Glucose Outcomes with the In-Home Use of a Hybrid Closed-Loop Insulin Delivery System in Adolescents and Adults with Type 1 Diabetes

Safety and Performance of the Omnipod Hybrid Closed-Loop System in Adults, Adolescents, and Children with Type 1 Diabetes Over 5 Days Under Free-Living Conditions

Background: The objective of this study was to assess the safety and performance of the Omnipod^® personalized model predictive control (MPC) algorithm in adults, adolescents, and children aged ≥6 years with type 1 diabetes (T1D) under free-living conditions using an investigational device. Materials and Methods: A 96-h hybrid closed-loop (HCL) study was conducted in a supervised hotel/rental home setting following a 7-day outpatient standard therapy (ST) phase. Eligible participants were aged 6-65 years with A1C <10.0% using insulin pump therapy or multiple daily injections. Meals during HCL were unrestricted, with boluses administered per usual routine. There was daily physical activity. The primary endpoints were percentage of time with sensor glucose <70 and ≥250 mg/dL. Results: Participants were 11 adults, 10 adolescents, and 15 children aged (mean ± standard deviation) 28.8 ± 7.9, 14.3 ± 1.3, and 9.9 ± 1.0 years, respectively. Percentage time ≥250 mg/dL during HCL was 4.5% ± 4.2%, 3.5% ± 5.0%, and 8.6% ± 8.8% per respective age group, a 1.6-, 3.4-, and 2.0-fold reduction compared to ST (P = 0.1, P = 0.02, and P = 0.03). Percentage time <70 mg/dL during HCL was 1.9% ± 1.3%, 2.5% ± 2.0%, and 2.2% ± 1.9%, a statistically significant decrease in adults when compared to ST (P = 0.005, P = 0.3, and P = 0.3). Percentage time 70-180 mg/dL increased during HCL compared to ST, reaching significance for adolescents and children: HCL 73.7% ± 7.5% vs. ST 68.0% ± 15.6% for adults (P = 0.08), HCL 79.0% ± 12.6% vs. ST 60.6% ± 13.4% for adolescents (P = 0.01), and HCL 69.2% ± 13.5% vs. ST 54.9% ± 12.9% for children (P = 0.003). Conclusions: The Omnipod personalized MPC algorithm was safe and performed well over 5 days and 4 nights of use by a cohort of participants ranging from youth aged ≥6 years to adults with T1D under supervised free-living conditions with challenges, including daily physical activity and unrestricted meals.

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

Objective: Safety data on Do-It-Yourself Artificial Pancreas Systems are missing. The most widespread in Europe is the AndroidAPS implementation of the OpenAPS algorithm. We used the UVA/Padova Type 1 Diabetes Simulator to in silico test safety and efficacy of this algorithm in different scenarios. Methods: We tested five configurations of the AndroidAPS algorithm differing in aggressiveness and patient's interaction with the system. All configurations were tested with insulin sensitivity variation of ±30%. The most promising configurations were tested in real-life scenarios: over- and underestimated bolus by 50%, bolus delivered 15 min before meal, and late bolus delivered 15 min after meal. Continuous Glucose Monitoring (CGM) time in ranges (TIRs) metrics were used to assess the glycemic control. Results: In silico testing showed that open-source closed-loop system AndroidAPS works effectively and safely. The best results were reached if AndroidAPS algorithm worked with microboluses and when half of calculated bolus was issued (mean glycemia 131 mg/dL, SD 27 mg/dL, TIR 91%, time between 54 and 70 mg/dL <1%, and low blood glucose index even <1). The meal bolus over- and underestimation as well as late bolus did not affect the TIR and, importantly, the time between 54 and 70 mg/dL. Conclusion: In silico testing proved that AndroidAPS implementation of the OpenAPS algorithm is safe and effective, and it showed a great potential to be tested in prospective home setting study.

Trends in mathematical modeling of host-pathogen interactions

Pathogenic microorganisms entail enormous problems for humans, livestock, and crop plants. A better understanding of the different infection strategies of the pathogens enables us to derive optimal treatments to mitigate infectious diseases or develop vaccinations preventing the occurrence of infections altogether. In this review, we highlight the current trends in mathematical modeling approaches and related methods used for understanding host-pathogen interactions. Since these interactions can be described on vastly different temporal and spatial scales as well as abstraction levels, a variety of computational and mathematical approaches are presented. Particular emphasis is placed on dynamic optimization, game theory, and spatial modeling, as they are attracting more and more interest in systems biology. Furthermore, these approaches are often combined to illuminate the complexities of the interactions between pathogens and their host. We also discuss the phenomena of molecular mimicry and crypsis as well as the interplay between defense and counter defense. As a conclusion, we provide an overview of method characteristics to assist non-experts in their decision for modeling approaches and interdisciplinary understanding.

Perioperative Considerations for Evolving Artificial Pancreas Devices

Type 1 diabetes mellitus is a lifelong condition. It requires intensive patient involvement including frequent glucose measurements and subcutaneous insulin dosing to provide optimal glycemic control to decrease short- and long-term complications of diabetes mellitus without causing hypoglycemia. Variations in insulin pharmacokinetics and responsiveness over time in addition to illness, stress, and a myriad of other factors make ideal glucose control a challenge. Control-to-range and control-to-target artificial pancreas devices (closed-loop artificial pancreas devices [C-APDs]) consist of a continuous glucose monitor, response algorithm, and insulin delivery device that work together to automate much of the glycemic management for an individual while continually adjusting insulin dosing toward a glycemic target. In this way, a C-APD can improve glycemic control and decrease the rate of hypoglycemia. The MiniMed 670G (Medtronic, Fridley, MN) system is currently the only Food and Drug Administration-cleared C-APD in the United States. In this system, insulin delivery is continually adjusted to a glucose concentration, and the patient inputs meal-time information to modify insulin delivery as needed. Data thus far suggest improved glycemic control and decreased hypoglycemic events using the system, with decreased need for patient self-management. Thus, the anticipated use of these devices is likely to increase dramatically over time. There are limited case reports of safe intraoperative use of C-APDs, but the Food and Drug Administration has not cleared any device for such use. Nonetheless, C-APDs may offer an opportunity to improve patient safety and outcomes through enhanced intraoperative glycemic control. Anesthesiologists should become familiar with C-APD technology to help develop safe and effective protocols for their intraoperative use. We provide an overview of C-APDs and propose an introductory strategy for intraoperative study of these devices.

