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Zhu T, Uduku C, Li K, Herrero P, Oliver N, Georgiou P. Enhancing self-management in type 1 diabetes with wearables and deep learning. NPJ Digit Med 2022; 5:78. [PMID: 35760819 PMCID: PMC9237131 DOI: 10.1038/s41746-022-00626-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 06/01/2022] [Indexed: 11/12/2022] Open
Abstract
People living with type 1 diabetes (T1D) require lifelong self-management to maintain glucose levels in a safe range. Failure to do so can lead to adverse glycemic events with short and long-term complications. Continuous glucose monitoring (CGM) is widely used in T1D self-management for real-time glucose measurements, while smartphone apps are adopted as basic electronic diaries, data visualization tools, and simple decision support tools for insulin dosing. Applying a mixed effects logistic regression analysis to the outcomes of a six-week longitudinal study in 12 T1D adults using CGM and a clinically validated wearable sensor wristband (NCT ID: NCT03643692), we identified several significant associations between physiological measurements and hypo- and hyperglycemic events measured an hour later. We proceeded to develop a new smartphone-based platform, ARISES (Adaptive, Real-time, and Intelligent System to Enhance Self-care), with an embedded deep learning algorithm utilizing multi-modal data from CGM, daily entries of meal and bolus insulin, and the sensor wristband to predict glucose levels and hypo- and hyperglycemia. For a 60-minute prediction horizon, the proposed algorithm achieved the average root mean square error (RMSE) of 35.28 ± 5.77 mg/dL with the Matthews correlation coefficients for detecting hypoglycemia and hyperglycemia of 0.56 ± 0.07 and 0.70 ± 0.05, respectively. The use of wristband data significantly reduced the RMSE by 2.25 mg/dL (p < 0.01). The well-trained model is implemented on the ARISES app to provide real-time decision support. These results indicate that the ARISES has great potential to mitigate the risk of severe complications and enhance self-management for people with T1D.
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Affiliation(s)
- Taiyu Zhu
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK.
| | - Chukwuma Uduku
- Division of Diabetes, Endocrinology and Metabolism, Faculty of Medicine, Imperial College London, London, UK
| | - Kezhi Li
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK. .,Institute of Health Informatics, University College London, London, UK.
| | - Pau Herrero
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | - Nick Oliver
- Division of Diabetes, Endocrinology and Metabolism, Faculty of Medicine, Imperial College London, London, UK
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK
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Zhu T, Li K, Chen J, Herrero P, Georgiou P. Dilated Recurrent Neural Networks for Glucose Forecasting in Type 1 Diabetes. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2020; 4:308-324. [PMID: 35415447 PMCID: PMC8982716 DOI: 10.1007/s41666-020-00068-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 01/10/2020] [Accepted: 01/15/2020] [Indexed: 12/20/2022]
Abstract
Diabetes is a chronic disease affecting 415 million people worldwide. People with type 1 diabetes mellitus (T1DM) need to self-administer insulin to maintain blood glucose (BG) levels in a normal range, which is usually a very challenging task. Developing a reliable glucose forecasting model would have a profound impact on diabetes management, since it could provide predictive glucose alarms or insulin suspension at low-glucose for hypoglycemia minimisation. Recently, deep learning has shown great potential in healthcare and medical research for diagnosis, forecasting and decision-making. In this work, we introduce a deep learning model based on a dilated recurrent neural network (DRNN) to provide 30-min forecasts of future glucose levels. Using dilation, the DRNN model gains a much larger receptive field in terms of neurons aiming at capturing long-term dependencies. A transfer learning technique is also applied to make use of the data from multiple subjects. The proposed approach outperforms existing glucose forecasting algorithms, including autoregressive models (ARX), support vector regression (SVR) and conventional neural networks for predicting glucose (NNPG) (e.g. RMSE = NNPG, 22.9 mg/dL; SVR, 21.7 mg/dL; ARX, 20.1 mg/dl; DRNN, 18.9 mg/dL on the OhioT1DM dataset). The results suggest that dilated connections can improve glucose forecasting performance efficiently.
