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NDT Advance Access originally published online on August 17, 2007
Nephrology Dialysis Transplantation 2008 23(1):161-168; doi:10.1093/ndt/gfm501
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© The Author [2007]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org


European rational approach for the genetics of diabetic complications—EURAGEDIC: patient populations and strategy

Lise Tarnow1, Per-Henrik Groop2,3, Samy Hadjadj4,5, Gbenga Kazeem6, Francois Cambien7,8, Michel Marre9,10, Carol Forsblom2,3, Hans-Henrik Parving13, David Trégouët7,8, Alexandre Thévard7,8, Martin Farrall6, Ivo Gut11, Dominique Gauguier6, Roger Cox12, Fumihiko Matsuda11, Mark Lathrop11, Nathalie Vionnet7,8 and for the EURAGEDIC Consortium

1Steno Diabetes Center, Copenhagen, Denmark, 2Helsinki University Central Hospital, Department of Medicine, Division of Nephrology, Helsinki, 3Folkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, Finland, 4CHU Poitiers, University Hospital, Endocrinology, Poitiers, 5INSERM, ERM 324, Poitiers, France, 6Welcome Trust Centre for Human Genetics, Oxford, UK, 7INSERM, UMR S 525, 8Université Pierre et Marie Curie-Paris 6, UMR S 525, F-75634, 9Department of Diabetology, Bichat Hospital 10INSERM, U695, Xavier Bichat University of Medicine, Paris, 11CNG, Evry, France, 12Mammalian Research Council, Oxford, UK and 13Department of Endocrinology, Rigshospitalet, Copenhagen, Denmark

Correspondence to: Lise Tarnow, Steno Diabetes Center, Niels Steensens Vej 2, DK-2820 Gentofte, Denmark. Email: ltar{at}steno.dk



   Abstract
Top
 Abstract
Introduction
 Subjects and methods
 Results
 Discussion
 Supplementary Materials
 Acknowledgements
 References
 
Background. Diabetic nephropathy is likely to be a complex genetic trait. To date, most diabetic nephropathy candidate gene studies have tested a limited number of genes and variants in small sized populations, or in populations that were poorly matched or phenotyped. The main objective of the EURAGEDIC study was to address these problems.

Methods. Single nucleotide polymorphisms (SNPs) in candidate genes were tested for association with overt diabetic nephropathy (persistent albuminuria >300 mg/24 h) in a large (n = 2499) Type 1 diabetes case/control study. Testing for transmission disequilibrium in 541 independent parent–offspring trios with or without diabetic nephropathy was applied for validation of consistency. Candidate genes were selected based on previous linkage studies, knowledge of metabolic pathways, and animal models. A comprehensive SNP discovery in more than 100 candidate genes was performed by direct sequencing.

Results. In total, 1176 cases with diabetic nephropathy and 1323 diabetic controls with longstanding normoalbuminuria were included from three European populations (Denmark, Finland, France). Data were collected on HbA1c, blood pressure, urinary albumin excretion rate, kidney function, retinopathy, smoking, medication and cardiovascular disease. To summarize the relevant non-genetic predictors for diabetic nephropathy a baseline phenotypic model fitted to EURAGEDIC data included the covariates: sex, diabetes duration, HbA1c and smoking as well as pair-wise interactions.

Conclusions. The EURAGEDIC study is designed and powered to identify and validate common alleles as genetic risk factors for diabetic nephropathy in Type 1 diabetic patients.

Keywords: diabetic nephropathy; genetics; type 1 diabetes



   Introduction
Top
 Abstract
 Introduction
Subjects and methods
 Results
 Discussion
 Supplementary Materials
 Acknowledgements
 References
 
The increased mortality in patients with Type 1 diabetes is predominantly caused by the poor prognosis for patients with diabetic nephropathy (DN). DN is characterized by persistent albuminuria, raised arterial blood pressure, a relentless decline in glomerular filtration rate and is associated with high cardiovascular morbidity and mortality [1,2]. The pathogenesis of this devastating micro-vascular complication is, however, not fully understood, although a multifactorial aetiology has generally been considered [1]. Epidemiological and familial studies suggest that genetic factors influence the risk to develop DN. The familial clustering of DN [3,4], together with that of elevated blood pressure [5], diabetes [6], and increased cardiovascular morbidity and early mortality [7] supports the hypothesis, that hereditary factors are involved in the liability to develop DN.

