Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

CRP polymorphisms and chronic kidney disease in the third national health and nutrition examination survey

  • Adriana M Hung1, 2Email author,
  • T Alp Ikizler1, 2,
  • Marie R Griffin1, 6,
  • Kimberly Glenn3,
  • Robert A Greevy1, 5,
  • Carlos G Grijalva6,
  • Edward D Siew1, 2 and
  • Dana C Crawford3, 4
BMC Medical Genetics201112:65

DOI: 10.1186/1471-2350-12-65

Received: 28 September 2010

Accepted: 11 May 2011

Published: 11 May 2011

Abstract

Background

CRP gene polymorphisms are associated with serum C-reactive protein concentrations and may play a role in chronic kidney disease (CKD) progression. We recently reported an association between the gene variant rs2808630 and CKD progression in African Americans with hypertensive kidney disease. This association has not been studied in other ethnic groups.

Methods

We used data from 5955 participants from Phase 2 of The Third National Health and Nutrition Examination Survey (1991-1994) to study the association between CRP polymorphisms and CKD prevalence in a population-based sample. The primary outcome was CKD defined as estimated glomerular filtration rate (eGFR) <60 ml/min or the presence of albuminuria. Secondary outcomes were the presence of albuminuria (any degree) and continuous eGFR. Six single nucleotide polymorphisms (SNPs) from the CRP gene, rs2808630, rs1205, rs3093066, rs1417938, rs3093058, and rs1800947, were evaluated.

Results

CRP rs2808630 AG compared to the referent AA genotype was associated with CKD in non-Hispanic blacks (n = 1649, 293 of whom had CKD) with an adjusted odds ratio (OR) of 3.09 (95% CI 1.65-5.8; p = 0.001). For the secondary outcomes, rs2808630 AG compared to the referent AA genotype was associated with albuminuria with an adjusted OR of 3.07 (95% CI 1.59-5.94; p = 0.002), however not with eGFR. There was no association between the SNPs and CKD, albuminuria or eGFR in non-Hispanic whites or Mexicans Americans.

Conclusions

In this cross-sectional study, the 3' flanking CRP gene variant rs2808630 was associated with CKD, mainly through its association with albuminuria in the non-Hispanic blacks. Despite not finding an association with eGFR, our results support our previous study demonstrating an association between CRP gene variant rs2808630 and CKD progression in a longitudinal cohort of African American with hypertensive kidney disease.

Background

Chronic kidney disease (CKD) represents a major public health problem worldwide. Even in its early stages, CKD is associated with poor outcomes and premature death mainly due to cardiovascular causes [1, 2]. Familial aggregation of CKD and end stage renal disease (ESRD) have been reported in populations throughout the world for all types of nephropathy, underscoring the importance of genetic factors [37]. There also appears to be genetic predisposition towards faster progression to ESRD which is strongly associated with race. For example, African Americans with a first degree relative with ESRD have a 9-fold increase in the risk of ESRD compared to a 3-5 fold increase in whites [8, 9]. Recently, genetic variations in two candidate genes both located within a 60 kb region of chromosome 22 have been identified as high risk for non-diabetic ESRD in African Americans[10]: the nonmuscle myosin heavy chain 9 (MYH9)[9] and the apolipoprotein L1 (APOL1)[10] genes. It is possible that additional genetic variants, such as those related to inflammatory pathways, may also be associated with CKD progression through common pathways and the risk of ESRD in general.

CKD is an inflammatory process, and biomarkers of inflammation increase as kidney function declines [11, 12]. Previous studies have shown that inflammation-related gene polymorphisms explain a proportion of trait variability observed for these biomarkers [1, 1319]. Given this relationship, it is likely that inflammation-related gene polymorphisms are also associated with CKD progression. We have previously demonstrated an association between a single nucleotide polymorphism rs2808630 in the CRP gene and CKD progression in the prospective African American Study of Kidney Disease and Hypertension (AASK) study [11], a randomized control trial designed to evaluate the effect of three different antihypertensive medications in the progression of hypertensive kidney disease [20]. In addition to being associated with decreased levels in general African American populations [15, 17], this SNP has been associated with decreased CRP in the AASK study [11] suggesting that the relationship between CKD and inflammation is complex and can be influenced by genetic factors.

In the current study, we used genetic data from the Third National Health and Nutrition Examination Survey (NHANES III), a large population-based survey, to further investigate our findings in non-Hispanic blacks, non-Hispanic whites and Mexican Americans. The primary goal of this study was to test the association between six CRP SNPs and the presence of CKD defined as either estimated glomerular filtration rate (eGFR) <60 ml/min/m2 or the presence of albuminuria according to National Kidney Foundation (NKF) consensus definitions [21]. Additionally, we evaluated the association between these polymorphisms with albuminuria (any degree) and continuous eGFR.

Methods

Study Population

The NHANES are surveys conducted by the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Prevention (CDC) to provide national estimates of the health and nutrition of the U.S. civilian, non-institutionalized population. NHANES III is a complex, multistage survey that oversampled minorities (non-Hispanic blacks and Mexican Americans), children, and the elderly. Sample weights are provided for each participant included in the survey to correct for non-response and to correct for the fact that each participant was not selected for the survey with equal probability. Participants were consented by the CDC at the time of the survey and sample collection, and consent included the storage of data and biological specimens for future research [22].

DNA samples were generated using cell lines created from blood samples of participants older than 12 years of age during NHANES III phase 2 (1991-1994). The total number of participants in NHANES III phase 2 was 16,530 and DNA was available for 7,159 participants. To account for this difference in DNA availability, the sample weights were recalculated using previously described methods [15]. All participants with DNA available had completed a household interview and a physical examination in the Mobile Examination Center.

We excluded participants younger than 18 years of age (n = 979) as the modification of diet in renal disease (MDRD) equation is not applicable to them, 35 participants with missing serum creatinine concentrations, 71 participants with missing albuminuria measurements and normal kidney function, and 119 pregnant women. Our exclusion criteria were similar to other studies in general [23, 24]. However we allowed participants age 18-20 years of age in the cohort, (different from other studies in the NHANES population [23, 24]) as they are usually included in studies of CKD progression [25, 26] and the MDRD equation applies to them. After exclusion criteria were applied, our final analysis included data from 5955 participants. The study protocol was reviewed and approved by the Center of Disease Control (CDC) Ethical Review Board. Because the study investigators did not have access to personal identifiers, the Vanderbilt University Internal Review Board and the VA Tennessee Valley Healthcare System approved the protocol as non-human subject research under the exempt category.

