Feasibility of closed-loop insulin delivery in type 2 diabetes: a randomized controlled study

Does fully closed-loop automated insulin delivery improve glycaemic control in patients with type 2 diabetes? A meta-analysis of randomized controlled trials

AIMS

This meta-analysis investigated the efficacy and safety of fully closed-loop automated insulin delivery (AID) in patients with type 2 diabetes.

MATERIALS AND METHODS

We systemically searched PubMed, Scopus, Web of Science, and Cochrane Central from inception until April 26, 2023. We included randomized controlled trials (RCTs) comparing fully closed-loop AID versus conventional insulin therapy. The outcomes were pooled as the mean difference (MD) and risk ratio with 95% confidence interval (CI) in the random effect model. Our primary outcome was the proportion of time in the target glucose range (5.6-10 mmol/L, 3.9-10 mmol/L, or 3.9-8 mmol/L, depending on the study). Key secondary outcomes included the proportion of time spent in hyperglycaemia or hypoglycaemia.

RESULTS

We included seven RCTs (three crossover and four parallel design), compromising 390 patients. Our analysis showed that compared to the control group, fully closed-loop AID increased the proportion of time spent within the target glucose range by additional 337 min per 24 h (MD = 23.39%, 95% CI [16.64%, 30.14%], p < 0.01), additional 108 min overnight (MD = 22.40%, 95% CI [12.88%, 31.91%], p < 0.01), and additional 258 min during the daytime period (MD = 26.85%, 95% CI [21.06%, 32.63%], p < 0.01). Compared to the control group, the overall time in hyperglycaemia was shortened by 326 min per 24 h (MD = -22.67%, 95% CI [-30.87%, -14.46%], p < 0.01). There was no significant difference between the two groups in terms of overall, overnight, and daytime periods spent in hypoglycaemia.

CONCLUSIONS

Our meta-analysis suggests that fully closed-loop AID may improve glycaemic control in patients with type 2 diabetes, particularly for those with more challenging diabetes management. Further research is required to establish the feasibility of implementing these systems in clinical practice. [Correction added on 26 August 2023 after first online publication: Under Results, the first sentence "We included seven RCTs (three crossover and one parallel designs)" has been changed to "We included seven RCTs (three crossover and four parallel designs)".].

Predictors of insulin pump initiation among people with type 2 diabetes from a US claims database using machine learning

OBJECTIVE

Insulin pump use is increasing among people with type 2 diabetes (T2D), albeit at a slower rate compared to people with type 1 diabetes (T1D). Factors associated with insulin pump initiation among people with T2D in the real-world are understudied.

METHODS

This retrospective, nested case-control study aimed to identify predictors of insulin pump initiation among people with T2D in the United States (US). Adults with T2D who were new to bolus insulin use were identified from the IBM MarketScan Commercial database (2015-2020). Candidate variables of pump initiation were entered into conditional logistic regression (CLR) and penalized CLR models.

RESULTS

Of the 32,104 eligible adults with T2D, 726 insulin pump initiators were identified and matched to 2,904 non-pump initiators using incidence density sampling. Consistent predictors of insulin pump initiation across the base case, sensitivity, and post hoc analyses included continuous glucose monitor (CGM) use, visiting an endocrinologist, acute metabolic complications, higher count of HbA1c tests, lower age, and fewer diabetes-related medication classes.

CONCLUSIONS

Many of these predictors could represent a clinical indication for treatment intensification, greater patient engagement in diabetes management, or proactive management by healthcare providers. Improved understanding of predictors for pump initiation may lead to more targeted efforts to improve access and acceptance of insulin pumps among persons with T2D.

The Clinical Impact of Flash Glucose Monitoring-a Digital Health App and Smartwatch Technology in Patients With Type 2 Diabetes: Scoping Review

BACKGROUND

Type 2 diabetes has a growing prevalence and confers significant cost burden to the health care system, raising the urgent need for cost-effective and easily accessible solutions. The management of type 2 diabetes requires significant commitment from the patient, caregivers, and the treating team to optimize clinical outcomes and prevent complications. Technology and its implications for the management of type 2 diabetes is a nascent area of research. The impact of some of the more recent technological innovations in this space, such as continuous glucose monitoring, flash glucose monitoring, web-based applications, as well as smartphone- and smart watch-based interactive apps has received limited attention in the research literature.

