Impaired insulin secretion is a fundamental defect in type 2 diabetes. The aim of this study was to investigate whether single nucleotide polymorphisms (SNPs) in the genes regulating insulin secretion (SLC2A2 [encoding GLUT2], GCK, TCF1 [encoding HNF-1α], HNF4A, GIP, and GLP1R) are associated with the conversion from impaired glucose tolerance (IGT) to type 2 diabetes in participants of the Finnish Diabetes Prevention Study. With the exception of SLC2A2, other genes were not associated with the risk of type 2 diabetes. All four SNPs of SLC2A2 predicted the conversion to diabetes, and rs5393 (AA genotype) increased the risk of type 2 diabetes in the entire study population by threefold (odds ratio 3.04, 95% CI 1.34–6.88, P = 0.008). The risk for type 2 diabetes in the AA genotype carriers was increased in the control group (5.56 [1.78–17.39], P = 0.003) but not in the intervention group. We conclude that the SNPs of SLC2A2 predict the conversion to diabetes in obese subjects with IGT.

Defective insulin secretion is a key feature in the pathogenesis of type 2 diabetes in addition to insulin resistance. Therefore, variants in the genes regulating insulin secretion are plausible candidate genes for type 2 diabetes. In the present study, single nucleotide polymorphisms (SNPs) in five genes regulating β-cell function were evaluated as risk factors for type 2 diabetes.

GLUT2 is a high-capacity facilitative glucose transporter expressed in liver, kidney, intestine, and pancreatic β-cells (1). In the pancreas, GLUT2 regulates entry of glucose into the pancreatic cell and thus insulin secretion. SNPs of the SLC2A2 gene (encoding GLUT2) have been associated with the risk of diabetes in some (2,3) but not in most (4–11) of the studies.

Glucokinase phosphorylates glucose to glucose-6-phosphate and plays a key role in the regulation of insulin secretion (12). The G-30A polymorphism in the pancreatic β-cell–specific promoter of the glucokinase (GCK) gene has been associated with reduced β-cell function (13), impaired glucose tolerance (14), and type 2 diabetes (15).

Hepatocyte nuclear factor 1α (HNF-1α) is an important transactivating factor that is involved in pancreatic β-cell glucose sensing because it increases transcription of many genes participating in the insulin secretion process. The G319S polymorphism of the TCF1 gene, encoding HNF-1α, has been associated with type 2 diabetes in Oji-Cree (16). However, there is no definite evidence that TCF1 is a major contributor to type 2 diabetes. HNF4A constitutes a part of a network of transcription factors, including HNF-1α, controlling gene expression in pancreatic β-cells and liver. Recent studies have indicated that SNPs in HNF4A are associated with the risk of type 2 diabetes (17–19)

Incretins, glucagon-like polypeptide-1 (GLP-1), and glucose-dependent insulinotropic polypeptide (GIP) are substances that are responsible for fast insulin response to ingested glucose (20). In patients with type 2 diabetes, the secretion of GLP-1 from intestinal mucosa and the responsiveness of the pancreatic β-cells to GIP are diminished. No data on the association of SNPs of incretin hormones, or their receptor genes, with the risk of type 2 diabetes are available.

In this study, we investigated whether SNPs in the SLC2A2, GCK, TCF1, HNF4A, GIP, and GLP-1 receptor (GLP-1R) genes predict the conversion from impaired glucose tolerance (IGT) to type 2 diabetes in participants of the Finnish Diabetes Prevention Study (DPS).

The main objective of the Finnish DPS was to investigate whether lifestyle intervention influences the conversion to type 2 diabetes during a 3-year follow-up (21). A total of 522 middle-aged (mean age 55 years) and overweight (BMI ≥25kg/m2) Finnish subjects with IGT (22) participated in the study and were randomized to an intervention or control group. The intervention group was given intensive and individualized nutritional counseling as well as individual advice to increase physical activity and to reduce weight, whereas the control group was given only general advice. An oral glucose tolerance test was performed at each annual follow-up visit, and the diagnosis of diabetes was confirmed with a second test. The study protocol was approved by the ethics committee of the National Public Health Institute in Helsinki, and all study subjects provided written informed consent.