Measurements and Definitions

The primary outcomefor this study was CKD defined as either an eGFR <60 ml/min or the presence of albuminuria in accordance to the NKF consensus definition which classifies all individuals with evidence of kidney damage (e.g. albuminuria) as having chronic kidney disease, irrespective of the level of GFR [21]. The focus of our study was to identify SNPs associated with common pathways for advanced CKD, and albuminuria is one of the main risk factors for CKD progression [2, 27]. Secondary outcomeswere a) albuminuria defined using sex-specific cut-offs of urine albumin creatinine ratio (ACR) ≥ 17 mg/g for men and ≥ 25 mg/g for women [24, 28, 29] and b) continuous eGFR. The choice of analyzing eGFR as a continuous trait was made to maximize the information available in the dataset, as there was a small number of participants with low GFR. We also performed an sensitivity analysis where eGFR was dichotomized as < or ≥ 60 ml/min.

NHANES creatinine was calibrated to the creatinine values of the Cleveland Clinic Laboratory [creatinine calibration formula: MDRDcr = -0.184 + 0.96 *NHANES III uncalibrated creatinine]. eGFR was calculated using the MDRD four-component equation: eGFR (mL/min/1.73 m2) = 175 × (serum creatinine)-1.154 × (Age)-0.203 × (0.742 if female) × (1.210 if African American). Calibration formula: MDRDcr = -0.184 + 0.96 *NHANES III uncalibrated creatinine. Urine albumin and creatinine were measured from a single random morning voided urine sample. Samples were obtained by a clean catch technique and shipped overnight on dry ice to a central laboratory and frozen for a later use. Urine albumin concentration was measured by means of solid-phase fluorescent immunoassay. Urine creatinine concentration was measured using the Jaffé method. Urine albumin creatinine ratio (ACR) is expressed in units of milligrams per gram. Similarly, CKD stages were categorized based on the current NKF classification system.

Genotype data

As previously reported, the CRP gene (2.3 kb) is located on chromosome 1q21-1q23. We accessed the genotype data for six CRP SNPs (rs1417938, rs1205, rs1800947 (Leu184Leu), rs2808630, rs3093058, and rs3093066) through the NHANES Genetic database maintained by the National Center for Health Statistics at the Centers for Disease Control and Prevention. All six SNPs selected were previously genotyped by Crawford et al [15] using Applied Biosystems TaqMan and Epoch MGB Eclipse Probe Systems (rs1800947). As previously reported [15], the overall average SNP genotyping call rate was 95%, and no SNP was out of Hardy Weinberg Equilibrium at p < 0.001 in all three subpopulations tested

Covariate Definitions

We ascertained known risk factors for CKD using NHANES measurements. Race/ethnicity was self-assigned as non-Hispanic white, non-Hispanic black, Mexican American, or other. C-reactive protein (CRP) assay used in for NHANES was a low sensitivity assay, with a lower limit of detection of 0.22 mg/dL. CRP values were right skew and were log transformed for the analysis. Diabetes mellitus (DM) was defined as a self-reported physician diagnosis of diabetes, use of diabetes medications (insulin or pills for diabetes), a plasma glucose from-first venipuncture >126 mg/dl or a random glucose from the biochemistry profile > 200 mg/dl. Hypertension was defined as any use of antihypertensive medications, the average of two systolic blood pressure measurements of 140 mm Hg or greater, or diastolic blood pressure of 90 mm Hg or greater taken during the examination. Smoking status was defined as "current" if the participant answered "yes" to the question "do you smoke now" or if the participant had cotinine levels >15 ng/mL. Body mass index (BMI) was classified as obese (≥30 kg/m2) or not obese. Triglycerides, calculated LDL cholesterol, and HDL cholesterol were treated as continuous variables. Cardiovascular disease (CVD) was considered positive if the participant reported ever having been told that he or she had had a heart attack, heart failure, or stroke or had undergone a surgical or percutaneous procedure for any of these conditions.

Statistical analysis

All results were weighted to account for the sampling design of NHANES III, which were recalculated by the CDC for NHANES III genetic study. All analyses were stratified by self reported race/ethnicity. Unadjusted and adjusted logistic regressions were conducted assuming an additive genetic model for each SNP (that is, with a change in the odds ratio or beta-coefficient per copy of the minor allele). Our prior research indicated that minor allele "G" is the "at-risk" allele for CKD progression compared with the major allele "A". Hence, SNPs were treated as categorical variables and coded as follows: 0 = homozygous major, 1 = heterozygous, and 2 = homozygous minor; the homozygous major category was considered the reference level. The primary outcome was CKD (any stage of CKD) treated as a binary outcome given the small number of participants with advance stages of CKD 4-5. For the secondary outcomes, continuous GFR was log transformed. Albuminuria was defined as a binary outcome (any degree of albuminuria). All analyses were performed either remotely using the Analytic Data Research by Email (ANDRE) portal of the CDC Research Data Center (RDC) in Hyattsville, MD or in person at the RDC using STATA 10 MP(College Station, TX), SAS/Genetics 9.1.3 (SAS Institute, Cary, NC), and SUDAAN 10 (Research Triangle Institute, NC). Our power calculation was focused in the association of the rs2808630 polymorphims and CKD in African Americans, based in our previous study (12) with a minor allele frequency of 0.14 for NHANES III. We estimated that we needed 253 African Americans with CKD and 253 African Americans without CKD in order to detect a genotype risk of 1.6 or higher, using an additive model. This model was chosen base in our previous study (12). Additionally given our small sample of African Americans with CKD (n = 293) we estimated that we will be under power for a dominant model requiring at least 354 controls and cases for the same genotype risk. We considered significance for a two sided alpha of 0.05 for all analyses and Bonferroni-adjusted alpha of 0.002 (0.05/29).

