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Filo M, Gupta A, Khammash M. Anti-windup strategies for biomolecular control systems facilitated by model reduction theory for sequestration networks. SCIENCE ADVANCES 2024; 10:eadl5439. [PMID: 39167660 PMCID: PMC11338268 DOI: 10.1126/sciadv.adl5439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 07/11/2024] [Indexed: 08/23/2024]
Abstract
Robust perfect adaptation, a system property whereby a variable adapts to persistent perturbations at steady state, has been recently realized in living cells using genetic integral controllers. In certain scenarios, such controllers may lead to "integral windup," an adverse condition caused by saturating control elements, which manifests as error accumulation, poor dynamic performance, or instabilities. To mitigate this effect, we here introduce several biomolecular anti-windup topologies and link them to control-theoretic anti-windup strategies. This is achieved using a novel model reduction theory that we develop for reaction networks with fast sequestration reactions. We then show how the anti-windup topologies can be realized as reaction networks and propose intein-based genetic designs for their implementation. We validate our designs through simulations on various biological systems, including models of patients with type I diabetes and advanced biomolecular proportional-integral-derivative (PID) controllers, demonstrating their efficacy in mitigating windup effects and ensuring safety.
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Lubasinski N, Thabit H, Nutter PW, Harper S. Blood Glucose Prediction from Nutrition Analytics in Type 1 Diabetes: A Review. Nutrients 2024; 16:2214. [PMID: 39064657 PMCID: PMC11280346 DOI: 10.3390/nu16142214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2024] [Revised: 07/06/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
INTRODUCTION Type 1 Diabetes (T1D) affects over 9 million worldwide and necessitates meticulous self-management for blood glucose (BG) control. Utilizing BG prediction technology allows for increased BG control and a reduction in the diabetes burden caused by self-management requirements. This paper reviews BG prediction models in T1D, which include nutritional components. METHOD A systematic search, utilizing the PRISMA guidelines, identified articles focusing on BG prediction algorithms for T1D that incorporate nutritional variables. Eligible studies were screened and analyzed for model type, inclusion of additional aspects in the model, prediction horizon, patient population, inputs, and accuracy. RESULTS The study categorizes 138 blood glucose prediction models into data-driven (54%), physiological (14%), and hybrid (33%) types. Prediction horizons of ≤30 min are used in 36% of models, 31-60 min in 34%, 61-90 min in 11%, 91-120 min in 10%, and >120 min in 9%. Neural networks are the most used data-driven technique (47%), and simple carbohydrate intake is commonly included in models (data-driven: 72%, physiological: 52%, hybrid: 67%). Real or free-living data are predominantly used (83%). CONCLUSION The primary goal of blood glucose prediction in T1D is to enable informed decisions and maintain safe BG levels, considering the impact of all nutrients for meal planning and clinical relevance.
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Affiliation(s)
- Nicole Lubasinski
- Department of Computer Science, The University of Manchester, Manchester M13 9PL, UK; (P.W.N.); (S.H.)
| | - Hood Thabit
- Diabetes, Endocrine and Metabolism Centre, Manchester Royal Infirmary, Manchester University NHS, Manchester M13 9WL, UK;
- Division of Diabetes, Endocrinology and Gastroenterology, School of Medical Science, The University of Manchester, Manchester M13 9NT, UK
| | - Paul W. Nutter
- Department of Computer Science, The University of Manchester, Manchester M13 9PL, UK; (P.W.N.); (S.H.)
| | - Simon Harper
- Department of Computer Science, The University of Manchester, Manchester M13 9PL, UK; (P.W.N.); (S.H.)
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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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4
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100 Years of insulin: A chemical engineering perspective. KOREAN J CHEM ENG 2023. [DOI: 10.1007/s11814-022-1308-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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A genetic mammalian proportional-integral feedback control circuit for robust and precise gene regulation. Proc Natl Acad Sci U S A 2022; 119:e2122132119. [PMID: 35687671 PMCID: PMC9214505 DOI: 10.1073/pnas.2122132119] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
To survive in the harsh environments they inhabit, cells have evolved sophisticated regulatory mechanisms that can maintain a steady internal milieu or homeostasis. This robustness, however, does not generally translate to engineered genetic circuits, such as the ones studied by synthetic biology. Here, we introduce an implementation of a minimal and universal gene regulatory motif that produces robust perfect adaptation for mammalian cells, and we improve on it by enhancing the precision of its regulation. The processes that keep a cell alive are constantly challenged by unpredictable changes in its environment. Cells manage to counteract these changes by employing sophisticated regulatory strategies that maintain a steady internal milieu. Recently, the antithetic integral feedback motif has been demonstrated to be a minimal and universal biological regulatory strategy that can guarantee robust perfect adaptation for noisy gene regulatory networks in Escherichia coli. Here, we present a realization of the antithetic integral feedback motif in a synthetic gene circuit in mammalian cells. We show that the motif robustly maintains the expression of a synthetic transcription factor at tunable levels even when it is perturbed by increased degradation or its interaction network structure is perturbed by a negative feedback loop with an RNA-binding protein. We further demonstrate an improved regulatory strategy by augmenting the antithetic integral motif with additional negative feedback to realize antithetic proportional–integral control. We show that this motif produces robust perfect adaptation while also reducing the variance of the regulated synthetic transcription factor. We demonstrate that the integral and proportional–integral feedback motifs can mitigate the impact of gene expression burden, and we computationally explore their use in cell therapy. We believe that the engineering of precise and robust perfect adaptation will enable substantial advances in industrial biotechnology and cell-based therapeutics.
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Sharma A, Singh HP, Nilam. A methodical survey of mathematical model-based control techniques based on open and closed loop control approach for diabetes management. INT J BIOMATH 2022. [DOI: 10.1142/s1793524522500516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Disturbance of blood sugar level is controlled through well-known biomechanical feedback loops: high levels of glucose in blood facilitate to release insulin from the pancreas which accelerates the absorption rate of cellular glucose. Low glucose levels encourage to release pancreatic glucagon which induces glycogen breakdown to glucose in the liver. These bio-control systems do not function properly in diabetic patients. Though the control of disease seems intuitively easy, in real life, due to many differences in structure by diet and fasting, exercise, medications, patient’s profile and other stressors, it is not that easy. The mathematical models of the glucose-insulin regulatory system follow the patient’s physiological conditions which make it difficult to identify and estimate all the model parameters. In this paper, we have given a systematic literature review on mathematical models of the diabetic patients, and various kinds of disease control techniques through the development of open and closed loop insulin deliver command system and optimization of exogenous insulin rate. It demonstrates the open and closed loop type model-based control strategies underlying the assumptions of the concerned models. The combination of mathematical model with control strategies such as genetic algorithm (GA), neural network (NN), sliding mode controller (SMC), model predictive controller (MPC), and fuzzy logic control (FLC) has been considered, which provides an overview of this area, highlighting the control profile over the diabetic model with promising clinical results, outlining key challenges, and identifying needs for the future research. Also, the significance of these control algorithms has been discussed in the presence of the noises, the controller’s robustness and various other disturbances. It provides substantial information on diabetes management through various control techniques.
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Affiliation(s)
- Ankit Sharma
- Department of Applied Mathematics, Delhi Technological University, Delhi 110042, India
| | | | - Nilam
- Department of Applied Mathematics, Delhi Technological University, Delhi 110042, India
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7
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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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Pompa M, Panunzi S, Borri A, De Gaetano A. A comparison among three maximal mathematical models of the glucose-insulin system. PLoS One 2021; 16:e0257789. [PMID: 34570804 PMCID: PMC8476045 DOI: 10.1371/journal.pone.0257789] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 09/13/2021] [Indexed: 11/24/2022] Open
Abstract
The most well-known and widely used mathematical representations of the physiology of a diabetic individual are the Sorensen and Hovorka models as well as the UVAPadova Simulator. While the Hovorka model and the UVAPadova Simulator only describe the glucose metabolism of a subject with type 1 diabetes, the Sorensen model was formulated to simulate the behaviour of both normal and diabetic individuals. The UVAPadova model is the most known model, accepted by the FDA, with a high level of complexity. The Hovorka model is the simplest of the three models, well documented and used primarily for the development of control algorithms. The Sorensen model is the most complete, even though some modifications were required both to the model equations (adding useful compartments for modelling subcutaneous insulin delivery) and to the parameter values. In the present work several simulated experiments, such as IVGTTs and OGTTs, were used as tools to compare the three formulations in order to establish to what extent increasing complexity translates into richer and more correct physiological behaviour. All the equations and parameters used for carrying out the simulations are provided.
