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.

Polymorphism rs11085226 in the gene encoding polypyrimidine tract-binding protein 1 negatively affects glucose-stimulated insulin secretion.

OBJECTIVE: Polypyrimidine tract-binding protein 1 (PTBP1) promotes stability and translation of mRNAs coding for insulin secretion granule proteins and thereby plays a role in ß-cells function. We studied whether common genetic variations within the PTBP1 locus influence insulin secretion, and/or proinsulin conversion. METHODS: We genotyped 1,502 healthy German subjects for four tagging single nucleotide polymorphisms (SNPs) within the PTBP1 locus (rs351974, rs11085226, rs736926, and rs123698) covering 100% of genetic variation with an r(2)≥0.8. The subjects were metabolically characterized by an oral glucose tolerance test with insulin, proinsulin, and C-peptide measurements. A subgroup of 320 subjects also underwent an IVGTT. RESULTS: PTBP1 SNP rs11085226 was nominally associated with lower insulinogenic index and lower cleared insulin response in the OGTT (p≤0.04). The other tested SNPs did not show any association with the analyzed OGTT-derived secretion parameters. In the IVGTT subgroup, SNP rs11085226 was accordingly associated with lower insulin levels within the first ten minutes following glucose injection (p = 0.0103). Furthermore, SNP rs351974 was associated with insulin levels in the IVGTT (p = 0.0108). Upon interrogation of MAGIC HOMA-B data, our rs11085226 result was replicated (MAGIC p = 0.018), but the rs351974 was not. CONCLUSIONS: We conclude that common genetic variation in PTBP1 influences glucose-stimulated insulin secretion. This underlines the importance of PTBP1 for beta cell function in vivo.

Glucose-raising genetic variants in MADD and ADCY5 impair conversion of proinsulin to insulin.

INTRODUCTION: Recent meta-analyses of genome-wide association studies revealed new genetic loci associated with fasting glycemia. For several of these loci, the mechanism of action in glucose homeostasis is unclear. The objective of the study was to establish metabolic phenotypes for these genetic variants to deliver clues to their pathomechanism. METHODS: In this cross-sectional study 1782 non-diabetic volunteers at increased risk for type 2 diabetes underwent an oral glucose tolerance test. Insulin, C-peptide and proinsulin were measured and genotyping was performed for 12 single nucleotide polymorphisms (SNP) in or near the genes GCK (rs4607517), DGKB (rs2191349), GCKR (rs780094), ADCY5 (rs11708067), MADD (rs7944584), ADRA2A (rs10885122), FADS1 (rs174550), CRY2 (rs11605924), SLC2A2 (rs11920090), PROX1 (rs340874), GLIS3 (rs7034200) and C2CD4B (rs11071657). Parameters of insulin secretion (AUC Insulin(0-30)/AUC Glucose(0-30), AUC C-peptide(0-120)/AUC Glucose(0-120)), proinsulin-to-insulin conversion (fasting proinsulin, fasting proinsulin/insulin, AUC Proinsulin(0-120)/AUCInsulin(0-120)) and insulin resistance (HOMA-IR, Matsuda-Index) were assessed. RESULTS: After adjustment for confounding variables, the effect alleles of the ADCY5 and MADD SNPs were associated with an impaired proinsulin-to-insulin conversion (p = 0.002 and p = 0.0001, respectively). GLIS3 was nominally associated with impaired proinsulin-to-insulin conversion and insulin secretion. The diabetogenic alleles of DGKB and PROX1 were nominally associated with reduced insulin secretion. Nominally significant effects on insulin sensitivity could be found for MADD and PROX1. DISCUSSION: By examining parameters of glucose-stimulated proinsulin-to-insulin conversion during an OGTT, we show that the SNP in ADCY5 is implicated in defective proinsulin-to-insulin conversion. In addition, we confirmed previous findings on the role of a genetic variant in MADD on proinsulin-to-insulin conversion. These effects may also be related to neighboring regions of the genome.

Glycemia determines the effect of type 2 diabetes risk genes on insulin secretion.

