Non-insulin-mediated glucose disappearance in subjects with IDDM. Discordance between experimental results and minimal model analysis

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

AIM

Glucagon-like peptide-1 receptor agonists provide multiple benefits to patients with type 2 diabetes, including improved glycaemic 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, subcutaneously) or saline (0.2 ml, subcutaneously) 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 a crossover design 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 showed that exenatide increased glucose effectiveness (Sg ) by 32% (p = .0008) but did not significantly affect insulin sensitivity (Si ). The exenatide-induced increase in insulin secretion made the largest contribution to interindividual variation in exenatide-induced acceleration of glucose disappearance while interindividual variation in the drug effect on Sg contributed to a lesser extent (β = 0.58 or 0.27, respectively).

CONCLUSIONS

This pilot study provides validation for the value of a frequently sampled intravenous glucose tolerance test (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 the effects of glucagon-like peptide-1 receptor agonists on glucose metabolism: first phase insulin secretion, glucose disappearance rates and glucose effectiveness.

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).

Origins and History of the Minimal Model of Glucose Regulation

It has long been hoped that our understanding of the pathogenesis of diabetes would be helped by the use of mathematical modeling. In 1979 Richard Bergman and Claudio Cobelli worked together to find a "minimal model" based upon experimental data from Bergman's laboratory. Model was chosen as the simplest representation based upon physiology known at the time. The model itself is two quasi-linear differential equations; one representing insulin kinetics in plasma, and a second representing the effects of insulin and glucose itself on restoration of the glucose after perturbation by intravenous injection. Model would only be sufficient if it included a delay in insulin action; that is, insulin had to enter a remote compartment, which was interstitial fluid (ISF). Insulin suppressed endogenous glucose output (by liver) slowly. Delay proved to be due to initial suppression of lipolysis; resultant lowering of free fatty acids reduced liver glucose output. Modeling also demanded that normalization of glucose after injection included an effect of glucose itself on glucose disposal and endogenous glucose production - these effects were termed "glucose effectiveness." Insulin sensitivity was calculated from fitting the model to intravenous glucose tolerance test data; the resulting insulin sensitivity index, SI, was validated with the glucose clamp method in human subjects. Model allowed us to examine the relationship between insulin sensitivity and insulin secretion. Relationship was described by a rectangular hyperbola, such that Insulin Secretion x Insulin Sensitivity = Disposition Index (DI). Latter term represents ability of the pancreatic beta-cells to compensate for insulin resistance due to factors such as obesity, pregnancy, or puberty. DI has a genetic basis, and predicts the onset of Type 2 diabetes. An additional factor was clearance of insulin by the liver. Clearance varies significantly among animal or human populations; using the model, clearance was shown to be lower in African Americans than Whites (adults and children), and may be a factor accounting for greater diabetes prevalence in African Americans. The research outlined in the manuscript emphasizes the powerful approach by which hypothesis testing, experimental studies, and mathematical modeling can work together to explain the pathogenesis of metabolic disease.

Choosing an optimal input for an intravenous glucose tolerance test to aid parameter identification

OBJECTIVE

The minimal model is used to estimate insulin sensitivity in patients with diabetes, following an intravenous glucose tolerance test (IVGTT). Issues have been reported regarding parameter estimation, including correlation between insulin sensitivity and action parameters. The objective was to reduce these issues, by modifying the input of glucose in the test.

METHODS

Data were available for 24 volunteers following an IVGTT and glucose clamp test. Correlation between parameters was explored using likelihood heatmaps. An integrated glucose-insulin model was used to simulate glucose and insulin concentrations following new glucose inputs. The improved input for the test was selected by finding the minimum inverse of the determinant of the Fisher information matrix.

KEY FINDINGS

When the minimal model was fitted to the IVGTT data, there was clear correlation between the insulin parameters. With the glucose clamp, all parameters were correlated and badly estimated. The modified input, a bolus dose followed by constant infusion, resulted in improvement in parameter estimation and reduction in parameter correlation.

CONCLUSIONS

It is possible to reduce the issues with parameter estimation in the minimal model by modifying the glucose input, leading to a simplified test deign and a reduction in the total amount of glucose infused.

Standardized Mixed-Meal Tolerance and Arginine Stimulation Tests Provide Reproducible and Complementary Measures of β-Cell Function: Results From the Foundation for the National Institutes of Health Biomarkers Consortium Investigative Series

OBJECTIVE

Standardized, reproducible, and feasible quantification of β-cell function (BCF) is necessary for the evaluation of interventions to improve insulin secretion and important for comparison across studies. We therefore characterized the responses to, and reproducibility of, standardized methods of in vivo BCF across different glucose tolerance states.

RESEARCH DESIGN AND METHODS

Participants classified as having normal glucose tolerance (NGT; n = 23), prediabetes (PDM; n = 17), and type 2 diabetes mellitus (T2DM; n = 22) underwent two standardized mixed-meal tolerance tests (MMTT) and two standardized arginine stimulation tests (AST) in a test-retest paradigm and one frequently sampled intravenous glucose tolerance test (FSIGT).

