HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) All Versions of this Article: CJN.02780806v1 2/3/509    most recent Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Scavini, M. Articles by Zager, P. G. Search for Related Content PubMed PubMed Citation Articles by Scavini, M. Articles by Zager, P. G.

The Burden of Chronic Kidney Disease among the Zuni Indians: The Zuni Kidney Project

Marina Scavini^*,,, Christine A. Stidley, Susan S. Paine^*, Vallabh O. Shah^*, Francesca Tentori^*,, Arlene Bobelu^,||, Thomas K. Welty^*, Jean W. MacCluer^¶, and Philip G. Zager^*

Department of Medicine, Divisions of ^* Nephrology; and Epidemiology and Biostatistics, University of New Mexico School of Medicine; and Dialysis Clinic, Inc., Albuquerque, New Mexico; San Raffaele Scientific Institute, Milan, Italy; ^|| Zuni Pueblo, Zuni, New Mexico; and ^¶ Southwest Foundation for Biomedical Research, San Antonio, Texas

Address correspondence to: Dr. Philip Zager, University of New Mexico, Division of Nephrology, 1500 Indian School NE, Albuquerque, NM 87102. Phone: 505-247-4044; Fax: 505-247-1297; E-mail: pzag{at}unm.edu

	Abstract

Top Abstract Introduction Materials and Methods Results Discussion Conclusion Disclosures References

 
The Zuni Indians of New Mexico are experiencing an epidemic of chronic kidney disease (CKD). The Zuni Pueblo created the Zuni Kidney Project (ZKP) to decrease the burden of CKD in the community. The aim of this study was to estimate the prevalence of CKD among Zuni Indians using National Kidney Foundation Kidney Disease Outcomes Quality Initiative criteria. The ZKP conducted a population-based, cross-sectional survey to estimate the prevalence of CKD and related risk factors among Zuni Indians aged 20 yr (n = 1113). GFR was estimated using equations based on serum creatinine, and urine albumin:creatinine ratio was calculated in a single spot urine sample. ESRD counts were obtained from health care providers. The age- and sex-adjusted prevalence of CKD among the Zuni Indians was >2.5-fold higher than that among the US composite population. The estimated prevalence of CKD stages 1 and 2 combined was three- to four-fold higher than that of CKD stages 3 and 4 combined. This ratio was significantly higher than that in the US composite population (1.4-fold). The prevalence of CKD stage 5 was eight-fold higher among the Zuni Indians than among the composite US population. The Zuni Indians have an expanded pool of CKD that contributes to the high burden of ESRD. The high prevalence of CKD stages 1 and 2 provides a unique opportunity to develop innovative treatment programs to reduce the burden of CKD in Zuni Pueblo.

	Introduction

Top Abstract Introduction Materials and Methods Results Discussion Conclusion Disclosures References

 
The current epidemic of chronic kidney disease (CKD) among American Indians is characterized by high rates of albuminuria and ESRD (1–3). The age- and gender-adjusted prevalence of ESRD among American Indians (2594 per million population) is second only to that among black individuals (4618 per million population) (4).

The National Kidney Foundation's Kidney Disease Outcomes Quality Initiative (NKF K/DOQI) has established guidelines to define five stages of CKD (5). The prevalence and distribution of CKD stages in the US population has been described using the Third National Health and Nutrition Examination Survey (NHANES III) population, a representative sample of noninstitutionalized US adults (6,7). Because of the small number of American Indians who participated in NHANES III, only limited data are available on the prevalence of CKD stages among this high-risk group (7).

The Zuni Indians of New Mexico are experiencing an epidemic of renal disease (8,9). In response to this epidemic, the Zuni Pueblo established the Zuni Kidney Project (ZKP) in collaboration with the University of New Mexico Health Sciences Center (UNMHSC), Southwest Foundation for Biomedical Research, Indian Health Service (IHS) and Dialysis Clinic Inc. The ZKP was funded by the National Institute of Diabetes and Digestive and Kidney Diseases and by Dialysis Clinic Inc. The ZKP conducted a population-based, cross-sectional survey of the Zuni Pueblo to estimate the prevalence of kidney disease and related risk factors (10). This survey reported high prevalence estimates for albuminuria and hematuria among Zuni Indians with and without diabetes (11,12).