Diabetic nephropathy is likely to be a complex genetic trait, that is, the disorder is the consequence of gene sequence variations at several genes in combination with unfavourable environmental factors. The high cumulative incidence (30–40%) might be accounted for by the effect of combinations of predisposing alleles with relatively high frequency and modest effect at several independent loci. Despite rapid progress in sequencing the human genome and in molecular genetic and bioinformatic technologies during past decades, the success to map and identify genes responsible for complex traits has been modest so far.

To date, most DN candidate gene studies may have lacked the thoroughness and the sensitivity to detect association with complex traits, as they have tested a limited number of genes and variants in small sized populations. In addition, some study populations have been poorly matched or phenotyped, which frequently have resulted in lack of replication of the weak associations detected [2].

Therefore, the main objective of the European Rational Approach for the Genetics of Diabetic Complications (EURAGEDIC) study was to address these problems by a combination of comprehensive SNP (single nucleotide polymorphism) discovery in candidate genes; testing a number of SNPs in two large independent patient cohorts (first- and second-line); adjustment for relevant confounders; and analysis of haplotypes where possible. With this approach, we strive to further characterize the biological pathways involved in the pathogenesis of micro- and macro-vascular complications of Type 1 diabetes, with a main focus on diabetic nephropathy.

The EURAGEDIC consortium comprises eight institutions from four different European countries combining all the necessary know-how and expertise to perform a comprehensive program of large-scale genetic studies of complex diseases. Within the consortium there are three clinical partners with access to large collections of well-characterized diabetic patients, an efficient technological platform for DNA analyses, expertise in statistical analysis for the studies in humans as well as expertise in genetic and physiological studies of rodent models.



   Subjects and methods
Top
 Abstract
 Introduction
 Subjects and methods
Results
 Discussion
 Supplementary Materials
 Acknowledgements
 References
 
Patient populations
Three European centres from Denmark [8], Finland [9] and France [10,11] contributed to the EURAGEDIC studies. All included subjects were of Caucasian descent.

Denmark
Since 1993 all Type 1 diabetic patients with diabetic nephropathy attending the Steno Diabetes Center in Copenhagen were asked to participate in a study of the genetics of diabetic complications. In total, 489 cases with established DN were enrolled. A control group of 463 longstanding Type 1 diabetic patients with persistent normoalbuminuria was recruited from the out-patient clinic. In addition, a nationwide survey of the Danish National Registry of Diagnosis was used to identify patients diagnosed with Type 1 diabetes and either diabetic kidney disease (DE102) or multiple complications (DE107). After verification in patient records a letter of invitation was sent for all patients with a Type 1 diabetic sibling and/or two living parents available for investigation. A total of 217 families with a proband with DN or a normoalbuminuric Type 1 diabetic proband were investigated in the trio study.

Finland
The FINNDIANE study is an ongoing multicentre, nationwide collection of Type 1 diabetic patients and their relatives recruited from 56 participating diabetic referral centres. Patients are recruited irrespective of their disease duration or complications. All patients with an ascertained renal status were considered and from this collection 387 cases with established diabetic nephropathy and 469 normoalbuminuric controls with duration of diabetes of >15 years were available for the case-control study. In addition, 202 pedigrees were classifiable for inclusion in the trio study.

France
Caucasian Type 1 diabetic patients participated in a cross-sectional study for the genetic study of diabetic complications, conducted in 17 diabetes clinics in France and Belgium between 1994 and 1995. In addition, a nationwide collection of Type 1 diabetic patients and their relatives was performed. In total, 300 cases and 391 controls were recruited. Furthermore, 122 families were available for the trio study. All probands fulfilled the EURAGEDIC definitions of cases and controls.

Non-diabetic control subjects
Three non-diabetic cohorts from the general populations (n = 736) were used to explore national differences in allele frequency (see details in Supplementary Materials).

The study was approved by the ethical committees in each of the participating countries, and all subjects gave written informed consent.

Definitions
Type 1 diabetes was considered present if the age at onset of diabetes was ≤ 35 years and the time to definitive insulin therapy ≤ 1 year.

Established diabetic nephropathy (cases) was defined by persistent albuminuria (>300 mg/24 h or >200 µg/min or >200 mg/l) in two out of three consecutive measurements on sterile urines, after >5 years diabetes duration. In patients with ongoing angiotensin-converting enzyme (ACE) inhibitors or angiotensin II receptor blockers (ARB) the last measurement of urinary albumin excretion before treatment initiation was used for classification. Patients with clinical suspicion of non-diabetic renal or urinary tract disease were excluded.

