BMC Medical Genetics

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Open AccessA genome-wide association for kidney function andendocrine-related traits in the NHLBI's Framingham Heart Study
Shih-Jen Hwang1, Qiong Yang3, James B Meigs2, Elizabeth N Pearce4 andCaroline S Fox*1,5
Address: 1National Heart Lung and Blood Institutes, Bethesda, MD, USA, 2Massachusetts General Hospital and Harvard Medical School , Boston,
MA, USA, 3Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA, 4Boston University School of Medicine,
Boston, MA, USA and 5Department of Endocrinology, Diabetes, and Hypertension, the Brigham and Women's Hospital, Harvard Medical School,
Boston, MA, USA
Email: Shih-Jen Hwang - hwangs2@nhlbi.nih.gov; Qiong Yang - qyang@bu.edu; James B Meigs - jmeigs@partner.org;
Elizabeth N Pearce - Elizabeth.pearce@bmc.org; Caroline S Fox* - foxca@nhlbi.nih.gov* Corresponding author

Published: 19 September 2007
<supplement>
S Vasan</editor><title><note>Research</note><p>The Framingham <url>http://www.biomedcentral.com/content/pdf/1471-2350-8-S1-info.pdf</url>
Heart Study 100,000 single nucleotide polymorphisms resource</p> </title> <editor>Emelia</supplement>
J Benjamin, Caroline S Fox, Daniel J Gottlieb, Cashell E Jaquish, Daniel Levy, James B Meigs, Christopher Newton-Cheh, Christopher J O'Donnell and Ramachandran

BMC Medical Genetics 2007, 8(Suppl 1):S10

doi:10.1186/1471-2350-8-S1-S10

This article is available from: http://www.biomedcentral.com/1471-2350/8/S1/S10© 2007 Hwang et al; licensee 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.

Abstract
Background: Glomerular filtration rate (GFR) and urinary albumin excretion (UAE) are markers
of kidney function that are known to be heritable. Many endocrine conditions have strong familial
components. We tested for association between the Affymetrix GeneChip Human Mapping 100K
single nucleotide polymorphism (SNP) set and measures of kidney function and endocrine traits.
Methods: Genotype information on the Affymetrix GeneChip Human Mapping 100K SNP set wasavailable on 1345 participants. Serum creatinine and cystatin-C (cysC; n = 981) were measured at
the seventh examination cycle (1998–2001); GFR (n = 1010) was estimated via the Modification ofDiet in Renal Disease (MDRD) equation; UAE was measured on spot urine samples during the sixth
examination cycle (1995–1998) and was indexed to urinary creatinine (n = 822). Thyroid
stimulating hormone (TSH) was measured at the third and fourth examination cycles (1981–1984;
1984–1987) and mean value of the measurements were used (n = 810). Age-sex-adjusted andmultivariable-adjusted residuals for these measurements were used in association with genotype
data using generalized estimating equations (GEE) and family-based association tests (FBAT)
models. We presented the results for association tests using additive allele model. We evaluatedassociations with 70,987 SNPs on autosomes with minor allele frequencies of at least 0.10, HardyWeinberg Equilibrium p-value ≥ 0.001, and call rates of at least 80%.
Results: The top SNPs associated with these traits using the GEE method were rs2839235 with
GFR (p-value 1.6*10-05), rs1158167 with cysC (p-value 8.5*10-09), rs1712790 with UAE (p-value
1.9*10-06), and rs6977660 with TSH (p-value 3.7*10-06), respectively. The top SNPs associated withthese traits using the FBAT method were rs6434804 with GFR(p-value 2.4*10-5), rs563754 withcysC (p-value 4.7*10-5), rs1243400 with UAE (p-value 4.8*10-6), and rs4128956 with TSH (p-value
3.6*10-5), respectively. Detailed association test results can be found at http://
www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. Four SNPs in or near the CST3
gene were highly associated with cysC levels (p-value 8.5*10-09 to 0.007).

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Conclusion: Kidney function traits and TSH are associated with SNPs on the Affymetrix
GeneChip Human Mapping 100K SNP set. These data will serve as a valuable resource forreplication as more SNPs associated with kidney function and endocrine traits are identified.

Background
Kidney disease affects 19 million adults in the UnitedStates [1]. Chronic kidney disease (CKD) is associatedwith cardiovascular disease [2-4], stroke [5], peripheralarterial disease [5,6], and all-cause mortality [7,8]. CVD
risk factors are associated with the development of kidneydisease [9], and the prevalence of traditional and novel
CVD risk factors is elevated among those with kidney disease [7,10]. Urinary albumin excretion (UAE) is an earlymarker of kidney function that predicts CKD progression[11-14]. While glomerular filtration rate (GFR) and UAE
are both measurements for kidney function, they represent different phenotypes and identify different subsets of
at-risk individuals [15].
Genetic factors play a role in the progression of renal disease. Familial aggregation of end-stage renal disease has
been identified [16]. Linkage analyses of kidney function
have been conducted [17-21], and novel loci have been
mapped to chromosomes 1 [18], 2 [21,22], 3 [17], 7 [22],
10 [19,20,22], and 18 [22]. In the Framingham Heart
Study, we have shown that kidney function is heritable[23], suggesting a role for genetic mechanisms in its etiology. Results of the linkage study from the Framingham
Heart Study suggested linkage between kidney disease anda locus on chromosome 4 with a LOD score of 2.2 [23].
Familial clustering of UAE has been observed in siblingsof subjects with diabetes [24], and UAE has been shown
to be heritable among the offspring of diabetic subjects[25]. Genome-wide linkage analyses have mapped novelloci to chromosomes 12 [26] and 19 [26] among families
enriched for hypertension. Among families with more
severe forms of nephropathy, suggestive evidence for linkage has been found on chromosome 10p [27] and 9q31–
32 [28]. In the Framingham Heart Study, we observed a
LOD score of 2.2 for UAE on chromosome 8 [29].
Thyroid disease, including Hashimoto's thyroiditis andGraves' disease, has a known familial component [30],and the same genes may underlie both conditions [31].
Measures of thyroid function have been shown to be heritable [32-34], and linkage has been reported to chromosome 18 for autoimmune thyroid disease in at least 2
studies [35,36].
As part of the Framingham Heart Study 100K Project, wesought to test the relation of multiple kidney and endocrine traits to 70,987 SNPs. In this manuscript, we focus
the results of association studies for GFR, UAE, cysC, andthyroid stimulating hormone (TSH), a sensitive measureof thyroid function.

