No association of multiple type 2 diabetes loci with type 1 diabetes

ONECUT1 variants beyond type 1 and type 2 diabetes: exploring clinical diversity and epigenetic associations in Arab cohorts

ONECUT1 gene, encoding hepatocyte nuclear factor 6, is involved in pancreas and liver development. ONECUT1 mutations impair the function of pancreatic β-cells and control a transcriptional/epigenetic machinery regulating endocrine development. Homozygous nonsense and missense mutations at ONECUT1_p.E231 and a homozygous frameshift mutation at ONECUT1_p.M289 were reported in neonatal diabetes individuals of French, Turkish, and Indian ethnicity, respectively. Additionally, heterozygous variants were observed in Northern European T2D patients, and Italian patients with neonatal diabetes and early-/late-onset T2D. Examining diverse populations, such as Arabs known for consanguinity, can generalize the ONECUT1 involvement in diabetes. Upon screening the cohorts of Kuwaiti T1D and MODY families, and of Kuwaiti and Qatari T2D individuals, we observed two homozygous variants-the deleterious missense rs202151356_p.H33Q in one MODY, one T1D, and two T2D individuals, and the synonymous rs61735385_p.P94P in two T2D individuals. Heterozygous variants were also observed. Examination of GTEx, NephQTL, mQTLdb and HaploReg highlighted the rs61735385_p.P94P variant as eQTL influencing the tissue-specific expression of ONECUT1, as mQTL influencing methylation at CpG sites in and around ONECUT1 with the nearest site at 677-bases 3' to rs61735385_p.P94P; as overlapping predicted binding sites for NF-kappaB and EBF on ONECUT1. DNA methylation profiles of peripheral blood from 19 MODY-X patients versus eight healthy individuals revealed significant hypomethylation at two CpG sites-one located 617-bases 3' to the p.P94P variant and 8,102 bases away from transcription start; and the other located 14,999 bases away from transcription start. Our study generalizes the association of ONECUT1 with clinical diversity in diabetes.

Coordination of cross-talk between metabolism and epigenetic regulation by the SIN3 complex

Post-translational modifications of histone proteins control the expression of genes. Metabolites from central and one-carbon metabolism act as donor moieties to modify histones and regulate gene expression. Thus, histone modification and gene regulation are connected to the metabolite status of the cell. Histone modifiers, such as the SIN3 complex, regulate genes involved in proliferation and metabolism. The SIN3 complex contains a histone deacetylase and a histone demethylase, which regulate the chromatin landscape and gene expression. In this chapter, we review the cross-talk between metabolic pathways that produce donor moieties, and epigenetic complexes regulating proliferation and metabolic genes. This cross-talk between gene regulation and metabolism is tightly controlled, and disruption of this cross-talk leads to metabolic diseases. We discuss promising therapeutics that directly regulate histone modifiers, and can affect the metabolic status of the cell, alleviating some metabolic diseases.

The common rs13266634 C > T variant in SLC30A8 contributes to the heterogeneity of phenotype and clinical features of both type 1 and type 2 diabetic subtypes

AIMS

T2D and T1D are phenotypically heterogeneous. This study aims to reveal the relationship between the common SLC30A8 rs13266634 variant and subgroups of T2D and T1D and their clinical characteristics.

METHODS

We included 3158 OGTT-based healthy controls, unrelated 1754 T2D, and 1675 autoantibody-positive T1D individuals. The associations between rs13266634 and subtypes of T2D, T1D, autoantibody status and glycemic-related quantitative traits were performed by binary logistic regression analysis under the additive model and multiple linear regression with appropriate adjustment.

RESULTS

We found that the T allele of rs13266634 was protectively associated with lean (OR = 0.810, P = 6.91E-04) but not obese T2D with considerable heterogeneity (P = 0.018). This allele also conferred significant protection with T1D of single (OR = 0.847, P = 9.76E-03), but not multi autoantibodies with substantial heterogeneity (P = 0.005). This variant significantly affected OGTT-related insulin release in lean (P = 2.66E-03, 3.88E-03 for CIR and DI, respectively) but not obese healthy individuals. Furthermore, rs13266634 T allele correlated with the risk of ZnT8A (OR = 1.440, P = 3.31E-05) and IA-2A (OR = 1.219, P = 1.32E-03) positivity, with more effect size in children/adolescents compared with adult-onset T1D subtypes.

CONCLUSIONS

These suggested that the SLC30A8 rs13266634 variant might be put into genetic risk scores to assess the risk of the subtypes of T1D and T2D and their related clinical features.

Autoantibodies to Zinc Transporter 8 and SLC30A8 Genotype in Type 1 Diabetes Childhood: A Pioneering Study in North Africa

BACKGROUND

Type 1 diabetes (T1D) occurs as a result of insulin deficiency due to destructive lesions of pancreatic β cells. In addition to classical autoantibodies (Abs) to islet cell antigens, antizinc transporter 8 Abs (ZnT8-Ab) have been recently described in T1D.

OBJECTIVE

As no data on ZnT8-Ab in Tunisian patients has been reported, we aim to evaluate the relationships between ZnT8-Ab, ZnT8 coding gene (SLC30A8) promoter polymorphism, and T1D risk in newly diagnosed children.

METHODS

ZnT8-Ab were measured in the serum of T1D newly affected children (n = 156) who were admitted to the pediatric department of the Hedi Chaker University Hospital of Sfax. Rs13266634 was genotyped in T1D children and 79 of their first-degree parents. The SPSS software was used to analyze the serological data. Allelic association analysis was conducted with family-based association tests implemented in the FBAT program v1.5.1.

RESULTS

ZnT8-Ab was detected in 66/156 (42.3%) of T1D newly diagnosed children. Among them, 6 (9%) presented ZnT8-Ab as the only humoral marker. The inclusion of ZnT8-Ab increased the number of Ab-positive patients to 90% and reduced the negative ones by 27%. There was no evidence of any overtransmission of any allele of the rs13266634 C/T polymorphism from parents to affected T1D children, nor of any correlation with any clinical or serological parameter. After the T1D disease onset age adjustment, a significant association was observed with the C allele suggesting that it could have a susceptibility role.

CONCLUSION

ZnT8-Ab appears as a relevant diagnostic marker for T1D in Tunisian children, especially at the onset of the disease as teenagers.

Association of ABCC8 and KCNJ11 gene variants with type 1 diabetes in south Indians

Background

Type 1 diabetes mellitus (TIDM) is a polygenic disorder with the involvement of several genetic and environmental risk factors. Mutation in genes namely ABCC8 and KCNJ11 disrupt the potentiality of KATP channel and regulates the secretion of insulin by detecting a change in the blood glucose level and consequently maintains glucose homeostasis. The present study was designed to investigate the association of ABCC8 and KCNJ11gene polymorphisms with type 1 diabetes. A case-control study was conducted enrolling 60 cases suffering from T1DM and 60 healthy controls of comparable age and sex. Gene variations were determined by PCR-RFLP and ARMS-PCR method.

Results

The ABCC8-3C > T (rs1799854) variation was found to be significantly associated with T1DM (p<0.01) and “CT” genotype was found to be predominant in T1DM with a threefold increased risk to diabetes and the association was statistically significant. However, we did not find any significant association of C>T (rs1801261) polymorphism of ABCC8 with T1DM. A significant association was observed for genetic variation at rs5219 C>T polymorphism and the frequency of TT genotype was found to be significantly higher in patients (46.7%) than in controls (21.7%), indicating the significant role of the KCNJ11 rs5219 variant in T1DM susceptibility (p<0.001), but we did not observe any significant association of G>A (rs5215) polymorphism of KCNJ11 with T1DM. In addition, haplotype analysis of the two genes revealed four haplotypes such as T-C-G-T, T-C-A-T, C-C-G-T, and T-T-G-T as risk haplotypes for type 1 diabetes (p<0.02) potentially making individual effects of these variants on the disease susceptibility, thereby indicating the synergistic role of these genes in the regulation of glucose homeostasis.

Conclusions

The present study highlights the importance of personalized medicine based on individual genetic profile.

Hepatopathy Associated With Type 1 Diabetes: Distinguishing Non-alcoholic Fatty Liver Disease From Glycogenic Hepatopathy

Autoimmune destruction of pancreatic β-cells results in the permanent loss of insulin production in type 1 diabetes (T1D). The daily necessity to inject exogenous insulin to treat hyperglycemia leads to a relative portal vein insulin deficiency and potentiates hypoglycemia which can induce weight gain, while daily fluctuations of blood sugar levels affect the hepatic glycogen storage and overall metabolic control. These, among others, fundamental characteristics of T1D are associated with the development of two distinct, but in part clinically similar hepatopathies, namely non-alcoholic fatty liver disease (NAFLD) and glycogen hepatopathy (GlyH). Recent studies suggest that NAFLD may be increasingly common in T1D because more people with T1D present with overweight and/or obesity, linked to the metabolic syndrome. GlyH is a rare but underdiagnosed complication hallmarked by extremely brittle metabolic control in, often young, individuals with T1D. Both hepatopathies share clinical similarities, troubling both diagnosis and differentiation. Since NAFLD is increasingly associated with cardiovascular and chronic kidney disease, whereas GlyH is considered self-limiting, awareness and differentiation between both condition is important in clinical care. The exact pathogenesis of both hepatopathies remains obscure, hence licensed pharmaceutical therapy is lacking and general awareness amongst physicians is low. This article aims to review the factors potentially contributing to fatty liver disease or glycogen storage disruption in T1D. It ends with a proposal for clinicians to approach patients with T1D and potential hepatopathy.

