Related Articles

OBJECTIVE

We have previously observed that genetic profiles determined by the combination of five functionally significant single nucleotide polymorphisms (SNPs) (rs1800795, rs5498, rs5361, rs1024611, and rs679620) of genes encoding prototypical inflammatory molecules are associated with history of ischemic stroke. Here we tested the ability of this multigenic model to predict stroke risk in a large population-based prospective cohort of subjects with type 2 diabetes.

RESEARCH DESIGN AND METHODS

This study was conducted using a prospective cohort of individuals with type 2 diabetes participating in the Go-DARTS (Genetics of Diabetes Audit and Research in Tayside Scotland) study, which includes genetic and clinical information of patients with diabetes within the Tayside region of Scotland, U.K. The above-mentioned inflammatory SNPs were investigated in 2,182 Go-DARTS participants. We created an inflammatory risk score (IRS), ranging from 0 to 5, according to the number of “at-risk” genotypes concomitantly carried by a given individual. The primary outcome was the occurrence of fatal or nonfatal stroke of any kind. Mean follow-up time was 6.2 ± 1.1 years.

RESULTS

The incidence of stroke increased according to the IRS. The IRS was significantly and independently associated with increased stroke risk after adjustment for other conventional risk factors (hazard ratio 1.34 [95% CI 1.1–1.7]; P = 0.009). The highest hazard ratio for stroke was found in subjects concomitantly carrying >3 proinflammatory variations and in subjects without previous cardiovascular diseases.

CONCLUSIONS

This large prospective cohort study provides evidence that SNPs of genes encoding prototypical inflammatory molecules may be used to create multigenic models that predict stroke risk in subjects with type 2 diabetes.

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.

OBJECTIVE

Metformin is actively transported into the liver by the organic cation transporter (OCT)1 (encoded by SLC22A1). In 12 normoglycemic individuals, reduced-function variants in SLC22A1 were shown to decrease the ability of metformin to reduce glucose excursion in response to oral glucose. We assessed the effect of two common loss-of-function polymorphisms in SLC22A1 on metformin response in a large cohort of patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

The Diabetes Audit and Research in Tayside Scotland (DARTS) database includes prescribing and biochemistry information and clinical phenotypes of all patients with diabetes within Tayside, Scotland, from 1992 onwards. R61C and 420del variants of SLC22A1 were genotyped in 3,450 patients with type 2 diabetes who were incident users of metformin. We assessed metformin response by modeling the maximum A1C reduction in 18 months after starting metformin and investigated whether a treatment target of A1C <7% was achieved. Sustained metformin effect on A1C between 6 and 42 months was also assessed, as was the time to metformin monotherapy failure. Covariates were SLC22A1 genotype, BMI, average drug dose, adherence, and creatinine clearance.

RESULTS

A total of 1,531 patients were identified with a definable metformin response. R61C and 420del variants did not affect the initial A1C reduction (P = 0.47 and P = 0.92, respectively), the chance of achieving a treatment target (P = 0.83 and P = 0.36), the average A1C on monotherapy up to 42 months (P = 0.44 and P = 0.75), or the hazard of monotherapy failure (P = 0.85 and P = 0.56).

CONCLUSIONS

The SLC22A1 loss-of-function variants, R61C and 420del, do not attenuate the A1C reduction achieved by metformin in patients with type 2 diabetes.

OBJECTIVE

Mutations in the HNF1A gene are the most common cause of maturity-onset diabetes of the young (MODY). There is a substantial variation in the age at diabetes diagnosis, even within families where diabetes is caused by the same mutation. We investigated the hypothesis that common polygenic variants that predispose to type 2 diabetes might account for the difference in age at diagnosis.

RESEARCH DESIGN AND METHODS

Fifteen robustly associated type 2 diabetes variants were successfully genotyped in 410 individuals from 203 HNF1A-MODY families, from two study centers in the U.K. and Norway. We assessed their effect on the age at diagnosis both individually and in a combined genetic score by summing the number of type 2 diabetes risk alleles carried by each patient.

RESULTS

We confirmed the effects of environmental and genetic factors known to modify the age at HNF1A-MODY diagnosis, namely intrauterine hyperglycemia (−5.1 years if present, P = 1.6 × 10−10) and HNF1A mutation position (−5.2 years if at least two isoforms affected, P = 1.8 × 10−2). Additionally, our data showed strong effects of sex (females diagnosed 3.0 years earlier, P = 6.0 × 10−4) and age at study (0.3 years later diagnosis per year increase in age, P = 4.7 × 10−38). There were no strong individual single nucleotide polymorphism effects; however, in the combined genetic score model, each additional risk allele was associated with 0.35 years earlier diabetes diagnosis (P = 5.1 × 10−3).

CONCLUSIONS

We show that type 2 diabetes risk variants of modest effect sizes reduce the age at diagnosis in HNF1A-MODY. This is one of the first studies to demonstrate that clinical characteristics of a monogenic disease can be modified by common polygenic variants.

OBJECTIVE

The causal nature of associations between circulating triglycerides, insulin resistance, and type 2 diabetes is unclear. We aimed to use Mendelian randomization to test the hypothesis that raised circulating triglyceride levels causally influence the risk of type 2 diabetes and raise normal fasting glucose levels and hepatic insulin resistance.

RESEARCH DESIGN AND METHODS

We tested 10 common genetic variants robustly associated with circulating triglyceride levels against the type 2 diabetes status in 5,637 case and 6,860 control subjects and four continuous outcomes (reflecting glycemia and hepatic insulin resistance) in 8,271 nondiabetic individuals from four studies.

