Home use of a bihormonal bionic pancreas versus insulin pump therapy in adults with type 1 diabetes: a multicentre randomised crossover trial

Intestinal Microbiota Play an Important Role in the Treatment of Type I Diabetes in Mice With BefA Protein

More and more studies have shown that the intestinal microbiota is the main factor in the pathogenesis of type 1 diabetes mellitus (T1DM). Beta cell expansion factor A (BefA) is a protein expressed by intestinal microorganisms. It has been proven to promote the proliferation of β-cells and has broad application prospects. However, as an intestinal protein, there have not been studies and reports on its application in diabetes and its mechanism of action. In this study, a T1DM model induced by multiple low-dose STZ (MLD-STZ) injections was established, and BefA protein was administered to explore its therapeutic effect in T1DM and the potential mechanism of intestinal microbiota. BefA protein significantly reduced the blood glucose, maintained the body weight, and improved the glucose tolerance of the mice. At the same time, the BefA protein significantly increased the expression of ZO-1, Occludin, and significantly reduced the expression of TLR-4, Myd88, and p-p65/p65. BefA protein significantly reduced the relative expression of pro-inflammatory cytokines IL-1β, IL-6 and TNF-α. In addition, our high-throughput sequencing shows for the first time that the good hypoglycemic effect of BefA protein is strongly related to the increase in the abundance of the beneficial gut bacteria Lactobacillus, Bifidobacterium and Oscillospria and the decrease in the abundance of the opportunistic pathogen Acinetobacter. Our group used animal models to verify the hypoglycemic effect of BefA protein, and first explored the potential mechanism of intestinal microbiota in BefA protein treatment.

Innovative features and functionalities of an artificial pancreas system: What do youth and parents want?

AIMS

Participant-driven solutions may help youth and families better engage and maintain use of diabetes technologies. We explored innovative features and functionalities of an ideal artificial pancreas (AP) system suggested by youth with type 1 diabetes and parents.

METHODS

Semi-structured interviews were conducted with 39 youth, ages 10-25 years, and 44 parents. Interviews were recorded, transcribed and coded using thematic analysis.

RESULTS

Youth (72% female, 82% non-Hispanic white) were (M ± SD) ages 17.0 ± 4.7 years, with diabetes for 9.4 ± 4.9 years, and HbA_1c of 68 ± 11 mmol/mol (8.4 ± 1.1%); 79% were pump-treated and 82% were continuous glucose monitor users. Of parents, 91% were mothers and 86% were non-Hispanic white, with a child 10.6 ± 4.5 years old. Youth and parents suggested a variety of innovative features and functionalities for an ideal AP system related to (1) enhancing the appeal of user interface, (2) increasing automation of new glucose management functionalities, and (3) innovative and commercial add-ons for greater convenience. Youth and parents offered many similar suggestions, including integration of ketone testing, voice activation, and location-tracking into the system. Youth seemed more driven by increasing convenience and normalcy while parents expressed more concerns with safety.

CONCLUSIONS

Youth and parents expressed creative solutions for an ideal AP system to increase ease of use, enhance normalcy, and reduce burden of management. Designers of AP systems will likely benefit from incorporating the desired preferences by end users to optimize acceptance and usability by young persons with diabetes.

Diabetes in Youth: A Global Perspective

Diabetes is a common disease among pediatric populations in the United States and worldwide. The incidence of type 1 and type 2 diabetes is increasing, with disproportional increases in racial/ethnic subpopulations. As the prevalence of obesity continue to increase, type 2 diabetes now represents a major form of pediatric diabetes. The management of diabetes in youth centers on maintaining glycemic control to prevent acute and chronic complications. This article summarizes the epidemiology, etiology, management, and complications of type 1 and type 2 diabetes in youth, as well as future directions and opportunities.

Glucose-Fueled Peptide Assembly: Glucagon Delivery via Enzymatic Actuation

Nature achieves remarkable function from the formation of transient, nonequilibrium materials realized through continuous energy input. The role of enzymes in catalyzing chemical transformations to drive such processes, often as part of stimuli-directed signaling, governs both material formation and lifetime. Inspired by the intricate nonequilibrium nanostructures of the living world, this work seeks to create transient materials in the presence of a consumable glucose stimulus under enzymatic control of glucose oxidase. Compared to traditional glucose-responsive materials, which typically engineer degradation to release insulin under high-glucose conditions, the transient nanofibrillar hydrogel materials here are stabilized in the presence of glucose but destabilized under conditions of limited glucose to release encapsulated glucagon. In the context of blood glucose control, glucagon offers a key antagonist to insulin in responding to hypoglycemia by signaling the release of glucose stored in tissues so as to restore normal blood glucose levels. Accordingly, these materials are evaluated in a prophylactic capacity in diabetic mice to release glucagon in response to a sudden drop in blood glucose brought on by an insulin overdose. Delivery of glucagon using glucose-fueled nanofibrillar hydrogels succeeds in limiting the onset and severity of hypoglycemia in mice. This general strategy points to a new paradigm in glucose-responsive materials, leveraging glucose as a stabilizing cue for responsive glucagon delivery in combating hypoglycemia. Moreover, compared to most fundamental reports achieving nonequilibrium and/or fueled classes of materials, the present work offers a rare functional example using a disease-relevant fuel to drive deployment of a therapeutic.

