The effect of insulin feedback on closed loop glucose control

The effects of targeted vagus nerve stimulation on glucose homeostasis in STZ-induced diabetic rodents

During type 1 diabetes, an autoimmune attack destroys pancreatic β-cells leading to the inability to maintain glucose homeostasis. These β-cells are neuroresponsive endocrine cells which normally secrete insulin partially in response to input from the vagus nerve. This neural pathway can be utilized as a point of therapeutic intervention by delivering exogenous stimulation to drive increased insulin secretion. In this study, a cuff electrode was implanted on the pancreatic branch of the vagus nerve just prior to pancreatic insertion in rats, and a continuous glucose meter was implanted into the descending aorta. Streptozotocin (STZ) was used to induce a diabetic state, and changes in blood glucose were assessed using various stimulation parameters. Stimulation driven changes in hormone secretion, pancreatic blood flow, and islet cell populations were assessed. We found increased changes in the rate of blood glucose change during stimulation which subsided after stimulation ended paired with increased concentration of circulating insulin. We did not observe increased pancreatic perfusion, which suggests that the modulation of blood glucose was due to the activation of b-cells rather than changes in the extra-organ transport of insulin. Pancreatic neuromodulation showed potentially protective effects by reducing deficits in islet diameter, and ameliorating insulin loss after STZ treatment.

Near-optimal feedback control for postprandial glucose regulation in type 1 diabetes

This paper is focused on feedback control of postprandial glucose levels for patients with type 1 Diabetes Mellitus. There are two important limitations that make this a challenging problem. First, the slow subcutaneous insulin pharmacokinetics that introduces a significant lag into the control loop. Second, the positivity constraint on the control action, meaning that it is not possible to remove insulin from the body. In this paper, both issues are explicitly considered in the design process using the internal model control framework, to derive a near-optimal feedback controller. Optimality is understood here as minimizing the blood glucose peak after a meal intake and, at the same time, preventing glucose values below a prescribed threshold. It is shown how the proposed controller approaches the optimal closed-loop performance as a limit case. The theoretical results are supported by a numerical example and the feasibility of the overall strategy under uncertainties is illustrated using an extended version UVa/Padova metabolic simulator.

Internal model control based module for the elimination of meal and exercise announcements in hybrid artificial pancreas systems

BACKGROUND AND OBJECTIVES

Hybrid artificial pancreas systems outperform current insulin pump therapies in blood glucose regulation in type 1 diabetes. However, subjects still have to inform the system about meals intake and exercise to achieve reasonable control. These patient announcements may result in overburden and compromise controller performance if not provided timely and accurately. Here, a hybrid artificial pancreas is extended with an add-on module that releases subjects from meals and exercise announcements.

METHODS

The add-on module consists of an internal-model controller that generates a "virtual" control action to compensate for disturbances. This "virtual" action is converted into insulin delivery, rescue carbohydrates suggestions, or insulin-on-board limitations, depending on a switching logic based on glucose measurements and predictions. The controller parameters are tuned by optimization and then related to standard parameters from the open-loop therapy. This module is implemented in a hybrid artificial pancreas system proposed by our research group for validation. This hybrid system extended with the add-on module is compared with the hybrid controller with carbohydrate counting errors (hybrid) and the hybrid controller with an alternative unannounced meal compensation module based on a meal detection algorithm (meal detector). The validation used the educational version of the UVa/Padova simulator to simulate the three controllers under two scenarios: one with only meals and another with meals and exercise. The exercise was modeled as a temporal increase of the insulin sensitivity resulting in the glucose drop usually related to an aerobic exercise.

RESULTS

For the scenario with only meals, the three controllers achieved similar time in range (proposed: 85.1 [77.9,88.1]%, hybrid: 84.0 [75.9,86.4]%, meal detector: 81.9 [79.3,83.8]%, median [interquartile range]) with low time in moderate hypoglycemia. Under the scenario with meals and exercise, the proposed module reduces 4.61% the time in hypoglycemia achieved with the other controllers, suggesting an acceptable amount of rescues (27.2 [23.7, 31.0] g).

CONCLUSIONS

The proposed add-on module achieved promising results: it outperformed the meal-detector-based controller, even achieving a postprandial performance as good as the hybrid controller (with carbohydrate counting errors). Also, the rescue suggestion feature of the module mitigated exercise-induced hypoglycemia with admissible rescue amounts.

An overview of advancements in closed-loop artificial pancreas system

Type 1 diabetes (T1D) is one of the world's health problems with a prevalence of 1.1 million for children and young adults under the age of 20. T1D is a health problem characterized by autoimmunity and the destruction of pancreatic cells that produce insulin. The available treatment is to maintain blood glucose within the desired normal range. To meet bolus and basal requirements, T1D patients may receive multiple daily injections (MDI) of fast-acting and long-acting insulin once or twice daily. In addition, insulin pumps can deliver multiple doses a day without causing injection discomfort in individuals with T1D. T1D patients have also monitored their blood glucose levels along with insulin replacement treatment using a continuous glucose monitor (CGM). However, this CGM has some drawbacks, like the sensor needs to be replaced after being inserted under the skin for seven days and needs to be calibrated (for some CGMs). The treatments and monitoring devices mentioned creating a lot of workloads to maintain blood glucose levels in individuals with T1D. Therefore, to overcome these problems, closed-loop artificial pancreas (APD) devices are widely used to manage blood glucose in T1D patients. Closed-loop APD consists of a glucose sensor, an insulin infusion device, and a control algorithm. This study reviews the progress of closed-loop artificial pancreas systems from the perspective of device properties, uses, testing procedures, regulations, and current market conditions.

A Reinforcement Learning Based System for Blood Glucose Control without Carbohydrate Estimation in Type 1 Diabetes: In Silico Validation

Type 1 Diabetes (T1D) is a chronic autoimmune disease, which requires the use of exogenous insulin for glucose regulation. In current hybrid closed-loop systems, meal entry is manual which adds cognitive burden to the persons living with T1D. In this study, we proposed a control system based on Proximal Policy Optimisation (PPO) that controls both basal and bolus insulin infusion and only requires meal announcement, thus eliminating the need for carbohydrate estimation. We evaluated the system on a challenging meal scenario, using an open-source simulator based on the UVA/Padova 2008 model and achieved a mean Time in Range value of 65% for the adult subject cohort, while maintaining a moderate hypoglycemic and hyperglycemic risk profile. The approach shows promise and welcomes further research towards the translation to a real-life artificial pancreas. Clinical relevance- This was an in-silico analysis towards the development of an autonomous artificial pancreas system for glucose control. The proposed system show promise in eliminating the need for estimating the carbohydrate content in meals.

Current Status and Emerging Options for Automated Insulin Delivery Systems

Combining technologies including rapid insulin analogs, insulin pumps, continuous glucose monitors, and control algorithms has allowed for the creation of automated insulin delivery (AID) systems. These systems have proven to be the most effective technology for optimizing metabolic control and could hold the key to broadly achieving goal-level glycemic control for people with type 1 diabetes. The use of AID has exploded in the past several years with several options available in the United States and even more in Europe. In this article, we review the largest studies involving these AID systems, and then examine future directions for AID with an emphasis on usability.

