Autonomous and continuous adaptation of a bihormonal bionic pancreas in adults and adolescents with type 1 diabetes

Point-Counterpoint: The Need for Do-It-Yourself (DIY) Open Source (OS) AID Systems in Type 1 Diabetes Management

In the last decade, technology developed by people with diabetes and their loved ones has added to the options for diabetes management. One such example is that of automated insulin delivery (AID) algorithms, which were created and shared as open source by people living with type 1 diabetes (T1D) years before commercial systems were first available. Now, numerous options for commercial systems exist in some countries, yet tens of thousands of people with diabetes are still choosing Open-Source AID (OS-AID), previously called "do-it-yourself" (DIY) systems, which are noncommercial versions of these open-source AID systems. In this article, we provide point and counterpoint perspectives regarding (1) safety and efficacy, (2) regulation and support, (3) user choice and flexibility, (4) access and affordability, and (5) patient and provider education, for open source and commercial AID systems. The perspectives reflected here include that of a person living with T1D who uses and has developed OS-AID systems, a physician-researcher based in the United States who conducts clinical trials to support development of commercial AID systems and supports people with diabetes using all types of AID, and an endocrinologist with T1D who uses both systems and treats people with diabetes using all types of AID.

Randomized Trial of the Insulin-Only iLet Bionic Pancreas for the Treatment of Cystic Fibrosis- Related Diabetes

OBJECTIVE

Cystic fibrosis-related diabetes (CFRD) affects up to 50% of adults with cystic fibrosis and adds significant morbidity and treatment burden. We evaluated the safety and efficacy of automated insulin delivery with the iLet bionic pancreas (BP) in adults with CFRD in a single-center, open-label, random-order, crossover trial.

RESEARCH DESIGN AND METHODS

Twenty participants with CFRD were assigned in random order to 14 days each on the BP or their usual care (UC). No restrictions were placed on diet or activity. The primary outcome was the percent time sensor-measured glucose was in target range 70-180 mg/dL (time in range [TIR]) on days 3-14 of each arm, and key secondary outcomes included mean continuous glucose monitoring (CGM) glucose and the percent time sensor-measured glucose was in hypoglycemic range <54 mg/dL.

RESULTS

TIR was significantly higher in the BP arm than the UC arm (75 ± 11% vs. 62 ± 22%, P = 0.001). Mean CGM glucose was lower in the BP arm than in the UC arm (150 ± 19 vs. 171 ± 45 mg/dL, P = 0.007). There was no significant difference in percent time with sensor-measured glucose <54 mg/dL (0.27% vs. 0.36%, P = 1.0), although self-reported symptomatic hypoglycemia episodes were higher during the BP arm than the UC arm (0.7 vs. 0.4 median episodes per day, P = 0.01). No episodes of diabetic ketoacidosis or severe hypoglycemia occurred in either arm.

CONCLUSIONS

Adults with CFRD had improved glucose control without an increase in CGM-measured hypoglycemia with the BP compared with their UC, suggesting that this may be an important therapeutic option for this patient population.

Mitigating iftar-related glycemic excursions in adolescents and young adults with type 1 diabetes on MiniMed™ 780G advanced hybrid closed loop system: a randomized clinical trial for adjunctive oral vildagliptin therapy during Ramadan fasting

BACKGROUND

Ramadan Iftar meal typically causes glucose excursions. Dipeptidyl peptidase-4 inhibitors increase glucagon-like peptide-1 and thus, decrease blood glucose levels with low risk of hypoglycemia.

AIM

To investigate the efficacy and safety of vildagliptin as an add-on therapy on glucose excursions of Iftar Ramadan meals among adolescents and young adults with type 1 diabetes mellitus (T1DM) using advanced hybrid closed-loop (AHCL) treatment.

METHODS

Fifty T1DM patients on MiniMed™ 780G AHCL were randomly assigned either to receive vildagliptin (50 mg tablet) with iftar meal during Ramadan month or not. All participants received pre-meal insulin bolus based on insulin-to-carbohydrate ratio (ICR) for each meal constitution.

RESULTS

Vildagliptin offered blunting of post-meal glucose surges (mean difference - 30.3 mg/dL [- 1.7 mmol/L] versus - 2.9 mg/dL [- 0.2 mmol/L] in control group; p < 0.001) together with concomitant exceptional euglycemia with time in range (TIR) significantly increased at end of Ramadan in intervention group from 77.8 ± 9.6% to 84.7 ± 8.3% (p = 0.016) and time above range (180-250 mg/dL) decreased from 13.6 ± 5.1% to 9.7 ± 3.6% (p = 0.003) without increasing hypoglycemia. A significant reduction was observed in automated daily correction boluses and total bolus dose by 23.9% and 16.3% (p = 0.015 and p < 0.023, respectively) with less aggressive ICR settings within intervention group at end of Ramadan. Coefficient of variation was improved from 37.0 ± 9.4% to 31.8 ± 7.1%; p = 0.035). No severe hypoglycemia or diabetic ketoacidosis were reported.

CONCLUSION

Adjunctive vildagliptin treatment mitigated postprandial hyperglycemia compared with pre-meal bolus alone. Vildagliptin significantly increased TIR while reducing glycemic variability without compromising safety. Trial registration This trial was registered under ClinicalTrials.gov Identifier no. NCT06021119.

Current Technologies for Managing Type 1 Diabetes Mellitus and Their Impact on Quality of Life-A Narrative Review

Type 1 diabetes mellitus is a chronic autoimmune disease that affects millions of people and generates high healthcare costs due to frequent complications when inappropriately managed. Our paper aimed to review the latest technologies used in T1DM management for better glycemic control and their impact on daily life for people with diabetes. Continuous glucose monitoring systems provide a better understanding of daily glycemic variations for children and adults and can be easily used. These systems diminish diabetes distress and improve diabetes control by decreasing hypoglycemia. Continuous subcutaneous insulin infusions have proven their benefits in selected patients. There is a tendency to use more complex systems, such as hybrid closed-loop systems that can modulate insulin infusion based on glycemic readings and artificial intelligence-based algorithms. It can help people manage the burdens associated with T1DM management, such as fear of hypoglycemia, exercising, and long-term complications. The future is promising and aims to develop more complex ways of automated control of glycemic levels to diminish the distress of individuals living with diabetes.

Continuous subcutaneous insulin infusions: Closing the loop

CONTEXT

Continuous subcutaneous insulin infusions (CSIIs) and continuous glucose monitors (CGMs) have revolutionized the management of diabetes mellitus (DM). Over the last two decades the development of advanced, small, and user-friendly technology has progressed substantially, essentially closing the loop in the fasting and post-absorptive state, nearing the promise of an artificial pancreas. The momentum was mostly driven by the diabetes community itself, to improve its health and quality of life.

EVIDENCE ACQUISITION

Literature regarding CSII and CGM was reviewed.

EVIDENCE SYNTHESIS

Management of DM aims to regulate blood glucose to prevent long term micro and macrovascular complications. CSIIs combined with CGMs provide an integrated system to maintain tight glycemic control in a safe and uninterrupted fashion, while minimizing hypoglycemic events. Recent advances have allowed to 'close the loop' by better mimicking endogenous insulin secretion and glucose level regulation. Evidence supports sustained improvement in glycemic control with reduced episodes of hypoglycemia using these systems, while improving quality of life. Ongoing work in delivery algorithms with or without counterregulatory hormones will allow for further layers of regulation of the artificial pancreas.

