Patient decision-making of CGM sensor driven insulin therapies in type 1 diabetes: In silico assessment.

In type 1 diabetes (T1D) therapy, continuous glucose monitoring (CGM) sensors, which provide glucose concentration in the subcutis every 1-5 min for 7 consecutive days, should allow in principle a more efficient insulin dosing than that based on the conventional 3-4 self-monitoring of blood glucose (SMBG) measurements per day. However, CGM, at variance with SMBG, is still not approved for insulin dosing in T1D management because regulatory agencies, e.g. FDA, are looking for more factual evidence on its safety. An in silico assessment of SMBG- vs CGM-driven insulin therapy can be a first step. Here we present a simulation model of T1D patient decision-making obtained by interconnecting models of glucose-insulin dynamics, SMBG and CGM measurement errors, carbohydrates-counting errors, insulin boluses time variability and forgetfulness, and subcutaneous insulin pump delivery. Inter- and intra- patient variability of model parameters are considered. The T1D patient decision-making model allows to run realistic multi-day simulations scenarios in a population of virtual subjects. We present the first results of simulations run in 20 virtual subjects over a 7-day period, which demonstrates that additional information brought by CGM (trend and hypo/hyperglycemic warnings) with respect to SMBG produces a statistically significant increment (about of 9%) of time spent by the patient in the euglycemic range (70-180 mg/dl).

Assessment of insulin action on carbohydrate metabolism: physiological and non-physiological methods.

Carbohydrate metabolism in humans is regulated by insulin secretion from pancreatic ß-cells and glucose disposal by insulin-sensitive tissues. Insulin facilitates glucose utilization in peripheral tissues and suppresses hepatic glucose production. Any defects in insulin action predispose an individual to glucose intolerance and Type 2 diabetes mellitus. Early detection of defects in insulin action could provide opportunities to prevent or delay progression of the disease state. There are different approaches to assess insulin action. Initial methods, such as peripheral insulin concentration and simple indices, have several limitations. Subsequently, researchers developed methodologies using intravenous glucose infusion to determine glucose fluxes. However, these methodologies are limited by being non-physiological. Newer, innovative techniques that have been developed are more sophisticated and physiological. By modelling glucose kinetics using isotope dilution techniques, several robust parameters can be obtained that are physiologically relevant and sound. This brief review summarizes most of the non-physiological and physiological methodologies used to measure the variables of insulin action.

Minimal model assessment of hepatic insulin extraction during an oral test from standard insulin kinetic parameters.

In this article, a first aim was to develop a minimal modeling approach to noninvasively assess hepatic insulin extraction in 204 healthy subjects studied with a standard meal by coupling the already available meal C-peptide minimal model with a new insulin model. The ingredients of this model are posthepatic IDR, which in turn is described in terms of pancreatic ISR and hepatic insulin extraction HE, and a linear monocompartmental model of insulin kinetics. Even if ISR is provided by the C-peptide minimal model, the simultaneous assessment of HE and insulin kinetics is critical, since compensations may arise between parameters describing these two processes. Therefore, as a second aim of this study, a method was developed to predict standard values of insulin kinetic parameters in an individual on the basis of the individual's anthropometric characteristics. The statistical analysis, based on linear regression of insulin kinetic parameters estimated from IM-IVGTT data performed on the same subjects, demonstrated that insulin kinetic parameters can be accurately predicted from age and body surface area. Once kinetic parameters of the new insulin model were fixed to these values, HE profile and indexes during a meal were reliably estimated in each individual, indicating a significant suppression during the meal since the overall index of HE, equal to 60 +/- 1% in the basal state, is reduced to 40 +/- 1% during a meal. However, standard parameters provide an approximation of the individual one; thus, the third aim was to define the impact on estimated indexes of using standard instead of individually estimated values. Our results showed that the 25% uncertainty affecting as an average insulin kinetic parameters of an individual, when they are predicted from age and body surface area, translates into a similar relative uncertainty in the individual's hepatic insulin extraction indexes.

