Disruption of GIP/GIPR axis in human adipose tissue is linked to obesity and insulin resistance.

CONTEXT Glucose-dependent insulinotropic peptide (GIP) has a central role in glucose homeostasis through its amplification of insulin secretion; however, its physiological role in adipose tissue is unclear. OBJECTIVE Our objective was to define the function of GIP in human adipose tissue in relation to obesity and insulin resistance. DESIGN GIP receptor (GIPR) expression was analyzed in human sc adipose tissue (SAT) and visceral adipose (VAT) from lean and obese subjects in 3 independent cohorts. GIPR expression was associated with anthropometric and biochemical variables. GIP responsiveness on insulin sensitivity was analyzed in human adipocyte cell lines in normoxic and hypoxic environments as well as in adipose-derived stem cells obtained from lean and obese patients. RESULTS GIPR expression was downregulated in SAT from obese patients and correlated negatively with body mass index, waist circumference, systolic blood pressure, and glucose and triglyceride levels. Furthermore, homeostasis model assessment of insulin resistance, glucose, and G protein-coupled receptor kinase 2 (GRK2) emerged as variables strongly associated with GIPR expression in SAT. Glucose uptake studies and insulin signaling in human adipocytes revealed GIP as an insulin-sensitizer incretin. Immunoprecipitation experiments suggested that GIP promotes the interaction of GRK2 with GIPR and decreases the association of GRK2 to insulin receptor substrate 1. These effects of GIP observed under normoxia were lost in human fat cells cultured in hypoxia. In support of this, GIP increased insulin sensitivity in human adipose-derived stem cells from lean patients. GIP also induced GIPR expression, which was concomitant with a downregulation of the incretin-degrading enzyme dipeptidyl peptidase 4. None of the physiological effects of GIP were detected in human fat cells obtained from an obese environment with reduced levels of GIPR. CONCLUSIONS GIP/GIPR signaling is disrupted in insulin-resistant states, such as obesity, and normalizing this function might represent a potential therapy in the treatment of obesity-associated metabolic disorders.

A Nested Case-Control Study of Metabolically Defined Body Size Phenotypes and Risk of Colorectal Cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC).

BACKGROUND Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown. METHODS AND FINDINGS The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed. CONCLUSIONS These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.