CONCLUSION

Ongoing efforts to develop an artificial pancreas have created effective tools to improve the management of DM. CSIIs and CGMs are useful in diverse populations ranging from children to the elderly, as well as in various clinical contexts. Individually and more so together, these have had a tremendous impact in the management of DM, while avoiding treatment fatigue. However, cost and accessibility are still a hindrance to its wider application.

Bihormonal Artificial Pancreas With Closed-Loop Glucose Control vs Current Diabetes Care After Total Pancreatectomy: A Randomized Clinical Trial

Importance

Glucose control in patients after total pancreatectomy is problematic because of the complete absence of α- and β-cells, leading to impaired quality of life. A novel, bihormonal artificial pancreas (BIHAP), using both insulin and glucagon, may improve glucose control, but studies in this setting are lacking.

Objective

To assess the efficacy and safety of the BIHAP in patients after total pancreatectomy.

Design, Setting, and Participants

This randomized crossover clinical trial compared the fully closed-loop BIHAP with current diabetes care (ie, insulin pump or pen therapy) in 12 adult outpatients after total pancreatectomy. Patients were recruited between August 21 and November 16, 2020. This first-in-patient study began with a feasibility phase in 2 patients. Subsequently, 12 patients were randomly assigned to 7-day treatment with the BIHAP (preceded by a 5-day training period) followed by 7-day treatment with current diabetes care, or the same treatments in reverse order. Statistical analysis was by Wilcoxon signed rank and Mann-Whitney U tests, with significance set at a 2-sided P < .05.

Main Outcomes and Measures

The primary outcome was the percentage of time spent in euglycemia (70-180 mg/dL [3.9-10 mmol/L]) as assessed by continuous glucose monitoring.

Results

In total, 12 patients (7 men and 3 women; median [IQR] age, 62.5 [43.1-74.0] years) were randomly assigned, of whom 3 did not complete the BIHAP phase and 1 was replaced. The time spent in euglycemia was significantly higher during treatment with the BIHAP (median, 78.30%; IQR, 71.05%-82.61%) than current diabetes care (median, 57.38%; IQR, 52.38%-81.35%; P = .03). In addition, the time spent in hypoglycemia (<70 mg/dL [3.9 mmol/L]) was lower with the BIHAP (median, 0.00% [IQR, 0.00%-0.07%] vs 1.61% [IQR, 0.80%-3.81%]; P = .004). No serious adverse events occurred.

Conclusions and Relevance

Patients using the BIHAP after total pancreatectomy experienced an increased percentage of time in euglycemia and a reduced percentage of time in hypoglycemia compared with current diabetes care, without apparent safety risks. Larger randomized trials, including longer periods of treatment and an assessment of quality of life, should confirm these findings.

Trial Registration

trialregister.nl Identifier: NL8871.

Adjunctive Therapies to Optimize Closed-loop Glucose Control

Closed-loop insulin delivery systems are fast becoming the standard of care in the management of type 1 diabetes and have led to significant improvements in diabetes management. Nevertheless, there is still room for improvement for the closed-loop systems to optimize treatment and meet target glycemic control. Adjunct treatments have been introduced as an alternative method to insulin-only treatment methods to overcome diabetes treatment challenges and improve clinical and patient reported outcomes during closed-loop treatment. The adjunct treatment agents mostly consist of medications that are already approved for type 2 diabetes treatment and aim to complete the missing physiologic factors, such as the entero-endocrine system, that regulate glycemia in addition to insulin. This paper will review many of these adjunct therapies, including the basic mechanisms of action, potential benefits, side effects, and the evidence supporting their use during closed-loop treatment.

The Bihormonal Bionic Pancreas Improves Glycemic Control in Individuals With Hyperinsulinism and Postpancreatectomy Diabetes: A Pilot Study

OBJECTIVE

To determine whether the bihormonal bionic pancreas (BHBP) improves glycemic control and reduces hypoglycemia in individuals with congenital hyperinsulinism (HI) and postpancreatectomy diabetes (PPD) compared with usual care (UC).

RESEARCH DESIGN AND METHODS

Ten subjects with HI and PPD completed this open-label, crossover pilot study. Coprimary outcomes were mean glucose concentration and time with continuous glucose monitoring (CGM) glucose concentration <3.3 mmol/L.

RESULTS

Mean (SD) CGM glucose concentration was 8.3 (0.7) mmol/L in the BHBP period versus 9 (1.8) mmol/L in the UC period (P = 0.13). Mean (SD) time with CGM glucose concentration <3.3 mmol/L was 0% (0.002) in the BHBP period vs. 1.3% (0.018) in the UC period (P = 0.11).

CONCLUSIONS

Relative to UC, the BHBP resulted in comparable glycemic control in our population.

A fully artificial pancreas versus a hybrid artificial pancreas for type 1 diabetes: a single-centre, open-label, randomised controlled, crossover, non-inferiority trial

BACKGROUND

For people with type 1 diabetes, there is currently no automated insulin delivery system that does not require meal input. We aimed to assess the efficacy of a novel faster-acting insulin aspart (Fiasp) plus pramlintide fully closed-loop system that does not require meal input.

METHODS

In this open-label, randomised controlled, crossover, non-inferiority trial we compared the Fiasp (Novo Nordisk, Bagsværd, Denmark) plus pramlintide closed-loop system with no meal input (fully artificial pancreas) and the Fiasp-alone closed-loop system with precise carbohydrate counting (hybrid artificial pancreas). Adults (≥18 years) who had a clinical diagnosis of type 1 diabetes for at least 12 months, had glycated haemoglobin 12% or lower, and had been on insulin pump therapy for at least 6 months were enrolled at McGill University Health Centre, Montreal, QC, Canada. The Fiasp plus pramlintide fully closed-loop system delivered pramlintide in a basal-bolus manner with a fixed ratio of 10 μg:U relative to insulin. A research staff member counted the carbohydrate content of meals to input in the hybrid closed-loop system. Participants completed the two full-day crossover interventions in a random order allocated by a computer-generated code implementing a blocked randomisation (block size of four). The primary outcome was the percentage of time spent within the glucose target range (3·9-10·0 mmol/L), with a 6% non-inferiority margin, assessed in all participants who completed both interventions. This trial is registered with ClinicalTrials.gov, NCT03800875.

FINDINGS

Between Feb 8, 2019, and Sept 19, 2020, we enrolled 28 adults, of whom 24 completed both interventions and were included in analyses. The percentage of time spent in the target range was 74·3% (IQR 61·5-82·8) with the fully closed-loop system versus 78·1% (66·3-87·5) with the hybrid Fiasp-alone closed-loop system (paired difference 2·6%, 95% CI -2·4 to 12·2; non-inferiority p=0·28). Eight (33%) participants had at least one hypoglycaemia event (<3·3 mmol/L) with the fully closed-loop system compared with 14 (58%) participants with the hybrid closed-loop system (2200-2200 h). Non-mild nausea was reported by three (13%) participants and non-mild bloating by one (4%) participant with the fully closed-loop system compared with zero participants with the hybrid closed-loop system.