Absence of diabetic nephropathy (controls) was defined as persistent normoalbuminuria (urinary albumin excretion rate: <30 mg/24 h or <20 µg/min or <20 mg/l) after at least 15 years of diabetes duration in patients not treated with ACE inhibitors or ARBs.

Clinical characteristics
A minimal set of clinical parameters available at the three sites at the time of inclusion (cross-sectional data) was defined including age, age at diabetes onset, height, weight, insulin dose, HbA1c, systolic blood pressure, diastolic blood pressure, antihypertensive treatment, urinary albumin excretion rate, serum/plasma creatinine, renal replacement therapy, retinopathy, smoking status and cardiovascular complications (stroke, myocardial infarction, amputation).

In the majority of patients urinary albumin excretion rate was measured on timed urine samples in a central laboratory by validated methods, i.e. enzyme immunoassay (Denmark), radioimmunoassay (Finland) and nephelometry (France). The remaining 16% of the French patients (n = 88) were classified based on repeated measurements of urinary concentrations of albumin in a morning spot urine sample.

Blood pressure was measured twice in the resting state with a standard sphygmomanometer and appropriate cuff size. HbA1c was analysed by standard high performance liquid chromatography (HPLC) techniques locally with normal values in the range from 4% to 7% in all centres. Serum/plasma creatinine concentration was determined by a modified Jaffe's method centrally in each country. For descriptive purposes, the last available creatinine data was considered, even in patients who had previously had a kidney graft. Renal replacement therapy was defined as kidney transplantation or dialysis. Patients in renal replacement therapy were classified as cases, irrespective of their actual creatinine concentration. Retinopathy was graded based on fundus photography or direct ophtalmoscopy by an experienced ophthalmologist as nil, simplex or proliferative retinopathy. Former and current smokers of one or more cigarettes per day were classified as smokers, all others as non-smokers. Cardiovascular disease was considered present in patients with a history of admission for stroke, myocardial infarction or vascular amputations.

Study design
The EURAGEDIC study design involved two phases for the genotyping of DNA variants, a first-line case-control study and a second-line trio study. The analysis strategy is a two-stage sequential design with stopping for futility. All selected variants were tested in the first-line study, and further in the second-line cohorts if the results of the first-line univariate analyses yielded a P-value less double equals0.1. The power of the first-line study was 71% for a genotype risk ratio (GRR) = 1.3 and a minor allele frequency of ≥0.25 with an {alpha} of 0.000125. To maximize the power of the first-line case-control study, 119 cases and 196 controls from the trio studies were included. When markers were genotyped in both the first and second-line studies these probands were solely included in the trio analyses.

For the trio study, families with at least one Type 1 diabetic proband with DN or with persistent long-lasting normoalbuminuria were considered and trios with both parents were recruited. When only one parent was available, the probands’ siblings were recruited in order to be able to infer probabilities for the missing parental genotype. The inclusion of trios with Type 1 diabetic probands affected or unaffected with DN allows us to search for opposite transmission patterns and reduces the risk of Type 1 diabetes loci from being detected [12].

Selection of candidate genes
The study is a systematic investigation of more than one hundred candidate genes identified through studies of susceptibility loci suggested by linkage studies, knowledge of metabolic pathways and data from animal models [13] (Supplementary Material—Table).

Positional candidate genes
Identified through several whole genome screens performed for Type 1 and 2 diabetes, insulin resistance, obesity, vascular complications of diabetes, blood pressure and hyperlipidaemia. Regions of particular interest include regions on chromosome 3q, 7q, 18q and 20p [14–19].

Functional candidate genes
Selected based on existing knowledge of metabolic pathways involved in the pathophysiology of diabetic complications. Among several options, three major avenues of investigation have been identified: (i) genes involved in glucose metabolism, including diabetes susceptibility genes or metabolism of glucose in target tissues, (ii) genes involved in the risk for vascular disease in the general population, including genes regulating blood pressure and cardiovascular risk and (iii) genes involved in the structure and growth of target tissues.

Animal models
New candidate genes are identified through the characterization of genes that exhibit altered transcriptional levels in the kidneys of diabetic and control rats (GK-rat) and mice (ENU mouse). Furthermore, the validation of the effect of impaired glucose homeostasis on the expression of these genes is ongoing. Thereby, new genes and pathways involved in the pathogenesis of diabetic complications in animal models are revealed. With the progress of the comparative genetic maps of rat, mouse and human genomes the human homologues of genes identified in the pathogenesis of diabetic complications in rodents will become available.