Determining the Perception and Willingness of Primary Care Providers to Prescribe Advanced Diabetes Technologies

Advanced diabetes technologies have produced increasingly favorable outcomes compared to older treatments. Disparities in practice resources have led to a treatment disparity by clinical setting, where endocrinologists typically prescribe far more such technologies than primary care providers (PCPs). Fully automated artificial pancreas systems (APS), which combine technologies to deliver and adjust insulin dosing continuously in response to automatic and continuous glucose monitoring, may be more straightforward for PCPs to prescribe and manage, therefore extending their benefit to more patients. We aimed to assess willingness of PCPs to prescribe advanced diabetes technologies through a cross-sectional survey of PCPs from 4 geographically diverse centers. While respondents were uncomfortable initiating (63 of 72, 88%) or adjusting (64 of 72, 89%) traditional insulin pumps, their views on APS were quite different: 71 of 76 (93%) saw advantages to prescribing APS by PCPs rather than only endocrinologists. Most would consider prescribing APS for type 1 diabetes (58 of 76, 76%) and type 2 diabetes (52 of 76, 68%). No differences were seen among attendings, residents, or nurse practitioners. APS were much more acceptable than traditional insulin pumps among this primary care sample. If successful, primary care management of closed-loop APS would greatly increase access to such therapies and reduce disparities among those patients who face more difficulty accessing subspecialty care than they do primary care.

Dual-hormone artificial pancreas for management of type 1 diabetes: Recent progress and future directions

Over the last few years, technological advances have led to tremendous improvement in the management of type 1 diabetes (T1D). Artificial pancreas systems have been shown to improve glucose control compared with conventional insulin pump therapy. However, clinically significant hypoglycemic and hyperglycemic episodes still occur with the artificial pancreas. Postprandial glucose excursions and exercise-induced hypoglycemia represent major hurdles in improving glucose control and glucose variability in many patients with T1D. In this regard, dual-hormone artificial pancreas systems delivering other hormones in addition to insulin (glucagon or amylin) may better reproduce the physiology of the endocrine pancreas and have been suggested as an alternative tool to overcome these limitations in clinical practice. In addition, novel ultra-rapid-acting insulin analogs with a more physiological time-action profile are currently under investigation for use in artificial pancreas devices, aiming to address the unmet need for further improvements in postprandial glucose control. This review article aims to discuss the current progress and future outlook in the development of novel ultra-rapid insulin analogs and dual-hormone closed-loop systems, which offer the next steps to fully closing the loop in the artificial pancreas.

Impact of Hybrid Closed Loop Therapy on Hypoglycemia Awareness in Individuals with Type 1 Diabetes and Impaired Hypoglycemia Awareness

Objective: This study evaluated the efficacy of using a hybrid closed loop (HCL) system in restoring hypoglycemia awareness in individuals with impaired awareness of hypoglycemia (IAH). Research Design and Methods: Participants with IAH (Gold score ≥4) were recruited into a randomized crossover pilot study. They participated in two 8-week periods using a HCL system (Medtronic 670G™) (intervention) and standard insulin pump therapy (control). Hyperinsulinemic hypoglycemic clamp studies were undertaken at baseline and at the end of each study period for the evaluation of the counter-regulatory hormonal and symptomatic responses to hypoglycemia. Results: Seventeen participants (mean age [standard deviation] 35.8 years [11.2 years]) were included in the study. Peak epinephrine levels (median, interquartile range [IQR]) in response to hypoglycemia were similar postintervention and control periods; 234.7 pmol/L (109.2; 938.9) versus 188.3 pmol/L (133.7; 402.9), P = 0.233. However, both peak adrenergic and neuroglycopenic symptom scores were higher after intervention; 5.0 (4.5; 9.0) versus 4.0 (4.0; 5.5), P = 0.009, and 8.5 (6.0; 15.0) versus 6.5 (6.0; 7.0) P = 0.014, respectively. Self-reported hypoglycemia awareness improved: median (IQR) Gold score was 4.0 (3.0; 5.5) versus 5.5 (4.5; 6.0); intervention versus control, P = 0.033. Time spent <3.9 and <3.0 mmol/L was lower in the intervention group than in control, P = 0.002. Other patient-reported outcomes (hypoglycemia fear and diabetes treatment satisfaction) did not change. Conclusions: A short-term use of a HCL system failed to demonstrate an improvement in counter-regulatory hormonal responses. However, higher hypoglycemia symptom scores during controlled hypoglycemia, better self-reported hypoglycemia awareness, and less time spent in hypoglycemia suggest the potential benefits of a HCL system in people with IAH. Trial Registration: anzctr.org.au Identifier: ACTRN12616000909426.

