Fighting the uphill battle to cure type 1 diabetes.

Three complementary approaches for type 1 diabetes. Immunotherapy targets the pathogenic immune cells or inflammatory cytokines to revert type 1 diabetes. An artificial pancreas delivers insulin automatically using continuous glucose monitoring, a controlling algorithm, and an insulin pump. Beta cell replacement therapy varies depending on the cell sources: allogeneic, or xenogeneic islet; beta-like cells derived from ESCs or iPSCs.

Efficacy, safety, and user experience of DIY or open-source artificial pancreas systems: a systematic review.

The do-it-yourself artificial pancreas system (DIYAPS) is a patient-driven initiative with the potential to revolutionise diabetes management, automating insulin delivery with existing pumps and CGM combined with open-source algorithms. Given the considerable interest in this topic within the diabetes community, we have conducted a systematic review of DIYAPS efficacy, safety, and user experience. Following recognised procedures and reporting standards, we identified 10 eligible publications of 730 participants within the peer-reviewed literature. Overall, studies reported improvements in time in range, HbA1c (glycated haemoglobin), reduced hypoglycaemia, and improved quality of life with DIYAPS use. While results were positive, the identified studies were small, and the majority were observational and at high risk of bias. Further research including well-designed randomised trials comparing DIYAPS with appropriate comparators is recommended.

Glucose outcomes of a learning-type artificial pancreas with an unannounced meal in type 1 diabetes.

BACKGROUND AND OBJECTIVES: Glycemic control with unannounced meals is the major challenge for artificial pancreas. In this study, we described the performance and safety of learning-type model predictive control (L-MPC) for artificial pancreas challenged by an unannounced meal in type 1 diabetes (T1D). METHODS: This closed-loop (CL) system was tested in 29 T1D patients at one site in a 4 h inpatient open-label study. Participants used an L-MPC CL system for 6 days after 2-day system identification using open-loop (OL) insulin system. During the CL period, the L-MPC system was started from 8:00 am to noon each day. At 9:00 am, each participant consumed 50 g of carbohydrates with no prandial insulin bolus. At 9:30 am on CL-Day 4 or CL-Day 6, participants rode bicycles for 20 minutes or drank 50 ml of beer, in a random order. RESULTS: As the primary outcome, TIR on CL-Day 3 was 65.2±23.3%, which was 9.8 points higher (95% CI 1.8 to 17.8; P = 0.019) than that on CL-Day 1. The time of glucose >10 mmol/L was decreased by 11.0% (95% CI -18.7 to 3.3; P = 0.007), and mean glucose level was decreased by 1.1 mmol/L (95% CI -1.1 to 0.5; P = 0.000). The total daily insulin dosage showed no significant difference (-0.1U, 95% CI -1.34 to 1.32; P = 0.982). Compared with OL-Day1 with a postprandial bolus, the TIR was increased by 13.7 points (95% CI 1.4 to 26.0; P = 0.030), the time of glucose >10 mmol/L and the mean glucose level were also decreased. Compared with the exercise day (CL-Day E, 62.0 ± 23.3%; P = 0.347) or alcohol day (CL-Day A, 64.0 ± 23.6%; P = 0.756), there was no statistically significant difference in terms of TIR, time of glucose >10 mmol/L and mean glucose level. No severe hypoglycemic events occurred and hypoglycemic episodes were not increased by using closed-loop insulin system. CONCLUSION: The L-MPC CL insulin system achieved good glycemic control challenged by an unannounced meal.

Effect of frequency of sensor use on glycaemic control in individuals on sensor-augmented pump therapy with and without Predictive Low Glucose Management System.

Improved frequency of sensor use improves glycaemic control. Furthermore, there is no deterioration of glycaemic control with increased sensor use in individuals on Predictive Low Glucose Management (PLGM) system. Younger children are more likely to have better sensor uptake than older children.