Single nucleotide polymorphism (SNP) discovery and SNP selection for genotyping
Genes selected for study were examined for polymorphisms from databases and by a SNP discovery platform. All exons and flanking intron sequences, 5' and 3' untranslated regions as well as promoter regions of each gene were screened by direct sequencing, using two sets of pooled DNA (see Supplementary Material for details). Sequencing reactions were performed according to the dye terminator methods using an ABI PRISM 3700 Analyzer (Applied Biosystems, Foster City, CA, USA). Alignment of experimental results, SNP discovery and genotyping were performed by the Genalys Software [20]. Haplotype structure and frequencies were determined from data concerning pooled DNA using the expectation maximization algorithm [21]. SNPs tagging the most frequent haplotypes (at least 5% in one population) were selected for genotyping. In addition, all non-synonymous variants that were detected in at least one diseased population were systematically investigated. We also examined 94 SNP genomic control markers in non-genic regions spaced throughout the genome to control for possible stratification within each population.

SNP genotyping was performed at the CNG (Centre National de Genotypage, Evry, France) using automated high throughput methods. Single SNP genotyping methods include the TaqMan, Amplifluor and MALDI-mass spectrometry platforms, whereas SNPlex method was used for multiplex SNP genotyping. All liquid handling was performed robotically in 384 well plates.

A success rate of >85% for genotyping was accepted. This threshold was selected to balance the loss of genotypic information with the relatively large efforts needed to retype individual samples using high-throughput technologies. Furthermore, quality control included 192 replicate samples genotyped blindly, DNAs from both cases and controls on each plate and to check for Hardy–Weinberg equilibrium in cases and controls, in each population, and on each individual plate. For trios, microsatellite markers (Panel 16, LMSV2, Applied Biosystems, Courtaboeuf, France) were genotyped to verify the family relationship. Nine trios that exhibited genotype patterns which were incompatible with the putative family structure were excluded.

Due to low DNA resource left, DNA sharing is not possible through the EURAGEDIC project, but data sharing is welcome to improve scientific knowledge. Requests should be addressed to the corresponding author to consider collaborations.

Statistical analyses
All phenotypic data are presented as mean (SD), except for serum creatinine and urinary albumin excretion rate, which due to their non-normal distribution are expressed as geometric means with 95% confidence intervals (CI) and log transformed before analysis. Comparisons of continuous baseline phenotypic covariates between cases and controls were performed with unpaired Student's t-test or a non-parametric test of equality of medians. Chi-squared test statistic was employed to compare categorical variables between cases and controls. Test of homogeneity of covariates across the three populations was performed using one-way ANOVA (Analysis of variance) or Chi-squared test as appropriate.

To summarize the relevant non-genetic predictors for diabetic nephropathy in the EURAGEDIC cohorts multivariate logistic regression models were fitted to data to obtain the baseline phenotypic model (BPM). The covariates used are sex, diabetes duration (in years), HbA1c and smoking status. The ‘survey’ suite of STATA procedures was used for this analysis using population (France, Denmark and Finland) to define the model strata; thus the BPM presents a (linear) population-weighted model of disease risk relevant for the EURAGEDIC samples. Model selection was carried out using a hierarchical backward elimination procedure with main and two-way interaction terms. A further series of models was fitted to each population group separately to allow the risks associated with differing levels of covariates to vary across the three populations.

After obtaining the BPM the effect of each genetic marker will be considered in the light of the BPM. That is, for each marker a logistic regression model will be fitted to estimate its odds ratio (OR) adjusting for the covariates in the fitted BPM.

A post-hoc power calculation for the first- and second-line studies combined (Table 1), using a multiplicative model of genetic risk and an {alpha} of 0.000125, revealed that the EURAGEDIC study has good power (~85%) to detect relatively small genetic differences: genotype risk ratio, GRR = 1.3 for an allele frequency > 0.25. Multiple testing error will be corrected for using the effective number of independent tests after taking into account linkage disequilibrium between markers [13].


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  		Table 1. Post-hoc power calculations for the EURAGEDIC first-line case-control study and the trio-study combined

 
The STATA (versions 7 and 9) statistical package was used to perform the analyses.



