Post genome-wide association studies of novel genes associated with type 2 diabetes show gene-gene interaction and high predictive value

CDKAL1 gene rs7756992 A/G polymorphism and type 2 diabetes mellitus: a meta-analysis of 62,567 subjects

The Cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like (CDKAL1) gene rs7756992 A/G polymorphism has been suggested to be associated with type 2 diabetes mellitus (T2DM), but the individual studies results are still controversial. To explore the association of CDKAL1 gene rs7756992 A/G polymorphism with T2DM, a meta-analysis involving 62,567 subjects from 21 separate studies was conducted. In the whole population, a significant association was found between CDKAL1 gene rs7756992 A/G polymorphism and T2DM under allelic (OR: 1.180, 95% CI: 1.130–1.230, P = 1.60 × 10⁻¹⁴), recessive (OR: 1.510, 95% CI: 1.380–1.660, P = 8.41 × 10⁻¹⁸), dominant (OR: 1.175, 95% CI: 1.109–1.246, P = 6.30 × 10⁻⁸), homozygous (OR: 1.400, 95% CI: 1.282–1.530, P = 8.02 × 10⁻¹⁴), and heterozygous genetic models (OR: 1.101, 95% CI: 1.040–1.166, P = 0.001). CDKAL1 gene rs7756992 A/G polymorphism was significantly associated with T2DM. The person with G allele of CDKAL1 gene rs7756992 A/G polymorphism might be predisposed to T2DM.

Glucagon gene polymorphism modifies the effects of smoking and physical activity on risk of type 2 diabetes mellitus in Han Chinese

Few genome-wide association studies have considered interactions between multiple genetic variants and environmental factors associated with disease. The interaction was examined between a glucagon gene (GCG) polymorphism and smoking, alcohol consumption and physical activity and the association with risk of type 2 diabetes mellitus (T2DM) in a case-control study of Chinese Han subjects. The rs12104705 polymorphism of GCG and interactions with environmental variables were analyzed for 9619 participants by binary multiple logistic regression. Smoking with the C-C haplotype of rs12104705 was associated with increased risk of T2DM (OR=1.174, 95% CI=1.013-1.361). Moderate and high physical activity with the C-C genotype was associated with decreased risk of T2DM as compared with low physical activity with the genotype (OR=0.251, 95% CI=0.206-0.306 and OR=0.190, 95% CI=0.164-0.220). However, the interaction of drinking and genotype was not associated with risk of T2DM. Genetic polymorphism in rs12104705 of GCG may interact with smoking and physical activity to modify the risk of T2DM.

Predicting risk of type 2 diabetes mellitus with genetic risk models on the basis of established genome-wide association markers: a systematic review

This study aimed to evaluate the predictive performance of genetic risk models based on risk loci identified and/or confirmed in genome-wide association studies for type 2 diabetes mellitus. A systematic literature search was conducted in the PubMed/MEDLINE and EMBASE databases through April 13, 2012, and published data relevant to the prediction of type 2 diabetes based on genome-wide association marker-based risk models (GRMs) were included. Of the 1,234 potentially relevant articles, 21 articles representing 23 studies were eligible for inclusion. The median area under the receiver operating characteristic curve (AUC) among eligible studies was 0.60 (range, 0.55-0.68), which did not differ appreciably by study design, sample size, participants' race/ethnicity, or the number of genetic markers included in the GRMs. In addition, the AUCs for type 2 diabetes did not improve appreciably with the addition of genetic markers into conventional risk factor-based models (median AUC, 0.79 (range, 0.63-0.91) vs. median AUC, 0.78 (range, 0.63-0.90), respectively). A limited number of included studies used reclassification measures and yielded inconsistent results. In conclusion, GRMs showed a low predictive performance for risk of type 2 diabetes, irrespective of study design, participants' race/ethnicity, and the number of genetic markers included. Moreover, the addition of genome-wide association markers into conventional risk models produced little improvement in predictive performance.

Association between type 2 diabetes and rs10811661 polymorphism upstream of CDKN2A/B: a meta-analysis

To assess the association between type 2 diabetes and rs10811661 polymorphism, upstream of CDKN2A/B, a literature-based search was conducted to collect data. The pooled OR (odds ratio) and 95 % confidence intervals (CI) were used to assess the strength of association between rs10811661 polymorphism and type 2 diabetes. OR with 95 % CI were performed for allele contrasts, additive genetic model, dominant genetic model and recessive genetic model, respectively. The effect model was used if there was heterogeneity between studies. Funnel plots were used to predict publication bias. 17 studies with 29,990 cases and 40,977 controls were enrolled in this meta-analysis. Significant association was found in all of the four genetic models: allele contrast (OR = 1.21, 95 % CI 1.18-1.24), additive genetic model (OR = 1.51, 95 % CI 1.40-1.63), dominant genetic model (OR = 1.37, 95 % CI 1.28-1.47) and recessive genetic model (OR = 1.25, 95 % CI 1.21-1.29). The meta-analysis indicated that rs10811661 polymorphism was significantly associated with the risk of type 2 diabetes.

Added value of novel circulating and genetic biomarkers in type 2 diabetes prediction: a systematic review

AIMS

To provide a systematic overview of the added value of novel circulating and genetic biomarkers in predicting type 2 diabetes (T2DM).

METHODS

We searched MEDLINE and EMBASE (January 2000 to September 2012) for studies that reported a measure of improvement in the performance of T2DM risk prediction models subsequent to adding novel biomarkers to traditional risk factors. We extracted data on study methods and metrics of incremental predictive value of novel biomarkers.

RESULTS

We included 34 publications from 30 studies. All studies reported a change in the area under the receiver-operating characteristic curve, which was modest, ranging from -0.004 to 0.1, with claims of statistically significant improvements in eleven studies. The net reclassification index was evaluated in 11 studies, and ranged from -2.2% to 10.2% after inclusion of genetic markers in six studies (statistically significant in two cases), and from -0.5% to 27.5% after inclusion of non-genetic markers in five studies (non-significant in two studies). The integrated discrimination index (0-2.04) was reported in eight studies, being statistically significant in five of these.

CONCLUSIONS

Currently known novel circulating and genetic biomarkers do not substantially improve T2DM risk prediction above and beyond the ability of traditional risk factors.

ACE I/D and MTHFR C677T polymorphisms are significantly associated with type 2 diabetes in Arab ethnicity: a meta-analysis

In this meta-analysis study, SNPs were investigated for their association with type 2 diabetes (T2D) in both Arab and Caucasian ethnicities. A total of 55 SNPs were analyzed, of which 11 fulfilled the selection criteria, and were used for analysis. It was found that TCF7L2 rs7903146 was significantly associated with a pooled OR of 1.155 (95%C.I.=1.059-1.259), p<0.0001 and I(2)=78.30% among the Arab population, whereas among Caucasians, the pooled OR was 1.45 (95%C.I.=1.386-1.516), p<0.0001 and I(2)=77.20%. KCNJ11 rs5219 was significantly associated in both the populations with a pooled OR of 1.176(1.092-1.268), p<0.0001 and I(2)=32.40% in Caucasians and a pooled OR of 1.28(1.111-1.475), p=0.001 among Arabs. The ACE I/D polymorphism was found to be significantly associated with a pooled OR of 1.992 (95%C.I.=1.774-2.236), p<0.0001 and I(2)=83.20% among the Arab population, whereas among Caucasians, the pooled OR was 1.078 (95%C.I.=0.993-1.17), p=0.073 and I(2)=0%. Similarly, MTHFR C677T polymorphism was also found to be significantly associated among Arabs with a pooled OR of 1.924 (95%C.I.=1.606-2.304), p<0.0001 and I(2)=27.20%, whereas among Caucasians, the pooled OR was 0.986 (95%C.I.=0.868-1.122), p=0.835 and I(2)=0%. Meanwhile PPARG-2 Pro12Ala, CDKN2A/2B rs10811661, IGF2BP2 rs4402960, HHEX rs7923837, CDKAL1 rs7754840, EXT2 rs1113132 and SLC30A8 rs13266634 were found to have no significant association with T2D among Arabs. In conclusion, it seems from this study that both Arabs and Caucasians have different SNPs associated with T2D. Moreover, this study sheds light on the profound necessity for further investigations addressing the question of the genetic components of T2D in Arabs.

