Assessing performance of closed-loop insulin delivery systems by continuous glucose monitoring: drawbacks and way forward

Recent advances in the precision control strategy of artificial pancreas

The scientific diagnosis and treatment of patients with diabetes require frequent blood glucose testing and insulin delivery to normoglycemia. Therefore, an artificial pancreas with a continuous blood glucose (BG) monitoring function is an urgent research target in the medical industry. The problem of closed-loop algorithmic control of the BG with a time delay is a key and difficult issue that needs to be overcome in the development of an artificial pancreas. Firstly, the composition, structure, and control characteristics of the artificial pancreas are introduced. Subsequently, the research progress of artificial pancreas control algorithms is reviewed, and the characteristics, advantages, and disadvantages of proportional-integral-differential control, model predictive control, and artificial intelligence control are compared and analyzed to determine whether they are suitable for the practical application of the artificial pancreas. Additionally, key advancements in areas such as blood glucose data monitoring, adaptive models, wearable devices, and fully automated artificial pancreas systems are also reviewed. Finally, this review highlights that meal prediction, control safety, integration, streamlining the optimization of control algorithms, constant temperature preservation of insulin, and dual-hormone artificial pancreas are issues that require further attention in the future.

Novel gene-based therapeutic approaches for the management of hepatic complications in diabetes: Reviewing recent advances

Diabetes mellitus is a chronic metabolic disorder marked by hyperglycemia and systemic complications, including hepatic dysfunction, significantly contributing to disease progression and morbidity. This article reviews recent advances in gene-based therapeutic strategies targeting hepatic complications in diabetes, offering a promising approach for precision medicine by addressing underlying molecular mechanisms. Traditional treatments for hepatic complications in diabetes often manage symptoms rather than molecular causes, showing limited efficacy. Gene-based therapies are poised to correct dysfunctional pathways and restore hepatic function. Fundamental gene therapy approaches include gene silencing via small interfering RNAs (siRNAs) to target hepatic glucose production, lipid metabolism, and inflammation. Viral vectors can restore insulin sensitivity and reduce oxidative stress in diabetic livers. Genome editing, especially CRISPR-Cas9, allows the precise modification of disease-associated genes, offering immense potential for hepatic complication treatment. Strategies using CRISPR-Cas9 to enhance insulin receptor expression and modulate aberrant lipid regulatory genes are explored. Safety challenges in gene-based therapies, such as off-target effects and immune responses, are discussed. Advances in nanoparticle-based delivery systems and targeted gene editing techniques offer solutions to enhance specificity and minimize adverse effects. In conclusion, gene-based therapeutic approaches are a transformative direction in managing hepatic complications in diabetes. Further research is needed to optimize efficacy, safety, and long-term outcomes. Nevertheless, these innovative strategies promise to improve the lives of individuals with diabetes by addressing hepatic dysfunction's genetic root causes.

Neural Networks With Gated Recurrent Units Reduce Glucose Forecasting Error Due to Changes in Sensor Location

BACKGROUND

Continuous glucose monitors (CGMs) have become important tools for providing estimates of glucose to patients with diabetes. Recently, neural networks (NNs) have become a common method for forecasting glucose values using data from CGMs. One method of forecasting glucose values is a time-delay feedforward (FF) NN, but a change in the CGM location on a participant can increase forecast error in a FF NN.

METHODS

In response, we examined a NN with gated recurrent units (GRUs) as a method of reducing forecast error due to changes in sensor location.

RESULTS

We observed that for 13 participants with type 2 diabetes wearing blinded CGMs on both arms for 12 weeks (FreeStyle Libre Pro-Abbott), GRU NNs did not produce significantly different errors in glucose prediction due to sensor location changes (P < .05).

CONCLUSION

We observe that GRU NNs can mitigate error in glucose prediction due to differences in CGM location.

