Diabetes
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Published online November 14, 2007
Diabetes 57:235-243, 2008
DOI: 10.2337/db07-0313
© 2008 by the American Diabetes Association
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Online-Only Appendix
Right arrow All Versions of this Article:
db07-0313v1
57/1/235    most recent
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow Request Permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Schelling, J. R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Schelling, J. R.
Social Bookmarking
Add to CiteULike   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?

Genome-Wide Scan for Estimated Glomerular Filtration Rate in Multi-Ethnic Diabetic Populations

The Family Investigation of Nephropathy and Diabetes (FIND)

Jeffrey R. Schelling, Hanna E. Abboud, Susanne B. Nicholas, Madeleine V. Pahl, John R. Sedor, Sharon G. Adler, Nedal H. Arar, Donald W. Bowden, Robert C. Elston, Barry I. Freedman, Katrina A.B. Goddard, Xiuqing Guo, Robert L. Hanson, Eli Ipp, Sudha K. Iyengar, Gyungah Jun, W.H. Linda Kao, Balakuntalam S. Kasinath, Paul L. Kimmel, Michael J. Klag, William C. Knowler, Robert G. Nelson, Rulan S. Parekh, Shannon R. Quade, Stephen S. Rich, Mohammed F. Saad, Marina Scavini, Michael W. Smith, Kent Taylor, Cheryl A. Winkler, Philip G. Zager, Vallabh O. Shah on behalf of the Family Investigation of Nephropathy and Diabetes Research Group*

Address correspondence and reprint requests to Dr. Sudha Iyengar, Department of Epidemiology and Biostatistics, Case Western Reserve University, Wolstein Research Building, Rm. 1300, 10900 Euclid Ave., Cleveland, OH 44106-7281. E-mail: ski{at}case.edu

Key Words: ARB, angiotensin receptor blocker • CKD, chronic kidney diseases • eGFR, estimated glomerular filtration rate • ESRD, end-stage renal disease • FIND, Family Investigation of Nephropathy and Diabetes • GFR, glomerular filtration rate • IBD, identity by descent • LOD, logarithm of odds • MDRD, Modification of Diet in Renal Disease


   ABSTRACT
TOP
 ABSTRACT
RESEARCH DESIGN AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
OBJECTIVE— Diabetic nephropathy, the most common cause of end-stage renal disease, aggregates in families and specific ethnic groups. Deconstructing diabetic nephropathy into intermediate, quantitative phenotypes may increase feasibility of detecting susceptibility loci by genetic screens. Glomerular filtration rate (GFR), which characterizes diabetic nephropathy, was employed as a quantitative trait in a preliminary whole-genome scan.

RESEARCH DESIGN AND METHODS— Estimated GFR (eGFR) was calculated for 882 diabetic sibpairs (mean age 57 years) of African-American (25.6% of total), American Indian (8.6%), European-American (14.2%), and Mexican-American (51.6%) descent enrolled in the initial phase of the Family Investigation of Nephropathy and Diabetes (FIND). A whole-genome scan was performed using 404 microsatellite markers (average spacing 9 cM) and model-free linkage analysis.

RESULTS— For all ethnicities combined, strong evidence for linkage was observed on chromosomes 1q43 (P = 3.6 x 10–3), 7q36.1 (P = 2.1 x 10–4), 8q13.3 (P = 4.6 x 10–4), and 18q23.3 (P = 2.7 x 10–3). Mexican-American families, who comprised the major ethnic subpopulation in FIND, contributed to linkage on chromosomes 1q43, 2p13.3, 7q36.1, 8q13.3, and 18q23.3, whereas African-American and American-Indian families displayed linkage peaks on chromosomes 11p15.1 and 15q22.3, respectively.

CONCLUSIONS— We have demonstrated multiple chromosomal regions linked to eGFR in a multi-ethnic collection of families ascertained by a proband with diabetic nephropathy. Identification of genetic variants within these loci that are responsible for the linkage signals could lead to predictive tests or novel therapies for subsets of patients at risk for diabetic nephropathy.

End-stage renal disease (ESRD) is the final outcome for multiple chronic kidney diseases (CKD), including one-third of patients with type 1 and type 2 diabetes. Nearly 45% of incident ESRD cases in the U.S. are due to diabetic nephropathy (www.usrds.org/2005/pdf/02_incid_prev_05), and <5% of ESRD cases are caused by Mendelian inheritance of known susceptibility genes. ESRD is therefore considered a complex disease in the remaining 95% of cases, with hereditary and environmental factors contributing to clinical outcomes. Evidence to support a genetic component to diabetic nephropathy is abundant, including concordance of diabetic nephropathy between diabetic siblings (1,2) and clustering of diabetic nephropathy within families and specific ethnic groups (3–5).

Whole-genome screens have been conducted to identify diabetic nephropathy genes in Pima Indian and African-American populations (6,7), and each study employed composite phenotype definitions for diabetic nephropathy. These screens have yielded candidate loci but no major susceptibility genes to date. A potential disadvantage of this study design is that establishing a categorical phenotype definition can be difficult, and inaccurate phenotype classification can reduce the likelihood of identifying candidate loci. An alternative approach is measurement of a quantitative trait, which represents an intermediate phenotype to predict an aspect of the complex disease. Although intermediate, quantitative traits may also be heterogeneous. Some potential advantages to this strategy are that these traits may be more proximal to underlying genes that regulate CKD, controlled by fewer loci, affected by fewer environmental factors, and more likely to reflect disease pathophysiology (8–10). Importantly, analysis using continuous, quantitative traits can increase power to detect genetic effects. Although most often applied to model organisms, this strategy has been successfully implemented to identify complex human disease loci (8,11–14).