OBJECTIVE

This scoping review aims to explore the literature available on type 2 diabetes, flash glucose monitoring, and digital health technology to improve diabetic clinical outcomes and inform future research in this area.

METHODS

A scoping review was undertaken by searching Ovid MEDLINE and CINAHL databases. A second search using all identified keywords and index terms was performed on Ovid MEDLINE (January 1966 to July 2021), EMBASE (January 1980 to July 2021), Cochrane Central Register of Controlled Trials (CENTRAL; the Cochrane Library, latest issue), CINAHL (from 1982), IEEE Xplore, ACM Digital Libraries, and Web of Science databases.

RESULTS

There were very few studies that have explored the use of mobile health and flash glucose monitoring in type 2 diabetes. These studies have explored somewhat disparate and limited areas of research, and there is a distinct lack of methodological rigor in this area of research. The 3 studies that met the inclusion criteria have addressed aspects of the proposed research question.

CONCLUSIONS

This scoping review has highlighted the lack of research in this area, raising the opportunity for further research in this area, focusing on the clinical impact and feasibility of the use of multiple technologies, including flash glucose monitoring in the management of patients with type 2 diabetes.

Automated Insulin Delivery (AID) Systems: Use and Efficacy in Children and Adults with Type 1 Diabetes and Other Forms of Diabetes in Europe in Early 2023

Type 1 diabetes (T1D) patients' lifestyle and prognosis has remarkably changed over the years, especially after the introduction of insulin pumps, in particular advanced hybrid closed loop systems (AHCL). Emerging data in literature continuously confirm the improvement of glycemic control thanks to the technological evolution taking place in this disease. As stated in previous literature, T1D patients are seen to be more satisfied thanks to the use of these devices that ameliorate not only their health but their daily life routine as well. Limited findings regarding the use of new devices in different age groups and types of patients is their major limit. This review aims to highlight the main characteristics of each Automated Insulin Delivery (AID) system available for patients affected by Type 1 Diabetes Mellitus. Our main goal was to particularly focus on these systems' efficacy and use in different age groups and populations (i.e., children, pregnant women). Recent studies are emerging that demonstrate their efficacy and safety in younger patients and other forms of diabetes.

Consensus Recommendations for the Use of Automated Insulin Delivery Technologies in Clinical Practice

The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage.

Automated Insulin Delivery Systems as a Treatment for Type 2 Diabetes Mellitus: A Review

OBJECTIVE

Approximately 6.3% of the worldwide population has type 2 diabetes mellitus (T2DM), and the number of people requiring insulin is increasing. Automated insulin delivery (AID) systems integrate continuous subcutaneous insulin infusion and continuous glucose monitoring with a predictive control algorithm to provide more physiologic glycemic control. Personalized glycemic targets are recommended in T2DM owing to the heterogeneity of the disease. Based on the success of hybrid closed-loop systems in improving glycemic control and safety in type 1 diabetes mellitus, there has been further interest in the use of these systems in people with T2DM.

METHODS

We performed a review of AID systems with a focus on the T2DM population.

RESULTS

In 5 randomized controlled trials, AID systems improve time in range and reduce glycemic variability, without increasing insulin requirements or the risk of hypoglycemia.

CONCLUSION

AID systems in T2DM are safe and effective in hospitalized and closely monitored settings. Home studies of longer duration are required to assess for long-term benefit and identify target populations of benefit.

