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Single-Nucleotide Polymorphism rs7754840 of CDKAL1 Is Associated with Impaired Insulin Secretion in Nondiabetic Offspring of Type 2 Diabetic Subjects and in a Large Sample of Men with Normal Glucose Tolerance

Alena Stanáková, Jussi Pihlajamäki, Johanna Kuusisto, Norbert Stefan, Andreas Fritsche, Hans Häring, Francesco Andreozzi, Elena Succurro, Giorgio Sesti, Trine Welløv Boesgaard, Torben Hansen, Oluf Pedersen, Per Anders Jansson, Ann Hammarstedt, Ulf Smith, Markku Laakso for the EUGENE2 Consortium

Department of Medicine (A.S., J.P., J.K., M.L.), University of Kuopio and Kuopio University Hospital, 70210 Kuopio, Finland; Department of Internal Medicine (N.S., A.F., H.H.), Division of Endocrinology, Diabetology, Nephrology, Vascular Medicine, and Clinical Chemistry, University of Tubingen, D-72076 Tubingen, Germany; Department of Experimental and Clinical Medicine (F.A., E.S., G.S.), University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy; Steno Diabetes Centre (T.W.B., T.H., O.P.), DK-2820 Gentofte Copenhagen, Denmark; and The Lundberg Laboratory for Diabetes Research (P.A.J., A.H., U.S.), Department of Internal Medicine, Sahlgrenska University Hospital, SE-413 45 Gothenburg, Sweden

Adress all correspondence and requests for reprints to: Markku Laakso, M.D., Academy Professor, Department of Medicine, University of Kuopio, 70210 Kuopio, Finland. E-mail: markku.laakso{at}uku.fi.

	Abstract

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Context: CDKAL1 is a recently discovered susceptibility gene for type 2 diabetes.

Objective: Our objective was to investigate the impact of rs7754840 of CDKAL1 on insulin secretion, insulin sensitivity, and risk of type 2 diabetes.

Design and Settings: Study 1 (the EUGENE2 study) was a cross-sectional study including subjects from five white populations in Europe (Denmark, Finland, Germany, Italy, and Sweden). Study 2 is an ongoing prospective study of Finnish men.

Participants: In study 1, 846 nondiabetic offspring of type 2 diabetic patients (age 40 ± 10 yr; body mass index 26.7 ± 5.0 kg/m²) participated. In study 2, subjects included 3900 middle-aged men (533 type 2 diabetic and 3367 nondiabetic subjects).

Interventions: Interventions included iv glucose-tolerance test (IVGTT), oral glucose-tolerance test (OGTT), and euglycemic-hyperinsulinemic clamp in study 1 and OGTT in study 2.

Main Outcome Measures: Parameters of insulin secretion, insulin resistance, and glucose tolerance status were assessed.

Results: In study 1, carriers of the GC and CC genotypes of rs7754840 had 11 and 24% lower first-phase insulin release in an IVGTT compared with that in carriers of the GG genotype (P = 0.002). The C allele was also associated with higher glucose area under the curve in an OGTT (P = 0.016). In study 2, rs7754840 was significantly associated with type 2 diabetes (P = 0.022) and markers of impaired insulin release [insulinogenic index (IGI), P = 0.012] in 2405 men with normal glucose tolerance.

Conclusions: rs7754840 of CDKAL1 was associated with markers of impaired insulin secretion in two independent studies. Furthermore, rs7754840 was associated with type 2 diabetes in Finnish men (study 2). Therefore, CDKAL1 is likely to increase the risk of type 2 diabetes by impairing insulin secretion.

	Introduction

Top Abstract Introduction Subjects and Methods Results Discussion References

 
The heretability of type 2 diabetes mellitus is well established, and intense efforts have been concentrated to identify genetic risk factors for this disease (1). Recent genome-wide association (GWA) studies have reported new type 2 diabetes-susceptibility genes, replicated in several different populations (2, 3, 4, 5, 6). One of these genes is CDKAL1 (cyclin-dependent kinase 5 regulatory subunit associated protein 1-like 1).

