.


  Results Top


[Table 1] shows a comparison between control group I and patient group II regarding age, sex, and BMI, and there was no statistically significant difference between patient and control groups regarding age and sex. In addition, there was a high statistically significant difference in BMI between the patient and control groups.
Table 1 Comparison between control group I and patient group II regarding age, sex, and BMI

Click here to view


[Table 2] shows a comparison between control group I and whole patient group II, with a highly statistically significant difference in mean serum urea, mean serum creatinine, mean serum HDL, mean serum LDL, mean serum TG, mean serum SGOT, mean serum insulin, and HOMA index and CBC in the patient group in comparison with the control group. It also shows a statistically significant difference in cholesterol in the patient group in comparison with the control group.
Table 2 Comparison between patients and control group regarding different laboratory parameters

Click here to view


[Table 3] shows a highly statistically significant difference in mean serum chemerin and mean serum hs-CRP in the patient group in comparison with the control group.
Table 3 Comparison between control group I and patient group II regarding serum chemerin and serum high-sensitivity C-reactive protein

Click here to view


[Table 4] reveals a nonsignificant difference between the three groups regarding age and sex. In addition, there was a highly significant increase in BMI in both patient groups compared with the control group.
Table 4 Comparison between the three studied groups control I, under hemodialysis IIa, and renal impairment IIb groups regarding age, sex, and BMI

Click here to view


[Table 5] shows a highly statistically significant difference between control group I, under hemodialysis group IIa, and renal impairment group IIb in CBC, mean serum urea, mean serum creatinine, mean serum SGPT, and mean serum SGOT. There was a highly statistically significant difference between control group I, under hemodialysis IIa, and renal impairment IIb in mean serum concentrations of cholesterol, TG, HDL, LDL, fasting serum insulin (FSI), and mean HOMA. However, there was no statistically significant difference between control group, under hemodialysis group, and renal impairment group in mean fasting blood glucose (FBG).
Table 5 Comparison between the three studied groups, control I, under hemodialysis IIa, and renal impairment IIb groups, regarding laboratory data

Click here to view


[Table 6] reveals that comparisons between the control group I and both patient groups and between the two patient groups showed highly statistically significant difference in CBC, mean serum urea, and mean serum creatinine (P<0.01), and a statistically significance in mean serum SGPT when comparing the control group with group IIa (P<0.05). The comparison between the control group I and renal impairment IIb group showed a highly statistically significant difference in mean serum urea and mean serum creatinine (P<0.01). Post-hoc analysis for laboratory parameters among the three studied groups: The comparison between the control group versus the under hemodialysis group showed a high statistical significance in TG, HDL, LDL, FSI, and HOMA index (P<0.01). The comparison between the control group versus renal impairment group showed high statistical significance in cholesterol, HDL, LDL, FSI, and HOMA index (P<0.01). The comparison between the patients under hemodialysis versus renal impairment patients showed high statistical significance in cholesterol, TG, and HDL (P<0.01). It also showed a statistical significance in LDL (P<0.05).
Table 6 Post-hoc analysis for the laboratory data between the three studied groups

Click here to view


[Table 7] shows a comparison between the three studied groups regarding mean serum chemerin and mean serum hs-CRP. There was a high statistically significant difference between the control group, under hemodialysis group, and renal impairment group in serum chemerin and serum hs-CRP.
Table 7 Comparison between the three studied groups regarding serum chemerin and serum high-sensitivity C-reactive protein

Click here to view


[Table 8] shows a post-hoc analysis for serum chemerin and serum hs-CRP among three studied groups, which show a highly statistical significance on both studied parameters (P<0.01).
Table 8 Post-hoc analysis for serum chemerin and serum high-sensitivity C-reactive protein among the three studied groups

Click here to view


[Table 9] demonstrates the correlation between serum chemerin and studied parameters in the under hemodialysis group, renal impairment group, and in all patients’ group; there was a significant positive correlation between chemerin and urea, creatinine, FSI, HOMA index, and hs-CRP among the hemodialysis patients (P<0.01). In addition, there was a positive correlation between serum chemerin and creatinine, FSI, HOMA index, and hs-CRP in renal impairment patients (P<0.01). There was also a correlation between chemerin and white blood cells, red blood cells (RBCs), Hb, urea, creatinine, cholesterol, TG, LDL, HDL, FBG, FSI, SGOT, SGPT, and hs-CRP in all patients’ group (P<0.01).
Table 9 Correlation between chemerin and the studied parameters in under hemodialysis group, renal impairment group, and in all patients group

Click here to view


[Table 10] and [Figure 1] shows that serum chemerin level is considered to have better positive predictive value, sensitivity, and specificity.
Table 10 Receiver operating characteristic curve between patients and controls regarding serum chemerin

Click here to view
Figure 1 Receiver operating characteristic (ROC) curve analysis between patients under hemodialysis and patients with renal impairment regarding serum chemerin.

