Manual closed-loop insulin delivery in children and adolescents with type 1 diabetes: a phase 2 randomised crossover trial.

BACKGROUND: Closed-loop systems link continuous glucose measurements to insulin delivery. We aimed to establish whether closed-loop insulin delivery could control overnight blood glucose in young people. METHODS: We undertook three randomised crossover studies in 19 patients aged 5-18 years with type 1 diabetes of duration 6.4 years (SD 4.0). We compared standard continuous subcutaneous insulin infusion and closed-loop delivery (n=13; APCam01); closed-loop delivery after rapidly and slowly absorbed meals (n=7; APCam02); and closed-loop delivery and standard treatment after exercise (n=10; APCam03). Allocation was by computer-generated random code. Participants were masked to plasma and sensor glucose. In APCam01, investigators were masked to plasma glucose. During closed-loop nights, glucose measurements were fed every 15 min into a control algorithm calculating rate of insulin infusion, and a nurse adjusted the insulin pump. During control nights, patients' standard pump settings were applied. Primary outcomes were time for which plasma glucose concentration was 3.91-8.00 mmol/L or 3.90 mmol/L or lower. Analysis was per protocol. This trial is registered, number ISRCTN18155883. FINDINGS: 17 patients were studied for 33 closed-loop and 21 continuous infusion nights. Primary outcomes did not differ significantly between treatment groups in APCam01 (12 analysed; target range, median 52% [IQR 43-83] closed loop vs 39% [15-51] standard treatment, p=0.06;

Tight glycaemic control by an automated algorithm with time-variant sampling in medical ICU patients.

OBJECTIVE: Tight glycaemic control (TGC) in critically ill patients improves clinical outcome, but is difficult to establish The primary objective of the present study was to compare glucose control in medical ICU patients applying a computer-based enhanced model predictive control algorithm (eMPC) extended to include time-variant sampling against an implemented glucose management protocol. DESIGN: Open randomised controlled trial. SETTING: Nine-bed medical intensive care unit (ICU) in a tertiary teaching hospital. PATIENTS AND PARTICIPANTS: Fifty mechanically ventilated medical ICU patients. INTERVENTIONS: Patients were included for a study period of up to 72 h. Patients were randomised to the control group (n = 25), treated by an implemented insulin algorithm, or to the eMPC group (n = 25), using the laptop-based algorithm. Target range for blood glucose (BG) was 4.4-6.1 mM. Efficacy was assessed by mean BG, hyperglycaemic index (HGI) and BG sampling interval. Safety was assessed by the number of hypoglycaemic-episodes < 2.2 mM. Each participating nurse filled-in a questionnaire regarding the usability of the algorithm. MEASUREMENTS AND MAIN RESULTS: BG and HGI were significantly lower in the eMPC group [BG 5.9 mM (5.5-6.3), median (IQR); HGI 0.4 mM (0.2-0.9)] than in control patients [BG 7.4 mM (6.9-8.6), p < 0.001; HGI 1.6 mM (1.1-2.4), p < 0.001]. One hypoglycaemic episode was detected in the eMPC group; no such episodes in the control group. Sampling interval was significantly shorter in the eMPC group [eMPC 117[Symbol: see text]min (+/- 34), mean (+/- SD), vs 174 min (+/- 27); p < 0.001]. Thirty-four nurses filled-in the questionnaire. Thirty answered the question of whether the algorithm could be applied in daily routine in the affirmative. CONCLUSIONS: The eMPC algorithm was effective in maintaining tight glycaemic control in severely ill medical ICU patients.

A simulation model of glucose regulation in the critically ill.