This study was conducted to estimate the prevalence of CKD among the Zuni Indians using K/DOQI criteria (5). To accomplish this, we estimated GFR using equations based on serum creatinine among ZKP survey participants (10). We also estimated the prevalence of albuminuria among survey participants with normal and decreased renal excretory function.

	Materials and Methods

Top Abstract Introduction Materials and Methods Results Discussion Conclusion Disclosures References

 
Study Population
The Zuni Indians are an American Indian tribe who live in a rural portion of western New Mexico. Rates of emigration and immigration among the Zuni are low. The Zuni Indians raise maize and wheat and engage in sheep herding and jewelry making. Traditional Zuni life is oriented around a matrilineal clan system and a ceremonial system based on a belief in the "ancient ones." In 2000, the Zuni Tribe had approximately 10,000 tribal members, 85% of whom live in the Pueblo (13) and receive their health care through IHS. The median age of the population was 26 yr (13).

Population Sample
Between January 1999 and April 2002, we recruited 1664 participants in the ZKP population-based, cross-sectional survey (10). The sample was representative of the Zuni Indians, as for age, gender, and education (10–12,14). Participants who were non-Zuni Indians (n = 54), aged <20 yr (n = 483), or missing variables that are required to estimate GFR (n = 14) were excluded from this analyses. Therefore, this study was restricted to 1113 participants, who accounted for 17.9% of the Zuni Indians aged 20 yr.

Data Collection
We collected data from the following sources: (1) ZKP population-based, cross-sectional survey, which has been previously described (n = 1664) (10); (2) Zuni Dialysis Unit, which provided de-identified information on Zuni ESRD patients maintained on hemodialysis (n = 83) or peritoneal dialysis (n = 6); and (3) UNMHSC Transplant Services, which provided de-identified information on Zuni patients who received renal transplantation (n = 9). The study was approved by the Human Research Review Committee of the UNMHSC, the Zuni Tribal Council, and the institutional review board of the IHS. Participants were asked to sign an informed consent before entering the study.

Laboratory Methods
Creatinine in serum and urine and serum glucose were measured by colorimetric methods (15). During a 28-mo period, the median coefficients of variation of the serum creatinine standards were 3.6% (interquartile range [IQR] 2.2) for creatinine values in the normal range (approximately 1.0 mg/dl; n = 3146 measurements) and 1.4% (IQR 0.4) for values in the high range (approximately 5.8 mg/dl; n = 3200 measurements). Glycosylated hemoglobin (HbA_1c) was measured by latex immunoagglutination inhibition. Urine albumin was measured in spot urine samples by rate nephelometry and expressed as urine albumin to creatinine ratio (UACR) (16,17).

Estimation of GFR
No specific prediction equation is available to estimate GFR among American Indians. Therefore, we estimated GFR using two serum creatinine–based equations: (1) MDRD-C (18): Estimated GFR (eGFR; ml/min per 1.73 m²) = 186.3 x [serum creatinine (mg/dl)]^–1.154 x age (years)^–0.203 x (0.742 if female); and (2) MDRD-AI: eGFR (ml/min per 1.73 m²) = 186.3 x [serum creatinine (mg/dl)]^–1.154 x age (years)^–0.203 x (0.742 if female) x 1.106.

The MDRD-C equation is the four-variable equation developed by the Modification of Diet in Renal Disease (MDRD) Study for white individuals (18). The MDRD-AI equation is the four-variable MDRD equation modified for American Indians. The rationale for this modified equation is based on studies that were conducted by Nelson et al. (19) among the Pima Indians. These investigators compared GFR as measured by iothalamate clearance (iGFR) with GFR estimates that were obtained with the four-variable MDRD equation with and without the black term. The measured GFR were intermediate between the estimates that were obtained with and without the term for black race. Specifically, eGFR as estimated using the MDRD formula without the black term underestimated iGFR by a median of 11%, and the equation with the black term overestimated iGFR by a median of 8%. Therefore, we used a multiplicative term of 1.106 that is halfway between the MDRD term for white race (1.0) and that for black race (1.212).

