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Vaughan N. Virtual Reality Meets Diabetes. J Diabetes Sci Technol 2024:19322968231222022. [PMID: 38193465 DOI: 10.1177/19322968231222022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
BACKGROUND This article provides a detailed summary of virtual reality (VR) and augmented reality (AR) applications in diabetes. The purpose of this comparative review is to identify application areas, direction and provide foundation for future virtual reality tools in diabetes. METHOD Features and benefits of each VR diabetes application are compared and discussed, following a thorough review of literature on virtual reality for diabetes using multiple databases. The weaknesses of existing VR applications are discussed and their strengths identified so that these can be carried forward. A novel virtual reality diabetes tool prototype is also developed and presented. RESULTS This research identifies three major categories where VR is being used in diabetes: education, prevention and treatment. Within diabetes education, there are three target groups: clinicians, adults with diabetes and children with diabetes. Both VR and AR have shown benefits in areas of Type 1 and Type 2 diabetes. CONCLUSIONS Virtual reality and augmented reality in diabetes have demonstrated potential to enhance training of diabetologists and enhance education, prevention and treatment for adults and children with Type 1 or Type 2 diabetes. Future research can continually build on virtual and augmented reality diabetes applications by integrating wide stakeholder inputs and diverse digital platforms. Several areas of VR diabetes are in early stages, with advantages and opportunities. Further VR diabetes innovations are encouraging to enhance training, management and treatment of diabetes.
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Affiliation(s)
- Neil Vaughan
- Department of Clinical and Biomedical Science, NIHR Exeter Biomedical Research Centre, Exeter Centre of Excellence in Diabetes, University of Exeter, Exeter, UK
- Royal Academy of Engineering, London, UK
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Cobelli C, Kovatchev B. Developing the UVA/Padova Type 1 Diabetes Simulator: Modeling, Validation, Refinements, and Utility. J Diabetes Sci Technol 2023; 17:1493-1505. [PMID: 37743740 PMCID: PMC10658679 DOI: 10.1177/19322968231195081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Arguably, diabetes mellitus is one of the best quantified human conditions. In the past 50 years, the metabolic monitoring technologies progressed from occasional assessment of average glycemia via HbA1c, through episodic blood glucose readings, to continuous glucose monitoring (CGM) producing data points every few minutes. The high-temporal resolution of CGM data enabled increasingly intensive treatments, from decision support assisting insulin injection or oral medication, to automated closed-loop control, known as the "artificial pancreas." Throughout this progress, mathematical models and computer simulation of the human metabolic system became indispensable for the technological progress of diabetes treatment, enabling every step, from assessment of insulin sensitivity via the now classic Minimal Model of Glucose Kinetics, to in silico trials replacing animal experiments, to automated insulin delivery algorithms. In this review, we follow these developments, beginning with the Minimal Model, which evolved through the years to become large and comprehensive and trigger a paradigm change in the design of diabetes optimization strategies: in 2007, we introduced a sophisticated model of glucose-insulin dynamics and a computer simulator equipped with a "population" of N = 300 in silico "subjects" with type 1 diabetes. In January 2008, in an unprecedented decision, the Food and Drug Administration (FDA) accepted this simulator as a substitute to animal trials for the pre-clinical testing of insulin treatment strategies. This opened the field for rapid and cost-effective development and pre-clinical testing of new treatment approaches, which continues today. Meanwhile, animal experiments for the purpose of designing new insulin treatment algorithms have been abandoned.
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Affiliation(s)
| | - Boris Kovatchev
- Center for Diabetes Technology,
University of Virginia, Charlottesville, VA, USA
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Riddell MC, Shakeri D, Scott SN. A Brief Review on the Evolution of Technology in Exercise and Sport in Type 1 Diabetes: Past, Present, and Future. Diabetes Technol Ther 2022; 24:289-298. [PMID: 34809493 DOI: 10.1089/dia.2021.0427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
One hundred years ago, insulin was first used to successfully lower blood glucose levels in young people living with what was then called juvenile diabetes. While insulin was not a cure for diabetes, it allowed individuals to resume a near normal life and have some freedom to eat more liberally and gain the strength they needed to live a more active lifestyle. Since then, a number of therapeutic and technical advances have arisen to further improve the health and wellbeing of individuals living with type 1 diabetes, allowing many to participate in sport at the local, regional, national or international level of competition. This review and commentary highlights some of the key advances in diabetes management in sport over the last 100 years since the discovery of insulin.