Towards the Integration of an Islet-Based Biosensor in Closed-Loop Therapies for Patients With Type 1 Diabetes

In diabetes mellitus (DM) treatment, Continuous Glucose Monitoring (CGM) linked with insulin delivery becomes the main strategy to improve therapeutic outcomes and quality of patients' lives. However, Blood Glucose (BG) regulation with CGM is still hampered by limitations of algorithms and glucose sensors. Regarding sensor technology, current electrochemical glucose sensors do not capture the full spectrum of other physiological signals, i.e., lipids, amino acids or hormones, relaying the general body status. Regarding algorithms, variability between and within patients remains the main challenge for optimal BG regulation in closed-loop therapies. This work highlights the simulation benefits to test new sensing and control paradigms which address the previous shortcomings for Type 1 Diabetes (T1D) closed-loop therapies. The UVA/Padova T1DM Simulator is the core element here, which is a computer model of the human metabolic system based on glucose-insulin dynamics in T1D patients. That simulator is approved by the US Food and Drug Administration (FDA) as an alternative for pre-clinical testing of new devices and closed-loop algorithms. To overcome the limitation of standard glucose sensors, the concept of an islet-based biosensor, which could integrate multiple physiological signals through electrical activity measurement, is assessed here in a closed-loop insulin therapy. This investigation has been addressed by an interdisciplinary consortium, from endocrinology to biology, electrophysiology, bio-electronics and control theory. In parallel to the development of an islet-based closed-loop, it also investigates the benefits of robust control theory against the natural variability within a patient population. Using 4 meal scenarios, numerous simulation campaigns were conducted. The analysis of their results then introduces a discussion on the potential benefits of an Artificial Pancreas (AP) system associating the islet-based biosensor with robust algorithms.

A Randomized Crossover Trial Comparing Glucose Control During Moderate-Intensity, High-Intensity, and Resistance Exercise With Hybrid Closed-Loop Insulin Delivery While Profiling Potential Additional Signals in Adults With Type 1 Diabetes

OBJECTIVE

To compare glucose control with hybrid closed-loop (HCL) when challenged by high intensity exercise (HIE), moderate intensity exercise (MIE), and resistance exercise (RE) while profiling counterregulatory hormones, lactate, ketones, and kinetic data in adults with type 1 diabetes.

RESEARCH DESIGN AND METHODS

This study was an open-label multisite randomized crossover trial. Adults with type 1 diabetes undertook 40 min of HIE, MIE, and RE in random order while using HCL (Medtronic MiniMed 670G) with a temporary target set 2 h prior to and during exercise and 15 g carbohydrates if pre-exercise glucose was <126 mg/dL to prevent hypoglycemia. Primary outcome was median (interquartile range) continuous glucose monitoring time-in-range (TIR; 70-180 mg/dL) for 14 h post-exercise commencement. Accelerometer data and venous glucose, ketones, lactate, and counterregulatory hormones were measured for 280 min post-exercise commencement.

RESULTS

Median TIR was 81% (67, 93%), 91% (80, 94%), and 80% (73, 89%) for 0-14 h post-exercise commencement for HIE, MIE, and RE, respectively (n = 30), with no difference between exercise types (MIE vs. HIE; P = 0.11, MIE vs. RE, P = 0.11; and HIE vs. RE, P = 0.90). Time-below-range was 0% for all exercise bouts. For HIE and RE compared with MIE, there were greater increases, respectively, in noradrenaline (P = 0.01 and P = 0.004), cortisol (P < 0.001 and P = 0.001), lactate (P ≤ 0.001 and P ≤ 0.001), and heart rate (P = 0.007 and P = 0.015). During HIE compared with MIE, there were greater increases in growth hormone (P = 0.024).

CONCLUSIONS

Under controlled conditions, HCL provided satisfactory glucose control with no difference between exercise type. Lactate, counterregulatory hormones, and kinetic data differentiate type and intensity of exercise, and their measurement may help inform insulin needs during exercise. However, their potential utility as modulators of insulin dosing will be limited by the pharmacokinetics of subcutaneous insulin delivery.

Wearable Glucose Monitoring and Implantable Drug Delivery Systems for Diabetes Management

The global cost of diabetes care exceeds $1 trillion each year with more than $327 billion being spent in the United States alone. Despite some of the advances in diabetes care including continuous glucose monitoring systems and insulin pumps, the technology associated with managing diabetes has largely remained unchanged over the past several decades. With the rise of wearable electronics and novel functional materials, the field is well-poised for the next generation of closed-loop diabetes care. Wearable glucose sensors implanted within diverse platforms including skin or on-tooth tattoos, skin-mounted patches, eyeglasses, contact lenses, fabrics, mouthguards, and pacifiers have enabled noninvasive, unobtrusive, and real-time analysis of glucose excursions in ambulatory care settings. These wearable glucose sensors can be integrated with implantable drug delivery systems, including an insulin pump, glucose responsive insulin release implant, and islets transplantation, to form self-regulating closed-loop systems. This review article encompasses the emerging trends and latest innovations of wearable glucose monitoring and implantable insulin delivery technologies for diabetes management with a focus on their advanced materials and construction. Perspectives on the current unmet challenges of these strategies are also discussed to motivate future technological development toward improved patient care in diabetes management.

Development platform for artificial pancreas algorithms

BACKGROUND AND AIMS

Assessing algorithms of artificial pancreas systems is critical in developing automated and fault-tolerant solutions that work outside clinical settings. The development and evaluation of algorithms can be facilitated with a platform that conducts virtual clinical trials. We present in this paper a clinically validated cloud-based distributed platform that supports the development and comprehensive testing of single and dual-hormone algorithms for type 1 diabetes mellitus (T1DM).

METHODS

The platform is built on principles of object-oriented design and runs user algorithms in real-time virtual clinical trials utilizing a multi-threaded environment enabled by concurrent execution over a cloud infrastructure. The platform architecture isolates user algorithms located on personal machines from proprietary patient data running on the cloud. Users import a plugin into their algorithms (Matlab, Python, or Java) to connect to the platform. Once connected, users interact with a graphical interface to design experimental protocols for their trials. Protocols include trial duration in days, mealtimes and amounts, variability in mealtimes and amounts, carbohydrate counting errors, snacks, and onboard insulin levels.

RESULTS

The platform facilitates development by solving the ODE model in the cloud on large CPU-optimized machines, providing a 62% improvement in memory, speed and CPU utilization. Users can easily debug & modify code, test multiple strategies, and generate detailed clinical performance reports. We validated and integrated into the platform a glucoregulatory system of ordinary differential equations (ODEs) parameterized with clinical data to mimic the inter and intra-day variability of glucose responses of 15 T1DM patients.

CONCLUSION

The platform utilizes the validated patient model to conduct virtual clinical trials for the rapid development and testing of closed-loop algorithms for T1DM.

Six Months of Hybrid Closed-Loop Versus Manual Insulin Delivery With Fingerprick Blood Glucose Monitoring in Adults With Type 1 Diabetes: A Randomized, Controlled Trial

OBJECTIVE

To investigate glycemic and psychosocial outcomes with hybrid closed-loop (HCL) versus user-determined insulin dosing with multiple daily injections (MDI) or insulin pump (i.e., standard therapy for most adults with type 1 diabetes).