RESULTS

From the MMTT, insulin secretion in T2DM was >86% lower compared with NGT or PDM (P < 0.001). Insulin sensitivity (Si) decreased from NGT to PDM (∼50%) to T2DM (93% lower [P < 0.001]). In the AST, insulin secretory response to arginine at basal glucose and during hyperglycemia was lower in T2DM compared with NGT and PDM (>58%; all P < 0.001). FSIGT showed decreases in both insulin secretion and Si across populations (P < 0.001), although Si did not differ significantly between PDM and T2DM populations. Reproducibility was generally good for the MMTT, with intraclass correlation coefficients (ICCs) ranging from ∼0.3 to ∼0.8 depending on population and variable. Reproducibility for the AST was very good, with ICC values >0.8 across all variables and populations.

CONCLUSIONS

Standardized MMTT and AST provide reproducible and complementary measures of BCF with characteristics favorable for longitudinal interventional trials use.

The forgotten role of glucose effectiveness in the regulation of glucose tolerance

Glucose effectiveness (SG) is the ability of glucose per se to stimulate its own uptake and to suppress its own production under basal/constant insulin concentrations. In an individual, glucose tolerance is a function of insulin secretion, insulin action and SG. Under conditions of declining insulin secretion and action (e.g. type 2 diabetes), the degree of SG assumes increasing significance in determining the level of glucose tolerance both in fasted and postprandial states. Although the importance of SG has been recognized for years, mechanisms that contribute to SG are poorly understood. Research data on modulation of SG and its impact in glucose intolerance is limited. In this review, we will focus on the role of SG in the regulation of glucose tolerance, its evaluation, and potential advantages of therapies that can enhance glucose-induced stimulation of glucose uptake and suppression of its own production in conditions of impaired insulin secretion and action.

A novel hierarchal-based approach to measure insulin sensitivity and secretion in at-risk populations

The pathogenesis of type 2 diabetes is characterized by insulin resistance and insulin secretory dysfunction. Few existing metabolic tests measure both characteristics, and no such tests are inexpensive enough to enable widespread use. A hierarchical approach uses 2 down-sampled tests in the dynamic insulin sensitivity and secretion test (DISST) family to first determine insulin sensitivity (SI) using 4 glucose measurements. Second the insulin secretion is determined for only participants with reduced SI using 3 C-peptide measurements from the original test. The hierarchical approach is assessed via its ability to classify 214 individual test responses of 71 females with an elevated risk of type 2 diabetes into 5 bins with equivalence to the fully sampled DISST. Using an arbitrary SI cut-off, 102 test responses were reassayed for C-peptide and unique insulin secretion characteristics estimated. The hierarchical approach correctly classified 84.5% of the test responses and 94.4% of the responses of individuals with increased fasting glucose. The hierarchical approach is a low-cost methodology for measuring key characteristics of type 2 diabetes. Thus the approach could provide an economical approach to studying the pathogenesis of type 2 diabetes, or in early risk screening. As the higher cost test uses the same clinical protocol as the low-cost test, the cost of the additional information is limited to the assay cost of C-peptide, and no additional procedures or callbacks are required.

Population pharmacodynamic modeling of hyperglycemic clamp and meal tolerance tests in patients with type 2 diabetes mellitus

In this study, glucose and insulin concentration-time profiles in subjects with type 2 diabetes mellitus (T2DM) under meal tolerance test (MTT) and hyperglycemic clamp (HGC) conditions were co-modeled simultaneously. Blood glucose and insulin concentrations were obtained from 20 subjects enrolled in a double-blind, placebo-controlled, randomized, two-way crossover study. Patients were treated with palosuran or placebo twice daily for 4 weeks and then switched to the alternative treatment after a 4-week washout period. The MTT and HGC tests were performed 1 h after drug administration on days 28 and 29 of each treatment period. Population data analysis was performed using NONMEM. The HGC model incorporates insulin-dependent glucose clearance and glucose-induced insulin secretion. This model was extended for the MTT, in which glucose absorption was described using a transit compartment with a mean transit time of 62.5 min. The incretin effect (insulin secretion triggered by oral glucose intake) was also included, but palosuran did not influence insulin secretion or sensitivity. Glucose clearance was 0.164 L/min with intersubject and interoccasion variability of 9.57% and 31.8%. Insulin-dependent glucose clearance for the HGC was about 3-fold greater than for the MTT (0.0111 vs. 0.00425 L/min/[mU/L]). The maximal incretin effect was estimated to enhance insulin secretion 2-fold. The lack of palosuran effect coupled with a population-based analysis provided quantitative insights into the variability of glucose and insulin regulation in patients with T2DM following multiple glucose tolerance tests. Application of these models may also prove useful in antihyperglycemic drug development and assessing glucose-insulin homeostasis.

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.

