The "Glucositter" overnight automated closed loop system for type 1 diabetes: a randomized crossover trial

Precision medicine in type 1 diabetes

First envisioned by early diabetes clinicians, a person-centred approach to care was an aspirational goal that aimed to match insulin therapy to each individual's unique requirements. In the 100 years since the discovery of insulin, this goal has evolved to include personalised approaches to type 1 diabetes diagnosis, treatment, prevention and prediction. These advances have been facilitated by the recognition of type 1 diabetes as an autoimmune disease and by advances in our understanding of diabetes pathophysiology, genetics and natural history, which have occurred in parallel with advancements in insulin delivery, glucose monitoring and tools for self-management. In this review, we discuss how these personalised approaches have improved diabetes care and how improved understanding of pathogenesis and human biology might inform precision medicine in the future.

Assessing the effect of closed-loop insulin delivery from onset of type 1 diabetes in youth on residual beta-cell function compared to standard insulin therapy (CLOuD study): a randomised parallel study protocol

INTRODUCTION

Management of newly diagnosed type 1 diabetes (T1D) in children and adolescents is challenging for patients, families and healthcare professionals. The objective of this study is to determine whether continued intensive metabolic control using hybrid closed-loop (CL) insulin delivery following diagnosis of T1D can preserve C-peptide secretion, a marker of residual beta-cell function, compared with standard multiple daily injections (MDI) therapy.

METHODS AND ANALYSIS

The study adopts an open-label, multicentre, randomised, parallel design, and aims to randomise 96 participants aged 10-16.9 years, recruited within 21 days of diagnosis with T1D. Following a baseline mixed meal tolerance test (MMTT), participants will be randomised to receive 24 months treatment with conventional MDI therapy or with CL insulin delivery. A further 24-month optional extension phase will be offered to all participants to continue with the allocated treatment. The primary outcome is the between group difference in area under the stimulated C-peptide curve (AUC) of the MMTT at 12 months post diagnosis. Analyses will be conducted on an intention-to-treat basis. Key secondary outcomes are between group differences in time spent in target glucose range (3.9-10 mmol/L), glycated haemoglobin (HbA1c) and time spent in hypoglycaemia (<3.9 mmol/L) at 12 months. Secondary efficacy outcomes include between group differences in stimulated C-peptide AUC at 24 months, time spent in target glucose range, glucose variability, hypoglycaemia and hyperglycaemia as recorded by periodically applied masked continuous glucose monitoring devices, total, basal and bolus insulin dose, and change in body weight. Cognitive, emotional and behavioural characteristics of participants and parents will be evaluated, and a cost-utility analysis performed to support adoption of CL as a standard treatment modality following diagnosis of T1D.

ETHICS AND DISSEMINATION

Ethics approval has been obtained from Cambridge East Research Ethics Committee. The results will be disseminated by peer-reviewed publications and conference presentations.

TRIAL REGISTRATION NUMBER

NCT02871089; Pre-results.

Diabetes Technology: Monitoring, Analytics, and Optimal Control

Over the past 50 years, the diabetes technology field progressed remarkably through self-monitoring of blood glucose (SMBG), continuous subcutaneous insulin infusion (CSII), risk and variability analysis, mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). This review follows these developments, beginning with an overview of the functioning of the human metabolic system in health and in diabetes and of its detailed quantitative network modeling. The review continues with a brief account of the first AP studies that used intravenous glucose monitoring and insulin infusion, and with notes about CSII and CGM-the technologies that made possible the development of contemporary AP systems. In conclusion, engineering lessons learned from AP research, and the clinical need for AP systems to prove their safety and efficacy in large-scale clinical trials, are outlined.

Assessing the efficacy, safety and utility of 6-month day-and-night automated closed-loop insulin delivery under free-living conditions compared with insulin pump therapy in children and adolescents with type 1 diabetes: an open-label, multicentre, multinational, single-period, randomised, parallel group study protocol

INTRODUCTION

Closed-loop systems titrate insulin based on sensor glucose levels, providing novel means to reduce the risk of hypoglycaemia while improving glycaemic control. We will assess effectiveness of 6-month day-and-night closed-loop insulin delivery compared with usual care (conventional or sensor-augmented pump therapy) in children and adolescents with type 1 diabetes.