Methods
Overall, 1345 participants were genotyped for the Affymetrix GeneChip Human Mapping 100K SNP set. For thismanuscript, we focused on GFR from examination 7, UAE
from examination 6, serum cysC from examination 7, andmean TSH from examinations 3 and 4. Phenotypes wereavailable in 1010 participants for GFR at exam cycle 7,822 participants for UAE at exam cycle 6, 981 participants
for cysC at exam cycle 7, and 810 participants for mean
TSH at exam cycles 3 and 4. Details about the selection
process and genotyping are provided in the Overview[37]. Age-sex- and multivariable-adjusted residuals weregenerated; we present here only the results for multivariable-adjusted traits (all available results can be found in thewebsite http://www.ncbi.nlm.nih.gov/projects/gap/cgibin/study.cgi?id=phs000007). We evaluated associations
with 70,987 SNPs on autosomes with minor allele frequencies of at least 0.10, HWE p-value ≥ 0.001, and genotypic call rates of at least 80%.
Phenotype assessment
Serum creatinine was measured using the modified Jaffe
method at exams 2 (1978–1981), 5 (1991–1995), 6(1995–1998), and 7 (1998–2001), and glomerular filtration rate (GFR) was estimated using the simplified Modification of Diet in Renal Disease Study equation [38,39].
CKD was defined based on the National Kidney Foundation Kidney Disease Outcome Quality Initiative working
group, and modified slightly as previously described [9].
Urinary albumin concentration (UAE) was measured byimmuno-turbidmetry (Tina-quant Albumin assay; Roche
Diagnostics, Indianapolis, IN) during the sixth examination cycle (1995–1998). Urinary albumin was indexed tourinary creatinine (as the urine albumin/creatinine ratio,
UACR) in order to account for differences in urine concentration. UACR is a validated and reliable single-sample
measure of urinary albumin excretion and is highly correlated with albumin excretion rates assessed by 24-h urine
collection [40,41]. Cystatin-C (cysC) was measured using
particle enhanced immunonephelometry (Dade Behring
BN 100 nephelometer; Dade Behring – Cystatin C reagent) with an inter-assay and intra-assay coefficient of variation of 3.3 and 2.4%, respectively. We have previously
published correlates of CKD in the Framingham Heart
Study, including hypertension, diabetes, smoking, obesity, and low HDL cholesterol [9,42].

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TSH was measured using a chemoluminescence assay(London Diagnostics, Eden Prairie, Minn) with a lower
limit of detection of 0.01 mU/L. Luteinizing hormone(LH), follicle stimulating hormone (FSH), and dehydroepiandrosterone sulfate (DHEAS) were measured as previously described [43,44]. Briefly, DHEAS concentrations
were measured on serum samples via radioimmunoassay(Diagnostic Products Corp, CA). Calcium and phosphorous were measured at the second examination cycle using
a standard colorimetric method (Roche Diagnostics,
Alameda, CA), and uric acid was measured at the second
examination cycle using an autoanalyzer with a phosphotungstic acid reagent.
Genotyping
Genotyping was performed using the 100K Affymetrix
GeneChip. Please see the Overview [37] for details.
Statistical methods
Phenotypes used for the analysis were created by generating normalized residuals. We generated both age-sex
adjusted and multivariable adjusted residuals for each
trait. Table 1 shows the covariates included in the multivariable adjustment; all data in this manuscript represents
the multivariable-adjusted traits. All association analyses
were performed using the generalized estimating equations or family based association tests; details are provided in the Overview [37]. Methods to verify family
structure, generate identity-by-descent for these 1345 participants with genotype information as well as the markers used for linkage analysis, is detailed in the Overview[37]. To assess the clustering of significance between each
SNP and phenotypes that were repeatedly measured inseveral examination cycles (see the third table in this article), we generated the geometric mean of p-values for
SNPs that fit the following criteria: at least 4 out of 6 p-values of <0.01 in GEE or FBAT analyses for 6 GFR traits(change in serum creatinine from exam 2 to 7; GFR at
exam 2; GFR at exam 5; GFR at exam 6; GFR at exam 7;
mean GFR exams 2, 5, 6, 7); one out of two UACR traits(UACR; UACR in a sample enriched for hypertension);
three out of three of TSH traits (TSH at exam 3; TSH at
exam 4; mean TSH at exams 3 and 4). Among the GFR
traits, Pearson correlation coefficients ranged from 0.18
(p < 0.001) between GFR at exam 2 and exam 7, to 0.77
(p < 0.001) for the mean of GFR at exams 2, 5, 6, and 7
and GFR at exam 7. Linkage analysis was performed using
the variance components methods on a subset of 100K
markers and Marshfield short-tandem repeats; please see
the Overview [37] for more details. Partial R2, the adjusted
percentage of the phenotype variation explained by the
genotype variation, was estimated by subtracting theadjusted R2 value for a model that excludes the genotype
from the R2 value for a model that includes the genotype.

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Results
A description of all traits and phenotypes, including relevant examination cycles and multivariable-adjustments, ispresented in Table 1. The median eGFR among individuals with CKD in our sample is 53.7 ml/min/1.73 m2.
Table 2a presents the top 25 SNPs with the lowest p-values
obtained via GEE for GFR, cysC, UAE, and mean TSH;
additional results can be found on the National Center for
Biotechnology
Information
websitehttp://
www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. The top SNP associated with
GFR, cysC, and UAE were rs2829235 (p-value 1.6*10-05),
rs1158167 (p-value 8.5*10-09) near the cysC precursor
gene family (CST3, CST4, CST9), and rs1712790 (p-value
1.9*10-06), respectively (Table 2a). The top SNP to be
associated with mean TSH was rs6977660 (p-value
3.7*10-06). Three SNPs were not shown on Table 2a dueto the linkage disequilibrium (LD) (r2 > 0.8) with theother top SNPs. These three SNPs were significantly associated with UAE at exam 6: rs9305355 (p-value 2.1*10-5)
in LD with rs9305354, rs725304 (p-value 2.5*10-5) in LD
with rs723464, and rs725307 (p-value 3.2 *10-5) in LD
with rs723464. Table 2b presents the top SNPs based on
the FBAT procedure. One SNP, rs10511594 (p = 9.0*10-5)
was in LD (r2 > 0.8) with rs7865184 (p = 4.0*10-5) for
mean TSH.
Table 2c presents all traits examined with LOD scores of atleast 2.5. One locus on chromosome 1 (nearest marker on
the 100K GeneChip, rs10489578) was linked to GFR witha LOD score of 3.08. We observed a LOD score of 4.28 foruric acid to chromosome 2 (nearest marker rs10495487),
a location we have previously identified using Marshfield
linkage analysis to uric acid [45].
Table 3 presents the top SNPs for our multiple phenotype
analysis for GFR, UAE, and TSH with a total of 24 SNPs
showing consistently significant associations with multiple related phenotypes. Tables 4a and 4b present results
looking at replication of genes that have been associatedwith kidney traits in the published literature. Four SNPs inor near the CST3 gene were highly correlated with cysClevels (p-value 8.5*10-09 to 0.007). All four SNPs haveminor allele frequencies greater than 10% and none werein linkage disequilibrium (defined by R2 > 0.8) as shownon Table 4. The proportion of the cysC variation that canbe explained by these SNPs is shown in Table 4.
rs1158167 accounts for 2.5% of the cysC variation. We
found nominal significance between a SNP near the APOE
gene and CKD (p = 0.04).
Additional findings
We also identified several other plausible candidate genesthat appear in our list of top 500 SNPs for each trait (seehttp://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/Page 3 of 10(page number not for citation purposes)