Click here to view


[Table 11] and [Figure 2] show that chemerin level is considered to have a better positive predictive value, sensitivity, and slightly better specificity.
Table 11 Receiver operating characteristic curve between patients under hemodialysis and patients with renal impairment regarding serum chemerin

Click here to view
Figure 2 Receiver operating characteristic (ROC) curve between patients under hemodialysis and patients with renal impairment regarding serum chemerin.

Click here to view



  Discussion Top


We aimed in the present work to study serum chemerin levels and to determine their relation to patients with CKD.

In this study, we compared three studied groups as regards age, sex, and BMI, and we found no significant difference in both age and sex, but we found a significant increase in BMI in patients more than the control group, which may be because of the increase in water loading and obesity in the diseased group, which was in contrast to the study by Dorte et al. [21], who showed that CKD patients had a significantly lower BMI compared with control patients, which results from the environmental and ethnic difference.

In our study, we found a statistical significance as regards Hb concentration, white blood cells, RBCs, FBG, FSI, and HOMA index between the control group and the patient groups. This was found also by Kilpatrick et al. [22] and Dorte et al. [21].

The studies found that patients in the renal impairment group and under hemodialysis group had anemia due to decreased erythropoietin (the most important factor), iron deficiency, folate deficiency, hemolysis, and bone marrow fibrosis because of the shortened life span of RBCs.

As regards lipid profile (cholesterol, TGs, HDL, LDL), there were significant differences between the control group and patient group, which was in agreement with Kilpatrick et al. [22] and Dorte et al. [21]. Dysregulation of lipid metabolism in the patient group can contribute to atherogenic diathesis and possibly to progression of renal disease and impaired energy metabolism in CRF. Hyperlipidemia can potentially accelerate progression of renal disease by several mechanisms. First, reabsorption of fatty acids, phospholipids, and cholesterol contained in the filtered proteins (albumin and lipoproteins) by tubular epithelial cells can stimulate tubulointerstitial inflammation, foam cell formation, and tissue injury. Second, accumulation of lipoproteins in glomerular mesangium can promote matrix production and glomerulosclerosis [23].

In this study, we found that chemerin level was significantly higher among patients in the under hemodialysis group compared with the other two groups. This was supported by Dörte et al. [21] and Fouque et al. [24], who reported that chemerin was more than two-fold higher in CKD patients compared with the control group. Studies found that elevated serum chemerin levels may be a consequence of impaired kidney in patients. Impaired clearance or catabolism of chemerin in kidney may lead to the accumulation of chemerin in the blood. This suggests that elevated serum chemerin levels are significantly associated with serum creatinine and urea in renal impairment and under hemodialysis patients. In our study, we found that chemerin level was significantly higher among the under hemodialysis patient group compared with renal impairment patient group because the serum urea and creatinine were higher in the under hemodialysis patient group compared with the renal impairment patient group, as the sample from the under hemodialysis patient group was collected before dialysis.

In our study, there is a significant positive correlation between chemerin level versus serum creatinine and urea. Dörte et al. [21] and Pfau et al. [15] support the same result which that referred to infiltration of kidney glomeruli by inflammatory cell like monocyte and macrophage result in glomerular injury and tubule-interstitial damage that decrease the functional capability of kidney to excrete waste products. Another explanation was obtained by the Pfau et al. [15] who postulated that impaired kidney function by overproduction and impaired degradation of extracellular matrix components leads to their accumulation in basement membrane and mesangial region in glomerulus or may be because of the presence of hyperglycemia, glomerular hypertension, advanced glycation end products, and activation of polyol pathway [15].

In our study, there is a significant positive correlation between chemerin level and cholesterol, TG, HDL, and LDL This was supported by Xu et al. [25], who reported that chemerin is a proinflammatory cytokine activating immune cells, and it might play a role in the inflammation of adipose tissue that occurs in obesity.

In addition, in our study, there is a positive significant correlation between chemerin level concentrations and FBG, FSI, and HOMA-IR index in the CKD group patients. This result agrees with Sell and Eckel [6]; Dorte et al. [21]; Lehrke et al. [26]; and Weigert et al. [27]. Sell and Eckel attributed this to the fact that chemerin induces insulin resistance in peripheral tissue such as skeletal muscle and inhibits glucose uptake. Another explanation was obtained by Weigert et al. [27], who postulated that, in adipocytes, chemerin has the opposite effect, where it increases insulin-stimulated glucose uptake, and in turn stimulates insulin sensitivity. Hence, the increase in the level of circulating chemerin is a compensatory metabolism in patient with insulin resistance.