RESEARCH DESIGN AND METHODS

Adults with type 1 diabetes using MDI or insulin pump without continuous glucose monitoring (CGM) were randomized to 26 weeks of HCL (Medtronic 670G) or continuation of current therapy. The primary outcome was masked CGM time in range (TIR; 70-180 mg/dL) during the final 3 weeks.

RESULTS

Participants were randomized to HCL (n = 61) or control (n = 59). Baseline mean (SD) age was 44.2 (11.7) years, HbA_1c was 7.4% (0.9%) (57 [10] mmol/mol), 53% were women, and 51% used MDI. HCL TIR increased from (baseline) 55% (13%) to (26 weeks) 70% (10%) with the control group unchanged: (baseline) 55% (12%) and (26 weeks) 55% (13%) (difference 15% [95% CI 11, 19]; P < 0.0001). For HCL, HbA_1c was lower (median [95% CI] difference -0.4% [-0.6, -0.2]; -4 mmol/mol [-7, -2]; P < 0.0001) and diabetes-specific positive well-being was higher (difference 1.2 [95% CI 0.4, 1.9]; P < 0.0048) without a deterioration in diabetes distress, perceived sleep quality, or cognition. Seventeen (9 device-related) versus 13 serious adverse events occurred in the HCL and control groups, respectively.

CONCLUSIONS

In adults with type 1 diabetes, 26 weeks of HCL improved TIR, HbA_1c, and their sense of satisfaction from managing their diabetes compared with those continuing with user-determined insulin dosing and self-monitoring of blood glucose. For most people living with type 1 diabetes globally, this trial demonstrates that HCL is feasible, acceptable, and advantageous.

Closed-loop insulin delivery systems in children and adolescents with type 1 diabetes

Introduction: Optimal glycemic control remains challenging in children and adolescents with type 1 diabetes due to highly variable day-to-day and night-to-night insulin requirements. This hurdle could be addressed by glucose-responsive insulin delivery based on real-time continuous glucose measurements.Areas covered: This review summaries recent advances of closed-loop systems in children and adolescents with type 1 diabetes, using both single- and dual-hormone closed-loop systems. The main outcomes, proportions of time spent in target range 70-180 mg/dl, and time spent in hypoglycemia below 70 mg/dl, are assessed particularly during unsupervised free-living randomized controlled trials.Expert opinion: Noteworthy and clinically meaningful translation of experimental investigations from controlled in-hospital settings to unrestricted home studies have been achieved over the past years, resulting in the regulatory approval of the first hybrid closed-loop system also in the pediatric population and with several other advanced devices in the pipeline. Large multinational and pivotal clinical trials including broad age populations are underway to facilitate the use of closed-loop systems in routine clinical practice.

Artificial Pancreas Control Strategies Used for Type 1 Diabetes Control and Treatment: A Comprehensive Analysis

This paper presents a comprehensive survey about the fundamental components of the artificial pancreas (AP) system including insulin administration and delivery, glucose measurement (GM), and control strategies/algorithms used for type 1 diabetes mellitus (T1DM) treatment and control. Our main focus is on the T1DM that emerges due to pancreas’s failure to produce sufficient insulin due to the loss of beta cells (β-cells). We discuss various insulin administration and delivery methods including physiological methods, open-loop, and closed-loop schemes. Furthermore, we report several factors such as hyperglycemia, hypoglycemia, and many other physical factors that need to be considered while infusing insulin in human body via AP systems. We discuss three prominent control algorithms including proportional-integral- derivative (PID), fuzzy logic, and model predictive, which have been clinically evaluated and have all shown promising results. In addition, linear and non-linear insulin infusion control schemes have been formally discussed. To the best of our knowledge, this is the first work which systematically covers recent developments in the AP components with a solid foundation for future studies in the T1DM field.

Chatteringfree hybrid adaptive neuro-fuzzy inference system-particle swarm optimisation data fusion-based BG-level control

In this study, a closed-loop control scheme is proposed for the glucose-insulin regulatory system in type-1 diabetic mellitus (T1DM) patients. Some innovative hybrid glucose-insulin regulators have combined artificial intelligence such as fuzzy logic and genetic algorithm with well known Palumbo model to regulate the blood glucose (BG) level in T1DM patients. However, most of these approaches have focused on the glucose reference tracking, and the qualitative of this tracking such as chattering reduction of insulin injection has not been well-studied. Higher-order sliding mode (HoSM) controllers have been employed to attenuate the effect of chattering. Owing to the delayed nature and non-linear property of glucose-insulin mechanism as well as various unmeasurable disturbances, even the HoSM methods are partly successful. In this study, data fusion of adaptive neuro-fuzzy inference systems optimised by particle swarm optimisation has been presented. The excellent performance of the proposed hybrid controller, i.e. desired BG-level tracking and chattering reduction in the presence of daily glucose-level disturbances is verified.

Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

The artificial pancreas (AP) is a system intended to control blood glucose levels through automated insulin infusion, reducing the burden of subjects with type 1 diabetes to manage their condition. To increase patients' safety, some systems limit the allowed amount of insulin active in the body, known as insulin-on-board (IOB). The safety auxiliary feedback element (SAFE) layer has been designed previously to avoid overreaction of the controller and thus avoiding hypoglycemia. In this work, a new method, so-called “dynamic rule-based algorithm,” is presented in order to adjust the limits of IOB in real time. The algorithm is an extension of a previously designed method which aimed to adjust the limits of IOB for a meal with 60 grams of carbohydrates (CHO). The proposed method is intended to be applied on hybrid AP systems during 24 h operation. It has been designed by combining two different strategies to set IOB limits for different situations: (1) fasting periods and (2) postprandial periods, regardless of the size of the meal. The UVa/Padova simulator is considered to assess the performance of the method, considering challenging scenarios. In silico results showed that the method is able to reduce the time spent in hypoglycemic range, improving patients' safety, which reveals the feasibility of the approach to be included in different control algorithms.

Simulation Software for Assessment of Nonlinear and Adaptive Multivariable Control Algorithms: Glucose - Insulin Dynamics in Type 1 Diabetes

A simulator for testing automatic control algorithms for nonlinear systems with time-varying parameters, variable time delays, and uncertainties is developed. It is based on simulation of virtual patients with Type 1 diabetes (T1D). Nonlinear models are developed to describe glucose concentration (GC) variations based on user-defined scenarios for meal consumption, insulin administration, and physical activity. They compute GC values and physiological variables, such as heart rate, skin temperature, accelerometer, and energy expenditure, that are indicative of physical activities affecting GC dynamics. This is the first simulator designed for assessment of multivariable controllers that consider supplemental physiological variables in addition to GC measurements to improve glycemic control. Virtual patients are generated from distributions of identified model parameters using clinical data. The simulator will enable testing and evaluation of new control algorithms proposed for automated insulin delivery as well as various control algorithms for nonlinear systems with uncertainties, time-varying parameters and delays.