Classification of Participants
CKD was defined according to the NKF K/DOQI guidelines as (1) presence of markers of kidney damage in urine or blood or (2) eGFR <60 ml/min per 1.73 m² (5). CKD stages were defined according to NKF K/DOQI guidelines: Stage 1, kidney damage with normal or increased GFR (90 ml/min per 1.73 m²); stage 2, kidney damage with mildly decreased GFR (60 to 89 ml/min per 1.73 m²); stage 3, moderately decreased GFR (30 to 59 ml/min per 1.73 m²); stage 4, severely decreased GFR (15 to 29 ml/min per 1.73 m²); and stage 5, kidney failure (GFR <15 ml/min per 1.73 m² or renal replacement therapy) (5).

Albuminuria was classified according to the American Diabetes Association recommendations (normal: UACR <0.03; microalbuminuria: 0.03 UACR <0.3; macroalbuminuria: UACR 0.3) (20). Because of its cross-sectional design, results from the ZKP survey did not allow us to determine whether the microalbuminuria was persistent. To estimate the persistence of microalbuminuria over time, we examined agreement in UACR findings in a subset of survey participants (n = 228) who were enrolled in a subsequent ZKP case-control study. A second urine sample was collected after a median period of 130 d (25th percentile 84 d; 75th percentile 287 d). Among participants who had microalbuminuria during the initial survey, the proportion with micro- or macroalbuminuria at follow-up was 68% (95% confidence interval [CI] 55.4 to 78.2). We then used a 0.68 factor to compute the prevalence of persistent microalbuminuria among participants who had an eGFR 60 ml/min per 1.73 m² (6).

Participants were classified as having diabetes when they had a history of diabetes (excluding gestational diabetes), random glucose 200 mg/dl, or HbA_1c >7.0% (20,21). Among the Pima Indians without a previous diagnosis of diabetes, the probability of having diabetes was 50% for individuals with HbA_1c of 6.0 to 6.9% and 98% for individuals with HbA_1c of 7.0 to 7.9% (22). Therefore, we classified participants with HbA_1c between 6.0 and 7.0%, a random glucose <200 mg/dl, and no history of diabetes as having an indeterminate diabetes status.

Data Quality
Participants with an implausibly high eGFR (eGFR >200 ml/min per 1.73 m²; n = 1) were assigned a maximum value of 200 ml/min per 1.73 m² (6). Participants with eGFR <15 ml/min per 1.73 m² (n = 2 by MDRD-C equation; n = 1 by MDRD-AI equation) were likely not to be in steady state with regard to serum creatinine and were not included in eGFR distributions. When prevalence of decreased eGFR was stratified by diabetes, we excluded 24 participants who were missing diabetes status and 67 participants with indeterminate diabetes status. When we estimated the prevalence of CKD, we excluded participants who did not have a UACR determination (n = 17).

Statistical Analyses
Prevalence estimates of decreased eGFR and CKD were expressed as percentages with 95% CI. Estimates of the variances and covariances were first obtained using Taylor series linearization to adjust for dependencies that were created by sampling within families. Because these results were very similar to those without adjustment for the sampling design and some proportions were very small, exact binomial proportions were used instead. When appropriate, estimated prevalence of decreased eGFR and CKD were age-adjusted (4-yr intervals) using the 2000 Zuni Tribal Census, with variance estimates adjusted by the finite population correction factor because >10% of the eligible population was enrolled in the survey (13). Estimated prevalence of decreased eGFR and CKD were also adjusted to the 2000 US census to allow comparison with NHANES III data (6,23). Statistical analyses were carried out in SAS (SAS Institute, Cary, NC) and SUDAAN (RTI, Research Triangle Park, NC) to account for the complex study design. The level of statistical significance was P < 0.05.

	Results

Top Abstract Introduction Materials and Methods Results Discussion Conclusion Disclosures References

 
Demographic Characteristics of ZKP Survey Participants
The demographic characteristics of ZKP survey participants are shown (Table 1). Female participants (median age 38 yr; IQR 22 yr) were older than male (median age 35 yr; IQR 16 yr) participants (P < 0.002). As we previously reported, prevalence of diabetes was higher among female (27.9%; 95% CI 23.6 to 30.6) than male (15.7%; 95% CI 12.4 to 18.9) participants (P < 0.001) (11,14).

