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.

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.

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 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.