Outpatient glycemic control with a bionic pancreas in type 1 diabetes

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

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

Automated Insulin Delivery in Children with Type 1 Diabetes

The advent of insulin pump therapy marked an important milestone in diabetes treatment in the past few decades and has become the tipping point for the development of automated insulin delivery systems (AID). Standalone insulin pump systems have evolved over the course of years and have been replaced by modern high-technology insulin pumps with continuous glucose monitor interface allowing real-time insulin dose adjustment to optimize treatment. This review summarizes evidence from AID studies conducted in children with type 1 diabetes and discusses the outlook for future generation AID systems from a pediatric treatment perspective.

Role of Composite Glycemic Indices: A Comparison of the Comprehensive Glucose Pentagon Across Diabetes Types and HbA1c Levels

Background: Complex changes of glycemia that occur in diabetes are not fully captured by any single measure. The Comprehensive Glucose Pentagon (CGP) measures multiple aspects of glycemia to generate the prognostic glycemic risk (PGR), which constitutes the relative risk of hypoglycemia combined with long-term complications. We compare the components of CGP and PGR across type 1 and type 2 diabetes. Methods: Participants: n = 60 type 1 and n = 100 type 2 who underwent continuous glucose monitoring (CGM). Mean glucose, coefficient of variation (%CV), intensity of hypoglycemia (INT_hypo), intensity of hyperglycemia (INT_hyper), time out-of-range (TOR <3.9 and >10 mmol/L), and PGR were calculated. PGR (median, interquartile ranges [IQR]) for diabetes types, and HbA1c classes were compared. Results: While HbA1c was lower in type 1 (type 1 vs. type 2: 8.0 ± 1.6 vs. 8.6 ± 1.7, P = 0.02), CGM-derived mean glucoses were similar across both groups (P > 0.05). TOR, %CV, INT_hypo, and INT_hyper were all higher in type 1 [type 1 vs. type 2: 665 (500, 863) vs. 535 (284, 823) min/day; 39% (33, 46) vs. 29% (24, 34); 905 (205, 2951) vs. 18 (0, 349) mg/dL × min²; 42,906 (23,482, 82,120) vs. 30,166 (10,276, 57,183) mg/dL × min², respectively, all P < 0.05]. Across each HbA1c class, the PGR remained consistently and significantly higher in type 1. While mean glucose remained the same across HbA1c classes, %CV, TOR, INT_hyper, and INT_hypo were significantly higher for type 1. Even within the same HbA1c class, the variation (IQR) of each parameter in type 1 was wider. The PGR increased across diabetes groups; type 2 on orals versus type 2 on insulin versus type 1 (PGR: 1.6 vs. 2.2 vs. 2.9, respectively, P < 0.05). Conclusion: Composite indices such as the CGP capture significant differences in glycemia independent of HbA1c and mean glucose. The use of such indices must be explored in both the clinical and research settings.

Time in range: a new parameter to evaluate blood glucose control in patients with diabetes

The International Consensus in Time in Range (TIR) was recently released and defined the concept of the time spent in the target range between 70 and 180 mg/dL while reducing time in hypoglycemia, for patients using Continuous Glucose Monitoring (CGM). TIR was validated as an outcome measures for clinical Trials complementing other components of glycemic control like Blood glucose and HbA1c. The challenge is to implement this practice more widely in countries with a limited health public and private budget as it occurs in Brazil. Could CGM be used intermittently? Could self-monitoring blood glucose obtained at different times of the day, with the amount of data high enough be used? More studies should be done, especially cost-effective studies to help understand the possibility of having sensors and include TIR evaluation in clinical practice nationwide.

Improving Clinical Outcomes in Newly Diagnosed Pediatric Type 1 Diabetes: Teamwork, Targets, Technology, and Tight Control-The 4T Study

Many youth with type 1 diabetes (T1D) do not achieve hemoglobin A1c (HbA1c) targets. The mean HbA1c of youth in the USA is higher than much of the developed world. Mean HbA1c in other nations has been successfully modified following benchmarking and quality improvement methods. In this review, we describe the novel 4T approach-teamwork, targets, technology, and tight control-to diabetes management in youth with new-onset T1D. In this program, the diabetes care team (physicians, nurse practitioners, certified diabetes educators, dieticians, social workers, psychologists, and exercise physiologists) work closely to deliver diabetes education from diagnosis. Part of the education curriculum involves early integration of technology, specifically continuous glucose monitoring (CGM), and developing a curriculum around using the CGM to maintain tight control and optimize quality of life.

Severe Hypoglycemia: Is It Still a Threat for Children and Adolescents With Type 1 Diabetes?

Severe hypoglycemia is defined as a condition with serious cognitive dysfunction, such as a convulsion and coma, requiring external help from other persons. This condition is still lethal and is reported to be the cause of death in 4-10% in children and adolescents with type 1 diabetes. The incidence of severe hypoglycemia in the pediatric population was previously reported as high as more than 50-100 patient-years; however, there was a decline in the frequency of severe hypoglycemia during the past decades, and relationship with glycemic control became weaker than previously reported. A lot of studies have shown the neurological sequelae with severe hypoglycemia as cognitive dysfunction and abnormalities in brain structure. This serious condition also provides negative psychosocial outcomes and undesirable compensatory behaviors. Various possible factors, such as younger age, recurrent hypoglycemia, nocturnal hypoglycemia, and impaired awareness of hypoglycemia, are possible risk factors for developing severe hypoglycemia. A low HbA_1c level is not a predictable value for severe hypoglycemia. Prevention of severe hypoglycemia remains one of the most critical issues in the management of pediatric patients with type 1 diabetes. Advanced technologies, such as continuous glucose monitoring (CGM), intermittently scanned CGM, and sensor-augmented pump therapy with low-glucose suspend system, potentially minimize the occurrence of severe hypoglycemia without worsening overall glycemic control. Hybrid closed-loop system must be the most promising tool for achieving optimal glycemic control with preventing the occurrence of severe hypoglycemia in pediatric patients with type 1 diabetes.

