, Volume 54, Issue 5, pp 1032–1042

Receptor for advanced glycation end-products (RAGE) provides a link between genetic susceptibility and environmental factors in type 1 diabetes

  • J. M. Forbes
  • J. Söderlund
  • F. Y. T. Yap
  • M. Knip
  • S. Andrikopoulos
  • J. Ilonen
  • O. Simell
  • R. Veijola
  • K. C. Sourris
  • M. T. Coughlan
  • C. Forsblom
  • R. Slattery
  • S. T. Grey
  • M. Wessman
  • H. Yamamoto
  • A. Bierhaus
  • M. E. Cooper
  • P.-H. Groop



This group of studies examines human genetic susceptibility conferred by the receptor for advanced glycation end-products (RAGE) in type 1 diabetes and investigates how this may interact with a western environment.


We analysed the AGER gene, using 13 tag SNPs, in 3,624 Finnish individuals from the FinnDiane study, followed by AGER associations with a high risk HLA genotype (DR3)-DQA1*05-DQB1*02/DRB1*0401-DQB1*0302 (n = 546; HLA-DR3/DR4), matched in healthy newborn infants from the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) Study (n = 373) using allelic analysis. We also studied islets and circulating RAGE in NODLt mice.


The rs2070600 and rs17493811 polymorphisms predicted increased risk of type 1 diabetes, whereas the rs9469089 SNP was related to decreased risk, on a high risk HLA background. Children from the DIPP study also showed a decline in circulating soluble RAGE levels, at seroconversion to positivity for type 1 diabetes-associated autoantibodies. Islet RAGE and circulating soluble RAGE levels in prediabetic NODLt mice decreased over time and were prevented by the AGE lowering therapy alagebrium chloride. Alagebrium chloride also decreased the incidence of autoimmune diabetes and restored islet RAGE levels.


These studies suggest that inherited AGER gene polymorphisms may confer susceptibility to environmental insults. Declining circulating levels of soluble RAGE, before the development of overt diabetes, may also be predictive of clinical disease in children with high to medium risk HLA II backgrounds and this possibility warrants further investigation in a larger cohort.


Advanced glycation Alagebrium chloride Autoimmune diabetes Children Insulin NOD Polymorphism 



Carboxymethyl lysine


Diabetes Prediction and Prevention Study


High mobility group protein B1


Hardy–Weinberg equilibrium


Minor allele frequencies


Receptor for advanced glycation end-products


Single nucleotide polymorphism


Type 1 diabetes occurs as the result of a complex disease process where genetic and environmental factors lead to an autoimmune response which, to date, remains incompletely defined [1]. Although some major genetic determinants of type 1 diabetes, such as alleles of the major histocompatibility locus (HLA) at the HLA-DRB1 and DQB1 loci [2] and more recently the HLA-B*39 locus [3], have been identified, these only account for some 40–50% of the familial clustering. In addition, approximately 70% of monozygotic twins are discordant for the development of type 1 diabetes [4], which indicates that environmental triggers are also likely to be important. Indeed, the risk of developing type 1 diabetes is still increasing by 3–4% annually in developed nations [5], which remains unexplained.

One such environmental contributor to type 1 diabetes may be AGEs. Western diets provide excesses of these non-enzymatic modifications [6], which contribute flavour and colour to foodstuffs (e.g. roasted meat or coffee), while more recently AGE modifications also add functional properties, such as improved emulsification. Dietary AGEs are absorbed intestinally and are a prominent contributor to the body’s AGE pool [7], in addition to the natural ageing process [8], redox imbalances [9], non-diabetes related diseases including renal impairment [10] and hyperglycaemia [11]. There is increasing evidence to suggest that AGEs also promote beta cell dysfunction [12, 13]. Furthermore, a reduced incidence of diabetes in a mouse model of autoimmune diabetes, the NOD mouse (NODLt) [14], has been reported following dietary restriction of AGEs. However, the specific mechanisms whereby insulin secretory pathways are damaged by AGEs are not known.

AGEs can exert their biological effects via receptors such as the receptor for advanced glycation end-products (RAGE) [15], although these compounds may have greater effects via modulation of RAGE production per se [16]. RAGE is a multi-ligand receptor involved in host-pathogen defence, in addition to cellular apoptosis [17]. Thus, a recent study has identified a blockade of the late stages of adoptively transferred autoimmune diabetes with the ‘decoy’ soluble RAGE receptor [18]. In addition, a small molecule inhibitor of RAGE [19] or antibodies against another RAGE ligand, HMGB1 [20], have been shown to delay islet destruction in hyperglycaemic NODLt mice. Furthermore, the heritability of insulin resistance [21], another factor postulated to be contributing to the increasing rate of type 1 diabetes incidence [22], is associated with specific RAGE polymorphisms.

Genetic susceptibility to type 1 diabetes remains to be fully defined, in particular, the contribution of certain genotypes in the context of our increasingly Westernised environment. It is therefore unknown if a specific RAGE genotype may play a role in susceptibility to type 1 diabetes; we have now investigated this issue using a combination of human genetics, molecular biology and integrative pathophysiology.


Human study

FinnDiane/DIPP design and recruitment

Characteristics of the 2,921 unrelated type 1 diabetic patients from the nationwide Finnish Diabetic Nephropathy (FinnDiane) study group [23] are provided in Table 1. Age of diagnosis of 30 years or below was used as a criterion for inclusion in the analysis. A total of 703 unrelated control samples were obtained from the Finnish Red Cross Blood Service. These controls represent a healthy non-diabetic Finnish population of European descent given the strict criteria that the Red Cross Blood Service practices regarding blood donors. Notably, there was no population stratification or admixture applied to their selection which might influence the genetic results (see Electronic supplementary material [ESM]).
Table 1

Patient characteristics: patients with T1D taken from the FinnDiane study









Sex, male (%)




Age (years)

40.3 ± 9.9



Age at T1D diagnosis (years)

12.8 ± 7.6



aFinnish Red Cross Blood Donors

bNewborn infants from the Type 1 DIPP

T1D, Type 1 diabetes

Since AGER is located within the HLA class III region [24], we further analysed these AGER associations outlined above on a matched high risk HLA background. To achieve this, newborn infants (n = 373), who had the highest risk HLA genotype (DR3)-DQA1*05-DQB1*02/DRB1*0401-DQB1*0302 (from n = 546; HLA-DR3/DR4 [25]), were matched with unaffected newborn infants with the identical HLA genotype, from the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) Study (n = 373) [26]. A subgroup of children was also used for circulating soluble RAGE analyses. We included all children with the high risk HLA genotype identified from the DIPP study as controls. However, unfortunately some participants with the high risk HLA background had to be excluded because it was not possible to extract sufficient quantities of DNA for the analyses from the miniscule blood samples available. All children studied were from Finland and no other children were excluded from the present study. The ethics committees of all participating centres approved the study protocol and all patients or guardians gave written consent. The studies follow the Declaration of Helsinki.