Results

Participants' characteristics are shown in table 1. From the 5955 participants that met criteria for our study, a total of 1,175 (20%) qualified as having CKD. More specifically, 7% of the participants (n = 406) had a GFR < 60 ml/min and 16% (n = 961) had albuminuria. Chronic kidney disease patients were older, 81% (n = 951) had albuminuria, had a higher prevalence of diabetes mellitus, hypertension, cardiovascular disease, higher body mass index, higher CRP levels, and lower smoking exposure compared to participants without CKD. Genotype frequencies by race/ethnicity are displayed in table 2. There were no statistically significant differences in the clinical characteristics among the different race/ethnicity groups by CKD status (additional file 1, table S1). Genotype frequencies by race for participants with and without CKD are displayed in table 2. Genotype frequencies by race for participants with and without albuminuria are displayed in additional file 1 table S4.
Table 1

Characteristics of the study participants ≥ 18 years of age, NHANES III DNA bank, 1991-1994, stratified by chronic kidney disease (CKD) status.

Characteristics

Total (n = 5955)

§ CKD (n = 1175)

No CKD (n = 4780)

p-value

Age, median (IQR) years

42 (30, 62)

67 (48,77)

38 (28,48)

<0.0001

Gender (males) (%)

44

42

44

0.01

Race/Ethnicity (%)

   

0.02

Non-Hispanic White % (n)

39% (2338)

47% (556)

37% (1782)

 

Non-Hispanic Black % (n)

28% (1649)

25% (293)

28% (1356)

 

Mexican American % (n)

28% (1685)

23% (272)

30% (1413)

 

Others % (n)

5% (283)

5% (54)

5% (229)

 

Serum Creatinine, mg/dL, median (IQR)

1 (0.9, 1.2)

1.2 (1,1.4)

1 (0.9,1.2)

<0.0001

Median eGFR ml/min/1.73 m2 (IQR)

93 (78, 109)

66 (53,87)

88 (77,104)

<0.0001

Urine albumin to creatinine ratio (IQR)

5.38 (2.66, 11.79)

35 (17, 87)

4 (21,74)

<0.0001

Albuminuria

   

<0.0001

None, % (n)

84% (5004)

19% (224)

100% (4780)

 

Microalbuminuria, % (n)

14% (813)

69%(813)

0 (0)

 

Macroalbuminuria, % (n)

2% (138)

12%(138)

0 (0)

 

Any form of albuminuria, % (n)

16% (951)

81%(951)

0 (0)

 

Low GFR

    

GFR < 60 ml/min (%)

7

35

0

<0.0001

CKD stage III (eGFR 30-59 ml/min) (%)

6.5

3

0

 

CKD stage IV (eGFR 15-29 ml/min) (%)

0.4

1

0

 

CKD stage V (eGFR < 15 ml/min) (%)

0.1

1

0

 

Systolic BP median (IQR), mmHg

119 (108, 134)

136 (120, 155)

117 (108,129)

<0.0001

Diastolic BP median (IQR), mmHg

62 (64, 80)

74 (66, 83)

72 (65,79)

0.07

Hypertension, %

22

46

15

<0.0001

BMI, median (IQR)

26.5 (23, 30)

27 (24, 31)

26 (23, 30)

0.02

C-reactive protein level (%)

   

<0.0001

<0.22 mg/dl (%)

62

53

65

 

0.22-0.99 mg/dl (%)

29

34

27

 

>1.00 mg/dl (%)

9

13

7

 

Current Smoking (%)

20

18

19

0.002

Prevalent CHD, (%)

8

19

4

<0.0001

LDL median (IQR),, mg/dL

122 (99, 146)

133 (106, 157)

120 (98, 144)

0.13

Diabetes (%)

11

26

7

<0.0001

*Categorical variables were tested using the Cochran-Mantel-Haenszel Test and continuous variables were tested using the Taylor Linearization method with SUDAAN. All tests were weighed.

§ CKD defined as either by decrease glomerular filtration rate (eGFR) or by the presence of albuminuria

Table 2

CRP polymorphisms, location and genotype frequencies for all participants by CKD status.

SNP

rs2808630

rs1205

rs1800947*

rs3093058

rs3093066

rs1417938

Location

3' flanking region

3' flanking region

Exon 2

5' flanking region

5' flanking region

Intron 1

Genotypes

AA

AG

GG

GG

AG

AA

GG

CG

CC

AA

AT

TT

CC

AC

AA

AA

AT

TT

Non-Hispanic Whites

N

270

221

43

241

236

57

475

**

**

537

5

-

498

9

-

259

239

48

CKD, GFP

50.75

42.68

6.57

46.17

42.19

11.63

93.19

**

**

98.36

1.64

-

97.69

2.31

-

47.44

43.7

8.79

N

867

686

133

694

808

190

1544

**

**

1718

6

-

1610

15

-

851

730

148

No-CKD,GFP

52.13

40.38

7.49

42.16

46.51

11.33

94.43

**

**

99.55

0.45

-

98.94

1.06

-

49.22

42.22

8.56

Non-Hispanic Blacks

 

rs2808630

rs1205

rs1800947*

rs3093058

rs3093066

rs1417938

N

181

96

8

190

86

9

**

**

-

212

71

8

154

105

15

221

62

**

CKD, GFP

61.45

36.4

2.14

67.06

29.39

3.55

99

**

-

76.01

22.89

1.1

56.35

38.37

5.28

77.75

20.65

**

N

950

323

26

804

397

79

**

**

-

915

356

34

751

443

67

1024

272

21

No-CKD,GFP

72.24

25.42

2.34

62.68

31.26

6.06

99

**

-

69.67

27.66

2.67

60.67

34.12

5.21

77.54

21.75

0.4

Mexicans Americans

 

rs2808630

rs1205

rs1800947*

rs3093058

rs3093066

rs1417938

N

167

89

11

102

137

31

253

7

-

260

8

**

269

**

**

110

130

24

CKD, GFP

61.12

33.75

5.13

42.21

47.09

10.70

97.66

2.3

-

95.81

3.91

**

99.25

**

**

41.67

49.23

9.1

N

852

434

57

562

621

168

1266

54

-

1348

37

**

1308

**

**

591

616

155

No-CKD,GFP

62.48

33.04

4.48

41.65

45.65

12.7

95.6

4.4

-

97.08

2.92

**

96.59

**

**

43.39

45.23

11.3

** Data user agreement with the CDC does not allow outputs with counts less than 5.

N:number of individuals with the specific genotype. GFP:genotype frequency percent