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

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

Glycaemic variability: The under-recognized therapeutic target in type 1 diabetes care

Type 1 diabetes mellitus (T1DM) remains one of the most challenging long-term conditions to manage. Despite robust evidence to demonstrate that near normoglycaemia minimizes, but does not completely eliminate, the risk of complications, its achievement has proved almost impossible in a real-world setting. HbA1c to date has been used as the gold standard marker of glucose control and has been shown to reflect directly the risk of diabetes complications. However, it has been recognized that HbA1c is a crude marker of glucose control. Continuous glucose monitoring (CGM) provides the ability to measure and observe inter- and intraday glycaemic variability (GV), a more meaningful measure of glycaemic control, more relevant to daily living for those with T1DM. This paper reviews the relationship between GV and hypoglycaemia, and micro- and macrovascular complications. It also explores the impact on GV of CGM, insulin pumps, closed-loop technologies, and newer insulins and adjunctive therapies. Looking to the future, there is an argument that GV should become a key determinant of therapeutic success. Further studies are required to investigate the pathological and psychological benefits of reducing GV.

A machine-learning approach to predict postprandial hypoglycemia

BACKGROUND

For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set.

METHODS

We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance.

RESULTS

In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models.

CONCLUSION

In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.

Controlling the AP Controller: Controller Performance Assessment and Modification

BACKGROUND

Despite recent advances in closed-loop control of blood glucose concentration (BGC) in people with type 1 diabetes (T1D), online performance assessment and modification of artificial pancreas (AP) control systems remain a challenge as the metabolic characteristics of users change over time.

METHODS

A controller performance assessment and modification system (CPAMS) analyzes the glucose concentration variations and controller behavior, and modifies the parameters of the control system used in the multivariable AP system. Various indices are defined to quantitatively evaluate the controller performance in real time. Controller performance assessment and modification system also incorporates online learning from historical data to anticipate impending disturbances and proactively counteract their effects.

RESULTS

Using a multivariable simulation platform for T1D, the CPAMS is used to enhance the BGC regulation in people with T1D by means of automated insulin delivery with an adaptive learning predictive controller. Controller performance assessment and modification system increases the percentage of time in the target range (70-180) mg/dL by 52.3% without causing any hypoglycemia and hyperglycemia events.

CONCLUSIONS

The results demonstrate a significant improvement in the multivariable AP controller performance by using CPAMS.

Patient Perception and Satisfaction With Insulin Pump System: Pilot User Experience Survey

BACKGROUND

The goal of this study was to assess patient perspectives and satisfaction with the MiniMed 670G insulin pump. Those participants who used the pump as part of a hybrid closed loop were also asked to provide their views on the automatic feature (auto mode).

METHODS

Adults with type 1 diabetes mellitus using the Medtronic™ 670G pump were asked about their experience with the device using a semi-structured survey developed by the research team. Responses were quantified to identify emergent themes.

RESULTS

Seventeen participants used the pump as part of a hybrid closed loop system, while four participants used the pump in combination with a nonintegrated continuous glucose monitoring system. Overall, participants indicated a high level of satisfaction with the pump (14/21) mostly because of improvements in blood glucose (BG) control (15/21). Least liked features were physical design and structure (6/21), frequency of user input (5/21), alert frequency (4/21), and difficulty of use (3/21). Those using the hybrid closed loop were satisfied with the auto mode feature (11/17), mostly because of improvements in BG control (9/17). The least liked features of the auto mode technology were that blood glucose levels remained elevated (5/17) and the frequency of alerts (4/17).

CONCLUSION

Participants indicated a high level of satisfaction with the pump and its auto mode featured mostly because of improvements in BG control. They also pointed out some key aspects of the device that are of potential clinical or commercial relevance. Additional research is needed to further evaluate users' perspectives on this new device.

Diagnosis and management of pancreatic cystic neoplasms: current evidence and guidelines

Pancreatic cystic neoplasms (PCN) are a heterogeneous group of pancreatic cysts that include intraductal papillary mucinous neoplasms, mucinous cystic neoplasms, serous cystic neoplasms and other rare cystic lesions, all with different biological behaviours and variable risk of progression to malignancy. As more pancreatic cysts are incidentally discovered on routine cross-sectional imaging, optimal surveillance for patients with PCN is becoming an increasingly common clinical problem, highlighting the need to balance cancer prevention with the risk of (surgical) overtreatment. This Review summarizes the latest developments in the diagnosis and management of PCN, including the quality of available evidence. Also discussed are the most important differences between the PCN guidelines from the American Gastroenterological Association, the International Association of Pancreatology and the European Study Group on Cystic Tumours of the Pancreas, including diagnostic and follow-up strategies and indications for surgery. Finally, new developments in the management of patients with PCN are addressed.

Feasibility Studies of an Insulin-Only Bionic Pancreas in a Home-Use Setting

BACKGROUND

We tested the safety and performance of the "insulin-only" configuration of the bionic pancreas (BP) closed-loop blood-glucose control system in a home-use setting to assess glycemic outcomes using different static and dynamic glucose set-points.

