Reduced glucose effectiveness associated with reduced insulin release: an artifact of the minimal-model method

Acute pharmacodynamic responses to exenatide: Drug-induced increases in insulin secretion and glucose effectiveness

Background

GLP1R agonists provide multiple benefits to patients with type 2 diabetes - including improved glycemic control, weight loss, and decreased risk of major adverse cardiovascular events. Because drug responses vary among individuals, we initiated investigations to identify genetic variants associated with the magnitude of drug responses.

Methods

Exenatide (5 µg, sc) or saline (0.2 mL, sc) was administered to 62 healthy volunteers. Frequently sampled intravenous glucose tolerance tests were conducted to assess the impact of exenatide on insulin secretion and insulin action. This pilot study was designed as a crossover study in which participants received exenatide and saline in random order.

Results

Exenatide increased first phase insulin secretion 1.9-fold (p=1.9×10 -9 ) and accelerated the rate of glucose disappearance 2.4-fold (p=2×10 -10 ). Minimal model analysis demonstrated that exenatide increased glucose effectiveness (S g ) by 32% (p=0.0008) but did not significantly affect insulin sensitivity (S i ). The exenatide-induced increase in insulin secretion made the largest contribution to inter-individual variation in exenatide-induced acceleration of glucose disappearance while inter-individual variation in the drug effect on S g contributed to a lesser extent (β=0.58 or 0.27, respectively).

Conclusions

This pilot study provides validation for the value of an FSIGT (including minimal model analysis) to provide primary data for our ongoing pharmacogenomic study of pharmacodynamic effects of semaglutide ( NCT05071898 ). Three endpoints provide quantitative assessments of GLP1R agonists' effects on glucose metabolism: first phase insulin secretion, glucose disappearance rates, and glucose effectiveness.

Registration

NCT02462421 (clinicaltrials.gov).

Funding

American Diabetes Association (1-16-ICTS-112); National Institute of Diabetes and Digestive and Kidney Disease (R01DK130238, T32DK098107, P30DK072488).

Insulin sensitivity index in type 1 diabetes and following human islet transplantation: comparison of the minimal model to euglycemic clamp measures

Insulin sensitivity is impaired in type 1 diabetes (T1D) and may be enhanced by islet transplantation, an effect best explained by improved metabolic control. While the minimal model index of insulin sensitivity, SI, has been used in studies of T1D, it has not before been evaluated against gold-standard measures derived from the euglycemic clamp. We sought to determine how well minimal model SI derived from an insulin-modified frequently sampled intravenous glucose tolerance (FSIGT) test compared with total body and peripheral insulin sensitivity estimates derived from the hyperinsulinemic-euglycemic clamp in subjects with T1D and following islet transplantation. Twenty-one T1D subjects were evaluated, including a subgroup (n = 12) studied again after intrahepatic islet transplantation, with results compared with normal controls (n = 11 for the FSIGT). The transplant recipients received 9,648 ± 666 islet equivalents/kg with reduction in HbA1c from 7.1 ± 0.2 to 5.5 ± 0.1% (P < 0.01) and 10/12 were insulin independent. FSIGT-derived SI was reduced in T1D pre- compared with posttransplant and with normal [1.76 ± 0.45 vs. 4.21 ± 0.34 vs. 4.45 ± 0.81 × 10(-4)(μU/ml)(-1)·min(-1); P < 0.01 for both]. Similarly, clamp-derived total body, and by the isotopic dilution method with [6,6-(2)H2]glucose, peripheral insulin sensitivity increased in T1D from pre- to posttransplant (P < 0.05 for both). The predictive power (r(2)) between volume-corrected SIC and measures of total and peripheral insulin sensitivity was 0.66 and 0.70, respectively (P < 0.00001 for both). That the minimal model SIC is highly correlated to the clamp-derived measures indicates that the FSIGT is an appropriate methodology for the determination of insulin sensitivity in T1D and following islet transplantation.

Ethnic differences in the relationship between insulin sensitivity and insulin response: a systematic review and meta-analysis

OBJECTIVE

Human blood glucose levels have likely evolved toward their current point of stability over hundreds of thousands of years. The robust population stability of this trait is called canalization. It has been represented by a hyperbolic function of two variables: insulin sensitivity and insulin response. Environmental changes due to global migration may have pushed some human subpopulations to different points of stability. We hypothesized that there may be ethnic differences in the optimal states in the relationship between insulin sensitivity and insulin response.

