In Silico Trials of an Open-Source Android-Based Artificial Pancreas: A New Paradigm to Test Safety and Efficacy of Do-It-Yourself Systems

Point-Counterpoint: The Need for Do-It-Yourself (DIY) Open Source (OS) AID Systems in Type 1 Diabetes Management

In the last decade, technology developed by people with diabetes and their loved ones has added to the options for diabetes management. One such example is that of automated insulin delivery (AID) algorithms, which were created and shared as open source by people living with type 1 diabetes (T1D) years before commercial systems were first available. Now, numerous options for commercial systems exist in some countries, yet tens of thousands of people with diabetes are still choosing Open-Source AID (OS-AID), previously called "do-it-yourself" (DIY) systems, which are noncommercial versions of these open-source AID systems. In this article, we provide point and counterpoint perspectives regarding (1) safety and efficacy, (2) regulation and support, (3) user choice and flexibility, (4) access and affordability, and (5) patient and provider education, for open source and commercial AID systems. The perspectives reflected here include that of a person living with T1D who uses and has developed OS-AID systems, a physician-researcher based in the United States who conducts clinical trials to support development of commercial AID systems and supports people with diabetes using all types of AID, and an endocrinologist with T1D who uses both systems and treats people with diabetes using all types of AID.

Accuracy of an Off-Label Transmitter and Data Manager Paired With an Intermittent Scanned Continuous Glucose Monitor in Adults With Type 1 Diabetes

BACKGROUND

This work evaluates the accuracy and agreement between the FreeStyle Libre sensor (FSL) and an off-label converted real-time continuous glucose monitor (c-rtCGM) device consisting of the MiaoMiao transmitter and the xDrip+ application which can be coupled to the FSL.

METHODS

Four weeks of glucose data were collected from 21 participants with type 1 diabetes using the c-rtCGM and FSL: two weeks with a single initial calibration (uncalibrated) and two weeks with a daily calibration (calibrated). Accuracy and agreement evaluation included mean absolute relative difference (MARD), the %20/20 rule, Bland-Altman plots, and the Consensus Error Grid analysis.

RESULTS

Values reported by the c-rtCGM system compared with the FSL resulted in an overall MARD of 12.06% and 84.71% of the results falling within Consensus Error Grid Zone A when the device is calibrated. For uncalibrated devices, an overall MARD of 17.49% was obtained. Decreased accuracy was shown in the hypoglycemic range and for rates of change greater than 2 mg/dL/min. The between-device bias also incremented with increasing glucose values.

CONCLUSION

Measurements recorded by the c-rtCGM were found to be accurate when compared with FSL data only when performing daily c-rtCGM device calibrations. High drops in accuracy and agreement between devices occurred when the c-rtCGM was not calibrated.

Developing the UVA/Padova Type 1 Diabetes Simulator: Modeling, Validation, Refinements, and Utility

Arguably, diabetes mellitus is one of the best quantified human conditions. In the past 50 years, the metabolic monitoring technologies progressed from occasional assessment of average glycemia via HbA1c, through episodic blood glucose readings, to continuous glucose monitoring (CGM) producing data points every few minutes. The high-temporal resolution of CGM data enabled increasingly intensive treatments, from decision support assisting insulin injection or oral medication, to automated closed-loop control, known as the "artificial pancreas." Throughout this progress, mathematical models and computer simulation of the human metabolic system became indispensable for the technological progress of diabetes treatment, enabling every step, from assessment of insulin sensitivity via the now classic Minimal Model of Glucose Kinetics, to in silico trials replacing animal experiments, to automated insulin delivery algorithms. In this review, we follow these developments, beginning with the Minimal Model, which evolved through the years to become large and comprehensive and trigger a paradigm change in the design of diabetes optimization strategies: in 2007, we introduced a sophisticated model of glucose-insulin dynamics and a computer simulator equipped with a "population" of N = 300 in silico "subjects" with type 1 diabetes. In January 2008, in an unprecedented decision, the Food and Drug Administration (FDA) accepted this simulator as a substitute to animal trials for the pre-clinical testing of insulin treatment strategies. This opened the field for rapid and cost-effective development and pre-clinical testing of new treatment approaches, which continues today. Meanwhile, animal experiments for the purpose of designing new insulin treatment algorithms have been abandoned.

The First Regulatory Clearance of an Open-Source Automated Insulin Delivery Algorithm

Open-source Automated Insulin Dosing (OS-AID) algorithms are made publicly accessible so that every facet of their operation can be understood. Currently, commercial AID algorithms are kept proprietary trade secrets, despite the role they take in making life and death decisions for people living with type 1 diabetes. Loop was the second OS-AID algorithm, developed initially by Nate Racklyeft and Pete Schwamb. In 2018, the nonprofit organization Tidepool (Palo Alto, CA) announced the launch of the "Tidepool Loop" initiative with the aim to generate real-world evidence and obtain regulatory clearance. By the end of 2020, the U.S. Food and Drug Administration received Tidepool's application for an interoperable automated glycemic controller based on Loop. After 2 years, the FDA approved the Tidepool Loop on January 23, 2023.