Wave of Wearables: Clinical Management of Patients and the Future of Connected Medicine

The future of connected health care will involve the collection of patient data or enhancement of clinician workflows through various biosensors and displays found on wearable electronic devices, many of which are marketed directly to consumers. The adoption of wearables in health care is being driven by efforts to reduce health care costs, improve care quality, and increase clinician efficiency. Wearables have significant potential to achieve these goals but are currently limited by lack of widespread integrations into electronic health records, biosensor data collection types, and a lack of scientifically rigorous literature showing benefit.

Online Glucose Prediction Using Computationally Efficient Sparse Kernel Filtering Algorithms in Type-1 Diabetes

Streaming data from continuous glucose monitoring (CGM) systems enable the recursive identification of models to improve estimation accuracy for effective predictive glycemic control in patients with type-1 diabetes. A drawback of conventional recursive identification techniques is the increase in computational requirements, which is a concern for online and real-time applications such as the artificial pancreas systems implemented on handheld devices and smartphones where computational resources and memory are limited. To improve predictions in such computationally constrained hardware settings, efficient adaptive kernel filtering algorithms are developed in this paper to characterize the nonlinear glycemic variability by employing a sparsification criterion based on the information theory to reduce the computation time and complexity of the kernel filters without adversely deteriorating the predictive performance. Furthermore, the adaptive kernel filtering algorithms are designed to be insensitive to abnormal CGM measurements, thus compensating for measurement noise and disturbances. As such, the sparsification-based real-time model update framework can adapt the prediction models to accurately characterize the time-varying and nonlinear dynamics of glycemic measurements. The proposed recursive kernel filtering algorithms leveraging sparsity for improved computational efficiency are applied to both in-silico and clinical subjects, and the results demonstrate the effectiveness of the proposed methods.

9. Pharmacologic Approaches to Glycemic Treatment: 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.

Faster Compared With Standard Insulin Aspart During Day-and-Night Fully Closed-Loop Insulin Therapy in Type 1 Diabetes: A Double-Blind Randomized Crossover Trial

OBJECTIVE

We evaluated the safety and efficacy of day-and-night fully closed-loop insulin therapy using faster (Faster-CL) compared with standard insulin aspart (Standard-CL) in young adults with type 1 diabetes.

RESEARCH DESIGN AND METHODS

In a double-blind, randomized, crossover trial, 20 participants with type 1 diabetes on insulin pump therapy (11 females, aged 21.3 ± 2.3 years, HbA_1c 7.5 ± 0.5% [58.5 ± 5.5 mmol/mol]) underwent two 27-h inpatient periods with unannounced afternoon moderate-vigorous exercise and unannounced/uncovered meals. We compared Faster-CL and Standard-CL in random order. During both interventions, the fuzzy-logic control algorithm DreaMed GlucoSitter was used. Glucose sensor data were analyzed by intention-to-treat principle with the difference (between Faster-CL and Standard-CL) in proportion of time in range 70-180 mg/dL (TIR) over 27 h as the primary end point.

RESULTS

The proportion of TIR was similar for both arms: 53.3% (83% overnight) in Faster-CL and 57.9% (88% overnight) in Standard-CL (P = 0.170). The proportion of time in hypoglycemia <70 mg/dL was 0.0% for both groups. Baseline-adjusted interstitial prandial glucose increments 1 h after meals were greater in Faster-CL compared with Standard-CL (P = 0.017). The gaps between measured plasma insulin and estimated insulin-on-board levels at the beginning, at the end, and 2 h after the exercise were smaller in the Standard-CL group (P = 0.029, P = 0.003, and P = 0.004, respectively). No severe adverse events occurred.

CONCLUSIONS

Fully closed-loop insulin delivery using either faster or standard insulin aspart was safe and efficient in achieving near-normal glucose concentrations outside postprandial periods. The closed-loop algorithm was better adjusted to the standard insulin aspart.

7. Diabetes Technology: 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.

HbA1c variability in adults with type 1 diabetes on continuous subcutaneous insulin infusion (CSII) therapy compared to multiple daily injection (MDI) treatment

OBJECTIVE

To determine if continuous subcutaneous insulin infusion (CSII) therapy is associated with lower glycated haemoglobin (HbA1c) variability (long-term glycaemic variability; GV) relative to multiple daily injection (MDI) treatment in adults with type 1 diabetes mellitus (T1DM).

DESIGN

Retrospective audit.

SETTING AND PARTICIPANTS

Clinic records from 506 adults with T1DM from two tertiary Australian hospitals.

OUTCOME MEASURES

Long-term GV was assessed by HbA1c SD and coefficient of variation (CV) in adults on established MDI or CSII therapy, and in a subset changing from MDI to CSII.

