Abstract

Background. Circulating chemerin, a novel adipokine linked to obesity, glucose tolerance and hyperlipidaemia, was recently reported to be increased in chronic kidney disease (CKD) patients. We explored possible links between chemerin and various clinical, nutritional and biochemical markers as well as its association with 5-year all-cause mortality.

Methods. Fasting plasma samples were obtained from 252 CKD Stage 5 patients [median age 56 years, male 61%, glomerular filtration rate (GFR) 7 mL/min] enrolled at the initiation of dialysis. Serum chemerin was measured using commercial ELISA. Chemerin levels were related to clinical status and biomarkers of inflammation, glucose and lipid metabolism and body composition (body mass index and total and truncal fat mass by dual-energy X-ray absorptiometry). Survival, censored for transplantation, was recorded for a follow-up time of 5 years.

Results. In univariate regression, circulating chemerin (119 ± 26 ng/mL) was positively correlated with cholesterol (ρ = 0.21; P = 0.001), triglycerides (ρ = 0.22; P = 0.0007), apolipoprotein B (ρ = 0.33; P < 0.0001), high-sensitivity C-reactive protein (ρ = 0.18; P = 0.006), white blood cell count (ρ = 0.23; P < 0.001), insulin (ρ = 0.18; P < 0.05) and homeostatic model assessment (HOMA) index (ρ = 0.17; P < 0.05), whereas we found a negative correlation with GFR (ρ = −0.28; P = 0.007), high-density lipoprotein cholesterol (ρ = −0.15; P < 0.05) and homocysteine (ρ = −0.25; P = 0.001). Moreover, a high chemerin predicted a better survival (log-rank χ2 = 3.85; P < 0.05). Also, in a Cox model, adjustments for age, sex and CRP did not alter this finding (hazard ratio = 1.98 [95% confidence interval = 1.13–3.50], P = 0.01). However, adjusting for GFR made the model non-significant.

Conclusions. We report that, in incident dialysis patients, an elevated chemerin is associated with a survival advantage despite its significant positive association with markers of inflammation and dyslipidaemia.

Introduction

Adipose tissue is not only an energy storage depot but also an active endocrine organ that produces and releases numerous metabolically active peptide hormones—adipokines, chemokines and cytokines. Although peptide hormone levels detected by immunoassays rise significantly in the context of chronic kidney disease (CKD), the consequences of the peptide hormone accumulation remains unclear [1]. Meanwhile, it is well known that a loss of glomerular filtration rate (GFR) leads to a marked state of dysmetabolism comprising insulin resistance and dyslipidaemia with high circulating triacylglycerols, high low-density lipoprotein levels and low high-density lipoprotein (HDL) levels [1], changes that confer an additive risk of cardiovascular disease (CVD) and mortality in CKD [2,3].

In the general population, obesity is associated with low-grade inflammation that is likely the result of an adipose tissue immune reaction and an important contributing factor to cardiovascular complications [4,5]. In CKD patients, increased fat mass is also associated with a state of low-grade inflammation [6], whereas the impact of an increased fat mass on survival remains debated [7,8]. The recent discovery of chemerin as a chemoattractant adipokine linking increased adipose tissue mass to adipose tissue infiltration of immunocompetent cells and inflammation [9,10] suggests a plausible novel pathway linking increased uraemic fat mass with inflammation, insulin resistance and dyslipidaemia. To date, only one study has reported circulating chemerin in a CKD population, finding increased levels in haemodialysis (HD) patients as well as an inverse correlation with residual renal function (RRF) [11]. However, as links between circulating chemerin and inflammation, body composition and metabolism were not investigated, we explored these associations as well as the possibility that chemerin predicted 5-year mortality in an observational cohort study of incident dialysis patients.

