Cobelli, Claudio

To provide an understanding of both the preclinical and clinical aspects of closed-loop artificial pancreas systems, we provide a discussion of this topic as part of this two-part Bench to Clinic narrative. Here, the Bench narrative provides an in-depth understanding of insulin-glucose-glucagon physiology in conditions that mimic the free-living situation to the extent possible in type 1 diabetes that will help refine and improve future closed-loop system algorithms. In the Clinic narrative, Doyle and colleagues compare and evaluate technology used in current closed-loop studies to gain further momentum toward outpatient trials and eventual approval for widespread use.

The simultaneous assessment of insulin action, secretion, and hepatic extraction is key to understanding postprandial glucose metabolism in nondiabetic and diabetic humans. We review the oral minimal method (i.e., models that allow the estimation of insulin sensitivity, β-cell responsivity, and hepatic insulin extraction from a mixed-meal or an oral glucose tolerance test). Both of these oral tests are more physiologic and simpler to administer than those based on an intravenous test (e.g., a glucose clamp or an intravenous glucose tolerance test). The focus of this review is on indices provided by physiological-based models and their validation against the glucose clamp technique. We discuss first the oral minimal model method rationale, data, and protocols. Then we present the three minimal models and the indices they provide. The disposition index paradigm, a widely used β-cell function metric, is revisited in the context of individual versus population modeling. Adding a glucose tracer to the oral dose significantly enhances the assessment of insulin action by segregating insulin sensitivity into its glucose disposal and hepatic components. The oral minimal model method, by quantitatively portraying the complex relationships between the major players of glucose metabolism, is able to provide novel insights regarding the regulation of postprandial metabolism.

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.

People with NIDDM are resistant to insulin. The present studies sought to determine whether the ability of glucose to regulate its own metabolism in the presence of basal insulin concentrations is impaired. To address this question, basal insulin concentrations were maintained constant with an exogenous insulin infusion, while endogenous hormone secretion was inhibited by somatostatin. The integrated glycemic response above baseline during identical prandial glucose infusions was greater (1,411 ± 94 vs. 938 ± 45 mmol/l per 5 h; P < 0.01) in the diabetic subjects than in the nondiabetic subjects, indicating a decrease in net glucose effectiveness. [6-3H]glucose also was infused to determine whether the decrease in net glucose effectiveness was due to a decrease in the ability of glucose to stimulate its own uptake and/or to suppress its own production. Despite identical rates of tracer infusion, the increment in plasma concentration of [6-3H]glucose was higher (4.50 ± 0.29 vs. 3.16 ± 0.21 × 105 dpm/ml per 5 h; P < 0.05) in the diabetic subjects than in the nondiabetic subjects. This was due to both a decrease (P < 0.05) in the ability of glucose to stimulate its own disappearance via mass action and to a greater (P < 0.01) inhibitory effect of glucose on its own clearance. The increase in glucose concentration resulted in prompt and comparable suppression of endogenous glucose production in both groups. Under these optimized conditions, indexes of glucose effectiveness calculated with both the “cold” and “hot” minimal models also were lower (P < 0.05) in the diabetic subjects than in the nondiabetic subjects and were highly correlated (r = 0.94–0.99; P < 0.001) with the indexes of glucose effectiveness calculated from the increments above baseline of glucose and [6-3H]glucose concentration. We conclude that the ability of glucose to regulate its own metabolism in the presence of basal insulin concentrations is abnormal in people with NIDDM.

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 μmol/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.

The accuracy of continuous interstitial fluid (ISF) glucose sensing is an essential component of current and emerging open- and closed-loop systems for type 1 diabetes. An important determinant of sensor accuracy is the physiological time lag of glucose transport from the vascular to the interstitial space. We performed the first direct measurement of this phenomenon to our knowledge in eight healthy subjects under an overnight fasted condition. Microdialysis catheters were inserted into the abdominal subcutaneous space. After intravenous bolus administrations of glucose tracers, timed samples of plasma and ISF were collected sequentially and analyzed for tracer enrichments. After accounting for catheter dead space and assay noise, the mean time lag of tracer appearance in the interstitial space was 5.3–6.2 min. We conclude that in the overnight fasted state in healthy adults, the physiological delay of glucose transport from the vascular to the interstitial space is 5–6 min. Physiological delay between blood glucose and ISF glucose, therefore, should not be an obstacle to sensor accuracy in overnight or fasting-state closed-loop systems of insulin delivery or open-loop therapy assessment for type 1 diabetes.

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.