CRPgenotypes and CKD defined as either by the presence of albuminuria or low GFR

Among non-Hispanic blacks, rs2808630 was associated with CKD both in the unadjusted (table 3) and adjusted analysis (table 4). More specifically, the AG genotype had an unadjusted odds ratio of 1.68 (95% CI: 1.34-2.12; p = 0.0001) for the presence of CKD compared to the AA genotype (table 3). Similarly the AG genotype was associated with CKD with an odds ratio of 2.94 (95% CI: 1.61 to 5.38; p = 0.001) compared to the AA genotype for the fully adjusted model, for other known risk factors for CKD. Both of these associations remained significant even after Bonferroni adjustment for the significance level (p ≤ 0.002). This significant association did not change when adjusting for CRP (table 4). Other independent predictors in the model for the SNP rs2808630 and CKD included age 1.05 (1.03,1.07), hypertension 2.21 (1.33, 3.08) and diabetes 2.44 (1.11, 5.35). History of cardiovascular disease 1.42 (0.66,3.0.8), obesity 0.77 (0.48,1.25), LDL cholesterol mg/dL 1.0 (0.9, 1.01), smoking 1.11 (0.63, 1.97) and CRP levels (mg/dL) 0.96 (0.81, 1.14) were not independent predictor in this model.
Table 3

Unadjusted and weighed association of CRP gene polymorphism and CKD, additive genetic model

Presence of CKD (logistic regression) for each SNP

 

No

  

No

  

No

  
  

Non-Hispanic Whites

¥ p value

 

Non-Hispanic Blacks

¥ p value

 

Mexican American

¥ p value

rs2808630

         

AA

1137

ref

 

1131

ref

 

1019

Ref

 

AG

907

1.09 (0.78-1.52)

0.6

419

1.68 (1.34 - 2.12)

0.0001*

523

1.04(0.58-1.88)

0.9

GG

176

0.90 (0.52-1.57)

0.7

34

1.08 (0.43 - 2.71)

0.9

68

1.17(0.57-2.40)

0.7

rs1205

         

GG

935

ref

 

994

ref

 

664

Ref

 

AG

1044

0.88 (0.61-1.28)

0.5

483

1.60 (0.66-3.88)

0.3

758

1.22 (0.76-1.97)

0.4

AA

247

1.07 (0.77-1.48)

0.7

88

1.82 (0.82-4.06)

0.1

199

1.20(0.77-1.89)

0.4

rs1417938

         

AA

1110

ref

 

1245

ref

 

701

Ref

 

AT

969

1.07 (0.76-1.50)

0.7

334

1.08(0.080-1.44)

0.6

746

1.21 (0.92-1.58)

0.2

TT

196

1.08 (0.61-1.93)

0.8

23

0.43(0.09-1.99)

0.3

179

1.10 (0.48-2.50)

0.8

rs1800947

         

GG

2019

ref

 

1508

ref

 

1519

Ref

 

CG

**

1.21 (0.56 - 2.66)

0.6

20

0.55 (0.13-2.26)

0.4

61

0.52 (0.20-1.38)

0.2

rs3093058

         

AA

2255

ref

 

1127

ref

 

1608

Ref

 

AT

11

3.69 (0.74-18.44)

0.09

427

0.76 (0.60-0.96)

0.02

**

1.36 (0.59-3.15)

0.5

TT

-

_

 

42

0.38 (0.13-1.07)

0.07

**

_

 

rs3093066

         

CC

2108

ref

 

905

ref

 

1577

Ref

 

CA

24

2.20 (0.65-7.47)

0.2

548

1.11 (0.56-2.21)

0.8

**

0.22 (0.05-0.97)

0.03

AA

-

_

 

82

0.92 (0.54-1.55)

0.7

**

_

 

¥ Unadjusted for multiple comparisons

*Statistical significance with Bonferroni correction (α/n = 0.05/29) p-value ≤ 0.002

Table 4

Adjusted and weighted association between CRP polymorphism rs2808630 and rs3093058 and CKD in African Americans Participants.

Models

Genotype

OR

95% CI

¥ p-value

rs2808630

    

Fully adjusted model

AA

ref**

  
 

AG

2.94

1.61, 5.38

0.001*

 

GG

2.22

0.68, 7.23

0.2

Fully adjusted model plus Log transformed CRP

AA

ref**

  
 

AG

3.09

1.65, 5.80

0.001*

 

GG

2.41

0.79, 7.39

0.1

rs3093058

    

Fully adjusted model

AA

ref**

  
 

AT

0.44

0.49,1.44

0.5

 

TT

0.15

0.02, 1.36

0.09

Fully adjusted model plus Log transformed CRP

AA

ref**

  
 

AT

0.8

0.45, 1.41

0.4

 

TT

0.15

0.02,1.39

0.09

¥ Unadjusted for multiple comparisons

*Statistical significance with Bonferroni correction (α/n = 0.05/29) p-value ≤ 0.002

Number of participants by rs2808630 genotype: AA = 1131; AG 419, and GG = 34

**ref. reference group homozygous for the major allele.

Fully adjusted model: adjusted for age, sex, diabetes, hypertension, cardiovascular disease, BMI, smoking and LDL cholesterol

In contrast, rs3093058 was associated with a decreased risk of CKD in the unadjusted analysis [AG vs. AA genotype, 0.76 (95% CI 0.60-0.96; p = 0.02)] (table 3). However, this association was not significant after Bonferroni adjustment or after adjustment for known risk factors for CKD. None of the other SNPs were associated with the risk of CKD in African Americans. There was no association observed between the studied SNPs and CKD in non-Hispanic whites or Mexicans Americans.