Weight change was calculated from the baseline value to the last weight measurement available, which varied from 1 to 3 years based on a new diagnosis of diabetes before the 3-year follow-up visit. For those who did not convert to diabetes, weight change was calculated as a difference in weight between baseline and 3 years.

DNA analysis.

DNA sample was available from 507 subjects (intervention group 259 and control group 248 subjects). SNPs of SLC2A2 (promoter SNPs rs5393 and rs5394 and exon SNPs rs5400 [T110I] and rs5404 [T198T]), TCF1 (rs1169288 and rs2464196), HNF4A (rs1884614, rs2144908, and rs1885088), GIP (rs2291725), and GLP-1R (rs6923761 and rs1042044) were genotyped using the TaqMan Allelic Discrimination Assays (Applied Biosystems). Genotyping reaction was amplified on a GeneAmp PCR system 2700 (95°C for 10 min, followed by 40 cycles of 95°C 15 s and 60°C 1 min), and fluorescence was detected on an ABI Prism 7000 sequence detector (Applied Biosystems). SNP rs1799884 (G-30A) of GCK was genotyped with restriction fragment–length polymorphism method using Alw21I restriction enzyme. The primer and probe sequences are available from the authors by request.

Statistical analysis.

All data were analyzed with the SPSS/Win programs (version 10.0, SPSS, Chicago, IL). Results are given as means ± SD or percentages. Variables, which were not normally distributed, were logarithmically transformed before statistical analyses. ANOVA was used to compare three groups, and Student’s t test was used for independent samples or χ2 test to compare two groups. Nonparametric Mann-Whitney U test and Kruskal-Wallis H test were applied to compare changes in weight and glucose levels (%). Logistic regression analysis was performed to evaluate whether the SNPs investigated predicted the conversion to type 2 diabetes.

The genotype distributions of SLC2A2, GCK, TCF1, HNF4A, GIP, and GLP-1R did not differ between the intervention and control groups, and they were in Hardy-Weinberg equilibrium.

Altogether, 72 of 479 subjects whose DNA and follow-up data were available converted from IGT to type 2 diabetes during the 3-year follow-up. SNPs rs5393 and rs5400 of SLC2A2 were associated with the conversion to type 2 diabetes in the entire study population (P = 0.020 and P = 0.031, respectively) (Table 1). In the control group, all four SNPs of SLC2A2 predicted the development of type 2 diabetes (P values from 0.004 to 0.017). In contrast, no differences were found in the conversion to type 2 diabetes between the genotypes in the intervention group. SNPs of other genes investigated or their haplotypes (HNF4A, data not shown) did not predict the conversion to type 2 diabetes.

Logistic regression analyses showed that the risk genotypes of the four SNPs of SLC2A2 were associated with a two- to threefold risk of developing type 2 diabetes in the entire study population and a four- to fivefold risk in the control group (Table 2). Moreover, subjects carrying the risk genotypes of SLC2A2 in the control group had increased plasma glucose levels both at fasting and at 120 min in an oral glucose tolerance test. The difference was most prominent for rs5404 (fasting: GG 4.42 ± 13.30% vs. A allele −0.36 ± 12.20%, P = 0.018; 120 min: GG 7.47 ± 29.70% vs. A allele −4.15 ± 26.44%, P = 0.015). No differences in insulin levels, insulin secretion (homeostasis model assessment of insulin secretion), or weight change among the genotypes were found (data not shown). SNPs in other genes investigated were not associated with differences in baseline characteristics in the study groups.

Next, we tested the effect of the intervention on the conversion to type 2 diabetes in carriers and noncarriers of the risk genotypes of SLC2A2 (Table 3). The subjects possessing the risk genotypes of all four SNPs had a 3.5-fold risk of developing type 2 diabetes if they were in the control group, compared with that in the intervention group (P < 0.001). No differences were found between the study groups in the conversion to type 2 diabetes in noncarriers of the risk genotypes.

