Automated overnight closed-loop glucose control in young children with type 1 diabetes

Open-source automated insulin delivery: international consensus statement and practical guidance for health-care professionals

Open-source automated insulin delivery systems, commonly referred to as do-it-yourself automated insulin delivery systems, are examples of user-driven innovations that were co-created and supported by an online community who were directly affected by diabetes. Their uptake continues to increase globally, with current estimates suggesting several thousand active users worldwide. Real-world user-driven evidence is growing and provides insights into safety and effectiveness of these systems. The aim of this consensus statement is two-fold. Firstly, it provides a review of the current evidence, description of the technologies, and discusses the ethics and legal considerations for these systems from an international perspective. Secondly, it provides a much-needed international health-care consensus supporting the implementation of open-source systems in clinical settings, with detailed clinical guidance. This consensus also provides important recommendations for key stakeholders that are involved in diabetes technologies, including developers, regulators, and industry, and provides medico-legal and ethical support for patient-driven, open-source innovations.

Automated insulin dosing systems: Advances after a century of insulin

The daily complexities of insulin therapy and glucose variability in type 1 diabetes still pose significant challenges, despite advancements in modern insulin analogues. Minimising hypoglycaemia and optimising time spent within target glucose range are recommended to reduce the risk of diabetes-related complications and distress. Access to structured education and adjuvant diabetes technologies, such as insulin pumps and glucose sensors, are recommended by National Institute for Health and Care Excellence (NICE) to enable people with type 1 diabetes achieve their glycaemic goals. One hundred years after the discovery of insulin, automated insulin dosing (AID, a.k.a. closed loop or artificial pancreas) systems are a reality with a number of systems available and being used in usual clinical practice. Evidence from randomised clinical trials and real-world prospective studies support efficacy, effectiveness and safety of AID systems. Qualitative evaluations reveal treatment satisfaction and positive effects on quality of life. Current insulin-only AID systems still require carbohydrate and activity announcement (hybrid closed loop) due to the inherent pharmacokinetic limitations of rapid-acting insulin analogies. Ultra-rapid acting insulin and adjunctive use of other therapies (e.g. glucagon, pramlitide) are being evaluated to achieve full closed loop. Open-source AID (OS-AID) systems have been developed by the diabetes community, driven by a desire for safety and to accelerate technological advancement. In addition to effectiveness and safety, real-world prospective studies suggest that OS-AID systems fulfil unmet needs of commercially approved systems. The development, ongoing challenges and expectations of AID are outlined in this review.

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.

Closed loop control in adolescents and children during winter sports: Use of the Tandem Control-IQ AP system

OBJECTIVE

Artificial pancreas (AP) systems have been shown to improve glycemic control throughout the day and night in adults, adolescents, and children. However, AP testing remains limited during intense and prolonged exercise in adolescents and children. We present the performance of the Tandem Control-IQ AP system in adolescents and children during a winter ski camp study, where high altitude, low temperature, prolonged intense activity, and stress challenged glycemic control.

METHODS

In a randomized controlled trial, 24 adolescents (ages 13-18 years) and 24 school-aged children (6-12 years) with Type 1 diabetes (T1D) participated in a 48 hours ski camp (∼5 hours skiing/day) at three sites: Wintergreen, VA; Kirkwood, and Breckenridge, CO. Study participants were randomized 1:1 at each site. The control group used remote monitored sensor-augmented pump (RM-SAP), and the experimental group used the t: slim X2 with Control-IQ Technology AP system. All subjects were remotely monitored 24 hours per day by study staff.

RESULTS

The Control-IQ system improved percent time within range (70-180 mg/dL) over the entire camp duration: 66.4 ± 16.4 vs 53.9 ± 24.8%; P = .01 in both children and adolescents. The AP system was associated with a significantly lower average glucose based on continuous glucose monitor data: 161 ± 29.9 vs 176.8 ± 36.5 mg/dL; P = .023. There were no differences between groups for hypoglycemia exposure or carbohydrate interventions. There were no adverse events.

CONCLUSIONS

The use of the Control-IQ AP improved glycemic control and safely reduced exposure to hyperglycemia relative to RM-SAP in pediatric patients with T1D during prolonged intensive winter sport activities.

Neonatal Diabetes Mellitus

Neonatal diabetes is a rare cause of hyperglycemia in the neonatal period. It is caused by mutations in genes that encode proteins playing critical roles in normal functions of pancreatic beta cells. Neonatal diabetes is divided into temporary and permanent subtypes. Treatment is based on the correction of fluid-electrolyte disturbances and hyperglycemia. Patients respond to insulin or sulfonylurea treatment according to the mutation type. Close glucose monitoring and education of caregivers about diabetes are vital.

Closed-Loop Control During Intense Prolonged Outdoor Exercise in Adolescents With Type 1 Diabetes: The Artificial Pancreas Ski Study

OBJECTIVE

Intense exercise is a major challenge to the management of type 1 diabetes (T1D). Closed-loop control (CLC) systems (artificial pancreas) improve glycemic control during limited intensity and short duration of physical activity (PA). However, CLC has not been tested during extended vigorous outdoor exercise common among adolescents.