View larger version (31K): [in this window] [in a new window] [as a PowerPoint slide]   Figure 1. Distribution of serum creatinine among female () and male () survey participants.

View larger version (37K): [in this window] [in a new window] [as a PowerPoint slide]   Figure 2. Distribution of GFR estimated using the Modification of Diet in Renal Disease equation for white individuals (MDRD-C; ) and MDRD equation for American Indians (MDRD-AI; ) among female (top) and male (bottom) participants. eGFR, estimated GFR; MDRD-C, no correction factor; MDRD-AI, correction factor = 1.106.

	Discussion

Top Abstract Introduction Materials and Methods Results Discussion Conclusion Disclosures References

Several possible factors may have contributed to these findings. First, high levels of anxiety and poor functional status among patients with CKD stages 3 and 4 may have led to significant selection bias with underrepresentation of these CKD groups (10,11,14). Furthermore, as postulated by Coresh et al. (6), anxiety and illness may also lead to nonparticipation and underrepresentation of patients with CKD stage 5. However, we minimized this risk by estimating the prevalence of ESRD from the records of the Zuni Dialysis Unit and the UNMHSC Transplant Services, where virtually all Zuni Indians with ESRD receive their care. Second, kidney function of Zuni Indians with CKD stages 3 and 4 may deteriorate rapidly, so the duration of these stages is short and their prevalence is low. Among the Pima Indians with overt albuminuria, the mean rate of GFR loss was three-fold higher than that reported among MDRD participants (24,25). Therefore, because disease progression is faster among patients with diabetic versus nondiabetic CKD, it is likely that, overall, the Zuni Indians have a faster progression because of the high prevalence of diabetic kidney disease (26). Third, the mortality rate among Zuni Indians with CKD stages 3 and 4 may be high, resulting in a lower prevalence of these CKD stages than expected. In fact, it is well established that CKD is associated with an excess risk for cardiovascular disease (CVD), even after adjustment for traditional CVD risk factors (27). Strong Heart Study investigators reported high rates of albuminuria and increased CVD mortality among American Indians (2,28). Fourth, the IHS, the Zuni Diabetes Program, and the ZKP all have increased the awareness of kidney disease; this may have led to better control of risk factors for progression of CKD (e.g., blood glucose, arterial BP) and, potentially, to slow disease progression. Finally, it should be noted that American Indians with ESRD have improved survival compared with non-Hispanic white individuals; this may have contributed to the observed high prevalence of CKD stage 5 (4).

The decision to use the four-variable MDRD equation to assess eGFR was based on several cultural and logistic reasons. The use of radioactive tracers for research purposes is not culturally acceptable to the Zuni Indians. The remote setting of the Zuni Pueblo practically precludes the possibility to measure GFR with unlabeled iothalamate (29). Finally, serum cystatin C measurements were not widely available at the time the survey was conducted (30). We acknowledge that the MDRD equation has not been validated in the following settings: (1) Among American Indians, (2) in the full range of eGFR expected in a population, and (3) among individuals with diabetes. However, Nelson et al. (19) demonstrated that, among the Pima Indians, the four-variable MDRD equation adequately predicted GFR as measured by the clearance of unlabeled iothalamate. Because no validated formula is available to estimate eGFR among American Indians, the use of a modified MDRD equation is reasonable. Recently, Poggio et al. (31) demonstrated that the four-variable MDRD equation performs well among individuals with diabetes but underestimated by 9.0 ml/min per 1.73 m² the GFR as measured by iothalamate clearance in individuals with normal GFR. Furthermore, the lack of standardization of our creatinine assay to the MDRD laboratory assay may have led to errors in estimating GFR between –5.5 and –8.1 ml/min per 1.73 m², with larger errors at higher levels of eGFR (32). This may have resulted in underestimation of GFR in this study. However, because the majority of participants with eGFR between 60 and 69 ml/min per 1.73 m² had either persistent microalbuminuria or overt proteinuria, a bias in eGFR of 9.0 ml/min per 1.73 m² would only minimally affect the estimated prevalence of CKD. It should be noted that a small proportion of participants with CKD had only a decreased eGFR. In fact, the prevalence of CKD reported in this study, which is based on both UACR and eGFR, is similar to what we previously reported using albuminuria alone (11).