Examining Sensor Agreement in Neural Network Blood Glucose Prediction

Successful measurements of interstitial glucose are a key component in providing effective care for patients with diabetes. Recently, there has been significant interest in using neural networks to forecast future glucose values from interstitial measurements collected by continuous glucose monitors (CGMs). While prediction accuracy continues to improve, in this work we investigated the effect of physiological sensor location on neural network blood glucose forecasting. We used clinical data from patients with Type 2 Diabetes who wore blinded FreeStyle Libre Pro CGMs (Abbott) on both their right and left arms continuously for 12 weeks. We trained patient-specific prediction algorithms to test the effect of sensor location on neural network forecasting (N = 13, Female = 6, Male = 7). In 10 of our 13 patients, we found at least one significant (P < .05) increase in forecasting error in algorithms which were tested with data taken from a different location than data which was used for training. These reported results were independent from other noticeable physiological differences between subjects (eg, height, age, weight, blood pressure) and independent from overall variance in the data. From these results we observe that CGM location can play a consequential role in neural network glucose prediction.

Current Advances in the Management of Diabetes Mellitus

Diabetes mellitus (DM) underscores a rising epidemic orchestrating critical socio-economic burden on countries globally. Different treatment options for the management of DM are evolving rapidly because the usual methods of treatment have not completely tackled the primary causes of the disease and are laden with critical adverse effects. Thus, this narrative review explores different treatment regimens in DM management and the associated challenges. A literature search for published articles on recent advances in DM management was completed with search engines including Web of Science, Pubmed/Medline, Scopus, using keywords such as DM, management of DM, and gene therapy. Our findings indicate that substantial progress has been made in DM management with promising results using different treatment regimens, including nanotechnology, gene therapy, stem cell, medical nutrition therapy, and lifestyle modification. However, a lot of challenges have been encountered using these techniques, including their optimization to ensure optimal glycemic, lipid, and blood pressure modulation to minimize complications, improvement of patients' compliance to lifestyle and pharmacologic interventions, safety, ethical issues, as well as an effective delivery system among others. In conclusion, lifestyle management alongside pharmacological approaches and the optimization of these techniques is critical for an effective and safe clinical treatment plan.

Perceptions and Understanding of Diabetes Mellitus Technology in Adults with Type 1 or Type 2 DM: A Pilot Survey from Pakistan

INTRODUCTION

Diabetes mellitus technology (DMT) is increasingly used for routine management in developed countries, yet its uptake in developing countries is not as consistent. Multiple factors may influence this, including country specific patient perception regarding DMT. We conducted a pilot study in Pakistan to understand this important question which has not been studied yet.

METHODS

A cross-sectional pilot study was conducted in Pakistan. An anonymous survey exploring perceptions of diabetes technology was circulated on social media platforms, collecting responses over 2 weeks. Target population included adults (≥18 years) living in Pakistan, with DM1 or 2.

RESULTS

A total of 40 responses were received. The majority (36/40) reported using conventional glucometers. Nine used continuous glucose monitoring (CGM). Thirty-two of 40 patients believed DMT improved diabetes care, 22 felt it helped decreased risk of Diabetes-related complications. 15/40 stated that DMT results in increased cost of care. Sixteen reported their diabetes care teams had never discussed wearable DMT options whereas 11 disliked them because they did not want a device on their self.

CONCLUSION

In our pilot study we have identified broad themes of opportunity and challenges to DMT use in Pakistan. Patients' perceptions regarding DMT were generally positive but significant barriers to its acceptance included high cost, lack of discussion between doctor and patient about available technology and personal hesitation. Limitations of our study include sampling bias (online survey) and small sample size, but this data can help inform larger studies, to look at this important topic in greater detail.

Nanomedicine-Based Strategies for Diabetes: Diagnostics, Monitoring, and Treatment

Traditional methods for diabetes management require constant and tedious glucose monitoring (GM) and insulin injections, impacting quality of life. The global diabetic population is expected to increase to 439 million, with approximately US$490 billion in healthcare expenditures by 2030, imposing a significant burden on healthcare systems worldwide. Recent advances in nanotechnology have emerged as promising alternative strategies for the management of diabetes. For example, implantable nanosensors are being developed for continuous GM, new nanoparticle (NP)-based imaging approaches that quantify subtle changes in β cell mass can facilitate early diagnosis, and nanotechnology-based insulin delivery methods are being explored as novel therapies. Here, we provide a holistic summary of this rapidly advancing field compiling all aspects pertaining to the management of diabetes.

