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Serum Fetuin-A Levels Are Associated with Vascular Calcifications and Predict Cardiovascular Events in Renal Transplant Recipients Céline Marećhal,* Georg Schlieper,‡ Pauline Nguyen,* Thilo Kru¨ger,‡ Emmanuel Coche,† Annie Robert,§ Jorgen Floege,‡ Eric Goffin,* Michel Jadoul,* and Olivier Devuyst* *Division of

Summary Nephrology and Background and objectives Vascular calcifications predict cardiovascular disease, the major cause of death in †Department of Radiology, Cliniques renal transplant recipients (RTRs). We studied the determinants of fetuin-A, a potent circulating calcificaUniversitaires Saint-Luc, tion inhibitor encoded by the AHSG gene, and tested its association with vascular calcifications and longUniversite´ catholique term survival and cardiovascular events (CVEs) in RTRs. de Louvain Medical Design, setting, participants, & measurements Two hundred seventy-seven prevalent RTRs from a single center were included. CVEs and deaths were prospectively recorded during a 5-year follow-up. Results Independent determinants of lower serum fetuin-A levels were lower plasma cholesterol, the AHSG rs4918 G allele, and history of smoking. Low serum fetuin-A level was a determinant of aortic calcifications (assessed using spiral CT). Low fetuin-A levels (ⱕ0.47 g/L, first quintile) were independently associated with CVEs and deaths (hazard ratio ⫽ 1.83; 95% confidence interval, 1.07 to 3.04). The association was confirmed for all-cause mortality, and the major adverse cardiovascular endpoints were analyzed separately. Patients with low fetuin-A and high high-sensitivity C-reactive protein (⬎4.36 mg/L, fourth quintile) levels had a 3.5-fold increased risk of all-cause mortality and CVEs. In the presence of inflammation, CVE-free survival was influenced by common variants in the AHSG gene. Conclusions These data show that low fetuin-A levels are independently associated with aortic calcifications and a higher risk of CVEs and mortality. They support fetuin-A as a circulating biomarker able to identify RTRs at risk for vascular calcifications and CVEs. Clin J Am Soc Nephrol 6: 974 –985, 2011. doi: 10.2215/CJN.06150710

Introduction Cardiovascular disease is the leading cause of premature death in renal transplant recipients (RTRs), with a 3.5 to 5% annual risk of fatal or nonfatal event (1,2). A high prevalence of vascular calcifications (VCs) has been shown in patients with chronic kidney disease (CKD) and in RTRs, with higher calcification scores than in age- and gender-matched nonrenal patients with coronary heart disease (3–9). The VCs are strong predictors of cardiovascular disease and all-cause mortality in hemodialysis and peritoneal dialysis patients (10 –12) but also in RTRs (13). Various mechanisms are involved in the high propensity of CKD patients to develop VCs, in addition to classical risk factors for atherosclerosis. These factors include systemic and local inflammation, oxidative stress, uremic toxins, and advanced glycation endproducts, resulting in osteogenic conversion and/or apoptosis of vascular smooth muscle cells (VSMCs). In this context, both living and death VSMCs stimulate the mineralization process in the 974

Copyright © 2011 by the American Society of Nephrology

arterial wall of CKD vessels by complex mechanisms (14,15). Recent evidence, however, suggests that the VC process is counterbalanced, in vivo, by circulating or local inhibitors that include fetuin-A, matrix Gla protein, osteoprotegerin, and inorganic pyrophosphate (16,17). Fetuin-A, also known as ␣2-Heremans Schmid glycoprotein (AHSG), is a glycoprotein encoded by the AHSG gene predominantly expressed in the liver (18,19). Serum fetuin-A acts as a negative acute phase reactant, being thus down-regulated in systemic inflammation (19). Fetuin-A knockout mice showed mild, soft tissue calcifications at baseline and developed severe calcifications of vital organs when crossed to the calcification-sensitive DBA/2 strain or fed on a mineral and vitamin D–rich diet (20). Furthermore, fetuin-A knockout mice do not exhibit VCs, unless there is damage of the vascular wall, as in ApoE and fetuin-A double knockout mice (21). The inhibitory role of fetuin-A on VCs may include inhibition of calcium phosphate precipitation in the se-

School, Brussels, Belgium; ‡Division of Nephrology, RWTH University Hospital Aachen, Aachen, Germany; and § Department of Epidemiology and biostatistics, Ecole de Sante´ Publique, Universite´ catholique de Louvain, Brussels, Belgium Correspondence: Dr. O. Devuyst, Division of Nephrology, Cliniques Universitaires Saint-Luc, UCL Medical School, 10 Avenue Hippocrate, B1200 Brussels, Belgium. Phone: 32-2-764-5450; Fax: 32-2-764-5455; E-mail: olivier.devuyst@ uclouvain.be