INTERPRETATION

The Fiasp plus pramlintide fully closed-loop system was not non-inferior to the Fiasp-alone hybrid closed-loop system for the overall percentage of time in the glucose target range. However, participants still spent a high percentage of time within the target range with the fully-closed loop system. Outpatient studies comparing the fully closed-loop hybrid systems with patient-estimated, rather than precise, carbohydrate counting are warranted.

FUNDING

Diabetes Canada.

Improvements in Glycemic Control Achieved by Altering the tmax Setting in the iLet® Bionic Pancreas When Using Fast-Acting Insulin Aspart: A Randomized Trial

INTRODUCTION

We investigated the safety of, and glucose control by, the insulin-only configuration of the iLet^® bionic pancreas delivering fast-acting insulin aspart (faster aspart), using the same insulin-dosing algorithm but different time to maximal serum drug concentration (t_max) settings, in adults with type 1 diabetes.

METHODS

We performed a single-center, single-blinded, crossover (two 7-day treatment periods) escalation trial over three sequential cohorts. Participants from each cohort were randomized to a default t_max setting (t₆₅ [t_max = 65 min]) followed by a non-default t_max setting (t₅₀ [t_max = 50 min; cohort 1], t₄₀ [t_max = 40 min; cohort 2], t₃₀ [t_max = 30 min; cohort 3]), or vice versa, all with faster aspart. Each cohort randomized eight new participants if escalation-stopping criteria were not met in the previous cohort.

RESULTS

Overall, 24 participants were randomized into three cohorts. Two participants discontinued treatment, one due to reported 'low blood glucose' during the first treatment period of cohort 3 (t₃₀). Mean time in low sensor glucose (< 54 mg/dl, primary endpoint) was < 1.0% for all t_max settings. Mean sensor glucose in cohorts 1 and 2 was significantly lower at non-default versus default t_max settings, with comparable insulin dosing. The mean time sensor glucose was in range (70-180 mg/dl) was > 70% for all cohorts, except the default t_max setting in cohort 1. No severe hypoglycemic episodes were reported. Furthermore, there were no clinically significant differences in adverse events between the groups.

CONCLUSION

There were no safety concerns with faster aspart in the iLet at non-default t_max settings. Improvements were observed in mean sensor glucose without increases in low sensor glucose at non-default t_max settings.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03816761.

Reducing the need for carbohydrate counting in type 1 diabetes using closed-loop automated insulin delivery (artificial pancreas) and empagliflozin: A randomized, controlled, non-inferiority, crossover pilot trial

AIM

To assess whether adding empagliflozin to closed-loop automated insulin delivery could reduce the need for carbohydrate counting in type 1 diabetes (T1D) without worsening glucose control.

MATERIALS AND METHODS

In an open-label, crossover, non-inferiority trial, 30 adult participants with T1D underwent outpatient automated insulin delivery interventions with three random sequences of prandial insulin strategy days: carbohydrate counting, simple meal announcement (no carbohydrate counting) and no meal announcement. During each sequence of prandial insulin strategies, participants were randomly assigned empagliflozin (25 mg/day) or not, and crossed over to the comparator. Mean glucose for carbohydrate counting without empagliflozin (control) was compared with no meal announcement with empagliflozin (in the primary non-inferiority comparison) and simple meal announcement with empagliflozin (in the conditional primary non-inferiority comparison).

RESULTS

Participants were aged 40 ± 15 years, had 27 ± 15 years diabetes duration and HbA1c of 7.6% ± 0.7% (59 ± 8 mmol/mol). The system with no meal announcement and empagliflozin was not non-inferior (and thus reasonably considered inferior) to the control arm (mean glucose 10.0 ± 1.6 vs. 8.5 ± 1.5 mmol/L; non-inferiority p = .94), while simple meal announcement and empagliflozin was non-inferior (8.5 ± 1.4 mmol/L; non-inferiority p = .003). Use of empagliflozin on the background of automated insulin delivery with carbohydrate counting was associated with lower mean glucose, corresponding to a 14% greater time in the target range. While no ketoacidosis was observed, mean fasting ketones levels were higher on empagliflozin (0.22 ± 0.18 vs. 0.13 ± 0.11 mmol/L; p < .001).

CONCLUSIONS

Empagliflozin added to automated insulin delivery has the potential to eliminate the need for carbohydrate counting and improves glycaemic control in conjunction with carbohydrate counting, but does not allow for the elimination of meal announcement.

Diabetes education in pediatrics: How to survive diabetes

Diabetes mellitus is the most common abnormal carbohydrate metabolism disorder affecting millions of people worldwide. It is characterized by hyperglycemia as a result of ß-cell destruction or dysfunction by both genetic and environmental factors. Over time chronic hyperglycemia leads to microvascular (i.e., retinopathy, nephropathy and neuropathy) and macrovascular (i.e., ischemic heart disease, peripheral vascular disease, and cerebrovascular disease) complications of diabetes. Diabetes complication trials showed the importance of achieving near-normal glycemic control to prevent and/or reduce diabetes-related morbidity and mortality. There is a staggering rate of increased incidence of diabetes in youth, raising concerns for future generations' health, quality of life and its enormous economic burden. Despite advancements in the technology, diabetes management remains cumbersome. Training individuals with diabetes to gain life-long survival skills requires a comprehensive and ongoing diabetes education by a multidisciplinary team. Diabetes education and training start at the time of diagnosis of diabetes and should be continuous throughout the course of disease. The goal is to empower the individuals and families to gain diabetes self-management skills. Diabetes education must be individualized depending on the individual's age, education, family dynamics, and support. In this article, we review the history of diabetes, etiopathogenesis and clinical presentation of both type 1 and type 2 diabetes in children as well as adolescents. We then focus on diabetes management with education methods and materials.

Role of Glucagon in Automated Insulin Delivery

Treatment of type 1 diabetes with exogenous insulin often results in unpredictable daily glucose variability and hypoglycemia, which can be dangerous. Automated insulin delivery systems can improve glucose control while reducing burden for people with diabetes. One approach to improve treatment outcomes is to incorporate the counter-regulatory hormone glucagon into the automated delivery system to help prevent the hypoglycemia that can be induced by the slow pharmacodynamics of insulin action. This article explores the advantages and disadvantages of incorporating glucagon into dual-hormone automated hormone delivery systems.

The Efficacy of Basal Rate and Carbohydrate Ratio Learning Algorithm for Closed-Loop Insulin Delivery (Artificial Pancreas) in Youth with Type 1 Diabetes in a Diabetes Camp

Background: Optimizing programmed basal rates and carbohydrate ratios may improve the performance of the artificial pancreas. We tested, in a diabetes camp, the efficacy of a learning algorithm that updates daily basal rates and carbohydrate ratios in the artificial pancreas. Materials and Methods: We conducted a randomized crossover trial in campers and counselors aged 8-21 years with type 1 diabetes on pump therapy. Participants underwent 2 days of artificial pancreas alone and 6 days of artificial pancreas with learning. During the artificial pancreas with learning, programmed basal rates and carbohydrate ratios were updated daily based on the learning algorithm's recommendations. All algorithm recommendations were reviewed for safety by camp physicians. The primary outcome was the time in target range (3.9-10 mmol/L) of the last 2 days of each intervention. Results: Thirty-four campers (age 13.9 ± 3.9, hemoglobin A1c 8.3% ± 0.2%) were included. Ninety-six percent of algorithm recommendations were approved by the camp physicians. Participants were in closed-loop mode 74% of the time. There was no difference between interventions in time in target (55%-55%; P = 0.71) nor in hypoglycemia events (0.8-0.9 events per day; P = 0.63). This was despite changes in programmed basal rate ranging from -21% to +117%, and changes in breakfast, lunch, and supper carbohydrate ratios from -17% to +40%, -36% to +37%, and -35% to +63%, respectively. Morever, postprandial hyperglycemia and hypoglycemia did not decrease in participants whose carbohydrate ratios were decreased (more insulin boluses) and increased (less insulin boluses), respectively. Conclusions: In camp settings, despite adjustments to programmed basal rates and carbohydrate ratios, the learning algorithm did not change glycemia, which may point toward limited effect of these adjustments in environments with large day-to-day variability in insulin needs. Longer randomized studies in real-world settings are required to further assess the efficacy of automatic adjustments of programmed basal rates and carbohydrate ratios.