Focused research is underway to improve the delivery of tight glycaemic control at the intensive care unit. A major component is the development of safe, efficacious and effective insulin titration algorithms, which are normally evaluated in time-consuming resource-demanding clinical studies. Simulation studies with virtual critically ill patients can substantially accelerate the development process. For this purpose, we created a model of glucoregulation in the critically ill. The model includes five submodels: a submodel of endogenous insulin secretion, a submodel of insulin kinetics, a submodel of enteral glucose absorption, a submodel of insulin action and a submodel of glucose kinetics. Model parameters are estimated utilizing prior knowledge and data collected routinely at the intensive care unit to represent the high intersubject and temporal variation in insulin needs in the critically ill. Bayesian estimation combined with the regularization method is used to estimate (i) time-invariant model parameters and (ii) a time-varying parameter, the basal insulin concentration, which represents the temporal variation in insulin sensitivity. We propose a validation process to validate virtual patients developed for the purpose of testing glucose controllers. The parameter estimation and the validation are exemplified using data collected in six critically ill patients treated at a medical intensive care unit. In conclusion, a novel glucoregulatory model has been developed to create a virtual population of critically ill facilitating in silico testing of glucose controllers at the intensive care unit.

Blood glucose control by a model predictive control algorithm with variable sampling rate versus a routine glucose management protocol in cardiac surgery patients: a randomized controlled trial.

CONTEXT: Elevated blood glucose levels occur frequently in the critically ill. Tight glucose control by intensive insulin treatment markedly improves clinical outcome. OBJECTIVE AND DESIGN: This is a randomized controlled trial comparing blood glucose control by a laptop-based model predictive control algorithm with a variable sampling rate [enhanced model predictive control (eMPC); version 1.04.03] against a routine glucose management protocol (RMP) during the peri- and postoperative periods. SETTING: The study was performed at the Department of Cardiac Surgery, University Hospital. PATIENTS: A total of 60 elective cardiac surgery patients were included in the study. INTERVENTIONS: Elective cardiac surgery and treatment with continuous insulin infusion (eMPC) or continuous insulin infusion combined with iv insulin boluses (RMP) to maintain euglycemia (target range 4.4-6.1 mmol/liter) were performed. There were 30 patients randomized for eMPC and 30 for RMP treatment. Blood glucose was measured in 1- to 4-h intervals as requested by each algorithm during surgery and postoperatively over 24 h. MAIN OUTCOME MEASURES: Mean blood glucose, percentage of time in target range, and hypoglycemia events were used. RESULTS: Mean blood glucose was 6.2 +/- 1.1 mmol/liter in the eMPC vs. 7.2 +/- 1.1 mmol/liter in the RMP group (P < 0.05); percentage of time in the target range was 60.4 +/- 22.8% for the eMPC vs. 27.5 +/- 16.2% for the RMP group (P < 0.05). No severe hypoglycemia (blood glucose < 2.9 mmol/liter) occurred during the study. Mean insulin infusion rate was 4.7 +/- 3.3 IU/h in the eMPC vs. 2.6 +/- 1.7 IU/h in the RMP group (P < 0.05). Mean sampling interval was 1.5 +/- 0.3 h in the eMPC vs. 2.1 +/- 0.2 h in the RMP group (P < 0.05). CONCLUSIONS: Compared with RMP, the eMPC algorithm was more effective and comparably safe in maintaining euglycemia in cardiac surgery patients.

Multicentric, randomized, controlled trial to evaluate blood glucose control by the model predictive control algorithm versus routine glucose management protocols in intensive care unit patients.

OBJECTIVE: To evaluate a fully automated algorithm for the establishment of tight glycemic control in critically ill patients and to compare the results with different routine glucose management protocols of three intensive care units (ICUs) across Europe (Graz, Prague, and London). RESEARCH DESIGN AND METHODS: Sixty patients undergoing cardiac surgery (age 67 +/- 9 years, BMI 27.7 +/- 4.9 kg/m2, 17 women) with postsurgery blood glucose levels >120 mg/dl (6.7 mmol/l) were investigated in three different ICUs (20 per center). Patients were randomized to either blood glucose management (target range 80-110 mg/dl [4.4-6.1 mmol/l]) by the fully automated model predictive control (MPC) algorithm (n = 30, 10 per center) or implemented routine glucose management protocols (n = 30, 10 per center). In all patients, arterial glucose was measured hourly to describe the glucose profile until the end of the ICU stay but for a maximum period of 48 h. RESULTS: Compared with routine protocols, MPC treatment resulted in a significantly higher percentage of time within the target glycemic range (% median [min-max]: 52 [17-92] vs. 19 [0-71]) over 0-24 h (P < 0.01). Improved glycemic control with MPC treatment was confirmed in patients remaining in the ICU for 48 h (0-24 h: 50 [17-71] vs. 21 [4-67], P < 0.05, and 24-48 h: 65 [38-96] vs. 25 [8-79], P < 0.05, for MPC [n = 16] vs. routine protocol [n = 13], respectively). Two hypoglycemic events (<54 mg/dl [3.0 mmol/l]) were observed with routine protocol treatment. No hypoglycemic event occurred with MPC. CONCLUSIONS: The data suggest that the MPC algorithm is safe and effective in controlling glycemia in critically ill postsurgery patients.