RESEARCH DESIGN AND METHODS

Skiing presents unique metabolic challenges: intense prolonged PA, cold, altitude, and stress/fear/excitement. In a randomized controlled trial, 32 adolescents with T1D (ages 10-16 years) participated in a 5-day ski camp (∼5 h skiing/day) at two sites: Wintergreen, VA, and Breckenridge, CO. Participants were randomized to the University of Virginia CLC system or remotely monitored sensor-augmented pump (RM-SAP). The CLC and RM-SAP groups were coarsely paired by age and hemoglobin A_1c (HbA_1c). All subjects were remotely monitored 24 h per day by the study physicians and clinical team.

RESULTS

Compared with physician-monitored open loop, percent time in range (70-180 mg/dL) improved using CLC: 71.3 vs. 64.7% (+6.6% [95% CI 1-12]; P = 0.005), with maximum effect late at night. Hypoglycemia exposure and carbohydrate treatments were improved overall (P = 0.001 and P = 0.007) and during the daytime with strong ski level effects (P = 0.0001 and P = 0.006); ski/snowboard proficiency was balanced between groups but with a very strong site effect: naive in Virginia and experienced in Colorado. There was no adverse event associated with CLC; the participants' feedback was overwhelmingly positive.

CONCLUSIONS

CLC in adolescents with T1D improved glycemic control and reduced exposure to hypoglycemia during prolonged intensive winter sport activities, despite the added challenges of cold and altitude.

An artificial pancreas for automated blood glucose control in patients with Type 1 diabetes

Automated glucose control in patients with Type 1 diabetes is much-coveted by patients, relatives and healthcare professionals. It is the expectation that a system for automated control, also know as an artificial pancreas, will improve glucose control, reduce the risk of diabetes complications and markedly improve patient quality of life. An artificial pancreas consists of portable devices for glucose sensing and insulin delivery which are controlled by an algorithm residing on a computer. The technology is still under development and currently no artificial pancreas is commercially available. This review gives an introduction to recent progress, challenges and future prospects within the field of artificial pancreas research.

The rise, fall, and resurgence of immunotherapy in type 1 diabetes

Despite considerable effort to halt or delay destruction of β-cells in autoimmune type 1 diabetes (T1D), success remains elusive. Over the last decade, we have seen a proliferation of knowledge on the pathogenesis of T1D that emerged from studies performed in non-obese diabetic (NOD) mice. However, while results of these preclinical studies appeared to hold great promise and boosted patients' hopes, none of these approaches, once tested in clinical settings, induced remission of autoimmune diabetes in individuals with T1D. The primary obstacles to translation reside in the differences between the human and murine autoimmune responses and in the contribution of many environmental factors associated with the onset of disease. Moreover, inaccurate dosing as well as inappropriate timing and uncertain length of drug exposure have played a central role in the negative outcomes of such therapeutic interventions. In this review, we summarize the most important approaches tested thus far in T1D, beginning with the most successful preclinical studies in NOD mice and ending with the latest disappointing clinical trials in humans. Finally, we highlight recent stem cell-based trials, for which expectations in the scientific community and among individuals with T1D are high.

Closed-loop system in the management of diabetes: past, present, and future

Intensive insulin therapy (IIT) has been shown to reduce micro- and macrovascular complications in patients with type 1 diabetes mellitus (T1DM). However, IIT is associated with a significant increase in severe hypoglycemic events, resulting in increased morbidity and mortality. Optimization of glycemic control without hypoglycemia (especially nocturnal) should be the next major goal for subjects on insulin treatment. The use of insulin pumps along with continuous glucose monitors (CGMs) has made it easier but requires significant resources and patient education. Research is ongoing to close the loop by integrating the pump and the CGM using different algorithms. The currently available closed-loop system is the threshold suspend. Steps needed to achieve a near-perfect closed-loop are (1) a control-to-range system that will reduce the incidence and/or severity of hyper- and/or hypoglycemia by adjusting the insulin dose and (2) a control-to-target system, a fully automated or hybrid system that sets target glucose levels to individual needs and maintains glucose levels throughout the day using insulin (unihormonal) alone or with other hormones such as glucagon or possibly pramlintide (bihormonal). Future research is also focusing on better insulin delivery devices (pumps), more accurate CGMs, better predictive algorithms, and ultra-rapid-acting insulin analogs to make the closed-loop system as physiological as possible.

An integrated multivariable artificial pancreas control system

The objective was to develop a closed-loop (CL) artificial pancreas (AP) control system that uses continuous measurements of glucose concentration and physiological variables, integrated with a hypoglycemia early alarm module to regulate glucose concentration and prevent hypoglycemia. Eleven open-loop (OL) and 9 CL experiments were performed. A multivariable adaptive artificial pancreas (MAAP) system was used for the first 6 CL experiments. An integrated multivariable adaptive artificial pancreas (IMAAP) system consisting of MAAP augmented with a hypoglycemia early alarm system was used during the last 3 CL experiments. Glucose values and physical activity information were measured and transferred to the controller every 10 minutes and insulin suggestions were entered to the pump manually. All experiments were designed to be close to real-life conditions. Severe hypoglycemic episodes were seen several times during the OL experiments. With the MAAP system, the occurrence of severe hypoglycemia was decreased significantly (P < .01). No hypoglycemia was seen with the IMAAP system. There was also a significant difference (P < .01) between OL and CL experiments with regard to percentage of glucose concentration (54% vs 58%) that remained within target range (70-180 mg/dl). Integration of an adaptive control and hypoglycemia early alarm system was able to keep glucose concentration values in target range in patients with type 1 diabetes. Postprandial hypoglycemia and exercise-induced hypoglycemia did not occur when this system was used. Physical activity information improved estimation of the blood glucose concentration and effectiveness of the control system.

