Richard N. Bergman

A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants.

Identifying the genetic variants that increase the risk of type 2 diabetes (T2D) in humans has been a formidable challenge. Adopting a genome-wide association strategy, we genotyped 1161 Finnish T2D cases and 1174 Finnish normal glucose-tolerant (NGT) controls with >315,000 single-nucleotide polymorphisms (SNPs) and imputed genotypes for an additional >2 million autosomal SNPs. We carried out association analysis with these SNPs to identify genetic variants that predispose to T2D, compared our T2D association results with the results of two similar studies, and genotyped 80 SNPs in an additional 1215 Finnish T2D cases and 1258 Finnish NGT controls. We identify T2D-associated variants in an intergenic region of chromosome 11p12, contribute to the identification of T2D-associated variants near the genes IGF2BP2 and CDKAL1 and the region of CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk. This brings the number of T2D loci now confidently identified to at least 10.

Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes

Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D). Established associations to common and rare variants explain only a small proportion of the heritability of T2D. As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and ∼2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975. We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P = 5.0 × 10−14), CDC123-CAMK1D (P = 1.2 × 10−10), TSPAN8-LGR5 (P = 1.1 × 10−9), THADA (P = 1.1 × 10−9), ADAMTS9 (P = 1.2 × 10−8) and NOTCH2 (P = 4.1 × 10−8) gene regions. Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.

Newly identified loci that influence lipid concentrations and risk of coronary artery disease

To identify genetic variants influencing plasma lipid concentrations, we first used genotype imputation and meta-analysis to combine three genome-wide scans totaling 8,816 individuals and comprising 6,068 individuals specific to our study (1,874 individuals from the FUSION study of type 2 diabetes and 4,184 individuals from the SardiNIA study of aging-associated variables) and 2,758 individuals from the Diabetes Genetics Initiative, reported in a companion study in this issue. We subsequently examined promising signals in 11,569 additional individuals. Overall, we identify strongly associated variants in eleven loci previously implicated in lipid metabolism (ABCA1, the APOA5-APOA4-APOC3-APOA1 and APOE-APOC clusters, APOB, CETP, GCKR, LDLR, LPL, LIPC, LIPG and PCSK9) and also in several newly identified loci (near MVK-MMAB and GALNT2, with variants primarily associated with high-density lipoprotein (HDL) cholesterol; near SORT1, with variants primarily associated with low-density lipoprotein (LDL) cholesterol; near TRIB1, MLXIPL and ANGPTL3, with variants primarily associated with triglycerides; and a locus encompassing several genes near NCAN, with variants strongly associated with both triglycerides and LDL cholesterol). Notably, the 11 independent variants associated with increased LDL cholesterol concentrations in our study also showed increased frequency in a sample of coronary artery disease cases versus controls.

Quantification of the Relationship Between Insulin Sensitivity and β-Cell Function in Human Subjects: Evidence for a Hyperbolic Function

To determine the relationship between insulin sensitivity and β-cell function, we quantified the insulin sensitivity index using the minimal model in 93 relatively young, apparently healthy human subjects of varying degrees of obesity (55 male, 38 female; 18–44 yr of age; body mass index 19.5–52.2 kg/m2) and with fasting glucose levels <6.4 mM. SI was compared with measures of body adiposity and β-cell function. Although lean individuals showed a wide range of SI, body mass index and SI were related in a curvilinear manner (P < 0.0001) so that on average, an increase in body mass index was associated generally with a lower value for SI. The relationship between the SI and the β-cell measures was more clearly curvilinear and reciprocal for fasting insulin (P < 0.0001), first-phase insulin response (AIRglucose; P < 0.0001), glucose potentiation slope (n = 56; P < 0.005), and β-cell secretory capacity (AIRmax; n = 43; P < 0.0001). The curvilinear relationship between SI and the β-cell measures could not be distinguished from a hyperbola, i.e., SI × β-cell function = constant. This hyperbolic relationship described the data significantly better than a linear function (P < 0.05). The nature of this relationship is consistent with a regulated feedback loop control system such that for any difference in SI, a proportionate reciprocal difference occurs in insulin levels and responses in subjects with similar carbohydrate tolerance. We conclude that in human subjects with normal glucose tolerance and varying degrees of obesity, β-cell function varies quantitatively with differences in insulin sensitivity. Because the function governing this relationship is a hyperbola, when insulin sensitivity is high, large changes in insulin sensitivity produce relatively small changes in insulin levels and responses, whereas when insulin sensitivity is low, small changes in insulin sensitivity produce relatively large changes in insulin levels and responses. Percentile plots based on knowledge of this interaction are presented for evaluating β-cell function in populations and over time.