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Clin J Am Soc Nephrol 6: 974 –985, May, 2011

rum (20), binding of the bone-derived hydroxyapatite (22), and limitation of dedifferentiation and apoptosis of the VSMCs (23). Serum fetuin-A levels are lower in CKD patients than in healthy controls, possibly because of lowgrade inflammation as suggested by the negative correlation with C-reactive protein (CRP) level (24). Several studies have documented an inverse association between serum fetuin-A levels and survival of dialysis patients (24 –27). These studies yielded discordant information about the importance of inflammation for the association of fetuin-A and mortality. The discrepancy could reflect variable inflammation parameters and the possibility that fetuin-A levels may be determined by inflammation-independent mechanisms, including genetic factors. Of interest, polymorphisms in the AHSG gene impact on fetuin-A level in dialysis patients (25). In this study, we investigated the determinants of circulating fetuin-A levels, including variants in the ASHG gene, their association with VCs, and the occurrence of death and cardiovascular events (CVEs) in a cohort of 277 RTRs followed for 5 years.

Materials and Methods Patients The prevalent Brussels Renal Transplant Cohort was initiated from February 3, 2004 to January 27, 2005. All RTRs with a functional graft attending the outpatient clinic of the Saint-Luc Academic Hospital (UCL, Brussels) for their annual or bi-annual in-depth control were asked to enter the study. The protocol was approved by the Ethics Committee of the UCL Medical School, and written informed consent was obtained from all patients. Exclusion criteria were age under 18, residing abroad, or being a recipient of a multiorgan transplant (28). Clinical and Biologic Parameters Demographic, clinical, biologic, and medical history parameters including history of CVEs (defined as myocardial, cerebrovascular, or lower limb necrosis or revascularization or documented transient ischemic attack) (29) were recorded and measured as described previously (28). Medical charts were reviewed by a single investigator; blood sampling and all other investigations were obtained on the day of inclusion. Serum analysis for high-sensitivity CRP (hsCRP) was performed by immunonephelometry using a standard (Dade Behring Holding, Liederbach, Germany). Serum fetuin-A level was measured by nephelometry as described previously (20). Fetuin-A level was obtained in 277 patients. The 277 RTRs underwent chest multislice spiral CT on a 16-slice scanner (16 Brilliance Power; Philips Medical systems, Cleveland, OH) at the time of inclusion. Individual scanning of thoracic aorta and the four branches of main coronary arteries was done as described previously (28). Follow-Up and Endpoints All of the patients included (100%) were followed until April 30, 2010, with a mean follow-up of 4.9 ⫾ 1.6 years. The occurrence of new CVEs and deaths was determined during this period, on the basis of hospital medical charts and systematic telephone contact with each referring neph-

Fetuin-A and CVE in RTR, Marećhal et al. 975

rologist. Endpoints were all-cause mortality and the onset of any CVE. Patients with graft rejection (n ⫽ 21, 7.6%) were not censored during the follow-up. Genotyping Nine single nucleotide polymorphisms (SNPs) in AHSG were chosen from the public database HapMap (http:// hapmap.ncbi.nlm.nih.gov) to cover the entire gene. On the basis of the linkage disequilibrium (LD), four SNPs capturing the common genetic variation were selected (rs2248690, rs4831, rs2070635, and rs4918; Figure 1). The allele and genotype frequencies for each SNP were tested for departure from Hardy-Weinberg equilibrium using a ␹2 test. We also compared the allele frequencies in our cohort with those of a white population in the National Center for Biotechnology Information public database (http:// www.ncbi.nlm.nih.gov/projects/SNP/snp_gf.cgi). Estimates of LD between SNPs were determined by calculating D⬘ and r2 statistics. Haplotype analysis was performed using the maximum likelihood method to estimate haplotype frequencies and haplotype mean effect for the serum fetuin-A level. The LD measure and the haplotype analyses were realized with PHASE v2.1, Haploview v4.1, and THESIAS v3.1. Total DNA was extracted from peripheral blood leukocytes (Gentra Systems Puregene, Minneapolis, MN). The primer pairs were designed using primer3 Software (Supplemental Table 1) to amplify by PCR the selected SNPs in the promoter (rs2248690), exon 1 (rs4831), intron 4 (rs2070635), and exon 7 (rs4918) of the AHSG gene (Figure 1). Statistical Analyses Results are presented as means ⫾ SD or n (%) as appropriate. Variables presenting a right skewed distribution were log-transformed. Univariate analysis was performed using Pearson’s cross product correlation, t test, and ␹2 test when applicable. All variables reaching the P ⬍ 0.2 level in univariate analysis entered the multivariate models. Multiple stepwise linear regression was used to assess significant determinants of fetuin-A level. All determinants of coronary artery calcifications (CACs) and aortic calcifications (AOCs) entered the multivariate models (28). Multivariate analysis under a hierarchically well-formulated model strategy with interaction and confounding assessment was performed. Survival analysis was made according to Kaplan-Meier curves, which were compared by the Mantel (log-rank) test or the Cox proportional hazards model, which calculates hazard ratios and their 95% confidence intervals. To find factors influencing survival, Cox univariate analysis was first performed with different parameters. Factors with a P ⬍ 0.2 in univariate analysis were entered in a forward/backward selection procedure based on the likelihood ratio test to select variables to enter in the Cox multivariate analysis. We divided the cohort into quintiles for fetuin-A and hsCRP to identify the threshold level at which risk increases (Supplemental Figures 1 and 2) and to define the cut-off values for both parameters. The first quintile was used to define a low fetuin-A level (ⱕ0.47 g/L), whereas the fourth quintile was used as a cut-off (⬎4.36 mg/L) for high hsCRP levels.