A wearable, minimally-invasive, fully electrochemically-controlled feedback minisystem for diabetes management

Diabetes is a chronic disease affecting 10% of the population globally, and can lead to serious damage in the heart, kidneys, eyes, blood vessels or nerves. Commercial artificial closed-loop feedback systems can significantly improve diabetes management and save lives. However, they are large and expensive for users. Here, we demonstrate for the first time a wearable, minimally-invasive, fully electrochemically-controlled feedback minisystem for diabetes management. Both the working principles of the sensor and pump in the feedback system are based on electrochemical reactions. The smart minisystem was constructed based on integrating the thermoplastic polyurethane hollow microneedles with an electrochemical biosensing device on its outer layer and an electrochemical micropump facing the inner layer of the microneedles. The sensing device was constructed based on sputtering thin metal films through a shadow mask and electroplating Prussian blue on the surface of the microneedles, followed by the immobilization of glucose oxidase on the working electrode. The electrochemical micropump was constructed by sputtering the interdigital electrodes, followed by sealing with a thin elastic film, which was further integrated with the inner channels of the microneedles. Both the sensor and the pump were electrically powered. Via being controlled by a printed circuit board, the biosensing device monitored the levels of interstitial glucose continuously to drive the electrochemical pump to deliver insulin intelligently, in order to control blood glucose within the normal range. The closed-loop feedback system was studied for its capability in maintaining the blood glucose levels of diabetic rats under various physiological conditions. The utility of the intelligent feedback system was successfully demonstrated on diabetic rats for controlling the blood glucose levels within the normal range. The minisystem is wearable, small, cost-effective, precise, stable and painless. It is anticipated that this approach opens a new paradigm for the development of closed-loop diabetes minisystems and may lead to a compelling future for diabetes management.

The automated pancreas: A review of technologies and clinical practice

Insulin pumps and glucose sensors are effective in improving diabetes therapy and reducing acute complications. The combination of both devices using an algorithm-driven interoperable controller makes automated insulin delivery (AID) systems possible. Many AID systems have been tested in clinical trials and have proven safety and effectiveness. However, currently, none of these systems are available for routine use in children younger than 6 years in Europe. For continued use, both users and prescribers must have sound knowledge of the features of the individual AID systems. Presently, all systems require various user interactions (e.g. meal announcements) because fully automated systems are not yet developed. Open-source systems are non-regulated variants to circumvent existing regulatory conditions. There are risks here for both users and prescribers. To evaluate AID therapy, the metric data of the glucose sensors, 'time in target range' and 'glucose management index', are novel recognized and suitable parameters allowing a consultation based on real glucose and insulin pump download data from the daily life of people with diabetes. Read out via cloud-based software or automatic download of such individual treatment data provides the ideal technical basis for shared decision-making through telemedicine, which must be further evaluated for general use.

Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence

Since Banting and Best isolated insulin in the 1920s, dramatic progress has been made in the treatment of type 1 diabetes mellitus (T1DM). However, dose titration and timely injection to maintain optimal glycemic control are often challenging for T1DM patients and their families because they require frequent blood glucose checks. In recent years, technological advances in insulin pumps and continuous glucose monitoring systems have created paradigm shifts in T1DM care that are being extended to develop artificial pancreas systems (APSs). Numerous studies that demonstrate the superiority of glycemic control offered by APSs over those offered by conventional treatment are still being published, and rapid commercialization and use in actual practice have already begun. Given this rapid development, keeping up with the latest knowledge in an organized way is confusing for both patients and medical staff. Herein, we explore the history, clinical evidence, and current state of APSs, focusing on various development groups and the commercialization status. We also discuss APS development in groups outside the usual T1DM patients and the administration of adjunct agents, such as amylin analogues, in APSs.

Technology in the management of type 2 diabetes: Present status and future prospects