RESULTS

Individuals carrying greater numbers of triglyceride-raising alleles had increased circulating triglyceride levels (SD 0.59 [95% CI 0.52–0.65] difference between the 20% of individuals with the most alleles and the 20% with the fewest alleles). There was no evidence that the carriers of greater numbers of triglyceride-raising alleles were at increased risk of type 2 diabetes (per weighted allele odds ratio [OR] 0.99 [95% CI 0.97–1.01]; P = 0.26). In nondiabetic individuals, there was no evidence that carriers of greater numbers of triglyceride-raising alleles had increased fasting insulin levels (SD 0.00 per weighted allele [95% CI −0.01 to 0.02]; P = 0.72) or increased fasting glucose levels (0.00 [−0.01 to 0.01]; P = 0.88). Instrumental variable analyses confirmed that genetically raised circulating triglyceride levels were not associated with increased diabetes risk, fasting glucose, or fasting insulin and, for diabetes, showed a trend toward a protective association (OR per 1-SD increase in log10 triglycerides: 0.61 [95% CI 0.45–0.83]; P = 0.002).

CONCLUSIONS

Genetically raised circulating triglyceride levels do not increase the risk of type 2 diabetes or raise fasting glucose or fasting insulin levels in nondiabetic individuals. One explanation for our results is that raised circulating triglycerides are predominantly secondary to the diabetes disease process rather than causal.

Continuing advances in genetic discovery have uncovered several dozen loci that are associated with Type 2 diabetes, including genetic variants that appear to modify responses to commonly prescribed diabetes medications. The use of an individual’s genetic information to guide therapy choices raises the possibility of ‘personalized medicine’, wherein each patient’s treatment plan is tailored based on genotype results. However, before such a model of care can be implemented, research is needed to more clearly quantify the association of genetic variation with treatment outcomes and adverse effects. In this article, we review a study examining the association of genetic variation in the cytochrome P450 2C9 enzyme with glycemic response to sulfonylureas in a large cohort of patients with Type 2 diabetes from the Genetics of Diabetes Audit and Research Tayside Study (Go-DARTS).

Objective

The causal nature of associations between circulating triglycerides, insulin resistance and type 2 diabetes is unclear. We aimed to use Mendelian randomization to test the hypothesis that raised circulating triglyceride levels causally influence the risk of type 2 diabetes, raised normal fasting glucose levels, and hepatic insulin resistance.

Research design and methods

We tested 10 common genetic variants robustly associated with circulating triglyceride levels against type 2 diabetes status in 5637 cases, 6860 controls, and four continuous outcomes (reflecting glycemia and hepatic insulin resistance) in 8271 non-diabetic individuals from four studies.

Results

Individuals carrying greater numbers of triglyceride-raising alleles had increased circulating triglyceride levels (0.59 SD [95% CI: 0.52, 0.65] difference between the 20% of individuals with the most alleles and the 20% with the fewest alleles). There was no evidence that carriers of greater numbers of triglyceride-raising alleles were at increased risk of type 2 diabetes (per weighted allele odds ratio (OR) 0.99 [95% CI: 0.97, 1.01]; P = 0.26). In non-diabetic individuals, there was no evidence that carriers of greater numbers of triglyceride-raising alleles had increased fasting insulin levels (0.00 SD per weighted allele [95% CI: −0.01, 0.02]; P = 0.72) or increased fasting glucose levels (0.00 SD per weighted allele [95% CI: −0.01, 0.01]; P = 0.88). Instrumental variable analyses confirmed that genetically raised circulating triglyceride levels were not associated with increased diabetes risk, fasting glucose or fasting insulin, and, for diabetes, showed a trend towards a protective association (OR per 1 SD increase in log10-triglycerides: 0.61 [95% CI: 0.45, 0.83]; P = 0.002).

Conclusion

Genetically raised circulating triglyceride levels do not increase the risk of type 2 diabetes, or raise fasting glucose or fasting insulin levels in non-diabetic individuals. One explanation for our results is that raised circulating triglycerides are predominantly secondary to the diabetes disease process rather than causal.

OBJECTIVE

Two independent genome-wide association studies for type 2 diabetes in Japanese subjects have recently identified common variants in the KCNQ1 gene that are strongly associated with type 2 diabetes. Here we studied whether a common variant in KCNQ1 would influence BMI as well as insulin secretion and action and predict future type 2 diabetes in subjects from Sweden and Finland.

RESEARCH DESIGN AND METHODS

Risk of type 2 diabetes conferred by KCNQ1 rs2237895 was studied in 2,830 type 2 diabetic case subjects and 3,550 control subjects from Sweden (Malmö Case-Control) and prospectively in 16,061 individuals from the Malmö Preventive Project (MPP). Association between genotype and insulin secretion/action was assessed cross- sectionally in 3,298 nondiabetic subjects from the Prevalence, Prediction and Prevention of Diabetes (PPP)-Botnia Study and longitudinally in 2,328 nondiabetic subjects from the Botnia Prospective Study (BPS). KCNQ1 expression (n = 18) and glucose-stimulated insulin secretion (n = 19) were measured in human islets from nondiabetic cadaver donors.