We acknowledge that a cross-sectional survey with a single spot urine sample cannot accurately assess the prevalence of persistent microalbuminuria. This limitation may have significantly decreased the precision of our estimated prevalence of CKD stage 1. However, when microalbuminuria was measured twice in a subset of survey participants, the persistence rate (68%) was similar to that reported by Coresh et al. (6) among a subset of the NHANES III population. Furthermore, we recently reported that among the Zuni Indians, there is an excellent agreement between the classification of albuminuria using one versus three spot urine samples (33).

We recognize that some Zuni Indians with glomerulonephritis may present with intermittent hematuria (8,9). However, we decided not to include hematuria as an indicator of CKD for the following reasons: (1) Using isolated hematuria as a marker for CKD may have significantly biased the precision of our prevalence estimates (12), (2) medical chart review often did not provide sufficient evidence to exclude a urologic cause for isolated hematuria, and (3) dipstick analysis is only semiquantitative and not specific.

We acknowledge that by not measuring fasting glucose we may have underestimated the proportion of participants with diabetes. However, this may not have had a major impact on the accuracy of our estimates. First, the prevalence of undiagnosed diabetes among the Zuni Indians is relatively low because of increased diabetes awareness (14). Second, most individuals with early diabetes were likely to be classified as undetermined diabetes status, allowing us to obtain conservative estimates among individuals with diabetes and individuals with normal glucose tolerance.

	Conclusion

Top Abstract Introduction Materials and Methods Results Discussion Conclusion Disclosures References

 
The expanded pool of CKD among the Zuni Indians has significant public health implications. Because the majority of the individuals with CKD are in stage 1 or 2, prevention and early treatment programs will likely succeed in reducing the burden of ESRD among the Zuni Indians. Our findings will support the efforts of this community to develop community-based screening and early implementation of primary and secondary prevention programs.

	Disclosures

Top Abstract Introduction Materials and Methods Results Discussion Conclusion Disclosures References

 
None.

	Acknowledgments

 
This study was supported in part by the National Institutes of Health (DK 49347, 07/01/1997 to 06/30/2004), UNM Clinical Research Center for Research Resources, UNM General Clinical Research Center (grant 5M01 RR 00997), National Institute of Environmental Health Sciences (grant P30 ES-012072), and Dialysis Clinic Inc.

We sincerely thank the Zuni people for welcoming us into their community; without their support, this project would not have been possible. We acknowledge the contributions and support of the Zuni Governors (M. Bowekaty and A. Quetawki) and the Zuni Tribal Council; support of Antonia M. Harford, MD, James W. Whitfield, MD, Andrew S. Narva, MD, David S. Kessler, MD, and the Zuni PHS Hospital staff; the advice provided by the Strong Heart Study investigators; and the excellent technical contribution of B. Brennan, BS, D. Dalton, BS, J. Ghahate, M. Helbert, M. Lamey, BA, S. McClelland, MSN, CFNP, W. McCurdy, BS, T. Peynetsa, D. Solomon, M. Waikaniwa, and K. Utterback.

	Footnotes

 
Published online ahead of print. Publication date available at www.cjasn.org.

Received August 14, 2006. Accepted March 1, 2007.

	References

Top Abstract Introduction Materials and Methods Results Discussion Conclusion Disclosures References

 