Real-World Use of Do-It-Yourself Artificial Pancreas Systems in Children and Adolescents With Type 1 Diabetes: Online Survey and Analysis of Self-Reported Clinical Outcomes.

BACKGROUND: Patient-driven initiatives have made uptake of Do-it-Yourself Artificial Pancreas Systems (DIYAPS) increasingly popular among people with diabetes of all ages. Observational studies have shown improvements in glycemic control and quality of life among adults with diabetes. However, there is a lack of research examining outcomes of children and adolescents with DIYAPS in everyday life and their social context. OBJECTIVE: This survey assesses the self-reported clinical outcomes of a pediatric population using DIYAPS in the real world. METHODS: An online survey was distributed to caregivers to assess the hemoglobin A1c levels and time in range (TIR) before and after DIYAPS initiation and problems during DIYAPS use. RESULTS: A total of 209 caregivers of children from 21 countries responded to the survey. Of the children, 47.4% were female, with a median age of 10 years, and 99.4% had type 1 diabetes, with a median duration of 4.3 years (SD 3.9). The median duration of DIYAPS use was 7.5 (SD 10.0) months. Clinical outcomes improved significantly, including the hemoglobin A1c levels (from 6.91% [SD 0.88%] to 6.27% [SD 0.67]; P<.001) and TIR (from 64.2% [SD 15.94] to 80.68% [SD 9.26]; P<.001). CONCLUSIONS: Improved glycemic outcomes were found across all pediatric age groups, including adolescents and very young children. These findings are in line with clinical trial results from commercially developed closed-loop systems.

Adaptive fuzzy integral sliding mode control of blood glucose level in patients with type 1 diabetes: In silico studies.

Currently, artificial pancreas is an alternative treatment instead of insulin therapy for patients with type 1 diabetes mellitus. Closed-loop control of blood glucose level (BGL) is one of the difficult tasks in biomedical engineering field due to nonlinear time-varying dynamics of insulin-glucose relation that is combined with time delays and model uncertainties. In this paper, we propose a novel adaptive fuzzy integral sliding mode control scheme for BGL regulation. System dynamics is identified online using fuzzy logic systems. The presented method is evaluated in silico studies by nine different virtual patients in three different groups for two continuous days. Simulation results demonstrate effective performance of the proposed control scheme of BGL regulation in presence of simultaneous meal and physical exercise disturbances. Comparison of the proposed control method with proportional-integral-derivative (PID) control and model predictive control (MPC) shows a superiority of the adaptive fuzzy integral sliding mode control with regard to two conventional methods of BGL regulation (PID and MPC) and sliding mode control.

Diabetes Technological Revolution: Winners and Losers?

Over recent years there has been an explosion in availability of technical devices to support diabetes self-management. But with this technology revolution comes new hurdles. On paper, the available diabetes technologies should mean that the vast majority of people with type 1 diabetes have optimal glycemic control and are using their preferred therapy choices. Yet, it does not appear to be universally the case. In parallel, suboptimal glycemic control remains stubbornly widespread. Barriers to improvement include access to technology, access to expert diabetes health care professionals, and prohibitive insurance costs. Until access can be improved to ensure the technologies are available and usable by those that need them, there are many people with diabetes who are still losing out.

Glucose-responsive insulin delivery for type 1 diabetes: The artificial pancreas story.

Insulin replacement therapy is integral to the management of type 1 diabetes, which is characterised by absolute insulin deficiency. Optimal glycaemic control, as assessed by glycated haemoglobin, and avoidance of hyper- and hypoglycaemic excursions have been shown to prevent diabetes-related complications. Insulin pump use has increased considerably over the past decade with beneficial effects on glycaemic control, quality of life and treatment satisfaction. The advent and progress of ambulatory glucose sensor technology has enabled continuous glucose monitoring based on real-time glucose levels to be integrated with insulin therapy. Low glucose and predictive low glucose suspend systems are currently used in clinical practice to mitigate against hypoglycaemia, and provide the first step towards feedback glucose control. The more advanced technology approach, an artificial pancreas or a closed-loop system, gradually increases and decreases insulin delivery in a glucose-responsive fashion to mitigate against hyper- and hypoglycaemia. Randomised outpatient clinical trials over the past 5 years have demonstrated the feasibility, safety and efficacy of the approach, and the recent FDA approval of the first single hormone closed-loop system establishes a new standard of care for people with type 1 diabetes.