METHODS AND ANALYSIS

The trial adopts an open-label, multicentre, multinational (UK and USA), randomised, single-period, parallel design. Participants (n=130) are children and adolescents (aged ≥6 and <19 years) with type 1 diabetes for at least 1 year, and insulin pump use for at least 3 months with suboptimal glycaemic control (glycated haemoglobin ≥58 mmol/mol (7.5%) and ≤86 mmol/mol (10%)). After a 2-3 week run-in period, participants will be randomised to 6-month use of hybrid closed-loop insulin delivery, or to usual care. Analyses will be conducted on an intention-to-treat basis. The primary outcome is glycated haemoglobin at 6 months. Other key endpoints include time in the target glucose range (3.9-10 mmol/L, 70-180 mg/dL), mean sensor glucose and time spent above and below target. Secondary outcomes include SD and coefficient of variation of sensor glucose levels, time with sensor glucose levels <3.5 mmol/L (63 mg/dL) and <3.0 mmol/L (54 mg/dL), area under the curve of glucose <3.5 mmol/L (63 mg/dL), time with glucose levels >16.7 mmol/L (300 mg/dL), area under the curve of glucose >10.0 mmol/L (180 mg/dL), total, basal and bolus insulin dose, body mass index z-score and blood pressure. Cognitive, emotional and behavioural characteristics of participants and caregivers and their responses to the closed-loop and clinical trial will be assessed. An incremental cost-effectiveness ratio for closed-loop will be estimated.

ETHICS AND DISSEMINATION

Cambridge South Research Ethics Committee and Jaeb Center for Health Research Institutional Review Office approved the study. The findings will be disseminated by peer-review publications and conference presentations.

TRIAL REGISTRATION NUMBER

NCT02925299; Pre-results.

Automated closed-loop control of diabetes: the artificial pancreas

The incidence of Diabetes Mellitus is on the rise worldwide, which exerts enormous health toll on the population and enormous pressure on the healthcare systems. Now, almost hundred years after the discovery of insulin in 1921, the optimization problem of diabetes is well formulated as maintenance of strict glycemic control without increasing the risk for hypoglycemia. External insulin administration is mandatory for people with type 1 diabetes; various medications, as well as basal and prandial insulin, are included in the daily treatment of type 2 diabetes. This review follows the development of the Diabetes Technology field which, since the 1970s, progressed remarkably through continuous subcutaneous insulin infusion (CSII), mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). All of these developments included significant engineering advances and substantial bioelectronics progress in the sensing of blood glucose levels, insulin delivery, and control design. The key technologies that enabled contemporary AP systems are CSII and CGM, both of which became available and sufficiently portable in the beginning of this century. This powered the quest for wearable home-use AP, which is now under way with prototypes tested in outpatient studies during the past 6 years. Pivotal trials of new AP technologies are ongoing, and the first hybrid closed-loop system has been approved by the FDA for clinical use. Thus, the closed-loop AP is well on its way to become the digital-age treatment of diabetes.

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Background

Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis.

Objective

The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges.

Methods

A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review.

Results

We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results.

Conclusions

We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.

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.

Home Use of Day-and-Night Hybrid Closed-Loop Insulin Delivery in Suboptimally Controlled Adolescents With Type 1 Diabetes: A 3-Week, Free-Living, Randomized Crossover Trial

OBJECTIVE

This study evaluated the feasibility, safety, and efficacy of day-and-night hybrid closed-loop insulin delivery in adolescents with type 1 diabetes under free-living conditions.

RESEARCH DESIGN AND METHODS

In an open-label randomized crossover study, 12 suboptimally controlled adolescents on insulin pump therapy (mean ± SD age 14.6 ± 3.1 years; HbA_1c 69 ± 8 mmol/mol [8.5 ± 0.7%]; duration of diabetes 7.8 ± 3.5 years) underwent two 21-day periods in which hybrid closed-loop insulin delivery was compared with sensor-augmented insulin pump therapy in random order. During the closed-loop intervention, a model predictive algorithm automatically directed insulin delivery between meals and overnight. Participants used a bolus calculator to administer prandial boluses.

