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Epidemiology and Outcomes |
,







,
* Department of Medicine,
Division of Nephrology,
Department of Epidemiology and Biostatistics, ** Department of Biopharmaceutical Sciences, and 
Center for Human Genetics, Department Dermatology, and Cardiovascular Research Institute, University of California at San Francisco, 
Lung Biology Center, San Francisco General Hospital, and
General Internal Medicine Section, San Francisco Veterans Affairs Medical Center, San Francisco, California; || Collaborative Health Studies Coordinating Center, Department of Biostatistics, ¶ Departments of Epidemiology and Laboratory Medicine, and |||| Departments of Medicine, Epidemiology, and Health Services, Cardiovascular Health Research Unit, University of Washington, Seattle, Washington; and 
Renal Section, VA Pittsburgh Healthcare System, and Renal-Electrolyte Division, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
Address correspondence to: Dr. Michael G. Shlipak, General Internal Medicine Section 111A1, VA Medical Center, 4150 Clement Street, San Francisco, CA 94124. Phone: 415-221-4810, ext. 3381; Fax: 415-379-5573; E-mail: shlip{at}itsa.ucsf.edu
Received for publication May 18, 2006. Accepted for publication September 7, 2006.
| Abstract |
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0.05 mg/dl per yr (odds ratio [OR] 0.94; 95% confidence interval [CI] 0.83 to 1.06) or with change in eGFR
3 ml/min per 1.73 m2 per yr (OR 1.02; 95% CI 0.92 to 1.13). In contrast, self reported African-American race was strongly associated with increased risk for kidney disease progression compared with white individuals for change in creatinine (OR 1.77; 95% CI 1.33 to 2.36) and for change in eGFR (OR 3.21; 95% CI 2.54 to 4.06). Among self-identified African Americans, low income (<$8000/yr) was strongly associated with prevalent kidney dysfunction by cystatin C >1.29 g/dl (adjusted OR 2.7; 95% CI 1.0 to 7.5) or by eGFR <60 ml/min per 1.73 m2 (adjusted OR 3.2; 95% CI 1.1 to 9.4) compared with those with incomes >$35,000/yr. Alleles that are known to be present more frequently in the African ancestral group were not associated with kidney dysfunction or kidney disease progression. Rather, kidney dysfunction in elderly African Americans seems more attributable to differences in environmental and social factors. | Introduction |
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| Materials and Methods |
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65 yr and participated in the Cardiovascular Health Study. Study design details were published previously (14). The initial cohort was enrolled from January 1989 to June 1990, and an additional 687 African American participants were enrolled by June 1993. Of the 862 African Americans, 736 participants were included in this study. Fifty-two participants either refused genetic testing or lacked available DNA samples and 74 had missing measures of renal function. All study protocols were approved by the appropriate institutional review boards.
Primary Predictor Variable: Selection of Ancestry-Informative Markers and Genotype Analysis
Twenty-four biallelic single-nucleotide peptide (SNP) markers were identified either from the National Center for Biotechnology Information SNP database (http://www.ncbi.nlm.nih.gov/SNP/) or from previously reported literature as being highly informative for ancestry (6,15). A detailed description of the source of markers and marker validation has been published (16). Briefly, the markers were chosen on the basis of the known allele frequency differences (
values) among African, European, and Native American populations.
is defined as the absolute value of allele frequency differences between two populations and is a measure of a markers informativeness for admixture analysis. A
of 1 suggests complete ancestry informativeness, and a
of 0 suggests no informativeness. These markers are spaced sufficiently distant throughout the genome that they offer independent association about genetic background or ancestry (16). Detailed information on these markers can be found in the dbSNP Web site under submitter handle "PSU-ANTH" or "HapMap-UCSF-WU-FP-TDI." Genotyping was performed using the AcycloPrime-FP method under standard conditions (16).