Materials and methods

Patients

We analysed a cross-sectional cohort study of CKD Stage 5 patients enrolled at the initiation of renal replacement therapy in the renal programme of the Karolinska University Hospital Huddinge [8]. Informed consent was previously obtained from each subject. Chemerin was analysed in 252 patients aged 56 years (interquartile range to 45–64), male 61% and with a median GFR of 7 mL/min (interquartile range to 5–9). Patients with a GFR <5 mL/min were defined as functionally anuric. Patients younger than 18 years and older than 70 years, those with HIV or hepatitis B/C and/or signs of acute infection, as well as patients unwilling to participate in the study were all excluded. The causes of kidney disease were diabetic nephropathy (31%), chronic glomerular nephritis (20%), polycystic kidney disease (12%), nephrosclerosis (5%) and other (25%) or unknown causes (7%). Clinical history of diabetes mellitus (DM) or CVD (obtained from the patient’s medical records) was documented in 33 or 39% of the patients. Of the diabetic patients, 85% were on subcutaneous insulin, while the rest had oral anti-diabetic or no treatment. The majority of patients were on antihypertensive medications (calcium channel blocker, 49%; beta-blockers, 67%; angiotensin-converting enzyme inhibiter, 68%), 29% of patients were treated with statins, while 23% were prescribed sevelamer hydrochloride. For each patient included, we also obtained outcome data from the time of inclusion until 10 October 2009 or until 5 years of follow-up, whichever came first. Follow-up was censored for transplantation and no patient was lost to follow-up. We divided the 252 patients into two groups by serum chemerin level below or above its 33rd percentile, 128.6 ng/mL.

Clinical assessments

Subjective global assessment (SGA) was used to evaluate signs of overall protein–energy wasting [12]. Body mass index (BMI) was defined as the weight in kilogrammes divided by the square of the height in metres. Body composition was assessed by measuring total fat mass and truncal fat mass by dual-energy X-ray absorptiometry using the DPX-L device (Lunar Corp., Madison, WI). Handgrip strength was measured using a Harpenden Handgrip Dynamometer (Yamar, Jackson, MI) in the dominant hand. Arterial systolic and diastolic blood pressures (BP) were measured three times in the morning after a 15-min resting period, and the mean values used.

Laboratory analyses

Fasting blood samples were collected and stored at −70°C until biochemical analysis. Serum levels of chemerin were measured using commercial ELISA (Millipore, St Charles, MI; intra- and inter-individual CV 5 and 4–6%, respectively). GFR was estimated as the mean of urea and creatinine clearances calculated from 24-h urinary collections. The serum tumour necrosis factor (TNF)-α and insulin levels were measured on an Immulite Automatic Analyser (Siemens Medical Solutions Diagnostics, Deerfield, IL), and HOMA was then computed as: HOMA index = fasting plasma glucose (mmol/L) × fasting plasma insulin (µU/mL)/22.5. Soluble vascular cell adhesion molecule-1 (sVCAM-1) was measured with an ELISA assay from R&D Systems Europe Ltd (Abington, UK), while plasma concentrations of total homocysteine and total cysteine were determined using high-performance liquid chromatography as previously described [13]. Serum albumin (bromcresol purple), high-sensitivity C-reactive protein (hsCRP) (nephelometry) and remaining blood chemistry were analysed with routine procedures at the Clinical Chemistry Laboratory, Karolinska University Hospital Huddinge.

Statistical analysis

All variables were expressed as the mean ± standard deviation or median (interquartile range), as appropriate. Statistical significance was set at the level of P < 0.05. Differences between groups were analysed by one-way ANOVA, followed by a post hoc test if ANOVA was significant. Comparisons between groups were performed using the Wilcoxon rank sum test. Spearman’s rank correlation (ρ) was used to determine correlations between chemerin and other continuous variables. Finally, Kaplan–Meier survival curves, spline curve and multivariate Cox regression analyses were used to assess survival and presented as χ2 and unadjusted and adjusted hazard ratios (HR; 95% confidence intervals [95% CI]). All statistical analyses were performed with the SAS statistical software (Version 9.2, SAS Institute Inc., Cary, NC).

