Man, Chiara Dalla

Background: A simulation model of the glucose—insulin system in normal life conditions can be very useful in diabetes research, e.g., testing insulin infusion algorithms and decision support systems and assessing glucose sensor performance and patient and student training. A new meal simulation model has been proposed that incorporates state-of-the-art quantitative knowledge on glucose metabolism and its control by insulin at both organ/tissue and whole-body levels. This article presents the interactive simulation software GIM (glucose insulin model), which implements this model. Methods: The model is implemented in MATLAB, version 7.0.1, and is designed with a windows interface that allows the user to easily simulate a 24-hour daily life of a normal, type 2, or type 1 diabetic subject. A Simulink version is also available. Three meals a day are considered. Both open- and closed-loop controls are available for simulating a type 1 diabetic subject. Results: Software options are described in detail. Case studies are presented to illustrate the potential of the software, e.g., compare a normal subject vs an insulin-resistant subject or open-loop vs closed-loop insulin infusion in type 1 diabetes treatment. Conclusions: User-friendly software that implements a state-of-the-art physiological model of the glucose-insulin system during a meal has been presented. The GIM graphical interface makes its use extremely easy for investigators without specific expertise in modeling.

OBJECTIVE The goal was to develop a new index of insulin sensitivity in patients with type 1 diabetes estimated from continuous glucose monitoring (CGM) and subcutaneous insulin delivery data under carefully controlled conditions. RESEARCH DESIGN AND METHODS The database consists of 12 subjects with type 1 diabetes, studied during breakfast, lunch, and dinner, in a clinical research unit, wearing both subcutaneous insulin pump and CGM device. Frequent blood samples were drawn for measurements of plasma glucose and insulin concentrations in order to estimate insulin sensitivity with the oral minimal model (SIMM). The new index of insulin sensitivity (SISP) was calculated with a simple algebraic formula for each meal, using only CGM and insulin pump data and compared with SIMM. RESULTS SI SP was well correlated with SIMM (r = 0.825; P < 10−8), and diurnal pattern was also similar to SIMM. CONCLUSIONS A novel method for estimating insulin sensitivity in subjects with type 1 diabetes on sensor-augmented insulin pump therapy has been presented. This new index correlates well with the reference oral minimal model estimate of insulin sensitivity. The knowledge of patient-specific insulin sensitivity and its diurnal variation can help in optimizing insulin therapy in type 1 diabetes and could also inform next-generation closed-loop control systems.

The contribution of elevated glucagon-like peptide 1 (GLP-1) to postprandial glucose metabolism after Roux-en-Y gastric bypass (RYGB) has been the subject of uncertainty. We used exendin-9,39, a competitive antagonist of GLP-1, to examine glucose metabolism, islet hormone secretion, and gastrointestinal transit in subjects after RYGB and in matched control subjects. Subjects were studied in the presence or absence of exendin-9,39 infused at 300 pmol/kg/min. Exendin-9,39 resulted in an increase in integrated postprandial glucose concentrations post-RYGB (3.6 ± 0.5 vs. 2.0 ± 0.4 mol/6 h, P = 0.001). Exendin-9,39 decreased insulin concentrations (12.3 ± 2.2 vs. 18.1 ± 3.1 nmol/6 h, P = 0.002) and the β-cell response to glucose (ϕTotal, 13 ± 1 vs. 11 ± 1 × 10−9 min−1, P = 0.01) but did not alter the disposition index (DI). In control subjects, exendin-9,39 also increased glucose (2.2 ± 0.4 vs. 1.7 ± 0.3 mol/6 h, P = 0.03) without accompanying changes in insulin concentrations, resulting in an impaired DI. Post-RYGB, acceleration of stomach emptying during the first 30 min by exendin-9,39 did not alter meal appearance, and similarly, suppression of glucose production and stimulation of glucose disappearance were unaltered in RYGB subjects. These data indicate that endogenous GLP-1 has effects on glucose metabolism and on gastrointestinal motility years after RYGB. However, it remains uncertain whether this explains all of the changes after RYGB.

Background: In type 1 diabetes mellitus (T1DM), physical activity (PA) lowers the risk of cardiovascular complications but hinders the achievement of optimal glycemic control, transiently boosting insulin action and increasing hypoglycemia risk. Quantitative investigation of relationships between PA-related signals and glucose dynamics, tracked using, for example, continuous glucose monitoring (CGM) sensors, have been barely explored. Subjects and Methods: In the clinic, 20 control and 19 T1DM subjects were studied for 4 consecutive days. They underwent low-intensity PA sessions daily. PA was tracked by the PA monitoring system (PAMS), a system comprising accelerometers and inclinometers. Variations on glucose dynamics were tracked estimating first- and second-order time derivatives of glucose concentration from CGM via Bayesian smoothing. Short-time effects of PA on glucose dynamics were quantified through the partial correlation function in the interval (0, 60 min) after starting PA. Results: Correlation of PA with glucose time derivatives is evident. In T1DM, the negative correlation with the first-order glucose time derivative is maximal (absolute value) after 15 min of PA, whereas the positive correlation is maximal after 40–45 min. The negative correlation between the second-order time derivative and PA is maximal after 5 min, whereas the positive correlation is maximal after 35–40 min. Control subjects provided similar results but with positive and negative correlation peaks anticipated of 5 min. Conclusions: Quantitative information on correlation between mild PA and short-term glucose dynamics was obtained. This represents a preliminary important step toward incorporation of PA information in more realistic physiological models of the glucose–insulin system usable in T1DM simulators, in development of closed-loop artificial pancreas control algorithms, and in CGM-based prediction algorithms for generation of hypoglycemic alerts.