Several groups have assessed heritability (h2) of estimated glomeruluar filtration rate (eGFR) as a quantitative trait. Hunt et al. (15) reported h2 = 0.33, 0.36, and 0.53 for three successive creatinine clearance examinations in a healthy Utah pedigree. These investigators subsequently analyzed eGFR from a similar (healthy and European-American) population at three time points over a 10-year period, demonstrating h2 ranging from 0.25 to 0.31 (16). DeWan et al. (17) calculated heritability of 0.17 in African-American and 0.18 in European-American hypertensive cohorts. In the Framingham Heart Study, Fox et al. (18) determined that eGFR heritability was 0.36. In a study of type 2 diabetic European-American subjects, Langefeld et al. (19) calculated h2 of eGFR, by the Modifican of Diet in Renal Disease equation, to be 0.75 after adjusting for age, sex, blood pressure, medications, and A1C levels. Bochud et al. (20) estimated heritability in families with hypertension, similarly adjusting for age and sex, and revealing h2 = 0.41 for inulin clearance, 0.52 for creatinine clearance, and 0.82 for creatinine clearance estimated by the Cockcroft-Gault formula. More recently, the Joslin group demonstrated significant eGFR heritability (h2 = 0.45) in families enriched for type 2 diabetes (21). Collectively, these data indicate that glomerular filtration rate (GFR) is heritable, and it is reasonable to screen for genes regulating GFR and/or renal disease progression.

Many of these heritability studies simultaneously demonstrated linkage with quantitative eGFR phenotypes. In the HyperGEN study involving 1,100 hypertensive EA and AA subjects, a locus on 3q27 was identified (17). Follow-up studies by this same group, with added subjects and denser genotyping, revealed linkage to 3p (22) and 7q (23) in the African-American cohort. In two studies by Hunt et al., which examined pedigrees from EA families in Utah, loci on chromosomes 2q21.3 (16) and 10q23.3 (15) were identified. The Framingham Heart Study identified linkage on chromosome 3q26.3 with creatinine clearance and on chromosome 4q23.3 with eGFR (18). However, these populations were largely devoid of diabetic subjects, and mean eGFRs of the study populations were in the normal range. The FIND consortium was established to identify diabetic nephropathy susceptibility genes in four ethnic groups (Hispanic American, African American, European American, and American Indian) (24). In contrast to other studies, families were ascertained for diabetic ESRD or severe diabetic nephropathy, and many participants have low eGFR. A whole-genome scan was performed with microsatellite markers. Here, we report the results of a linkage analysis of eGFR.


   RESEARCH DESIGN AND METHODS
TOP
 ABSTRACT
 RESEARCH DESIGN AND METHODS
RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 
The FIND family study population and study design have been described in detail (24). Briefly, samples were collected from the eight participating investigation centers. The predominant recruitment strategy in the family-based linkage portion of FIND was to enroll probands with diabetic nephropathy and diabetic siblings with or without diabetic nephropathy. A total of 941 subjects, comprising 882 sibpairs, were included in this analysis (Table 1). Approval for recruitment was obtained from institutional review boards at each participating institution. Subjects were recruited and samples collected according to the Declaration of Helsinki principles, and a certificate of confidentiality was filed at the National Institutes of Health.


View this table:
[in this window]
[in a new window]

  		TABLE 1 Description of genotyped families

 
Diabetes definition.
Diabetes was self-reported and corroborated in most subjects by prevalent treatment with insulin and/or oral hypoglycemic agents. Diabetes duration was obtained from the medical history and confirmed by medical record review. In diabetic patients not treated with hypoglycemic agents, diabetes was diagnosed by A1C screening and fasting plasma glucose confirmation at study entry. Subjects with A1C >7.0% were considered diabetic, whereas those with A1C <6.0% were considered nondiabetic. Subjects with A1C concentration in the range from >6.0 to <7.0% underwent fasting plasma glucose and/or oral glucose tolerance testing. Assay results for diabetes diagnosis were interpreted according to American Diabetes Association criteria (25). Subjects with either type 1 or type 2 diabetes were included, though nearly all enrollees were believed to have type 2 diabetes (26).

Proband phenotype criteria.
Detailed criteria have been published previously (24). Briefly, parameters for inclusion in the FIND family study required diagnoses of diabetic nephropathy, as defined by kidney biopsy revealing diabetic nephropathy plus overt proteinuria (>0.5 g protein/g creatinine); by CKD due to diabetic nephropathy (but not yet ESRD), based upon either diabetic retinopathy with proteinuria (>1.0 g protein/g creatinine) or diabetes duration ≥10 years with proteinuria (>3.0 g protein/g creatinine); or by ESRD due to diabetic nephropathy (as defined by one of the following: 1) onset of diabetes ≥5 years before renal replacement therapy plus diabetic retinopathy [documented microaneurysms, proliferative diabetic retinopathy, macular edema, or prior retinal laser photocoagulation], 2) onset of diabetes ≥5 years before renal replacement therapy with documented proteinuria [>3.0 g protein/g creatinine], or 3) diabetic retinopathy with proteinuria [>3.0 g protein/g creatinine]). Demographic and clinical characteristics are shown in Table 2.


View this table:
[in this window]
[in a new window]

  		TABLE 2 Description of genotyped participants by phenotype

 
Diabetic sibling phenotype criteria.
Diabetic siblings were considered to be either affected or unaffected by diabetic nephropathy. Criteria for siblings affected for diabetic nephropathy included the following: 1) kidney biopsy consistent with diabetic nephropathy, 2) microalbuminuria (>30 mg albumin/g creatinine), or 3) serum creatinine concentration ≥1.6 mg/dl for men and ≥1.4 mg/dl for women. Diabetic siblings were classified as unaffected by diabetic nephropathy by virtue of diabetes duration ≥10 years, serum creatinine concentration <1.6 mg/dl for men and <1.4 mg/dl for women, and urine albumin–to–creatinine ratio <30 mg/g. Diabetic siblings with normal urine albumin excretion and diabetes duration <10 years were considered indeterminate but included in the linkage analysis.

eGFR.
For most subjects, eGFR was calculated by the Modification of Diet in Renal Disease (MDRD) equation (27): eGFR (ml/min per 1.73 m2) = 186 x (plasma creatinine)–1.154 x (age)–0.203 x (0.742 if female) x (1.210 if African American). eGFR was assumed to be 5.0 ml/min per 1.73 m2 for patients receiving hemodialysis or for transplant recipients (n = 333).