Efficacy and safety of Android artificial pancreas system use at home among adults with type 1 diabetes mellitus in China: protocol of a 26-week, free-living, randomised, open-label, two-arm, two-phase, crossover trial

INTRODUCTION

Do-it-yourself artificial pancreas system (DIY APS) is built using commercially available insulin pump, continuous glucose monitoring (CGM) and an open-source algorithm. Compared with commercial products, DIY systems are affordable, allow personalised settings and provide updated algorithms, making them a more promising therapy for most patients with type 1 diabetes mellitus (T1DM). Many small and self-reported observational studies have found that their real-world use was associated with potential metabolic and psychological benefits. However, rigorous-designed studies are urgently needed to confirm its efficacy and safety.

METHODS AND ANALYSIS

In this 26-week randomised, open-label, two-arm, two-phase, crossover trial, participants aged 18-75 years, with T1DM and glycated haemoglobin (HbA1c) 7-11%, will use AndroidAPS during one 12-week period and sensor-augmented pump during another 12-week period. This study will recruit at least 24 randomised participants. AndroidAPS consists of three components: (1) real-time CGM; (2) insulin pump; (3) AndroidAPS algorithm implemented in Android smartphone. The primary endpoint is time in range (3.9-10.0 mmol/L) derived from CGM. The main secondary endpoints include percentage of sensor glucose values below, within and above target range; mean sensor glucose value; measures of glycaemic variability and centralised HbA1c. Safety endpoints mainly include the frequency of hypoglycaemia events, diabetic ketoacidosis and other serious adverse events.

ETHICS AND DISSEMINATION

This study has been approved by the Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University. There will be verbal and written information regarding the trial given to each participant. The study will be disseminated through peer-reviewed publications and conference presentations.

OVERALL STATUS

Recruiting.

STUDY START

11 February 2023.

PRIMARY COMPLETION

31 July 2024.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT05726461).

A systematic review of cardiac in-silico clinical trials

Computational models of the heart are now being used to assess the effectiveness and feasibility of interventions through in-silico clinical trials (ISCTs). As the adoption and acceptance of ISCTs increases, best practices for reporting the methodology and analysing the results will emerge. Focusing in the area of cardiology, we aim to evaluate the types of ISCTs, their analysis methods and their reporting standards. To this end, we conducted a systematic review of cardiac ISCTs over the period of 1 January 2012-1 January 2022, following the preferred reporting items for systematic reviews and meta-analysis (PRISMA). We considered cardiac ISCTs of human patient cohorts, and excluded studies of single individuals and those in which models were used to guide a procedure without comparing against a control group. We identified 36 publications that described cardiac ISCTs, with most of the studies coming from the US and the UK. In 75% of the studies, a validation step was performed, although the specific type of validation varied between the studies. ANSYS FLUENT was the most commonly used software in 19% of ISCTs. The specific software used was not reported in 14% of the studies. Unlike clinical trials, we found a lack of consistent reporting of patient demographics, with 28% of the studies not reporting them. Uncertainty quantification was limited, with sensitivity analysis performed in only 19% of the studies. In 97% of the ISCTs, no link was provided to provide easy access to the data or models used in the study. There was no consistent naming of study types with a wide range of studies that could potentially be considered ISCTs. There is a clear need for community agreement on minimal reporting standards on patient demographics, accepted standards for ISCT cohort quality control, uncertainty quantification, and increased model and data sharing.

First Use of Open-Source Automated Insulin Delivery AndroidAPS in Full Closed-Loop Scenario; Pancreas4ALL Randomized Pilot Study

Objective: We evaluated the safety and feasibility of open-source automated insulin delivery AndroidAPS in adolescents and young adults with type 1 diabetes (T1D) and compared its efficacy in three different scenarios: hybrid closed loop (HCL) with meal boluses, meal announcement only (MA), and full closed loop (FCL). Research Design and Methods: In an open-label, prospective, randomized crossover trial (clinicaltrials.gov NCT04835350), 16 adolescents with T1D (10 females) with mean age of 17 years (range 15-20), glycated hemoglobin 56 mmol/mol (range 43-75), and mean duration of diabetes 5.9 years (9-15) underwent three distinct 3-day periods of camp living, comparing the above-mentioned scenarios of AndroidAPS. We used modified and locked version of AndroidAPS 3.1.03, which was called Pancreas4ALL for study purposes. The order of MA and FCL periods was assigned randomly. The primary endpoints were feasibility and safety of the system represented by percentage of time of glucose control by the system and time in hypoglycemia below 3 mmol/L. Results: The glycemia was controlled by the system 95% time of the study and the proportion of time below 3 mmol/L did not exceed 1% over the whole study period (0.72%). The HCL scenario reached significantly higher percentage of time below 3 mmol/L (HCL 1.05% vs. MA 0.0% vs. FCL 0.0%; P = 0.05) compared to other scenarios. No difference was observed among the scenarios in the percentage of time between 3.9 and 10 mmol/L (HCL 83.3% vs. MA 79.85% vs. FCL 81.03%, P = 0.58) corresponding to mean glycemia (HCL 6.65 mmol/L vs. MA 7.34 mmol/L vs. FCL 7.05 mmol/L, P = 0.28). No difference was observed in the mean daily dose of insulin or in the daily carbohydrate intake. No serious adverse event occurred during the study period. Conclusions: Our pilot study showed that FCL might be a realistic mode of treatment for people with T1D.