Is it possible to constantly and accurately monitor blood sugar levels, in people with Type 1 diabetes, with a discrete device (non-invasive or invasive)?

Real-time continuous glucose monitors using subcutaneous needle-type sensors continue to develop. The limitations of currently available systems, however, include time lag behind changes in blood glucose, the invasive nature of such systems, and in some cases, their accuracy. Non-invasive techniques have been developed, but, to date, no commercial device has been successful. A key research priority for people with Type 1 diabetes identified by the James Lind Alliance was to identify ways of monitoring blood glucose constantly and accurately using a discrete device, invasive or non-invasive. Integration of such a sensor is important in the development of a closed-loop system and the technology must be rapid, selective and acceptable for continuous use by individuals. The present review provides an update on existing continuous glucose-sensing technologies, and an overview of emergent techniques, including their accuracy and limitations.

Recent Avenues in Novel Patient-Friendly Techniques for the Treatment of Diabetes

Background:

Diabetes is one of the most common chronic metabolic disorders which affect the quality of human life worldwide. As per the WHO report, between 1980 to 2014, the number of diabetes patients increases from 108 million to 422 million, with a global prevalence rate of 8.5% per year. Diabetes is the prime reason behind various other diseases like kidney failure, stroke, heart disorders, glaucoma, etc. It is recognized as the seventh leading cause of death throughout the world. The available therapies are painful (insulin injections) and inconvenient due to higher dosing frequency. Thus, to find out a promising and convenient treatment, extensive investigations are carried out globally by combining novel carrier system (like microparticle, microneedle, nanocarrier, microbeads etc.) and delivery devices (insulin pump, stimuli-responsive device, inhalation system, bioadhesive patch, insulin pen etc.) for more precise diagnosis and painless or less invasive treatment of disease.

Objective:

The review article is made with an objective to compile information about various upcoming and existing modern technologies developed to provide greater patient compliance and reduce the undesirable side effect of the drug. These devices evade the necessity of daily insulin injection and offer a rapid onset of action, which sustained for a prolonged duration of time to achieve a better therapeutic effect.

Conclusion:

Despite numerous advantages, various commercialized approaches, like Afrezza (inhalation insulin) have been a failure in recent years. Such results call for more potential work to develop a promising system. The novel approaches range from the delivery of non-insulin blood glucose lowering agents to insulin-based therapy with minimal invasion are highly desirable.

Targeting glucose control in preterm infants: pilot studies of continuous glucose monitoring

OBJECTIVE

Hyperglycaemia is common in very preterm infants and is associated with adverse outcomes. Preventing hyperglycaemia without increasing the risk of hypoglycaemia is difficult. Real time tracking with continuous glucose monitors (CGM) may improve glucose control. We assessed the feasibility and safety of CGM to target glucose control in preterm infants, to inform a randomised controlled trial (RCT).

DESIGN

We performed a single centre study in very preterm infants during the first week of life. Accuracy was assessed by comparison of CGM with blood glucose levels (n=20 infants). In a separate pilot study of efficacy (n=20), real-time CGM combined with a paper guideline to target glucose control (2.6-10 mmol/L) was compared with standard neonatal care (masked CGM). Questionnaires were used to assess staff acceptability.

RESULTS

No concerns were raised about infection or skin integrity at sensor site. The sensor performed well compared with point-of-care blood glucose measurements, mean bias of -0.27 (95% CI -0.35 to -0.19). Per cent time in target range (sensor glucose 2.6-10 mmol/L) was greater with CGM than POC (77% vs 59%, respectively) and per cent time sensor glucose >10 mmol/L was less with CGM than POC (24% vs 40%, respectively). The CGM also detected clinically unsuspected episodes of hypoglycaemia. Staff reported that the use of the CGM positively improved clinical care.

CONCLUSIONS

This study suggests that CGM has sufficient accuracy and utility in preterm infants to warrant formal testing in a RCT.

