Interactions between genetic background, insulin resistance and ß-cell function.

An interaction between genes and the environment is a critical component underlying the pathogenesis of the hyperglycaemia of type 2 diabetes. The development of more sophisticated techniques for studying gene variants and for analysing genetic data has led to the discovery of some 40 genes associated with type 2 diabetes. Most of these genes are related to changes in ß-cell function, with a few associated with decreased insulin sensitivity and obesity. Interestingly, using quantitative traits based on continuous measures rather than dichotomous ones, it has become evident that not all genes associated with changes in fasting or post-prandial glucose are also associated with a diagnosis of type 2 diabetes. Identification of these gene variants has provided novel insights into the physiology and pathophysiology of the ß-cell, including the identification of molecules involved in ß-cell function that were not previously recognized as playing a role in this critical cell.

Arm length is associated with type 2 diabetes mellitus in Japanese-Americans.

AIMS/HYPOTHESIS: The aim of the study was to examine the association of type 2 diabetes mellitus with arm length as a marker for early life environment and development. METHODS: This was a cross-sectional analysis of 658 second- and third-generation Japanese-Americans (349 men and 309 women). Different arm length (total, upper and forearm length) and leg length (total and lower leg length) measurements were performed. Type 2 diabetes was defined by the use of hypoglycaemic medication, fasting plasma glucose (FPG) ≥ 7 mmol/l or glucose at 2 h ≥ 11.1 mmol/l during an OGTT. Persons meeting the criteria for impaired glucose tolerance were excluded from these analyses (FPG <7 mmol/l and 2 h glucose during an OGGT <11.1 but ≥ 7.8 mmol/l). Multivariable logistic regression was used to estimate associations between prevalence of diabetes and limb length while adjusting for possible confounders. RESULTS: A total of 145 individuals had diabetes. On univariate analysis, arm and leg length were not associated with diabetes. After adjustment for age, sex, computed tomography-measured intra-abdominal fat area, height, weight, smoking status and family history of diabetes, total arm length and upper arm length were inversely related to diabetes (OR for a 1 SD increase 0.49, 95% CI 0.29, 0.84 for total arm length, and OR 0.56, 95% CI 0.36, 0.87 for upper arm length). Forearm length, height and leg length were not associated with diabetes after adjustment for confounding variables. CONCLUSIONS/INTERPRETATION: Our findings of associations between arm lengths and prevalence of type 2 diabetes supports a role for factors that determine bone growth or their correlates in the development of this condition.

Preeclampsia and risk of developing subsequent diabetes.

OBJECTIVE: Determine whether preeclampsia is associated with developing diabetes. METHODS: Subsequent diabetes was ascertained using ICD-9 codes, pharmacy and glucose data in a retrospective cohort study of 2,032 women with preeclampsia and 29,431 without preeclampsia. RESULTS: During a median follow-up of 8.2 years, 342 women developed diabetes. Preeclampsia was associated with a higher risk of diabetes adjusting for age, primigravidity, and gestational diabetes (hazard ratio, HR 1.82, 95%CI 1.26, 2.62) and in women without gestational diabetes (n = 30,109; HR 1.86, 95%CI 1.22, 2.84). CONCLUSION: Women with preeclampsia have greater risk of developing diabetes, even in the absence of gestational diabetes.

The beta cell lesion in type 2 diabetes: there has to be a primary functional abnormality.

The critical role of the beta cell in the pathogenesis of type 2 diabetes is now well established. When examined in patients with type 2 diabetes and individuals at increased risk, reductions in beta cell mass and abnormalities of beta cell function can both be demonstrated. The question of whether one alone is sufficient or both are necessary for the development of hyperglycaemia has been debated. Based on human and animal studies, it appears that neither alone is sufficient. Rather, for glucose to rise to the level at which diabetes would be diagnosed, defects in beta cell mass and in beta cell function are required.

An examination of beta-cell function measures and their potential use for estimating beta-cell mass.