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Choudhary S, Kalra V, Kumar M, Tiwary AK, Sood J, Silakari O. Bio-Inspired Strategies against Diabetes and Associated Complications: A Review. ACTA ACUST UNITED AC 2019; 13:273-282. [PMID: 31884934 DOI: 10.2174/1872211314666191224120145] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 12/13/2019] [Accepted: 12/16/2019] [Indexed: 01/22/2023]
Abstract
Bio-molecules are the most important target to be considered while designing any drug delivery system. The logic lies in using such bio-sensing or bio-mimicking systems in their formulations that can mimic the active site of those receptors to which the drug is going to bind. Polymers mimicking the active site of target enzymes are regarded as bio-inspired polymers and can be used to ameliorate many diseased conditions. Nowadays, this strategy is also being adopted against diabetes and its complications. Under hyperglycemic conditions, many pathways get activated which are responsible for the progression of diabetes-associated secondary complications viz. retinopathy, neuropathy, and nephropathy. The enzymes involved in the progression of these complications can be mimicked for their effective management. For an instance, Aldose Reductase (ALR2), a rate-limiting enzyme of the polyol pathway (downstream pathway) which gets over-activated under hyperglycemic condition is reported to be mimicked by using polymers which are having same functionalities in their structure. This review aims at critically appraising reports in which target mimicking bio-inspired formulations have been envisaged against diabetes and its complications. The information summarized in this review will provide an idea about the bio-sensing approaches utilized to manage blood glucose level and the utility of bio-inspired polymers for the management of diabetic complications (DC). Such type of information may be beneficial to pharmaceutical companies and academia for better development of targeted drug delivery systems with sustained-release property against these diseased conditions.
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Affiliation(s)
- Shalki Choudhary
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Vinni Kalra
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Manoj Kumar
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Ashok Kumar Tiwary
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Jatin Sood
- Formulation Research and Development Department, Peace Naturals Project Inc. The Cronos Group, Stayner, Ontario, Canada
| | - Om Silakari
- Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
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Herrero P, El-Sharkawy M, Daniels J, Jugnee N, Uduku CN, Reddy M, Oliver N, Georgiou P. The Bio-inspired Artificial Pancreas for Type 1 Diabetes Control in the Home: System Architecture and Preliminary Results. J Diabetes Sci Technol 2019; 13:1017-1025. [PMID: 31608656 PMCID: PMC6835194 DOI: 10.1177/1932296819881456] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND Artificial pancreas (AP) technology has been proven to improve glucose and patient-centered outcomes for people with type 1 diabetes (T1D). Several approaches to implement the AP have been described, clinically evaluated, and in one case, commercialized. However, none of these approaches has shown a clear superiority with respect to others. In addition, several challenges still need to be solved before achieving a fully automated AP that fulfills the users' expectations. We have introduced the Bio-inspired Artificial Pancreas (BiAP), a hybrid adaptive closed-loop control system based on beta-cell physiology and implemented directly in hardware to provide an embedded low-power solution in a dedicated handheld device. In coordination with the closed-loop controller, the BiAP system incorporates a novel adaptive bolus calculator which aims at improving postprandial glycemic control. This paper focuses on the latest developments of the BiAP system for its utilization in the home environment. METHODS The hardware and software architectures of the BiAP system designed to be used in the home environment are described. Then, the clinical trial design proposed to evaluate the BiAP system in an ambulatory setting is introduced. Finally, preliminary results corresponding to two participants enrolled in the trial are presented. RESULTS Apart from minor technical issues, mainly due to wireless communications between devices, the BiAP system performed well (~88% of the time in closed-loop) during the clinical trials conducted so far. Preliminary results show that the BiAP system might achieve comparable glycemic outcomes to the existing AP systems (~73% time in target range 70-180 mg/dL). CONCLUSION The BiAP system is a viable platform to conduct ambulatory clinical trials and a potential solution for people with T1D to control their glucose control in a home environment.
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Affiliation(s)
- Pau Herrero
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | - Mohamed El-Sharkawy
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | - John Daniels
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | - Narvada Jugnee
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Chukwuma N. Uduku
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Monika Reddy
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Nick Oliver
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK
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Moscardó V, Herrero P, Díez JL, Giménez M, Rossetti P, Georgiou P, Bondia J. Coordinated dual-hormone artificial pancreas with parallel control structure. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.06.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Artificial Pancreas: Current Progress and Future Outlook in the Treatment of Type 1 Diabetes. Drugs 2019; 79:1089-1101. [DOI: 10.1007/s40265-019-01149-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Majeed W, Thabit H. Closed-loop insulin delivery: current status of diabetes technologies and future prospects. Expert Rev Med Devices 2018; 15:579-590. [PMID: 30027775 DOI: 10.1080/17434440.2018.1503530] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
INTRODUCTION Type 1 diabetes is characterised by destruction of pancreatic beta cells, leading to insulin deficiency and hyperglycaemia. The mainstay of treatment remains lifelong insulin therapy as a sustainable cure has as yet proven elusive. The burden of daily management of type 1 diabetes has contributed to suboptimal outcomes for people living with the condition. Innovative technological approaches have been shown to improve glycaemic and patient-related outcomes. AREAS COVERED We discuss recent advances in technologies in type 1 diabetes including closed-loop systems, also known as the 'artificial pancreas. Its various components, technical aspects and limitations are reviewed. We also discuss its advent into clinical practice, and other systems in development. Evidence from clinical studies are summarised. EXPERT COMMENTARY The recent approval of a hybrid closed-loop system for clinical use highlights the significant progress made in this field. Results from clinical studies have shown safety and glycaemic benefit, however challenges remain around improving performance and acceptability. More data is required to establish long-term clinical efficacy and cost-effectiveness, to fulfil the expectations of people with type 1 diabetes.
