Efficacy Determinants of Subcutaneous Microdose Glucagon during Closed-Loop Control

Russell, Steven; El-Khatib, Firas; Nathan, David M.; Damiano, Edward R.

doi:10.1177/193229681000400602

Cited by 36 publications

(40 citation statements)

References 25 publications

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“…76 In addition, bihormonal closed-loop systems that infuse glucagon as well as insulin have been shown to be effective in preventing blood glucose levels < 70 mg/dL (3.9 mmol/L). [79][80][81] Many challenges remain for the development of a fully automated closed-loop system. These challenges include reducing the delay between glucose sensing and insulin delivery, improvements in CGM sensor accuracy, proving the clinical accuracy of control algorithms, further research in bihormonal delivery systems, and the development of ultrarapid-acting insulin analogs.…”

Section: Closed-loop Systemsmentioning

confidence: 99%

Recent Advances in the Prevention of Hypoglycemia in Type 1 Diabetes

Realsen¹,

Chase²

2011

Diabetes Technology & Therapeutics

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Iatrogenic hypoglycemia is one of the chief barriers to optimal glycemic control in people with type 1 diabetes (T1D). As a common contributor to morbidity and mortality in T1D, severe hypoglycemia (SH) is also a major fear for people with T1D and their families. Consequently, fear of hypoglycemia and hypoglycemia-avoidant behaviors are predominant limiting factors in achieving euglycemia in people with T1D. Nocturnal SH and hypoglycemia unawareness are prevalent obstacles in the detection of hypoglycemia which further impair the prevention and treatment of SH. Various strategies and technologies have already been developed to help detect and prevent hypoglycemia, including improved patient education, frequent self-monitoring of blood glucose levels, the use of rapid-acting and basal insulin analogs, continuous subcutaneous insulin infusion therapy, exercise-related insulin modifications, and continuous glucose monitors. The efficacy of these methods is well established, but further advances are still needed. The purpose of this review is to describe these currently available methods and to emphasize recent progress related to the prevention of hypoglycemia in T1D.

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Section: Closed-loop Systemsmentioning

confidence: 99%

Recent Advances in the Prevention of Hypoglycemia in Type 1 Diabetes

Realsen¹,

Chase²

2011

Diabetes Technology & Therapeutics

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“…Additionally, there are other factors that can affect the glycemic response of glucagon (22). It is well known that glucagon is the counter-regulatory hormone to insulin, but when insulin-on-board (IOB) is high, the effect of glucagon may be blunted, or even absent altogether (14,22,23).…”

Section: Introductionmentioning

confidence: 99%

Factors affecting the success of glucagon delivered during an automated closed-loop system in type 1 diabetes

Bakhtiani

Youssef

Duell

et al. 2015

Journal of Diabetes and its Complications

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Background In bi-hormonal closed-loop systems for treatment of diabetes, glucagon sometimes fails to prevent hypoglycemia. We evaluated glucagon responses during several closed-loop studies to determine factors, such as gain factors, responsible for glucagon success and failure. Methods We extracted data from four closed-loop studies, examining blood glucose excursions over the 50 minutes after each glucagon dose and defining hypoglycemic failure as glucose values < 60 mg/dl. Secondly, we evaluated hyperglycemic excursions within the same period, where glucose was > 180 mg/dl. We evaluated several factors for association with rates of hypoglycemic failure or hyperglycemic excursion. These factors included age, weight, HbA1c, duration of diabetes, gender, automation of glucagon delivery, glucagon dose, proportional and derivative errors (PE and DE), insulin on board (IOB), night vs. day delivery, and point sensor accuracy. Results We analyzed a total of 251 glucagon deliveries during 59 closed-loop experiments performed on 48 subjects. Glucagon successfully maintained glucose within target (60 – 180 mg/dl) in 195 (78%) of instances with 40 (16%) hypoglycemic failures and 16 (6%) hyperglycemic excursions. A multivariate logistic regression model identified PE (p<0.001), DE (p<0.001), and IOB (p<0.001) as significant determinants of success in terms of avoiding hypoglycemia. Using a model of glucagon absorption and action, simulations suggested that the success rate for glucagon would be improved by giving an additional 0.8 mcg/kg. Conclusion We conclude that glucagon fails to prevent hypoglycemia when it is given at a low glucose threshold and when glucose is falling steeply. We also confirm that high IOB significantly increases the risk for glucagon failures. Tuning of glucagon subsystem parameters may help reduce this risk.

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“…[12][13][14] Since automatic delivery systems give insulin doses frequently, there is a risk of giving excess insulin if the pending effect of previously administered insulin is underestimated. This is referred to as "stacking" of insulin, and leads to hypoglycemia.…”

mentioning

confidence: 99%

“…This is referred to as "stacking" of insulin, and leads to hypoglycemia. [12][13][14] Development of a wearable device to monitor interstitial insulin levels in real time would allow the pharmacokinetic characteristics of insulin to be calculated and fed back to the bionic pancreas to optimize insulin delivery. It would also allow early detection of failures in insulin delivery, such as due to an insulin infusion set becoming occluded or pulled out of the skin.…”

mentioning

confidence: 99%

Development of a Microsphere-Based System to Facilitate Real-Time Insulin Monitoring

Kahanovitz

Şeker

Marks

et al. 2015

J Diabetes Sci Technol

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Diabetes mellitus type 1 (T1D) is a disorder of glucose regulation characterized by autoimmune destruction of the insulinproducing pancreatic beta cells and the need for lifelong insulin replacement therapy. Achieving and maintaining near-normal blood glucose (BG) concentrations is critical for successful long term care of patients with diabetes mellitus and prevention of diabetic complications.1-7 However, the therapy required to achieve this goal is extremely demanding. Despite state-of-the-art insulin therapy using rapidacting insulin analogs, hyperglycemia and hypoglycemia are common in most people with T1D. 5-7The availability of insulin pumps and continuous glucose monitors (CGMs) have made possible the development of systems that automatically deliver insulin in response to real time glucose measurements. [8][9][10][11][12][13] These bionic pancreas (or artificial pancreas) devices measure BG frequently, estimate the proper insulin dose, and deliver insulin. However, development of control algorithms capable of appropriately regulating insulin dosing has been challenging due to the slow and unpredictable Background: The speed of insulin absorption after subcutaneous delivery is highly variable. Incorrect assumptions about insulin pharmacokinetics compromise effective glycemic regulation. Our ultimate goal is to develop a system to monitor insulin levels in vivo continuously, allowing pharmacokinetic parameters to be calculated in real time. We hypothesize that a bead-based detection system can be run on a flow-through microfluidic platform to measure insulin in subcutaneous fluid sampled via microdialysis. As a first step in development, we focused on microsphere-based measurement of insulin.

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Efficacy Determinants of Subcutaneous Microdose Glucagon during Closed-Loop Control

Cited by 36 publications

References 25 publications

Recent Advances in the Prevention of Hypoglycemia in Type 1 Diabetes

Recent Advances in the Prevention of Hypoglycemia in Type 1 Diabetes

Factors affecting the success of glucagon delivered during an automated closed-loop system in type 1 diabetes

Development of a Microsphere-Based System to Facilitate Real-Time Insulin Monitoring

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