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Affiliation(s)
- Marcello Pompa
- CNR-IASI, Laboratorio di Biomatematica, Consiglio Nazionale delle Ricerche, Istituto di Analisi dei Sistemi ed Informatica, Rome, Italy
- Università Cattolica del Sacro Cuore Rome, Rome, Italy
| | - Simona Panunzi
- CNR-IASI, Laboratorio di Biomatematica, Consiglio Nazionale delle Ricerche, Istituto di Analisi dei Sistemi ed Informatica, Rome, Italy
| | - Alessandro Borri
- CNR-IASI, Laboratorio di Biomatematica, Consiglio Nazionale delle Ricerche, Istituto di Analisi dei Sistemi ed Informatica, Rome, Italy
| | - Andrea De Gaetano
- CNR-IASI, Laboratorio di Biomatematica, Consiglio Nazionale delle Ricerche, Istituto di Analisi dei Sistemi ed Informatica, Rome, Italy
- CNR-IRIB, Consiglio Nazionale delle Ricerche, Istituto per la Ricerca e l’Innovazione Biomedica Palermo, Palermo, Italy
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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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10
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Modelling glucose dynamics during moderate exercise in individuals with type 1 diabetes. PLoS One 2021; 16:e0248280. [PMID: 33770092 PMCID: PMC7996980 DOI: 10.1371/journal.pone.0248280] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 02/24/2021] [Indexed: 12/17/2022] Open
Abstract
The artificial pancreas is a closed-loop insulin delivery system that automatically regulates glucose levels in individuals with type 1 diabetes. In-silico testing using simulation environments accelerates the development of better artificial pancreas systems. Simulation environments need an accurate model that captures glucose dynamics during exercise to simulate real-life scenarios. We proposed six variations of the Bergman Minimal Model to capture the physiological effects of moderate exercise on glucose dynamics in individuals with type 1 diabetes. We estimated the parameters of each model with clinical data using a Bayesian approach and Markov chain Monte Carlo methods. The data consisted of measurements of plasma glucose, plasma insulin, and oxygen consumption collected from a study of 17 adults with type 1 diabetes undergoing aerobic exercise sessions. We compared the models based on the physiological plausibility of their parameters estimates and the deviance information criterion. The best model features (i) an increase in glucose effectiveness proportional to exercise intensity, and (ii) an increase in insulin action proportional to exercise intensity and duration. We validated the selected model by reproducing results from two previous clinical studies. The selected model accurately simulates the physiological effects of moderate exercise on glucose dynamics in individuals with type 1 diabetes. This work offers an important tool to develop strategies for exercise management with the artificial pancreas.
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11
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Pinnaro C, Christensen GE, Curtis V. Modeling Ketogenesis for Use in Pediatric Diabetes Simulation. J Diabetes Sci Technol 2021; 15:303-308. [PMID: 31608650 PMCID: PMC8256079 DOI: 10.1177/1932296819882058] [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: 11/15/2022]
Abstract
BACKGROUND Simulation is being increasingly integrated into medical education. Diabetes simulation is well-received by trainees and has demonstrated improved clinical results, including reduced adult inpatient hyperglycemia. However, no pediatric-specific diabetes simulation programs exist for use in medical education. None of the existing diabetes models incorporate ketones as an input or an output, which is essential for use in teaching pediatric diabetes management. METHODS We created a pediatric diabetes simulation incorporating both blood sugar and urine ketones as output. Ketone output is implemented as a state variable but is obfuscated to simulate hospital experience. Blood sugar output is similar to other models and incorporates the current blood sugar, insulin on board (IOB) and carbohydrates on board (COB), and insulin and carbohydrate sensitivities. The program calculates all IOB and COB every 15 minutes based on user input and provides written summary feedback at the end of the simulation about inaccurate dosing and timing. RESULTS The simulation realistically incorporated both blood glucose and urine ketones in clinically valid and actionable formats. After completing this simulation, 16/17 pediatric residents indicated that they wanted more simulated diabetes cases integrated into their curriculum. CONCLUSION Implementing simulation into pediatric diabetes education was feasible and well-received. More work is needed to further study the role of simulation in pediatric diabetes education when used adjunctively or in lieu of lectures when time or resources are limited.
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Affiliation(s)
- Catherina Pinnaro
- Stead Family Department of
Pediatrics, Department of Endocrinology and Diabetes, University of Iowa,
IA, USA
- Catherina Pinnaro, MD, Stead Family
Department of Pediatrics, Department of Endocrinology and Diabetes,
University of Iowa, 2015-20 Boyd Tower, 200 Hawkins Drive, Iowa City,
IA 52242, USA.
| | - Gary E. Christensen
- Department of Electrical and
Computer Engineering, University of Iowa, IA, USA
- Department of Radiation Oncology,
University of Iowa, IA, USA
| | - Vanessa Curtis
- Stead Family Department of
Pediatrics, Department of Endocrinology and Diabetes, University of Iowa,
IA, USA
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12
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Zheng M, Ni B, Kleinberg S. Automated meal detection from continuous glucose monitor data through simulation and explanation. J Am Med Inform Assoc 2021; 26:1592-1599. [PMID: 31562509 PMCID: PMC6857509 DOI: 10.1093/jamia/ocz159] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 07/09/2019] [Accepted: 08/14/2019] [Indexed: 01/01/2023] Open
Abstract
Background Artificial pancreas systems aim to reduce the burden of type 1 diabetes by automating insulin dosing. These systems link a continuous glucose monitor (CGM) and insulin pump with a control algorithm, but require users to announce meals, without which the system can only react to the rise in blood glucose. Objective We investigate whether CGM data can be used to automatically infer meals in daily life even in the presence of physical activity, which can raise or lower blood glucose. Materials and Methods We propose a novel meal detection algorithm that combines simulations with CGM, insulin pump, and heart rate monitor data. When observed and predicted glucose differ, our algorithm uses simulations to test whether a meal may explain this difference. We evaluated our method on simulated data and real-world data from individuals with type 1 diabetes. Results In simulated data, we detected meals earlier and with higher accuracy than was found in prior work (25.7 minutes, 1.2 g error; compared with 48.3 minutes, 17.2 g error). In real-world data, we discovered a larger number of plausible meals than was found in prior work (30 meals, 76.7% accepted; compared with 33 meals, 39.4% accepted). Discussion Prior research attempted meal detection from CGM, but had delays and lower accuracy in real data or did not allow for physical activity. Our approach can be used to improve insulin dosing in an artificial pancreas and trigger reminders for missed meal boluses. Conclusions We demonstrate that meal information can be robustly inferred from CGM and body-worn sensor data, even in challenging environments of daily life.
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Affiliation(s)
- Min Zheng
- Computer Science, Stevens Institute of Technology, Hoboken, New Jersey, USA
| | - Baohua Ni
- Electronic Engineering, Tsinghua University, Beijing, China
| | - Samantha Kleinberg
- Computer Science, Stevens Institute of Technology, Hoboken, New Jersey, USA
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13
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Frank S, Jbaily A, Hinshaw L, Basu R, Basu A, Szeri AJ. Modeling the acute effects of exercise on glucose dynamics in healthy nondiabetic subjects. J Pharmacokinet Pharmacodyn 2021; 48:225-239. [PMID: 33394220 DOI: 10.1007/s10928-020-09726-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 11/04/2020] [Indexed: 11/25/2022]
Abstract
To shed light on how acute exercise affects blood glucose (BG) concentrations in nondiabetic subjects, we develop a physiological pharmacokinetic/pharmacodynamic model of postprandial glucose dynamics during exercise. We unify several concepts of exercise physiology to derive a multiscale model that includes three important effects of exercise on glucose dynamics: increased endogenous glucose production (EGP), increased glucose uptake in skeletal muscle (SM), and increased glucose delivery to SM by capillary recruitment (i.e. an increase in surface area and blood flow in capillary beds). We compare simulations to experimental observations taken in two cohorts of healthy nondiabetic subjects (resting subjects (n = 12) and exercising subjects (n = 12)) who were each given a mixed-meal tolerance test. Metabolic tracers were used to quantify the glucose flux. Simulations reasonably agree with postprandial measurements of BG concentration and EGP during exercise. Exercise-induced capillary recruitment is predicted to increase glucose transport to SM by 100%, causing hypoglycemia. When recruitment is blunted, as in those with capillary dysfunction, the opposite occurs and higher than expected BG levels are predicted. Model simulations show how three important exercise-induced phenomena interact, impacting BG concentrations. This model describes nondiabetic subjects, but it is a first step to a model that describes glucose dynamics during exercise in those with type 1 diabetes (T1D). Clinicians and engineers can use the insights gained from the model simulations to better understand the connection between exercise and glucose dynamics and ultimately help patients with T1D make more informed insulin dosing decisions around exercise.
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Affiliation(s)
- Spencer Frank
- Department of Mechanical Engineering at the University of California Berkeley, Berkeley, USA.
- Dexcom in San Diego, San Diego, CA, USA.
| | - Abdulrahman Jbaily
- Department of Mechanical Engineering at the University of California Berkeley, Berkeley, USA
- Dexcom in San Diego, San Diego, CA, USA
| | - Ling Hinshaw
- Division of Endocrinology at Mayo Clinic, Rochester, USA
| | - Rita Basu
- Division of Endocrinology at the University of Virginia School of Medicine, Charlottesville, USA
| | - Ananda Basu
- Division of Endocrinology at the University of Virginia School of Medicine, Charlottesville, USA
| | - Andrew J Szeri
- Department of Mechanical Engineering at the University of California Berkeley, Berkeley, USA
- Department of Mechanical Engineering at the University of British Columbia, Vancouver, Canada
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ESSAMBA MAH URSULE, WOAFO PAUL. NUMERICAL SIMULATION OF AN ELECTRODYNAMIC TRANSDUCER CONTROL OF INSULIN PROVISION IN THE BERGMAN’S AND THE CHENG’S MODELS FOR THE DYNAMICS OF THE COUPLE GLUCOSE-INSULIN IN DIABETICS. J MECH MED BIOL 2020. [DOI: 10.1142/s0219519420500554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper deals with the numerical simulation of a model of blood glucose level control of a diabetic person using an electrodynamic transducer. Two mathematical models describing the dynamics of the couple glucose–insulin are used: the Bergman’s and the Cheng’s models. First, the adaptive control is applied on the dynamics of a reservoir opener by an electrodynamic transducer. Then it is applied on the two models of the glucose–insulin dynamics. It is found that the control of the reservoir opener and that of the glycemia of a diabetic patient are efficient for some values of the control parameters.