The growing incidence of type 2 diabetes (T2D) is a significant health concern, representing 90% of diabetes cases worldwide. As the disease progresses, resultant insulin deficiency and hyperglycaemia necessitates insulin therapy in many cases. It has been recognized that a significant number of people who have a clinical requirement for insulin therapy, as well as their healthcare professionals, are reluctant to intensify treatment with insulin due to fear of hypoglycaemia, poor understanding of treatment regimens or lack of engagement, and are therefore at higher risk of developing complications from poor glycaemic control. Over the past decade, the rise of diabetes technologies, including dosing advisors, continuous glucose monitoring systems, insulin pumps and automated insulin delivery systems, has led to great improvements in the therapies available, particularly to those requiring insulin. Although the focus has largely been on delivering these therapies to the type 1 diabetes population, it is becoming increasingly recognized that people with T2D face similar challenges to achieve recommended glycaemic standards and also have the potential to benefit from these advances. In this review, we discuss diabetes technologies that are currently available for people with T2D and the evidence supporting their use, as well as future prospects. We conclude that there is a clinical need to extend the use of these technologies to the T2D population to curb the consequences of suboptimal disease management in this group.

Insulin Pump Therapy for Patients With Type 2 Diabetes Mellitus: Evidence, Current Barriers, and New Technologies

An increasing number of patients with type 2 diabetes mellitus (T2DM) use insulin pumps. The first insulin pumps especially designed for patients with T2DM have recently become available. However, national guidelines do not primarily recommend the use of continuous subcutaneous insulin infusion (CSII) for this patient group. The effectiveness of CSII in T2DM has not yet been convincingly demonstrated, despite some positive evidence. An overview and an assessment of various studies to date will be given. T2DM is a heterogeneous disease with a substantial phenotypic variability; therefore, it is difficult to provide general conclusions about the effectiveness of CSII in T2DM therapy. The pump types, characteristics, and associated barriers may play a relevant role for therapy outcome. Most advanced functions like various bolus dosages offered by conventional insulin pumps are not needed for T2DM treatment and complicate the device handling for this subject group. Additionally, new technologies like increased connectivity, advanced software features, and interoperability are currently becoming available representing further barriers. The implementation of this technological progress might be a benefit for pumps for T2DM as well. However, these have not been sufficiently examined either and increased security challenges due to integrated peripheral components should not be neglected in terms of a sound cybersecurity. Pump features and handling for patients with T2DM should be as easy as possible, indicating a need for insulin pumps specially designed for patients with T2DM. However, it has to be investigated if pumps designed for T2DM are more effective than other intensified insulin regimens.

Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study

BACKGROUND

Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data.

METHODS

We used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6).

RESULTS

Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%).

CONCLUSIONS

Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.

Practical implementation of automated closed-loop insulin delivery: A French position statement

Automated closed-loop (CL) insulin therapy has come of age. This major technological advance is expected to significantly improve the quality of care for adults, adolescents and children with type 1 diabetes. To improve access to this innovation for both patients and healthcare professionals (HCPs), and to promote adherence to its requirements in terms of safety, regulations, ethics and practice, the French Diabetes Society (SFD) brought together a French Working Group of experts to discuss the current practical consensus. The result is the present statement describing the indications for CL therapy with emphasis on the idea that treatment expectations must be clearly defined in advance. Specifications for expert care centres in charge of initiating the treatment were also proposed. Great importance was also attached to the crucial place of high-quality training for patients and healthcare professionals. Long-term follow-up should collect not only metabolic and clinical results, but also indicators related to psychosocial and human factors. Overall, this national consensus statement aims to promote the introduction of marketed CL devices into standard clinical practice.

Closed-loop management of inpatient hyperglycaemia

The prevalence of diabetes in the inpatient setting is increasing, and suboptimal glucose control in hospital is associated with increased morbidity and mortality. Attaining the recommended glucose levels is challenging with standard insulin therapy. Hypoglycaemia and hyperglycaemia are common and diabetes management in hospital can be a considerable workload burden for health-care professionals. Fully automated insulin delivery (closed-loop) has been shown to be safe, and achieves superior glucose control than standard insulin therapy in the hospital, including in those patients receiving haemodialysis and enteral or parenteral nutrition where glucose control can be particularly challenging. Evidence that the improved glucose control achieved using closed-loop systems can translate into improved clinical outcomes for patients is key to support widespread adoption of this technology. The closed-loop approach has the potential to provide a paradigm shift in the management of inpatient diabetes, particularly in the most challenging inpatient populations, and may reduce staff work burden and the health-care costs associated with inpatient diabetes.