Genotyping and analytic methods.
Genotyping and linkage analyses were conducted on 882 diabetic sibpairs. DNA was extracted for genotyping from either lymphoblast cell lines or leukocyte buffy coats. Genotyping was performed at the Center for Inherited Disease Research, including 404 microsatellite markers on 22 autosomes and two sex chromosomes, using the Marshfield Genetics, version 8, marker set from Research Genetics, with an average marker spacing of 9 cM.

All computer programs noted below are part of the S.A.G.E (Statistical Analysis for Genetic Epidemiology), version 5.0, program package. Differences between groups (probands vs. affected siblings and unaffected vs. affected siblings) were tested allowing for a sibling correlation, as implemented in ASSOC, using two-sided tests. Mendelian inconsistencies were identified with the MarkerInfo program. No departure from Hardy-Weinberg proportions was observed at any marker at a 1% significance level. Maximum likelihood estimation was performed with the FREQ program to estimate separate marker allele frequencies in all four ethnic groups (African American, European American, American Indian, and Mexican American). Before performing linkage analyses, errors in relationship specification were identified with the program RELTEST, using the entire genome scan. Multipoint identity by descent (IBD) allele sharing estimates were computed using the program GENIBD for each of the four ethnic groups. Linkage analyses were conducted with eGFR as a quantitative trait and the Haseman-Elston regression linkage test (within the program SIBPAL), using multipoint IBD sharing estimates. SIBPAL performs linear regression modeling of sibpair traits as a function of marker allele IBD sharing. With this method, a weighted average of the squared mean-corrected trait sum, minus the squared trait difference, was used as the dependent variable, the regression allowing for the nonindependence of sibpairs and the nonindependence of squared trait sums and differences (28). For regions suggestive of linkage, asymptotic P values from the Haseman-Elston test were validated by obtaining empirical P values in SIBPAL. To do this, we permuted the allele sharing among the sibling pairs within each sibship and across all sibships of the same size. For each permutation, we recalculated the test statistic to generate a distribution of test statistics. We determined the proportion of the replicates that are equal to or more extreme than the original test statistic and report this as the empirical P value. Based on the settings used in SIBPAL for the number of permutations, the empirical P value is estimated to be within 20% of the true value, with a 95% CI.

Quantitative eGFR was evaluated according to two different models. In the first model, the covariates diabetes duration, ACE inhibitor or angiotensin receptor blocker (ARB) use, BMI, and A1C concentration were incorporated. In the second model, a linear regression was performed before linkage, and only covariates that were significant at the 5% level (diabetes duration and ACE inhibitor or ARB use) were used. A separate linkage analysis was performed for each ethnicity, and P values were combined across ethnicities according to Fisher's method (29). The Fisher meta-analysis technique was utilized because it was not necessarily valid to treat all of the ethnic groups as a single sample owing to allele frequency and demographic differences. To corroborate pooled results obtained with Fisher's method, we also calculated a weighted average of the Haseman-Elston regression coefficients, with weights inversely proportional to the estimated variances of the coefficients, and analyzed the combined ethnicities together, with membership in ethnic group included in the Haseman-Elston regression as covariates (30).


   RESULTS
TOP
 ABSTRACT
 RESEARCH DESIGN AND METHODS
 RESULTS
DISCUSSION
 APPENDIX
 REFERENCES
 
Subjects were enrolled and samples collected from 378 families (Table 1); 52% were Mexican American (196 pedigrees), 25% African American (96 pedigrees), 14% European American (54 pedigrees), and 9% American Indian (32 pedigrees). As previously reported (30), African-American probands had significantly lower male-to-female ratio and higher BMI compared with the other groups (Table 2). Otherwise, probands had similar demographic characteristics among the four ethnic groups.

Within the 378 FIND families, diabetic siblings were enrolled and classified as probands, siblings with diabetic nephropathy, siblings unaffected by diabetic nephropathy, or siblings with insufficient diabetes duration to meet unaffected sibling criteria. Demographic and clinical data are shown for each of these groups in Table 2. Of note, unaffected siblings were significantly older than probands or affected siblings, and probands had significantly younger age of onset and shorter diabetes duration than unaffected siblings. As expected, mean eGFR was markedly lower in probands than in other groups (Table 2), especially if winsorized (at 5 ml/min per 1.73 m2) eGFR values were used for probands and affected siblings with ESRD (data not shown).

Because eGFR decline characterizes and predicts diabetic nephropathy, eGFR derived from the MDRD equation was employed in the quantitative trait linkage analysis. The mean eGFR value for the entire population was 77.5 ± 32.4 ml/min per 1.73 m2 when ESRD subjects were excluded from the calculation and 56.8 ± 43.9 ml/min per 1.73 m2 when winsorized eGFR values were included for ESRD subjects. Data obtained from the entire population (882 diabetic sibpairs) and analyzed using eGFR as a quantitative trait according to the first model (adjusted for covariates diabetes duration, ACE inhibitor/ARB use, BMI, and A1C) yielded suggestive evidence for linkage using multipoint IBD estimates on chromosomes 1q43, 7q36.1, 8q13.3, and 11p15.1 and a broad peak on 18q13–21 and 20p12.2 (not shown). Mexican-American families, who comprised the majority of the families, predominantly contributed to the 1q, 7q, 8q, 18q, and 20p linkage peaks. In the ethnicity-specific analysis of Mexican Americans, peaks on 8q (nominal P = 8.70 x 10–6) and 18q (nominal P = 6.40 x 10–6) reached Lander-Kruglyak threshold for significant linkage (31).