Experimental periodontitis induced by Porphyromonas gingivalis does not alter the onset or severity of diabetes in mice

BACKGROUND AND OBJECTIVE

Diabetes mellitus is believed to increase the risk and severity of periodontitis. However, less evidence is available on the converse effects of periodontitis on diabetes. The objective of the study was to investigate to what degree experimental periodontitis induced by Porphyromonas gingivalis might influence the onset and severity of diabetes in different mouse models.

MATERIAL AND METHODS

Twenty-eight male Tallyho/JngJ mice (type 2 diabetes), 20 male streptozotocin-induced diabetes C57BL/6J mice (type 1 diabetes) and 20 male C57BL/6J mice at 4 wks of age were evenly divided into two groups: periodontal infection and sham infection. Periodontitis was induced by Porphyromonas gingivalis W50 (P. gingivalis) oral inoculation before the development of diabetes. Sham-infected mice received vehicle as control. P. gingivalis in the oral cavity were identified by quantitative polymerase chain reaction. Fasting glucose, body weight and food intake levels were monitored and glucose tolerance tests were performed to assess glucose homeostasis for the onset and progression of diabetes. The level of alveolar bone loss and tumor necrosis factor-alpha were determined in week 20 when mice were killed.

RESULTS

Mice in the infection groups developed more alveolar bone loss than those in sham-infection groups (Tallyho p = 0.021; C57-STZ p = 0.014; C57 p = 0.035). Hyperglycemic mice exhibited significantly more bone loss compared to those normal glucose mice (Tallyho vs. C57 p = 0.029; C57-STZ vs. C57 p = 0.024). The level of tumor necrosis factor-alpha was consistent with that of periodontal bone loss and hyperglycemia. There was no significant effect of mouse species on the amount of bone loss at the same level of blood glucose. No statistically significant difference or trend in glucose metabolism was found between the infection and sham-infection group.

CONCLUSION

Diabetes enhanced the risk for periodontal disease induced by P. gingivalis. However, no converse impact was found between this periodontal infection and onset and severity of diabetes in both type 1 and 2 diabetes mice.

Genome-wide meta-analysis of genetic susceptible genes for Type 2 Diabetes

Background

Many genetic studies, including single gene studies and Genome-wide association studies (GWAS), aim to identify risk alleles for genetic diseases such as Type II Diabetes (T2D). However, in T2D studies, there is a significant amount of the hereditary risk that cannot be simply explained by individual risk genes. There is a need for developing systems biology approaches to integrate comprehensive genetic information and provide new insight on T2D biology.

Methods

We performed comprehensive integrative analysis of Single Nucleotide Polymorphisms (SNP's) individually curated from T2D GWAS results and mapped them to T2D candidate risk genes. Using protein-protein interaction data, we constructed a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. We then studied the relationship between these T2D genes and curated gene sets.

Results

We determined that T2D candidate risk genes are concentrated in certain parts of the genome, specifically in chromosome 20. Using the T2D genetic network, we identified highly-interconnected network "hub" genes. By incorporating T2D GWAS results, T2D pathways, and T2D genes' functional category information, we further ranked T2D risk genes, T2D-related pathways, and T2D-related functional categories. We found that highly-interconnected T2D disease network “hub” genes most highly associated to T2D genetic risks to be PI3KR1, ESR1, and ENPP1. The well-characterized TCF7L2, contractor to our expectation, was not among the highest-ranked T2D gene list. Many interacted pathways play a role in T2D genetic risks, which includes insulin signalling pathway, type II diabetes pathway, maturity onset diabetes of the young, adipocytokine signalling pathway, and pathways in cancer. We also observed significant crosstalk among T2D gene subnetworks which include insulin secretion, regulation of insulin secretion, response to peptide hormone stimulus, response to insulin stimulus, peptide secretion, glucose homeostasis, and hormone transport. Overview maps involving T2D genes, gene sets, pathways, and their interactions are all reported.

Conclusions

Large-scale systems biology meta-analyses of GWAS results can improve interpretations of genetic variations and genetic risk factors. T2D genetic risks can be attributable to the summative genetic effects of many genes involved in a broad range of signalling pathways and functional networks. The framework developed for T2D studies may serve as a guide for studying other complex diseases.

Quantitative assessment of the variation in IGF2BP2 gene and type 2 diabetes risk

Insulin-like growth factor 2 mRNA-binding protein 2 (IFG2BP2) belongs to an mRNA-binding protein family involved in the development and stimulation of insulin action, which has attracted considerable attention as a candidate gene for type 2 diabetes (T2D) since it was first identified through genome-wide association approach. The relationship between IFG2BP2 and T2D has been reported in various ethnic groups; however, these studies have yielded contradictory results. To investigate this inconsistency, we performed a meta-analysis of 35 studies involving a total of 175,965 subjects for two wildly studied polymorphisms (rs4402960 and rs1470579) of the IFG2BP2 to evaluate the effect of IFG2BP2 on genetic susceptibility for T2D. An overall random-effects per-allele OR of 1.13 (95% CI: 1.12-1.15; P < 10(-5)) and 1.09 (95% CI: 1.07-1.12; P < 10(-5)) was found for the two variants, respectively. Significant results were also observed using dominant or recessive genetic model. No significant results between study heterogeneity were found in most of the comparison. In the subgroup analysis by ethnicity, sample size, diagnostic criterion and mean age and BMI of cases, significantly increased risks were found for these polymorphisms in almost all genetic models. This meta-analysis demonstrated that these two common polymorphisms is a risk factor for developing T2D, but these associations vary in different ethnic populations.

Genetic prediction of common diseases. Still no help for the clinical diabetologist!

Genome-wide association studies (GWAS) have identified several loci associated with many common, multifactorial diseases which have been recently used to market genetic testing directly to the consumers. We here addressed the clinical utility of such GWAS-derived genetic information in predicting type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD) in diabetic patients. In addition, the development of new statistical approaches, novel technologies of genome sequencing and ethical, legal and social aspects related to genetic testing have been also addressed. Available data clearly show that, similarly to what reported for most common diseases, genetic testing offered today by commercial companies cannot be used as predicting tools for T2DM and CAD. Further studies taking into account the complex interaction between genes as well as between genetic and non-genetic factors, including age, obesity and glycemic control which seem to modify genetic effects on the risk of T2DM and CAD, might mitigate such negative conclusions. Also, addressing the role of relatively rare variants by next generation sequencing may help identify novel and strong genetic markers with an important role in genetic prediction. Finally, statistical tools concentrated on reclassifying patients might be a useful application of genetic information for predicting many common diseases. By now, prediction of such diseases, including those of interest for the clinical diabetologist, have to be pursued by using traditional clinical markers which perform well and are not costly.

IGF2BP2 alternative variants associated with glutamic acid decarboxylase antibodies negative diabetes in Malaysian subjects

Background

The association of Insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) common variants (rs4402960 and rs1470579) with type 2 diabetes (T2D) has been performed in different populations. The aim of this study was to evaluate the association of alternative variants of IGF2BP2; rs6777038, rs16860234 and rs7651090 with glutamic acid decarboxylase antibodies (GADA) negative diabetes in Malaysian Subjects.

Methods/Principal Findings

IGF2BP2; rs6777038, rs16860234 and rs7651090 single nucleotide polymorphisms (SNPs) were genotyped in 1107 GADA negative diabetic patients and 620 control subjects of Asian from Malaysia. The additive genetic model adjusted for age, race, gender and BMI showed that alternative variants; rs6777038, rs16860234 and rs7651090 of IGF2BP2 associated with GADA negative diabetes (OR = 1.21; 1.36; 1.35, P = 0.03; 0.0004; 0.0002, respectively). In addition, the CCG haplotype and diplotype CCG-TCG increased the risk of diabetes (OR = 1.51, P = 0.01; OR = 2.36, P = 0.009, respectively).

Conclusions/Significance

IGF2BP2 alternative variants were associated with GADA negative diabetes. The IGF2BP2 haplotypes and diplotypes increased the risk of diabetes in Malaysian subject.