All SNPs of SLC2A2 investigated were in strong linkage disequilibrium with each other (D′ >0.98, P < 0.001), and they formed seven haplotypes (ACCG, ACTG, ACTA, CCTG, CCTA, CTTG, and CTTA). None of these haplotypes increased the risk of type 2 diabetes beyond that of an individual SNP. The CTTA haplotype protected from the development of type 2 diabetes in the control group (odds ratio [OR] 0.27 [95% CI 0.09–0.79], P = 0.016).

In our recent study, we found that ATP-sensitive K+ channel genes, ABCC8 (encoding sulfonylurea receptor 1, SUR1) and KCNJ11 (encoding Kir6.2), predicted the conversion from IGT to type 2 diabetes (23). Since these genes regulate insulin secretion, we investigated whether they have an additive effect with rs5393 on the risk of type 2 diabetes. We found that the conversion to type 2 diabetes was significantly increased in the presence of both the AA genotype of rs5393 of SLC2A2 (the SNP which was most strongly associated with type 2 diabetes risk) and the GG genotype of rs3758947 of ABCC8 (Fig. 1). Subjects having both the SLC2A2 and ABCC8 risk genotypes had a 6.5-fold risk for the conversion to type 2 diabetes compared with those having neither of the risk genotypes (OR 6.49 [95% CI 1.52–27.73], P = 0.012). Instead, there was no significant additive effect between SLC2A2 and KCNJ11 (rs5219 SNP) on the risk of type 2 diabetes (P = 0.062).

This is the first report to demonstrate in a prospective study setting that in subjects with IGT, SNPs in SLC2A2 are associated with a threefold risk for developing type 2 diabetes. This increase in risk was observed in the entire study population and in the control group. Furthermore, the carriers of the high-risk genotypes benefited more from intervention than noncarriers. The SNPs in the GGK, TCF1, HNF4A, GIP, and GLP1R genes did not affect the conversion to type 2 diabetes.

The majority of previous studies have failed to show that SLC2A2 is associated with type 2 diabetes (4–11), and only two studies have shown a positive association (2,3). In U.K. Europid subjects, SNPs in SLC2A2 were associated with type 2 diabetes (2). In that study, the risk was associated with a minor allele of rs5400 (T110I) and rs5404 (T198T), whereas in our study the risk was attributable to the common homozygotes. We do not have any explanation for this discrepancy, but it may indicate differences in study populations. We gave our results without adjustment for the number of tests undertaken, which may lead to false-positive results due to multiple testing. However, the SNPs of SLC2A2 were consistently associated with the conversion to type 2 diabetes even after adjustment for confounding factors (Table 2), quite similar to the results reported in a previous study (2).

Our finding that the SNPs of SLC2A2 were associated with the risk for type 2 diabetes in the control group, but not in the intervention group, is very similar to our previous results on the ABCC8 gene (23). Furthermore, SNPs in SLC2A2 and ABCC8 contributed to the risk of type 2 diabetes independently of weight change. Risk genotypes of these genes had an additive effect on the conversion to type 2 diabetes, and a similar nonsignificant trend was observed with respect to SLC2A2 and KCNJ11 (Fig. 1). Our results may indicate that subjects with risk genotypes in this network of genes regulating insulin secretion (SLC2A2, ABCC8, and KCNJ11) are at particularly high risk for type 2 diabetes.

In the present study, we did not find any differences in fasting and 2-h insulin levels among the genotypes of SLC2A2. In the Finnish DPS, insulin secretion is influenced by the lifestyle intervention, which improved insulin sensitivity (24). Because the effect of SNPs of SLC2A2 was observed only in the control group, this may imply that the improvement in insulin sensitivity by lifestyle changes is also beneficial to the preservation of insulin secretion in β-cells.