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Table 1: Traits names, Framingham Heart Study examination cycle, and multivariable adjustments
Trait

Sample size

Exam cycle/s
Adjustment†

Offspring

Original Cohort

840–1010
854
840–1010
1010
981
912–1031
822
532

2, 5, 6, 72, 72, 5, 6, 7
7
7
1, 2
6
6

0
0
0
0
0
0
0
0

Age and sex; multivariable*
Age and sex; multivariable*
Age and sex; multivariable*
Age and sex; multivariable*
Age and sex; multivariable*
Age and sex; multivariable*
Age and sex; multivariable*
Age and sex; multivariable*

906
911
883

2
2
3

0
0
0

Mean of TSH exam 3 & 4

810

3, 4

0

Luteinizing hormone (LH) **Follicle stimulating hormone (FSH)**
Dehydroepiandrosterone sulfate (DHEAS)

508
509
850

3
3
3

0
0
0

Age and sex; age, sex, serum creatinine
Age and sex; age, sex, and serum creatinine
Age and sex; age, sex, body mass index, smoking, menopausal status, thyroid
hormone use
Age and sex; age, sex, body mass index, smoking, menopausal status, thyroid
hormone use
Age and multivariable***Age and multivariable***Age and sex; multivariable***Serum Creatinine
Change in serum creatinine
Glomerular Filtration Rate (GFR)
Chronic Kidney Disease
Cystatin C
Uric acid
Urinary Albumin Excretion
Urinary Albumin Excretion ≥ 30 mg/g;
Hypertension-enriched sample
Serum Calcium
Serum Phosphorus
Thyroid stimulation hormone (TSH)*Multivariable adjustment include age, sex, systolic blood pressure, hypertension treatment, HDL-cholesterol, smoking, diabetes, body mass index**Men and post-menopausal women only with natural menopause not using hormone replacement treatment or oral contraceptive pills*** Age, diabetes mellitus, impaired fasting glucose, smoking, systolic blood pressure, diastolic blood pressure, body-mass index, hypertension
treatment, prevalent cardiovascular disease, total cholesterol/HDL ratio and alcohol intake

study.cgi?id=phs000007). For GFR, we identified LRP1B(GEE p-value = 0.0006, rs1049688), ADRBK2 (GEE pvalue = 0.002, rs1048312), APOB (GEE p-value = 0.003,
rs1048312), several genes in the chromosome 17 cytokine
gene cluster including CCL3, CCL4, and CCL18 (GEE pvalue 0.004, rs1818816), SCARB1 (GEE p-value = 0.004,
rs1902569), NFKB1 (FBAT p-value = 0.001, rs230489),
TGFB1 (FBAT p-value = 0.003, rs2072239), and PPARG(FBAT p-value = 0.005, rs709157). For UAE, we also identified a SNP in the LRP1B gene (GEE p-value = 0.004,
rs1049687). For cysC, we identified SNPs in the LRP1B
gene (GEE p-value = 0.001, rs1463615), ANGPT1 (GEE pvalue = 0.005, rs4354281), NFKB1 (FBAT p-value = 0.004,
rs2991716), and PPARG (FBAT p-value = 0.004,
rs1051041). For mean TSH, we identified SNPs in
ADRBK2 (GEE p-value = 0.002, rs3888397), TRHDE (GEEp-value = 0.002, rs2044305) and DIO2 (GEE p-value =0.003, rs54566), the SCD4 gene (rs10516679 GEE pvalue = 0.0007 FBAT p-value = 0.02), VLDLR (FBAT pvalue = 0.002, rs4084415) and APOBEC2P (FBAT p-value= 0.005, rs722442).

Discussion
In our analysis of kidney-related traits, we have foundstrong evidence for association between multiple kidneyrelated traits and TSH with SNPs on the Affymetrix 100K
GeneChip. We found strong evidence for association
between cysC levels and 4 SNPs in or near the CST3 gene.
For UAE, we observed strong association with ADAM23, agene involved in the metalloproteinase family, which may
be involved in the pathophysiology of glomerulosclerosis[46], and PCDH9, a gene that is a member of the cadherin

superfamily. For TSH, we observed significant association
with the HSPA4L gene with a mean p-value for all three
TSH measurements, a gene that is part of the heat shockprotein family, which may be involved in the pathophysiology of thyroid disease [47]. We also observed association with the SCD4 gene, a gene involved in theconversion of saturated to monounsaturated fatty acids;
TSH is an important correlate of lipid levels [48].
In our linkage results, we observed a region we have previously noted for uric acid [45], albeit with a significantly
higher LOD score. We identified a LOD score of 2.78 on
chromosome 3, approximately 18 Mb away from a region
previously noted in association with kidney function in
hypertensive individuals [17], a region that lies within our
1.5 support LOD interval. We also report novel loci for
GFR and TSH.
We show significant association between cysC levels andthe CST3 gene, an observation that has been previously
noted [49]. Our top SNP reaches genome-wide significance, and may represent a true finding. In our candidate
gene approach, we found nominal significance for a SNP
near the APOE gene, a gene that has been associated withCKD [50]. Unfortunately, poor coverage of the APOE geneby the Affymetrix 100K Genechip precluded a more indepth test of association with SNPs in the APOE gene and
CKD.
Strengths of our study lie in our assessment of multiple
measures of kidney function and endocrine traits in asample unselected for these traits, thus reducing bias. We