Glucose Control in Adults with Type 1 Diabetes Using a Medtronic Prototype Enhanced-Hybrid Closed-Loop System: A Feasibility Study

Background: Experience from first-generation closed-loop (CL) systems informs refinements to enhance glucose control and user acceptance. A next-generation prototype enhanced-hybrid CL (E-HCL) system incorporates iterative changes to the Medtronic MiniMed 670G CL system, including automated correction boluses, lower target glucose level, and user enhancements. The aim was to explore safety, system performance, and glucose control using E-HCL in adults with type 1 diabetes. Methods: Twelve adults underwent this first in-human feasibility study. After a 1-week run-in using open-loop (OL), E-HCL was activated at the start of a supervised 1-week hotel phase, followed by 3 weeks free living at home. Supervised challenges included two meal interventions (unannounced and late meal bolus) and a sensor calibration intervention. Primary outcome was sensor glucose time-in-range (TIR); OL run-in and E-HCL at home were compared by Wilcoxon signed-rank test. Results: Twelve adults (seven men; median [interquartile range] age 48 [39, 57] years; HbA_1c 6.8 [6.2, 7.2]%, 51 [44, 55] mmol/mol; diabetes duration 31 [13, 41] years) completed the protocol. E-HCL resulted in greater TIR (85.3 [79.4, 88.4]% vs. 75.0 [66.6, 83.7]%, P = 0.003) and lower mean sensor glucose (123.0 [119.3, 129.6] mg/dL vs. 143.5 [135.8, 154.5] mg/dL, P = 0.002) than OL. Time spent <70 mg/dL increased using E-HCL (4.4 [3.3, 6.1]% vs. 3.0 [1.8, 3.8]%, P = 0.02) with no difference in time <54 mg/dL (P = 0.64). Time in CL was 99.98 [99.0, 100.0]%. All participants were satisfied using E-HCL. Conclusions: In adults with well-controlled HbA_1c levels, a prototype E-HCL resulted in high TIR, few CL exits, and positive user experiences at the expense of increased hypoglycemia (<70 mg/dL). E-HCL represents a positive step in the journey toward optimizing glucose control in people living with type 1 diabetes.

Automated Insulin Delivery Algorithms

IN BRIEF Automated insulin delivery (AID; also known as artificial pancreas) has improved the regulation of blood glucose concentrations, reduced the frequency of hyperglycemic and hypoglycemic episodes, and improved the quality of life of people with diabetes and their families. Three different types of algorithms-proportional-integral-derivative control, model predictive control, and fuzzy-logic knowledge-based systems-have been used in AID control systems. This article will highlight the foundations of these algorithms and discuss their strengths and limitations. Multivariable artificial pancreas and dual-hormone (insulin and glucagon) systems will be introduced.

Plasma-Insulin-Cognizant Adaptive Model Predictive Control for Artificial Pancreas Systems

An adaptive model predictive control (MPC) algorithm with dynamic adjustments of constraints and objective function weights based on estimates of the plasma insulin concentration (PIC) is proposed for artificial pancreas (AP) systems. A personalized compartment model that translates the infused insulin into estimates of PIC is integrated with a recursive subspace-based system identification to characterize the transient dynamics of glycemic measurements. The system identification approach is able to identify stable, reliable linear time-varying models from closed-loop data. An MPC algorithm using the adaptive models is designed to compute the optimal exogenous insulin delivery for AP systems without requiring any manually-entered meal information. A dynamic safety constraint derived from the estimation of PIC is incorporated in the adaptive MPC to improve the efficacy of the AP and prevent insulin overdosing. Simulation case studies demonstrate the performance of the proposed adaptive MPC algorithm.

Safety Evaluation of the MiniMed 670G System in Children 7-13 Years of Age with Type 1 Diabetes

OBJECTIVE

To evaluate the safety of in-home use of the MiniMed™ 670G system with SmartGuard™ technology in children with type 1 diabetes (T1D).

METHODS

Participants (N = 105, ages 7-13 years, mean age 10.8 ± 1.8 years) were enrolled at nine centers (eight in the United States and one in Israel) and completed a 2-week baseline run-in phase in Manual Mode followed by a 3-month study phase with Auto Mode enabled. Sensor glucose (SG), glycated hemoglobin (HbA_1c), percentage of SG values across glucose ranges, and SG variability, during the run-in and study phases were compared. Participants underwent frequent sample testing with i-STAT^® venous reference measurement during a hotel period (6 days/5 nights) to evaluate the system's continuous glucose monitoring performance.

RESULTS

Auto Mode was used a median of 81% of the time. From baseline to end of study, overall SG dropped by 6.9 ± 17.2 mg/dL (P < 0.001), HbA_1c decreased from 7.9% ± 0.8% to 7.5% ± 0.6% (P < 0.001), percentage of time in target glucose range (70-180 mg/dL) increased from 56.2% ± 11.4% to 65.0% ± 7.7% (P < 0.001), and the SG coefficient of variation decreased from 39.6% ± 5.4% to 38.5% ± 3.8% (P = 0.009). The percentage of SG values within target glucose range was 68.2% ± 9.1% and that of i-STAT reference values was 65.6% ± 17.7%. The percentage of values within 20%/20 of the i-STAT reference was 85.2%. There were no episodes of severe hypoglycemia or diabetic ketoacidosis during the study phase.

CONCLUSION

In-home use of MiniMed 670G system Auto Mode for 3 months by children with T1D, similar to MiniMed 670G system use by adolescents and adults with T1D, was safe and associated with reduced HbA_1c levels and increased time in target glucose range, compared with baseline.

Artificial Pancreas: Clinical Study in Latin America Without Premeal Insulin Boluses

BACKGROUND

Emerging therapies such as closed-loop (CL) glucose control, also known as artificial pancreas (AP) systems, have shown significant improvement in type 1 diabetes mellitus (T1DM) management. However, demanding patient intervention is still required, particularly at meal times. To reduce treatment burden, the automatic regulation of glucose (ARG) algorithm mitigates postprandial glucose excursions without feedforward insulin boluses. This work assesses feasibility of this new strategy in a clinical trial.

METHODS

A 36-hour pilot study was performed on five T1DM subjects to validate the ARG algorithm. Subjects wore a subcutaneous continuous glucose monitor (CGM) and an insulin pump. Insulin delivery was solely commanded by the ARG algorithm, without premeal insulin boluses. This was the first clinical trial in Latin America to validate an AP controller.

RESULTS

For the total 36-hour period, results were as follows: average time of CGM readings in range 70-250 mg/dl: 88.6%, in range 70-180 mg/dl: 74.7%, <70 mg/dl: 5.8%, and <50 mg/dl: 0.8%. Results improved analyzing the final 15-hour period of this trial. In that case, the time spent in range was 70-250 mg/dl: 94.7%, in range 70-180 mg/dl: 82.6%, <70 mg/dl: 4.1%, and <50 mg/dl: 0.2%. During the last night the time spent in range was 70-250 mg/dl: 95%, in range 70-180 mg/dl: 87.7%, <70 mg/dl: 5.0%, and <50 mg/dl: 0.0%. No severe hypoglycemia occurred. No serious adverse events were reported.

CONCLUSIONS

The ARG algorithm was successfully validated in a pilot clinical trial, encouraging further tests with a larger number of patients and in outpatient settings.

Efficacy and safety of the artificial pancreas in the paediatric population with type 1 diabetes

BACKGROUND

Type 1 diabetes (DM1) is one of the most common chronic diseases in childhood and requires life-long insulin therapy and continuous health care support. An artificial pancreas (AP) or closed-loop system (CLS) have been developed with the aim of improving metabolic control without increasing the risk of hypoglycaemia in patients with DM1. As the impact of APs have been studied mainly in adults, the aim of this review is to evaluate the efficacy and safety of the AP in the paediatric population with DM1.