Impact of Accelerating Insulin on an Artificial Pancreas System Without Meal Announcement: An In Silico Examination

BACKGROUND

Controlling postprandial blood glucose without the benefit of an appropriately sized premeal insulin bolus has been challenging given the delays in absorption and action of subcutaneously injected insulin during conventional and artificial pancreas (AP) system diabetes treatment. We aim to understand the impact of accelerating insulin and increasing aggressiveness of the AP controller as potential solutions to address the postprandial hyperglycemia challenge posed by unannounced meals through a simulation study.

METHODS

Accelerated rapid-acting insulin analogue is modeled within the UVA/Padova simulation platform by uniformly reducing its pharmacokinetic time constants (α multiplier) and used with a model predictive control, where the controller's aggressiveness depends on α. Two sets of single-meal simulations were performed: (1) where we only tune the controller's aggressiveness and (2) where we also accelerate insulin absorption and action to assess postprandial glycemic control during each intervention.

RESULTS

Mean percent of time spent within the 70 to 180 mg/dL postprandial glycemic range is significantly higher in set (2) than in set (1): 79.9, 95% confidence interval [77.0, 82.7] vs 88.8 [86.8, 90.9] ([Note to typesetter: Set all unnecessary math in text format and insert appropriate spaces between operators.] P < .05) for α = 2, and 81.4 [78.6, 84.3] vs 94.1 [92.6, 95.6] (P < .05) for α = 3. A decrease in percent of time below 70 mg/dL is also detected: 0.9 [0.4, 2.2] vs 0.6 [0.2, 1.4] (P = .23) for α = 2 and 1.4 [0.7, 2.8] vs 0.4 [0.1, 1.4] (P < .05) for α = 3.

CONCLUSION

These proof-of-concept simulations suggest that an AP without prandial insulin boluses combined with significantly faster insulin analogues could match the glycemic performance obtained with an optimal hybrid AP.

Improvements in Glycemic Control Achieved by Altering the tmax Setting in the iLet® Bionic Pancreas When Using Fast-Acting Insulin Aspart: A Randomized Trial

INTRODUCTION

We investigated the safety of, and glucose control by, the insulin-only configuration of the iLet^® bionic pancreas delivering fast-acting insulin aspart (faster aspart), using the same insulin-dosing algorithm but different time to maximal serum drug concentration (t_max) settings, in adults with type 1 diabetes.

METHODS

We performed a single-center, single-blinded, crossover (two 7-day treatment periods) escalation trial over three sequential cohorts. Participants from each cohort were randomized to a default t_max setting (t₆₅ [t_max = 65 min]) followed by a non-default t_max setting (t₅₀ [t_max = 50 min; cohort 1], t₄₀ [t_max = 40 min; cohort 2], t₃₀ [t_max = 30 min; cohort 3]), or vice versa, all with faster aspart. Each cohort randomized eight new participants if escalation-stopping criteria were not met in the previous cohort.

RESULTS

Overall, 24 participants were randomized into three cohorts. Two participants discontinued treatment, one due to reported 'low blood glucose' during the first treatment period of cohort 3 (t₃₀). Mean time in low sensor glucose (< 54 mg/dl, primary endpoint) was < 1.0% for all t_max settings. Mean sensor glucose in cohorts 1 and 2 was significantly lower at non-default versus default t_max settings, with comparable insulin dosing. The mean time sensor glucose was in range (70-180 mg/dl) was > 70% for all cohorts, except the default t_max setting in cohort 1. No severe hypoglycemic episodes were reported. Furthermore, there were no clinically significant differences in adverse events between the groups.

CONCLUSION

There were no safety concerns with faster aspart in the iLet at non-default t_max settings. Improvements were observed in mean sensor glucose without increases in low sensor glucose at non-default t_max settings.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03816761.

New Aspects of Diabetes Research and Therapeutic Development

Both type 1 and type 2 diabetes mellitus are advancing at exponential rates, placing significant burdens on health care networks worldwide. Although traditional pharmacologic therapies such as insulin and oral antidiabetic stalwarts like metformin and the sulfonylureas continue to be used, newer drugs are now on the market targeting novel blood glucose-lowering pathways. Furthermore, exciting new developments in the understanding of beta cell and islet biology are driving the potential for treatments targeting incretin action, islet transplantation with new methods for immunologic protection, and the generation of functional beta cells from stem cells. Here we discuss the mechanistic details underlying past, present, and future diabetes therapies and evaluate their potential to treat and possibly reverse type 1 and 2 diabetes in humans. SIGNIFICANCE STATEMENT: Diabetes mellitus has reached epidemic proportions in the developed and developing world alike. As the last several years have seen many new developments in the field, a new and up to date review of these advances and their careful evaluation will help both clinical and research diabetologists to better understand where the field is currently heading.