RESEARCH DESIGN AND METHODS

We identified studies that measured the insulin sensitivity index (SI) and acute insulin response to glucose (AIRg) in three major ethnic groups: Africans, Caucasians, and East Asians. We identified 74 study cohorts comprising 3,813 individuals (19 African cohorts, 31 Caucasian, and 24 East Asian). We calculated the hyperbolic relationship using the mean values of SI and AIRg in the healthy cohorts with normal glucose tolerance.

RESULTS

We found that Caucasian subpopulations were located around the middle point of the hyperbola, while African and East Asian subpopulations are located around unstable extreme points, where a small change in one variable is associated with a large nonlinear change in the other variable.

CONCLUSIONS

Our findings suggest that the genetic background of Africans and East Asians makes them more and differentially susceptible to diabetes than Caucasians. This ethnic stratification could be implicated in the different natural courses of diabetes onset.

Ethnic differences in glucose disposal, hepatic insulin sensitivity, and endogenous glucose production among African American and European American women

Intravenous glucose tolerance tests have demonstrated lower whole-body insulin sensitivity (S(I)) among African Americans (AA) compared with European Americans (EA). Whole-body S(I) represents both insulin-stimulated glucose disposal, primarily by skeletal muscle, and insulin's suppression of endogenous glucose production (EGP) by liver. A mathematical model was recently introduced that allows for distinction between disposal and hepatic S(I). The purpose of this study was to examine specific indexes of S(I) among AA and EA women to determine whether lower whole-body S(I) in AA may be attributed to insulin action at muscle, liver, or both. Participants were 53 nondiabetic, premenopausal AA and EA women. Profiles of EGP and indexes of Disposal S(I) and Hepatic S(I) were calculated by mathematical modeling and incorporation of a stable isotope tracer ([6,6-(2)H(2)]glucose) into the intravenous glucose tolerance test. Body composition was assessed by dual-energy x-ray absorptiometry. After adjustment for percentage fat, both Disposal S(I) and Hepatic S(I) were lower among AA (P = .009 for both). Time profiles for serum insulin and EGP revealed higher peak insulin response and corresponding lower EGP among AA women compared with EA. Indexes from a recently introduced mathematical model suggest that lower whole-body S(I) among nondiabetic AA women is due to both hepatic and peripheral components. Despite lower Hepatic S(I), AA displayed lower EGP, resulting from higher postchallenge insulin levels. Future research is needed to determine the physiological basis of lower insulin sensitivity among AA and its implications for type 2 diabetes mellitus risk.

Assessment of non-insulin-mediated glucose uptake: association with body fat and glycemic status

In the fasting state, approximately 83% of glucose uptake occurs via non-insulin-mediated mechanisms. A widely accepted static rate for NIMGU is 1.62 mg kg(-1)·min(-1). To investigate the variability of NIMGU, we examined differences by glucose tolerance, sex, age, race (American Indian/African American/Caucasian), and adiposity in 616 volunteers (including individuals with normal glucose regulation [NGR] and impaired glucose regulation [IGR] and diabetes mellitus [DM]) using data from euglycemic-hyperinsulinemic clamp experiments. NIMGU was determined by plotting basal glucose output and insulin action against fasting and steady-state clamp insulin. The intercept with the y-axis after extrapolation was interpreted as NIMGU at zero insulin. Body composition was determined by dual-energy x-ray absorptiometry; and glucose regulation, by a 75-g oral glucose tolerance test. Energy expenditure was measured by indirect calorimetry in a metabolic chamber. In individuals with NGR (n = 385), NIMGU was 1.63 mg kg(estimated metabolic body size (fat free mass + 17.7 kg))(-1) min(-1) (95% confidence interval, 1.59-1.66). NIMGU increased with IGR and DM (IGR: n = 189, 1.67 [1.62-1.72]; DM: n = 42, 2.39 [2.29-2.49]; P < .0001 across groups). NIMGU did not differ by sex (P = .13), age (P = .22), or race (P = .06); however, NIMGU was associated with percentage body fat (r(2) = 0.04, P < .0001). Furthermore, NIMGU was positively associated with 24-hour and sleep energy expenditure (r(2) = 0.002, P = .03; r(2) = 0.01, P < .01). Extrapolated NIMGU in individuals with NGR is remarkably consistent with previously published data. Our results indicate that NIMGU is associated with adiposity. NIMGU increases with declining glucose tolerance perhaps to preserve glucose uptake during increased insulin resistance.