RESULTS

Adults (n=506, (164 CSII), 50% women, mean±SD age 38.0±15.3 years, 17.0±13.7 years diabetes, mean HbA1c 7.8%±1.2% (62±13 mmol/mol) on CSII, 8.0%±1.5% (64±16 mmol/mol) on MDI) were followed for 4.1±3.6 years. CSII use was associated with lower GV (HbA1c SD: CSII vs MDI 0.5%±0.41% (6±6 mmol/mol) vs 0.7%±0.7% (9±8 mmol/mol)) and CV: CSII vs MDI 6.7%±4.6% (10±10 mmol/mol) vs 9.3%±7.3% (14±13 mmol/mol), both p<0.001. Fifty-six adults (73% female, age 36±13 years, 16±13 years diabetes, HbA1c 7.8%±0.8% (62±9 mmol/mol)) transitioned from MDI to CSII. Mean HbA1c fell by 0.4%. GV from 1 year post-CSII commencement decreased significantly, HbA1c SD pre-CSII versus post-CSII 0.7%±0.5% (8±5 mmol/mol) vs 0.4%±0.4% (5±4 mmol/mol); p<0.001, and HbA1c CV 9.2%±5.5% (13±8 mmol/mol) vs 6.1%±3.9% (9±5 mmol/mol); p<0.001.

CONCLUSIONS

In clinical practice with T1DM adults relative to MDI, CSII therapy is associated with lower HbA1c GV.

Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning

Techniques using machine learning for short term blood glucose level prediction in patients with Type 1 Diabetes are investigated. This problem is significant for the development of effective artificial pancreas technology so accurate alerts (e.g. hypoglycemia alarms) and other forecasts can be generated. It is shown that two factors must be considered when selecting the best machine learning technique for blood glucose level regression: (i) the regression model performance metrics being used to select the model, and (ii) the preprocessing techniques required to account for the imbalanced time spent by patients in different portions of the glycemic range. Using standard benchmark data, it is demonstrated that different regression model/preprocessing technique combinations exhibit different accuracies depending on the glycemic subrange under consideration. Therefore technique selection depends on the type of alert required. Specific findings are that a linear Support Vector Regression-based model, trained with normal as well as polynomial features, is best for blood glucose level forecasting in the normal and hyperglycemic ranges while a Multilayer Perceptron trained on oversampled data is ideal for predictions in the hypoglycemic range.

RETRACTED: Glycemic control outcomes of adults using the MiniMed™ 670G hybrid closed-loop (HCL) system: A single-center study

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. A number of individuals who fulfilled authorship requirements for the article were omitted by the sole author. Secondly, the conclusions drawn are statistically incorrect. The scientific inaccuracies include: incorrect use of available analysis sets, insufficient definition of statistical methodology (including in measurement of HbA1c), incorrect methodology, lack of statistical significance for correlations made, and data errors.

Efficacy of Hybrid Closed-Loop System in Adults with Type 1 Diabetes and Gastroparesis

We evaluated the efficacy of 670 G HCL on changes in HbA1c and continuous glucose monitor (CGM)-based glucose metrics at 3 and 6 months between five adults with T1D with gastroparesis and nine age-, sex-, and diabetes duration-matched T1D without gastroparesis. At baseline, there were no differences in age, gender, diabetes duration, and total daily insulin requirement between two groups. Median duration of gastroparesis diagnosis was 4.3 years (interquartile range [IQR]: 3.7, 5.9 years). Reduction in HbA1c [difference in HbA1c from baseline to 6 months, median (IQR): 0.3% (0.3%, 0.3%) vs. 0.5% (0.3%, 0.9%); P = 0.20] and CGM time spent in normoglycemia at 6 months [median (IQR): 73% (68%, 80%) vs. 67% (64%, 74%); P = 0.24] were not different between the groups. HCL has similar efficacy in glucose control in adults with T1D with gastroparesis and appears to be safe in this population.

One Year Clinical Experience of the First Commercial Hybrid Closed-Loop System

OBJECTIVE

In September 2016, the U.S. Food and Drug Administration approved the Medtronic 670G "hybrid" closed-loop system. In Auto Mode, this system automatically controls basal insulin delivery based on continuous glucose monitoring data but requires users to enter carbohydrates and blood glucose for boluses. To track real-world experience with this first commercial closed-loop device, we prospectively followed pediatric and adult patients starting the 670G system.

RESEARCH DESIGN AND METHODS

This was a 1-year prospective observational study of patients with type 1 diabetes starting the 670G system between May 2017 and May 2018 in clinic.

RESULTS

Of the total of 84 patients who received 670G and consented, 5 never returned for follow-up, with 79 (aged 9-61 years) providing data at 1 week and 3, 6, 9, and/or 12 months after Auto Mode initiation. For the 86% (68 out of 79) with 1-week data, 99% (67 out of 68) successfully started. By 3 months, at least 28% (22 out of 79) had stopped using Auto Mode; at 6 months, 34% (27 out of 79); at 9 months, 35% (28 out of 79); and by 12 months, 33% (26 out of 79). The primary reason for continuing Auto Mode was desire for increased time in range. Reasons for discontinuation included sensor issues in 62% (16 out of 26), problems obtaining supplies in 12% (3 out of 26), hypoglycemia fear in 12% (3 out of 26), multiple daily injection preference in 8% (2 out of 26), and sports in 8% (2 out of 26). At all visits, there was a significant correlation between hemoglobin A1c (HbA1c) and Auto Mode utilization.

CONCLUSIONS

While Auto Mode utilization correlates with improved glycemic control, a focus on usability and human factors is necessary to ensure use of Auto Mode. Alarms and sensor calibration are a major patient concern, which future technology should alleviate.

Proportion of Basal to Total Insulin Dose Is Associated with Metabolic Control, Body Mass Index, and Treatment Modality in Children with Type 1 Diabetes-A Cross-Sectional Study with Data from the International SWEET Registry

OBJECTIVES

To investigate in a large population the proportion of daily basal insulin dose (BD) to daily total insulin dose (TD) (BD/TD) and its association with glycated hemoglobin A1c (HbA1c), body mass index (BMI)- SDS, and treatment modality in children with type 1 diabetes.