RESULTS

The proportion of time that sensor glucose was in the target range (3.9-10 mmol/L; primary end point) was increased during the closed-loop intervention compared with sensor-augmented insulin pump therapy by 18.8 ± 9.8 percentage points (mean ± SD; P < 0.001), the mean sensor glucose level was reduced by 1.8 ± 1.3 mmol/L (P = 0.001), and the time spent above target was reduced by 19.3 ± 11.3 percentage points (P < 0.001). The time spent with sensor glucose levels below 3.9 mmol/L was low and comparable between interventions (median difference 0.4 [interquartile range -2.2 to 1.3] percentage points; P = 0.33). Improved glucose control during closed-loop was associated with increased variability of basal insulin delivery (P < 0.001) and an increase in the total daily insulin dose (53.5 [39.5-72.1] vs. 51.5 [37.6-64.3] units/day; P = 0.006). Participants expressed positive attitudes and experience with the closed-loop system.

CONCLUSIONS

Free-living home use of day-and-night closed-loop in suboptimally controlled adolescents with type 1 diabetes is safe, feasible, and improves glucose control without increasing the risk of hypoglycemia. Larger and longer studies are warranted.

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.

A new horizon for glucose monitoring

Regular self-monitoring of blood glucose is crucial for proper insulin dosing and gives a reliable foundation for reasonable glycaemic control. According to recent data, recommended values for glycated haemoglobin A1c as set by the professional associations remain out of the reach for a large proportion of the paediatric population. In the last decades, the treatment of type 1 diabetes has changed significantly as new devices gain a role in routine clinical care. Real-time glucose levels can be monitored with continuous glucose monitoring (CGM), which provides a broad spectrum of information on glucose trends on a moment-to-moment basis. This information can be useful for patients' decision making and clinicians' understanding of patients' conduct. However, several barriers, including the current price, impede a broader use of CGM in most regions of the world. This review summarizes data from randomized, controlled trials that included a paediatric population, and it provides some evidence-based visions for the possible broader utilization of CGM, also for incorporation into insulin delivery devices that enable a closed-loop insulin delivery.

Classification of Physical Activity: Information to Artificial Pancreas Control Systems in Real Time

Physical activity has a wide range of effects on glucose concentrations in type 1 diabetes (T1D) depending on the type (ie, aerobic, anaerobic, mixed) and duration of activity performed. This variability in glucose responses to physical activity makes the development of artificial pancreas (AP) systems challenging. Automatic detection of exercise type and intensity, and its classification as aerobic or anaerobic would provide valuable information to AP control algorithms. This can be achieved by using a multivariable AP approach where biometric variables are measured and reported to the AP at high frequency. We developed a classification system that identifies, in real time, the exercise intensity and its reliance on aerobic or anaerobic metabolism and tested this approach using clinical data collected from 5 persons with T1D and 3 individuals without T1D in a controlled laboratory setting using a variety of common types of physical activity. The classifier had an average sensitivity of 98.7% for physiological data collected over a range of exercise modalities and intensities in these subjects. The classifier will be added as a new module to the integrated multivariable adaptive AP system to enable the detection of aerobic and anaerobic exercise for enhancing the accuracy of insulin infusion strategies during and after exercise.

Meal Detection in Patients With Type 1 Diabetes: A New Module for the Multivariable Adaptive Artificial Pancreas Control System

A novel meal-detection algorithm is developed based on continuous glucose measurements. Bergman's minimal model is modified and used in an unscented Kalman filter for state estimations. The estimated rate of appearance of glucose is used for meal detection. Data from nine subjects are used to assess the performance of the algorithm. The results indicate that the proposed algorithm works successfully with high accuracy. The average change in glucose levels between the meals and the detection points is 16(±9.42) [mg/dl] for 61 successfully detected meals and snacks. The algorithm is developed as a new module of an integrated multivariable adaptive artificial pancreas control system. Meal detection with the proposed method is used to administer insulin boluses and prevent most of postprandial hyperglycemia without any manual meal announcements. A novel meal bolus calculation method is proposed and tested with the UVA/Padova simulator. The results indicate significant reduction in hyperglycemia.