The role of transcription factor 7-like 2 in metabolic disorders

Transcription factor 7-like 2 (TCF7L2), a member of the T cell factor/lymphoid enhancer factor family, generally forms a complex with β-catenin to regulate the downstream target genes as an effector of the canonical Wnt signalling pathway. TCF7L2 plays a vital role in various biological processes and functions in many organs and tissues, including the liver, islet and adipose tissues. Further, TCF7L2 down-regulates hepatic gluconeogenesis and promotes lipid accumulation. In islets, TCF7L2 not only affects the insulin secretion of the β-cells but also has an impact on other cells. In addition, TCF7L2 influences adipogenesis in adipose tissues. Thus, an out-of-control TCF7L2 expression can result in metabolic disorders. The TCF7L2 gene is composed of 17 exons, generating 13 different transcripts, and has many single-nucleotide polymorphisms (SNPs). The discovery that these SNPs have an impact on the risk of type 2 diabetes (T2D) has attracted thorough investigations in the study of TCF7L2. Apart from T2D, TCF7L2 SNPs are also associated with type 1, posttransplant and other types of diabetes. Furthermore, TCF7L2 variants affect the progression of other disorders, such as obesity, cancers, metabolic syndrome and heart diseases. Finally, the interaction between TCF7L2 variants and diet also needs to be investigated.

New Insights into the Genetics of Latent Autoimmune Diabetes in Adults

PURPOSE OF REVIEW

Diabetes is a spectrum of clinical manifestations, including latent autoimmune diabetes in adults (LADA). However, it has been questioned whether LADA exists or simply is a group of misclassified type 1 diabetes (T1D) and type 2 diabetes (T2D) patients. This review will provide an updated overview of the genetics of LADA, highlight what genetics tell us about LADA as a diabetes subtype, and point to future directions in the study of LADA.

RECENT FINDINGS

Recent studies have verified the genetic overlap between LADA and both T1D and T2D and have contributed identification of a novel LADA-specific locus, namely, PFKFB3, and subtype-specific signatures in the HLA region. Genetic risk scores comprising T1D-risk variants have been shown to be a promising tool for discriminating diabetes subtypes and identifying patients rapidly progressing to insulin dependence. Genetic data support the existence of LADA, but further studies are needed to fully determine the place of LADA in the diabetes spectrum.

Correlates of Insulin-Stimulated Glucose Disposal in Recent-Onset Type 1 and Type 2 Diabetes

CONTEXT AND OBJECTIVE

Not only type 2 diabetes (T2D), but also type 1 diabetes (T1D), can be associated with insulin resistance, as assessed using insulin-stimulated whole-body glucose disposal (M-value). We hypothesized that different factors would affect the M-value at the onset of T1D and T2D.

DESIGN AND PATIENTS

We examined 132 patients with T1D or T2D matched for sex, age, and body mass index with a known diabetes duration of <12 months. Multivariable linear regression analyses were applied to test the associations between glycemic control, blood lipid levels, adiponectin, and proinflammatory immune mediators and the M-value, obtained from the hyperinsulinemic-euglycemic clamp.

RESULTS

Despite comparable age, body mass index, and near-normoglycemic control, the mean M-value was lower in those with T2D than in those with T1D. Patients with T1D had a lower waist/hip ratio and serum triglycerides but higher serum adiponectin than patients with T2D. However, the circulating proinflammatory markers were not different. Even with adjustments for glucose-lowering treatments, the fasting blood glucose correlated negatively with the M-value in both groups. However, gamma-glutamyl transferase-independently of any treatments-correlated negatively only in T2D. In contrast, serum adiponectin correlated positively with the M-values.

CONCLUSIONS

Fasting glycemia correlated with insulin-stimulated glucose disposal in both diabetes types. However, altered liver and adipose tissue function were associated with insulin-stimulated glucose disposal only in T2D, underpinning the specific differences between these diabetes types.

SLC30A8 Gene rs13266634 C/T Polymorphism in Children with Type 1 Diabetes in Tamil Nadu, India

Objective

Zinc transporter 8 (ZnT8) is a multi-transmembrane protein situated in the insulin secretory granule of the islets of β-cells and is identified as a novel auto-antigen in type 1 diabetes (T1D). The gene coding for ZnT8, solute carrier family 30 member 8 (SLC30A8) is located on chromosome 8q24.11. This study aimed to identify the association of SLC30A8 rs13266634 C/T gene polymorphism with T1D in a sample of T1D children in Tamil Nadu, India.

Methods

The family based study was conducted in 121 T1D patients and 214 of their family members as controls. The SLC30A8 gene rs13266634 C/T polymorphism was evaluated by polymerase chain reaction-restriction fragment length polymorphism.

Results

No significant differences were observed in either allele (odds ratio: 0.92; confidence interval: 0.33-2.58; p=0.88) and genotype (CC: p=0.74; CT: p=0.82; TT: p=0.80) frequencies of rs13266634 C/T between T1D patients and controls. Transmission disequilibrium test has identified over-transmission of mutant T allele from parents to affected children (T: U=9:7) without statistical significance. Metaanalysis on the overall effects of rs13266634 C allele frequency was not different (p=0.10 and Pheterogeneity=0.99) in T1D patients as compared to the controls.

Conclusion

The present study along with the meta-analysis does not show any substantial association of the rs13266634 C/T polymorphism with T1D development in this population.

Genetic Aspects of Latent Autoimmune Diabetes in Adults: A Mini-Review

Diabetes is a multifactorial disease, caused by a complex interplay between environmental and genetic risk factors. Genetic determinants of particularly Type 1 Diabetes (T1D) and Type 2 Diabetes (T2D) have been studied extensively, whereas well-powered studies of Latent Autoimmune Diabetes in Adults (LADA) are lacking. So far available studies support a clear genetic overlap between LADA and T1D, however, with smaller effect sizes of the T1D-risk variants in LADA as compared to T1D. A genetic overlap between LADA and T2D is less clear. However, recent studies, including large numbers of LADA patients, provide different lines of evidence to support a genetic overlap between T2D and LADA. The genetic predisposition to LADA is yet to be explored in a study design, like a genome- wide association study, which allows for analyses of the genetic predisposition independently of prior hypothesis about potential candidate genes. This type of study may facilitate the discovery of risk variants associated with LADA independently of T1D and T2D, and is central in order to determine if LADA should be considered as an independent diabetic subtype. Extended knowledge about the genetic predisposition to LADA may also facilitate stratification of the heterogeneous group of LADA patients, which may assist the choice of treatment. This mini-review summarizes current knowledge of the genetics of LADA, and discusses the perspectives for future studies.

Genetic confirmation of T2DM meta-analysis variants studied in gestational diabetes mellitus in an Indian population

BACKGROUND

Meta-analysis is useful for combining the results of different studies statistically to confirm genuine associations in genetics. Based on earlier reports, we aimed to investigate the association between type 2 diabetes mellitus (T2DM) genetic variants identified in a previous meta-analysis in gestational diabetes mellitus (GDM) in an Indian woman.

MATERIAL AND METHODS

In this study, 137 pregnant women with GDM and 150 pregnant women were selected on the basis of their serum glucose levels. The six single nucleotide polymorphisms (SNPs) of different genes studied had known involvement in pancreatic β-cell function, particular pathways linked to T2DM, and other biological functions. Genomic DNA was isolated from the 287 women for polymerase chain reaction and restriction fragment length polymorphism analyses.

RESULTS

The rs7903146, rs13266634, rs2283228, rs5210 and rs179881 SNPs were found to be positively associated with GDM when calculated for genotype and allele frequencies (p < 0.05), but rs680 (ApaI) variant did not show statistically significant association (p = 0.31). The rs7903146, rs2283228, rs5210 and rs680 variants showed a strong association with oral glucose tolerance test values.

CONCLUSION

The SNPs studied in this GDM had the same role as those identified in a previous T2DM meta-analysis, and showed positive association in the Indian women. Meta-analyses should be implemented to assess the IGF2 gene in GDM subjects.

TCF7L2 Genetic Variants Contribute to Phenotypic Heterogeneity of Type 1 Diabetes

OBJECTIVE

The phenotypic diversity of type 1 diabetes suggests heterogeneous etiopathogenesis. We investigated the relationship of type 2 diabetes-associated transcription factor 7 like 2 (TCF7L2) single nucleotide polymorphisms (SNPs) with immunologic and metabolic characteristics at type 1 diabetes diagnosis.

RESEARCH DESIGN AND METHODS

We studied TrialNet participants with newly diagnosed autoimmune type 1 diabetes with available TCF7L2 rs4506565 and rs7901695 SNP data (n = 810; median age 13.6 years; range 3.3-58.6). We modeled the influence of carrying a TCF7L2 variant (i.e., having 1 or 2 minor alleles) on the number of islet autoantibodies and oral glucose tolerance test (OGTT)-stimulated C-peptide and glucose measures at diabetes diagnosis. All analyses were adjusted for known confounders.

RESULTS

The rs4506565 variant was a significant independent factor of expressing a single autoantibody, instead of multiple autoantibodies, at diagnosis (odds ratio [OR] 1.66 [95% CI 1.07, 2.57], P = 0.024). Interaction analysis demonstrated that this association was only significant in participants ≥12 years old (n = 504; OR 2.12 [1.29, 3.47], P = 0.003) but not younger ones (n = 306, P = 0.73). The rs4506565 variant was independently associated with higher C-peptide area under the curve (AUC) (P = 0.008) and lower mean glucose AUC (P = 0.0127). The results were similar for the rs7901695 SNP.

CONCLUSIONS

In this cohort of individuals with new-onset type 1 diabetes, type 2 diabetes-linked TCF7L2 variants were associated with single autoantibody (among those ≥12 years old), higher C-peptide AUC, and lower glucose AUC levels during an OGTT. Thus, carriers of the TCF7L2 variant had a milder immunologic and metabolic phenotype at type 1 diabetes diagnosis, which could be partly driven by type 2 diabetes-like pathogenic mechanisms.

Childhood adiposity and risk of type 1 diabetes: A Mendelian randomization study

BACKGROUND

The incidence of type 1 diabetes (T1D) is increasing globally. One hypothesis is that increasing childhood obesity rates may explain part of this increase, but, as T1D is rare, intervention studies are challenging to perform. The aim of this study was to assess this hypothesis with a Mendelian randomization approach that uses genetic variants as instrumental variables to test for causal associations.