STUDY DESIGN

Cross-sectional study in subjects with type 1 diabetes, age ≤18 years, and ≥2 years of diabetes duration, registered in the international multicenter Better control in Pediatric and Adolescent diabeteS: Working to crEate CEnTers of Reference registry in March 2018. Variables included region, sex, age, diabetes duration, treatment modality (multiple daily injections [MDI] or continuous subcutaneous insulin infusion [CSII]), self-monitoring blood glucose, HbA1c, BD/TD, and BMI-SDS. BMI was converted to BMI-SDS using World Health Organization charts as reference. Hierarchic linear regression models were applied with adjustment for age, sex, and diabetes duration.

RESULTS

A total of 19 687 children with type 1 diabetes (49% female, 49% CSII users) with median age 14.8 (11.5; 17.2) years and diabetes duration 6.0 (3.9; 9.0) years were included. HbA1c was 63 (55; 74) mmol/mol (7.9 [7.2; 8.9]%), and BMI-SDS 0.55 (-0.13; 1.21). Unadjusted, a lower BD/TD was associated with lower HbA1c, male sex, younger age, shorter diabetes duration, lower BMI-SDS, higher numbers of self-monitoring blood glucose and CSII (all P < .01). After adjustment for confounders, lower BD/TD was associated with lower HbA1c (P < .01) and lower BMI-SDS (P < .01) in children on CSII, but not on MDI.

CONCLUSIONS

Lower BD/TD is positively associated with lower HbA1c and lower BMI-SDS in children with type 1 diabetes on CSII. It remains to be investigated in a prospective study whether reducing BD/TD insulin will improve metabolic control and normalize body weight in children with type 1 diabetes.

Evidence on User-Led Innovation in Diabetes Technology (The OPEN Project): Protocol for a Mixed Methods Study

Background

Digital innovations in health care have traditionally followed a top-down pathway, with manufacturers leading the design and production of technology-enabled solutions and those living with chronic conditions involved only as passive recipients of the end product. However, user-driven open-source initiatives in health care are becoming increasingly popular. An example is the growing movement of people with diabetes, who create their own “Do-It-Yourself Artificial Pancreas Systems” (DIYAPS).

Objective

The overall aim of this study is to establish the empirical evidence base for the clinical effectiveness and quality-of-life benefits of DIYAPS and identify the challenges and possible solutions to enable their wider diffusion.

Methods

A research program comprising 5 work packages will examine the outcomes and potential for scaling up DIYAPS solutions. Quantitative and qualitative methodologies will be used to examine clinical and self-reported outcome measures of DIYAPS users. The majority of members of the research team live with type 1 diabetes and are active DIYAPS users, making Outcomes of Patients’ Evidence With Novel, Do-It-Yourself Artificial Pancreas Technology (OPEN) a unique, user-driven research project.

Results

This project has received funding from the European Commission’s Horizon 2020 Research and Innovation Program, under the Marie Skłodowska-Curie Action Research and Innovation Staff Exchange. Researchers with both academic and nonacademic backgrounds have been recruited to formulate research questions, drive the research process, and disseminate ongoing findings back to the DIYAPS community and other stakeholders.

Conclusions

The OPEN project is unique in that it is a truly patient- and user-led research project, which brings together an international, interdisciplinary, and intersectoral research group, comprising health care professionals, technical developers, biomedical and social scientists, the majority of whom are also living with diabetes. Thus, it directly addresses the core research and user needs of the DIYAPS movement. As a new model of cooperation, it will highlight how researchers in academia, industry, and the patient community can create patient-centric innovation and reduce disease burden together.

International Registered Report Identifier (IRRID)

PRR1-10.2196/15368

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System

BACKGROUND

Maintaining glycemic equilibrium can be challenging for people living with type 1 diabetes (T1D) as many factors (eg, length, type, duration, insulin on board, stress, and training) will impact the metabolic changes triggered by physical activity potentially leading to both hypoglycemia and hyperglycemia. Therefore, and despite the noted health benefits, many individuals with T1D do not exercise as much as their healthy peers. While technology advances have improved glucose control during and immediately after exercise, it remains one of the key limitations of artificial pancreas (AP) systems, largely because stopping insulin at the onset of exercise may not be enough to prevent impending, exercise-induced hypoglycemia.

METHODS

A hybrid AP algorithm with subject-specific exercise behavior recognition and anticipatory action is designed to prevent hypoglycemic events during and after moderate-intensity exercise. Our approach relies on a number of key innovations, namely, an activity informed premeal bolus calculator, personalized exercise pattern recognition, and a multistage model predictive control (MS-MPC) strategy that can transition between reactive and anticipatory modes. This AP design was evaluated on 100 in silico subjects from the most up-to-date FDA-accepted UVA/Padova metabolic simulator, emulating an outpatient clinical trial setting. Results with a baseline controller, a regular MPC (rMPC), are also included for comparison purposes.

RESULTS

In silico experiments reveal that the proposed MS-MPC strategy markedly reduces the number of exercise-related hypoglycemic events (8 vs 68).

CONCLUSION

An anticipatory mode for insulin administration of a monohormonal AP controller reduces the occurrence of hypoglycemia during moderate-intensity exercise.