Drug Delivery Strategies for the Treatment of Metabolic Diseases

Metabolic diseases occur when normal metabolic processes are disrupted in the human body, which can be congenital or acquired. The incidence of metabolic diseases worldwide has reached epidemic proportions. So far, various methods including systemic drug therapy and surgery are exploited to prevent and treat metabolic diseases. However, current pharmacotherapeutic options for treatment of these metabolic disorders remain limited and ineffective, especially reducing patient compliance to treatment. Therefore, it is desirable to exploit effective drug delivery approaches to effectively treat metabolic diseases and reduce side effects. This brief review summarizes novel delivery strategies including local, targeted, and oral drug delivery strategies, as well as intelligent stimulus-responsive drug delivery strategy, for the treatment of metabolic disorders including diabetes, obesity, and atherosclerosis.

Calibration of Minimally Invasive Continuous Glucose Monitoring Sensors: State-of-The-Art and Current Perspectives

Minimally invasive continuous glucose monitoring (CGM) sensors are wearable medical devices that provide real-time measurement of subcutaneous glucose concentration. This can be of great help in the daily management of diabetes. Most of the commercially available CGM devices have a wire-based sensor, usually placed in the subcutaneous tissue, which measures a "raw" current signal via a glucose-oxidase electrochemical reaction. This electrical signal needs to be translated in real-time to glucose concentration through a calibration process. For such a scope, the first commercialized CGM sensors implemented simple linear regression techniques to fit reference glucose concentration measurements periodically collected by fingerprick. On the one hand, these simple linear techniques required several calibrations per day, with the consequent patient's discomfort. On the other, only a limited accuracy was achieved. This stimulated researchers to propose, over the last decade, more sophisticated algorithms to calibrate CGM sensors, resorting to suitable signal processing, modelling, and machine-learning techniques. This review paper will first contextualize and describe the calibration problem and its implementation in the first generation of CGM sensors, and then present the most recently-proposed calibration algorithms, with a perspective on how these new techniques can influence future CGM products in terms of accuracy improvement and calibration reduction.

Closed-loop insulin delivery in inpatients with type 2 diabetes: a randomised, parallel-group trial

BACKGROUND

We assessed whether fully closed-loop insulin delivery (the so-called artificial pancreas) is safe and effective compared with standard subcutaneous insulin therapy in patients with type 2 diabetes in the general ward.

METHODS

For this single-centre, open-label, parallel-group, randomised controlled trial, we enrolled patients aged 18 years or older with type 2 diabetes who were receiving insulin therapy. Patients were recruited from general wards at Addenbrooke's Hospital, Cambridge, UK. Participants were randomly assigned (1:1) by a computer-generated minimisation method to receive closed-loop insulin delivery (using a model-predictive control algorithm to direct subcutaneous delivery of rapid-acting insulin analogue without meal-time insulin boluses) or conventional subcutaneous insulin delivery according to local clinical guidelines. The primary outcome was time spent in the target glucose concentration range of 5·6-10·0 mmol/L during the 72 h study period. Analyses were by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT01774565.

FINDINGS

Between Feb 20, 2015, and March 24, 2016, we enrolled 40 participants, of whom 20 were randomly assigned to the closed-loop intervention group and 20 to the control group. The proportion of time spent in the target glucose range was 59·8% (SD 18·7) in the closed-loop group and 38·1% (16·7) in the control group (difference 21·8% [95% CI 10·4-33·1]; p=0·0004). No episodes of severe hypoglycaemia or hyperglycaemia with ketonaemia occurred in either group. One adverse event unrelated to study devices occurred during the study (gastrointestinal bleed).

INTERPRETATION

Closed-loop insulin delivery without meal-time boluses is effective and safe in insulin-treated adults with type 2 diabetes in the general ward.

FUNDING

Diabetes UK; European Foundation for the Study of Diabetes; JDRF; National Institute for Health Research Cambridge Biomedical Research Centre; Wellcome Trust.

Proportional Insulin Infusion in Closed-Loop Control of Blood Glucose

A differential equation model is formulated that describes the dynamics of glucose concentration in blood circulation. The model accounts for the intake of food, expenditure of calories and the control of glucose levels by insulin and glucagon. These and other hormones affect the blood glucose level in various ways. In this study only main effects are taken into consideration. Moreover, by making a quasi-steady state approximation the model is reduced to a single nonlinear differential equation of which parameters are fit to data from healthy subjects. Feedback provided by insulin plays a key role in the control of the blood glucose level. Reduced β-cell function and insulin resistance may hamper this process. With the present model it is shown how by closed-loop control these defects, in an organic way, can be compensated with continuous infusion of exogenous insulin.