Disposition index, glucose effectiveness, and conversion to type 2 diabetes: the Insulin Resistance Atherosclerosis Study (IRAS)

OBJECTIVE

Disposition index (DI) and glucose effectiveness (S(G)) are risk factors for diabetes. However, the effect of DI and S(G) on future diabetes has not been examined in large epidemiological studies using direct measures.

RESEARCH DESIGN AND METHODS

Insulin sensitivity index (S(I)), acute insulin response (AIR), and S(G) were measured in 826 participants (aged 40-69 years) in the Insulin Resistance Atherosclerosis Study (IRAS) by the frequently sampled intravenous glucose tolerance test. DI was expressed as S(I) x AIR. At the 5-year follow-up examination, 128 individuals (15.5%) had developed diabetes.

RESULTS

The area under the receiver operating characteristic curve of a model with S(I) and AIR was similar to that of DI (0.767 vs. 0.774, P = 0.543). In a multivariate logistic regression model that included both DI and S(G), conversion to diabetes was predicted by both S(G) (odds ratio x 1 SD, 0.61 [0.47-0.80]) and DI (0.68 [0.54-0.85]) after adjusting for demographic variables, fasting and 2-h glucose concentrations, family history of diabetes, and measures of obesity. Age, sex, race/ethnicity, glucose tolerance status, obesity, and family history of diabetes did not have a significant modifying impact on the relation of S(G) and DI to incident diabetes.

CONCLUSIONS

The predictive power of DI is comparable to that of its components, S(I) and AIR. S(G) and DI independently predict conversion to diabetes similarly across race/ethnic groups, varying states of glucose tolerance, family history of diabetes, and obesity.

Systems engineering medicine: engineering the inflammation response to infectious and traumatic challenges

The complexity of the systemic inflammatory response and the lack of a treatment breakthrough in the treatment of pathogenic infection demand that advanced tools be brought to bear in the treatment of severe sepsis and trauma. Systems medicine, the translational science counterpart to basic science's systems biology, is the interface at which these tools may be constructed. Rapid initial strides in improving sepsis treatment are possible through the use of phenomenological modelling and optimization tools for process understanding and device design. Higher impact, and more generalizable, treatment designs are based on mechanistic understanding developed through the use of physiologically based models, characterization of population variability, and the use of control-theoretic systems engineering concepts. In this review we introduce acute inflammation and sepsis as an example of just one area that is currently underserved by the systems medicine community, and, therefore, an area in which contributions of all types can be made.

Reappraisal of the intravenous glucose tolerance index for a simple assessment of insulin sensitivity in mice

Mice are increasingly used in studies where measuring insulin sensitivity (IS) is a common procedure. The glucose clamp is labor intensive, cannot be used in large numbers of animals, cannot be repeated in the same mouse, and has been questioned as a valid tool for IS in mice; thus, the minimal model with 50-min intravenous glucose tolerance test (IVGTT) data was adapted for studies in mice. However, specific software and particular ability was needed. The aim of this study was to establish a simple procedure for evaluating IS during IVGTT in mice (CS(I)). IVGTTs (n = 520) were performed in NMRI and C57BL/6J mice (20-25g). After glucose injection (1 g/kg), seven samples were collected for 50 min for glucose and insulin measurements, analyzed with a minimal model that provided the validated reference IS (S(I)). By using the regression CS(I) = alpha(1) + alpha(2) x K(G)/AUC(D), where K(G) is intravenous glucose tolerance index and AUC(d) is the dynamic area under the curve, IS was calculated in 134 control animals randomly selected (regression CS(I) vs. S(I): r = 0.66, P < 0.0001) and yielded alpha(1) = 1.93 and alpha(2) = 0.24. K(G) is the slope of log (glucose(5-20)) and AUC(D) is the mean dynamic area under insulin curve in the IVGTT. By keeping fixed alpha(1) and alpha(2), CS(I) was validated in 143 control mice (4.7 +/- 0.2 min*microU(-1)*ml(-1), virtually identical to S(I): 4.7 +/- 0.3, r = 0.89, P < 0.0001); and in 123 mice in different conditions: transgenic, addition of neuropeptides, incretins, and insulin (CS(I): 6.0 +/- 0.4 vs. S(I): 6.1 +/- 0.4, r = 0.94, P < 0.0001). In the other 120 animals, CS(I) revealed its ability to segregate different categories, as does S(I). This easily usable formula for calculating CS(I) overcomes many experimental obstacles and may be a simple alternative to more complex procedures when large numbers of mice or repeated experiments in the same animals are required.