MAIN BODY

The real advantage of a CLS compared to last-generation sensor-augmented pumps is the gradual modulation of basal insulin infusion in response to glycaemic variations (towards both hyperglycaemia and hypoglycaemia), which has the aim of improving the proportion of time spent in the target glucose range and reducing the mean glucose level without increasing the risk of hypoglycaemia. Some recent studies demonstrated that also in children and adolescents an AP is able to reduce the frequency of hypoglycaemic events, an important limiting factor in reaching good metabolic control, particularly overnight. However, the advantages of the AP in reducing hyperglycaemia, increasing the time spent in the target glycaemic range and thus reducing glycated haemoglobin are less clear and require more clinical trials in the paediatric population, in particular in younger children.

CONCLUSIONS

Although the first results from bi-hormonal CLS are promising, long-term, head-to-head studies will have to prove their superiority over insulin-only approaches. More technological progress, the availability of more fast-acting insulin, further developments of algorithms that could improve glycaemic control after meals and physical activity are the most important challenges in reaching an optimal metabolic control with the use of the AP in children and adolescents.

Effect of 6 months of hybrid closed-loop insulin delivery in adults with type 1 diabetes: a randomised controlled trial protocol

INTRODUCTION

Manual determination of insulin dosing largely fails to optimise glucose control in type 1 diabetes. Automated insulin delivery via closed-loop systems has improved glucose control in short-term studies. The objective of the present study is to determine the effectiveness of 6 months' closed-loop compared with manually determined insulin dosing on time-in-target glucose range in adults with type 1 diabetes.

METHODS AND ANALYSIS

This open-label, seven-centre, randomised controlled parallel group clinical trial will compare home-based hybrid closed-loop versus standard diabetes therapy in Australia. Adults aged ≥25 years with type 1 diabetes using intensive insulin therapy (via multiple daily injections or insulin pump, total enrolment target n=120) will undertake a run-in period including diabetes and carbohydrate-counting education, clinical optimisation and baseline data collection. Participants will then be randomised 1:1 either to 26 weeks of MiniMed 670G hybrid closed-loop system therapy (Medtronic, Northridge, CA, USA) or continuation of their current diabetes therapy. The hybrid closed-loop system delivers insulin automatically to address basal requirements and correct to target glucose level, while bolus doses for meals require user initiation and carbohydrate estimation. Analysis will be intention to treat, with the primary outcome time in continuous glucose monitoring (CGM) target range (3.9-10.0 mmol/L) during the final 3 weeks of intervention. Secondary outcomes include: other CGM parameters, HbA_1c, severe hypoglycaemia, psychosocial well-being, sleep, cognition, electrocardiography, costs, quality of life, biomarkers of vascular health and hybrid closed-loop system performance. Semistructured interviews will assess the expectations and experiences of a subgroup of hybrid closed-loop users.

ETHICS AND DISSEMINATION

The study has Human Research Ethics Committee approval. The study will be conducted in accordance with the principles of the Declaration of Helsinki and Good Clinical Practice. Results will be disseminated at scientific conferences and via peer-reviewed publications.

TRIAL REGISTRATION NUMBER

ACTRN12617000520336; Pre-results.

Adaptive and Personalized Plasma Insulin Concentration Estimation for Artificial Pancreas Systems

BACKGROUND

The artificial pancreas (AP) system, a technology that automatically administers exogenous insulin in people with type 1 diabetes mellitus (T1DM) to regulate their blood glucose concentrations, necessitates the estimation of the amount of active insulin already present in the body to avoid overdosing.

METHOD

An adaptive and personalized plasma insulin concentration (PIC) estimator is designed in this work to accurately quantify the insulin present in the bloodstream. The proposed PIC estimation approach incorporates Hovorka's glucose-insulin model with the unscented Kalman filtering algorithm. Methods for the personalized initialization of the time-varying model parameters to individual patients for improved estimator convergence are developed. Data from 20 three-days-long closed-loop clinical experiments conducted involving subjects with T1DM are used to evaluate the proposed PIC estimation approach.

RESULTS

The proposed methods are applied to the clinical data containing significant disturbances, such as unannounced meals and exercise, and the results demonstrate the accurate real-time estimation of the PIC with the root mean square error of 7.15 and 9.25 mU/L for the optimization-based fitted parameters and partial least squares regression-based testing parameters, respectively.

CONCLUSIONS

The accurate real-time estimation of PIC will benefit the AP systems by preventing overdelivery of insulin when significant insulin is present in the bloodstream.

Multi-level Supervision and Modification of Artificial Pancreas Control System

Artificial pancreas (AP) systems provide automated regulation of blood glucose concentration (BGC) for people with type 1 diabetes (T1D). An AP includes three components: a continuous glucose monitoring (CGM) sensor, a controller calculating insulin infusion rate based on the CGM signal, and a pump delivering the insulin amount calculated by the controller to the patient. The performance of the AP system depends on successful operation of these three components. Many APs use model predictive controllers that rely on models to predict BGC and to calculate the optimal insulin infusion rate. The performance of model-based controllers depends on the accuracy of the models that is affected by large dynamic changes in glucose-insulin metabolism or equipment performance that may move the operating conditions away from those used in developing the models and designing the control system. Sensor errors and missing signals will cause calculation of erroneous insulin infusion rates. And the performance of the controller may vary at each sampling step and each period (meal, exercise, and sleep), and from day to day. Here we describe a multi-level supervision and controller modification (ML-SCM) module is developed to supervise the performance of the AP system and retune the controller. It supervises AP performance in 3 time windows: sample level, period level, and day level. At sample level, an online controller performance assessment sub-module will generate controller performance indexes to evaluate various components of the AP system and conservatively modify the controller. A sensor error detection and signal reconciliation module will detect sensor error and reconcile the CGM sensor signal at each sample. At period level, the controller performance is evaluated with information collected during a certain time period and the controller is tuned more aggressively. At the day level, the daily CGM ranges are further analyzed to determine the adjustable range of controller parameters used for sample level and period level. Thirty subjects in the UVa/Padova metabolic simulator were used to evaluate the performance of the ML-SCM module and one clinical experiment is used to illustrate its performance in a clinical environment. The results indicate that the AP system with an ML-SCM module has a safer range of glucose concentration distribution and more appropriate insulin infusion rate suggestions than an AP system without the ML-SCM module.

Optimizing Hybrid Closed-Loop Therapy in Adolescents and Emerging Adults Using the MiniMed 670G System

OBJECTIVE

The MiniMed 670G System is the first commercial hybrid closed-loop (HCL) system for management of type 1 diabetes. Using data from adolescent and young adult participants, we compared insulin delivery patterns and time-in-range metrics in HCL (Auto Mode) and open loop (OL). System alerts, usage profiles, and operational parameters were examined to provide suggestions for optimal clinical use of the system.

RESEARCH DESIGN AND METHODS

Data from 31 adolescent and young adult participants (14-26 years old) at three clinical sites in the 670G pivotal trial were analyzed. Participants had a 2-week run-in period in OL, followed by a 3-month in-home study phase with HCL functionality enabled. Data were compared between baseline OL and HCL use after 1 week, 1 month, 2 months, and 3 months.