Grading system to assess clinical performance of closed-loop glucose control.

Closed-loop control of the glucose concentration in type 1 diabetes has been the subject of extensive research over the last 3 decades. Building on the recent progress in continuous glucose sensing techniques, several prototypes of a closed-loop system have been developed. To complement existing measures of glucose control, we designed a grading system specifically designed to provide clinical assessment of closed-loop systems including that of glucose controllers. The system introduces six grades, A-F, describing the level of control and the therapeutic intervention during outside-meal and postprandial conditions. Grades A and B represent excellent and good glucose control, respectively, without the need for a corrective therapeutic action. Grade C represents suboptimal control with a recommendation for a corrective action. Grade D represents poor control requiring a corrective action. Grades E and F represent very poor and life-threatening control, respectively, with a need for an immediate corrective action or requiring external assistance. The outcome of grading is the quantification of time spent in each grade. The grading system is exemplified using data obtained with a model predictive controller within an in silico simulation environment. We conclude that the grading system provides suitable means to assess efficacy and safety of glucose controllers complementing existing measures of glucose control.

Insulin kinetics in type-I diabetes: continuous and bolus delivery of rapid acting insulin.

We investigated insulin lispro kinetics with bolus and continuous subcutaneous insulin infusion (CSII) modes of insulin delivery. Seven subjects with type-1 diabetes treated by CSII with insulin lispro have been studied during prandial and postprandial conditions over 12 hours. Eleven alternative models of insulin kinetics have been proposed implementing a number of putative characteristics. We assessed 1) the effect of insulin delivery mode, i.e., bolus or basal, on the insulin absorption rate, the effects of 2) insulin association state and 3) insulin dose on the rate of insulin absorption, 4) the remote insulin effect on its volume of distribution, 5) the effect of insulin dose on insulin disappearance, 6) the presence of insulin degradation at the injection site, and finally 7) the existence of two pathways, fast and slow, of insulin absorption. An iterative two-stage parameter estimation technique was used. Models were validated through assessing physiological feasibility of parameter estimates, posterior identifiability, and distribution of residuals. Based on the principle of parsimony, best model to fit our data combined the slow and fast absorption channels and included local insulin degradation. The model estimated that 67(53-82)% [mean (interquartile range)] of delivered insulin passed through the slow absorption channel [absorption rate 0.011(0.004-0.029) min(-1)] with the remaining 33% passed through the fast channel [absorption rate 0.021(0.011-0.040) min(-1)]. Local degradation rate was described as a saturable process with Michaelis-Menten characteristics [VMAX = 1.93(0.62 - 6.03) mU min(-1), KM = 62.6(62.6 - 62.6) mU]. Models representing the dependence of insulin absorption rate on insulin disappearance and the remote insulin effect on its volume of distribution could not be validated suggesting that these effects are not present or cannot be detected during physiological conditions.

Associations of glucose control with insulin sensitivity and pancreatic beta-cell responsiveness in newly presenting type 2 diabetes.