In-silico evaluation of an artificial pancreas achieving automatic glycemic control in patients with type 1 diabetes

Artificial pancreas (AP) is a useful tool for maintaining the blood glucose (BG) of patients with type 1 diabetes (T1D) within the euglycemic range. An intelligent controller has been developed based on general predictive control (GPC) for AP. This controller exhibits good performance with the UVA/Padova T1D mellitus simulator approved by the US Food and Drug Administration. In this work, the GPC controller was further evaluated under strict conditions, including a pump with noise and error, a CGM sensor with noise and error, a high carbohydrate intake, and a large population of 100 in-silico subjects. Test results showed that the subjects are in high risk for hypoglycemia. Thus, an insulin on board (IOB) calculator, as well as an adaptive control weighting parameter (AW) strategy, was introduced. The percentage of time spent in euglycemic range of the in-silico subjects was 86.0% ± 5.8%, and the patient group had a low risk of hypoglycemia with the GPC+IOB+AW controller. Moreover, the proposed AW strategy is more effective in hypoglycemia prevention and does not require any personalized data compared with the IOB calculator. Thus, the proposed controller realized an automatic control of the BG of patients with T1D without meal announcements and complex user interaction.

Minimal and Maximal Models to Quantitate Glucose Metabolism: Tools to Measure, to Simulate and to Run in Silico Clinical Trials

Several models have been proposed to describe the glucose system at whole-body, organ/tissue and cellular level, designed to measure non-accessible parameters (minimal models), to simulate system behavior and run in silico clinical trials (maximal models). Here, we will review the authors' work, by putting it into a concise historical background. We will discuss first the parametric portrait provided by the oral minimal models-building on the classical intravenous glucose tolerance test minimal models-to measure otherwise non-accessible key parameters like insulin sensitivity and beta-cell responsivity from a physiological oral test, the mixed meal or the oral glucose tolerance tests, and what can be gained by adding a tracer to the oral glucose dose. These models were used in various pathophysiological studies, which we will briefly review. A deeper understanding of insulin sensitivity can be gained by measuring insulin action in the skeletal muscle. This requires the use of isotopic tracers: both the classical multiple-tracer dilution and the positron emission tomography techniques are discussed, which quantitate the effect of insulin on the individual steps of glucose metabolism, that is, bidirectional transport plasma-interstitium, and phosphorylation. Finally, we will present a cellular model of insulin secretion that, using a multiscale modeling approach, highlights the relations between minimal model indices and subcellular secretory events. In terms of maximal models, we will move from a parametric to a flux portrait of the system by discussing the triple tracer meal protocol implemented with the tracer-to-tracee clamp technique. This allows to arrive at quasi-model independent measurement of glucose rate of appearance (Ra), endogenous glucose production (EGP), and glucose rate of disappearance (Rd). Both the fast absorbing simple carbs and the slow absorbing complex carbs are discussed. This rich data base has allowed us to build the UVA/Padova Type 1 diabetes and the Padova Type 2 diabetes large scale simulators. In particular, the UVA/Padova Type 1 simulator proved to be a very useful tool to safely and effectively test in silico closed-loop control algorithms for an artificial pancreas (AP). This was the first and unique simulator of the glucose system accepted by the U.S. Food and Drug Administration as a substitute to animal trials for in silico testing AP algorithms. Recent uses of the simulator have looked at glucose sensors for non-adjunctive use and new insulin molecules.

Practical Guidance on Open Source and Commercial Automated Insulin Delivery Systems: A Guide for Healthcare Professionals Supporting People with Insulin-Requiring Diabetes