We did not find any association of the SNPs of GCK, TCF1, HNF4A, GIP, or GLP-1R with type 2 diabetes. However, negative findings do not exclude the possibility that these genes may contribute to the risk of diabetes in other populations. SNPs in HNF4A have been shown to be associated with type 2 diabetes in other populations (18,19) and in one previous study on Finns (17). Different study designs and the relatively small sample size of the DPS may explain discrepant findings. For example, to replicate a positive association of SNPs of HNF4A and the conversion to type 2 diabetes in the present study, a sample size of ∼1,400 subjects would have been needed.

In conclusion, the high-risk genotypes of SLC2A2 predicted the conversion to type 2 diabetes in Finnish subjects with IGT. We also demonstrated that carriers of these risk genotypes benefited from the lifestyle intervention. This study gives further evidence that genes regulating insulin secretion are important for the risk of type 2 diabetes also in obese subjects with IGT, generally assumed to have the primary defect in insulin action.

FIG. 1.
FIG. 1. Conversion to type 2 diabetes according to SNP rs5393 of the GLUT2 (SLC2A2) gene and SNP rs3758947 of the SUR1 (ABCC8) gene (A) and according to SNP rs5393 of the GLUT2 (SLC2A2) gene and SNP rs5219 of the Kir6.2 (KCNJ11) gene (B). GLUT2+ (AA), SUR1+ (GG), and Kir6.2+ (23K allele) are high-risk genotypes.
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Conversion to type 2 diabetes according to SNP rs5393 of the GLUT2 (SLC2A2) gene and SNP rs3758947 of the SUR1 (ABCC8) gene (A) and according to SNP rs5393 of the GLUT2 (SLC2A2) gene and SNP rs5219 of the Kir6.2 (KCNJ11) gene (B). GLUT2+ (AA), SUR1+ (GG), and Kir6.2+ (23K allele) are high-risk genotypes.

FIG. 1.
FIG. 1. Conversion to type 2 diabetes according to SNP rs5393 of the GLUT2 (SLC2A2) gene and SNP rs3758947 of the SUR1 (ABCC8) gene (A) and according to SNP rs5393 of the GLUT2 (SLC2A2) gene and SNP rs5219 of the Kir6.2 (KCNJ11) gene (B). GLUT2+ (AA), SUR1+ (GG), and Kir6.2+ (23K allele) are high-risk genotypes.
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Conversion to type 2 diabetes according to SNP rs5393 of the GLUT2 (SLC2A2) gene and SNP rs3758947 of the SUR1 (ABCC8) gene (A) and according to SNP rs5393 of the GLUT2 (SLC2A2) gene and SNP rs5219 of the Kir6.2 (KCNJ11) gene (B). GLUT2+ (AA), SUR1+ (GG), and Kir6.2+ (23K allele) are high-risk genotypes.

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TABLE 1

Subjects with the specific risk genotype versus subjects without the risk genotype who converted to type 2 diabetes

