Contemporary studies focus increasingly on the development of artificial pancreas (AP) - an engineering system known as closed-loop control (CLC). The final goal - an ambulatory AP - has the potential to make a tremendous impact on the health and lives of people with type 1 diabetes. Our interdisciplinary international team has been at the forefront of CLC development, creating models, in silico testing platform, safety and control algorithms that represent the state of the art in AP development today. With this project, we bring the quest for ambulatory CLC to a new level, proposing to merge for the first time three key aspects of the optimal control in type 1 diabetes: human behavior, physiology and engineering Our primary goal is to build, test, and validate a new ambulatory CLC system that is informed by, and is adaptive to, real-time changes in behavior and physiology. Our underlying hypothesis is: the rate of behavioral events and the ensuing metabolic responses can be divided into hierarchical time scales, which can be translated into a modular engineering hierarchy with clearly identifiable and tractable control goals at each time scale. Phase 1 - Building assessment algorithms and control modules (primary time scale minutes-hours): We will first characterize the relationships of psycho-behavioral markers and acute behavioral events (e.g. meals, exercise) with the magnitude of physiological response and the need for real-time adaptation of CLC. Engineering tools will be designed responsible for the patient safety and prevention of hypoglycemia and for the 'health'of the AP system on both local and remote levels. We will develop a framework to address system transitions instigated by behavioral challenges and will conduct innovative physiological experiments to assess "dawn" phenomenon, glucose fluxes following complex carbohydrate meal, and hepatic glucagon sensitivity. Phase 2 - Judging the effect size of control components (primary time scale days-weeks): We will engineer an adaptive learning algorithm that recognizes patients'bio-behavioral patterns, such as meal and exercise timing, and diurnal variation in insulin sensitivity. Coordinated clinical studies and large-scale in silico experiments will estimate the effect size of inclusion into CLC of initialization and real-time adaptation control components. Specifically, we will assess the effect of using physiological and behavioral: (i) markers to initialize CLC and (ii) profiles to adjust insulin boluses and basal rate. Phase 3 - System validation and trial of long-term ambulatory CLC: A final multi-center trial will validate our system in patients'natural environment in preparation for its ultimate translation into clinical practice. The primary hypothesis driving Phase 3 is: compared to state-of-the-art sensor augmented open loop therapy, closed-loop control will reduce the frequency of hypoglycemia and will increase the time spent within the target range of 70-180 mg/dl, without compromising average glycemic control as measured by HbA1c.
The artificial pancreas based on closed-loop control, has the potential to make a tremendous impact on the health and lives of people with type 1 diabetes. The development of this technology has made significant strides over the last five years;however, it is still in infancy, currently being tested in inpatient clinical-research center setting. As the transition is made from the clinic to outpatient trials and then to approved ambulatory devices, additional strategies will need to be developed to optimize control and individualize treatment, requiring creative, medically-inspired engineering design and safety monitoring.
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|Hinshaw, Ling; Schiavon, Michele; Mallad, Ashwini et al. (2014) Effects of delayed gastric emptying on postprandial glucose kinetics, insulin sensitivity, and ?-cell function. Am J Physiol Endocrinol Metab 307:E494-502|
|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|
|Basu, Ananda; Kudva, Yogish C; Basu, Rita (2013) GPR40 modulators: new kid on the block. Diabetes Care 36:185-7|
|Dube, S; Errazuriz, I; Cobelli, C et al. (2013) Assessment of insulin action on carbohydrate metabolism: physiological and non-physiological methods. Diabet Med 30:664-70|
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