As increasing numbers of people with insulin-managed diabetes use automated insulin delivery (AID) systems or seek such technologies, healthcare providers are faced with a steep learning curve. Healthcare providers need to understand how to support these technologies to help inform shared decision making, discussing available options, implementing them in the clinical setting, and guiding users in special situations. At the same time, there is a growing diversity of commercial and open source automated insulin delivery systems that are evolving at a rapid pace. This practical guide seeks to provide a conversational framework for healthcare providers to first understand and then jointly assess AID system options with users and caregivers. Using this framework will help HCPs in learning how to evaluate potential new commercial or open source AID systems, while also providing a guide for conversations to help HCPs to assess the readiness and understanding of users for AID systems. The choice of an AID system is not as simple as whether the system is open source or commercially developed, and indeed there are multiple criteria to assess when choosing an AID system. Most importantly, the choices and preferences of the person living with diabetes should be at the center of any decision around the ideal automated insulin delivery system or any other diabetes technology. This framework highlights issues with AID use that may lead to burnout or perceived failures or may otherwise cause users to abandon the use of AID. It discusses the troubleshooting of basic AID system operation and discusses more advanced topics regarding how to maximize the time spent on AID systems, including how to optimize settings and behaviors for the best possible outcomes with AID technology for people with insulin-requiring diabetes. This practical approach article demonstrates how healthcare providers will benefit from assessing and better understanding all available AID system options to enable them to best support each individual.

Dynamic of Glucose Homeostasis in Virtual Patients: A Comparison between Different Behaviors

This work presents a mathematical model of homeostasis dynamics in healthy individuals, focusing on the generation of conductive data on glucose homeostasis throughout the day under dietary and physical activity factors. Two case studies on glucose dynamics for populations under conditions of physical activity and sedentary lifestyle were developed. For this purpose, two types of virtual populations were generated, the first population was developed according to the data of a total of 89 physical persons between 20 and 75 years old and the second was developed using the Monte Carlo approach, obtaining a total of 200 virtual patients. In both populations, each participant was classified as an active or sedentary person depending on the physical activity performed. The results obtained demonstrate the capacity of virtual populations in the generation of in-silico approximations similar to those obtained from in-vivo studies. Obtaining information that is only achievable through specific in-vivo experiments. Being a tool that generates information for the approach of alternatives in the prevention of the development of type 2 Diabetes.

Efficient Closed Loop Simulation of Do-It-Yourself Artificial Pancreas Systems

BACKGROUND

Numerical simulations, also referred to as in silico trials, are nowadays the first step toward approval of new artificial pancreas (AP) systems. One suitable tool to run such simulations is the UVA/Padova Type 1 Diabetes Metabolic Simulator (T1DMS). It was used by Toffanin et al. to provide data about safety and efficacy of AndroidAPS, one of the most wide-spread do-it-yourself AP systems. However, the setup suffered from slow simulation speed. The objective of this work is to speed up simulation by implementing the algorithm directly in MATLAB^®/Simulink^®.

METHOD

Firstly, AndroidAPS is re-implemented in MATLAB^® and verified. Then, the function is incorporated into T1DMS. To evaluate the new setup, a scenario covering 2 days in real time is run for 30 virtual patients. The results are compared to those presented in the literature.

RESULTS

Unit tests and integration tests proved the equivalence of the new implementation and the original AndroidAPS code. Simulation of the scenario required approximately 15 minutes, corresponding to a speed-up factor of roughly 1000 with respect to real time. The results closely resemble those presented by Toffanin et al. Discrepancies were to be expected because a different virtual population was considered. Also, some parameters could not be extracted from and harmonized with the original setup.

CONCLUSIONS

The new implementation facilitates extensive in silico trials of AndroidAPS due to the significant reduction of runtime. This provides a cheap and fast means to test new versions of the algorithm before they are shared with the community.

Closed-Loop Insulin Delivery Systems: Past, Present, and Future Directions

Closed-loop (artificial pancreas) systems for automated insulin delivery have been likened to the holy grail of diabetes management. The first iterations of glucose-responsive insulin delivery were pioneered in the 1960s and 1970s, with the development of systems that used venous glucose measurements to dictate intravenous infusions of insulin and dextrose in order to maintain normoglycemia. Only recently have these bulky, bedside technologies progressed to miniaturized, wearable devices. These modern closed-loop systems use interstitial glucose sensing, subcutaneous insulin pumps, and increasingly sophisticated algorithms. As the number of commercially available hybrid closed-loop systems has grown, so too has the evidence supporting their efficacy. Future challenges in closed-loop technology include the development of fully closed-loop systems that do not require user input for meal announcements or carbohydrate counting. Another evolving avenue in research is the addition of glucagon to mitigate the risk of hypoglycemia and allow more aggressive insulin dosing.

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

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

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.

An In Silico Head-to-Head Comparison of the Do-It-Yourself Artificial Pancreas Loop and Bio-Inspired Artificial Pancreas Control Algorithms

BACKGROUND

User-developed automated insulin delivery systems, also referred to as do-it-yourself artificial pancreas systems (DIY APS), are in use by people living with type 1 diabetes. In this work, we evaluate, in silico, the DIY APS Loop control algorithm and compare it head-to-head with the bio-inspired artificial pancreas (BiAP) controller for which clinical data are available.

METHODS

The Python version of the Loop control algorithm called PyLoopKit was employed for evaluation purposes. A Python-MATLAB interface was created to integrate PyLoopKit with the UVa-Padova simulator. Two configurations of BiAP (non-adaptive and adaptive) were evaluated. In addition, the Tandem Basal-IQ predictive low-glucose suspend was used as a baseline algorithm. Two scenarios with different levels of variability were used to challenge the algorithms on the adult (n = 10) and adolescent (n = 10) virtual cohorts of the simulator.

