Oral Glucose Minimal Model Research Articles

The Minimally-Invasive Oral Glucose Minimal Model: Estimation of Gastric Retention, Glucose Rate of Appearance, and Insulin Sensitivity From Type 1 Diabetes Data Collected in Real-Life Conditions.

Modeling the effect of meal composition on glucose excursion would help in designing decision support systems (DSS) for type 1 diabetes (T1D) management. In fact, macronutrients differently affect post-prandial gastric retention (GR), rate of appearance (R[Formula: see text]), and insulin sensitivity (S[Formula: see text]). Such variables can be estimated, in inpatient settings, from plasma glucose (G) and insulin (I) data using the Oral glucose Minimal Model (OMM) coupled with a physiological model of glucose transit through the gastrointestinal tract (reference OMM, R-OMM). Here, we present a model able to estimate those quantities in daily-life conditions, using minimally-invasive (MI) technologies, and validate it against the R-OMM. Forty-seven individuals with T1D (weight =78±13 kg, age =42±10 yr) underwent three 23-hour visits, during which G and I were frequently sampled while wearing continuous glucose monitoring (CGM) and insulin pump (IP). Using a Bayesian Maximum A Posteriori estimator, R-OMM was identified from plasma G and I measurements, and MI-OMM was identified from CGM and IP data. The MI-OMM fitted the CGM data well and provided precise parameter estimates. GR and R[Formula: see text] model parameters were not significantly different using the MI-OMM and R-OMM (p 0.05) and the correlation between the two S[Formula: see text] was satisfactory ( ρ =0.77). The MI-OMM is usable to estimate GR, R[Formula: see text], and S[Formula: see text] from data collected in real-life conditions with minimally-invasive technologies. Applying MI-OMM to datasets where meal compositions are available will allow modeling the effect of each macronutrient on GR, R[Formula: see text], and S[Formula: see text]. DSS could finally exploit this information to improve diabetes management.

A New Index of Insulin Sensitivity from Glucose Sensor and Insulin Pump Data: In Silico and In Vivo Validation in Youths with Type 1 Diabetes.

Background: Estimation of insulin sensitivity (SI) and its daily variation are key for optimizing insulin therapy in patients with type 1 diabetes (T1D). We recently developed a method for SI estimation from continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) data in adults with T1D (SISP) and validated it under restrained experimental conditions. Herein, we validate in vivo a new version of SISP performing well in daily life unrestrained conditions. Methods: The new SISP was tested in both simulated and real data. The simulated dataset consists of 100 virtual adults of the UVa/Padova T1D Simulator monitored during an open-loop experiment, whereas the real dataset consists of 10 youths with T1D monitored during a hybrid closed-loop meal study. In both datasets, participants underwent two consecutive meals (breakfast and lunch, at 7 and 11 am) with the same carbohydrate content (70 g). Plasma glucose and insulin were measured during each meal to estimate the oral glucose minimal model SI (SIMM). CGM and CSII data were used for SISP calculation, which was then validated against the gold standard SIMM. Results: SISP was estimated with good precision (median coefficient of variation <20%) in 100% of the real and 91% of the simulated meals. SISP and SIMM were highly correlated, both in the simulated and real datasets (R = 0.82 and R = 0.83, P < 0.001), and exhibited a similar intraday pattern. Conclusions: SISP is suitable for estimating SI in both closed- and open-loop settings, provided that the subject wears a CGM sensor and a subcutaneous insulin pump.

Identifiability Analysis of Three Control-Oriented Models for Use in Artificial Pancreas Systems.