RESULTS

Carbohydrate-to-insulin (C-to-I) ratios were more aggressive for all meals with HCL compared with baseline OL. Total daily insulin dose and basal-to-bolus ratio did not change during the trial. Time in range increased 14% with use of Auto Mode after 3 months (P < 0.001), and HbA_1c decreased 0.75%. Auto Mode exits were primarily due to sensor/insulin delivery alerts and hyperglycemia. The percentage of time in Auto Mode gradually declined from 87%, with a final use rate of 72% (-15%).

CONCLUSIONS

In transitioning young patients to the 670G system, providers should anticipate immediate C-to-I ratio adjustments while also assessing active insulin time. Users should anticipate occasional Auto Mode exits, which can be reduced by following system instructions and reliably bolusing for meals. Unique 670G system functionality requires ongoing clinical guidance and education from providers.

Overnight Glucose Control with Dual- and Single-Hormone Artificial Pancreas in Type 1 Diabetes with Hypoglycemia Unawareness: A Randomized Controlled Trial

BACKGROUND

The dual-hormone (insulin and glucagon) artificial pancreas may be justifiable in some, but not all, patients. We sought to compare dual- and single-hormone artificial pancreas systems in patients with hypoglycemia unawareness and documented nocturnal hypoglycemia.

METHODS

We conducted a randomized crossover trial comparing the efficacy of dual- and single-hormone artificial pancreas systems in controlling plasma glucose levels over the course of one night's sleep. We recruited 18 adult participants with hypoglycemia unawareness and 17 participants with hypoglycemia awareness, all of whom had documented nocturnal hypoglycemia during 2 weeks of screening. Outcomes were calculated using plasma glucose.

RESULTS

In participants with hypoglycemia unawareness, the median (interquartile range [IQR]) percentage of time that plasma glucose was below 4.0 mmol/L was 0% (0-0) on dual-hormone artificial pancreas nights and 0% (0-10) on single-hormone artificial pancreas nights (P = 0.20). Additionally, participants with hypoglycemia unawareness experienced two hypoglycemic events (<3.0 mmol/L) on dual-hormone artificial pancreas nights and three hypoglycemic events on single-hormone artificial pancreas nights. In participants with hypoglycemia awareness, the median (IQR) percentage of time that plasma glucose was below 4.0 mmol/L was 0% (0-0) on both dual- and single-hormone artificial pancreas nights. Hypoglycemia awareness participants experienced zero hypoglycemic events on dual-hormone artificial pancreas nights and one event on single-hormone artificial pancreas nights.

DISCUSSION

In this study, dual-hormone and single-hormone systems performed equally well in preventing nocturnal hypoglycemia in participants with hypoglycemia unawareness. Longer studies over the course of multiple days and nights may be needed to explore possible specific benefits in this population. ClinicalTrials.gov No. NCT02282254.

Multivariable Adaptive Artificial Pancreas System in Type 1 Diabetes

PURPOSE OF REVIEW

The review summarizes the current state of the artificial pancreas (AP) systems and introduces various new modules that should be included in future AP systems.

RECENT FINDINGS

A fully automated AP must be able to detect and mitigate the effects of meals, exercise, stress and sleep on blood glucose concentrations. This can only be achieved by using a multivariable approach that leverages information from wearable devices that provide real-time streaming data about various physiological variables that indicate imminent changes in blood glucose concentrations caused by meals, exercise, stress and sleep. The development of a fully automated AP will necessitate the design of multivariable and adaptive systems that use information from wearable devices in addition to glucose sensors and modify the models used in their model-predictive alarm and control systems to adapt to the changes in the metabolic state of the user. These AP systems will also integrate modules for controller performance assessment, fault detection and diagnosis, machine learning and classification to interpret various signals and achieve fault-tolerant control. Advances in wearable devices, computational power, and safe and secure communications are enabling the development of fully automated multivariable AP systems.

Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target

BACKGROUND

Postprandial (PP) control remains a challenge for closed-loop (CL) systems. Few studies with inconsistent results have systematically investigated the PP period.

OBJECTIVE

To compare a new CL algorithm with current pump therapy (open loop [OL]) in the PP glucose control in type 1 diabetes (T1D) subjects.

METHODS

A crossover randomized study was performed in two centers. Twenty T1D subjects (F/M 13/7, age 40.7 ± 10.4 years, disease duration 22.6 ± 9.9 years, and A1c 7.8% ± 0.7%) underwent an 8-h mixed meal test on four occasions. In two (CL1/CL2), after meal announcement, a bolus was given followed by an algorithm-driven basal infusion based on continuous glucose monitoring (CGM). Alternatively, in OL1/OL2 conventional pump therapy was used. Main outcome measures were as follows: glucose variability, estimated with the coefficient of variation (CV) of the area under the curve (AUC) of plasma glucose (PG) and CGM values, and from the analysis of the glucose time series; mean, maximum (C_max), and time to C_max glucose concentrations and time in range (<70, 70-180, >180 mg/dL).

RESULTS

CVs of the glucose AUCs were low and similar in all studies (around 10%). However, CL achieved greater reproducibility and better PG control in the PP period: CL1 = CL2<OL1<OL2 (PG_mean 123 ± 47 and 125 ± 44 vs. 152 ± 53 and 159 ± 54 mg/dL) and C_max OL 217.1 ± 67.0 mg/dL versus CL 183.3 ± 63.9 mg/dL, P < 0.0001. Time-in-range was higher with CL versus OL (80% vs. 64%; P < 0.001). Neither the time below 70 mg/dL (CL 6.1% vs. OL 3.2%; P > 0.05) nor the need for oral glucose was significantly different (CL 40.0% vs. OL 22.5% of meals; P = 0.054).

CONCLUSIONS

This novel CL algorithm effectively and consistently controls PP glucose excursions without increasing hypoglycemia. Study registered at ClinicalTrials.gov : study number NCT02100488.

Glucose Outcomes with the In-Home Use of a Hybrid Closed-Loop Insulin Delivery System in Adolescents and Adults with Type 1 Diabetes

BACKGROUND

The safety and effectiveness of the in-home use of a hybrid closed-loop (HCL) system that automatically increases, decreases, and suspends insulin delivery in response to continuous glucose monitoring were investigated.

METHODS

Adolescents (n = 30, ages 14-21 years) and adults (n = 94, ages 22-75 years) with type 1 diabetes participated in a multicenter (nine sites in the United States, one site in Israel) pivotal trial. The Medtronic MiniMed^® 670G system was used during a 2-week run-in phase without HCL control, or Auto Mode, enabled (Manual Mode) and, thereafter, with Auto Mode enabled during a 3-month study phase. A supervised hotel stay (6 days/5 nights) that included a 24-h frequent blood sample testing with a reference measurement (i-STAT) occurred during the study phase.