RESULTS

The C-allele of KCNQ1 rs2237895 was associated with increased risk of type 2 diabetes in both the Malmö Case-Control (odds ratio 1.23 [95% CI 1.12–1.34]; P = 5.6 × 10−6) and the prospective (1.14 [1.06–1.22]; P = 4.8 × 10−4) studies. Furthermore, the C-allele was associated with decreased insulin secretion (corrected insulin response [CIR] P = 0.013; disposition index [DI] P = 0.013) in the PPP-Botnia Study and in the BPS at baseline (CIR P = 3.6 × 10−4; DI P = 0.0058) and after follow-up (CIR P = 0.0018; DI P = 0.0030). C-allele carriers showed reduced glucose-stimulated insulin secretion in human islets (P = 2.5 × 10−6).

CONCLUSIONS

A common variant in the KCNQ1 gene is associated with increased risk of future type 2 diabetes in Scandinavians, which partially can be explained by an effect on insulin secretion.

OBJECTIVE—Type 2 diabetes is a common complex disorder with environmental and genetic components. We used a candidate gene–based approach to identify single nucleotide polymorphism (SNP) variants in 222 candidate genes that influence susceptibility to type 2 diabetes.

RESEARCH DESIGN AND METHODS—In a case-control study of 1,161 type 2 diabetic subjects and 1,174 control Finns who are normal glucose tolerant, we genotyped 3,531 tagSNPs and annotation-based SNPs and imputed an additional 7,498 SNPs, providing 99.9% coverage of common HapMap variants in the 222 candidate genes. Selected SNPs were genotyped in an additional 1,211 type 2 diabetic case subjects and 1,259 control subjects who are normal glucose tolerant, also from Finland.

RESULTS—Using SNP- and gene-based analysis methods, we replicated previously reported SNP-type 2 diabetes associations in PPARG, KCNJ11, and SLC2A2; identified significant SNPs in genes with previously reported associations (ENPP1 [rs2021966, P = 0.00026] and NRF1 [rs1882095, P = 0.00096]); and implicated novel genes, including RAPGEF1 (rs4740283, P = 0.00013) and TP53 (rs1042522, Arg72Pro, P = 0.00086), in type 2 diabetes susceptibility.

CONCLUSIONS—Our study provides an effective gene-based approach to association study design and analysis. One or more of the newly implicated genes may contribute to type 2 diabetes pathogenesis. Analysis of additional samples will be necessary to determine their effect on susceptibility.

OBJECTIVE

To examine the association between single nucleotide polymorphisms (SNPs) in the engulfment and cell motility 1 (ELMO1) gene, a locus previously shown to be associated with diabetic nephropathy in two ethnically distinct type 2 diabetic populations, and the risk of nephropathy in type 1 diabetes.

RESEARCH DESIGN AND METHODS

Genotypic data from a genome-wide association scan (GWAS) of the Genetics of Kidneys in Diabetes (GoKinD) study collection were analyzed for associations across the ELMO1 locus. In total, genetic associations were assessed using 118 SNPs and 1,705 individuals of European ancestry with type 1 diabetes (885 normoalbuminuric control subjects and 820 advanced diabetic nephropathy case subjects).

RESULTS

The strongest associations in ELMO1 occurred at rs11769038 (odds ratio [OR] 1.24; P = 1.7 × 10−3) and rs1882080 (OR 1.23; P = 3.2 × 10−3) located in intron 16. Two additional SNPs, located in introns 18 and 20, respectively, were also associated with diabetic nephropathy. No evidence of association for variants previously reported in type 2 diabetes was observed in our collection.

CONCLUSIONS

Using GWAS data from the GoKinD collection, we comprehensively examined evidence of association across the ELMO1 locus. Our investigation marks the third report of associations in ELMO1 with diabetic nephropathy, further establishing its role in the susceptibility of this disease. There is evidence of allelic heterogeneity, contributed by the diverse genetic backgrounds of the different ethnic groups examined. Further investigation of SNPs at this locus is necessary to fully understand the commonality of these associations and the mechanism(s) underlying their role in diabetic nephropathy.

Aims/hypothesis

Obesity is a major risk factor for type 2 diabetes. Recent genome-wide association (GWA) studies have identified multiple loci robustly associated with BMI and risk of obesity. However, information on their associations with type 2 diabetes is limited. Such information could help increase our understanding of the link between obesity and type 2 diabetes. We examined the associations of 12 obesity susceptibility loci, individually and in combination, with risk of type 2 diabetes in the population-based European Prospective Investigation of Cancer (EPIC) Norfolk cohort.

Methods

We genotyped 12 SNPs, identified by GWA studies of BMI, in 20,428 individuals (aged 39–79 years at baseline) with an average follow-up of 12.9 years, during which 729 individuals developed type 2 diabetes. A genetic predisposition score was calculated by adding the BMI-increasing alleles across the 12 SNPs. Associations with incidence of type 2 diabetes were examined by logistic regression models.

Results

Of the 12 SNPs, eight showed a trend with increased risk of type 2 diabetes, consistent with their BMI-increasing effects. Each additional BMI-increasing allele in the genetic predisposition score was associated with a 4% increased odds of developing type 2 diabetes (OR 1.041, 95% CI 1.005–1.078; p = 0.02). Adjustment for BMI completely abolished the association with incident type 2 diabetes (OR 1.003, 95% CI 0.967–1.039; p = 0.89).

Conclusions/interpretation

The genetic predisposition to obesity leads to increased risk of developing type 2 diabetes, which is completely mediated by its obesity-predisposing effect.

Electronic supplementary material

The online version of this article (doi:10.1007/s00125-011-2044-5) contains supplementary material, which is available to authorized users.

OBJECTIVE

Multiple single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D) susceptibility have been identified in predominantly European-derived populations. These SNPs have not been extensively investigated for individual and cumulative effects on T2D risk in African Americans.