Secondary Predictors
Using records from the 1992 to 1993 visit, we recorded age, gender, and race (self-reported), body-mass index, smoking (current smoker versus former smoker or never smoked), diagnosis of diabetes (history of diabetes, use of a hypoglycemic agent or insulin, or a fasting glucose level of
126 mg/dl), hypertension (systolic BP >140/90 mmHg or treated hypertension), LDL and HDL cholesterol levels, and C-reactive protein levels (14). Categorical variables were used for education, (less than ninth grade, high school, or more than high school), income (<$8000/yr, $8000 to $35,000, and >$35,000), and occupation white collar ([professional, technical, administrative, sales, or clerical), blue collar (craftsman, machine operator, laborer, farming, or forestry workers), and other (housewife or refusal to answer]).
Outcome: Measures of Kidney Function
All assays were performed in fasting serum specimens that were stored at 70°C. Cystatin C was measured by means of a particle-enhanced immunonephelometric assay (N Latex Cystatin C; Dade Behring, Deerfield, IL) with a nephelometer (BNII; Dade Behring). The range of detection of the assay is 0.195 to 7.330 mg/L. The reference range for young, healthy individuals was reported as 0.53 to 0.95 mg/L. The assay remained stable over five cycles of freezing and thawing. Serum creatinine was measured by a colorimetric method (Ektachem 700; Eastman Kodak, Rochester, NY). The mean coefficient of variation for monthly controls was 1.94% (range 1.16 to 3.90%). We used the Modification of Diet in Renal Disease (MDRD) equation (17) to estimate GFR.
Statistical Analyses
The proportion of African, European, and Native American ancestry for each individual was estimated by a maximum-likelihood method (18) with the program IAE3 (19). For each genotype, an expression for the probability of this genotype is derived on the basis of the allele frequency in each of the ancestral populations. Because the markers are independent, the probabilities for each of the genotypes can be multiplied to give an expression for the multilocus probability or likelihood of a certain ancestry. The log of the likelihood then is maximized for each individual. An individuals percentage of African ancestry was coded as a continuous variable.
We categorized the cohort by quartiles of African ancestry, and we estimated the mean value of kidney function (by cystatin C and eGFR separately) for each quartile of African ancestry in a cross-sectional manner from the 1992 or 1993 visit. We used multivariable linear regression with adjusted means to test whether African ancestry was independently associated with each measure of kidney function (in separate models) after adjustment for age, gender, smoking, diabetes, hypertension, education, income, and occupation. We used linear spline models to depict graphically the association between African ancestry and kidney function across the range of ancestry and kidney function, using spline knots at the quartiles of African ancestry.
A change in serum creatinine
0.05 mg/dl per yr or a change in eGFR
3 ml/min per 1.73 m2 per year was considered progression of kidney disease, on the basis of a previous study from this cohort (20). We determined the change in serum creatinine from baseline to follow-up visits. Length of follow-up was 7 yr for the original cohort and 4 yr for the African American cohort that was recruited in 1992 to 1993. Because there was a different length of follow-up for the original and the African American cohorts, we used linear regression to determine the average annual change in creatinine as the slope of the change in creatinine for each individual. We used multivariable logistic regression to study the association of African ancestry as well as self-reported race with kidney disease progression. In addition, to study the association between sociodemographic variables and kidney function, we used linear and logistic regression adjusting for age, gender, hypertension, diabetes, and smoking. All analyses were performed using S-Plus (release 6.1; Insightful, Seattle, WA) and SPSS statistical software (release 12.0.2; SPSS, Chicago, IL). Two-tailed P < 0.05 was considered significant.
| Results |
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The prevalence of reduced kidney function was high, with 31% (225) having cystatin C levels between 1.0 and 1.28 mg/L, 13% (97) having levels >1.29 mg/L, and 18% (128) having an eGFR <60 ml/min per 1.73 m2.