Realizing a Closed-Loop (Artificial Pancreas) System for the Treatment of Type 1 Diabetes

Recent, rapid changes in the treatment of type 1 diabetes have allowed for commercialization of an "artificial pancreas" that is better described as a closed-loop controller of insulin delivery. This review presents the current state of closed-loop control systems and expected future developments with a discussion of the human factor issues in allowing automation of glucose control. The goal of these systems is to minimize or prevent both short-term and long-term complications from diabetes and to decrease the daily burden of managing diabetes. The closed-loop systems are generally very effective and safe at night, have allowed for improved sleep, and have decreased the burden of diabetes management overnight. However, there are still significant barriers to achieving excellent daytime glucose control while simultaneously decreasing the burden of daytime diabetes management. These systems use a subcutaneous continuous glucose sensor, an algorithm that accounts for the current glucose and rate of change of the glucose, and the amount of insulin that has already been delivered to safely deliver insulin to control hyperglycemia, while minimizing the risk of hypoglycemia. The future challenge will be to allow for full closed-loop control with minimal burden on the patient during the day, alleviating meal announcements, carbohydrate counting, alerts, and maintenance. The human factors involved with interfacing with a closed-loop system and allowing the system to take control of diabetes management are significant. It is important to find a balance between enthusiasm and realistic expectations and experiences with the closed-loop system.

Artificial Pancreas Systems and Physical Activity in Patients with Type 1 Diabetes: Challenges, Adopted Approaches, and Future Perspectives

Physical activity is important for patients living with type 1 diabetes (T1D) but limited by the challenges associated with physical activity induced glucose variability. Optimizing glycemic control without increasing the risk of hypoglycemia is still a hurdle despite many advances in insulin formulations, delivery methods, and continuous glucose monitoring systems. In this respect, the artificial pancreas (AP) system is a promising therapeutic option for a safer practice of physical activity in the context of T1D. It is important that healthcare professionals as well as patients acquire the necessary knowledge about how the AP system works, its limits, and how glucose control is regulated during physical activity. This review aims to examine the current state of knowledge on exercise-related glucose variations especially hypoglycemic risk in T1D and to discuss their effects on the use and development of AP systems. Though effective and highly promising, these systems warrant further research for an optimized use around exercise.

Automated glycemic control with the bionic pancreas in cystic fibrosis-related diabetes: A pilot study

Cystic fibrosis-related diabetes (CFRD) is the most common extrapulmonary manifestation of cystic fibrosis. The current standard of care for CFRD involves treatment with insulin, typically via multiple daily injections. We conducted a small pilot study comparing usual care with automated glycemic control using the bihormonal (insulin and glucagon) and insulin-only configurations of the bionic pancreas. Both configurations of the bionic pancreas achieved good glycemic control, with mean glucose levels <150 mg/dl and minimal hypoglycemia. Subjects reported improved treatment satisfaction and reduced burden of diabetes management with the bionic pancreas. Further investigation of automated glycemic control in the treatment of CFRD is warranted.

Improving Glucose Prediction Accuracy in Physically Active Adolescents With Type 1 Diabetes

BACKGROUND

Physical activity presents a significant challenge for glycemic control in individuals with type 1 diabetes. As accurate glycemic predictions are key to successful automated decision-making systems (eg, artificial pancreas, AP), the inclusion of additional physiological variables in the estimation of the metabolic state may improve the glucose prediction accuracy during exercise.

METHODS

Predictor-based subspace identification is applied to a dynamic glucose prediction model including heart rate measurements along with variables representing the carbohydrate consumption and insulin boluses. To demonstrate the improvement in prediction ability due to the additional heart rate variable, the performance of the proposed modeling technique is evaluated with (SID-HR) and without heart rate (SID-2) as an additional input using experimental data involving adolescents at ski camp. Furthermore, the performance of the proposed approach is compared to that of the metabolic state observer (MSO) model currently used in the University of Virginia AP algorithm.

RESULTS

The addition of heart rate in the subspace-based model (SID-HR) yields a statistically significant improvement in the root-mean-square error compared to the SID-2 model (P < .001) and the standard MSO (P < .001). Furthermore, the SID-HR model performed favorably in comparison to the SID-2 and MSO models after accounting for its increased complexity.

CONCLUSIONS

Directly considering the effects of physical activity levels on glycemic dynamics through the inclusion of heart rate as an additional input variable in the glucose dynamics model improves the glucose prediction accuracy. The proposed methodology could improve exercise-informed model-based predictive control algorithms in artificial pancreas systems.

Optimal regulation of blood glucose level in Type I diabetes using insulin and glucagon

The Glucose-Insulin-Glucagon nonlinear model accurately describes how the body responds to exogenously supplied insulin and glucagon in patients affected by Type I diabetes. Based on this model, we design infusion rates of either insulin (monotherapy) or insulin and glucagon (dual therapy) that can optimally maintain the blood glucose level within desired limits after consumption of a meal and prevent the onset of both hypoglycemia and hyperglycemia. This problem is formulated as a nonlinear optimal control problem, which we solve using the numerical optimal control package PSOPT. Interestingly, in the case of monotherapy, we find the optimal solution is close to the standard method of insulin based glucose regulation, which is to assume a variable amount of insulin half an hour before each meal. We also find that the optimal dual therapy (that uses both insulin and glucagon) is better able to regulate glucose as compared to using insulin alone. We also propose an ad-hoc rule for both the dosage and the time of delivery of insulin and glucagon.

Technology for Augmenting Type 1 Diabetes Mellitus Management

Type 1 diabetes mellitus has witnessed significant progress in its management over the past several decades. This review highlights technologic advancements in type 1 diabetes management. Continuous glucose monitoring systems are now available at various functionality and cost levels, addressing diverse patient needs, including a recently US Food and Drug Administration (FDA)–approved implantable continuous glucose monitoring system (CGMS). Another dimension to these state-of-the-art technologies is CGMS and insulin pump integration. These integrations have allowed for CGMS-based adjustments to basal insulin delivery rates and suspension of insulin delivery when a low blood glucose event is predicted. This review also includes a brief discussion of upcoming technologies such as patch-based CGMS and insulin-glucagon dual-hormonal delivery.

Multivariable Artificial Pancreas for Various Exercise Types and Intensities

BACKGROUND

Exercise challenges people with type 1 diabetes in controlling their glucose concentration (GC). A multivariable adaptive artificial pancreas (MAAP) may lessen the burden.