Diabetes: Models, Signals, and Control

The control of diabetes is an interdisciplinary endeavor, which includes a significant biomedical engineering component, with traditions of success beginning in the early 1960s. It began with modeling of the insulin-glucose system, and progressed to large-scale in silico experiments, and automated closed-loop control (artificial pancreas). Here, we follow these engineering efforts through the last, almost 50 years. We begin with the now classic minimal modeling approach and discuss a number of subsequent models, which have recently resulted in the first in silico simulation model accepted as substitute to animal trials in the quest for optimal diabetes control. We then review metabolic monitoring, with a particular emphasis on the new continuous glucose sensors, on the analyses of their time-series signals, and on the opportunities that they present for automation of diabetes control. Finally, we review control strategies that have been successfully employed in vivo or in silico, presenting a promise for the development of a future artificial pancreas and, in particular, discuss a modular architecture for building closed-loop control systems, including insulin delivery and patient safety supervision layers. We conclude with a brief discussion of the unique interactions between human physiology, behavioral events, engineering modeling and control relevant to diabetes.

The future of open- and closed-loop insulin delivery systems

We have analysed several aspects of insulin-dependent diabetes mellitus, including the glucose metabolic system, diabetes complications, and previous and ongoing research aimed at controlling glucose in diabetic patients. An expert review of various models and control algorithms developed for the glucose homeostasis system is presented, along with an analysis of research towards the development of a polymeric insulin infusion system. Recommendations for future directions in creating a true closed-loop glucose control system are presented, including the development of multivariable models and control systems to more accurately describe and control the multi-metabolite, multi-hormonal system, as well as in-vivo assessments of implicit closed-loop control systems.

Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage

Insulin resistance contributes to the pathophysiology of diabetes and is a hallmark of obesity, metabolic syndrome, and many cardiovascular diseases. Therefore, quantifying insulin sensitivity/resistance in humans and animal models is of great importance for epidemiological studies, clinical and basic science investigations, and eventual use in clinical practice. Direct and indirect methods of varying complexity are currently employed for these purposes. Some methods rely on steady-state analysis of glucose and insulin, whereas others rely on dynamic testing. Each of these methods has distinct advantages and limitations. Thus, optimal choice and employment of a specific method depends on the nature of the studies being performed. Established direct methods for measuring insulin sensitivity in vivo are relatively complex. The hyperinsulinemic euglycemic glucose clamp and the insulin suppression test directly assess insulin-mediated glucose utilization under steady-state conditions that are both labor and time intensive. A slightly less complex indirect method relies on minimal model analysis of a frequently sampled intravenous glucose tolerance test. Finally, simple surrogate indexes for insulin sensitivity/resistance are available (e.g., QUICKI, HOMA, 1/insulin, Matusda index) that are derived from blood insulin and glucose concentrations under fasting conditions (steady state) or after an oral glucose load (dynamic). In particular, the quantitative insulin sensitivity check index (QUICKI) has been validated extensively against the reference standard glucose clamp method. QUICKI is a simple, robust, accurate, reproducible method that appropriately predicts changes in insulin sensitivity after therapeutic interventions as well as the onset of diabetes. In this Frontiers article, we highlight merits, limitations, and appropriate use of current in vivo measures of insulin sensitivity/resistance.