CRP genotypes and continuous GFR by MDRD equation

We examined the association between the selected CRP SNPs and continuous eGFR. There was no association detected with continuous eGFR and the tested CRP SNPs in the unadjusted or fully adjusted model (table 5). Additional unadjusted logistic regression for low eGFR (GFR < 60 ml/min) was performed as an alternative analysis for all CRP SNPs with no association detected (data not shown).
Table 5

Association between CRP variants and eGFR-MDRD

 

linear regression to eGFR (log transformed) for each SNP

 

Non-Hispanic Whites

 

Non-Hispanic Blacks

 

Mexican Americans

 
 

β(95% CI)

¥ p-value

β (95% CI)

¥ p-value

β(95% CI)

¥ p-value

rs2808630

      

Unadjusted

-0.001 (-0.02, 0.01)

0.55

-0.01 (-0.04,0.02)

0.61

-0.01 (-0.01, 0.03)

0.54

Fully adjusted

-0.01 (-0.04, 0.02)

0.46

0.00 (-0.04, 0.04)

0.94

0.01 (-0.01, 0.04)

0.20

rs1205

      

Unadjusted

-0.001 (-0.02, 0.01)

0.76

-0.01 (-0.03, 0.02)

0.53

-0.001(-0.01, 0.02)

0.55

Fully adjusted

-0.001 (-0.02, 0.02)

0.84

-0.01 (-0.04, 0.01)

0.32

0.01 (-0.02, 0.04)

0.55

rs1417938

      

Unadjusted

0.001 (-0.02, 0.02)

0.72

-0.01(-0.04, 0.02)

0.54

-0.01 (-0.03, 0.01)

0.51

Fully adjusted

0.01 (-0.01, 0.03)

0.35

-0.02 (-0.05, 0.01)

0.18

-0.001(-0.03, 0.03)

0.84

rs1800947

      

Unadjusted

-0.001 (-0.05, 0.04)

0.87

0.01 (-0.13, 0.16)

0.84

0.03 (- 0.02, 0.07)

0.26

Fully adjusted

0.01(-0.07, 0.09)

0.75

0.04 (-0.20, 0.27)

0.75

0.01 (-0.04, 0.06)

0.72

rs3093058

      

Unadjusted

-0.08 (-0.31, 0.14)

0.45

0.01 (-0.01, 0.04)

0.36

0.01 (-0.05, 0.06)

0.78

Fully adjusted

-0.04 (-0.13, 0.05)

0.36

-0.01 (-0.04, 0.02)

0.63

-0.03 (-0.11, 0.04)

0.34

rs3093066

      

Unadjusted

0.02 (-0.19, 0.23)

0.86

0.02 (-0.01, 0.04)

0.16

-0.03 (-0.09, 0.03)

0.37

Fully adjusted

0.06 (-0.13, 0.25)

0.53

0.01 (-0.02, 0.03)

0.68

-0.01 (-0.11, 0.08)

0.78

¥ Unadjusted for multiple comparisons

Fully adjusted: age, gender, diabetes, hypertension, cardiovascular disease, BMI, smoking, LDL cholesterol, log transformed C reactive protein and albuminuria

CRPgenotypes and albuminuria

We further evaluated the association between the CRP SNPs with albuminuria (any degree of albuminuria versus none). The rs2808630 SNP was the only CRP SNP significantly associated with albuminuria in non-Hispanic blacks. To examine the association of the rs2808630 with albuminuria, we adjusted all our models for eGFR. In the minimally adjusted analysis (model 1), the AG genotype had an OR 1.68 (95% CI 1.27-2.23; p = 0.0009) for the presence of albuminuria compared to the AA genotype. Similarly, the AG genotype of rs2808630 was associated with albuminuria in the fully adjusted model with an OR of 3.07 (95%CI 1.59-5.94; p = 0.002) compared to the AA genotype (table 6). These associations were significant after Bonferroni adjustment (p ≤ 0.002). Other independent predictors in the model for the SNP rs2808630 and albuminuria included age (years) 1.03 (1.01,1.06), hypertension 2.42 (1.35, 4.35) and diabetes 2.52 (1.12, 5.67). History of cardiovascular disease 1.47 (0.67, 3.26), body mass index(BMI) greater than thirty 0.82 (0.46, 1.41), LDL cholesterol mg/dL 1.0 (0.99, 1.00), smoking 1.18 (0.67, 2.06) and CRP levels (mg/dL), 1.24 (0.90, 1.70) were not independent predictor in this model. There was no association observed between the studied SNPs and albuminuria in non-Hispanic whites or Mexicans Americans.
Table 6

Adjusted and weighted association between CRP polymorphism rs2808630 and albuminuria in African Americans Participants.

Models

Genotype

OR

95% CI

¥ p-value

rs2808630

    

Adjusted for Age and gender (Model 1).

AA

ref*

  
 

AG

1.68

1.27, 2.23

0.0009

 

GG

0.99

0.47, 2.28

0.9

Adjusted for risk factors

AA

ref*

  
 

AG

2.95

1.54, 5.67

0.002

 

GG

1.78

0.50, 76.34

0.4

Adjusted for risk factors and Log transformed CRP (Model 2).

AA

ref*

  
 

AG

3.07

1.59, 5.94

0.002

 

GG

1.91

1.00, 6.54

0.3

*ref. reference group homozygous for the major allele.

¥ Unadjusted for multiple comparisons

* Statistical significance with Bonferroni correction (α/n = 0.05/29) p-value ≤ 0.002

Odd ratios are adjusted for:

Model 1:adjusted for age, sex and GFR.

Model 2:age, gender, diabetes, hypertension, cardiovascular disease, BMI, cotinine levels, NSAIDS exposure, LDL cholesterol, GFR and log transformed C reactive protein.

There were no statistically significant differences in the demographics or clinical characteristics among the different rs2808630 genotypes (table 7).
Table 7

Clinical Characteristics of African American Participants of the NHANES III Genetic Study (1991-1994) by rs2808630 genotype.