METHODS

The MAAP operates without any user input and computes insulin based on continuous glucose monitor and physical activity signals. To analyze performance, 18 60-h closed-loop experiments with 96 exercise sessions with three different protocols were completed. Each day, the subjects completed one resistance and one treadmill exercise (moderate continuous training [MCT] or high-intensity interval training [HIIT]). The primary outcome is time spent in each glycemic range during the exercise + recovery period. Secondary measures include average GC and average change in GC during each exercise modality.

RESULTS

The GC during exercise + recovery periods were within the euglycemic range (70-180 mg/dL) for 69.9% of the time and within a safe glycemic range for exercise (70-250 mg/dL) for 93.0% of the time. The exercise sessions are defined to begin 30 min before the start of exercise and end 2 h after start of exercise. The GC were within the severe hypoglycemia (<55 mg/dL), moderate hypoglycemia (55-70 mg/dL), moderate hyperglycemia (180-250 mg/dL), and severe hyperglycemia (>250 mg/dL) for 0.9%, 1.3%, 23.1%, and 4.8% of the time, respectively. The average GC decline during exercise differed with exercise type (P = 0.0097) with a significant difference between the MCT and resistance (P = 0.0075). To prevent large GC decreases leading to hypoglycemia, MAAP recommended carbohydrates in 59% of MCT, 50% of HIIT, and 39% of resistance sessions.

CONCLUSIONS

A consistent GC decline occurred in exercise and recovery periods, which differed with exercise type. The average GC at the start of exercise was above target (185.5 ± 56.6 mg/dL for MCT, 166.9 ± 61.9 mg/dL for resistance training, and 171.7 ± 41.4 mg/dL HIIT), making a small decrease desirable. Hypoglycemic events occurred in 14.6% of exercise sessions and represented only 2.22% of the exercise and recovery period.

Evaluation of an Artificial Pancreas with Enhanced Model Predictive Control and a Glucose Prediction Trust Index with Unannounced Exercise

BACKGROUND

We investigated the safety and efficacy of the addition of a trust index to enhanced Model Predictive Control (eMPC) Artificial Pancreas (AP) that works by adjusting the responsiveness of the controller's insulin delivery based on the confidence intervals around predictions of glucose trends. This constitutes a dynamic adaptation of the controller's parameters in contrast with the widespread AP implementation of individualized fixed controller tuning.

MATERIALS AND METHODS

After 1 week of sensor-augmented pump (SAP) use, subjects completed a 48-h AP admission that included three meals/day (carbohydrate range 29-57 g/meal), a 1-h unannounced brisk walk, and two overnight periods. Endpoints included sensor glucose percentage time 70-180, <70, >180 mg/dL, number of hypoglycemic events, and assessment of the trust index versus standard eMPC glucose predictions.

RESULTS

Baseline characteristics for the 15 subjects who completed the study (mean ± SD) were age 46.1 ± 17.8 years, HbA1c 7.2% ± 1.0%, diabetes duration 26.8 ± 17.6 years, and total daily dose (TDD) 35.5 ± 16.4 U/day. Mean sensor glucose percent time 70-180 mg/dL (88.0% ± 8.0% vs. 74.6% ± 9.4%), <70 mg/dL (1.5% ± 1.9% vs. 7.8% ± 6.0%), and number of hypoglycemic events (0.6 ± 0.6 vs. 6.3 ± 3.4), all showed statistically significant improvement during AP use compared with the SAP run-in (P < 0.001). On average, the trust index enhanced controller responsiveness to predicted hyper- and hypoglycemia by 26% (P < 0.005).

CONCLUSIONS

In this population of well-controlled patients, we conclude that eMPC with trust index AP achieved nearly 90% time in the target glucose range. Additional studies will further validate these results.

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.

Feasibility of Induced Pluripotent Stem Cell Therapies for Treatment of Type 1 Diabetes

IMPACT STATEMENT

This review of iPSCs to treat T1D provides a current assessment of the challenges and potential for this proposed new therapy.

Patient informaticians: Turning patient voice into patient action

Historically, patients have held a passive role within healthcare, seeking consultation from and following the directions of providers and their care teams. However, changes in culture, education, and technology are making it possible for patients to proactively develop and implement technologies and approaches for health management and quality of life enhancement—to act as patient informaticians. This perspective reviews the societal shifts facilitating the evolution of patient informaticians as discrete actors within healthcare, describes the work of patient informaticians and how this work differs from that of other patient roles (eg, patient advocates), considers examples of patient informaticians in action, and defines patient informaticians’ position relative to the healthcare system.

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.

Enzyme-Based Glucose Sensor: From Invasive to Wearable Device

Blood glucose concentration is a key indicator of patients' health, particularly for symptoms associated with diabetes mellitus. Because of the large number of diabetic patients, many approaches for glucose measurement have been studied to enable continuous and accurate glucose level monitoring. Among them, electrochemical analysis is prominent because it is simple and quantitative. This technology has been incorporated into commercialized and research-level devices from simple test strips to wearable devices and implantable systems. Although directly monitoring blood glucose assures accurate information, the invasive needle-pinching step to collect blood often results in patients (particularly young patients) being reluctant to adopt the process. An implantable glucose sensor may avoid the burden of repeated blood collections, but it is quite invasive and requires periodic replacement of the sensor owing to biofouling and its short lifetime. Therefore, noninvasive methods to estimate blood glucose levels from tears, saliva, interstitial fluid (ISF), and sweat are currently being studied. This review discusses the evolution of enzyme-based electrochemical glucose sensors, including materials, device structures, fabrication processes, and system engineering. Furthermore, invasive and noninvasive blood glucose monitoring methods using various biofluids or blood are described, highlighting the recent progress in the development of enzyme-based glucose sensors and their integrated systems.

Impact of erroneous meal insulin bolus with dual-hormone artificial pancreas using a simplified bolus strategy - A randomized controlled trial

Postprandial glucose control remains challenging for patients with type 1 diabetes (T1D). A simplified meal bolus approach with a dual-hormone (insulin and glucagon) closed-loop system (DH-CLS) has been tested; yet, the impact of categorization errors with this strategy is unknown. The objective was to compare, in a randomized controlled inpatient trial, DH-CLS with the simplified meal bolus approach for two different meals properly categorized or overestimated. We tested, in patients with T1D, the simplified strategy with two standardized breakfasts (n = 10 per meal) adequately categorized or overestimated: (1) 75 g and (2) 45 g of carbohydrate. No difference was observed for percentage of time <4.0 mmol/L over a 4-hour post-meal period (primary outcome; median [IQR]: 0[0-0] vs. 0[0-0] for both comparisons, p = 0.47 and 0.31 for the 75 g and 45 g meals, respectively). Despite higher meal insulin boluses with overestimation for both meals (9.2 [8.2-9.6] vs. 8.1 [7.3-9.1] U and 8.4 [7.2-10.4] vs. 4.8 [3.7-5.6] U; p < 0.05), mean glycemia, percentage of time in target range and glucagon infusion did not differ. Additional scenarios were tested in silico with comparable results. These results suggest that the DH-CLS with a simplified meal bolus calculation is probably able to avoid hypoglycemia in the event of meal size misclassification.