A closed-loop artificial pancreas using a proportional integral derivative with double phase lead controller based on a new nonlinear model of glucose metabolism

BACKGROUND

Most closed-loop insulin delivery systems rely on model-based controllers to control the blood glucose (BG) level. Simple models of glucose metabolism, which allow easy design of the control law, are limited in their parametric identification from raw data. New control models and controllers issued from them are needed.

METHODS

A proportional integral derivative with double phase lead controller was proposed. Its design was based on a linearization of a new nonlinear control model of the glucose-insulin system in type 1 diabetes mellitus (T1DM) patients validated with the University of Virginia/Padova T1DM metabolic simulator. A 36 h scenario, including six unannounced meals, was tested in nine virtual adults. A previous trial database has been used to compare the performance of our controller with their previous results. The scenario was repeated 25 times for each adult in order to take continuous glucose monitoring noise into account. The primary outcome was the time BG levels were in target (70-180 mg/dl).

RESULTS

Blood glucose values were in the target range for 77% of the time and below 50 mg/dl and above 250 mg/dl for 0.8% and 0.3% of the time, respectively. The low blood glucose index and high blood glucose index were 1.65 and 3.33, respectively.

CONCLUSION

The linear controller presented, based on the linearization of a new easily identifiable nonlinear model, achieves good glucose control with low exposure to hypoglycemia and hyperglycemia.

Clinical validation of the quick dynamic insulin sensitivity test

The quick dynamic insulin sensitivity test (DISTq) can yield an insulin sensitivity result immediately after a 30-min clinical protocol. The test uses intravenous boluses of 10 g glucose and 1 U insulin at t = 1 and 11 min, respectively, and measures glucose levels in samples taken at t = 0, 10, 20, and 30 min. The low clinical cost of the protocol is enabled via robust model formulation and a series of population-derived relationships that estimate insulin pharmacokinetics as a function of insulin sensitivity (SI). Fifty individuals underwent the gold standard euglycaemic clamp (EIC) and DISTq within an eight-day period. SI values from the EIC and two DISTq variants (four-sample DISTq and two-sample DISTq30) were compared with correlation, Bland-Altman and receiver operator curve analyses. DISTq and DISTq30 correlated well with the EIC [R = 0.76 and 0.75, and receiver operator curve c-index = 0.84 and 0.85, respectively]. The median differences between EIC and DISTq/DISTq30 SI values were 13% and 22%, respectively. The DISTq estimation method predicted individual insulin responses without specific insulin assays with relative accuracy and thus high equivalence to EIC SI values was achieved. DISTq produced very inexpensive, relatively accurate immediate results, and can thus enable a number of applications that are impossible with established SI tests.

A graphical method for practical and informative identifiability analyses of physiological models: a case study of insulin kinetics and sensitivity

BACKGROUND

Derivative based a-priori structural identifiability analyses of mathematical models can offer valuable insight into the identifiability of model parameters. However, these analyses are only capable of a binary confirmation of the mathematical distinction of parameters and a positive outcome can begin to lose relevance when measurement error is introduced. This article presents an integral based method that allows the observation of the identifiability of models with two-parameters in the presence of assay error.

METHODS

The method measures the distinction of the integral formulations of the parameter coefficients at the proposed sampling times. It can thus predict the susceptibility of the parameters to the effects of measurement error. The method is tested in-silico with Monte Carlo analyses of a number of insulin sensitivity test applications.

RESULTS

The method successfully captured the analogous nature of identifiability observed in Monte Carlo analyses of a number of cases including protocol alterations, parameter changes and differences in participant behaviour. However, due to the numerical nature of the analyses, prediction was not perfect in all cases.

CONCLUSIONS

Thus although the current method has valuable and significant capabilities in terms of study or test protocol design, additional developments would further strengthen the predictive capability of the method. Finally, the method captures the experimental reality that sampling error and timing can negate assumed parameter identifiability and that identifiability is a continuous rather than discrete phenomenon.

Design and clinical pilot testing of the model-based dynamic insulin sensitivity and secretion test (DISST)

BACKGROUND

Insulin resistance is a significant risk factor in the pathogenesis of type 2 diabetes. This article presents pilot study results of the dynamic insulin sensitivity and secretion test (DISST), a high-resolution, low-intensity test to diagnose insulin sensitivity (IS) and characterize pancreatic insulin secretion in response to a (small) glucose challenge. This pilot study examines the effect of glucose and insulin dose on the DISST, and tests its repeatability.

METHODS

DISST tests were performed on 16 subjects randomly allocated to low (5 g glucose, 0.5 U insulin), medium (10 g glucose, 1 U insulin) and high dose (20 g glucose, 2 U insulin) protocols. Two or three tests were performed on each subject a few days apart.

RESULTS

Average variability in IS between low and medium dose was 10.3% (p=.50) and between medium and high dose 6.0% (p=.87). Geometric mean variability between tests was 6.0% (multiplicative standard deviation (MSD) 4.9%). Geometric mean variability in first phase endogenous insulin response was 6.8% (MSD 2.2%). Results were most consistent in subjects with low IS.

