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

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 P-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 (P < 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 RYR2, GDI2, KIAA0232, COL4A1 and COL4A2 in GLP-1 stimulated insulin secretion.

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 < 9.2 × 10-7). These associations were significantly stronger compared with the individual metabolite components. One of the ratios, valine to phosphatidylcholine acyl-alkyl C32:2 (PC ae C32:2), in addition showed a directionally consistent positive association with OGTT-derived measures of insulin secretion and resistance (p ≤ 5.4 × 10-3) and prevalent type 2 diabetes (ORVal_PC ae C32:2 2.64 [ß 0.97 ± 0.09], p = 1.0 × 10-27). Furthermore, Val_PC ae C32:2 predicted incident diabetes independent of established risk factors in two epidemiological cohort studies (HRVal_PC ae C32:2 1.57 [ß 0.45 ± 0.06]; p = 1.3 × 10-15), leading to modest improvements in the receiver operating characteristics when added to a model containing a set of established risk factors in both cohorts (increases from 0.780 to 0.801 and from 0.862 to 0.865 respectively, when added to the model containing traditional risk factors + glucose). CONCLUSIONS/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.

A Genome-Wide Association Study of IVGTT-Based Measures of First-Phase Insulin Secretion Refines the Underlying Physiology of Type 2 Diabetes Variants.

Understanding the physiological mechanisms by which common variants predispose to type 2 diabetes requires large studies with detailed measures of insulin secretion and sensitivity. Here we performed the largest genome-wide association study of first-phase insulin secretion, as measured by intravenous glucose tolerance tests, using up to 5,567 individuals without diabetes from 10 studies. We aimed to refine the mechanisms of 178 known associations between common variants and glycemic traits and identify new loci. Thirty type 2 diabetes or fasting glucose-raising alleles were associated with a measure of first-phase insulin secretion at P < 0.05 and provided new evidence, or the strongest evidence yet, that insulin secretion, intrinsic to the islet cells, is a key mechanism underlying the associations at the HNF1A, IGF2BP2, KCNQ1, HNF1B, VPS13C/C2CD4A, FAF1, PTPRD, AP3S2, KCNK16, MAEA, LPP, WFS1, and TMPRSS6 loci. The fasting glucose-raising allele near PDX1, a known key insulin transcription factor, was strongly associated with lower first-phase insulin secretion but has no evidence for an effect on type 2 diabetes risk. The diabetes risk allele at TCF7L2 was associated with a stronger effect on peak insulin response than on C-peptide-based insulin secretion rate, suggesting a possible additional role in hepatic insulin clearance or insulin processing. In summary, our study provides further insight into the mechanisms by which common genetic variation influences type 2 diabetes risk and glycemic traits.

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 ß-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 × 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.

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.

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

AIMS: Compare metabolic responses after mixed versus liquid meals of similar caloric/nutritional content in healthy and type 2 diabetes (T2D) subjects. METHODS: Ten healthy men and 10 men with T2D received mixed and liquid meals after an overnight fast. Classical (insulinogenic index; insulin/glucose areas under curves, AUC(insulin)/AUC(glucose)) 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. RESULTS: Among healthy subjects, beta-cell function and OGIS were similar between meals. C-peptide (p=0.03), insulin (p=0.002), AUC(insulin)/AUC(glucose) (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 AUC(insulin)/AUC(glucose), rate sensitivity, and, in healthy subjects, PFR. CONCLUSIONS: Metabolic 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.

The impact of genetic variation in the G6PC2 gene on insulin secretion depends on glycemia.

CONTEXT: Single-nucleotide polymorphisms (SNPs) within the G6PC2 locus are associated with fasting glucose and insulin secretion. These SNPs are not associated with type 2 diabetes risk. OBJECTIVE: Our objective was to investigate whether the impact of the SNP on variables of glucose-stimulated insulin secretion is influenced by glucose tolerance status. DESIGN, SETTING, PARTICIPANTS, AND INTERVENTION: In this cross-sectional study, we genotyped 1505 healthy Caucasian subjects [normal glucose tolerance (NGT), 1098; impaired glucose tolerance (IGT)/impaired fasting glucose (IFG), 407] for SNP rs560887 within the G6PC2 locus. A subgroup of 326 subjects underwent an iv glucose tolerance test, and 512 participants took part in a hyperinsulinemic-euglycemic clamp. For replication, SNP rs560887 was genotyped in 457 subjects (NGT, 265; IGT, 192) from four independent German and Dutch studies who underwent a hyperglycemic clamp. MAIN OUTCOME MEASURE: Insulin secretion was evaluated. RESULTS: Carriers of the major G-allele exhibited increased fasting glycemia (P<0.0001). Insulin sensitivity and secretion were not associated with the SNP (P≥0.06). Glucose tolerance status and genotype interacted on insulin secretion (P=0.036), such that in NGT subjects, the minor A-allele of rs560887 was associated with decreased insulinogenic index (P=0.044), which was not the case in subjects with IFG/IGT (P=1.0). During the iv glucose tolerance test, an association of A-allele carriers with decreased first-phase insulin secretion was also observed only in NGT subjects (P=0.0053). Likewise, in the hyperglycemic clamp group, the A-allele was associated with decreased first-phase insulin secretion only in the NGT group (P=0.022) but not in the IGT group. CONCLUSIONS: The effects of hyperglycemia on insulin secretion override the more subtle effects of genetic variation in the G6PC2 locus on insulin secretion.