Results

Baseline parameters in chemerin groups

Baseline values for studied parameters are given in Table 1, both for the whole cohort (median age 56 years; males 61%) and in the low–medium or high chemerin tertile groups separately. Chemerin levels were lower in diabetic patients compared to non-diabetics (113 ± 25 vs 122 ± 26 ng/mL, P = 0.01), while GFR was higher in the diabetics (8 ± 3 vs 7 ± 3 mL/min, P = 0.03). Patients defined as anuria had higher chemerin levels compared to those with a GFR >5 mL/min (126 ± 24 vs 117 ± 27 ng/mL, P = 0.02), while patients prescribed sevelamer hydrochloride had lower levels of chemerin compared to the other patients (111 ± 26 vs 122 ± 26 ng/mL, P = 0.004). Chemerin levels were not statistically different between males and females.

Table 1

Clinical and laboratory characteristics of 252 included CKD Stage 5 patients, total and grouped according to chemerin level of 128.6 ng/mL

Characteristic Total CKD Stage 5 patients (n = 252) Low chemerin (n = 167) High chemerin (n = 85) P-value 
Chemerin (ng/mL) 119 ± 26 105 ± 17 147 ± 15 <0.0001 
Clinical status     
 Age (years) 56 (45–64) 57 (43–65) 54 (46–63) ns 
 Sex (%males) 61 62 59 ns 
 GFR (mL/min) 7 (5–9) 8 (6–9) 7 (5–8) 0.018 
Diabetic status     
 DM (%) 33 36 25 0.076 
 Fasting glucose (mmol/L)a 5.4 ± 1.4 5.5 ± 1.6 5.3 ± 0.9 ns 
 Fasting insulin (µU/mL)a 13 (9–19) 12 (9–18) 14 (11–22) 0.016 
 HOMA indexa 2.9 (2.0–4.7) 2.8 (1.8–4.3) 3.2 (2.3–5.2) 0.088 
Nutritional status     
 Wasting (SGA >1) (%) 25 23 27 ns 
 Handgrip strength (kg) 30 ± 12 30 ± 12 19 ± 10 ns 
 BMI (kg/m224.6 ± 4.3 24.2 ± 4.1 25.5 ± 4.7 0.057 
 Body fat mass (kg) 21 ± 10 21 ± 10 22 ± 9 ns 
 Lean body mass (kg) 49 ± 10 50 ± 10 48 ± 11 ns 
 Serum albumin (g/L) 33 ± 6 33 ± 5 32 ± 6 0.038 
 Serum creatinine (mg/dL) 743 ± 251 741 ± 285 746 ± 258 ns 
Blood lipids and adipokines     
 Cholesterol (mmol/L) 4.8 ± 1.3 4.6 ± 1.2 5.1 ± 1.5 0.011 
 Triacylglycerols (mmol/L) 1.9 ± 1.1 1.7 ± 0.9 2.2 ± 1.4 0.017 
 HDL cholesterol (mmol/L) 1.4 ± 0.6 1.4 ± 0.5 1.3 ± 0.6 ns 
 Apolipoprotein A (g/L) 1.3 ± 0.3 1.3 ± 0.3 1.3 ± 0.4 ns 
 Apolipoprotein B (g/L) 0.9 ± 0.3 0.8 ± 0.3 1.0 ± 0.4 0.0001 
 Leptin (ng/mL) 11 (5–27) 8 (4–21) 16 (6–39) 0.003 
Inflammatory biomarkers     
 WBCs (109/L) 8.1 ± 2.9 7.7 ± 2.7 8.7 ± 3.1 0.015 
 Thrombocytes (109/L) 274 ± 107 250 ± 88 323 ± 124 < 0.0001 