METHOD

This is an open-label non-randomized study with three consecutive intervention periods. Participants had consecutive weeks of usual care followed by the insulin-only BP with (1) an individualized static set-point of 115 or 130 mg/dL and (2) a dynamic set-point that automatically varied within 110 to 130 mg/dL, depending on hypoglycemic risk. Human factors (HF) testing was conducted using validated surveys. The last five days of each study arm were used for data analysis.

RESULTS

Thirteen participants were enrolled with a mean age of 28 years, mean A1c of 7.2%, and mean daily insulin dose of 0.6 U/kg (0.4-1.0 U/kg). The usual care arm had an average glucose of 145 ± 20 mg/dL, which increased in the static set-point arm (159 ± 8 mg/dL, P = .004) but not in the dynamic set-point arm (154 ± 10 mg/dL, P = ns). There was no significant difference in time spent in range (70-180 mg/dL) among the three study arms. There was less time <70 mg/dL with both the static (1.8% ± 1.4%, P = .009) and dynamic set-point (2.7±1.5, P = .051) arms compared to the usual-care arm (5.5% ± 4.2%). HF testing demonstrated preliminary user satisfaction and no increased risk of diabetes burden or distress.

CONCLUSIONS

The insulin-only configuration of the BP using either static or dynamic set-points and initialized only with body weight performed similarly to other published insulin-only systems.

Advances in technology for management of type 1 diabetes

Technological advances have had a major effect on the management of type 1 diabetes. In addition to blood glucose meters, devices used by people with type 1 diabetes include insulin pumps, continuous glucose monitors, and, most recently, systems that combine both a pump and a monitor for algorithm-driven automation of insulin delivery. In the next 5 years, as many advances are expected in technology for the management of diabetes as there have been in the past 5 years, with improvements in continuous glucose monitoring and more available choices of systems that automate insulin delivery. Expansion of the use of technology will be needed beyond endocrinology practices to primary-care settings and broader populations of patients. Tools to support decision making will also need to be developed to help patients and health-care providers to use the output of these devices to optimise diabetes management.

Mathematical modeling of the glucagon challenge test

A model for the homeostasis of glucose through the regulating hormones glucagon and insulin is described. It contains a subsystem that models the internalization of the glucagon receptor. Internalization is a mechanism in cell signaling, through which G-protein coupled receptors are taken from the surface of the cell to the endosome. The model is used to interpret data from a glucagon challenge test in which subjects have been under treatment with a novel glucagon receptor anti-sense drug which is aimed at reducing the number of receptors in the liver. It is shown how the receptor internalization results in tolerance of the blood glucose concentration to glucagon-induced hyperglycemia. We quantify the reduction of the number of receptors using the model and the data before and after treatment.

Outcome and long-term quality of life after total pancreatectomy (PANORAMA): a nationwide cohort study

BACKGROUND

The threshold to perform total pancreatectomy is rather high, predominantly because of concerns for long-term consequences of brittle diabetes on patients' quality of life. Contemporary data on postoperative outcomes, diabetes management, and long-term quality of life after total pancreatectomy from large nationwide series are, however, lacking.

METHODS

We performed a nationwide, retrospective cohort study among adults who underwent total pancreatectomy in 17 Dutch centers (2006-2016). Morbidity and mortality were analyzed, and long-term quality of life was assessed cross-sectionally using the following generic and disease-specific questionnaires: the 5-level version European quality of life 5-dimension and the European Organization for Research and Treatment in Cancer Quality of Life Questionnaire Cancer. Several questionnaires specifically addressing diabetic quality of life included the Problem Areas in Diabetes Scale 20, the Diabetes Treatment Satisfaction Questionnaire-status version, and the Hypoglycemia Fear Survey-II. Results were compared with the general population and patients with type 1 diabetes.

RESULTS

Overall, 148 patients after total pancreatectomy were included. The annual nationwide volume of total pancreatectomy increased from 5 in 2006 to 32 in 2015 (P < .05). The 30-day and 90-day mortality were 5% and 8%, respectively. The major complication rate was 32%. Quality of life questionnaires were completed by 60 patients (85%, median follow-up of 36 months). Participants reported lower global (73 vs 78, P = .03) and daily health status (0.83 vs 0.87, P < .01) compared to the general population. Quality of life did not differ based on time after total pancreatectomy (<3, 3-5, or >5 years). In general, patients were satisfied with their diabetes therapy and experienced similar diabetes-related distress as patients with type 1 diabetes.

CONCLUSION

This nationwide study found increased use of total pancreatectomy with a relatively high 90-day mortality. Long-term quality of life was lower compared to the general population, although differences were small. Diabetes-related distress and treatment satisfaction were similar to patients with type 1 diabetes.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Engineering the vasculature for islet transplantation

The microvasculature in the pancreatic islet is highly specialized for glucose sensing and insulin secretion. Although pancreatic islet transplantation is a potentially life-changing treatment for patients with insulin-dependent diabetes, a lack of blood perfusion reduces viability and function of newly transplanted tissues. Functional vasculature around an implant is not only necessary for the supply of oxygen and nutrients but also required for rapid insulin release kinetics and removal of metabolic waste. Inadequate vascularization is particularly a challenge in islet encapsulation. Selectively permeable membranes increase the barrier to diffusion and often elicit a foreign body reaction including a fibrotic capsule that is not well vascularized. Therefore, approaches that aid in the rapid formation of a mature and robust vasculature in close proximity to the transplanted cells are crucial for successful islet transplantation or other cellular therapies. In this paper, we review various strategies to engineer vasculature for islet transplantation. We consider properties of materials (both synthetic and naturally derived), prevascularization, local release of proangiogenic factors, and co-transplantation of vascular cells that have all been harnessed to increase vasculature. We then discuss the various other challenges in engineering mature, long-term functional and clinically viable vasculature as well as some emerging technologies developed to address them. The benefits of physiological glucose control for patients and the healthcare system demand vigorous pursuit of solutions to cell transplant challenges. STATEMENT OF SIGNIFICANCE: Insulin-dependent diabetes affects more than 1.25 million people in the United States alone. Pancreatic islets secrete insulin and other endocrine hormones that control glucose to normal levels. During preparation for transplantation, the specialized islet blood vessel supply is lost. Furthermore, in the case of cell encapsulation, cells are protected within a device, further limiting delivery of nutrients and absorption of hormones. To overcome these issues, this review considers methods to rapidly vascularize sites and implants through material properties, pre-vascularization, delivery of growth factors, or co-transplantation of vessel supporting cells. Other challenges and emerging technologies are also discussed. Proper vascular growth is a significant component of successful islet transplantation, a treatment that can provide life-changing benefits to patients.