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Affiliation(s)
- Waseem Majeed
- a Manchester Academic Health Science Centre , Manchester University Hospitals NHS Foundation Trust , Manchester , UK
| | - Hood Thabit
- a Manchester Academic Health Science Centre , Manchester University Hospitals NHS Foundation Trust , Manchester , UK.,b Division of Diabetes, Endocrinology and Gastroenterology, Faculty of Biology, Medicine and Health , University of Manchester , Manchester , UK
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Güemes A, Georgiou P. Review of the role of the nervous system in glucose homoeostasis and future perspectives towards the management of diabetes. Bioelectron Med 2018; 4:9. [PMID: 32232085 PMCID: PMC7098234 DOI: 10.1186/s42234-018-0009-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Accepted: 06/10/2018] [Indexed: 12/16/2022] Open
Abstract
Diabetes is a disease caused by a breakdown in the glucose metabolic process resulting in abnormal blood glucose fluctuations. Traditionally, control has involved external insulin injection in response to elevated blood glucose to substitute the role of the beta cells in the pancreas which would otherwise perform this function in a healthy individual. The central nervous system (CNS), however, also plays a vital role in glucose homoeostasis through the control of pancreatic secretion and insulin sensitivity which could potentially be used as a pathway for enhancing glucose control. In this review, we present an overview of the brain regions, peripheral nerves and molecular mechanisms by which the CNS regulates glucose metabolism and the potential benefits of modulating them for diabetes management. Development of technologies to interface to the nervous system will soon become a reality through bioelectronic medicine and we present the emerging opportunities for the treatment of type 1 and type 2 diabetes.
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Affiliation(s)
- Amparo Güemes
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, UK
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, UK
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Rawson TM, O’Hare D, Herrero P, Sharma S, Moore LSP, de Barra E, Roberts JA, Gordon AC, Hope W, Georgiou P, Cass AEG, Holmes AH. Delivering precision antimicrobial therapy through closed-loop control systems. J Antimicrob Chemother 2018; 73:835-843. [PMID: 29211877 PMCID: PMC5890674 DOI: 10.1093/jac/dkx458] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Sub-optimal exposure to antimicrobial therapy is associated with poor patient outcomes and the development of antimicrobial resistance. Mechanisms for optimizing the concentration of a drug within the individual patient are under development. However, several barriers remain in realizing true individualization of therapy. These include problems with plasma drug sampling, availability of appropriate assays, and current mechanisms for dose adjustment. Biosensor technology offers a means of providing real-time monitoring of antimicrobials in a minimally invasive fashion. We report the potential for using microneedle biosensor technology as part of closed-loop control systems for the optimization of antimicrobial therapy in individual patients.