Genetics of type 2 diabetes in European populations

Type 2 diabetes (T2D) has become a leading health problem throughout the world. It is caused by environmental and genetic factors, as well as interactions between the two. However, until very recently, the T2D susceptibility genes have been poorly understood. During the past 5 years, with the advent of genome-wide association studies (GWAS), a total of 58 T2D susceptibility loci have been associated with T2D risk at a genome-wide significance level (P < 5 × 10(-8) ), with evidence showing that most of these genetic variants influence pancreatic β-cell function. Most novel T2D susceptibility loci were identified through GWAS in European populations and later confirmed in other ethnic groups. Although the recent discovery of novel T2D susceptibility loci has contributed substantially to our understanding of the pathophysiology of the disease, the clinical utility of these loci in disease prediction and prognosis is limited. More studies using multi-ethnic meta-analysis, gene-environment interaction analysis, sequencing analysis, epigenetic analysis, and functional experiments are needed to identify new susceptibility T2D loci and causal variants, and to establish biological mechanisms.

Genetic risk score constructed using 14 susceptibility alleles for type 2 diabetes is associated with the early onset of diabetes and may predict the future requirement of insulin injections among Japanese individuals

OBJECTIVE

We evaluated the clinical usefulness of a genetic risk score (GRS) based on 14 well-established variants for type 2 diabetes.

RESEARCH DESIGN AND METHODS

We analyzed 14 SNPs at HHEX, CDKAL1, CDKN2B, SLC30A8, KCNJ11, IGF2BP2, PPARG, TCF7L2, FTO, KCNQ1, IRS-1, GCKR, UBE2E2, and C2CD4A/B in 1,487 Japanese individuals (724 patients with type 2 diabetes and 763 control subjects). A GRS was calculated according to the number of risk alleles by counting all 14 SNPs (T-GRS) as well as 11 SNPs related to β-cell function (β-GRS) and then assessing the association between each GRS and the clinical features.

RESULTS

Among the 14 SNPs, 4 SNPs were significantly associated with type 2 diabetes in the present Japanese sample (P < 0.0036). The T-GRS was significantly associated with type 2 diabetes (P = 5.9 × 10(-21)). Among the subjects with type 2 diabetes, the β-GRS was associated with individuals receiving insulin therapy (β = 0.0131, SE = 0.006, P = 0.0431), age at diagnosis (β = -0.608, SE = 0.204, P = 0.0029), fasting serum C-peptide level (β = -0.032, SE = 0.0140, P = 0.022), and C-peptide index (β = -0.031, SE = 0.012, P = 0.0125).

CONCLUSIONS

Our data suggest that the β-GRS is associated with reduced β-cell functions and may be useful for selecting patients who should receive more aggressive β-cell-preserving therapy.

Contribution of common variants of ENPP1, IGF2BP2, KCNJ11, MLXIPL, PPARγ, SLC30A8 and TCF7L2 to the risk of type 2 diabetes in Lebanese and Tunisian Arabs

BACKGROUND

While several type 2 diabetes mellitus (T2DM) susceptibility loci identified through genome-wide association studies (GWAS) have been replicated in many populations, their association in Arabs has not been reported. For this reason, the present study looked at the contribution of ENNP1 (rs1044498), IGF2BP2 (rs1470579), KCNJ11 (rs5219), MLXIPL (rs7800944), PPARγ (rs1801282), SLC30A8 (rs13266634) and TCF7L2 (rs7903146) SNPs to the risk of T2DM in Lebanese and Tunisian Arabs.

METHODS

Study subjects (case/controls) were Lebanese (751/918) and Tunisians (1470/838). Genotyping was carried out by the allelic discrimination method.

RESULTS

In Lebanese and Tunisians, neither ENNP1 nor MLXIPL was associated with T2DM, whereas TCF7L2 was significantly associated with an increased risk of T2DM in both the Lebanese [P < 0.001; OR (95% CI): 1.38 (1.20-1.59)] and Tunisians [P < 0.001; OR (95% CI): 1.36 (1.18-1.56)]. Differential associations of IGF2BP2, KCNJ11, PPARγ and SLC30A8 with T2DM were noted in the two populations. IGF2BP2 [P = 1.3 × 10(-5); OR (95% CI): 1.66 (1.42-1.94)] and PPARγ [P = 0.005; OR (95% CI): 1.41 (1.10-1.80)] were associated with T2DM in the Lebanese, but not Tunisians, while KCNJ11 [P = 8.0 × 10(-4); OR (95% CI): 1.27 (1.09-1.47)] and SLC30A8 [P = 1.6 × 10(-5); OR (95% CI): 1.37 (1.15-1.62)] were associated with T2DM in the Tunisians, but not Lebanese, after adjusting for gender and body mass index.

CONCLUSION

T2DM susceptibility loci SNPs identified through GWAS showed differential associations with T2DM in two Arab populations, thus further confirming the ethnic contributions of these variants to T2DM susceptibility.

An improved PSO algorithm for generating protective SNP barcodes in breast cancer

Background

Possible single nucleotide polymorphism (SNP) interactions in breast cancer are usually not investigated in genome-wide association studies. Previously, we proposed a particle swarm optimization (PSO) method to compute these kinds of SNP interactions. However, this PSO does not guarantee to find the best result in every implement, especially when high-dimensional data is investigated for SNP–SNP interactions.

Methodology/Principal Findings

In this study, we propose IPSO algorithm to improve the reliability of PSO for the identification of the best protective SNP barcodes (SNP combinations and genotypes with maximum difference between cases and controls) associated with breast cancer. SNP barcodes containing different numbers of SNPs were computed. The top five SNP barcode results are retained for computing the next SNP barcode with a one-SNP-increase for each processing step. Based on the simulated data for 23 SNPs of six steroid hormone metabolisms and signalling-related genes, the performance of our proposed IPSO algorithm is evaluated. Among 23 SNPs, 13 SNPs displayed significant odds ratio (OR) values (1.268 to 0.848; p<0.05) for breast cancer. Based on IPSO algorithm, the jointed effect in terms of SNP barcodes with two to seven SNPs show significantly decreasing OR values (0.84 to 0.57; p<0.05 to 0.001). Using PSO algorithm, two to four SNPs show significantly decreasing OR values (0.84 to 0.77; p<0.05 to 0.001). Based on the results of 20 simulations, medians of the maximum differences for each SNP barcode generated by IPSO are higher than by PSO. The interquartile ranges of the boxplot, as well as the upper and lower hinges for each n-SNP barcode (n = 3∼10) are more narrow in IPSO than in PSO, suggesting that IPSO is highly reliable for SNP barcode identification.

Conclusions/Significance

Overall, the proposed IPSO algorithm is robust to provide exact identification of the best protective SNP barcodes for breast cancer.

Strong association of common variants in the IGF2BP2 gene with type 2 diabetes in Lebanese Arabs

OBJECTIVE

Several genome-wide association studies and replication analyses have identified common variation at the insulin-like binding protein 2 (IGF2BP2) gene to be associated with type 2 diabetes (T2DM). The aim of this study was to replicate in a Lebanese Arab population identified associations of IGF2BP2 variants rs4402960 and rs1470579 with T2DM.

METHODS

This case-control study involved 544 T2DM patients and 606 control subjects. Genotyping was done by the allelic exclusion method.

RESULTS

T allele of rs440960 (P=6.5 × 10(-6)) and C allele of rs1470579 (P=5.3 × 10(-4)) were significantly associated with T2DM; both SNPs were in strong LD (D'=0.83, r(2)=0.58). While both IGF2BP2 SNPs were significantly associated with T2DM under additive and recessive models, only rs4402960 remained significantly associated with T2DM under the dominant model. Taking the common rs4402960/rs1470579 GA haplotype as reference, multivariate analysis confirmed the positive association of TC (P=0.009; OR, 1.43; 95%CI, 1.09-1.87), and TA (P<0.001; OR=5.49; 95%CI=2.09-14.39) haplotypes with increased T2DM risk. These differences remained significant after applying the Bonferroni correction for multiple testing.

CONCLUSION

We validate that IGF2BP2 susceptibility variants rs4402960 and rs1470579 associate with T2DM in Lebanese Arabs.

