By nature, the development of a closed-loop system (known as artificial pancreas) controlling blood glucose (BG) in diabetes is an interdisciplinary project involving physiology, behavioral science, and engineering. Consequently, this project represents an interdisciplinary and international effort of physicians, psychologists, mathematicians, and engineers from the United States, Italy, and France dedicated to the understanding of behavioral and biological prerequisites to individually-tailored closed-loop control of type 1 diabetes (T1DM). The principal idea of the proposed research is: in order to be successful, closed-loop control must adapt to individual physiologic characteristics and to the behavioral profile of each person. The keys to this adaptation are biosystem (patient) observation and modular control. Thus, we propose to lay the foundation for a modular system comprised of algorithmic observers of patients'behavior and metabolic state, and control modules responsible for insulin delivery and hypoglycemia prevention. Building this system, we will utilize our extensive expertise with in silico modeling and simulation of the human metabolism, and the experience from our recent closed-loop control studies. The development and testing of this system will be accomplished in four phases: Phase 1 will investigate patterns of behavioral events relevant to T1DM control. A field study will record meals, insulin injections, and exercise in parallel with continuous glucose monitoring, aiming to develop a learning algorithm - behavioral observer - which will track over time key recurrent elements of a person's routine. Phase 2 will investigate relationships of insulin sensitivity and impaired counterregulation with BG variability, aiming to develop algorithmic physiology observers, which will track specific parameters of glucose variability and recurrent hypoglycemia as markers of change in a person's insulin sensitivity and counterregulatory ability. Phase 3 will test in the field an advisory system providing personalized feedback to people with T1DM. The system will be informed by behavioral and physiology observers and will consist of three advisory modules: (i) evaluation of risk for hypoglycemia 24 hours ahead;(ii) bolus calculator suggesting pre-meal insulin doses, and (iii) basal rate advisor suggesting basal rate profiles for the next 24 hours. Phase 4 will focus on automated closed-loop control conducting a series of studies (outpatient &inpatient) testing sequentially three control modules responsible for: (i) detection and prevention of hypoglycemia 1-2 hours ahead, (ii) control of pre-meal insulin boluses, and (iii) control of basal rate and overnight steady state. We envision that, depending on patients'or physicians'choice, each observer or advisory/control module could be used separately, or within integrated open- or closed-loop control systems. A modular approach will permit incremental testing and deployment of system features, which will structure and facilitate system development.

Public Health Relevance

With the advances in insulin delivery and continuous glucose monitoring, research must now focus on integrating these technologies into systems alleviating the burden of everyday diabetes maintenance and ensuring optimal diabetes control - a task that requires studies of physiology, behavior, and engineering. Thus, we propose an interdisciplinary project, which will lay the foundation for a modular diabetes control system using algorithmic observation of patients'behavior and metabolic state to inform control modules responsible for insulin delivery and hypoglycemia prevention. We envision that, depending on patients'or physicians'choices, each module could be used separately, or within integrated advisory or closed-loop control systems. A modular approach will also permit incremental testing and deployment of system features, which will structure and facilitate the progress towards the automated closed-loop control commonly known as artificial pancreas.

National Institute of Health (NIH)
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Research Project (R01)
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Special Emphasis Panel (ZDK1-GRB-2 (O2))
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Arreaza-Rubin, Guillermo
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University of Virginia
Schools of Medicine
United States
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Brown, Sue A; Breton, Marc D; Anderson, Stacey M et al. (2017) Overnight Closed-Loop Control Improves Glycemic Control in a Multicenter Study of Adults With Type 1 Diabetes. J Clin Endocrinol Metab 102:3674-3682
Campos-Náñez, Enrique; Kovatchev, Boris P (2016) Impact of Meal Constituents on Artificial Pancreas Algorithms. Diabetes Technol Ther 18:607-609
Kovatchev, Boris P; Patek, Stephen D; Ortiz, Edward Andrew et al. (2015) Assessing sensor accuracy for non-adjunct use of continuous glucose monitoring. Diabetes Technol Ther 17:177-86
Brown, Sue A; Kovatchev, Boris P; Breton, Marc D et al. (2015) Multinight ""bedside"" closed-loop control for patients with type 1 diabetes. Diabetes Technol Ther 17:203-9
Kovatchev, Boris P (2015) Hypoglycemia Reduction and Accuracy of Continuous Glucose Monitoring. Diabetes Technol Ther 17:530-3
Kovatchev, Boris P; Renard, Eric; Cobelli, Claudio et al. (2014) Safety of outpatient closed-loop control: first randomized crossover trials of a wearable artificial pancreas. Diabetes Care 37:1789-96
Gonder-Frederick, Linda (2014) Lifestyle modifications in the management of type 1 diabetes: still relevant after all these years? Diabetes Technol Ther 16:695-8
Cobelli, Claudio; Renard, Eric; Kovatchev, Boris (2014) The artificial pancreas: a digital-age treatment for diabetes. Lancet Diabetes Endocrinol 2:679-81
Kovatchev, Boris P; Wakeman, Christian A; Breton, Marc D et al. (2014) Computing the surveillance error grid analysis: procedure and examples. J Diabetes Sci Technol 8:673-84
Hughes-Karvetski, Colleen; Patek, Stephen D; Breton, Marc D et al. (2013) Historical data enhances safety supervision system performance in T1DM insulin therapy risk management. Comput Methods Programs Biomed 109:220-5

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