 

AA (n = 1131)

AG (n = 416)

GG (n = 34)

p-value

Age, years, median (IQR)

33 (20,46)

34 (22,47)

38(29,55)

0.2

Gender, male (%)

43

42

50

0.9

Hypertension (%)

23

24

29

0.3

Diabetes (%)

9

11

* (*)

0.6

Cardiovascular Disease (%)

6

5

* (*)

0.8

Serum Creatinine, mg/dL (calibrated)

1.1 (0.9,1.2)

1 (0.9,1.2)

1.1 (1, 1.2)

0.39

Urine albumin to creatinine ratio

4.7 (2.2, 104)

5.2 (2.3, 15)

6.9 (3.4, 25)

0.03

Body Mass Index Kg/m2

26 (22, 30)

26 (22, 32)

26 (22, 32)

0.06

LDL cholesterol, mg/dL

121 (98, 145)

115 (95, 142)

100 (80, 169)

0.9

Smoking (%)

37

38

38

0.8

Categorical variables were tested using the Cochran-Mantel-Haenszel Test and continuous variables were Tested using the Taylor linearization method in SUDAAN with all analysis weighed.

Discussion

In this nationally-representative population, we show an association between the CRP SNP rs2808630 and the presence of CKD in non-Hispanic black participants defined either by low GFR < 60 ml/min or the presence of albuminuria. This result is concordant with the findings of our previous study in which rs2808630 was associated with CKD progression in a longitudinal prospective CKD cohort from the African American Study of Kidney Disease and Hypertension[11].

CKD is multifactorial, occurring in the context of chronic co-morbid conditions such as diabetes mellitus, cardiovascular disease, obesity and hypertension. Since all of these conditions impact vascular inflammation, CKD is considered as an inflammatory condition [12, 30]. Based on these observations, one can speculate that individuals prone to a higher inflammatory response due to genetic predisposition would have a higher risk of CKD progression. However, our current and previous study showed that the minor allele of SNP rs2808630, which is associated with lower CRP levels in the general population (18, 26, 20, 21), is associated with CKD, now in two independent cohorts[11] along with another replication study [31]. The association between rs2808630 and CKD status remained significant after adjustment for CRP levels, suggesting that the pathway for this association is not mediated by CRP. On the other hand, it may be related to other immune-modulatory molecules of the cytokine network, which involves a highly complex interaction of multiple cascades of gene activation and suppression [32].

Another possible explanation for our observations is that the association between inflammation and CKD is the consequence of the multiple co-morbidities and decreased clearance from the kidney. This might suggest that the inflammatory response itself may not be related to a genetic predisposition towards disease progression in kidney disease. It is also possible that the rs2808630 is in linkage disequilibrium with another SNP that increases the risk of CKD. CRP rs2808630 is located in the 3' flanking region of the gene and is not in strong linkage disequilibrium with any common variation found within 50 kb in the Yoruba International HapMap dataset, although it is in moderate LD (r2~0.5) with three intergenic common SNPs [33]. We cannot rule out the possibility that other common SNP(s) outside the 50 kb boundary may be in LD with rs2808630 or that a less common SNP (in the 1%-5% range) is in LD with rs2808630. After a preliminary exploration of the newly released 1000 Genomes Project Data for Yoruba [34] the latter possibility seems unlikely (data not shown).

In order to better understand the association of CRP rs2808630 with CKD, we studied the association of the rs2808630 with albuminuria and with continuous eGFR as independent outcomes. CRP rs2808630 was also associated with albuminuria. Albuminuria is a strong risk factor for CKD progression and could potentially represent a pathway for the association with CKD progression. Although genetic basis for GFR traits [35](9) and albuminuria traits [36, 37] have been recently identified and may differ, it has been recently highlighted that information regarding albuminuria should be incorporated in the CKD staging system in order to be able to predict those at risk for progression [27]. This relevant clinical observation highlights that gene variants associated with albuminuria may be part of the common pathway for CKD progression and underscores the need to include albuminuria in the outcome when studying genetic susceptibility for CKD progression through common pathways.

Finally, individuals with this CRP genotype may respond less to other interventions compared with patients that have the referent genotypes at these SNPs. Our previous study findings in the AASK trial suggested that African Americans with the minor allele for the rs2808630 benefited less from ACE inhibition. Unfortunately, NHANES III lacks detailed medication data, and we were not able to test for this association.

Recently, genome-wide association studies (GWAS) studies have identified a number of potential candidate genes and genomic regions that may contribute to the pathogenesis of CKD(9, 10, 12). Two important susceptibility genes for CKD progression to ESRD have been identified in African Americans for "non-diabetic kidney disease"[10]. These include MYH9 [9] and APOL1 [10]. In addition to the genes implicated in CKD progression to ESRD, GWAS meta-analyses has also yielded a large number of potential gene variants that are associated with both creatinine-based and cystatin C-based eGFR[35]. However, taken all together, these SNPs explain only about 1.43% of the GFR variability at the population level, suggesting that many other genetic components of kidney function remain to be identified. Furthermore it has been hypothesized that there are genes involved in disease initiation only (i.e UMOD) [35] and other genes involved in progression to ESRD.

The main strength of this study is that it is performed in a large population-based, nationally representative and ethnically diverse sample, which is rich in phenotypic information, allowing us to adjust in our analysis for the main risk factors known to be associated with CKD progression. However, our study is not without limitations. NHANES III is a population based sample designed to represent the health status of the non-institutionalized US population which follows a cross-sectional design and precludes the possibility of studying gene effect on disease progression. Secondly, eGFR was calculated using a single creatinine measurement. Given some variability involved in the measurement, a single value may increase the risk of misclassification of participants without CKD as CKD and overall dilute the association. Thirdly, because this is a population-based sample, the majority of the participants had normal kidney function (mean GFR 95 ± 25 ml/min) and only 7% had low eGFR, with the vast majority of them classified as stage 3. This may have limited our ability to detect an association with eGFR. In contrast, our previous study [11] was performed in a population of only African Americans, all of whom had established CKD (mean baseline GFR 43 47 ± 13.5 ml/min) followed logintudinally and with repeated measurements of the outcome using the gold standard to measure GFR (iothalamate I-125). Overall, these two studies are complementary to each other (a comparison of the difference between populations studies, design and outcome ascertaiment between AASK and NHANES is provided in our additional file1 tables S2 and S3). The fact that we were able to demonstrate an association with albuminuria is also highly relevant as this may potentially explain the relationship with CKD progression via a common pathway.