   Results
Top
 Abstract
 Introduction
 Subjects and methods
 Results
Discussion
 Supplementary Materials
 Acknowledgements
 References
 
First line case-control study
Clinical characteristics of Type 1 diabetic probands included in the case-control and the trio study are presented in Tables 2 and 3, respectively. In Table 2, the clinical characteristics of the subjects included in the first-line study are shown for each country separately.


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  		Table 2. Clinical characteristics of 2499 Type 1 diabetic patients with and without diabetic nephropathy included in the first-line case-control study

 

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  		Table 3. Clinical characteristics of 541 type 1 diabetic probands with and without diabetic nephropathy included in the second-line trio study

 
Baseline phenotypic model
The baseline phenotypic model (BPM) predicts risk of DN within the EURAGEDIC cohorts; these risks are a weighted-average across the three populations (Table 4). This model shows that male sex, long duration of diabetes, high HbA1c level and smoking are significant (P ≤ 0.007) main-effect predictors of developing DN in the EURAGEDIC cohort. There is also some evidence (P = 0.007) of an interaction between HbA1c level and diabetes duration. Analysis of each population shows that there are a few important differences in covariate-predicted risks across the three populations (Table 5), most notably with HbA1c levels. Mindful of the principle of parsimony and stressing the desire to model European-wide risk for DN, we propose that the EURAGEDIC BPM for the combined data set is appropriate for modeling individual-specific risks in these populations.


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  		Table 4. Summary of the fitted baseline phenotypic model (BPM) selected to be predictive for the EURAGEDIC cohorts – each genetic marker will be considered in the light of this EURAGEDIC BPM

 

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  		Table 5. Summary of the fitted baseline phenotypic model selected to be predictive for each EURAGEDIC population

 
Second line trio study
For the second-line study, 253 trios of probands with established nephropathy and 288 control trios were studied—clinical data are presented in Table 2.

As in the first-line case-control study, trio probands with nephropathy had poorer glycaemic control, higher blood pressures and elevated creatinine, received antihypertensive medication and smoked more frequently and had more severe retinopathy and cardiovascular disease as compared with normoalbuminuric Type 1 diabetic controls.

Applying a trio design implies that parents are alive and available for investigation. Therefore, as expected, patients in the second-line study were on average 6 years younger, had developed diabetes earlier and had shorter duration of diabetes in comparison with probands of the first-line study, P < 0.0001.



   Discussion
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
Supplementary Materials
 Acknowledgements
 References
 
The EURAGEDIC study is a large case-control study including more than 1000 Type 1 diabetic case-control pairs from three European countries (Denmark, Finland, France). Testing for transmission disequilibrium in 541 independent parent-offspring trios with or without diabetic nephropathy from the three countries is used to evaluate positive results from the case-control cohorts using a design and statistical analysis that are robust to population stratification and provide an opportunity to evaluate parent-of-origin effects. Our aim is to identify genetic susceptibility to DN through a strategy based upon comprehensive SNP discovery in a large number of candidate genes, testing a large number of SNPs, adjustment for relevant confounders, and analysis of haplotypes.

Besides good glycaemic control, the attenuation of the renin-angiotensin system with ACE inhibitors or ARB is important in the clinical management of early diabetic kidney disease. It was, therefore, not surprising that the study of candidate genes for DN began in 1994 with the study by Marre et al. [22] reporting a protective effect of the II genotype of the ACE-gene for the development of this complication. Since then a considerable number of reports on the association of the ACE/ID polymorphism and DN have been published and now, a decade after the initial paper, more than 50 studies have been carried out in different patient populations with predictable conflicting results [23].

Contributing to the confusion are major differences between studies in criteria for the diagnosis of diabetic kidney disease. The majority of previous studies includes patients with both micro- and macro-albuminuria among cases, despite existing studies [24] demonstrating that Type 1 diabetic patients with micro-albuminuria may not necessarily progress to overt DN. To reduce case heterogeneity in the EURAGEDIC study, patients with micro-albuminuria were not included and strict criteria for established DN have been applied in both the first-line and second-line study.

Selection of control subjects for the present study was similarly strict and required patients to have had Type 1 diabetes and persistent normoalbuminuria for more than 15 years, thereby minimizing the number of controls who will in the future eventually develop severe diabetic kidney disease. Furthermore, blockers of the renin–angiotensin system with the potential of urinary albumin excretion lowering were not allowed for in the control groups to avoid misclassification.