Total population (n = 479)
Intervention group (n = 241)
Control group (n = 238)
Conversion to diabetesPConversion to diabetesPConversion to diabetesP
SLC2A2       
    rs5393       
AA vs. C allele 64 vs. 8 (17.0 vs. 7.8) 0.020 17 vs. 4 (8.9 vs. 8.2) 0.878 47 vs. 4 (25.5 vs. 7.4) 0.004 
    rs5394       
        CC vs. T allele 64 vs. 8 (16.5 vs. 8.8) 0.064 17 vs. 4 (8.5 vs. 9.5) 0.838 47 vs. 4 (24.9 vs. 8.2) 0.011 
    rs5400 (T110I)       
        CC vs. T allele 61 vs. 11 (17.1 vs. 9.0) 0.031 17 vs. 4 (9.3 vs. 6.9) 0.573 44 vs. 7 (25.3 vs. 10.9) 0.017 
    rs5404 (T198T)       
        GG vs. A allele 64 vs. 8 (16.5 vs. 8.8) 0.064 17 vs. 4 (8.5 vs. 9.8) 0.795 47 vs. 4 (25.0 vs. 8.0) 0.009 
GCK       
    rs1799884 (G-30A)       
        GG vs. A allele 56 vs. 13 (16.0 vs. 11.0) 0.190 16 vs. 3 (9.0 vs. 5.3) 0.369 40 vs. 10 (23.1 vs. 16.4) 0.270 
TCF1       
    rs1169288 (I27L)       
        AA vs. C allele 27 vs. 45 (14.0 vs. 15.7) 0.190 9 vs. 12 (9.4 vs. 8.3) 0.767 18 vs. 33 (18.6 vs. 23.4) 0.370 
rs2464196 (S487N)       
        GG vs. A allele 35 vs. 37 (14.7 vs. 15.4) 0.843 11 vs. 10 (9.4 vs. 8.1) 0.713 24 vs. 27 (19.8 vs. 23.1) 0.542 
HNF4A       
    rs1884614       
        CC vs. T allele 51 vs. 21 (16.5 vs. 12.4) 0.224 16 vs. 5 (10.8 vs. 5.4) 0.145 35 vs. 16 (21.7 vs. 20.8) 0.866 
    rs2144908       
        GG vs. A allele 51 vs. 21 (16.6 vs. 12.3) 0.209 16 vs. 5 (11.0 vs. 5.3) 0.126 35 vs. 16 (21.6 vs. 21.1) 0.923 
    rs1885088       
        GG vs. A allele 52 vs. 20 (14.6 vs. 16.1) 0.691 15 vs. 6 (8.6 vs. 9.0) 0.934 37 vs. 14 (20.4 vs. 24.6) 0.509 
GIP       
    rs2291725 (G103S)       
        CC vs. T allele 20 vs. 52 (15.7 vs. 14.8) 0.792 8 vs. 13 (12.7 vs. 7.3) 0.192 12 vs. 39 (18.8 vs. 0.22.4) 0.541 
GLP-1R       
    rs6923761 (G168S)       
        GG vs. A allele 38 vs. 34 (15.9 vs. 14.2) 0.596 11 vs. 10 (8.4 vs. 9.1) 0.849 27 vs. 24 (25.0 vs. 18.5) 0.221 
    rs1042044 (L260F)       
        CC vs. A allele 21 vs. 51 (15.4 vs. 14.9) 0.874 3 vs. 18 (4.5 vs. 10.3) 0.159 18 vs. 33 (25.7 vs. 19.6) 0.298 
Total population (n = 479)
Intervention group (n = 241)
Control group (n = 238)
Conversion to diabetesPConversion to diabetesPConversion to diabetesP
SLC2A2       
    rs5393       
AA vs. C allele 64 vs. 8 (17.0 vs. 7.8) 0.020 17 vs. 4 (8.9 vs. 8.2) 0.878 47 vs. 4 (25.5 vs. 7.4) 0.004 
    rs5394       
        CC vs. T allele 64 vs. 8 (16.5 vs. 8.8) 0.064 17 vs. 4 (8.5 vs. 9.5) 0.838 47 vs. 4 (24.9 vs. 8.2) 0.011 
    rs5400 (T110I)       
        CC vs. T allele 61 vs. 11 (17.1 vs. 9.0) 0.031 17 vs. 4 (9.3 vs. 6.9) 0.573 44 vs. 7 (25.3 vs. 10.9) 0.017 
    rs5404 (T198T)       
        GG vs. A allele 64 vs. 8 (16.5 vs. 8.8) 0.064 17 vs. 4 (8.5 vs. 9.8) 0.795 47 vs. 4 (25.0 vs. 8.0) 0.009 
GCK       
    rs1799884 (G-30A)       
        GG vs. A allele 56 vs. 13 (16.0 vs. 11.0) 0.190 16 vs. 3 (9.0 vs. 5.3) 0.369 40 vs. 10 (23.1 vs. 16.4) 0.270 
TCF1       
    rs1169288 (I27L)       
        AA vs. C allele 27 vs. 45 (14.0 vs. 15.7) 0.190 9 vs. 12 (9.4 vs. 8.3) 0.767 18 vs. 33 (18.6 vs. 23.4) 0.370 
rs2464196 (S487N)       
        GG vs. A allele 35 vs. 37 (14.7 vs. 15.4) 0.843 11 vs. 10 (9.4 vs. 8.1) 0.713 24 vs. 27 (19.8 vs. 23.1) 0.542 
HNF4A       
    rs1884614       
        CC vs. T allele 51 vs. 21 (16.5 vs. 12.4) 0.224 16 vs. 5 (10.8 vs. 5.4) 0.145 35 vs. 16 (21.7 vs. 20.8) 0.866 
    rs2144908       
        GG vs. A allele 51 vs. 21 (16.6 vs. 12.3) 0.209 16 vs. 5 (11.0 vs. 5.3) 0.126 35 vs. 16 (21.6 vs. 21.1) 0.923 
    rs1885088       
        GG vs. A allele 52 vs. 20 (14.6 vs. 16.1) 0.691 15 vs. 6 (8.6 vs. 9.0) 0.934 37 vs. 14 (20.4 vs. 24.6) 0.509 
GIP       
    rs2291725 (G103S)       
        CC vs. T allele 20 vs. 52 (15.7 vs. 14.8) 0.792 8 vs. 13 (12.7 vs. 7.3) 0.192 12 vs. 39 (18.8 vs. 0.22.4) 0.541 
GLP-1R       
    rs6923761 (G168S)       
        GG vs. A allele 38 vs. 34 (15.9 vs. 14.2) 0.596 11 vs. 10 (8.4 vs. 9.1) 0.849 27 vs. 24 (25.0 vs. 18.5) 0.221 
    rs1042044 (L260F)       
        CC vs. A allele 21 vs. 51 (15.4 vs. 14.9) 0.874 3 vs. 18 (4.5 vs. 10.3) 0.159 18 vs. 33 (25.7 vs. 19.6) 0.298 