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Clinical Journal of the American Society of Nephrology

Figure 1. | Structure of the AHSG gene, polymorphisms, and haplotype analysis. (A) Structure of AHSG gene, with black boxes representing exons 1 to 7 and shaded regions representing the 5ⴕ- and 3ⴕ-untranslated regions. The genomic location of the nine SNPs covering the gene is indicated. (B) Linkage disequilibrium between the nine SNPs covering AHSG and selection of the four SNPs capturing the common genetic variation (rs2248690, rs4831, rs2070635, and rs4918). (C) Allele frequencies of the AHSG polymorphisms in the RTR cohort and the general population. HWE, Hardy-Weinberg equilibrium. *National Center for Biotechnology Information public data.

All statistical analyses were performed using SPSS 15.0 software. All tests were two-tailed, and P ⬍ 0.05 was considered as significant.

Results Characteristics of the Patients The demographic characteristics of the cohort were reported earlier (28). The 277 patients were 98% white, 61% male, and 53.0 ⫾ 12.8 years of age, with a time of transplantation of 7.8 ⫾ 6.5 years (Table 1). Drugs at inclusion included azathioprine in 29% (n ⫽ 81), mycophenolate mofetil in 43% (n ⫽ 119), cyclosporine in 49% (n ⫽ 136), tacrolimus in 41% (n ⫽ 113), and sirolimus in 8% (n ⫽ 21). Thirty patients (11%) were taking erythropoietin, 24% (n ⫽ 66) were taking vitamin D, and 39% (n ⫽ 108) were taking calcium supplements. A majority of patients (59%, n ⫽ 164) were taking angiotensin converting enzyme inhibitors and/or angiotensin receptor blockers as anti-hypertensive treatment (Table 1). The cause of ESRD was chronic glomerulonephritis (n ⫽ 95, 34%), chronic interstitial nephropathy (n ⫽ 80, 29%), polycystic kidney disease (n ⫽ 52, 19%), nephrosclerosis (n ⫽ 14, 5.0%), diabetic nephropathy (n ⫽ 11, 4.0%), and others/unknown (n ⫽ 25, 9.0%).

Determinants of Serum Fetuin-A Level Serum fetuin-A level, at the time of inclusion, averaged 0.58 ⫾ 0.13 g/L and showed a normal distribution in the 277 patients. It was positively correlated with plasma triglyceride (P ⫽ 0.012), diastolic BP (P ⫽ 0.029), and total cholesterol (P ⬍ 0.0001) levels (Table 1). There was no correlation between fetuin-A level and the duration of transplantation, the immunosupressive regimen and hypertensive drugs, erythropoietin, vitamin D, use of calcium, and inflammation markers including hsCRP, albumin, homocysteine, and fibrinogen. We next studied the potential influence of AHSG gene variation on serum fetuin-A level in this cohort. On the basis of LD, four SNPs capturing the common genetic variation in AHSG were selected (rs2248690, rs4831, rs2070635, and rs4918; see Figure 1). The genotype frequencies were consistent with those expected under HardyWeinberg equilibrium and the genotype distribution was similar to that in the European/white population (Figure 1). The serum fetuin-A level was influenced by the rs2248690, rs2070635, and rs4918 variants of AHSG, with a dose-dependent effect depending on the number of alleles (Figure 2). About 7% of the variability of fetuin-A



Table 1. Characteristics of renal transplant recipients and their univariate association with serum fetuin-A levels

Variables (n ⫽ 277) Demographics and comorbidity age (years) male gender (n ⫽ 170) history of smoking (n ⫽ 147) current smoking (n ⫽ 38) body mass index (kg/m2) diabetes (n ⫽ 41) history of CVE (n ⫽ 87) history of parathyroidectomy (n ⫽ 40) Physical examination systolic BP (mmHg) diastolic BP (mmHg) pulse pressure (mmHg) Drugs use of statin (n ⫽ 110) use of AVK (n ⫽ 18) use of prednisolone (n ⫽ 264) use of tacrolimus (n ⫽ 113) use of cyclosporine (n ⫽ 136) use of azathioprine (n ⫽ 81) use of sirolimus (n ⫽ 21) use of MMF (n ⫽ 119) time under MMF (years) EPO (n ⫽ 30) vitamin D (n ⫽ 66) calcium (n ⫽ 108) ACE-I and/or ARB (n ⫽ 164) loop diuretics (n ⫽ 52) ␤-blockers (n ⫽ 100) calcium channel blockers (n ⫽ 95) Kidney function and RRT characteristics MDRD (ml/min per 1.73 m2) time on dialysis (years) time of transplantation (months) creatinine (mg/dl) multiple TP (n ⫽ 24) Biological markers glucose (mg/dl) hsCRP (mg/L) albumin (g/dl) GOT (IU/L) GPT (IU/L) fibrinogen homocysteine (␮mol/L) total cholesterol (mg/dl) HDL cholesterol (mg/dl) triglycerides (mg/dl) 25(OH)vitD3 (ng/ml) 1–25(OH)vitD3 (pg/ml) PTH (pg/ml)