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

CONTEXT

A challenge for automated glycemic control in type 1 diabetes (T1D) is the large variation in insulin needs between individuals and within individuals at different times in their lives.

OBJECTIVES

The objectives of the study was to test the ability of a third-generation bihormonal bionic pancreas algorithm, initialized with only subject weight; to adapt automatically to the different insulin needs of adults and adolescents; and to evaluate the impact of optional, automatically adaptive meal-priming boluses.

DESIGN

This was a randomized controlled trial.

SETTING

The study was conducted at an inpatient clinical research center.

PATIENTS

Twelve adults and 12 adolescents with T1D participated in the study.

INTERVENTIONS

Subjects in each age group were randomized to automated glycemic control for 48 hours with or without automatically adaptive meal-priming boluses.

MAIN OUTCOME MEASURES

Mean plasma glucose (PG), time with PG less than 60 mg/dL, and insulin total daily dose were measured.

RESULTS

The 48-hour mean PG values with and without adaptive meal-priming boluses were 132 ± 9 vs 146 ± 9 mg/dL (P = .03) in adults and 162 ± 6 vs 175 ± 9 mg/dL (P = .01) in adolescents. Adaptive meal-priming boluses improved mean PG without increasing time spent with PG less than 60 mg/dL: 1.4% vs 2.3% (P = .6) in adults and 0.1% vs 0.1% (P = 1.0) in adolescents. Large increases in adaptive meal-priming boluses and shifts in the timing and size of automatic insulin doses occurred in adolescents. Much less adaptation occurred in adults. There was nearly a 4-fold variation in the total daily insulin dose across all cohorts (0.36-1.41 U/kg · d).

CONCLUSIONS

A single control algorithm, initialized only with subject weight, can quickly adapt to regulate glycemia in patients with TID and highly variable insulin requirements.

Bringing closed-loop home: recent advances in closed-loop insulin delivery

PURPOSE OF REVIEW

To highlight the recent advances in closed-loop research, the development and progress towards utilizing closed loop outside of the clinical research setting and at patients' homes.

RECENT FINDINGS

In spite of the modern insulin therapy in type 1 diabetes, hypoglycaemia is still a major limiting factor. This often leads to suboptimal glycaemic control and risk of diabetes complications. Closed loop has been shown to improve glycaemic control whilst avoiding hypoglycaemia. Incremental progress has been made in this field, from the use of automated systems and bihormonal closed-loop systems in clinical research facility settings under close supervision to the use of ambulatory closed-loop prototype at patients' homes in free-living conditions. Different population of patients with type 1 diabetes and control algorithm approaches have been studied, assessing the efficacy and safety. Transitional and home studies present different challenges at achieving better glycaemic outcome with closed loop. Improved glucose sensor reliability may accelerate the clinical use and faster insulin analogues increase the clinical utility.

SUMMARY

Results and experience with closed-loop insulin delivery have been encouraging, leading the way for future improvements and development in the field, to make closed loop suitable for use in clinical practice.

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.

Feasibility of overnight closed-loop therapy in young children with type 1 diabetes aged 3-6 years: comparison between diluted and standard insulin strength

OBJECTIVE

To assess feasibility of overnight closed-loop therapy in young children with type 1 diabetes and contrast closed loop using diluted versus standard insulin strength.

RESEARCH DESIGN AND METHODS

Eleven children (male 6; age range 3.75-6.96 years; glycated hemoglobin 60 (14) mmol/mol; body mass index SD score 1.0 (0.8); diabetes duration 2.2 (1.0) years, mean (SD); total daily dose 12.9 (10.6, 16.5) IU/day, median (IQR)) were studied at a clinical research facility on two occasions. In random order, participants received closed loop with diluted insulin aspart (CL_Dil; 20 IU/mL) or closed loop with standard aspart (CL_Std; 100 IU/mL) from 17:00 until 8:00 the following morning. Children consumed an evening meal at 17:00 (44 (12) gCHO) and an optional bedtime snack (6 (7) gCHO) identical on both occasions. Meal insulin boluses were calculated by standard pump bolus calculators. Basal rates on insulin pump were adjusted every 15 min as directed by a model-predictive-control algorithm informed by a real-time glucose sensor values.

RESULTS

Mean plasma glucose was 122 (24) mg/dL during CL_Dil vs 122 (23) mg/dL during CL_Std (p=0.993). The time spent in the target glucose range 70-145 mg/dL was 83 (70, 100)% vs 72 (54, 81)% (p=0.328). Time above 145 mg/dL was 13 (0, 27)% vs 19 (10, 45)% (p=0.477) and time spent below 70 mg/dL was 0.0 (0.0, 1.4)% vs 1.4 (0.0, 11.6)% (p=0.161). One asymptomatic hypoglycemia below 63 mg/dL occurred in one participant during CL_Dil versus six episodes in five participants during CL_Std (p=0.09). Glucose variability measured by CV of plasma glucose tended to be reduced during CL_Dil (20% (13, 31) vs 32% (24, 42), p=0.075).

