Integrated Approach to Close the Loop in Type 1 diabetes We have established in the current grant cycle, that early prandial hyper-glucagonemia is a feature in individuals with type 1 diabetes (T1D) contributing to prandial hyperglycemia. Likewise, plasma glucagon concentrations do not rise appropriately in response to declining glucose concentrations in T1D, placing them at risk for hypoglycemia. However, the causes of these abnormalities are speculated, but not proven to be due to concomitant ? cell dysfunction leading to abnormal glucagon secretion/kinetics. From a network-control view-point, this effect amounts to a disruption of the intra-islet ??? cell feedback control, which we have described in previous work. Therefore, our overarching goal in this application is to apply a novel isotope dilution method we have recently developed, to measure in vivo glucagon kinetics in T1D subjects across clinically relevant scenarios. This will allow quantifying the network deficiencies observed in T1D, and will in turn enable the development of a sophisticated multi-hormonal closed loop artificial pancreas (AP) taking into account the specifics of intra-islet functioning in health and in T1D. At any given time, plasma glucagon concentrations reflect a net balance between the rates of glucagon secretion, its' clearance and it's volume of distribution (VD). Unlike the C-peptide model that is used to measure insulin secretion, no such model is available to measure glucagon kinetics. Hence, it has not been possible to directly and reliably estimate these parameters in humans. Existing control algorithms utilizing glucagon in response to declining glucose concentrations are based on indirect estimations of glucagon kinetics. To directly measure these parameters, we will use novel tracer methods combined with organ catheterization technique, to determine whether, and if so, how glucagon appearance, hepatic extraction, clearance and/or VD is altered in T1D during post absorptive and postprandial states. Thereafter, we will determine systemic glucagon kinetics during subcutaneous (sq) administration of glucagon in T1D, as that is after all, the clinically germane route of administration of glucagon for multi-hormonal AP systems. These data will be used to estimate the parameters of our intra-islet network model and a glucagon secretion model, which will then enable computer simulation and AP algorithm design based on glucagon physiology in T1D. The algorithm will be designed to restore physiological glucagon profiles by using inhibitors of glucagon secretion to prevent excessive early prandial rise in glucagon and subsequent sq. glucagon infusions to restore apt late prandial rise in glucagon levels. Hence, we will build a new system model of glucagon kinetics, endogenous secretion and sq absorption that will be incorporated in an intra-islet network model specific to T1D, and will be then implemented into the FDA approved T1D simulator for in silico testing prior to future multi-hormonal AP clinical trials that will seek to normalize glucose excursions in T1D.

Public Health Relevance

Integrated Approach to Close the Loop in Type 1 diabetes The concept and practical application of dual-hormone (insulin + glucagon) closed-loop artificial pancreas algorithms are evolving and few such algorithms are currently being tested in clinical research trials for type 1 diabetes. However, critical knowledge-gaps exist regarding glucagon kinetics, ? cell function and intra-islet feedback network that are disrupted and abnormal in type 1 diabetes. The proposed experiments in this application will seek to bridge these gaps directly and in vivo, by applying a novel isotope dilution technique using glucagon tracers to estimate these parameters in type 1 diabetes. These physiological inputs will then be used to create biological models of glucagon secretion and kinetics, incorporated and tested into the FDA approved T1D simulator to develop a new dual hormone algorithm that will be tested in clinical trials in the future.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
5R01DK085516-07
Application #
9566156
Study Section
Clinical and Integrative Diabetes and Obesity Study Section (CIDO)
Program Officer
Arreaza-Rubin, Guillermo
Project Start
2009-09-30
Project End
2022-06-30
Budget Start
2018-07-01
Budget End
2019-06-30
Support Year
7
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Virginia
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
065391526
City
Charlottesville
State
VA
Country
United States
Zip Code
22904
Basu, Ananda; Joshi, Nisha; Miles, John et al. (2018) Paradigm Shifts in Nocturnal Glucose Control in Type 2 Diabetes. J Clin Endocrinol Metab 103:3801-3809
Ozaslan, Basak; Patek, Stephen D; Grabman, Jesse H et al. (2018) Body Mass Index Effect on Differing Responses to Psychological Stress in Blood Glucose Dynamics in Patients With Type 1 Diabetes. J Diabetes Sci Technol 12:657-664
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
Cramer, Angela M; Scalzo, Patricia; Bach, Sarah M et al. (2017) Patient- and Provider-Centered Design of an Outpatient Diabetes Technology Clinic. J Diabetes Sci Technol 11:499-505
Dadlani, Vikash; Kudva, Yogish C (2016) Role of Automation/Technology in Day-to-Day Diabetes Care. Diabetes Technol Ther 18:273-5
El Khatib, Moustafa M; Ohmine, Seiga; Jacobus, Egon J et al. (2016) Tumor-Free Transplantation of Patient-Derived Induced Pluripotent Stem Cell Progeny for Customized Islet Regeneration. Stem Cells Transl Med 5:694-702
Levine, James A (2016) The Baetylus Theorem-the central disconnect driving consumer behavior and investment returns in Wearable Technologies. Technol Invest 7:59-65
Chakkera, Harini A; Kudva, Yogish C; Chang, Yu-Hui H et al. (2016) Glucose homeostasis after simultaneous pancreas and kidney transplantation: a comparison of subjects with C-peptide-positive non-type 1 diabetes mellitus and type 1 diabetes mellitus. Clin Transplant 30:52-9
Simha, Vinaya; Kudva, Yogish C (2016) HDL Cholesterol Story Is Dead: Long Live HDL! Diabetes 65:2826-8
Gonder-Frederick, Linda A; Grabman, Jesse H; Kovatchev, Boris et al. (2016) Is Psychological Stress a Factor for Incorporation Into Future Closed-Loop Systems? J Diabetes Sci Technol 10:640-6

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