We examined the ability of indices of insulin sensitivity and pancreatic beta-cell responsiveness to explain interindividual variability of clinical measures of glucose control in newly presenting type 2 diabetes. Subjects with newly presenting type 2 diabetes (n = 65; 53 males and 12 females; age, 54 +/- 1 yr; body mass index, 30.5 +/- 0.7 kg/m(2); mean +/- SE) underwent an insulin-modified iv glucose tolerance test to determine minimal model-derived insulin sensitivity (S(I)), glucose effectiveness, first-phase insulin secretion, and disposition index. Subjects also underwent a standard meal tolerance test (MTT) to measure fasting/basal (M(0)) and postprandial (M(I)) pancreatic beta-cell responsiveness. Stepwise linear regression used these indices to explain interindividual variability of fasting and postprandial plasma glucose and insulin concentrations and glycated hemoglobin (HbA(1C)). All measures of pancreatic beta-cell responsiveness (M(0), M(I), and first-phase insulin secretion) were negatively correlated with fasting plasma glucose (P < 0.01) and positively correlated with fasting plasma insulin (FPI) and insulin responses to MTT (P < 0.05). S(I) demonstrated negative correlation with FPI (P < 0.001) but failed to correlate with any glucose variable. M(I) followed by disposition index (composite index of insulin sensitivity and pancreatic beta-cell responsiveness) were most informative in explaining interindividual variability. It was possible to explain 70-80% interindividual variability of fasting plasma glucose, FPI, HbA(1C), and insulin responses to MTT, and only 25-40% interindividual variability of postprandial glucose. In conclusion, postprandial insulin deficiency is the most powerful explanatory factor of deteriorating glucose control in newly presenting type 2 diabetes. Indices of insulin sensitivity and pancreatic beta-cell responsiveness explain fasting glucose and HbA(1C) well but fail to explain postprandial glucose.

Interstitial glucose kinetics in subjects with type 1 diabetes under physiologic conditions.

We investigated the dynamic relationship between interstitial glucose (IG) in the subcutaneous adipose tissue and plasma glucose (PG) during physiologic conditions in type 1 diabetes mellitus (T1DM). Nine subjects with T1DM (5/4 M/F; age, 33 +/- 13 years; body mass index, 26.6 +/- 4.3 kg/m(2); glycosylated hemoglobin [HbA(1c)], 8.6% +/- 0.9%; mean +/- SD) treated by continuous subcutaneous insulin infusion (CSII) with insulin lispro were studied over 12 hours after a standard meal (40 g carbohydrate [CHO]) and prandial insulin. IG was measured by open flow microperfusion. Nine compartment models were postulated to account for temporal variations in the IG/PG ratio. The models differed in the inclusion of physiologically motivated alterations of pathways entering/leaving the IG compartment in the adipose tissue. The best model included zero order (constant) glucose disposal from the interstitial fluid (ISF) and insulin-stimulated glucose transfer from plasma to the ISF. The former effect is expressed by a positive association between the IG/PG ratio and PG, eg, a decrease in PG from 9 to 3.3 mmol/L lowers the IG/PG ratio by 0.1. The latter effect results in the IG/PG ratio to be increased by 0.03 per 10 mU/L of plasma insulin. We were not able to detect the stimulatory effect of insulin on glucose disappearance from the ISF. In conclusion, we developed and quantified a model of IG kinetics in the adipose tissue applicable to physiologic conditions in subjects with T1DM.

Closing the loop: the adicol experience.

The objective of the project Advanced Insulin Infusion using a Control Loop (ADICOL) was to develop a treatment system that continuously measures and controls the glucose concentration in subjects with type 1 diabetes. The modular concept of the ADICOL's extracorporeal artificial pancreas consisted of a minimally invasive subcutaneous glucose system, a handheld PocketPC computer, and an insulin pump (D-Tron, Disetronic, Burgdorf, Switzerland) delivering subcutaneously insulin lispro. The present paper describes a subset of ADICOL activities focusing on the development of a glucose controller for semi-closed-loop control, an in silico testing environment, clinical testing, and system integration. An incremental approach was adopted to evaluate experimentally a model predictive glucose controller. A feasibility study was followed by efficacy studies of increasing complexity. The ADICOL project demonstrated feasibility of a semi-closed-loop glucose control during fasting and fed conditions with a wearable, modular extracorporeal artificial pancreas.