Are we optimizing gestational diabetes treatment with glyburide? The pharmacologic basis for better clinical practice.

Glyburide's pharmacokinetics (PK) and pharmacodynamics have not been studied in women with gestational diabetes mellitus (GDM). The objective of this study was to assess steady-state PK of glyburide, as well as insulin sensitivity, beta-cell responsivity, and overall disposition indices after a mixed-meal tolerance test (MMTT) in women with GDM (n = 40), nonpregnant women with type 2 diabetes mellitus (T2DM) (n = 26), and healthy pregnant women (n = 40, MMTT only). At equivalent doses, glyburide plasma concentrations were approximately 50% lower in pregnant women than in nonpregnant subjects. The average umbilical cord/maternal plasma glyburide concentration ratio at the time of delivery was 0.7 +/- 0.4. Insulin sensitivity was approximately fivefold lower in women with GDM as compared with healthy pregnant women. Despite comparable beta-cell responsivity indices, the average beta-cell function corrected for insulin resistance was more than 3.5-fold lower in women with glyburide-treated GDM than in healthy pregnant women. Women with GDM in whom glyburide treatment has failed may benefit from alternative medication or dosage escalation; however, fetal safety should be kept in mind.

Reconstructing insulin secretion rate after a glucose stimulus by an improved stochastic deconvolution method.

Reconstructing insulin secretion rate (ISR) after a glucose stimulus by deconvolution is difficult because of its biphasic pattern, i.e., a rapid secretion peak is followed by a slower release. Here, we refine a recently proposed stochastic deconvolution method by modeling ISR as the multiple integration of a white noise process with time-varying statistics. The unknown parameters are estimated from the data by employing a maximum likelihood criterion. A fast computational scheme implementing the method is presented. Monte Carlo simulation results are developed which numerically show a more reliable ISR profile reconstructed by the new method.

Bayesian identification of a population compartmental model of C-peptide kinetics.

When models are used to measure or predict physiological variables and parameters in a given individual, the experiments needed are often complex and costly. A valuable solution for improving their cost effectiveness is represented by population models. A widely used population model in insulin secretion studies is the one proposed by Van Cauter et al. (Diabetes 41:368-377, 1992), which determines the parameters of the two compartment model of C-peptide kinetics in a given individual from the knowledge of his/her age, sex, body surface area, and health condition (i.e., normal, obese, diabetic). This population model was identified from the data of a large training set (more than 200 subjects) via a deterministic approach. This approach, while sound in terms of providing a point estimate of C-peptide kinetic parameters in a given individual, does not provide a measure of their precision. In this paper, by employing the same training set of Van Cauter et al., we show that the identification of the population model into a Bayesian framework (by using Markov chain Monte Carlo) allows, at the individual level, the estimation of point values of the C-peptide kinetic parameters together with their precision. A successful application of the methodology is illustrated in the estimation of C-peptide kinetic parameters of seven subjects (not belonging to the training set used for the identification of the population model) for which reference values were available thanks to an independent identification experiment.

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.

Estimation of organ transport function for recirculating indicator dilution curves.

The transport function of an indicator through an organ allows the calculation of important physiological parameters, but its estimation, especially in the presence of recirculation, can be difficult. In this paper, we estimate the transport function of 3H-mannitol (an extracellular tracer of glucose) in the human leg skeletal muscle. To do so, an indicator bolus is administered into the femoral artery and its recirculating dilution curves are nonuniformly sampled in both the femoral artery and the femoral vein. A new deconvolution-based method is used to simultaneously estimate the indicator transport function and the organ plasma flow. Subsequently, the indicator mean transit time and distribution volume are calculated. The reliability of the method is assessed by Monte Carlo simulation. The ability to estimate parameters, like mean transit time and extracellular distribution volume, is critical to the study of pathophysiologic states such as diabetes, insulin resistance, and hypertension.