RESEARCH DESIGN AND METHODS

Seventeen index T2D risk variants were genotyped in 2,652 African American case subjects with T2D and 1,393 nondiabetic control subjects. Individual SNPs and cumulative risk allele loads were assessed for association with risk for T2D. Cumulative risk was assessed by counting risk alleles and evaluating the difference in cumulative risk scores between case subjects and control subjects. A second analysis weighted risk scores (ln [OR]) based on previously reported European-derived effect sizes.

RESULTS

Frequencies of risk alleles ranged from 8.6 to 99.9%. Eleven SNPs had ORs >1, and 5 from ADAMTS9, WFS1, CDKAL1, JAZF1, and TCF7L2 trended or had nominally significant evidence of T2D association (P < 0.05). Individuals carried between 13 and 29 risk alleles. Association was observed between T2D and increase in risk allele load (unweighted OR 1.04 [95% CI 1.01–1.08], P = 0.010; weighted 1.06 [1.03–1.10], P = 8.10 × 10−5). When TCF7L2 SNP rs7903146 was included as a covariate, the risk score was no longer associated with T2D in either model (unweighted 1.02 [0.98–1.05], P = 0.33; weighted 1.02 [0.98–1.06], P = 0.40).

CONCLUSIONS

The trend of increase in risk for T2D with increasing risk allele load is similar to observations in European-derived populations; however, these analyses indicate that T2D genetic risk is primarily mediated through the effect of TCF7L2 in African Americans.

Background

A limited number of studies have assessed the risk of common diseases when combining information from several predisposing polymorphisms. In most cases, individual polymorphisms only moderately increase risk (~20%), and they are thought to be unhelpful in assessing individuals' risk clinically. The value of analyzing multiple alleles simultaneously is not well studied. This is often because, for any given disease, very few common risk alleles have been confirmed.

Methods and Findings

Three common variants (Lys23 of KCNJ11, Pro12 of PPARG, and the T allele at rs7903146 of TCF7L2) have been shown to predispose to type 2 diabetes mellitus across many large studies. Risk allele frequencies ranged from 0.30 to 0.88 in controls. To assess the combined effect of multiple susceptibility alleles, we genotyped these variants in a large case-control study (3,668 controls versus 2,409 cases). Individual allele odds ratios (ORs) ranged from 1.14 (95% confidence interval [CI], 1.05 to 1.23) to 1.48 (95% CI, 1.36 to 1.60). We found no evidence of gene-gene interaction, and the risks of multiple alleles were consistent with a multiplicative model. Each additional risk allele increased the odds of type 2 diabetes by 1.28 (95% CI, 1.21 to 1.35) times. Participants with all six risk alleles had an OR of 5.71 (95% CI, 1.15 to 28.3) compared to those with no risk alleles. The 8.1% of participants that were double-homozygous for the risk alleles at TCF7L2 and Pro12Ala had an OR of 3.16 (95% CI, 2.22 to 4.50), compared to 4.3% with no TCF7L2 risk alleles and either no or one Glu23Lys or Pro12Ala risk alleles.

Conclusions

Combining information from several known common risk polymorphisms allows the identification of population subgroups with markedly differing risks of developing type 2 diabetes compared to those obtained using single polymorphisms. This approach may have a role in future preventative measures for common, polygenic diseases.

Combining information from several known common risk polymorphisms allows the identification of subgroups of the population with markedly differing risks of developing type 2 diabetes.

Editors' Summary

Background.

Diabetes is an important and increasingly common global health problem; the World Health Organization has estimated that about 170 million people currently have diabetes worldwide. One particular form, type 2 diabetes, develops when cells in the body become unable to respond to a hormone called insulin. Insulin is normally released by the pancreas and controls the ability of body cells to take in glucose (sugar). Therefore, when cells become insensitive to insulin as in people with type 2 diabetes, glucose levels in the body are not well controlled and may become dangerously high in the blood. These high levels can have long-term damaging effects on various organs in the body, particularly the eyes, nerves, heart, and kidneys. There are many different factors that affect whether someone is likely to develop type 2 diabetes. These factors can be broadly grouped into two categories: environmental and genetic. Environmental factors such as obesity, a diet high in sugar, and a sedentary lifestyle are all risk factors for developing type 2 diabetes in later life. Genetically, a number of variants in many different genes may affect the risk of developing the disease. Generally, these gene variants are common in human populations but each gene variant only mildly increases the risk that a person possessing it will get type 2 diabetes.

Why Was This Study Done?

The investigators performing this study wanted to understand how different gene variants combine to affect an individual's risk of getting type 2 diabetes. That is, if a person carries many different variants, does their overall risk increase a lot or only a little?

What Did the Researchers Do and Find?

First, the researchers surveyed the published reports to identify those gene variants for which there was strong evidence of an association with type 2 diabetes. They found mutations in three genes that had been shown reproducibly to be associated with type 2 diabetes in different studies: PPARG (whose product is involved in regulation of fat tissue), KCNJ11 (whose product is involved in insulin production), and TCF7L2 (whose product is thought to be involved in controlling sugar levels). Then, they compared two groups of white people in the UK: 2,409 people with type 2 diabetes (“cases”), and 3,668 people from the general population (“controls”). The researchers compared the two groups to see which individuals possessed which gene variants, and did statistical testing to work out to what extent having particular combinations of the gene variants affected an individual's chance of being a “case” versus a “control.” Their results showed that in the groups studied, having an ever-increasing number of gene variants increased the risk of developing diabetes. The risk that someone with none of the gene variants would develop type 2 diabetes was about 2%, while the chance for someone with all gene variants was about10%.