RESULTS

Adolescents (mean ± standard deviation [SD] 16.5 ± 2.29 years of age and 7.7 ± 4.15 years of diabetes) used the system for a median 75.8% (interquartile range [IQR] 68.0%-88.4%) of the time (2977 patient-days). Adults (mean ± SD 44.6 ± 12.79 years of age and 26.4 ± 12.43 years of diabetes) used the system for a median 88.0% (IQR 77.6%-92.7%) of the time (9412 patient-days). From baseline run-in to the end of study phase, adolescent and adult HbA_1c levels decreased from 7.7% ± 0.8% to 7.1% ± 0.6% (P < 0.001) and from 7.3% ± 0.9% to 6.8% ± 0.6% (P < 0.001, Wilcoxon signed-rank test), respectively. The proportion of overall in-target (71-180 mg/dL) sensor glucose (SG) values increased from 60.4% ± 10.9% to 67.2% ± 8.2% (P < 0.001) in adolescents and from 68.8% ± 11.9% to 73.8% ± 8.4% (P < 0.001) in adults. During the hotel stay, the proportion of in-target i-STAT^® blood glucose values was 67.4% ± 27.7% compared to SG values of 72.0% ± 11.6% for adolescents and 74.2% ± 17.5% compared to 76.9% ± 8.3% for adults. There were no severe hypoglycemic or diabetic ketoacidosis events in either cohort.

CONCLUSIONS

HCL therapy was safe during in-home use by adolescents and adults and the study phase demonstrated increased time in target, and reductions in HbA_1c, hyperglycemia and hypoglycemia, compared to baseline.

TRIAL REGISTRATION

Clinicaltrials.gov identifier: NCT02463097.

Continuous Glucose Monitoring Enables the Detection of Losses in Infusion Set Actuation (LISAs)

Reliable continuous glucose monitoring (CGM) enables a variety of advanced technology for the treatment of type 1 diabetes. In addition to artificial pancreas algorithms that use CGM to automate continuous subcutaneous insulin infusion (CSII), CGM can also inform fault detection algorithms that alert patients to problems in CGM or CSII. Losses in infusion set actuation (LISAs) can adversely affect clinical outcomes, resulting in hyperglycemia due to impaired insulin delivery. Prolonged hyperglycemia may lead to diabetic ketoacidosis-a serious metabolic complication in type 1 diabetes. Therefore, an algorithm for the detection of LISAs based on CGM and CSII signals was developed to improve patient safety. The LISA detection algorithm is trained retrospectively on data from 62 infusion set insertions from 20 patients. The algorithm collects glucose and insulin data, and computes relevant fault metrics over two different sliding windows; an alarm sounds when these fault metrics are exceeded. With the chosen algorithm parameters, the LISA detection strategy achieved a sensitivity of 71.8% and issued 0.28 false positives per day on the training data. Validation on two independent data sets confirmed that similar performance is seen on data that was not used for training. The developed algorithm is able to effectively alert patients to possible infusion set failures in open-loop scenarios, with limited evidence of its extension to closed-loop scenarios.

Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control

Development of an effective artificial pancreas (AP) controller to deliver insulin autonomously to people with type 1 diabetes mellitus is a difficult task. In this paper, three enhancements to a clinically validated AP model predictive controller (MPC) are proposed that address major challenges facing automated blood glucose control, and are then evaluated by both in silico tests and clinical trials. First, the core model of insulin-blood glucose dynamics utilized in the MPC is expanded with a medically inspired personalization scheme to improve controller responses in the face of inter- and intra-individual variations in insulin sensitivity. Next, the asymmetric nature of the short-term consequences of hypoglycemia versus hyperglycemia is incorporated in an asymmetric weighting of the MPC cost function. Finally, an enhanced dynamic insulin-on-board algorithm is proposed to minimize the likelihood of controller-induced hypoglycemia following a rapid rise of blood glucose due to rescue carbohydrate load with accompanying insulin suspension. Each advancement is evaluated separately and in unison through in silico trials based on a new clinical protocol, which incorporates induced hyper- and hypoglycemia to test robustness. The advancements are also evaluated in an advisory mode (simulated) testing of clinical data. The combination of the three proposed advancements show statistically significantly improved performance over the nonpersonalized controller without any enhancements across all metrics, displaying increased time in the 70-180 mg/dL safe glycemic range (76.9 versus 68.8%) and the 80-140 mg/dL euglycemic range (48.1 versus 44.5%), without a statistically significant increase in instances of hypoglycemia. The proposed advancements provide safe control action for AP applications, personalizing and improving controller performance without the need for extensive model identification processes.

Adjuvant Liraglutide and Insulin Versus Insulin Monotherapy in the Closed-Loop System in Type 1 Diabetes: A Randomized Open-Labeled Crossover Design Trial

BACKGROUND

The closed-loop (CL) system delivers insulin in a glucose-responsive manner and optimal postprandial glycemic control is difficult to achieve with the algorithm and insulin available. We hypothesized that adjunctive therapy with liraglutide, a once-daily glucagon-like peptide-1 agonist, would be more effective in normalizing postprandial hyperglycemia versus insulin monotherapy in the CL system, in patients with type 1 diabetes.

METHODS

This was a randomized, controlled, open-label, crossover design trial comparing insulin monotherapy versus adjuvant subcutaneous liraglutide 1.2 mg and insulin, using the CL system in 15 patients. Blood glucose (BG), insulin, and glucagon concentrations were analyzed.

RESULTS

The liraglutide arm was associated with overall decreased mean BG levels (P = .0002). The average BG levels from 8:00 pm (day 1) to 9:00 pm (day 2) were lower in the liraglutide arm (144.6 ± 36.31 vs 159.7 ± 50.88 mg/dl respectively; P = .0002). Two-hour postbreakfast and lunch BG profiles were better in the liraglutide arm (P < .05) and the insulin and glucagon assay values were lower (P < .0001). Postprandially, the area under the curve (AUC) for 2-hour postbreakfast and lunch BG levels were significant (P = .01, P = .03) and the AUC for glucagon, postbreakfast (P < .0001) and lunch (P < .05), was also significant. The incidence of hypoglycemia did not differ between arms (P = .83, Fisher's exact test). Overall, adjunct liraglutide therapy plus CL was well tolerated even with expected side effects.

CONCLUSION

This is a proof-of-concept study showing liraglutide can be a potential adjunctive therapy in addition to CL with insulin to reduce postprandial hyperglycemia in type 1 diabetes.

Mitigating Meal-Related Glycemic Excursions in an Insulin-Sparing Manner During Closed-Loop Insulin Delivery: The Beneficial Effects of Adjunctive Pramlintide and Liraglutide

OBJECTIVE

Closed-loop (CL) insulin delivery effectively maintains glucose overnight but struggles when challenged with meals. Use of single-day, 30-μg/meal pramlintide lowers meal excursions during CL. We sought to further elucidate the potential benefits of adjunctive agents after 3-4 weeks of outpatient dose titration.

RESEARCH DESIGN AND METHODS

Two CL studies were conducted: one evaluating adjunctive pramlintide and the other liraglutide. Ten subjects (age 16-23 years; A1C 7.2 ± 0.6% [55 ± 6.6 mmol/mol]) completed two 24-h sessions: one on CL alone and one on CL plus 60-μg pramlintide (CL + P), after a 3-4-week outpatient dose escalation. Eleven subjects (age 18-27 years; A1C 7.5 ± 0.9% [58 ± 9.8 mmol/mol]) were studied before and after treatment with 1.8 mg liraglutide (CL + L) after a similar 3-4-week dose escalation period. Timing and content of meals during CL were identical within experiments; meals were not announced.