A characteristic and dominant feature of type 2 diabetes is a reduction in beta-cell function that is associated with a decrease in beta-cell volume. A decline in the first-phase insulin response following intravenous glucose administration can be demonstrated as the fasting glucose concentration increases. This response is completely absent before the glucose threshold that defines diabetes has been reached and at a time when beta-cells are clearly still present, implying that a functional beta-cell lesion has to exist independent of beta-cell loss. Surgical or chemical reductions of up to 65% of beta-cell volume demonstrate that functional adaptation of the normal beta-cell prevents a rise in fasting glucose or reduction in first-phase insulin response. However, the ability of glucose to potentiate the beta-cell's response to non-glucose secretagogues is reduced and is more closely associated with the reduction in beta-cell volume. The future, in terms of prevention and treatment of type 2 diabetes, lies in the ability to prevent and revert both beta-cell loss and dysfunction. However, until beta-cell volume can be quantified reliably and non-invasively, we will need to rely on the ability of glucose to potentiate insulin release as the best surrogate estimate of the number of beta-cells.

Within-subject variability of measures of beta cell function derived from a 2 h OGTT: implications for research studies.

AIMS/HYPOTHESIS: Knowledge of the within-subject variability of a parameter is required to properly design and calculate sample sizes for longitudinal studies. We sought to determine the day-to-day variability of measures of beta cell function derived from an OGTT. METHODS: Thirty-seven adults (13 with normal glucose tolerance, ten with impaired glucose tolerance, 14 with type 2 diabetes) underwent a standard 2 h 75 g OGTT on two separate days (median time between tests, 7 days; range, 5-14). From these data, the reproducibility of several indices of beta cell function were determined: insulinogenic index (DeltaI(0-30)/DeltaG(0-30)), early C-peptide response (DeltaCP(0-30)/DeltaG(0-30)), incremental AUC insulin to glucose response (incAUC(ins)/incAUC(glu)), integrated insulin secretion response from 0 to 120 min (IS/Glu(0-120)) and indices of beta cell function derived from a mathematical model. RESULTS: Within-subject variability for DeltaI(0-30)/DeltaG(0-30) (CV 57.1%) was higher than DeltaCP(0-30)/DeltaG(0-30) (CV 34.7%). Measures integrated over the full 120 min of the OGTT, incAUC(ins)/incAUC(glu) (CV 24.9%) and IS/Glu(0-120) (CV 17.4%), demonstrated less variability. The mathematical model-derived measures of beta cell glucose sensitivity (CV 20.3%) and potentiation (CV 33.0%) showed moderate variability. The impact of the different measures' variability on sample size (30% change from baseline) is demonstrated by calculated sample sizes of 89 for DeltaI(0-30)/DeltaG(0-30), 37 for DeltaCP(0-30)/DeltaG(0-30), 21 for incAUC(ins)/incAUC(glu) and 11 for IS/Glu(0-120). CONCLUSIONS/INTERPRETATION: Some OGTT-derived indices of beta cell function, in particular the insulinogenic index, demonstrate high within-subject variability. Integrated measures that utilise multiple time points and measures that use C-peptide show less variability and may lead to a reduced sample size requirement.

Intra-abdominal fat accumulation predicts the development of the metabolic syndrome in non-diabetic Japanese-Americans.

AIMS/HYPOTHESIS: Intra-abdominal fat (IAF) is an important risk factor for CHD and type 2 diabetes, and in cross-sectional studies is associated with the metabolic syndrome (MetS). Our aim was to determine whether IAF accumulation predicts the future development of MetS in non-diabetic Japanese-Americans. SUBJECTS AND METHODS: We conducted a prospective study of 457 Japanese-American men and women (mean+/-SD: age 51.5 +/- 12.0 years, BMI 23.9 +/- 3.1 kg/m(2)) without diabetes or MetS at baseline. Of these, 408 completed a 5-year follow-up and 366 completed a 10-year follow-up. BMI, waist circumference, IAF and subcutaneous fat (SCF) areas by computed tomography, blood pressure, fasting plasma glucose, insulin, triacylglycerol and HDL-cholesterol were measured at baseline and at 5- and 10-year follow-up. MetS was defined using National Cholesterol Education Program Adult Treatment Panel III criteria. RESULTS: Incidence of MetS was 15.3% at 5 years and 17.8% at 10 years. A change of 1 SD in IAF area was associated with a 2.1-fold increase in the odds of MetS at 10 years (odds ratio = 2.08, 95% CI 1.41-3.07) after adjusting for age, sex, baseline IAF and the presence of each individual MetS criteria at baseline. This association was independent of changes in fasting insulin and SCF areas. CONCLUSIONS/INTERPRETATION: We conclude that IAF accumulation over time independently predicts the development of MetS and thus may play an important role in the development of MetS in Japanese-Americans.