Disturbed zinc homeostasis in diabetic patients by in vitro and in vivo analysis of insulinomimetic activity of zinc

Disturbances of zinc homeostasis have been observed in several diseases, including diabetes mellitus. To further characterize the association between zinc and diabetes, we recruited 75 patients with type 1 or type 2 diabetes and 75 nondiabetic sex-/age-matched control subjects in order to analyze differences concerning human zinc transporter 8 (hZnT-8) expression, single nucleotide polymorphisms (SNPs) in the genes of hZnT-8 as well as metallothionein 1A and serum/intracellular zinc. Furthermore, we investigated the relation between insulin and zinc homeostasis in type 2 diabetic subjects and consolidated our results by in vitro analysis of the effect of insulin on cellular zinc status and by analysis of the modulation of insulin signal transduction by intracellular zinc homeostasis. Concerning the expression of hZnT-8 and the SNPs analyzed, we did not observe any differences between diabetic and control subjects. Serum zinc was significantly lower in diabetic patients compared to controls, and intracellular zinc showed the same tendency. Interestingly, type 2 diabetes patients treated with insulin displayed lower serum zinc compared to those not injecting insulin. In vitro analyses showed that insulin leads to an increase in intracellular zinc and that insulin signaling was enhanced by elevated intracellular zinc concentrations. In conclusion, we show that type 1 and type 2 diabetic patients suffer from zinc deficiency, and our results indicate that zinc supplementation may qualify as a potential treatment adjunct in type 2 diabetes by promoting insulin signaling, especially in zinc-deficient subjects.

Association analysis of 31 common polymorphisms with type 2 diabetes and its related traits in Indian sib pairs

AIMS/HYPOTHESIS

Evaluation of the association of 31 common single nucleotide polymorphisms (SNPs) with fasting glucose, fasting insulin, HOMA-beta cell function (HOMA-β), HOMA-insulin resistance (HOMA-IR) and type 2 diabetes in the Indian population.

METHODS

We genotyped 3,089 sib pairs recruited in the Indian Migration Study from four cities in India (Lucknow, Nagpur, Hyderabad and Bangalore) for 31 SNPs in 24 genes previously associated with type 2 diabetes in European populations. We conducted within-sib-pair analysis for type 2 diabetes and its related quantitative traits.

RESULTS

The risk-allele frequencies of all the SNPs were comparable with those reported in western populations. We demonstrated significant associations of CXCR4 (rs932206), CDKAL1 (rs7756992) and TCF7L2 (rs7903146, rs12255372) with fasting glucose, with β values of 0.007 (p = 0.05), 0.01 (p = 0.01), 0.007 (p = 0.05), 0.01 (p = 0.003) and 0.08 (p = 0.01), respectively. Variants in NOTCH2 (rs10923931), TCF-2 (also known as HNF1B) (rs757210), ADAM30 (rs2641348) and CDKN2A/B (rs10811661) significantly predicted fasting insulin, with β values of -0.06 (p = 0.04), 0.05 (p = 0.05), -0.08 (p = 0.01) and -0.08 (p = 0.02), respectively. For HOMA-IR, we detected associations with TCF-2, ADAM30 and CDKN2A/B, with β values of 0.05 (p = 0.04), -0.07 (p = 0.03) and -0.08 (p = 0.02), respectively. We also found significant associations of ADAM30 (β = -0.05; p = 0.01) and CDKN2A/B (β = -0.05; p = 0.03) with HOMA-β. THADA variant (rs7578597) was associated with type 2 diabetes (OR 1.5; 95% CI 1.04, 2.22; p = 0.03).

CONCLUSIONS/INTERPRETATION

We validated the association of seven established loci with intermediate traits related to type 2 diabetes in an Indian population using a design resistant to population stratification.

Replication study of common variants in CDKAL1 and CDKN2A/2B genes associated with type 2 diabetes in Lebanese Arab population

We investigated the association of CDKAL1 (rs7754840 and rs7756992) and CDKN2A/2B (rs10811661) variants with T2DM. Higher MAF of rs7754840 and rs7756992 were seen in patients, and both were associated with T2DM under additive, dominant, and recessive models. CDKAL1 rs7754840 and rs7756992, but not CDKN2A/2B rs10811661, are associated with T2DM in Lebanese.

Genes related to diabetes may be associated with pancreatic cancer in a population-based case-control study in Minnesota

OBJECTIVES

Type 2 diabetes is associated with increased pancreatic cancer risk; however, the nature of this relationship is not clear. We examined the link between 10 diabetes-related single-nucleotide polymorphisms and pancreatic cancer in a case-control study conducted in 1994 to 1998.

METHODS

Cases (n = 162) were ascertained from hospitals in the Twin Cities and Mayo Clinic, Minn. Controls (n = 540) from the general population were frequency matched by age, sex, and race. Unconditional logistic regression provided odds ratios of pancreatic cancer and 95% confidence intervals (95% CIs).

RESULTS

In a multivariate-adjusted model, a significant association was observed only for rs780094 in the glucokinase regulator (GCKR) gene: odds ratios for pancreatic cancer were 1.00 for TT, 1.35 (95% CI, 0.71-2.58) for CT, and 2.14 (95% CI, 1.12-4.08) for CC genotypes (P trend = 0.01) and did not change after the adjustment for diabetes.

CONCLUSIONS

This study provides the first evidence that GCKR rs780094, a single-nucleotide polymorphism related to diabetes, may be associated with pancreatic cancer risk. Although the results from this analysis are preliminary, there is a biologic plausibility for such an association.

A methodological perspective on genetic risk prediction studies in type 2 diabetes: recommendations for future research

Fueled by the successes of genome-wide association studies, numerous studies have investigated the predictive ability of genetic risk models in type 2 diabetes. In this paper, we review these studies from a methodological perspective, focusing on the variables included in the risk models as well as the study designs and populations investigated. We argue and show that differences in study design and characteristics of the study population have an impact on the observed predictive ability of risk models. This observation emphasizes that genetic risk prediction studies should be conducted in those populations in which the prediction models will ultimately be applied, if proven useful. Of all genetic risk prediction studies to date, only a few were conducted in populations that might be relevant for targeting preventive interventions.

IGF2BP2 genetic variation and type 2 diabetes: a global meta-analysis

Insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) is involved in the stimulation of insulin action. Polymorphisms in the IGF2BP2 gene have been analyzed in numerous studies to assess the type 2 diabetes (T2D) risk attributed to these variants, but results are conflicting. To better understand the effect of rs4402960 polymorphism on T2D risk, we performed a comprehensive meta-analysis that included 35 published studies involving 70,261 cases and 100,567 controls. The relatively infrequent T variant was significantly associated with T2D with a per-allele odds ratio (OR) of 1.14 (95% confidence interval (CI): 1.12-1.16; p<10(-5)). Significant results were also observed for heterozygous (OR=1.17, 95% CI: 1.14-1.20; p<10(-5)) and homozygous (OR=1.23, 95% CI: 1.16-1.30; p<10(-5)) compared with wild type. In the subgroup analysis by ethnicity, significantly increased risks were found in East Asian, Caucasian and Indian populations. However, no significant associations were detected among other ethnicities. In the stratified analysis according to sample size, diagnostic criterion, mean body mass index, and age of cases significantly increased risks for the polymorphism were found in all genetic models. In conclusion, this meta-analysis suggests that rs4402960 polymorphism in IGF2BP2 is associated with elevated T2D risk, but these associations vary in different ethnic populations.