Formation and regeneration of the endocrine pancreas

The elaboration of the pancreas from epithelial buds to the intricate organ requires complex patterning information that controls fundamental cellular processes such as differentiation and proliferation of pancreatic progenitor cells. During pancreatic organogenesis, endocrine cells are generated from a population of pancreatic progenitor cells. The progenitor cells during the early development simultaneously receive multiple signals, some mitogenic and some inducing differentiation. These extrinsic signals are interpreted through an intrinsic mechanism that either commits the progenitor cell to the mitotic cell cycle or leads to exit from the cell cycle in order to differentiate. The endocrine cells that differentiate from progenitor cells are postmitotic, and direct lineage tracing analyses indicate that a population of progenitor cells persists throughout embryogenesis to allow the differentiation of new endocrine cells. At the end of embryogenesis an early postnatal period is characterized by high rates of beta cell proliferation leading to massive increases in beta cell mass. The beta cell mass expansion considerably slows down in adult animals, though variations in insulin demand due to physiological and pathological states such as pregnancy and obesity can lead to adaptive changes in the beta cells that include hyperplasia, hypertrophy, and increased insulin synthesis and secretion. Deciphering the mechanisms that regulate the plasticity of beta cell mass can be an important step in developing effective strategies to treat diabetes.

Integrated model of hepatic and peripheral glucose regulation for estimation of endogenous glucose production during the hot IVGTT

We have developed a new model to describe endogenous glucose kinetics during a labeled (hot) intravenous glucose tolerance test (IVGTT) to derive a time profile of endogenous glucose production (EGP). We reanalyzed data from a previously published study (P. Vicini, J. J. Zachwieja, K. E. Yarasheski, D. M. Bier, A. Caumo, and C. Cobelli. Am J Physiol Endocrinol Metab 276: E285-E294, 1999), in which insulin-modified [6,6-2H2]glucose-labeled IVGTTs (0.33 g/kg glucose) were performed in 10 normal subjects. In addition, a second tracer ([U-13C]glucose) was infused in a variable rate to clamp the endogenous glucose tracer-to-tracee ratio (TTR). Our new model describing endogenous glucose kinetics was incorporated into the two-compartment hot minimal-model structure. The model gave estimates of glucose effectiveness [1.54 +/- 0.31 (SE) ml x kg(-1) x min(-1)], insulin sensitivity (37.74 +/- 5.23 10(4) dl x kg(-1) x min(-1) x microU(-1) x ml), and a new parameter describing the sensitivity of EGP to the inhibitory effect of insulin (IC50 = 0.0195 +/- 0.0046 min(-1)). The model additionally provided an estimate of the time course of EGP showing almost immediate inhibition, followed by a secondary inhibitory effect caused by infusion of insulin, and a large overshoot as EGP returns to its basal value. Our estimates show very good agreement with those obtained via deconvolution and the model-independent TTR clamp technique. These results suggest that the new integrated model can serve as a simple one-step approach to obtain metabolic indexes while also providing a parametric description of EGP.

Loss of beta cell function as fasting glucose increases in the non-diabetic range

AIMS/HYPOTHESIS

Our aim was to define the level of glycaemia at which pancreatic insulin secretion, particularly first-phase insulin release, begins to decline.

METHODS

Plasma glucose and insulin concentrations were measured during an IVGTT in 553 men with non-diabetic fasting plasma glucose concentrations. In 466 of the men C-peptide was also estimated. IVGTT insulin secretion in first and late phases was assessed by: (i) the circulating insulin response; (ii) population parameter deconvolution analysis of plasma C-peptide concentrations; and (iii) a combined model utilising both insulin and C-peptide concentrations. Measurements of insulin sensitivity and elimination were also derived by modelling analysis.

RESULTS

As fasting plasma glucose (FPG) increased, IVGTT first-phase insulin secretion declined by 73%, 71% and 68% for the three methods respectively. The FPG values at which this decline began, determined by change point regression, were 4.97, 5.16 and 5.42 mmol/l respectively. The sensitivity of late-phase insulin secretion to glucose declined at FPG concentrations above 6.0 mmol/l. Insulin elimination, but not insulin sensitivity, varied with FPG.

CONCLUSIONS/INTERPRETATION

The range of FPG over which progressive loss of the first-phase response begins may be as low as 5.0 to 5.4 mmol/l, with late-phase insulin responses declining at FPG concentrations above 6.0 mmol/l.