RESULTS

Pramlintide delayed the time to peak plasma glucose (PG) excursion (CL 1.6 ± 0.5 h vs. CL + P 2.6 ± 0.9 h, P < 0.001) with concomitant blunting of peak postprandial increments in PG (P < 0.0001) and reductions in postmeal incremental PG area under the curve (AUC) (P = 0.0002). CL + L also led to reductions in PG excursions (P = 0.05) and incremental PG AUC (P = 0.004), with a 28% reduction in prandial insulin delivery. Outpatient liraglutide therapy led to a weight loss of 3.2 ± 1.8 kg, with a 26% reduction in total daily insulin dose.

CONCLUSIONS

Adjunctive pramlintide and liraglutide treatment mitigated postprandial hyperglycemia during CL control; liraglutide demonstrated the additional benefit of weight loss in an insulin-sparing manner. Further investigations of these and other adjunctive agents in long-term outpatient CL studies are needed.

Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas

OBJECTIVE

To evaluate two widely used control algorithms for an artificial pancreas (AP) under nonideal but comparable clinical conditions.

RESEARCH DESIGN AND METHODS

After a pilot safety and feasibility study (n = 10), closed-loop control (CLC) was evaluated in a randomized, crossover trial of 20 additional adults with type 1 diabetes. Personalized model predictive control (MPC) and proportional integral derivative (PID) algorithms were compared in supervised 27.5-h CLC sessions. Challenges included overnight control after a 65-g dinner, response to a 50-g breakfast, and response to an unannounced 65-g lunch. Boluses of announced dinner and breakfast meals were given at mealtime. The primary outcome was time in glucose range 70-180 mg/dL.

RESULTS

Mean time in range 70-180 mg/dL was greater for MPC than for PID (74.4 vs. 63.7%, P = 0.020). Mean glucose was also lower for MPC than PID during the entire trial duration (138 vs. 160 mg/dL, P = 0.012) and 5 h after the unannounced 65-g meal (181 vs. 220 mg/dL, P = 0.019). There was no significant difference in time with glucose <70 mg/dL throughout the trial period.

CONCLUSIONS

This first comprehensive study to compare MPC and PID control for the AP indicates that MPC performed particularly well, achieving nearly 75% time in the target range, including the unannounced meal. Although both forms of CLC provided safe and effective glucose management, MPC performed as well or better than PID in all metrics.

Automated Overnight Closed-Loop Control Using a Proportional-Integral-Derivative Algorithm with Insulin Feedback in Children and Adolescents with Type 1 Diabetes at Diabetes Camp

OBJECTIVE

This study determined the feasibility and efficacy of an automated proportional-integral-derivative with insulin feedback (PID-IFB) controller in overnight closed-loop (OCL) control of children and adolescents with type 1 diabetes over multiple days in a diabetes camp setting.

RESEARCH DESIGN AND METHODS

The Medtronic (Northridge, CA) Android™ (Google, Mountain View, CA)-based PID-IFB system consists of the Medtronic Minimed Revel™ 2.0 pump and Enlite™ sensor, a control algorithm residing on an Android phone, a translator, and remote monitoring capabilities. An inpatient study was completed for 16 participants to determine feasibility. For the camp study, subjects with type 1 diabetes were randomized to either OCL or sensor-augmented pump therapy (control conditions) per night for up to 6 nights at diabetes camp.

RESULTS

During the camp study, 21 subjects completed 50 OCL nights and 52 control nights. Based on intention to treat, the median time spent in range, from 70 to 150 mg/dL, was greater during OCL at 66.4% (n = 55) versus 50.6% (n = 52) during the control period (P = 0.004). A per-protocol analysis allowed for assessment of algorithm performance with the median percentage time in range, 70-150 mg/dL, being 75.5% (n = 37) for OCL versus 47.6% (n = 32) for the control period (P < 0.001). There was less time spent in the hypoglycemic ranges <60 mg/dL and <70 mg/dL during OCL compared with the control period (P = 0.003 and P < 0.001, respectively).

CONCLUSIONS

The PID-IFB controller is effective in improving time spent in range as well as reducing nocturnal hypoglycemia during the overnight period in children and adolescents with type 1 diabetes in a diabetes camp setting.

Hybrid Closed-Loop Insulin Delivery in Type 1 Diabetes During Supervised Outpatient Conditions

BACKGROUND

Efficacy and safety of the Medtronic Hybrid Closed-Loop (HCL) system were tested in subjects with type 1 diabetes in a supervised outpatient setting.

METHODS

The HCL system is a prototype research platform that includes a sensor-augmented insulin pump in communication with a control algorithm housed on an Android-based cellular device. Nine subjects with type 1 diabetes (5 female, mean age 53.3 years, mean A1C 7.2%) underwent 9 studies totaling 571 hours of closed-loop control using either default or personalized parameters. The system required meal announcements with estimates of carbohydrate (CHO) intake that were based on metabolic kitchen quantification (MK), dietician estimates (D), or subject estimates (Control). Postprandial glycemia was compared for MK, D, and Control meals.

RESULTS

The overall sensor glucose mean was 145 ± 43, the overall percentage time in the range 70-180 mg/dL was 80%, the overall percentage time <70 mg/dL was 0.79%. Compared to intervals of default parameter use (225 hours), intervals of personalized parameter use (346 hours), sensor glucose mean was 158 ± 49 and 137 ± 37 mg/dL (P < .001), respectively, and included more time in range (87% vs 68%) and less time below range (0.54% vs 1.18%). Most subjects underestimated the CHO content of meals, but postprandial glycemia was not significantly different between MK and matched Control meals (P = .16) or between D and matched Control meals (P = .76). There were no episodes of severe hypoglycemia.

CONCLUSIONS

The HCL system was efficacious and safe during this study. Personally adapted HCL parameters were associated with more time in range and less time below range than default parameters. Accurate estimates of meal CHO did not contribute to improved postprandial glycemia.

The artificial pancreas: evaluating risk of hypoglycaemia following errors that can be expected with prolonged at-home use

AIMS

Artificial pancreas systems show benefit in closely monitored at-home studies, but may not have sufficient power to assess safety during infrequent, but expected, system or user errors. The aim of this study was to assess the safety of an artificial pancreas system emulating the β-cell when the glucose value used for control is improperly calibrated and participants forget to administer pre-meal insulin boluses.

METHODS

Artificial pancreas control was performed in a clinic research centre on three separate occasions each lasting from 10 p.m. to 2 p.m. Sensor glucose values normally used for artificial pancreas control were replaced with scaled blood glucose values calculated to be 20% lower than, equal to or 33% higher than the true blood glucose. Safe control was defined as blood glucose between 3.9 and 8.3 mmol/l.

RESULTS

Artificial pancreas control resulted in fasting scaled blood glucose values not different from target (6.67 mmol/l) at any scaling factor. Meal control with scaled blood glucose 33% higher than blood glucose resulted in supplemental carbohydrate to prevent hypoglycaemia in four of six participants during breakfast, and one participant during the night. In all instances, scaled blood glucose reported blood glucose as safe.

CONCLUSIONS

Outpatient trials evaluating artificial pancreas performance based on sensor glucose may not detect hypoglycaemia when sensor glucose reads higher than blood glucose. Because these errors are expected to occur, in-hospital artificial pancreas studies using supplemental carbohydrate in anticipation of hypoglycaemia, which allow safety to be assessed in a controlled non-significant environment should be considered as an alternative. Inpatient studies provide a definitive alternative to model-based computer simulations and can be conducted in parallel with closely monitored outpatient artificial pancreas studies used to assess benefit.