Design Considerations for Artificial Pancreas Pivotal Studies.

The development of artificial pancreas systems has evolved to the point that pivotal studies designed to assess efficacy and safety are in progress or soon to be initiated. These pivotal studies are intended to provide the necessary data to gain clearance from the U.S. Food and Drug Administration, coverage by payers, and adoption by patients and clinicians. Although there will not be one design that is appropriate for every system, there are certain aspects of protocol design that will be considerations in all pivotal studies designed to assess efficacy and safety. One key aspect of study design is the intervention to be used by the control group. A case can be made that the control group should use the currently available best technology, which is sensor-augmented pump therapy. However, an equally, if not more, compelling case can be made that the control intervention should be usual care. In this Perspective, we elaborate on this issue and provide a pragmatic approach to the design of clinical trials of artificial pancreas systems.

Use of an artificial pancreas among adolescents for a missed snack bolus and an underestimated meal bolus.

OBJECTIVE: The objective of this study was to evaluate the safety and performance of the artificial pancreas (AP) in adolescents with type 1 diabetes (T1D) following insulin omission for food. RESEARCH DESIGN AND METHODS: In a randomized, cross-over trial, adolescents with T1D aged 13-18 yr were enrolled in a randomized, cross-over trial. On separate days, received either usual care (UC) through their home insulin pump or used an AP system (Diabetes Assistant platform, continuous glucose monitor, and insulin pump). Approximately 1 h after admission, participants in both groups received an unannounced snack of 30 g carbohydrate, and 4 h later they received an 80 g lunch, for which both groups only received 75% of the calculated insulin dose to cover carbohydrates. On the UC day (but not the AP day), they received their full high blood glucose (BG) correction factor at lunch. Each admission lasted approximately 8 h. RESULTS: A total of 16 participants completed the trial. On the AP day (compared to UC), mean BG was lower (197 ± 10 vs. 235 ± 14 mg/dL) and time in range 70-180 mg/dL was higher (43% ± 7 vs. 19% ± 7) (both p < 0.05) overall; these results held in the time following the snack and meal (also p < 0.05). During the trial, there were no differences between groups in the rate of hypoglycemia <70 mg/dL. CONCLUSIONS: The AP provided improvements in short-term glycemic control without increases in hypoglycemia following missed insulin for food in adolescents. Thus, the AP partly compensates for missed insulin boluses for food, a common occurrence in adolescent diabetes care. Further testing is needed in longer-term settings.

Enhancing glucose sensor models: modeling the drop-outs.

BACKGROUND: Computer simulation environments have been used in the development of many artificial pancreas systems. A glucose sensor model is an essential part of these environments, and different models have been proposed. However, not one of these models accounts for drop-outs of sensor readings, a well-known phenomenon caused by physical pressure on the sensor site. In this work, we have proposed an enhanced model that accounts for drop-outs and demonstrated its improvement over the existing one-compartment model. MATERIALS AND METHODS: Potential drop-outs were augmented to the existing model, and their incidences and magnitudes were estimated simultaneously with the model parameters using the Bayesian approach. Drop-outs and model parameters were estimated from data collected from 15 subjects with type 1 diabetes who underwent an artificial pancreas study. Model fitting and parameter estimates were contrasted between the enhanced model and the existing one-compartment model. RESULTS: Both models achieved similar parameter estimates (P=not significant) and were all physiologically plausible. The enhanced model further estimated 1.71 drop-outs per day, which improved model fit (weighted residual reduced from [minimum -4%, maximum 3%] to [-3%, 2%]) and reduced significantly the deviance information criteria from 2739.72 to 1456.00. CONCLUSIONS: The enhanced model improves fitting of glucose levels and should allow more realistic simulations that assesses artificial pancreas systems.