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Table 2: Most significant results for GFR (examination 7), UAE (examination 6), cysC (examination 7), and mean TSH (examinations 3
and 4) by GEE (2a), FBAT (2b) and linkage (2c) analyses
2a. Top 25 SNPs for association with GFR (examination 7), UAE (examination 6), cysC (examination 7), and mean TSH (examinations 3 and 4) basedon the lowest p value of the GEE test. Corresponding phenotype names on the web are GFRMV7 (GFR), UAELNMV6 (UAE), CYSCMV7 (CysC), andTSHMEAN34MV (TSH)

TRAIT

SNP

Physical Location (Mb)

Chromosome

P value – FBATP value – GEE

GENE

CysC

rs1158167

23,526,189

20

0.006

8.5*10-09

CST9L|CST9|CST3

UAE

rs1712790

114,126,679

11

0.014

1.9*10-06

FAM55B

TSH

rs6977660

19,578,720

7

0.010

3.7*10-06

TSH

rs9322817

105,338,926

6

0.502

6.5*10-06

HACE1

TSH

rs10499559

21,882,699

7

0.068

8.3*10-06

RAPGEF5

UAE

rs9305354

28,397,067

21

0.013

8.4*10-06

CysC

rs2145231

23,573,547

20

0.011

1.1*10-05

UAE

rs723464

133,940,196

4

0.000

1.1*10-05

UAE

rs2113379

207,177,180

2

0.003

1.4*10-05

ADAM23

GFR

rs2839235

46,625,020

21

0.055

1.6*10-05

PCNT2

TSH

rs10493147

129,095,104

4

0.040

2.1*10-05

HSPA4L

0.005

2.8*10-05

TTC21ADNAJC11TSH

rs784490

39,148,534

3

CST9|CST3|CST4

UAE

rs278021

6,698,929

1

0.958

2.9*10-05

UAE

rs1856190

33,598,615

9

0.127

3.0*10-05

UAE

rs10485409

91,562,132

6

0.147

3.1*10-05

UAE

rs2785980

216,088,914

1

4.8*10-04

3.7*10-05

UAE

rs837678

191,168,583

3

0.012

3.8*10-05

GFR

rs3095160

49,844,269

13

0.002

3.8*10-05

UAE

rs2761171

99,278,898

13

0.078

4.1*10-05

CLYBL
PABPC3

LEPREL1

GFR

rs10507344

24,623,069

13

0.017

4.1*10-05

GFR

rs890945

157,924,801

5

0.036

4.7*10-05

UAE

rs10502192

114,127,562

11

0.051

4.9*10-05

FAM55B

GFR

rs10489639

157,492,600

1

0.194

4.9*10-05

CD48

TSH

rs9308765

118,759,439

2

0.353

5.1*10-05

TSH

rs3908399

12,849,275

20

8.0*10-04

5.2*10-05

2b. Top 25 SNPs for association with GFR (examination 7), UAE (examination 6), cysC (examination 7), and mean TSH (examinations 3 and 4) basedon the lowest p-value of the FBAT test. Corresponding phenotype names on the web are GFRMV7 (GFR), UAELNMV6 (UAE), CYSCMV7 (CysC), andTSHMEAN34MV (TSH)

TRAIT

SNP

GEE

GENE

UAE

rs1243400

UAE

rs827640

Physical Location (Mb)

Chromosome

P value - FBATP value - GEE

9,016,664

10

4.8*10-06

0.036

10

1.5*10-05

0.047

9,028,017

GENE

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Table 2: Most significant results for GFR (examination 7), UAE (examination 6), cysC (examination 7), and mean TSH (examinations 3
and 4) by GEE (2a), FBAT (2b) and linkage (2c) analyses (Continued)
UAE

rs7315682

32,577,224

12

2.1*10-05

0.28

FGD4

GFR

rs6434804

196,589,899

2

2.4*10-05

0.003

DNAH7

1

2.5*10-05

0.253

SLC35F3

1.0*10-05

UAE

rs1543468

230,655,160

UAE

rs723464

133,940,196

4

2.7*10-05

TSH

rs4128956

131,848,063

93.6*10-05

0.355

TSH

rs7865184

14,677,867

9

3.9*10-05

0.082

CysC

rs563754

61,068,849

18

4.7*10-05

0.002

GFR

rs2885618

41,244,839

18

4.8*10-05

1.0*10-04

TSH

rs701801

100,166,859

10

5.8*10-05

0.090
0.192

ZDHHC21

HPS1

UAE

rs33855

171,124,642

5

6.2*10-05

GFR

rs10483741

61,059,454

14

6.3*10-05

0.298

PRKCH

CysC

rs2121267

46,461,040

2

6.6*10-05

0.002

EPAS1

CysC

rs1400306

83,256,163

11

7.2*10-05

0.061

TSH

rs1223531

13,255,521

6

8.0*10-05

0.123

CysC

rs10504244

59,147,328

8

8.3*10-05

0.005

12

9.1*10-05

0.087
0.088

UAE

rs2374688

106,175,669

UAE

rs10519012

58,333,458

15

1.0*10-04

CysC

rs1931619

82,682,388

6

1.0*10-04

0.05

UAE

rs10501264

41,387,406

11

1.1*10-04

7.2*10-04

UAE

rs10492025

111,735,770

12

1.1*10-04

0.41

UAE

rs2056694

128,517,768

71.1*10-04

0.1

UAE

rs8113386

16,332,871

19

1.1*10-04

0.053

2

1.3 *10-04

0.64

TSH

rs295136

200,966,508

PHACTR1

BTBD11

RPH3A

KLF2

2c. Magnitude and Location of Peak LOD scores for regions in which LOD exceeds 2.5.* Corresponding phenotype names on the web are
PHOSPHORUSMV2 (serum phosphorous), URICACIDMV2 (uric acid), SCRLNMVNL6 (serum creatinine exam 6), LHMV3 (luteinizing hormone),GFRMVNL6 (GFR exam 6), CALCIUMMV2 (serum calcium), GFRMV7 (GFR exam 7), and SCRLNMVNL5 (serum creatinine exam 5)

Trait

SNP

Chromosome

Physicallocation (Mb)

LOD 1.5(Lower; Mb)

LOD 1.5(Upper; Mb)

LOD

Serum Phosphorous

rs754958

8134307953

130413367

139409406

4.33

Uric acidrs10495487

223109451072413787803

4.28

Serum creatinine (exam 6)

rs10489578

1229251990

224108350

230301885

3.35

Luteinizing Hormone

rs10515134

1752218447

45043525

59136652

3.17

GFR (exam 6)

rs10489578

1229251990

220881707

230301885

3.08

Serum Calcium

rs10484370

618686366

10181754

21792915

3.03

GFR (exam 7)

rs10511176

3100809191

73551217

103238533

2.79

Serum creatinine (exam 5)

rs10502302

182542886

1562773857290

2.51*All traits are multivariable-adjusted; see Table 1 for specific covariate adjustments.