Mean ⫾ SD or %

r with Fetuin-A

53 ⫾ 13 61 53 14 26 ⫾ 5 15 31 14

⫺0.07

136 ⫾ 21 82 ⫾ 12 54 ⫾ 16

0.06 0.13 ⫺0.03

0.342 0.029 0.657

40 7 95 41 49 29 8 43 2⫾3 11 24 39 59 19 36 34

⫺0.03

0.260 0.682 0.220 0.192 0.503 0.056 0.163 0.366 0.607 0.205 0.389 0.980 0.089 0.197 0.842 0.554

51 ⫾ 20 2⫾2 92.8 ⫾ 77.8 1.7 ⫾ 0.8 9

0.08 0.00 0.00 ⫺0.10

0.204 0.921 0.970 0.092 0.958

101 ⫾ 36 3.6 ⫾ 7.3 4 ⫾ 0.3 26 ⫾ 22 26 ⫾ 35 324 ⫾ 79 16 ⫾ 6 203 ⫾ 43 59 ⫾ 18 150 ⫾ 198 17 ⫾ 10 34 ⫾ 17 56 ⫾ 46

0.02 ⫺0.05 0.08 0.09 0.07 0.06 ⫺0.01 0.42 0.03 0.16 ⫺0.12 0.09 ⫺0.05

0.785 0.625 0.187 0.124 0.241 0.376 0.869 ⬍0.0001 0.667 0.012 0.051 0.161 0.781

levels is accounted for by the rs4918 variant. Five haplotypes of AHSG had a frequency ⬎5%, with the ACGC haplotype (A-rs2248690, C-rs4831, G-rs2070635, and C-rs4918) being the most frequent (Table 2). The haplotype carrying the T allele of rs224869, the A allele of rs2070635, and the G allele of rs4918 (256Ser) had the most significant effect to lower the expected serum fetuin-A level (P ⫽ 0.00005; Table 2). The inclusion of all variables with P ⬍ 0.2 (gender, history and current smoking, diastolic BP, use of tacrolimus, azathioprine, and sirolimus, creatinine, total cholesterol, triglyceride,

0.06

P 0.237 0.055 0.062 0.132 0.304 0.745 0.937 0.524

albumin, glutamate oxalacetate transaminase, 25[OH] and 1,25[OH] vitamin D3, angiotensin converting enzyme inhibitor and/or angiotensin receptor blocker treatment, rs2248690 T allele, rs2070635 A allele, rs4918 G allele, and the TCAG haplotype) in a stepwise linear regression analysis showed that the independent determinants of lower serum fetuin-A levels were a lower level of cholesterol, the AHSG rs4918 G allele, and a history of smoking (Table 3). It must be noted that the robust correlation between serum fetuin-A levels and total cholesterol levels was not influenced by the administration of statins in this cohort (Figure 3).

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Table 1. (Continued)

Variables (n ⫽ 277)

Mean ⫾ SD or %

r with Fetuin-A

P

10 ⫾ 1 3⫾1

0.03 ⫺0.05

0.596 0.440

calcium (mg/dl) phosphate (mg/dl)

Results are presented as means ⫾ SD (units) or % (n) as appropriate. Variables presenting a right skewed distribution were logtransformed. Univariate analysis was performed using Pearson’s cross product correlation and t test. AVK, anti-vitamin K; MMF, mycophenolate mofetil; EPO, erythropoietin; ACE-I, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blockers; MDRD, Modification of Diet in Renal Disease; TP, transplantation; GOT, glutamate oxalacetate transaminase; GPT, glutamate pyruvate transaminase; PTH, parathyroid hormone.

Influence of Serum Fetuin-A Level on VCs The potential influence of serum fetuin-A on VCs in RTRs was next addressed. By univariate analysis, serum fetuin-A levels negatively correlated with CAC (r ⫽ ⫺0.12; P ⫽ 0.045) and AOC (r ⫽ ⫺0.15; P ⫽ 0.011). All determinants of CAC (age, history of CVEs, time on dialysis, gender, current use of statins, multiple transplantation, diabetes duration, history of smoking, and history of parathyroidectomy), and AOC (age, history of CVEs, time on dialysis, history of smoking, pulse pressure, total time under mycophenolate mofetil, multiple transplantation, and current use of anti-vitamin K) (28) were entered into a multivariate analysis. Multiple linear regression (Table 4) showed that lower serum fetuin-A level was an indepen-

dent predictor of higher AOC (P ⫽ 0.008) but not of CAC (P ⫽ 0.384). It must be noted that the inclusion of the fetuin-A determinant improved the model for AOC (F change ⫽ 7.13, P ⫽ 0.008), confirming the predictive value of serum fetuin-A level on that parameter.