Clinical results of an automated artificial pancreas using technosphere inhaled insulin to mimic first-phase insulin secretion

OBJECTIVE

The purpose of this study was to investigate whether or not adding a fixed preprandial dose of inhaled insulin to a fully automated closed loop artificial pancreas would improve the postprandial glucose control without adding an increased risk of hypoglycemia.

RESEARCH DESIGN AND METHODS

Nine subjects with T1DM were recruited for the study. The patients were on closed-loop control for 24 hours starting around 4:30 pm. Mixed meals (~50 g CHO) were given at 6:30 pm and 7:00 am the following day. For the treatment group each meal was preceded by the inhalation of one 10 U dose of Technosphere Insulin (TI). Subcutaneous insulin delivery was controlled by a zone model predictive control algorithm (zone-MPC). At 11:00 am, the patient exercised for 30 ± 5 minutes at 50% of predicted heart rate reserve.

RESULTS

The use of TI resulted in increasing the median percentage time in range (70-180 mg/dl, BG) during the 5-hour postprandial period by 21.6% (81.6% and 60% in the with/without TI cases, respectively, P = .06) and reducing the median postprandial glucose peak by 33 mg/dl (172 mg/dl and 205 mg/dl in the with and without TI cases, respectively, P = .004). The median percentage time in range 80-140 mg/dl during the entire study period was 67.5% as compared to percentage time in range without the use of TI of 55.2% (P = .03).

CONCLUSIONS

Adding preprandial TI (See video supplement) to an automated closed-loop AP system resulted in superior postprandial control as demonstrated by lower postprandial glucose exposure without addition hypoglycemia.

Type 1 Diabetes in the Young: Organization of Two National Centers in Israel and Slovenia

Type 1 diabetes is a chronic autoimmune disease that affects mainly young people. In the last 50 years, a steady increase of the T1D incidence in the young is reported worldwide, with an average 4 % increase annually. In addition, the mean age at the diagnosis is decreasing. Studies show that good metabolic control is important not only for delaying the chronic complications of diabetes but also for improving the quality of life of patients and their families. Continuous education, together with modern technology, is crucial in achieving these goals. Longitudinal data on glycated hemoglobin (HbA1c), along with the data on severe hypoglycemia and severe diabetic ketoacidosis, can describe the quality of care in a defined population. Two national reference diabetes centres taking care of children, adolescents and young adults with diabetes in Israel and Slovenia are described.

Reduced Worries of Hypoglycaemia, High Satisfaction, and Increased Perceived Ease of Use after Experiencing Four Nights of MD-Logic Artificial Pancreas at Home (DREAM4)

AIMS

This study assesses the impact of using an AP-system at home on fear of hypoglycaemia. In addition, satisfaction and acceptance of the new technology are evaluated.

METHODS

In a multicentre, multinational study of 75 patients using the MD-Logic AP during four consecutive nights in home setting 59 of them (aged 10-54 years, 54% male, HbA1c 7.89 ± 0.69% [62.72 ± 7.51 mmol/mol], diabetes duration 11.6 ± 8.4 yrs) answered standardized questionnaires (HFS, adapted TAM, and AP satisfaction) before and after using the AP.

RESULTS

After experiencing the AP in home setting worries of hypoglycaemia were significantly reduced (before 1.04 ± 0.53 versus after 0.90 ± 0.63; P = 0.017). Perceived ease of use as a measure of acceptance with the AP significantly increased after personal experience (before 4.64 ± 0.94 versus after 5.06 ± 1.09; P = 0.002). The overall satisfaction mean score after using the AP was 3.02 ± 0.54 (range 0-4), demonstrating a high level of satisfaction with this technology.

CONCLUSIONS

The four-night home-based experience of using MD Logic AP was associated with reduced worries of hypoglycaemia, high level of satisfaction, and increased perceived ease of use of the new technology in children, adolescents, and adults.

Multicenter closed-loop insulin delivery study points to challenges for keeping blood glucose in a safe range by a control algorithm in adults and adolescents with type 1 diabetes from various sites

BACKGROUND

The Control to Range Study was a multinational artificial pancreas study designed to assess the time spent in the hypo- and hyperglycemic ranges in adults and adolescents with type 1 diabetes while under closed-loop control. The controller attempted to keep the glucose ranges between 70 and 180 mg/dL. A set of prespecified metrics was used to measure safety.