METHODS AND FINDINGS

We created a genetic instrument of 23 single nucleotide polymorphisms (SNPs) associated with childhood adiposity in children aged 2-10 years. Summary-level association results for these 23 SNPs with childhood-onset (<17 years) T1D were extracted from a meta-analysis of genome-wide association study with 5,913 T1D cases and 8,828 reference samples. Using inverse-variance weighted Mendelian randomization analysis, we found support for an effect of childhood adiposity on T1D risk (odds ratio 1.32, 95% CI 1.06-1.64 per standard deviation score in body mass index [SDS-BMI]). A sensitivity analysis provided evidence of horizontal pleiotropy bias (p = 0.04) diluting the estimates towards the null. We therefore applied Egger regression and multivariable Mendelian randomization methods to control for this type of bias and found evidence in support of a role of childhood adiposity in T1D (odds ratio in Egger regression, 2.76, 95% CI 1.40-5.44). Limitations of our study include that underlying genes and their mechanisms for most of the genetic variants included in the score are not known. Mendelian randomization requires large sample sizes, and power was limited to provide precise estimates. This research has been conducted using data from the Early Growth Genetics (EGG) Consortium, the Genetic Investigation of Anthropometric Traits (GIANT) Consortium, the Tobacco and Genetics (TAG) Consortium, and the Social Science Genetic Association Consortium (SSGAC), as well as meta-analysis results from a T1D genome-wide association study.

CONCLUSIONS

This study provides genetic support for a link between childhood adiposity and T1D risk. Together with evidence from observational studies, our findings further emphasize the importance of measures to reduce the global epidemic of childhood obesity and encourage mechanistic studies.

Importance of Zinc Transporter 8 Autoantibody in the Diagnosis of Type 1 Diabetes in Latin Americans

There is a scarcity of data of zinc transporter-8 autoantibody (ZnT8A) on mixed populations such as Brazilian. Therefore, we evaluated the relevance of ZnT8A for type 1 diabetes (T1D) diagnosis and the role of ZnT8 coding gene (SLC30A8) in T1D predisposition. Patients with T1D (n = 629; diabetes duration = 11 (6-16) years) and 651 controls were genotyped for SLC30A8 rs16889462 and rs2466295 variants (BeadXpress platform). ZnT8 triple antibody was measured by ELISA; glutamic acid decarboxylase (GAD65A) and protein tyrosine phosphatase (IA-2A) autoantibodies by radioimmunoassay.

RESULTS

Znt8A was detected in 68.7% of recent-onset T1D patients and 48.9% of the entire patient cohort, similar to GAD65A (68.3% and 47.2%) and IA-2A (64.8% and 42.4%) positivities respectively. ZnT8A was the only antibody in 8.4% of patients. Znt8A and IA2A frequencies and titers were independent of gender and ethnicity, whereas GAD65A titers were greater in females. The diabetes duration-dependent decline in ZnT8A frequency was similar to GAD65A and IA-2A. The SLC30A8 rs2466293 AG + GG genotypes were associated with T1D risk in non-European descents (56.2% × 42.9%; p = 0.018), and the GG genotype with higher ZnT8A titers in recent-onset T1D: 834.5 IU/mL (711.3-2190.0) × 281 IU/mL (10.7-726.8); p = 0.027. Conclusion ZnT8A detection increases T1D diagnosis rate even in mixed populations. SLC30A8 rs2466293 was associated with T1D predisposition in non-European descents.

Dissecting heterogeneity in paediatric Type 1 diabetes: association of TCF7L2 rs7903146 TT and low-risk human leukocyte antigen (HLA) genotypes

AIMS

To test the hypothesis that non-obese individuals with childhood-onset Type 1 diabetes and the rs7903146 TT genotype would be less likely to have high-risk human leukocyte antigen (HLA) genotypes and alleles.

METHODS

We studied a cohort of 105 non-obese participants in the T1D Exchange Biobank Residual Insulin Study who had childhood-onset Type 1 diabetes [mean (sd) age at onset and recruitment, respectively, 9.9 (4.15) and 14.4 (4.13) years; 84.8% non-Hispanic white]. We analysed islet autoantibodies (glutamic acid decarboxylase 65, islet cell autoantigen 512/islet antigen-2 and zinc transporter 8), non-fasting random C-peptide levels, HLA type and TCF7L2 single nucleotide polymorphism rs7903146 in this cohort.

RESULTS

None of the 13 individuals with the rs7903146 TT genotype carried the highest Type 1 diabetes risk HLA genotype, i.e. DRB1*03:01/DR4 (DRB1*0401, *04:05 or *04:02), compared with 29.4% (27/92) of those without it (P=0.023). The DRB1*03:01 allele was present in 15.4% (2/13) of individuals with the single nucleotide polymorphism, compared with 59.8% (55/92) of those without it (P=0.003). Analyses restricted to autoantibody-positive individuals (n=80) yielded similar results. The HLA DRB1*15:01 allele, which affords dominant protection against Type 1 diabetes, was found in one participant, who had multiple islet autoantibodies and carried the rs7903146 TT genotype.

CONCLUSIONS

These findings further support the hypothesis that TCF7L2 gene variation contributes to diabetogenesis in a subset of young people with Type 1 diabetes, opening possible new pathways for therapy and prevention.

The prevalence and predictive value of the SLC30A8 R325W polymorphism and zinc transporter 8 autoantibodies in the development of GDM and postpartum type 1 diabetes

The objectives were to evaluate possible associations between the SLC30A8 R325W polymorphism and gestational diabetes mellitus (GDM) as well as postpartum development of type 2 diabetes. Furthermore, we wanted to confirm the prevalence of zinc transporter 8 autoantibodies (ZnT8A), as previously reported, in a larger population and study its predictive value in relation to other β cell specific autoantibodies in postpartum development of type 1 diabetes. Women diagnosed with GDM (n = 776) and women without diabetes (n = 511) were included in the study. Autoantibodies were analyzed in all women using enzyme-linked immunosorbent assay. DNA was extracted when possible from women with GDM (n = 536) and all of the controls. R325W was detected through polymerase chain reaction and specific restriction digestion. The R325W C-allele were more frequent in women with GDM compared to in controls (OR 1.47, 95 % CI 1.16-1.88, p = 0.0018) but not significantly increased in women with GDM and postpartum development of type 2 diabetes. Autoantibodies were found in 6.8 % (53/776) of the women with GDM and approximately 3.2 % (25/776) were ZnT8A positive. Approximately 19 % (10/53) of the autoantibody positive women with GDM developed postpartum type 1 diabetes. In conclusion, this is the first study to report a significant association between the R325W C-allele and increased risk of developing GDM. All of the autoantibody positive women with GDM who developed postpartum type 1 diabetes were positive for autoantibodies against glutamic acid decarboxylase (GADA). Thus ZnT8A did not have any additional predictive value in postpartum development of type 1 diabetes.

Islet biology, the CDKN2A/B locus and type 2 diabetes risk

Type 2 diabetes, fuelled by the obesity epidemic, is an escalating worldwide cause of personal hardship and public cost. Diabetes incidence increases with age, and many studies link the classic senescence and ageing protein p16(INK4A) to diabetes pathophysiology via pancreatic islet biology. Genome-wide association studies (GWASs) have unequivocally linked the CDKN2A/B locus, which encodes p16 inhibitor of cyclin-dependent kinase (p16(INK4A)) and three other gene products, p14 alternate reading frame (p14(ARF)), p15(INK4B) and antisense non-coding RNA in the INK4 locus (ANRIL), with human diabetes risk. However, the mechanism by which the CDKN2A/B locus influences diabetes risk remains uncertain. Here, we weigh the evidence that CDKN2A/B polymorphisms impact metabolic health via islet biology vs effects in other tissues. Structured in a bedside-to-bench-to-bedside approach, we begin with a summary of the evidence that the CDKN2A/B locus impacts diabetes risk and a brief review of the basic biology of CDKN2A/B gene products. The main emphasis of this work is an in-depth look at the nuanced roles that CDKN2A/B gene products and related proteins play in the regulation of beta cell mass, proliferation and insulin secretory function, as well as roles in other metabolic tissues. We finish with a synthesis of basic biology and clinical observations, incorporating human physiology data. We conclude that it is likely that the CDKN2A/B locus influences diabetes risk through both islet and non-islet mechanisms.

Variants in Genes Controlling Oxidative Metabolism Contribute to Lower Hepatic ATP Independent of Liver Fat Content in Type 1 Diabetes

Type 1 diabetes has been recently linked to nonalcoholic fatty liver disease (NAFLD), which is known to associate with insulin resistance, obesity, and type 2 diabetes. However, the role of insulin resistance and hyperglycemia for hepatic energy metabolism is yet unclear. To analyze early abnormalities in hepatic energy metabolism, we examined 55 patients with recently diagnosed type 1 diabetes. They underwent hyperinsulinemic-normoglycemic clamps with [6,6-(2)H2]glucose to assess whole-body and hepatic insulin sensitivity. Hepatic γATP, inorganic phosphate (Pi), and triglyceride concentrations (hepatocellular lipid content [HCL]) were measured with multinuclei magnetic resonance spectroscopy ((31)P/(1)H-MRS). Glucose-tolerant humans served as control (CON) (n = 57). Whole-body insulin sensitivity was 44% lower in patients than in age- and BMI-matched CON. Hepatic γATP was 15% reduced (2.3 ± 0.6 vs. 2.7 ± 0.6 mmol/L, P < 0.001), whereas hepatic Pi and HCL were similar in patients when compared with CON. Across all participants, hepatic γATP correlated negatively with glycemia and oxidized LDL. Carriers of the PPARG G allele (rs1801282) and noncarriers of PPARGC1A A allele (rs8192678) had 21 and 13% lower hepatic ATP concentrations. Variations in genes controlling oxidative metabolism contribute to a reduction in hepatic ATP in the absence of NAFLD, suggesting that alterations in hepatic mitochondrial function may precede diabetes-related liver diseases.