Prediction of Hypoglycemia During Aerobic Exercise in Adults With Type 1 Diabetes

BACKGROUND

Fear of exercise related hypoglycemia is a major reason why people with type 1 diabetes (T1D) do not exercise. There is no validated prediction algorithm that can predict hypoglycemia at the start of aerobic exercise.

METHODS

We have developed and evaluated two separate algorithms to predict hypoglycemia at the start of exercise. Model 1 is a decision tree and model 2 is a random forest model. Both models were trained using a meta-data set based on 154 observations of in-clinic aerobic exercise in 43 adults with T1D from 3 different studies that included participants using sensor augmented pump therapy, automated insulin delivery therapy, and automated insulin and glucagon therapy. Both models were validated using an entirely new validation data set with 90 exercise observations collected from 12 new adults with T1D.

RESULTS

Model 1 identified two critical features predictive of hypoglycemia during exercise: heart rate and glucose at the start of exercise. If heart rate was greater than 121 bpm during the first 5 min of exercise and glucose at the start of exercise was less than 182 mg/dL, it predicted hypoglycemia with 79.55% accuracy. Model 2 achieved a higher accuracy of 86.7% using additional features and higher complexity.

CONCLUSIONS

Models presented here can assist people with T1D to avoid exercise related hypoglycemia. The simple model 1 heuristic can be easily remembered (the 180/120 rule) and model 2 is more complex requiring computational resources, making it suitable for automated artificial pancreas or decision support systems.

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

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

Automated Insulin Delivery Algorithms

IN BRIEF Automated insulin delivery (AID; also known as artificial pancreas) has improved the regulation of blood glucose concentrations, reduced the frequency of hyperglycemic and hypoglycemic episodes, and improved the quality of life of people with diabetes and their families. Three different types of algorithms-proportional-integral-derivative control, model predictive control, and fuzzy-logic knowledge-based systems-have been used in AID control systems. This article will highlight the foundations of these algorithms and discuss their strengths and limitations. Multivariable artificial pancreas and dual-hormone (insulin and glucagon) systems will be introduced.

Insulin-and-Glucagon Artificial Pancreas Versus Insulin-Alone Artificial Pancreas: A Short Review

IN BRIEF The advantage of the insulin-and-glucagon artificial pancreas is based on the rapid effect of subcutaneous glucagon delivery in preventing hypoglycemia compared to suspension of insulin delivery. In short-term studies, the dual-hormone artificial pancreas reduced daytime hypoglycemia, especially during exercise, compared to the insulin-alone artificial pancreas, but the insulin-alone system seemed sufficient in eliminating nocturnal hypoglycemia. The comparative benefits of the single- and dual-hormone systems for improving A1C and preventing severe hypoglycemia remain unknown.

New uses and formulations of glucagon for hypoglycaemia

Hypoglycaemia is the more frequent complication of insulin therapy and the main barrier to tight glycaemic control. Injectable glucagon and oral intake of carbohydrates are the recommended treatments for severe and non-severe hypoglycaemia episodes, respectively. Nasal glucagon is currently being developed as a ready-to-use device, to simplify severe hypoglycaemia rescue. Stable forms of liquid glucagon could open the field for different approaches for mild to moderate hypoglycaemia treatment, such as mini-doses of glucagon or continuous subcutaneous glucagon infusion as a part of dual-hormone closed-loop systems. Pharmaceutical companies are developing stable forms of native glucagon or glucagon analogues for that purpose.

A Century of Diabetes Technology: Signals, Models, and Artificial Pancreas Control

Arguably, diabetes mellitus is one of the best-quantified human conditions: elaborate in silico models describe the action of the human metabolic system; real-time signals such as continuous glucose monitoring are readily available; insulin delivery is being automated; and control algorithms are capable of optimizing blood glucose fluctuation in patients' natural environments. The transition of the artificial pancreas (AP) to everyday clinical use is happening now, and is contingent upon seamless concerted work of devices encompassing the patient in a digital treatment ecosystem. This review recounts briefly the story of diabetes technology, which began a century ago with the discovery of insulin, progressed through glucose monitoring and subcutaneous insulin delivery, and is now rapidly advancing towards fully automated clinically viable AP systems.