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Affiliation(s)
- URSULE ESSAMBA MAH
- Lab. Modelling and Simulation in Engineering, Biomimetics and Prototypes and TWAS Research Unit, Faculty of Sciences, University of Yaoundé I, Cameroon
| | - PAUL WOAFO
- Lab. Modelling and Simulation in Engineering, Biomimetics and Prototypes and TWAS Research Unit, Faculty of Sciences, University of Yaoundé I, Cameroon
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15
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Isfahani MK, Zekri M, Marateb HR, Faghihimani E. A Hybrid Dynamic Wavelet-Based Modeling Method for Blood Glucose Concentration Prediction in Type 1 Diabetes. JOURNAL OF MEDICAL SIGNALS & SENSORS 2020; 10:174-184. [PMID: 33062609 PMCID: PMC7528985 DOI: 10.4103/jmss.jmss_62_19] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 12/12/2019] [Accepted: 01/10/2020] [Indexed: 11/07/2022]
Abstract
Background: Diabetes mellitus (DM) is a chronic disease that affects public health. The prediction of blood glucose concentration (BGC) is essential to improve the therapy of type 1 DM (T1DM). Methods: Having considered the risk of hyper- and hypo-glycemia, we provide a new hybrid modeling approach for BGC prediction based on a dynamic wavelet neural network (WNN) model, including a heuristic input selection. The proposed models include a hybrid dynamic WNN (HDWNN) and a hybrid dynamic fuzzy WNN (HDFWNN). These wavelet-based networks are designed based on dominant wavelets selected by the genetic algorithm-orthogonal least square method. Furthermore, the HDFWNN model structure is improved using fuzzy rule induction, an important innovation in the fuzzy wavelet modeling. The proposed networks are tested on real data from 12 T1DM patients and also simulated data from 33 virtual patients with an UVa/ Padova simulator, an approved simulator by the US Food and Drug Administration. Results: A comparison study is performed in terms of new glucose-based assessment metrics, such as gFIT, glucose-weighted form of ESODn (gESODn), and glucose-weighted R2 (gR2). For real patients’ data, the values of the mentioned indices are accomplished as gFIT = 0.97 ± 0.01, gESODn = 1.18 ± 0.38, and gR2 = 0.88 ± 0.07. HDFWNN, HDWNN and jump NN method showed the prediction error (root mean square error [RMSE]) of 11.23 ± 2.77 mg/dl, 10.79 ± 3.86 mg/dl and 16.45 ± 4.33 mg/dl, respectively. Conclusion: Furthermore, the generalized estimating equation and post hoc tests show that proposed models perform better compared with other proposed methods.
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Affiliation(s)
| | - Maryam Zekri
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Hamid Reza Marateb
- Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.,Department of Automatic Control, Biomedical Engineering Research Center, Polytechnic University of Catalonia, Barcelona Tech, Barcelona, Spain
| | - Elham Faghihimani
- Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
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Ebrahimi N, Ozgoli S, Ramezani A. Model free sliding mode controller for blood glucose control: Towards artificial pancreas without need to mathematical model of the system. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 195:105663. [PMID: 32750632 DOI: 10.1016/j.cmpb.2020.105663] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 07/10/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND The mechanism of glucose regulation in human blood is a nonlinear complicated biological system with uncertain parameters and external disturbances which cannot be imitated accurately by a simple mathematical model. So to achieve an artificial pancreas, a method that does not need a model is necessary. METHODS In this paper, a model free third order terminal sliding mode controller is developed and applied to blood glucose regulation system. So in this paper, a data driven control method is proposed which doesn't need a pre specified mathematical model of the system. The proposed method uses a third order terminal sliding mode controller to overcome the problem in finite time without chattering. It also uses a disturbance estimation technique to reject external disturbances. The sliding mode algorithm is equipped with a regression algorithm to release its need to model of the system. It is proved theoretically that the method is stable and the error converges to zero. In order to determine the parameters needed in this method, an algorithm is provided. RESULTS Simulation studies are carried out with different scenarios and compared with Model Free Adaptive Control method. At the first scenario, the proposed method is applied to a virtual type- 1 diabetic patient without considering of external disturbances. The blood glucose level of 110 mg/dl is considered as the goal and it is illustrated that the desired glucose concentration is obtained. It is illustrated that the proposed method shows better performance against Model Free Adaptive Controller. Then in the next scenario, blood glucose of the patient is controlled in presence of three meal times during a day with different values of carbohydrate. The maximum of the blood glucose in this scenario is obtained as 168.5 mg/dl and the minimum of it stays on 85.5 Mg/dl. So the patient blood glucose level is almost within acceptable range (70-180 mg/dl) unlike the Model Free Adaptive Controller. In the last scenario, 22 tests are done for different patients (by randomly varying simulator parameters in ± 40% range) and the control performance is evaluated by the well-known Control Variability Grid Analysis CVGA. For all of them, the blood glucose remains in the green zone (safe region) of the CVGA . CONCLUSION Simulation results show that the proposed method acts robustly and can overcome uncertainties and external disturbances. The blood glucose level remains in safe region in all case. So the proposed method can be used in an artificial pancreas.
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Affiliation(s)
- Nahid Ebrahimi
- Systems, Life Sciences and Control Engineering (SyLiCon) LAB, Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
| | - Sadjaad Ozgoli
- Systems, Life Sciences and Control Engineering (SyLiCon) LAB, Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
| | - Amin Ramezani
- Systems, Life Sciences and Control Engineering (SyLiCon) LAB, Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
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17
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Sánchez OD, Ruiz-Velázquez E, Alanís AY, Quiroz G, Torres-Treviño L. Parameter estimation of a meal glucose-insulin model for TIDM patients from therapy historical data. IET Syst Biol 2019; 13:8-15. [PMID: 30774111 DOI: 10.1049/iet-syb.2018.5038] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
The effect of meal on blood glucose concentration is a key issue in diabetes mellitus because its estimation could be very useful in therapy decisions. In the case of type 1 diabetes mellitus (T1DM), the therapy based on automatic insulin delivery requires a closed-loop control system to maintain euglycaemia even in the postprandial state. Thus, the mathematical modelling of glucose metabolism is relevant to predict the metabolic state of a patient. Moreover, the eating habits are characteristic of each person, so it is of interest that the mathematical models of meal intake allow to personalise the glycaemic state of the patient using therapy historical data, that is, daily measurements of glucose and records of carbohydrate intake and insulin supply. Thus, here, a model of glucose metabolism that includes the effects of meal is analysed in order to establish criteria for data-based personalisation. The analysis includes the sensitivity and identifiability of the parameters, and the parameter estimation problem was resolved via two algorithms: particle swarm optimisation and evonorm. The results show that the mathematical model can be a useful tool to estimate the glycaemic status of a patient and personalise it according to her/his historical data.
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Affiliation(s)
- Oscar D Sánchez
- CUCEI, Universidad de Guadalajara, Av. Revolución 1500, Col. Universitaria, 44430 Guadalajara, Jal., México
| | - Eduardo Ruiz-Velázquez
- CUCEI, Universidad de Guadalajara, Av. Revolución 1500, Col. Universitaria, 44430 Guadalajara, Jal., México.
| | - Alma Y Alanís
- CUCEI, Universidad de Guadalajara, Av. Revolución 1500, Col. Universitaria, 44430 Guadalajara, Jal., México
| | - Griselda Quiroz
- FIME, Universidad Autónoma de Nuevo León, Av. Universidad S/N, Ciudad Universitaria, 66455 San Nicolás de los Garza, Nuevo León, N.L., México
| | - Luis Torres-Treviño
- FIME, Universidad Autónoma de Nuevo León, Av. Universidad S/N, Ciudad Universitaria, 66455 San Nicolás de los Garza, Nuevo León, N.L., México
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Rashid M, Samadi S, Sevil M, Hajizadeh I, Kolodziej P, Hobbs N, Maloney Z, Brandt R, Feng J, Park M, Quinn L, Cinar A. Simulation Software for Assessment of Nonlinear and Adaptive Multivariable Control Algorithms: Glucose - Insulin Dynamics in Type 1 Diabetes. Comput Chem Eng 2019; 130:106565. [PMID: 32863472 PMCID: PMC7449052 DOI: 10.1016/j.compchemeng.2019.106565] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
A simulator for testing automatic control algorithms for nonlinear systems with time-varying parameters, variable time delays, and uncertainties is developed. It is based on simulation of virtual patients with Type 1 diabetes (T1D). Nonlinear models are developed to describe glucose concentration (GC) variations based on user-defined scenarios for meal consumption, insulin administration, and physical activity. They compute GC values and physiological variables, such as heart rate, skin temperature, accelerometer, and energy expenditure, that are indicative of physical activities affecting GC dynamics. This is the first simulator designed for assessment of multivariable controllers that consider supplemental physiological variables in addition to GC measurements to improve glycemic control. Virtual patients are generated from distributions of identified model parameters using clinical data. The simulator will enable testing and evaluation of new control algorithms proposed for automated insulin delivery as well as various control algorithms for nonlinear systems with uncertainties, time-varying parameters and delays.