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BMC Medical Genetics 2007, 8(Suppl 1):S10

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Table 3: SNPs showing the top 8 significant association with multiple measurements of GFR, UACR, or TSH phenotypes.*
Corresponding phenotype names on the web are GFRMV7 (GFR), UAELNMV6 (UAE), and TSHMEAN34MV (TSH).

Trait

chromosome

SNP (rsID)

GFR
GFR
GFR
GFR
GFR
GFR
GFR
GFR
UAE
UAE
UAE
UAE
UAE
UAE
UAE
UAE
TSH
TSH
TSH
TSH
TSH
TSH
TSH
TSH

21
17
13
7
7
13
18
2
11
6
21
11
1
4
21
6
7
4
7
6
2
6
7
7

rs2839235
rs10512437
rs2480555
rs10486135
rs727087
rs1005066
rs2885618
rs10496887
rs1712790
rs10485409
rs9305354
rs10502192
rs2077678
rs723464
rs9305355
rs10484587
rs6977660
rs10493147
rs10499559
rs9322817
rs9308765
rs6942231
rs10486365
rs10486653

Physical Location
46625020
27046466
70785310
11301740
824457070790573
41244839
142198571
114126679
9156213228397067
114127562
75246848
133940196
28397088
143183270
19578720
129095104
21882699
105338926
118759439
105298507
19574604
34484903

Genes (in or near)

Mean p-value (GEE)

PCNT2

6.3*10-4
0.002
0.003
0.004
0.004
0.004
0.004
0.005
9.1*10-07
1.0*10-05
1.9*10-05
3.6*10-05
4.4*10-05
4.9*10-05
5.0*10-05
5.2*10-05
1.6*10-05
2.1*10-05
2.8*10-05
7.4*10-05
7.7*10-05
1.6*10-04
1.9*10-04
2.7*10-04

DACH1
ICA1
DACH1
SETBP1
LRP1B
FAM55D
EPHA7
FAM55DAIG1
HSPA4LDNAH11INSIG2

BMPER

Mean p-value (FBAT)
0.281
0.197
0.006
0.142
0.223
0.022
0.024
0.091
0.009
0.067
0.018
0.041
0.022
0.000
0.011
0.032
0.022
0.019
0.111
0.576
0.404
0.541
0.221
0.252

*see details in methods for criteria for generating mean p-value

Table 4: Results on Association Analysis for Candidate Genes

4a. Results of GEE analysis between SNPs in the CST3 and APOE candidate genes and the kidney function traits with p-value <0.05. Corresponding phenotype names on the web are CYSMV7 (CysC) and CKDMV7 (CKD).
Candidategene

PHENOTYPE

SNP

CST3
CST3
CST3
CST3
APOE

CysC
CysC
CysC
CysC
CKD

rs1158167
rs2145231
rs726217
rs911122
rs3760626

CHROMOSO
ME

Location

Minor allele
frequency(%)

P_value(GEE)

Partial R2

20
20
20
20
19

23,526,189
23,573,547
23,532,116
23,573,746
50,148,945

21
15
38
37
45

8.0 *10-9
1.1*10-5
3.1*10-4
0.007
0.04

2.5
1.2
0.8
1.1
-

4b. Results of FBAT analysis between SNPs in the CST3 candidate gene and kidney function traits with p-value < 0.05.
Corresponding phenotype names on the web are CYSMV7 (CysC) and UAEGE30HTNMV6 (UAE ≥ 30 mg/g)
Candidategene

PHENOTYPE

SNP

CST3
CST3
CST3

CysC
CysC
UAE ≥ 30*

rs1158167
rs2145231
rs911122

CHROMOSO
ME

LocationP-value20
20
20

23,526,189
23,573,547
23,573,746

0.006
0.011
0.032

*In a sample enriched for hypertensive individuals

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BMC Medical Genetics 2007, 8(Suppl 1):S10

also have excellent assessment of potential confounders
that we are able to adjust for in our residual creation.
Because the Framingham Heart Study has measured multiple traits, we are able to examine phenotype clustering.
Limitations exist as well. Kidney function was ascertainedby a single serum creatinine measure, which may lead tomisclassification. Our sample was not selected for CKD,
and as a result, affected individuals had moderate CKD as
reflected by the median eGFR of 53.7 ml/min/1.73 m2
among participants with CKD. The MDRD equation,
which was used to estimate GFR, has been shown tounderestimate GFR by 29% in healthy individuals [51];therefore, we may have introduced additional misclassification into our trait definition. We used a spot urine specimen to assess UAE instead of a 24-hour collection.
However, spot UAE approximates 24-hour collections[40], and are not prone to the error inherent in collecting
24-hour urine specimens. We used cysC as a continuous
trait and did not use transforming equations to estimate
GFR, as most existing equations have been developed insmall, selected samples [52,53], or developed using
immunoturbimetric method [53,54] instead of nephelometry and therefore we did not feel as though they
were appropriate for use in our large population-based
cohort. Further, we used cystatin C as a marker of kidney
function but can not rule out that it may also reflect cardiovascular disease risk above and beyond its relation tokidney function [55-59]. Our focus on multivariable
models may have led us to miss important bivariate associations between SNPs and measures of kidney function.
Given that our findings have not yet been replicated,
many p-values may represent false positive findings. We
used TSH as an indicator of thyroid function, as we do nothave measures of free thyroxine or a reliable assessment of
thyroid disease in our study sample. Our sample is neither
ethnically diverse nor nationally representative, and it isuncertain how our results would apply to other ethnic
groups. However, in genetics studies, sample homogeneity is beneficial in order to reduce population stratification. For limitations pertaining to our genotyping or
statistical methods, please see the Overview [37].

http://www.biomedcentral.com/1471-2350/8/S1/S10

tration rate; LD = linkage disequilibrium; LH = luteinizing
hormone; MDRD = Modification of Diet in Renal Disease;
SNP = single nucleotide polymorphism; TSH = thyroid
stimulating hormone; UAE = urinary albumin excretion.