OBJECTIVE: Several single nucleotide polymorphisms (SNPs) in diabetes risk genes reduce glucose- and/or incretin-induced insulin secretion. Here, we investigated interactions between glycemia and such diabetes risk polymorphisms. RESEARCH DESIGN AND METHODS: Insulin secretion was assessed by insulinogenic index and areas under the curve of C-peptide/glucose in 1,576 subjects using an oral glucose tolerance test (OGTT). Participants were genotyped for 10 diabetes risk SNPs associated with ß-cell dysfunction: rs5215 (KCNJ11), rs13266634 (SLC30A8), rs7754840 (CDKAL1), rs10811661 (CDKN2A/2B), rs10830963 (MTNR1B), rs7903146 (TCF7L2), rs10010131 (WFS1), rs7923837 (HHEX), rs151290 (KCNQ1), and rs4402960 (IGF2BP2). Furthermore, the impact of the interaction between genetic variation in TCF7L2 and glycemia on changes in insulin secretion was tested in 315 individuals taking part in a lifestyle intervention study. RESULTS: For the SNPs in TCF7L2 and WFS1, we found a significant interaction between glucose control and insulin secretion (all P ≤ 0.0018 for glucose × genotype). When plotting insulin secretion against glucose at 120 min OGTT, the compromising SNP effects on insulin secretion are most apparent under high glucose. In the longitudinal study, rs7903146 in TCF7L2 showed a significant interaction with baseline glucose tolerance upon change in insulin secretion (P = 0.0027). Increased glucose levels at baseline predicted an increase in insulin secretion upon improvement of glycemia by lifestyle intervention only in carriers of the risk alleles. CONCLUSIONS: For the diabetes risk genes TCF7L2 and WFS1, which are associated with impaired incretin signaling, the level of glycemia determines SNP effects on insulin secretion. This indicates the increasing relevance of these SNPs during the progression of prediabetes stages toward clinically overt type 2 diabetes.

Evaluation of fasting state-/oral glucose tolerance test-derived measures of insulin release for the detection of genetically impaired ß-cell function.

BACKGROUND: To date, fasting state- and different oral glucose tolerance test (OGTT)-derived measures are used to estimate insulin release with reasonable effort in large human cohorts required, e.g., for genetic studies. Here, we evaluated twelve common (or recently introduced) fasting state-/OGTT-derived indices for their suitability to detect genetically determined ß-cell dysfunction. METHODOLOGY/PRINCIPAL FINDINGS: A cohort of 1364 White European individuals at increased risk for type 2 diabetes was characterized by OGTT with glucose, insulin, and C-peptide measurements and genotyped for single nucleotide polymorphisms (SNPs) known to affect glucose- and incretin-stimulated insulin secretion. One fasting state- and eleven OGTT-derived indices were calculated and statistically evaluated. After adjustment for confounding variables, all tested SNPs were significantly associated with at least two insulin secretion measures (p≤0.05). The indices were ranked according to their associations' statistical power, and the ranks an index obtained for its associations with all the tested SNPs (or a subset) were summed up resulting in a final ranking. This approach revealed area under the curve (AUC)(Insulin(0-30))/AUC(Glucose(0-30)) as the best-ranked index to detect SNP-dependent differences in insulin release. Moreover, AUC(Insulin(0-30))/AUC(Glucose(0-30)), corrected insulin response (CIR), AUC(C-Peptide(0-30))/AUC(Glucose(0-30)), AUC(C-Peptide(0-120))/AUC(Glucose(0-120)), two different formulas for the incremental insulin response from 0-30 min, i.e., the insulinogenic indices (IGI)(2) and IGI(1), and insulin 30 min were significantly higher-ranked than homeostasis model assessment of ß-cell function (HOMA-B; p<0.05). AUC(C-Peptide(0-120))/AUC(Glucose(0-120)) was best-ranked for the detection of SNPs involved in incretin-stimulated insulin secretion. In all analyses, HOMA-ß displayed the highest rank sums and, thus, scored last. CONCLUSIONS/SIGNIFICANCE: With AUC(Insulin(0-30))/AUC(Glucose(0-30),) CIR, AUC(C-Peptide(0-30))/AUC(Glucose(0-30)), AUC(C-Peptide(0-120))/AUC(Glucose(0-120)), IGI(2), IGI(1), and insulin 30 min, dynamic measures of insulin secretion based on early insulin and C-peptide responses to oral glucose represent measures which are more appropriate to assess genetically determined ß-cell dysfunction than fasting measures, i.e., HOMA-B. Genes predominantly influencing the incretin axis may possibly be best detected by AUC(C-Peptide(0-120))/AUC(Glucose(0-120)).