Exendin-(9,39) is a competitive antagonist of glucagon-like peptide-1 (GLP-1) at its receptor. However, it is unclear if it has direct and unique effects of its own. We tested the hypothesis that exendin-(9,39) and GLP-1-(9,36)amide have direct effects on hormone secretion and β-cell function as well as glucose metabolism in healthy subjects. Glucose containing [3-3H]glucose was infused to mimic the systemic appearance of glucose after a meal. Saline, GLP-1-(9,36)amide, or exendin-(9,39) at 30 pmol/kg/min (Ex 30) or 300 pmol/kg/min (Ex 300) were infused in random order on separate days. Integrated glucose concentrations were slightly but significantly increased by exendin-(9,39) (365 ± 43 vs. 383 ± 35 vs. 492 ± 49 vs. 337 ± 50 mmol per 6 h, saline, Ex 30, Ex 300, and GLP-1-[9,36]amide, respectively; P = 0.05). Insulin secretion did not differ among groups. However, insulin action was lowered by exendin-(9,39) (25 ± 4 vs. 20 ± 4 vs. 18 ± 3 vs. 21 ± 4 10−4 dL/kg[min per μU/mL]; P = 0.02), resulting in a lower disposition index (DI) during exendin-(9,39) infusion (1,118 ± 118 vs. 816 ± 83 vs. 725 ± 127 vs. 955 ± 166 10−14 dL/kg/min2 per pmol/L; P = 0.003). Endogenous glucose production and glucose disappearance did not differ significantly among groups. We conclude that exendin-(9,39), but not GLP-1-(9,36)amide, decreases insulin action and DI in healthy humans.

We recently demonstrated a diurnal pattern to insulin action (i.e., insulin sensitivity [SI]) in healthy individuals with higher SI at breakfast than at dinner. To determine whether such a pattern exists in type 1 diabetes, we studied 19 subjects with C-peptide–negative diabetes (HbA1c 7.1 ± 0.6%) on insulin pump therapy with normal gastric emptying. Identical mixed meals were ingested during breakfast, lunch, and dinner at 0700, 1300, and 1900 h in randomized Latin square of order on 3 consecutive days when measured daily physical activity was equal. The triple tracer technique enabled measurement of glucose fluxes. Insulin was administered according to the customary insulin:carbohydrate ratio for each participant. Although postprandial glucose excursions did not differ among meals, insulin concentration was higher (P < 0.01) and endogenous glucose production less suppressed (P < 0.049) at breakfast than at lunch. There were no differences in meal glucose appearance or in glucose disappearance between meals. Although there was no statistical difference (P = 0.34) in SI between meals in type 1 diabetic subjects, the diurnal pattern of SI taken across the three meals in its entirety differed (P = 0.016) from that of healthy subjects. Although the pattern in healthy subjects showed decreasing SI between breakfast and lunch, the reverse SI pattern was observed in type 1 diabetic subjects. The results suggest that in contrast to healthy subjects, SI diurnal pattern in type 1 diabetes is specific to the individual and cannot be extrapolated to the type 1 diabetic population as a whole, implying that artificial pancreas algorithms may need to be personalized.

Recently, we proposed a model describing the suppression of endogenous glucose production (EGP) during a meal. It assumes that EGP suppression depends on glucose concentration and its rate of change and on delayed insulin action. Hepatic insulin sensitivity (SILmeal) can be derived from EGP model parameters. This model was shown to adequately describe EGP profiles measured with multiple tracer techniques; however, SILmealhas never been compared directly with its euglycemic hyperinsulinemic clamp counterpart (SILclamp). To do so, 62 subjects with different degrees of glucose tolerance underwent a triple-tracer mixed meal. Fifty-seven subjects also underwent a labeled ([3-3H]glucose) euglycemic hyperinsulinemic clamp. From the triple-tracer meal data, virtually model-independent estimates of EGP were obtained using the tracer-to-tracee clamp technique, and the EGP model was identified in each subject. Model fit was satisfactory, and SILmealwas estimated with good precision. Correlation between SILclampand SILmealwas good ( r = 0.72, P < 0.001); however, SILmealwas lower than SILclamp(4.60 ± 0.64 vs. 8.73 ± 1.07 10−4dl·kg−1·min−1per μU/ml, P < 0.01). This difference may be due to different ranges of insulin explored during the two tests (ΔIclamp= 15.60 ± 1.61 vs. ΔImeal= 83.37 ± 10.71 μU/ml) as well as steady- vs. non-steady-state glucose and insulin profiles. In conclusion, the new EGP model provides an estimate of hepatic insulin sensitivity during a meal that is in good agreement with that derived in the same individuals with a hyperinsulinemic clamp. When used in conjunction with the minimal model, the approach potentially enables estimation of hepatic insulin sensitivity from a single-tracer labeled meal or oral glucose tolerance test.

We designed an experiment to examine the effect of bile acid sequestration with Colesevelam on fasting and postprandial glucose metabolism in type 2 diabetes. To do so, we tested the hypothesis that Colesevelam increases the disposition index (DI), and this increase is associated with increased glucagon-like peptide-1 (GLP-1) concentrations. Thirty-eight subjects on metformin monotherapy were studied using a double-blind, placebo-controlled, parallel-group design. Subjects were studied before and after 12 weeks of Colesevelam or placebo using a labeled triple-tracer mixed meal to measure the rate of meal appearance (Meal Ra), endogenous glucose production (EGP), and glucose disappearance (Rd). Insulin sensitivity and β-cell responsivity indices were estimated using the oral minimal model and then used to calculate DI. Therapy with Colesevelam was associated with a decrease in fasting (7.0 ± 0.2 vs. 6.6 ± 0.2 mmol/L; P = 0.004) and postprandial glucose concentrations (3,145 ± 138 vs. 2,896 ± 127 mmol/6 h; P = 0.01) in the absence of a change in insulin concentrations. Minimal model–derived indices of insulin secretion and action were unchanged. Postprandial GLP-1 concentrations were not altered by Colesevelam. Although EGP and Rd were unchanged, integrated Meal Ra was decreased by Colesevelam (5,191 ± 204 vs. 5,817 ± 204 μmol/kg/6 h; P = 0.04), suggesting increased splanchnic sequestration of meal-derived glucose.

OBJECTIVE Physical activity (PA), even at low intensity, promotes health and improves hyperglycemia. However, the effect of low-intensity PA captured with accelerometery on glucose variability in healthy individuals and patients with type 1 diabetes has not been examined. Quantifying the effects of PA on glycemic variability would improve artificial endocrine pancreas (AEP) algorithms. RESEARCH DESIGN AND METHODS We studied 12 healthy control subjects (five males, 37.7 ± 13.7 years of age) and 12 patients with type 1 diabetes (five males, 37.4 ± 14.2 years of age) for 88 h. Participants performed PA approximating a threefold increase over their basal metabolic rate. PA was captured using a PA-monitoring system, and interstitial fluid glucose concentrations were captured with continuous glucose monitors. In random order, one meal per day was followed by inactivity, and the other meals were followed by walking. Glucose and PA data for a total of 216 meals were analyzed from 30 min prior to meal ingestion to 270 min postmeal. RESULTS In healthy subjects, the incremental glucose area under the curve was 4.5 mmol/L/270 min for meals followed by walking, whereas it was 9.6 mmol/L/270 min (P = 0.022) for meals followed by inactivity. The corresponding glucose excursions for those with type 1 diabetes were 7.5 mmol/L/270 min and 18.4 mmol/L/270 min, respectively (P &lt; 0.001). CONCLUSIONS Walking significantly impacts postprandial glucose excursions in healthy populations and in those with type 1 diabetes. AEP algorithms incorporating PA may enhance tight glycemic control end points.

The mechanisms by which common genetic variation predisposes to type 2 diabetes remain unclear. The disease-associated variants in TCF7L2 (rs7903146) and WFS1 (rs10010131) have been shown to affect response to exogenous glucagon-like peptide 1 (GLP-1), while variants in KCNQ1 (rs151290, rs2237892, and rs2237895) alter endogenous GLP-1 secretion. We set out to validate these observations using a model of GLP-1–induced insulin secretion. We studied healthy individuals using a hyperglycemic clamp and GLP-1 infusion. In addition, we measured active and total GLP-1 in response to an oral challenge in nondiabetic subjects. After genotyping the relevant single nucleotide polymorphisms, generalized linear regression models and repeated-measures ANCOVA models incorporating potential confounders, such as age and BMI, were used to assess the associations, if any, of response with genotype. These variants did not alter GLP-1 concentrations in response to oral intake. No effects on β-cell responsiveness to hyperglycemia and GLP-1 infusion were apparent. Diabetes-associated variation (T allele at rs7903146) in TCF7L2 may impair the ability of hyperglycemia to suppress glucagon (45 ± 2 vs. 47 ± 2 vs. 60 ± 5 ng/L for CC, CT, and TT, respectively, P = 0.02). In nondiabetic subjects, diabetes-associated genetic variation does not alter GLP-1 concentrations after an oral challenge or its effect on insulin secretion.