 Fibrinogen (g/L) 5.0 ± 1.3 4.9 ± 1.3 5.4 ± 1.4 0.006 
 hsCRP (mg/L) 4.9 (1.7–14) 4.1 (1.2–11.2) 6.7 (1.9–17) 0.04 
 TNF-α (pg/mL) 13 ± 16 14 ± 21 11 ± 6 ns 
Cardiovascular markers     
 CVD (%) 39 39 40 ns 
 Systolic BP (mmHg) 151 ± 22 150 ± 21 153 ± 25 ns 
 Diastolic BP (mmHg) 88 ± 12 87 ± 13 89 ± 12 ns 
 Homocysteine (µmol/L) 29 (21–41) 31 (23–44) 24 (17–36) 0.003 
 Cysteine (µmol/L) 369 ± 122 394 ± 124 333 ± 108 0.002 
 sVCAM-1 (ng/mL) 1375 ± 538 1483 ± 582 1398 ± 416 ns 
Characteristic Total CKD Stage 5 patients (n = 252) Low chemerin (n = 167) High chemerin (n = 85) P-value 
Chemerin (ng/mL) 119 ± 26 105 ± 17 147 ± 15 <0.0001 
Clinical status     
 Age (years) 56 (45–64) 57 (43–65) 54 (46–63) ns 
 Sex (%males) 61 62 59 ns 
 GFR (mL/min) 7 (5–9) 8 (6–9) 7 (5–8) 0.018 
Diabetic status     
 DM (%) 33 36 25 0.076 
 Fasting glucose (mmol/L)a 5.4 ± 1.4 5.5 ± 1.6 5.3 ± 0.9 ns 
 Fasting insulin (µU/mL)a 13 (9–19) 12 (9–18) 14 (11–22) 0.016 
 HOMA indexa 2.9 (2.0–4.7) 2.8 (1.8–4.3) 3.2 (2.3–5.2) 0.088 
Nutritional status     
 Wasting (SGA >1) (%) 25 23 27 ns 
 Handgrip strength (kg) 30 ± 12 30 ± 12 19 ± 10 ns 
 BMI (kg/m224.6 ± 4.3 24.2 ± 4.1 25.5 ± 4.7 0.057 
 Body fat mass (kg) 21 ± 10 21 ± 10 22 ± 9 ns 
 Lean body mass (kg) 49 ± 10 50 ± 10 48 ± 11 ns 
 Serum albumin (g/L) 33 ± 6 33 ± 5 32 ± 6 0.038 
 Serum creatinine (mg/dL) 743 ± 251 741 ± 285 746 ± 258 ns 
Blood lipids and adipokines     
 Cholesterol (mmol/L) 4.8 ± 1.3 4.6 ± 1.2 5.1 ± 1.5 0.011 
 Triacylglycerols (mmol/L) 1.9 ± 1.1 1.7 ± 0.9 2.2 ± 1.4 0.017 
 HDL cholesterol (mmol/L) 1.4 ± 0.6 1.4 ± 0.5 1.3 ± 0.6 ns 
 Apolipoprotein A (g/L) 1.3 ± 0.3 1.3 ± 0.3 1.3 ± 0.4 ns 
 Apolipoprotein B (g/L) 0.9 ± 0.3 0.8 ± 0.3 1.0 ± 0.4 0.0001 
 Leptin (ng/mL) 11 (5–27) 8 (4–21) 16 (6–39) 0.003 
Inflammatory biomarkers     
 WBCs (109/L) 8.1 ± 2.9 7.7 ± 2.7 8.7 ± 3.1 0.015 
 Thrombocytes (109/L) 274 ± 107 250 ± 88 323 ± 124 < 0.0001 
 Fibrinogen (g/L) 5.0 ± 1.3 4.9 ± 1.3 5.4 ± 1.4 0.006 
 hsCRP (mg/L) 4.9 (1.7–14) 4.1 (1.2–11.2) 6.7 (1.9–17) 0.04 
 TNF-α (pg/mL) 13 ± 16 14 ± 21 11 ± 6 ns 
Cardiovascular markers     
 CVD (%) 39 39 40 ns 
 Systolic BP (mmHg) 151 ± 22 150 ± 21 153 ± 25 ns 
 Diastolic BP (mmHg) 88 ± 12 87 ± 13 89 ± 12 ns 
 Homocysteine (µmol/L) 29 (21–41) 31 (23–44) 24 (17–36) 0.003 
 Cysteine (µmol/L) 369 ± 122 394 ± 124 333 ± 108 0.002 
 sVCAM-1 (ng/mL) 1375 ± 538 1483 ± 582 1398 ± 416 ns 