CDKAL1 is located on chromosome 6p22.3 and encodes a 65-kDa protein. Although the function of the CDKAL1 gene is unknown, its protein product shares protein domain similarity with cyclin-dependent kinase 5 (CDK5) regulatory subunit-associated protein 1 (CDK5RAP1), a neuronal protein that specifically inhibits activation of CDK5 (7). CDK5 is a small serine/threonine protein kinase recognized as an essential molecule in the brain. Furthermore, it has several extraneuronal effects (8), and it is thought to play a role in the pathophysiology of β-cell dysfunction and predisposition to type 2 diabetes (9). CDKAL1 expression in human pancreatic islets (3) supports the notion that CDKAL1 and CDK5-mediated pathways in β-cells are related.

Recent GWA studies have identified several single-nucleotide polymorphisms (SNPs) in intron 5 of CDKAL1 associated with type 2 diabetes (3, 4, 5, 6). In an Icelandic study, Steinthorsdottir et al. (4) reported an association of the G allele of rs7756992 with type 2 diabetes, and this association was replicated in the combined data from five case-control studies of the European ancestry. Moreover, the GG homozygotes had an approximately 20% lower corrected insulin response to an oral glucose load than did the carriers of the C allele, suggesting that this variant confers the risk for type 2 diabetes via impaired insulin secretion (4).

The results obtained from the GWA studies of the Diabetes Genetic Initiative (DGI) (5), Finland-United States Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics (FUSION) (6), Wellcome Trust Case Control Consortium (WTCCC), and United Kingdom Type 2 Diabetes Genetics Consortium (UKT2D) (3) show evidence for an association of additional four SNPs of CDKAL1 (rs7754840, rs10946398, rs4712523, and rs9465871) with type 2 diabetes. The strongest associations were observed for two of them, rs7754840 and rs10946398 (5, 3), that are in complete linkage disequilibrium (r² = 1.0 according to HapMap-CEU). Furthermore, in the DGI scan, the risk C allele of rs7754840 was associated with reduced insulin secretion (assessed from the IGI) in nondiabetic subjects (5).

These results, as well as a probable role of CDKAL1 protein product in CDK5-mediated regulation of pancreatic β-cell function, suggest that variants in CDKAL1 may influence the risk of type 2 diabetes by impairing insulin secretion. However, in previous studies, the assessment of insulin secretion was based on variables derived from an oral glucose tolerance test (OGTT), which does not specify mechanisms of insulin secretion. Similarly, no accurate estimation of insulin sensitivity [except for homeostasis model assessment for insulin resistance (HOMA-IR) index] (4) has been performed in these studies. Therefore, we investigated whether rs7754840, the most plausible type 2 diabetes-associated polymorphism in CDKAL1, affects insulin secretion and insulin sensitivity in 846 nondiabetic offspring of type 2 diabetic patients from five different white populations in Europe (the EUGENE2 Consortium). Furthermore, we replicated the findings in a relatively large population-based study that also allowed us to investigate the association of rs7754840 with type 2 diabetes.

	Subjects and Methods

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Subjects

Study 1: the EUGENE2 study Subjects included in this study were healthy nondiabetic offspring of patients with type 2 diabetes. One of the parents (a proband) had to have type 2 diabetes and the spouse a normal glucose tolerance in an OGTT (>85% of subjects) or a lack of history of type 2 diabetes in the first-degree relatives. An OGTT was performed after a 12-h fast. The probands (n = 536) were randomly selected among type 2 diabetic subjects living in the regions of five study centers in Europe. They were recruited over a 4-yr period (in 2000–2004) through advertisement in public media and in the hospitals. The acceptance rate of diabetic volunteers to be included was at least 70% in the different centers. Type 2 diabetes among the probands was defined according to the World Health Organization (WHO) criteria (10). Next, the offspring (children of a diabetic proband and his/her spouse) were invited to the study. Altogether, 846 offspring were included in the study as follows: Catanzaro, Italy (n = 110); Copenhagen, Denmark (n = 270); Gothenburg, Sweden (n = 100); Kuopio, Finland (n = 217); and Tubingen, Germany (n = 149). The study protocol was approved by appropriate Institutional Review Boards. All study subjects gave an informed consent.