Common variants at 30 loci contribute to polygenic dyslipidemia

Blood low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglyceride levels are risk factors for cardiovascular disease. To dissect the polygenic basis of these traits, we conducted genome-wide association screens in 19,840 individuals and replication in up to 20,623 individuals. We identified 30 distinct loci associated with lipoprotein concentrations (each with P < 5 × 10−8), including 11 loci that reached genome-wide significance for the first time. The 11 newly defined loci include common variants associated with LDL cholesterol near ABCG8, MAFB, HNF1A and TIMD4; with HDL cholesterol near ANGPTL4, FADS1-FADS2-FADS3, HNF4A, LCAT, PLTP and TTC39B; and with triglycerides near AMAC1L2, FADS1-FADS2-FADS3 and PLTP. The proportion of individuals exceeding clinical cut points for high LDL cholesterol, low HDL cholesterol and high triglycerides varied according to an allelic dosage score (P < 10−15 for each trend). These results suggest that the cumulative effect of multiple common variants contributes to polygenic dyslipidemia.

Toward Physiological Understanding of Glucose Tolerance: Minimal-Model Approach

Glucose tolerance depends on a complex interaction among insulin secretion from the β-cells, clearance of the hormone, and the actions of insulin to accelerate glucose disappearance and inhibit endogenous glucose production. An additional factor, less well recognized, is the ability of glucose per se, independent of changes in insulin, to increase glucose uptake and suppress endogenous output (glucose effectiveness). These factors can be measured in the intact organism with physiologically based minimal models of glucose utilization and insulin kinetics. With the glucose minimal model, insulin sensitivity (SI) and glucose effectiveness (SG) are measured by computer analysis of the frequently sampled intravenous glucose tolerance test. The test involves intravenous injection of glucose followed by tolbutamide or insulin and frequent blood sampling. SI varied from a high of 7.6 × 10−4 min−1 · μU−1 · ml−1 in young Whites to 2.3 × 10−4 min−1 · μU−1 · ml−1 in obese nondiabetic subjects; in all of the nondiabetic subjects, SG was normal. In subjects with non-insulin-dependent diabetes mellitus (NIDDM), not only was SI reduced 90% below normal (0.61 ± 0.16 × 10−4 min−1 · μU−1 · ml−1), but in this group alone, SG was reduced (from 0.026 ± 0.008 to 0.014 ± 0.002 min−1); thus, defects in SI and SG are synergistic in causing glucose intolerance in NIDDM. One assumption of the minimal model is that the time delay in insulin action on glucose utilization in vivo is due to sluggish insulin transport across the capillary endothelium. This was tested by comparing insulin concentrations in plasma with those in lymph (representing interstitial fluid) during euglycemic-hyperinsulinemic glucose clamps. Lymph insulin was lower than plasma insulin at basal (12 vs. 18 μU/ml) and at steady state, indicating significant loss of insulin from the interstitial space, presumably due to cellular uptake of the insulin-receptor complex. Additionally, during clamps, lymph insulin changed more slowly than plasma insulin, but the rate of glucose utilization followed a time course identical with that of lymph (r = .96) rather than plasma (r = .71). Thus, lymph insulin, which may be reflective of interstitial fluid, is the signal to which insulin-sensitive tissues are responding. These studies support the concept that, at physiological insulin levels, the time for insulin to cross the capillary endothelium is the process that determines the rate of insulin action in vivo. In separate experiments, a similar intimate relationship was found between lymph insulin and glucose utilization estimated from the minimal model, supporting the accuracy of the minimal model as a mathematical representation of insulin action in vivo. Additional factors in glucose tolerance are insulin secretion and clearance. We proposed a model of insulin/C-peptide kinetics, derived from the original conception of Eaton and Polonsky, in which determination of C-peptide kinetics in each individual is unnecessary if insulin and C-peptide kinetics are modeled simultaneously. Prehepatic insulin secretion after glucose injection was calculated in healthy women; total insulin secretion was 22.2 nmol; first-phase insulin averaged 38% of total, but there was wide variation among healthy subjects. The ability to determine insulin secretion, insulin action, and glucose effectiveness from modeling allowed us to investigate their interaction. We propose that in healthy individuals, there is a balance between secretion and insulin action such that insulin secretion × insulin sensitivity = constant. Thus, with insulin resistance, it is proposed that a normal β-cell will increase its sensitivity to glucose appropriately, staving off impaired glucose tolerance. This concept is supported by data in healthy pregnant women, in whom the reciprocal relationship is shown to exist and impaired glucose tolerance is not observed despite substantial insulin resistance (S, reduced to 1.8 × 10−4 min−1 · μU−1 · ml−1). In subjects at risk for diabetes, e.g., HLA-identical siblings of insulin-dependent diabetic subjects and Pima Indians, insulin sensitivity × secretion < normal constant value. Additionally, Pima Indians with the lowest sensitivity/secretion product appear to be at highest risk for developing NIDDM. Finally, with simulation of the models, we examined the relative importance of individual and compound defects of SI, SG, and insulin secretion to glucose intolerance. Although no individual defect (≤80%) of these factors causes diabetic glucose tolerance (KG < 1), compound defects are remarkably synergistic, with a combined SI/SG defect being the most severe (KG = 0.60), and an SG defect being a requisite component for diabetic glucose tolerance.