The carriage of risk variants of CDKAL1 impairs beta-cell function in both diabetic and non-diabetic patients and reduces response to non-sulfonylurea and sulfonylurea agonists of the pancreatic KATP channel

On chromosome 6q22.3, a cluster of single-nucleotide polymorphisms located in intron 5 of the cyclin-dependent kinase 5 (CDK5) regulatory subunit-associated protein 1-like 1 (CDKAL1) gene were shown to confer susceptibility to type 2 diabetes in multiple ethnic groups. The diabetogenic role of CDKAL1 variants is suggested to consist in lower insulin secretion probably due to the insufficient inhibition of the CDK5 activity. In this study, we assessed the association of several SNPs of CDKAL1 with T2D in 772 Russian affected patients and 773 normoglycemic controls using a Taqman-based allelic discrimination assay. We showed association of the minor allele C of rs10946398 (Odds Ratio (OR) = 1.21, 95% CI = 1.04-1.4, P = 0.016), allele C of rs7754840 (OR = 1.18, 95% CI = 1.01-1.37, P = 0.038), and allele G of rs7756992 (OR = 1.21, 95% CI = 1.04-1.42, P = 0.017) with higher diabetes risk thereby replicating the predisposing role of CDKAL1 in etiology of T2D. These alleles contribute to three haplotypes (CCA, CGG, and CCG) related to higher diabetes risk (OR = 1.48, 2.12, and 1.95). Combinations of these haplotypes between each other form the group of high-risk haplogenotypes whose carriers had decreased HOMA-β compared to other CDKAL1 variants in both diabetic (38.6 ± 19.3 vs. 48.2 ± 21.2, P(adjusted) = 0.019-0.044) and non-diabetic (91.8 ± 42.1 vs. 108 ± 47.2, P(adjusted) = 0.0054-0.01) patients. The carriage of the risk haplogenotypes of CDKAL1 was associated with reduced response to non-sulfonylurea and sulfonylurea agonists of the pancreatic KATP channel. These data suggest that CDKAL1 is involved in the pathogenesis of T2D through impaired beta-cell function.

Association between IGF2BP2 rs4402960 polymorphism and risk of type 2 diabetes mellitus: a meta-analysis

BACKGROUND AND AIMS

Genome-wide association studies recently found IGF2BP2 rs4402960 polymorphism associated with enhanced risk of type 2 diabetes mellitus (T2DM). Numerous studies have been published to replicate the association. However, results were inconsistent and inconclusive. To clarify the relationship of IGF2BP2 rs4402960 polymorphism and T2DM, we conducted this meta-analysis.

METHODS

We searched PubMed for all eligible studies up to February 2011. Forty eight independent study groups from 28 case-control studies and two prospective studies were identified. Pooled odds ratio (OR) and 95% confidential interval (95% CI) were adopted to evaluate the association.

RESULTS

The pooled results indicated that the rs4402960 polymorphism of the IGF2BP2 gene was related to increased risk of T2DM for T allele vs. G allele (OR = 1.13, 95% CI 1.11-1.15) under additive genetic model. Significant associations were also found under dominant (OR = 1.17, 95% CI 1.14-1.20) and recessive (OR = 1.20, 95% CI 1.15-1.25) genetic models. There was no significant heterogeneity among all studies. Subgroup analyses stratified by ethnicity showed that significant increased risks were observed in European, East Asian and South Asian populations, and the effect sizes were similar. For Africans, no significant association was detected under all genetic models.

CONCLUSIONS

Our meta-analysis suggested that IGF2BP2 rs4402960 polymorphism conferred elevated risk of T2DM, especially in European, East Asian and South Asian populations.

Annotating individual human genomes

Advances in DNA sequencing technologies have made it possible to rapidly, accurately and affordably sequence entire individual human genomes. As impressive as this ability seems, however, it will not likely amount to much if one cannot extract meaningful information from individual sequence data. Annotating variations within individual genomes and providing information about their biological or phenotypic impact will thus be crucially important in moving individual sequencing projects forward, especially in the context of the clinical use of sequence information. In this paper we consider the various ways in which one might annotate individual sequence variations and point out limitations in the available methods for doing so. It is arguable that, in the foreseeable future, DNA sequencing of individual genomes will become routine for clinical, research, forensic, and personal purposes. We therefore also consider directions and areas for further research in annotating genomic variants.

New type 2 diabetes risk genes provide new insights in insulin secretion mechanisms

Type 2 diabetes results from the inability of beta cells to increase insulin secretion sufficiently to compensate for insulin resistance. Insulin resistance is thought to result mainly from environmental factors, such as obesity. However, there is compelling evidence that the decline of both insulin sensitivity and insulin secretion have also a genetic component. Recent genome-wide association studies identified several novel risk genes for type 2 diabetes. The vast majority of these genes affect beta cell function by molecular mechanisms that remain unknown in detail. Nevertheless, we and others could show that a group of genes affect glucose-stimulated insulin secretion, a group incretin-stimulated insulin secretion (incretin sensitivity or secretion) and a group proinsulin-to-insulin conversion. The most important so far type 2 diabetes risk gene, TCF7L2, interferes with all three mechanisms. In addition to advancing knowledge in the pathophysiology of type 2 diabetes, the discovery of novel genetic determinants of diabetes susceptibility may help understanding of gene-environment, gene-therapy and gene-gene interactions. It was also hoped that it could make determination of the individual risk for type 2 diabetes feasible. However, the allelic relative risks of most genetic variants discovered so far are relatively low. Thus, at present, clinical criteria assess the risk for type 2 diabetes with greater sensitivity and specificity than the combination of all known genetic variants.

SLC30A8 polymorphism and type 2 diabetes risk: evidence from 27 study groups

BACKGROUND AND AIMS

Intense research has been performed to identify the genetic risk factors in type 2 diabetes, and a single nucleotide polymorphism (SNP) in SLC30A8 (rs13266634) was reported to be associated with type 2 diabetes mellitus. However, published data on the association between SLC30A8 polymorphism and the risk of type 2 diabetes were inconsistent. Therefore, we conducted this meta-analysis to derive a more precise estimation of the relationship.

METHODS AND RESULTS

We searched PubMed through October 2009 to identify all relevant papers. Odds ratios (ORs) and 95% confidence intervals (CIs) were extracted under an additive genetic model. In the current meta-analysis, we identified a total of 27 groups including 42,609 cases and 69,564 controls. In analyses of the case-control studies by ethnicity, the results indicated that SLC30A8 polymorphism was related to elevate risks of type 2 diabetes both in Europeans (OR=1.15, 95% CI 1.11-1.18, P<0.001) and Asians (OR=1.15, 95% CI 1.11-1.19, P<0.001). Next, we separated hospital-based case-control studies from population-based case-control studies, however, there was no apparent difference between population-based case-control study groups (OR=1.15, 95% CI 1.12-1.17, P<0.001) and hospital-based case-control study groups (OR=1.16, 95% CI 1.07-1.25, P<0.001).

CONCLUSION

Our present meta-analysis provided evidence that SLC30A8 (rs13266634) C allele carriers could elevate the risk of type 2 diabetes, especially in Europeans and Asians.

Association between rs13266634 C/T polymorphisms of solute carrier family 30 member 8 (SLC30A8) and type 2 diabetes, impaired glucose tolerance, type 1 diabetes--a meta-analysis

AIMS

To investigate the association of solute carrier family 30 member 8 (SLC30A8) rs13266634 C/T polymorphism with type 2 diabetes (T2DM), impaired glucose tolerance (IGT), and type 1 diabetes (T1DM).

METHODS

We searched all the publications about the association between SLC30A8 and diabetes from PubMed, and evaluated the association between SLC30A8 rs13266634 C/T polymorphism and T2DM, IGT and T1DM, respectively, by meta-analysis of all the validated studies. Allelic and genotypic comparisons between cases and controls were evaluated.

RESULTS

Thirty six studies were included in the meta-analysis: 31 studies were analysed for rs13266634 C/T polymorphism with T2DM, 3 studies with IGT and 4 studies with T1DM. The pooled odds ratios (ORs) for allelic and genotypic comparisons (including additive model, co-dominant model, dominant model and recessive model) showed that rs13266634 C/T polymorphism was significantly associated with increased T2DM risk: OR=1.15, 95% confidence interval (CI)=1.13-1.17, P<0.001, P(heterogeneity)=0.041, OR=1.34, 95% CI=1.26-1.41, P<0.001, P(heterogeneity)=0.908, OR=1.20, 95% CI=1.16-1.24, P<0.001, P(heterogeneity)=0.699, and OR=1.23, 95% CI=1.17-1.30, P<0.001, P(heterogeneity)=0.801, respectively. In subgroup analyses, we found that rs13266634 C/T polymorphism was associated with T2DM risk both in Asian and European subgroup (P<0.001), but not in African (P>0.05). And the pooled odds ratio (OR) for allelic frequency comparison showed that rs13266634 C/T polymorphism was also significantly associated with IGT: OR=1.15, 95% CI=1.06-1.26, P<0.001, P(heterogeneity)=0.364. Meanwhile, our meta-analysis did not suggest that rs13266634 C/T polymorphism was associated with T1DM risk (P>0.05): OR=1.02, 95% CI=0.98-1.06, P=0.328, P(heterogeneity)=0.488 for allelic frequency comparison.