Although strict pre-defined phenotypic inclusion criteria were agreed upon, several interesting differences in clinical characteristics exist between the three patient populations in the EURAGEDIC study. Since different strategies for patient recruitment have been applied in the three countries [8–11], sampling bias is likely to contribute to differences in nephropathy risk, retinopathy and renal replacement therapy patterns. To adjust for possible heterogeneity of gene-associations across populations, it was decided to perform tests for allelic association with disease for each of the three populations separately and to combine results in a weighted analysis. Furthermore, for each polymorphism a logistic regression model was fitted to estimate its odds ratio adjusting for the covariates and their interactions in the fitted BPM. The BPM was selected to be predictive of DN for the EURAGEDIC cohorts, and is thus only of relevance to the interpretation of this study. Interestingly, the analysis of clinical characteristics clearly identified a consistent association between smoking and DN, both in the first- and second-line cohorts, which was not fully established hitherto in type 1 diabetic patients [25,26].

The statistical strategy applied in the EURAGEDIC study strives to establish confirmed association given the large number of candidate genes and SNPs that will be tested. Therefore, a two-stage approach was chosen, and all SNP markers showing suggestive evidence of association in the first-line case-control study (P ≤ 0.1) was to be characterized in the second-line family-based trio study as well. A newly developed method [27] to combine results from the first-line and second-line studies was then used to assess overall evidence for association. One major limitation to the overall study is the relatively small number of trios included, due to unexpected difficulties in the recruitment phase.

A post-hoc power calculation for the first- and second-line studies combined revealed that the EURAGEDIC study has good power (~85%) to detect relatively small genetic differences (GRR = 1.3) provided that the allele is frequent in the population. Another limitation to the EURAGEDIC study is the relatively high OR detectable with the number of patients included. In order to have 85% power to detect a GRR of 1.2, inclusion of about twice the number of cases (n ~ 2000) would be necessary.

Population stratification is a source of potential confounding in case-control studies. Ethnic diversity between cases and controls within each country would potentially lead to stratification effects and inflation of the type 1 error. The association for the genomic control markers in the EURAGEDIC was compatible with the expectation of no association (data not shown) indicating that stratification within one or more of the populations is an unlikely source of positive association. Genetic diversity across countries can be dealt with by calculating weighted risks across the three countries; marked genetic diversity is likely to increase the type 2 error. In addition, our collection of trio families provides a valuable resource in which to robustly test potential diabetic nephropathy susceptibility alleles.

Large-scale cohorts of type 1 diabetic patients dedicated to complications are available for genetic studies. The GoKinD cohort, using a very similar strategy, focuses on American diabetic patients [28]. The DCCT/EDIC cohort, also in North America, is focusing on the development of DN, including microalbuminuria, using a follow-up design [29]. Other important cohorts such as the Pittsburgh Epidemiology of Diabetes Complications Study [30] or the EURODIAB complication study [31] also presented the association of DN with genetic markers. In this context, the EURAGEDIC cohort is of peculiar importance due to its large size, its careful phenotypic definition of cases and controls, its family-based and cross-sectional combined approaches and its multinational European recruitment.

In conclusion, the EURAGEDIC study based on a systematic selection and exploration of candidate genes for diabetic complications in large collections of well-characterized Type 1 diabetic patients has the power to identify and validate the contribution of common alleles as genetic risk factors for DN.



   Supplementary Materials
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Supplementary Materials
Acknowledgements
 References
 
For Supplementary Materials, please refer NDT online.



   Acknowledgements
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Supplementary Materials
 Acknowledgements
References
 
The EURAGEDIC study was made possible through funding from the European Commission through contract number QLG2-CT-2001-01669.

All type 1 diabetes patients and their family are sincerely acknowledged.

The Danish Diabetes Association and the Sehested Hansen Foundation are thanked for their continued support to this study.

The French Diabetes Association is thanked for its support to this study (bourse AFD recherche 2000). The list of participating centers in France and Belgium is available in references 10 and 11. A. Boland and A. Lemainque (CNG) are acknowledged for handling DNA bank, and for micro-satellite and Taqman ® genotyping. V. Laneuze and C. Demer for secretarial assistance.

Oluf Pedersen, Steno Diabetes Center, Gentofte, Denmark and Ludovic Drouet, Hôpital Lariboisière, Paris France are kindly acknowledged for providing non-diabetic individuals from population-based studies.