Parameter estimation in distributed models of blood-tissue exchange: a Monte Carlo strategy to assess precision.

Distributed parameter models of blood-tissue exchange are increasingly used to interpret multiple tracer dilution data in regional kinetic studies. To derive a measure of the precision with which the model parameters are estimated is therefore of paramount importance. The standard approach to deriving precision of estimates does not take into account the fact that some of the model parameters are fixed. Thus, the precision of parameter estimates is not realistic and, in all likelihood, it is overestimated. The aim of this study is to describe a Monte Carlo method devised to obtain a theoretically sound measure of the precision of estimates, which takes into account both measurement error and the uncertainty associated with the fixed parameters. The fixed parameter values are taken from a probability distribution. By letting the fixed parameters vary according to their distribution, a large number of synthetic datasets is generated. Noise is then added. Estimating the parameters in each of these synthetic datasets allows the derivation of a Monte Carlo mean and standard deviation, which provides a realistic measure of precision. The methodology is illustrated for a simulated data case study dealing with the estimation of the capillary permeability-surface area product in a two tracer experiment.

Visceral adipose tissue impairs insulin secretion and insulin sensitivity but not energy expenditure in obesity.

In obesity, a central pattern of fat distribution is mostly associated with hyperinsulinemia, insulin resistance, and hyperlipemia, thus promoting the development of non-insulin-dependent diabetes mellitus and cardiovascular disease. In addition, in obesity, changes in energy expenditure are hypothesized to be involved in the development or maintenance of excessive body fat storage. In this study, abdominal fat distribution by computed tomographic (CT) scan was used to study the relation between the visceral fat depot, insulin secretion, and insulin sensitivity in a group of obese subjects with normal glucose tolerance (n = 26; body mass index [BMI], 39 +/- 1 kg/m2) and a group of normal-weight control subjects (n = 9; BMI, 23 +/- 1 kg/m2). The minimal model method was used to assess insulin sensitivity, S(I), and first-phase (phi1) and second-phase (phi2) beta-cell sensitivity from plasma glucose, insulin, and C-peptide concentrations measured during an intravenous glucose tolerance test ([IVGTT] 0.33 g/kg body weight). Moreover, we evaluated the relationships between these parameters and the resting metabolic rate (RMR) and glucose-induced thermogenesis (GIT) measured by indirect calorimetry. The data show the following: (1) in obese subjects, phi1 is greater but not statistically different from the value in control subjects (252 +/- 41 v 157 +/- 25 dimensionless 10(9)); (2) phi2 is significantly higher in obese subjects (27 +/- 4 v 14 +/- 2 min(-1) x 10(9), P < .05), with a positive correlation between the amount of visceral adipose tissue (VAT) and phi2 (r = .49, P < .05); (3) S(I) is decreased in the obese group (2.8 +/- 0.3 v 9.7 +/- 1.6 10(-4) x min(-1)/microU x mL(-1)), P < .0001), with a negative correlation of S(I) with the adiposity index BMI (r = -.67, P < .0001) and VAT (r = .56, P < .05); (4) RMR, expressed in absolute terms, was significantly increased in obese versus lean subjects (5.9 +/- 0.2 v 4.6 +/- 0.3 kJ/min, P < .01), whereas when RMR was adjusted for fat-free mass (FFM), the difference between the two groups disappeared (0.09 +/- 0.003 v 0.09 +/- 0.002 kJ/min x kg FFM). We did not observe any difference in GIT between lean and obese subjects. Moreover, GIT was significantly correlated with FFM (r = .69, P < .005), but not with BMI. The amount of VAT did not correlate with RMR or GIT. In conclusion, these results suggest that in obese subjects with normal glucose tolerance, insulin sensitivity is impaired and the beta-cell hyperresponse to glucose is mainly due to an enhanced second-phase beta-cell secretion. The degree of visceral fat deposition seems to affect insulin secretion and worsens insulin sensitivity, but does not influence energy expenditure.