What Do These Findings Mean?

These results show that the risk of developing type 2 diabetes is greater if an individual possesses all of the gene variants that were examined in this study. The analysis also suggests that using information on all three variants, rather than just one, is likely to be more accurate in predicting future risk. How this genetic information should be used alongside other well-known preventative measures such as altered lifestyle requires further study.

Additional Information.

Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030374.

NHS Direct patient information on diabetes

National Diabetes Information Clearinghouse information on type 2 diabetes

World Health Organization Diabetes Programme

Centers for Disease ControlDiabetes Public Health Resource

Mitochondrial dysfunction has been observed in skeletal muscle of people with diabetes and insulin-resistant individuals. Furthermore, inherited mutations in mitochondrial DNA can cause a rare form of diabetes. However, it is unclear whether mitochondrial dysfunction is a primary cause of the common form of diabetes. To date, common genetic variants robustly associated with type 2 diabetes (T2D) are not known to affect mitochondrial function. One possibility is that multiple mitochondrial genes contain modest genetic effects that collectively influence T2D risk. To test this hypothesis we developed a method named Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA; http://www.broadinstitute.org/mpg/magenta). MAGENTA, in analogy to Gene Set Enrichment Analysis, tests whether sets of functionally related genes are enriched for associations with a polygenic disease or trait. MAGENTA was specifically designed to exploit the statistical power of large genome-wide association (GWA) study meta-analyses whose individual genotypes are not available. This is achieved by combining variant association p-values into gene scores and then correcting for confounders, such as gene size, variant number, and linkage disequilibrium properties. Using simulations, we determined the range of parameters for which MAGENTA can detect associations likely missed by single-marker analysis. We verified MAGENTA's performance on empirical data by identifying known relevant pathways in lipid and lipoprotein GWA meta-analyses. We then tested our mitochondrial hypothesis by applying MAGENTA to three gene sets: nuclear regulators of mitochondrial genes, oxidative phosphorylation genes, and ∼1,000 nuclear-encoded mitochondrial genes. The analysis was performed using the most recent T2D GWA meta-analysis of 47,117 people and meta-analyses of seven diabetes-related glycemic traits (up to 46,186 non-diabetic individuals). This well-powered analysis found no significant enrichment of associations to T2D or any of the glycemic traits in any of the gene sets tested. These results suggest that common variants affecting nuclear-encoded mitochondrial genes have at most a small genetic contribution to T2D susceptibility.

Author Summary

Mitochondria play a crucial role in metabolic homeostasis, and alteration of mitochondrial function is a hallmark of diabetes. While mitochondrial activity is reduced in people with diabetes, it is unclear whether mitochondrial dysfunction is a cause or effect of type 2 diabetes. Genome-wide association studies for type 2 diabetes have explained ≈10% of the heritability of the disease, but none of the loci are known to affect mitochondrial activity. It is possible though that a mitochondrial contribution is hidden in the remaining 90%. Hence, we tested the hypothesis that multiple mitochondria-related genes encoded in the nucleus, each having a weak effect (hard to detect individually), can collectively influence type 2 diabetes. To address this, we developed a computational method (MAGENTA) that allowed us to adequately analyze large collective datasets of human genetic variation obtained from collaborative studies of type 2 diabetes and related glycemic traits. Despite the increased sensitivity of MAGENTA compared to single-DNA variant analysis, we found no support for a causal relationship between mitochondrial dysfunction and type 2 diabetes. These results may help steer future efforts in understanding the pathogenesis of the disease. MAGENTA is broadly applicable to testing associations between other biological pathways and common diseases or traits.

Aim

Genome-wide association studies have identified > 30 common variants associated with Type 2 diabetes (> 5% minor allele frequency). These variants have small effects on individual risk and do not account for a large proportion of the heritable component of the disease. Monogenic forms of diabetes are caused by mutations that occur in < 1:2000 individuals and follow strict patterns of inheritance. In contrast, the role of low frequency genetic variants (minor allele frequency 0.1–5%) in Type 2 diabetes is not known. The aim of this study was to assess the role of low frequency PDX1 (also called IPF1) variants in Type 2 diabetes.

Methods

We sequenced the coding and flanking intronic regions of PDX1 in 910 patients with Type 2 diabetes and 878 control subjects.

Results

We identified a total of 26 variants that occurred in 5.3% of individuals, 14 of which occurred once. Only D76N occurred in > 1%. We found no difference in carrier frequency between patients (5.7%) and control subjects (5.0%) (P = 0.46). There were also no differences between patients and control subjects when analyses were limited to subsets of variants. The strongest subset were those variants in the DNA binding domain where all five variants identified were only found in patients (P = 0.06).

Conclusion

Approximately 5% of UK individuals carry a PDX1 variant, but there is no evidence that these variants, either individually or cumulatively, predispose to Type 2 diabetes. Further studies will need to consider strategies to assess the role of multiple variants that occur in < 1 in 1000 individuals.

OBJECTIVE

Recent genome-wide association studies have resulted in a dramatic increase in our knowledge of the genetic loci involved in type 2 diabetes. In a complementary approach to these single-marker studies, we attempted to identify biological pathways associated with type 2 diabetes. This approach could allow us to identify additional risk loci.