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Affiliation(s)
- Mudassir Rashid
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
| | - Sediqeh Samadi
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
| | - Mert Sevil
- Dept of Biomedical Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 606016
| | - Iman Hajizadeh
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
| | - Paul Kolodziej
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
| | - Nicole Hobbs
- Dept of Biomedical Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 606016
| | - Zacharie Maloney
- Dept of Biomedical Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 606016
| | - Rachel Brandt
- Dept of Biomedical Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 606016
| | - Jianyuan Feng
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
| | - Minsun Park
- College of Nursing, University of Illinois at Chicago, Chicago, IL, USA, 60612
| | - Laurie Quinn
- College of Nursing, University of Illinois at Chicago, Chicago, IL, USA, 60612
| | - Ali Cinar
- Dept of Chemical and Biological Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 60616
- Dept of Biomedical Engineering, Illinois Institute of Technology, 10 W 33rd Street, Chicago, IL, USA, 606016
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Fujii M, Murakami Y, Karasawa Y, Sumitomo Y, Fujita S, Koyama M, Uda S, Kubota H, Inoue H, Konishi K, Oba S, Ishii S, Kuroda S. Logical design of oral glucose ingestion pattern minimizing blood glucose in humans. NPJ Syst Biol Appl 2019; 5:31. [PMID: 31508240 PMCID: PMC6718521 DOI: 10.1038/s41540-019-0108-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 08/06/2019] [Indexed: 12/22/2022] Open
Abstract
Excessive increase in blood glucose level after eating increases the risk of macroangiopathy, and a method for not increasing the postprandial blood glucose level is desired. However, a logical design method of the dietary ingestion pattern controlling the postprandial blood glucose level has not yet been established. We constructed a mathematical model of blood glucose control by oral glucose ingestion in three healthy human subjects, and predicted that intermittent ingestion 30 min apart was the optimal glucose ingestion patterns that minimized the peak value of blood glucose level. We confirmed with subjects that this intermittent pattern consistently decreased the peak value of blood glucose level. We also predicted insulin minimization pattern, and found that the intermittent ingestion 30 min apart was optimal, which is similar to that of glucose minimization pattern. Taken together, these results suggest that the glucose minimization is achieved by suppressing the peak value of insulin concentration, rather than by enhancing insulin concentration. This approach could be applied to design optimal dietary ingestion patterns.
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Affiliation(s)
- Masashi Fujii
- Molecular Genetic Research Laboratory, Graduate School of Science, The University of Tokyo, Tokyo, 113-0033 Japan
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, 113-0033 Japan
- Present Address: Department of Integrated Sciences for Life, Graduate School of Integrated Sciences for Life, Hiroshima University, Hiroshima, 739-8526 Japan
| | - Yohei Murakami
- Department of Systems Science, Graduate School of Informatics, Kyoto University, Kyoto, 606-8501 Japan
| | - Yasuaki Karasawa
- Department of Neurosurgery, The University of Tokyo Hospital, The University of Tokyo, Tokyo, 113-0033 Japan
- Department of Rehabilitation, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033 Japan
| | - Yohei Sumitomo
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, 113-0033 Japan
| | - Suguru Fujita
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, 113-0033 Japan
| | - Masanori Koyama
- Department of Mathematics, Graduate School of Science and Engineering, Ritsumeikan University, Shiga, 525-8577 Japan
| | - Shinsuke Uda
- Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, Fukuoka, 812-8582 Japan
| | - Hiroyuki Kubota
- Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, Fukuoka, 812-8582 Japan
| | - Hiroshi Inoue
- Metabolism and Nutrition Research Unit, Institute for Frontier Science Initiative, Kanazawa University, Ishikawa, 920-8640 Japan
| | - Katsumi Konishi
- Faculty of Computer and Information Sciences, Hosei University, Tokyo, 184-8584 Japan
| | - Shigeyuki Oba
- Department of Systems Science, Graduate School of Informatics, Kyoto University, Kyoto, 606-8501 Japan
| | - Shin Ishii
- Department of Systems Science, Graduate School of Informatics, Kyoto University, Kyoto, 606-8501 Japan
- CREST, Japan Science and Technology Agency, Tokyo, 113-0033 Japan
| | - Shinya Kuroda
- Molecular Genetic Research Laboratory, Graduate School of Science, The University of Tokyo, Tokyo, 113-0033 Japan
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, 113-0033 Japan
- CREST, Japan Science and Technology Agency, Tokyo, 113-0033 Japan
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20
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Mandal S, Sutradhar A. Robust multi-objective blood glucose control in Type-1 diabetic patient. IET Syst Biol 2019; 13:136-146. [PMID: 31170693 PMCID: PMC8687400 DOI: 10.1049/iet-syb.2018.5093] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
In this study, an automatic robust multi‐objective controller has been proposed for blood glucose (BG) regulation in Type‐1 Diabetic Mellitus (T1DM) patient through subcutaneous route. The main objective of this work is to control the BG level in T1DM patient in the presence of unannounced meal disturbances and other external noises with a minimum amount of insulin infusion rate. The multi‐objective output‐feedback controller with H∞, H2 and pole‐placement constraints has been designed using linear matrix inequality technique. The designed controller for subcutaneous insulin delivery was tested on in silico adult and adolescent subjects of UVa/Padova T1DM metabolic simulator. The experimental results show that the closed‐loop system tracks the reference BG level very well and does not show any hypoglycaemia effect even during the long gap of a meal at night both for in silico adults and adolescent. In the presence of 50 gm meal disturbance, average adult experience normoglycaemia 92% of the total simulation time and hypoglycaemia 0% of total simulation time. The robustness of the controller has been tested in the presence of irregular meals and insulin pump noise and error. The controller yielded robust performance with a lesser amount of insulin infusion rate than the other designed controllers reported earlier.
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Affiliation(s)
- Sharmistha Mandal
- Electrical Engineering Department, Aliah University, Salt Lake, Kolkata, India.
| | - Ashoke Sutradhar
- Electrical Engineering Department, Indian Institute of Engineering Science and Technology, Howrah, India
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21
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Glucose-responsive insulin by molecular and physical design. Nat Chem 2019; 9:937-943. [PMID: 28937662 DOI: 10.1038/nchem.2857] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Accepted: 07/11/2017] [Indexed: 12/15/2022]
Abstract
The concept of a glucose-responsive insulin (GRI) has been a recent objective of diabetes technology. The idea behind the GRI is to create a therapeutic that modulates its potency, concentration or dosing relative to a patient's dynamic glucose concentration, thereby approximating aspects of a normally functioning pancreas. From the perspective of the medicinal chemist, the GRI is also important as a generalized model of a potentially new generation of therapeutics that adjust potency in response to a critical therapeutic marker. The aim of this Perspective is to highlight emerging concepts, including mathematical modelling and the molecular engineering of insulin itself and its potency, towards a viable GRI. We briefly outline some of the most important recent progress toward this goal and also provide a forward-looking viewpoint, which asks if there are new approaches that could spur innovation in this area as well as to encourage synthetic chemists and chemical engineers to address the challenges and promises offered by this therapeutic approach.
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Bhattacharjee A, Easwaran A, Leow MKS, Cho N. Design of an online-tuned model based compound controller for a fully automated artificial pancreas. Med Biol Eng Comput 2019; 57:1437-1449. [PMID: 30895514 DOI: 10.1007/s11517-019-01972-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 03/06/2019] [Indexed: 11/25/2022]
Abstract
This paper deals with the development of a control algorithm that can predict optimal insulin doses without patients' intervention in fully automated artificial pancreas system. An online-tuned model based compound controller comprising an online-tuned internal model control (IMC) algorithm and an enhanced IMC (eIMC) algorithm along with a meal detection module is proposed. Volterra models, used to develop IMC and eIMC algorithms, are developed online using recursive least squares (RLS) filter. The time domain kernels, computed online using RLS filter, are converted into frequency domain to obtain Volterra transfer function (VTF). VTFs are used to develop both IMC and eIMC algorithms. The compound controller is designed in such a way that eIMC predicts insulin doses when the glucose rate increase detector of meal detection module is positive, otherwise conventional IMC takes the control action. Experimental results show that the compound controller performs robustly in the presence of higher and irregular amounts of meal disturbances at random times, very high actuator and sensor noises and also with the variation in insulin sensitivity. The combination of compound control strategy and meal detection module compensates the shortcomings of both slow subcutaneous insulin action that causes postprandial hyperglycemia, and delayed peak of action that causes hypoglycaemia. Graphical Abstract A fully-automated artificial pancreas system containing glucose sensor, insulin pump and control algorithm. Block diagram showing the control algorithm i.e., online-tuned compound IMC comprising enhanced IMC, conventional IMC and meal detection module, developed in the present work.