Study 2: a population-based cross-sectional study of 3900 men We collected an independent random sample of Finnish men from an ongoing study (started in March 2005) in Kuopio (the METSIM Study, the Metabolic Syndrome in Men) to replicate our findings and to investigate the association of CDKAL1 with type 2 diabetes. These subjects were randomly selected from the population register of Kuopio (population of 90,000) including all subjects. This sample included 3900 subjects (3367 nondiabetic and 533 type 2 diabetic men), aged from 50–70 yr. Type 2 diabetes was defined according to the WHO criteria (10). The protocol included a 1-d visit to the Clinical Research Unit of the University of Kuopio. This study was approved by the Ethics Committee of the University of Kuopio and was in accordance with the Helsinki Declaration.

Measurements, metabolic studies, and calculations

Study 1 All study centers followed the same protocol. Blood pressure was measured in a sitting position after a 5-min rest with a mercury sphygmomanometer. Height and weight were measured to the nearest 0.5 cm and 0.1 kg, respectively. Body mass index (BMI) was calculated as weight (kilograms) divided by height (meters) squared. Fasting blood samples were drawn after 12 h fasting followed by an OGTT (75 g glucose) to evaluate glucose tolerance status (samples for plasma glucose and insulin at 0, 30, 90, and 120 min). On the second occasion after the 12-h fast, an iv glucose tolerance test (IVGTT) was performed to determine the first-phase insulin secretion capacity after an overnight fast. A bolus of glucose (300 mg/kg in a 50% solution) was given within 30 sec into the antecubital vein. Samples for the measurement of plasma glucose and insulin (arterialized venous blood) were drawn at –5, 0, 2, 4, 6, 8, 10, 20, 30, 40, 50, and 60 min. At 60 min after the glucose bolus, the euglycemic-hyperinsulinemic clamp started (insulin infusion of 240 pmol/m²·min) for 120 min to evaluate insulin sensitivity (11), presented as M-value. This protocol has been validated previously (12). Glucose was clamped at 5.0 mmol/liter for the next 120 min by infusion of 20% glucose at various rates according to glucose measurements performed at 5-min intervals. The mean amount of glucose infused during the last hour was used to calculate the rates of whole-body glucose uptake. In the Copenhagen center, the euglycemic clamp was not performed, and insulin sensitivity was assessed as insulin sensitivity index (S_I) derived from an IVGTT (13). Glucose tolerance status in all study subjects was assessed according to the WHO criteria (10).

Study 2 Clinical measurements were performed as described above. During a 2-h OGTT (75 g glucose), samples for plasma glucose and insulin were drawn at 0, 30, and 120 min to evaluate the degree of glucose tolerance and insulin response to an oral glucose load. Neither IVGTT nor euglycemic-hyperinsulinemic clamp was performed in this study.

Calculations The incremental areas under the glucose and insulin curve during an OGTT (studies 1 and 2) and IVGTT (study 1) were calculated by the trapezoidal method. A marker of insulin resistance, HOMA-IR, was calculated in all subjects (studies 1 and 2), using the following equation: fasting insulin x fasting glucose/22.5. Three markers of insulin secretion were calculated, the IGI, HOMA-β index, and corrected insulin response (CIR) to an oral glucose load. The IGI was calculated as a ratio of increments of insulin and glucose levels during the first 30 min of an OGTT as follows: [(insulin 30 min – fasting insulin)/(glucose 30 min – fasting glucose)] (14). HOMA-β index was calculated according to the following formula: (20 x fasting insulin level)/(fasting plasma glucose – 3.5). CIR was calculated according to the following formula: [(insulin 30 min x 100)/glucose 30 min x (glucose 30 min – 3.89)] (15). Disposition index (16) was calculated according to the following formula: [(M-value x insulin area under the curve (AUC) during the first 10 min of an IVGTT)/1000].