The challenges of achieving postprandial glucose control using closed-loop systems in patients with type 1 diabetes

For patients with type 1 diabetes, closed-loop delivery systems (CLS) combining an insulin pump, a glucose sensor and a dosing algorithm allowing a dynamic hormonal infusion have been shown to improve glucose control when compared with conventional therapy. Yet, reducing glucose excursion and simplification of prandial insulin doses remain a challenge. The objective of this literature review is to examine current meal-time strategies in the context of automated delivery systems in adults and children with type 1 diabetes. Current challenges and considerations for post-meal glucose control will also be discussed. Despite promising results with meal detection, the fully automated CLS has yet failed to provide comparable glucose control to CLS with carbohydrate-matched bolus in the post-meal period. The latter strategy has been efficient in controlling post-meal glucose using different algorithms and in various settings, but at the cost of a meal carbohydrate counting burden for patients. Further improvements in meal detection algorithms or simplified meal-priming boluses may represent interesting avenues. The greatest challenges remain in regards to the pharmacokinetic and dynamic profiles of available rapid insulins as well as sensor accuracy and lag-time. New and upcoming faster acting insulins could provide important benefits. Multi-hormone CLS (eg, dual-hormone combining insulin with glucagon or pramlintide) and adjunctive therapy (eg, GLP-1 and SGLT2 inhibitors) also represent promising options. Meal glucose control with the artificial pancreas remains an important challenge for which the optimal strategy is still to be determined.

Automated blood glucose control in type 1 diabetes: A review of progress and challenges

Since the 2000s, research teams worldwide have been working to develop closed-loop (CL) systems able to automatically control blood glucose (BG) levels in patients with type 1 diabetes. This emerging technology is known as artificial pancreas (AP), and its first commercial version just arrived in the market. The main objective of this paper is to present an extensive review of the clinical trials conducted since 2011, which tested various implementations of the AP for different durations under varying conditions. A comprehensive table that contains key information from the selected publications is provided, and the main challenges in AP development and the mitigation strategies used are discussed. The development timelines for different AP systems are also included, highlighting the main evolutions over the clinical trials for each system.

Impact of an Automated Bihormonal Delivery System on Psychosocial Outcomes in Adults with Type 1 Diabetes

OBJECTIVES

The study assessed the psychosocial impact of the bihormonal bionic pancreas (BP) on adults in a real-world outpatient setting.

RESEARCH DESIGN AND METHODS

Thirty-nine adults with type 1 diabetes at four study centers across the U.S. participated in a two-arm, random-order, crossover design study: 11 days with the BP and 11 days with their usual care (UC). Psychosocial questionnaires were administered before the first study arm, at the end of the first study arm, and at the end of the second study arm.

RESULTS

The mean age of participants was 33 years; mean diabetes duration was 17 years; and 84% were non-Hispanic Caucasian. Significantly greater improvements in psychosocial outcomes were found following the use of BP versus UC; these included reductions in diabetes-related distress (P < 0.001) with the greatest drops in distress related to hypoglycemia and to eating constraints, and greater treatment satisfaction (P < 0.05). The majority of BP users described their experience as positive with a reduction in worrying about low (72%) and high (69%) blood sugars. The majority reported trusting the device (69%) and feeling less burdened by the BP than their usual method of diabetes care (64%). Concerns about the burden of the BP were also expressed, with >75% of users highlighting the burden of carrying around the equipment and the need to change glucagon daily, and more than half of the participants reporting concerns about wearability, discomfort, the time it took to correct out-of-range numbers, and "glitches" in the system.

CONCLUSIONS

Overall, participants report substantial psychosocial benefits accruing from the BP relative to their usual method of diabetes care. However, participants also reported a number of burdens associated with the system. Future versions of the BP device should be designed with the goal of addressing these concerns, and studies with larger, more diverse samples, and with more technology-naive participants are needed.

A Comparison of Time Delay in Three Continuous Glucose Monitors for Adolescents and Adults

BACKGROUND

The physiologic delay in glucose diffusion from the blood to the interstitial fluid and instrumental factors contribute to the delay between changes in plasma glucose (PG) and measurements made by continuous glucose monitors (CGMs). This study compared the duration of this delay for three CGMs.

METHODS

A total of 24 healthy adolescent and adult subjects with type 1 diabetes wore three CGM devices simultaneously for 48 hours: Dexcom G4 Platinum, Abbott Navigator, and Medtronic Enlite. The time delay between PG and CGM-estimated plasma glucose (CGMG) was estimated by comparing time-shifted CGMG with reference PG taken every 15 minutes.

RESULTS

The delay estimated by our approach was larger for the Navigator than for the G4 Platinum in adolescents (7.7 ± 1.1 versus 5.6 ± 0.9 min, P = .0396) and adults (10.9 ± 1.1 versus 8.1 ± 0.7 min, P = .0107). The delay was nominally longer for the Navigator than for the Enlite in both the adolescent (7.7 ± 1.1 versus 4.3 ± 1.0 min, P = .0728) and adult (10.9 ± 1.1 versus 8.3 ± 0.9 min, P = .111) populations, but these differences were not statistically significant. There was no difference in the delay between G4 Platinum and Enlite. Adolescents had shorter delays than adults for all three devices. There was a significant correlation between longer delay and increasing age for the G4 Platinum and Navigator.

CONCLUSIONS

There are differences in the estimated PG to CGMG time delays between CGM devices in the same subjects. The delay between PG and CGMG is smaller for adolescents than for adults. The PG-to-CGMG time delay is influenced by both instrument and host factors.

Effect of artificial pancreas systems on glycaemic control in patients with type 1 diabetes: a systematic review and meta-analysis of outpatient randomised controlled trials

BACKGROUND

Closed-loop artificial pancreas systems have been in development for several years, including assessment in numerous varied outpatient clinical trials. We aimed to summarise the efficacy and safety of artificial pancreas systems in outpatient settings and explore the clinical and technical factors that can affect their performance.

METHODS

We did a systematic review and meta-analysis of randomised controlled trials comparing artificial pancreas systems (insulin only or insulin plus glucagon) with conventional pump therapy (continuous subcutaneous insulin infusion [CSII] with blinded continuous glucose monitoring [CGM] or unblinded sensor-augmented pump [SAP] therapy) in adults and children with type 1 diabetes. We searched Medline, Embase, and the Cochrane Central Register of Controlled Trials for studies published from 1946, to Jan 1, 2017. We excluded studies not published in English, those involving pregnant women or participants who were in hospital, and those testing adjunct medications other than glucagon. The primary outcome was the mean difference in percentage of time blood glucose concentration remained in target range (3·9-10 mmol/L or 3·9-8 mmol/L, depending on the study), assessed by random-effects meta-analysis. This study is registered with PROSPERO, number 2015:CRD42015026854.

FINDINGS

We identified 984 reports; after exclusions, 27 comparisons from 24 studies (23 crossover and one parallel design) including a total of 585 participants (219 in adult studies, 265 in paediatric studies, and 101 in combined studies) were eligible for analysis. Five comparisons assessed dual-hormone (insulin and glucagon), two comparisons assessed both dual-hormone and single-hormone (insulin only), and 20 comparisons assessed single-hormone artificial pancreas systems. Time in target was 12·59% higher with artificial pancreas systems (95% CI 9·02-16·16; p<0·0001), from a weighted mean of 58·21% for conventional pump therapy (I²=84%). Dual-hormone artificial pancreas systems were associated with a greater improvement in time in target range compared with single-hormone systems (19·52% [95% CI 15·12-23·91] vs 11·06% [6·94 to 15·18]; p=0·006), although six of seven comparisons compared dual-hormone systems to CSII with blinded CGM, whereas 21 of 22 single-hormone comparisons had SAP as the comparator. Single-hormone studies had higher heterogeneity than dual-hormone studies (I² 79% vs 66%). Bias assessment characteristics were incompletely reported in 12 of 24 studies, no studies masked participants to the intervention assignment, and masking of outcome assessment was not done in 12 studies and was unclear in 12 studies.