Serum Fetuin-A Level and CVEs and deaths Forty-one RTRs (14.8%) died during follow-up (4.9 ⫾ 1.6 years), and 37 patients (13.4%) had a nonfatal CVE (Supplemental Table 2). Patients with a CVE during follow-up (n ⫽ 53) were older, more frequently men, had a more frequent history of CVEs and of diabetes, had a higher systolic BP and pulse pressure, were more frequently using

Figure 2. | Influence of AHSG variants on serum fetuin-A levels. The variants rs4918 (exon 7), rs2248690 (promoter), and rs2070635 (intron 4) exert a dose-dependent effect on serum fetuin-A levels in the cohort.



Table 2. Frequency of the AHSG gene haplotypes and effect of the main haplotypes on serum fetuin-A levels

Haplotypes

Frequency (%)

Expected FetuinA Mean

Haplotype Additive Effect (95% Confidence Interval)

P

ACGC TCAG AGAC ACAG ACAC

43.4 21.0 15.8 9.7 7.8

0.31 (0.30 to 0.33) 0.25 (0.23 to 0.28) 0.27 (0.24 to 0.30) 0.25 (0.21 to 0.28) 0.35 (0.32 to 0.38)

⫺0.06 (⫺0.09 to ⫺0.03) ⫺0.04 (⫺0.08 to ⫺0.004) ⫺0.06 (⫺0.10 to ⫺0.03) 0.04 (0.0003 to 0.08)

0.00005 0.03 0.001 0.05

Likelihood-ratio test: ␹2 ⫽ 35.12, df ⫽ 4, P ⫽ 0.0001. Haplotype order: rs2248690 (A/T), rs4831(C/G), rs2070635 (A/G), rs4918 (C/ G). The haplotype analysis was performed using the maximum likelihood method to estimate haplotype frequencies and haplotype mean effect for the serum fetuin-A level.

Table 3. Determinants of serum fetuin-A levels in renal transplant recipients

Step Number

Factors

B

95% Confidence Interval

P

1 2 3

Total cholesterol rs4918 G allele History of smoking

0.41 ⫺0.25 ⫺0.15

0.44 to 0.75 ⫺0.10 to ⫺0.04 ⫺0.07 to ⫺0.01

⬍0.0001 ⬍0.0001 0.006

R2 ⫽ 0.255; F ⫽ 30.48; P ⬍ 0.0001. All significant variables in univariate analysis (P ⬍ 0.2) entered the multivariate models. Multiple stepwise linear regression model was used to assess significant determinants of fetuin-A.

Figure 3. | Relationship between serum fetuin-A level and total plasma cholesterol. (A) Relationship between quartiles of serum fetuin-A levels and total plasma cholesterol levels. (B) Correlation between serum fetuin-A and total cholesterol according to the use of statin.

loop diuretics, had higher levels of hsCRP, homocysteine, parathyroid hormone, and had lower levels of 25(OH)vitamin D3 (Table 5). Using Kaplan-Meier survival curves, we assessed the association between serum fetuin-A levels and survival or occurrence of CVEs in this cohort. All-cause mortality and occurrence of CVEs were significantly increased (log rank ⫽ 6.09; P ⫽ 0.014) in RTRs with low serum fetuin-A levels (ⱕ0.47 g/L, first quintile; Figure 4). Multivariate Cox regression showed that low levels of fe-

tuin-A were an independent predictor of all-cause mortality and occurrence of CVEs after adjustments for history of CVEs, use of anti-vitamin K, homocysteine, hsCRP, CACs, and systolic BP (Supplemental Table 3). The predictive power of low fetuin-A levels was confirmed when analyzing cardiovascular mortality and CVEs (53 events) and all-cause mortality (41 events). We next evaluated whether the risk conferred by low fetuin-A level could be modulated by inflammation. Because of the statistical significance (P ⬍ 0.001) of the inter-

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Table 4. Determinants of aortic calcifications in renal transplant recipients

B (95% Confidence Interval)

P

⫺0.89 (⫺1.55 to ⫺0.24) 0.06 (0.05 to 0.07) 0.44 (0.23 to 0.65) 0.07 (0.03 to 0.11) 0.37 (0.19 to 0.54) 0.01 (0.00 to 0.01) ⫺0.04 (⫺0.08 to ⫺0.01) 0.41 (0.10 to 0.71) 0.41 (0.05 to 0.78)

0.008 ⬍0.0001 ⬍0.0001 ⬍0.0001 ⬍0.0001 0.021 0.020 0.009 0.026

Fetuin-A (g/L) Age (years) History of CVE Time on dialysis (years) History of smoking Pulse pressure (mmHg) Total time under MMF (years) Multiple TP Current use of AVK

R2 ⫽ 0.665, F ⫽ 58.837, P ⬍ 0.0001. All determinants aortic calcifications (AOC) entered the multivariate models. Multivariate analysis under a hierarchically well-formulated model strategy with interaction and confounding assessment was performed. MMF, mycophenolate mofetil; TP, transplantation; AVK, anti-vitamin K.