Laboratory determinations

Study 1 Glucose was measured by the glucose oxidase method (glucose and lactate analyzer 2300 Stat Plus; Yellow Springs Instrument Co., Inc., Yellow Springs, OH). Because plasma insulin levels were measured by different methods (except for the Gothenburg center having their insulin measured in Tubingen), the assay applied in Tubingen (microparticle enzyme immunoassay; Abbott Laboratories, Tokyo, Japan) was selected as a reference assay. Catanzaro, Copenhagen, and Kuopio centers sent 40–100 fasting and postglucose challenge plasma insulin samples to Tubingen for parallel analysis. Plasma insulin levels from these three centers were converted to plasma insulin levels corresponding to the Tubingen assay by linear regression analysis, which gave the best fit (linear correlation between the Tubingen assay and method applied in the centers were the following: 0.87 for Catanzaro, 0.96 for Copenhagen, and 0.98 for Kuopio).

Study 2 Plasma glucose levels in the fasting state and during the OGTT were measured by the glucose oxidase method (2300 Stat Plus; Yellow Springs Instruments). To determine plasma insulin, blood was collected in EDTA-containing tubes. After centrifugation, the plasma was stored at –70 C until analysis. Plasma insulin concentration was determined by a commercial double-antibody solid-phase RIA (Phadeseph Insulin RIA 100; Pharmacia Diagnostics, Uppsala, Sweden).

DNA analysis (studies 1 and 2)

Screening of rs7754840 was performed with the TaqMan allelic discrimination assay (Applied Biosystems, Foster City, CA). Genotyping reaction was amplified on a GeneAmp PCR system 2700 (95C for 10 min, followed by 40 cycles of 95 C for 15 sec and 60 C for 1 min), and fluorescence was detected on an ABI Prism 7000 sequence detector (Applied Biosystems). Genotyping success rate was 99.7 and 100% in studies 1 and 2, and the error rate was 0% in both studies (6.3% of all samples were re-genotyped in study 1, and 4.7% in study 2).

Statistical analysis (studies 1 and 2)

Data analyses were carried out with the SPSS 14.0 for Windows programs. The results for continuous variables are given as means ± SD. Odds ratios (ORs) are presented with 95% confidence intervals (CI). Insulin levels, HOMA-IR, HOMA-β, IGI, and CIR were logarithmically transformed for statistical analyses. The differences between the groups were assessed by the ANOVA and t test for continuous variables and by the ² test for noncontinuous variables. Linear mixed model, general linear univariate model, and logistic regression analysis was applied to adjust for confounding factors. For mixed-model analysis, we included the center and pedigree (coded as a family number) as random factors, the genotype and gender as fixed factors, and age, BMI, and HOMA-IR as covariates. For the general univariate model, we included the genotype and gender as fixed factors and age, BMI, and HOMA-IR as covariates. Pearson correlation coefficient was used to examine the relationship between continuous variables.

	Results

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Study 1

Baseline characteristics Characteristics of study subjects (n = 846) by center are presented in Table 1. Altogether, 43.5% of subjects were men and 56.5% women (P < 0.001 among the centers). The mean age was 40 ± 10 yr (P < 0.001), BMI 26.7 ± 5.0 kg/m² (P < 0.001), fasting plasma glucose 5.1 ± 0.5 mmol/liter (P < 0.001), and 2-h plasma glucose 6.3 ± 1.5 mmol/liter (P = 0.167). Because the centers differed with respect to age, gender distribution, and BMI, all results were adjusted for these variables and additionally for center and familial relationship (and HOMA-IR index, when appropriate). Altogether, 698 subjects had normal glucose tolerance (NGT), 20 had impaired fasting glucose (IFG), and 128 had impaired glucose tolerance (IGT); i.e. 17% had abnormal glucose tolerance. The frequency of the minor C allele of rs7754840 was 0.33 (96 homozygous and 373 heterozygous carriers of the C allele among 846 subjects). The genotype distribution followed the Hardy-Weinberg equilibrium (P = 0.911).

