Coverage of Prandial Insulin Requirements: An Elusive Goal

The continuing quest for better subcutaneously administered prandial insulins: a review of recent developments and potential clinical implications

The class of rapid-acting insulin analogues were introduced more than 20 years ago to control postprandial plasma glucose (PPG) excursions better than unmodified regular human insulin. Insulins, lispro, aspart and glulisine all achieved an earlier onset of action, greater peak effect and shorter duration of action resulting in lower PPG levels and a reduced risk of late postprandial hypoglycaemia. However, the subcutaneous absorption rate of these analogues still fails to match the physiological profile of insulin in the systemic circulation following a meal. Recent reformulations of aspart and lispro have generated a second generation of more rapid-acting insulin analogue candidates, including fast-acting aspart (faster aspart), ultra-rapid lispro and BioChaperone Lispro. These modifications have the potential to mimic physiological prandial insulin secretion better with an even earlier onset of action with improved PPG control, shorter duration of effect and reduced risk of hypoglycaemia. Recent phase 3 trials in type 1 and type 2 diabetes show that faster aspart and ultra-rapid lispro compared with conventional aspart and lispro, achieved fewer PPG excursions with a small increase in post-meal hypoglycaemia but similar or marginally superior glycated haemoglobin levels, and suggest the need for parallel optimization of basal insulin replacement. Phase 1 trials for BioChaperone Lispro are equally encouraging with phase 3 trials yet to be initiated. Comparative analysis of the clinical and pharmacological evidence for these new prandial insulin candidates in the treatment of type 1 and type 2 diabetes is the main focus of this review.

Randomized Outpatient Trial of Single- and Dual-Hormone Closed-Loop Systems That Adapt to Exercise Using Wearable Sensors

OBJECTIVE

Automated insulin delivery is the new standard for type 1 diabetes, but exercise-related hypoglycemia remains a challenge. Our aim was to determine whether a dual-hormone closed-loop system using wearable sensors to detect exercise and adjust dosing to reduce exercise-related hypoglycemia would outperform other forms of closed-loop and open-loop therapy.

RESEARCH DESIGN AND METHODS

Participants underwent four arms in randomized order: dual-hormone, single-hormone, predictive low glucose suspend, and continuation of current care over 4 outpatient days. Each arm included three moderate-intensity aerobic exercise sessions. The two primary outcomes were percentage of time in hypoglycemia (<70 mg/dL) and in a target range (70-180 mg/dL) assessed across the entire study and from the start of the in-clinic exercise until the next meal.

RESULTS

The analysis included 20 adults with type 1 diabetes who completed all arms. The mean time (SD) in hypoglycemia was the lowest with dual-hormone during the exercise period: 3.4% (4.5) vs. 8.3% (12.6) single-hormone (P = 0.009) vs. 7.6% (8.0) predictive low glucose suspend (P < 0.001) vs. 4.3% (6.8) current care where pre-exercise insulin adjustments were allowed (P = 0.49). Time in hypoglycemia was also the lowest with dual-hormone during the entire 4-day study: 1.3% (1.0) vs. 2.8% (1.7) single-hormone (P < 0.001) vs. 2.0% (1.5) predictive low glucose suspend (P = 0.04) vs. 3.1% (3.2) current care (P = 0.007). Time in range during the entire study was the highest with single-hormone: 74.3% (8.0) vs. 72.0% (10.8) dual-hormone (P = 0.44).

CONCLUSIONS

The addition of glucagon delivery to a closed-loop system with automated exercise detection reduces hypoglycemia in physically active adults with type 1 diabetes.

Future of Automated Insulin Delivery Systems

Advances in continuous glucose monitoring (CGM) have brought on a paradigm shift in the management of type 1 diabetes. These advances have enabled the automation of insulin delivery, where an algorithm determines the insulin delivery rate in response to the CGM values. There are multiple automated insulin delivery (AID) systems in development. A system that automates basal insulin delivery has already received Food and Drug Administration approval, and more systems are likely to follow. As the field of AID matures, future systems may incorporate additional hormones and/or multiple inputs, such as activity level. All AID systems are impacted by CGM accuracy and future CGM devices must be shown to be sufficiently accurate to be safely incorporated into AID. In this article, we summarize recent achievements in AID development, with a special emphasis on CGM sensor performance, and discuss the future of AID systems from the point of view of their input-output characteristics, form factor, and adaptability.