Outcome Measures for Artificial Pancreas Clinical Trials: A Consensus Report

Research on and commercial development of the artificial pancreas (AP) continue to progress rapidly, and the AP promises to become a part of clinical care. In this report, members of the JDRF Artificial Pancreas Project Consortium in collaboration with the wider AP community 1) advocate for the use of continuous glucose monitoring glucose metrics as outcome measures in AP trials, in addition to HbA1c, and 2) identify a short set of basic, easily interpreted outcome measures to be reported in AP studies whenever feasible. Consensus on a broader range of measures remains challenging; therefore, reporting of additional metrics is encouraged as appropriate for individual AP studies or study groups. Greater consistency in reporting of basic outcome measures may facilitate the interpretation of study results by investigators, regulatory bodies, health care providers, payers, and patients themselves, thereby accelerating the widespread adoption of AP technology to improve the lives of people with type 1 diabetes.

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Retrofitting of continuous glucose monitoring traces allows more accurate assessment of glucose control in outpatient studies

BACKGROUND

Glucose control in artificial pancreas (AP) studies is commonly assessed by metrics such as the percentage of time with blood glucose (BG) concentration below 70 mg/dL or in the nearly normal range 70-180 mg/dL (in brief, time in hypoglycemia and time in target, respectively). In outpatient studies these control metrics can be computed only from continuous glucose monitoring (CGM) sensor data, with the risk of an unfair assessment because of their inaccuracy. The aim of the present article is to show that the control metrics can be much more accurately determined if CGM data are preprocessed by a recently proposed retrofitting algorithm.

SUBJECTS AND METHODS

Data from 47 type 1 diabetes subjects are considered. Subjects were studied in a closed-loop control trial prescribing three 24-h admissions. Glucose concentration was monitored using the Dexcom(®) (San Diego, CA) SEVEN(®) Plus CGM sensor. Frequent BG reference values were collected in parallel with the YSI analyzer (Yellow Springs Instrument, Yellow Springs, OH). To simulate the few reference values available in outpatient conditions, we down-sampled the YSI data and provided to the retrofitting algorithm only the reference values that would have been collected in outpatient protocols. Time in hypoglycemia, time in target, mean, and SD of glucose profile were computed on the basis of both the original and the retrofitted CGM traces and compared with those computed using the frequently obtained YSI data.

RESULTS

Using the retrofitted traces, the average error affecting the estimation of time in hypoglycemia and time in target was approximately halved with respect to the original CGM traces (from 4.5% to 1.9% and from 8.7% to 4.4%, respectively). Error in mean and SD was reduced even further, from 10.0 mg/dL to 3.5 mg/dL and from 8.6 mg/dL to 2.9 mg/dL, respectively.

CONCLUSIONS

The improved accuracy of retrofitted CGM with respect to the original CGM traces allows a more reliable assessment of glucose control in outpatient AP studies.

Managing diabetes with nanomedicine: challenges and opportunities

Nanotechnology-based approaches hold substantial potential for improving the care of patients with diabetes. Nanoparticles are being developed as imaging contrast agents to assist in the early diagnosis of type 1 diabetes. Glucose nanosensors are being incorporated in implantable devices that enable more accurate and patient-friendly real-time tracking of blood glucose levels, and are also providing the basis for glucose-responsive nanoparticles that better mimic the body's physiological needs for insulin. Finally, nanotechnology is being used in non-invasive approaches to insulin delivery and to engineer more effective vaccine, cell and gene therapies for type 1 diabetes. Here, we analyse the current state of these approaches and discuss key issues for their translation to clinical practice.