CONCLUSIONS

These findings suggest that DISST may be an easily performed dynamic test to quantify IS with high resolution, especially among those with reduced IS.

Glucose effectiveness and insulin sensitivity measurements derived from the non-insulin-assisted minimal model and the clamp techniques are concordant

BACKGROUND

We investigated the concordance between glucose effectiveness (SG) and insulin sensitivity (SI), derived from the unmodified dynamic non-insulin-assisted intravenous glucose tolerance test (IVGTT) implemented by SG(MM) and SI(MM); simulation analysis and modelling/conversational interaction (SAAM/CONSAM) versus the eu/hyperglycaemic basal insulinaemic and the euglycaemic hyperinsulinaemic clamp (SG(CLAMP) and SI(CLAMP)).

METHODS

Twenty-seven of 30 normoglycaemic subjects completed a (1) euglycaemic hyperinsulinaemic clamp, (2) 6-h eu/hyperglycaemic near-normoinsulinaemic pancreatic clamp with hyperglycaemia present over the final 2 h of the clamp (Day 2 study), (3) identical clamp to (2) but with euglycaemia maintained over the entire 6 h (Day 3 study) and (4) IVGTT. SG(CLAMP) was calculated in two ways based on data from study (2) alone (Day 2 SG(CLAMP210-240')) or from data from study day (2) and (3) (Day 2-3 SG(CLAMP330-360')).

RESULTS

SG(MM) was unrelated to the magnitude of endogenous insulin release (AIR). The single-day (Day 2) and two-day (Day 2 and 3) SG(CLAMP) protocols correlated (r = 0.72, p = 0.003), but SG(CLAMP210-240') was significantly (p = 0.001) higher than SG(CLAMP330-360'). Employing the Day 2 and 3 SG(CLAMP) protocol, the whole body SG(CLAMP330-360') was similar to SG(MM) (1.80 ± 0.82 versus 1.73 ± 0.58 dL/min) and correlated (r = 0.45, p < 0.02). SG(CLAMP210-240') did not correlate with SG(MM) (r = 0.24). SI(MM) and SI(CLAMP) were similar (0.093 ± 0.060 versus 0.087 ± 0.029 dL/min per mU/L) and correlated (r = 0.76, p < 0.001).

CONCLUSIONS

The time-dependent increase in glucose disposal observed during a prolonged 6-h clamp significantly influences the estimation of SG(CLAMP), and significant concordance coefficients are observed between SG(MM), and SG(CLAMP330-360'), and SI(MM) and SI(CLAMP).

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.

Pancreatic network control of glucagon secretion and counterregulation

Glucagon counterregulation (GCR) is a key protection against hypoglycemia compromised in insulinopenic diabetes by an unknown mechanism. In this work, we present an interdisciplinary approach to the analysis of the GCR control mechanisms. Our results indicate that a pancreatic network which unifies a few explicit interactions between the major islet peptides and blood glucose (BG) can replicate the normal GCR axis and explain its impairment in diabetes. A key and novel component of this network is an alpha-cell auto-feedback, which drives glucagon pulsatility and mediates triggering of pulsatile GCR by hypoglycemia via a switch-off of the beta-cell suppression of the alpha-cells. We have performed simulations based on our models of the endocrine pancreas which explain the in vivo GCR response to hypoglycemia of the normal pancreas and the enhancement of defective pulsatile GCR in beta-cell deficiency by switch-off of intrapancreatic alpha-cell suppressing signals. The models also predicted that reduced insulin secretion decreases and delays the GCR. In conclusion, based on experimental data we have developed and validated a model of the normal GCR control mechanisms and their dysregulation in insulin deficient diabetes. One advantage of this construct is that all model components are clinically measurable, thereby permitting its transfer, validation, and application to the study of the GCR abnormalities of the human endocrine pancreas in vivo.

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.

The cellular fate of glucose and its relevance in type 2 diabetes

Type 2 diabetes is a complex disorder with diminished insulin secretion and insulin action contributing to the hyperglycemia and wide range of metabolic defects that underlie the disease. The contribution of glucose metabolic pathways per se in the pathogenesis of the disease remains unclear. The cellular fate of glucose begins with glucose transport and phosphorylation. Subsequent pathways of glucose utilization include aerobic and anaerobic glycolysis, glycogen formation, and conversion to other intermediates in the hexose phosphate or hexosamine biosynthesis pathways. Abnormalities in each pathway may occur in diabetic subjects; however, it is unclear whether perturbations in these may lead to diabetes or are a consequence of the multiple metabolic abnormalities found in the disease. This review is focused on the cellular fate of glucose and relevance to human type 2 diabetes.

Glucose action and adrenocortical biosynthesis in women with polycystic ovary syndrome

OBJECTIVE

To determine if insulin or glucose action plays a role in adrenocortical steroidogenesis in the polycystic ovary syndrome (PCOS).

DESIGN

Prospective cohort study.

SETTING

Academic medical center.

PATIENT(S)

Nine reproductive-aged patients with PCOS and nine age-, race-, and body mass index-matched controls.