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Affiliation(s)
- T M Rawson
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, UK
| | - D O’Hare
- Department of Bioengineering, Imperial College London, London, UK
| | - P Herrero
- Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, UK
| | - S Sharma
- College of Engineering, Swansea University, Swansea, UK
| | - L S P Moore
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, UK
- Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, Acton, UK
| | - E de Barra
- Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, Acton, UK
| | - J A Roberts
- University of Queensland Centre for Clinical Research, Faculty of Medicine and Centre for Translational Pharmacodynamics, School of Pharmacy, The University of Queensland, Brisbane, Australia
- Royal Brisbane and Women’s Hospital, Brisbane, Australia
| | - A C Gordon
- Section of Anaesthetics, Pain Medicine & Intensive Care, Imperial College London, London, UK
| | - W Hope
- Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK
| | - P Georgiou
- Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, UK
| | - A E G Cass
- Department of Chemistry & Institute of Biomedical Engineering, Imperial College London, Kensington Campus, London, UK
| | - A H Holmes
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, UK
- Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, Acton, UK
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Herrero P, Bondia J, Oliver N, Georgiou P. A coordinated control strategy for insulin and glucagon delivery in type 1 diabetes. Comput Methods Biomech Biomed Engin 2017; 20:1474-1482. [PMID: 28929796 DOI: 10.1080/10255842.2017.1378352] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Type 1 diabetes is an autoimmune condition characterised by a pancreatic insulin secretion deficit, resulting in high blood glucose concentrations, which can lead to micro- and macrovascular complications. Type 1 diabetes also leads to impaired glucagon production by the pancreatic α-cells, which acts as a counter-regulatory hormone to insulin. A closed-loop system for automatic insulin and glucagon delivery, also referred to as an artificial pancreas, has the potential to reduce the self-management burden of type 1 diabetes and reduce the risk of hypo- and hyperglycemia. To date, bihormonal closed-loop systems for glucagon and insulin delivery have been based on two independent controllers. However, in physiology, the secretion of insulin and glucagon in the body is closely interconnected by paracrine and endocrine associations. In this work, we present a novel biologically-inspired glucose control strategy that accounts for such coordination. An in silico study using an FDA-accepted type 1 simulator was performed to evaluate the proposed coordinated control strategy compared to its non-coordinated counterpart, as well as an insulin-only version of the controller. The proposed coordinated strategy achieves a reduction of hyperglycemia without increasing hypoglycemia, when compared to its non-coordinated counterpart.
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Affiliation(s)
- Pau Herrero
- a Centre for Bio-Inspired Technology, Institute of Biomedical Engineering , Imperial College London , London , UK
| | - Jorge Bondia
- b Institut Universitari d'Automàtica i Informàtica Industrial , Universitat Politècnica de València , València , Spain
| | - Nick Oliver
- c Charing Cross Hospital, Imperial College Healthcare NHS Trust , London , UK
| | - Pantelis Georgiou
- a Centre for Bio-Inspired Technology, Institute of Biomedical Engineering , Imperial College London , London , UK
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Herrero P, Bondia J, Adewuyi O, Pesl P, El-Sharkawy M, Reddy M, Toumazou C, Oliver N, Georgiou P. Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra-day variability. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 146:125-131. [PMID: 28688482 PMCID: PMC6522376 DOI: 10.1016/j.cmpb.2017.05.010] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 04/02/2017] [Accepted: 05/25/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Current prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain. METHODS In this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake. RESULTS Overall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4vs. 131.8 ± 4.2mg/dl; percentage time in target [70, 180]mg/dl, 82.0 ± 7.0vs. 89.5 ± 4.2; percentage time above target 17.7 ± 7.0vs. 10.2 ± 4.1. Adolescents: mean glucose 158.2 ± 21.4vs. 140.5 ± 13.0mg/dl; percentage time in target, 65.9 ± 12.9vs. 77.5 ± 12.2; percentage time above target, 31.7 ± 13.1vs. 19.8 ± 10.2. Note that no increase in percentage time in hypoglycemia was observed. CONCLUSION Using an adaptive meal bolus calculator within a closed-loop control system has the potential to improve glycemic control in type 1 diabetes when compared to its non-adaptive counterpart.
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Affiliation(s)
- Pau Herrero
- Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom.
| | - Jorge Bondia
- Institut Universitari d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, València, Spain
| | - Oloruntoba Adewuyi
- Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Peter Pesl
- Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Mohamed El-Sharkawy
- Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Monika Reddy
- Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Chris Toumazou
- Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Nick Oliver
- Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
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Oviedo S, Vehí J, Calm R, Armengol J. A review of personalized blood glucose prediction strategies for T1DM patients. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33:e2833. [PMID: 27644067 DOI: 10.1002/cnm.2833] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 09/15/2016] [Accepted: 09/16/2016] [Indexed: 06/06/2023]
Abstract
This paper presents a methodological review of models for predicting blood glucose (BG) concentration, risks and BG events. The surveyed models are classified into three categories, and they are presented in summary tables containing the most relevant data regarding the experimental setup for fitting and testing each model as well as the input signals and the performance metrics. Each category exhibits trends that are presented and discussed. This document aims to be a compact guide to determine the modeling options that are currently being exploited for personalized BG prediction.