Although BMI has been implicated as an independent risk factor for ESRD (32), it was not a statistically significant covariate in the regression analysis of either the total or ethnicity-specific samples in this study. In addition, A1C is not considered a reliable measure of blood glucose control in ESRD patients (33). Therefore, the linkage analysis was repeated after adjusting only for diabetes duration and ACE inhibitor/ARB use. In these analyses, suggestive evidence for linkage in the entire population persisted at 1q43, 7q36.1, 8q13.3, 11p15.1, 18q22.3 (Fig. 1 and Table 3), and 2p13.3. As seen in Fig. 1 and Table 3, Mexican Americans primarily contributed to the linkage signals on chromosomes 1q, 2p, 7q, 8q, and 18q, while African-American and American Indian families contributed to linkage peaks on 11p and 15q22.3, respectively.


Figure 1
View larger version (51K):
[in this window]
[in a new window]

  		FIG. 1. Multipoint results of the genome-wide linkage scan for eGFR on 22 autosomes after adjusting for covariates diabetes duration and ACE inhibitor or ARB use. For each chromosome, genetic distance (cM) is plotted on the x-axis against –log10 (P value) on the y-axis, with corresponding LOD score scale. Data are presented for each ethnicity separately and for ethnicity-combined analyses. Lower horizontal dotted lines represent suggestive evidence (P = 7.4 x 10–4), and upper horizontal lines show threshold for evidence (P = 2.2 x 10–5) of linkage by Lander-Kruglyak criteria (ref. 31).

 

View this table:
[in this window]
[in a new window]

  		TABLE 3 Linkage analysis results for eGFR with nominal significance

 
Linkage results are shown for eGFR in ethnicity-specific diabetic populations (Fig. 2). Data are presented without covariate adjustment (baseline model) and with pair-mean diabetes duration and ACE inhibitor/ARB use as covariates (adjusted model). For all chromosome regions displayed, covariate adjustments resulted in increased evidence for linkage.


Figure 2
View larger version (29K):
[in this window]
[in a new window]

  		FIG. 2. Detailed genome scans for linkage with eGFR. A: Chromosomes 1, 2, 7, 8, and 18 data for Mexican Americans. B: Chromosome 11 for African Americans. C: chromosome 15 for American Indians. Curves representing data adjusted for the covariates diabetes duration and ACE inhibitor/ARB use are shown with solid lines, whereas curves for unadjusted data are shown with broken lines. Chromosomal position in cM is shown along the upper x-axis, microsatellite marker locations are shown along the lower x-axis, and –log P values are shown along the y-axis, with corresponding LOD score scale. Lower horizontal dotted lines represent suggestive evidence (P = 7.4 x 10–4), and upper horizontal lines show threshold for evidence (P = 2.2 x 10–5) of linkage by Lander-Kruglyak criteria (ref. 31).

 

   DISCUSSION
TOP
 ABSTRACT
 RESEARCH DESIGN AND METHODS
 RESULTS
 DISCUSSION
APPENDIX
 REFERENCES
 
In the current report, several major chromosomal intervals, the most prominent at 1q43, 7q36.1, 8q13.3, and 18q23.3, significantly contributed to eGFR phenotype. The findings were influenced primarily by Mexican Americans, who comprised the largest subpopulation in our cohort. The 1q locus appears to be unique, with no obvious overlap with previous diabetic nephropathy genome scans (Table 4). As discussed in more detail below, the 18q23.3 peak replicated our prior findings in FIND (30) based on a composite diabetic nephropathy definition that included criteria other than eGFR (24). Interestingly, the 7q peak was not detected in the FIND population diabetic nephropathy scan (30), and the locus on 8q showed modest support but was not sufficient to meet Lander-Kruglyak evidence for significance (31). Taken together, because there are few common linkage peaks between the two FIND analyses, we speculate that GFR and other aspects of the diabetic nephropathy phenotype may be regulated by different genes. However, our failure to replicate these diabetic nephropathy linkage signals from FIND or from other collections of diabetic nephropathy patients may reflect the modest sample size used in this study (see power analysis in the online appendix [available at http://dx.doi.org/10.2337/db07-0313]) and the differences in phenotypes and ascertainment strategies (34).


View this table:
[in this window]
[in a new window]

  		TABLE 4 Comparison with other diabetic nephropathy and diabetic nephropathy–related genome scans and candidate gene studies

 
Several studies have identified 7q susceptibility loci in scans employing diabetic nephropathy phenotypes defined by albuminuria (6–35), and a very recent genome scan of eGFR demonstrated linkage to 7q37 in African Americans (23) (Table 4). Krolewski et al. (35) analyzed albuminuria as a quantitative trait and discovered a linkage peak at 7q36.2, which yielded a logarithm of odds (LOD) score in excess of 3.0. Despite the difference in phenotype definitions, the 7q peak described by Krolewski et al. is replicated in our study (Table 4) and coincides with the locus for the NOS3 gene (36–38). In the genome scan involving the Pima Indian population, a single peak of linkage was identified at 7q33 (6), which is considerably more centromeric.

The Mexican-American cohort also exhibited linkage at 18q23, consistent with linkage peaks in genome scans for type 2 diabetes (39) and diabetic nephropathy (7,40) in European-American and African-American populations, as well as the FIND linkage scan for a composite diabetic nephropathy phenotype (30). In addition, Ewens et al. (41) found three polymorphisms in the BCL2 gene, which resides at 18q21.33, that were significantly associated with diabetic nephropathy in European-American families. Although European-American and African-American families did not contribute significantly to the 18q peak in this study (Fig. 1), the data from all of the studies collectively suggest the presence of race-independent diabetes and diabetic nephropathy genes in this region. Of note, our peak is centered ~900 kb from the carnosinase gene (CNDP1) (Table 4). Polymorphisms in CNDP1 have been associated with resistance to diabetic nephropathy in European Americans (42,43).