CONCLUSIONS

In this feasibility study, closed-loop therapy maintained good overnight glucose control with tendency towards reduced hypoglycemia and reduced glucose variability using diluted insulin.

TRIAL REGISTRATION NUMBER

clinicaltrials.gov Identifier: NCT01557634.

A review of artificial pancreas technologies with an emphasis on bi-hormonal therapy

Since the discovery of insulin, great progress has been made to improve the accuracy and safety of automated insulin delivery systems to help patients with type 1 diabetes achieve their treatment goals without causing hypoglycaemia. In recent years, bioengineering technology has greatly advanced diabetes management, with the development of blood glucose meters, continuous glucose monitors, insulin pumps and control systems for automatic delivery of one or more hormones. New insulin analogues have improved subcutaneous absorption characteristics, but do not completely eliminate the risk of hypoglycaemia. Insulin effect is counteracted by glucagon in non-diabetic individuals, while glucagon secretion in those with type 1 diabetes is impaired. The use of glucagon in the artificial pancreas is therefore a logical and feasible option for preventing and treating hypoglycaemia. However, commercially available glucagon is not stable in aqueous solution for long periods, forming potentially cytotoxic fibrils that aggregate quickly. Therefore, a more stable formulation of glucagon is needed for long-term use and storage in a bi-hormonal pump. In addition, a model of glucagon action in type 1 diabetes is lacking, further limiting the inclusion of glucagon into systems employing model-assisted control. As a result, although several investigators have been working to help develop bi-hormonal systems for patients with type 1 diabetes, most continue to utilize single hormone systems employing only insulin. This article seeks to focus on the attributes of glucagon and its use in bi-hormonal systems.

Algorithms for a closed-loop artificial pancreas: the case for model predictive control

The relative merits of model predictive control (MPC) and proportional-integral-derivative (PID) control are discussed, with the end goal of a closed-loop artificial pancreas (AP). It is stressed that neither MPC nor PID are single algorithms, but rather are approaches or strategies that may be implemented very differently by different engineers. The primary advantages to MPC are that (i) constraints on the insulin delivery rate (and/or insulin on board) can be explicitly included in the control calculation; (ii) it is a general framework that makes it relatively easy to include the effect of meals, exercise, and other events that are a function of the time of day; and (iii) it is flexible enough to include many different objectives, from set-point tracking (target) to zone (control to range). In the end, however, it is recognized that the control algorithm, while important, represents only a portion of the effort required to develop a closed-loop AP. Thus, any number of algorithms/approaches can be successful--the engineers involved in the design must have experience with the particular technique, including the important experience of implementing the algorithm in human studies and not simply through simulation studies.

Model-based closed-loop glucose control in type 1 diabetes: the DiaCon experience

BACKGROUND

To improve type 1 diabetes mellitus (T1DM) management, we developed a model predictive control (MPC) algorithm for closed-loop (CL) glucose control based on a linear second-order deterministic-stochastic model. The deterministic part of the model is specified by three patient-specific parameters: insulin sensitivity factor, insulin action time, and basal insulin infusion rate. The stochastic part is identical for all patients but identified from data from a single patient. Results of the first clinical feasibility test of the algorithm are presented.

METHODS

We conducted two randomized crossover studies. Study 1 compared CL with open-loop (OL) control. Study 2 compared glucose control after CL initiation in the euglycemic (CL-Eu) and hyperglycemic (CL-Hyper) ranges, respectively. Patients were studied from 22:00-07:00 on two separate nights.

RESULTS

Each study included six T1DM patients (hemoglobin A1c 7.2% ± 0.4%). In study 1, hypoglycemic events (plasma glucose < 54 mg/dl) occurred on two OL and one CL nights. Average glucose from 22:00-07:00 was 90 mg/dl [74-146 mg/dl; median (interquartile range)] during OL and 108 mg/dl (101-128 mg/dl) during CL (determined by continuous glucose monitoring). However, median time spent in the range 70-144 mg/dl was 67.9% (3.0-73.3%) during OL and 80.8% (70.5-89.7%) during CL. In study 2, there was one episode of hypoglycemia with plasma glucose <54 mg/dl in a CL-Eu night. Mean glucose from 22:00-07:00 and time spent in the range 70-144 mg/dl were 121 mg/dl (117-133 mg/dl) and 69.0% (30.7-77.9%) in CL-Eu and 149 mg/dl (140-193 mg/dl) and 48.2% (34.9-72.5%) in CL-Hyper, respectively.

CONCLUSIONS

This study suggests that our novel MPC algorithm can safely and effectively control glucose overnight, also when CL control is initiated during hyperglycemia.

Use of a "fuzzy logic" controller in a closed-loop artificial pancreas

BACKGROUND

Most current model-based approaches to closed-loop artificial pancreas systems rely on mathematical equations describing the human glucoregulatory system; however, incorporating the various physiological parameters (e.g., illness, stress) into these models has been problematic. We evaluated a fully automated "fuzzy logic" (FL) closed-loop insulin dosing controller that does not require differential equations of the glucoregulatory system and allows clinicians to personalize dosing aggressiveness to meet individual patient requirements.