INTERPRETATION

Artificial pancreas systems uniformly improved glucose control in outpatient settings, despite heterogeneous clinical and technical factors.

FUNDING

None.

Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra-day variability

BACKGROUND AND OBJECTIVE

Current prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain.

METHODS

In this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake.

RESULTS

Overall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4vs. 131.8 ± 4.2mg/dl; percentage time in target [70, 180]mg/dl, 82.0 ± 7.0vs. 89.5 ± 4.2; percentage time above target 17.7 ± 7.0vs. 10.2 ± 4.1. Adolescents: mean glucose 158.2 ± 21.4vs. 140.5 ± 13.0mg/dl; percentage time in target, 65.9 ± 12.9vs. 77.5 ± 12.2; percentage time above target, 31.7 ± 13.1vs. 19.8 ± 10.2. Note that no increase in percentage time in hypoglycemia was observed.

CONCLUSION

Using an adaptive meal bolus calculator within a closed-loop control system has the potential to improve glycemic control in type 1 diabetes when compared to its non-adaptive counterpart.

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.

Outpatient Closed-Loop Control with Unannounced Moderate Exercise in Adolescents Using Zone Model Predictive Control

BACKGROUND

The artificial pancreas (AP) has the potential to improve glycemic control in adolescents. This article presents the first evaluation in adolescents of the Zone Model Predictive Control and Health Monitoring System (ZMPC+HMS) AP algorithms, and their first evaluation in a supervised outpatient setting with frequent exercise.

MATERIALS AND METHODS

Adolescents with type 1 diabetes underwent 3 days of closed-loop control (CLC) in a hotel setting with the ZMPC+HMS algorithms on the Diabetes Assistant platform. Subjects engaged in twice-daily exercise, including soccer, tennis, and bicycling. Meal size (unrestricted) was estimated and entered into the system by subjects to trigger a bolus, but exercise was not announced.

RESULTS

Ten adolescents (11.9-17.7 years) completed 72 h of CLC, with data on 95 ± 14 h of sensor-augmented pump (SAP) therapy before CLC as a comparison to usual therapy. The percentage of time with continuous glucose monitor (CGM) 70-180 mg/dL was 71% ± 10% during CLC, compared to 57% ± 16% during SAP (P = 0.012). Nocturnal control during CLC was safe, with 0% (0%, 0.6%) of time with CGM <70 mg/dL compared to 1.1% (0.0%, 14%) during SAP. Despite large meals (estimated up to 120 g carbohydrate), only 8.0% ± 6.9% of time during CLC was spent with CGM >250 mg/dL (16% ± 14% during SAP). The system remained connected in CLC for 97% ± 2% of the total study time. No adverse events or severe hypoglycemia occurred.

CONCLUSIONS

The use of the ZMPC+HMS algorithms is feasible in the adolescent outpatient environment and achieved significantly more time in the desired glycemic range than SAP in the face of unannounced exercise and large announced meal challenges.

Use of Wearable Sensors and Biometric Variables in an Artificial Pancreas System

An artificial pancreas (AP) computes the optimal insulin dose to be infused through an insulin pump in people with Type 1 Diabetes (T1D) based on information received from a continuous glucose monitoring (CGM) sensor. It has been recognized that exercise is a major challenge in the development of an AP system. The use of biometric physiological variables in an AP system may be beneficial for prevention of exercise-induced challenges and better glucose regulation. The goal of the present study is to find a correlation between biometric variables such as heart rate (HR), heat flux (HF), skin temperature (ST), near-body temperature (NBT), galvanic skin response (GSR), and energy expenditure (EE), 2D acceleration-mean of absolute difference (MAD) and changes in glucose concentrations during exercise via partial least squares (PLS) regression and variable importance in projection (VIP) in order to determine which variables would be most useful to include in a future artificial pancreas. PLS and VIP analyses were performed on data sets that included seven different types of exercises. Data were collected from 26 clinical experiments. Clinical results indicate ST to be the most consistently important (important for six out of seven tested exercises) variable over all different exercises tested. EE and HR are also found to be important variables over several types of exercise. We also found that the importance of GSR and NBT observed in our experiments might be related to stress and the effect of changes in environmental temperature on glucose concentrations. The use of the biometric measurements in an AP system may provide better control of glucose concentration.

Type 1 Diabetes - A Clinical Perspective

Type 1 diabetes is a disease in which autoimmune destruction of pancreatic β-cells leads to insulin deficiency. Controlling blood glucose with an acceptable range is a major goal of therapy. Measurements of hemoglobin A1c and blood glucose levels are used for both the diagnosis and the long-term management of the disease. This chapter briefly describes the pathophysiology, diagnosis, and management of type 1 diabetes.

Home use of a bihormonal bionic pancreas versus insulin pump therapy in adults with type 1 diabetes: a multicentre randomised crossover trial

BACKGROUND

The safety and effectiveness of a continuous, day-and-night automated glycaemic control system using insulin and glucagon has not been shown in a free-living, home-use setting. We aimed to assess whether bihormonal bionic pancreas initialised only with body mass can safely reduce mean glycaemia and hypoglycaemia in adults with type 1 diabetes who were living at home and participating in their normal daily routines without restrictions on diet or physical activity.

METHODS

We did a random-order crossover study in volunteers at least 18 years old who had type 1 diabetes and lived within a 30 min drive of four sites in the USA. Participants were randomly assigned (1:1) in blocks of two using sequentially numbered sealed envelopes to glycaemic regulation with a bihormonal bionic pancreas or usual care (conventional or sensor-augmented insulin pump therapy) first, followed by the opposite intervention. Both study periods were 11 days in length, during which time participants continued all normal activities, including athletics and driving. The bionic pancreas was initialised with only the participant's body mass. Autonomously adaptive dosing algorithms used data from a continuous glucose monitor to control subcutaneous delivery of insulin and glucagon. The coprimary outcomes were the mean glucose concentration and time with continuous glucose monitoring (CGM) glucose concentration less than 3·3 mmol/L, analysed over days 2-11 in participants who completed both periods of the study. This trial is registered with ClinicalTrials.gov, number NCT02092220.

FINDINGS

We randomly assigned 43 participants between May 6, 2014, and July 3, 2015, 39 of whom completed the study: 20 who were assigned to bionic pancreas first and 19 who were assigned to the comparator first. The mean CGM glucose concentration was 7·8 mmol/L (SD 0·6) in the bionic pancreas period versus 9·0 mmol/L (1·6) in the comparator period (difference 1·1 mmol/L, 95% CI 0·7-1·6; p<0·0001), and the mean time with CGM glucose concentration less than 3·3 mmol/L was 0·6% (0·6) in the bionic pancreas period versus 1·9% (1·7) in the comparator period (difference 1·3%, 95% CI 0·8-1·8; p<0·0001). The mean nausea score on the Visual Analogue Scale (score 0-10) was greater during the bionic pancreas period (0·52 [SD 0·83]) than in the comparator period (0·05 [0·17]; difference 0·47, 95% CI 0·21-0·73; p=0·0024). Body mass and laboratory parameters did not differ between periods. There were no serious or unexpected adverse events in the bionic pancreas period of the study.