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Affiliation(s)
- Michael C Riddell
- School of Kinesiology and Health Science, Muscle Health Research Centre, York University, Toronto, Canada
| | - Dorsa Shakeri
- School of Kinesiology and Health Science, Muscle Health Research Centre, York University, Toronto, Canada
| | - Sam N Scott
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Bern University Hospital, University of Bern, Bern, Switzerland
- Team Novo Nordisk Professional Cycling Team, Atlanta, Georgia, USA
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Alonso-Bastida A, Adam-Medina M, Posada-Gómez R, Salazar-Piña DA, Osorio-Gordillo GL, Vela-Valdés LG. Dynamic of Glucose Homeostasis in Virtual Patients: A Comparison between Different Behaviors. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:716. [PMID: 35055537 PMCID: PMC8775377 DOI: 10.3390/ijerph19020716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 12/23/2021] [Accepted: 01/01/2022] [Indexed: 02/04/2023]
Abstract
This work presents a mathematical model of homeostasis dynamics in healthy individuals, focusing on the generation of conductive data on glucose homeostasis throughout the day under dietary and physical activity factors. Two case studies on glucose dynamics for populations under conditions of physical activity and sedentary lifestyle were developed. For this purpose, two types of virtual populations were generated, the first population was developed according to the data of a total of 89 physical persons between 20 and 75 years old and the second was developed using the Monte Carlo approach, obtaining a total of 200 virtual patients. In both populations, each participant was classified as an active or sedentary person depending on the physical activity performed. The results obtained demonstrate the capacity of virtual populations in the generation of in-silico approximations similar to those obtained from in-vivo studies. Obtaining information that is only achievable through specific in-vivo experiments. Being a tool that generates information for the approach of alternatives in the prevention of the development of type 2 Diabetes.
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Affiliation(s)
- Alexis Alonso-Bastida
- Electronic Engineering Department, TecNM/CENIDET, Cuernavaca 62490, Morelos, Mexico; (M.A.-M.); (G.-L.O.-G.); (L.G.V.-V.)
| | - Manuel Adam-Medina
- Electronic Engineering Department, TecNM/CENIDET, Cuernavaca 62490, Morelos, Mexico; (M.A.-M.); (G.-L.O.-G.); (L.G.V.-V.)
| | | | | | - Gloria-Lilia Osorio-Gordillo
- Electronic Engineering Department, TecNM/CENIDET, Cuernavaca 62490, Morelos, Mexico; (M.A.-M.); (G.-L.O.-G.); (L.G.V.-V.)
| | - Luis Gerardo Vela-Valdés
- Electronic Engineering Department, TecNM/CENIDET, Cuernavaca 62490, Morelos, Mexico; (M.A.-M.); (G.-L.O.-G.); (L.G.V.-V.)