DNA isolation and genotyping

DNA was isolated from whole blood with either a Puregene DNA Purification Kit (Gentra Systems, Minneapolis, MN, USA) according to the manufacturer’s protocol or with a phenol-chloroform protocol [27].

Tag SNPs for genotyping were selected from the HapMap Project’s NCBI build 34 and 35 for the CEU (Utah, USA residents with ancestry from northern and western Europe populations; n = 180), as defined by Gabriel et al. [28] to encompass the genetic variation of the entire AGER gene. Minor allele frequencies (MAFs) were required to be ≥0.05. Genotyping was performed with TaqMan SNP Genotyping Assays (Applied Biosystems, Foster City, CA, USA) with an ABI Prism 7900HT Sequence Detection System (Perkin-Elmer, Foster City, CA, USA) and the Sequenom multiplex platform (Sequenom, Hamburg, Germany) according to the manufacturers’ instructions.

HLA genotyping for the two high risk haplotypes was performed with a sequence specific hybridisation method using oligonucleotide probes specific for DQA1*05 and DQB1*02 (DR3-DQ2 haplotype) and DRB1*0401 and DQB1*0302 (DR4-DQ8 haplotype) as described previously [25].

Soluble RAGE analysis

A series of children from the DIPP study were analysed for plasma soluble RAGE. The group comprised 15 children (12 boys) who progressed to clinical type 1 diabetes during prospective observation (patients) and 15 autoantibody-negative controls (non-progressors) who remained non-diabetic and were matched with the patients for sex, date of birth and HLA genotype. Altogether 14 children carried the high risk genotype (DQA1*05-DQB1*02/DQB1*0302) and 16 patients carried moderate risk genotypes (HLADQB1*0302/x≠ DQB1*02, *0301, *0602 or *0603). All children provided three plasma samples. Among the patients, the first sample was obtained before seroconversion to autoantibody positivity at a mean age of 1.1 (range 0.3–3.0) years, the second sample soon after the seroconversion at a mean age of 2.4 (range 0.7–5.3) years and the third sample soon after the diagnosis of clinical type 1 diabetes at a mean age of 5.2 (range 2.0–8.9) years. In the control patients, the samples were obtained at the corresponding ages. Human plasma samples were assayed undiluted according to the manufacturer’s instructions contained within the Human RAGE ELISA (R&D Systems, Minneapolis, MN, USA). The inter-assay CV was 7.8% while the intra-assay CV was 5.7%. The limit of sensitivity was 4.12 pg/ml.

Statistical analysis

Hardy–Weinberg equilibrium (HWE) for SNPs was tested in the control individuals (n = 703), and which were considered to be in HWE when p > 0.05 using Haploview 4.0 (MIT/Harvard Broad Institute, Boston, MA, USA) [29]. Testing for allelic associations was carried out with logistic regression adjusted for sex in SPSS 15.0 (Chicago, IL, USA).

Due to the strong linkage disequilibrium between SNPs in the HLA region and the HLA class II loci, it was necessary to exclude an effect of the HLA on the associations. Therefore, we matched patients and references for the high risk HLA genotype (DR3)-DQA1*05-DQB1*02/DRB1*0401-DQB1*0302. Logistic regression was used to evaluate allelic odds ratios.

The issue of sufficient power to detect association between AGER SNPs and type 1 diabetes was evaluated using the Genetic Power Calculator (http://pngu.mgh.harvard.edu/∼purcell/gpc/; [30]) with the module of case–control for discrete traits. Bonferroni correction for multiple comparisons was applied to the FinnDiane cohort under the conditions of 13 SNPs, α level 0.05, and three performed tests. Human soluble RAGE concentrations were analysed by repeated measures ANOVA.

Rodent study

We next studied islet and circulating RAGE production in a spontaneous experimental model which resembles type 1 diabetes, the NODLt mouse, since the genetic loci thought to be responsible for susceptibility to diabetes in these mice include the AGER gene sequence [31, 32]. NODLt or NODScid mice fed standard mouse chow (containing the AGE, carboxymethyl lysine (CML): 93.39 nmol [mol lysine]–1 [100 mg]–1) were studied prediabetes at 4, 8 and 12 weeks of age (n = 6 per group). In addition, groups of 4-week-old NODLt mice were randomised to receive either no treatment (vehicle) or the AGE-lowering therapy alagebrium chloride (1 mg kg–1 day–1 i.p.) and followed for 6 weeks (prediabetes, n = 10 per group). For incidence studies, littermate NODLt mice (n = 20 per group) were studied until the diagnosis of diabetes was confirmed or until day 200 of age, with one group randomised to receive alagebrium chloride (1 mg kg–1 day–1 i.p.) for the study duration from week 4 of age. Mice were given ad libitum access to food and water and maintained on 12 h dark–light cycles. Plasma glucose was measured by autoanalyser (Beckman Coulter LX20PRO, Brea, CA, USA) and plasma insulin concentrations were measured by radioimmunoassay (Linco Research, MO, USA).

Circulating RAGE and AGE ELISA

Soluble RAGE in mouse serum (RAGE sandwich ELISA kit, R&D systems, Minneapolis, MN, USA) was assayed undiluted according to the manufacturer’s instructions. The inter-assay CV was 6.7% and the intra-assay CV was 5.9%. The limit of sensitivity of the assay was 1 pg/ml. The AGE, CML, was assessed in serum or plasma using an in-house ELISA. The limit of detection of the assay was 8.0 nmol/mol lysine. The inter-assay CV was 7.3%. The intra-assay coefficient of variation was 5.5%.


Formalin fixed paraffin sections [33] were incubated with primary antibody (AGE [34], 1:1,000; goat anti-RAGE 1:2,500, Chemicon, Temecula, CA, USA) overnight at 4°C. Tissue sections were consecutively stained with biotinylated IgG for 10 min and avidin–biotin horseradish peroxidase complex for 15 min (Vectastain ABC Elite kit, Vector Laboratories, Burlingame, CA, USA) before 3,3′-diaminobenzidine tetrahydrochloride treatment (DAB; Sigma Chemical Co., St Louis, MI, USA). Negative control sections omitted the primary antibody. Positive control tissues were also included. Quantitation of islet immunostaining was completed by computer-aided densitometry (Image Pro Plus 6.0, Media Cybernetics, Bethesda, MD, USA) where all islets were analysed (×100) and results expressed as proportional to the area of positive staining.