Evaluation of glucose controllers in virtual environment: methodology and sample application.

OBJECTIVE: Adaptive systems to deliver medical treatment in humans are safety-critical systems and require particular care in both the testing and the evaluation phase, which are time-consuming, costly, and confounded by ethical issues. The objective of the present work is to develop a methodology to test glucose controllers of an artificial pancreas in a simulated (virtual) environment. MATERIAL AND METHODS: A virtual environment comprising a model of the carbohydrate metabolism and models of the insulin pump and the glucose sensor is employed to simulate individual glucose excursions in subjects with type 1 diabetes. The performance of the control algorithm within the virtual environment is evaluated by considering treatment and operational scenarios. RESULTS: The developed methodology includes two dimensions: testing in relation to specific life style conditions, i.e. fasting, post-prandial, and life style (metabolic) disturbances; and testing in relation to various operating conditions, i.e. expected operating conditions, adverse operating conditions, and system failure. We define safety and efficacy criteria and describe the measures to be taken prior to clinical testing. The use of the methodology is exemplified by tuning and evaluating a model predictive glucose controller being developed for a wearable artificial pancreas focused on fasting conditions. CONCLUSION: Our methodology to test glucose controllers in a virtual environment is instrumental in anticipating the results of real clinical tests for different physiological conditions and for different operating conditions. The thorough testing in the virtual environment reduces costs and speeds up the development process.

Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes.

A nonlinear model predictive controller has been developed to maintain normoglycemia in subjects with type 1 diabetes during fasting conditions such as during overnight fast. The controller employs a compartment model, which represents the glucoregulatory system and includes submodels representing absorption of subcutaneously administered short-acting insulin Lispro and gut absorption. The controller uses Bayesian parameter estimation to determine time-varying model parameters. Moving target trajectory facilitates slow, controlled normalization of elevated glucose levels and faster normalization of low glucose values. The predictive capabilities of the model have been evaluated using data from 15 clinical experiments in subjects with type 1 diabetes. The experiments employed intravenous glucose sampling (every 15 min) and subcutaneous infusion of insulin Lispro by insulin pump (modified also every 15 min). The model gave glucose predictions with a mean square error proportionally related to the prediction horizon with the value of 0.2 mmol L(-1) per 15 min. The assessment of clinical utility of model-based glucose predictions using Clarke error grid analysis gave 95% of values in zone A and the remaining 5% of values in zone B for glucose predictions up to 60 min (n = 1674). In conclusion, adaptive nonlinear model predictive control is promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.

Evaluating glycemic control algorithms by computer simulations.

BACKGROUND: Numerous guidelines and algorithms exist to achieve glycemic control. Their strengths and weaknesses are difficult to assess without head-to-head comparison in time-consuming clinical trials. We hypothesized that computer simulations may be useful. METHODS: Two open-label randomized clinical trials were replicated using computer simulations. One study compared performance of the enhanced model predictive control (eMPC) algorithm at two intensive care units in the United Kingdom and Belgium. The other study compared three glucose control algorithms-eMPC, Matias (the absolute glucose protocol), and Bath (the relative glucose change protocol)-in a single intensive care unit. Computer simulations utilized a virtual population of 56 critically ill subjects derived from routine data collected at four European surgical and medical intensive care units. RESULTS: In agreement with the first clinical study, computer simulations reproduced the main finding and discriminated between the two intensive care units in terms of the sampling interval (1.3 h vs. 1.8 h, United Kingdom vs. Belgium; P < 0.01). Other glucose control metrics were comparable between simulations and clinical results. The principal outcome of the second study was also reproduced. The eMPC demonstrated better performance compared with the Matias and Bath algorithms as assessed by the time when plasma glucose was in the target range between 4.4 and 6.1 mmol/L (65% vs. 43% vs. 42% [P < 0.001], eMPC vs. Matias vs. Bath) without increasing the risk of severe hypoglycemia. CONCLUSIONS: Computer simulations may provide resource-efficient means for preclinical evaluation of algorithms for glycemic control in the critically ill.