Current treatment options and challenges in patients with Type 1 diabetes: Pharmacological, technical advances and future perspectives

Type 1 diabetes mellitus imposes a significant burden of complications and mortality, despite important advances in treatment: subjects affected by this disease have also a worse quality of life-related to disease management. To overcome these challenges, different new approaches have been proposed, such as new insulin formulations or innovative devices. The introduction of insulin pumps allows a more physiological insulin administration with a reduction of HbA1c level and hypoglycemic risk. New continuous glucose monitoring systems with better accuracy have allowed, not only better glucose control, but also the improvement of the quality of life. Integration of these devices with control algorithms brought to the creation of the first artificial pancreas, able to independently gain metabolic control without the risk of hypo- and hyperglycemic crisis. This approach has revolutionized the management of diabetes both in terms of quality of life and glucose control. However, complete independence from exogenous insulin will be obtained only by biological approaches that foresee the replacement of functional beta cells obtained from stem cells: this will be a major challenge but the biggest hope for the subjects with type 1 diabetes. In this review, we will outline the current scenario of innovative diabetes management both from a technological and biological point of view, and we will also forecast some cutting-edge approaches to reduce the challenges that hamper the definitive cure of diabetes.

'Smart' insulin-delivery technologies and intrinsic glucose-responsive insulin analogues

Insulin replacement therapy for diabetes mellitus seeks to minimise excursions in blood glucose concentration above or below the therapeutic range (hyper- or hypoglycaemia). To mitigate acute and chronic risks of such excursions, glucose-responsive insulin-delivery technologies have long been sought for clinical application in type 1 and long-standing type 2 diabetes mellitus. Such 'smart' systems or insulin analogues seek to provide hormonal activity proportional to blood glucose levels without external monitoring. This review highlights three broad strategies to co-optimise mean glycaemic control and time in range: (1) coupling of continuous glucose monitoring (CGM) to delivery devices (algorithm-based 'closed-loop' systems); (2) glucose-responsive polymer encapsulation of insulin; and (3) mechanism-based hormone modifications. Innovations span control algorithms for CGM-based insulin-delivery systems, glucose-responsive polymer matrices, bio-inspired design based on insulin's conformational switch mechanism upon insulin receptor engagement, and glucose-responsive modifications of new insulin analogues. In each case, innovations in insulin chemistry and formulation may enhance clinical outcomes. Prospects are discussed for intrinsic glucose-responsive insulin analogues containing a reversible switch (regulating bioavailability or conformation) that can be activated by glucose at high concentrations.

New closed-loop insulin systems

Advances in diabetes technologies have enabled the development of automated closed-loop insulin delivery systems. Several hybrid closed-loop systems have been commercialised, reflecting rapid transition of this evolving technology from research into clinical practice, where it is gradually transforming the management of type 1 diabetes in children and adults. In this review we consider the supporting evidence in terms of glucose control and quality of life for presently available closed-loop systems and those in development, including dual-hormone closed-loop systems. We also comment on alternative 'do-it-yourself' closed-loop systems. We remark on issues associated with clinical adoption of these approaches, including training provision, and consider limitations of presently available closed-loop systems and areas for future enhancements to further improve outcomes and reduce the burden of diabetes management.

Feasibility of large experimental animal models in testing novel therapeutic strategies for diabetes

Diabetes is among the top 10 causes of death in adults and caused approximately four million deaths worldwide in 2017. The incidence and prevalence of diabetes is predicted to increase. To alleviate this potentially severe situation, safer and more effective therapeutics are urgently required. Mice have long been the mainstay as preclinical models for basic research on diabetes, although they are not ideally suited for translating basic knowledge into clinical applications. To validate and optimize novel therapeutics for safe application in humans, an appropriate large animal model is needed. Large animals, especially pigs, are well suited for biomedical research and share many similarities with humans, including body size, anatomical features, physiology, and pathophysiology. Moreover, pigs already play an important role in translational studies, including clinical trials for xenotransplantation. Progress in genetic engineering over the past few decades has facilitated the development of transgenic animals, including porcine models of diabetes. This article discusses features that attest to the attractiveness of genetically modified porcine models of diabetes for testing novel treatment strategies using recent technical advances.

Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An In Silico Validation

People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although the artificial pancreas and continuous glucose monitoring have been proven to be effective in achieving closed-loop control, significant challenges still remain due to the high complexity of glucose dynamics and limitations in the technology. In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In particular, the delivery strategies are developed by double Q-learning with dilated recurrent neural networks. For designing and testing purposes, the FDA-accepted UVA/Padova Type 1 simulator was employed. First, we performed long-term generalized training to obtain a population model. Then, this model was personalized with a small data-set of subject-specific data. In silico results show that the single and dual-hormone delivery strategies achieve good glucose control when compared to a standard basal-bolus therapy with low-glucose insulin suspension. Specifically, in the adult cohort (n = 10), percentage time in target range 70, 180 mg/dL improved from 77.6% to 80.9% with single-hormone control, and to 85.6% with dual-hormone control. In the adolescent cohort (n = 10), percentage time in target range improved from 55.5% to [Formula: see text] with single-hormone control, and to 78.8% with dual-hormone control. In all scenarios, a significant decrease in hypoglycemia was observed. These results show that the use of deep reinforcement learning is a viable approach for closed-loop glucose control in T1D.