Real-time reverse transcription-polymerase chain reaction

RNA obtained from pancreatic islet tissue collected immediately was later used to synthesise cDNA with the Superscript First strand synthesis system for RT-PCR (Gibco BRL, Grand Island, NY, USA). Gene expression for AGER was analysed by real-time quantitative RT-PCR performed with the TaqMan system based on real-time detection of accumulated fluorescence (ABI Prism 7700, Perkin-Elmer) normalised to 18 S as previously [35]. For mouse Ager (NM_007425), the probe was 6-FAM CACAGCCCGGATTG-MGB, the forward primer was 5′-GCTGTAGCTGGTGGTCAGAACA-3′ and the reverse primer was 5′-CCCCTTACAGCTTAGCACAAGTG-3′.

Islet extraction

Pancreatic islets were isolated as previously described [36]. Briefly, 10 ml of cold Hanks balanced salt solution (HBBS) containing 0.75 mg/ml collagenase type V (Sigma Chemical Co.) was injected into the bile duct. The pancreas was digested at 37°C for 10–20 min and then disrupted and filtered through a 500 μm mesh. The pancreatic islets were separated from exocrine tissue by histopaque density gradient, for which the islets were suspended in histopaque 1.119 g/l, followed by layering of histopaque 1.083 g/l and histopaque 1.077 g/l (Sigma). The hand-picked islets were then used for flow cytometry as outlined below and RNA extraction [37]. For flow cytometry, purified islets were washed (10 mM EDTA in Hanks’ balanced salt solution) and stained using goat anti-RAGE (Santa Cruz Biotechnology, Santa Cruz, CA, USA) followed by rabbit anti-goat IgG, F(ab′)2, FITC conjugated (Chemicon International, Temecula, CA, USA).

Statistical analysis

Mouse results are expressed as mean ± standard deviation unless otherwise specified. All analyses of rodent data with more than two groups were performed by ANOVA followed by post hoc analysis with Tukey’s test. Student’s t test or Mann–Whitney analyses were used to compare two groups where appropriate and are labelled as such. A logrank (Mantel–Cox) test was used to analyse diabetes incidence in NOD mice. A value of p < 0.05 was considered to be statistically significant (GraphPad Prism; GraphPad Software, San Diego, CA, USA).


AGER gene polymorphisms and type 1 diabetes susceptibility


Characteristics of individuals from the FinnDiane study have been previously described in detail [38] and are summarised in Table 1. We observed that all 13 SNPs for AGER were strongly associated with type 1 diabetes (Table 2), even after Bonferroni correction for multiple comparisons (cut-off p = 0.0013). Regarding the HLA frequencies, 19% of the FinnDiane type 1 diabetic patients have the (DR3)-DQA1*05-DQB1*02/DRB1*0401-DQB1*0302 genotype. This percentage of the high risk DR3/DR4 genotype is small given that this is only one of several other HLA haplotypes/genotypes which are known risk factors for the development of type 1 diabetes [39]. Therefore, in the FinnDiane cohort, 72.8% of all patients belong to the genetic risk group, as described by Näntö-Salonen et al. [39]. HWE was tested in the controls and all SNPs were in HWE.
Table 2

SNP associations between AGER and type 1 diabetes: analysis of genomic DNA from patients with T1D from the FinnDiane cohort and from newborn infants with the highest risk HLA genotype (DR3)-DQA1*05-DQB1*02/DRB1*0401-DQB1*0302 obtained as part of the Finnish Type 1 DIPP

Position NCBI35 Chr 6 (bp)

SNP (alleles)

Minora allele

SNP location

The FinnDiane cohort

HLA–DR3/DR4 matched

OR (95% CI)b

MAF controls/patients

p value

MAF controls/patients

p value

Sample size (n)








rs204990 (G/T)




1.82 × 10−11 c



0.932 (0.773–1.125)


rs3132940 (C/A)




1.45 × 10−39 c



1.404 (0.598–3.298)


rs12663103 (T/C)




6.30 × 10−10 c





rs204993 (T/C)




1.27 × 10−23 c



1.088 (0.901–1.314)


rs204994 (G/A)




2.82 × 10−9 c



0.937 (0.777–1.131)


rs1800625 (T/C)




2.76 × 10−9 c



0.933 (0.772–1.128)


rs1800624 (T/A)




5.89 × 10−5 c



0.839 (0.677–1.040)


rs2070600 (G/A)




2.71 × 10−6 c



1.452 (1.068–1.973)


rs1035798 (C/T)




1.53 × 10−4 c



0.848 (0.685–1.051)


rs9469089 (G/C)




3.03 × 10−16 c


3.65 × 10−5

0.423 (0.282–0.637)


rs2269423 (G/T)




1.36 × 10−30 c



0.964 (0.741–1.254)


rs17493811 (C/G)




6.34 × 10−4 c



1.518 (1.038–2.220)


rs3134947 (G/A)




1.23 × 10−8 c



0.926 (0.767–1.118)

In the FinnDiane cohort, the p values are from logistic regression analyses, adjusted for sex, and reflect the joint effect of AGER and HLA

The DIPP HLA DR3/DR4 matched set show the effect of AGER conditional for HLA haplotype

aThe minor allele is the opposite in the DR3/DR4 matched data

bThe allelic ORs with 95% CIs are modelled for the respective minor allele

cSNPs in the FinnDiane cohort that survive the stringent Bonferroni correction for multiple testing nucleotide polymorphism

MAF, minor allele frequency


We performed allelic analyses, which identified that three SNPs (rs2070600, rs9469089 and rs17493811) were still associated with type 1 diabetes (p = 0.017, 3.65 × 10−5, and 0.031, respectively) in HLA-matched children from the DIPP (Table 2). In particular, the rs2070600 and rs17493811 polymorphisms predicted increased risk of type 1 diabetes (ORs 1.452 and 1.518, respectively) whereas rs9469089 was related to decreased risk (OR 0.423) in the high risk HLA setting. However, using less stringent HLA adjustment, i.e. analysing those individuals with the risk alleles DQB1*02 and/or DQB1*0302 (type 1 diabetes patients n = 2,660 and controls n = 294), all AGER SNPs apart from rs204993 remained associated with type 1 diabetes following HLA matching (data not shown).