SUBJECTS AND METHODS

This pilot study evaluated the FL controller in the setting of bed rest in a very controlled environment. Two carbohydrate-controlled meals were given (30 g at 8 a.m. and 60 g at 2 p.m. without meal announcement or premeal bolus. The primary end point of the study was avoidance of hypoglycemia, defined at <60 mg/dL. Multiple end points related to the frequency and severity of hyperglycemia and hypoglycemia were also assessed.

RESULTS

Of the 12 subjects we recruited, 10 were enrolled, and seven completed the study. Two of the enrolled subjects were discontinued because of hypoglycemia; the other was discontinued because of sensor failure. Seven of the 10 subjects who completed the study had average blood glucose values of 165 mg/dL and were within a specified target blood glucose range (70-200 mg/dL) for 76% of the 24-h study period.

CONCLUSIONS

Our findings suggest that the FL controller provides a viable alternative to model-based controllers as a component of a closed-loop insulin delivery system. Furthermore, our FL controller allows clinicians to easily specify the level of glucose control based on each patient's clinical needs.

Predicting the optimal basal insulin infusion pattern in children and adolescents on insulin pumps

OBJECTIVE

We aimed at developing and cross-validating a mathematical prediction model for an optimal basal insulin infusion pattern for children with type 1 diabetes on continuous subcutaneous insulin infusion therapy (CSII).

RESEARCH DESIGN AND METHODS

We used the German/Austrian DPV-Wiss database for quality control and scientific surveys in pediatric diabetology and retrieved all CSII patients <20 years of age (November 2009). A total of 1,248 individuals from our previous study were excluded (dataset 1), resulting in 6,063 CSII patients (dataset 2) (mean age 10.6 ± 4.3 years). Only the most recent basal insulin infusion rates (BRs) were considered. BR patterns were identified and corresponding patients sorted by unsupervised clustering. Logistic regression analysis was applied to calculate the probabilities for each BR pattern. Equations were based on both independent datasets separately, and probabilities for BR patterns were cross-validated using typical test patients.

RESULTS

Of the 6,063 children, 5,903 clustered in one of four major circadian BR patterns, confirming our previous study. The oldest age-group (mean age 12.8 years) was represented by 2,490 patients (42.18%) with a biphasic dawn-dusk pattern (BC). A broad single insulin maximum at 9-10 p.m. (F) was unveiled by 853 patients (14.45%) (mean age 6.3 years). Logistic regression analysis revealed that age, to a lesser extent duration of diabetes, and partly sex predicted BR patterns. Cross-validation revealed almost identical probabilities for BR patterns BC and F in the two datasets but some variation in the remaining two BR patterns.

CONCLUSIONS

Reconfirmation of four key BR patterns in two very large independent cohorts supports that these patterns are realistic approximations of the circadian distribution of insulin needs in children with type 1 diabetes. Prediction of an optimal pattern a priori can improve initiation and clinical follow-up of CSII in children and adolescents. In addition, these BR patterns represent valuable information for insulin-infusion algorithms in closed-loop CSII.

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

BACKGROUND

Tight glucose control is needed to prevent long-term diabetes complications; this is hindered by the risk of hypoglycemia, especially at night.

OBJECTIVE

To assess the safety and efficacy of the closed-loop MD-Logic Artificial Pancreas (MDLAP), controlling nocturnal glucose levels in patients with type 1 diabetes mellitus (T1DM).

RESEARCH DESIGN AND METHODS

This was a randomized, multicenter, multinational, crossover trial conducted in Slovenia, Germany, and Israel. Twelve patients with T1DM (age 23.8 ± 15.6 yr; duration of diabetes 13.5 ± 11.9 yr; A1c 8.1 ± 0.8%, mean ±  SD) were randomly assigned to participate in two sequential overnight sessions: one using continuous subcutaneous insulin infusion (CSII) and the other, closed-loop insulin delivery by MDLAP. The primary outcome was the number of hypoglycemic events below 63 mg/dL. Endpoints analyses were based on sensor glucose readings.

RESULTS

Three events of nocturnal hypoglycemia occurred during CSII and none during the closed-loop control (p = 0.18). The percentage of time spent in the near normal range of 63-140 mg/dL was significantly higher in the overnight closed-loop sessions [76% (54-85)] than during CSII therapy [29% (11-44)] [p = 0.02, median (interquartile range)]. The mean overnight glucose level was reduced by 36 mg/dL with closed-loop insulin delivery (p = 0.02) with a significantly less glucose variability when compared with the CSII nights (p < 0.001).

CONCLUSION

The results of this study demonstrate the ability of the MDLAP to safely improve overnight glucose control without increased risk of hypoglycemia in patients with T1DM at three different national, geographic, and clinical centers (ClinicalTrials.gov number, NCT 01238406).

Nocturnal glucose control with an artificial pancreas at a diabetes camp

BACKGROUND

Recent studies have shown that an artificial-pancreas system can improve glucose control and reduce nocturnal hypoglycemia. However, it is not known whether such results can be replicated in settings outside the hospital.