MAIN OUTCOME MEASURE(S)

Insulin-modified frequently sampled intravenous glucose tolerance testing and an acute 60-minute ACTH-(1-24) stimulation test. From the glucose tolerance test, glucose and insulin were measured and the insulin sensitivity index, glucose effectiveness, and acute insulin response to glucose were determined. Dehydroepiandrosterone sulfate (DHEAS) basally and 17-hydroxypregnenolone, 17-hydroxyprogesterone, DHEA, androstenedione, and cortisol during ACTH testing at 0 and 60 minute (steroid(0) and steroid(60)) were determined. The net change in steroid during the ACTH test was calculated.

RESULT(S)

The insulin sensitivity index had limited correlation with adrenocortical variables in both groups. In patients with PCOS, glucose effectiveness was positively associated with DHEAS, cortisol(0), cortisol(60), change in cortisol, DHEA(0), DHEA(60), change in DHEA, 17-hydroxyprenenolone(60), change in 17-hydroxypregnenolone, DHEA(0), androstenedione(0), 17-hydroxyprenenolone(0), 17-hydroxyprogesterone(0), 17-hydroxyprenenolone(60), and 17-hydroxyprogesterone(60).

CONCLUSION(S)

Adrenocortical biosynthesis, basally and in response to ACTH, appears to be closely associated with glucose effectiveness in PCOS. A common factor determining both the effectiveness of glucose to control its own production or uptake and adrenocortical biosynthesis may be aberrant in PCOS.

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.

Bayesian two-compartment and classic single-compartment minimal models: comparison on insulin modified IVGTT and effect of experiment reduction

Models describing plasma glucose and insulin concentration of an intravenous glucose tolerance test (IVGTT) allow a noninvasive cost-effective approach to estimate important indexes characterizing the efficiency of glucose-insulin control system, i.e., glucose effectiveness (S(G)) and insulin sensitivity (S(I)). To overcome some limitations of the classic single compartment minimal model (1CMM) of glucose kinetics , a two-compartment Bayesian minimal model (2CBMM) has been recently proposed for the standard IVGTT. This study aims to assess 2CBMM ability to describe the insulin-modified IVGTT (IM-IVGTT) which is the protocol of choice since it allows to study insulinopenic states. Both a full-length IM-IVGTT (240 min) as well as a reduced version (90 min) of it are studied. Results of the maximum a posteriori identification of IM-IVGTT (240 min) in 13 normals agree with those of standard IVGTT, i.e., a 42% decrease (P < 0.002) of S(G) and a 13% increase (P < 0.006) of S(I) with respect to ICMM. When identified from IM-IVGTT (90 min), 2CBMM not only provides S(G) and S(I) estimates 46% lower (P < 0.002) and 41% higher (P < 0.002) than 1CMM ones respectively, but also seems to overcome some limitations of the 240 min-based identification that probably arise because the minimal model is unable to properly account for the hyperglycemic hormonal response taking place in the second half of IM-IVGTT.

QUICKI is a useful index of insulin sensitivity in subjects with hypertension

Insulin resistance may link disorders of metabolic homeostasis such as diabetes and obesity with disorders of hemodynamic homeostasis such as hypertension. Thus it is of interest to validate simple methods for quantifying insulin sensitivity in hypertensive patients. The quantitative insulin-sensitivity check index (QUICKI) is a novel mathematical transformation of fasting blood glucose and insulin levels. In obese and diabetic subjects, QUICKI has a significantly better linear correlation with glucose clamp determinations of insulin sensitivity than minimal-model estimates. To determine whether QUICKI is also useful in hypertensive subjects, we performed glucose clamps and frequently sampled intravenous glucose tolerance tests (FSIVGTT) on 27 hypertensive subjects taken off antihypertensive medication. Indexes of insulin sensitivity derived from glucose clamp studies (SIClamp) were compared with QUICKI, minimal-model analysis of FSIVGTTs (SIMM), and homeostasis model assessment (HOMA). The correlation between QUICKI and SIClamp ( r = 0.84) was significantly better than that between SIMM and SIClamp( r = 0.65; P < 0.028). The correlation between QUICKI and SIClamp was comparable to that between 1/HOMA and SIClamp ( r = 0.82). When studies were repeated in 14 subjects who had resumed antihypertensive medications, the percent changes in SIClamp for each of these patients were significantly correlated with percent changes in QUICKI ( r = 0.61) and HOMA ( r = −0.54) but not SIMM ( r = −0.18). We conclude that QUICKI is a simple, robust index of insulin sensitivity that is useful for evaluating and following the insulin resistance of hypertensive subjects in both research studies and clinical practice.

Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans

Insulin resistance plays an important role in the pathophysiology of diabetes and is associated with obesity and other cardiovascular risk factors. The "gold standard" glucose clamp and minimal model analysis are two established methods for determining insulin sensitivity in vivo, but neither is easily implemented in large studies. Thus, it is of interest to develop a simple, accurate method for assessing insulin sensitivity that is useful for clinical investigations. We performed both hyperinsulinemic isoglycemic glucose clamp and insulin-modified frequently sampled iv glucose tolerance tests on 28 nonobese, 13 obese, and 15 type 2 diabetic subjects. We obtained correlations between indexes of insulin sensitivity from glucose clamp studies (SI(Clamp)) and minimal model analysis (SI(MM)) that were comparable to previous reports (r = 0.57). We performed a sensitivity analysis on our data and discovered that physiological steady state values [i.e. fasting insulin (I(0)) and glucose (G(0))] contain critical information about insulin sensitivity. We defined a quantitative insulin sensitivity check index (QUICKI = 1/[log(I(0)) + log(G(0))]) that has substantially better correlation with SI(Clamp) (r = 0.78) than the correlation we observed between SI(MM) and SI(Clamp). Moreover, we observed a comparable overall correlation between QUICKI and SI(Clamp) in a totally independent group of 21 obese and 14 nonobese subjects from another institution. We conclude that QUICKI is an index of insulin sensitivity obtained from a fasting blood sample that may be useful for clinical research.

Minimal model SG overestimation and SI underestimation: improved accuracy by a Bayesian two-compartment model

The intravenous glucose tolerance test (IVGTT) single-compartment minimal model (1CMM) method has recently been shown to overestimate glucose effectiveness and underestimate insulin sensitivity. Undermodeling, i.e., use of single- instead of two-compartment description of glucose kinetics, has been advocated to explain these limitations. We describe a new two-compartment minimal model (2CMM) into which we incorporate certain available knowledge on glucose kinetics. 2CMM is numerically identified using a Bayesian approach. Twenty-two standard IVGTT (0.30 g/kg) in normal humans were analyzed. In six subjects, the clamp-based index of insulin sensitivity (ScI) was also measured. 2CMM glucose effectiveness (S2G) and insulin sensitivity (S2I) were, respectively, 60% lower (P < 0.0001) and 35% higher (P < 0.0001) than the corresponding 1CMM S1G and S1I indexes: 2.81 +/- 0.29 (SE) vs. S1G = 4.27 +/- 0.33 ml. min(-1). kg(-1) and S2I = 11.67 +/- 1.71 vs. S1I = 8.68 +/- 1.62 10(2) ml. min(-1). kg(-1) per microU/ml. S2I was not different from ScI = 12.61 +/- 2.13 10(2) ml. min(-1). kg(-1) per microU/ml (nonsignificant), whereas S1I was 60% lower (P < 0.02). In conclusion, a new 2CMM has been presented that improves the accuracy of glucose effectiveness and insulin sensitivity estimates of the classic 1CMM from a standard IVGTT in normal humans.

Undermodeling affects minimal model indexes: insights from a two-compartment model

The classic (hereafter cold) and the labeled (hereafter hot) minimal models are powerful tools to investigate glucose metabolism. The cold model provides, from intravenous glucose tolerance test (IVGTT) data, indexes of glucose effectiveness (SG) and insulin sensitivity (SI) that measure the effect of glucose and insulin, respectively, to enhance glucose disappearance and inhibit endogenous glucose production. The hot model provides, from hot IVGTT data, indexes of glucose effectiveness (SG*) and insulin sensitivity (SI*) that, respectively, measure the effects of glucose and insulin on glucose disappearance only. Recent reports call for a reexamination of some of the assumptions of the minimal models. We have previously pointed out the criticality of the single-compartment description of glucose kinetics on which both the minimal models are founded. In this paper we evaluate the impact of single-compartment undermodeling on SG, SI*, and by using a two-compartment model to describe the glucose system. The relationships of the minimal model indexes to the analogous indexes measured with the glucose clamp technique are also examined. Theoretical analysis and simulation studies indicate that cold indexes are more affected than hot indexes by undermodeling. In particular, care must be exercised in the physiological interpretation of SG, because this index is a local descriptor of events taking place in the initial portion of the IVGTT. As a consequence, SG not only reflects glucose effect on glucose uptake and production but also the rapid exchange of glucose between the accessible and nonaccessible glucose pools that occurs in the early part of the test.

Prognostic importance of insulin-mediated glucose uptake in aged patients with congestive heart failure secondary to mitral and/or aortic valve disease