Of the few whole-genome scans for eGFR that have been conducted, overlap between chromosome regions identified, including the results described in this study, is limited. The first scan by Hunt et al. identified linkage of creatinine clearance to chromosome 10 in families from Utah ascertained for cardiovascular disease risk, with relatively normal GFR (15). A second scan by the same group, which analyzed three creatinine-based GFR estimates (by the MDRD equation) over a 10-year period, showed linkage to chromosome 2q but only for the third examination (16). DeWan et al. (17) analyzed creatinine clearance but in a mixed African-American and European-American population ascertained for hypertension and at increased risk for cardiovascular diseases. These investigators identified linkage with chromosome 3q27 but not chromosomes 2 or 10. Fox et al. (18) used multipoint variance component linkage analysis in 330 families from the community-based Framingham Heart Study offspring cohort, using a 10-cM genome-wide scan, for serum creatinine, eGFR, and creatinine clearance measured from 1998 to 2001. eGFR was estimated using the simplified MDRD equation, and creatinine clearance was estimated using the Cockcroft-Gault equation. The peak LOD scores for serum creatinine, eGFR, and creatinine clearance were 2.28 at 176 cM on chromosome 4, 2.19 at 78 cM on chromosome 4, and 1.91 at 103 cM on chromosome 3, respectively. This group recently reported the results of linkage analysis with 11,200 markers in this same cohort. A marker (rs10489578) near 1q43 peak in the FIND genome scan was linked to both eGFR (LOD 3.08) and serum creatinine (LOD 3.35) (44). Finally, Placha et al. (21) reported a genome scan of eGFR, estimated by serum cystatin C, MDRD, and Cockroft-Gault equations, in 63 extended Caucasian pedigrees (406 individuals with type 2 diabetes and 428 without diabetes) with normal eGFR values. This group detected strong evidence for linkage among diabetic relatives on chromosome 2q and suggestive evidence on 10q in proximity to the region previously identified by Hunt et al. (15) and 18p. When diabetic and nondiabetic relatives were combined in the analysis, evidence for linkage was also found on chromosome 7p. Linkage peaks in the current study may not replicate previous findings owing to differences in ethnic composition of the populations studied and relatively modest sizes of most prior studies. In contrast to earlier reports, the FIND study ascertained for subjects with diabetes and renal insufficiency.

Another difference between prior eGFR genetics studies and this study is the method of GFR estimation. We chose the MDRD equation to estimate GFR because serum creatinine was easily obtained for all study subjects and because it is currently the most accurate creatinine-based index of eGFR (45). This formula was originally derived from a study population of 1,628 predominantly Caucasian patients with multiple causes of CKD (27) and has subsequently been validated in African-American (46) and American Indian (47) cohorts. In several recent reports, accuracy of the equation was analyzed in patients with relatively normal GFR (48) and in patients with diabetes (49). Each concluded that the MDRD study underestimated GFR in the normal range. However, our cohort has a relatively depressed mean eGFR, approaching the range in the population from which the formula was derived. Furthermore, statistical analyses for genetic studies rely on phenotype ranking. Therefore, to the extent that the MDRD equation uniformly underestimates the absolute GFR value, the rank order is unlikely to change; thus, one would expect the results to be unaffected. The definitive study, which includes the entire FIND family sample with approximately four times as many participants, is ongoing. In this analysis, a single nucleotide polymorphism genotyping platform will replace the microsatellite technology.

A potential limitation of this study is that eGFR was determined on the basis of a single random sample collection. Although validation with serial measures would be ideal, quality control studies demonstrated negligible variability on repeat creatinine measurement of the same serum sample (data not shown). An additional point is that we ascertained for diabetic nephropathy according to criteria that included diabetes duration. It is therefore possible that we omitted subjects with the most severe eGFR phenotypes, who may have died before achieving duration criteria. It has been suggested that such a study design may bias results toward discovery of survival rather than disease genes (50). Selection bias in relation to the participating centers and subsequently enrolled diabetic sibpairs cannot be ruled out. Finally, data were adjusted for a number of confounding variables, such as diabetes duration, antihypertensive therapy, BMI, and A1C concentration, but other potentially relevant parameters, such as blood pressure and cardiovascular diseases, were not available. Like many common diseases, diabetic nephropathy is caused by a combination of genetic and environmental risk factors. Both linkage and association designs have been used to identify common disease genes. Linkage studies are not as powerful as association studies for identification of genes contributing to risk for common, complex diseases, which are caused by variants with effect sizes in the order of odds ratios of 1.15–1.4. FIND was initiated when whole-genome association studies were not feasible. At that time, family-based study designs had the advantage of searching the whole genome in an unbiased manner without predetermining the identity of specific candidates, and association designs were only used to evaluate candidate disease genes. Given recent successes in gene mapping for common disease using whole-genome association, the FIND consortium is now also performing a whole-genome association. The results of linkage analysis reported in this paper will be valuable in the analyses of whole-genome association data and should accelerate identification of genetic variants that regulate diabetic nephropathy pathogenesis.

In conclusion, several linkage peaks for eGFR were identified from the multi-ethnic FIND cohort, which represents the largest diabetic nephropathy genetics study to date. After adjustment for diabetes duration and ACE inhibitor/ARB use, the peaks on chromosomes 1q43, 7q36.1, and 8q21.3 are very close to attaining Lander-Kruglyak significance for linkage (31) within the Mexican-American cohort and achieve genome-wide significance at the 5% level, strongly suggesting that unique diabetic nephropathy alleles cluster within these loci. We speculate that fine-mapping and candidate gene and sequencing analyses should permit identification of mutations within these loci, which could ultimately lead to specific predictive tests and/or therapies for a subset of patients with diabetic nephropathy. Further studies investigating the association of GFR and genetic loci in large U.S. populations of patients with diabetic kidney disease will be necessary to confirm the findings.


   APPENDIX
TOP
 ABSTRACT
 RESEARCH DESIGN AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
REFERENCES
 
Members of the Family Investigation of Nephropathy and Diabetes Research Group
Genetic analysis and data coordinating center.
Case Western Reserve University, Cleveland, Ohio: S.K. Iyengar (Principal Investigator [PI], R.C. Elston (Co-investigator [CI], K.A.B. Goddard (CI), J.M. Olson (CI), S. Ialacci (Program Coordinator [PC]), C. Fondran, A. Horvath, G. Jun, K. Kramp, S.R.E. Quade, M. Slaughter, and E. Zaletel.

