Closed-Loop Control Modalities in Type 1 Diabetes: Efficacy and System Acceptance In 2009 we initiated one of the first NIH studies dedicated to engineering and clinical testing of closed-loop control (CLC) of type 1 diabetes. Since then, we have achieved key milestones and derived conclusions which enabled further research in this rapidly growing field. Notably, we proposed the idea that the artificial pancreas is not a single all-in-one device but a network encompassing the patient in a digital treatment ecosystem that can offer and alter different treatment modalities in real time depending on the patient's clinical state. This new notion was reflected in: (1) Our modular engineering design of CLC algorithms, which now allows various treatment modalities to be initiated and swapped without interruption; (2) The Diabetes Assistant (DiAs) - the first portable CLC hub using a smart phone to run control algorithms and specifically designed to be operated by the patient, which is now used in a number of outpatient studies in the U.S. and in Europe, and (3) The Unified Safety System (USS Virginia) - the first CLC algorithm engineered to adapt its mode of operation during the course of every night, first mitigating after-dinner hyperglycemia and then sliding the patient to a target morning glucose of 120mg/dl, thereby resetting his/her metabolic state for a new day. Using these technologies, we now propose to compare in a randomized cross-over trial the long-term efficacy of three treatment modalities - sensor-augmented pump (SAP) vs. USS+SAP during the day(d) vs. USS+CLC(d). We plan to randomize 84 patients with type 1 diabetes into two different treatment sequences: SAP,USS+SAP(d),USS+CLC(d),USS+SAP(d) and USS+SAP(d),USS+CLC(d),USS+SAP(d),SAP. Each treatment modality will continue for 2 months - sufficient time to address the following specific aims: SA1: Overnight CLC achieved by USS+SAP(d) will be superior to SAP alone in terms of: (1) Improved HbA1c without increasing the risk for hypoglycemia; (2) Reduced incidence and risk for hypoglycemia overnight, and (3) Reduced fear of hypoglycemia and improved diabetes quality of life scores. SA2: CLC during the day achieved by USS+CLC(d) will preserve the benefits of USS+SAP(d) and will be superior to USS+SAP(d) in terms of: (1) Increased time within target range of 70-180mg/dl during the day; (2) Reduced risk for hypoglycemia during and after exercise, and (3) Reduced postprandial glucose variability. SA3: CLC system acceptance evaluated by focus-group interviews and technology acceptance scores will be: (1) Superior, for USS+SAP(d) compared to SAP alone, i.e. adding USS overnight will increase patients' acceptance of CLC, and (2) Marginally inferior, for USS+CLC(d) compared to USS+SAP(d); i.e. some patients would prefer SAP alone during the day due to perceived increased system complexity. Overall, we expect to establish that a distinct overnight CLC modality (USS Virginia) combined with SAP therapy during the day is a viable precursor to future adaptable therapeutic schemes, achieving glycemic control that is superior to SAP alone and optimal balance between system complexity and perceived benefits.

Public Health Relevance

Closed-Loop Control Modalities in Type 1 Diabetes: Efficacy and System Acceptance The future of the artificial pancreas (AP) as optimal treatment for type 1 diabetes is within reach. To fulfil their promise to all, AP systems need to prove their efficacy i rigorous clinical trials with outcomes targeting key parameters of glucose control-hemoglobin A1c and risk for hypoglycemia. We therefore propose to deploy the novel technologies we have developed in the past several years, in a long-term study aiming to establish that optimal balance between AP system complexity and benefits to the patient is achieved by a system that automatically takes over a person's blood glucose control in the evening and then stabilizes and 'resets' blood glucose levels to normal by the morning. Such an approach follows the natural wake-sleep circadian cycle, takes advantage of overnight steady-state glucose levels, and is proven with our current technology.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
2R01DK085623-06
Application #
8878487
Study Section
Clinical and Integrative Diabetes and Obesity Study Section (CIDO)
Program Officer
Arreaza-Rubin, Guillermo
Project Start
2009-09-28
Project End
2019-02-28
Budget Start
2015-03-01
Budget End
2016-02-29
Support Year
6
Fiscal Year
2015
Total Cost
Indirect Cost
Name
University of Virginia
Department
Psychiatry
Type
Schools of Medicine
DUNS #
065391526
City
Charlottesville
State
VA
Country
United States
Zip Code
22904
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
Kovatchev, Boris P (2015) Hypoglycemia Reduction and Accuracy of Continuous Glucose Monitoring. Diabetes Technol Ther 17:530-3
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
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
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
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
Renard, Eric; Cobelli, Claudio; Kovatchev, Boris P (2013) Closed loop developments to improve glucose control at home. Diabetes Res Clin Pract 102:79-85

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