Monogenic Diabetes: What It Teaches Us on the Common Forms of Type 1 and Type 2 Diabetes

To date, more than 30 genes have been linked to monogenic diabetes. Candidate gene and genome-wide association studies have identified > 50 susceptibility loci for common type 1 diabetes (T1D) and approximately 100 susceptibility loci for type 2 diabetes (T2D). About 1-5% of all cases of diabetes result from single-gene mutations and are called monogenic diabetes. Here, we review the pathophysiological basis of the role of monogenic diabetes genes that have also been found to be associated with common T1D and/or T2D. Variants of approximately one-third of monogenic diabetes genes are associated with T2D, but not T1D. Two of the T2D-associated monogenic diabetes genes-potassium inward-rectifying channel, subfamily J, member 11 (KCNJ11), which controls glucose-stimulated insulin secretion in the β-cell; and peroxisome proliferator-activated receptor γ (PPARG), which impacts multiple tissue targets in relation to inflammation and insulin sensitivity-have been developed as major antidiabetic drug targets. Another monogenic diabetes gene, the preproinsulin gene (INS), is unique in that INS mutations can cause hyperinsulinemia, hyperproinsulinemia, neonatal diabetes mellitus, one type of maturity-onset diabetes of the young (MODY10), and autoantibody-negative T1D. Dominant heterozygous INS mutations are the second most common cause of permanent neonatal diabetes. Moreover, INS gene variants are strongly associated with common T1D (type 1a), but inconsistently with T2D. Variants of the monogenic diabetes gene Gli-similar 3 (GLIS3) are associated with both T1D and T2D. GLIS3 is a key transcription factor in insulin production and β-cell differentiation during embryonic development, which perturbation forms the basis of monogenic diabetes as well as its association with T1D. GLIS3 is also required for compensatory β-cell proliferation in adults; impairment of this function predisposes to T2D. Thus, monogenic forms of diabetes are invaluable "human models" that have contributed to our understanding of the pathophysiological basis of common T1D and T2D.

Association of polymorphism rs7903146 gene TCF7L2 with low concentrations of autoantibodies in latent autoimmune diabetes of adults (LADA)

Aim. To determine the frequencies of alleles and genotypes of polymorphic marker rs7903146 of the TCF7L2 gene in latent autoimmune diabetes in adults (LADA) and healthy individuals. The aims of the study were also to compare the distribution of alleles and genotypes and to explore the association with the development of LADA.Materials and methods. A total of 96 patients (46 females and 50 males) with LADA and 201 healthy individuals were examined. A quantitative determination of autoantibodies GADA, ICA, IA-2A and ZnT8 in the serum of LADA patients was performed. All patients underwent genotyping of rs7903146 of the TCF7L2 genes.Results. There was an increased frequency of the T allele and genotype T+ of marker rs7903146 of the TCF7L2 gene in patients with LADA with low concentrations of autoantibodies compared to a group of patients with high concentrations and with controls. We observed significant associations of the T allele and genotype T+ with LADA in patients with low concentrations of autoantibodies [p = 0.02; odds ratio (OR) = 1.85; 95% confidence interval (CI) = 1.10–3.13 and p = 0.04; OR = 2.14; 95% CI = 1.01–4.53 for the T allele and genotype T+, respectively).Conclusion. The results of the study suggest that LADA patients with low concentrations of autoantibodies have a genetically pre-determined similarity with patients with type 2 diabetes.

Structural Properties of Gene Promoters Highlight More than Two Phenotypes of Diabetes

Genome-wide association studies (GWAS) published in the last decade raised the number of loci associated with type 1 (T1D) and type 2 diabetes (T2D) to more than 50 for each of these diabetes phenotypes. The environmental factors seem to play an important role in the expression of these genes, acting through transcription factors that bind to promoters. Using the available databases we examined the promoters of various genes classically associated with the two main diabetes phenotypes. Our comparative analyses have revealed significant architectural differences between promoters of genes classically associated with T1D and T2D. Nevertheless, five gene promoters (about 16%) belonging to T1D and six gene promoters (over 19%) belonging to T2D have shown some intermediary structural properties, suggesting a direct relationship to either LADA (Latent Autoimmune Diabetes in Adults) phenotype or to non-autoimmune type 1 phenotype. The distribution of these promoters in at least three separate classes seems to indicate specific pathogenic pathways. The image-based patterns (DNA patterns) generated by promoters of genes associated with these three phenotypes support the clinical observation of a smooth link between specific cases of typical T1D and T2D. In addition, a global distribution of these DNA patterns suggests that promoters of genes associated with T1D appear to be evolutionary more conserved than those associated with T2D. Though, the image based patterns obtained by our method might be a new useful parameter for understanding the pathogenetic mechanism and the diabetogenic gene networks.

Genetics of type 2 diabetes-pitfalls and possibilities

Type 2 diabetes (T2D) is a complex disease that is caused by a complex interplay between genetic, epigenetic and environmental factors. While the major environmental factors, diet and activity level, are well known, identification of the genetic factors has been a challenge. However, recent years have seen an explosion of genetic variants in risk and protection of T2D due to the technical development that has allowed genome-wide association studies and next-generation sequencing. Today, more than 120 variants have been convincingly replicated for association with T2D and many more with diabetes-related traits. Still, these variants only explain a small proportion of the total heritability of T2D. In this review, we address the possibilities to elucidate the genetic landscape of T2D as well as discuss pitfalls with current strategies to identify the elusive unknown heritability including the possibility that our definition of diabetes and its subgroups is imprecise and thereby makes the identification of genetic causes difficult.

Association between diabetes and amyotrophic lateral sclerosis in Sweden

BACKGROUND AND PURPOSE

Energy metabolism is altered in patients with amyotrophic lateral sclerosis (ALS) but the role of diabetes is largely unknown.

METHODS

A population-based case-control study was conducted of 5108 ALS cases and 25,540 individually matched population controls during 1991-2010. Information on ALS and pre-existing diabetes was retrieved from the Swedish Patient Register to explore the association of ALS with diabetes overall and with insulin-dependent or non-insulin-dependent diabetes specifically. Variation of the association by diabetes duration and age was also studied.

RESULTS

In total, 224 ALS cases (4.39%) and 1437 controls (5.63%) had diabetes before the index date, leading to an overall inverse association between diabetes and ALS risk [odds ratio (OR) 0.79, 95% confidence interval (CI) 0.68-0.91]. The association was strong for non-insulin-dependent diabetes (OR 0.66, 95% CI 0.53-0.81) but not for insulin-dependent diabetes (OR 0.83, 95% CI 0.60-1.15) and varied as a function of diabetes duration, with the strongest association observed around 6 years after first ascertainment of diabetes. The association was age-specific; the inverse association was noted only amongst individuals aged 70 or older. In contrast, for younger individuals (<50 years), pre-existing insulin-dependent diabetes was associated with a higher ALS risk (OR 5.38, 95% CI 1.87-15.51).

CONCLUSIONS

Our study suggests that there is an association between diabetes and ALS, and highlights the importance of taking into account age, insulin dependence and diabetes duration. Future studies should explore whether the association is independent of body mass index.

KCNJ11: Genetic Polymorphisms and Risk of Diabetes Mellitus

Diabetes mellitus (DM) is a major worldwide health problem and its prevalence has been rapidly increasing in the last century. It is caused by defects in insulin secretion or insulin action or both, leading to hyperglycemia. Of the various types of DM, type 2 occurs most frequently. Multiple genes and their interactions are involved in the insulin secretion pathway. Insulin secretion is mediated through the ATP-sensitive potassium (KATP) channel in pancreatic beta cells. This channel is a heteromeric protein, composed of four inward-rectifier potassium ion channel (Kir6.2) tetramers, which form the pore of the KATP channel, as well as sulfonylurea receptor 1 subunits surrounding the pore. Kir6.2 is encoded by the potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) gene, a member of the potassium channel genes. Numerous studies have reported the involvement of single nucleotide polymorphisms of the KCNJ11 gene and their interactions in the susceptibility to DM. This review discusses the current evidence for the contribution of common KCNJ11 genetic variants to the development of DM. Future studies should concentrate on understanding the exact role played by these risk variants in the development of DM.

Type 2 diabetes susceptibility gene variants predispose to adult-onset autoimmune diabetes

AIMS/HYPOTHESIS

Latent autoimmune diabetes in adults (LADA) is phenotypically a hybrid of type 1 and type 2 diabetes. Genetically LADA is poorly characterised but does share genetic predisposition with type 1 diabetes. We aimed to improve the genetic characterisation of LADA and hypothesised that type 2 diabetes-associated gene variants also predispose to LADA, and that the associations would be strongest in LADA patients with low levels of GAD autoantibodies (GADA).

METHODS

We assessed 41 type 2 diabetes-associated gene variants in Finnish (phase I) and Swedish (phase II) patients with LADA (n = 911) or type 1 diabetes (n = 406), all diagnosed after the age of 35 years, as well as in non-diabetic control individuals 40 years or older (n = 4,002).

RESULTS

Variants in the ZMIZ1 (rs12571751, p = 4.1 × 10(-5)) and TCF7L2 (rs7903146, p = 5.8 × 10(-4)) loci were strongly associated with LADA. Variants in the KCNQ1 (rs2237895, p = 0.0012), HHEX (rs1111875, p = 0.0024 in Finns) and MTNR1B (rs10830963, p = 0.0039) loci showed the strongest association in patients with low GADA, supporting the hypothesis that the disease in these patients is more like type 2 diabetes. In contrast, variants in the KLHDC5 (rs10842994, p = 9.5 × 10(-4) in Finns), TP53INP1 (rs896854, p = 0.005), CDKAL1 (rs7756992, p = 7.0 × 10(-4); rs7754840, p = 8.8 × 10(-4)) and PROX1 (rs340874, p = 0.003) loci showed the strongest association in patients with high GADA. For type 1 diabetes, a strong association was seen for MTNR1B (rs10830963, p = 3.2 × 10(-6)) and HNF1A (rs2650000, p = 0.0012).