A stochastic deconvolution method to reconstruct insulin secretion rate after a glucose stimulus.

Insulin secretion rate (ISR) is not directly measurable in man but it can be reconstructed from C-peptide (CP) concentration measurements by solving an input estimation problem by deconvolution. The major difficulties posed by the estimation of ISR after a glucose stimulus, e.g., during an intravenous glucose tolerance test (IVGTT), are the ill-conditioning of the problem, the nonstationary pattern of the secretion rate, and the nonuniform/infrequent sampling schedule. In this work, a nonparametric method based on the classic Phillips-Tikhonov regularization approach is presented. The problem of nonuniform/infrequent sampling is addressed by a novel formulation of the regularization method which allows the estimation of quasi time-continuous input profiles. The input estimation problem is stated into a Bayesian context, where the a priori known nonstationary characteristics of ISR after the glucose stimulus are described by a stochastic model. Deconvolution is tackled by linear minimum variance estimation, thus allowing the derivation of new statistically based regularization criteria. Finally, a Monte-Carlo strategy is implemented to assess the uncertainty of the estimated ISR arising from CP measurement error and impulse response parameters uncertainty.

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.

Direct assessment of liver glycogen storage by 13C nuclear magnetic resonance spectroscopy and regulation of glucose homeostasis after a mixed meal in normal subjects.

Despite extensive recent studies, understanding of the normal postprandial processes underlying immediate storage of substrate and maintenance of glucose homeostasis in humans after a mixed meal has been incomplete. The present study applied 13C nuclear magnetic resonance spectroscopy to measure sequential changes in hepatic glycogen concentration, a novel tracer approach to measure postprandial suppression of hepatic glucose output, and acetaminophen to trace the pathways of hepatic glycogen synthesis to elucidate the homeostatic adaptation to the fed state in healthy human subjects. After the liquid mixed meal, liver glycogen concentration rose from 207 +/- 22 to 316 +/- 19 mmol/liter at an average rate of 0.34 mmol/liter per min and peaked at 318 +/- 31 min, falling rapidly thereafter (0.26 mmol/liter per min). The mean increment at peak represented net glycogen synthesis of 28.3 +/- 3.7 g (approximately 19% of meal carbohydrate content). The contribution of the direct pathway to overall glycogen synthesis was 46 +/- 5 and 68 +/- 8% between 2 and 4 and 4 and 6 h, respectively. Hepatic glucose output was completely suppressed within 30 min of the meal. It increased steadily from 60 to 255 min from 0.31 +/- 32 to 0.49 +/- 18 mg/kg per min then rapidly returned towards basal levels (1.90 +/- 0.04 mg/kg per min). This pattern of change mirrored precisely the plasma glucagon/insulin ratio. These data provide for the first time a comprehensive picture of normal carbohydrate metabolism in humans after ingestion of a mixed meal.

Assessment of insulin action and glucose effectiveness in diabetic and nondiabetic humans.

Insulin concentrations in humans continuously change and typically increase only when glucose also increases such as with eating. In this setting, it is not known whether the severity of hepatic and extrahepatic insulin resistance is comparable and whether the ability of glucose to regulate its own uptake and release is defective in non-insulin-dependent diabetes mellitus (NIDDM). To address this question, NIDDM and nondiabetic subjects were studied when glucose concentrations were clamped at either 5 mM (euglycemia) or varied so as to mimic the glucose concentrations observed in nondiabetic humans after food ingestion (hyperglycemia). Insulin was infused so as to simulate a "nondiabetic" postprandial profile. During euglycemia, insulin increased glucose disposal in nondiabetic but not diabetic subjects indicating marked extrahepatic resistance. In contrast, insulin-induced suppression of glucose release was only minimally less (P < 0.05) in diabetic than nondiabetic subjects (-1.06 +/- 0.09 vs. -1.47 +/- 0.21 nmol.kg-1 per 4 h). Hyperglycemia substantially enhanced disposal in both groups. Glucose effectiveness measured as the magnitude of enhancement of disposal (0.59 +/- 0.18 vs. 0.62 +/- 0.17 nmollkg-1 per 4 h) and suppression of release (-0.36 +/- 0.12 vs. -0.14 +/- 0.12 nmol.kg-1 per 4 h) did not differ in the diabetic and nondiabetic subjects. In conclusion, when assessed in the presence of a physiological insulin profile, people with NIDDM demonstrate: (a) profound extrahepatic insulin resistance, (b) modest hepatic insulin resistance, and (c) normal ability of glucose to stimulate its own uptake and suppress its own release.