Using the Novel Approach of an Artificial Pancreas to Manage Type 1 Diabetes Mellitus in Pregnancy

Recent National Institute for Health and Care Excellence (NICE) guidelines suggest that insulin pump therapy should be used in pregnant women with Type 1 diabetes mellitus (T1DM) who do not achieve optimal glycaemic control with multiple daily injection (MDI) therapy. Furthermore, a landmark trial has confirmed that prospective continuous glucose monitoring (CGM) may be beneficial for women using both MDI and insulin pumps during pregnancy, with positive effects on neonatal outcomes. More recently, overnight use of an artificial pancreas (AP) with a model-predictive control algorithm has been shown to improve the amount of time women spend within the overnight glucose target range (3.5–7.8 mmol/L) during pregnancy. However, preliminary studies where the AP is used day and night have shown a high degree of interindividual variability in response to the intervention, and further randomised trials are needed to understand which women are suitable candidates for CGM, insulin pump, and AP technology. It is understood that improvements in maternal glycaemic control can minimise the risk of adverse neonatal outcomes. Given the substantial improvements in glycaemic control with AP use outside of pregnancy, the recent advances in AP technology provide hope that AP systems will improve the effectiveness of continuous subcutaneous insulin infusion and CGM during pregnancy. Further research is needed to evaluate whether AP can optimise glucose control and neonatal outcomes in T1DM pregnancy. This paper will discuss emerging technologies available for the management of T1DM in pregnancy.

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.

Biologically Inspired and Chemically Derived Methods for Glucose-Responsive Insulin Therapy

The controlled delivery of therapeutics in a manner responsive to physiological indicators has promise in realizing new therapeutic approaches to combat disease. This approach is especially relevant in the context of diabetes. Natural fluctuations in blood glucose seen in the healthy state, complete with peaks and troughs, are poorly regulated as a result of detrimental production or ineffective signaling of the insulin hormone. While several manifestations of diabetes are treated with regularly administered exogenous insulin, the present standard of care results in suboptimal glycemic management that poorly recreates natural hormone control, leading to long-term instability and a significantly increased risk for secondary health complications. New synthetic technologies that make insulin available only when needed, and at the exact dose required, have been explored under the broad vision of realizing a "fully synthetic pancreas." Yet, many challenges remain to realizing a technology that is appropriately responsive, safe, and well integrated into a manageable routine. Herein, many of the approaches explored thus far to sense physiological blood glucose and elicit response through the release of therapeutic insulin are summarized. The approaches point to a new, autonomous approach to managing diabetes with biomimetic therapy.

Plasma-Insulin-Cognizant Adaptive Model Predictive Control for Artificial Pancreas Systems

An adaptive model predictive control (MPC) algorithm with dynamic adjustments of constraints and objective function weights based on estimates of the plasma insulin concentration (PIC) is proposed for artificial pancreas (AP) systems. A personalized compartment model that translates the infused insulin into estimates of PIC is integrated with a recursive subspace-based system identification to characterize the transient dynamics of glycemic measurements. The system identification approach is able to identify stable, reliable linear time-varying models from closed-loop data. An MPC algorithm using the adaptive models is designed to compute the optimal exogenous insulin delivery for AP systems without requiring any manually-entered meal information. A dynamic safety constraint derived from the estimation of PIC is incorporated in the adaptive MPC to improve the efficacy of the AP and prevent insulin overdosing. Simulation case studies demonstrate the performance of the proposed adaptive MPC algorithm.

Assessing patient-reported outcomes for automated insulin delivery systems: the psychometric properties of the INSPIRE measures

AIM

Participants in clinical trials assessing automated insulin delivery systems report perceived benefits and burdens that reflect their experiences and may predict their likelihood of uptake and continued use of this novel technology. Despite the importance of understanding their perspectives, there are no available validated and reliable measures assessing the psychosocial aspects of automated insulin delivery systems. The present study assesses the initial psychometric properties of the INSPIRE measures, which were developed for youth and adults with Type 1 diabetes, as well as parents and partners.

METHODS

Data from 292 youth, 159 adults, 150 parents of youth and 149 partners of individuals recruited from the Type 1 Diabetes Exchange Registry were analysed. Participants completed INSPIRE questionnaires and measures of quality of life, fear of hypoglycaemia, diabetes distress, glucose monitoring satisfaction. Exploratory factor analysis assessed factor structures. Associations between INSPIRE scores and other measures, HbA_1c , and technology use assessed concurrent and discriminant validity.

RESULTS

Youth, adult, parent and partner measures assess positive expectancies of automated insulin delivery systems. Measures range from 17 to 22 items and are reliable (α = 0.95-0.97). Youth, adult and parent measures are unidimensional; the partner measure has a two-factor structure (perceptions of impact on partners versus the person with diabetes). Measures showed concurrent and discriminant validity.

CONCLUSIONS

INSPIRE measures assessing the positive expectancies of automated insulin delivery systems for youth, adults, parents and partners have meaningful factor structures and are internally consistent. The developmentally sensitive INSPIRE measures offer added value as clinical trials test newer systems, systems become commercially available and clinicians initiate using these systems.

DREAM5: An open-label, randomized, cross-over study to evaluate the safety and efficacy of day and night closed-loop control by comparing the MD-Logic automated insulin delivery system to sensor augmented pump therapy in patients with type 1 diabetes at home

AIMS

Previous DREAM studies demonstrated the safety and efficacy of the CE marked MD-Logic closed-loop system (DreaMed GlucoSitter) in different settings for overnight glycaemic control. The present study aimed to evaluate the system for day and night use for 60 hours during the weekend at home compared to sensor-augmented pump (SAP) therapy in participants with type 1 diabetes.

METHODS

This was a prospective, multicentre, crossover, controlled study (clinicaltrials.gov NCT01238406). All participants were connected in randomized order for one weekend to SAP therapy or the MD-Logic System. In the intervention arm only, the amount of carbohydrate was entered into the bolus calculator; the rest of insulin delivery was automated and wireless via a tablet computer. The primary endpoint was percentage of glucose values between 70 and 180 mg/dL.