CONCLUSIONS/INTERPRETATION

LADA and adult-onset type 1 diabetes share genetic risk variants with type 2 diabetes, supporting the idea of a hybrid form of diabetes and distinguishing them from patients with classical young-onset type 1 diabetes.

Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes

Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15 × 10(-94) Collapse

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MESH Headings

• Black or African American/genetics
• Diabetes Mellitus, Type 2/genetics
• Diabetes Mellitus, Type 2/pathology
• Genome-Wide Association Study
• HLA-B27 Antigen/genetics
• HMGA2 Protein/genetics
• Humans
• KCNQ1 Potassium Channel/genetics
• Mutant Chimeric Proteins/genetics
• Polymorphism, Single Nucleotide
• Transcription Factor 7-Like 2 Protein/genetics

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Affiliation(s)

Maggie C. Y. Ng Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Daniel Shriner Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Brian H. Chen Program on Genomics and Nutrition, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America Center for Metabolic Disease Prevention, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America
Jiang Li Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Wei-Min Chen Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
Xiuqing Guo Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
Jiankang Liu Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Suzette J. Bielinski Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
Lisa R. Yanek The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
Michael A. Nalls Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
Mary E. Comeau Center for Public Health Genomics, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Laura J. Rasmussen-Torvik Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Richard A. Jensen Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Medicine, University of Washington, Seattle, Washington, United States of America
Daniel S. Evans San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California, United States of America
Yan V. Sun Department of Epidemiology and Biomedical Informatics, Emory University, Atlanta, Georgia, United States of America
Ping An Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Sanjay R. Patel Division of Sleep Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
Yingchang Lu The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Jirong Long Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
Loren L. Armstrong Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Lynne Wagenknecht Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Lingyao Yang Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Beverly M. Snively Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Nicholette D. Palmer Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Poorva Mudgal Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Carl D. Langefeld Center for Public Health Genomics, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Keith L. Keene Department of Biology, Center for Health Disparities, East Carolina University, Greenville, North Carolina, United States of America
Barry I. Freedman Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Josyf C. Mychaleckyj Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
Uma Nayak Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
Leslie J. Raffel Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
Mark O. Goodarzi Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
Y-D Ida Chen Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
Herman A. Taylor Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America Jackson State University, Tougaloo College, Jackson, Mississippi, United States of America
Adolfo Correa Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Mario Sims Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
David Couper Collaborative Studies Coordinating Center, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
James S. Pankow Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, United States of America
Eric Boerwinkle Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
Adebowale Adeyemo Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Ayo Doumatey Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Guanjie Chen Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Rasika A. Mathias The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America Division of Allergy and Clinical Immunology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
Dhananjay Vaidya The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
Andrew B. Singleton Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
Alan B. Zonderman Laboratory of Personality and Cognition, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America
Robert P. Igo Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
John R. Sedor Department of Medicine, Case Western Reserve University, MetroHealth System campus, Cleveland, Ohio, United States of America Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, Ohio, United States of America
the FIND Consortium
Edmond K. Kabagambe Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
David S. Siscovick Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Medicine, University of Washington, Seattle, Washington, United States of America Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
Barbara McKnight Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
Kenneth Rice Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
Yongmei Liu Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
Wen-Chi Hsueh Department of Medicine, University of California, San Francisco, California, United States of America
Wei Zhao Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
Lawrence F. Bielak Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
Aldi Kraja Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Michael A. Province Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Erwin P. Bottinger The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Omri Gottesman The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Qiuyin Cai Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
Wei Zheng Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
William J. Blot Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee; International Epidemiology Institute, Rockville, Maryland, United States of America
William L. Lowe Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Jennifer A. Pacheco Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Dana C. Crawford Center for Human Genetics Research and Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
the eMERGE Consortium
the DIAGRAM Consortium
Elin Grundberg Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
the MuTHER Consortium
Stephen S. Rich Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
M. Geoffrey Hayes Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
Xiao-Ou Shu Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
Ruth J. F. Loos The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
Ingrid B. Borecki Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
Patricia A. Peyser Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
Steven R. Cummings San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California, United States of America
Bruce M. Psaty Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America Department of Medicine, University of Washington, Seattle, Washington, United States of America Department of Epidemiology, University of Washington, Seattle, Washington, United States of America Department of Health Services, University of Washington, Seattle, Washington, United States of America
Myriam Fornage Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
Sudha K. Iyengar Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
Michele K. Evans Health Disparities Unit, National Institute on Aging, National Institutes of Health, Baltimore Maryland, United States of America
Diane M. Becker The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
W. H. Linda Kao Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
James G. Wilson Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Jerome I. Rotter Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
Michèle M. Sale Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America Department of Medicine, University of Virginia, Charlottesville, Virginia, United States of America Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
Simin Liu Program on Genomics and Nutrition, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America Department of Epidemiology, University of California Los Angeles, Los Angeles, California, United States of America Departments of Epidemiology and Medicine, Brown University, Providence, Rhode Island, United States of America
Charles N. Rotimi Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
Donald W. Bowden Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
for the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium

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Genetic insights into cardiometabolic risk factors

Many biochemical traits are recognised as risk factors, which contribute to or predict the development of disease. Only a few are in widespread use, usually to assist with treatment decisions and motivate behavioural change. The greatest effort has gone into evaluation of risk factors for cardiovascular disease and/or diabetes, with substantial overlap as 'cardiometabolic' risk. Over the past few years many genome-wide association studies (GWAS) have sought to account for variation in risk factors, with the expectation that identifying relevant polymorphisms would improve our understanding or prediction of disease; others have taken the direct approach of genomic case-control studies for the corresponding diseases. Large GWAS have been published for coronary heart disease and Type 2 diabetes, and also for associated biomarkers or risk factors including body mass index, lipids, C-reactive protein, urate, liver function tests, glucose and insulin. Results are not encouraging for personal risk prediction based on genotyping, mainly because known risk loci only account for a small proportion of risk. Overlap of allelic associations between disease and marker, as found for low density lipoprotein cholesterol and heart disease, supports a causal association, but in other cases genetic studies have cast doubt on accepted risk factors. Some loci show unexpected effects on multiple markers or diseases. An intriguing feature of risk factors is the blurring of categories shown by the correlation between them and the genetic overlap between diseases previously thought of as distinct. GWAS can provide insight into relationships between risk factors, biomarkers and diseases, with potential for new approaches to disease classification.

Genetics of diabetes--are we missing the genes or the disease?

Diabetes is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. The chronic hyperglycemia of diabetes is associated with long-term damage, dysfunction, and failure of different organs, especially the eyes, kidneys, nerves, heart, and blood vessels. Several pathogenic processes are involved in the development of diabetes. These range from autoimmune destruction of the beta-cells of the pancreas with consequent insulin deficiency to abnormalities that result in resistance to insulin action (American Diabetes Association, 2011). The vast majority of cases of diabetes fall into two broad categories. In type 1 diabetes (T1D), the cause is an absolute deficiency of insulin secretion, whereas in type 2 diabetes (T2D), the cause is a combination of resistance to insulin action and an inadequate compensatory insulin secretory response. However, the subdivision into two main categories represents a simplification of the real situation, and research during the recent years has shown that the disease is much more heterogeneous than a simple subdivision into two major subtypes assumes. Worldwide prevalence figures estimate that there are 280 million diabetic patients in 2011 and more than 500 million in 2030 (http://www.diabetesatlas.org/). In Europe, about 6-8% of the population suffer from diabetes, of them about 90% has T2D and 10% T1D, thereby making T2D to the fastest increasing disease in Europe and worldwide. This epidemic has been ascribed to a collision between the genes and the environment. While our knowledge about the genes is clearly better for T1D than for T2D given the strong contribution of variation in the HLA region to the risk of T1D, the opposite is the case for T2D, where our knowledge about the environmental triggers (obesity, lack of exercise) is much better than the understanding of the underlying genetic causes. This lack of knowledge about the underlying genetic causes of diabetes is often referred to as missing heritability (Manolio et al., 2009) which exceeds 80% for T2D but less than 25% for T1D. In the following review, we will discuss potential sources of this missing heritability which also includes the possibility that our definition of diabetes and its subgroups is imprecise and thereby making the identification of genetic causes difficult.

Overlap of genetic susceptibility to type 1 diabetes, type 2 diabetes, and latent autoimmune diabetes in adults

Despite the notion that there is a degree of commonality to the biological etiology of type 1 diabetes (T1D) and type 2 diabetes (T2D), the lack of overlap in the genetic factors underpinning each of them suggests very distinct mechanisms. A disorder considered to be at the "intersection" of these two diseases is "latent autoimmune diabetes in adults" (LADA). Interestingly, genetic signals from both T1D and T2D are also seen in LADA, including the key HLA and transcription factor 7-like 2 (TCF7L2) loci, but the magnitudes of these effects are more complex than just pointing to LADA as being a simple admixture of T1D and T2D. We review the current status of the understanding of the genetics of LADA and place it in the context of what is known about the genetics of its better-studied "cousins," T1D and T2D, especially with respect to the myriad of discoveries made over the last decade through genome-wide association studies.

Association of TCF7L2 variation with single islet autoantibody expression in children with type 1 diabetes

BACKGROUND

The transcription factor 7-like 2 (TCF7L2) gene has the strongest genetic association with type 2 diabetes. TCF7L2 also associates with latent autoimmune diabetes in adults, which often presents with a single islet autoantibody, but not with classical type 1 diabetes.