Simulation environment to evaluate closed-loop insulin delivery systems in type 1 diabetes.

BACKGROUND: Closed-loop insulin delivery systems linking subcutaneous insulin infusion to real-time continuous glucose monitoring need to be evaluated in humans, but progress can be accelerated with the use of in silico testing. We present a simulation environment designed to support the development and testing of closed-loop insulin delivery systems in type 1 diabetes mellitus (T1DM). METHODS: The principal components of the simulation environment include a mathematical model of glucose regulation representing a virtual population with T1DM, the glucose measurement model, and the insulin delivery model. The simulation environment is highly flexible. The user can specify an experimental protocol, define a population of virtual subjects, choose glucose measurement and insulin delivery models, and specify outcome measures. The environment provides graphical as well as numerical outputs to enable a comprehensive analysis of in silico study results. The simulation environment is validated by comparing its predictions against a clinical study evaluating overnight closed-loop insulin delivery in young people with T1DM using a model predictive controller. RESULTS: The simulation model of glucose regulation is described, and population values of 18 synthetic subjects are provided. The validation study demonstrated that the simulation environment was able to reproduce the population results of the clinical study conducted in young people with T1DM. CONCLUSIONS: Closed-loop trials in humans should be preceded and concurrently guided by highly efficient and resource-saving computer-based simulations. We demonstrate validity of population-based predictions obtained with our simulation environment.

Intensive insulin therapy: enhanced Model Predictive Control algorithm versus standard care.

OBJECTIVE: To investigate the effectiveness of an enhanced software Model Predictive Control (eMPC) algorithm for intravenous insulin infusion, targeted at tight glucose control in critically ill patients, over 72 h, in two intensive care units with different management protocols. DESIGN: Comparison with standard care in a two center open randomized clinical trial. SETTING: Two adult intensive care units in University Hospitals. PATIENTS AND PARTICIPANTS: Thirty-four critically ill patients with hyperglycaemia (glucose >120 mg/dL) or already receiving insulin infusion. INTERVENTIONS: Patients were randomized, within each ICU, to intravenous insulin infusion advised by eMPC algorithm or the ICU's standard insulin infusion administration regimen. MEASUREMENTS AND RESULTS: Arterial glucose concentration was measured at study entry and when advised by eMPC or measured as part of standard care. Time-weighted average glucose concentrations in patients receiving eMPC advised insulin infusions were similar [104 mg/dL (5.8 mmol/L)] in both ICUs. eMPC advised glucose measurement interval was significantly different between ICUs (1.1 vs. 1.8 h, P < 0.01). The standard care insulin algorithms resulted in significantly different time-weighted average glucose concentrations between ICUs [128 vs. 99 mg/dL (7.1 vs. 5.5 mmol/L), P < 0.01]. CONCLUSIONS: In this feasibility study the eMPC algorithm provided similar, effective and safe tight glucose control over 72 h in critically ill patients in two different ICUs. Further development is required to reduce glucose sampling interval while maintaining a low risk of hypoglycaemia.

In silico testing--impact on the progress of the closed loop insulin infusion for critically ill patients project.

BACKGROUND: In silico testing was used extensively in the European Commission-funded Closed Loop Insulin Infusion for Critically Ill Patients (Clinicip) project, which aimed to develop prototype systems for closed loop glucose control in the critically ill. This article presents two examples of how the simulation environment was utilized in this project. METHODS: The in silico simulation environment was used to simulate a 48-hour clinical trial in a surgical intensive care unit to achieve tight glycemic control. A set of 10 critically ill synthetic subjects was selected for two different studies. In the first study, two sets of clinical trials were simulated using two versions of a model predictive control (MPC)-based glucose control algorithm: MPC Version 0.1.5 with hourly glucose measurements and updated MPC Version 1.4.3 with variable 1- to 4-hour glucose sampling. In the second study, four sets of clinical trials were simulated with four levels of measurement error at 2, 5, 7, and 15% coefficient of variation corresponding to the measurement error of commercially available glucose measuring devices. RESULTS: In the first study, more frequent glucose measurements associated with MPC Version 0.1.5 facilitated more efficacious and safer glucose control compared to that obtained with the prolonged and variable glucose sampling rate associated with MPC Version 1.4.3. In the second study, a marked deterioration in safety measures was observed in studies performed with a measurement error of 15%. CONCLUSIONS: The presented simulation studies highlighted two important uses of in silico simulation environment in the Clinicip project. The impressive progress and successful completion of the Clinicip project would not be possible without computer-based simulations.