Conclusion

The CRP gene variant rs2808630 was associated with the presence of CKD in non-Hispanic black participants. Larger longitudinal studies of individuals with established CKD are needed to further evaluate the role of CRP polymorphisms in CKD progression in an ethnically diverse population, including non-Hispanic whites and Mexican Americans. Further studies are also required to evaluate if this association is with CKD in general or with specific disease etiologies such as diabetes and hypertension. Finally, pharmacogenetic studies evaluating the role of CRP polymorphisms in the response to specific medications such as ACE inhibition with regards to renoprotection are also needed.

Declarations

Acknowledgements

The findings and conclusions in this paper are those of the author(s) and do not necessarily represent the views of the National Center for Health Statistics, Centers for Disease Control and Prevention.

We thank Karen E. Davis and Research Data Center at the CDC for assistance with the NHANES III genetic data. The Vanderbilt University Center for Human Genetics Research, Computational Genomics Core provided computational and/or analytical support for this work. A.M.H. was supported by a Research Career Development Award and a Clinical Research Center of Excellence from the Department of Veterans Affairs and DK077373.

Authors’ Affiliations

(1)
Veterans Administration Tennessee Valley Healthcare System Nashville
(2)
Division of Nephrology, Vanderbilt University
(3)
Center for Human Genetics Research
(4)
Department of Molecular Physiology and Biophysics, Vanderbilt University
(5)
Department of Biostatistics, Vanderbilt University
(6)
Division of Pharmacoepidemiology, Department of Preventive Medicine, Vanderbilt University