Arterial wall thickness is associated with insulin resistance in type 2 diabetic patients

The aim of the present study was to investigate the independent association of the intimal-medial thickness of carotid and femoral arteries (CA-IMT and FA-IMT), a marker of atheroscelosis, with insulin resistance in type 2 diabetic patients. We evaluated CA-IMT and FA-IMT by high-resolution ultrasonography and insulin resistance determined by euglycemic hyperinsulinemic clamp in 119 type 2 diabetic subjects, 71 males and 48 females (age, 54 +/- 12 (SD) years). In simple regression analyses, CA-IMT and FA-IMT were significantly inversely correlated with insulin sensitivity index (CA-IMT, r = -0.225, p = 0.010; FA-IMT, r = -0.186, p = 0.043, respectively). Multiple regression analysis was performed with the logarithm of CA-IMT or FA-IMT as a dependent variable and insulin sensitivity index as an independent variable along with known clinical risk factors. Insulin sensitivity index exhibited a significant independent contribution to log (CA-IMT) (beta = -0.204, p = 0.033) and to log (FA-IMT) (beta = -0.237, p = 0.010) in these models (CA-IMT, R(2) = 0.347, p < 0.0001; FA-IMT, R(2) = 0.398, p < 0.0001, respectively). In conclusion, insulin resistance is associated with both CA-IMT and FA-IMT in type 2 diabetic patients, suggesting that it is an independent risk factor for the development of atherosclerosis in type 2 diabetes.

Impaired basal glucose effectiveness but unaltered fasting glucose release and gluconeogenesis during short-term hypercortisolemia in healthy subjects

Excess cortisol has been demonstrated to impair hepatic and extrahepatic insulin action. To determine whether glucose effectiveness and, in terms of endogenous glucose release (EGR), gluconeogenesis, also are altered by hypercortisolemia, eight healthy subjects were studied after overnight infusion with hydrocortisone or saline. Glucose effectiveness was assessed by a combined somatostatin and insulin infusion protocol to maintain insulin concentration at basal level in the presence of prandial glucose infusions. Despite elevated insulin concentrations (P < 0.05), hypercortisolemia resulted in higher glucose (P < 0.05) and free fatty acid concentrations (P < 0.05). Furthermore, basal insulin concentrations were higher during hydrocortisone than during saline infusion (P < 0.01), indicating the presence of steroid-induced insulin resistance. Postabsorptive glucose production (P = 0.64) and the fractional contribution of gluconeogenesis to EGR (P = 0.33) did not differ on the two study days. During the prandial glucose infusion, the integrated glycemic response above baseline was higher in the presence of hydrocortisone than during saline infusion (P < 0.05), implying a decrease in net glucose effectiveness (4.42 +/- 0.52 vs. 6.65 +/- 0.83 ml.kg-1.min-1; P < 0.05). To determine whether this defect is attributable to an impaired ability of glucose to suppress glucose production, to stimulate its own uptake, or both, glucose turnover and "hot" (labeled) indexes of glucose effectiveness (GE) were calculated. Hepatic GE was lower during cortisol than during saline infusion (2.39 +/- 0.24 vs. 3.82 +/- 0.51 ml.kg-1.min-1; P < 0.05), indicating a defect in the ability of glucose to restrain its own production. In addition, in the presence of excess cortisol, glucose disappearance was inappropriate for the prevailing glucose concentration, implying a decrease in glucose clearance (P < 0.05). The decrease in glucose clearance was confirmed by the higher increment in [3-3H]glucose during hydrocortisone than during saline infusion (P < 0.05), despite the administration of identical tracer infusion rates. In conclusion, short-term hypercortisolemia in healthy individuals with normal beta-cell function decreases insulin action but does not alter rates of EGR and gluconeogenesis. In addition, cortisol impairs the ability of glucose to suppress its own production, which due to accumulation of glucose in the glucose space results in impaired peripheral glucose clearance. These results suggest that cortisol excess impairs glucose tolerance by decreasing both insulin action and glucose effectiveness.