RESULTS

The ITT population comprised 48 (19 males, 29 females) adolescents and adults experienced in sensor use: (median, [IQR]): age, 16.1years [13.2-18.5]; diabetes duration, 9.4 years [5.0-12.7]; pump use, 5.4 years [3.1-9.4]; HbA1c, 7.6% [7.0-8.1]. A significant increase in the percentage of time within target range (70-180 mg/dL) (66.6% vs 59.9%, P = 0.002) was observed with the closed-loop system vs control weekends with unchanged percentage of time below 70 mg/dL (2.3% vs 1.5%, P = 0.369). Mean weekend glucose level per participant was significantly lower (153 [142-175] vs 164 [150-186] mg/dL, P = 0.003). No safety signals were observed.

CONCLUSIONS

The MD-Logic system was safe and associated with better glycaemic control than SAP therapy for day and night use. The absence of remote monitoring did not lead to safety signals in adapting basal rates nor in administration of automated bolus corrections.

Preserved glucose response to low-dose glucagon after exercise in insulin-pump-treated individuals with type 1 diabetes: a randomised crossover study

AIMS/HYPOTHESIS

This study aimed to compare the increase in plasma glucose after a subcutaneous injection of 200 μg glucagon given after 45 min of cycling with resting (study 1) and to investigate the effects of glucagon when injected before compared with after 45 min of cycling (study 2). We hypothesised that: (1) the glucose response to glucagon would be similar after cycling and resting; and (2) giving glucagon before the activity would prevent the exercise-induced fall in blood glucose during exercise and for 2 h afterwards.

METHODS

Fourteen insulin-pump-treated individuals with type 1 diabetes completed three visits in a randomised, placebo-controlled, participant-blinded crossover study. They were allocated by sealed envelopes. Baseline values were (mean and range): HbA_1c 54 mmol/mol (43-65 mmol/mol) or 7.1% (6.1-8.1%); age 45 years (23-66 years); BMI 26 kg/m² (21-30 kg/m²); and diabetes duration 26 years (8-51 years). At each visit, participants consumed a standardised breakfast 2 h prior to 45 min of cycling or resting. A subcutaneous injection of 200 μg glucagon was given before or after cycling or after resting. The glucose response to glucagon was compared after cycling vs resting (study 1) and before vs after cycling (study 2).

RESULTS

The glucose response to glucagon was higher after cycling compared with after resting (mean ± SD incremental peak: 2.6 ± 1.7 vs 1.8 ± 2.0 mmol/l, p = 0.02). As expected, plasma glucose decreased during cycling (-3.1 ± 2.8 mmol/l) but less so when glucagon was given before cycling (-0.9 ± 2.8 mmol/l, p = 0.002). The number of individuals reaching glucose values ≤3.9 mmol/l was the same on the 3 days.

CONCLUSIONS/INTERPRETATION

Moderate cycling for 45 min did not impair the glucose response to glucagon compared with the glucose response after resting. The glucose fall during cycling was diminished by a pre-exercise injection of 200 μg glucagon; however, no significant difference was seen in the number of events of hypoglycaemia.

TRIAL REGISTRATION

Clinicaltrials.gov NCT02882737 FUNDING: The study was funded by the Danish Diabetes Academy founded by Novo Nordisk foundation and by an unrestricted grant from Zealand Pharma.

Model individualization for artificial pancreas

BACKGROUND AND OBJECTIVE

The inter-subject variability characterizing the patients affected by type 1 diabetes mellitus makes automatic blood glucose control very challenging. Different patients have different insulin responses, and a control law based on a non-individualized model could be ineffective. The definition of an individualized control law in the context of artificial pancreas is currently an open research topic. In this work we consider two novel identification approaches that can be used for individualizing linear glucose-insulin models to a specific patient.

METHODS

The first approach belongs to the class of black-box identification and is based on a novel kernel-based nonparametric approach, whereas the second is a gray-box identification technique which relies on a constrained optimization and requires to postulate a model structure as prior knowledge. The latter is derived from the linearization of the average nonlinear adult virtual patient of the UVA/Padova simulator. Model identification and validation are based on in silico data collected during simulations of clinical protocols designed to produce a sufficient signal excitation without compromising patient safety. The identified models are evaluated in terms of prediction performance by means of the coefficient of determination, fit, positive and negative max errors, and root mean square error.

RESULTS

Both identification approaches were used to identify a linear individualized glucose-insulin model for each adult virtual patient of the UVA/Padova simulator. The resulting model simulation performance is significantly improved with respect to the performance achieved by a linear average model.

CONCLUSIONS

The approaches proposed in this work have shown a good potential to identify glucose-insulin models for designing individualized control laws for artificial pancreas.

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

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

Low doses of dasiglucagon consistently increase plasma glucose levels from hypoglycaemia and euglycaemia in people with type 1 diabetes mellitus

AIM

To characterize the pharmacokinetic and pharmacodynamic properties of dasiglucagon, a novel, stable and liquid formulated glucagon analogue, during hypoglycaemic and euglycaemic conditions in adult patients with type 1 diabetes mellitus.

RESEARCH DESIGN AND METHODS

In this randomized double-blind trial, 17 patients received four single subcutaneous doses (0.03, 0.08, 0.2 and 0.6 mg) of dasiglucagon (4 mg/mL formulation) under euglycaemic (plasma glucose [PG] 5.6 mmol/L [100 mg/dL]) or hypoglycaemic (PG 3.1-3.7 mmol/L [56-66 mg/dL]) conditions. For comparison, three doses (0.03, 0.08 and 0.2 mg) of a commercial glucagon formulation (Eli Lilly) were investigated at euglycaemia.