RESEARCH DESIGN AND METHODS

We used individual level genotype data generated from the Wellcome Trust Case Control Consortium (WTCCC) type 2 diabetes study, consisting of 393,143 autosomal SNPs, genotyped across 1,924 case subjects and 2,938 control subjects. We sought additional evidence from summary level data available from the Diabetes Genetics Initiative (DGI) and the Finland-United States Investigation of NIDDM Genetics (FUSION) studies. Statistical analysis of pathways was performed using a modification of the Gene Set Enrichment Algorithm (GSEA). A total of 439 pathways were analyzed from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and BioCarta databases.

RESULTS

After correcting for the number of pathways tested, we found no strong evidence for any pathway showing association with type 2 diabetes (top Padj = 0.31). The candidate WNT-signaling pathway ranked top (nominal P = 0.0007, excluding TCF7L2; P = 0.002), containing a number of promising single gene associations. These include CCND2 (rs11833537; P = 0.003), SMAD3 (rs7178347; P = 0.0006), and PRICKLE1 (rs1796390; P = 0.001), all expressed in the pancreas.

CONCLUSIONS

Common variants involved in type 2 diabetes risk are likely to occur in or near genes in multiple pathways. Pathway-based approaches to genome-wide association data may be more successful for some complex traits than others, depending on the nature of the underlying disease physiology.

OBJECTIVE

To test if knowledge of type 2 diabetes genetic variants improves disease prediction.

RESEARCH DESIGN AND METHODS

We tested 40 single nucleotide polymorphisms (SNPs) associated with diabetes in 3,471 Framingham Offspring Study subjects followed over 34 years using pooled logistic regression models stratified by age (<50 years, diabetes cases = 144; or ≥50 years, diabetes cases = 302). Models included clinical risk factors and a 40-SNP weighted genetic risk score.

RESULTS

In people <50 years of age, the clinical risk factors model C-statistic was 0.908; the 40-SNP score increased it to 0.911 (P = 0.3; net reclassification improvement (NRI): 10.2%, P = 0.001). In people ≥50 years of age, the C-statistics without and with the score were 0.883 and 0.884 (P = 0.2; NRI: 0.4%). The risk per risk allele was higher in people <50 than ≥50 years of age (24 vs. 11%; P value for age interaction = 0.02).

CONCLUSIONS

Knowledge of common genetic variation appropriately reclassifies younger people for type 2 diabetes risk beyond clinical risk factors but not older people.

AIMS—To evaluate whether diabetes mellitus is a risk factor for the development of primary open angle glaucoma or ocular hypertension (OHT).
METHODS—A historical cohort study of an unselected population comprising all residents of the Tayside region of Scotland was performed using record linkage techniques followed by case note review. Ascertainment of prevalent diabetes was achieved using the Diabetes Audit and Research in Tayside Study (DARTS) validated regional diabetes register. Glaucoma and treated OHT were defined by encashment of community prescriptions and the statutory surgical procedure coding database.
RESULTS—The study population comprised 6631 diabetic subjects and 166 144 non-diabetic subjects aged >40 years without glaucoma or OHT at study entry. 65 patients with diabetes and 958 without diabetes were identified as new cases of glaucoma or treated OHT during the 24 month study period, yielding a standardised morbidity ratio of 127 (95% CI, 96-158). Case note review demonstrated non-differential misclassification of prevalent glaucoma and OHT as incident disease (diabetic cohort 20%, non-diabetic cohort 24%; p=0.56) primarily as a result of non-compliance in medically treated disease. Removing misclassified cases and adjusting for age yielded an incidence of primary open angle glaucoma in diabetes of 1.1/1000 patient years (95% CI, 0.89-1.31) compared to 0.7/1000 patient years (95% CI, 0.54-0.86) in the non-diabetic cohort; RR 1.57 (95% CI, 0.99-2.48).
CONCLUSIONS—This study failed to confirm an association between diabetes mellitus and primary open angle glaucoma and ocular hypertension. A non-significant increase in diagnosed and treated disease in the diabetic population was observed, but evidence was also found that detection bias contributes to this association.



OBJECTIVE

Wolfram syndrome 1 (WFS1) single nucleotide polymorphisms (SNPs) are associated with risk of type 2 diabetes. In this study we aimed to refine this association and investigate the role of low-frequency WFS1 variants in type 2 diabetes risk.

RESEARCH DESIGN AND METHODS

For fine-mapping, we sequenced WFS1 exons, splice junctions, and conserved noncoding sequences in samples from 24 type 2 diabetic case and 68 control subjects, selected tagging SNPs, and genotyped these in 959 U.K. type 2 diabetic case and 1,386 control subjects. The same genomic regions were sequenced in samples from 1,235 type 2 diabetic case and 1,668 control subjects to compare the frequency of rarer variants between case and control subjects.

RESULTS

Of 31 tagging SNPs, the strongest associated was the previously untested 3′ untranslated region rs1046320 (P = 0.008); odds ratio 0.84 and P = 6.59 × 10−7 on further replication in 3,753 case and 4,198 control subjects. High correlation between rs1046320 and the original strongest SNP (rs10010131) (r2 = 0.92) meant that we could not differentiate between their effects in our samples. There was no difference in the cumulative frequency of 82 rare (minor allele frequency [MAF] <0.01) nonsynonymous variants between type 2 diabetic case and control subjects (P = 0.79). Two intermediate frequency (MAF 0.01–0.05) nonsynonymous changes also showed no statistical association with type 2 diabetes.