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Corbett JP, Garcia-Tirado J, Colmegna P, Diaz Castaneda JL, Breton MD. Using an Online Disturbance Rejection and Anticipation System to Reduce Hyperglycemia in a Fully Closed-Loop Artificial Pancreas System. J Diabetes Sci Technol 2022; 16:52-60. [PMID: 34861786 PMCID: PMC8875044 DOI: 10.1177/19322968211059159] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
INTRODUCTION Hyperglycemia following meals is a recurring challenge for people with type 1 diabetes, and even the most advanced available automated systems currently require manual input of carbohydrate amounts. To progress toward fully automated systems, we present a novel control system that can automatically deliver priming boluses and/or anticipate eating behaviors to improve postprandial full closed-loop control. METHODS A model predictive control (MPC) system was enhanced by an automated bolus system reacting to early glucose rise and/or a multistage MPC (MS-MPC) framework to anticipate historical patterns. Priming was achieved by detecting large glycemic disturbances, such as meals, and delivering a fraction of the patient's total daily insulin (TDI) modulated by the disturbance's likelihood (bolus priming system [BPS]). In the anticipatory module, glycemic disturbance profiles were generated from historical data using clustering to group days with similar behaviors; the probability of each cluster is then evaluated at every controller step and informs the MS-MPC framework to anticipate each profile. We tested four configurations: MPC, MPC + BPS, MS-MPC, and MS-MPC + BPS in simulation to contrast the effect of each controller module. RESULTS Postprandial time in range was highest for MS-MPC + BPS: 60.73 ± 25.39%, but improved with each module: MPC + BPS: 56.95±25.83 and MS-MPC: 54.83 ± 26.00%, compared with MPC: 51.79 ± 26.12%. Exposure to hypoglycemia was maintained for all controllers (time below 70 mg/dL <0.5%), and improvement came primarily from a reduction in postprandial time above range (MS-MPC + BPS: 39.10 ± 25.32%, MPC + BPS: 42.99 ± 25.81%, MS-MPC: 45.09 ± 25.96%, MPC: 48.18 ± 26.09%). CONCLUSIONS The BPS and anticipatory disturbance profiles improved blood glucose control and were most efficient when combined.
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Affiliation(s)
- John P. Corbett
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
- John P. Corbett, PhD, Center for Diabetes Technology, University of Virginia, 560 Ray C. Hunt Drive, Charlottesville, VA 22903, USA.
| | - Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Patricio Colmegna
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | | | - Marc D. Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
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Sun X, Rashid M, Hobbs N, Brandt R, Askari MR, Cinar A. Incorporating Prior Information in Adaptive Model Predictive Control for Multivariable Artificial Pancreas Systems. J Diabetes Sci Technol 2022; 16:19-28. [PMID: 34861777 PMCID: PMC8875040 DOI: 10.1177/19322968211059149] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Adaptive model predictive control (MPC) algorithms that recursively update the glucose prediction model are shown to be promising in the development of fully automated multivariable artificial pancreas systems. However, the recursively updated glycemic prediction models do not explicitly consider prior knowledge in the identification of the model parameters. Prior information of the glycemic effects of meals and physical activity can improve model accuracy and yield better glycemic control algorithms. METHODS A glucose prediction model based on regularized partial least squares (rPLS) method where the prior information is encoded as the regularization term is developed to provide accurate predictions of the future glucose concentrations. An adaptive MPC is developed that incorporates dynamic trajectories for the glucose setpoint and insulin dosing constraints based on the estimated plasma insulin concentration (PIC). The proposed adaptive MPC algorithm is robust to disturbances caused by unannounced meals and physical activities even in cases with missing glucose measurements. The effectiveness of the proposed adaptive MPC based on rPLS is investigated with in silico subjects of the multivariable glucose-insulin-physiological variables simulator (mGIPsim). RESULTS The efficacy of the proposed adaptive MPC strategy in regulating the blood glucose concentration (BGC) of people with T1DM is assessed using the average percent time in range (TIR) for glucose, defined as 70 to 180 mg/dL inclusive, and the average percent time in hypoglycemia (<70 and >54 mg/dL) and level 2 hypoglycemia (≤54 mg/dL). The TIR for a cohort of 20 virtual subjects of mGIPsim is 81.9% ± 7.4% (with no hypoglycemia or severe hypoglycemia) for the proposed MPC compared with 73.9% ± 7.6% (0.2% ± 0.1% in hypoglycemia and 0.1% ± 0.1% in level 2 hypoglycemia) for an MPC based on a recursive autoregressive exogenous (ARX) model. CONCLUSIONS The adaptive MPC algorithm that incorporates prior knowledge in the recursive updating of the glucose prediction model can contribute to the development of fully automated artificial pancreas systems that can mitigate meal and physical activity disturbances.
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Affiliation(s)
- Xiaoyu Sun
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Nicole Hobbs
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Rachel Brandt
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mohammad Reza Askari
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Ali Cinar
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
- Ali Cinar, PhD, Department of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd St, Chicago, IL 60616, USA.