RESULTS

Both BiAP and Loop improve, or maintain, glycemic control when compared with Basal-IQ. Under the scenario with lower variability, BiAP and Loop perform relatively similarly. However, BiAP, and in particular its adaptive configuration, outperformed Loop in the scenario with higher variability by increasing the percentage time in glucose target range 70-180 mg/dL (BiAP-Adaptive vs Loop vs Basal-IQ) (adults: 89.9% ± 3.2%* vs 79.5% ± 5.3%* vs 67.9% ± 8.3%; adolescents: 74.6 ± 9.5%* vs 53.0% ± 7.7% vs 55.4% ± 12.0%, where * indicates the significance of P < .05 calculated in sequential order) while maintaining the percentage time below range (adults: 0.89% ± 0.37% vs 1.72% ± 1.26% vs 3.41 ± 1.92%; adolescents: 2.87% ± 2.77% vs 4.90% ± 1.92% vs 4.17% ± 2.74%).

CONCLUSIONS

Both Loop and BiAP algorithms are safe and improve glycemic control when compared, in silico, with Basal-IQ. However, BiAP appears significantly more robust to real-world challenges by outperforming Loop and Basal-IQ in the more challenging scenario.

The Evolution of Insulin Administration in Type 1 Diabetes

Insulin has been utilized in the treatment of type 1 diabetes (T1D) for 100 years. While there is still no cure for T1D, insulin administration has undergone a remarkable evolution which has contributed to improvements in quality of life and life expectancy in individuals with T1D. The advent of faster-acting and longer-acting insulins allowed for the implementation of insulin regimens more closely resembling normal insulin physiology. These improvements afforded better glycemic control, which is crucial for limiting microvascular complications and improving T1D outcomes. Suspension of insulin delivery in response to actual and forecasted hypoglycemia has improved quality of life and mitigated hypoglycemia without compromising glycemic control. Advances in continuous glucose monitoring (CGM) and insulin pumps, efforts to model glucose and insulin kinetics, and the application of control theory to T1D have made the automation of insulin delivery a reality. This review will summarize the past, present, and future of insulin administration in T1D.

How It Started, How It Is Going: The Future of Artificial Pancreas Systems (Automated Insulin Delivery Systems)

Originally, the future of automated insulin delivery (AID) systems, or artificial pancreas systems (APS), was having them at all, in any form. We've learned in the last half dozen years that the future of all artificial pancreas systems holds higher time in range, less work required to manage automated insulin delivery systems to improve quality of life, and the ability to input critical information back into the system itself. The data and user experience stories make it clear: APS works. APS are an improvement over other diabetes therapy methods when they are made available, accessible, and affordable. Understanding the unmet expectations of current users of first generation APS technology may also aid in the development of improved technology and user experiences for the future of APS.

Why #WeAreNotWaiting-Motivations and Self-Reported Outcomes Among Users of Open-source Automated Insulin Delivery Systems: Multinational Survey

Background

Automated insulin delivery (AID) systems have been shown to be safe and effective in reducing hyperglycemia and hypoglycemia but are not universally available, accessible, or affordable. Therefore, user-driven open-source AID systems are becoming increasingly popular.

Objective

This study aims to investigate the motivations for which people with diabetes (types 1, 2, and other) or their caregivers decide to build and use a personalized open-source AID.

Methods

A cross-sectional web-based survey was conducted to assess personal motivations and associated self-reported clinical outcomes.

Results

Of 897 participants from 35 countries, 80.5% (722) were adults with diabetes and 19.5% (175) were caregivers of children with diabetes. Primary motivations to commence open-source AID included improving glycemic outcomes (476/509 adults, 93.5%, and 95/100 caregivers, 95%), reducing acute (443/508 adults, 87.2%, and 96/100 caregivers, 96%) and long-term (421/505 adults, 83.3%, and 91/100 caregivers, 91%) complication risk, interacting less frequently with diabetes technology (413/509 adults, 81.1%; 86/100 caregivers, 86%), improving their or child’s sleep quality (364/508 adults, 71.6%, and 80/100 caregivers, 80%), increasing their or child’s life expectancy (381/507 adults, 75.1%, and 84/100 caregivers, 84%), lack of commercially available AID systems (359/507 adults, 70.8%, and 79/99 caregivers, 80%), and unachieved therapy goals with available therapy options (348/509 adults, 68.4%, and 69/100 caregivers, 69%). Improving their own sleep quality was an almost universal motivator for caregivers (94/100, 94%). Significant improvements, independent of age and gender, were observed in self-reported glycated hemoglobin (HbA_1c), 7.14% (SD 1.13%; 54.5 mmol/mol, SD 12.4) to 6.24% (SD 0.64%; 44.7 mmol/mol, SD 7.0; P<.001), and time in range (62.96%, SD 16.18%, to 80.34%, SD 9.41%; P<.001).