Figure 4. | Kaplan-Meier estimates for long-term influence of fetuin-A level on all-cause mortality and CVEs.

action between CRP and fetuin-A to predict the outcomes, we decided to stratify the results. After stratification for hsCRP levels, patients with lower fetuin-A levels (ⱕ0.47 g/L) and with higher hsCRP levels (⬎4.36 mg/L, fourth quintile) had worse survival (log rank ⫽ 14.10, P ⫽ 0.003; Figure 5), with a risk of death and CVEs 3.48 times higher compared with patients with higher fetuin-A and lower hsCRP (Supplemental Table 4). Although the AHSG variants influence fetuin-A levels, alone they had no influence on all-cause mortality and CVEs in this cohort. However, the association of the deleterious rs4918 G allele with higher hsCRP level was significantly associated (log rank ⫽ 11.37, P ⫽ 0.010) with more CVEs and a worse survival (Figure 6). The influence of AHSG and hsCRP on CVE-free survival was also shown for the rs2248690 T

allele (log rank ⫽ 9.09, P ⫽ 0.028) and for the TCAG haplotype (log rank ⫽ 8.98, P ⫽ 0.030).

Discussion To the best of our knowledge, this study is the first to show that low serum fetuin-A levels are independently associated with AOC and predict a higher risk of long-term CVEs and deaths in RTRs. The risk conferred by low fetuin-A is modulated by inflammation. We also showed that fetuin-A levels are determined by a common haplotype of the AHSG gene, as well as by low plasma cholesterol and a history of smoking. In the presence of inflammation, the CVE-free survival is influenced by common variants in AHSG. The strengths of our study include a prospective study design, a 5-year follow-up, the use of



Table 5. Comparison between patients with CVE and without CVE during follow-up

Variables (n ⫽ 277) Demographics and comorbidity age (years) male gender history of smoking current smoking body mass index (kg/m2) diabetes history of CVE history of parathyroidectomy Physical examination systolic BP (mmHg) diastolic BP (mmHg) pulse pressure (mmHg) Drugs use of statin use of AVK use of prednisolone use of tacrolimus use of cyclosporine use of azathioprine use of sirolimus use of MMF EPO vitamin D calcium ACE-I and/or ARB loop diuretics ␤-blockers calcium channel blockers Kidney function and RRT characteristics MDRD (ml/min per 1.73 m2) time on dialysis (years) time of transplantation (years) creatinine (mg/dl) multiple TP Biological markers glucose (mg/dl) hsCRP (mg/L) albumin (g/dl) GOT (IU/L) GPT (IU/L) fibrinogen homocysteine (␮mol/L) total cholesterol (mg/dl) HDL cholesterol (mg/dl) triglycerides (mg/dl) 25(OH)vitD3 (ng/ml) 1–25(OH)2vitD3 (pg/ml) PTH (pg/ml) calcium (mg/dl)

No CVEs (n ⫽ 224) (Mean ⫾ SD or %)

CVEs (n ⫽ 53) (Mean ⫾ SD or %)

P

51 ⫾ 13 55 51 13 26 ⫾ 5 13 24 13

59 ⫾ 11 87 62 17 27 ⫾ 5 25 64 21

⬍0.001 ⬍0.001 0.168 0.505 0.103 0.033 ⬍0.001 0.117

134 ⫾ 19 82 ⫾ 12 52 ⫾ 16

145 ⫾ 23 83 ⫾ 16 62 ⫾ 16

⬍0.001 0.560 ⬍0.001

37 5 95 39 50 31 7 43 11 25 40 59 16 35 34

51 11 93 49 43 23 9 43 11 17 34 59 32 40 38

0.085 0.124 0.275 0.214 0.365 0.314 0.567 0.943 0.898 0.214 0.404 0.906 0.010 0.634 0.630

52 ⫾ 19 2⫾2 7.8 ⫾ 6.4 1.6 ⫾ 0.8 9

49 ⫾ 21 3⫾3 7.5 ⫾ 6.7 1.8 ⫾ 0.8 6

0.268 0.379 0.731 0.064 0.653

101 ⫾ 37 29 ⫾ 4.9 4 ⫾ 0.3 27 ⫾ 24 27 ⫾ 38 323 ⫾ 78 16 ⫾ 4 204 ⫾ 43 60 ⫾ 17 153 ⫾ 219 18 ⫾ 10 34 ⫾ 18 51 ⫾ 42 10 ⫾ 1