Data are n (%), unless otherwise indicated.

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TABLE 2

SNPs in SLC2A2 as predictors for the development of type 2 diabetes (logistic regression analysis)

ORTotal population
PORIntervention group
PORControl group
P
95% CI95% CI95% CI
rs5393 (AA vs. C allele)*          
Model 1 (univariate) 2.44 1.13–5.26 0.023 1.09 0.35–3.41 0.878 4.29 1.47–12.51 0.008 
Model 2 (multivariate)† 3.04 1.34–6.88 0.008 1.17 0.35–3.89 0.820 5.56 1.78–17.39 0.003 
rs5394 (CC vs. T allele)*          
Model 1 (univariate) 2.05 0.95–4.44 0.069 0.89 0.28–2.79 0.838 3.72 1.27–10.90 0.016 
Model 2 (multivariate)† 2.54 1.12–5.79 0.026 0.85 0.25–2.86 0.786 4.91 1.56–15.46 0.007 
rs5400 (CC vs. T allele)*          
Model 1 (univariate) 2.08 1.06–4.10 0.034 1.38 0.45–4.29 0.575 2.76 1.17–6.49 0.020 
Model 2 (multivariate)† 2.60 1.26–5.35 0.009 1.40 0.42–4.64 0.580 3.78 1.50–9.56 0.005 
rs5404 (GG vs. A allele)*          
Model 1 (univariate) 2.05 0.95–4.44 0.069 0.86 0.27–2.70 0.795 3.83 1.31–11.22 0.014 
Model 2 (multivariate)† 2.57 1.13–5.85 0.025 0.84 0.25–2.82 0.772 5.07 1.61–15.92 0.005 
ORTotal population
PORIntervention group
PORControl group
P
95% CI95% CI95% CI
rs5393 (AA vs. C allele)*          
Model 1 (univariate) 2.44 1.13–5.26 0.023 1.09 0.35–3.41 0.878 4.29 1.47–12.51 0.008 
Model 2 (multivariate)† 3.04 1.34–6.88 0.008 1.17 0.35–3.89 0.820 5.56 1.78–17.39 0.003 
rs5394 (CC vs. T allele)*          
Model 1 (univariate) 2.05 0.95–4.44 0.069 0.89 0.28–2.79 0.838 3.72 1.27–10.90 0.016 
Model 2 (multivariate)† 2.54 1.12–5.79 0.026 0.85 0.25–2.86 0.786 4.91 1.56–15.46 0.007 
rs5400 (CC vs. T allele)*          
Model 1 (univariate) 2.08 1.06–4.10 0.034 1.38 0.45–4.29 0.575 2.76 1.17–6.49 0.020 
Model 2 (multivariate)† 2.60 1.26–5.35 0.009 1.40 0.42–4.64 0.580 3.78 1.50–9.56 0.005 
rs5404 (GG vs. A allele)*          
Model 1 (univariate) 2.05 0.95–4.44 0.069 0.86 0.27–2.70 0.795 3.83 1.31–11.22 0.014 
Model 2 (multivariate)† 2.57 1.13–5.85 0.025 0.84 0.25–2.82 0.772 5.07 1.61–15.92 0.005 
*