Data presented as the mean ± standard deviation or median (interquartile range). ns, not significant; GFR, glomerular filtration ratio; hsCRP, high-sensitivity C-reactive protein; TNF-α, tumour necrosis factor-alpha; CVD, cardiovascular disease; sVCAM-1, soluble vascular cell adhesion molecule-1.

a

Only in non-diabetic patients (n = 167).

Univariate correlations with chemerin

In Table 2 and Figure 1, univariate correlations between circulating chemerin and investigated parameters are presented. Briefly, chemerin was not correlated with body composition markers or BP, while there were positive correlations with insulin resistance, fasting triglycerides, cholesterol, apolipoprotein B, leptin, CRP, white blood cell (WBC) counts, fibrinogen and thrombocyte counts. Negative associations were found between chemerin with GFR, HDL cholesterol, TNF-α and thiols (homocysteine and cysteine). While we also performed subgroup analysis of univariate correlations only in patients with or without DM, as well as in those with and without RRF, the results were not greatly different from those reported above, and the small number of patients meant that statistical power was a concern. These data are, therefore, not reported further.

Table 2

Spearman rank correlations for selected clinical and laboratory makers with serum chemerin level in 252 CKD Stage 5 patients

Parameter Spearman ρ P-value 
Clinical status   
 Age −0.084 ns 
 GFR −0.276 0.007 
Diabetic statusa   
 Fasting glucose 0.062 ns 
 Fasting insulin 0.184 0.017 
 HOMA index 0.165 <0.05 
Nutritional status   
 Handgrip strength −0.003 ns 
 BMI 0.074 ns 
 Body fat mass 0.028 ns 
 Truncal fat mass 0.012 ns 
 Lean body mass −0.080 ns 
 Serum albumin −0.096 ns 
 Serum creatinine 0.042 ns 
Blood lipids and adipokines   
 Cholesterol 0.206 0.001 
 Triacylglycerols 0.216 0.0007 
 HDL cholesterol −0.153 0.016 
 Apolipoprotein A −0.070 ns 
 Apolipoprotein B 0.334 <0.0001 
 Leptin 0.225 0.002 
Inflammatory biomarkers   
 WBCs 0.228 <0.001 
 Thrombocytes 0.352 <0.0001 
 Fibrinogen 0.184 0.005 
 hsCRP 0.175 0.006 
 TNF-α −0.234 0.003 
Cardiovascular markers   
 Homocysteine −0.249 0.001 
 Cysteine −0.286 0.0002 
 sVCAM-1 −0.198 0.015 
Parameter Spearman ρ P-value 
Clinical status   
 Age −0.084 ns 
 GFR −0.276 0.007 
Diabetic statusa   
 Fasting glucose 0.062 ns 
 Fasting insulin 0.184 0.017 
 HOMA index 0.165 <0.05 
Nutritional status   
 Handgrip strength −0.003 ns 
 BMI 0.074 ns 
 Body fat mass 0.028 ns 
 Truncal fat mass 0.012 ns 
 Lean body mass −0.080 ns 
 Serum albumin −0.096 ns 
 Serum creatinine 0.042 ns 
Blood lipids and adipokines   
 Cholesterol 0.206 0.001 
 Triacylglycerols 0.216 0.0007 
 HDL cholesterol −0.153 0.016 
 Apolipoprotein A −0.070 ns 
 Apolipoprotein B 0.334 <0.0001 
 Leptin 0.225 0.002 
Inflammatory biomarkers   
 WBCs 0.228 <0.001 
 Thrombocytes 0.352 <0.0001 
 Fibrinogen 0.184 0.005 
 hsCRP 0.175 0.006 
 TNF-α −0.234 0.003 
Cardiovascular markers   
 Homocysteine −0.249 0.001 
 Cysteine −0.286 0.0002 
 sVCAM-1 −0.198 0.015 

ns, not significant; GFR, glomerular filtration ratio; hsCRP, high-sensitivity C-reactive protein; TNF-α, tumour necrosis factor-alpha; CVD, cardiovascular disease; sVCAM-1, soluble vascular cell adhesion molecule-1.

a

Only in non-diabetic patients (n = 167).