Advances in technology for management of type 1 diabetes

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

Diabetic Gastroparesis

This review covers the epidemiology, pathophysiology, clinical features, diagnosis, and management of diabetic gastroparesis, and more broadly diabetic gastroenteropathy, which encompasses all the gastrointestinal manifestations of diabetes mellitus. Up to 50% of patients with type 1 and type 2 DM and suboptimal glycemic control have delayed gastric emptying (GE), which can be documented with scintigraphy, 13C breath tests, or a wireless motility capsule; the remainder have normal or rapid GE. Many patients with delayed GE are asymptomatic; others have dyspepsia (i.e., mild to moderate indigestion, with or without a mild delay in GE) or gastroparesis, which is a syndrome characterized by moderate to severe upper gastrointestinal symptoms and delayed GE that suggest, but are not accompanied by, gastric outlet obstruction. Gastroparesis can markedly impair quality of life, and up to 50% of patients have significant anxiety and/or depression. Often the distinction between dyspepsia and gastroparesis is based on clinical judgement rather than established criteria. Hyperglycemia, autonomic neuropathy, and enteric neuromuscular inflammation and injury are implicated in the pathogenesis of delayed GE. Alternatively, there are limited data to suggest that delayed GE may affect glycemic control. The management of diabetic gastroparesis is guided by the severity of symptoms, the magnitude of delayed GE, and the nutritional status. Initial options include dietary modifications, supplemental oral nutrition, and antiemetic and prokinetic medications. Patients with more severe symptoms may require a venting gastrostomy or jejunostomy and/or gastric electrical stimulation. Promising newer therapeutic approaches include ghrelin receptor agonists and selective 5-hydroxytryptamine receptor agonists.

MiniMed 670G hybrid closed loop artificial pancreas system for the treatment of type 1 diabetes mellitus: overview of its safety and efficacy

Introduction: Automated insulin delivery for people with type 1 diabetes has been a major goal in the diabetes technology field for many years. While a fully automated system has not yet been accomplished, the MiniMed™ 670G artificial pancreas (AP) system is the first commercially available insulin pump that automates basal insulin delivery, while still requiring user input for insulin boluses. Determining the safety and efficacy of this system is essential to the development of future devices striving for more automation. Areas Covered: This review will provide an overview of how the MiniMed 670G system works including its safety and efficacy, how it compares to similar devices, and anticipated future advances in diabetes technology currently under development. Expert Opinion: The ultimate goal of advanced diabetes technologies is to reduce the burden and amount of management required of patients with diabetes. In addition to reducing patient workload, achieving better glucose control and improving hemoglobin A1c (HbA1c) values are essential for reducing the threat of diabetes-related complications further down the road. Current devices come close to reaching these goals, but understanding the unmet needs of patients with diabetes will allow future technologies to achieve these goals more quickly.

Diabetes Technology: Review of the 2019 American Diabetes Association Standards of Medical Care in Diabetes

DESCRIPTION

The American Diabetes Association (ADA) annually updates its Standards of Medical Care in Diabetes to provide clinicians, patients, researchers, payers, and other interested parties with evidence-based recommendations for the diagnosis and management of patients with diabetes.

METHODS

The ADA Professional Practice Committee comprises physicians, adult and pediatric endocrinologists, diabetes educators, registered dietitians, epidemiologists, pharmacists, and public health experts. To develop the 2019 standards, the committee continuously searched MEDLINE through November 2018 to consider and review studies, particularly high-quality trials including persons with diabetes, for potential incorporation into recommendations. It also solicited feedback from the larger clinical community.

RECOMMENDATIONS

This synopsis focuses on selected guidance relating to use of diabetes technology in adults with diabetes. Recommendations address self-monitoring of blood glucose, continuous glucose monitors, and automated insulin delivery 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.

Reducing Hypoglycemia in the Real World: A Retrospective Analysis of Predictive Low-Glucose Suspend Technology in an Ambulatory Insulin-Dependent Cohort

Objective: Analyze real-world usage and impact of a predictive low-glucose suspend (PLGS) insulin delivery system for maintenance of euglycemia and prevention of hypoglycemic events in people with insulin-dependent diabetes. Methods: Retrospective analysis of Tandem Basal-IQ users who uploaded at least 21 days of PLGS usage data between August 31, 2018, and March 14, 2019 (N = 8132). Insulin delivery and sensor-glucose concentrations were analyzed. The times spent below 70 mg/dL, between 70 and 180 mg/dL, and above 180 mg/dL were assessed. Subgroup analyses were conducted to examine matched pre-/postoutcomes with experienced users (n = 1371) and performance over time for a mixed subgroup with >9 weeks of data (n = 3563). Results: The mean age of patients was 32.4 years, 52% were female, 96% had type 1 diabetes, and 4% had type 2 diabetes. Mean duration on PLGS was 65 days. Algorithm introduction led to a 45% median relative risk reduction in sensor time <70 mg/dL, pre/post (% <70:2.0, 1.1), while the mean glucose remained stable (168 and 168 mg/dL). Mean frequency of hypoglycemic events decreased from one every 9 days to one every 30 days. Total daily insulin dose decreased from 43.4 to 42.3 U in the pre/post subgroup. Manual override of the system was low (4.5%). The number of daily suspensions remained stable (4.9). Conclusions: Introduction of PLGS resulted in effective and sustained prevention of hypoglycemia without a significant increase in mean blood glucose and may be considered for people with type 1 diabetes at risk for hypoglycemia.

Hyperglycaemia is an under-appreciated but modifiable risk factor in managing people with type 1 diabetes and fragility fractures

There are two major musculoskeletal effects of Type 1 diabetes mellitus (T1DM) - fragility fractures and impaired fracture union. Fractures in these patients are a significant and limb threatening injury. Traditionally, they have been treated with prolonged immobilisation and as rigid as possible internal fixation. Recently, hyperglycaemia has been recognised as the most significant modifiable risk factor in treating patients with T1DM and fractured limbs. This article reviews this association further and outlines the role of orthopaedic surgeons in minimising orthopaedic-related complications.