Competing interests
The authors declare that they have no competing interests.

Authors' contributions
SH generated the phenotype data, participated in the analysis, and drafted the manuscript. CF helped generate thephenotype data, interpret the results, and draft the manuscript. QY generated the phenotype data, interpreted theresults, and helped draft the manuscript. JBM helped generate the phenotype data, interpret the results, and revised
the manuscript critically for important intellectual content; and has given final approval of the version to be published. EP assisted in the acquisition and cleaning of theTSH data and critically reviewed a draft of the manuscript.
All authors gave final approval to the manuscript.

Acknowledgements
The Framingham Study is supported by N01-HC 25195. Dr Meigs is supported by an American Diabetes Association Career Development Award.
The study was also supported by donation of urinary albumin excretion
assay reagents from Roche Diagnostics Inc. A portion of the research was
conducted using the BU Linux Cluster for Genetic Analysis (LinGA) fundedby the NIH NCRR (National Center for Research Resources) Shared
Instrumentation grant (1S10RR163736-01A1). The investigators would liketo recognize the Framingham Heart Study participants and the following
collaborators: Martin Larson, Daniel Levy, Emelia J. Benjamin, Joanne M.
Murabito, and Ramachandran S. Vasan.
This article has been published as part of BMC Medical Genetics Volume 8
Supplement 1, 2007: The Framingham Heart Study 100,000 single nucleotide polymorphisms resource. The full contents of the supplement areavailable online at http://www.biomedcentral.com/1471-2350/8?issue=S1.

References
1.

2.

Conclusion
Kidney function traits and TSH are associated with SNPs
on the Affymetrix 100K SNP GeneChip. Replication of
association between these traits and SNPs requires followup in independent samples. These data will serve as a valuable resource for replication as more SNPs associatedwith kidney function and endocrine traits are identified.

3.

4.

5.

Abbreviations
CKD = chronic kidney disease; cysC = cystatin-C; DHEAS= dehydroepiandrosterone sulfate; FBAT = family-based
association tests; FSH = follicle stimulating hormone; GEE= generalized estimating equations; GFR = glomerular fil-6.

Coresh J, Astor BC, Greene T, Eknoyan G, Levey AS: Prevalence ofchronic kidney disease and decreased kidney function in theadult US population: Third National Health and Nutrition
Examination Survey. Am J Kidney Dis 2003, 41:1-12.
Manjunath G, Tighiouart H, Ibrahim H, MacLeod B, Salem DN, Griffith
JL, Coresh J, Levey AS, Sarnak MJ: Level of kidney function as arisk factor for atherosclerotic cardiovascular outcomes in
the community. J Am Coll Cardiol 2003, 41:47-55.
Manjunath G, Tighiouart H, Coresh J, MacLeod B, Salem DN, Griffith
JL, Levey AS, Sarnak MJ: Level of kidney function as a risk factor
for cardiovascular outcomes in the elderly. Kidney Int 2003,
63:1121-1129.
Mann JF, Gerstein HC, Pogue J, Bosch J, Yusuf S: Renal insufficiency
as a predictor of cardiovascular outcomes and the impact oframipril: the HOPE randomized trial. Ann Intern Med 2001,
134:629-636.
Fried LF, Shlipak MG, Crump C, Bleyer AJ, Gottdiener JS, Kronmal
RA, Kuller LH, Newman AB: Renal insufficiency as a predictor of
cardiovascular outcomes and mortality in elderly individuals.
J Am Coll Cardiol 2003, 41:1364-1372.
O'Hare AM, Vittinghoff E, Hsia J, Shlipak MG: Renal insufficiency
and the risk of lower extremity peripheral arterial disease:results from the heart and estrogen/progestin replacement
study (HERS). J Am Soc Nephrol 2004, 15:1046-1051.

Page 8 of 10(page number not for citation purposes)

BMC Medical Genetics 2007, 8(Suppl 1):S10

7.

8.

9.
10.

11.

12.

13.

14.

15.

16.
17.

18.

19.
20.

21.

22.

23.

24.