Conclusions

These results highlight the unmet needs of people with diabetes, provide new insights into the evolving phenomenon of open-source AID technology, and indicate improved clinical outcomes. This study may inform health care professionals and policy makers about the opportunities provided by open-source AID systems.

International Registered Report Identifier (IRRID)

RR2-10.2196/15368

Pre-school and school-aged children benefit from the switch from a sensor-augmented pump to an AndroidAPS hybrid closed loop: A retrospective analysis

OBJECTIVE

Data on closed loop systems in young children with type 1 diabetes (T1D) are limited. We tested the efficacy and safety of an open-source, do-it-yourself automated insulin delivery system AndroidAPS in preschool and school-aged children.

RESEARCH DESIGN AND METHODS

This retrospective study analyzed diabetes control in 18 preschool (3-7 years) and 18 school-aged children (8-14 years) with T1D who switched from a sensor-augmented pump (SAP) to AndroidAPS. We compared the CGM parameters and HbA1c levels 3 months before and 6 months after the initiation of AndroidAPS therapy and evaluated frequency of severe adverse events during AndroidAPS use, the most frequent reasons for its interruption, and the experience and psychosocial benefits of AndroidAPS use.

RESULTS

General glycemic control was significantly improved after the switch from SAP to AndroidAPS. Time in range (TIR) increased in both preschool (70.8%-78.6%, p = 0.004) and school-aged children (77.2%-82.9%, p < 0.001), whereas HbA1c levels decreased (preschool children 53.8-48.5 mmol/mol, p < 0.001; school-aged children 52.6-45.1 mmol/mol, p = 0.001). Time spent in range of 3.0-3.8 mmol/L increased slightly in school children (2.6%-3.8%, p = 0.040), but not in preschool children (3.0%-3.0%, p = 0.913). Time spent at <3 mmol/L remained unchanged in both preschool (0.95%-0.67%, p = 0.432) and school-aged children (0.8%-0.8%, p = 1.000). No episodes of severe hypoglycemia or DKA and significant improvement of quality of life were reported by AndroidAPS users.

CONCLUSIONS

AndroidAPS seems effective for T1D control both in preschool and school-age children but further validation by prospective studies is necessary.

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.

Full closed loop open-source algorithm performance comparison in pigs with diabetes

Understanding how automated insulin delivery (AID) algorithm features impact glucose control under full closed loop delivery represents a critical step toward reducing patient burden by eliminating the need for carbohydrate entries at mealtimes. Here, we use a pig model of diabetes to compare AndroidAPS and Loop open‐source AID systems without meal announcements. Overall time‐in‐range (70–180 mg/dl) for AndroidAPS was 58% ± 5%, while time‐in‐range for Loop was 35% ± 5%. The effect of the algorithms on time‐in‐range differed between meals and overnight. During the overnight monitoring period, pigs had an average time‐in‐range of 90% ± 7% when on AndroidAPS compared to 22% ± 8% on Loop. Time‐in‐hypoglycemia also differed significantly during the lunch meal, whereby pigs running AndroidAPS spent an average of 1.4% (+0.4/−0.8)% in hypoglycemia compared to 10% (+3/−6)% for those using Loop. As algorithm design for closed loop systems continues to develop, the strategies employed in the OpenAPS algorithm (known as oref1) as implemented in AndroidAPS for unannounced meals may result in a better overall control for full closed loop systems.

Safety and glycemic outcomes of do-it-yourself AndroidAPS hybrid closed-loop system in adults with type 1 diabetes

Background

The aim of the study was to assess the safety and glycemic outcomes with the use of a Do-It-Yourself (DIY) Hybrid Closed-Loop (HCL) system based on the AndroidAPS application in type 1 diabetes (T1D).

Methods

Single-center clinical trial, with 3-week run-in and 12-week study period. DIY HCL system consisted of the Dana Diabecare RS insulin pump, Dexcom G5 continuous glucose monitoring system and AndroidAPS application. Primary outcome was safety: incidences of severe hypoglycemia, diabetic ketoacidosis, time spent in glycemia <54 mg/dl. Secondary endpoints included percentage of time in range (TIR) 70–180 mg/dl, time below 70 mg/dl, HbA1c, insulin requirements, and body weight.