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Affiliation(s)
- Silvia Oviedo
- Institut d'Informàtica i Aplicacions, Parc Científic i Tecnològic de la Universitat de Girona, 17003, Girona, Spain
| | - Josep Vehí
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Edifici P4, 17071, Girona, Spain
| | - Remei Calm
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Edifici P4, 17071, Girona, Spain
| | - Joaquim Armengol
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, Edifici P4, 17071, Girona, Spain
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Cass AE, Sharma S. Microneedle Enzyme Sensor Arrays for Continuous In Vivo Monitoring. Methods Enzymol 2017; 589:413-427. [DOI: 10.1016/bs.mie.2017.02.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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Herrero P, Pesl P, Reddy M, Oliver N, Georgiou P, Toumazou C. Advanced Insulin Bolus Advisor Based on Run-To-Run Control and Case-Based Reasoning. IEEE J Biomed Health Inform 2015; 19:1087-96. [PMID: 24956470 DOI: 10.1109/jbhi.2014.2331896] [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: 12/14/2022]
Abstract
This paper presents an advanced insulin bolus advisor for people with diabetes on multiple daily injections or insulin pump therapy. The proposed system, which runs on a smartphone, keeps the simplicity of a standard bolus calculator while enhancing its performance by providing more adaptability and flexibility. This is achieved by means of applying a retrospective optimization of the insulin bolus therapy using a novel combination of run-to-run (R2R) that uses intermittent continuous glucose monitoring data, and case-based reasoning (CBR). The validity of the proposed approach has been proven by in-silico studies using the FDA-accepted UVa-Padova type 1 diabetes simulator. Tests under more realistic in-silico scenarios are achieved by updating the simulator to emulate intrasubject insulin sensitivity variations and uncertainty in the capillarity measurements and carbohydrate intake. The CBR(R2R) algorithm performed well in simulations by significantly reducing the mean blood glucose, increasing the time in euglycemia and completely eliminating hypoglycaemia. Finally, compared to an R2R stand-alone version of the algorithm, the CBR(R2R) algorithm performed better in both adults and adolescent populations, proving the benefit of the utilization of CBR. In particular, the mean blood glucose improved from 166 ± 39 to 150 ± 16 in the adult populations (p = 0.03) and from 167 ± 25 to 162 ± 23 in the adolescent population (p = 0.06). In addition, CBR(R2R) was able to completely eliminate hypoglycaemia, while the R2R alone was not able to do it in the adolescent population.
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Reddy M, Herrero P, Sharkawy ME, Pesl P, Jugnee N, Pavitt D, Godsland IF, Alberti G, Toumazou C, Johnston DG, Georgiou P, Oliver NS. Metabolic Control With the Bio-inspired Artificial Pancreas in Adults With Type 1 Diabetes: A 24-Hour Randomized Controlled Crossover Study. J Diabetes Sci Technol 2015; 10:405-13. [PMID: 26581881 PMCID: PMC4773972 DOI: 10.1177/1932296815616134] [Citation(s) in RCA: 23] [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: 11/16/2022]
Abstract
BACKGROUND The Bio-inspired Artificial Pancreas (BiAP) is a closed-loop insulin delivery system based on a mathematical model of beta-cell physiology and implemented in a microchip within a low-powered handheld device. We aimed to evaluate the safety and efficacy of the BiAP over 24 hours, followed by a substudy assessing the safety of the algorithm without and with partial meal announcement. Changes in lactate and 3-hydroxybutyrate concentrations were investigated for the first time during closed-loop. METHODS This is a prospective randomized controlled open-label crossover study. Participants were randomly assigned to attend either a 24-hour closed-loop visit connected to the BiAP system or a 24-hour open-loop visit (standard insulin pump therapy). The primary outcome was percentage time spent in target range (3.9-10 mmol/l) measured by sensor glucose. Secondary outcomes included percentage time in hypoglycemia (<3.9 mmol/l) and hyperglycemia (>10 mmol/l). Participants were invited to attend for an additional visit to assess the BiAP without and with partial meal announcements. RESULTS A total of 12 adults with type 1 diabetes completed the study (58% female, mean [SD] age 45 [10] years, BMI 25 [4] kg/m(2), duration of diabetes 22 [12] years and HbA1c 7.4 [0.7]% [58 (8) mmol/mol]). The median (IQR) percentage time in target did not differ between closed-loop and open-loop (71% vs 66.9%, P = .9). Closed-loop reduced time spent in hypoglycemia from 17.9% to 3.0% (P < .01), but increased time was spent in hyperglycemia (10% vs 28.9%, P = .01). The percentage time in target was higher when all meals were announced during closed-loop compared to no or partial meal announcement (65.7% [53.6-80.5] vs 45.5% [38.2-68.3], P = .12). CONCLUSIONS The BiAP is safe and achieved equivalent time in target as measured by sensor glucose, with improvement in hypoglycemia, when compared to standard pump therapy.