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

BACKGROUND: Glucose control in artificial pancreas (AP) studies is commonly assessed by metrics such as the percentage of time with blood glucose (BG) concentration below 70 mg/dL or in the nearly normal range 70-180 mg/dL (in brief, time in hypoglycemia and time in target, respectively). In outpatient studies these control metrics can be computed only from continuous glucose monitoring (CGM) sensor data, with the risk of an unfair assessment because of their inaccuracy. The aim of the present article is to show that the control metrics can be much more accurately determined if CGM data are preprocessed by a recently proposed retrofitting algorithm. SUBJECTS AND METHODS: Data from 47 type 1 diabetes subjects are considered. Subjects were studied in a closed-loop control trial prescribing three 24-h admissions. Glucose concentration was monitored using the Dexcom(®) (San Diego, CA) SEVEN(®) Plus CGM sensor. Frequent BG reference values were collected in parallel with the YSI analyzer (Yellow Springs Instrument, Yellow Springs, OH). To simulate the few reference values available in outpatient conditions, we down-sampled the YSI data and provided to the retrofitting algorithm only the reference values that would have been collected in outpatient protocols. Time in hypoglycemia, time in target, mean, and SD of glucose profile were computed on the basis of both the original and the retrofitted CGM traces and compared with those computed using the frequently obtained YSI data. RESULTS: Using the retrofitted traces, the average error affecting the estimation of time in hypoglycemia and time in target was approximately halved with respect to the original CGM traces (from 4.5% to 1.9% and from 8.7% to 4.4%, respectively). Error in mean and SD was reduced even further, from 10.0 mg/dL to 3.5 mg/dL and from 8.6 mg/dL to 2.9 mg/dL, respectively. CONCLUSIONS: The improved accuracy of retrofitted CGM with respect to the original CGM traces allows a more reliable assessment of glucose control in outpatient AP studies.

Systematically in silico comparison of unihormonal and bihormonal artificial pancreas systems.

Automated closed-loop control of blood glucose concentration is a daily challenge for type 1 diabetes mellitus, where insulin and glucagon are two critical hormones for glucose regulation. According to whether glucagon is included, all artificial pancreas (AP) systems can be divided into two types: unihormonal AP (infuse only insulin) and bihormonal AP (infuse both insulin and glucagon). Even though the bihormonal AP is widely considered a promising direction, related studies are very scarce due to this system's short research history. More importantly, there are few studies to compare these two kinds of AP systems fairly and systematically. In this paper, two switching rules, P-type and PD-type, were proposed to design the logic of orchestrates switching between insulin and glucagon subsystems, where the delivery rates of both insulin and glucagon were designed by using IMC-PID method. These proposed algorithms have been compared with an optimal unihormonal system on virtual type 1 diabetic subjects. The in silico results demonstrate that the proposed bihormonal AP systems have outstanding superiorities in reducing the risk of hypoglycemia, smoothing the glucose level, and robustness with respect to insulin/glucagon sensitivity variations, compared with the optimal unihormonal AP system.

A generic telemedicine infrastructure for monitoring an artificial pancreas trial.

Telemedicine systems are seen as a possible solution for the remote monitoring of physiological parameters and can be particularly useful for chronic patients treated at home. Implementing those systems however has always required spending a great effort on the underlying infrastructure instead of focusing on the application cores as perceived by their users. This paper proposes an abstract unifying infrastructure for telemedicine services which is loosely based on the multi-agent paradigm. It provides the capability of transferring to the clinic any remotely acquired information, and possibly sending back updates to the patient. The infrastructure is a layered one, with the bottom layer acting at the data level and implemented in terms of a software library targeting a wide set of hardware devices. On top of this infrastructure several services can be written shaping the functionality of the telemedicine application while at the highest level, adhering to a simple agent model, it is possible to reuse those functional components porting the application to different platforms. The infrastructure has been successfully used for implementing a telemonitoring service for a randomized controlled study aimed at testing the effectiveness of the artificial pancreas as a treatment within the AP@home project funded by the European Union.