View larger version (32K): [in this window] [in a new window]   FIG. 1. Plasma glucose (A) and insulin (B) levels during an OGTT and glucose (C) and insulin (D) levels AUC during an OGTT according to SNP rs7754840 in all subjects. P values are adjusted for age, BMI, gender, family, and center and are calculated over the three genotype groups (ANOVA). *, P < 0.05; **, P < 0.01. In A and B, black squares and solid line are for GG, white triangles and dashed line for GC, and white circles and dotted line for CC. In C and D, black bars are for GG, striped bars for GC, and white bars for CC. Insulin levels are log-transformed in statistical analyses. Data are given as adjusted means ± SEM.

View larger version (20K): [in this window] [in a new window]   FIG. 2. Insulin sensitivity measured by clamp (A), the first-phase and second-phase glucose levels AUC over basal glucose (B), and first-phase and second-phase insulin levels AUC over basal insulin (C) during the IVGTT according to SNP rs7754840 in all subjects (Copenhagen is excluded from analyses of insulin sensitivity and second-phase insulin secretion). P values are adjusted for age, BMI, gender, family, and center and are calculated over the three genotype groups (ANOVA). Insulin levels are log-transformed in statistical analyses. Black bars are for GG, striped bars for GC, and white bars for CC. Data are given as adjusted means ± SEM.

We performed all statistical analyses also by excluding subjects with IGT or IFG. The results of analyses of insulin sensitivity data and OGTT data did not differ substantially from those presented above. The difference in glucose AUC during the OGTT remained significant (P = 0.005), being higher in carriers of the C allele. Regarding the IVGTT data, the percentage change in the first-phase insulin release between the genotypes was comparable with that observed in the entire study group in the additive model, because each copy of the C allele decreased the first-phase insulin release approximately by 11%. However, the overall comparison over the genotypes was no more statistically significant, probably due to a lower number of cases. The difference in glucose AUC over basal glucose level during 10–60 min of the IVGTT remained significant (P = 0.013) under the additive model, with 15% increase in CC homozygotes compared with GG homozygotes, similar to the results from the entire study group.

When analyzing a subgroup of subjects with IGT and/or IFG (n = 148), no significant differences of examined parameters between the CDKAL1 genotypes were observed, most probably due to a small sample size. However, when more stringent criteria for fasting plasma glucose were used (cutoff point of 5.6 mmol/liter, the number of subjects with IGT and/or IFG was 234), the differences in the first-phase insulin release, IGI, and CIR between the genotypes observed in the entire study group persisted and became even more significant (adjusted P values of 0.009, 0.003, and 0.006, respectively).

Study 2

Baseline characteristics An independent sample of 3900 middle-aged Finnish men from the ongoing population-based study was studied. Of 3367 nondiabetic subjects (mean age 59.0 ± 5.8 yr; BMI 26.9 ± 3.8 kg/m²), 2405 subjects (71.4%) had NGT, 632 (18.8%) had IFG, and 330 (9.8%) had IGT. The 533 type 2 diabetic subjects were significantly older (61.1 ± 5.6 vs. 58.6 ± 5.8 yr; P < 0.001) and more obese (BMI 30.3 ± 5.4 vs. 26.4 ± 30.3 kg/m²; P < 0.001) than subjects with NGT.

Association of rs7754840 with type 2 diabetes First, we investigated the association of rs7754840 with type 2 diabetes in 2405 subjects with NGT and 533 subjects with type 2 diabetes. We observed a significant association of rs7754840 with type 2 diabetes under the recessive model [OR 1.346 (CI 1.044–1.120); P = 0.022], indicating a 1.3-fold higher risk in CC homozygotes than in carriers of the G allele. In the additive model, the effect was significant only when comparing GG and CC homozygotes [OR 1.422 (CI 1.072–1.882); P = 0.014] and remained significant also after the adjustment for age and BMI (P = 0.038). Under the dominant model, no significant association was observed. The frequency of the genotypes differed nominally between the subjects with NGT and subjects with type 2 diabetes (frequencies of the C allele were 0.37 vs. 0.41, respectively; P = 0.047). The genotype distribution in both groups followed the Hardy-Weinberg equilibrium.