Participating investigation centers.
Case Western Reserve University, Cleveland, Ohio: J.R. Sedor (PI), J.R. Schelling (CI), A.R. Sehgal (CI), A. Pickens (PC), L. Humbert, and L. Goetz-Fradley. Harbor-University of California Los Angeles (UCLA) Medical Center, Los Angeles, California: S. Adler (PI), H.E. Collins-Schramm (CI) (UCLA, Davis, CA), E. Ipp (CI), H. Li (CI) (UCLA, Davis, CA), M. Pahl (CI) (UCLA, Irvine, CA), M.F. Seldin (CI) (UCLA, Davis, CA), J. LaPage (PC), B. Walker (PC), C. Garcia, J. Gonzalez, and L. Ingram-Drake. Johns Hopkins University, Baltimore, Maryland: M. Klag (PI), R. Parekh (PI), L. Kao (CI), L. Meoni (CI), T. Whitehead, and J. Chester (PC). National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona: W.C. Knowler (PI), R.L. Hanson (CI), R.G. Nelson (CI), J. Wolford (CI), L. Jones (PC), R. Juan, R. Lovelace, C. Luethe, L.M. Phillips, J. Sewemaenewa, I. Sili, and B. Waseta. University of California Los Angeles, Los Angeles, California: M.F. Saad (PI), S.B. Nicholas (PI), X. Guo (CI), J. Rotter (CI), K. Taylor (CI), D. Wang (CI), M. Budgett, and F. Hariri (PC). University of New Mexico, Albuquerque, New Mexico: P. Zager (PI), V. Shah (CI), M. Scavini (CI), and A. Bobelu (PC). University of Texas Health Science Center at San Antonio, San Antonio, Texas: H. Abboud (PI), N. Arar (CI), R. Duggirala (CI), B.S. Kasinath (CI), R. Plaetke (CI), M. Stern (CI), C. Jenkinson (CI), C. Goyes (PC), V. Sartorio, T. Abboud, and L. Hernandez. Wake-Forest University, Winston-Salem, North Carolina: B.I. Freedman (PI), D.W. Bowden (CI), S.C. Satko (CI), S.S. Rich (CI), S. Warren (PC), S. Viverette, G. Brooks, R. Young, and M. Spainhour. Laboratory of Genomic Diversity, National Cancer Institute, Frederick, Maryland: C. Winkler (PI), M.W. Smith (CI), M. Thompson, R. Hanson (PC), and B. Kessing. National Institute of Diabetes and Digestive and Kidney Diseases program office, Bethesda, Maryland: J.P. Briggs, P.L. Kimmel, and R. Rasooly. External Advisory Committee: D. Warnock (chair), R. Chakraborty, G.M. Dunston, S.J. O'Brien (ad hoc), and R. Spielman.


   ACKNOWLEDGMENTS
 
This study was supported by grant U01DK57292-05 from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and, in part, by the Intramural Research Program of the NIDDK. This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health (NIH), under contract N01-CO-12400. This research was supported, in part, by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research. This work was also supported by the National Center for Research Resources for the General Clinical Research Center (GCRC) grants: Case Western Reserve University, M01-RR-000080; Wake Forest University, M01-RR-07122; Harbor–University of California, Los Angeles Medical Center, M01-RR-00425; College of Medicine, University of California, Irvine, M01-RR-00827–29; University of New Mexico, HSC M01-RR-00997; and Frederic C. Bartter, M01-RR-01346. Genotyping was performed by the Center for Inherited Disease Research, which is fully funded through a federal contract from the NIH to Johns Hopkins University (N01-HG-65403). The results of this analysis were obtained by using the S.A.G.E. package of genetic epidemiology software, which is supported by a U.S. Public Health Service Resource Grant (RR03655) from the National Center for Research Resources.

We thank all FIND participants.


   FOOTNOTES
 
Published ahead of print at http://diabetes.diabetesjournals.org on. DOI: 10.2337/db07-0313.

Additional information for this article can be found in an online appendix at http://dx.doi.org/10.2337/db07-0313.

* A complete list of members of the Family Investigation of Nephropathy and Diabetes Research Group and their respective affiliations can be found in the APPENDIX. Back

The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. government.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received for publication March 14, 2007 and accepted in revised form October 8, 2007