Insulin sensitivity and glucose effectiveness from three minimal models: effects of energy restriction and body fat in adult male rhesus monkeys

The minimal model of glucose disappearance (MINMOD version 3; MM3) and both the one-compartment (1CMM) and the two-compartment (2CMM) minimal models were used to analyze stable isotope-labeled intravenous glucose tolerance test (IVGTT) data from year 10 of a study of the effect of dietary restriction (DR) in male rhesus monkeys. Adult monkeys were energy restricted (R; n = 12) on a semipurified diet to approximately 70% of control (C) intake (ad libitum-fed monkeys; n = 12). Under ketamine anesthesia, fasting insulin levels were greater among C monkeys. Insulin sensitivity estimates from all models were greater in R than C monkeys, whereas glucose effectiveness estimates were not consistently greater in R monkeys. Fasting plasma glucose as well as hepatic glucose production and clearance rates did not differ between groups. Body fat, in part, statistically mediated the effect of DR to enhance insulin sensitivity indexes. Precision of estimation and intermodel relationships among insulin sensitivity and glucose effectiveness estimates were in the ranges of those reported previously for humans and dogs, suggesting that the models may provide valid estimates for rhesus monkeys as well. The observed insulin sensitivity indexes from all models, elevated among R vs. C monkeys, may be explained, at least in part, by the difference in body fat content between these groups after chronic DR.

Methods for clinical assessment of insulin sensitivity and beta-cell function

The quantitative assessment of insulin sensitivity (IS) and beta-cell function (BCF) is fundamental in the study of metabolic disorders. The most relevant experimental tests and data analysis methods for assessing both IS and BCF are described and their characteristic features discussed. Advantages and limitations of each method are comparatively reviewed to help investigators choose the most suitable test for their needs. The problem of properly relating BCF to IS is also addressed. Particular attention is paid to the oral glucose tolerance test, which has recently received considerable interest. The role of mathematical models in IS and BCF assessment is also emphasized.

S(G), S(I), and EGP of exercise-trained middle-aged men estimated by a two-compartment labeled minimal model

To examine the effects of physical training on glucose effectiveness (S(G)), insulin sensitivity (S(I)), and endogenous glucose production (EGP) in middle-aged men, stable-labeled frequently sampled intravenous glucose tolerance tests (FSIGTT) were performed on 11 exercise-trained middle-aged men and 12 age-matched sedentary men. The time course of EGP during the FSIGTT was estimated by nonparametric stochastic deconvolution. Glucose uptake-specific indexes of glucose effectiveness (S(2*)(G) x 10(2): 0.81 +/- 0.08 vs. 0.60 +/- 0.05 dl. min(-1). kg(-1), P < 0.05) and insulin sensitivity [S(2*)(I) x 10(4): 24.59 +/- 2.98 vs. 11.89 +/- 2.36 dl. min(-1). (microU/ml)(-1). kg(-1), P < 0.01], which were analyzed using the two-compartment minimal model, were significantly greater in the trained group than in the sedentary group. Plasma clearance rate (PCR) of glucose was consistently greater in the trained men than in sedentary men throughout FSIGTT. Compared with sedentary controls, EGP of trained middle-aged men was higher before glucose load. The EGP of the two groups was similarly suppressed by approximately 70% within 10 min, followed by an additional suppression after insulin infusion. EGP returned to basal level at approximately 60 min in the trained men and at 100 min in the controls, followed by its overshoot, which was significantly greater in the trained men than in the controls. In addition, basal EGP was positively correlated with S(2*)(G) . The higher basal EGP and greater EGP overshoot in trained middle-aged men appear to compensate for the increased insulin-independent (S(2*)(G)) and -dependent (S(2*)(I)) glucose uptake to maintain glucose homeostasis.

Contribution to glucose tolerance of insulin-independent vs. insulin-dependent mechanisms in mice

To study the contributions of insulin-dependent vs. insulin-independent mechanisms to intravenous glucose tolerance (K(G)), 475 experiments in mice were performed. An intravenous glucose bolus was given either alone or with exogenous insulin or with substances modulating insulin secretion and sensitivity. Seven samples were taken over 50 min. Insulin [suprabasal area under the curve (DeltaAUC(ins))] ranged from 0 to 100 mU. ml(-1). 50 min. After validation against the euglycemic hyperinsulinemic clamp, the minimal model of net glucose disappearance was exploited to analyze glucose and insulin concentrations to measure the action of glucose per se independent of dynamic insulin (S(G)) and the combined effect of insulin sensitivity (S(I)) and secretion. Sensitivity analysis showed that insulin [through disposition index (DI)] contributed to glucose tolerance by 29 +/- 4% in normal conditions. In conditions of elevated hyperinsulinemia, contribution by insulin increased on average to 69%. K(G) correlated with DI but was saturated for DeltaAUC(ins) above 15 mU. ml(-1). 50 min. Insulin sensitivity related to DeltaAUC(ins) in a hyperbolic manner, whereas S(G) did not correlate with the insulin peak in the physiological range. Thus glucose tolerance in vivo is largely mediated by mechanisms unrelated to dynamic insulin and saturates with high insulin.