Assessment of insulin action in NIDDM in the presence of dynamic changes in insulin and glucose concentration.

Both glucose and insulin are important regulators of glucose uptake and hepatic glucose release. Because insulin concentrations rarely if ever increase under daily living conditions, unless glucose concentrations also increase, we sought to determine whether hepatic and extrahepatic responses to changes in insulin and glucose concentration are impaired in patients with non-insulin-dependent diabetes mellitus (NIDDM). To address this question, glucose metabolism was measured in diabetic and nondiabetic subjects. A computer-driven infusion system was used to produce a nondiabetic postprandial insulin profile in both groups while sufficient exogenous glucose was infused to mimic nondiabetic postprandial glucose concentrations. Although NIDDM was associated with greater (P < 0.05) hepatic glucose release both before and during the prandial insulin infusion, suppression did not differ in the diabetic and nondiabetic subjects (-1.06 +/- 0.20 vs. -0.86 +/- 0.15 mmol/kg every 4 h). In contrast, stimulation of both glucose disappearance (0.77 +/- 0.27 vs. 1.68 +/- 0.27 mmol/kg every 4 h) and forearm glucose uptake (187 +/- 81 vs. 550 +/- 149 mumol/dl every 4 h) was lower (P < 0.05) in diabetic than in nondiabetic subjects. Thus, despite increased basal rates of glucose production, obese individuals with NIDDM had decreased stimulation of glucose disappearance but normal suppression of hepatic glucose release in response to nondiabetic prandial glucose and insulin concentrations. These data indicate that the increase in glucose that occurs with carbohydrate ingestion is likely to compensate for hepatic but not extrahepatic insulin resistance.

Methods for assessment of the rate of onset and offset of insulin action during nonsteady state in humans.

Measurement of glucose turnover under non-steady-state conditions has proven problematic. When the mass of the glucose pool is not changing (i.e., glucose concentrations are constant) non-steady-state error can be minimized if all glucose entering the circulation has the same specific activity as plasma [radioactive infused glucose (hot-GINF) method]. Alternatively, a second tracer can be used to measure the effective volume of glucose [variable-pV method of Issekutz (T. Issekutz, R. Issekutz, and D. Elahi. (Can. J. Physiol. 52:215-224, 1974)]. To determine whether these techniques provide concordant assessments of insulin action under non-steady-state conditions, glucose turnover was measured in six subjects. After initiation of insulin (0.6 mU.kg-1 x min-1), both methods indicated similar rates of suppression of hepatic glucose release, which was complete by approximately 100-120 min. In contrast, the traditional fixed-pV method of Steele (R. Steele, J. Wall, R. DeBodo, and N. Altszuler. Am. J. Physiol. 187:15-24 1956) underestimated turnover (P < 0.01) resulting in apparent complete suppression of glucose release within approximately 40 min (P < 0.01 vs. other methods). The hot-GINF and variable-pV methods also yielded similar estimates of turnover after discontinuation of insulin. Both indicated that resumption of hepatic glucose release was slower (P < 0.01) and fall of glucose uptake faster (P < 0.01) than suggested by the fixed-pV method. Thus both the hot-GINF and variable-pV methods avoid non-steady-state error introduced by the fixed-pV method and provide concordant assessments of the rate of onset and offset of insulin action.