Results

In total 12 subjects (5 men, 7 women) were enrolled, mean age 31.3±6.7, 95%CI(27.7–34.9) years, mean diabetes duration 16.1±5.7, 95%CI(13.0–19.2) years. No episodes of severe hypoglycemia or ketoacidosis were observed. Percentage of time spent in glycemia below 54mg/dl was not increased. Average sensor glycemia was lower in the study period than baseline (141.1 ± 8.4, 95%CI(136.3–145.9) vs. 153.3 ± 17.9, 95%CI(143.2–163.4), mg/dl p<0.001). TIR 70–180 mg/dl was improved by 11.3%, 95%CI(2.8%-19.8%) (from 68.0 ± 12.7 to 79.3 ± 6.4%, p<0.001), without increasing hypoglycemia time. The HbA1c level decreased by -0.5%, 95%CI(-0.9%–-0.1%) (from 6.8 ± 0.5 to 6.3 ± 0.4%, p<0.001). Additionally, in the last 4 weeks of the study period participants significantly improved and showed TIR 70–180 mg/dl 82.1 ± 5.6%, 95%CI(78.9–85.3), time <54 mg/dl 0.30 (0.20–0.55)%, median 95%CI(0.1–0.7) and <70 mg/dl 1.90 (1.10–3.05)%, median 95%CI(0.7–3.2). The insulin requirement and body weight did not change in the study.

Conclusions

The study revealed safety of the Do-It-Yourself HCL system AndroidAPS in adults with T1D, limited to well-controlled, highly selected and closely monitored patients. The use of AndroidAPS significantly improved HbA1c, time in range and average sensor glycemia without increasing hypoglycemia. As both patients and their medical team are gaining experience using the system over time, they improve glycemic control.

Trial registration

German Clinical Trials Register: no. DRKS00015439; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00015439.

Effect of nutrition on postprandial glucose control in hospitalized patients with type 2 diabetes receiving fully automated closed-loop insulin therapy

Fully automated closed-loop insulin delivery may offer a novel way to manage diabetes in hospital. However, postprandial glycaemic control remains challenging. We aimed to assess the effect of nutritional intake on postprandial glucose control in hospitalized patients with type 2 diabetes receiving fully closed-loop insulin therapy. The effects of different meal types and macronutrient composition on sensor glucose time-in-target (TIT, 3.9-10.0 mmol/L) and mean sensor glucose were assessed with hierarchical linear models using a Bayesian estimation approach. TIT was lower and the mean sensor glucose slightly higher, after breakfast compared with lunch and dinner, whereas the insulin dose was higher. Across meals, when carbohydrates were replaced by fat, or to a lesser extent by protein, postprandial glucose control improved. For breakfast, a 3.9% improvement in TIT was observed when 10% of the energy from carbohydrates was replaced by fat. Improvements were slightly lower during lunch and dinner (3.2% and 3.4%) or when carbohydrates were replaced by protein (2.2 and 2.7%, respectively). We suggest that reducing carbohydrate at the expense of fat or protein, could further improve glucose control during fully closed-loop insulin therapy in hospital.

The Padova Type 2 Diabetes Simulator from Triple-Tracer Single-Meal Studies: In Silico Trials Also Possible in Rare but Not-So-Rare Individuals

Background:In silico trials in type 2 diabetes (T2D) would be useful for testing diabetes treatments and accelerating the development of new antidiabetic drugs. In this study, we present a T2D simulator able to reproduce the variability observed in a T2D population. The simulator also allows to safely experiment on virtual subjects with severe (and possibly rare) pathological conditions. Methods: A meal simulation model of glucose, insulin, and C-peptide systems, made of 15 differential equations and 39 parameters, has been identified using a system decomposition and forcing function Bayesian strategy on data of 51 T2D subjects undergoing a single triple-tracer mixed meal. One hundred T2D in silico subjects have been generated from the joint distribution of estimated model parameters. A case study is presented to illustrate the simulator use for testing a virtual drug (improving insulin action and secretion) in a subpopulation of rare, extremely impaired, T2D subjects. Results: The model well fitted T2D data and parameters were estimated with precision. Simulated plasma glucose, insulin, and C-peptide well matched the data (e.g., median [25^th-75^th percentile] glucose area under the curves of 6.9 [6.1-8.5] 10⁴ mg/dL·min in silico vs. 7.0 [5.6-8.2] 10⁴ mg/dL·min in vivo). The potential use of the simulator was shown in a case study, in which the (virtual) antidiabetic drug dose was optimized for very insulin-resistant T2D subjects. Conclusions: We have developed a T2D simulator that captures the behavior of T2D population during a meal, both in terms of average and intersubject variability. The simulator represents a cost-effective way to test new antidiabetic drugs, before moving to human trials.