Effect of mild exercise training on glucose effectiveness in healthy men

OBJECTIVE

To detect whether mild exercise training improves glucose effectiveness (S(G)), which is the ability of hyperglycemia to promote glucose disposal at basal insulin, in healthy men.

RESEARCH DESIGN AND METHODS

Eight healthy men (18-25 years of age) underwent ergometer training at lactate threshold (LT) intensity for 60 min/day for 5 days/week for 6 weeks. An insulin-modified intravenous glucose tolerance test was performed before as well as at 16 h and 1 week after the last training session. S(G) and insulin sensitivity (S(I)) were estimated using a minimal-model approach.

RESULTS

After the exercise training, VO(2max) and VO(2) at LT increased by 5 and 34%, respectively (P < 0.05). The mild exercise training improves S(G) measured 16 h after the last training session, from 0.018 +/- 0.002 to 0.024 +/- 0.001 min(-1) (P < 0.05). The elevated S(G) after exercise training tends to be maintained regardless of detraining for 1 week (0.023 +/- 0.002 min(-1), P = 0.09). S(I) measured at 16 h after the last training session significantly increased (pre-exercise training, 13.9 +/- 2.2; 16 h, 18.3 +/- 2.4, x10(-5). min(-1). pmol/l(-1), P < 0.05) and still remained elevated 1 week after stopping the training regimen (18.6 +/- 2.2, x10(-5). min(-1). pmol/l(-1), P < 0.05).

CONCLUSIONS

Mild exercise training at LT improves S(G) in healthy men with no change in the body composition. Improving not only S(I) but also S(G) through mild exercise training is thus considered to be an effective method for preventing glucose intolerance.

Glucose effectiveness assessed under dynamic and steady state conditions. Comparability of uptake versus production components

Glucose tolerance is determined by both insulin action and insulin-independent effects, or "glucose effectiveness," which includes glucose-mediated stimulation of glucose uptake (Rd) and suppression of hepatic glucose output (HGO). Despite its importance to tolerance, controversy surrounds accurate assessment of glucose effectiveness. Furthermore, the relative contributions of glucose's actions on Rd and HGO under steady state and dynamic conditions are unclear. We performed hyperglycemic clamps and intravenous glucose tolerance tests in eight normal dogs, and assessed glucose effectiveness by two independent methods. During clamps, glucose was raised to three successive 90-min hyperglycemic plateaus by variable labeled glucose infusion rate; glucose effectiveness (GE) was quantified as the slope of the dose-response relationship between steady state glucose and glucose infusion rate (GE[CLAMP(total)]), Rd (GE[CLAMP(uptake)]) or HGO (GE[CLAMP(HGO)]). During intravenous glucose tolerance tests, tritiated glucose (1.2 microCi/kg) was injected with cold glucose (0.3 g/kg); glucose and tracer dynamics were analyzed using a two-compartment model of glucose kinetics to obtain Rd and HGO components of glucose effectiveness. All experiments were performed during somatostatin inhibition of islet secretion, and basal insulin and glucagon replacement. During clamps, Rd rose from basal (2.54+/-0.20) to 3.95+/-0.54, 6.76+/-1.21, and 9.48+/-1.27 mg/min per kg during stepwise hyperglycemia; conversely, HGO declined to 2.06+/-0.17, 1.17+/-0.19, and 0.52+/-0.33 mg/min per kg. Clamp-based glucose effectiveness was 0.0451+/-0.0061, 0.0337+/-0.0060, and 0.0102+/-0.0009 dl/min per kg for GE[CLAMP(total)], GE[CLAMP(uptake)], and GE[CLAMP(HGO)], respectively. Glucose's action on Rd dominated overall glucose effectiveness (72.2+/-3.3% of total), a result virtually identical to that obtained during intravenous glucose tolerance tests (71.6+/-6.1% of total). Both methods yielded similar estimates of glucose effectiveness. These results provide strong support that glucose effectiveness can be reliably estimated, and that glucose-stimulated Rd is the dominant component during both steady state and dynamic conditions.