Circulating soluble RAGE concentrations

Children from the DIPP study who progressed to type 1 diabetes (diagnosis; 5.2 ± 2.2 years) had a decline in circulating soluble RAGE levels with concomitant seroconversion to positivity for type 1 diabetes-associated autoantibodies (islet cell antibodies, insulin autoantibodies, antibodies to glutamic acid decarboxylase and/or antibodies to islet antigen 2; Fig. 1).
Fig. 1

Human participants who progress to type 1 diabetes show a decline in soluble RAGE at seroconversion to autoantibody positivity. Serial plasma samples before seroconversion (white bars; mean age 1.1 ± 0.6 years), following initial seroconversion (black bars; mean age 2.4 ± 1.2 years) to positivity for diabetes-associated autoantibodies (islet cell antibodies, insulin autoantibodies, antibodies to glutamic acid decarboxylase, and/or antibodies to islet antigen 2) and at clinical diagnosis of type 1 diabetes (hatched bar; mean age 5.2 ± 2.2 years), in the progressors (n = 15) and samples obtained at corresponding ages in autoantibody-negative non-progressors (hatched bar; n = 15), were analysed for soluble RAGE concentrations. p = 0.025 vs initial sample; p = 0.02 vs initial sample; ***p < 0.001 vs sample obtained at seroconversion to autoantibody positivity. sRAGE, soluble RAGE

Islet RAGE and circulating soluble RAGE decline prediabetes in NODLt mice

We first identified that islet RAGE expression was decreased in NODLt mice and, to a significantly lesser extent, in NODScid mice by week 8, compared with 4-week-old NOD mice (Fig. 2 a,b and d). However, by week 12, islet RAGE expression was significantly higher in both NODLt and NODScid (Fig. 2c and d). In NODLt mice, the loss of islet RAGE expression between weeks 4 and 12 was prevented with the AGE-lowering therapy, alagebrium chloride (Fig. 2e). In addition, the islet expression of the gene encoding RAGE (AGER) declined over time and was below detectable levels by week 8 of age in NODLt mice (4 weeks, 1.14 ± 0.63 fold induction; n = 4 per group; p < 0.001 4 vs 8 or 12 weeks) as assessed by real-time RT-PCR [40]. There was also a significant decline in the levels of circulating soluble RAGE from 4 to 6 weeks of age in NODLt mice (Fig. 2f) that was not seen in NODScid mice (Fig. 2f). Alagebrium chloride also significantly improved circulating soluble RAGE concentrations at week 10 of age (Fig. 2g). There was a positive association between the loss of islet RAGE and declining circulating soluble RAGE in NODLt mice (r = 0.85, p < 0.001). In addition, serum concentrations of the AGE, CML, were elevated at both week 8 and 12 weeks of age in NODLt mice as compared with NODScid mice (Fig. 2h), which was also ameliorated by treatment to reduce AGE accumulation with alagebrium chloride at week 10 (NODLt, 4,826 ± 12,61 nmol/mol lysine vs NODLt treated with alagebrium chloride, 2,059 ± 589 nmol/mol lysine, p = 0.026 Student’s t test). There was also a decline in plasma concentrations of the RAGE ligand HMGB1 seen at week 8 in NODLt mice when compared with NODScid mice (Fig. 2i). Furthermore, alagebrium chloride markedly decreased the incidence of autoimmune diabetes in NODLt mice from 50% to 10% (Fig. 2j).
Fig. 2

Prediabetic NOD mice have a deficiency in islet RAGE production. a–e Flow cytometry for islet RAGE levels; a–c representative histograms in prediabetic NODLt mouse islets (n = 5 per group) at 4 weeks (a), 8 weeks (b) and 12 weeks (c). d,e Percentage of cell surface RAGE-positive islets in (d) NODLt (white bars) and NODScid (black bars) mice at weeks 4, 8 and 12 and (e) at week 10 (n = 10 per group, after treatment with alagebrium chloride (ALT), an inhibitor of RAGE ligand (AGE) accumulation. f,g Plasma concentrations of circulating soluble RAGE by ELISA; (f) time course in prediabetic NODLt and NODScid mice (NODLt, white bars; NODScid, black bars) (g) At week 10 following ALT therapy. h Time-course plasma concentrations of CML in prediabetic NODLt and NODScid mice (n = 5–8 per group) (NODLt, white bars; NODScid, black bars). i Plasma concentrations of the RAGE ligand, HMGB1, in NODLt and NODScid mice (n = 10 per group) (NODLt, white bars; NODScid, black bars). j Diabetes incidence in untreated NODLt mice (square, dashed line) or in NODLt mice administered with ALT (circle, continuous line) (n = 20 per group). p = 0.0283 vs NODLt, Student’s t test, two tailed; **p < 0.01 vs 4 W NODLt; ***p < 0.001 vs 4 W NODLt, p = 0.0238 vs 4 W, Mann–Whitney test; p = 0.0016 vs untreated NODLt, Student’s t test, two tailed; §p = 0.009 vs NODLt + ALT, logrank (Mantel–Cox) test; ††p < 0.01 vs 8 W NODLt; ‡‡p < 0.01 vs 12 W NODLt, *p < 0.05 vs 4 W NODScid. sRAGE, soluble RAGE; W, week

Immunohistochemistry also demonstrated a decline in islet RAGE staining in NODLt mice at weeks 8 and 12 prediabetes, which was not seen in NODScid mice (see Fig. 3a–g). RAGE immunostaining in islets was localised primarily to alpha cells in both NODLt and NODScid mice (Fig. 3). Islet AGE immunohistochemistry demonstrated increased concentrations of CML at all prediabetic time points in NODScid mice (Fig. 3h), although only a small proportion of AGE staining in NODScid mice was cell surface bound as determined by flow cytometry compared with NODLt mice (Fig. 3i). There was also a significant, but transient, increase in plasma glucose concentrations seen at week 8 in NODLt (4 week NODLt, 2.7 ± 0.5 mmol/l vs 8 week NODLt, 7.2 ± 2.1 mmol/l, p < 0.05; vs 12 week NODLt, 6.9 ± 1.1 mmol/l, NS). There was no difference seen in plasma glucose (8 week NODScid, 7.4 ± 4.2 mmol/l; 12 week NODScid, 6.1 ± 2.6 mmol/l) or glycated haemoglobin concentrations (Table 3) between NODLt and NODScid mice at either 8 or 12 weeks of age. NODLt mice were heavier than NODScid mice by 12 weeks of the study (Table 3).
Fig. 3