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Affiliation(s)
| | | | - Melvin Khee-Shing Leow
- Nanyang Technological University, Singapore, Singapore.,Department of Endocrinology, Tan Tock Seng Hospital, Singapore, Singapore.,Singapore Institute for Clinical Sciences, A*STAR, Singapore, Singapore.,Office of Clinical Sciences, Duke-NUS Graduate Medical School, Singapore, Singapore.,Lee Kong Chian School of Medicine-Imperial College London, London, SW7 2DD, UK
| | - Namjoon Cho
- Nanyang Technological University, Singapore, Singapore
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23
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Shirin A, Della Rossa F, Klickstein I, Russell J, Sorrentino F. Optimal regulation of blood glucose level in Type I diabetes using insulin and glucagon. PLoS One 2019; 14:e0213665. [PMID: 30893335 PMCID: PMC6426249 DOI: 10.1371/journal.pone.0213665] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 02/26/2019] [Indexed: 12/11/2022] Open
Abstract
The Glucose-Insulin-Glucagon nonlinear model accurately describes how the body responds to exogenously supplied insulin and glucagon in patients affected by Type I diabetes. Based on this model, we design infusion rates of either insulin (monotherapy) or insulin and glucagon (dual therapy) that can optimally maintain the blood glucose level within desired limits after consumption of a meal and prevent the onset of both hypoglycemia and hyperglycemia. This problem is formulated as a nonlinear optimal control problem, which we solve using the numerical optimal control package PSOPT. Interestingly, in the case of monotherapy, we find the optimal solution is close to the standard method of insulin based glucose regulation, which is to assume a variable amount of insulin half an hour before each meal. We also find that the optimal dual therapy (that uses both insulin and glucagon) is better able to regulate glucose as compared to using insulin alone. We also propose an ad-hoc rule for both the dosage and the time of delivery of insulin and glucagon.
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Affiliation(s)
- Afroza Shirin
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
- * E-mail:
| | - Fabio Della Rossa
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
| | - Isaac Klickstein
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
| | - John Russell
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
| | - Francesco Sorrentino
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
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24
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Kadota R, Sugita K, Uchida K, Yamada H, Yamashita M, Kimura H. A mathematical model of type 1 diabetes involving leptin effects on glucose metabolism. J Theor Biol 2018; 456:213-223. [PMID: 30098320 DOI: 10.1016/j.jtbi.2018.08.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 08/03/2018] [Accepted: 08/06/2018] [Indexed: 12/14/2022]
Abstract
Leptin, a hormone released from fat cells in adipose tissues, was recently found to be capable of normalizing glucose metabolism in animals. Clinical data on patients with lipodystrophy indicates that leptin may have a positive effect on glucose metabolism in individuals with diabetes. There are growing expectations that leptin can improve the current insulin treatment for patients with type 1 diabetes. We investigated this possibility through in silico experiments based on a mathematical model of diabetes, which is currently the only mode of research that eliminates human risk. A model of the brain-centered glucoregulatory system, in which leptin plays a central role, was constructed and integrated within a conventional model of insulin/glucose dynamics. The model has been validated using experimental data from animal studies. The in silico combination experiments showed excellent therapeutic performance over insulin monotherapy.
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Affiliation(s)
- Rei Kadota
- Faculty of Science and Engineering, Waseda University, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo, Japan
| | - Kazuma Sugita
- Faculty of Science and Engineering, Waseda University, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo, Japan
| | - Kenko Uchida
- Faculty of Science and Engineering, Waseda University, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo, Japan
| | - Hitoshi Yamada
- TOYOTA MOTOR CORPORATION, 1, Toyota-cho, Toyota, Aichi, Japan
| | | | - Hidenori Kimura
- Faculty of Science and Engineering, Waseda University, Building 55S, Room 706A, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo 169-8555, Japan.
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25
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Artificial pancreas clinical trials: Moving towards closed-loop control using insulin-on-board constraints. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.05.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Campos-Náñez E, Layne JE, Zisser HC. In Silico Modeling of Minimal Effective Insulin Doses Using the UVA/PADOVA Type 1 Diabetes Simulator. J Diabetes Sci Technol 2018; 12:376-380. [PMID: 28952380 PMCID: PMC5851227 DOI: 10.1177/1932296817735341] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
BACKGROUND The objective of this study was to identify the minimum basal insulin infusion rates and bolus insulin doses that would result in clinically relevant changes in blood glucose levels in the most insulin sensitive subjects with type 1 diabetes. METHODS The UVA/PADOVA Type 1 Diabetes Simulator in silico population of children, adolescents, and adults was administered a basal insulin infusion rate to maintain blood glucose concentrations at 120 mg/dL (6.7 mmol/L). Two scenarios were modeled independently after 1 hour of simulated time: (1) basal insulin infusion rates in increments of 0.01 U/h were administered and (2) bolus doses in increments of 0.01 U were injected. Subjects were observed for 4 hours to determine insulin delivery required to change blood glucose by 12.5 mg/dL (0.7 mmol/L) and 25 mg/dL (1.4 mmol/L) in only 5% of the in silico population. RESULTS The basal insulin infusion rates required to change blood glucose by 12.5 mg/dL and 25 mg/dL in 5% of children, adolescents, and adults were 0.03, 0.11, and 0.10 U/h and 0.06, 0.21, and 0.19 U/h, respectively. The bolus insulin doses required to change blood glucose by the target amounts in the respective populations were 0.10, 0.28, and 0.30 U and 0.19, 0.55, and 0.60 U. CONCLUSIONS In silico modeling suggests that only a very small percentage of individuals with type 1 diabetes, corresponding to children with high insulin sensitivity and low body weight, will exhibit a clinically relevant change in blood glucose with very low basal insulin rate changes or bolus doses.
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Affiliation(s)
- Enrique Campos-Náñez
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
- Enrique Campos-Náñez, PhD, Center for Diabetes Technology, University of Virginia, PO Box 400888 Charlottesville, VA 22904-0888, USA.
| | | | - Howard C. Zisser
- Verily Life Sciences, San Francisco, CA, USA
- University of California, Santa Barbara, Santa Barbara, CA, USA
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27
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Nath A, Biradar S, Balan A, Dey R, Padhi R. Physiological Models and Control for Type 1 Diabetes Mellitus: A Brief Review. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.ifacol.2018.05.077] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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28
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Viceconti M, Cobelli C, Haddad T, Himes A, Kovatchev B, Palmer M. In silico assessment of biomedical products: The conundrum of rare but not so rare events in two case studies. Proc Inst Mech Eng H 2017; 231:455-466. [PMID: 28427321 DOI: 10.1177/0954411917702931] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
In silico clinical trials, defined as "The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention," have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognized as inadequate, as for example, the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical assessment of artificial pancreas technologies, and the second, an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patients' phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern.
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Affiliation(s)
- Marco Viceconti
- 1 Department of Mechanical Engineering, INSIGNEO Institute for in silico Medicine, The University of Sheffield, Sheffield, UK
| | - Claudio Cobelli
- 2 Department of Information Engineering, University of Padova, Padova, Italy
| | | | | | - Boris Kovatchev
- 4 Center for Diabetes Technology, The University of Virginia, Charlottesville, VA, USA
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Bakh NA, Bisker G, Lee MA, Gong X, Strano MS. Rational Design of Glucose-Responsive Insulin Using Pharmacokinetic Modeling. Adv Healthc Mater 2017; 6. [PMID: 28841775 DOI: 10.1002/adhm.201700601] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Revised: 06/30/2017] [Indexed: 11/08/2022]
Abstract
A glucose responsive insulin (GRI) is a therapeutic that modulates its potency, concentration, or dosing of insulin in relation to a patient's dynamic glucose concentration, thereby approximating aspects of a normally functioning pancreas. Current GRI design lacks a theoretical basis on which to base fundamental design parameters such as glucose reactivity, dissociation constant or potency, and in vivo efficacy. In this work, an approach to mathematically model the relevant parameter space for effective GRIs is induced, and design rules for linking GRI performance to therapeutic benefit are developed. Well-developed pharmacokinetic models of human glucose and insulin metabolism coupled to a kinetic model representation of a freely circulating GRI are used to determine the desired kinetic parameters and dosing for optimal glycemic control. The model examines a subcutaneous dose of GRI with kinetic parameters in an optimal range that results in successful glycemic control within prescribed constraints over a 24 h period. Additionally, it is demonstrated that the modeling approach can find GRI parameters that enable stable glucose levels that persist through a skipped meal. The results provide a framework for exploring the parameter space of GRIs, potentially without extensive, iterative in vivo animal testing.
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Affiliation(s)
- Naveed A. Bakh
- Department of Chemical Engineering; Massachusetts Institute of Technology; 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Gili Bisker
- Department of Chemical Engineering; Massachusetts Institute of Technology; 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Michael A. Lee
- Department of Chemical Engineering; Massachusetts Institute of Technology; 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Xun Gong
- Department of Chemical Engineering; Massachusetts Institute of Technology; 77 Massachusetts Avenue Cambridge MA 02139 USA
| | - Michael S. Strano
- Department of Chemical Engineering; Massachusetts Institute of Technology; 77 Massachusetts Avenue Cambridge MA 02139 USA
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30
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31
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Marchetti L, Reali F, Dauriz M, Brangani C, Boselli L, Ceradini G, Bonora E, Bonadonna RC, Priami C. A Novel Insulin/Glucose Model after a Mixed-Meal Test in Patients with Type 1 Diabetes on Insulin Pump Therapy. Sci Rep 2016; 6:36029. [PMID: 27824066 PMCID: PMC5099899 DOI: 10.1038/srep36029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Accepted: 10/10/2016] [Indexed: 11/30/2022] Open
Abstract
Current closed-loop insulin delivery methods stem from sophisticated models of the glucose-insulin (G/I) system, mostly based on complex studies employing glucose tracer technology. We tested the performance of a new minimal model (GLUKINSLOOP 2.0) of the G/I system to characterize the glucose and insulin dynamics during multiple mixed meal tests (MMT) of different sizes in patients with type 1 diabetes (T1D) on insulin pump therapy (continuous subcutaneous insulin infusion, CSII). The GLUKINSLOOP 2.0 identified the G/I system, provided a close fit of the G/I time-courses and showed acceptable reproducibility of the G/I system parameters in repeated studies of identical and double-sized MMTs. This model can provide a fairly good and reproducible description of the G/I system in T1D patients on CSII, and it may be applied to create a bank of “virtual” patients. Our results might be relevant at improving the architecture of upcoming closed-loop CSII systems.