References

  1. Balakrishnan VS, Guo D, Rao M, et al: Cytokine gene polymorphisms in hemodialysis patients: association with comorbidity, functionality, and serum albumin. Kidney Int. 2004, 65: 1449-60. 10.1111/j.1523-1755.2004.00531.x.View ArticlePubMedGoogle Scholar
  2. Hemmelgarn BR, Manns BJ, Lloyd A, et al: Relation between kidney function, proteinuria, and adverse outcomes. JAMA. 303: 423-9.
  3. Gumprecht J, Zychma MJ, Moczulski DK, Gosek K, Grzeszczak W: Family history of end-stage renal disease among hemodialyzed patients in Poland. J Nephrol. 2003, 16: 511-5.PubMedGoogle Scholar
  4. Harjutsalo V, Katoh S, Sarti C, Tajima N, Tuomilehto J: Population-based assessment of familial clustering of diabetic nephropathy in type 1 diabetes. Diabetes. 2004, 53: 2449-54. 10.2337/diabetes.53.9.2449.View ArticlePubMedGoogle Scholar
  5. Ramirez SP, McClellan W, Port FK, Hsu SI: Risk factors for proteinuria in a large, multiracial, southeast Asian population. J Am Soc Nephrol. 2002, 13: 1907-17. 10.1097/01.ASN.0000018406.20282.C8.View ArticlePubMedGoogle Scholar
  6. Scolari F, Amoroso A, Savoldi S, et al: Familial clustering of IgA nephropathy: further evidence in an Italian population. Am J Kidney Dis. 1999, 33: 857-65. 10.1016/S0272-6386(99)70417-8.View ArticlePubMedGoogle Scholar
  7. Vijay V, Snehalatha C, Shina K, Lalitha S, Ramachandran A: Familial aggregation of diabetic kidney disease in Type 2 diabetes in south India. Diabetes Res Clin Pract. 1999, 43: 167-71. 10.1016/S0168-8227(99)00007-8.View ArticlePubMedGoogle Scholar
  8. Freedman BI, Soucie JM, Stone SM, Pegram S: Familial clustering of end-stage renal disease in blacks with HIV-associated nephropathy. Am J Kidney Dis. 1999, 34: 254-8. 10.1016/S0272-6386(99)70352-5.View ArticlePubMedGoogle Scholar
  9. Kao WH, Klag MJ, Meoni LA, et al: MYH9 is associated with nondiabetic end-stage renal disease in African Americans. Nat Genet. 2008, 40: 1185-92. 10.1038/ng.232.View ArticlePubMedGoogle Scholar
  10. Freedman BI, Kopp JB, Langefeld CD, et al: The apolipoprotein L1 (APOL1) gene and nondiabetic nephropathy in African Americans. J Am Soc Nephrol. 21: 1422-6.
  11. Hung AM, Crawford DC, Griffin MR, et al: CRP Polymorphisms and Progression of Chronic Kidney Disease in African Americans. Clin J Am Soc Nephrol. 2010, 5 (1): 24-33. 10.2215/CJN.01900309.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Tonelli M, Sacks F, Pfeffer M, Jhangri GS, Curhan G: Biomarkers of inflammation and progression of chronic kidney disease. Kidney Int. 2005, 68: 237-45. 10.1111/j.1523-1755.2005.00398.x.View ArticlePubMedGoogle Scholar
  13. Brull DJ, Montgomery HE, Sanders J, et al: Interleukin-6 gene -174g>c and -572g>c promoter polymorphisms are strong predictors of plasma interleukin-6 levels after coronary artery bypass surgery. Arterioscler Thromb Vasc Biol. 2001, 21: 1458-63. 10.1161/hq0901.094280.View ArticlePubMedGoogle Scholar
  14. Carlson CS, Aldred SF, Lee PK, et al: Polymorphisms within the C-reactive protein (CRP) promoter region are associated with plasma CRP levels. Am J Hum Genet. 2005, 77: 64-77. 10.1086/431366.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Crawford DC, Sanders CL, Qin X, et al: Genetic variation is associated with C-reactive protein levels in the Third National Health and Nutrition Examination Survey. Circulation. 2006, 114: 2458-65. 10.1161/CIRCULATIONAHA.106.615740.View ArticlePubMedGoogle Scholar
  16. Kathiresan S, Larson MG, Vasan RS, et al: Contribution of clinical correlates and 13 C-reactive protein gene polymorphisms to interindividual variability in serum C-reactive protein level. Circulation. 2006, 113: 1415-23. 10.1161/CIRCULATIONAHA.105.591271.View ArticlePubMedGoogle Scholar
  17. Lange LA, Carlson CS, Hindorff LA, et al: Association of polymorphisms in the CRP gene with circulating C-reactive protein levels and cardiovascular events. JAMA. 2006, 296: 2703-11. 10.1001/jama.296.22.2703.View ArticlePubMedGoogle Scholar
  18. Liu Y, Berthier-Schaad Y, Fallin MD, et al: IL-6 haplotypes, inflammation, and risk for cardiovascular disease in a multiethnic dialysis cohort. J Am Soc Nephrol. 2006, 17: 863-70. 10.1681/ASN.2005050465.View ArticlePubMedGoogle Scholar
  19. Zacho J, Tybjaerg-Hansen A, Jensen JS, Grande P, Sillesen H, Nordestgaard BG: Genetically elevated C-reactive protein and ischemic vascular disease. N Engl J Med. 2008, 359: 1897-908. 10.1056/NEJMoa0707402.View ArticlePubMedGoogle Scholar
  20. Appel LJ, Wright JT, Greene T, et al: Long-term effects of renin-angiotensin system-blocking therapy and a low blood pressure goal on progression of hypertensive chronic kidney disease in African Americans. Arch Intern Med. 2008, 168: 832-9. 10.1001/archinte.168.8.832.View ArticlePubMedGoogle Scholar
  21. NKF: Nationa Kidney Foundation K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002, 39: S1-266.Google Scholar
  22. CDC: National Health and Nutrition Examination Survey III (NHANES) DNA Specimens: Guidelines for Proposals to Use Samples and Proposed Cost Schedule. Federal Register. 2002, 67: 51585-51589.Google Scholar
  23. Chu AY, Parekh RS, Astor BC, et al: Association of APOE polymorphism with chronic kidney disease in a nationally representative sample: a Third National Health and Nutrition Examination Survey (NHANES III) Genetic Study. BMC Med Genet. 2009, 10: 108-10.1186/1471-2350-10-108.View ArticlePubMedPubMed CentralGoogle Scholar
  24. de Boer IH, Ioannou GN, Kestenbaum B, Brunzell JD, Weiss NS: 25-Hydroxyvitamin D levels and albuminuria in the Third National Health and Nutrition Examination Survey (NHANES III). Am J Kidney Dis. 2007, 50: 69-77. 10.1053/j.ajkd.2007.04.015.View ArticlePubMedGoogle Scholar
  25. Fried LF, Duckworth W, Zhang JH, et al: Design of combination angiotensin receptor blocker and angiotensin-converting enzyme inhibitor for treatment of diabetic nephropathy (VA NEPHRON-D). Clin J Am Soc Nephrol. 2009, 4: 361-8. 10.2215/CJN.03350708.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Gassman JJ, Greene T, Wright JT, et al: Design and statistical aspects of the African American Study of Kidney Disease and Hypertension (AASK). J Am Soc Nephrol. 2003, 14: S154-65. 10.1097/01.ASN.0000070080.21680.CB.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Tonelli M, Muntner P, Lloyd A, et al: Using proteinuria and estimated glomerular filtration rate to classify risk in patients with chronic kidney disease: a cohort study. Ann Intern Med. 2011, 154: 12-21.View ArticlePubMedGoogle Scholar
  28. Mattix HJ, Hsu CY, Shaykevich S, Curhan G: Use of the albumin/creatinine ratio to detect microalbuminuria: implications of sex and race. J Am Soc Nephrol. 2002, 13: 1034-9.PubMedGoogle Scholar
  29. Warram JH, Gearin G, Laffel L, Krolewski AS: Effect of duration of type I diabetes on the prevalence of stages of diabetic nephropathy defined by urinary albumin/creatinine ratio. J Am Soc Nephrol. 1996, 7: 930-7.PubMedGoogle Scholar
  30. Hung A, Pupim L, Yu C, et al: Determinants of C-reactive protein in chronic hemodialysis patients: relevance of dialysis catheter utilization. Hemodial Int. 2008, 12: 236-43. 10.1111/j.1542-4758.2008.00260.x.View ArticlePubMedGoogle Scholar
  31. Ned RM, Yesupriya A, Imperatore G, et al: Inflammation gene variants and susceptibility to albuminuria in the U.S. population: analysis in the Third National Health and Nutrition Examination Survey (NHANES III), 1991-1994. BMC Med Genet. 2010, 11: 155-10.1186/1471-2350-11-155.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Balakrishnan VS, Rao M: Genetics and reverse epidemiology among patients on chronic hemodialysis. Semin Dial. 2007, 20: 570-6. 10.1111/j.1525-139X.2007.00361.x.View ArticlePubMedGoogle Scholar
  33. Frazer KA, Ballinger DG, Cox DR, et al: A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007, 449: 851-61. 10.1038/nature06258.View ArticlePubMedGoogle Scholar
  34. Durbin RM, Abecasis GR, Altshuler DL, et al: A map of human genome variation from population-scale sequencing. Nature. 2010, 467: 1061-73. 10.1038/nature09534.View ArticlePubMedGoogle Scholar
  35. Kottgen A, Glazer NL, Dehghan A, et al: Multiple loci associated with indices of renal function and chronic kidney disease. Nat Genet. 2009, 41 (6): 712-7. 10.1038/ng.377.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Leon JM, Freedman BI, Miller MB, et al: Genome scan of glomerular filtration rate and albuminuria: the HyperGEN study. Nephrol Dial Transplant. 2007, 22: 763-71. 10.1093/ndt/gfl674.View ArticlePubMedGoogle Scholar
  37. Peralta CA, Li Y, Wassel C, et al: Differences in albuminuria between Hispanics and whites: an evaluation by genetic ancestry and country of origin: the multi-ethnic study of atherosclerosis. Circ Cardiovasc Genet. 2010, 3: 240-7. 10.1161/CIRCGENETICS.109.914499.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2350/12/65/prepub

Copyright

© Hung et al; licensee BioMed Central Ltd. 2011

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.