[What is the current state of the artificial pancreas in diabetes care?] / Wo steht das künstliche Pankreas in der Diabetestherapie?

The artificial pancreas (also referred to as closed-loop system) brings us one step closer to the decade-long dream of automated insulin delivery. The closed-loop system directs subcutaneous insulin delivery corresponding to the glucose concentration using a control algorithm. Evidence shows that closed-loop systems substantially improve glucose control and quality of life; however, fully automated closed-loop systems have not yet been accomplished. Active input from patients is required for mealtime insulin dosing and corrections. This article provides an overview on the current state of development of the artificial pancreas in the treatment of diabetes.

Closed-loop insulin delivery in end-of-life care: a case report.

BACKGROUND: Glucose management for people with diabetes approaching the end of life can be very challenging. The aim is to balance a minimally invasive approach with avoidance of symptomatic hypo- and hyperglycaemia. CASE REPORT: We present a case of a hospitalized individual whose glucose was managed with closed-loop insulin delivery within a randomized controlled trial setting during a period of terminal illness. During the time in which closed-loop insulin delivery was used, glucose control was safe, with no glucose-related harm. The mean ± sd sensor glucose for this individual was 11.3 ± 4.3 mmol/l, percentage of time spent in target glucose range between 6 and 15 mmol/l was 70.5%, time spent in hypoglycaemia was 2.0% and time spent in significant hyperglycaemia >20 mmol/l was 2.6%. CONCLUSION: Closed-loop systems can accommodate personalized glucose targets and highly variable insulin requirements. Factory-calibrated continuous glucose sensors and insulin pump therapy are less intrusive than finger-stick glucose measurements and insulin injections, respectively. Closed-loop systems may provide a safer and less burdensome approach to glucose management towards the end of life.

Bolusing frequency and amount impacts glucose control during hybrid closed-loop.

AIM: To compare bolus insulin delivery patterns during closed-loop home studies in adults with suboptimally [HbA1c 58-86 mmol/mol (7.5%-10%)] and well-controlled [58 mmol/mol (< 7.5%)] Type 1 diabetes. METHODS: Retrospective analysis of daytime and night-time insulin delivery during home use of closed-loop over 4 weeks. Daytime and night-time controller effort, defined as amount of insulin delivered by closed-loop relative to usual basal insulin delivery, and daytime bolus effort, defined as total bolus insulin delivery relative to total daytime insulin delivery were compared between both cohorts. Correlation analysis was performed between individual bolus behaviour (bolus effort and frequency) and daytime controller efforts, and proportion of time spent within and below sensor glucose target range. RESULTS: Individuals with suboptimally controlled Type 1 diabetes had significantly lower bolus effort (P = 0.038) and daily bolus frequency (P < 0.001) compared with those with well-controlled diabetes. Controller effort during both daytime (P = 0.007) and night-time (P = 0.005) were significantly higher for those with suboptimally controlled Type 1 diabetes. Time when glucose was within the target range (3.9-10.0 mmol/L) during daytime correlated positively with bolus effort (r = 0.37, P = 0.016) and bolus frequency (r = 0.33, P = 0.037). Time when glucose was below the target range during daytime was comparable in both groups (P = 0.36), and did not correlate significantly with bolus effort (r = 0.28, P = 0.066) or bolus frequency (r = -0.21, P = 0.19). CONCLUSION: More frequent bolusing and higher proportion of insulin delivered as bolus during hybrid closed-loop use correlated positively with time glucose was in target range. This emphasises the need for user input and educational support to benefit from this novel therapeutic modality.

Gluclas: A software for computer-aided modulation of glucose infusion in glucose clamp experiments.

