Novel Use of Glucagon in a Closed-Loop System for Prevention of Hypoglycemia in Type 1 Diabetes

Abstract

OBJECTIVETo minimize hypoglycemia in subjects with type 1 diabetes by automated glucagon delivery in a closed-loop insulin delivery system.RESEARCH DESIGN AND METHODSAdult subjects with type 1 diabetes underwent one closed-loop study with insulin plus placebo and one study with insulin plus glucagon, given at times of impending hypoglycemia. Seven subjects received glucagon using high-gain parameters, and six subjects received glucagon in a more prolonged manner using low-gain parameters. Blood glucose levels were measured every 10 min and insulin and glucagon infusions were adjusted every 5 min. All subjects received a portion of their usual premeal insulin after meal announcement.RESULTSAutomated glucagon plus insulin delivery, compared with placebo plus insulin, significantly reduced time spent in the hypoglycemic range (15 ± 6 vs. 40 ± 10 min/day, P = 0.04). Compared with placebo, high-gain glucagon delivery reduced the frequency of hypoglycemic events (1.0 ± 0.6 vs. 2.1 ± 0.6 events/day, P = 0.01) and the need for carbohydrate treatment (1.4 ± 0.8 vs. 4.0 ± 1.4 treatments/day, P = 0.01). Glucagon given with low-gain parameters did not significantly reduce hypoglycemic event frequency (P = NS) but did reduce frequency of carbohydrate treatment (P = 0.05).CONCLUSIONSDuring closed-loop treatment in subjects with type 1 diabetes, high-gain pulses of glucagon decreased the frequency of hypoglycemia. Larger and longer-term studies will be required to assess the effect of ongoing glucagon treatment on overall glycemic control.

Algorithms for a Closed-Loop Artificial Pancreas: The Case for Proportional-Integral-Derivative Control

“…The use of glucagon is not strictly speaking an MPC versus PID question, as glucagon can be used with PID control. Castle and coauthors 20 included glucagon in their fading memory proportional derivative controller ( Figure 2C, blue and light blue lines, with PID and MPC results from previous panels included for reference), albeit the fading memory proportional derivative algorithm does notas is evident in the name-include integral action. A discussion of the benefit of integral action is deferred until later in this report.…”

Recent Trends in Blood Glucose Control Studies

“…Some research groups [26,[30][31][32] have developed feedback blood glucose control systems combining CSII and continuous glucose monitoring (CGM) that adjust subcutaneous insulin infusion rates based on blood glucose measurements obtained from a micro needle implanted subcutaneously. Most of the control methods utilized in these systems are model predictive control (MPC) and proportional, integral and differential (PID) control.…”

Pharmacokinetics Modeling of Exogenous Glucagon in Type 1 Diabetes Mellitus Patients

“…However, the parameters of half-saturation glucagon amounts are fixed as constants of 10 -3 (k 3 [mg], from compartment 2 to compart- Model 7 has the same structure as Model 6 and replaces the linear transport from compartment 1 to compartment 2 with a Michaelis-Menten function (saturation at high doses). The Michaelis-Menten parameters are the maximal transport rate k 2 (mg/min) and the half-saturation amount k 3 (mg).…”

“…More recently, glucagon has also been used in bihormonal artificial pancreas (AP) systems, where insulin and glucagon are both injected using subcutaneous (SC) pumps and dosed based on model prediction from continuous glucose monitoring data and history of injections. Castle et al 3 were the first to perform such a study and demonstrate that glucagon interventions can be successfully added to an AP system as a protection against hypoglycemia. Further studies were carried by El-Khatib et al 4 and Ward et al, 5 as well as the most recent randomized crossover trial by Haidar et al, 6 which also showed improvement of glucose control against hypoglycemia with nocturnal glucagon plus insulin closed-loop strategy over open-loop insulin infusion.…”

“…As research interest and implementation of glucagon intervention in the treatment of T1DM patients grow, [3][4][5][6][7][8][9] especially in the field of modeling predictive AP systems, it becomes necessary to establish a robust and easily identifiable pharmacokinetic model of exogenous glucagon transport. In addition, such a model will provide the foundation for designing bihormonal strategies in AP systems, 10 leading to an easier regulatory pathway to clinical trials as well as accelerated system design and optimization.…”