CONCLUSIONS

Our meta-analysis results revealed the significant association between rs13266634 C/T polymorphism and T2DM and IGT, but did not support the association between this polymorphism and T1DM.

Genetics of Type 2 Diabetes

BACKGROUND

Type 2 diabetes (T2D) is a complex disorder that is affected by multiple genetic and environmental factors. Extensive efforts have been made to identify the disease-affecting genes to better understand the disease pathogenesis, find new targets for clinical therapy, and allow prediction of disease.

CONTENT

Our knowledge about the genes involved in disease pathogenesis has increased substantially in recent years, thanks to genomewide association studies and international collaborations joining efforts to collect the huge numbers of individuals needed to study complex diseases on a population level. We have summarized what we have learned so far about the genes that affect T2D risk and their functions. Although more than 40 loci associated with T2D or glycemic traits have been reported and reproduced, only a minor part of the genetic component of the disease has been explained, and the causative variants and affected genes are unknown for many of the loci.

SUMMARY

Great advances have recently occurred in our understanding of the genetics of T2D, but much remains to be learned about the disease etiology. The genetics of T2D has so far been driven by technology, and we now hope that next-generation sequencing will provide important information on rare variants with stronger effects. Even when variants are known, however, great effort will be required to discover how they affect disease risk.

Genetic variants of diabetes risk and incident cardiovascular events in chronic coronary artery disease

Objective

To determine whether information from genetic risk variants for diabetes is associated with cardiovascular events incidence.

Methods

From the about 30 known genes associated with diabetes, we genotyped single-nucleotide polymorphisms at the 10 loci most associated with type-2 diabetes in 425 subjects from the MASS-II Study, a randomized study in patients with multi-vessel coronary artery disease. The combined genetic information was evaluated by number of risk alleles for diabetes. Performance of genetic models relative to major cardiovascular events incidence was analyzed through Kaplan-Meier curve comparison and Cox Hazard Models and the discriminatory ability of models was assessed for cardiovascular events by calculating the area under the ROC curve.

Results

Genetic information was able to predict 5-year incidence of major cardiovascular events and overall-mortality in non-diabetic individuals, even after adjustment for potential confounders including fasting glycemia. Non-diabetic individuals with high genetic risk had a similar incidence of events then diabetic individuals (cumulative hazard of 33.0 versus 35.1% of diabetic subjects). The addition of combined genetic information to clinical predictors significantly improved the AUC for cardiovascular events incidence (AUC = 0.641 versus 0.610).

Conclusions

Combined information of genetic variants for diabetes risk is associated to major cardiovascular events incidence, including overall mortality, in non-diabetic individuals with coronary artery disease.

Clinical Trial Registration Information

Medicine, Angioplasty, or Surgery Study (MASS II). Unique identifier: ISRCTN66068876 URL.

Genetic risk profiling for prediction of type 2 diabetes

Type 2 diabetes (T2D) is a common disease caused by a complex interplay between many genetic and environmental factors. Candidate gene studies and recent collaborative genome-wide association efforts revealed at least 38 common single nucleotide polymorphisms (SNPs) associated with increased risk of T2D. Genetic testing of multiple SNPs is considered a potentially useful tool for early detection of individuals at high diabetes risk leading to improved targeting of preventive interventions.

Prediction model for knee osteoarthritis based on genetic and clinical information

INTRODUCTION

Osteoarthritis (OA) is the most common bone and joint disease influenced by genetic and environmental factors. Recent association studies have uncovered the genetic factors behind OA, its susceptibility genes, which would enable us to predict disease occurrence based on genotype information. However, most previous studies have evaluated the effects of only a single susceptibility gene, and hence prediction based on such information is not as reliable. Here, we constructed OA-prediction models based on genotype information from a case-control association study and tested their predictability.

METHODS

We genotyped risk alleles of the three susceptibility genes, asporin (ASPN), growth differentiation factor 5 (GDF5), and double von Willebrand factor A domains (DVWA) for a total of 2,158 Japanese subjects (933 OA and 1,225 controls) and statistically analyzed their effects. After that, we constructed prediction models by using the logistic regression analysis.

RESULTS

When the effects of each allele were assumed to be the same and multiplicative, each additional risk allele increased the odds ratio (OR) by a factor of 1.23 (95% confidence interval (CI), 1.12 to 1.34). Individuals with five or six risk alleles showed significantly higher susceptibility when compared with those with zero or one, with an OR of 2.67 (95% CI, 1.46 to 4.87; P = 0.0020). Statistical evaluation of the prediction power of models showed that a model using only genotyping data had poor predictability. We obtained a model with good predictability by incorporating clinical data, which was further improved by rigorous age adjustment.

CONCLUSIONS

Our results showed that consideration of adjusted clinical information, as well as increases in the number of risk alleles to be integrated, is critical for OA prediction by using data from case-control studies. To the authors' knowledge, this is the first report of the OA-prediction model combining both genetic and clinical information.

Genome-wide association study (GWAS)-identified disease risk alleles do not compromise human longevity

A set of currently known alleles increasing the risk for coronary artery disease, cancer, and type 2 diabetes as identified by genome-wide association studies was tested for compatibility with human longevity. Here, we show that nonagenarian siblings from long-lived families and singletons older than 85 y of age from the general population carry the same number of disease risk alleles as young controls. Longevity in this study population is not compromised by the cumulative effect of this set of risk alleles for common disease.

Genetic epidemiology of age-related osteoporosis and its clinical applications

Osteoporosis is an important and complex disorder that is highly prevalent worldwide. This disease poses a major challenge to modern medicine and its treatment is associated with high costs. Numerous studies have endeavored to decipher the pathogenesis of this disease. The clinical assessment of patients often incorporates information about a family history of osteoporotic fractures. Indeed, the observation of an increased risk of fracture in an individual with a positive parental history of hip fracture provides strong evidence for the heritability of osteoporosis. The onset and progression of osteoporosis are generally controlled by multiple genetic and environmental factors, as well as interactions between them, with rare cases determined by a single gene. In an attempt to identify the genetic markers of complex diseases such as osteoporosis, there has been a move away from traditional linkage mapping studies and candidate gene association studies to higher-density genome-wide association studies. The advent of high-throughput technology enables genotyping of millions of DNA markers in the human genome, and consequently the identification and characterization of causal variants and loci that underlie osteoporosis. This Review presents an overview of the major findings since 2007 and clinical applications of these genome-wide linkage and association studies.

Wnt signaling in pancreatic islets

The Wnt signaling pathway is critically important not only for stem cell amplification, differentiation, and migration, but also is important for organogenesis and the development of the body plan. Beta-catenin/TCF7L2-dependent Wnt signaling (the canonical pathway) is involved in pancreas development, islet function, and insulin production and secretion. The glucoincretin hormone glucagon-like peptide-1 and the chemokine stromal cell-derived factor-1 modulate canonical Wnt signaling in beta-cells which is obligatory for their mitogenic and cytoprotective actions. Genome-wide association studies have uncovered 19 gene loci that confer susceptibility for the development of type 2 diabetes. At least 14 of these diabetes risk alleles encode proteins that are implicated in islet growth and functioning. Seven of them are either components of, or known target genes for, Wnt signaling. The transcription factor TCF7L2 is particularly strongly associated with risk for diabetes and appears to be fundamentally important in both canonical Wnt signaling and beta-cell functioning. Experimental loss of TCF7L2 function in islets and polymorphisms in TCF7L2 alleles in humans impair glucose-stimulated insulin secretion, suggesting that perturbations in the Wnt signaling pathway may contribute substantially to the susceptibility for, and pathogenesis of, type 2 diabetes. This review focuses on considerations of the hormonal regulation of Wnt signaling in islets and implications for mutations in components of the Wnt signaling pathway as a source for risk-associated alleles for type 2 diabetes.