RESEARCH DESIGN AND METHODS

We studied 53 individuals for approximately 22 h each during clinical research center admissions. Plasma glucose level was measured every 15-30 min (YSI clinical laboratory analyzer instrument [YSI, Inc., Yellow Springs, OH]). During the admission, subjects received three mixed meals (1 g of carbohydrate/kg of body weight; 100 g maximum) with meal announcement and automated insulin dosing by the controller.

RESULTS

For adults, the mean of subjects' mean glucose levels was 159 mg/dL, and mean percentage of values 71-180 mg/dL was 66% overall (59% daytime and 82% overnight). For adolescents, the mean of subjects' mean glucose levels was 166 mg/dL, and mean percentage of values in range was 62% overall (53% daytime and 82% overnight). Whereas prespecified criteria for safety were satisfied by both groups, they were met at the individual level in adults only for combined daytime/nighttime and for isolated nighttime. Two adults and six adolescents failed to meet the daytime criterion, largely because of postmeal hyperglycemia, and another adolescent failed to meet the nighttime criterion.

CONCLUSIONS

The control-to-range system performed as expected: faring better overnight than during the day and performing with variability between patients even after individualization based on patients' prior settings. The system had difficulty preventing postmeal excursions above target range.

Assessing the effectiveness of 3 months day and night home closed-loop insulin delivery in adults with suboptimally controlled type 1 diabetes: a randomised crossover study protocol

INTRODUCTION

Despite therapeutic advances, many people with type 1 diabetes are still unable to achieve optimal glycaemic control, limited by the occurrence of hypoglycaemia. The objective of the present study is to determine the effectiveness of day and night home closed-loop over the medium term compared with sensor-augmented pump therapy in adults with type 1 diabetes and suboptimal glycaemic control.

METHODS AND ANALYSIS

The study will adopt an open label, three-centre, multinational, randomised, two-period crossover study design comparing automated closed-loop glucose control with sensor augmented insulin pump therapy. The study will aim for 30 completed participants. Eligible participants will be adults (≥18 years) with type 1 diabetes treated with insulin pump therapy and suboptimal glycaemic control (glycated haemoglobin (HbA1c)≥7.5% (58 mmol/mmol) and ≤10% (86 mmol/mmol)). Following a 4-week optimisation period, participants will undergo a 3-month use of automated closed-loop insulin delivery and sensor-augmented pump therapy, with a 4-6 week washout period in between. The order of the interventions will be random. All analysis will be conducted on an intention to treat basis. The primary outcome is the time spent in the target glucose range from 3.9 to 10.0 mmol/L based on continuous glucose monitoring levels during the 3 months free living phase. Secondary outcomes include HbA1c changes; mean glucose and time spent above and below target glucose levels. Further, participants will be invited at baseline, midpoint and study end to participate in semistructured interviews and complete questionnaires to explore usability and acceptance of the technology, impact on quality of life and fear of hypoglycaemia.

ETHICS AND DISSEMINATION

Ethical approval has been obtained at all sites. Before screening, all participants will be provided with oral and written information about the trial. The study will be disseminated by peer-review publications and conference presentations.

TRIAL REGISTRATION NUMBER

NCT01961622 (ClinicalTrials.gov).

A randomized trial of a home system to reduce nocturnal hypoglycemia in type 1 diabetes

OBJECTIVE

Overnight hypoglycemia occurs frequently in individuals with type 1 diabetes and can result in loss of consciousness, seizure, or even death. We conducted an in-home randomized trial to determine whether nocturnal hypoglycemia could be safely reduced by temporarily suspending pump insulin delivery when hypoglycemia was predicted by an algorithm based on continuous glucose monitoring (CGM) glucose levels.

RESEARCH DESIGN AND METHODS

Following an initial run-in phase, a 42-night trial was conducted in 45 individuals aged 15-45 years with type 1 diabetes in which each night was assigned randomly to either having the predictive low-glucose suspend system active (intervention night) or inactive (control night). The primary outcome was the proportion of nights in which ≥1 CGM glucose values ≤60 mg/dL occurred.