OGTT data Next we investigated the effect of different genotypes on glucose and insulin levels in an OGTT and markers of insulin secretion and insulin resistance in subjects with NGT. In subjects with NGT, plasma glucose AUC did not differ significantly between the genotypes (GG vs. GC vs. CC: 821.4 ± 103.7 vs. 823.4 ± 104.8 vs. 827.1 ± 102.5 mmol/liter·min; P = 0.694). A significant difference in insulin AUC between the genotypes was observed (GG vs. GC vs. CC: 37.5 ± 25.8 vs. 33.9 ± 23.2 vs. 34.3 ± 23.3 pmol/liter·min x 10³; P < 0.001). After the adjustment for age, BMI, and HOMA-IR index, the effect remained significant (P_adjusted = 0.041). Furthermore, we observed an association of the C allele with significantly lower values of IGI (Fig. 3), being reduced by 9% in GC heterozygotes compared with GG homozygotes and by 2% in CC homozygotes compared with GC heterozygotes (P < 0.001; P_adjusted = 0.012). We performed statistical analyses also under the dominant model for IGI values, and the effect remained significant (P_adjusted = 0.004). Accordingly, the C allele was associated with reduced corrected insulin response to an oral glucose load (GG vs. GC vs. CC: 212.4 ± 261.5 vs. 198.4 ± 219.8 vs. 190.9 ± 167.3; P = 0.001; P_adjusted = 0.016). HOMA-IR differed significantly between the genotypes (GG vs. GC vs. CC: 1.85 ± 1.29 vs. 1.70 ± 1.13 vs. 1.69 ± 1.17; P = 0.010). Analyses of all nondiabetic subjects provided very similar results.

View larger version (23K): [in this window] [in a new window]   FIG. 3. Insulinogenic index values according to SNP rs7754840 in subjects with normal glucose tolerance from the replication sample of Finnish middle-aged men. P value is adjusted for age, BMI, and HOMA-IR and is calculated over the three genotype groups (ANOVA). Insulinogenic index and HOMA-IR values are log-transformed in statistical analysis. Black bars are for GG, striped bars for GC, and white bars for CC. Data are given as adjusted means ± SEM.

	Discussion

Top Abstract Introduction Subjects and Methods Results Discussion References

Association of SNPs of CDKAL1 with estimates of insulin release derived from an OGTT has been previously assessed in two studies (4, 5). In the Danish case-control Inter99 study, Steinthorsdottir et al. (4) reported that homozygous carriers of the risk allele of rs7756992 had an estimated 22% lower CIR to an oral glucose load than did the noncarriers (P = 3.5 x 10^–9). This effect was close to recessive (heterozygous carriers showed only 2%, nonsignificant, reduction of CIR compared with noncarriers) and was present in individuals with type 2 diabetes as well as controls. In the GWA scan of the DGI group (5), the risk C allele of rs7754840 was associated with reduced insulin secretion, measured as an IGI, in subjects without type 2 diabetes (P = 0.01). Both SNPs (rs7756992 and rs7754840) are located in intron 5 of CDKAL1, and a considerable linkage disequilibrium (LD) exists between them (r² = 0.677 according to HapMap-CEU). Our results agree with these findings. However, the effect of rs7754840 observed in our study was additive rather than recessive, because each copy of the C allele decreased the first-phase insulin release approximately by 12%. We confirmed the association of rs7754840 with CIR, which was decreased by 26% in CC homozygotes compared with GG homozygotes, and this effect was additive too. We also replicated the association of the C allele of rs7754840 with reduced values of IGI.