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Affiliation(s)
- Monika Reddy
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, UK
| | - Pau Herrero
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Institute of Biomedical Engineering, Imperial College London, UK
| | - Mohamed El Sharkawy
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Institute of Biomedical Engineering, Imperial College London, UK
| | - Peter Pesl
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Institute of Biomedical Engineering, Imperial College London, UK
| | - Narvada Jugnee
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, UK
| | - Darrell Pavitt
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, UK
| | - Ian F Godsland
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, UK
| | - George Alberti
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, UK
| | - Christofer Toumazou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Institute of Biomedical Engineering, Imperial College London, UK
| | - Desmond G Johnston
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, UK
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Institute of Biomedical Engineering, Imperial College London, UK
| | - Nick S Oliver
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, UK
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Bakhtiani PA, El Youssef J, Duell AK, Branigan DL, Jacobs PG, Lasarev MR, Castle JR, Ward WK. Factors affecting the success of glucagon delivered during an automated closed-loop system in type 1 diabetes. J Diabetes Complications 2015; 29:93-8. [PMID: 25264232 PMCID: PMC4281277 DOI: 10.1016/j.jdiacomp.2014.09.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2014] [Revised: 08/31/2014] [Accepted: 09/03/2014] [Indexed: 12/31/2022]
Abstract
BACKGROUND In bi-hormonal closed-loop systems for treatment of diabetes, glucagon sometimes fails to prevent hypoglycemia. We evaluated glucagon responses during several closed-loop studies to determine factors, such as gain factors, responsible for glucagon success and failure. METHODS We extracted data from four closed-loop studies, examining blood glucose excursions over the 50min after each glucagon dose and defining hypoglycemic failure as glucose values<60 mg/dl. Secondly, we evaluated hyperglycemic excursions within the same period, where glucose was>180 mg/dl. We evaluated several factors for association with rates of hypoglycemic failure or hyperglycemic excursion. These factors included age, weight, HbA1c, duration of diabetes, gender, automation of glucagon delivery, glucagon dose, proportional and derivative errors (PE and DE), insulin on board (IOB), night vs. day delivery, and point sensor accuracy. RESULTS We analyzed a total of 251 glucagon deliveries during 59 closed-loop experiments performed on 48 subjects. Glucagon successfully maintained glucose within target (60-180 mg/dl) in 195 (78%) of instances with 40 (16%) hypoglycemic failures and 16 (6%) hyperglycemic excursions. A multivariate logistic regression model identified PE (p<0.001), DE (p<0.001), and IOB (p<0.001) as significant determinants of success in terms of avoiding hypoglycemia. Using a model of glucagon absorption and action, simulations suggested that the success rate for glucagon would be improved by giving an additional 0.8μg/kg. CONCLUSION We conclude that glucagon fails to prevent hypoglycemia when it is given at a low glucose threshold and when glucose is falling steeply. We also confirm that high IOB significantly increases the risk for glucagon failures. Tuning of glucagon subsystem parameters may help reduce this risk.
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Affiliation(s)
- P A Bakhtiani
- Harold Schnitzer Diabetes Center, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239
| | - J El Youssef
- Harold Schnitzer Diabetes Center, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239.
| | - A K Duell
- Harold Schnitzer Diabetes Center, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239
| | - D L Branigan
- Harold Schnitzer Diabetes Center, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239
| | - P G Jacobs
- Department of Biomedical Engineering, Oregon Health and Science University, 33030 SW Bond Ave., Portland, OR 97239
| | - M R Lasarev
- Oregon Institute of Occupational Health Sciences, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239
| | - J R Castle
- Harold Schnitzer Diabetes Center, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239
| | - W K Ward
- Harold Schnitzer Diabetes Center, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239
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Reddy M, Herrero P, El Sharkawy M, Pesl P, Jugnee N, Thomson H, Pavitt D, Toumazou C, Johnston D, Georgiou P, Oliver N. Feasibility study of a bio-inspired artificial pancreas in adults with type 1 diabetes. Diabetes Technol Ther 2014; 16:550-7. [PMID: 24801544 PMCID: PMC4135321 DOI: 10.1089/dia.2014.0009] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BACKGROUND This study assesses proof of concept and safety of a novel bio-inspired artificial pancreas (BiAP) system in adults with type 1 diabetes during fasting, overnight, and postprandial conditions. In contrast to existing glucose controllers in artificial pancreas systems, the BiAP uses a control algorithm based on a mathematical model of β-cell physiology. The algorithm is implemented on a miniature silicon microchip within a portable hand-held device that interfaces the components of the artificial pancreas. MATERIALS AND METHODS In this nonrandomized open-label study each subject attended for a 6-h fasting study followed by a 13-h overnight and post-breakfast study on a separate occasion. During both study sessions the BiAP system was used, and microboluses of insulin were recommended every 5 min by the control algorithm according to subcutaneous sensor glucose levels. The primary outcome was percentage time spent in the glucose target range (3.9-10.0 mmol/L). RESULTS Twenty subjects (55% male; mean [SD] age, 44 [10] years; duration of diabetes, 22 [12] years; glycosylated hemoglobin, 7.4% [0.7%] [57 (7) mmol/mol]; body mass index, 25 [4] kg/m(2)) participated in the fasting study, and the median (interquartile range) percentage time in target range was 98.0% (90.8-100.0%). Seventeen of these subjects then participated in the overnight/postprandial study, where 70.7% (63.9-77.4%) of time was spent in the target range and, reassuringly, 0.0% (0.0-2.3%) of time was spent in hypoglycemia (<3.9 mmol/L). CONCLUSIONS The BiAP achieves safe glycemic control during fasting, overnight, and postprandial conditions.