Advances in Type 1 Diabetes Technology Over the Last Decade

The past 10 years have witnessed rapid advances in the technology used to treat patients with type 1 diabetes (T1D). While the disease burden is still high, these advances have contributed to improvements in both glycaemic control and quality of life for many of those affected. New technologies allow for individualisation of care, as patients are able to work with their providers to determine which systems best fit their lifestyle and needs. In addition, thanks to improved glucose monitoring technologies, patients can now simultaneously improve glycaemic control and reduce hypoglycaemia, thereby mitigating risk for acute and chronic complications. Technological advances in T1D care are rapidly moving us toward increasingly automated devices, which offer the promise of reduced disease burden. In this article, we review advances in glucose monitoring, insulin and glucagon delivery, and the applications and algorithms seeking to integrate novel technologies.

Analysis of Prevalence, Magnitude and Timing of the Dawn Phenomenon in Adults and Adolescents With Type 1 Diabetes: Descriptive Analysis of 2 Insulin Pump Trials

OBJECTIVES

To better understand the dawn phenomenon in type 1 diabetes, we sought to determine its prevalence, timing and magnitude in studies specifically designed to assess basal insulin requirements in patients using insulin pumps.

METHODS

Thirty-three participants from 2 sensor-augmented insulin pump studies were analyzed. Twenty participants were obtained from a methodologically ideal semiautomated basal analysis trial in which basal rates were determined from repeated fasting tests (the derivation set) and 13 from an artificial pancreas trial in which duration of fasting was variable (the "confirmation" set). Prevalence was determined for the total cohort and for individual trials using the standard definition of an increase in insulin exceeding 20% and lasting ≥90 minutes. Among cases, time of onset and percent change in the magnitude of basal delivery were determined.

RESULTS

Seventeen participants (52%) experienced the dawn phenomenon (11 of 20 [55%] in the derivation set and 6 of 13 [46%] in the confirmation set). Time of onset was 3 AM (interquartile range [IQR], 3 to 4:15 AM) in the derivation set and 3 AM (IQR, 3 to 4 AM) in the confirmation set. The magnitude of the dawn phenomenon was a 58.1% (IQR, 28.8% to 110.6%) increase in insulin requirements in the derivation set and 65.5% (IQR, 45.6% to 87.4%) in the confirmation set.

CONCLUSIONS

The dawn phenomenon occurs in approximately half of patients with type 1 diabetes; when present, it has predictable timing of onset (generally 3 AM) and a substantial, but highly variable, magnitude. These findings imply that optimization of glycemic control requires clinical emphasis on fasted overnight basal insulin assessment.

A Clinical Overview of Insulin Pump Therapy for the Management of Diabetes: Past, Present, and Future of Intensive Therapy

IN BRIEF Insulin pump therapy is advancing rapidly. This article summarizes the variety of insulin pump technologies available to date and discusses important clinical considerations for each type of technology.

Efficacy and Safety of Real-Life Usage of MiniMed 670G Automode in Children with Type 1 Diabetes Less than 7 Years Old

The Medtronic MiniMed 670G system with SmartGuard™ (Medtronic, Northridge, CA) is a commercial hybrid closed-loop (HCL) system approved for use in 2018 for children >7 years. Studies of this HCL system in subjects >7 years old show improvement in glycemic control, but no study has described its use in younger children. This is a retrospective analysis of patients with type 1 diabetes (T1D) <7 years of age who used the 670G HCL system at Seattle Children's Hospital for 3 months. We compared 2-week data from Carelink™ while in manual mode (MM) with suspend before low active with those in auto mode (AM). We used two tailed t-test to compare variables related to glycemic control. Sixteen children were reviewed [age of AM start: average 4.3 years (range 2-6); 10 male]. The average time in AM was 6.3 ± 2.9 months (range 3-12). There was a statistically significant change for A1c [MM 7.9% (62.8 mmol/mol), AM 7.4% (57.4 mmol/mol); P-value <0.001], percentage time in range (MM 42.8%, AM 56.2%; P-value <0.001), percentage hypoglycemia (MM 1.3%, AM 2.4%; P-value 0.04), and average sensor glucose [MM 200 mg/dL (11.1 mmol/L), AM 176 mg/dL (9.8 mmol/L); P-value <0.001]. No serious adverse reports noted. This case series showed improvement in glycemic control in very young children using the 670G HCL. We did note more hypoglycemia although no serious adverse events, such as hypoglycemic seizure, were reported. A HCL system can be used in young children with T1D safely and effectively and should be an option for children <7 years.

A Technological Revolution: The Integration of New Treatments to Manage Type 1 Diabetes

Intensive insulin treatment and frequent self-monitoring of blood glucose (SMBG) have been recognized as pillars of diabetes treatment. Many patients with type 1 diabetes (T1D) struggle to achieve targeted glycemic control. Technology has vastly changed how these tenets to treatment can occur. Continuous subcutaneous insulin infusion (CSII) pumps and continuous glucose monitoring (CGM) can be used in place of their counterparts, multiple daily injections and SMBG. We present a review of CSII, CGM, and of different levels of integration among these two therapies, ranging from low glucose suspension devices to hybrid closed loop insulin delivery. Analysis of the various tools, their effect on glycemic control, and a guide to integrate them into pediatric clinical practice is presented. Although a cure for T1D remains the ultimate goal, technology holds the promise of keeping youth with T1D in targeted control and minimize the burden of this chronic medical condition. [Pediatr Ann. 2019;48(8):e311-e318.].