In our study, there is a positive correlation between chemerin concentration levels and hs-CRP index in end-stage renal disease patients. This was in agreement with the study by Bozaoglu et al. [14], who suggested that chemerin is a chemotactic agent that was recently identified as the ligand of ChemR23, a serpentine receptor expressed by activated macrophages and monocyte-derived dendritic cells, as previously mentioned. This fact suggests a key role of the ChemR23/chemerin axis in directing plasmacytoid dendritic cell trafficking, which can play a significant role in regulating the immune response by enhancing chemoattraction of the cells of the immune response toward sites of pathological inflammation.

The diagnostic performance of serum chemerin in detecting patients with renal impairment and under hemodialysis and control healthy persons revealed that the best cutoff level for chemerin in patient groups was greater than 147.5 ng/ml, with a diagnostic sensitivity, diagnostic specificity, positive predictive value, negative predictive value, and efficiency of 100, 100, 100, and 100%, respectively, and an area under the curve of 100.

The diagnostic performance of serum chemerin in detecting patients with renal impairment and under hemodialysis reveals that the best cutoff level for chemerin was 249 ng/ml, with a diagnostic sensitivity, diagnostic specificity, positive predictive value, negative predictive value, and efficiency of 100, 90, 90.9, and 100%, respectively, and an AUC of 99.2.

An association of chemerin serum levels with metabolic syndrome-related parameters, including BMI [28], fasting insulin (FI), TGs [29], HDL-cholesterol [28], leptin [29], and C.reactive protein (CRP) [28], has been shown. In agreement with these findings, chemerin is positively correlated with BMI, FI, and CRP, whereas GFR remains independently associated with circulating chemerin. Interestingly, GFR also independently predicts chemerin serum levels in the CKD patients, which indicates that renal function is a significant predictor of circulating chemerin not only in subjects with (near) normal glomerular filtration but also in patients with end-stage renal disease.


  Conclusion Top


We found a significantly higher chemerin level in patients with impaired kidney function compared with normal control group and much increase in patients under hemodialysis compared with the other two groups.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

 
  References Top

1.
Bacchetta J, Sea JL, Chun RF, Lisse TS, Wesseling-Perry K, Gales B et al. FGF23 inhibits extra-renal synthesis of 1,25-dihydroxyvitamin D in human monocytes. J Bone Miner Res 2012; 28:46–55.  Back to cited text no. 1
    
2.
Levey AS, Eckardt KU, Tsukamoto Y, Levin A, Coresh J, Rossert J et al. Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney Int 2005; 67:2089–2100.  Back to cited text no. 2
    
3.
Levey AS, Coresh J, Balk E, Kausz AT, Levin A, Steffes MW et al. National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann Intern Med 2003; 139:137–147.  Back to cited text no. 3
    
4.
Johnson D. CKD screening and management: overview [chapter 4]. In Daugirdas J. Handbook of chronic kidney disease management. Lippincott Williams & Wilkins; 2011. 32–43.  Back to cited text no. 4
    
5.
Remuzzi G, Benigni A, Remuzzi A. Mechanisms of progression and regression of renal lesions of chronic nephropathies and diabetes. J Clin Investig 2006; 116:288–296.  Back to cited text no. 5
    
6.
Sell H, Eckel J. Chemotactic cytokines, obesity and type 2 diabetes: in vivo and in vitro evidence for a possible causal correlation? Proc Nutr Soc 2009; 24:1–7.  Back to cited text no. 6
    
7.
Wittamer V, Franssen JD, Vulcano M, Mirjolet JF, Le poul E, Migeotte L et al. Specific recruitment of antigen-presenting cells by chemerin, a novel processed ligand from human inflammatory fluids. J Exp Med 2003; 198:977–985.  Back to cited text no. 7
    
8.
Zabel BA, Allen SJ, Kulig P, Allen JA, Cichy J. Chemerin activation by serine proteases of the coagulation, fibrinolytic, and inflammatory cascades. J Biol Chem J 2005; 280:34661–34666.  Back to cited text no. 8
    
9.
Goralski KB et al. Chemerin: a novel adipokine that regulates adipogenesis and adipocyte metabolism. J Biol Chem 2007; 282:28175–28188.  Back to cited text no. 9
    
10.
Cash JL, Hart R, Russ A, Dixon JPC. Synthetic chemerin-derived peptides suppress inflammation through ChemR23. J Exp Med 2008; 205:767–775.  Back to cited text no. 10
    
11.
Du XY, Zabel BA, Mylest T, Allen SJ, Handel T, Lee P et al. Regulation of chemerin bioactivity by plasma corboxypeptidas n, corbox b (activated thrombin activable fibrinolysis inhibitor), and platelets. J Bio Chem 2009; 284:751–758.  Back to cited text no. 11
    
12.
Yoshimura T, Oppenhein JJ. Chemerin reveals it chimeric nature. J Exp Med 2008; 205:2187–2190.  Back to cited text no. 12
    