The FinnDiane study is supported by grants from the Folkhälsan Research Foundation, Samfundet Folkhälsan, the Wilhelm and Else Stockmann Foundation, the Liv och Hälsa Foundation and the Finnish Medical Society (Finska Läkaresällskapet). We acknowledge all the physicians and nurses at each centre participating in the collection of patients (see Supplementary Materials):

Conflict of interest statement. None declared.



   References
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Supplementary Materials
 Acknowledgements
 References
 

  1. Parving HH, Osterby R, Ritz E. Diabetic Nephropathy. In: The Kidney.—Brenner BM, ed. (2003) 7th edn. Philadelphia: WB Saunders. 1777–1818.
  2. Altshuler D, Daly M, Kruglyak L. Guilt by association. Nat Genet (2000) 26:135–137.[CrossRef][Web of Science][Medline]
  3. 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 (1989) 320:1161–1165.[Abstract]
  4. Harjutsalo V, Katoh S, Sarti C, Tajima N, Tuomilehto J. Population-based assessment of familial clustering of diabetic nephropathy in type 1 diabetes. Diabetes (2004) 53:2449–2454.[Abstract/Free Full Text]
  5. Viberti GC, Keen H, Wiseman M. Raised arterial pressure in parents of proteinuric insulin dependent diabetic patients. Br Med J (1987) 295:515–517.[Abstract/Free Full Text]
  6. Fagerudd JA, Pettersson-Fernholm KJ, Gronhagen-Riska C, Groop PH. The impact of a family history of Type II (non-insulin-dependent) diabetes mellitus on the risk of diabetic nephropathy in patients with Type I (insulin-dependent) diabetes mellitus. Diabetologia (1999) 42:519–526.[CrossRef][Web of Science][Medline]
  7. Tarnow L, Rossing P, Nielsen FS, Fagerudd JA, Poirier O, Parving HH. Cardiovascular morbidity and early mortality cluster in parents of type 1 diabetic patients with diabetic nephropathy. Diabetes Care (2000) 23:30–33.[Abstract]
  8. Lajer M, Tarnow L, Fleckner J, et al. Association of aldose reductase gene Z+2 polymorphism with reduced susceptibility to diabetic nephropathy in Caucasian Type 1 diabetic patients. Diabet Med (2004) 21:867–873.[CrossRef][Web of Science][Medline]
  9. Pettersson-Fernholm K, Forsblom C, Hudson BI, Perola M, Grant PJ, Groop PH. The functional -374 T/A RAGE gene polymorphism is associated with proteinuria and cardiovascular disease in type 1 diabetic patients. Diabetes (2003) 52:891–894.[Abstract/Free Full Text]
  10. Hadjadj S, Pean F, Gallois Y, et al. Different patterns of insulin resistance in relatives of type 1 diabetic patients with retinopathy or nephropathy: the Genesis France-Belgium Study. Diabetes Care (2004) 27:2661–2668.[Abstract/Free Full Text]
  11. Marre M, Jeunemaitre X, Gallois Y, et al. Contribution of genetic polymorphism in the renin-angiotensin system to the development of renal complications in insulin-dependent diabetes: Genetique de la Nephropathie Diabetique (GENEDIAB) study group. J Clin Invest (1997) 99:1585–1595.[Web of Science][Medline]
  12. Rogus JJ, Moczulski D, Freire MB, Yang Y, Warram JH, Krolewski AS. Diabetic nephropathy is associated with AGT polymorphism T235: results of a family-based study. Hypertension (1998) 31:627–631.[Abstract/Free Full Text]
  13. Nyholt DR. A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet (2004) 74:765–769.[CrossRef][Web of Science][Medline]
  14. Imperatore G, Hanson RL, Pettitt DJ, Kobes S, Bennett PH, Knowler WC. Sib-pair linkage analysis for susceptibility genes for microvascular complications among Pima Indians with type 2 diabetes. Pima Diabetes Genes Group. Diabetes (1998) 47:821–830.[Abstract]
  15. Moczulski DK, Rogus JJ, Antonellis A, Warram JH, Krolewski A. Major susceptibility locus for nephropathy in type 1 diabetes on chromosome 3q. Results of novel discordant sib-pair analysis. Diabetes (1998) 47:1164–1169.[Abstract]