Automated glucose control in the ICU: effect of nutritional protocol and measurement error.

Tight glycaemic control has been shown to reduce mortality and morbidity in critically ill subjects. Using in silico computational approach, the objective of this study was to evaluate the effect of nutrition and the measurement error on glucose control. In silico simulation environment describing 21 synthetic subjects was used to simulate a 48 h clinical trial with an adaptive model predictive controller in the intensive care unit. Two types of nutritional protocols, simple and complex, and various levels of the measurement error (ME) were evaluated. The simple nutritional protocol resulted in more efficacious glucose control compared to that obtained with the complex nutritional protocol. A considerable deterioration was noted with the increasing level of the ME. Severe hypoglycaemia episodes (<2.8 mM) were observed with the ME>10%. We conclude that nutritional protocol should be kept simple to facilitate efficacious glucose control with an adaptive model predictive controller. The measurement error of the glucose measuring device should be less or equal to 10%

Partitioning glucose distribution/transport, disposal, and endogenous production during IVGTT.

We have separated the effect of insulin on glucose distribution/transport, glucose disposal, and endogenous production (EGP) during an intravenous glucose tolerance test (IVGTT) by use of a dual-tracer dilution methodology. Six healthy lean male subjects (age 33 +/- 3 yr, body mass index 22.7 +/- 0.6 kg/m(2)) underwent a 4-h IVGTT (0.3 g/kg glucose enriched with 3-6% D-[U-(13)C]glucose and 5-10% 3-O-methyl-D-glucose) preceded by a 2-h investigation under basal conditions (5 mg/kg of D-[U-(13)C]glucose and 8 mg/kg of 3-O-methyl-D-glucose). A new model described the kinetics of the two glucose tracers and native glucose with the use of a two-compartment structure for glucose and a one-compartment structure for insulin effects. Insulin sensitivities of distribution/transport, disposal, and EGP were similar (11.5 +/- 3.8 vs. 10.4 +/- 3.9 vs. 11.1 +/- 2.7 x 10(-2) ml small middle dot kg(-1) small middle dot min(-1) per mU/l; P = nonsignificant, ANOVA). When expressed in terms of ability to lower glucose concentration, stimulation of disposal and stimulation of distribution/transport accounted each independently for 25 and 30%, respectively, of the overall effect. Suppression of EGP was more effective (P < 0.01, ANOVA) and accounted for 50% of the overall effect. EGP was suppressed by 70% (52-82%) (95% confidence interval relative to basal) within 60 min of the IVGTT; glucose distribution/transport was least responsive to insulin and was maximally activated by 62% (34-96%) above basal at 80 min compared with maximum 279% (116-565%) activation of glucose disposal at 20 min. The deactivation of glucose distribution/transport was slower than that of glucose disposal and EGP (P < 0.02) with half-times of 207 (84-510), 12 (7-22), and 29 (16-54) min, respectively. The minimal-model insulin sensitivity was tightly correlated with and linearly related to sensitivity of EGP (r = 0.96, P < 0.005) and correlated positively but nonsignificantly with distribution/transport sensitivity (r = 0.73, P = 0.10) and disposal sensitivity (r = 0.55, P = 0.26). We conclude that, in healthy subjects during an IVGTT, the two peripheral insulin effects account jointly for approximately one-half of the overall insulin-stimulated glucose lowering, each effect contributing equally. Suppression of EGP matches the effect in the periphery.