RESULTS

Dasiglucagon led to a dose-dependent and rapid increase in PG levels across all doses tested (mean increases 30 minutes post-dosing of 2.2 to 4.4 mmol/L [39-80 mg/dL] from euglycaemia and 1.3 to 5.2 mmol/L [24-94 mg/dL] from hypoglycaemia), which was higher than the rises elicited by similar doses of commercial glucagon (1.7-3.9 mmol/L [30-71 mg/dL]). The median time (range) to an increase in PG of >1.1 mmol/L (20 mg/dL) was <20 (18-19.5) minutes with 0.03 mg dasiglucagon and, with higher doses, the median times ranged from 9 to 15 minutes (commercial glucagon 13-14 minutes). In hypoglycaemia, 0.03 and 0.08 mg dasiglucagon re-established normoglycaemia (PG ≥3.9 mmol/L [70 mg/dL]) within median times of 14 and 10 minutes, respectively. Nausea and vomiting occurred more frequently with dasiglucagon than with commercial glucagon at identical doses which might be attributable to dasiglucagon's higher potency.

CONCLUSION

Dasiglucagon rapidly increased PG at doses of 0.03 to 0.6 mg in a dose-dependent manner and, therefore, is a good candidate for use in dual-hormone artificial pancreas systems.

Technology for Augmenting Type 1 Diabetes Mellitus Management

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

Optimal predictive low glucose management settings during physical exercise in adolescents with type 1 diabetes

OBJECTIVES

To assess the optimal setting of the predictive low glucose management (PLGM) algorithm for preventing exercise-induced hypoglycemia in adolescents with type 1 diabetes.

METHODS

Thirty-four adolescents, 15 to 20 years, wearing PLGM system, were followed during 3 days exercise during a diabetes camp. PLGM threshold was set at 70 mg/dL between 8 am and 10 pm and 90 mg/dL during 10 pm and 8 am Adolescents were divided into group A and B, with PLGM threshold at 90 and 70 mg/dL, respectively, during exercise. Time spent in hypoglycemia and AUC for time slots 8 am to 1 pm, 1 to 4 pm, 4 to 11 pm, 11 pm to 3 am, 3 to 8 am, in 3 days were compared between groups by Wilcoxon rank sum test.

RESULTS

We analyzed 31 patients (median age 15.0 years, 58.1% males, median diabetes duration 7.0 years, hemoglobin A1c [HbA1c] 7.1%). No significant difference has been observed in time spent in hypoglycemia between groups using threshold 70 or 90. Time spent in target was similar in both groups, as well as time spent in hypo or hyperglycemia. The trends of blood glucose over the 3 days in the 2 groups over-lapped without significant differences.

CONCLUSIONS

A PLGM threshold of 90 mg/dL during the night was associated with reduced time in hypoglycemia in adolescents doing frequent physical exercise, while maintaining 65.1% time in range during the day. However, a threshold of 70 mg/dL seems to be safe in the duration of the physical exercise. PLGM system in adolescents with type 1 diabetes was effective to prevent hypoglycemia during and after exercise, irrespective of the PLGM thresholds used.

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.

Effectiveness of artificial pancreas in the non-adult population: A systematic review and network meta-analysis

OBJECTIVE

Artificial pancreas is a technology that minimizes user input by bridging continuous glucose monitoring and insulin pump treatment, and has proven safety in the adult population. The purpose of this systematic review and meta-analysis is to evaluate the efficacy of closed-loop (CL) systems in the glycemic control of non-adult type 1 diabetes patients in both a pairwise and network meta-analysis (NMA) context and investigate various parameters potentially affecting the outcome.

METHODS

Literature was systematically searched using the MEDLINE (1966-2018), Scopus (2004-2018), Cochrane Central Register of Controlled Trials (CENTRAL) (1999-2018), Clinicaltrials.gov (2008-2018) and Google Scholar (2004-2018) databases. Studies comparing the glycemic control in CL (either single- or dual-hormone) with continuous subcutaneous insulin infusion (CSII) in people with diabetes (PWD) aged <18 years old were deemed eligible. The primary outcome analysis was conducted with regard to time spent in the target glycemic range. All outcomes were evaluated in NMA in order to investigate potential between-algorithm differences. Pairwise meta-analysis and meta-regression were performed using the RevMan 5.3 and Open Meta-Analyst software. For NMA, the package pcnetmetain R 3.5.1 was used.

RESULTS

The meta-analysis was based on 25 studies with a total of 504 PWD. The CL group was associated with significantly higher percentage of time spent in the target glycemic range (Mean (SD): 67.59% (SD: 8.07%) in the target range and OL PWD spending 55.77% (SD: 11.73%), MD: -11.97%, 95% CI [-18.40, -5.54%]) and with lower percentages of time in hyperglycemia (MD: 3.01%, 95% CI [1.68, 4.34%]) and hypoglycemia (MD: 0.67%, 95% CI [0.21, 1.13%]. Mean glucose was also decreased in the CL group (MD: 0.75 mmol/L, 95% CI [0.18-1.33]). The NMA arm of the study showed that the bihormonal modality was superior to other algorithms and standard treatment in lowering mean glucose and increasing time spent in the target range. The DiAs platform was superior to PID in controlling hypoglycemia and mean glucose. Time in target range and mean glucose were unaffected by the confounding factors tested.

CONCLUSIONS

The findings of this meta-analysis suggest that artificial pancreas systems are superior to the standard sensor-augmented pump treatment of type 1 diabetes mellitus in non-adult PWD. Between-algorithm differences are also addressed, implying a superiority of the bihormonal treatment modality. Future large-scale studies are needed in the field to verify these outcomes and to determine the optimal algorithm to be used in the clinical setting.