CDKAL1 and type 2 diabetes: a global meta-analysis

CDKAL1 (cyckin-dependent kinase 5 regulatory subunit-associated protein 1-like 1) has been shown to be associated with type 2 diabetes in various ethnic groups; however, contradictory results have been reported. We performed a comprehensive meta-analysis of 21 studies for rs7756992, 17 studies for rs7754840 and 10 studies for rs10946398 variants of the CDKAL1 gene to evaluate the effect of CDKAL1 on genetic susceptibility for type 2 diabetes. We found a significant association of rs7756992, rs7754840 and rs10946398 in CDKAL1 with type 2 diabetes (odds ratio (OR) = 1.15, 95% confidence interval (CI) = 1.07-1.23, P < 0.0001; OR = 1.14, 95%CI = 1.06-1.24, P = 0.001, and OR = 1.12, 95%CI = 1.07-1.18, P < 0.0001, respectively). We conclude that there are significant associations between CDKAL1 polymorphisms and type 2 diabetes, but these associations vary in different ethnic populations.

Meta-analysis and functional effects of the SLC30A8 rs13266634 polymorphism on isolated human pancreatic islets

BACKGROUND

The C-allele of rs13266634 located in SLC30A8 (ZNT8) has been strongly associated with decreased insulin release and with type 2 diabetes (T2D) susceptibility in some but not all studies. To shed further light on this issue, we performed a meta-analysis of the association between rs13266634 and T2D in different ethnic groups and assessed the relationships between SLC30A8 genotypes and some properties of isolated human islets.

METHODS

From 32 original articles, a total of 77,234 control individuals and 44,945 subjects with T2D were studied in meta-analysis. To assess the relationships between SLC30A8 genotype and islet cell phenotype, insulin secretion in response to glucose, glucose plus arginine and glucose plus glibenclamide was determined in pancreatic islets isolated from 82 multiorgan donors genotyped for the rs13266634 polymorphism. Quantitative expression of SLC30A8, Insulin and Glucagon mRNA was also measured.

RESULTS

Overall, each SLC30A8 risk allele was associated with a 14% increased risk for T2D (P=2.78 x 10(-34)). The population risk of T2D attributable to this polymorphism was estimated at 9.5% in Europeans and 8.1% in East Asians. Basal and stimulated insulin secretion from human islets as well as islet expressions of SLC30A8, Insulin and Glucagon were not affected by the presence of the polymorphism. However, SLC30A8 expression was positively correlated with Insulin (r=0.75, P=6.43 x 10(-6)) and Glucagon (r: 0.70, P=4.89 x 10(-5)) levels.

CONCLUSIONS

The SLC30A8 rs13266634 polymorphism is among the most confirmed genetic markers of T2D in Europeans and East Asians. In isolated human islets, the risk C-allele does not affect ex-vivo insulin secretion and SLC30A8 expression, which is correlated with that of insulin and glucagon.

Combining genetic markers and clinical risk factors improves the risk assessment of impaired glucose metabolism

BACKGROUND

Although several candidate gene polymorphisms (SNPs) have been associated with increased risk of type 2 diabetes mellitus (T2DM), relatively few studies have assessed the ability of T2DM candidate genes to assess the risk of impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and T2DM beyond the information provided by clinical risk factors.

OBJECTIVE

To test whether the inclusion of genetic markers in a regression model provides a better assessment of the risk of IFG, IGT, and T2DM than a model based only on non-genetic risk factors commonly assessed in clinical settings.

METHODS

Subjects (n = 485; 213 parents, 272 offspring) from the Quebec Family Study, not known to haveT2DM, were measured for several risk factors and underwent an oral glucose tolerance test. Thirty-eight SNPs in 25 susceptibility/ candidate genes previously reported to be associated with T2DM were genotyped. In order to identify risk factors associated with IFG/IGT/T2DM, two logistic regression models were tested: a full model (FM) including age, sex, body mass index (BMI), systolic and diastolic blood pressure, smoking status, and the 38 SNPs; and a reduced model (RM), in which the SNPs were dropped, which allowed us to test the null-hypothesis that the markers are not associated with the risk of IFG/IGT/T2DM. Performances of the models were compared by using a likelihood ratio test and the receiver-operating characteristic curves (ROC).The area under the curve (AUC) was calculated from the ROC curve.

RESULTS

The analyses showed that age (P < 0.0001), BMI (P < 0.0001), and six variants (IGF2BP2 rs4402960, P = 0.002; ADIPOQ+276 G>T, P = 0.004; UCP2Ala55Val, P = 0.01; CDKN2AI2B rs3731201, P = 0.02; rs495490, P = 0.02, and rsl 0811661, P = 0.03) were significantly associated with the risk of IFG/IGT/T2DM. Dropping genetic markers from the analysis significantly reduced the fit of the model to the data (chi-square = 38.98, P < 0.00001 contrasting RM to FM), suggesting that the genetic markers are significantly associated with the risk of IFG/IGT/T2DM. Furthermore, the AUC was higher for FM than for RM (0.85 (95% CI 0.81-0.89) versus 0.81 (95% CI 0.76-0.85), P = 0.004).

CONCLUSION

Our results suggest that combining genetic markers with traditional clinical risk factors has the potential to improve our ability to assess the risk of complex diseases such as T2DM.

Gene-gene interactions lead to higher risk for development of type 2 diabetes in an Ashkenazi Jewish population

Background

Evidence has accumulated that multiple genetic and environmental factors play important roles in determining susceptibility to type 2 diabetes (T2D). Although variants from candidate genes have become prime targets for genetic analysis, few studies have considered their interplay. Our goal was to evaluate interactions among SNPs within genes frequently identified as associated with T2D.

Methods/Principal Findings

Logistic regression was used to study interactions among 4 SNPs, one each from HNF4A[rs1884613], TCF7L2[rs12255372], WFS1[rs10010131], and KCNJ11[rs5219] in a case-control Ashkenazi sample of 974 diabetic subjects and 896 controls. Nonparametric multifactor dimensionality reduction (MDR) and generalized MDR (GMDR) were used to confirm findings from the logistic regression analysis. HNF4A and WFS1 SNPs were associated with T2D in logistic regression analyses [P<0.0001, P<0.0002, respectively]. Interaction between these SNPs were also strong using parametric or nonparametric methods: the unadjusted odds of being affected with T2D was 3 times greater in subjects with the HNF4A and WFS1 risk alleles than those without either (95% CI = [1.7–5.3]; P≤0.0001). Although the univariate association between the TCF7L2 SNP and T2D was relatively modest [P = 0.02], when paired with the HNF4A SNP, the OR for subjects with risk alleles in both SNPs was 2.4 [95% CI = 1.7–3.4; P≤0.0001]. The KCNJ11 variant reached significance only when paired with either the HNF4A or WFSI SNPs: unadjusted ORs were 2.0 [95% CI = 1.4–2.8; P≤0.0001] and 2.3 [95% CI = 1.2-4.4; P≤0.0001], respectively. MDR and GMDR results were consistent with the parametric findings.

Conclusions

These results provide evidence of strong independent associations between T2D and SNPs in HNF4A and WFS1 and their interaction in our Ashkenazi sample. We also observed an interaction in the nonparametric analysis between the HNF4A and KCNJ11 SNPs (P≤0.001), demonstrating that an independently non-significant variant may interact with another variant resulting in an increased disease risk.

Genomics of heart failure

Cardiovascular disease is the leading cause of death in men and women, and heart failure (HF) is associated with high rates of morbidity and mortality. Most common forms of HF are non-mendelian and the evidence for heritability is modest. Study of the genetic susceptibility to HF has been limited to patients with rare familial forms of HF and candidate gene association studies in patients with distinct subtypes of HF. However, with the completion of the human genome project and the development of the HapMap template, new large-scale genome-wide association studies are possible. This article reviews the status of these and other important developments in genomics, in particular genome-wide sequencing, and other "omics".

Assessing the impact of a combined analysis of four common low-risk genetic variants on autism risk

Background

Autism is a complex disorder characterized by deficits involving communication, social interaction, and repetitive and restrictive patterns of behavior. Twin studies have shown that autism is strongly heritable, suggesting a strong genetic component. In other disease states with a complex etiology, such as type 2 diabetes, cancer and cardiovascular disease, combined analysis of multiple genetic variants in a genetic score has helped to identify individuals at high risk of disease. Genetic scores are designed to test for association of genetic markers with disease.