Closed-loop glucose control: psychological and behavioral considerations.

Since 2000, the diabetes community has witnessed tremendous technological advances that have revolutionized diabetes management. Currently, closed-loop glucose control (CLC) systems, which link continuous subcutaneous insulin infusion and continuous glucose monitoring, are the newest, cutting edge technology aimed at reducing glycemic variability and improving daily management of diabetes. Although advances in knowledge and technology in the treatment of diabetes have improved exponentially, adherence to diabetes regimens remains complex and often difficult to predict. Human factors, such as patient perceptions and behavioral self-regulation, are central to adherence to prescribed regimens, as well as to adoption and utilization of diabetes technology, and they will continue to be crucial as diabetes management evolves. Thus, the aims of this article are three-fold: (1) to review psychological and behavioral factors that have influenced adoption and utilization of past technologies, (2) to examine three theoretical frameworks that may help in conceptualizing relevant patient factors in diabetes management, and (3) to propose patient-selection factors that will likely affect future CLC systems.

Automatic bolus and adaptive basal algorithm for the artificial pancreatic ß-cell.

BACKGROUND: The current basal and bolus insulin pump therapy is dependent on user intervention; because of its open-loop nature, the therapy does not accommodate insulin variability and unmeasured meal disturbances. To conquer these challenges, an automatic bolus and adaptive basal (ABAB) therapy is proposed to regulate glucose levels for people with type 1 diabetes mellitus. METHODS: The basal insulin profile is adjusted by the proposed algorithm every 30 min based on interstitial glucose level and its rate of change. An automated bolus is suggested by the system if a meal is detected or a hyperglycemia event occurs. A conservative insulin bolus is administered, the size of which is determined based on glucose prediction and the subject-specific correction factor. One hour later, the algorithm checks whether another bolus is needed. To prevent overdelivery, insulin-on-board is used as a safety constraint. RESULTS: The ABAB therapy was compared with the optimal open-loop therapy and missed-bolus scenario on 100 adult subjects from the Food and Drug Administration-accepted University of Virginia/Padova Metabolic Simulator. The ABAB therapy presented superior performance according to the control-variability grid analysis. In addition, the ABAB therapy shows excellent robustness to insulin sensitivity rise: the hypoglycemia percentage was only 3.3% even when insulin sensitivity was increased by 20%. Independent of user intervention, the ABAB therapy is a good candidate for the first generation of an artificial pancreas. The proposed therapy shows excellent robustness to insulin dosing mismatches.

Effect of intensive insulin therapy using a closed-loop glycemic control system in hepatic resection patients: a prospective randomized clinical trial.

OBJECTIVE Intensive insulin therapy (IIT) reduces morbidity and mortality in patients in surgical intensive care units. The aim of this study is to assess the effect of IIT using a closed-loop system in hepatectomized patients. RESEARCH DESIGN AND METHODS Patients were randomly assigned to receive IIT using a closed-loop system: an artificial pancreas (AP group) or conventional insulin therapy using the sliding-scale method (SS group). RESULTS The incidence of surgical-site infection in the AP group was significantly lower than that in the SS group. The length of hospitalization required for patients in the AP group was significantly shorter than that in the SS group. CONCLUSIONS Total hospital costs for patients in the AP group were significantly lower than for patients in the SS group. IIT using a closed-loop system maintained near-normoglycemia and contributed to a reduction in the incidence of SSI and total hospital costs due to shortened hospitalization.