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.

Fully Closed Loop Glucose Control With a Bihormonal Artificial Pancreas in Adults With Type 1 Diabetes: An Outpatient, Randomized, Crossover Trial

OBJECTIVE

To demonstrate the performance and safety of a bihormonal (insulin and glucagon) artificial pancreas (AP) in adults with type 1 diabetes.

RESEARCH DESIGN AND METHODS

In this outpatient, randomized, crossover trial, 2-week fully closed loop glucose control (AP therapy) was compared with 2-week open loop control (patient's normal insulin pump therapy with a glucose sensor if they had one).

RESULTS

A total of 23 patients were included in the analysis. Time in range (70-180 mg/dL [3.9-10 mmol/L]) was significantly higher during closed loop (median 86.6% of time [interquartile range 84.9-88.5]) compared with open loop (53.9% [49.7-67.2]; P < 0.0001).

CONCLUSIONS

Compared with insulin pump therapy, the bihormonal AP provided superior glucose control, without meal or exercise announcements, and was safe in adults with type 1 diabetes.

Time in range centered diabetes care

Optimal glycemic control remains challenging and elusive for many people with diabetes. With the comprehensive clinical evidence on safety and efficiency in large populations, and with broader reimbursement, the adoption of continuous glucose monitoring (CGM) is rapidly increasing. Standardized visual reporting and interpretation of CGM data and clear and understandable clinical targets will help professionals and individuals with diabetes use diabetes technology more efficiently, and finally improve long-term outcomes with less everyday disease burden. For the majority of people with type 1 or type 2 diabetes, time in range (between 70 and 180 mg/dL, or 3.9 and 10 mmol/L) target of more than 70% is recommended, with each incremental increase of 5% towards this target being clinically meaningful. At the same time, the goal is to minimize glycemic excursions: a recommended target for a time below range (< 70 mg/dL or < 3.9 mmol/L) is less than 4%, and time above range (> 180 mg/dL or 10 mmol/L) less than 25%, with less stringent goals for older individuals or those at increased risk. These targets should be individualized: the personal use of CGM with the standardized data presentation provides all necessary means to accurately tailor diabetes management to the needs of each individual with diabetes.

13. Children and Adolescents: Standards of Medical Care in Diabetes-2021

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc21-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc21-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Developing Insulin Delivery Devices with Glucose Responsiveness

Individuals with type 1 and advanced type 2 diabetes require daily insulin therapy to maintain blood glucose levels in normoglycemic ranges to prevent associated morbidity and mortality. Optimal insulin delivery should offer both precise dosing in response to real-time blood glucose levels as well as a feasible and low-burden administration route to promote long-term adherence. A series of glucose-responsive insulin delivery mechanisms and devices have been reported to increase patient compliance while mitigating the risk of hypoglycemia. This review discusses currently available insulin delivery devices, overviews recent developments towards the generation of glucose-responsive delivery systems, and provides commentary on the opportunities and barriers ahead regarding the integration and translation of current glucose-responsive insulin delivery designs.

New and Emerging Technologies in Type 1 Diabetes

There has been a rapid advancement in the pace of development of new diabetes technologies and therapies for the management of type 1 diabetes over the past decade. The Diabetes Control and Complications Trial conclusively established that tight glycemic control with intensive insulin therapy decreases the rates of diabetes complications in proportion to glycemic control, and diabetes technologies have accordingly been developed to help patients reach these goals. In this review, the authors discuss new diabetes therapeutics and technologies, including new insulin analogues, insulin pumps, continuous glucose monitoring systems, and automated insulin delivery systems."

Closed-loop insulin delivery systems in children and adolescents with type 1 diabetes

Introduction: Optimal glycemic control remains challenging in children and adolescents with type 1 diabetes due to highly variable day-to-day and night-to-night insulin requirements. This hurdle could be addressed by glucose-responsive insulin delivery based on real-time continuous glucose measurements.Areas covered: This review summaries recent advances of closed-loop systems in children and adolescents with type 1 diabetes, using both single- and dual-hormone closed-loop systems. The main outcomes, proportions of time spent in target range 70-180 mg/dl, and time spent in hypoglycemia below 70 mg/dl, are assessed particularly during unsupervised free-living randomized controlled trials.Expert opinion: Noteworthy and clinically meaningful translation of experimental investigations from controlled in-hospital settings to unrestricted home studies have been achieved over the past years, resulting in the regulatory approval of the first hybrid closed-loop system also in the pediatric population and with several other advanced devices in the pipeline. Large multinational and pivotal clinical trials including broad age populations are underway to facilitate the use of closed-loop systems in routine clinical practice.