   REFERENCES
TOP
 ABSTRACT
 RESEARCH DESIGN AND METHODS
 RESULTS
 DISCUSSION
 APPENDIX
 REFERENCES
 

  1. Seaquist ER, Goetz FC, Rich S, Barbosa J: Familial clustering of diabetic kidney disease: evidence for genetic susceptibility to diabetic nephropathy. N Engl J Med 320:1161–1165, 1989[Abstract]
  2. Quinn M, Angelico MC, Warram JH, Krolewski AS: Familial factors determine the development of diabetic nephropathy in patients with IDDM. Diabetologia 39:940–945, 1996[Medline]
  3. Nelson RG, Newman JM, Knowler WC, Sievers ML, Kunzelman CL, Pettitt DJ, Moffett CD, Teutsch SM, Bennett PH: Incidence of end-stage renal disease in type 2 (non-insulin-dependent) diabetes mellitus in Pima Indians. Diabetologia 31:730–736, 1988[Medline]
  4. Covic AMC, Iyengar SK, Olson JM, Sehgal AR, Constantiner M, Jedrey C, Kara M, Sabbagh E, Sedor JR, Schelling JR: A family-based strategy to identify genes for diabetic nephropathy. Am J Kidney Dis 37:638–647, 2001[Medline]
  5. Satko SG, Langefeld CD, Daeihagh P, Bowden DW, Rich SS, Freedman BI: Nephropathy in siblings of African Americans with overt type 2 diabetic nephropathy. Am J Kidney Dis 40:489–494, 2002[Medline]
  6. Imperatore G, Hanson RL, Pettitt DJ, Kobes S, Bennett PH, Knowler WC, the Pima Diabetes Genes Group: Sib-pair linkage analysis for susceptibility genes for microvascular complications among Pima Indians with type 2 diabetes. Diabetes 47:821–830, 1998[Abstract]
  7. Bowden DW, Colicigno CJ, Langefeld CD, Sale MM, Williams A, Anderson PJ, Rich SS, Freedman BI: A genome scan for diabetic nephropathy in African Americans. Kidney Int 66:1517–1526, 2004[Medline]
  8. Ghosh S, Schork NJ: Genetic analysis of NIDDM: the study of quantitative traits. Diabetes 45:1–14, 1996[Abstract]
  9. Carlson CS, Eberle MA, Kruglyak L, Nickerson DA: Mapping complex disease loci in whole-genome association studies. Nature 429:446–452, 2004[Medline]
  10. Lander ES, Schork NJ: Genetic dissection of complex traits. Science 265:2037–2048, 1994[Abstract/Free Full Text]
  11. Pratley RE, Thompson DB, Prochazka M, Baier L, Mott D, Ravussin E, Sakul H, Ehm MG, Burns DK, Foroud T, Garvey WT, Hanson RL, Knowler WC, Bennett PH, Bogardus C: An autosomal genomic scan for loci linked to prediabetic phenotypes in Pima Indians. J Clin Invest 101:1757–1764, 1998[Medline]
  12. Rotter JI, Bu X, Cantor RM, Warden CH, Brown J, Gray RJ, Blanche PJ, Krauss RM, Lusis AJ: Multilocus genetic determinants of LDL particle size in coronary artery disease families. Am J Hum Genet 58:585–594, 1996[Medline]
  13. Fogarty DG, Rich SS, Hanna L, Warram JH, Krolewski AS: Urinary albumin excretion in families with type 2 diabetes is heritable and genetically correlated to blood pressure. Kidney Int 57:250–257, 2000[Medline]
  14. Mahtani MM, Widen E, Lehto M, Thomas J, McCarthy M, Brayer J, Bryant B, Chan G, Daly M, Forsblom C, Kanninen T, Kirby A, Kruglyak L, Munnelly K, Parkkonen M, Reeve-Daly MP, Weaver A, Brettin T, Duyk G, Lander ES, Groop LC: Mapping of a gene for type 2 diabetes associated with an insulin secretion defect by a genome scan in Finnish families. Nat Genet 14:90–94, 1996[Medline]
  15. 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 62:1143–1148, 2002[Medline]
  16. Hunt SC, Coon H, Hasstedt SI, 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 17:511–515, 2004[Medline]
  17. 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 68:136–144, 2001[Medline]
  18. 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 15:2457–2461, 2004[Abstract/Free Full Text]
  19. Langefeld CD, Beck SR, Bowden DW, Rich SS, Wagenknecht LE, Freedman BI: Heritability of GFR and albuminuria in Caucasians with type 2 diabetes mellitus. Am J Kidney Dis 43:796–800, 2004[Medline]
  20. Bochud M, Elston RC, Maillard M, Bovet P, Schild L, Shamlaye C, Burnier M: Heritability of renal function in hypertensive families of African descent in the Seychelles (Indian Ocean). Kidney Int 67:61–69, 2005[Medline]
  21. Placha G, Poznik GD, Dunn J, Smiles A, Krolewski B, Glew T, Puppula S, 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 serun cystatin C levels in extended families with type 2 diabetes. Diabetes 55:3358–3365, 2006[Abstract/Free Full Text]
  22. DeWan AT, Arnett DK, Miller MB, Peacock JM, Atwood LD, Province MA, Lewis CE, Hunt SC, Eckfeldt JH: Refined mapping of suggestive linkage to renal function in African Americans: the HyperGEN study. Am J Hum Genet 71:204–205, 2002[Medline]
  23. Leon JM, Freedman BI, Miller MB, North KE, Hunt SC, Eckfeldt JH, Lewis CE, Kraja AT, Djousse L, Arnett DK: Genome scan of glomerular filtration rate and albuminuria: the HyperGEN study. Nephrol Dial Transplant 22:763–771, 2006[Medline]
  24. Knowler WC, Coresh J, Elston RC, Freedman BI, Iyengar SK, Kimmel PL, Olson JM, Plaetke R, Sedor JR, Seldin MF: The Family Investigation of Nephropathy and Diabetes (FIND): design and methods. J Diabetes Complications 19:1–9, 2005[Medline]
  25. Expert Committee on the Diagnosis and Classification of Diabetes Mellitus: Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 20:1183–1197, 1997[Medline]
  26. Knowler WC, Ipp E, the FIND Research Group: Diabetes types and autoimmunity in the Family Investigation of Nephropathy in Diabetes (Abstract). J Am Soc Nephrol 16:149A, 2005
  27. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D: Modification of Diet in Renal Disease Study Group: A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Ann Intern Med 130:461–470, 1999[Abstract/Free Full Text]
  28. Shete S, Jacobs KB, Elston RC: Adding further power to the Haseman and Elston method for detecting linkage in larger sibships: weighting sums and differences. Hum Hered 55:79–85, 2003[Medline]
  29. Elston RC: On Fisher's method of combining P-values. Biometrical J 33:339–345, 1991