Single-Hormone Artificial Pancreas Use in Diabetes: Clinical Efficacy and Remaining Challenges

IN BRIEF Artificial pancreas systems are rapidly developing and constitute the most promising technology for insulin-requiring diabetes management. Single-hormone systems (SH-AP) that deliver only insulin and have a hybrid design that necessitates patients' interventions around meals and exercise are the first to appear on the market. Trials with SH-AP have demonstrated improvement in time spent with blood glucose levels within target ranges, with a concomitant decrease in hypoglycemia. Longer and larger trials involving different patient populations are ongoing to further advance this important technology.

Diabetes Technology: Monitoring, Analytics, and Optimal Control

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

Artificial Pancreas Systems for People With Type 2 Diabetes: Conception and Design of the European CLOSE Project

In the last 10 years tremendous progress has been made in the development of artificial pancreas (AP) systems for people with type 1 diabetes (T1D). The pan-European consortium CLOSE (Automated Glu cose Contro l at H ome for People with Chronic Disea se) is aiming to develop integrated AP solutions (APplus) tailored to the needs of people with type 2 diabetes (T2D). APplus comprises a product and service package complementing the AP system by obligatory training as well as home visits and telemedical consultations on demand. Outcome predictors and performance indicators shall help to identify people who could benefit most from AP usage and facilitate the measurement of AP impact in diabetes care. In a first step CLOSE will establish a scalable APplus model case working at the interface between patients, homecare service providers, and payers in France. CLOSE will then scale up APplus by pursuing geographic distribution, targeting additional audiences, and enhancing AP functionalities and interconnectedness. By being part of the European Institute of Innovation and Technology (EIT) Health public-private partnership, CLOSE is committed to the EIT "knowledge triangle" pursuing the integrated advancement of technology, education, and business creation. Putting stakeholders, education, and impact into the center of APplus advancement is considered key for achieving wide AP use in T2D care.

Analysis of "Artificial Pancreas (AP) Systems for People With Type 2 Diabetes: Conception and Design of the European CLOSE Project"

In an article in Journal of Diabetes Science and Technology, Schliess and coauthors describe the conception and design of the European Automated Glu cose Contro l at H ome for People with Chronic Di sease (CLOSE) initiative for the implementation of artificial pancreas (AP) systems for people with diabetes. The CLOSE consortium aims to develop integrated AP solutions (APplus) tailored to the needs of individuals with type 2 diabetes (T2D) by developing superior risk- and cost-benefit scenarios for AP operation to achieve acceptance by users and caregivers and a high likelihood for reimbursement. CLOSE is integrating the AP platform into the center of a comprehensive product and service package specifically tailored to defined T2D patient groups and care environments, leading to an interactive collaboration with users, health care providers, and other stakeholders in diabetes care. This is a very ambitious but well-conceived and delineated project which takes into consideration most of the relevant factors that may influence AP implementation in T2D care.

The International Diabetes Closed-Loop Study: Testing Artificial Pancreas Component Interoperability

BACKGROUND

Use of artificial pancreas (AP) requires seamless interaction of device components, such as continuous glucose monitor (CGM), insulin pump, and control algorithm. Mobile AP configurations also include a smartphone as computational hub and gateway to cloud applications (e.g., remote monitoring and data review and analysis). This International Diabetes Closed-Loop study was designed to demonstrate and evaluate the operation of the inControl AP using different CGMs and pump modalities without changes to the user interface, user experience, and underlying controller.

METHODS

Forty-three patients with type 1 diabetes (T1D) were enrolled at 10 clinical centers (7 United States, 3 Europe) and 41 were included in the analyses (39% female, >95% non-Hispanic white, median T1D duration 16 years, median HbA1c 7.4%). Two CGMs and two insulin pumps were tested by different study participants/sites using the same system hub (a smartphone) during 2 weeks of in-home use.