Our aim is to analyze the identifiability of three commonly used control-oriented models for glucose control in patients with type 1 diabetes (T1D). Structural and practical identifiability analysis were performed on three published control-oriented models for glucose control in patients with type 1 diabetes (T1D): the subcutaneous oral glucose minimal model (SOGMM), the intensive control insulin-nutrition-glucose (ICING) model, and the minimal model control-oriented (MMC). Structural identifiability was addressed with a combination of the generating series (GS) approach and identifiability tableaus whereas practical identifiability was studied by means of (1) global ranking of parameters via sensitivity analysis together with the Latin hypercube sampling method (LHS) and (2) collinearity analysis among parameters. For practical identifiability and model identification, continuous glucose monitor (CGM), insulin pump, and meal records were selected from a set of patients (n = 5) on continuous subcutaneous insulin infusion (CSII) that underwent a clinical trial in an outpatient setting. The performance of the identified models was analyzed by means of the root mean square (RMS) criterion. A reliable set of identifiable parameters was found for every studied model after analyzing the possible identifiability issues of the original parameter sets. According to an importance factor ([Formula: see text]), it was shown that insulin sensitivity is not the most influential parameter from the dynamical point of view, that is, is not the parameter impacting the outputs the most of the three models, contrary to what is assumed in the literature. For the test data, the models demonstrated similar performance with most RMS values around 20 mg/dl (min: 15.64 mg/dl, max: 51.32 mg/dl). However, MMC failed to identify the model for patient 4. Also, considering the three models, the MMC model showed the higher parameter variability when reidentified every 6 hours. This study shows that both structural and practical identifiability analysis need to be considered prior to the model identification/individualization in patients with T1D. It was shown that all the studied models are able to represent the CGM data, yet their usefulness in a hypothetical artificial pancreas could be a matter of debate. In spite that the three models do not capture all the dynamics and metabolic effects as a maximal model (ie, our FDA-accepted UVa/Padova simulator), SOGMM and ICING appear to be more appealing than MMC regarding both the performance and parameter variability after reidentification. Although the model predictions of ICING are comparable to the ones of the SOGMM model, the large parameter set makes the model prone to overfitting if all parameters are identified. Moreover, the existence of a high nonlinear function like [Formula: see text] prevents the use of tools from the linear systems theory.

Glycemic control after metabolic surgery: a Granger causality and graph analysis.

The purpose of this study was to examine the contribution of nonesterified fatty acids (NEFA) and incretin to insulin resistance and diabetes amelioration after malabsorptive metabolic surgery that induces steatorrhea. In fact, NEFA infusion reduces glucose-stimulated insulin secretion, and high-fat diets predict diabetes development. Six healthy controls, 11 obese subjects, and 10 type 2 diabetic (T2D) subjects were studied before and 1 mo after biliopancreatic diversion (BPD). Twenty-four-hour plasma glucose, NEFA, insulin, C-peptide, glucagon-like peptide-1 (GLP-1), and gastric inhibitory polypeptide (GIP) time courses were obtained and analyzed by Granger causality and graph analyses. Insulin sensitivity and secretion were computed by the oral glucose minimal model. Before metabolic surgery, NEFA levels had the strongest influence on the other variables in both obese and T2D subjects. After surgery, GLP-1 and C-peptide levels controlled the system in obese and T2D subjects. Twenty-four-hour GIP levels were markedly reduced after BPD. Finally, not only did GLP-1 levels play a central role, but also insulin and C-peptide levels had a comparable relevance in the network of healthy controls. After BPD, insulin sensitivity was completely normalized in both obese and T2D individuals. Increased 24-h GLP-1 circulating levels positively influenced glucose homeostasis in both obese and T2D subjects who underwent a malabsorptive bariatric operation. In the latter, the reduction of plasma GIP levels also contributed to the improvement of glucose metabolism. It is possible that the combination of a pharmaceutical treatment reducing GIP and increasing GLP-1 plasma levels will contribute to better glycemic control in T2D. The application of Granger causality and graph analyses sheds new light on the pathophysiology of metabolic surgery.

Modeling the oral glucose tolerance test in normal and impaired glucose tolerant states: a population approach

Objective: The conventional approach to analyzing data from oral glucose tolerance testing (OGTT) requires model identification in each individual separately (standard two stage, STS), ignoring knowledge about the population as a whole. In practice, however, the OGTT is sparsely sampled and individual estimates are often not resolvable from available data. This weakness is often encountered in large scale trials or epidemiological studies, leading to either multiple imputations or simply much less data available for analysis.Methods: We have applied a population approach, nonlinear mixed effects modeling, to plasma glucose, insulin and C-peptide data obtained from a 120 minute OGTT undertaken by 106 subjects with varying glucose tolerance. This method provides estimates of population means, variances and covariances of model parameters and empirical Bayes estimates of individual parameter values, as well as measures of intra-individual (within-subject) and inter-individual (between-subject) variability. The recently developed oral glucose minimal model was used to evaluate insulin sensitivity, and a combined model approach was used to assess β-cell secretion.Results: Applying these models allowed for the reconstruction of insulin secretion and glucose absorption profiles and gave population indexes of insulin sensitivity (SI = 6.51 ± 1.20 × 10−4 min−1·μU−1·ml), fractional hepatic extraction of insulin (F = 0.522 ± 0.291) and fractional insulin clearance (kI = 0.258 ± 0.151 min−1). Whereas the traditional approach to parameter estimation failed to recover estimates in more than one third of the population, the population approach provided individual estimates in all subjects. Examination of the empirical Bayes estimates showed that individual parameter estimates were able to differentiate well between individuals at glucose tolerant states ranging from euglycemia to overt type 2 diabetes.Conclusions: Our findings suggest that population analysis is a powerful tool for obtaining accurate assessments of indexes of insulin sensitivity and β-cell function from the OGTT, especially in epidemiological studies with large numbers of sparsely sampled subjects.

