1,721,321 research outputs found
Minimal and Maximal Models to Quantitate Glucose Metabolism: Tools to Measure, to Simulate and to Run in Silico Clinical Trials
Full text linkSeveral models have been proposed to describe the glucose system at whole-body, organ/tissue and cellular level, designed to measure non-accessible parameters (minimal models), to simulate system behavior and run in silico clinical trials (maximal models). Here, we will review the authors’ work, by putting it into a concise historical background. We will discuss first the parametric portrait provided by the oral minimal models—building on the classical intravenous glucose tolerance test minimal models—to measure otherwise non-accessible key parameters like insulin sensitivity and beta-cell responsivity from a physiological oral test, the mixed meal or the oral glucose tolerance tests, and what can be gained by adding a tracer to the oral glucose dose. These models were used in various pathophysiological studies, which we will briefly review. A deeper understanding of insulin sensitivity can be gained by measuring insulin action in the skeletal muscle. This requires the use of isotopic tracers: both the classical multiple-tracer dilution and the positron emission tomography techniques are discussed, which quantitate the effect of insulin on the individual steps of glucose metabolism, that is, bidirectional transport plasma-interstitium, and phosphorylation. Finally, we will present a cellular model of insulin secretion that, using a multiscale modeling approach, highlights the relations between minimal model indices and subcellular secretory events. In terms of maximal models, we will move from a parametric to a flux portrait of the system by discussing the triple tracer meal protocol implemented with the tracer-to-tracee clamp technique. This allows to arrive at quasi-model independent measurement of glucose rate of appearance (Ra), endogenous glucose production (EGP), and glucose rate of disappearance (Rd). Both the fast absorbing simple carbs and the slow absorbing complex carbs are discussed. This rich data base has allowed us to build the UVA/Padova Type 1 diabetes and the Padova Type 2 diabetes large scale simulators. In particular, the UVA/Padova Type 1 simulator proved to be a very useful tool to safely and effectively test in silico closed-loop control algorithms for an artificial pancreas (AP). This was the first and unique simulator of the glucose system accepted by the U.S. Food and Drug Administration as a substitute to animal trials for in silico testing AP algorithms. Recent uses of the simulator have looked at glucose sensors for non-adjunctive use and new insulin molecules
A run-to-run algorithm for insulin to carbohydrate ratio adaptation in sensor-augmented pump therapy of type 1 diabetes
No full textThe Padova Type 2 Diabetes Simulator from Triple-Tracer Single-Meal Studies: In Silico Trials Also Possible in Rare but Not-So-Rare Individuals
Full text linkBackground: In silico trials in type 2 diabetes (T2D) would be useful for testing diabetes treatments and accelerating the development of new antidiabetic drugs. In this study, we present a T2D simulator able to reproduce the variability observed in a T2D population. The simulator also allows to safely experiment on virtual subjects with severe (and possibly rare) pathological conditions. Methods: A meal simulation model of glucose, insulin, and C-peptide systems, made of 15 differential equations and 39 parameters, has been identified using a system decomposition and forcing function Bayesian strategy on data of 51 T2D subjects undergoing a single triple-tracer mixed meal. One hundred T2D in silico subjects have been generated from the joint distribution of estimated model parameters. A case study is presented to illustrate the simulator use for testing a virtual drug (improving insulin action and secretion) in a subpopulation of rare, extremely impaired, T2D subjects. Results: The model well fitted T2D data and parameters were estimated with precision. Simulated plasma glucose, insulin, and C-peptide well matched the data (e.g., median [25th-75th percentile] glucose area under the curves of 6.9 [6.1-8.5] 104 mg/dL·min in silico vs. 7.0 [5.6-8.2] 104 mg/dL·min in vivo). The potential use of the simulator was shown in a case study, in which the (virtual) antidiabetic drug dose was optimized for very insulin-resistant T2D subjects. Conclusions: We have developed a T2D simulator that captures the behavior of T2D population during a meal, both in terms of average and intersubject variability. The simulator represents a cost-effective way to test new antidiabetic drugs, before moving to human trials