RESULTS

The major difference between the system components was the stability of their wireless connections with the smartphone. The two sensors achieved similar rates of connectivity as measured by percentage time in closed loop (75% and 75%); however, the two pumps had markedly different closed-loop adherence (66% vs. 87%). When connected, all system configurations achieved similar glycemic outcomes on AP control (73% [mean] time in range: 70-180 mg/dL, and 1.7% [median] time <70 mg/dL).

CONCLUSIONS

CGMs and insulin pumps can be interchangeable in the same Mobile AP system, as long as these devices achieve certain levels of reliability and wireless connection stability.

Automated closed-loop control of diabetes: the artificial pancreas

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

2020 vision - An overview of prospects for diabetes management and prevention in the next decade

After a century of medical progress, people nowadays live longer with diabetes than ever before. However, current preventative approaches, compounded in part by increased life-expectancy, are failing to reduce the prevalence of diabetes. Cardiovascular sequelae account for many of the four million deaths annually attributable to diabetes. Evidence indicates that certain glucose-lowering medications can improve vascular outcomes in some people with type 2 diabetes, which, together with better understanding of using multiple therapies concurrently, offers opportunities for beneficial personalization of medication regimens. However, further well-designed long-term studies are needed to evaluate cardiovascular benefits and safety of new and older medications, particularly in users typical of everyday diabetes care. Although there are numerous other promising advances in pharmacotherapies and biotechnology, these will probably be unaffordable for most people with diabetes globally. Therefore, effective national public health approaches will be essential to reducing the incidence of diabetes and its associated burdens; these may entail politically controversial measures to change unhealthy lifestyle behaviours. Stakeholders could learn from past failures and emulate successes in other health-care initiatives. Without early action at all levels, we face a future in which approaching one-quarter of humans will have diabetes, with more than half afflicted during their lifetime.

Analyzing the Potential of Advanced Insulin Dosing Strategies in Patients With Type 2 Diabetes: Results From a Hybrid In Silico Study

BACKGROUND

The ongoing improvement of continuous glucose monitoring (CGM) sensors and of insulin pumps are paving the way for a fast implementation of artificial pancreas (AP) for type 1 diabetes (T1D) patients. The case for type 2 diabetes (T2D) patients is less obvious since usually some residual beta cell function allows for simpler therapy approaches, and even multiple daily injections (MDI) therapy is not very widespread. However, the number of insulin dependent T2D patients is vastly increasing and therefore a need for understanding chances and challenges of an automated insulin therapy arises. Based on this background, this article analyzes conditions under which the use of more advanced therapeutic approaches, particularly AP, could bring a substantial improvement and should be considered as a viable therapy option.

METHOD

Data of 14 insulin-treated T2D patients on MDI wearing a CGM device and deviation analysis methods were used to estimate the expected improvements in the clinical outcome by using self-monitoring of blood glucose (SMBG) with advanced carbohydrate counting, a full AP or intermediate approaches, either CGM measurements with MDI therapy or SMBG with insulin pump. HbA1C and time in range (70-140 mg/dl, 70-180 mg/dl, respectively) were used as a performance measure. Outcome measures beyond glycemic control (eg, compliance, patient acceptance) have not been analyzed in this study.

RESULTS

AP has the potential to improve the condition of many poorly controlled insulin-treated T2D patients. However, as the interpatient variability is much higher than in T1D, a prescreening is recommended to select suitable patients.

CONCLUSIONS

Clinical criteria need to be developed for inclusion/exclusion of T2D patients for AP related therapies.

A review of personalized blood glucose prediction strategies for T1DM patients

This paper presents a methodological review of models for predicting blood glucose (BG) concentration, risks and BG events. The surveyed models are classified into three categories, and they are presented in summary tables containing the most relevant data regarding the experimental setup for fitting and testing each model as well as the input signals and the performance metrics. Each category exhibits trends that are presented and discussed. This document aims to be a compact guide to determine the modeling options that are currently being exploited for personalized BG prediction.