104 ⫾ 34 6.6 ⫾ 13.0 4 ⫾ 0.4 24 ⫾ 11 23 ⫾ 17 329 ⫾ 82 19 ⫾ 9 201 ⫾ 42 59 ⫾ 20 135 ⫾ 72 14 ⫾ 7 32 ⫾ 15 75 ⫾ 42 9⫾1

0.350 0.037 0.584 0.514 0.891 0.674 0.003 0.562 0.529 0.988 0.012 0.446 0.001 0.462

CT-based calcium mass score, and the comprehensive data on study participants allowing for adjustment for known risk factors (including lead time of dialysis, duration of transplantation, and residual renal function). Our first aim was to identity the determinants of serum fetuin-A levels in RTRs. Based on similar immunoenzymometric assays, the mean serum fetuin-A level in this cohort (0.58 g/L) is in the same range as values reported in hemodialysis and peritoneal dialysis patients (24,27).

These values are slightly lower (24) or similar (25) than in healthy controls. The fact that serum fetuin-A levels are similar in dialysis patients and RTRs in our cohort support recent observations made in transplanted children (30) and adults (31). Our results showed that circulating fetuin-A levels are determined by variants in the ASHG gene, plasma cholesterol levels, and a history of smoking, independently of inflammation. It must be noted that plasma cholesterol levels and smoking history did not change

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Table 5. (Continued)

Variables (n ⫽ 277) phosphate (mg/dl) fetuin-A

No CVEs (n ⫽ 224) (Mean ⫾ SD or %)

CVEs (n ⫽ 53) (Mean ⫾ SD or %)

P

3⫾1 0.59 ⫾ 0.13

3⫾1 0.56 ⫾ 0.15

0.880 0.175

Results are presented as means ⫾ SD (units) or % (n) as appropriate. Variables presenting a right skewed distribution were logtransformed. Univariate analysis was performed using ␹2 Pearson and t test. AVK, anti-vitamin K; MMF, mycophenolate mofetil; EPO, erythropoietin; ACE-I, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blockers; MDRD, Modification of Diet in Renal Disease; TP, transplantation; GOT, glutamate oxalacetate transaminase; GPT, glutamate pyruvate transaminase; PTH, parathyroid hormone.

significantly over time in this cohort. These data suggest that a gene– environment interaction regulates circulating fetuin-A levels in this population. The influence of the rs4918 variant (Thr256Ser) of the AHSG gene on circulating fetuin-A levels was first evidenced in ESRD patients (25). We confirm and extend this observation in a larger cohort of RTRs, based on the influence of several variants and a common haplotype. In particular, the G allele of the rs4918 variant (frequency, 0.31) accounts for approximately 7% of the variability of fetuin-A levels and remains independently associated with this parameter after adjustment for multiple factors. The positive correlation between circulating fetuin-A and cholesterol levels in this cohort was also observed in a cohort of 222 prevalent hemodialysis patients (32) and supports the link between fetuin-A and dyslipidemia (33,34). Possible factors accounting for this correlation include regular physical activity, which participates in the maintenance of low levels of circulating fetuin-A together with lower plasma cholesterol and enhanced insulin sensitivity (35), nutritional status (25), weight control, and amount of liver fat (36,37). The negative correlation between fetuin-A levels and history of smoking, which has not been reported before, may reflect the consequences of tobacco smoking on liver function, physical activity, and/or weight loss (38). Although fetuin-A acts as a negative acute phase reactant, there is no correlation between fetuin-A levels and inflammation markers in this cohort. The latter observation confirms that genetic factors can determine fetuin-A levels independently of inflammation (39). The RTRs, examined on average 8 years after transplantation, also show lower hsCRP levels and presumably milder inflammation than ESRD patients (25–27). Several lines of evidence have shown that fetuin-A is a potent inhibitor of calcifications both in vivo and in vitro (20,23). Accordingly, our second aim was to investigate the relationship between circulating fetuin-A and VCs in RTRs. By multiple linear regression, we observed that low serum fetuin-A is a predictor of aortic— but not coronary— calcifications. Ketteler et al. (24) showed that sera with low serum fetuin-A concentrations show an impaired ex vivo capacity to inhibit CaxPO4 precipitation. Correlations between low fetuin-A levels and vascular or valvular calcifications have been shown in smaller cohorts of ESRD patients after renal replacement therapy (26,40,41). These findings are in line with ultrastructural analyses performed on iliac artery segments of dialysis patients, showing the close spatial relationship between fetuin-A and