Weiner DE, Tighiouart H, Amin MG, Stark PC, MacLeod B, Griffith
JL, Salem DN, Levey AS, Sarnak MJ: Chronic kidney disease as arisk factor for cardiovascular disease and all-cause mortality:
a pooled analysis of community-based studies. J Am Soc Nephrol 2004, 15:1307-1315.
Culleton BF, Larson MG, Wilson PW, Evans JC, Parfrey PS, Levy D:
Cardiovascular disease and mortality in a community-based
cohort with mild renal insufficiency.
Kidney Int 1999,
56:2214-2219.
Fox CS, Larson MG, Leip EP, Culleton B, Wilson PW, Levy D: Predictors of new-onset kidney disease in a community-based
population. JAMA 2004, 291:844-850.
Muntner P, Hamm LL, Kusek JW, Chen J, Whelton PK, He J: Theprevalence of nontraditional risk factors for coronary heartdisease in patients with chronic kidney disease. Ann Intern Med
2004, 140:9-17.
Adler AI, Stevens RJ, Manley SE, Bilous RW, Cull CA, Holman RR:
Development and progression of nephropathy in type 2 diabetes: The United Kingdom Prospective Diabetes Study(UKPDS 64). Kidney Int 2003, 63:225-232.
Nelson RG, Bennett PH, Beck GJ, Tan M, Knowler WC, Mitch WE,
Hirschman GH, Myers BD: Development and progression ofrenal disease in Pima Indians with non-insulin-dependent
diabetes mellitus. Diabetic Renal Disease Study Group. N
Engl J Med 1996, 335:1636-1642.
Peterson JC, Adler S, Burkart JM, Greene T, Hebert LA, Hunsicker
LG, King AJ, Klahr S, Massry SG, Seifter JL: Blood pressure control,
proteinuria, and the progression of renal disease. The Modification of Diet in Renal Disease Study. Ann Intern Med 1995,
123:754-762.
Maschio G, Alberti D, Janin G, Locatelli F, Mann JF, Motolese M, Ponticelli C, Ritz E, Zucchelli P: Effect of the angiotensin-convertingenzyme inhibitor benazepril on the progression of chronic
renal insufficiency. The Angiotensin-Converting-Enzyme
Inhibition in Progressive Renal Insufficiency Study Group. N
Engl J Med 1996, 334:939-945.
Garg AX, Kiberd BA, Clark WF, Haynes RB, Clase CM: Albuminuria and renal insufficiency prevalence guides population
screening: results from the NHANES III. Kidney Int 2002,
61:2165-2175.
Lei HH, Perneger TV, Klag MJ, Whelton PK, Coresh J: Familial
aggregation of renal disease in a population-based case-control study. J Am Soc Nephrol 1998, 9:1270-1276.
DeWan AT, Arnett DK, Atwood LD, Province MA, Lewis CE, Hunt
SC, Eckfeldt J: A genome scan for renal function among hypertensives: the HyperGEN study. Am J Hum Genet 2001,
68:136-144.
Cohn DH, Shohat T, Yahav M, Ilan T, Rechavi G, King L, Shohat M: A
locus for an autosomal dominant form of progressive renal
failure and hypertension at chromosome 1q21. Am J Hum
Genet 2000, 67:647-651.
Freedman BI, Rich SS, Yu H, Roh BH, Bowden DW: Linkage heterogeneity of end-stage renal disease on human chromosome
10. Kidney Int 2002, 62:770-774.
Hunt SC, Hasstedt SJ, Coon H, Camp NJ, Cawthon RM, Wu LL, Hopkins PN: Linkage of creatinine clearance to chromosome 10
in Utah pedigrees replicates a locus for end-stage renal disease in humans and renal failure in the fawn-hooded rat. Kidney Int 2002, 62:1143-1148.
Hunt SC, Coon H, Hasstedt SJ, Cawthon RM, Camp NJ, Wu LL, Hopkins PN: Linkage of serum creatinine and glomerular filtration rate to chromosome 2 in Utah pedigrees. Am J Hypertens
2004, 17:511-515.
Placha G, Poznik GD, Dunn J, Smiles A, Krolewski B, Glew T, PuppalaS, Schneider J, Rogus JJ, Rich SS, Duggirala R, Warram JH, Krolewski
AS: A genome-wide linkage scan for genes controlling variation in renal function estimated by serum cystatin C levels in
extended families with type 2 diabetes. Diabetes 2006,
55:3358-3365.
Fox CS, Yang Q, Cupples LA, Guo CY, Larson MG, Leip EP, Wilson
PW, Levy D: Genomewide Linkage Analysis to Serum Creatinine, GFR, and Creatinine Clearance in a Community-Based
Population: The Framingham Heart Study. J Am Soc Nephrol
2004, 15:2457-2461.
Pontiroli AE, Monti LD, Pizzini A, Piatti P: Familial clustering ofarterial blood pressure, HDL cholesterol, and pro-insulin but

http://www.biomedcentral.com/1471-2350/8/S1/S10

25.
26.

27.

28.

29.

30.
31.

32.

33.

34.

35.

36.

37.

38.

39.

40.
41.

42.

not of insulin resistance and microalbuminuria in siblings ofpatients with type 2 diabetes. Diabetes Care 2000, 23:1359-1364.
Forsblom CM, Kanninen T, Lehtovirta M, Saloranta C, Groop LC:
Heritability of albumin excretion rate in families of patientswith Type II diabetes. Diabetologia 1999, 42:1359-1366.
Freedman BI, Beck SR, Rich SS, Heiss G, Lewis CE, Turner S, Province
MA, Schwander KL, Arnett DK, Mellen BG: A genome-wide scanfor urinary albumin excretion in hypertensive families. Hypertension 2003, 42:291-296.
Iyengar SK, Fox KA, Schachere M, Manzoor F, Slaughter ME, Covic
AM, Orloff SM, Hayden PS, Olson JM, Schelling JR, Sedor JR: Linkage
analysis of candidate loci for end-stage renal disease due todiabetic nephropathy. J Am Soc Nephrol 2003, 14:S195-S201.
Chung KW, Ferrell RE, Ellis D, Barmada M, Moritz M, Finegold DN,
Jaffe R, Vats A: African American hypertensive nephropathy
maps to a new locus on chromosome 9q31–q32. Am J Hum
Genet 2003, 73:420-429.
Fox CS, Yang Q, Guo CY, Cupples LA, Wilson PW, Levy D, Meigs JB:
Genome-wide linkage analysis to urinary microalbuminuria
in a community-based sample: The Framingham Heart
Study. Kidney Int 2005, 67:70-74.
Hall R, Stanbury JB: Familial studies of autoimmune thyroiditis.
Clin Exp Immunol 1967, 2(Suppl-25):.
Tomer Y, Barbesino G, Greenberg DA, Concepcion E, Davies TF:
Mapping the major susceptibility loci for familial Graves' andHashimoto's diseases: evidence for genetic heterogeneityand gene interactions. J Clin Endocrinol Metab 1999, 84:4656-4664.
Samollow PB, Perez G, Kammerer CM, Finegold D, Zwartjes PW,
Havill LM, Comuzzie AG, Mahaney MC, Goring HH, Blangero J, Foley
TP, Barmada MM: Genetic and environmental influences on
thyroid hormone variation in Mexican Americans. J Clin Endocrinol Metab 2004, 89:3276-3284.
Hansen PS, Brix TH, Sorensen TI, Kyvik KO, Hegedus L: Major
genetic influence on the regulation of the pituitary-thyroid
axis: a study of healthy Danish twins. J Clin Endocrinol Metab
2004, 89:1181-1187.
Meikle AW, Stringham JD, Woodward MG, Nelson JC: Hereditaryand environmental influences on the variation of thyroid
hormones in normal male twins. J Clin Endocrinol Metab 1988,
66:588-592.
Vaidya B, Imrie H, Perros P, Young ET, Kelly WF, Carr D, Large DM,
Toft AD, Kendall-Taylor P, Pearce SH: Evidence for a new Graves
disease susceptibility locus at chromosome 18q21. Am J Hum
Genet 2000, 66:1710-1714.
Hadj KH, Rebai A, Kaffel N, Abid M, Ayadi H: Evidence for linkage
and association between autoimmune thyroid diseases andthe 18q12–q21 region in a large Tunisian family. Int J Immunogenet 2006, 33:25-32.
Cupples LA, Arruda H, Benjamin EJ, D'Agostino RB Sr, Demissie S,
DeStefano AL, Dupuis J, Falls K, Fox CS, Gottlieb DJ, Govindaraju DR,
Guo CY, Hwang SJ, Kathiresan S, Kiel DP, Larson MG, Laramie JM,
Levy D, Lunetta KL, Mailman MD, Manning AK, Meigs JB, Murabito JM,
Newton-Cheh C, O'Connor GT, O'Donnell CJ, Pandey MA, Qiong Y,
Seshadri S, Vasan RS, Wang ZY, Wolf PA, Atwood LD: The Framingham Heart Study 100K SNP genome-wide association
study resource: Overview of 17 phenotype working groupreports. BMC Med Genet 2007, 8(Suppl 1):S1.
Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D: A moreaccurate method to estimate glomerular filtration rate fromserum creatinine: a new prediction equation. Modification ofDiet in Renal Disease Study Group. Ann Intern Med 1999,
130:461-470.
K/DOQI clinical practice guidelines for chronic kidney disease : evaluation, classification, and stratification. Kidney
Disease Outcome Quality Initiative. Am J Kidney Dis 2002,
39:S1-246.
Nathan DM, Rosenbaum C, Protasowicki VD: Single-void urine
samples can be used to estimate quantitative microalbuminuria. Diabetes Care 1987, 10:414-418.
Bakker AJ: Detection of microalbuminuria. Receiver operating characteristic curve analysis favors albumin-to-creatinine ratio over albumin concentration. Diabetes Care 1999,
22:307-313.
Parikh NI, Hwang SJ, Larson MG, Meigs JB, Levy D, Fox CS: Cardiovascular disease risk factors in chronic kidney disease: over-Page 9 of 10(page number not for citation purposes)