BACKGROUND AND OBJECTIVE: The glucose clamp (GC) is an experimental technique for assessing several aspects of glucose metabolism. In these experiments, investigators face the non-trivial challenge of accurately adjusting the rate of intravenous glucose infusion to drive subjects' blood glucose (BG) concentration towards a desired plateau level. In this work we present Gluclas, an open-source software to support researchers in the modulation of glucose infusion rate (GIR) during GC experiments. METHODS: Gluclas uses a proportional-integrative-derivative controller to provide GIR suggestions based on BG measurements. The controller embeds an anti-wind-up scheme to account for actuator physical limits and suitable corrections of control action to accommodate for possible sampling jitter due to manual measurement and actuation. The software also provides a graphic user interface to increase its usability. A preliminary validation of the controller is performed for different clamp scenarios (hyperglycemic, euglycemic, hypoglycemic) on a simulator of glucose metabolism in healthy subjects, which also includes models of measurement error and sampling delay for increased realism. In silico trials are performed on 50 virtual subjects. We also report the results of the first in-vivo application of the software in three subjects undergoing a hypoglycemic clamp. RESULTS: In silico, during the plateau period, the coefficient of variation (CV) is in median below 5% for every protocol, with 5% being considered the threshold for sufficient quality. In terms of median [5th percentile, 95th percentile], average BG level during the plateau period is 12.18 [11.58 - 12.53] mmol/l in the hyperglycemic clamp (target: 12.4 mmol/), 4.92 [4.51 - 5.14] mmol/l in the euglycemic clamp (target: 5.5 mmol/) and 2.38 [2.33 - 2.64] in the hypoglycemic clamp (target: 2.5 mmol/). Results in vivo are consistent with those obtained in silico during the plateau period: average BG levels are between 2.56 and 2.68 mmol/l (target: 2.5 mmol/l); CV is below 5% for all three experiments. CONCLUSIONS: Gluclas offered satisfactory control for tested GC protocols. Although its safety and efficacy need to be further validated in vivo, this preliminary validation suggest that Gluclas offers a reliable and non-expensive solution for reducing investigator bias and improving the quality of GC experiments.

A PID Control Algorithm for a Post-Prandial Hypoglycemic Clamp Study.

Post-prandial hypoglycemia occurs 2-5 hours after food intake, in not only insulin-treated patients with diabetes but also other metabolic disorders. For example, postprandial hypoglycemia is an increasingly recognized late metabolic complication of bariatric surgery (also known as PBH), particularly gastric bypass. Underlying mechanisms remain incompletely understood to date. Besides excessive insulin exposure, impaired counter-regulation may be a further pathophysiological feature. To test this hypothesis, we need standardized postprandial hypoglycemic clamp procedures in affected and unaffected individuals allowing to reach identical predefined postprandial hypoglycemic trajectories. Generally, in these experiments, clinical investigators manually adjust glucose infusion rate (GIR) to clamp blood glucose (BG) to a target hypoglycemic value. Nevertheless, reaching the desired target by manual adjustment may be challenging and possible glycemic undershoots when approaching hypoglycemia can be a safety concern for patients. In this study, we developed a PID algorithm to assist clinical investigators in adjusting GIR to reach the predefined trajectory and hypoglycemic target. The algorithm is developed in a manual mode to permit the clinical investigator to interfere. We test the controller in silico by simulating glucose-insulin dynamics in PBH and healthy nonsurgical individuals. Different scenarios are designed to test the robustness of the algorithm to different sources of variability and to errors, e.g. outliers in the BG measurements, sampling delays or missed measurements. The results prove that the PID algorithm is capable of accurately and safely reaching the target BG level, on both healthy and PBH subjects, with a median deviation from reference of 2.8% and 2.4% respectively.Clinical relevance- This control algorithm enables standardized, accurate and safe postprandial hypoglycemic clamps, as evidenced in silico in PBH patients and controls.

Hepatic and intramyocellular glycogen stores in adults with type 1 diabetes and healthy controls.

Glycogen levels in liver and skeletal muscle assessed non-invasively using magnetic resonance spectroscopy after a 48-h pre-study period including a standardized diet and withdrawal from exercise did not differ between individuals with well-controlled Type 1 DM and matched healthy controls.