METHODS

In this multicenter, multinational, randomized, crossover trial, we assessed the short-term safety and efficacy of an artificial pancreas system for control of nocturnal glucose levels in patients (10 to 18 years of age) with type 1 diabetes at a diabetes camp. In two consecutive overnight sessions, we randomly assigned 56 patients to receive treatment with an artificial pancreas on the first night and a sensor-augmented insulin pump (control) on the second night or to the reverse order of therapies on the first and second nights. Thus, all the patients received each treatment in a randomly assigned order. The primary end points were the number of hypoglycemic events (defined as a sensor glucose value of <63 mg per deciliter [3.5 mmol per liter] for at least 10 consecutive minutes), the time spent with glucose levels below 60 mg per deciliter (3.3 mmol per liter), and the mean overnight glucose level for individual patients.

RESULTS

On nights when the artificial pancreas was used, versus nights when the sensor-augmented insulin pump was used, there were significantly fewer episodes of nighttime glucose levels below 63 mg per deciliter (7 vs. 22) and significantly shorter periods when glucose levels were below 60 mg per deciliter (P=0.003 and P=0.02, respectively, after adjustment for multiplicity). Median values for the individual mean overnight glucose levels were 126.4 mg per deciliter (interquartile range, 115.7 to 139.1 [7.0 mmol per liter; interquartile range, 6.4 to 7.7]) with the artificial pancreas and 140.4 mg per deciliter (interquartile range, 105.7 to 167.4 [7.8 mmol per liter; interquartile range, 5.9 to 9.3]) with the sensor-augmented pump. No serious adverse events were reported.

CONCLUSIONS

Patients at a diabetes camp who were treated with an artificial-pancreas system had less nocturnal hypoglycemia and tighter glucose control than when they were treated with a sensor-augmented insulin pump. (Funded by Sanofi and others; ClinicalTrials.gov number, NCT01238406.).

Challenges and Recent Progress in the Development of a Closed-loop Artificial Pancreas

Pursuit of a closed-loop artificial pancreas that automatically controls the blood glucose of individuals with type 1 diabetes has intensified during the past six years. Here we discuss the recent progress and challenges in the major steps towards a closed-loop system. Continuous insulin infusion pumps have been widely available for over two decades, but "smart pump" technology has made the devices easier to use and more powerful. Continuous glucose monitoring (CGM) technology has improved and the devices are more widely available. A number of approaches are currently under study for fully closed-loop systems; most manipulate only insulin, while others manipulate insulin and glucagon. Algorithms include on-off (for prevention of overnight hypoglycemia), proportional-integral-derivative (PID), model predictive control (MPC) and fuzzy logic based learning control. Meals cause a major "disturbance" to blood glucose, and we discuss techniques that our group has developed to predict when a meal is likely to be consumed and its effect. We further examine both physiology and device-related challenges, including insulin infusion set failure and sensor signal attenuation. Finally, we discuss the next steps required to make a closed-loop artificial pancreas a commercial reality.

The use of an automated, portable glucose control system for overnight glucose control in adolescents and young adults with type 1 diabetes

OBJECTIVE

A key milestone in progress towards providing an efficacious and safe closed-loop artificial pancreas system for outpatient use is the development of fully automated, portable devices with fault detection capabilities to ensure patient safety. The ability to remotely monitor the operation of the closed-loop system would facilitate future physician-supervised home studies.

RESEARCH DESIGN AND METHODS

This study was designed to investigate the efficacy and safety of a fully automated, portable, closed-loop system. The Medtronic Portable Glucose Control System (PGCS) consists of two subcutaneous glucose sensors, a control algorithm based on proportional-integral-derivative with insulin feedback operating from a BlackBerry Storm smartphone platform, Bluetooth radiofrequency translator, and an off-the-shelf Medtronic Paradigm Veo insulin pump. Participants with type 1 diabetes using insulin pump therapy underwent two consecutive nights of in-clinic, overnight, closed-loop control after a baseline open-loop assessment.

RESULTS

Eight participants attended for 16 overnight studies. The PGCS maintained mean overnight plasma glucose levels of 6.4 ± 1.7 mmol/L (115 ± 31 mg/dL). The proportion of time with venous plasma glucose <3.9, between 3.9 and 8 (70 and 144 mg/dL), and >8 mmol/L was 7, 78, and 15%, respectively. The proportion of time the sensor glucose values were maintained between 3.9 and 8 mmol/L was greater for closed-loop than open-loop (84.5 vs. 46.7%; P < 0.0001), and time spent <3.3 mmol/L was also reduced (0.9 vs. 3%; P < 0.0001).

CONCLUSIONS

These results suggest that the PGCS, an automated closed-loop device, is safe and effective in achieving overnight glucose control in patients with type 1 diabetes.

Evaluation of a portable ambulatory prototype for automated overnight closed-loop insulin delivery in young people with type 1 diabetes

OBJECTIVE

To evaluate an ambulatory, portable prototype, overnight automated closed-loop (CL) system and to explore optimal time of CL initiation.