Improving drug-like properties of insulin and GLP-1 via molecule design and formulation and improving diabetes management with device & drug delivery

There is an increased incidence of diabetes worldwide. The discovery of insulin revolutionized the management of diabetes, the revelation of glucagon-like peptide-1 (GLP-1) and introduction of GLP-1 receptor agonists to clinical practice was another breakthrough. Continued translational research resulted in better understanding of diabetes, which, in combination with cutting-edge biology, chemistry, and pharmaceutical tools, have allowed for the development of safer, more effective and convenient insulins and GLP-1. Advances in self-administration of insulin and GLP-1 receptor agonist therapies with use of drug-device combination products have further improved the outcomes of diabetes management and quality of life for diabetic patients. The synergies of insulin and GLP-1 receptor agonist actions have led to development of devices that can deliver both molecules simultaneously. New chimeric GLP-1-incretins and insulin-GLP-1-incretin molecules are also being developed. The objective of this review is to summarize molecular designs to improve the drug-like properties of insulin and GLP-1 and to highlight the continued advancement of drug-device combination products to improve diabetes management.

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.

Artificial Pancreas Project at Cambridge 2013

The development and clinical testing of closed-loop systems (the artificial pancreas) is underpinned by advances in continuous glucose monitoring and benefits from concerted academic and industry collaborative efforts. This review describes the progress of the Artificial Pancreas Project at the University of Cambridge from 2006 to 2014. Initial studies under controlled laboratory conditions, designed to collect representative safety and performance data, were followed by short to medium free-living unsupervised outpatient studies demonstrating the safety and efficacy of closed-loop insulin delivery using a model predictive control algorithm. Accompanying investigations included assessment of the psychosocial impact and key factors affecting glucose control such as insulin kinetics and glucose absorption. Translation to other disease conditions such as critical illness and Type 2 diabetes took place. It is concluded that innovation of iteratively enhanced closed-loop systems will provide tangible means to improve outcomes and quality of life in people with Type 1 diabetes and their families in the next decade.

Modelling endogenous insulin concentration in type 2 diabetes during closed-loop insulin delivery

Background

Closed-loop insulin delivery is an emerging treatment for type 1 diabetes (T1D) evaluated clinically and using computer simulations during pre-clinical testing. Efforts to make closed-loop systems available to people with type 2 diabetes (T2D) calls for the development of a new type of simulators to accommodate differences between T1D and T2D. Presented here is the development of a model of posthepatic endogenous insulin concentration, a component omitted in T1D simulators but key for simulating T2D physiology.

Methods

We evaluated six competing models to describe the time course of endogenous insulin concentration as a function of the plasma glucose concentration and time. The models were fitted to data collected in insulin-naive subjects with T2D who underwent two 24-h visits and were treated, in a random order, by either closed-loop insulin delivery or glucose-lowering oral agents. The model parameters were estimated using a Bayesian approach, as implemented in the WinBUGS software. Model selection criteria were used to identify the best model describing our clinical data.

Results

The selected model successfully described endogenous insulin concentration over 24 h in both study periods and provided plausible parameter estimates. Model-derived results were in concordance with a clinical finding which revealed increased posthepatic endogenous insulin concentration during the control study period (P < 0.05). The modelling results indicated that the excess amount of insulin can be attributed to the glucose-independent effect as the glucose-dependent effect was similar between visits (P > 0.05).

Conclusions

A model to describe endogenous insulin concentration in T2D including components of posthepatic glucose-dependent and glucose-independent insulin secretion was identified and validated. The model is suitable to be incorporated in a simulation environment for evaluating closed-loop insulin delivery in T2D.

Electronic supplementary material

The online version of this article (doi:10.1186/s12938-015-0009-5) contains supplementary material, which is available to authorized users.