Hepatic insulin sensitivity in healthy and prediabetic subjects: from a dual- to a single-tracer oral minimal model.

Recently, a model was proposed to assess hepatic insulin sensitivity during a meal, i.e., the ability of insulin to suppress glucose production (EGP), SI (P). The model was developed on EGP data obtained from a triple-tracer meal and the tracer-to-tracee clamp technique and validated against the euglycemic hyperinsulinemic clamp. The aim of this study was to assess whether SI (P) can be obtained from plasma concentrations measured after a single-tracer meal by incorporating the above EGP model into the oral glucose minimal model by describing both glucose production and disposal (OMM(PD)). Triple-tracer meal data of two databases (20 healthy and 60 healthy and prediabetic subjects) were used. Virtually model-independent EGP estimates were calculated. OMM(PD) was identified on exogenous and endogenous glucose concentrations, providing indices of SI (P), disposal insulin sensitivity (SI (D)), and EGP. The model fitted the data well, and SI (P) and SI (D) were estimated with precision in both databases (SI (P) = 5.48 ± 0.54 10(-4) dl·kg(-1)·min(-1) per μU/ml and SI (D) = 9.93 ± 2.18 10(-4) dl·kg(-1)·min(-1) per μU/ml in healthy; SI (P) = 5.41 ± 3.55 10(-4) dl·kg(-1)·min(-1) per μU/ml and SI (D) = 5.34 ± 6.17 10(-4) dl·kg(-1)·min(-1) per μU/ml, in healthy and prediabetic subjects). Estimated SI (P) and that derived from the triple-tracer EGP model were very similar on average. Moreover, the time course of EGP normalized to basal EGP (EGPb), and EGP/EGPb agreed with the results obtained using the triple-tracer method. In this study, we have demonstrated that SI (P), SI (D), and EGP/EGPb time course can be estimated reliably from a single-tracer meal protocol in both healthy and prediabetic subjects.

Association between thyrotropin levels and insulin sensitivity in euthyroid obese adolescents.

Thyrotropin (TSH) levels display a positive association with body mass index (BMI), and the prevalence of isolated hyperthyrotropinemia is higher in obese adolescents compared to their normal weight controls. However, the metabolic significance of the higher TSH in obese adolescents is less clear. The objective of this study was to determine the relationship between TSH concentrations and insulin sensitivity, lipids, and adipokines in euthyroid, non-diabetic, obese adolescents. Thirty-six euthyroid, non-diabetic, obese adolescents between the ages of 12 and 18 years underwent a 75 g oral glucose tolerance test. Insulin sensitivity (Si) and pancreatic β-cell function as assessed by disposition index (DI) were measured using the oral glucose minimal model approach. Cholesterol (total, low-density lipoprotein [LDL-C], and high-density lipoprotein [HDL-C]), triglycerides (TG), interleukin-6 (IL-6), total and high molecular weight (HMW) adiponectin, and retinol binding protein-4 (RBP4) were also determined. Associations between measures of thyroid function and Si, DI, lipids, and adipokines were computed using Pearson's correlation coefficient and multiple regression analysis. The mean age of the subjects was 14.3±1.88 years, and the mean BMI was 32.5±4.65 kg/m2; 97% were non-Hispanic white and 47% were male. The mean TSH was 2.7±1.2 mIU/L. Increasing serum TSH was correlated with decreasing Si (log Si) in the entire cohort (p=0.03), but this relationship persisted only in males (p=0.02). The correlation between TSH and Si in males remained significant after adjusting for BMI (p=0.02). There was no correlation between TSH and pancreatic β-cell function as assessed by DI (p=0.48). TSH correlated positively with LDL-C (p=0.04) and IL-6 (p=0.03), but these associations vanished or weakened after adjusting for BMI (LDL-C p-value=0.44; IL-6 p-value=0.07). This study suggests a sex-specific association between TSH and insulin sensitivity in euthyroid, non-diabetic, obese adolescent males. Prospective studies are warranted to explore further this sexual dimorphism in the relationship between thyroid function and insulin sensitivity and to determine if obese adolescents with insulin resistance receiving thyroid supplements for hypothyroidism would benefit from targeting TSH levels in the lower half of normal range.