Preliminary evaluation of a new semi-closed-loop insulin therapy system over the prandial period in adult patients with type 1 diabetes: the WP6.0 Diabeloop study

There is room for improvement in the algorithms used in closed-loop insulin therapy during the prandial period. This pilot study evaluated the efficacy and safety of the Diabeloop algorithm (model predictive control type) during the postprandial period. This 2-center clinical trial compared interstitial glucose levels over two 5-hour periods (with/without the algorithm) following a calibrated lunch. On the control day, the amount of insulin delivered by the pump was determined according to the patient's usual parameters. On the test day, 50% or 75% of the theoretical bolus required was delivered, while the algorithm, informed of carbohydrate intake, proposed changes to insulin delivery every 15 minutes using modeling to forecast glucose levels. The primary endpoint was percentage of time spent at near normoglycemia (70-180 mg/dl). Twelve patients with type 1 diabetes (9 men, age 35.6 ± 12.7 years, HbA1c 7.3 ± 0.8%) were included. The percentage of time spent in the target range was 84.5 ± 20.8 (test day) versus 69.2 ± 33.9% (control day, P = .11). The percentage of time spent in hypoglycemia < 70 mg/dl was 0.2 ± 0.8 (test) versus 4.4 ± 8.2% (control, P = .18). Interstitial glucose at the end of the test (5 hours) was 127.5 ± 40.1 (test) versus 146 ± 53.5 mg/dl (control, P = .25). The insulin doses did not differ, and no differences were observed between the 50% and 75% boluses. In a semi-closed-loop configuration with manual priming boluses (25% or 50% reduction), the Diabeloop v1 algorithm was as successful as the manual method in determining the prandial bolus, without any exposure to excessive hypoglycemic risk.

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).

Home use of closed-loop insulin delivery for overnight glucose control in adults with type 1 diabetes: a 4-week, multicentre, randomised crossover study

BACKGROUND

Closed-loop insulin delivery is a promising option to improve glycaemic control and reduce the risk of hypoglycaemia. We aimed to assess whether overnight home use of automated closed-loop insulin delivery would improve glucose control.

METHODS

We did this open-label, multicentre, randomised controlled, crossover study between Dec 1, 2012, and Dec 23, 2014, recruiting patients from three centres in the UK. Patients aged 18 years or older with type 1 diabetes were randomly assigned to receive 4 weeks of overnight closed-loop insulin delivery (using a model-predictive control algorithm to direct insulin delivery), then 4 weeks of insulin pump therapy (in which participants used real-time display of continuous glucose monitoring independent of their pumps as control), or vice versa. Allocation to initial treatment group was by computer-generated permuted block randomisation. Each treatment period was separated by a 3-4 week washout period. The primary outcome was time spent in the target glucose range of 3·9-8·0 mmol/L between 0000 h and 0700 h. Analyses were by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT01440140.

FINDINGS

We randomly assigned 25 participants to initial treatment in either the closed-loop group or the control group, patients were later crossed over into the other group; one patient from the closed-loop group withdrew consent after randomisation, and data for 24 patients were analysed. Closed loop was used over a median of 8·3 h (IQR 6·0-9·6) on 555 (86%) of 644 nights. The proportion of time when overnight glucose was in target range was significantly higher during the closed-loop period compared to during the control period (mean difference between groups 13·5%, 95% CI 7·3-19·7; p=0·0002). We noted no severe hypoglycaemic episodes during the control period compared with two episodes during the closed-loop period; these episodes were not related to closed-loop algorithm instructions.

INTERPRETATION

Unsupervised overnight closed-loop insulin delivery at home is feasible and could improve glucose control in adults with type 1 diabetes.

FUNDING

Diabetes UK.

Day and night home closed-loop insulin delivery in adults with type 1 diabetes: three-center randomized crossover study

OBJECTIVE

To evaluate the feasibility of day and night closed-loop insulin delivery in adults with type 1 diabetes under free-living conditions.

RESEARCH DESIGN AND METHODS

Seventeen adults with type 1 diabetes on insulin pump therapy (means ± SD age 34 ± 9 years, HbA1c 7.6 ± 0.8%, and duration of diabetes 19 ± 9 years) participated in an open-label multinational three-center crossover study. In a random order, participants underwent two 8-day periods (first day at the clinical research facility followed by 7 days at home) of sensor-augmented insulin pump therapy (SAP) or automated closed-loop insulin delivery. The primary end point was the time when sensor glucose was in target range between 3.9 and 10.0 mmol/L during the 7-day home phase.