Pharmacokinetics of insulin lispro in type 2 diabetes during closed-loop insulin delivery

Insulin pharmacokinetics is not well understood during continuous subcutaneous insulin infusion in type 2 diabetes (T2D). We analyzed data collected in 11 subjects with T2D [6 male, 9 white European and two of Indian ethnicity; age 59.7(12.1) years, BMI 30.1(3.9) kg/m(2), fasting C-peptide 1002.2(365.8) pmol/l, fasting plasma glucose 9.6(2.2) mmol/l, diabetes duration 8.0(6.2) years and HbA1c 8.3(0.8)%; mean(SD)] who underwent a 24-h study investigating closed-loop insulin delivery at the Wellcome Trust Clinical Research Facility, Cambridge, UK. Subcutaneous delivery of insulin lispro was modulated every 15 min according to a model predictive control algorithm. Two complementary insulin assays facilitated discrimination between exogenous (lispro) and endogenous plasma insulin concentrations measured every 15-60 min. Lispro pharmacokinetics was represented by a linear two-compartment model whilst parameters were estimated using a Bayesian approach applying a closed-form model solution. The time-to-peak of lispro absorption (t(max)) was 109.6 (75.5-120.5) min [median (interquartile range)] and the metabolic clearance rate (MCR(I)) 1.26 (0.87-1.56)×10(-2) l/kg/min. MCR(I) was negatively correlated with fasting C-peptide (r(s)=-0.84; P=.001) and with fasting plasma insulin concentration (r(s)=-0.79; P=.004). In conclusion, compartmental modelling adequately represents lispro kinetics during continuous subcutaneous insulin infusion in T2D. Fasting plasma C-peptide or fasting insulin may be predictive of lispro metabolic clearance rate in T2D but further investigations are warranted.

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.

Artificial pancreas: fuzzy logic and control of glycemia

PURPOSE OF REVIEW

This review describes the latest efforts, challenges, and experience of using automated insulin delivery systems at outpatient settings and home studies. This is an important step in getting recognition of these systems as a routine therapy for patients with type 1 diabetes.

RECENT FINDINGS

Almost 3 years elapsed since the first description of closed-loop use outside the protecting environment of the hospital, at a diabetes camp. In this period, several different approaches of closed-loop systems were used at outpatient settings. The low-glucose suspend feature on the pump showed a reduction in the risk of nocturnal hypoglycemia. Closed-loop systems with diverse control algorithms with a single or bihormonal approach showed an improvement in glycemic control. The improvement was demonstrated during the overnight use and during the 24-h use. The outpatient studies with closed-loop systems, especially overnight at home, demonstrated that the current configurations are already safe and efficient for daily use. Technological advancement should undoubtedly lead to even better performance.

SUMMARY

Studies using closed-loop systems at patients' home are currently being carried out. The preliminary results of these experiments are encouraging and enhance our confidence in this tool as suitable for use in clinical daily practice.

Identifying and meeting the challenges of insulin therapy in type 2 diabetes

Type 2 diabetes mellitus (T2DM) is a chronic illness that requires clinical recognition and treatment of the dual pathophysiologic entities of altered glycemic control and insulin resistance to reduce the risk of long-term micro- and macrovascular complications. Although insulin is one of the most effective and widely used therapeutic options in the management of diabetes, it is used by less than one-half of patients for whom it is recommended. Clinician-, patient-, and health care system-related challenges present numerous obstacles to insulin use in T2DM. Clinicians must remain informed about new insulin products, emerging technologies, and treatment options that have the potential to improve adherence to insulin therapy while optimizing glycemic control and mitigating the risks of therapy. Patient-related challenges may be overcome by actively listening to the patient’s fears and concerns regarding insulin therapy and by educating patients about the importance, rationale, and evolving role of insulin in individualized self-treatment regimens. Enlisting the services of Certified Diabetes Educators and office personnel can help in addressing patient-related challenges. Self-management of diabetes requires improved patient awareness regarding the importance of lifestyle modifications, self-monitoring, and/or continuous glucose monitoring, improved methods of insulin delivery (eg, insulin pens), and the enhanced convenience and safety provided by insulin analogs. Health care system-related challenges may be improved through control of the rising cost of insulin therapy while making it available to patients. To increase the success rate of treatment of T2DM, the 2012 position statement from the American Diabetes Association and the European Association for the Study of Diabetes focused on individualized patient care and provided clinicians with general treatment goals, implementation strategies, and tools to evaluate the quality of care.