METHODS

We aimed to test if TCF7L2 is associated with single islet autoantibody expression in pediatric type 1 diabetes. We studied 71 prospectively recruited children who had newly diagnosed type 1 diabetes and evidence of islet autoimmunity, that is, expressed ≥1 islet autoantibody to insulin, glutamic acid decarboxylase 65, islet cell autoantigen 512, or zinc transporter 8. TCF7L2 rs7903146 alleles were identified. Data at diagnosis were cross-sectionally analyzed.

RESULTS

We found that 21.1% of the children with autoimmune type 1 diabetes expressed a single islet autoantibody. The distribution of TCF7L2 rs7903146 genotypes in children with a single autoantibody (n=15) was 40% CC, 26.7% CT and 33.3% TT, compared with children with ≥2 islet autoantibodies (50% CC, 42.9% CT and 7.1% TT, p=0.024). Furthermore, compared with children with ≥2 autoantibodies, single-autoantibody children had characteristics reflecting milder autoimmune destruction of β-cells. Restricting to lean children (body mass index<85th centile; n=36), 45.5% of those expressing a single autoantibody were rs7903146 TT homozygotes, compared with 0% of those with ≥2 autoantibodies (p<0.0001).

CONCLUSION

These results suggest that, in children with only mild islet autoimmunity, mechanisms associated with TCF7L2 genetic variation contribute to diabetogenesis, and this contribution is larger in the absence of obesity.

Analysis of the expression of candidate genes for type 1 diabetes susceptibility in T cells

Type 1 diabetes is characterized by T-cell-mediated autoimmune destruction of pancreatic β-cells. Currently, approximately 50 type 1 diabetes susceptibility genes or chromosomal regions have been identified. However, the functions of type 1 diabetes susceptibility genes in T cells are elusive. In this study, we evaluated the correlation between type 1 diabetes susceptibility genes and T-cell signaling. The expression levels of 22 candidate type 1 diabetes susceptibility genes in T cells from nonobese diabetic (NOD), control C57BL/6 (B6), and NOD-control F1 hybrid mice were analyzed in response to 2 key immunoregulatory cytokines: interleukin-2 (IL-2) and transforming growth factor β (TGF-β). Exogenous gene expression studies were also performed in EL4 and Jurkat E6.1 T-cell lines. Significant differences in the expression of Clec16a, Dlk1, Il2, Ptpn22, Rnls, and Zac1 (also known as Plagl1) were observed in T cells derived from the 3 strains of mice, and TGF-β differentially influenced the expression of Ctla4, Foxp3, Il2, Ptpn22, Sh2b3, and Zac1. We found that TGF-β induced Zac1 expression in both primary T cells and EL4 cells and that exogenous expression of Zac1 and ZAC1 in T-cell lines altered the expression of Il2 and DLK1, respectively. The results of our study indicate the possibility that additional genetic pathways underlying type 1 diabetes susceptibility, including those involving Clec16a, Dlk1, Rnls, Sh2b3, and Zac1 under IL-2 and TGF-β signaling in T cells, may be shared between human and NOD mice.

Diabetes and infections-hepatitis C: is there type 2 diabetes excess in hepatitis C infection?

Individual epidemiologic studies as well as the pooled analysis of observational studies have indicated the association between type 2 diabetes (T2D) and hepatitis C virus infection (HCV). Whether HCV infection is the cause of diabetes or diabetic patients are more prone to get HCV infection is still in question. The objective of the present review was to provide answers to this issue, based on available evidence from epidemiologic, molecular, experimental and therapeutic studies. Our current understanding of how chronic HCV infection could induce T2D is incomplete, but it seems twofold based on both direct and indirect roles of the virus. HCV may directly induce insulin resistance (IR) through its proteins. HCV core protein was shown to stimulate suppressor of cytokine signaling, resulting in ubiquitination and degradation of tyrosine kinase phosphorylated insulin receptor substrates (IRS1/2) in proteasomes. HCV-nonstructural protein could increase protein phosphatase 2A which has been shown to inactivate the key enzyme Akt by dephosphorylating it. Insulin signaling defects in hepatic IRS-1 tyrosine phosphorylation and PI3-kinase association/activation may contribute to IR, which leads to the development of T2D in patients with HCV infection. The peroxisome proliferator-activated receptors (PPARs) are also implicated. PPARα/γ, together with their obligate partner RXR, are the main nuclear receptors expressed in the liver. PPARα upregulates glycerol-3-phosphate dehydrogenase, glycerol kinase, and glycerol transport proteins, which allows for glucose synthesis during fasting states. Decreased activity of PPARs could attribute to HCV-induced IR. Immune-mediated mechanisms may be involved in the indirect role of HCV in inducing IR. It is speculated that TNF-alpha plays a major role in the pathogenesis of IR through lowering IRS1/2. Furthermore, HCV infection- triggered ER stress could lead to the activation of PP2A, which inhibits both Akt and the AMP-activated kinase, the regulators of gluconeogenesis. In summary, we illustrate that HCV infection is accompanied by multiple defects in the upstream insulin signaling pathway in the liver that may contribute to the observed prevalence of IR and diabetes. Future studies are needed to resolve this issue.

GLIS3, a susceptibility gene for type 1 and type 2 diabetes, modulates pancreatic beta cell apoptosis via regulation of a splice variant of the BH3-only protein Bim

Mutations in human Gli-similar (GLIS) 3 protein cause neonatal diabetes. The GLIS3 gene region has also been identified as a susceptibility risk locus for both type 1 and type 2 diabetes. GLIS3 plays a role in the generation of pancreatic beta cells and in insulin gene expression, but there is no information on the role of this gene on beta cell viability and/or susceptibility to immune- and metabolic-induced stress. GLIS3 knockdown (KD) in INS-1E cells, primary FACS-purified rat beta cells, and human islet cells decreased expression of MafA, Ins2, and Glut2 and inhibited glucose oxidation and insulin secretion, confirming the role of this transcription factor for the beta cell differentiated phenotype. GLIS3 KD increased beta cell apoptosis basally and sensitized the cells to death induced by pro-inflammatory cytokines (interleukin 1β + interferon-γ) or palmitate, agents that may contribute to beta cell loss in respectively type 1 and 2 diabetes. The increased cell death was due to activation of the intrinsic (mitochondrial) pathway of apoptosis, as indicated by cytochrome c release to the cytosol, Bax translocation to the mitochondria and activation of caspases 9 and 3. Analysis of the pathways implicated in beta cell apoptosis following GLIS3 KD indicated modulation of alternative splicing of the pro-apoptotic BH3-only protein Bim, favouring expression of the pro-death variant Bim_S via inhibition of the splicing factor SRp55. KD of Bim abrogated the pro-apoptotic effect of GLIS3 loss of function alone or in combination with cytokines or palmitate. The present data suggest that altered expression of the candidate gene GLIS3 may contribute to both type 1 and 2 type diabetes by favouring beta cell apoptosis. This is mediated by alternative splicing of the pro-apoptotic protein Bim and exacerbated formation of the most pro-apoptotic variant Bim_S.

Pancreatic beta cell dysfunction and death is a central event in the pathogenesis of diabetes. Genome-wide association studies have identified a large number of associations between specific loci and the two main forms of diabetes, namely type 1 and type 2 diabetes, but the mechanisms by which these candidate genes predispose to diabetes remain to be clarified. The GLIS3 gene region has been identified as a susceptibility risk locus for both type 1 and type 2 diabetes—it is actually the only locus showing association with both forms of diabetes and the regulation of blood glucose. We show that decreased expression of GLIS3 may contribute to diabetes by favouring beta cell apoptosis. This is mediated by the mitochondrial pathway of apoptosis, activated via alternative splicing (a process by which exons are joined in multiple ways, leading to the generation of several proteins by a single gene) of the pro-apoptotic protein Bim, which favours formation of the most pro-apoptotic variant. The present data provides the first evidence that a susceptibility gene for diabetes may contribute to disease via regulation of alternative splicing of a pro-apoptotic gene in pancreatic beta cells.

Zinc transporter 8 autoantibodies and their association with SLC30A8 and HLA-DQ genes differ between immigrant and Swedish patients with newly diagnosed type 1 diabetes in the Better Diabetes Diagnosis study

We examined whether zinc transporter 8 autoantibodies (ZnT8A; arginine ZnT8-RA, tryptophan ZnT8-WA, and glutamine ZnT8-QA variants) differed between immigrant and Swedish patients due to different polymorphisms of SLC30A8, HLA-DQ, or both. Newly diagnosed autoimmune (≥1 islet autoantibody) type 1 diabetic patients (n = 2,964, <18 years, 55% male) were ascertained in the Better Diabetes Diagnosis study. Two subgroups were identified: Swedes (n = 2,160, 73%) and immigrants (non-Swedes; n = 212, 7%). Non-Swedes had less frequent ZnT8-WA (38%) than Swedes (50%), consistent with a lower frequency in the non-Swedes (37%) of SLC30A8 CT+TT (RW+WW) genotypes than in the Swedes (54%). ZnT8-RA (57 and 58%, respectively) did not differ despite a higher frequency of CC (RR) genotypes in non-Swedes (63%) than Swedes (46%). We tested whether this inconsistency was due to HLA-DQ as 2/X (2/2; 2/y; y is anything but 2 or 8), which was a major genotype in non-Swedes (40%) compared with Swedes (14%). In the non-Swedes only, 2/X (2/2; 2/y) was negatively associated with ZnT8-WA and ZnT8-QA but not ZnT8-RA. Molecular simulation showed nonbinding of the relevant ZnT8-R peptide to DQ2, explaining in part a possible lack of tolerance to ZnT8-R. At diagnosis in non-Swedes, the presence of ZnT8-RA rather than ZnT8-WA was likely due to effects of HLA-DQ2 and the SLC30A8 CC (RR) genotypes.

Association between autoantibodies to the Arginine variant of the Zinc transporter 8 (ZnT8) and stimulated C-peptide levels in Danish children and adolescents with newly diagnosed type 1 diabetes

BACKGROUND

The zinc transporter 8 (ZnT8) was recently identified as a common autoantigen in type 1 diabetes (T1D) and inclusion of ZnT8 autoantibodies (ZnT8Ab) was found to increase the diagnostic specificity of T1D.