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Affiliation(s)
- Luca Marchetti
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy
| | - Federico Reali
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy.,Department of Mathematics, University of Trento, Trento, Italy
| | - Marco Dauriz
- Department of Medicine, Section of Endocrinology, University of Verona School of Medicine, Verona, Italy
| | - Corinna Brangani
- Department of Medicine, Section of Endocrinology, University of Verona School of Medicine, Verona, Italy
| | - Linda Boselli
- Department of Medicine, Section of Endocrinology, University of Verona School of Medicine, Verona, Italy
| | - Giulia Ceradini
- Department of Medicine, Section of Endocrinology, University of Verona School of Medicine, Verona, Italy
| | - Enzo Bonora
- Department of Medicine, Section of Endocrinology, University of Verona School of Medicine, Verona, Italy.,Division of Endocrinology and Metabolic Diseases, Azienda Ospedaliera Universitaria Integrata, Verona, Italy
| | - Riccardo C Bonadonna
- Department of Clinical and Experimental Medicine, University of Parma, Parma, Italy.,Division of Endocrinology, Azienda Ospedaliera Universitaria of Parma, Italy
| | - Corrado Priami
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy.,Department of Mathematics, University of Trento, Trento, Italy
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32
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Cescon M, DeSalvo DJ, Ly TT, Maahs DM, Messer LH, Buckingham BA, Doyle FJ, Dassau E. Early Detection of Infusion Set Failure During Insulin Pump Therapy in Type 1 Diabetes. J Diabetes Sci Technol 2016; 10:1268-1276. [PMID: 27621142 PMCID: PMC5094340 DOI: 10.1177/1932296816663962] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Insulin infusion set failure resulting in prolonged hyperglycemia or diabetic ketoacidosis can occur with pump therapy in type 1 diabetes. Set failures are frequently characterized by variable and unpredictable patterns of increasing glucose values despite increased insulin infusion. Early detection may minimize the risk of prolonged hyperglycemia, an important consideration for automated insulin delivery and closed-loop applications. METHODS A novel algorithm designed to alert the patient to the onset of infusion set failure was developed based upon continuous glucose sensor values and insulin delivered from an insulin pump. The method was calibrated on 12 weeks of infusion set wear without failures recorded by 4 patients in ambulatory conditions and prospectively validated on 18 weeks of infusion set wear with and without failures belonging to 9 other subjects in ambulatory conditions. RESULTS The algorithm, evaluated retrospectively, identified a failure 2.52 ± 1.91 days ahead of the actual event as recorded by the clinical team, corresponding to 50% sensitivity, 66% specificity and 55% accuracy. If set failure alarms had been activated in real time, the average time >180 mg/dl would be reduced from 82.7 ± 40.9 hours/week/subject (without alarm) to 58.8 ± 31.1 hours/week/subject (with alarm), corresponding to a potential 29% reduction in time spent >180mg/dl. CONCLUSION The proposed method for early detection of infusion set failure based on glucose sensor and insulin data demonstrated favorable results on retrospective data and may be implemented as an additional safeguard in a future fully automated closed-loop system.
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Affiliation(s)
- Marzia Cescon
- Department Chemical Engineering & Institute for Collaborative Biotechnologies, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Daniel J DeSalvo
- Pediatric Diabetes and Endocrinology, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA
| | - Trang T Ly
- Division of Pediatric Endocrinology, Stanford School of Medicine, Stanford, CA, USA
| | - David M Maahs
- Barbara Davis Center for Diabetes, University of Colorado Denver, Aurora, CO, USA
| | - Laurel H Messer
- Barbara Davis Center for Diabetes, University of Colorado Denver, Aurora, CO, USA
| | - Bruce A Buckingham
- Division of Pediatric Endocrinology, Stanford School of Medicine, Stanford, CA, USA
| | - Francis J Doyle
- Department Chemical Engineering & Institute for Collaborative Biotechnologies, University of California, Santa Barbara, Santa Barbara, CA, USA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Eyal Dassau
- Department Chemical Engineering & Institute for Collaborative Biotechnologies, University of California, Santa Barbara, Santa Barbara, CA, USA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
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Zecchin C, Facchinetti A, Sparacino G, Cobelli C. How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study. J Diabetes Sci Technol 2016; 10:1149-60. [PMID: 27381030 PMCID: PMC5032963 DOI: 10.1177/1932296816654161] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND In type 1 diabetes (T1D) management, short-term glucose prediction can allow to anticipate therapeutic decisions when hypo/hyperglycemia is imminent. Literature prediction methods mainly use past continuous glucose monitoring (CGM) readings. Sophisticated algorithms can use information on insulin delivered and meal carbohydrate (CHO) content. The quantification of how much insulin and CHO information improves glucose prediction is missing in the literature and is investigated, in an open-loop setting, in this proof-of-concept study. METHODS We adopted a versatile literature prediction methodology able to utilize a variety of inputs. We compared predictors that use (1) CGM; (2) CGM and insulin; (3) CGM and CHO; and (4) CGM, insulin, and CHO. Data of 15 T1D subjects in open-loop setup were used. Prediction was evaluated via absolute error and temporal gain focusing on meal/night periods. The relative importance of each individual input of the predictor was evaluated with a sensitivity analysis. RESULTS For a prediction horizon (PH) ≥ 30 minutes, insulin and CHO information improves prediction accuracy of 10% and double the temporal gain during the 2 hours following the meal. During the night the 4 methods did not give statistically different results. When PH ≥ 45 minutes, the influence of CHO information on prediction is 5-fold that of insulin. CONCLUSIONS In an open-loop setting, with PH ≥ 30 minutes, information on CHO and insulin improves short-term glucose prediction in the 2-hour time window following a meal, but not during the night. CHO information improves prediction significantly more than insulin.
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Affiliation(s)
- Chiara Zecchin
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Andrea Facchinetti
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Giovanni Sparacino
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
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Visentin R, Giegerich C, Jäger R, Dahmen R, Boss A, Grant M, Dalla Man C, Cobelli C, Klabunde T. Improving Efficacy of Inhaled Technosphere Insulin (Afrezza) by Postmeal Dosing: In-silico Clinical Trial with the University of Virginia/Padova Type 1 Diabetes Simulator. Diabetes Technol Ther 2016; 18:574-85. [PMID: 27333446 PMCID: PMC5035370 DOI: 10.1089/dia.2016.0128] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BACKGROUND Technosphere(®) insulin (TI), an inhaled human insulin with a fast onset of action, provides a novel option for the control of prandial glucose. We used the University of Virginia (UVA)/Padova simulator to explore in-silico the potential benefit of different dosing regimens on postprandial glucose (PPG) control to support the design of further clinical trials. Tested dosing regimens included at-meal or postmeal dosing, or dosing before and after a meal (split dosing). METHODS Various dosing regimens of TI were compared among one another and to insulin lispro in 100 virtual type-1 patients. Individual doses were identified for each regimen following different titration rules. The resulting postprandial glucose profiles were analyzed to quantify efficacy and the risk for hypoglycemic events. RESULTS This approach allowed us to assess the benefit/risk for each TI dosing regimen and to compare results with simulations of insulin lispro. We identified a new titration rule for TI that could significantly improve the efficacy of treatment with TI. CONCLUSION In-silico clinical trials comparing the treatment effect of different dosing regimens with TI and of insulin lispro suggest that postmeal dosing or split dosing of TI, in combination with an appropriate titration rule, can achieve a superior postprandial glucose control while providing a lower risk for hypoglycemic events than conventional treatment with subcutaneously administered rapid-acting insulin products.
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Affiliation(s)
- Roberto Visentin
- Department of Information Engineering, University of Padova, Padova, Italy
| | | | - Robert Jäger
- Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany
| | | | | | | | - Chiara Dalla Man
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
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Colmegna PH, Sánchez-Peña RS, Gondhalekar R, Dassau E, Doyle FJ. Reducing Glucose Variability Due to Meals and Postprandial Exercise in T1DM Using Switched LPV Control: In Silico Studies. J Diabetes Sci Technol 2016; 10:744-53. [PMID: 27022097 PMCID: PMC5038547 DOI: 10.1177/1932296816638857] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [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 Time-varying dynamics is one of the main issues for achieving safe blood glucose control in type 1 diabetes mellitus (T1DM) patients. In addition, the typical disturbances considered for controller design are meals, which increase the glucose level, and physical activity (PA), which increases the subject's sensitivity to insulin. In previous works the authors have applied a linear parameter-varying (LPV) control technique to manage unannounced meals. METHODS A switched LPV controller that switches between 3 LPV controllers, each with a different level of aggressiveness, is designed to further cope with both unannounced meals and postprandial PA. Thus, the proposed control strategy has a "standard" mode, an "aggressive" mode, and a "conservative" mode. The "standard" mode is designed to be applied most of the time, while the "aggressive" mode is designed to deal only with hyperglycemia situations. On the other hand, the "conservative" mode is focused on postprandial PA control. RESULTS An ad hoc simulator has been developed to test the proposed controller. This simulator is based on the distribution version of the UVA/Padova model and includes the effect of PA based on Schiavon.(1) The test results obtained when using this simulator indicate that the proposed control law substantially reduces the risk of hypoglycemia with the conservative strategy, while the risk of hyperglycemia is scarcely affected. CONCLUSIONS It is demonstrated that the announcement, or anticipation, of exercise is indispensable for letting a mono-hormonal artificial pancreas deal with the consequences of postprandial PA. In view of this the proposed controller allows switching into a conservative mode when notified of PA by the user.
