Common Variants in the Type 2 Diabetes KCNQ1 Gene Are Associated with Impairments in Insulin Secretion During Hyperglycaemic Glucose Clamp

Background: Genome-wide association studies in Japanese populations recently identified common variants in the KCNQ1 gene to be associated with type 2 diabetes. We examined the association of these variants within KCNQ1 with type 2 diabetes in a Dutch population, investigated their effects on insulin secretion and metabolic traits and on the risk of developing complications in type 2 diabetes patients. Methodology: The KCNQ1 variants rs151290, rs2237892, and rs2237895 were genotyped in a total of 4620 type 2 diabetes patients and 5285 healthy controls from the Netherlands. Data on macrovascular complications, nephropathy and retinopathy were available in a subset of diabetic patients. Association between genotype and insulin secretion/action was assessed in the additional sample of 335 individuals who underwent a hyperglycaemic clamp. Principal Findings: We found that all the genotyped KCNQ1 variants were significantly associated with type 2 diabetes in our Dutch population, and the association of rs151290 was the strongest (OR 1.20, 95% CI 1.07-1.35, p = 0.002). The risk C-allele of rs151290 was nominally associated with reduced first-phase glucose-stimulated insulin secretion, while the non-risk T-allele of rs2237892 was significantly correlated with increased second-phase glucose-stimulated insulin secretion (p = 0.025 and 0.0016, respectively). In addition, the risk C-allele of rs2237892 was associated with higher LDL and total cholesterol levels (p = 0.015 and 0.003, respectively). We found no evidence for an association of KCNQ1 with diabetic complications. Conclusions: Common variants in the KCNQ1 gene are associated with type 2 diabetes in a Dutch population, which can be explained at least in part by an effect on insulin secretion. Furthermore, our data suggest that KCNQ1 is also associated with lipid metabolism

Metabolite ratios as potential biomarkers for type 2 diabetes:a DIRECT study

Aims/hypothesis Circulating metabolites have been shown to reflect metabolic changes during the development of type 2 diabetes. In this study we examined the association of metabolite levels and pairwise metabolite ratios with insulin responses after glucose, glucagon-like peptide-1 (GLP-1) and arginine stimulation. We then investigated if the identified metabolite ratios were associated with measures of OGTT-derived beta cell function and with prevalent and incident type 2 diabetes. Methods We measured the levels of 188 metabolites in plasma samples from 130 healthy members of twin families (from the Netherlands Twin Register) at five time points during a modified 3 h hyperglycaemic clamp with glucose, GLP-1 and arginine stimulation. We validated our results in cohorts with OGTT data (n = 340) and epidemiological case–control studies of prevalent (n = 4925) and incident (n = 4277) diabetes. The data were analysed using regression models with adjustment for potential confounders. Results There were dynamic changes in metabolite levels in response to the different secretagogues. Furthermore, several fasting pairwise metabolite ratios were associated with one or multiple clamp-derived measures of insulin secretion (all p Conclusion/interpretation In this study we have shown that the Val_PC ae C32:2 metabolite ratio is associated with an increased risk of type 2 diabetes and measures of insulin secretion and resistance. The observed effects were stronger than that of the individual metabolites and independent of known risk factors.</p

Postprandial metabolic responses to mixed versus liquid meal tests in healthy men and men with type 2 diabetes

AbstractAimsCompare metabolic responses after mixed versus liquid meals of similar caloric/nutritional content in healthy and type 2 diabetes (T2D) subjects.MethodsTen healthy men and 10 men with T2D received mixed and liquid meals after an overnight fast. Classical (insulinogenic index; insulin/glucose areas under curves, AUCinsulin/AUCglucose) and model-based (beta-cell glucose sensitivity; rate sensitivity; potentiation factor ratio, PFR) beta-cell function estimates were calculated. Between-meal differences in glucose, insulin, C-peptide, triglyceride (TG), beta-cell function and oral glucose insulin sensitivity (OGIS) and between-meal correlations for beta-cell function and OGIS were evaluated.ResultsAmong healthy subjects, beta-cell function and OGIS were similar between meals. C-peptide (p=0.03), insulin (p=0.002), AUCinsulin/AUCglucose (p=0.004) and insulin secretion (p=0.04) were higher after the liquid meal. Among T2D subjects, glucose, insulin, C-peptide, beta-cell function, and OGIS were similar. PFR was higher (p=0.004) and TG increased more slowly (p=0.002) after the liquid meal. OGIS and beta-cell function were correlated during both meals in both groups (r=0.66–0.98), except incremental AUCinsulin/AUCglucose, rate sensitivity, and, in healthy subjects, PFR.ConclusionsMetabolic responses after mixed or liquid meals of similar content were highly correlated in T2D and healthy subjects. In T2D, the liquid meal produced beta-cell function estimates generally similar to the mixed meal

Integrative network analysis highlights biological processes underlying GLP-1 stimulated insulin secretion : A DIRECT study