Predicting and Preventing Nocturnal Hypoglycemia in Type 1 Diabetes Using Big Data Analytics and Decision Theoretic Analysis

Background: Despite new glucose sensing technologies, nocturnal hypoglycemia is still a problem for people with type 1 diabetes (T1D) as symptoms and sensor alarms may not be detected while sleeping. Accurately predicting nocturnal hypoglycemia before sleep may help minimize nighttime hypoglycemia. Methods: A support vector regression (SVR) model was trained to predict, before bedtime, the overnight minimum glucose and overnight nocturnal hypoglycemia for people with T1D. The algorithm was trained on continuous glucose measurements and insulin data collected from 124 people (22,804 valid nights of data) with T1D. The minimum glucose threshold for announcing nocturnal hypoglycemia risk was derived by applying a decision theoretic criterion to maximize expected net benefit. Accuracy was evaluated on a validation set from 10 people with T1D during a 4-week trial under free-living sensor-augmented insulin-pump therapy. The primary outcome measures were sensitivity and specificity of prediction, the correlation between predicted and actual minimum nocturnal glucose, and root-mean-square error. The impact of using the algorithm to prevent nocturnal hypoglycemia is shown in-silico. Results: The algorithm predicted 94.1% of nocturnal hypoglycemia events (<3.9 mmol/L, 95% confidence interval [CI], 71.3-99.9) with an area under the receiver operating characteristic curve of 0.86 (95% CI, 0.75-0.98). Correlation between actual and predicted minimum glucose was high (R = 0.71, P < 0.001). In-silico simulations showed that the algorithm could reduce nocturnal hypoglycemia by 77.0% (P = 0.006) without impacting time in target range (3.9-10 mmol/L). Conclusion: An SVR model trained on a big data set and optimized using decision theoretic criterion can accurately predict at bedtime if overnight nocturnal hypoglycemia will occur and may help reduce nocturnal hypoglycemia.

Dual-Hormone Closed-Loop System Using a Liquid Stable Glucagon Formulation Versus Insulin-Only Closed-Loop System Compared With a Predictive Low Glucose Suspend System: An Open-Label, Outpatient, Single-Center, Crossover, Randomized Controlled Trial

OBJECTIVE

To assess the efficacy and feasibility of a dual-hormone (DH) closed-loop system with insulin and a novel liquid stable glucagon formulation compared with an insulin-only closed-loop system and a predictive low glucose suspend (PLGS) system.

RESEARCH DESIGN AND METHODS

In a 76-h, randomized, crossover, outpatient study, 23 participants with type 1 diabetes used three modes of the Oregon Artificial Pancreas system: 1) dual-hormone (DH) closed-loop control, 2) insulin-only single-hormone (SH) closed-loop control, and 3) PLGS system. The primary end point was percentage time in hypoglycemia (<70 mg/dL) from the start of in-clinic aerobic exercise (45 min at 60% VO_2max) to 4 h after.

RESULTS

DH reduced hypoglycemia compared with SH during and after exercise (DH 0.0% [interquartile range 0.0-4.2], SH 8.3% [0.0-12.5], P = 0.025). There was an increased time in hyperglycemia (>180 mg/dL) during and after exercise for DH versus SH (20.8% DH vs. 6.3% SH, P = 0.038). Mean glucose during the entire study duration was DH, 159.2; SH, 151.6; and PLGS, 163.6 mg/dL. Across the entire study duration, DH resulted in 7.5% more time in target range (70-180 mg/dL) compared with the PLGS system (71.0% vs. 63.4%, P = 0.044). For the entire study duration, DH had 28.2% time in hyperglycemia vs. 25.1% for SH (P = 0.044) and 34.7% for PLGS (P = 0.140). Four participants experienced nausea related to glucagon, leading three to withdraw from the study.

CONCLUSIONS

The glucagon formulation demonstrated feasibility in a closed-loop system. The DH system reduced hypoglycemia during and after exercise, with some increase in hyperglycemia.

A clinical review of the t:slim X2 insulin pump

Insulin pumps are commonly used for intensive insulin therapy to treat type 1 diabetes in adults and youth. Insulin pump technologies have advanced dramatically in the last several years to integrate with continuous glucose monitors (CGM) and incorporate control algorithms. These control algorithms automate some insulin delivery in response to the glucose information received from the CGM to reduce the occurrence of hypoglycemia and hyperglycemia and improve overall glycemic control. The t:slim X2 insulin pump system became commercially available in 2016. It is an innovative insulin pump technology that can be updated remotely by the user to install new software onto the pump device as new technologies become available. Currently, the t:slim X2 pairs with the Dexcom G6 CGM and there are two advanced software options available: Basal-IQ, which is a predictive low glucose suspend (PLGS) technology, and Control-IQ, which is a Hybrid Closed Loop (HCL) technology. This paper will describe the different types of advanced insulin pump technologies, review how the t:slim X2 insulin pump works, and summarize the clinical studies leading to FDA approval and commercialization of the Basal-IQ and Control-IQ technologies.