Circadian variability of insulin sensitivity: physiological input for in silico artificial pancreas.

Closed-loop control clinical research trials have been considerably accelerated by in silico trials using the Food and Drug Administration-accepted type 1 diabetes mellitus (T1DM) simulator. We have recently demonstrated that postprandial insulin sensitivity (SI) in T1DM subjects was lower at breakfast (B) than lunch (L) and dinner (D), but not significantly, because of the small population size. The goal of this study was therefore to incorporate this novel information into the University of Virginia/Padova T1DM simulator and to reproduce in silico the observed circadian variability. Twenty T1DM subjects received an identical mixed meal at B, L, and D. SI was calculated for each meal using the oral glucose minimal model. Seven SI daily patterns were identified, and their probabilities were estimated. Each in silico subject was linked to a time-varying SI profile, while random deviations of up to 40% were allowed. Simulations were compared with experimental data. The integrated area above the basal glucose curve values were 2.60 ± 0.91 (B), 1.38 ± 0.91 (L), and 1.44 ± 1.07 (D) 10(4) min · mg/dL in silico versus 2.87 ± 1.65 (B), 1.98 ± 1.56 (L), and 2.16 ± 2.00 (D) 10(4) min · mg/dL in vivo. Incremental peak glucose values were 109 ± 33 (B), 80 ± 29 (L), and 81 ± 30 (D) mg/dL in silico versus 136 ± 39 (B), 126 ± 37 (L), and 125 ± 48 (D) mg/dL in vivo. The incorporation of a time-varying SI into the simulator makes this technology suitable for running multiple-meal scenarios, thus enabling a more robust design of artificial pancreas algorithms.

A concerted decline in insulin secretion and action occurs across the spectrum of fasting and postchallenge glucose concentrations

Individuals with impaired fasting glucose (IFG) are at increased risk of developing diabetes over the subsequent decade. However, there is uncertainty as to the mechanisms contributing to the development of diabetes. We sought to quantitate insulin secretion and action across the prediabetic range of fasting glucose. We studied a cohort of 173 individuals with a fasting glucose concentration <7·0 mM after an overnight fast using a 75-g oral glucose tolerance test (OGTT). Insulin action (S(i)) was estimated using the oral glucose minimal model, and β-cell responsivity indices (φ) were estimated using the oral C-peptide minimal model. The disposition index (DI) for each individual was calculated. The relationship of DI, φ and S(i) with fasting and postchallenge glucose, as well as other covariates, was explored using a generalized linear regression model. In this cross-sectional study, S(i) and DI were inversely related to fasting glucose concentrations. On the other hand, φ was unrelated to fasting glucose concentrations. S(i), φ and DI were all inversely related to area above basal glucose concentrations after glucose challenge. Multiple parameters including body composition and gender contributed to the variability of S(i) and DI at a given fasting or postchallenge glucose concentration. Defects in insulin secretion and action interact with body composition and gender to influence postchallenge glucose concentrations. There is considerable heterogeneity of insulin secretion and action for a given fasting glucose likely because of patient subsets with isolated IFG and normal glucose tolerance.

Response letter to comment on: Villareal et al. (2009) TCF7L2 Variant rs7903146 Affects the Risk of Type 2 Diabetes by Modulating Incretin Action. Diabetes; 59:479–485

We appreciate Knop's (1) interest in our recent article (2). Although his suggestion that the impaired incretin response observed in subjects with the TCF7L2 variant rs7903146 may be due to insulin resistance and impaired glucose tolerance (IGT) is interesting, this hypothesis is not supported by our data. Insulin sensitivity was measured in both groups using the oral glucose minimal model, which has been validated against the clamp technique (3), and no differences were found (14.2 vs. 15.3 × 103 min−1 per pmol/l; P = 0.42). Although there was a tendency toward a higher BMI in the group with the TCF7L2 variant, the difference did not reach statistical significance. …