CONCLUSIONS

We identified six highly correlated SNPs that show strong and comparable associations with risk of type 2 diabetes, but further refinement of these associations will require large sample sizes (>100,000) or studies in ethnically diverse populations. Low frequency variants in WFS1 are unlikely to have a large impact on type 2 diabetes risk in white U.K. populations, highlighting the complexities of undertaking association studies with low-frequency variants identified by resequencing.

OBJECTIVE

Despite extensive evidence for genetic susceptibility to diabetic nephropathy, the identification of susceptibility genes and their variants has had limited success. To search for genes that contribute to diabetic nephropathy, a genome-wide association scan was implemented on the Genetics of Kidneys in Diabetes collection.

RESEARCH DESIGN AND METHODS

We genotyped ∼360,000 single nucleotide polymorphisms (SNPs) in 820 case subjects (284 with proteinuria and 536 with end-stage renal disease) and 885 control subjects with type 1 diabetes. Confirmation of implicated SNPs was sought in 1,304 participants of the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) study, a long-term, prospective investigation of the development of diabetes-associated complications.

RESULTS

A total of 13 SNPs located in four genomic loci were associated with diabetic nephropathy with P < 1 × 10−5. The strongest association was at the FRMD3 (4.1 protein ezrin, radixin, moesin [FERM] domain containing 3) locus (odds ratio [OR] = 1.45, P = 5.0 × 10−7). A strong association was also identified at the CARS (cysteinyl-tRNA synthetase) locus (OR = 1.36, P = 3.1 × 10−6). Associations between both loci and time to onset of diabetic nephropathy were supported in the DCCT/EDIC study (hazard ratio [HR] = 1.33, P = 0.02, and HR = 1.32, P = 0.01, respectively). We demonstratedexpression of both FRMD3 and CARS in human kidney.

CONCLUSIONS

We identified genetic associations for susceptibility to diabetic nephropathy at two novel candidate loci near the FRMD3 and CARS genes. Their identification implicates previously unsuspected pathways in the pathogenesis of this important late complication of type 1 diabetes.

Background

Recent advance in genetic studies added the confirmed susceptible loci for type 2 diabetes to eighteen. In this study, we attempt to analyze the independent and joint effect of variants from these loci on type 2 diabetes and clinical phenotypes related to glucose metabolism.

Methods/Principal Findings

Twenty-one single nucleotide polymorphisms (SNPs) from fourteen loci were successfully genotyped in 1,849 subjects with type 2 diabetes and 1,785 subjects with normal glucose regulation. We analyzed the allele and genotype distribution between the cases and controls of these SNPs as well as the joint effects of the susceptible loci on type 2 diabetes risk. The associations between SNPs and type 2 diabetes were examined by logistic regression. The associations between SNPs and quantitative traits were examined by linear regression. The discriminative accuracy of the prediction models was assessed by area under the receiver operating characteristic curves. We confirmed the effects of SNPs from PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 on risk for type 2 diabetes, with odds ratios ranging from 1.114 to 1.406 (P value range from 0.0335 to 1.37E-12). But no significant association was detected between SNPs from WFS1, FTO, JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30 and type 2 diabetes. Analyses on the quantitative traits in the control subjects showed that THADA SNP rs7578597 was association with 2-h insulin during oral glucose tolerance tests (P = 0.0005, empirical P = 0.0090). The joint effect analysis of SNPs from eleven loci showed the individual carrying more risk alleles had a significantly higher risk for type 2 diabetes. And the type 2 diabetes patients with more risk allele tended to have earlier diagnostic ages (P = 0.0006).

Conclusions/Significance

The current study confirmed the association between PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 and type 2 diabetes. These type 2 diabetes risk loci contributed to the disease additively.

OBJECTIVE

Interactions between genetic and environmental factors lead to immune dysregulation causing type 1 diabetes and other autoimmune disorders. Recently, many common genetic variants have been associated with type 1 diabetes risk, but each has modest individual effects. Familial clustering of type 1 diabetes has not been explained fully and could arise from many factors, including undetected genetic variation and gene interactions.

RESEARCH DESIGN AND METHODS

To address this issue, the Type 1 Diabetes Genetics Consortium recruited 3,892 families, including 4,422 affected sib-pairs. After genotyping 6,090 markers, linkage analyses of these families were performed, using a novel method and taking into account factors such as genotype at known susceptibility loci.

RESULTS

Evidence for linkage was robust at the HLA and INS loci, with logarithm of odds (LOD) scores of 398.6 and 5.5, respectively. There was suggestive support for five other loci. Stratification by other risk factors (including HLA and age at diagnosis) identified one convincing region on chromosome 6q14 showing linkage in male subjects (corrected LOD = 4.49; replication P = 0.0002), a locus on chromosome 19q in HLA identical siblings (replication P = 0.006), and four other suggestive loci.

CONCLUSIONS

This is the largest linkage study reported for any disease. Our data indicate there are no major type 1 diabetes subtypes definable by linkage analyses; susceptibility is caused by actions of HLA and an apparently random selection from a large number of modest-effect loci; and apart from HLA and INS, there is no important susceptibility factor discoverable by linkage methods.

Background

Type 2 diabetes (T2D) is a multifactorial disease in which environmental triggers interact with genetic variants in the predisposition to the disease. A number of common variants have been associated with T2D but our knowledge of their ability to predict T2D prospectively is limited.