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Affiliation(s)
- Monika Reddy
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, London, United Kingdom
| | - Pau Herrero
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Mohamed El Sharkawy
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Peter Pesl
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Narvada Jugnee
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, London, United Kingdom
| | - Hazel Thomson
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, London, United Kingdom
| | - Darrell Pavitt
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, London, United Kingdom
| | - Christofer Toumazou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Desmond Johnston
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, London, United Kingdom
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Nick Oliver
- Division of Diabetes, Endocrinology and Metabolism, Imperial College London, London, United Kingdom
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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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Jacobs PG, El Youssef J, Castle J, Bakhtiani P, Branigan D, Breen M, Bauer D, Preiser N, Leonard G, Stonex T, Ward WK. Automated control of an adaptive bihormonal, dual-sensor artificial pancreas and evaluation during inpatient studies. IEEE Trans Biomed Eng 2014; 61:2569-81. [PMID: 24835122 DOI: 10.1109/tbme.2014.2323248] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Automated control of blood glucose in patients with type-1 diabetes has not yet been fully implemented. The aim of this study was to design and clinically evaluate a system that integrates a control algorithm with off-the-shelf subcutaneous sensors and pumps to automate the delivery of the hormones glucagon and insulin in response to continuous glucose sensor measurements. The automated component of the system runs an adaptive proportional derivative control algorithm which determines hormone delivery rates based on the sensed glucose measurements and the meal announcements by the patient. We provide details about the system design and the control algorithm, which incorporates both a fading memory proportional derivative controller (FMPD) and an adaptive system for estimating changing sensitivity to insulin based on a glucoregulatory model of insulin action. For an inpatient study carried out in eight subjects using Dexcom SEVEN PLUS sensors, prestudy HbA1c averaged 7.6, which translates to an estimated average glucose of 171 mg/dL. In contrast, during use of the automated system, after initial stabilization, glucose averaged 145 mg/dL and subjects were kept within the euglycemic range (between 70 and 180 mg/dL) for 73.1% of the time, indicating improved glycemic control. A further study on five additional subjects in which we used a newer and more reliable glucose sensor (Dexcom G4 PLATINUM) and made improvements to the insulin and glucagon pump communication system resulted in elimination of hypoglycemic events. For this G4 study, the system was able to maintain subjects' glucose levels within the near-euglycemic range for 71.6% of the study duration and the mean venous glucose level was 151 mg/dL.
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20
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Pagkalos I, Herrero P, Toumazou C, Georgiou P. Bio-Inspired glucose control in diabetes based on an analogue implementation of a β-cell model. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2014; 8:186-195. [PMID: 24686302 DOI: 10.1109/tbcas.2014.2301377] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
This paper presents a bio-inspired method for in-vivo control of blood glucose based on a model of the pancreatic β-cell. The proposed model is shown to be implementable using low-power analogue integrated circuits in CMOS, realizing a biologically faithful implementation which captures all the behaviours seen in physiology. This is then shown to be capable of glucose control using an in silico population of diabetic subjects achieving 93% of the time in tight glycemic target (i.e., [70, 140] mg/dl) . The proposed controller is then compared with a commonly used external physiological insulin delivery (ePID) controller for glucose control. Results confirm equivalent, or superior, performance in comparison with ePID. The system has been designed in a commercially available 0.35 μm CMOS process and achieves an overall power consumption of 1.907 mW.