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Affiliation(s)
- Patricio H Colmegna
- National Scientific and Technical Research Council, Buenos Aires, Argentina Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Buenos Aires, Argentina
| | - Ricardo S Sánchez-Peña
- National Scientific and Technical Research Council, Buenos Aires, Argentina Centro de Sistemas y Control, Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina
| | - Ravi Gondhalekar
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Eyal Dassau
- John A. Paulson School of Engineering & Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Francis J Doyle
- John A. Paulson School of Engineering & Applied Sciences, Harvard University, Cambridge, MA, USA
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Mamykina L, Levine ME, Davidson PG, Smaldone AM, Elhadad N, Albers DJ. Data-driven health management: reasoning about personally generated data in diabetes with information technologies. J Am Med Inform Assoc 2016; 23:526-31. [PMID: 26984049 DOI: 10.1093/jamia/ocv187] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Accepted: 11/03/2015] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE To investigate how individuals with diabetes and diabetes educators reason about data collected through self-monitoring and to draw implications for the design of data-driven self-management technologies. MATERIALS AND METHODS Ten individuals with diabetes (six type 1 and four type 2) and 2 experienced diabetes educators were presented with a set of self-monitoring data captured by an individual with type 2 diabetes. The set included digital images of meals and their textual descriptions, and blood glucose (BG) readings captured before and after these meals. The participants were asked to review a set of meals and associated BG readings, explain differences in postprandial BG levels for these meals, and predict postprandial BG levels for the same individual for a different set of meals. Researchers compared conclusions and predictions reached by the participants with those arrived at by quantitative analysis of the collected data. RESULTS The participants used both macronutrient composition of meals, most notably the inclusion of carbohydrates, and names of dishes and ingredients to reason about changes in postprandial BG levels. Both individuals with diabetes and diabetes educators reported difficulties in generating predictions of postprandial BG; their predictions varied in their correlations with the actual captured readings from r = 0.008 to r = 0.75. CONCLUSION Overall, the study showed that identifying trends in the data collected with self-monitoring is a complex process, and that conclusions reached by both individuals with diabetes and diabetes educators are not always reliable. This suggests the need for new ways to facilitate individuals' reasoning with informatics interventions.
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Affiliation(s)
- Lena Mamykina
- Department of Biomedical Informatics, Columbia University
| | | | | | | | - Noemie Elhadad
- Department of Biomedical Informatics, Columbia University
| | - David J Albers
- Department of Biomedical Informatics, Columbia University
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Merck CA, Kleinberg S. Causal Explanation Under Indeterminism: A Sampling Approach. PROCEEDINGS OF THE ... AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE. AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE 2016; 2016:1037-1043. [PMID: 31001456 PMCID: PMC6465960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
One of the key uses of causes is to explain why things happen. Explanations of specific events, like an individual's heart attack on Monday afternoon or a particular car accident, help assign responsibility and inform our future decisions. Computational methods for causal inference make use of the vast amounts of data collected by individuals to better understand their behavior and improve their health. However, most methods for explanation of specific events have provided theoretical approaches with limited applicability. In contrast we make two main contributions: an algorithm for explanation that calculates the strength of token causes, and an evaluation based on simulated data that enables objective comparison against prior methods and ground truth. We show that the approach finds the correct relationships in classic test cases (causal chains, common cause, and backup causation) and in a realistic scenario (explaining hyperglycemic episodes in a simulation of type 1 diabetes).
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Zarkogianni K, Litsa E, Mitsis K, Wu PY, Kaddi CD, Cheng CW, Wang MD, Nikita KS. A Review of Emerging Technologies for the Management of Diabetes Mellitus. IEEE Trans Biomed Eng 2015; 62:2735-49. [PMID: 26292334 PMCID: PMC5859570 DOI: 10.1109/tbme.2015.2470521] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVE High prevalence of diabetes mellitus (DM) along with the poor health outcomes and the escalated costs of treatment and care poses the need to focus on prevention, early detection and improved management of the disease. The aim of this paper is to present and discuss the latest accomplishments in sensors for glucose and lifestyle monitoring along with clinical decision support systems (CDSSs) facilitating self-disease management and supporting healthcare professionals in decision making. METHODS A critical literature review analysis is conducted focusing on advances in: 1) sensors for physiological and lifestyle monitoring, 2) models and molecular biomarkers for predicting the onset and assessing the progress of DM, and 3) modeling and control methods for regulating glucose levels. RESULTS Glucose and lifestyle sensing technologies are continuously evolving with current research focusing on the development of noninvasive sensors for accurate glucose monitoring. A wide range of modeling, classification, clustering, and control approaches have been deployed for the development of the CDSS for diabetes management. Sophisticated multiscale, multilevel modeling frameworks taking into account information from behavioral down to molecular level are necessary to reveal correlations and patterns indicating the onset and evolution of DM. CONCLUSION Integration of data originating from sensor-based systems and electronic health records combined with smart data analytics methods and powerful user centered approaches enable the shift toward preventive, predictive, personalized, and participatory diabetes care. SIGNIFICANCE The potential of sensing and predictive modeling approaches toward improving diabetes management is highlighted and related challenges are identified.
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Affiliation(s)
| | | | | | | | | | | | - May D. Wang
- Contact information for the corresponding author: , Phone: 404-385-2954, Fax: 404-894-4243, Address: Suite 4106, UA Whitaker Building, 313 Ferst Drive, Atlanta, GA 30332, USA
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39
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Colmegna PH, Sanchez-Pena RS, Gondhalekar R, Dassau E, Doyle FJ. Switched LPV Glucose Control in Type 1 Diabetes. IEEE Trans Biomed Eng 2015; 63:1192-1200. [PMID: 26452196 DOI: 10.1109/tbme.2015.2487043] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE The purpose of this paper is to regulate the blood glucose level in Type 1 Diabetes Mellitus patients with a practical and flexible procedure that can switch among a finite number of distinct controllers, depending on the user's choice. METHODS A switched linear parameter-varying controller with multiple switching regions, related to hypo-, hyper-, and euglycemia situations, is designed. The key feature is to arrange the controller into a framework that provides stability and performance guaranty. RESULTS The closed-loop performance is tested on the complete in silico adult cohort of the UVA/Padova metabolic simulator, which has been accepted by the U.S. Food and Drug Administration in lieu of animal trials. The outcome produces comparable or improved results with respect to previous works. CONCLUSION The strategy is practical because it is based on a model tuned only with a priori patient information in order to cover the interpatient uncertainty. Results confirm that this control structure yields tangible improvements in minimizing risks of hyper- and hypoglycemia in scenarios with unannounced meals. SIGNIFICANCE This flexible procedure opens the possibility of taking into account, at the design stage, unannounced meals and/or patients' physical exercise.
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40
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Lv D, Kulkarni SD, Chan A, Keith S, Pettis R, Kovatchev BP, Farhi LS, Breton MD. Pharmacokinetic Model of the Transport of Fast-Acting Insulin From the Subcutaneous and Intradermal Spaces to Blood. J Diabetes Sci Technol 2015; 9:831-40. [PMID: 25759184 PMCID: PMC4525663 DOI: 10.1177/1932296815573864] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [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
Pharmacokinetic (PK) models describing the transport of insulin from the injection site to blood assist clinical decision making and are part of in silico platforms for developing and testing of insulin delivery strategies for treatment of patients with diabetes. The ability of these models to accurately describe all facets of the in vivo insulin transport is therefore critical for their application. Here, we propose a new model of fast-acting insulin analogs transport from the subcutaneous and intradermal spaces to blood that can accommodate clinically observed biphasic appearance and delayed clearance of injected insulin, 2 phenomena that are not captured by existing PK models. To develop the model we compare 9 insulin transport PK models which describe hypothetical insulin delivery pathways potentially capable of approximating biphasic appearance of exogenous insulin. The models are tested with respect to their ability to describe clinical data from 10 healthy volunteers which received 1 subcutaneous and 2 intradermal insulin injections on 3 different occasions. The optimal model, selected based on information and posterior identifiability criteria, assumes that insulin is delivered at the administrative site and is then transported to the bloodstream via 2 independent routes (1) diffusion-like process to the blood and (2) combination of diffusion-like processes followed by an additional compartment before entering the blood. This optimal model accounts for biphasic appearance and delayed clearance of exogenous insulin. It agrees better with the clinical data as compared to commonly used models and is expected to improve the in silico development and testing of insulin treatment strategies, including artificial pancreas systems.