vascular microcalcifications (42). Distinct pathophysiology mechanisms operate in different vascular beds (14,42). The fact that low serum fetuin-A is a predictor of aortic— but not coronary— calcification in this cohort must be pointed out. Risk factors for coronary and aortic calcifications are partly similar but also specific to each site in the general population. For instance, age and gender exert a significantly different influence on these two types of calcifications. Furthermore, genetic heritability is much higher for aortic calcifications than for coronary calcifications (43). These differences may reflect the fact that coronary calcifications occur generally in the intima, whereas calcifications in noncoronary arteries such as the aorta can involve both the intima and the media layers of the artery, thus reflecting different pathogenic processes (43). Of note, Schlieper et al. (42) observed an association beween microcalcification of the media and inhibitors of calcification like fetuin-A. Thus, different pathophysiology mechanisms operating in different vascular beds probably account for the association between fetuin-A levels and aortic calcifications but not coronary calcifications. The third aim of this study was to test whether fetuin-A levels could predict long-term survival and CVEs in RTRs. Based on an approximately 5-year follow-up, we showed that low fetuin-A levels are an independent predictor of survival and occurrence of new CVEs in this cohort of RTRs, after adjustment for traditional risk factors. The predictive power of fetuin-A is similar for all-cause death and for CVEs and cardiovascular death. The incidence of CVEs in our cohort, 3.8 events per 100 patient-years, is moderately lower than the 4.4 value recently observed in the Oregon Health and Science University cohort (44). The difference may be explained by the fact that the latter cohort included patients at the time of transplantation, whereas our patients had an approximately 8-year posttransplant vintage, with lower risk than in the early post-transplantation period. These data support earlier studies, suggesting that low serum fetuin-A levels are associated with cardiovascular mortality in ESRD patients irrespective of type of dialysis or ethnicity (24 –27). In contrast to its association with incident CVEs, fetuin-A is not associated with prevalent CVEs in this cohort. This could be explained by a survival bias, considering the long transplantation vintage and the fact that 31% of patients had a history of nonfatal CVEs at inclusion.



Figure 5. | Kaplan-Meier estimates of the influence of fetuin-A level and inflammation on all-cause mortality and CVEs: stratification for hsCRP.

Figure 6. | Kaplan-Meier estimates of the influence of AHSG rs4918 variant and inflammation on all-cause mortality and CVEs.

984


The demonstration that inflammation (monitored by hsCRP) significantly modulates the effect of low fetuin-A on survival in our cohort of RTRs must be stressed. The potential influence of inflammation on the association of fetuin-A with mortality in dialysis patients is debated (39): two studies of prevalent dialysis patients have shown an influence of CRP levels on the prognostic value of fetuin-A (24,26), whereas two studies of incident dialysis patients showed no dependence (25,27). Our data clearly show that the association of fetuin-A with all-cause mortality and CVEs is independent of hsCRP levels but that patients with the combination of lower fetuin-A and higher hsCRP levels have an approximately 3.5-fold increase in the risk of death compared with patients with higher fetuin-A and lower hsCRP. The influence of these two factors is also shown by the stratification by hsCRP levels of the risk conferred by the AHSG genotype. Of note, the prescription of immunosuppressive drugs remained unchanged during follow-up, whereas the percentage of patients taking statins raised from 40 to 55%. Several limitations of our study should be acknowledged. First, our study population of RTRs is typical of those followed in European centers, which are mostly white and with a lower prevalence of diabetes than in the United States. Second, hsCRP and fetuin-A were measured once at the inclusion, so that we could not assess the effect of variation over time. The facts that the background noise is greater with a single measure and that the measure was obtained on average after 8 years of transplantation (meaning a certain degree of stabilization) support the relevance of our data. Third, we used an indirect estimation of GFR to assess renal function, which may affect the influence of cardiovascular risk factors (45). Fourth, as discussed above, the inclusion of prevalent but not incident RTRs may introduce a survival bias. Finally, although our cohort is the largest thus far, its size limits the genetic analysis of the influence of AHSG variants. It must be pointed, however, that the genetic results have a strong biologic counterpart (influence on circulating fetuin-A levels) and support the clinical data showing the influence of fetuin-A on calcification and outcomes. Additional studies will thus be necessary to address these limitations and confirm our findings. In conclusion, this study shows that common variants in the AHSG gene and inflammation-independent mechanisms lead to low serum fetuin-A levels in RTRs. In turn, low serum fetuin-A levels are associated with aortic calcifications and predict an increased risk for death and CVEs. The later risk is even further increased if inflammation coexists with low fetuin-A levels. Acknowledgments The authors thank H. Debaix, N. Kanaan, M. Mourad, Y. Pirson, and members of the UCL Nephrology Collaborative group for the careful joint follow-up of the patients as well as Prof. W. JahnenDechent for the gift of the anti-fetuin-A antibody. This work was financially supported by the Fonds National de la Recherche Scientifique Me´dicale (FRSM), the Fondation Saint-Luc, the EU project Grant Genomic Strategies for Treatment and Prevention of Cardiovascular Death in Uremia and End-stage Renal Disease (GENECURE) (FP6 LSHM CT 2006 037697), the ‘Fondation

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Received: July 18, 2010 Accepted: December 13, 2010 Published online ahead of print. Publication date available at www.cjasn.org.