AGE and RAGE localisation in pancreatic islets in NOD mice. Photomicrographs of immunohistochemistry for RAGE in (a–c) NODScid mice at (a) week 4 (b) week 8 and (c) week 12; and (d–f) NODLt mice at (d) week 4 (e) week 8 and (f) week 12. g Quantification of islets. h Quantification of islet CML by immunohistochemistry. i Cell surface binding of AGEs to islets by flow cytometry. *p < 0.05 NODScid vs NODLt within the same time point; p < 0.05 vs 4 W NODLt; p < 0.05 vs 4 W NODScid. W, week

Table 3

Metabolic variables for NOD mouse groups

Mouse group

GHb (%)

BW (g)


 4 weeks

2.6 ± 0.1

18.3 ± 1.3

 8 weeks

2.2 ± 0.1

23.7 ± 1.7*

 12 weeks

2.4 ± 0.1

27.7 ± 1.0*†


 4 weeks

2.8 ± 0.3

18.1 ± 1.6

 8 weeks

3.1 ± 0.1

23.4 ± 1.6*

 12 weeks

2.8 ± 0.1

24.0 ± 1.3*‡

Data are mean±SD

Glycated haemoglobin and final body weights at the study end-points

n = 10 mice per group

*p < 0.05 vs respective 4 week group; p = 0.05 vs 8 week NODLt, p = 0.05 at 12 weeks for NODLt vs NODScid

GHb, glycated haemoglobin; BW, body weight


Many chronic diseases are likely to be polygenic, including type 1 diabetes, where less than 50% of the familial clustering can be attributed to currently reported genotypes. This highlights the importance of discovering other candidate genes and environmental contributors. Within the present study, we have identified three polymorphisms in the AGER gene, which encodes for the proteins RAGE and soluble RAGE, that may partly explain the genetic susceptibility to type 1 diabetes in humans, even in individuals with a high risk HLA class II background. Of particular interest is the finding that a temporal decline in circulating concentrations of the protective isoform of RAGE (termed soluble RAGE) at seroconversion to produce autoantibodies, predicted the clinical onset of type 1 diabetes in patients within high to moderate risk class II HLA genotypes. These findings are reminiscent of changes in NODLt mice, in whom seroconversion appears concurrently with a decrease in circulating soluble RAGE concentrations [41]. There was also an inverse relationship between circulating CML (an AGE) and either soluble RAGE concentrations or islet RAGE expression seen in both NODLt and NODScid mice. Interestingly, despite elevated circulating AGE levels, islet AGE concentrations were also lower in NODLt mice compared with NODScid mice, suggesting that RAGE may be involved in the cellular uptake or intracellular production of AGEs in pancreatic islet cells.

A decline in circulating soluble RAGE has also been shown in children during active autoimmune disease, such as Kawasaki disease or systemic onset juvenile idiopathic arthritis [42]. Decreases in plasma soluble RAGE were also seen in samples from children who progressed to type 1 diabetes at seroconversion to autoantibodies, compared with samples obtained before seroconversion. At the diagnosis of diabetes, those individuals who progressed to type 1 diabetes in the DIPP study had an increase in plasma soluble RAGE, probably as the result of active inflammation, as has been seen previously in individuals with type 2 diabetes [43]. Therefore, although tantalising, the changes in soluble RAGE seen within the present study in children who progressed to type 1 diabetes need to be confirmed in larger patient cohorts. In addition, since soluble RAGE concentrations were not measured in the individuals from whom we have the AGER genotype data, Mendelian randomisation was not possible in the present populations.

The genetic loci thought to be responsible for susceptibility to autoimmune diabetes in NODLt mice include the AGER gene sequence encoding RAGE proteins [31, 32]. The presence of the serine residue within the G82S polymorphism, identified using AGER rs2070600 in humans, has previously been associated with lower [44] or elevated [43] circulating soluble RAGE concentrations. Interestingly, this SNP was also associated with a higher risk of type 1 diabetes in our human cohorts in the present study. In our human studies, it is difficult to increase the numbers of patients and controls studied, given the key requirement to control for high risk HLA genotypes to delineate the association between AGER for type 1 diabetes. It is nevertheless intriguing that, even in such a small HLA-matched sample, there were clear genotypic differences and changes in circulating soluble RAGE concentrations between those with type 1 diabetes and those without. However, this finding now needs to be replicated in larger human populations.

One cannot discount, however, the possibility that changes in the proteases responsible for the cleavage of membrane RAGE to soluble RAGE [45] may also be altering the circulating soluble RAGE pool in our diabetic individuals and mice, independent of changes in the AGER gene. Indeed, this possibility should be further investigated in subsequent studies, in particular given the effects of AGEs on circulating RAGE concentrations in NODLt mice observed in the present study. Interestingly, prediabetic NODLt mice in the present study also had a transient decline in islet RAGE expression at seroconversion to autoantibody positivity which was not seen in NODScid mice. Furthermore, we have determined that circulating soluble RAGE concentrations correlate with islet RAGE expression. In the future, it would be desirable to confirm these findings using a NODLt mouse with an islet specific knockout of the gene encoding RAGE (AGER). It would also have been highly desirable to confirm a loss of islet RAGE expression in human participants with type 1 diabetes, although we have shown that decreases in circulating soluble RAGE concentrations in children with medium to high risk HLA II genetic backgrounds may be predictive of this loss in islet RAGE expression. Therefore, at this stage we can only speculate as to the association of RAGE with type 1 diabetes.

Some previous studies in adoptively transferred autoimmune diabetes and syngeneic islet transplants in hyperglycaemic NODLt mice have shown benefits from the interruption of RAGE ligand binding with small molecule inhibitors of RAGE [19] or exogenous soluble RAGE [18]. In adoptively transferred diabetes [18], however, it could only be concluded that RAGE ligands may be involved in the differentiation of T cells to a mature phenotype. Indeed, NODScid mice also have neither T nor B cells, providing further evidence that RAGE expression on T cells, such as Th1 [18, 19], may also be important in the pathogenesis of diabetes, given that NODScid mice do not develop autoimmune diabetes.

Prediabetic NODLt mice in the present study showed increases in circulating RAGE ligands, AGEs, that were not seen with respect to another RAGE ligand, HMGB1. This was surprising given the previous association of HMGB1 to autoimmune diabetes in NODLt mice [20]. However, another ligand of RAGE is the amylin polypeptide, which is co-produced with insulin by the pancreatic beta cells in response to elevation of plasma glucose [46]. Moreover, there is speculation that insulin may also bind to RAGE, since there is evidence of amyloidosis composed of iatrogenic A-Ins type amyloid [47]. Indeed, other investigators have suggested that proinflammatory RAGE and HLA II DRB1 polymorphisms may synergise to activate the immune response in diabetes complications [48]. However, whether this affects the immune system during the development of autoimmune diabetes or type 1 diabetes remains to be determined.