Previous studies have demonstrated that insulin resistance is a common feature of congestive heart failure (CHF), but the clinical significance of such insulin resistance is still debated. We tested the hypothesis that insulin-mediated glucose uptake (IMGU) is a prognostic factor in CHF in aged patients. For this purpose 174 aged patients with CHF participated in a cross-sectional and a longitudinal study of 24 months' duration. In this latter study survival analysis was calculated comparing subjects at the first and second tertile of IMGU with those at third tertile. All subjects underwent anthropometric (body mass index, waist/hip ratio), cardiovascular (arterial blood pressure, 24-hour Holter monitoring, peak VO2, left ventricular ejection fraction, echocardiography), and metabolic (determination of fasting plasma glucose, insulin, catecholamine, free fatty acids, tumor necrosis factor-alpha concentrations, and assessment of IMGU by euglycemic hyperinsulinemic glucose clamp) investigations. In the cross-sectional study, IMGU correlated with age (r = -0.33, p <0.001), body mass index (r = -0.46 p <0.001), ventricular premature complexes (r = -0.78, p <0.001), left ventricular ejection fraction (r = -0.15, p <0.05), fasting plasma norepinephrine (r = -0.75, p <0.001), tumor necrosis factor-alpha (r = -0.45, p <0.001), free fatty acids (r = -0.54, p <0.001), and peak VO2 (r = 0.67, p <0.001). In the longitudinal study patients at the first and second tertile of IMGU had a lower probability of survival than patients at the third tertile (p <0.03). Cox regression analysis showed IMGU to be a prognostic factor independent of fasting plasma norepinephrine, tumor necrosis factor-alpha, free fatty acid concentration, New York Heart Association class, peak VO2, and left ventricle ejection fraction (relative risk 1.1, 95% confidence intervals 1.0 to 2.1). In conclusion, our study demonstrates that insulin resistance is a common feature of CHF most likely due to elevated plasma norepinephrine and tumor necrosis factor-alpha concentrations, and that IMGU is an independent prognostic factor in CHF.

Glucose effectiveness and insulin sensitivity from the minimal models: consequences of undermodeling assessed by Monte Carlo simulation

The unlabeled (cold) minimal model (MM) and the labeled (hot) minimal model (HMM) are a powerful tool to investigate in vivo metabolism from a standard intravenous glucose tolerance test (IVGTT) or hot IVGTT (HIVGTT). They allow to estimate metabolic indexes of the glucose-insulin system, namely glucose effectiveness (GE) and insulin sensitivity (IS) (of uptake and production those of MM, and of uptake only those of HMM). Here, the consequences of the single-compartment glucose kinetics approximation used in the MM's are investigated via Monte Carlo simulation, using a physiologic reference model (RM) of the system. RM allows to generate noisy synthetic plasma concentrations of glucose, tracer glucose, and insulin during IVGTT and HIVGTT, which are then analyzed with MM and HMM. The MM and HMM GE and IS are then compared with the RM ones. Results of 400 runs show that: 1) correlation of MM GE with the RM index is weak; 2) MM IS is well correlated with the RM index, but severely underestimates it; 3) HMM clearance rate is correlated with RM clearance; and 4) HMM IS is well correlated and only slightly overestimates the RM index. These results demonstrate that GE of MM is most affected by the single-compartment approximation and the indexes of HMM are more robust than those of MM.

Overestimation of minimal model glucose effectiveness in presence of insulin response is due to undermodeling

Glucose effectiveness is an important determinant of glucose tolerance that can be derived from minimal model analysis of an intravenous glucose tolerance test (IVGTT). However, recent evidence suggests that glucose effectiveness is overestimated by minimal model analysis. Here we compare a new model-independent estimate of glucose effectiveness with the minimal model estimate by reanalyzing published data in which insulin-dependent diabetic subjects were each given IVGTTs under two conditions (Quon, M. J., C. Cochran, S. I. Taylor, and R. C. Eastman. Diabetes 43: 890-896, 1994). In one case, a basal insulin level was maintained (BI-IVGTT). In the second case, a dynamic insulin response was recreated (DI-IVGTT). Our results show that minimal model glucose effectiveness is very similar to the model-independent measurement during a BI-IVGTT but is three times higher during a DI-IVGTT. To investigate the causes of minimal model overestimation in the presence of a dynamic insulin response, Monte Carlo simulation studies on a two-compartment model of glucose kinetics with various insulin response patterns were performed. Results suggest that minimal model overestimation is due to single-compartment representation of glucose kinetics that results in a critical oversimplification in the presence of increasingly dynamic insulin secretion patterns.

State-space models of insulin and glucose responses to diets of varying nutrient content in men and women

Discrete-time state-space models were developed to describe contemporaneous responses of plasma insulin and glucose of normal human subjects. Male and female subjects ingested three consecutive identical meals from isocaloric diets classified as high-carbohydrate, high-fat, high-protein, or standard. Distinctly different glucose and insulin responses were measured in men and women. A seven-state system of linear equations, three in insulin and four in glucose, was identified and estimated to describe responses in men. A six-state system, three in insulin and three in glucose, describes responses in women. Model simulations at 15-min intervals closely match measured concentrations over a 12-h period. Effects of diet content and meal timing on insulin and glucose concentrations were quantified. Dynamic insulin and glucose responses to isocaloric meals of pure carbohydrate, fat, and protein diets were projected on the basis of models developed from mixed diets. The symmetry of the projections indicates that positive excursions in glucose concentrations associated with carbohydrate intake may be matched with negative excursions associated with fat and protein intake to help manage postmeal glucose excursions.