Use of oral glucose minimal model–derived index of insulin sensitivity in subjects with early type 1 diabetes mellitus

Insulin resistance plays an important role during various stages of the type 1 diabetes mellitus disease process. Unfortunately, many of the techniques used to measure insulin sensitivity are experimentally laborious and time-consuming and are thus impractical for larger clinical and population studies. Therefore, in this study, we obtain estimates of insulin sensitivity from a simpler experiment, the oral glucose tolerance test (OGTT), and compare them with those from a frequently sampled intravenous glucose tolerance test (FSIGT) in a population of subjects defined as having early type 1 diabetes mellitus (abnormal 2-hour glucose on OGTT) and a group of healthy controls. A total of 19 subjects were studied. Eight antibody-positive first- or second-degree relatives of patients with type 1 diabetes mellitus and 11 healthy controls underwent both a 3-hour OGTT and an insulin-modified FSIGT on separate days. Indices of insulin sensitivity ( S I) were estimated from the recently derived oral glucose minimal model and the original minimal model of glucose kinetics for the OGTT and FSIGT, respectively. Estimates of S I from the OGTT correlated closely with those from the FSIGT in both early type 1 diabetes mellitus ( r s=0.76, P = .04) and healthy control ( r s = 0.67, P = .03) populations. This preliminary study demonstrates the usefulness of OGTT-derived estimates of insulin sensitivity in an early type 1 diabetes mellitus population. Given the simplicity of the OGTT relative to the traditional methods of measuring S I, the oral glucose minimal model may be appropriate for large population studies and clinical trials.

Insulin sensitivity by oral glucose minimal models: validation against clamp

Measuring insulin sensitivity in the presence of physiological changes in glucose and insulin concentrations, e.g., during a meal or OGTT, is important to better understand insulin resistance in a variety of metabolic conditions. Recently, two oral minimal models have been proposed to measure overall insulin sensitivity (S(I)) and its selective effect on glucose disposal (S(I)*) from oral tests. S(I) and S(I)* have been successfully validated against multiple tracer meal estimates, but validation against euglycemic hyperinsulinemic clamp estimates is lacking. Here, we do so in 21 subjects who underwent both a multiple-tracer OGTT and a labeled euglycemic hyperinsulinemic clamp. Correlation between minimal-model S(I), S(I) and corresponding clamp estimates S(I)(*clamp), S(I)(*clamp) was satisfactory, respectively r = 0.81, P < 0.001, and r = 0.71, P < 0.001. S(I) was significantly lower than S(I)(clamp) (8.08 +/- 0.89 vs. 13.66 +/- 1.69 10(-4) dl.kg(-1).min(-1) per microU/ml, P = 0.0002), whereas S(I) and S(I)(*clamp) were very similar (8.17 +/- 1.59 vs. 8.84 +/- 1.39 10(-4) dl.kg(-1).min(-1) per microU/ml, P = 0.52). These results add credibility to the oral minimal-model method as a simple and reliable physiological tool to estimate S(I) and S(I)*, also in large-scale clinical trials.

Measurement of selective effect of insulin on glucose disposal from labeled glucose oral test minimal model

The oral glucose minimal model (OMM) measures insulin sensitivity (S(I)) and the glucose rate of appearance (R(a)) of ingested glucose in the presence of physiological changes of insulin and glucose concentrations. However, S(I) of OMM measures the overall effect of insulin on glucose utilization and glucose production. In this study we show that, by adding a tracer to the oral dose, e.g., of a meal, and by using the labeled version of OMM, OMM* to interpret the data, one can measure the selective effect of insulin on glucose disposal, S(I)*. Eighty-eight individuals underwent both a triple-tracer meal with the tracer-to-tracee clamp technique, providing a model-independent reference of the R(a) of ingested glucose (R(a meal)(ref)) and an insulin-modified labeled intravenous glucose tolerance test (IVGTT*). We show that OMM* provides not only a reliable means of tracing the R(a) of ingested glucose (R(a meal)) but also accurately measures S(I)*. We do so by comparing OMM* R(a meal) with the model-independent R(a meal)(ref) provided by the tracer-to-tracee clamp technique, while OMM* S(I)* is compared with both S(I)(* ref), obtained by using as known input R(a meal)(ref), and with S(I)* measured during IVGTT*.