Methods and Findings

By using a Cox proportional hazard model, common variants in the PPARG (P12A), CAPN10 (SNP43 and 44), KCNJ11 (E23K), UCP2 (−866G>A), and IRS1 (G972R) genes were studied for their ability to predict T2D in 2,293 individuals participating in the Botnia study in Finland. After a median follow-up of 6 y, 132 (6%) persons developed T2D. The hazard ratio for risk of developing T2D was 1.7 (95% confidence interval [CI] 1.1–2.7) for the PPARG PP genotype, 1.5 (95% CI 1.0–2.2) for the CAPN10 SNP44 TT genotype, and 2.6 (95% CI 1.5–4.5) for the combination of PPARG and CAPN10 risk genotypes. In individuals with fasting plasma glucose ≥ 5.6 mmol/l and body mass index ≥ 30 kg/m2, the hazard ratio increased to 21.2 (95% CI 8.7–51.4) for the combination of the PPARG PP and CAPN10 SNP43/44 GG/TT genotypes as compared to those with the low-risk genotypes with normal fasting plasma glucose and body mass index < 30 kg/m2.

Conclusion

We demonstrate in a large prospective study that variants in the PPARG and CAPN10 genes predict future T2D. Genetic testing might become a future approach to identify individuals at risk of developing T2D.

In a large prospective study, Lyssenko and colleagues show that variants in the PPARG and CAPN10 genes can help predict whether a person will develop Type 2 diabetes.

Background

Elevated plasma levels of asymmetric dimethylarginine (ADMA) has been reported to be associated with insulin resistance and micro/macrovascular diabetic complications, and may predict cardiovascular events in type 2 diabetic patients. Dimethylarginine dimethylaminohydrolase 1 (DDAH1) is the major enzyme eliminating ADMA in humans, but the effect of genetic variations in DDAH1 on type 2 diabetes and its long-term outcome are unknown.

Methods

From July 2006 to June 2009, we assessed the association between polymorphisms in DDAH1 and type 2 diabetes in 814 consecutive unrelated subjects, including 309 type 2 diabetic patients and 505 non-diabetic individuals. Six single nucleotide polymorphisms (SNPs) in DDAH1, rs233112, rs1498373, rs1498374, rs587843, rs1403956, and rs1241321 were analyzed. Plasma ADMA levels were determined by high performance liquid chromatography. Insulin sensitivity was assessed by the homeostasis model assessment of insulin resistance (HOMA-IR).

Results

Among the 6 SNPs, only rs1241321 was significantly associated with a decreased risk of type 2 diabetes (AA vs GG+AG, OR = 0.64, 95% CI 0.47-0.86, p = 0.004). The association remained unchanged after adjustment for plasma ADMA level. The fasting plasma glucose and log HOMA-IR tended to be lower in subjects carrying the homozygous AA genotype of rs1241321 compared with the GG+AG genotypes. Over a median follow-up period of 28.2 months, there were 44 all-cause mortality and 50 major adverse cardiovascular events (MACE, including cardiovascular death, non-fatal myocardial infarction and stroke). Compared with the GG and AG genotypes, the AA genotype of rs1241321 was associated with reduced risk of MACE (HR = 0.31, 95% CI: 0.11-0.90, p = 0.03) and all-cause mortality (HR = 0.18, 95% CI: 0.04-0.80, p = 0.02) only in subgroup with type 2 diabetes. One common haplotype (GGCAGC) was found to be significantly associated with a decreased risk of type 2 diabetes (OR = 0.67, 95% CI = 0.46-0.98, p = 0.04).

Conclusions

Our results provide the first evidence that SNP rs1241321 in DDAH1 is associated with type 2 diabetes and its long-term outcome.

Aims

There are a large number of common genetic variants that have been robustly associated with low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, or triglyceride concentrations. The majority of these have been identified or confirmed in recent genome-wide association studies, but few studies have assessed the combined effect of known lipid variants. We hypothesized that these variants would influence both the need for interventions and myocardial infarction (MI) outcomes. We aimed to estimate combined effects of proven SNPs on LDL, HDL, and triglyceride concentrations and MI history in a representative older population.

Methods and results

In the InCHIANTI Study of Aging (age ≥65 years), we calculated individual dyslipidaemia risk allele counts for increased LDL (range 4–14, n = 594), reduced HDL (5–16, n = 635), and increased triglycerides (7–16, n = 611). Lipid levels were compared with ATPIII National Cholesterol Education Panel (NCEP) intervention guidelines. Individual variants and the APOE haplotype explained <2.1% of the variance in their respective lipid concentrations, with the exception of the CETP SNP rs1800775 and HDL levels (4.76%). Combined risk allele counts outperformed the largest single-SNP effects for LDL (explaining 7.1% of variance) and triglycerides (4.8%), but not HDL (3.4%). Risk alleles were divided as near as possible into quartiles. The 31% of respondents with 10 or more LDL increasing alleles were more likely to have LDL levels above the intervention threshold (OR 3.00, 95% CI 1.67–5.39, P = 2.5 × 10−4), compared with the 21% with 7 or less risk alleles. Similarly, the 35% with 13 or more triglyceride risk alleles were more likely to exceed NCEP intervention thresholds (OR 2.98, 95% CI 1.43–6.22, P = 0.004) compared with the 24% with 10 or less alleles. The number of individuals reporting an MI event was small (n = 67), but an event was more common in the 36% of respondents who had the highest combined risk allele score for all three lipids (OR 3.68, 95% CI 1.21–11.2, P = 0.021) compared with the lowest risk 22%.

Conclusion

In a representative older population, the cumulative effects of proven LDL- and triglyceride-altering genetic variants increased the odds of crossing the lipid-level threshold for therapeutic intervention by approximately three-fold.