rs5393 genotypes were coded as 0 = the C allele, 1 = the AA genotype; rs5394 genotypes 0 = T allele, 1 = CC genotype; rs5400 genotypes 0 =T allele, 1 = CC genotype; rs5404 genotypes 0 = A allele, 1 = GG genotype.

†

Adjusted for age, sex, weight at baseline, and weight change.

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TABLE 3

The effect of intervention on the conversion to diabetes (%) and OR calculated by logistic regression analysis according to the SNPs of SLC2A2

ControlInterventionOR (95% CI)PControlInterventionOR (95% CI)P
rs5393 AA genotype    C allele    
 25.5 8.9 3.53 (1.94–6.42) <0.001 7.4 8.2 0.90 (0.21–3.81) 0.886 
rs5394 CC genotype    T allele    
 24.9 8.5 3.54 (1.95–6.43) <0.001 8.2 9.5 0.84 (0.20–3.61) 0.819 
rs5400 CC genotype    T allele    
 25.3 9.3 3.31 (1.81–6.05) <0.001 10.9 6.9 1.66 (0.46–5.98) 0.440 
rs5404 GG genotype    A allele    
 25.0 8.5 3.59 (1.98–6.52) <0.001 8.0 9.8 0.80 (0.19–3.44) 0.769 
ControlInterventionOR (95% CI)PControlInterventionOR (95% CI)P
rs5393 AA genotype    C allele    
 25.5 8.9 3.53 (1.94–6.42) <0.001 7.4 8.2 0.90 (0.21–3.81) 0.886 
rs5394 CC genotype    T allele    
 24.9 8.5 3.54 (1.95–6.43) <0.001 8.2 9.5 0.84 (0.20–3.61) 0.819 
rs5400 CC genotype    T allele    
 25.3 9.3 3.31 (1.81–6.05) <0.001 10.9 6.9 1.66 (0.46–5.98) 0.440 
rs5404 GG genotype    A allele    
 25.0 8.5 3.59 (1.98–6.52) <0.001 8.0 9.8 0.80 (0.19–3.44) 0.769 
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This study was financially supported by grants from the Academy of Finland (38387 and 46558 to J.T., 40758 to M.U.), the EVO fund of the Kuopio University Hospital (5106 to M.U., 5194 to M.L.), the Ministry of Education, the Finnish Diabetes Research Foundation, the Juho Vainio Foundation, the Yrjö Jahnsson Foundation, and the European Union (EUGENE2, LSHM-CT-2004-512013 to M.L.).

We thank Teemu Kuulasmaa, MSc, for his help in statistical analyses of the data and Kaija Eirola and Leena Uschanoff for their help in genotyping the subjects.

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