Fig. 1

Univariate correlations between circulating chemerin levels and (A) GFR and (B) apolipoprotein B (Apo-B) in 252 patients starting dialysis therapy.

Fig. 1

Univariate correlations between circulating chemerin levels and (A) GFR and (B) apolipoprotein B (Apo-B) in 252 patients starting dialysis therapy.

Survival analysis

During a mean follow-up of 28 months (1–70 months), 63 patients (25%) died. Survival analysis showed a significant protective effect for survival with high circulating chemerin levels in a spline curve (Figure 2A), as well as in patients within the high chemerin group in Kaplan–Meier survival analysis (log-rank χ2 = 3.85, P < 0.05) (Figure 2B). This was confirmed in a Cox proportional hazards model (Figure 3) where the low chemerin group was associated with a higher mortality (HR = 1.72 [95% CI = 0.99–2.96], P = 0.05). This significant association remained even after adjustment for age, sex and CRP levels (HR = 1.98 [95% CI = 1.13–3.50], P = 0.01) and tended to remain also after adjustment for CVD and diabetes (HR = 1.75 [95% CI = 0.98–3.15], P = 0.06). However, adjusting for GFR made the model non-significant (HR = 1.33 [95% CI = 0.72–2.47], P = 0.35).

Fig. 2

Survival curves for all-cause mortality among 252 incident dialysis patients according to the chemerin levels (A) by Spline curve for HR with the 95% CI adjusted for age, sex, CVD and inflammation, and (B) by Kaplan–Meier survival curves for high or low chemerin group.

Fig. 2

Survival curves for all-cause mortality among 252 incident dialysis patients according to the chemerin levels (A) by Spline curve for HR with the 95% CI adjusted for age, sex, CVD and inflammation, and (B) by Kaplan–Meier survival curves for high or low chemerin group.

Fig. 3

In crude and adjusted Cox proportional hazards models, using the high chemerin group as reference, a low chemerin levels predicted mortality, also following adjustments for age and sex (Model 1), for CRP levels (Model 2) and for CVD and diabetes (Model 3), but the model became non-significant after adjustment for GFR (Model 4).

Fig. 3

In crude and adjusted Cox proportional hazards models, using the high chemerin group as reference, a low chemerin levels predicted mortality, also following adjustments for age and sex (Model 1), for CRP levels (Model 2) and for CVD and diabetes (Model 3), but the model became non-significant after adjustment for GFR (Model 4).

Discussion

In the first study of the metabolic associations of an elevated circulating chemerin level in the context of uraemia, we demonstrate that high chemerin levels predict a better survival in CKD patients. Furthermore, we report associations between circulating chemerin levels and GFR, insulin resistance, blood lipids and inflammatory markers, but not with body fat.

The main finding of our analysis is the seemingly paradoxical protective effect of a high circulating chemerin in a 5-year follow-up of all-cause mortality. As a high chemerin did not correlate with either higher fat or muscle mass, it is unlikely that the observed protective effect of high chemerin is due to differences in body composition. Interestingly, although patients with a high chemerin had higher hsCRP, the positive predictive value of chemerin did not change following adjustment for inflammation. While multiple mechanisms may explain the complex relationship between chemerin and survival, it is interesting to note that blood lipids including cholesterol, triglycerides and apolipoprotein B were the strongest correlates of chemerin in univariate analysis. A low cholesterol concentration is connected to poor survival in wasting diseases including CKD [14] and uraemic hypertriglyceridaemia has been reported to be associated with increased survival [15], while a high apolipoprotein B/A ratio was recently suggested to be a marker of short-term outcome in incident dialysis patients [16]. Our data confirms the previous report in 60 HD patients [11] of a negative association between chemerin and GFR, including a consistent increase in chemerin levels in patients with anuria, but suggests that, as adjustment for GFR attenuates the positive predictive value of high chemerin levels on survival, RRF is the major determinant of the link between chemerin and outcome in CKD.