Method

The accumulation of multiple risk alleles markedly increases the risk of being affected, and compared with studying polymorphisms individually, it improves the identification of subgroups of individuals at greater risk. In the present study, we show that this approach can be applied to autism by specifically looking at a high-risk population of children who have siblings with autism. A two-sample study design and the generation of a genetic score using multiple independent genes were used to assess the risk of autism in a high-risk population.

Results

In both samples, odds ratios (ORs) increased significantly as a function of the number of risk alleles, with a genetic score of 8 being associated with an OR of 5.54 (95% confidence interval [CI] 2.45 to 12.49). The sensitivities and specificities for each genetic score were similar in both analyses, and the resultant area under the receiver operating characteristic curves were identical (0.59).

Conclusions

These results suggest that the accumulation of multiple risk alleles in a genetic score is a useful strategy for assessing the risk of autism in siblings of affected individuals, and may be better than studying single polymorphisms for identifying subgroups of individuals with significantly greater risk.

Chromosome 7p linkage and association study for diabetes related traits and type 2 diabetes in an African-American population enriched for nephropathy

BACKGROUND

Previously we performed a linkage scan of 638 African American affected sibling pairs (ASP) with type 2 diabetes (T2D) enriched for end-stage renal disease (ESRD). Ordered subset linkage analysis (OSA) revealed a linkage peak on chromosome 7p in the subset of families with earlier age of T2D diagnosis.

METHODS

We fine mapped this region by genotyping 11 additional polymorphic markers in the same ASP and investigated a total of 68 single nucleotide polymorphisms (SNPs) in functional candidate genes (GCK1, IL6, IGFBP1 and IGFBP3) for association with age of T2D diagnosis, age of ESRD diagnosis, duration of T2D to onset of ESRD, body mass index (BMI) in African American cases and T2D-ESRD in an African American case-control cohort. OSA of fine mapping markers supported linkage at 28 cM on 7p (near D7S3051) in early-onset T2D families (max. LOD = 3.61, P = 0.002). SNPs in candidate genes and 70 ancestry-informative markers (AIMs) were evaluated in 577 African American T2D-ESRD cases and 596 African American controls.

RESULTS

The most significant association was observed between ESRD age of diagnosis and SNP rs730497, located in intron 1 of the GCK1 gene (recessive T2D age-adjusted P = 0.0006). Nominal associations were observed with GCK1 SNPs and T2D age of diagnosis (BMI-adjusted P = 0.014 to 0.032). Also, one IGFBP1 and four IGFBP3 SNPs showed nominal genotypic association with T2D-ESRD (P = 0.002-0.049). After correcting for multiple tests, only rs730497 remanined significant.

CONCLUSION

Variant rs730947 in the GCK1 gene appears to play a role in early ESRD onset in African Americans.

Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study

OBJECTIVES

To assess the performance of a panel of common single nucleotide polymorphisms (genotypes) associated with type 2 diabetes in distinguishing incident cases of future type 2 diabetes (discrimination), and to examine the effect of adding genetic information to previously validated non-genetic (phenotype based) models developed to estimate the absolute risk of type 2 diabetes.

DESIGN

Workplace based prospective cohort study with three 5 yearly medical screenings.

PARTICIPANTS

5535 initially healthy people (mean age 49 years; 33% women), of whom 302 developed new onset type 2 diabetes over 10 years.

OUTCOME MEASURES

Non-genetic variables included in two established risk models-the Cambridge type 2 diabetes risk score (age, sex, drug treatment, family history of type 2 diabetes, body mass index, smoking status) and the Framingham offspring study type 2 diabetes risk score (age, sex, parental history of type 2 diabetes, body mass index, high density lipoprotein cholesterol, triglycerides, fasting glucose)-and 20 single nucleotide polymorphisms associated with susceptibility to type 2 diabetes. Cases of incident type 2 diabetes were defined on the basis of a standard oral glucose tolerance test, self report of a doctor's diagnosis, or the use of anti-diabetic drugs.

RESULTS

A genetic score based on the number of risk alleles carried (range 0-40; area under receiver operating characteristics curve 0.54, 95% confidence interval 0.50 to 0.58) and a genetic risk function in which carriage of risk alleles was weighted according to the summary odds ratios of their effect from meta-analyses of genetic studies (area under receiver operating characteristics curve 0.55, 0.51 to 0.59) did not effectively discriminate cases of diabetes. The Cambridge risk score (area under curve 0.72, 0.69 to 0.76) and the Framingham offspring risk score (area under curve 0.78, 0.75 to 0.82) led to better discrimination of cases than did genotype based tests. Adding genetic information to phenotype based risk models did not improve discrimination and provided only a small improvement in model calibration and a modest net reclassification improvement of about 5% when added to the Cambridge risk score but not when added to the Framingham offspring risk score.

CONCLUSION

The phenotype based risk models provided greater discrimination for type 2 diabetes than did models based on 20 common independently inherited diabetes risk alleles. The addition of genotypes to phenotype based risk models produced only minimal improvement in accuracy of risk estimation assessed by recalibration and, at best, a minor net reclassification improvement. The major translational application of the currently known common, small effect genetic variants influencing susceptibility to type 2 diabetes is likely to come from the insight they provide on causes of disease and potential therapeutic targets.

Antidiabetic effects of IGFBP2, a leptin-regulated gene

We tested whether leptin can ameliorate diabetes independent of weight loss by defining the lowest dose at which leptin treatment of ob/ob mice reduces plasma glucose and insulin concentration. We found that a leptin dose of 12.5 ng/hr significantly lowers blood glucose and that 25 ng/hr of leptin normalizes plasma glucose and insulin without significantly reducing body weight, establishing that leptin exerts its most potent effects on glucose metabolism. To find possible mediators of this effect, we profiled liver mRNA using microarrays and identified IGF Binding Protein 2 (IGFBP2) as being regulated by leptin with a similarly high potency. Overexpression of IGFBP2 by an adenovirus reversed diabetes in insulin-resistant ob/ob, Ay/a, and diet-induced obese mice, as well as insulin-deficient streptozotocin-treated mice. Hyperinsulinemic clamp studies showed a 3-fold improvement in hepatic insulin sensitivity following IGFBP2 treatment of ob/ob mice. These results show that IGFBP2 can regulate glucose metabolism, a finding with potential implications for the pathogenesis and treatment of diabetes.

Combined risk allele score of eight type 2 diabetes genes is associated with reduced first-phase glucose-stimulated insulin secretion during hyperglycemic clamps

OBJECTIVE

At least 20 type 2 diabetes loci have now been identified, and several of these are associated with altered beta-cell function. In this study, we have investigated the combined effects of eight known beta-cell loci on insulin secretion stimulated by three different secretagogues during hyperglycemic clamps.

RESEARCH DESIGN AND METHODS

A total of 447 subjects originating from four independent studies in the Netherlands and Germany (256 with normal glucose tolerance [NGT]/191 with impaired glucose tolerance [IGT]) underwent a hyperglycemic clamp. A subset had an extended clamp with additional glucagon-like peptide (GLP)-1 and arginine (n = 224). We next genotyped single nucleotide polymorphisms in TCF7L2, KCNJ11, CDKAL1, IGF2BP2, HHEX/IDE, CDKN2A/B, SLC30A8, and MTNR1B and calculated a risk allele score by risk allele counting.

RESULTS

The risk allele score was associated with lower first-phase glucose-stimulated insulin secretion (GSIS) (P = 7.1 x 10(-6)). The effect size was equal in subjects with NGT and IGT. We also noted an inverse correlation with the disposition index (P = 1.6 x 10(-3)). When we stratified the study population according to the number of risk alleles into three groups, those with a medium- or high-risk allele score had 9 and 23% lower first-phase GSIS. Second-phase GSIS, insulin sensitivity index and GLP-1, or arginine-stimulated insulin release were not significantly different.

CONCLUSIONS

A combined risk allele score for eight known beta-cell genes is associated with the rapid first-phase GSIS and the disposition index. The slower second-phase GSIS, GLP-1, and arginine-stimulated insulin secretion are not associated, suggesting that especially processes involved in rapid granule recruitment and exocytosis are affected in the majority of risk loci.