OBJECTIVES

The main aims were to determine whether ZnT8Ab vary during follow-up 1 year after diagnosis, and to relate the reactivity of three types of ZnT8Ab to the residual stimulated C-peptide levels during the first year after diagnosis.

SUBJECTS

A total of 129 newly diagnosed T1D patients <15 years was followed prospectively 1, 3, 6, and 12 months after diagnosis.

METHODS

Hemoglobin A1c, meal-stimulated C-peptide, ZnT8Ab, and other pancreatic autoantibodies were measured at each visit. Patients were genotyped for the rs13266634 variant at the SLC30A8 gene and HLA-DQ alleles.

RESULTS

The levels of all ZnT8Ab [ZnT8Arg (arginine), ZnT8Trp (tryptophan), ZnT8Gln (glutamine)] tended to decrease during disease progression. A twofold higher level of ZnT8Arg and ZnT8Gln was associated with 4.6%/5.2% (p = 0.02), 5.3%/8.2% (p = 0.02) and 8.9%/9.7% (p = 0.004) higher concentrations of stimulated C-peptide 3, 6, and 12 months after diagnosis. The TT genotype carriers of the SLC30A8 gene had 45.8% (p = 0.01) and 60.1% (p = 0.002) lower stimulated C-peptide 6 and 12 months after diagnosis compared to the CC and the CT genotype carriers in a recessive model.

CONCLUSIONS

The levels of the Arg variant of the ZnT8 autoantibodies are associated with higher levels of stimulated C-peptide after diagnosis of T1D and during follow-up. Carriers of the TT genotype of the SLC30A8 gene predict lower stimulated C-peptide levels 12 months after diagnosis.

IGF2BP2 and IGF2 genetic effects in diabetes and diabetic nephropathy

OBJECTIVE

The IGF2BP2 gene is located on chromosome 3q27.2 within a region linked to type 1 diabetes (T1D), type 2 diabetes (T2D) and diabetic nephropathy (DN). Its protein functionally binds to 5'-UTR of the imprinting IGF2 gene. The present study aims to evaluate the IGF2BP2-IGF2 genetic effects in diabetes and DN.

MATERIALS AND METHODS

Three cohorts including T1D with and without DN (n=1139) of European descents from the GoKinD study, Swedish T1D with and without DN (n=303) and Czech control subjects without diabetes, T1D, T2D with and without DN (n=1418) were enrolled in TaqMan genotyping experiments for IGF2BP2 rs4402960 and IGF2 rs10770125. Igf2bp2 gene expression in kidney tissues of db/db and control mice at the ages of 5 and 26 weeks was examined with real time RT-PCR and Western blot.

RESULTS

An association of IGF2BP2 rs4402960 with T2D in the Czech population was replicated. This IGF2BP2 polymorphism (P=0.037, OR=0.69 95% CI 0.49-0.98) was found to be associated with DN in male not in female patients with T1D selected from the GoKinD study. In the analyses of combined the GoKinD, Czech and Swedish populations, the association between IGF2BP2 polymorphism and DN in male patients with T1D was still significant (P=0.030, OR=0.73, 95% CI 0.54-0.97). IGF2 rs10770125 was also associated with DN in male T1D patients of the GoKinD population (P=0.038, OR=0.67 95% CI 0.46-0.98). There might be a genetic interaction between IGF2BP2 and IGF2 (P=0.05). The Igf2bp2 gene expression levels were increased in the kidneys of db/db mice compared to controls at the age of 5weeks but not at 26 weeks.

CONCLUSIONS

The present study has replicated the association of IGF2BP2 rs4402960 with T2D in the Czech population and provided data suggesting that IGF2BP2 may have genetic interaction with IGF2 with a protective effect against DN in male patients with T1D.

Association of variants in HLA-DQA1-DQB1, PTPN22, INS, and CTLA4 with GAD autoantibodies and insulin secretion in nondiabetic adults of the Botnia Prospective Study

OBJECTIVE

Previously, we observed an association between family history of type 1 diabetes and development of non-insulin-dependent diabetes. The aims of this study were to assess whether type 1 diabetes susceptibility gene variants explain this association and investigate the effect of the variants on insulin secretion and presence of glutamic acid decarboxylase autoantibodies (GADA) in nondiabetic adults.

DESIGN AND METHODS

Polymorphisms in INS (rs689), PTPN22 (rs2476601), CTLA4 (rs3087243), and the HLA-DQA1-DQB1 regions (rs2187668 and rs7454108 tagging HLA-DQ2.5 and HLA-DQ8 respectively) were genotyped in the Botnia Prospective Study (n=2764), in which initially nondiabetic participants were followed for a mean of 8.1 years.

RESULTS

The variants did not explain the association between family history of type 1 diabetes and development of non-insulin-dependent diabetes. In these nondiabetic adults, HLA-DQ AND PTPN22 risk genotypes were associated with GADA (HLA-DQ2.5/HLA-DQ8 or HLA-DQ8: OR (95% CI): 1.7 (1.3-2.3), P=0.0004; PTPN22 CT/TT: OR: 1.6 (1.2-2.2), P=0.003; P values were adjusted for sex, age, BMI, and follow-up time). A higher genetic risk score was associated with lower insulin secretion (insulinogenic index: 13.27 (16.27) vs 12.69 (15.27) vs 10.98 (13.06), P=0.02) and better insulin sensitivity index (risk score of 0-1 vs 2-3 vs 4-6: 142 (111) vs 144 (118) vs 157 (127), P=0.01) at baseline and a poorer capacity to compensate for the increased insulin demand after follow-up.

CONCLUSIONS

In nondiabetic adults, HLA-DQ2.5/HLA-DQ8 and PTPN22 CT/TT genotypes were associated with GADA.

Genetic association of zinc transporter 8 (ZnT8) autoantibodies in type 1 diabetes cases

AIMS/HYPOTHESIS

Autoantibodies to zinc transporter 8 (ZnT8A) are associated with risk of type 1 diabetes. Apart from the SLC30A8 gene itself, little is known about the genetic basis of ZnT8A. We hypothesise that other loci in addition to SLC30A8 are associated with ZnT8A.

METHODS

The levels of ZnT8A were measured in 2,239 British type 1 diabetic individuals diagnosed before age 17 years, with a median duration of diabetes of 4 years. Cases were tested at over 775,000 loci genome wide (including 53 type 1 diabetes associated regions) for association with positivity for ZnT8A. ZnT8A were also measured in an independent dataset of 855 family members with type 1 diabetes.

RESULTS

Only FCRL3 on chromosome 1q23.1 and the HLA class I region were associated with positivity for ZnT8A. rs7522061T>C was the most associated single nucleotide polymorphism (SNP) in the FCRL3 region (p = 1.13 × 10(-16)). The association was confirmed in the family dataset (p ≤ 9.20 × 10(-4)). rs9258750A>G was the most associated variant in the HLA region (p = 2.06 × 10(-9) and p = 0.0014 in family cases). The presence of ZnT8A was not associated with HLA-DRB1, HLA-DQB1, HLA-A, HLA-B or HLA-C (p > 0.05). Unexpectedly, the two loci associated with the presence of ZnT8A did not alter risk of having type 1 diabetes, and the 53 type 1 diabetes risk loci did not influence positivity for ZnT8A, despite them being disease specific.

CONCLUSIONS/INTERPRETATION

ZnT8A are not primary pathogenic factors in type 1 diabetes. Nevertheless, ZnT8A testing in combination with other autoantibodies facilitates disease prediction, despite the biomarker not being under the same genetic control as the disease.

Tyrphostin AG490 agent modestly but significantly prevents onset of type 1 in NOD mouse; implication of immunologic and metabolic effects of a Jak-Stat pathway inhibitor

Previously, we have reported that the Jak-Stat signaling pathway is defective in NOD mice. In this study, prediabetic female NOD mice (4 weeks) were treated by intraperitoneal injection either with AG490 or DMSO three times per week for 4 consecutive weeks, followed by once a week for an additional 6 weeks. The onset of diabetes was attenuated in NOD mice treated with AG490 relative to DMSO treated control mice (p < 0.02). From an immunological standpoint, AG490 induced the expression of Foxp3 in CD4(+)CD25(-) T-cells and down-regulated expression of co-stimulatory molecules in dendritic cells (DC) both in vitro and in vivo. AG490 treated CD4+CD25- T-cells and DC in vitro, acquired regulatory functions; namely, the ability to suppress proliferation of a responding cell population in vitro. AG490 treatment resulted in significant reduction of blood glucose values and increased expression of PPARγ in splenocytes and markedly increased expression PPARγ2 but not PPARγ1 in adipocyte in vitro. Presence of multiple Stat5 DNA binding consensus sequences within the promoter region of the PPARγ gene in human and in mouse suggests that PPARγ is downstream to the Jak-Stat signaling pathway. This study highlights a critical role of the Jak-Stat signaling pathway in the pathogenesis of T1D and suggests that blocking the Jak-Stat signaling pathway by AG490 as a tyrosine kinase inhibitor may provide an effective means for preventing autoimmune T1D via both immunological and metabolic effects.

E23K polymorphism of the KCNJ11 gene in Korean children with type 1 diabetes

BACKGROUND

This study was undertaken to evaluate the association of the E23K polymorphism of KCNJ11 and type 1 diabetes in a Korean population.

METHODS

Clinical variables from 70 Korean children with type 1 diabetes were analyzed. Patients' DNA was screened for the E23 locus in the KCNJ11 gene. Each genotype frequency and clinical characteristics according to the genotypes were compared between the patient and control groups.

RESULTS

The genotype frequencies of the KCNJ11 E23 polymorphic locus in the patient group were 30.0% for EE, 44.3% for EK, and 25.7% for KK. We detected no differences in genotype frequencies between the patient and control groups. Additionally, in the patient group, no difference was detected in the clinical phenotypes among the three genotypes.