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Garcia-Tirado J, Lv D, Corbett JP, Colmegna P, Breton MD. Advanced hybrid artificial pancreas system improves on unannounced meal response - In silico comparison to currently available system. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 211:106401. [PMID: 34560603 DOI: 10.1016/j.cmpb.2021.106401] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 09/01/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Glycemic control, especially meal-related disturbance rejection, has proven to be a major challenge for people with type 1 diabetes. In this manuscript, we introduce a novel, personalized, advanced hybrid insulin infusion system (a.k.a. artificial pancreas) based on the Model Predictive Control (MPC) methodology to adjust insulin infusion while automatically rejecting uninformed meals. METHODS The proposed advanced hybrid closed-loop system relies on the integration of three key elements: (i) an adaptive personalized MPC control law that modulates the control strength depending on recent past control actions, glucose measurements, and its derivative, (ii) an automatic Bolus Priming System (BPS) that commands additional insulin injections safely upon the detection of enabling metabolic conditions (e.g., an unacknowledged meal), and (iii) a new hyperglycemia mitigation system to avoid prevailing hyperglycemia. The benefits of the proposed system are demonstrated through simulations and tests using the most up-to-date Type 1 UVA/Padova simulator as preclinical stage prior to in vivo clinical tests. We used a legacy algorithm (USS Virginia), currently used in clinical care, as a benchmark controller. RESULTS Overall, the proposed control strategy enhanced by an automatic BPS improves glycemic control when compared with an available system. When a large meal is not announced (80g CHO), the proposed controller outperformed the legacy controller in time-in-target-range TIR (postprandial and overnight) and time-in-tight-range TTR (overall, postprandial, and overnight). CONCLUSION The integration of a novel BPS into an advanced control system allowed to automatically reject unannounced meals. Exhaustive simulation studies indicated the safety and feasibility of the proposed controller to be deployed in human clinical trials.
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Affiliation(s)
- Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.
| | - Dayu Lv
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.
| | - John P Corbett
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA; Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA, USA.
| | - Patricio Colmegna
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.
| | - Marc D Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.
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Colmegna P, Cengiz E, Garcia-Tirado J, Kraemer K, Breton MD. Impact of Accelerating Insulin on an Artificial Pancreas System Without Meal Announcement: An In Silico Examination. J Diabetes Sci Technol 2021; 15:833-841. [PMID: 32546001 PMCID: PMC8258534 DOI: 10.1177/1932296820928067] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Controlling postprandial blood glucose without the benefit of an appropriately sized premeal insulin bolus has been challenging given the delays in absorption and action of subcutaneously injected insulin during conventional and artificial pancreas (AP) system diabetes treatment. We aim to understand the impact of accelerating insulin and increasing aggressiveness of the AP controller as potential solutions to address the postprandial hyperglycemia challenge posed by unannounced meals through a simulation study. METHODS Accelerated rapid-acting insulin analogue is modeled within the UVA/Padova simulation platform by uniformly reducing its pharmacokinetic time constants (α multiplier) and used with a model predictive control, where the controller's aggressiveness depends on α. Two sets of single-meal simulations were performed: (1) where we only tune the controller's aggressiveness and (2) where we also accelerate insulin absorption and action to assess postprandial glycemic control during each intervention. RESULTS Mean percent of time spent within the 70 to 180 mg/dL postprandial glycemic range is significantly higher in set (2) than in set (1): 79.9, 95% confidence interval [77.0, 82.7] vs 88.8 [86.8, 90.9] ([Note to typesetter: Set all unnecessary math in text format and insert appropriate spaces between operators.] P < .05) for α = 2, and 81.4 [78.6, 84.3] vs 94.1 [92.6, 95.6] (P < .05) for α = 3. A decrease in percent of time below 70 mg/dL is also detected: 0.9 [0.4, 2.2] vs 0.6 [0.2, 1.4] (P = .23) for α = 2 and 1.4 [0.7, 2.8] vs 0.4 [0.1, 1.4] (P < .05) for α = 3. CONCLUSION These proof-of-concept simulations suggest that an AP without prandial insulin boluses combined with significantly faster insulin analogues could match the glycemic performance obtained with an optimal hybrid AP.