RESULTS

During the home phase, the percentage of time when glucose was in target range was significantly higher during closed-loop compared with SAP (median 75% [interquartile range 61-79] vs. 62% [53-70], P = 0.005). Mean glucose (8.1 vs. 8.8 mmol/L, P = 0.027) and time spent above target (P = 0.013) were lower during closed loop, while time spent below target was comparable (P = 0.339). Increased time in target was observed during both daytime (P = 0.017) and nighttime (P = 0.013).

CONCLUSIONS

Compared with SAP, 1 week of closed-loop insulin delivery at home reduces mean glucose and increases time in target without increasing the risk of hypoglycemia in adults with relatively well-controlled type 1 diabetes.

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.

Evaluation of stochastic adjustment for glucose sensor bias during closed-loop insulin delivery

BACKGROUND

In outpatient studies of closed-loop insulin delivery systems, it is not typically practical to obtain blood glucose measurements for an outcome measure. Using a continuous glucose monitoring (CGM) device as both part of the intervention and as the outcome in a clinical trial can give a biased estimate of the treatment effect. A stochastic adjustment has been proposed to correct this problem.

MATERIALS AND METHODS

We performed Monte Carlo simulations to assess the performance of the stochastic adjustment in various scenarios where the CGM device was used passively and when it was used to inform insulin delivery. The resulting bias for using CGM to estimate the percentage of glucose values inside a target range was compared with and without the proposed stochastic adjustment.

RESULTS

CGM bias for estimating the percentage of glucose values 70-180 mg/dL ranged from -6% to +4% in the various scenarios studied. In some circumstances, stochastic adjustment did indeed reduce this CGM bias. However, in other circumstances, stochastic adjustment made the bias worse. Stochastic adjustment tended to underestimate the true percentage of glucose values in range for most, but not all, scenarios considered in these simulations.

CONCLUSIONS

Stochastic adjustment is not a general solution to the problem of CGM bias. The proposed adjustment relies on an implicit assumption that usually does not hold. The appropriate level of adjustment depends on how efficacious the closed-loop system is, which is not typically known in practice.

Night glucose control with MD-Logic artificial pancreas in home setting: a single blind, randomized crossover trial-interim analysis

BACKGROUND

Artificial pancreas (AP) systems have shown an improvement in glucose control and a reduced risk of nocturnal hypoglycemia under controlled conditions but remain to be evaluated under daily-life conditions.

OBJECTIVE

To assess the feasibility, safety, and efficacy of the MD-Logic AP in controlling nocturnal glucose levels in the patient's home.

METHODS

Two-arm study, each covering four consecutive nights comparing the MD-Logic AP ('closed-loop' arm) with sensor-augmented pump therapy ('control' arm). Fifteen patients (mean age 19 ± 10.4 yr, A1c 7.5 ± 0.5% or 58 ± 5.9 mmol/mol, diabetes duration 9.9 ± 8.2 yr) were randomly assigned either to 'Group A' (first 'closed-loop', then 'control' arm) or to 'Group B' (vice versa). Investigators were masked to treatment intervention. Primary endpoints were the time spent with glucose levels below 70 mg/dL and the percentage of nights in which the mean overnight glucose levels were within 90-140 mg/dL. Endpoint analyses were based on unmodified sensor glucose readings of the four study nights.

RESULTS

Time of glucose levels spent below 70 mg/dL was significantly shorter on the closed-loop nights than on control nights, median and interquartile range 3.8 (0, 11.6) and 48.7 (0.6, 67.9) min, respectively; p = 0.0034. The percentage of individual nights in which mean overnight glucose level was within 90-140 mg/dL was 67 (33, 88), and 50 (25, 75), under closed-loop and control nights, respectively, with no statistical difference. Secondary endpoint analyses demonstrated significant improvements in hypoglycemia parameters. No serious adverse events were reported.

CONCLUSION

This interim analysis demonstrates the feasibility, safety, and efficiency of the MD-Logic AP system in home use, and demonstrates an improvement over sensor-augmented pump therapy. (ClinicalTrials.gov identifier NCT01726829).