Glucose kinetics during a meal: One vs two compartment minimal model
No full textDuring IVGTT the two compartment model performs better than the single compartment one. Here the aim is to test 1 vs 2 compartment model during a more gentle perturbation such an oral test. 1 and 2 compartment minimal model, both unlabeled and labeled, were identified with glucose rate of appearance, i.e. the input of the model, assumed known from a model-independent method. For unlabeled data both models perform similarly while for labeled data the 2 compartment model is better. Among model-based metabolic indices differences were noted in the inhibition of endogenous glucose production due to glucose itself predicted by one and two compartment models
Physiology-based run-to-run adaptation of insulin to carbohydrate ratio improves type 1 diabetes therapy: Results from an in silico study
No full textThe insulin to carbohydrate ratio (CR) is a parameter used by patients with type 1 diabetes (T1D) to calculate the pre-meal insulin bolus and compensate postprandial glucose excursion. However, CR is known to vary over time, within and between days, hence tracking its variations is important for optimizing glucose control. Physicians periodically tune this parameter, by trial and error, based on empirical guidelines and patient's diary, but contemporary diabetes technology has the potential to move to CR adaptation. The aim of this work is to propose an algorithm to adapt patient's CR to physiological and/or behavioral changes based on minimally-invasive everyday-life technology data. We developed a run-to-run (R2R) algorithm for CR adaptation exploiting a physiology-based method for CR optimization. The algorithm retrospectively evaluates the quality of glycemic control and proposes, every 2 days, an adaptation of patient's CR by using patient's minimally-invasive data. The performance of the algorithm was assessed in silico using the single-day University of Virginia/Padova T1D simulator (Visentin et al., J Diabetes Sci Technol 2018) which incorporates a model for intra-day variability of insulin sensitivity and dawn phenomenon. The feasibility and robustness of the algorithm was tested in a 35-day scenario (7 days of run-in), with 3 meals per day, in 100 in silico subjects by including inter-day variability of insulin sensitivity (Toffanin et al., IEEE Trans Biomed Eng 2018) together with suboptimal CR or basal insulin infusion rate. Different values of the R2R gain (lambda) were tested, ranging from pmb0 to 1. In all simulations, CR adaptation improves glycemic control in a significant percentage of virtual subjects, within 5 weeks. Moreover, the method was safe also in case of suboptimal insulin infusion rate. Based on simulation results, a good compromise between safety and efficacy was achieved with lambda between 0.5 and 1. The proposed R2R algorithm for CR adaptation proved to be effective in silico. These results need to be confirmed clinically. The method can potentially be used in conjunction with algorithms for basal insulin adaptation and/or closed-loop control
Artificial Pancreas: In Silico Study Shows No Need of Meal Announcement and Improved Time in Range of Glucose with Intraperitoneal vs. Subcutaneous Insulin Delivery
Full text linkContemporary Artificial Pancreas (AP) consists of a subcutaneous (SC) glucose sensor, a SC insulin pump and a control algorithm. Even the most advanced systems are far from optimal, in particular due to the non-physiologic nature of SC route. While SC insulin delivery is convenient and minimally
invasive, it introduces delays to insulin action that make tight control difficult, particularly during meals. In addition frequent
patient interventions are needed, e.g., at mealtime. The intraperitoneal (IP) insulin delivery could address this major challenge since it exhibits a faster pharmacokinetics/pharmacodynamics, hence making easier to quickly respond to glycemic disturbances. A 1-day hospital closed-loop study has shown significant improvements of IP glucose control vs SC AP, and that meal announcement is not necessary. However, the IP AP has not been tested in more realistic everyday life conditions. In this work we have performed an in silico study of 14 days of an IP AP by using the UVA/Padova simulator which includes intra- and inter-day variability of insulin sensitivity and several real life scenarios. We show superiority of IP AP vs SC AP in terms of quality of glucose control (time in range 87% IP vs 80% SC) without the need of a meal announcement
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