METHODS

We performed a randomized crossover study and compared automated overnight glucose control started at the time of an evening-meal or at bedtime. Eight young people with type 1 diabetes (T1D) on insulin pump therapy [M = 4; age = 14.3 (1.7) yr; HbA1c = 8.2 (1.3)%; mean (SD)] were studied on two occasions at clinical research facility. A standardized self-selected evening meal [70 (11)g CHO] and snack [22 (4)g CHO] accompanied by prandial insulin boluses were given at 18:00 and 21:00 hours, respectively. In random order, automated CL was started at 18:00 or 21:00 hours and ran until 8:00 hours the next day. Basal insulin delivery was automatically adjusted by a model predictive control algorithm based on real-time continuous glucose monitor readings.

RESULTS

Overnight plasma glucose levels (between 21:00 and 08:00 hours) were within the target range (71-145 mg/dL) for 82 (59, 98)% of time when CL started at 18:00 hours and 64 (48, 70)% when CL started at 21:00 hours [median (IQR), p = 0.036]. Time spent above 180 mg/dL [8 (0, 17) vs. 13 (3, 26)%, p = 0.310] or below 70 mg/dL [0 (0,7) vs. 0 (0, 8)%, p = 1.000] did not differ between the two occasions. Mean overnight glucose [121 (14) vs. 137 (13) mg/dL, p = 0.731) was also similar. Overnight insulin infusion rates were comparable [0.8 (0.5, 1.3) vs. 0.8 (0.6, 1.4) U/h, p = 0.263]. No interruptions to CL delivery were observed.

CONCLUSION

Automated CL delivery can be applied reliably and safely to control glucose levels overnight in young people with T1D. Tighter glucose levels may be achieved with an earlier time of CL initiation.

Real-time state estimation and long-term model adaptation: a two-sided approach toward personalized diagnosis of glucose and insulin levels

BACKGROUND

With continuous glucose sensors (CGSs), it is possible to obtain a dynamical signal of the patient's subcutaneous glucose concentration in real time. How could that information be exploited? We suggest a model-based diagnosis system with a twofold objective: real-time state estimation and long-term model parameter identification.

METHODS

To obtain a dynamical model, Bergman's nonlinear minimal model (considering plasma glucose G, insulin I, and interstitial insulin X) is extended by two states describing first and second insulin response. Furthermore, compartments for oral glucose and subcutaneous insulin inputs as well as for subcutaneous glucose measurement are added. The observability of states and external inputs as well as the identifiability of model parameters are assessed using the empirical observability Gramian. Signals are estimated for different nondiabetic and diabetic scenarios by unscented Kalman filter.

RESULTS

(1) Observability of different state subsets is evaluated, e.g., from CGSs, {G, I} or {G, X} can be observed and the set {G, I, X} cannot. (2) Model parameters are included, e.g., it is possible to estimate the second-phase insulin response gain k(G2) additionally. This can be used for model adaptation and as a diagnostic parameter that is almost zero for diabetes patients. (3) External inputs are considered, e.g., oral glucose is theoretically observable for nondiabetic patients, but estimation scenarios show that the time delay of 1 h limits application.

CONCLUSIONS

A real-time estimation of states (such as plasma insulin I) and parameters (such as k(G2)) is possible, which allows an improved real-time state prediction and a personalized model.

Feasibility study of automated overnight closed-loop glucose control under MD-logic artificial pancreas in patients with type 1 diabetes: the DREAM Project

BACKGROUND

Artificial pancreas systems may offer a potential major impact on the normalization of metabolic control and preventing hypoglycemic events. This study aims to establish near-normal overnight glucose control and reduce the risk of nocturnal hypoglycemia using the MD-Logic Artificial Pancreas (MDLAP), an algorithm that was developed by our research group. This inpatient feasibility study is the first step towards implementing an overnight closed-loop MDLAP system at the patient's home.

SUBJECTS AND METHODS

Seven patients with type 1 diabetes (three adolescents and four adults; mean±SD age, 20.6±4.7 years; duration of diabetes, 9.6±2.6 years; body mass index, 24.3±3.9 kg/m(2); and glycated hemoglobin, 7.8±0.8%) participated in a total of 14 closed-loop overnight sessions. Each participant underwent two closed-loop inpatient sessions starting at dinner alone and at dinner following exercise. The closed-loop inpatient sessions were compared with data derived from nights spent at home with an open-loop system in a similar scenario to the study protocol.

RESULTS

The mean percentage of time spent in the near normal glucose range of 63-140 mg/dL was 83±16%, and the median (interquartile range) was 85% (78-92%) for the overnight closed-loop sessions compared with 34±31% and 27% (6-57%) in the homecare open-loop setting, respectively. During the overnight closed-loop sessions at dinner alone 92±9% of the sensor values ranged within target range, compared with 73±19% for the sessions following exercise (P=0.03). No hypoglycemic (<63 mg/dL) events occurred during the closed-loop sessions.

CONCLUSION

Closed-loop insulin delivery under MDLAP is a feasible and safe solution to control overnight glycemia.

The identifiable virtual patient model: comparison of simulation and clinical closed-loop study results

BACKGROUND

Optimizing a closed-loop insulin delivery algorithm for individuals with type 1 diabetes can be potentially facilitated by a mathematical model of the patient. However, model simulation studies that evaluate changes to the control algorithm need to produce conclusions similar to those that would be obtained from a clinical study evaluating the same modification. We evaluated the ability of a low-order identifiable virtual patient (IVP) model to achieve this goal.