13.
Takahashi M, Takahashi Y, Takahashi K, Zolotaryov FN, Hong KS, Kitazawa R et al. Chemerin enhances insulin signaling and potentiates insulin-stimulated glucose uptake in 3T3-L1 adipocytes. FEBS Lett 2008; 582:573–578.  Back to cited text no. 13
    
14.
Bozaoglu K, Bolton K, McMillan J, Zimmet P, Jowett J, Collier G et al. Chemerin is a novel adipokine associated with obesity and metabolic syndrome. Endocrinology 2007; 148:4687–4694.  Back to cited text no. 14
    
15.
Pfau D, Bachmann A, Lossner U, Kratzsch J, Bluher M, Stumvoll M, Fasshauer M. Serum levels of the adipokine chemerin in relation to renal function. Diabetes Care 2009; 33:171–173.  Back to cited text no. 15
    
16.
Friedwald WT, Levy RI, Frederickson DS. Estimation of the concentration of low density lipoprotein cholesterol in plasma without use of the preparative ultracentrifuge. Clin Chem 1972; 18:499.  Back to cited text no. 16
    
17.
Perez-Fontan M, Cordido F, Rodriguez-Carmona A, Peteiro J, Garcia-Naveiro R, Garcia-Buela J. Plasma ghrelin in patients undergoing haemodialysis and peritoneal dialysis. Nephrol Dial Transplant 2004; 19:2095–2100.  Back to cited text no. 17
    
18.
Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Clinical Neph 2004; 27:1487–1495.  Back to cited text no. 18
    
19.
Grad E, Pachino RM, Fitzgerald GA, Danenberg HD. Role of thromboxane receptor in C-reactive protein-induced thrombosis. Arterioscler Thromb Vasc Biol 2012; 32:2468–2474.  Back to cited text no. 19
    
20.
Chu SH, Lee MK, Ahnk Y, Im JA, Park MS, Lee DC et al. Chemerin and adiponectin contribute reciprocally to metabolic syndrome. PloS One 2012; 7:e34710.  Back to cited text no. 20
    
21.
Dorte P, Anette B, Matthias B, Micheal S, Jurgen K. Serum levels of the adipokine chemerin in relation to renal function. Diabetes Care 2012; 33:171–173.  Back to cited text no. 21
    
22.
Kilpatrick RD, McAllister CJ, Kovesdy CP, Derose SF, Kopple JD, Kalantar-Zadeh K. Association between serum lipids and survival in hemodialysis patients and impact of race. J Am Soc Nephrol 2007; 18:293–303.  Back to cited text no. 22
    
23.
Adlar AI, Stevens RJ, Manley SE, Bilous RW, Cull CA, Holman RR. Development and progression of nephropathy in type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS 64). Kidney Int 2003; 63:225–232.  Back to cited text no. 23
    
24.
Fouque D, Kalantar-Zadeh K, Kopple J, Cano N, Chauveau P, Cuppari L. A proposed nomenclature and diagnostic criteria for protein–energy wasting in acute and chronic kidney disease. Kidney Int 2008; 73:391–398.  Back to cited text no. 24
    
25.
Xu H, Barnes GT, Yang Q, Tan G, Yang D, Chou CJ et al. Chronic inflammation in fat plays a crucial role in the development of obesity–related insulin resistance. J Clin Invest 2003; 112:1821–1830.  Back to cited text no. 25
    
26.
Lehrke M, Becker A, Greif M, Stark R, Laubender RP, Von Ziegler F, Lebherz C et al. Chemerin is associated with markers of inflammation and components of the metabolic syndrome but does not predict coronary atherosclerosis. Eur J Endocrinol 2009; 161:339–344.  Back to cited text no. 26
    
27.
Weigert J, Neumeier M, Wanninger J, Filarsky M, Bauer S, Wiest R et al. Systemic chemerin is related to inflammation rather than obesity in type 2 diabetes. Clin Endocrinol 2010; 395:106–110.  Back to cited text no. 27
    
28.
Ikizler TA. Resolved: being fat is good for dialysis patients: the Godzilla effect: pro. J Am Soc Nephrol 2008; 19:1059–1062.  Back to cited text no. 28
    
29.
Axelsson J, Rashid Qureshi A, Suliman ME, Honda H, Pecoits-Filho R, Heimburger O et al. Truncal fat mass as a contributor to inflammation in end-stage renal disease. Am J Clin Nutr 2004; 80:1222–1229.  Back to cited text no. 29
    


    Figures

  [Figure 1], [Figure 2]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9], [Table 10], [Table 11]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Patients and methods
Results
Discussion
Conclusion
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed835    
    Printed9    
    Emailed0    
    PDF Downloaded98    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]