Power spectral analysis of heart-rate variations improves assessment of diabetic cardiac autonomic neuropathy.

Power spectral analysis (PSA) of heart-rate variations has recently proved a useful tool in evaluating cardiovascular autonomic activity. It offers the possibility of examining both the functioning of parasympathetic and sympathetic pathways through breakdown into two frequency bands, and of their effects on heart-rate cyclic variability. We applied an autoregressive model for PSA to study overall autonomic tone in 20 male age-matched control subjects and 53 insulin-dependent (type I) diabetic subjects, subdivided into three groups of 20, 15, and 18, each group presenting different degrees of autonomic involvement. We found that: 1) power spectrum density (PSD) values at high-frequency bands (parasympathetic dependent) were similar in diabetic subjects without cardiac autonomic neuropathy (CAN) and in control subjects, but differed significantly from diabetic subjects with mild CAN and severe CAN, both standing and lying; 2) PSD values at low frequency (mainly sympathetic dependent) were similar, or slightly different, in diabetic subjects without CAN and in control subjects, but differed significantly from diabetic subjects with mild and severe CAN, both standing and lying; 3) as an expression of parasympathetic versus sympathetic coherence, correlations, both standing and lying, existed between PSD values at low- and high-frequency bands in control and diabetic subjects without CAN, but not in diabetic subjects with CAN; and 4) different degrees of correlation characterized the PSD values of high and low frequencies versus traditional cardiovascular test values in the diabetic subjects. The best correlation was between PSD low-frequency values and the lying-to-standing maneuver.(ABSTRACT TRUNCATED AT 250 WORDS)

Glucose disposal, beta-cell secretion, and hepatic insulin extraction in cirrhosis: a minimal model assessment.

Factors controlling glucose metabolism after IV load were studied in nine patients with compensated cirrhosis and in six age-matched controls. The time courses of glucose, insulin, and C peptide were analyzed by means of the minimal model technique. In cirrhosis, insulin sensitivity was reduced by approximately 70% and glucose-dependent glucose uptake (glucose effectiveness) by 45%. Decreased glucose effectiveness explained 65% of the variance of glucose disappearance and correlated with the ratio of urinary creatinine to height, an independent measure of muscle mass (r = 0.839). beta-cell responsiveness to glucose, measured on C-peptide kinetics, was variable and increased on average by 170% and 107% (first-phase and second-phase, respectively). The total amount of insulin secreted by beta-cells in the course of the study was nearly doubled, whereas the basal insulin secretion rate was in the normal range. The time courses of hepatic extraction of insulin did not differ between groups, and basal extraction was on average 58% in controls and 56% in patients with cirrhosis. It was reduced to 30% in a single patient who had severe hepatocellular failure and large spontaneous portosystemic shunting. We conclude that the alterations in glucose metabolism of cirrhosis include a decreased insulin sensitivity, a reduced glucose effectiveness, and an increased pancreatic responsiveness to glucose, leading to hyperinsulinemia. The hepatic extraction of insulin is reduced only in the very advanced stages of the disease, possibly because of a large reserve capacity of the hepatic parenchyma.

Is the "pool-fraction" paradigm a valid model for assessment of in vivo turnover in non-steady state?

Quantification of in vivo turnover of endogenous substances in nonsteady state is of fundamental importance for understanding a variety of physiological and clinical metabolic situations. Toward this end, a pool-fraction model has become a paradigm in the glucose and ketone body areas. We discuss the basic assumptions on which the pool-fraction model is based and the criteria on which it has been validated. Specific comments are then made on its current and potential use for quantifying the non-steady-state turnover of glucose, ketone bodies, and insulin. We conclude that the quantitative reliability of predictions provided by the pool-fraction model is quite poor and that new developments are needed for quantifying the non-steady-state situation.