RESULTS

Overnight hypoglycemia with at least one CGM value ≤60 mg/dL occurred on 196 of 942 (21%) intervention nights versus 322 of 970 (33%) control nights (odds ratio 0.52 [95% CI 0.43-0.64]; P < 0.001). Median hypoglycemia area under the curve was reduced by 81%, and hypoglycemia lasting >2 h was reduced by 74%. Overnight sensor glucose was >180 mg/dL during 57% of control nights and 59% of intervention nights (P = 0.17), while morning blood glucose was >180 mg/dL following 21% and 27% of nights, respectively (P < 0.001), and >250 mg/dL following 6% and 6%, respectively. Morning ketosis was present <1% of the time in each arm.

CONCLUSIONS

Use of a nocturnal low-glucose suspend system can substantially reduce overnight hypoglycemia without an increase in morning ketosis.

Artificial pancreas using a personalized rule-based controller achieves overnight normoglycemia in patients with type 1 diabetes

OBJECTIVE

This study assessed the efficacy of a closed-loop (CL) system consisting of a predictive rule-based algorithm (pRBA) on achieving nocturnal and postprandial normoglycemia in patients with type 1 diabetes mellitus (T1DM). The algorithm is personalized for each patient's data using two different strategies to control nocturnal and postprandial periods.

RESEARCH DESIGN AND METHODS

We performed a randomized crossover clinical study in which 10 T1DM patients treated with continuous subcutaneous insulin infusion (CSII) spent two nonconsecutive nights in the research facility: one with their usual CSII pattern (open-loop [OL]) and one controlled by the pRBA (CL). The CL period lasted from 10 p.m. to 10 a.m., including overnight control, and control of breakfast. Venous samples for blood glucose (BG) measurement were collected every 20 min.

RESULTS

Time spent in normoglycemia (BG, 3.9-8.0 mmol/L) during the nocturnal period (12 a.m.-8 a.m.), expressed as median (interquartile range), increased from 66.6% (8.3-75%) with OL to 95.8% (73-100%) using the CL algorithm (P<0.05). Median time in hypoglycemia (BG, <3.9 mmol/L) was reduced from 4.2% (0-21%) in the OL night to 0.0% (0.0-0.0%) in the CL night (P<0.05). Nine hypoglycemic events (<3.9 mmol/L) were recorded with OL compared with one using CL. The postprandial glycemic excursion was not lower when the CL system was used in comparison with conventional preprandial bolus: time in target (3.9-10.0 mmol/L) 58.3% (29.1-87.5%) versus 50.0% (50-100%).

CONCLUSIONS

A highly precise personalized pRBA obtains nocturnal normoglycemia, without significant hypoglycemia, in T1DM patients. There appears to be no clear benefit of CL over prandial bolus on the postprandial glycemia.

Evaluating the Performance of a Novel Embedded Closed-loop System

The objective was to assess the reliability of a novel automated closed-loop glucose control system developed within the AP@home consortium in adults with type 1 diabetes. Eight adults with type 1 diabetes on insulin pump therapy (3 men; ages 40.5 ± 14.3 years; HbA1c 8.2 ± 0.8%) participated in an open-label, single-center, single-arm, 12-hour overnight study performed at the clinical research facility. A standardized evening meal (80 g CHO) accompanied by prandial insulin boluses were given at 19:00 followed by an optional snack of 15 g at 22:00 without insulin bolus. Automated closed-loop glucose control was started at 19:00 and continued until 07:00 the next day. Basal insulin delivery (Accu-Chek Spirit, Roche) was automatically adjusted by Cambridge model predictive control algorithm, running on a purpose-built embedded device, based on real-time continuous glucose monitor readings (Dexcom G4 Platinum). Closed-loop system was operational as intended over 99% of the time. Overnight plasma glucose levels (22:00 to 07:00) were within the target range (3.9 to 8.0 mmol/l) for 75.4% (37.5, 92.9) of the time without any time spent in hypoglycemia (<3.9 mmol/l). Mean overnight glucose was 7.8 ± 1.3 mmol/l. For the entire 12-hour closed-loop period (19:00 until 07:00) plasma glucose levels were within the target range (3.9 to 10.0 mmol/l) for 84.4% (63.3, 100) of time. There were no adverse events noted during the trial. We observed a high degree of reliability of the automated closed-loop system. The time spent in target glucose level overnight was comparable to results of previously published studies. Further developments to miniaturize the system for home studies are warranted.