BMC Medical Genetics 2007, 8(Suppl 1):S10

43.

44.

45.

46.
47.
48.

49.

50.
51.

52.
53.

54.

55.

56.

57.

58.

59.

all burden and rates of treatment and control. Arch Intern Med
2006, 166:1884-1891.
Amin S, Zhang Y, Sawin CT, Evans SR, Hannan MT, Kiel DP, Wilson
PW, Felson DT: Association of hypogonadism and estradiol
levels with bone mineral density in elderly men from the
Framingham study. Ann Intern Med 2000, 133:951-963.
Arnlov J, Pencina MJ, Amin S, Nam BH, Benjamin EJ, Murabito JM,
Wang TJ, Knapp PE, D'Agostino RB Sr, Bhasin S, Vasan RS: Endogenous sex hormones and cardiovascular disease incidence inmen. Ann Intern Med 2006, 145:176-184.
Yang Q, Guo CY, Cupples LA, Levy D, Wilson PW, Fox CS:
Genome-wide search for genes affecting serum uric acid levels: the Framingham Heart Study.
Metabolism 2005,
54:1435-1441.
Camp TM, Smiley LM, Hayden MR, Tyagi SC: Mechanism of matrix
accumulation and glomerulosclerosis in spontaneously
hypertensive rats. J Hypertens 2003, 21:1719-1727.
Ginsberg J, Labedz T, Brindley DN: Phosphorylation of Heat
Shock Protein-90 by TSH in FRTL-5 Thyroid Cells. Thyroid
2006, 16:737-742.
Kanaya AM, Harris F, Volpato S, Perez-Stable EJ, Harris T, Bauer DC:
Association between thyroid dysfunction and total cholesterol level in an older biracial population: the health, aging
and body composition study. Arch Intern Med 2002, 162:773-779.
Eriksson P, Deguchi H, Samnegard A, Lundman P, Boquist S, Tornvall
P, Ericsson CG, Bergstrand L, Hansson LO, Ye S, Hamsten A:
Human evidence that the cystatin C gene is implicated in
focal progression of coronary artery disease. Arterioscler
Thromb Vasc Biol 2004, 24:551-557.
Hsu CC, Kao WH, Coresh J, Pankow JS, Marsh-Manzi J, Boerwinkle
E, Bray MS: Apolipoprotein E and progression of chronic kidney disease. JAMA 2005, 293:2892-2899.
Rule AD, Larson TS, Bergstralh EJ, Slezak JM, Jacobsen SJ, Cosio FG:
Using serum creatinine to estimate glomerular filtration
rate: accuracy in good health and in chronic kidney disease.
Ann Intern Med 2004, 141:929-937.
Rule AD, Bergstralh EJ, Slezak JM, Bergert J, Larson TS: Glomerular
filtration rate estimated by cystatin C among different clinical presentations. Kidney Int 2006, 69:399-405.
Hoek FJ, Kemperman FA, Krediet RT: A comparison between
cystatin C, plasma creatinine and the Cockcroft and Gault
formula for the estimation of glomerular filtration rate.
Nephrol Dial Transplant 2003, 18:2024-2031.
Grubb A, Bjork J, Lindstrom V, Sterner G, Bondesson P, Nyman U: A
cystatin C-based formula without anthropometric variables
estimates glomerular filtration rate better than creatinine
clearance using the Cockcroft-Gault formula. Scand J Clin Lab
Invest 2005, 65:153-162.
Shlipak MG, Sarnak MJ, Katz R, Fried LF, Seliger SL, Newman AB, Siscovick DS, Stehman-Breen C: Cystatin C and the risk of deathand cardiovascular events among elderly persons. N Engl J
Med 2005, 352:2049-2060.
Shlipak MG, Wassel Fyr CL, Chertow GM, Harris TB, Kritchevsky SB,
Tylavsky FA, Satterfield S, Cummings SR, Newman AB, Fried LF:
Cystatin C and mortality risk in the elderly: the health, aging,and body composition study. J Am Soc Nephrol 2006, 17:254-261.
Fried LF, Katz R, Sarnak MJ, Shlipak MG, Chaves PH, Jenny NS, Stehman-Breen C, Gillen D, Bleyer AJ, Hirsch C, Siscovick D, Newman
AB: Kidney function as a predictor of noncardiovascular mortality. J Am Soc Nephrol 2005, 16:3728-3735.
O'Hare AM, Newman AB, Katz R, Fried LF, Stehman-Breen CO,
Seliger SL, Siscovick DS, Shlipak MG: Cystatin C and incident
peripheral arterial disease events in the elderly: results fromthe Cardiovascular Health Study. Arch Intern Med 2005,
165:2666-2670.
Shlipak MG, Katz R, Fried LF, Jenny NS, Stehman-Breen CO, Newman
AB, Siscovick D, Psaty BM, Sarnak MJ: Cystatin-C and mortality inelderly persons with heart failure. J Am Coll Cardiol 2005,
45:268-271.

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