Insulin Sensitivity and Atherosclerosis

Background Reduced insulin sensitivity has been proposed as an important risk factor in the development of atherosclerosis. However, insulin sensitivity is related to many other cardiovascular risk factors, including plasma insulin levels, and it is unclear whether an independent role of insulin sensitivity exists. Large epidemiological studies that measure insulin sensitivity directly have not been conducted. Methods and Results The Insulin Resistance Atherosclerosis Study (IRAS) evaluated insulin sensitivity (SI) by the frequently sampled intravenous glucose tolerance test with analysis by the minimal model of Bergman. IRAS measured intimal-medial thickness (IMT) of the carotid artery as an index of atherosclerosis by use of noninvasive B-mode ultrasonography. These measures, as well as factors that may potentially confound or mediate the relationship between insulin sensitivity and atherosclerosis, were available in relation to 398 black, 457 Hispanic, and 542 non-Hispanic white IRAS participants. Ther...

Estimation of Endogenous Glucose Production During Hyperinsulinemic-Euglycemic Glucose Clamps: Comparison of Unlabeled and Labeled Exogenous Glucose Infusates

Tracer methodology has been applied extensively to the estimation of endogenous glucose production (Ra) during euglycemic glucose clamps. The accuracy of this approach has been questioned due to the observation of significantly negative estimates for Ra when insulin levels are high. We performed hyperinsulinemic (300 μU/ml)-euglycemic glucose clamps for 180 min in normal dogs and compared the standard approach, an unlabeled exogenous glucose infusate (cold GINF protocol, n = 12), to a new approach in which a tracer (D-[3-3H]glucose) was added to the exogenous glucose used for clamping (hot GINF protocol, n = 10). Plasma glucose, insulin and glucagon concentrations, and glucose infusion rates were similar for the two protocols. Plasma glucose specific activity was 20 ± 1% of basal (at 120–180 min) in the cold GINF studies, and 44 ± 3 to 187 ± 5% of basal in the hot GINF studies. With the one-compartment, fixed pool volume model of Steele, Ra, for the cold GINF studies was –2.4 ± 0.7 mg · min−1 · kg−1 at 25 min and remained significantly negative until 110 min (P < .05). For the hot GINF studies, Ra was never significantly less than zero (P > .05) and was greater than in the cold GINF studies at 20–90 min (P < .05). There was substantially less between-(78%) and within- (40%) experiment variation for the hot GINF studies compared with the cold GINF studies. An alternate approach (regression method) to the application of the one-compartment model, which allows for a variable and estimable effective distribution volume, yielded Ra estimates that were suppressed 60–100% from basal. In conclusion, the one-compartment, fixed pool volume model of glucose kinetics is inadequate for the estimation of Ra during euglycemic glucose clamps. Two new strategies for estimating Ra from the one-compartment model, the hot GINF protocol and the regression method calculation, yielded more accurate and physiologically plausible estimates of Ra than currently used methodology.

The Insulin Resistance Atherosclerosis Study (IRAS). Objectives, design, and recruitment results

The Insulin Resistance Atherosclerosis Study (IRAS) is the first epidemiologic study designed to assess the relationships between insulin resistance, insulinemia, glycemia, other components of the insulin resistance syndrome, and prevalent cardiovascular disease (CVD) in a large multiethnic cohort. Over 1600 men and women were recruited from four geographic areas to represent a range of glucose tolerance (normal, impaired, and diabetic) and ethnicity (hispanic, non-Hispanic white, and African-American). Insulin resistance was assessed directly using the frequently sampled intravenous glucose tolerance test with minimal model analysis. Intimal-medial carotid artery wall thickness, an indicator of atherosclerosis, was measured using B-mode ultrasonography. Prevalent CVD was assessed by questionnaire and resting electrocardiography. This report describes the design of the study and provides the recruitment results. Forthcoming cross-sectional analyses will help to disentangle the association between insulin resistance and CVD, apart from the concomitant hyperinsulinemia and related CVD risk factors.

A better index of body adiposity.

Obesity is a growing problem in the United States and throughout the world. It is a risk factor for many chronic diseases. The BMI has been used to assess body fat for almost 200 years. BMI is known to be of limited accuracy, and is different for males and females with similar %body adiposity. Here, we define an alternative parameter, the body adiposity index (BAI = ((hip circumference)/((height)1.5)–18)). The BAI can be used to reflect %body fat for adult men and women of differing ethnicities without numerical correction. We used a population study, the “BetaGene” study, to develop the new index of body adiposity. %Body fat, as measured by the dual‐energy X‐ray absorptiometry (DXA), was used as a “gold standard” for validation. Hip circumference (R = 0.602) and height (R = −0.524) are strongly correlated with %body fat and therefore chosen as principal anthropometric measures on which we base BAI. The BAI measure was validated in the “Triglyceride and Cardiovascular Risk in African‐Americans (TARA)” study of African Americans. Correlation between DXA‐derived %adiposity and the BAI was R = 0.85 for TARA with a concordance of C_b = 0.95. BAI can be measured without weighing, which may render it useful in settings where measuring accurate body weight is problematic. In summary, we have defined a new parameter, the BAI, which can be calculated from hip circumference and height only. It can be used in the clinical setting even in remote locations with very limited access to reliable scales. The BAI estimates %adiposity directly.