Review of the Omnipod® 5 Automated Glucose Control System Powered by Horizon™ for the treatment of Type 1 diabetes

Type 1 diabetes (T1D) is a medical condition that requires constant management, including monitoring of blood glucose levels and administration of insulin. Advancements in diabetes technology have offered methods to reduce the burden on people with T1D. Several hybrid closed-loop systems are commercially available or in clinical trials, each with unique features to improve care for patients with T1D. This article reviews the Omnipod® 5 Automated Glucose Control System Powered by Horizon™ and the safety and efficacy data to support its use in the management of T1D.

Closed-loop control in insulin pumps for type-1 diabetes mellitus: safety and efficacy

INTRODUCTION

Type 1 diabetes is a lifelong disease with high management burden. The majority of people with type 1 diabetes fail to achieve glycemic targets. Algorithm-driven automated insulin delivery (closed-loop) systems aim to address these challenges. This review provides an overview of commercial and emerging closed-loop systems.

AREAS COVERED

We review safety and efficacy of commercial and emerging hybrid closed-loop systems. A literature search was conducted and clinical trials using day-and-night closed-loop systems during free-living conditions were used to report on safety data. We comment on efficacy where robust randomized controlled trial data for a particular system are available. We highlight similarities and differences between commercial systems.

EXPERT OPINION

Study data shows that hybrid closed-loop systems are safe and effective, consistently improving glycemic control when compared to standard therapy. While a fully closed-loop system with minimal burden remains the end-goal, these hybrid closed-loop systems have transformative potential in diabetes care.

Where Do We Stand with Closed-Loop Systems and Their Challenges?

Treatments for type 1 diabetes have advanced significantly over recent years. There are now multiple hybrid closed-loop systems commercially available and additional systems are in development. Challenges remain, however. This review outlines the recent advances in closed-loop systems and outlines the remaining challenges, including post-prandial hyperglycemia and exercise-related dysglycemia.

Evolution of Insulin Delivery Devices: From Syringes, Pens, and Pumps to DIY Artificial Pancreas

The year 2021 will mark 100 years since the discovery of insulin. Insulin, the first medication to be discovered for diabetes, is still the safest and most potent glucose-lowering therapy. The major challenge of insulin despite its efficacy has been the occurrence of hypoglycemia, which has resulted in sub-optimal dosages being prescribed in the vast majority of patients. Popular devices used for insulin administration are syringes, pens, and pumps. An artificial pancreas (AP) with a closed-loop delivery system with > 95% time in range is believed to soon become a reality. The development of closed-loop delivery systems has gained momentum with recent advances in continuous glucose monitoring (CGM) and computer algorithms. This review discusses the evolution of syringes, disposable, durable pens and connected pens, needles, tethered and patch insulin pumps, bionic pancreas, alternate controller-enabled infusion (ACE) pumps, and do-it-yourself artificial pancreas systems (DIY-APS).

Preventing undesirable behavior of intelligent machines

Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple data analysis and pattern recognition tools to complex systems that achieve superhuman performance on various tasks. Ensuring that they do not exhibit undesirable behavior-that they do not, for example, cause harm to humans-is therefore a pressing problem. We propose a general and flexible framework for designing machine learning algorithms. This framework simplifies the problem of specifying and regulating undesirable behavior. To show the viability of this framework, we used it to create machine learning algorithms that precluded the dangerous behavior caused by standard machine learning algorithms in our experiments. Our framework for designing machine learning algorithms simplifies the safe and responsible application of machine learning.

Advances in artificial pancreas systems

The artificial pancreas for managing type 1 diabetes has progressed from research into clinical practice, revealing areas for future advancements.

Role of Glucagon in Automated Insulin Delivery

Treatment of type 1 diabetes with exogenous insulin often results in unpredictable daily glucose variability and hypoglycemia, which can be dangerous. Automated insulin delivery systems can improve glucose control while reducing burden for people with diabetes. One approach to improve treatment outcomes is to incorporate the counter-regulatory hormone glucagon into the automated delivery system to help prevent the hypoglycemia that can be induced by the slow pharmacodynamics of insulin action. This article explores the advantages and disadvantages of incorporating glucagon into dual-hormone automated hormone delivery systems.

Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management

PURPOSE OF REVIEW

Machine learning (ML) is increasingly being studied for the screening, diagnosis, and management of diabetes and its complications. Although various models of ML have been developed, most have not led to practical solutions for real-world problems. There has been a disconnect between ML developers, regulatory bodies, health services researchers, clinicians, and patients in their efforts. Our aim is to review the current status of ML in various aspects of diabetes care and identify key challenges that must be overcome to leverage ML to its full potential.