INTERPRETATION

Relative to conventional and sensor-augmented insulin pump therapy, the bihormonal bionic pancreas, initialised only with participant weight, was able to achieve superior glycaemic regulation without the need for carbohydrate counting. Larger and longer studies are needed to establish the long-term benefits and risks of automated glycaemic management with a bihormonal bionic pancreas.

FUNDING

National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health, and National Center for Advancing Translational Sciences.

Mitigating Reductions in Glucose During Exercise on Closed-Loop Insulin Delivery: The Ex-Snacks Study

OBJECTIVE

To assess whether snacking could be used with closed-loop (CL) insulin delivery to avoid exercise-induced reductions in plasma glucose (PG), as well as elevations in PG at the end of exercise.

RESEARCH DESIGN AND METHODS

Twelve type 1 diabetes (T1D) subjects (age 13-36 years, duration 10.7 ± 8.4 years, A1c 7.4% ± 0.8% [57 ± 8.7 mmol/mol]) underwent two 105-min exercise studies while under CL control: CL alone and CL+snack. Exercise, commenced at 3 PM, consisted of four 15-min periods of brisk treadmill walking to 65%-70% HR_max (separated by three 5-min rest periods), followed by a 30-min recovery period. Fifteen to 30 g carbohydrate (Gatorade) was provided on snacking visits just before and midway through the exercise period. PG and insulin were measured every 15-20 min during the exercise studies.

RESULTS

Baseline PG levels were similar for CL alone (164 ± 16 mg/dL) versus CL+snack (172 ± 11 mg/dL). During exercise, PG levels fell by 53 ± 10 mg/dL without snacking versus a modest 10 ± 13 mg/dL increase in PG with snacking (P = 0.0005); similar differences in the change in PG levels were observed at the end of recovery period. Hypoglycemia requiring rescue treatment (PG ≤60 mg/dL) during exercise occurred in three nonsnacking visits versus none with snacking. During the 75-min exercise period, insulin delivered was 1.8 ± 0.4 U for the CL+snack admission compared to 0.7 ± 0.1 U during CL alone (P = 0.002).

CONCLUSION

These results support the use of a simple snacking strategy to avoid exercise-induced lowering of PG while on CL insulin delivery. Persistent insulin infusion during exercise with snacking also appears to be effective in limiting increases in PG at the end of exercise.

Efficacy of single-hormone and dual-hormone artificial pancreas during continuous and interval exercise in adult patients with type 1 diabetes: randomised controlled crossover trial

AIMS/HYPOTHESIS

The aim of this study was to assess whether the dual-hormone (insulin and glucagon) artificial pancreas reduces hypoglycaemia compared with the single-hormone (insulin alone) artificial pancreas during two types of exercise.

METHODS

An open-label randomised crossover study comparing both systems in 17 adults with type 1 diabetes (age, 37.2 ± 13.6 years; HbA_1c, 8.0 ± 1.0% [63.9 ± 10.2 mmol/mol]) during two exercise types on an ergocycle and matched for energy expenditure: continuous (60% [Formula: see text] for 60 min) and interval (2 min alternating periods at 85% and 50% [Formula: see text] for 40 min, with two 10 min periods at 45% [Formula: see text] at the start and end of the session). Blocked randomisation (size of four) with a 1:1:1:1 allocation ratio was computer generated. The artificial pancreas was applied from 15:30 hours until 19:30 hours; exercise was started at 18:00 hours and announced 20 min earlier to the systems. The study was conducted at the Institut de recherches cliniques de Montréal.

RESULTS

During single-hormone control compared with dual-hormone control, exercise-induced hypoglycaemia (plasma glucose <3.3 mmol/l with symptoms or <3.0 mmol/l regardless of symptoms) was observed in four (23.5%) vs two (11.8%) interventions (p = 0.5) for continuous exercise and in six (40%) vs one (6.25%) intervention (p = 0.07) for interval exercise. For the pooled analysis (single vs dual hormone), the median (interquartile range) percentage time spent at glucose levels below 4.0 mmol/l was 11% (0.0-46.7%) vs 0% (0-0%; p = 0.0001) and at glucose levels between 4.0 and 10.0 mmol/l was 71.4% (53.2-100%) vs 100% (100-100%; p = 0.003). Higher doses of glucagon were needed during continuous (0.126 ± 0.057 mg) than during interval exercise (0.093 ± 0.068 mg) (p = 0.03), with no reported side-effects in all interventions.

CONCLUSIONS/INTERPRETATION

The dual-hormone artificial pancreas outperformed the single-hormone artificial pancreas in regulating glucose levels during announced exercise in adults with type 1 diabetes.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01930110 FUNDING: : Société Francophone du Diabète and Diabète Québec.

A Simplified Semiquantitative Meal Bolus Strategy Combined with Single- and Dual-Hormone Closed-Loop Delivery in Patients with Type 1 Diabetes: A Pilot Study

BACKGROUND

Single- and dual-hormone closed-loop systems can improve glycemic control and have the potential to reduce carbohydrate-counting burden for patients with type 1 diabetes; however, simplification of meal insulin calculation should not compromise glycemic control.

METHODS

We compared in a randomized outpatient pilot trial: (1) a single-hormone closed-loop system accompanied with carbohydrate-content matched boluses versus accompanied with a simplified meal bolus strategy, and (2) a dual-hormone closed-loop system accompanied with carbohydrate-content matched boluses versus accompanied with a simplified meal bolus strategy. Carbohydrate-matched boluses were based on the participant's carbohydrate meal content estimation whereas the simplified strategy involved the selection, by participants, of a semi-quantitative meal carbohydrate-content size: snack, regular, large, or very large meal. Each participant also underwent sensor-augmented pump therapy. Basal insulin delivery was more aggressive with the simplified bolus. The primary outcome was mean sensor glucose level over a 15-h daytime period.

RESULTS

Twelve participants were recruited (48.2 ± 16.0 years old; HbA1c 7.4% ± 0.9%) to compare the two bolus strategies during single- and dual-hormone closed-loop delivery. A similar mean sensor glucose level (15 h) was achieved with the carbohydrate-matched boluses and simplified strategy using single-hormone (median [interquartile]: 7.6 [7.2-8.1] vs. 8.0 [7.0-8.6] mmol/L; P = 0.90) and dual-hormone closed-loop systems (7.6 [6.7-9.1] vs. 7.0 [6.4-8.2] mmol/L; P = 0.08). Exploratory analyses showed that, as compared with sensor-augmented pump therapy, there was an increased time spent in hypoglycemia with the simplified strategy but not with the carbohydrate-matched boluses.

CONCLUSIONS

Though the algorithm employed in this pilot study may lead to an increased risk for hypoglycemia, this strategy has the potential to reduce the carbohydrate-counting burden in patients with type 1 diabetes while generally maintaining adequate glucose control. Longer outpatient studies with an improved algorithm are needed.