Cross-Sectional Association of African Ancestry with Kidney Function
The mean level of kidney function (by cystatin C or eGFR) did not vary significantly across quartiles of African ancestry in unadjusted and adjusted analyses (Table 1). We also compared adjusted mean values of each kidney measure across the observed range of African ancestry using linear splines. There was no significant association between genetic African ancestry and renal function by either cystatin C or eGFR (Figures 1 and 2)
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Association of African Ancestry and Self-Reported Race with Kidney Disease Progression
Among 542 African American participants with repeated measures of serum creatinine, 11% had change in serum creatinine
0.05 mg/dl per yr and 20% had a change in eGFR
3 ml/min per 1.73 m2 per yr. There was no significant association between genetic African ancestry and progression of kidney disease in this cohort either by change in serum creatinine or by change in eGFR. However, self-reported African American race was significantly associated with kidney disease progression when compared with white individuals (Table 2). We also conducted an analysis using change in creatinine and change in eGFR as continuous variables, and the results were unchanged.
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1.29 mg/L (odds ratio 0.81; 95% confidence interval 0.56 to 1.17) and for eGFR <60 ml/min per 1.73 m2 (odds ratio 0.78; 95% confidence interval 0.55 to 1.11).
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| Discussion |
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Previous work has shown a greater burden of kidney disease in African Americans compared with white individuals (2,3). Our results are in accordance with previous data that suggest an association of self-identified African American race with faster progression of kidney disease (3,4), and these findings also were reported previously in the CHS Cohort (20). Many factors have been cited as potential explanations for these disparities, including sociodemographic factors, access to care, hypertension control, and genetics (3,21). In particular, the association between socioeconomic status (particularly income) and kidney disease has been described at the individual (22) and the area level (23). Because income but not African ancestry was associated with kidney dysfunction among African Americans, African Americans may be exposed and susceptible disproportionately to environmental, social, and health care access factors that affect the development and the progression of kidney disease (24).
The strength of our study is its novel method for addressing the association of genetic ancestry (rather than self-reported race) with kidney dysfunction and kidney disease progression. In addition, we used two different measures of kidney function and thereby expanded the range of kidney function levels at which these associations could be tested. Although our study is negative, it does not rule out the possible genetic contribution to kidney disease. In fact, future analyses still may lead to admixture mapping of important loci (25). The higher burden of ESRD in African Americans still may be due, in part, to genetic reasons and environmental reasons or geneenvironment interactions. Until these are elucidated, clinicians should continue to monitor for and treat early renal insufficiency aggressively according to accepted guidelines in this high-risk population. Future studies should be conducted in other populations that may lead to admixture mapping of important loci or elucidation of these possible geneenvironment interactions.
Our study also has certain limitations. We used a relatively limited number of genetic markers, which may result in imprecise estimates for individual ancestry and may have biased our results toward the null (16,26). However, simulation studies indicate that even a limited number of highly informative markers (FST >0.5) may provide estimates of ancestry correlated to true ancestry (26). The African American population has approximately 80% African ancestry, and there are relatively few individuals with <50% African ancestry. Therefore, we are unable to test the full range of ancestry, which limits our capacity to detect nonlinear effects at the lower end of African ancestry. Other populations, such as certain Latino groups, may be useful to elucidate the effects of African ancestry across the lower ranges (27). Variation within the ancestral populations (i.e., between African subgroups) cannot be captured by our method; therefore, information on African ancestry may be limited to informativeness from the ancestral populations genotyped. We did not include white individuals in our study, but previous analyses showed that non-Hispanic white individuals have <5% African ancestry (16,28); therefore, it is unlikely that there is sufficient African ancestry among Europeans to perform a similar analysis within that group (16). In addition, our study may have been biased by a survivor effect. That is, because our population is aged >65 yr, this cohort may not allow for the study of genetic differences in kidney function and progression in young African Americans, among whom disparities in kidney disease compared with white individuals are extreme. However, even among older individuals, African Americans also have a higher burden of kidney disease compared with white individuals (2). Further studies in younger cohorts and perhaps with larger numbers of makers may be required.
| Conclusion |
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| Acknowledgments |
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This study was presented as a poster at the American Heart Association Council on Prevention and Epidemiology; March 2, 2006; Phoenix, AZ.
A full list of participating CHS investigators and institutions can be found at http://www.chs-nhlbi.org. M.S. and R.K. had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
| Footnotes |
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| References |
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