RECENT FINDINGS

ML has led to impressive progress in development of automated insulin delivery systems and diabetic retinopathy screening tools. Compared with these, use of ML in other aspects of diabetes is still at an early stage. The Food & Drug Administration (FDA) is adopting some innovative models to help bring technologies to the market in an expeditious and safe manner. ML has great potential in managing diabetes and the future is in furthering the partnership of regulatory bodies with health service researchers, clinicians, developers, and patients to improve the outcomes of populations and individual patients with diabetes.

Therapeutic Inertia in Pediatric Diabetes: Challenges to and Strategies for Overcoming Acceptance of the Status Quo

Despite significant advances in therapies for pediatric type 1 diabetes, achievement of glycemic targets remains elusive, and management remains burdensome for patients and their families. This article identifies common challenges in diabetes management at the patient-provider and health care system levels and proposes practical approaches to overcoming therapeutic inertia to enhance health outcomes for youth with type 1 diabetes.

13. Children and Adolescents: Standards of Medical Care in Diabetes-2020

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc20-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc20-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

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.

Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement

BACKGROUND

Either under standard basal-bolus treatment or hybrid closed-loop control, subjects with type 1 diabetes are required to count carbohydrates (CHOs). However, CHO counting is not only burdensome but also prone to errors. Recently, an artificial pancreas algorithm that does not require premeal insulin boluses-the so-called automatic regulation of glucose (ARG)-was introduced. In its first pilot clinical study, although the exact CHO counting was not required, subjects still needed to announce the meal time and classify the meal size.

METHOD

An automatic switching signal generator (SSG) is proposed in this work to remove the manual mealtime announcement from the control strategy. The SSG is based on a Kalman filter and works with continuous glucose monitoring readings only.

RESULTS

The ARG algorithm with unannounced meals (ARG_um) was tested in silico under the effect of different types of mixed meals and intrapatient variability, and contrasted with the ARG algorithm with announced meals (ARG_am). Simulations reveal that, for slow-absorbing meals, the time in the euglycemic range, [70-180] mg/dL, increases using the unannounced strategy (ARG_am: 78.1 [68.6-80.2]% (median [IQR]) and ARG_um: 87.8 [84.5-90.6]%), while similar results were found with fast-absorbing meals (ARG_am: 87.4 [86.0-88.9]% and ARG_um: 87.6 [86.1-88.8]%). On the other hand, when intrapatient variability is considered, time in euglycemia is also comparable (ARG_am: 81.4 [75.4-83.5]% and ARG_um: 80.9 [77.0-85.1]%).

CONCLUSION

In silico results indicate that it is feasible to perform an in vivo evaluation of the ARG algorithm with unannounced meals.

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.

Glucose Control in Adults with Type 1 Diabetes Using a Medtronic Prototype Enhanced-Hybrid Closed-Loop System: A Feasibility Study

Background: Experience from first-generation closed-loop (CL) systems informs refinements to enhance glucose control and user acceptance. A next-generation prototype enhanced-hybrid CL (E-HCL) system incorporates iterative changes to the Medtronic MiniMed 670G CL system, including automated correction boluses, lower target glucose level, and user enhancements. The aim was to explore safety, system performance, and glucose control using E-HCL in adults with type 1 diabetes. Methods: Twelve adults underwent this first in-human feasibility study. After a 1-week run-in using open-loop (OL), E-HCL was activated at the start of a supervised 1-week hotel phase, followed by 3 weeks free living at home. Supervised challenges included two meal interventions (unannounced and late meal bolus) and a sensor calibration intervention. Primary outcome was sensor glucose time-in-range (TIR); OL run-in and E-HCL at home were compared by Wilcoxon signed-rank test. Results: Twelve adults (seven men; median [interquartile range] age 48 [39, 57] years; HbA_1c 6.8 [6.2, 7.2]%, 51 [44, 55] mmol/mol; diabetes duration 31 [13, 41] years) completed the protocol. E-HCL resulted in greater TIR (85.3 [79.4, 88.4]% vs. 75.0 [66.6, 83.7]%, P = 0.003) and lower mean sensor glucose (123.0 [119.3, 129.6] mg/dL vs. 143.5 [135.8, 154.5] mg/dL, P = 0.002) than OL. Time spent <70 mg/dL increased using E-HCL (4.4 [3.3, 6.1]% vs. 3.0 [1.8, 3.8]%, P = 0.02) with no difference in time <54 mg/dL (P = 0.64). Time in CL was 99.98 [99.0, 100.0]%. All participants were satisfied using E-HCL. Conclusions: In adults with well-controlled HbA_1c levels, a prototype E-HCL resulted in high TIR, few CL exits, and positive user experiences at the expense of increased hypoglycemia (<70 mg/dL). E-HCL represents a positive step in the journey toward optimizing glucose control in people living with type 1 diabetes.