Importantly, lowering AGEs using alagebrium chloride in the present study not only increased circulating soluble RAGE concentrations in NODLt mice at seroconversion to autoantibodies, but also reduced the incidence of autoimmune diabetes. Taken together, one could speculate that it is important therapeutically to prevent ligand-induced chronic modulation of RAGE to protect islet function. Also of interest was the fact that it is unlikely that increases in plasma glucose are driving the elevations in plasma AGEs seen within the present study, as is classically seen in diabetes complications [33, 49, 50], since elevations in circulating AGEs by 8 weeks of age were also evident in NODScid mice, which do not develop diabetes. These results raise the possibility that precipitation of diabetes could occur as the result of either genetic or environmental modulation of RAGE.

Another limitation of the present study is that so-called high risk HLA haplotypes are ‘losing power’ in the younger age groups developing type 1 diabetes [51]. However, FinnDiane is collecting data on adult type 1 diabetic patients, rather than paediatric patients, and therefore the mean duration of type 1 diabetes is relatively high (24.4 years, or median of 24.2 years), meaning that less than 10% of the patients studied were born in the late 1970s and 1980s, when the HLA trend mentioned above was primarily observed. Furthermore, given that the controls for the FinnDiane study were recruited through the Finnish blood bank, we have little epidemiological data relating to these individuals.

Thus, in summary, the data presented here show that inherited AGER gene polymorphisms should be considered as novel contributors to susceptibility to type 1 diabetes. This group of studies also suggests that excesses of environmental factors such as AGEs might modulate changes in islet and circulating RAGE expression, which may contribute to insulin secretory defects and ultimately the development of overt diabetes. In addition, declining circulating levels of soluble RAGE, which binds excesses of AGEs, at seroconversion before the development of overt diabetes, may warrant investigation in larger human cohorts as a predictor of clinical type 1 diabetes. Thus, we not only propose a novel mechanism for the development of diabetes, but also suggest a potential treatment, the AGE lowering therapy alagebrium chloride, currently under clinical investigation, albeit for other medical indications. Furthermore, we believe that these results challenge current thoughts in this area, by hypothesising that genetic or environmental declines in RAGE may be important for the development of type 1 diabetes. These findings are also likely to have implications for the pathogenesis of other chronic diseases in which this receptor is considered to play a pivotal role.



We are indebted to M Parkkonen and R Sallinen, University of Helsinki, Helsinki, Finland, who contributed to the genotyping of the FinnDiane patients. The authors would also like to thank K Gilbert, V Thallas and M Arnstein, Baker IDI Heart and Diabetes Institute, Melbourne, Australia, for their technical expertise. Thanks also to A Blair for assistance with rat and mouse IV/IPGTTs. This work was completed with support from an innovative grant from the Juvenile Diabetes Research Foundation (JDRF; 5-2010-163). J Forbes is a JDRF Career Development Fellow. S Andrikopoulos is a recipient of an RD Wright Fellowship from the National Health and Medical Research Council of Australia (NHMRC) of Australia. M Cooper is a JDRF Scholar and an NHMRC Australia Fellow. The FinnDiane is funded by the Folkhälsan Research Foundation, Wilhelm and Else Stockmann Foundation, Sigrid Juselius Foundation, European Commission (LSHB-CT-2003-503364, LSHB-CT-2006-037681), Academy of Finland (214335 and 124280 to M.W.), Medicinska understödsföreningen Liv och Hälsa, Signe and Ane Gyllenberg Foundation, Waldemar von Frenkell Foundation and Governmental Grants for Health Sciences Research. We acknowledge all the physicians and nurses at each participating centre for their invaluable role in patient recruitment, collection of samples and data (see ESM). The DIPP Study is supported by grants from the Juvenile Diabetes Research Foundation (grants 4-1998-274, 4-1999-731, 4-2001-435), Emil Aaltonen Foundation, Academy of Finland, Jalmari and Rauha Ahokas Foundation, Diabetes Research Foundation, Finland, Signe and Ane Gyllenberg Foundation, Yrjö Jahnsson Foundation, Sigrid Juselius Foundation, Novo Nordisk Foundation, Päivikki and Sakari Sohlberg Foundation, and Special Research Funds for the Oulu, Tampere and Turku University Hospitals.

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Supplementary material

125_2011_2058_MOESM1_ESM.pdf (105 kb)
ESM 1(PDF 105 kb)