  16. Vardarli I, Baier LJ, Hanson RL, et al. Gene for susceptibility to diabetic nephropathy in type 2 diabetes maps to 18q22.3-23. Kidney Int (2002) 62:2176–2183.[CrossRef][Web of Science][Medline]
  17. Vionnet N, Hani EH, Dupont S, et al. Genomewide search for type 2 diabetes-susceptibility genes in French whites: evidence for a novel susceptibility locus for early-onset diabetes on chromosome 3q27-qter and independent replication of a type 2-diabetes locus on chromosome 1q21-q24. Am J Hum Genet (2000) 67:1470–1480.[CrossRef][Web of Science][Medline]
  18. Bowden DW, Colicigno CJ, Langefeld CD, et al. A genome scan for diabetic nephropathy in African Americans. Kidney Int (2004) 66:1517–1526.[CrossRef][Web of Science][Medline]
  19. Osterholm AM, He B, Pitkaniemi J, et al. Genome-wide scan for type 1 diabetic nephropathy in the Finnish population reveals suggestive linkage to a single locus on chromosome 3q. Kidney Int (2007) 71:140–145.[CrossRef][Web of Science][Medline]
  20. Takahashi M, Matsuda F, Margetic N, Lathrop M. Automated identification of single nucleotide polymorphisms from sequencing data. Proc IEEE Comput Soc Bioinform Conf (2002) 1:87–193.[Medline]
  21. Dempster A, Laird N, Rubin D. Maximum likelihood from incomplete data via the EM algorithm. J Royal Stat Society (1977) 39:1–38.
  22. Marre M, Bernadet P, Gallois Y, et al. Relationships between angiotensin I converting enzyme gene polymorphism, plasma levels, and diabetic retinal and renal complications. Diabetes (1994) 43:384–388.[Abstract]
  23. Ng DP, Tai BC, Koh D, Tan KW, Chia KS. Angiotensin-I converting enzyme insertion/deletion polymorphism and its association with diabetic nephropathy: a meta-analysis of studies reported between 1994 and 2004 and comprising 14,727 subjects. Diabetologia (2005) 48:1008–1016.[CrossRef][Web of Science][Medline]
  24. Perkins BA, Ficociello LH, Silva KH, Finkelstein DM, Warram JH, Krolewski AS. Regression of microalbuminuria in type 1 diabetes. N Engl J Med (2003) 348:2285–2293.[Abstract/Free Full Text]
  25. Karamanos B, Porta M, Songini M, et al. Different risk factors of microangiopathy in patients with type I diabetes mellitus of short versus long duration. The EURODIAB IDDM Complications Study. Diabetologia (2000) 43:348–355.[CrossRef][Web of Science][Medline]
  26. Orchard TJ, Dorman JS, Maser RE, et al. Factors associated with avoidance of severe complications after 25 yr of IDDM. Pittsburgh Epidemiology of Diabetes Complications Study I. Diabetes Care (1990) 13:741–747.[Abstract]
  27. Kazeem GR, Farrall M. Integrating case-control and TDT studies. Ann Hum Genet (2005) 69:329–335.[Web of Science][Medline]
  28. Mueller PW, Rogus JJ, Cleary PA, et al. Genetics of Kidneys in Diabetes (GoKinD) study: a genetics collection available for identifying genetic susceptibility factors for diabetic nephropathy in type 1 diabetes. J Am Soc Nephrol (2006) 17:1782–1790.[Abstract/Free Full Text]
  29. Boright AP, Paterson AD, Mirea L, et al. Genetic variation at the ACE gene is associated with persistent microalbuminuria and severe nephropathy in type 1 diabetes: the DCCT/EDIC Genetics Study. Diabetes (2005) 54:1238–1244.[Abstract/Free Full Text]
  30. Orchard TJ, Chang YF, Ferrell RE, Petro N, Ellis DE. Nephropathy in type 1 diabetes: a manifestation of insulin resistance and multiple genetic susceptibilities? Further evidence from the Pittsburgh Epidemiology of Diabetes Complication Study. Kidney Int (2002) 62:963–970.[CrossRef][Web of Science][Medline]
  31. Soedamah-Muthu S, Clohoum HM, Taskinen M-R, Idzior-Walus B, Fuller JH, and the EURODIAB IDDM Complications Study Group. Differences in HDL-cholesterol:apoA-I+apoAII ratio and apoE phenotype with albuminuric status in type I diabetic patients. Diabetologia (2000) 43:1353–1359.[CrossRef][Web of Science][Medline]
Received for publication: 30. 1.07
Accepted in revised form: 3. 7.07


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