Minimal model estimate of glucose effectiveness: role of the minimal model volume and of the second hidden compartment

The following is an abstract of the article discussed in the subsequent letter:

Finegood, Diane T., and Dan Tzur. Reduced glucose effectiveness associated with reduced insulin release: an artifact of the minimal-model method. Am. J. Physiol. 271 ( Endocrinol. Metab. 34): E485–E495, 1996.—We previously demonstrated that minimal model-derived estimates of glucose effectiveness (SG), based on the frequently sampled intravenous glucose tolerance test (SG FSIGT), were reduced in islet-transplanted or streptozotocin-treated dogs and in patients with insulin-dependent diabetes mellitus. To ascertain the validity of our observations, we compared SG FSIGT with estimates based on a basal hormone replacement glucose clamp (SG BRCLAMP) and a basal hormone replacement glucose tolerance test (SG BRGTT) in normal control (CNTL, n = 12) and streptozotocin-treated dogs with normal fasting plasma glucose (STZ-Rx, n = 9). SG FSIGT was reduced in STZ-Rx compared with CNTL ( P < 0.05). However, neither SG BRCLAMP nor SG BRGTT was reduced in the STZ-Rx group ( P > 0.05). Comparison of protocols for each subject indicated that SG FSIGT was greater than either SG BRCLAMP or SG BRGTT in control ( P < 0.002) but not in STZ-Rx dogs ( P > 0.1). The relationship of SG FSIGT to insulin secretory function suggests that our previous conclusion that SG FSIGT was reduced in subjects with limited insulin release may be an artifact of the minimal-model method. Our results suggest that caution must be exercised in the interpretation of differences in minimal-model estimates of SG between subject groups with significantly different levels of insulin secretory function.

Metabolism of oral glucose in pancreas transplant recipients with normal and impaired glucose tolerance

To gain insight into the pathophysiology of impaired glucose tolerance in pancreas transplantation, glucose kinetics and insulin secretion were assessed after an oral glucose load in four combined pancreas-kidney recipients with impaired glucose tolerance (IPx), in five combined pancreas-kidney recipients with normal glucose tolerance, in six nondiabetic kidney transplant recipients, and in eight normal subjects employing a dual isotope technique, beta-Cell function was evaluated by calculating prehepatic insulin secretion rates, which subsequently were correlated to the ambient glucose concentrations to obtain an index of beta-cell responsiveness. Oxidative and nonoxidative glucose metabolism were assessed by indirect calorimetry. Basal insulin secretion rates, the glucose-stimulated early insulin secretion rates, as well as beta-cell responsiveness were markedly reduced in IPx than in the glucose-tolerant transplant subjects. Total systemic glucose appearance was similar in the groups with apparently comparable inhibition of systemic glucose release and increase in exogenous glucose appearance. The hyperglycemic response in IPx was due to a significant reduction in the glucose disappearance rates during the first 2 h after glucose ingestion. Nonoxidative glucose metabolism increased significantly less in IPx than in glucose-tolerant groups. Glucagon secretion was less suppressed in the early part of the study in IPx, which may have contributed to the excessive hyperglycemia. In conclusion, IPx after pancreas transplantation was characterized by 1) impaired early insulin secretion, 2) reduced beta-cell responsiveness, 3) reduced glucose uptake, 4) impaired nonoxidative glucose metabolism, and 5) impaired early inhibition of glucagon secretion.

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.

The hot but not the cold minimal model allows precise assessment of insulin sensitivity in NIDDM subjects

Assessment of insulin sensitivity in subjects with non-insulin-dependent diabetes mellitus (NIDDM) is of paramount importance but intrinsically difficult. The standard (hereafter cold) minimal model, in conjunction with an insulin-modified protocol, has been recently proposed, but the estimates of insulin sensitivity showed poor precision (Saad et al. Diabetes 43: 1114-1121, 1994). We propose the tracer (hereafter hot) minimal model as a highly reliable method to estimate insulin sensitivity (SI*) and fractional glucose clearance (SG*), reflecting glucose disposal only, in NIDDM subjects. A [6,6- 2H2] glucose-labeled insulin-modified intravenous glucose tolerance test was performed in seven NIDDM subjects. In particular, SI* was 1.07 +/- 0.34 x10(-4)min(-1).microU-1.ml estimated with an average precision (mean coefficient of variation of 12%, range 4-22%), whereas the cold minimal model SI was 0.96 +/- 0.26 x 10(-4) min-1. microU-1.ml (mean coefficient of variation of 105%, range 3-353%). Another advantage of the hot indexes with respect to the cold indexes is their ability to reflect glucose and insulin effect on glucose disposal only, and not also on hepatic glucose production. Finally, we also studied by simulation the effect of glucose urinary loss on cold and hot minimal model indexes; only cold glucose effectiveness (SG) was significantly affected, resulting in a mean approximately 40% lower. The hot minimal model appears therefore more reliable than the cold model for assessing glucose tolerance in NIDDM subjects. In particular its ability to dissect disposal from production processes, coupled with the very good precision of the estimated metabolic indexes, supports the clinical use of this method in NIDDM subjects.