  1. 1.
    Mathis D, Vence L, Benoist C (2001) Beta-cell death during progression to diabetes. Nature 414:792–798PubMedCrossRefGoogle Scholar
  2. 2.
    Davies JL, Kawaguchi Y, Bennett ST et al (1994) A genome-wide search for human type 1 diabetes susceptibility genes. Nature 371:130–136PubMedCrossRefGoogle Scholar
  3. 3.
    Nejentsev S, Howson JM, Walker NM et al (2007) Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A. Nature 450:887–892PubMedCrossRefGoogle Scholar
  4. 4.
    Hyttinen V, Kaprio J, Kinnunen L, Koskenvuo M, Tuomilehto J (2003) Genetic liability of type 1 diabetes and the onset age among 22,650 young Finnish twin pairs: a nationwide follow-up study. Diabetes 52:1052–1055PubMedCrossRefGoogle Scholar
  5. 5.
    Harjutsalo V, Sjoberg L, Tuomilehto J (2008) Time trends in the incidence of type 1 diabetes in Finnish children: a cohort study. Lancet 371:1777–1782PubMedCrossRefGoogle Scholar
  6. 6.
    Maillard L (1912) Action des acides amines sur les sucres: formation des melanoidines par voie. CR Acad Sci 154:66–68, article in FrenchGoogle Scholar
  7. 7.
    Henle T (2005) Protein-bound advanced glycation endproducts (AGEs) as bioactive amino acid derivatives in foods. Amino Acids 29:313–322PubMedCrossRefGoogle Scholar
  8. 8.
    Sell D, Monnier V (1989) Structure and elucidation of a senescence cross-link from human extracellular matrix. J Biol Chem 264:21597–21602PubMedGoogle Scholar
  9. 9.
    Baynes JW, Thorpe SR (1999) Role of oxidative stress in diabetic complications: a new perspective on an old paradigm. Diabetes 48:1–9PubMedCrossRefGoogle Scholar
  10. 10.
    Miyata T, Wada Y, Cai Z et al (1997) Implication of an increased oxidative stress in the formation of advanced glycation end products in patients with end-stage renal failure. Kidney Int 51:1170–1181PubMedCrossRefGoogle Scholar
  11. 11.
    Brownlee M (2001) Biochemistry and molecular cell biology of diabetic complications. Nature 414:813–820PubMedCrossRefGoogle Scholar
  12. 12.
    Kaneto H, Fujii J, Myint T et al (1996) Reducing sugars trigger oxidative modification and apoptosis in pancreatic beta-cells by provoking oxidative stress through the glycation reaction. Biochem J 320:855–863PubMedGoogle Scholar
  13. 13.
    Matsuoka T, Kajimoto Y, Watada H et al (1997) Glycation-dependent, reactive oxygen species-mediated suppression of the insulin gene promoter activity in HIT cells. J Clin Invest 99:144–150PubMedCrossRefGoogle Scholar
  14. 14.
    Peppa M, He C, Hattori M, McEvoy R, Zheng F, Vlassara H (2003) Fetal or neonatal low-glycotoxin environment prevents autoimmune diabetes in NOD mice. Diabetes 52:1441–1448PubMedCrossRefGoogle Scholar
  15. 15.
    Chavakis T, Bierhaus A, Al-Fakhri N et al (2003) The pattern recognition receptor (RAGE) is a counterreceptor for leukocyte integrins: a novel pathway for inflammatory cell recruitment. J Exp Med 198:1507–1515PubMedCrossRefGoogle Scholar
  16. 16.
    Bierhaus A, Illmer T, Kasper M et al (1997) Advanced glycation end product (AGE)-mediated induction of tissue factor in cultured endothelial cells is dependent on RAGE. Circulation 96:2262–2271PubMedGoogle Scholar
  17. 17.
    Hofmann MA, Drury S, Fu C et al (1999) RAGE mediates a novel proinflammatory axis: a central cell surface receptor for S100/calgranulin polypeptides. Cell 97:889–901PubMedCrossRefGoogle Scholar
  18. 18.
    Chen Y, Yan SS, Colgan J et al (2004) Blockade of late stages of autoimmune diabetes by inhibition of the receptor for advanced glycation end products. J Immunol 173:1399–1405PubMedGoogle Scholar
  19. 19.
    Chen Y, Akirav EM, Chen W et al (2008) RAGE ligation affects T cell activation and controls T cell differentiation. J Immunol 181:4272–4278PubMedGoogle Scholar
  20. 20.
    Han J, Zhong J, Wei W et al (2008) Extracellular high-mobility group box 1 acts as an innate immune mediator to enhance autoimmune progression and diabetes onset in NOD mice. Diabetes 57:2118–2127PubMedCrossRefGoogle Scholar
  21. 21.
    Sullivan CM, Futers TS, Barrett JH, Hudson BI, Freeman MS, Grant PJ (2005) RAGE polymorphisms and the heritability of insulin resistance: the Leeds family study. Diab Vasc Dis Res 2:42–44PubMedCrossRefGoogle Scholar
  22. 22.
    Wilkin TJ (2001) The accelerator hypothesis: weight gain as the missing link between type I and type II diabetes. Diabetologia 44:914–922PubMedCrossRefGoogle Scholar
  23. 23.
    Groop PH, Thomas MC, Moran JL et al (2009) The presence and severity of chronic kidney disease predicts all-cause mortality in type 1 diabetes. Diabetes 58:1651–1658PubMedCrossRefGoogle Scholar
  24. 24.
    The MHC sequencing consortium (1999) Complete sequence and gene map of a human major histocompatibility complex. Nature 401:921–923CrossRefGoogle Scholar
  25. 25.
    Hermann R, Turpeinen H, Laine AP et al (2003) HLA DR-DQ-encoded genetic determinants of childhood-onset type 1 diabetes in Finland: an analysis of 622 nuclear families. Tissue Antigens 62:162–169PubMedCrossRefGoogle Scholar
  26. 26.
    Kukko M, Virtanen SM, Toivonen A et al (2004) Geographical variation in risk HLA-DQB1 genotypes for type 1 diabetes and signs of beta-cell autoimmunity in a high-incidence country. Diab Care 27:676–681CrossRefGoogle Scholar
  27. 27.
    Vandenplas S, Wiid I, Grobler-Rabie A et al (1984) Blot hybridisation analysis of genomic DNA. J Med Genet 21:164–172PubMedCrossRefGoogle Scholar
  28. 28.
    Gabriel SB, Schaffner SF, Nguyen H et al (2002) The structure of haplotype blocks in the human genome. Science 296:2225–2229PubMedCrossRefGoogle Scholar
  29. 29.
    Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21:263–265PubMedCrossRefGoogle Scholar
  30. 30.
    Purcell S, Cherny SS, Sham PC (2003) Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics 19:149–150Google Scholar
  31. 31.
    Hattori M, Buse JB, Jackson RA et al (1986) The NOD mouse: recessive diabetogenic gene in the major histocompatibility complex. Science 231:733–735PubMedCrossRefGoogle Scholar
  32. 32.
    Sugaya K, Fukagawa T, Matsumoto K et al (1994) Three genes in the human MHC class III region near the junction with the class II: gene for receptor of advanced glycosylation end products, PBX2 homeobox gene and a notch homolog, human counterpart of mouse mammary tumor gene int-3. Genomics 23:408–419PubMedCrossRefGoogle Scholar
  33. 33.