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Affiliation(s)
- Patricio Colmegna
- Center for Diabetes Technology, University of Virginia, Charlottesville, USA
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Patricio Colmegna, PhD, Center for Diabetes Technology, University of Virginia, 560 Ray C Hunt Dr, Charlottesville, VA 22903, USA.
| | - Eda Cengiz
- Division of Pediatric Endocrinology and Diabetes, Yale University School of Medicine, New Haven, CT, USA
- Bahcesehir University School of Medicine, Istanbul, Turkey
| | - Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, USA
| | - Kristen Kraemer
- Division of Pediatric Endocrinology and Diabetes, Yale University School of Medicine, New Haven, CT, USA
| | - Marc D. Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, USA
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Garcia-Tirado J, Brown SA, Laichuthai N, Colmegna P, Koravi CL, Ozaslan B, Corbett JP, Barnett CL, Pajewski M, Oliveri MC, Myers H, Breton MD. Anticipation of Historical Exercise Patterns by a Novel Artificial Pancreas System Reduces Hypoglycemia During and After Moderate-Intensity Physical Activity in People with Type 1 Diabetes. Diabetes Technol Ther 2021; 23:277-285. [PMID: 33270531 PMCID: PMC7994426 DOI: 10.1089/dia.2020.0516] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Objective: Physical activity is a major challenge to glycemic control for people with type 1 diabetes. Moderate-intensity exercise often leads to steep decreases in blood glucose and hypoglycemia that closed-loop control systems have so far failed to protect against, despite improving glycemic control overall. Research Design and Methods: Fifteen adults with type 1 diabetes (42 ± 13.5 years old; hemoglobin A1c 6.6% ± 1.0%; 10F/5M) participated in a randomized crossover clinical trial comparing two hybrid closed-loop (HCL) systems, a state-of-the-art hybrid model predictive controller and a modified system designed to anticipate and detect unannounced exercise (APEX), during two 32-h supervised admissions with 45 min of planned moderate activity, following 4 weeks of data collection. Primary outcome was the number of hypoglycemic episodes during exercise. Continuous glucose monitor (CGM)-based metrics and hypoglycemia are also reported across the entire admissions. Results: The APEX system reduced hypoglycemic episodes overall (9 vs. 33; P = 0.02), during exercise (5 vs. 13; P = 0.04), and in the 4 h following (2 vs. 11; P = 0.02). Overall CGM median percent time <70 mg/dL decreased as well (0.3% vs. 1.6%; P = 0.004). This protection was obtained with no significant increase in time >180 mg/dL (18.5% vs. 16.6%, P = 0.15). Overnight control was notable for both systems with no hypoglycemia, median percent in time 70-180 mg/dL at 100% and median percent time 70-140 mg/dL at ∼96% for both. Conclusions: A new closed-loop system capable of anticipating and detecting exercise was proven to be safe and feasible and outperformed a state-of-the-art HCL, reducing participants' exposure to hypoglycemia during and after moderate-intensity physical activity. ClinicalTrials.gov NCT03859401.
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Affiliation(s)
- Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Sue A. Brown
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
- Division of Endocrinology and Metabolism, Department of Medicine, University of Virginia, Charlottesville, Virginia, USA
| | - Nitchakarn Laichuthai
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
- Excellence Center in Diabetes, Hormone, and Metabolism, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, and Division of Endocrinology and Metabolism, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Patricio Colmegna
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Chaitanya L.K. Koravi
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Basak Ozaslan
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - John P. Corbett
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Charlotte L. Barnett
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Michael Pajewski
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Mary C. Oliveri
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Helen Myers
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
| | - Marc D. Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
- Address correspondence to: Marc D. Breton, PhD, Center for Diabetes Technology, University of Virginia, PO Box 400888, Charlottesville, VA 22904-4888, USA
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Visentin R, Cobelli C, Dalla Man C. The Padova Type 2 Diabetes Simulator from Triple-Tracer Single-Meal Studies: In Silico Trials Also Possible in Rare but Not-So-Rare Individuals. Diabetes Technol Ther 2020; 22:892-903. [PMID: 32324063 DOI: 10.1089/dia.2020.0110] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Background: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.
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Affiliation(s)
- Roberto Visentin
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Chiara Dalla Man
- Department of Information Engineering, University of Padova, Padova, Italy
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