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

Background

Patient-driven initiatives have made uptake of Do-it-Yourself Artificial Pancreas Systems (DIYAPS) increasingly popular among people with diabetes of all ages. Observational studies have shown improvements in glycemic control and quality of life among adults with diabetes. However, there is a lack of research examining outcomes of children and adolescents with DIYAPS in everyday life and their social context.

Objective

This survey assesses the self-reported clinical outcomes of a pediatric population using DIYAPS in the real world.

Methods

An online survey was distributed to caregivers to assess the hemoglobin A_1c levels and time in range (TIR) before and after DIYAPS initiation and problems during DIYAPS use.

Results

A total of 209 caregivers of children from 21 countries responded to the survey. Of the children, 47.4% were female, with a median age of 10 years, and 99.4% had type 1 diabetes, with a median duration of 4.3 years (SD 3.9). The median duration of DIYAPS use was 7.5 (SD 10.0) months. Clinical outcomes improved significantly, including the hemoglobin A_1c levels (from 6.91% [SD 0.88%] to 6.27% [SD 0.67]; P<.001) and TIR (from 64.2% [SD 15.94] to 80.68% [SD 9.26]; P<.001).

Conclusions

Improved glycemic outcomes were found across all pediatric age groups, including adolescents and very young children. These findings are in line with clinical trial results from commercially developed closed-loop systems.

Modeling the variability of insulin sensitivity for people with Type 1 Diabetes based on clinical data from an artificial pancreas study

Type 1 Diabetes is an autoimmune disease that eliminates endogenous insulin production. Without the crucial hormone insulin, which is necessary to equilibrate the blood glucose level, the patient must inject insulin subcutaneously. Treatment must be personalized (timing and size of insulin delivery) to achieve glycaemic equilibrium and avoid long-term comorbidities. Patients are educated on Functional Insulin Therapy (FIT) in order to independently adjust insulin delivery several times a day (at least prior to each meal and physical activity). Among personalized parameters, the Correction Factor is used to occasionally correct hyperglycemia via the injection of an insulin dose (bolus) and its value determines the bolus size. Although well-known in common diabetes practice for chronically poorly controlled patients, the phenomenon of "hyperglycemia induces insulin resistance" on a short term basis in patients with rather well controlled diabetes is presented here. Using a new database of evidence, we show that the insulin sensitivity factor, depends on the current level of glycaemia. This opens the door to refining dosing rules for patients and insulin delivery devices in artificial pancreas systems.

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

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

Advanced Technology in the Management of Diabetes: Which Comes First-Continuous Glucose Monitor or Insulin Pump?

PURPOSE OF REVIEW

In this article, we consider advanced technologies for the management of diabetes.

RECENT FINDINGS

Specifically, we pose the question of which should come first: an insulin pump (CSII) or a continuous glucose monitor (CGM)? Historical perspective on both insulin delivery and glucose measurement is provided. Recently published clinical trials are reviewed. Practical issues including quality of life, patient education, and out-of-pocket cost are discussed. Based on available evidence and clinical experience, we favor CGM as a first-line technology recommendation for the treatment of type 1 diabetes (T1D).

Information and communication technology enabling partnership in person-centred diabetes management: building a theoretical framework from an inductive case study in The Netherlands

OBJECTIVES

The aim of this paper is to construct a theoretical framework for information and communication technology (ICT)-enabled partnership towards diabetes management.

DESIGN

We conducted an inductive case study and held interviews on the development and use of an artificial pancreas (AP) system for diabetes management.

SETTING

The study was carried out in the Netherlands with users of an AP system.

PARTICIPANTS

We interviewed six patients with type 1 diabetes, five healthcare professionals (two medical specialists and three diabetes nurses), and one policy advisor from the Ministry of Health, Welfare and Sport.

RESULTS

We built a new theoretical framework for ICT-enabled person-centred diabetes management, covering the central themes of self-managing the disease, shared analysing of (medical) data and experiencing the partnership. We found that ICT yielded new activities of data sharing and a new role for data professionals in the provision of care as well as contributed to carefree living thanks to the semiautomated management enabled by the device. Our data suggested that to enable the partnership through ICT, organisational adjustments need to be made such as the development of new ICT services and a viable financial model to support these services.

CONCLUSION

The management of diabetes through ICT requires an adjustment of the partnership between persons with the chronic condition and the healthcare professional(s) in such a way that the potential for self-managing the condition by analysing the newly available (medical) data (from the AP system) together leads to an experience of partnership between patients and healthcare professionals.

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

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

Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning

Tight blood glucose control reduces the risk of microvascular and macrovascular complications in patients with type 1 diabetes. However, this is very difficult due to the large intra-individual variability and other factors that affect glycaemic control. The main limiting factor to achieve strict control of glucose levels in patients on intensive insulin therapy is the risk of severe hypoglycaemia. Therefore, hypoglycaemia is the main safety problem in the treatment of type 1 diabetes, negatively affecting the quality of life of patients suffering from this disease. Decision support tools based on machine learning methods have become a viable way to enhance patient safety by anticipating adverse glycaemic events. This study proposes the application of four machine learning algorithms to tackle the problem of safety in diabetes management: (1) grammatical evolution for the mid-term continuous prediction of blood glucose levels, (2) support vector machines to predict hypoglycaemic events during postprandial periods, (3) artificial neural networks to predict hypoglycaemic episodes overnight, and (4) data mining to profile diabetes management scenarios. The proposal consists of the combination of prediction and classification capabilities of the implemented approaches. The resulting system significantly reduces the number of episodes of hypoglycaemia, improving safety and providing patients with greater confidence in decision-making.