CONCLUSION

Although a rather small sample size constituted a limitation of this study, the association of the E23K polymorphism with type 1 diabetes was not statistically significant in the Korean population evaluated.

Lack of association of type 2 diabetes susceptibility genotypes and body weight on the development of islet autoimmunity and type 1 diabetes

AIM

To investigate whether type 2 diabetes susceptibility genes and body weight influence the development of islet autoantibodies and the rate of progression to type 1 diabetes.

METHODS

Genotyping for single nucleotide polymorphisms (SNP) of the type 2 diabetes susceptibility genes CDKAL1, CDKN2A/2B, FTO, HHEX-IDE, HMGA2, IGF2BP2, KCNJ11, KCNQ1, MTNR1B, PPARG, SLC30A8 and TCF7L2 was obtained in 1350 children from parents with type 1 diabetes participating in the BABYDIAB study. Children were prospectively followed from birth for islet autoantibodies and type 1 diabetes. Data on weight and height were obtained at 9 months, 2, 5, 8, 11, and 14 years of age.

RESULTS

None of type 2 diabetes risk alleles at the CDKAL1, CDKN2A/2B, FTO, HHEX-IDE, HMGA2, IGF2BP2, KCNJ11, KCNQ1, MTNR1B, PPARG and SLC30A8 loci were associated with the development of islet autoantibodies or diabetes. The type 2 diabetes susceptible genotype of TCF7L2 was associated with a lower risk of islet autoantibodies (7% vs. 12% by age of 10 years, P = 0.015, P(corrected) = 0.18). Overweight children at seroconversion did not progress to diabetes faster than non-overweight children (HR: 1.08; 95% CI: 0.48-2.45, P>0.05).

CONCLUSIONS

These findings do not support an association of type 2 diabetes risk factors with islet autoimmunity or acceleration of diabetes in children with a family history of type 1 diabetes.

ZnT8 and type 1 diabetes

Zinc is essential for the proper storage, secretion, and the action of insulin and is transported from cytoplasm to insulin secretory granules in the pancreatic β-cells by SLC30A zinc transporters (ZnT). ZnT8 is specifically expressed in the pancreatic β-cells and has been identified as a novel target autoantigen in patients with type 1 diabetes. Autoantibodies to ZnT8 (ZnT8A) are detected in 50-60% of Japanese patients with acute-onset and 20% with slow-onset type 1 diabetes. Furthermore, humoral autoreactivity to ZnT8 is unique in terms of a key determinant, which is not reported on other islet autoantigens such as insulin, glutamic acid decarboxylase, or the protein tyrosine phosphatase-related molecules IA-2. Type 2 diabetes-associated nonsynonymous single nucleotide polymorphism in SLC30A8 (the gene of ZnT8), rs13266634 (Arg325Trp), modulates ZnT8A specificities thereby indicating that this amino acid substitution has the critical role in antibody binding. The humoral autoreactivity to ZnT8 depends on the clinical phenotype, which may provide clues to understand the role of this protein in the pathogenesis of type 1 diabetes.

Decrease in thyroid adenoma associated (THADA) expression is a marker of dedifferentiation of thyroid tissue

Background

Thyroid adenoma associated (THADA) has been identified as the target gene affected by chromosome 2p21 translocations in thyroid adenomas, but the role of THADA in the thyroid is still elusive. The aim of this study was to quantify THADA gene expression in normal tissues and in thyroid hyper- and neoplasias, using real-time PCR.

Methods

For the analysis THADA and 18S rRNA gene expression assays were performed on 34 normal tissue samples, including thyroid, salivary gland, heart, endometrium, myometrium, lung, blood, and adipose tissue as well as on 85 thyroid hyper- and neoplasias, including three adenomas with a 2p21 translocation. In addition, NIS (sodium-iodide symporter) gene expression was measured on 34 of the pathological thyroid samples.

Results

Results illustrated that THADA expression in normal thyroid tissue was significantly higher (p < 0.0001, exact Wilcoxon test) than in the other tissues. Significant differences were also found between non-malignant pathological thyroid samples (goiters and adenomas) and malignant tumors (p < 0.001, Wilcoxon test, t approximation), anaplastic carcinomas (ATCs) and all other samples and also between ATCs and all other malignant tumors (p < 0.05, Wilcoxon test, t approximation). Furthermore, in thyroid tumors THADA mRNA expression was found to be inversely correlated with HMGA2 mRNA. HMGA2 expression was recently identified as a marker revealing malignant transformation of thyroid follicular tumors. A correlation between THADA and NIS has also been found in thyroid normal tissue and malignant tumors.

Conclusions

The results suggest THADA being a marker of dedifferentiation of thyroid tissue.

Genome-wide association studies (GWAS): impact on elucidating the aetiology of diabetes

It has proven to be challenging to isolate the genes underlying the genetic components conferring susceptibility to type 1 and type 2 diabetes. Unlike previous approaches, 'genome-wide association studies' have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with a number of novel disease-associated variants being largely replicated by independent groups. This review provides an overview of these recent breakthroughs in the context of type 1 and type 2 diabetes, and outlines strategies on how these findings will be applied to impact clinical care for these two highly prevalent disorders.

Genetic analysis of adult-onset autoimmune diabetes

OBJECTIVE

In contrast with childhood-onset type 1 diabetes, the genetics of autoimmune diabetes in adults are not well understood. We have therefore investigated the genetics of diabetes diagnosed in adults positive for autoantibodies.

RESEARCH DESIGN AND METHODS

GAD autoantibodies (GADAs), insulinoma-associated antigen-2 antibodies (IA-2As), and islet cell autoantibodies were measured at time of diagnosis. Autoantibody-positive diabetic subjects (n = 1,384) and population-based control subjects (n = 2,235) were genotyped at 20 childhood-onset type 1 diabetes loci and FCRL3, GAD2, TCF7L2, and FTO.

RESULTS

PTPN22 (1p13.2), STAT4 (2q32.2), CTLA4 (2q33.2), HLA (6p21), IL2RA (10p15.1), INS (11p15.5), ERBB3 (12q13.2), SH2B3 (12q24.12), and CLEC16A (16p13.13) were convincingly associated with autoimmune diabetes in adults (P ≤ 0.002), with consistent directions of effect as reported for pediatric type 1 diabetes. No evidence of an HLA-DRB1*03/HLA-DRB1*04 (DR3/4) genotype effect was obtained (P = 0.55), but it remained highly predisposing (odds ratio 26.22). DR3/4 was associated with a lower age at diagnosis of disease, as was DR4 (P = 4.67 × 10(-6)) but not DR3. DR3 was associated with GADA positivity (P = 6.03 × 10(-6)) but absence of IA-2A (P = 3.22 × 10(-7)). DR4 was associated with IA-2A positivity (P = 5.45 × 10(-6)).

CONCLUSIONS

Our results are consistent with the hypothesis that the genetics of autoimmune diabetes in adults and children are differentiated by only relatively few age-dependent genetic effects. The slower progression toward autoimmune insulin deficiency in adults is probably due to a lower genetic load overall combined with subtle variation in the HLA class II gene associations and autoreactivity.

Gestational diabetes mellitus is associated with TCF7L2 gene polymorphisms independent of HLA-DQB1*0602 genotypes and islet cell autoantibodies

AIMS

To test whether the TCF7L2 gene was associated with gestational diabetes, whether the association between TCF7L2 and gestational diabetes was independent of HLA-DQB1*0602 and islet cell autoantibodies, as well as maternal age, number of pregnancies, family history of diabetes and the HLA-DQB1 genotypes, and to test whether the distribution of HLA-DQB1 alleles was affected by country of birth.

METHODS

We genotyped the rs7903146, rs12255372 and rs7901695 single nucleotide polymorphisms of the TCF7L2 gene in 826 mothers with gestational diabetes and in 1185 healthy control subjects in the Diabetes Prediction in Skåne Study. The mothers were also typed for HLA-DQB1 genotypes and tested for islet cell autoantibodies against GAD65, insulinoma-associated antigen-2 and insulin.

RESULTS

The heterozygous genotypes CT, GT and TC of the rs7903146 (T is risk for Type 2 diabetes), rs12255372 (T is risk for Type 2 diabetes) and rs7901695 (C is risk for Type 2 diabetes), respectively, as well as the homozygous genotypes TT, TT and CC of the rs7903146, rs12255372 and rs7901695, respectively, were strongly associated with gestational diabetes (P < 0.0001). These associations remained statistically significant after adjusting for maternal age, number of pregnancies, family history of diabetes and HLA-DQ genotypes and were independent of the presence of islet cell autoantibodies. No interaction was observed between TCF7L2 and HLA-DQB1*0602, which was shown to be negatively associated with gestational diabetes in mothers born in Sweden (P = 0.010).

CONCLUSIONS

The TCF7L2 was associated with susceptibility for gestational diabetes independently of the presence of HLA-DQB1*0602 and islet cell autoantibodies and other factors such as maternal age, number of pregnancies, family history of diabetes and other HLA-DQ genotypes. The HLA-DQB1*0602 was negatively associated with gestational diabetes in mothers born in Sweden.

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.

Histone deacetylase (HDAC) inhibition as a novel treatment for diabetes mellitus

Both common forms of diabetes have an inflammatory pathogenesis in which immune and metabolic factors converge on interleukin-1β as a key mediator of insulin resistance and β-cell failure. In addition to improving insulin resistance and preventing β-cell inflammatory damage, there is evidence of genetic association between diabetes and histone deacetylases (HDACs); and HDAC inhibitors (HDACi) promote β-cell development, proliferation, differentiation and function and positively affect late diabetic microvascular complications. Here we review this evidence and propose that there is a strong rationale for preclinical studies and clinical trials with the aim of testing the utility of HDACi as a novel therapy for diabetes.