Plasma pancreatic polypeptide levels are associated with differences in body fat distribution in human subjects.

AIMS/HYPOTHESIS: Pancreatic polypeptide (PP) is produced by the F-cells of the pancreas, and its plasma concentration has been used as a marker of parasympathetic activity. Recent work in rodents suggests that there is both sympathetic and parasympathetic innervation of white adipose tissue and that parasympathetic activity is anabolic resulting in lipid accumulation. We have examined whether in humans increased PP levels are associated with increased intra-abdominal fat (IAF), and thereby insulin resistance. MATERIALS AND METHODS: We measured PP levels in 177 non-diabetic subjects (75 male/102 female; age 32-75 years) 3 min after an i.v. glucose bolus during a frequently sampled intravenous glucose tolerance test. IAF and s.c. fat (SCF) areas were measured by CT scan. The insulin sensitivity index (S (I)) was quantified using Bergman's minimal model. RESULTS: PP levels were higher in men than in women (96.2 +/- 72.2 vs 76.1 +/- 55.0 pg/ml, mean +/- SD, p = 0.037), as was IAF area (124.7 +/- 67.4 vs 83.0 +/- 57.7 cm(2), p < 0.001). While PP levels were significantly associated with IAF (r = 0.16, p = 0.031), WHR (r = 0.30, p < 0.001) and age (r = 0.37, p < 0.01), they were not associated with SCF (r = 0.02, p = 0.829). The association between PP and IAF was not independent of age and/or sex. S(I) was negatively associated with PP levels (r = -0.17, p = 0.026) and IAF area (r = -0.65, p < 0.001). The association between S(I) and PP disappeared after adjusting for IAF area, indicating that S(I) was not a major determinant of PP levels. CONCLUSIONS/INTERPRETATION: In humans, age and sex may modulate the association between plasma PP level and IAF area, suggesting that they may be determinants of parasympathetic activity and thus IAF accumulation.

Resistin is not associated with insulin sensitivity or the metabolic syndrome in humans.

AIMS/HYPOTHESIS: The aim of this study was to further elucidate the relationship between resistin and insulin sensitivity, body fat distribution and the metabolic syndrome in humans. METHODS: We measured plasma resistin levels in 177 non-diabetic subjects (75 male, 102 female; age 32-75 years). BMI, waist circumference, blood pressure, lipids, glucose, plasminogen-activator inhibitor 1 (PAI-1), adiponectin and leptin levels were also measured. The insulin sensitivity index (S(I)) was quantified using Bergman's minimal model. Intra-abdominal fat (IAF) and subcutaneous fat (SQF) areas were quantified by CT scan. Presence of metabolic syndrome criteria was determined using the National Cholesterol Education Program Adult Treatment Panel III guidelines. RESULTS: When subjects were divided into categories based on BMI (< or > or =27.5 kg/m(2)) and S(I) (< or > or = 7 x 10(-5) min(-1) [pmol/l](-1)), resistin levels did not differ between the lean, insulin-sensitive (n=53, 5.36+/-0.3 ng/ml), lean, insulin-resistant (n=67, 5.70+/-0.4 ng/ml) and obese, insulin-resistant groups (n=48, 5.94+/-0.4 ng/ml; ANOVA p=0.65). Resistin correlated with age (r=-0.22, p<0.01), BMI (r=0.16, p=0.03) and SQF (r=0.19, p=0.01) but not with S(I) (p=0.31) or IAF (p=0.52). Resistin did not correlate with the number of metabolic syndrome criteria or any of the individual metabolic syndrome criteria. In contrast, adiponectin, PAI-1 and leptin each correlated with IAF, SQF and S(I). Additionally, the number of metabolic syndrome criteria correlated with adiponectin (r=-0.32, p<0.001), leptin (r=0.31, p<0.001) and PAI-1 (r=0.26, p=0.001). CONCLUSIONS/INTERPRETATION: In contrast to other adipokines, resistin is only weakly associated with body fat and is unlikely to be a major mediator of insulin resistance or the metabolic syndrome in humans.