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21
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Pedersen MG, Cobelli C. Multiscale modelling of insulin secretion during an intravenous glucose tolerance test. Interface Focus 2014; 3:20120085. [PMID: 24427529 DOI: 10.1098/rsfs.2012.0085] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Dysfunctional insulin secretion from pancreatic β-cells plays a major role in the development of diabetes. The intravenous glucose tolerance test (IVGTT) is a widely used clinical test to assess β-cell function. The analysis of IVGTT data is conveniently performed using mathematical models, which need to be fairly simple to enable parameter identifiability (minimal models), but should at the same time have sound biological foundation at the cellular level. Using mathematical analysis and model reduction, we show here that our recent mathematical model of insulin secretory granule dynamics in β-cells provides mechanistic underpinning for our minimal model of pancreatic insulin secretion during an IVGTT.
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Affiliation(s)
- Morten Gram Pedersen
- Department of Information Engineering , University of Padova , Via Gradenigo 6/b, Padova 35131 , Italy
| | - Claudio Cobelli
- Department of Information Engineering , University of Padova , Via Gradenigo 6/b, Padova 35131 , Italy
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22
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Steil GM, Grodsky GM. The artificial pancreas: is it important to understand how the β cell controls blood glucose? J Diabetes Sci Technol 2013; 7:1359-69. [PMID: 24124965 PMCID: PMC3876382 DOI: 10.1177/193229681300700528] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
It has been more than 7 years since the first fully automated closed-loop insulin delivery system that linked subcutaneous insulin delivery and glucose sensing was published. Since the initial report, the physiologic insulin delivery (PID) algorithm used to emulate the β cell has been modified from the original proportional-integral-derivative terms needed to fit the β cell's biphasic response to a hyperglycemic clamp to include terms emulating cephalic phase insulin release and the effect of insulin per se to inhibit insulin secretion. In this article, we compare the closed-loop glucose profiles obtained as each new term has been added, reassess the ability of the revised PID model to describe the β cells' insulin response to a hyperglycemic clamp, and look for the first time at its ability to describe the response to a hypoglycemic clamp. We also consider changes that might be added to the model based on perfused pancreas data. We conclude that the changes introduced in the PID model have systematically improved the closed-loop meal response. We note that the changes made do not adversely affect the ability of the model to fit hyperglycemic clamp data but are necessary to fit the response to a hypoglycemic clamp. Finally, we note a number of β cell characteristics observed in the perfused pancreas have not been included in the model. We suggest that continuing the effort to understand and incorporate aspects of how the β cell achieves glucose control can provide valuable insights into how improvements in future artificial pancreas algorithms might be achieved.
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Affiliation(s)
- Garry M Steil
- Boston Children's Hospital, Attn: Medicine Critical Care, 333 Longwood Ave., Boston, MA 022115.
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Herrero P, Georgiou P, Oliver N, Reddy M, Johnston D, Toumazou C. A composite model of glucagon-glucose dynamics for in silico testing of bihormonal glucose controllers. J Diabetes Sci Technol 2013; 7:941-51. [PMID: 23911175 PMCID: PMC3879758 DOI: 10.1177/193229681300700416] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND The utility of simulation environments in the development of an artificial pancreas for type 1 diabetes mellitus (T1DM) management is well established. The availability of a simulator that incorporates glucagon as a counterregulatory hormone to insulin would allow more efficient design of bihormonal glucose controllers. Existing models of the glucose regulatory system that incorporates glucagon action are difficult to identify without using tracer data. In this article, we present a novel model of glucagon-glucose dynamics that can be easily identified with standard clinical research data. METHODS The minimal model of plasma glucose and insulin kinetics was extended to account for the action of glucagon on net endogenous glucose production by incorporating a new compartment. An existing subcutaneous insulin absorption model was used to account for subcutaneous insulin delivery. The same model of insulin pharmacokinetics was employed to model the pharmacokinetics of subcutaneous glucagon absorption. Finally, we incorporated an existing gastrointestinal absorption model to account for meal intake. Data from a closed-loop artificial pancreas study using a bihormonal controller on T1DM subjects were employed to identify the composite model. To test the validity of the proposed model, a bihormonal controller was designed using the identified model. RESULTS Model parameters were identified with good precision, and an excellent fitting of the model with the experimental data was achieved. The proposed model allowed the design of a bihormonal controller and demonstrated its ability to improve glycemic control over a single-hormone controller. CONCLUSIONS A novel composite model, which can be easily identified with standard clinical data, is able to account for the effect of exogenous insulin and glucagon infusion on glucose dynamics. This model represents another step toward the development of a bihormonal artificial pancreas.
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Affiliation(s)
- Pau Herrero
- Center for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Institute of Biomedical Engineering, Imperial College London, South Kensington Campus, London, UK.
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