Closed-loop artificial pancreas systems: engineering the algorithms

In this two-part Bench to Clinic narrative, recent advances in both the preclinical and clinical aspects of artificial pancreas (AP) development are described. In the preceding Bench narrative, Kudva and colleagues provide an in-depth understanding of the modified glucoregulatory physiology of type 1 diabetes that will help refine future AP algorithms. In the Clinic narrative presented here, we compare and evaluate AP technology to gain further momentum toward outpatient trials and eventual approval for widespread use. We enumerate the design objectives, variables, and challenges involved in AP development, concluding with a discussion of recent clinical advancements. Thanks to the effective integration of engineering and medicine, the dream of automated glucose regulation is nearing reality. Consistent and methodical presentation of results will accelerate this success, allowing head-to-head comparisons that will facilitate adoption of the AP as a standard therapy for type 1 diabetes.

Overnight closed-loop insulin delivery in young people with type 1 diabetes: a free-living, randomized clinical trial

OBJECTIVE

To evaluate feasibility, safety, and efficacy of overnight closed-loop insulin delivery in free-living youth with type 1 diabetes.

RESEARCH DESIGN AND METHODS

Overnight closed loop was evaluated at home by 16 pump-treated adolescents with type 1 diabetes aged 12-18 years. Over a 3-week period, overnight insulin delivery was directed by a closed-loop system, and on another 3-week period sensor-augmented therapy was applied. The order of interventions was random. The primary end point was time when adjusted sensor glucose was between 3.9 and 8.0 mmol/L from 2300 to 0700 h.

RESULTS

Closed loop was constantly applied over at least 4 h on 269 nights (80%); sensor data were collected over at least 4 h on 282 control nights (84%). Closed loop increased time spent with glucose in target by a median 15% (interquartile range -9 to 43; P < 0.001). Mean overnight glucose was reduced by a mean 14 (SD 58) mg/dL (P < 0.001). Time when glucose was <70 mg/dL was low in both groups, but nights with glucose <63 mg/dL for at least 20 min were less frequent during closed loop (10 vs. 17%; P = 0.01). Despite lower total daily insulin doses by a median 2.3 (interquartile range -4.7 to 9.3) units (P = 0.009), overall 24-h glucose was reduced by a mean 9 (SD 41) mg/dL (P = 0.006) during closed loop.

CONCLUSIONS

Unsupervised home use of overnight closed loop in adolescents with type 1 diabetes is safe and feasible. Glucose control was improved during the day and night with fewer episodes of nocturnal hypoglycemia.

The artificial pancreas: is it important to understand how the β cell controls blood glucose?

It has been more than 7 years since the first fully automated closed-loop insulin delivery system that linked subcutaneous insulin delivery and glucose sensing was published. Since the initial report, the physiologic insulin delivery (PID) algorithm used to emulate the β cell has been modified from the original proportional-integral-derivative terms needed to fit the β cell's biphasic response to a hyperglycemic clamp to include terms emulating cephalic phase insulin release and the effect of insulin per se to inhibit insulin secretion. In this article, we compare the closed-loop glucose profiles obtained as each new term has been added, reassess the ability of the revised PID model to describe the β cells' insulin response to a hyperglycemic clamp, and look for the first time at its ability to describe the response to a hypoglycemic clamp. We also consider changes that might be added to the model based on perfused pancreas data. We conclude that the changes introduced in the PID model have systematically improved the closed-loop meal response. We note that the changes made do not adversely affect the ability of the model to fit hyperglycemic clamp data but are necessary to fit the response to a hypoglycemic clamp. Finally, we note a number of β cell characteristics observed in the perfused pancreas have not been included in the model. We suggest that continuing the effort to understand and incorporate aspects of how the β cell achieves glucose control can provide valuable insights into how improvements in future artificial pancreas algorithms might be achieved.