  30. Iyengar SK, Abboud HE, Goddard KA, Saad MF, Adler SG, Arar NH, Bowden DW, Duggirala R, Elston RC, Hanson RL, Ipp E, Kao L, Kimmel PL, Klag MJ, Knowler WC, Nelson RG, Nicholas SB, Pahl MV, Parekh RS, Rasooly RS, Rich SS, Scavini M, Schelling JR, Sedor JR, Sehgal AR, Shah VO, Smith MW, Winkler CA, Zager PG, Freedman BI: Genome-wide scans for diabetic nephropathy and albuminuria in multi-ethnic populations: the FIND study. Diabetes 56:1577–1585, 2006
  31. Lander E, Kruglyak L: Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet 11:241–247, 1995[Medline]
  32. Hsu CY, McCulloch CE, Iribarren C, Darbinian J, Go AS: Body mass index and risk for end-stage renal disease. Ann Intern Med 144:21–28, 2006[Abstract/Free Full Text]
  33. Hsu CY, Vittinghoff E, Lin F, Shlipak MG: The incidence of end-stage renal disease is increasing faster than the prevalence of chronic renal insufficiency. Ann Intern Med 141:95–101, 2004[Abstract/Free Full Text]
  34. Iyengar SK, Freedman BI, Sedor JR: Mining the genome for susceptibility to diabetic nephropathy: the role of large-scale studies and consortia. Semin Nephrol 27:208–222, 2007[Medline]
  35. Krolewski AS, Poznik GD, Placha G, Canani L, Dunn J, Walker W, Smiles A, Krolewski B, Fogarty DG, Moczulski D, Araki S, Makita Y, Ng DP, Rogus J, Duggirala R, Rich SS, Warram JH: A genome-wide linkage scan for genes controlling variation in urinary albumin excretion in type II diabetes. Kidney Int 69:129–136, 2006[Medline]
  36. Zanchi A, Moczulski DK, Hanna LS, Wantman M, Warram JH, Krolewski AS: Risk of advanced diabetic nephropathy in type 1 diabetes is associated with endothelial nitric oxide synthase gene polymorphism. Kidney Int 57:405–413, 2000[Medline]
  37. Asakimori Y, Yorioka N, Taniguchi Y, Ito T, Ogata S, Kyuden Y, Kohno N: T-786–>C polymorphism of the endothelial nitric oxide synthase gene influences the progression of renal disease. Nephron 91:747–751, 2002[Medline]
  38. Liu Y, Burdon KP, Langefeld CD, Beck SR, Wagenknecht LE, Rich SS, Bowden DW, Freedman BI: T-786C polymorphism of the endothelial nitric oxide synthase gene is associated with albuminuria in the diabetes heart study. J Am Soc Nephrol 16:1085–1090, 2005[Abstract/Free Full Text]
  39. Sale MM, Freedman BI, Langefeld CD, Williams AH, Hicks PJ, Colicigno CJ, Beck SR, Brown WM, Rich SS, Bowden DW: A genome-wide scan for type 2 diabetes in African-American families reveals evidence for a locus on chromosome 6q. Diabetes 53:830–837, 2004[Abstract/Free Full Text]
  40. Vardarli I, Baier LJ, Hanson RL, Akkoyun M, Fischer C, Rohmeiss P, Basci A, Bartram CR, Van der Woude FJ, Janssen B: Gene for susceptibility to diabetic nephropathy in type 2 diabetes maps to 18q22.3–23. Kidney Int 62:2176–2183, 2002[Medline]
  41. Ewens KG, George RA, Sharma K, Ziyadeh FN, Spielman RS: Assessment of 115 candidate genes for diabetic nephropathy by transmission/disequilibrium test. Diabetes 54:3305–3318, 2005[Abstract/Free Full Text]
  42. Janssen B, Hohenadel D, Brinkkoetter P, Peters V, Rind N, Fischer C, Rychlik I, Cerna M, Romzova M, De Heer E, Baelde H, Bakker SJ, Zirie M, Rondeau E, Mathieson P, Saleem MA, Meyer J, Koppel H, Sauerhoefer S, Bartram CR, Nawroth P, Hammes HP, Yard BA, Zschocke J, Van der Woude FJ: Carnosine as a protective factor in diabetic nephropathy: association with a leucine repeat of the carnosinase gene CNDP1. Diabetes 54:2320–2327, 2005[Abstract/Free Full Text]
  43. Freedman BI, Hicks PJ, Sale MM, Pierson ED, Langefeld CD, Rich SS, Xu J, McDonough C, Janssen B, Yard BA, Van der Woude FJ, Bowden DW: A leucine repeat in the carnosinase gene CNDP1 is associated with diabetic end-stage renal disease in European Americans. Nephrol Dial Transplant 22:1131–1135, 2007[Abstract/Free Full Text]
  44. Hwang SJ, Yang Q, Meigs JB, Pearce EN, Fox CS: A genome-wide association for kidney function and endocrine-related traits in the NHLBI's Framingham Heart Study. BMC Med Genet 8 (Suppl. 1):S10, 2007
  45. Stevens LA, Coresh J, Greene T, Levey AS: Assessing kidney function–measured and estimated glomerular filtration rate. N Engl J Med 354:2473–2483, 2006[Free Full Text]
  46. Lewis J, Agodoa L, Cheek D, Greene T, Middleton J, O'Connor D, Ojo A, Phillips R, Sika M, Wright J Jr.: Comparison of cross-sectional renal function measurements in African Americans with hypertensive nephrosclerosis and of primary formulas to estimate glomerular filtration rate. Am J Kidney Dis 38:744–753, 2001[Medline]
  47. Nelson RG, Greene T, Beck GJ, Van Lente F, Wang X, Knowler WC: Estimating GFR by the MDRD and Crockroft-Gault equations in Pima Indians (Abstract). J Am Soc Nephrol 14:134A, 2003
  48. 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 141:929–937, 2004[Abstract/Free Full Text]
  49. Vervoort G, Willems HL, Wetzels JF: Assessment of glomerular filtration rate in healthy subjects and normoalbuminuric diabetic patients: validity of a new (MDRD) prediction equation. Nephrol Dial Transplant 17:1909–1913, 2002[Abstract/Free Full Text]
  50. Rogus JJ, Warram JH, Krolewski AS: Genetic studies of late diabetic complications: the overlooked importance of diabetes duration before complication onset. Diabetes 51:1655–1662, 2002[Abstract/Free Full Text]

Add to CiteULike CiteULike   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati    What's this?


This article has been cited by other articles:


Home page
 DiabetesHome page
H. Kato, H. Si, T. Hostetter, and K. Susztak
Smad1 as a Biomarker for Diabetic Nephropathy
Diabetes, June 1, 2008; 57(6): 1459 - 1460.
[Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Online-Only Appendix
Right arrow All Versions of this Article:
db07-0313v1
57/1/235    most recent
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow Request Permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Schelling, J. R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Schelling, J. R.
Social Bookmarking
Add to CiteULike   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE