Chiara Dalla Man

Meal Simulation Model of the Glucose-Insulin System

A simulation model of the glucose-insulin system in the postprandial state can be useful in several circumstances, including testing of glucose sensors, insulin infusion algorithms and decision support systems for diabetes. Here, we present a new simulation model in normal humans that describes the physiological events that occur after a meal, by employing the quantitative knowledge that has become available in recent years. Model parameters were set to fit the mean data of a large normal subject database that underwent a triple tracer meal protocol which provided quasi-model-independent estimates of major glucose and insulin fluxes, e.g., meal rate of appearance, endogenous glucose production, utilization of glucose, insulin secretion. By decomposing the system into subsystems, we have developed parametric models of each subsystem by using a forcing function strategy. Model results are shown in describing both a single meal and normal daily life (breakfast, lunch, dinner) in normal. The same strategy is also applied on a smaller database for extending the model to type 2 diabetes.

Increased prevalence of insulin resistance and nonalcoholic fatty liver disease in Asian-Indian men.

Type 2 diabetes mellitus (T2DM) is strongly associated with obesity in most, but not all, ethnic groups, suggesting important ethnic differences in disease susceptibility. Although it is clear that insulin resistance plays a major role in the pathogenesis of T2DM and that insulin resistance is strongly associated with increases in hepatic (HTG) and/or intramyocellular lipid content, little is known about the prevalence of insulin resistance and potential differences in intracellular lipid distribution among healthy, young, lean individuals of different ethnic groups. To examine this question, 482 young, lean, healthy, sedentary, nonsmoking Eastern Asians (n = 49), Asian-Indians (n = 59), Blacks (n = 48), Caucasians (n = 292), and Hispanics (n = 34) underwent an oral glucose tolerance test to assess whole-body insulin sensitivity by an insulin sensitivity index. In addition, intramyocellular lipid and HTG contents were measured by using proton magnetic resonance spectroscopy. The prevalence of insulin resistance, defined as the lower quartile of insulin sensitivity index, was ≈2- to 3-fold higher in the Asian-Indians compared with all other ethnic groups, and this could entirely be attributed to a 3- to 4-fold increased prevalence of insulin resistance in Asian-Indian men. This increased prevalence of insulin resistance in the Asian-Indian men was associated with an ≈2-fold increase in HTG content and plasma IL-6 concentrations compared with Caucasian men. These data demonstrate important ethnic and gender differences in the pathogenesis of insulin resistance in Asian-Indian men and have important therapeutic implications for treatment of T2DM and for the development of steatosis-related liver disease in this ethnic group.

Model Predictive Control of Type 1 Diabetes: An in Silico Trial:

Background: The development of artificial pancreas has received a new impulse from recent technological advancements in subcutaneous continuous glucose monitoring and subcutaneous insulin pump delivery systems. However, the availability of innovative sensors and actuators, although essential, does not guarantee optimal glycemic regulation. Closed-loop control of blood glucose levels still poses technological challenges to the automatic control expert, most notable of which are the inevitable time delays between glucose sensing and insulin actuation. Methods: A new in silico model is exploited for both design and validation of a linear model predictive control (MPC) glucose control system. The starting point is a recently developed meal glucose-insulin model in health, which is modified to describe the metabolic dynamics of a person with type 1 diabetes mellitus. The population distribution of the model parameters originally obtained in healthy 204 patients is modified to describe diabetic patients. Individual models of virtual patients are extracted from this distribution. A discrete-time MPC is designed for all the virtual patients from a unique input-output-linearized approximation of the full model based on the average population values of the parameters. The in silico trial simulates 4 consecutive days, during which the patient receives breakfast, lunch, and dinner each day. Results: Provided that the regulator undergoes some individual tuning, satisfactory results are obtained even if the control design relies solely on the average patient model. Only the weight on the glucose concentration error needs to be tuned in a quite straightforward and intuitive way. The ability of the MPC to take advantage of meal announcement information is demonstrated. Imperfect knowledge of the amount of ingested glucose causes only marginal deterioration of performance. In general, MPC results in better regulation than proportional integral derivative, limiting significantly the oscillation of glucose levels. Conclusions: The proposed in silico trial shows the potential of MPC for artificial pancreas design. The main features are a capability to consider meal announcement information, delay compensation, and simplicity of tuning and implementation.

Fully Integrated Artificial Pancreas in Type 1 Diabetes: Modular Closed-Loop Glucose Control Maintains Near Normoglycemia

Integrated closed-loop control (CLC), combining continuous glucose monitoring (CGM) with insulin pump (continuous subcutaneous insulin infusion [CSII]), known as artificial pancreas, can help optimize glycemic control in diabetes. We present a fundamental modular concept for CLC design, illustrated by clinical studies involving 11 adolescents and 27 adults at the Universities of Virginia, Padova, and Montpellier. We tested two modular CLC constructs: standard control to range (sCTR), designed to augment pump plus CGM by preventing extreme glucose excursions; and enhanced control to range (eCTR), designed to truly optimize control within near normoglycemia of 3.9–10 mmol/L. The CLC system was fully integrated using automated data transfer CGM→algorithm→CSII. All studies used randomized crossover design comparing CSII versus CLC during identical 22-h hospitalizations including meals, overnight rest, and 30-min exercise. sCTR increased significantly the time in near normoglycemia from 61 to 74%, simultaneously reducing hypoglycemia 2.7-fold. eCTR improved mean blood glucose from 7.73 to 6.68 mmol/L without increasing hypoglycemia, achieved 97% in near normoglycemia and 77% in tight glycemic control, and reduced variability overnight. In conclusion, sCTR and eCTR represent sequential steps toward automated CLC, preventing extremes (sCTR) and further optimizing control (eCTR). This approach inspires compelling new concepts: modular assembly, sequential deployment, testing, and clinical acceptance of custom-built CLC systems tailored to individual patient needs.

Downregulation of the Longevity-Associated Protein Sirtuin 1 in Insulin Resistance and Metabolic Syndrome: Potential Biochemical Mechanisms

OBJECTIVE Sirtuins (SIRTs) are NAD+-dependent deacetylases that regulate metabolism and life span. We used peripheral blood mononuclear cells (PBMCs) to determine ex vivo whether insulin resistance/metabolic syndrome influences SIRTs. We also assessed the potential mechanisms linking metabolic alterations to SIRTs in human monocytes (THP-1) in vitro. RESEARCH DESIGN AND METHODS SIRT1-SIRT7 gene and protein expression was determined in PBMCs of 54 subjects (41 with normal glucose tolerance and 13 with metabolic syndrome). Insulin sensitivity was assessed by the minimal model analysis. Subclinical atherosclerosis was assessed by carotid intima-media thickness (IMT). In THP-1 cells exposed to high glucose or fatty acids in vitro, we explored SIRT1 expression, p53 acetylation, Jun NH2-terminal kinase (JNK) activation, NAD+ levels, and nicotinamide phosphoribosyltransferase (NAMPT) expression. The effects of SIRT1 induction by resveratrol and of SIRT1 gene silencing were also assessed. RESULTS In vivo, insulin resistance and metabolic syndrome were associated with low PBMC SIRT1 gene and protein expression. SIRT1 gene expression was negatively correlated with carotid IMT. In THP-1 cells, high glucose and palmitate reduced SIRT1 and NAMPT expression and reduced the levels of intracellular NAD+ through oxidative stress. No effect was observed in cells exposed to linoleate or insulin. High glucose and palmitate increased p53 acetylation and JNK phosphorylation; these effects were abolished in siRNA SIRT1–treated cells. Glucose- and palmitate-mediated effects on NAMPT and SIRT1 were prevented by resveratrol in vitro. CONCLUSIONS Insulin resistance and subclinical atherosclerosis are associated with SIRT1 downregulation in monocytes. Glucotoxicity and lypotoxicity play a relevant role in quenching SIRT1 expression.

GIM, Simulation Software of Meal Glucose—Insulin Model

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.

Multinational Study of Subcutaneous Model-Predictive Closed-Loop Control in Type 1 Diabetes Mellitus: Summary of the Results

Background: In 2008–2009, the first multinational study was completed comparing closed-loop control (artificial pancreas) to state-of-the-art open-loop therapy in adults with type 1 diabetes mellitus (T1DM). Methods: The design of the control algorithm was done entirely in silico, i.e., using computer simulation experiments with N = 300 synthetic “subjects” with T1DM instead of traditional animal trials. The clinical experiments recruited 20 adults with T1DM at the Universities of Virginia (11); Padova, Italy (6); and Montpellier, France (3). Open-loop and closed-loop admission was scheduled 3–4 weeks apart, continued for 22 h (14.5 h of which were in closed loop), and used a continuous glucose monitor and an insulin pump. The only difference between the two sessions was that insulin dosing was performed by the patient under a physicians supervision during open loop, whereas insulin dosing was performed by a control algorithm during closed loop. Results: In silico design resulted in rapid (less than 6 months compared to years of animal trials) and cost-effective system development, testing, and regulatory approvals in the United States, Italy, and France. In the clinic, compared to open-loop, closed-loop control reduced nocturnal hypoglycemia (blood glucose below 3.9 mmol/liter) from 23 to 5 episodes (p < .01) and increased the amount of time spent overnight within the target range (3.9 to 7.8 mmol/liter) from 64% to 78% (p = .03). Conclusions: In silico experiments can be used as viable alternatives to animal trials for the preclinical testing of insulin treatment strategies. Compared to open-loop treatment under identical conditions, closed-loop control improves the overnight regulation of diabetes.

Pathogenesis of Pre-Diabetes: Mechanisms of Fasting and Postprandial Hyperglycemia in People With Impaired Fasting Glucose and/or Impaired Glucose Tolerance

Thirty-two subjects with impaired fasting glucose (IFG) and 28 subjects with normal fasting glucose (NFG) ingested a labeled meal and 75 g glucose (oral glucose tolerance test) on separate occasions. Fasting glucose, insulin, and C-peptide were higher (P < 0.05) in subjects with IFG than in those with NFG, whereas endogenous glucose production (EGP) did not differ, indicating hepatic insulin resistance. EGP was promptly suppressed, and meal glucose appearance comparably increased following meal ingestion in both groups. In contrast, glucose disappearance (Rd) immediately after meal ingestion was lower (P < 0.001) in subjects with IFG/impaired glucose tolerance (IGT) and IFG/diabetes but did not differ in subjects with IFG/normal glucose tolerance (NGT) or NFG/NGT. Net insulin action (Si) and insulin-stimulated glucose disposal (Si*) were reduced (P < 0.001, ANOVA) in subjects with NFG/IGT, IFG/IGT, and IFG/diabetes but did not differ in subjects with NFG/NGT or IFG/NGT. Defective insulin secretion also contributed to lower postprandial Rd since disposition indexes were lower (P < 0.001, ANOVA) in subjects with NFG/IGT, IFG/IGT, and IFG/diabetes but did not differ in subjects with NFG/NGT and IFG/NGT. We conclude that postprandial hyperglycemia in individuals with early diabetes is due to lower rates of glucose disappearance rather than increased meal appearance or impaired suppression of EGP, regardless of their fasting glucose. In contrast, insulin secretion, action, and the pattern of postprandial turnover are essentially normal in individuals with isolated IFG.

The UVA/PADOVA Type 1 Diabetes Simulator New Features

Objective: Recent studies have provided new insights into nonlinearities of insulin action in the hypoglycemic range and into glucagon kinetics as it relates to response to hypoglycemia. Based on these data, we developed a new version of the UVA/PADOVA Type 1 Diabetes Simulator, which was submitted to FDA in 2013 (S2013). Methods: The model of glucose kinetics in hypoglycemia has been improved, implementing the notion that insulin-dependent utilization increases nonlinearly when glucose decreases below a certain threshold. In addition, glucagon kinetics and secretion and action models have been incorporated into the simulator: glucagon kinetics is a single compartment; glucagon secretion is controlled by plasma insulin, plasma glucose below a certain threshold, and glucose rate of change; and plasma glucagon stimulates with some delay endogenous glucose production. A refined statistical strategy for virtual patient generation has been adopted as well. Finally, new rules for determining insulin to carbs ratio (CR) and correction factor (CF) of the virtual patients have been implemented to better comply with clinical definitions. Results: S2013 shows a better performance in describing hypoglycemic events. In addition, the new virtual subjects span well the real type 1 diabetes mellitus population as demonstrated by good agreement between real and simulated distribution of patient-specific parameters, such as CR and CF. Conclusions: S2013 provides a more reliable framework for in silico trials, for testing glucose sensors and insulin augmented pump prediction methods, and for closed-loop single/dual hormone controller design, testing, and validation.

Evaluating the Efficacy of Closed-Loop Glucose Regulation via Control-Variability Grid Analysis

Background: Advancements in subcutaneous continuous glucose monitoring and subcutaneous insulin delivery are stimulating the development of a minimally invasive artificial pancreas that facilitates optimal glycemic regulation in diabetes. The key component of such a system is the blood glucose controller for which different design strategies have been investigated in the literature. In order to evaluate and compare the efficacy of the various algorithms, several performance indices have been proposed. Methods: A new tool—control-variability grid analysis (CVGA)—for measuring the quality of closed-loop glucose control on a group of subjects is introduced. It is a method for visualization of the extreme glucose excursions caused by a control algorithm in a group of subjects, with each subject presented by one data point for any given observation period. A numeric assessment of the overall level of glucose regulation in the population is given by the summary outcome of the CVGA. Results: It has been shown that CVGA has multiple uses: Comparison of different patients over a given time period, of the same patient over different time periods, of different control laws, and of different tuning of the same controller on the same population. Conclusions: Control-variability grid analysis provides a summary of the quality of glycemic regulation for a population of subjects and is complementary to measures such as area under the curve or low/high blood glucose indices, which characterize a single glucose trajectory for a single subject.