1. Nelson RG, Morgenstern H, Bennett PH: An epidemic of proteinuria in Pima Indians with type 2 diabetes mellitus. Kidney Int54 :2081 –2088,1998[CrossRef][Medline]
2. Robbins DC, Knowler WC, Lee ET, Yeh J, Go OT, Welty T, Fabsitz R, Howard BV: Regional differences in albuminuria among American Indians: An epidemic of renal disease. Kidney Int49 :557 –563,1996[Medline]
3. Hoehner CM, Greenlund KJ, Rith-Najarian S, Casper ML, McClellan WM: Association of the insulin resistance syndrome and microalbuminuria among nondiabetic Native Americans. The Inter-Tribal Heart Project. J Am Soc Nephrol13 :1626 –1634,2002[Abstract/Free Full Text]
4. US Renal Data System: 2005 Annual Data Report: Atlas of End-Stage Renal Disease in the United States, Bethesda, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases,2005
5. K/DOQI clinical practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. Am J Kidney Dis39 :S1 –S266,2002[CrossRef][Medline]
6. Coresh J, Astor BC, Greene T, Eknoyan G, Levey AS: Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third National Health and Nutrition Examination Survey. Am J Kidney Dis41 :1 –12,2003[Medline]
7. Ezzati TM, Massey JT, Waksberg J, Chu A, Maurer KR: Sample design: Third National Health and Nutrition Examination Survey. Vital Health Stat21 –35,1992
8. Hoy WE, Megill DM, Hughson MD: Epidemic renal disease of unknown etiology in the Zuni Indians. Am J Kidney Dis9 :485 –496,1987[Medline]
9. Pasinski R, Pasinski M: End-stage renal disease among the Zuni Indians: 1973–1983. Arch Intern Med147 :1093 –1096,1987[Abstract/Free Full Text]
10. Stidley CA, Shah VO, Narva AS, Dalton D, MacCluer JW, Bobelu A, Scavini M, Welty TK, Zager PG: A population-based, cross-sectional survey of the Zuni Pueblo: A collaborative approach to an epidemic of kidney disease. Am J Kidney Dis39 :358 –368,2002[Medline]
11. Shah VO, Scavini M, Stidley CA, Tentori F, Welty TK, MacCluer JW, Narva AS, Bobelu A, Albert CP, Kessler DS, Harford AM, Wong CS, Harris AA, Paine S, Zager PG: Epidemic of diabetic and nondiabetic renal disease among the Zuni Indians: The Zuni Kidney Project. J Am Soc Nephrol14 :1320 –1329,2003[Abstract/Free Full Text]
12. Tentori F, Stidley CA, Scavini M, Shah VO, Narva AS, Paine S, Bobelu A, Welty TK, MacCluer JW, Zager PG: Prevalence of hematuria among Zuni Indians with and without diabetes: The Zuni kidney Project. Am J Kidney Dis41 :1195 –1204,2003[CrossRef][Medline]
13. Office of the Zuni Tribal Census. Tribal Census2000
14. Scavini M, Stidley CA, Shah VO, Narva AS, Tentori F, Kessler DS, Bobelu A, Albert CP, Bobelu J, Jamon E, Natachu K, Neha D, Waikaniwa M, Welty TK, MacCluer JW, Zager PG: Prevalence of diabetes is higher among female than male Zuni indians. Diabetes Care26 :55 –60,2003[Abstract/Free Full Text]
15. Tietz Textbook of Clinical Chemistry, 3rd Ed., Philadelphia, WB Saunders,1999
16. Watts GF, Bennett JE, Rowe DJ, Morris RW, Gatling W, Shaw KM, Polak A: Assessment of immunochemical methods for determining low concentrations of albumin in urine. Clin Chem32 :1544 –1548,1986[Abstract/Free Full Text]
17. Schwab SJ, Christensen RL, Dougherty K, Klahr S: Quantitation of proteinuria by use of protein-to-creatinine ratios in single urine samples. Arch Intern Med147 :943 –944,1987[Abstract/Free Full Text]
18. Levey AS, Greene T, Kusek J, Beck GJ: A Simplified equation to predict glomerular filtration rate from serum creatinine [Abstract]. J Am Soc Nephrol11 :155A –944,2000
19. Nelson RG, Greene T, Beck GJ, Van Lente F, Wang X, Knowler WC: Estimating GFR by the MDRD and Cockroft-Gault Equations in Pima Indians [Abstract]. J Am Soc Nephrol14 :134A –944,2003