Just as its namesake, the mythological multi-headed beast, chemerin has a dual nature as an adipokine and a chemokine. While chemerin was initially discovered in human inflammatory fluids [17] and reported as a proinflammatory molecule [18], later studies revealed the existence of an anti-inflammatory chemerin cleavage product, chemerin 15 [19]. Likewise, chemerin was recently identified as a novel adipokine regulating adipogenesis and enhancing insulin signalling in fat [10,20,21], but later studies reported that chemerin may also induce insulin resistance in human skeletal muscle cells [22]. This duality may be the explanation for the differences in association between chemerin with various markers of inflammation in the present study. Like most previous studies [23,24], we found a positive correlation between chemerin and hsCRP, as well as the other investigated acute-phase reactants fibrinogen, thrombocytes and WBCs. However, chemerin was negatively associated with the locally acting proinflammatory cytokine TNF-α, which has been suggested to interact biologically with the chemerin receptor (ChemR23) [25]. In the present study, chemerin was also negatively associated with the plasma thiols, homocysteine and cysteine. As low plasma thiols predict a poor survival in CKD [26,27], and decreased metabolism of thiol compounds has been reported in dyslipidaemic albeit non-uraemic patients [28], our findings regarding cholesterol, triglycerides and apolipoprotein B as well as thiols suggest a possible link between chemerin and lipid dysmetabolism in our patients.

It is also noteworthy that our study did not show any correlations between circulating chemerin and multiple markers of body composition, contrary to earlier reports in other patient populations [29,30], and that the use of sevelamer hydrochloride, a novel phosphate binder with putative anti-diabetic effects, was associated with lower chemerin levels. The lack of association between chemerin and body composition is made more complex by the strong associations between leptin and chemerin in our study. However, as recent data have suggested that leptin is almost exclusively cleared from the circulation by the kidney [31], reduced GFR may link these two peptides as well as obscure any relationship to body fat.

A number of limitations of the present study should be acknowledged. First, this is a cross-sectional study of a relatively small number of patients and cause–effect relationships could not be determined. Secondly, we did not measure other markers of chemerin signalling, such as chemerin 15 and the chemerin receptors, nor did we study functional differences between tissues other than blood or intracellular pathways. Our study should, therefore, be regarded as hypothesis generating in nature. As such, it encourages further study of the role of adipose tissue inflammation in metabolic complications of CKD, especially dyslipidaemia.

Conclusion

In summary, we report that an elevated chemerin appears to have a protective effect on survival in incident dialysis patients, either directly or through its association with other factors. As with the many other association studies showing links between circulating proteins and outcomes, mechanistic studies will now be needed to evaluate the possible therapeutic and predictive value of these findings.

We would like to thank the patients and personnel involved in the creation of this cohort. Especially, we thank our research staff at KBC (Annika Nilsson, Ann-Kristin Emmot and Ulrika Jensen) and KFC (Monica Eriksson and Ann-Christin Bragfors-Helin). The authors were supported by grants from the Uehara Memorial Foundation (T.Y.), the Swedish Society of Medical Research (J.A.) and Sanofi-Aventis (J.A.). We also benefited from a grant from Baxter Healthcare Inc. (Deerfield, IL, USA) to the Karolinska Institutet.

Conflict of interest statement. B.L. is an employee of Baxter Healthcare Inc. P.S. is a member of the scientific advisory board of Gambro AB. J.A. is the recipient of honoraria from Baxter and a research grant from Sanofi-Aventis. None of the other authors have any conflicts of interest to declare.

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