<div><p>Glucagon-like peptide 1 (GLP-1) stimulated insulin secretion has a considerable heritable component as estimated from twin studies, yet few genetic variants influencing this phenotype have been identified. We performed the first genome-wide association study (GWAS) of GLP-1 stimulated insulin secretion in non-diabetic individuals from the Netherlands Twin register (n = 126). This GWAS was enhanced using a tissue-specific protein-protein interaction network approach. We identified a beta-cell protein-protein interaction module that was significantly enriched for low gene scores based on the GWAS <i>P</i>-values and found support at the network level in an independent cohort from Tübingen, Germany (n = 100). Additionally, a polygenic risk score based on SNPs prioritized from the network was associated (<i>P</i> < 0.05) with glucose-stimulated insulin secretion phenotypes in up to 5,318 individuals in MAGIC cohorts. The network contains both known and novel genes in the context of insulin secretion and is enriched for members of the focal adhesion, extracellular-matrix receptor interaction, actin cytoskeleton regulation, Rap1 and PI3K-Akt signaling pathways. Adipose tissue is, like the beta-cell, one of the target tissues of GLP-1 and we thus hypothesized that similar networks might be functional in both tissues. In order to verify peripheral effects of GLP-1 stimulation, we compared the transcriptome profiling of ob/ob mice treated with liraglutide, a clinically used GLP-1 receptor agonist, versus baseline controls. Some of the upstream regulators of differentially expressed genes in the white adipose tissue of ob/ob mice were also detected in the human beta-cell network of genes associated with GLP-1 stimulated insulin secretion. The findings provide biological insight into the mechanisms through which the effects of GLP-1 may be modulated and highlight a potential role of the beta-cell expressed genes <i>RYR2</i>, <i>GDI2</i>, <i>KIAA0232</i>, <i>COL4A1</i> and <i>COL4A2</i> in GLP-1 stimulated insulin secretion.</p></div

The CTRB1/2 locus affects diabetes susceptibility and treatment via the incretin pathway

The incretin hormone glucagon-like peptide 1 (GLP-1) promotes glucose homeostasis and enhances -cell function. GLP-1 receptor agonists (GLP-1 RAs) and dipeptidyl peptidase-4 (DPP-4) inhibitors, which inhibit the physiological inactivation of endogenous GLP-1, are used for the treatment of type 2 diabetes. Using the Metabochip, we identified three novel genetic loci with large effects (30-40%) on GLP-1-stimulated insulin secretion during hyperglycemic clamps in nondiabetic Caucasian individuals (TMEM114; CHST3 and CTRB1/2; n = 232; all P 8.8 x 10(-7)). rs7202877 near CTRB1/2, a known diabetes risk locus, also associated with an absolute 0.51 +/- 0.16% (5.6 +/- 1.7 mmol/mol) lower A1C response to DPP-4 inhibitor treatment in G-allele carriers, but there was no effect on GLP-1 RA treatment in type 2 diabetic patients (n = 527). Furthermore, in pancreatic tissue, we show that rs7202877 acts as expression quantitative trait locus for CTRB1 and CTRB2, encoding chymotrypsinogen, and increases fecal chymotrypsin activity in healthy carriers. Chymotrypsin is one of the most abundant digestive enzymes in the gut where it cleaves food proteins into smaller peptide fragments. Our data identify chymotrypsin in the regulation of the incretin pathway, development of diabetes, and response to DPP-4 inhibitor treatment

Results from network analysis of GLP-1 stimulated insulin secretion GWAS.

<p>A) The beta-cell specific GLP-1 response consensus network, annotated with the top enriched KEGG pathways: Focal adhesion (green), ECM-receptor interaction (blue) and Rap1 signaling (purple). Arrows indicate genes that were identified as upstream regulators of differentially expressed genes in the transcriptome analyses of the liraglutide treated mice versus baseline controls. B) The KEGG pathways enriched (BH adjusted <i>P</i>-value < 1 × 10⁻³) within the GLP-1 response consensus network, compared to the whole beta-cell PPI network. C) The red line denotes the combined z-score in the Tübingen validation cohort for 28 consensus network SNPs with discovery GWAS <i>P</i> < 5 × 10⁻⁴ compared to 100,000 z-scores obtained from randomly selected sets of SNPs from the beta-cell network (histogram), empirical <i>P</i>-value = 0.012. D) Top panel: Top regulators for networks of differentially expressed genes in the liraglutide treated mice transcriptome experiment. Bottom panel: Prioritized network modules from human and mouse experiments map to connective tissue and focal adhesion related pathways.</p

Clinical characteristics of the study groups.

<p>Clinical characteristics of the study groups.</p

Clinical characteristics of the study participants.

<p>The data are presented as mean ± SD. BMI: Body Mass Index. HbA_1c: haemoglobin A_1c. HDL: high density lipoprotein. LDL: low density lipoprotein. T2D: type 2 diabetes.</p