Capabilities of Next-Generation Patch Pump: Improved Precision, Instant Occlusion Detection, and Dual-Hormone Therapy

Insulin pumps allow patients to attain better blood glucose control with more lifestyle flexibility. Their size and cost, however, limit their usefulness. Current CSII pumps are bulky, intrusive, and expensive. SFC Fluidics is addressing these problems by developing a new type of wearable patch pump based on the patented electro-chemiosmotic (ECO) microfluidic pumping technology. This nonmechanical pumping technology allows accurate and precise delivery of very small amounts of insulin and/or other drugs, including concentrated insulin. The pump engine is small and can be made inexpensively from injection molded parts, allowing its use in a disposable or semidisposable pod format. In addition, a single ECO pump engine can be used to deliver two drugs through independent pathways. Other features of SFC Fluidics' pod include latching safety valves that prevent accidental overdosing of insulin due to pressure changes and an instantaneous occlusion sensor that can immediately detect delivery failure at the first missed dose. These features allow for the development of a series of patch pumps that will offer users the benefit of CSII therapy in a more discreet and reliable patch pump form.

A steady decline in pancreas transplantation rates

BACKGROUND/OBJECTIVES

After years of growth in many pancreas transplant programs, UNOS has reported declining transplant numbers in the USA. This precipitating trend urges for an evaluation of the transplant numbers and scientific productivity in the Eurotransplant region and the UK.

METHODS

We performed a trend analysis of pancreas transplantation rates, between 1997 and 2016, adjusting for changes in population size, and an analysis of scientific publications in this field. We used information from the UNOS, Eurotransplant, and UK transplant registry and bibliometric information from the Web of Science database.

RESULTS

Between 2004 and 2016 there was an average annual decline in pancreas transplantation rates per million inhabitants of 3.3% in the USA and 2.5% in the Eurotransplant region. In the UK, transplant numbers showed an average annual decline of 1.0% from 2009 to 2016. Publications in Q1 journals showed an annual change of -2.1% and +20.1%, before 2004, and a change of -3.8% and -5.5%, between 2004 and 2016, for USA and Eurotransplant publications, respectively.

CONCLUSIONS

Adjusting pancreas transplantation rates for changes in population size showed a clear decline in transplant numbers in both the USA and Eurotransplant region, with first signs of decline in the UK. Following this trend, the number of scientific publications in this field have declined worldwide.

Going beyond HbA1c to understand the benefits of advanced diabetes therapies

The gold standard for monitoring overall glycemia is HbA1c. However, HbA1c has several important limitations, giving more weight to the prior 2 to 3 months rather than short-term glycemic control. In addition, the level of the HbA1c does not reflect the important interpersonal differences in its relationship with mean glucose, and HbA1c is affected by many common clinical conditions (anemia, uremia) that can interfere with the accuracy of its measurement in the laboratory. The development and refinement of continuous glucose monitoring (CGM), a glucose- and patient-centric technology, over the past two decades have permitted the creation of new single and composite metrics, such as the percentage of time in range and the glucose pentagon, respectively, which provide clinically relevant insights into short-term glycemic control. In addition, CGM creates new outcome metrics for clinical management and investigational studies (percentage of time in hypoglycemia, percentage of time in target range) that can accurately and meaningfully report the effects of an intervention, whether that is a drug, a device, or a psychosocial program, and CGM provides the key input to drive algorithm-based insulin delivery. Finally, CGM linked with artificial intelligence permits real-time feedback to patients about modifiable patterns of glycemic excursions.

Considering Plasma Insulin Concentrations in Adaptive Model Predictive Control for Artificial Pancreas Systems

Adaptive and personalized model predictive control (MPC) algorithms that explicitly consider insulin dosing constraints are necessary to prevent overdose-induced hypoglycemia in type 1 diabetes. A personalized plasma insulin concentration (PIC) estimator is integrated with adaptive models to characterize the temporal dynamics of PIC and blood glucose concentration (BGC). The dynamic profile trajectories of the PIC and BGC are used to adapt the control problems and constraints on-line to accurately reflect the concurrent metabolic state of the individuals and improve glucose regulation. The adaptive MPC algorithm, explicitly considering the PIC in the insulin dose computations, is demonstrated to effectively control BGC without any manual user input on meal information while avoiding excessive insulin administration that can cause hypoglycemia. Simulation results demonstrate the ability of the proposed approach with an average 71.14 % of time spent in the target range (BGC ϵ [70, 180]mg/dL).

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.

Hepatic Glucagon Receptor Signaling Enhances Insulin-Stimulated Glucose Disposal in Rodents

Glucagon receptor (GCGR) agonists cause hyperglycemia but also weight loss. However, GCG-like peptide 1 receptor (GLP1R)/GCGR mixed agonists do not exhibit the diabetogenic effects often attributed to GCGR activity. Thus, we sought to investigate the effect of glucagon agonism on insulin action and glucose homeostasis. Acute GCGR agonism induced immediate hyperglycemia, followed by improved glucose tolerance and enhanced glucose-stimulated insulin secretion. Moreover, acute GCGR agonism improved insulin tolerance in a dose-dependent manner in both lean and obese mice. Improved insulin tolerance was independent of GLP1R, FGF21, and hepatic glycogenolysis. Moreover, we observed increased glucose infusion rate, disposal, uptake, and suppressed endogenous glucose production during euglycemic clamps. Mice treated with insulin and GCGR agonist had enhanced phosphorylation of hepatic AKT at Ser473; this effect was reproduced in isolated mouse primary hepatocytes and resulted in increased AKT kinase activity. These data reveal that GCGR agonism enhances glucose tolerance, in part, by augmenting insulin action, with implications for the use of GCGR agonism in therapeutic strategies for diabetes.