Dynamics of insulin action in hypertension: assessment from minimal model interpretation of intravenous glucose tolerance test data

Assessment of glucose effectiveness from short IVGTT in individuals with different degrees of glucose tolerance

AIMS

Minimal model analysis of intravenous glucose tolerance test (IVGTT) data represents the reference method to assess insulin sensitivity (S_I) and glucose effectiveness (S_G) that quantify the insulin-dependent and insulin-independent processes of glucose disappearance, respectively. However, test duration (3 h) and need for modeling expertise limit the applicability of this method. Aim of this study was providing a simple predictor of S_G applicable to short test (1 h), as previously done with S_I.

METHODS

Three groups of subjects reflecting different glucose tolerance degrees underwent a 3 h IVGTT: subjects with normal glucose tolerance (NGT, n = 164), with defective glucose regulation (DGR, n = 191), and with type 2 diabetes (T2D, n = 39). Minimal model analysis provided reference S_G and its components at zero (GEZI) and basal (BIE) insulin. The simple predictor CS_G (calculated S_G) was described by the formula CS_G = α₀ + α₁ × K_G/G_peak, being K_G the glucose disappearance rate (between 10 and 50 min) and G_peak the maximum of the glucose curve during the test; α₀ and α₁ coefficients were provided by linear regression analysis.

RESULTS

CS_G and S_G showed a markedly significant relationship in the whole dataset (r = 0.72, p < 0.0001) and in the single groups (r = 0.70 in NGT, r = 0.71 in DGR and r = 0.70 in T2D, p < 0.0001 for all); α₁ × K_G/G_peak was significantly related to GEZI (r ≥ 0.60).

CONCLUSIONS

The interest for insulin-independent glucose disappearance is increasing, due to the recent availability of SGLT2 pharmacological agents, lowering glycemic levels without requiring insulin action. This study proposes a reliable predictor of S_G based on IVGTT lasting 1 h only, and not requiring mathematical modeling skills.

IVGTT-based simple assessment of glucose tolerance in the Zucker fatty rat: Validation against minimal models

For the assessment of glucose tolerance from IVGTT data in Zucker rat, minimal model methodology is reliable but time- and money-consuming. This study aimed to validate for the first time in Zucker rat, simple surrogate indexes of insulin sensitivity and secretion against the glucose-minimal-model insulin sensitivity index (SI) and against first- (Φ1) and second-phase (Φ2) β-cell responsiveness indexes provided by C-peptide minimal model. Validation of the surrogate insulin sensitivity index (ISI) and of two sets of coupled insulin-based indexes for insulin secretion, differing from the cut-off point between phases (FPIR3-SPIR3, t = 3 min and FPIR5-SPIR5, t = 5 min), was carried out in a population of ten Zucker fatty rats (ZFR) and ten Zucker lean rats (ZLR). Considering the whole rat population (ZLR+ZFR), ISI showed a significant strong correlation with SI (Spearman's correlation coefficient, r = 0.88; P<0.001). Both FPIR3 and FPIR5 showed a significant (P<0.001) strong correlation with Φ1 (r = 0.76 and r = 0.75, respectively). Both SPIR3 and SPIR5 showed a significant (P<0.001) strong correlation with Φ2 (r = 0.85 and r = 0.83, respectively). ISI is able to detect (P<0.001) the well-recognized reduction in insulin sensitivity in ZFRs, compared to ZLRs. The insulin-based indexes of insulin secretion are able to detect in ZFRs (P<0.001) the compensatory increase of first- and second-phase secretion, associated to the insulin-resistant state. The ability of the surrogate indexes in describing glucose tolerance in the ZFRs was confirmed by the Disposition Index analysis. The model-based validation performed in the present study supports the utilization of low-cost, insulin-based indexes for the assessment of glucose tolerance in Zucker rat, reliable animal model of human metabolic syndrome.

Short-term glucagon stimulation test of C-peptide effect on glucose utilization in patients with type 1 diabetes mellitus

This work aimed to evaluate the use of a four-point glucagon stimulation test of C-peptide effect on glucose utilization in type 1 diabetic patients using a new mathematical model. A group of 32 type 1 diabetic patients and a group of 10 healthy control subjects underwent a four-point glucagon stimulation test with blood sampling at 0, 6, 15 and 30 min after 1 mg glucagon bolus intravenous administration. Pharmacokinetic and pharmacokinetic/pharmacodynamic models of C-peptide effect on glucose utilization versus area under curve (AUC) were used. A two-sample t test and ANOVA with Bonferroni correction were used to test the significance of differences between parameters. A significant difference between control and patient groups regarding the coefficient of whole-body glucose utilization and AUC C-peptide/AUC glucose ratio (p ≪ 0.001 and p = 0.002, respectively) was observed. The high correlation (r = 0.97) between modeled coefficient of whole-body glucose utilization and numerically calculated AUC C-peptide/AUC glucose ratio related to entire cohort indicated the stability of used method. The short-term four-point glucagon stimulation test allows the numerically calculated AUC C-peptide/AUC glucose ratio and/or the coefficient of whole-body glucose utilization calculated from model to be used to diagnostically identify type 1 diabetic patients.

Non-invasive continuous glucose monitoring: improved accuracy of point and trend estimates of the Multisensor system

Non-invasive continuous glucose monitoring (NI-CGM) sensors are still at an early stage of development, but, in the near future, they could become particularly appealing in diabetes management. Solianis Monitoring AG (Zurich, Switzerland) has proposed an approach for NI-CGM based on a multi-sensor concept, embedding primarily dielectric spectroscopy and optical sensors. This concept requires a mathematical model able to estimate glucose levels from the 150 channels directly measured through the Multisensor. A static multivariate linear regression model (with order and parameters common to the entire population of subjects) was proposed for such a scope (Caduff et al., Biosens Bioelectron 26:3794-3800, 2011). The aim of this work is to evaluate the accuracy in the estimation of glucose levels and trends that the NI-CGM Multisensor platform can achieve by exploiting different techniques for model identification, namely, ordinary least squares, subset variable selection, partial least squares and least absolute shrinkage and selection operator (LASSO). Data collected in human beings monitored for a total of 45 study days were used for model identification and model test. Several metrics of standard use in the diabetes scientific community to measure point and clinical accuracy of glucose sensors were used to assess the models. Results indicate that the LASSO technique is superior to the others shrinking many channel weights to zero thus leading to smoother glucose profiles and resulting in a more robust model to possible artifacts in the Multisensor data. Although, as expected, the performance of the NI-CGM system with the LASSO model is not yet comparable with that of enzyme-based needle glucose sensors, glucose trends are satisfactorily estimated. Considering the non-invasive nature of the multi-sensor platform, this result can have an immediate impact in the current clinical practice, e.g., to integrate sparse self-monitoring of blood glucose data with an indication of the glucose trend to aid the diabetic patient in dealing with, or even preventing in the short time scale, the threats of critical events such as hypoglycaemia.