    Forbes JM, Thallas V, Thomas MC et al (2003) The breakdown of preexisting advanced glycation end products is associated with reduced renal fibrosis in experimental diabetes. FASEB J 17:1762–1764PubMedGoogle Scholar
  34. 34.
    van der Pijl JW, Daha MR, van den Born J et al (1998) Extracellular matrix in human diabetic nephropathy: reduced expression of heparan sulphate in skin basement membrane. Diabetologia 41:791–798PubMedCrossRefGoogle Scholar
  35. 35.
    Candido R, Forbes JM, Thomas MC et al (2003) A breaker of advanced glycation end products attenuates diabetes-induced myocardial structural changes. Circ Res 92:785–792PubMedCrossRefGoogle Scholar
  36. 36.
    Kebede M, Favaloro J, Gunton JE et al (2008) Fructose-1, 6-bisphosphatase overexpression in pancreatic beta-cells results in reduced insulin secretion: a new mechanism for fat-induced impairment of beta-cell function. Diabetes 57:1887–1895PubMedCrossRefGoogle Scholar
  37. 37.
    Kooptiwut S, Kebede M, Zraika S et al (2005) High glucose-induced impairment in insulin secretion is associated with reduction in islet glucokinase in a mouse model of susceptibility to islet dysfunction. J Mol Endocrinol 35:39–48PubMedCrossRefGoogle Scholar
  38. 38.
    Pettersson-Fernholm K, Forsblom C, Hudson BI, Perola M, Grant PJ, Groop PH (2003) The functional -374 T/A RAGE gene polymorphism is associated with proteinuria and cardiovascular disease in type 1 diabetic patients. Diabetes 52:891–894PubMedCrossRefGoogle Scholar
  39. 39.
    Nanto-Salonen K, Kupila A, Simell S et al (2008) Nasal insulin to prevent type 1 diabetes in children with HLA genotypes and autoantibodies conferring increased risk of disease: a double-blind, randomised controlled trial. Lancet 372:1746–1755PubMedCrossRefGoogle Scholar
  40. 40.
    Forbes JM, Thorpe SR, Thallas-Bonke V et al (2005) Modulation of soluble receptor for advanced glycation end products by angiotensin-converting enzyme-1 inhibition in diabetic nephropathy. J Am Soc Nephrol 16:2363–2372PubMedCrossRefGoogle Scholar
  41. 41.
    Reddy S, Bibby N, Elliott RB (1990) Longitudinal study of islet cell antibodies and insulin autoantibodies and development of diabetes in non-obese diabetic (NOD) mice. Clin Exp Immunol 81:400–405PubMedCrossRefGoogle Scholar
  42. 42.
    Wittkowski H, Hirono K, Ichida F et al (2007) Acute Kawasaki disease is associated with reverse regulation of soluble receptor for advance glycation end products and its proinflammatory ligand S100A12. Arthritis Rheum 56:4174–4181PubMedCrossRefGoogle Scholar
  43. 43.
    Gaens KH, Ferreira I, van der Kallen CJ et al (2009) Association of polymorphism in the receptor for advanced glycation end products (RAGE) gene with circulating RAGE levels. J Clin Endocrinol Metab 94:5174–5180PubMedCrossRefGoogle Scholar
  44. 44.
    Jang Y, Kim JY, Kang SM et al (2007) Association of the Gly82Ser polymorphism in the receptor for advanced glycation end products (RAGE) gene with circulating levels of soluble RAGE and inflammatory markers in nondiabetic and nonobese Koreans. Metabolism 56:199–205PubMedCrossRefGoogle Scholar
  45. 45.
    Zhang L, Bukulin M, Kojro E et al (2008) Receptor for advanced glycation end products is subjected to protein ectodomain shedding by metalloproteinases. J Biol Chem 283:35507–35516PubMedCrossRefGoogle Scholar
  46. 46.
    Yan SD, Zhu H, Zhu A et al (2000) Receptor-dependent cell stress and amyloid accumulation in systemic amyloidosis. Nat Med 6:643–651PubMedCrossRefGoogle Scholar
  47. 47.
    Shikama Y, Kitazawa J, Yagihashi N et al (2010) Localized amyloidosis at the site of repeated insulin injection in a diabetic patient. Intern Med 49:397–401PubMedCrossRefGoogle Scholar
  48. 48.
    Damiani G, Campo I, Zorzetto M et al (2007) Pro-inflammatory variants of DRB1 and RAGE genes are associated with susceptibility to pediatric type 1 diabetes: a new hypothesis on the adaptive role of autoimmunity. Riv Biol 100:285–304PubMedGoogle Scholar
  49. 49.
    Forbes JM, Soulis T, Thallas V et al (2001) Renoprotective effects of a novel inhibitor of advanced glycation. Diabetologia 44:108–114PubMedCrossRefGoogle Scholar
  50. 50.
    Fu MX, Wells-Knecht KJ, Blackledge JA, Lyons TJ, Thorpe SR, Baynes JW (1994) Glycation, glycoxidation, and cross-linking of collagen by glucose. Kinetics, mechanisms, and inhibition of late stages of the Maillard reaction. Diabetes 43:676–683PubMedCrossRefGoogle Scholar
  51. 51.
    Fourlanos S, Varney MD, Tait BD et al (2008) The rising incidence of type 1 diabetes is accounted for by cases with lower-risk human leukocyte antigen genotypes. Diab Care 31:1546–1549CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • J. M. Forbes
    • 1
    • 2
  • J. Söderlund
    • 3
    • 4
  • F. Y. T. Yap
    • 1
  • M. Knip
    • 3
    • 5
    • 6
  • S. Andrikopoulos
    • 7
  • J. Ilonen
    • 8
    • 14
  • O. Simell
    • 9
  • R. Veijola
    • 10
  • K. C. Sourris
    • 1
  • M. T. Coughlan
    • 1
  • C. Forsblom
    • 3
    • 4
  • R. Slattery
    • 2
  • S. T. Grey
    • 11
  • M. Wessman
    • 3
  • H. Yamamoto
    • 12
  • A. Bierhaus
    • 13
  • M. E. Cooper
    • 1
    • 2
  • P.-H. Groop
    • 1
    • 3
    • 4
  1. 1.Diabetes Complications DivisionBaker IDI Heart and Diabetes InstituteMelbourneAustralia
  2. 2.Department of Immunology and MedicineMonash UniversityMelbourneAustralia
  3. 3.Department of Diabetes Genetics, Folkhälsan Institute of GeneticsUniversity of HelsinkiHelsinkiFinland
  4. 4.Department of Medicine, Division of NephrologyHelsinki University Central HospitalHelsinkiFinland
  5. 5.Hospital for Children and AdolescentsUniversity of HelsinkiHelsinkiFinland
  6. 6.Department of PaediatricsTampere University HospitalTampereFinland
  7. 7.Department of Medicine(AH/NH) University of Melbourne, Heidelberg Repatriation HospitalHeidelberg HeightsAustralia
  8. 8.Immunogenetics LaboratoryUniversity of TurkuTurkuFinland
  9. 9.Department of PaediatricsUniversity of TurkuTurkuFinland
  10. 10.Department of PaediatricsUniversity of OuluOuluFinland
  11. 11.Gene Therapy and AutoImmunity GroupGarvan InstituteDarlinghurstAustralia
  12. 12.Department of Biochemistry and Molecular Vascular BiologyKanazawa University Graduate School of Medical ScienceKanazawaJapan
  13. 13.Department of Internal Medicine I and Clinical ChemistryUniversity of HeidelbergHeidelbergGermany
  14. 14.Department of Clinical MicrobiologyUniversity of Eastern FinlandKuopioFinland

Personalised recommendations