Dietary-fat-induced obesity in mice results in beta cell hyperplasia but not increased insulin release: evidence for specificity of impaired beta cell adaptation.

AIMS/HYPOTHESIS: Increased dietary fat intake is associated with obesity and insulin resistance, but studies have shown that the subsequent increase in insulin release is not appropriate for this obesity-induced insulin resistance. We therefore sought to determine whether the impaired beta cell adaptation is due to inadequate expansion of the beta cell population or to a lack of an adaptive increase in insulin release. METHODS: Male mice were fed diets containing increasing amounts of fat (15, 30 or 45% of energy intake) for 1 year, after which islet morphology and secretory function were assessed. RESULTS: Increased dietary fat intake was associated with a progressive increase in body weight (p<0.001). Fractional beta cell area (total beta cell area/section area) was increased with increasing dietary fat (1.36+/-0.39, 2.46+/-0.40 and 4.93+/-1.05%, p<0.001), due to beta cell hyperplasia, and was positively and highly correlated with body weight (r2=0.68, p<0.005). In contrast, insulin release following i.p. glucose did not increase with increasing dietary fat (118+/-32, 108+/-47 and 488+/-200 pmol/l per mmol/l, p=0.07) and did not correlate with body weight (r2=0.11). When this response was examined relative to fractional beta cell area (insulin release/fractional beta cell area), it did not increase but rather tended to decrease with increasing dietary fat (157+/-55, 43+/-13 and 97+/-53 [pmol/l per mmol/l]/%, p=0.06) and did not correlate with body weight (r2=0.02). CONCLUSIONS/INTERPRETATION: Long-term fat feeding is associated with an increase in the beta cell population but an inadequate functional adaptation. Thus, a functional rather than a morphological abnormality appears to underlie dietary-fat-induced beta cell dysfunction.

Relationship of adiponectin to body fat distribution, insulin sensitivity and plasma lipoproteins: evidence for independent roles of age and sex.

AIMS/HYPOTHESIS: Increased intra-abdominal fat is associated with insulin resistance and an atherogenic lipoprotein profile. Circulating concentrations of adiponectin, an adipocyte-derived protein, are decreased with insulin resistance. We investigated the relationships between adiponectin and leptin, body fat distribution, insulin sensitivity and lipoproteins. METHODS: We measured plasma adiponectin, leptin and lipid concentrations, intra-abdominal and subcutaneous fat areas by CT scan, and insulin sensitivity index (S(I)) in 182 subjects (76 M/106F). RESULTS: Adiponectin concentrations were higher in women than in men (7.4+/-2.9 vs 5.4+/-2.3 micro g/ml, p<0.0001) as were leptin concentrations (19.1+/-13.7 vs 6.9+/-5.1 ng/ml, p<0.0001). Women were more insulin sensitive (S(I): 6.8+/-3.9 vs 5.9+/-4.4 x 10(-5) min(-1)/(pmol/l), p<0.01) and had more subcutaneous (240+/-133 vs 187+/-90 cm(2), p<0.01), but less intra-abdominal fat (82+/-57 vs 124+/-68 cm(2), p<0.0001). By simple regression, adiponectin was positively correlated with age ( r=0.227, p<0.01) and S(I) ( r=0.375, p<0.0001), and negatively correlated with BMI ( r=-0.333, p<0.0001), subcutaneous ( r=-0.168, p<0.05) and intra-abdominal fat ( r=-0.35, p<0.0001). Adiponectin was negatively correlated with triglycerides ( r=-0.281, p<0.001) and positively correlated with HDL cholesterol ( r=0.605, p<0.0001) and Rf, a measure of LDL particle buoyancy ( r=0.474, p<0.0001). By multiple regression analysis, adiponectin was related to age ( p<0.0001), sex ( p<0.005) and intra-abdominal fat ( p<0.01). S(I) was related to intra-abdominal fat ( p<0.0001) and adiponectin ( p<0.0005). Both intra-abdominal fat and adiponectin contributed independently to triglycerides, HDL cholesterol and Rf. CONCLUSION/INTERPRETATION: These data suggest that adiponectin concentrations are determined by intra-abdominal fat mass, with additional independent effects of age and sex. Adiponectin could link intra-abdominal fat with insulin resistance and an atherogenic lipoprotein profile.