20. American Diabetes Association: Standards of medical care for patients with diabetes mellitus. Diabetes Care29[Suppl 1] :S4 –S42,2006
21. Rohlfing CL, Little RR, Wiedmeyer HM, England JD, Madsen R, Harris MI, Flegal KM, Eberhardt MS, Goldstein DE: Use of GHb (HbA1c) in screening for undiagnosed diabetes in the US population. Diabetes Care23 :187 –191,2000[Abstract]
22. Hanson RL, Nelson RG, McCance DR, Beart JA, Charles MA, Pettitt DJ, Knowler WC: Comparison of screening tests for non-insulin-dependent diabetes mellitus. Arch Intern Med153 :2133 –2140,1993[Abstract/Free Full Text]
23. Bureau of US Census: Census 2000 Summary File (SF 1). Available at: http://www.census.gov/Press-Release/www/2001/sumfile1.html. Accessed May 25,2006
24. Nelson RG, Bennett PH, Beck GJ, Tan M, Knowler WC, Mitch WE, Hirschman GH, Myers BD: Development and progression of renal disease in Pima Indians with non-insulin-dependent diabetes mellitus. Diabetic Renal Disease Study Group. N Engl J Med335 :1636 –1642,1996[Abstract/Free Full Text]
25. Levey AS, Gassman JJ, Hall PM, Walker WG: Assessing the progression of renal disease in clinical studies: Effects of duration of follow-up and regression to the mean. Modification of Diet in Renal Disease (MDRD) Study Group. J Am Soc Nephrol1 :1087 –1094,1991[Abstract]
26. Shah VO, Pathak D, Tentori F, Stephens G, Paine S, Bobelu A, Zager P: Changing patterns of diabetic and non-diabetic ESRD among Zuni Indians; the Genetics of Kidney Disease in Zuni Indians (GKDZI) study [Abstract]. J Am Soc Nephrol17 :638A –1094,2006
27. Levin A, Djurdjev O, Barrett B, Burgess E, Carlisle E, Ethier J, Jindal K, Mendelssohn D, Tobe S, Singer J, Thompson C: Cardiovascular disease in patients with chronic kidney disease: Getting to the heart of the matter. Am J Kidney Dis38 :1398 –1407,2001[Medline]
28. Howard BV, Lee ET, Cowan LD, Devereux RB, Galloway JM, Go OT, Howard WJ, Rhoades ER, Robbins DC, Sievers ML, Welty TK: Rising tide of cardiovascular disease in American Indians. The Strong Heart Study. Circulation99 :2389 –2395,1999[Abstract/Free Full Text]
29. Wilson DM, Bergert JH, Larson TS, Liedtke RR: GFR determined by nonradiolabeled iothalamate using capillary electrophoresis. Am J Kidney Dis30 :646 –652,1997[Medline]
30. Perkins BA, Nelson RG, Ostrander BE, Blouch KL, Krolewski AS, Myers BD, Warram JH: Detection of renal function decline in patients with diabetes and normal or elevated GFR by serial measurements of serum cystatin C concentration: Results of a 4-year follow-up study. J Am Soc Nephrol16 :1404 –1412,2005[Abstract/Free Full Text]
31. Poggio ED, Wang X, Greene T, Van Lente F, Hall PM: Performance of the modification of diet in renal disease and Cockcroft-Gault equations in the estimation of GFR in health and in chronic kidney disease. J Am Soc Nephrol16 :459 –466,2005[Abstract/Free Full Text]
32. Murthy K, Stevens LA, Stark PC, Levey AS: Variation in the serum creatinine assay calibration: A practical application to glomerular filtration rate estimation. Kidney Int68 :1884 –1887,2005[CrossRef][Medline]
33. Stephens G, Tentori F, Paine S, Pathak D, Shah V, Zager P: Single versus multiple urine samples for determination of intermediate renal phenotype in genetic epidemiology studies: Results from the Genetics of Kidney Disease in Zuni Indians (GKDZI) study [Abstract]. J Am Soc Nephrol17 :153A –1887,2006[CrossRef]

This Article Abstract Full Text (PDF) All Versions of this Article: CJN.02780806v1 2/3/509    most recent Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Scavini, M. Articles by Zager, P. G. Search for Related Content PubMed PubMed Citation Articles by Scavini, M. Articles by Zager, P. G.