METHODS

Ten adult subjects (42.5 ± 11.5 years of age; 18.0 ± 13.5 years diabetes; 6.9 ± 0.8% hemoglobin A1c) previously characterized with the IVP model were studied following the procedures independently reported in a pediatric study assessing proportional-integral-derivative control with and without a 50% meal insulin bolus. Peak postprandial glucose levels with and without the meal bolus and use of supplemental carbohydrate to treat hypoglycemia were compared using two-way analysis of variance and chi-square tests, respectively.

RESULTS

The meal bolus decreased the peak postprandial glucose levels in both the adult-simulation and pediatricclinical study (231 ± 38 standard deviation to 205 ± 33 mg/dl and 226 ± 51 to 194 ± 47 mg/dl, respectively; p = .0472). No differences were observed between the peak postprandial levels obtained in the two studies (clinical and simulation study not different, p = .57; interaction p = .83) or in the use of supplemental carbohydrate (3 occurrences in 17 patient days of closed-loop control in the clinical-pediatric study; 7 occurrences over 20 patient days in the adult-simulation study, p = .29).

CONCLUSIONS

Closed-loop simulations using an IVP model can predict clinical study outcomes in patients studied independently from those used to develop the model.

Management of diabetes mellitus in infants

Diabetes mellitus diagnosed during the first 2 years of life differs from the disease in older children regarding its causes, clinical characteristics, treatment options and needs in terms of education and psychosocial support. Over the past decade, new genetic causes of neonatal diabetes mellitus have been elucidated, including monogenic β-cell defects and chromosome 6q24 abnormalities. In patients with KCNJ11 or ABCC8 mutations and diabetes mellitus, oral sulfonylurea offers an easy and effective treatment option. Type 1 diabetes mellitus in infants is characterized by a more rapid disease onset, poorer residual β-cell function and lower rate of partial remission than in older children. Insulin therapy in infants with type 1 diabetes mellitus or other monogenic causes of diabetes mellitus is a challenge, and novel data highlight the value of continuous subcutaneous insulin infusion in this very young patient population. Infants are entirely dependent on caregivers for insulin therapy, nutrition and glucose monitoring, which emphasizes the need for appropriate education and psychosocial support of parents. To achieve optimal long-term metabolic control with low rates of acute and chronic complications, continuous and structured diabetes care should be provided by a multidisciplinary health-care team.

Meta-analysis of overnight closed-loop randomized studies in children and adults with type 1 diabetes: the Cambridge cohort

AIM

We reviewed the safety and efficacy of overnight closed-loop insulin delivery compared with conventional continuous subcutaneous insulin infusion (CSII) in two distinct age groups with type 1 diabetes mellitus (T1DM), young people aged 5 to 18 years and adults, combining data of previously published randomized studies.

METHODS

We evaluated four randomized crossover studies in 17 children and adolescents [13.4 ± 3.6 years; mean ± standard deviation (SD)] and 24 adults (37.5 ± 9.1 years) on 45 closed-loop (intervention) and 45 CSII (control) visits. Each subject attended for two overnight study visits, using either closed-loop or conventional pump therapy, in random order. In each age group, studies were designed to mimic realistic likely scenarios. In the children and adolescent studies, closed loop was used following a standard evening meal and following 40 min of moderate-intensity exercise. In the adult studies, closed loop was commenced following a 60 g carbohydrate meal or a 100 g carbohydrate meal accompanied by alcohol. The primary outcome measure was time for which plasma glucose was within target range (3.91-8.0 mmol/liter).

RESULTS

Overnight closed loop increased the time in target plasma glucose in both young (from 40% to 60%, p = .002) and adults (from 50% to 76%, p < .001) compared with conventional CSII. Combined analysis showed an increase from 43% to 71% with closed loop (p < .001). Additionally, closed loop reduced the time spent below 3.91 mmol/liter and above 8.0 mmol/liter, from 4.1% to 2.1% (p = .01) and 33% to 20% (p = .03), respectively. Glycemic variability, as measured by the SD of plasma glucose, was lower during closed loop compared with CSII (1.5 versus 2.1 mmol/liter, p = .007).

CONCLUSIONS

Overnight closed loop may improve glycemic control and reduce nocturnal hypoglycemia in both young people and adults with T1DM.

Closed-loop insulin delivery: from bench to clinical practice

Automated closed-loop insulin delivery, also referred to as the 'artificial pancreas', has been an important but elusive goal of diabetes treatment for many decades. Research milestones include the conception of continuous glucose monitoring in the early 1960s, followed by the production of the first commercial hospital-based artificial pancreas in the late 1970s that combined intravenous glucose sensing and insulin delivery. In the past 10 years, research into the artificial pancreas has gained substantial momentum and focused on the subcutaneous route for glucose measurement and insulin delivery, which reflects technological advances in interstitial glucose monitoring and the increasing use of the continuous subcutaneous insulin infusion. This Review discusses the design of an artificial pancreas, its components and clinical results, as well as the advantages and disadvantages of different types of automated closed-loop systems and potential future advances. The introduction of the artificial pancreas into clinical practice will probably occur gradually, starting with simpler approaches, such as overnight control of blood glucose concentration and temporary pump shut-off, that are adapted to more complex situations, such as glycemic control during meals and exercise.