Randomized trial of a dual-hormone artificial pancreas with dosing adjustment during exercise compared with no adjustment and sensor-augmented pump therapy

AIMS

To test whether adjusting insulin and glucagon in response to exercise within a dual-hormone artificial pancreas (AP) reduces exercise-related hypoglycaemia.

MATERIALS AND METHODS

In random order, 21 adults with type 1 diabetes (T1D) underwent three 22-hour experimental sessions: AP with exercise dosing adjustment (APX); AP with no exercise dosing adjustment (APN); and sensor-augmented pump (SAP) therapy. After an overnight stay and 2 hours after breakfast, participants exercised for 45 minutes at 60% of their maximum heart rate, with no snack given before exercise. During APX, insulin was decreased and glucagon was increased at exercise onset, while during SAP therapy, subjects could adjust dosing before exercise. The two primary outcomes were percentage of time spent in hypoglycaemia (<3.9 mmol/L) and percentage of time spent in euglycaemia (3.9-10 mmol/L) from the start of exercise to the end of the study.

RESULTS

The mean (95% confidence interval) times spent in hypoglycaemia (<3.9 mmol/L) after the start of exercise were 0.3% (-0.1, 0.7) for APX, 3.1% (0.8, 5.3) for APN, and 0.8% (0.1, 1.4) for SAP therapy. There was an absolute difference of 2.8% less time spent in hypoglycaemia for APX versus APN (p = .001) and 0.5% less time spent in hypoglycaemia for APX versus SAP therapy (p = .16). Mean time spent in euglycaemia was similar across the different sessions.

CONCLUSIONS

Adjusting insulin and glucagon delivery at exercise onset within a dual-hormone AP significantly reduces hypoglycaemia compared with no adjustment and performs similarly to SAP therapy when insulin is adjusted before exercise.

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

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 50min 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 0.8μg/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.

A review of artificial pancreas technologies with an emphasis on bi-hormonal therapy

Since the discovery of insulin, great progress has been made to improve the accuracy and safety of automated insulin delivery systems to help patients with type 1 diabetes achieve their treatment goals without causing hypoglycaemia. In recent years, bioengineering technology has greatly advanced diabetes management, with the development of blood glucose meters, continuous glucose monitors, insulin pumps and control systems for automatic delivery of one or more hormones. New insulin analogues have improved subcutaneous absorption characteristics, but do not completely eliminate the risk of hypoglycaemia. Insulin effect is counteracted by glucagon in non-diabetic individuals, while glucagon secretion in those with type 1 diabetes is impaired. The use of glucagon in the artificial pancreas is therefore a logical and feasible option for preventing and treating hypoglycaemia. However, commercially available glucagon is not stable in aqueous solution for long periods, forming potentially cytotoxic fibrils that aggregate quickly. Therefore, a more stable formulation of glucagon is needed for long-term use and storage in a bi-hormonal pump. In addition, a model of glucagon action in type 1 diabetes is lacking, further limiting the inclusion of glucagon into systems employing model-assisted control. As a result, although several investigators have been working to help develop bi-hormonal systems for patients with type 1 diabetes, most continue to utilize single hormone systems employing only insulin. This article seeks to focus on the attributes of glucagon and its use in bi-hormonal systems.

The effect of rising vs. falling glucose level on amperometric glucose sensor lag and accuracy in Type 1 diabetes

BACKGROUND

Because declining glucose levels should be detected quickly in persons with Type 1 diabetes, a lag between blood glucose and subcutaneous sensor glucose can be problematic. It is unclear whether the magnitude of sensor lag is lower during falling glucose than during rising glucose.

METHODS

Initially, we analysed 95 data segments during which glucose changed and during which very frequent reference blood glucose monitoring was performed. However, to minimize confounding effects of noise and calibration error, we excluded data segments in which there was substantial sensor error. After these exclusions, and combination of data from duplicate sensors, there were 72 analysable data segments (36 for rising glucose, 36 for falling). We measured lag in two ways: (1) the time delay at the vertical mid-point of the glucose change (regression delay); and (2) determination of the optimal time shift required to minimize the difference between glucose sensor signals and blood glucose values drawn concurrently.

RESULTS

Using the regression delay method, the mean sensor lag for rising vs. falling glucose segments was 8.9 min (95%CI 6.1-11.6) vs. 1.5 min (95%CI -2.6 to 5.5, P<0.005). Using the time shift optimization method, results were similar, with a lag that was higher for rising than for falling segments [8.3 (95%CI 5.8-10.7) vs. 1.5 min (95% CI -2.2 to 5.2), P<0.001]. Commensurate with the lag results, sensor accuracy was greater during falling than during rising glucose segments.

CONCLUSIONS

In Type 1 diabetes, when noise and calibration error are minimized to reduce effects that confound delay measurement, subcutaneous glucose sensors demonstrate a shorter lag duration and greater accuracy when glucose is falling than when rising.