OGTT applied in previous studies (4, 5) does not allow us to estimate insulin sensitivity accurately, neither the first nor second phase of insulin secretion. We showed that the differences in insulin secretion between the genotypes of rs7754840 could be mainly due to impaired first-phase insulin release in carriers of the risk allele under the additive model. Moreover, the association of CDKAL1 with the disposition index suggests that CDKAL1 may influence the ability of the β-cells to compensate for insulin resistance. Several prospective studies have indicated that impaired first-phase insulin secretion is an independent predictor for the progression from normal or impaired glucose tolerance to type 2 diabetes (17, 18, 19). We also replicated our results in a large independent sample of either nondiabetic or normoglycemic Finnish men, in whom the C allele was associated with reduced markers of insulin secretion derived from an OGTT and also with increased risk of type 2 diabetes when comparing subjects with NGT and type 2 diabetes. Thus, our findings support the association of CDKAL1 gene with the risk of type 2 diabetes and provide a plausible mechanism explaining this association.

Mechanisms underlying the association of rs7754840 with impaired insulin secretion and the risk of type 2 diabetes are not fully understood. This SNP is located within large (90-kb) intron 5 in CDKAL1 gene and is in strong LD with nearby SNPs and in a complete LD with rs10946398 that has been also convincingly associated with type 2 diabetes in combined FUSION-DGI-WTCCC/UKT2D data (3). Considering the similarity of CDKAL1 protein product and CDK5RAP1 (CDK5 inhibitor), it is possible that CDKAL1 is involved in CDK5-mediated regulation of β-cell function. Inhibition of CDK5 activity seems to have a positive impact on insulin gene expression and secretion during glucotoxic conditions (20). Furthermore, in the study of Ubeda et al. (9), inhibition of CDK5 activity prevented the loss of insulin gene expression in an experimental model of glucotoxicity. However, additional studies are needed to fully elucidate the function of CDKAL1 in CDK5-mediated pathways in pancreatic β-cells.

The limitation of the present study is that our data include five different Caucasian populations, and thus, genetic differences between the populations (population stratification) might influence our results. However, we replicated the findings of study 1 in a large independent sample of Finns (study 2). Moreover, similar results have been previously reported in another large sample of Danes (4). Study 2 included only men, which might be a limitation of the study. On the other hand, there is no indication in previous studies that the association of CDKAL1 with type 2 diabetes or parameters of glucose metabolism is dependent on gender (3, 4, 5, 6).

In summary, our findings indicate that in a relatively large and metabolically well-characterized sample of offspring of type 2 diabetic patients, rs7754840 of CDKAL1 is related to impaired first-phase insulin release, thus providing a reliable explanation for the association between CDKAL1 and type 2 diabetes risk reported by recent GWA studies and replicated in our large population-based cohort of Finnish men.

	Acknowledgments

 
The EUGENE2 consortium (www.eugene2.com) consists of the laboratories of U. Smith (Sweden), M. Laakso (Finland), H. Häring (Germany), G. Sesti (Italy), O. Pedersen (Denmark), J. Zierath (Sweden), H.-G. Joost (Germany), F. Beguinot (Italy), E. Van Obberghen (France), J. Auwerx (France), F. Bosch (Spain), and P. Lind (Sweden).

	Footnotes

 
This study was supported by grants from the EUGENE2 project funded by the European Community (LSHM-CT-2004-512013) and The Kuopio University Hospital (EVO Grant 5207 to M.L.).

Disclosure Statement: All authors have nothing to declare.

First Published Online February 19, 2008

Abbreviations: AUC, Area under the curve; BMI, body mass index; CDK5, cyclin-dependent kinase 5; CDK5RAP1, CDK5 regulatory subunit-associated protein 1; CI, confidence interval; CIR, corrected insulin response; DGI, Diabetes Genetic Initiative; FUSION, Finland-United States Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics; GWA, genome-wide association; HOMA-IR, homeostasis model assessment for insulin resistance; IFG, impaired fasting glucose; IGI, insulinogenic index; IGT, impaired glucose tolerance; IVGTT, iv glucose tolerance test; LD, linkage disequilibrium; NGT, normal glucose tolerance; OGTT, oral glucose tolerance test; S_I, insulin sensitivity index; SNP, single-nucleotide polymorphism; UKT2D, United Kingdom Type 2 Diabetes Genetics Consortium; WTCCC, Wellcome Trust Case Control Consortium.

Received October 3, 2007.

Accepted February 8, 2008.

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