Interstitium versus Blood Equilibrium in Glucose Concentration and its Impact on Subcutaneous Continuous Glucose Monitoring Systems

The relationship between both interstitial and blood glucose remains a debated topic, on which there is still no consensus. The experimental evidence suggests that blood and interstitial fluid glucose levels are correlated by a kinetic equilibrium, which as a consequence has a time and magnitude gradient in glucose concentration between blood and interstitium. Furthermore, this equilibrium can be perturbed by several physiological effects (such as foreign body response, wound-healing effect, etc.), with a consequent reduction of interstitial fluid glucose versus blood glucose correlation. In the present study, the impact of operating in the interstitium on continuous glucose monitoring systems (CGMs) will be discussed in depth, both for the application of CGMs in the management of diabetes and in other critical areas, such as tight glycaemic control in critically ill patients.

Remote glucose monitoring in camp setting reduces the risk of prolonged nocturnal hypoglycemia

OBJECTIVE

This study tested the feasibility and effectiveness of remote continuous glucose monitoring (CGM) in a diabetes camp setting.

SUBJECTS AND METHODS

Twenty campers (7-21 years old) with type 1 diabetes were enrolled at each of three camp sessions lasting 5-6 days. On alternating nights, 10 campers were randomized to usual wear of a Dexcom (San Diego, CA) G4™ PLATINUM CGM system, and 10 were randomized to remote monitoring with the Dexcom G4 PLATINUM communicating with the Diabetes Assistant, a cell phone platform, to allow wireless transmission of CGM values. Up to 15 individual graphs and sensor values could be displayed on a single remote monitor or portable tablet. An alarm was triggered for values <70 mg/dL, and treatment was given for meter-confirmed hypoglycemia. The primary end point was to decrease the duration of hypoglycemic episodes <50 mg/dL.

RESULTS

There were 320 nights of CGM data and 197 hypoglycemic events. Of the remote monitoring alarms, 79% were true (meter reading of <70 mg/dL). With remote monitoring, 100% of alarms were responded to, whereas without remote monitoring only 54% of alarms were responded to. The median duration of hypoglycemic events <70 mg/dL was 35 min without remote monitoring and 30 min with remote monitoring (P=0.078). Remote monitoring significantly decreased prolonged hypoglycemic events, eliminating all events <50 mg/dL lasting longer than 30 min as well as all events <70 mg/dL lasting more than 2 h.

CONCLUSIONS

Remote monitoring is feasible at diabetes camps and effective in reducing the risk of prolonged nocturnal hypoglycemia. This technology will facilitate forthcoming studies to evaluate the efficacy of automated closed-loop systems in the camp setting.

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.

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.

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.

Performance comparison of the medtronic sof-sensor and enlite glucose sensors in inpatient studies of individuals with type 1 diabetes

OBJECTIVE

Knowledge of the accuracy of continuous glucose monitoring (CGM) devices is important for its use as a management tool for individuals with diabetes and for its use to assess outcomes in clinical studies. Using data from several inpatient studies, we compared the accuracy of two sensors, the Medtronic Enlite™ using MiniMed Paradigm(®) Veo™ calibration and the Sof-Sensor(®) glucose sensor using Guardian(®) REAL-Time CGM calibration (all from Medtronic Diabetes, Northridge, CA).

SUBJECTS AND METHODS

Nocturnal data were analyzed from eight inpatient studies in which both CGM and reference glucose measurements were available. The analyses included 1,666 CGM-reference paired glucose values for the Enlite in 54 participants over 69 nights and 3,627 paired values for the Sof-Sensor in 66 participants over 91 nights.

RESULTS

The Enlite sensor tended to report glucose levels lower than the reference over the entire range of glucose values, whereas the Sof-Sensor values tended to be higher than reference values in the hypoglycemic range and lower than reference values in the hyperglycemic range. The overall median sensor-reference difference was -15 mg/dL for the Enlite and -1 mg/dL for the Sof-Sensor (P<0.001). The median relative absolute difference was 15% for the Enlite versus 12% for the Sof-Sensor (P=0.06); 66% of Enlite values and 73% of Sof-Sensor values met International Organization for Standardization criteria.

CONCLUSIONS

We found that the Enlite tended to be biased low over the entire glucose range, whereas the Sof-Sensor showed the more typical sensor pattern of being biased high in the hypoglycemic range and biased low in the hyperglycemic range.

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).