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Affiliation(s)
- Dayu Lv
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Sandip D Kulkarni
- Department of Bioengineering, University of Maryland College Park, College Park, MD, USA
| | - Alice Chan
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Stephen Keith
- Beckton Dickinson Technologies, Research Triangle Park NC, USA
| | - Ron Pettis
- Beckton Dickinson Technologies, Research Triangle Park NC, USA
| | - Boris P Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Leon S Farhi
- Department of Medicine, Division of Endocrinology and Metabolism, University of Virginia, Charlottesville, VA, USA
| | - Marc D Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
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Tuo J, Sun H, Shen D, Wang H, Wang Y. Optimization of insulin pump therapy based on high order run-to-run control scheme. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 120:123-134. [PMID: 25981797 DOI: 10.1016/j.cmpb.2015.04.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2014] [Revised: 04/08/2015] [Accepted: 04/20/2015] [Indexed: 06/04/2023]
Abstract
BACKGROUND AND OBJECTIVES Continuous subcutaneous insulin infusion (CSII) pump is widely considered a convenience and promising way for type 1 diabetes mellitus (T1DM) subjects, who need exogenous insulin infusion. In the standard insulin pump therapy, there are two modes for insulin infusion: basal and bolus insulin. The basal-bolus therapy should be individualized and optimized in order to keep one subject's blood glucose (BG) level within the normal range; however, the optimization procedure is troublesome and it perturb the patients a lot. Therefore, an automatic adjustment method is needed to reduce the burden of the patients, and run-to-run (R2R) control algorithm can be used to handle this significant task. METHODS In this study, two kinds of high order R2R control methods are presented to adjust the basal and bolus insulin simultaneously. For clarity, a second order R2R control algorithm is first derived and studied. Furthermore, considering the differences between weekdays and weekends, a seventh order R2R control algorithm is also proposed and tested. RESULTS In order to simulate real situation, the proposed method has been tested with uncertainties on measurement noise, drifts, meal size, meal time and snack. The proposed method can converge even when there are ±60 min random variations in meal timing or ±50% random variations in meal size. CONCLUSIONS According to the robustness analysis, one can see that the proposed high order R2R has excellent robustness and could be a promising candidate to optimize insulin pump therapy.
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Affiliation(s)
- Jianyong Tuo
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, PR China
| | - Huiling Sun
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, PR China
| | - Dong Shen
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, PR China
| | - Hui Wang
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, PR China
| | - Youqing Wang
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, PR China.
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42
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Fang Q, Yu L, Li P. A new insulin-glucose metabolic model of type 1 diabetes mellitus: An in silico study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 120:16-26. [PMID: 25896293 DOI: 10.1016/j.cmpb.2015.03.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2014] [Revised: 03/12/2015] [Accepted: 03/30/2015] [Indexed: 06/04/2023]
Abstract
Diabetes mellitus is a serious metabolic disease that threatens people's health. The artificial pancreas system (APS) has been generally considered as the ultimate cure of type 1 diabetes mellitus (T1DM). The simulation model of insulin-glucose metabolism is an essential part of an APS as it processes the measured glucose level and generates control signal to the insulin infusion system. This paper presents a new insulin-glucose metabolic model using model reduction methods applied to the popular but complex Cobelli's model. The performances of three different model reduction methods, namely Padé approximation, Routh approximation and system identification, are compared. The results of in silico simulation based on 30 virtual patients of three groups for adults, adolescents, and children show that the approximation error between this new model and the original Cobelli's model is so small that can be neglected. It can be concluded that the proposed simplified model can describe the insulin-glucose metabolism process rather accurately as well as can be easily implemented and integrated into an APS to make the APS technology more mature and closer to clinical use. The FPGA implementation, testing and further simplification possibility will be explored in the next stage of research.
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Affiliation(s)
- Qiang Fang
- School of Electrical and Computing Engineering, RMIT University, Melbourne, VIC 3000, Australia.
| | - Lei Yu
- University of Chinese Academy of Sciences, Beijing, China
| | - Peng Li
- University of Chinese Academy of Sciences, Beijing, China
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Fravolini ML, Fabietti PG. An iterative learning strategy for the auto-tuning of the feedforward and feedback controller in type-1 diabetes. Comput Methods Biomech Biomed Engin 2014; 17:1464-82. [PMID: 23282162 DOI: 10.1080/10255842.2012.753064] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
This paper proposes a scheme for the control of the blood glucose in subjects with type-1 diabetes mellitus based on the subcutaneous (s.c.) glucose measurement and s.c. insulin administration. The tuning of the controller is based on an iterative learning strategy that exploits the repetitiveness of the daily feeding habit of a patient. The control consists of a mixed feedback and feedforward contribution whose parameters are tuned through an iterative learning process that is based on the day-by-day automated analysis of the glucose response to the infusion of exogenous insulin. The scheme does not require any a priori information on the patient insulin/glucose response, on the meal times and on the amount of ingested carbohydrates (CHOs). Thanks to the learning mechanism the scheme is able to improve its performance over time. A specific logic is also introduced for the detection and prevention of possible hypoglycaemia events. The effectiveness of the methodology has been validated using long-term simulation studies applied to a set of nine in silico patients considering realistic uncertainties on the meal times and on the quantities of ingested CHOs.
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Affiliation(s)
- M L Fravolini
- a Department of Electronic and Information Engineering , University of Perugia , Via G. Duranti No. 93, 06125 Perugia , Italy
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Lunze K, Woitok A, Walter M, Brendel MD, Afify M, Tolba R, Leonhardt S. Analysis and modelling of glucose metabolism in diabetic Göttingen minipigs. Biomed Signal Process Control 2014. [DOI: 10.1016/j.bspc.2014.04.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Liu SW, Huang HP, Lin CH, Chien IL. Modified control algorithms for patients with type 1 diabetes mellitus undergoing exercise. J Taiwan Inst Chem Eng 2014. [DOI: 10.1016/j.jtice.2014.06.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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A switching hybrid control method for automatic blood glucose regulation in diabetic Göttingen minipigs. Biomed Signal Process Control 2014. [DOI: 10.1016/j.bspc.2014.05.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Colmegna P, Sanchez Pena RS, Gondhalekar R, Dassau E, Doyle Iii FJ. Reducing risks in type 1 diabetes using H∞ control. IEEE Trans Biomed Eng 2014; 61:2939-47. [PMID: 25020013 DOI: 10.1109/tbme.2014.2336772] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
A control scheme was designed in order to reduce the risks of hyperglycemia and hypoglycemia in type 1 diabetes mellitus (T1DM). This structure is composed of three main components: an H∞ robust controller, an insulin feedback loop (IFL), and a safety mechanism (SM). A control-relevant model that is employed to design the robust controller is identified. The identification procedure is based on the distribution version of the UVA/Padova metabolic simulator using the simulation adult cohort. The SM prevents dangerous scenarios by acting upon a prediction of future glucose levels, and the IFL modifies the loop gain in order to reduce postprandial hypoglycemia risks. The procedure is tested on the complete alic>in silico adult cohort of the UVA/Padova metabolic simulator, which has been accepted by the Food and Drug Administration (FDA) in lieu of animal trials.
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Greenwood NJC, Gunton JE. A computational proof of concept of a machine-intelligent artificial pancreas using Lyapunov stability and differential game theory. J Diabetes Sci Technol 2014; 8:791-806. [PMID: 25562888 PMCID: PMC4764243 DOI: 10.1177/1932296814536271] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND This study demonstrated the novel application of a "machine-intelligent" mathematical structure, combining differential game theory and Lyapunov-based control theory, to the artificial pancreas to handle dynamic uncertainties. METHODS Realistic type 1 diabetes (T1D) models from the literature were combined into a composite system. Using a mixture of "black box" simulations and actual data from diabetic medical histories, realistic sets of diabetic time series were constructed for blood glucose (BG), interstitial fluid glucose, infused insulin, meal estimates, and sometimes plasma insulin assays. The problem of underdetermined parameters was side stepped by applying a variant of a genetic algorithm to partial information, whereby multiple candidate-personalized models were constructed and then rigorously tested using further data. These formed a "dynamic envelope" of trajectories in state space, where each trajectory was generated by a hypothesis on the hidden T1D system dynamics. This dynamic envelope was then culled to a reduced form to cover observed dynamic behavior. A machine-intelligent autonomous algorithm then implemented game theory to construct real-time insulin infusion strategies, based on the flow of these trajectories through state space and their interactions with hypoglycemic or near-hyperglycemic states. RESULTS This technique was tested on 2 simulated participants over a total of fifty-five 24-hour days, with no hypoglycemic or hyperglycemic events, despite significant uncertainties from using actual diabetic meal histories with 10-minute warnings. In the main case studies, BG was steered within the desired target set for 99.8% of a 16-hour daily assessment period. Tests confirmed algorithm robustness for ±25% carbohydrate error. For over 99% of the overall 55-day simulation period, either formal controller stability was achieved to the desired target or else the trajectory was within the desired target. CONCLUSIONS These results suggest that this is a stable, high-confidence way to generate closed-loop insulin infusion strategies.
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Affiliation(s)
- Nigel J C Greenwood
- School of Mathematics and Physics, University of Queensland, Brisbane, Australia Neuromathix, NeuroTech Research Pty Ltd
| | - Jenny E Gunton
- Westmead Clinical School, University of Sydney, Sydney, Australia Diabetes and Transcription Factors Group, Garvan Institute of Medical Research, Darlinghurst, Australia St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Kensington, Australia Diabetes and Endocrinology, Westmead Hospital, Sydney, Australia
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LMI Based Robust Blood Glucose Regulation in Type-1 Diabetes Patient with Daily Multi-meal Ingestion. ACTA ACUST UNITED AC 2014. [DOI: 10.1007/s40031-014-0083-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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