The Do-It-Yourself Artificial Pancreas: A Comprehensive Review

Diabetes technology (DT) has accomplished tremendous progress in the past decades, aiming to convert these technologies as viable treatment options for the benefit of patients with diabetes (PWD). Despite the advances, PWD face multiple challenges with the efficient management of type 1 diabetes. Most of the promising and innovative technological developments are not accessible to a larger proportion of PWD. The slow pace of development and commercialization, overpricing, and lack of peer support are few such factors leading to inequitable access to the innovations in DT. Highly motivated and tech-savvy members of the diabetes community have therefore come up with the #WeAreNotWaiting movement and started developing their own do-it-yourself artificial pancreas systems (DIYAPS) integrating continuous glucose monitoring (CGM), insulin pumps, and smartphone technology to run openly shared algorithms to achieve appreciable glycemic control and quality of life (QoL). These systems use tailor-made interventions to achieve automated insulin delivery (AID) and are not commercialized or regulated. Online social network megatrends such as GitHub, CGM in the Cloud, and Twitter have been providing platforms to share these open source technologies and user experiences. Observational studies, anecdotal evidence, and real-world patient stories revealed significant improvements in time in range (TIR), time in hypoglycemia (TIHypo), HbA1c levels, and QoL after the initiation of DIYAPS. But this unregulated do-it-yourself (DIY) approach is perceived with great circumspection by healthcare professionals (HCP), regulatory bodies, and device manufacturers, making users the ultimate risk-bearers. The use of the regularized CGM and insulin pump with unauthorized algorithms makes them off-label and has been a matter of great concern. Besides these, lack of safety data, funding or insurance coverage, ethical, and legal issues are roadblocks to the unanimous acceptance of these systems among patients with type 1 diabetes (T1D). A multi-agency approach is necessary to evaluate the risks, and to delineate the incumbency and liability of clinicians, regulatory bodies, and manufacturers associated with the use of DIYAPS. Understanding the potential of DIYAPS as the need of the present time, many regional and international agencies have come with strategies to appraise its safety as well as to support education and training on its use. Here we provide a comprehensive description of the DIYAPS-including their origin, existing literature, advantages, and disadvantages that can help the industry leaders, clinicians, and PWD to make the best use of these systems.

CREATE (Community deRivEd AutomaTEd insulin delivery) trial. Randomised parallel arm open label clinical trial comparing automated insulin delivery using a mobile controller (AnyDANA-loop) with an open-source algorithm with sensor augmented pump therapy in type 1 diabetes

Background

Commercialised automated insulin delivery (AID) systems have demonstrated improved outcomes in type 1 diabetes (T1D), however, they have limited capacity for algorithm individualisation, and can be prohibitively expensive if an individual is without access to health insurance or health funding subsidy. Freely available open-source algorithms, which have the ability to individualise algorithm parameters paired with commercial insulin pumps, and continuous glucose monitoring make up the so-called "do it yourself" (DIY) approach to AID. Limited data on the open-source approach have shown promising results, but data from a large randomised control trial are lacking.

Methods

The CREATE (Community deRivEd AutomaTEd insulin delivery) trial is an open-labelled, randomised, parallel 24-week, multi-site trial comparing sensor augmented pump therapy (SAPT) to our AnyDANA-loop. The three components of AnyDANA-loop are: 1) OpenAPS algorithm implemented in a smartphone (a version of AndroidAPS), 2) DANA-i™ insulin pump and, 3) Dexcom G6^R continuous glucose monitor (CGM). The primary outcome measure is the percentage of time in target sensor glucose range (3.9 -10mmol/L). Secondary outcomes include psycho-social factors and platform performance. Analysis of online collective learning, characteristic of the open-source approach, is planned. 100 participants with T1D aged 7 - 70 years (age stratified into children/adolescents 7-15 years and adults 16-70 years), will be recruited from four sites in New Zealand. A 24-week continuation phase follows, to assess long-term safety.

Use of a do-it-yourself artificial pancreas system is associated with better glucose management and higher quality of life among adults with type 1 diabetes

BACKGROUND

Previous studies show that the use of do-it-yourself artificial pancreas system (DIYAPS) may be associated with better glycemic control characterized by improved estimated hemoglobin A1c (eHbA1c) and time in range among adults with type 1 diabetes (T1D). However, few studies have demonstrated the changes in laboratory-measured HbA1c, which is a more accepted index for glycemic control, after using a DIYAPS.

METHODS

This is a retrospective before-after study approaching patients who reported self-use of AndroidAPS. The main inclusion criteria included: T1D; aged ⩾18 years; having complete record of ⩾3 months of continuous AndroidAPS use; with laboratory-measured HbA1c and quality of life scale data before and after 3 months of AndroidAPS use; and not pregnant. The primary outcome was the change in HbA1c between baseline and 3 months after initiation of AndroidAPS use.

RESULTS

Overall, 15 patients (10 females) were included; the median age was 32.2 years (range: 19.2-69.4), median diabetes duration was 9.7 years (range: 1.8-23.7) and median baseline HbA1c was 7.3% (range: 6.4-10.1). The 3 months of AndroidAPS use was associated with substantial reductions in HbA1c [6.79% (SD: 1.29) versus 7.63% (SD: 1.06), p = 0.002] and glycemic variability when compared with sensor-augmented pump therapy. A lower level of fear of hypoglycemia [22.13 points (SD: 6.87) versus 26.27 points (SD: 5.82), p = 0.010] was also observed after using AndroidAPS.

CONCLUSIONS

The 3 months of AndroidAPS use was associated with significant improvements in glucose management and quality of life among adults with T1D.