Gene variants in the novel type 2 diabetes loci CDC123/CAMK1D, THADA, ADAMTS9, BCL11A, and MTNR1B affect different aspects of pancreatic beta-cell function.

OBJECTIVE: Recently, results from a meta-analysis of genome-wide association studies have yielded a number of novel type 2 diabetes loci. However, conflicting results have been published regarding their effects on insulin secretion and insulin sensitivity. In this study we used hyperglycemic clamps with three different stimuli to test associations between these novel loci and various measures of beta-cell function. RESEARCH DESIGN AND METHODS: For this study, 336 participants, 180 normal glucose tolerant and 156 impaired glucose tolerant, underwent a 2-h hyperglycemic clamp. In a subset we also assessed the response to glucagon-like peptide (GLP)-1 and arginine during an extended clamp (n = 123). All subjects were genotyped for gene variants in JAZF1, CDC123/CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, NOTCH2/ADAMS30, DCD, VEGFA, BCL11A, HNF1B, WFS1, and MTNR1B. RESULTS: Gene variants in CDC123/CAMK1D, ADAMTS9, BCL11A, and MTNR1B affected various aspects of the insulin response to glucose (all P < 6.9 x 10(-3)). The THADA gene variant was associated with lower beta-cell response to GLP-1 and arginine (both P < 1.6 x 10(-3)), suggesting lower beta-cell mass as a possible pathogenic mechanism. Remarkably, we also noted a trend toward an increased insulin response to GLP-1 in carriers of MTNR1B (P = 0.03), which may offer new therapeutic possibilities. The other seven loci were not detectably associated with beta-cell function. CONCLUSIONS: Diabetes risk alleles in CDC123/CAMK1D, THADA, ADAMTS9, BCL11A, and MTNR1B are associated with various specific aspects of beta-cell function. These findings point to a clear diversity in the impact that these various gene variants may have on (dys)function of pancreatic beta-cells.

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

OBJECTIVE: At least 20 type 2 diabetes loci have now been identified, and several of these are associated with altered beta-cell function. In this study, we have investigated the combined effects of eight known beta-cell loci on insulin secretion stimulated by three different secretagogues during hyperglycemic clamps. RESEARCH DESIGN AND METHODS: A total of 447 subjects originating from four independent studies in the Netherlands and Germany (256 with normal glucose tolerance [NGT]/191 with impaired glucose tolerance [IGT]) underwent a hyperglycemic clamp. A subset had an extended clamp with additional glucagon-like peptide (GLP)-1 and arginine (n = 224). We next genotyped single nucleotide polymorphisms in TCF7L2, KCNJ11, CDKAL1, IGF2BP2, HHEX/IDE, CDKN2A/B, SLC30A8, and MTNR1B and calculated a risk allele score by risk allele counting. RESULTS: The risk allele score was associated with lower first-phase glucose-stimulated insulin secretion (GSIS) (P = 7.1 x 10(-6)). The effect size was equal in subjects with NGT and IGT. We also noted an inverse correlation with the disposition index (P = 1.6 x 10(-3)). When we stratified the study population according to the number of risk alleles into three groups, those with a medium- or high-risk allele score had 9 and 23% lower first-phase GSIS. Second-phase GSIS, insulin sensitivity index and GLP-1, or arginine-stimulated insulin release were not significantly different. CONCLUSIONS: A combined risk allele score for eight known beta-cell genes is associated with the rapid first-phase GSIS and the disposition index. The slower second-phase GSIS, GLP-1, and arginine-stimulated insulin secretion are not associated, suggesting that especially processes involved in rapid granule recruitment and exocytosis are affected in the majority of risk loci.

The heritability of HbA1c and fasting blood glucose in different measurement settings.

In an extended twin study we estimated the heritability of fasting HbA1c and blood glucose levels. Blood glucose was assessed in different settings (at home and in the clinic). We tested whether the genetic factors influencing fasting blood glucose levels overlapped with those influencing HbA1c and whether the same genetic factors were expressed across different settings. Fasting blood glucose was measured at home and during two visits to the clinic in 77 healthy families with same-sex twins and siblings, aged 20 to 45 years. HbA1c was measured during the first clinic visit. A 4-variate genetic structural equation model was used that estimated the heritability of each trait and the genetic correlations among traits. Heritability explained 75% of the variance in HbA1c. The heritability of fasting blood glucose was estimated at 66% at home and lower in the clinic (57% and 38%). Fasting blood glucose levels were significantly correlated across settings (0.34 < r < 0.54), mostly due to a common set of genes that explained between 53% and 95% of these correlations. Correlations between HbA1c and fasting blood glucoses were low (0.11 < r < 0.23) and genetic factors influencing HbA1c and fasting glucose were uncorrelated. These results suggest that in healthy adults the genes influencing HbA1c and fasting blood glucose reflect different aspects of the glucose metabolism. As a consequence these two glycemic parameters can not be used interchangeably in diagnostic procedures or in studies attempting to find genes for diabetes. Both contribute unique (genetic) information.