Molecular and Materials Engineering for Delivery of Peptide Drugs to Treat Type 2 Diabetes

Type 2 diabetes is exploding globally. Despite numerous treatment options, nearly half of type 2 diabetics are unsuccessful at properly managing the disease, primarily due to a lack of patient compliance, driven by adverse side effects as well as complicated and frequent dosing schedules. Improving the delivery of type 2 diabetes drugs has the potential to increase patient compliance and thus, greatly enhance health outcomes and quality of life. This review focuses on molecular and materials engineering strategies that have been implemented to improve the delivery of peptide drugs to treat type 2 diabetes. Peptide drugs benefit from high potency and specificity but suffer from instability and short half-lives that limit their utility as therapeutics and pose a significant delivery challenge. Several approaches have been developed to improve the availability and efficacy of antidiabetic peptides and proteins in vivo. These methods are reviewed herein and include devices, which sustain the release of peptides in long term, and molecular engineering strategies, which prolong circulation time and slow the release of therapeutic peptides. By optimizing the delivery of these peptides and proteins using these approaches, long-term glucose control can be achieved in type 2 diabetes patients.

Hybrid Closed-Loop Control Is Safe and Effective for People with Type 1 Diabetes Who Are at Moderate to High Risk for Hypoglycemia

Background: Typically, closed-loop control (CLC) studies excluded patients with significant hypoglycemia. We evaluated the effectiveness of hybrid CLC (HCLC) versus sensor-augmented pump (SAP) in reducing hypoglycemia in this high-risk population. Methods: Forty-four subjects with type 1 diabetes, 25 women, 37 ± 2 years old, HbA1c 7.4% ± 0.2% (57 ± 1.5 mmol/mol), diabetes duration 19 ± 2 years, on insulin pump, were enrolled at the University of Virginia (N = 33) and Stanford University (N = 11). Eligibility: increased risk of hypoglycemia confirmed by 1 week of blinded continuous glucose monitor (CGM); randomized to 4 weeks of home use of either HCLC or SAP. Primary/secondary outcomes: risk for hypoglycemia measured by the low blood glucose index (LBGI)/CGM-based time in ranges. Results: Values reported: mean ± standard deviation. From baseline to the final week of study: LBGI decreased more on HCLC (2.51 ± 1.17 to 1.28 ± 0.5) than on SAP (2.1 ± 1.05 to 1.79 ± 0.98), P < 0.001; percent time below 70 mg/dL (3.9 mmol/L) decreased on HCLC (7.2% ± 5.3% to 2.0% ± 1.4%) but not on SAP (5.8% ± 4.7% to 4.8% ± 4.5%), P = 0.001; percent time within the target range 70-180 mg/dL (3.9-10 mmol/L) increased on HCLC (67.8% ± 13.5% to 78.2% ± 10%) but decreased on SAP (65.6% ± 12.9% to 59.6% ± 16.5%), P < 0.001; percent time above 180 mg/dL (10 mmol/L) decreased on HCLC (25.1% ± 15.3% to 19.8% ± 10.1%) but increased on SAP (28.6% ± 14.6% to 35.6% ± 17.6%), P = 0.009. Mean glucose did not change significantly on HCLC (144.9 ± 27.9 to 143.8 ± 14.4 mg/dL [8.1 ± 1.6 to 8.0 ± 0.8 mmol/L]) or SAP (152.5 ± 24.3 to 162.4 ± 28.2 [8.5 ± 1.4 to 9.0 ± 1.6]), P = ns. Conclusions: Compared with SAP therapy, HCLC reduced the risk and frequency of hypoglycemia, while improving time in target range and reducing hyperglycemia in people at moderate to high risk of hypoglycemia.

Utilizing the Ambulatory Glucose Profile to Standardize and Implement Continuous Glucose Monitoring in Clinical Practice

Use of continuous glucose monitoring (CGM) is recognized as a valuable component of diabetes self-management and is increasingly considered a standard of care for individuals with diabetes who are treated with intensive insulin therapy. As the clinical use of CGM technology expands, consistent and standardized glycemic metrics and glucose profile visualization have become increasingly important. A common set of CGM metrics has been proposed by an international expert panel in 2017, including standard definitions of time in ranges, glucose variability, and adequacy of data collection. We describe the core CGM metrics, as well as the standardized glucose profile format consolidating 2 weeks of CGM measurements, referred to as the ambulatory glucose profile (AGP), which was also recommended by the CGM expert panel. We present an updated AGP report featuring the core CGM metrics and a visualization of glucose patterns that need clinical attention. New tools for use by clinicians and patients to interpret AGP data are reviewed. Strategies based on the authors' experience in implementing CGM technology across the clinical care spectrum are highlighted.

The impact of using a closed-loop system on food choices and eating practices among people with Type 1 diabetes: a qualitative study involving adults, teenagers and parents

AIMS

We explored whether, how and why moving onto and using a hybrid day-and-night closed-loop system affected people's food choices and dietary practices to better understand the impact of this technology on everyday life and inform recommendations for training and support given to future users.

METHODS

Twenty-four adults, adolescents and parents were interviewed before commencing use of the closed-loop system and following its 3-month use. Data were analysed thematically and longitudinally.

RESULTS

While participants described preparing and/or eating similar meals to those consumed prior to using a closed-loop, many described feeling more normal and less burdened by diabetes in dietary situations. Individuals also noted how the use of this technology could lead to deskilling (less precise carbohydrate counting) and less healthy eating (increased snacking and portion sizes and consumption of fatty, energy-dense foods) because of the perceived ability of the system to deal with errors in carbohydrate counting and address small rises in blood glucose without a corrective dose needing to be administered.

CONCLUSIONS

While there may be quality-of-life benefits to using a closed-loop, individuals might benefit from additional nutritional and behavioural education to help promote healthy eating. Refresher training in carbohydrate counting may also be necessary to help ensure that users are able to undertake diabetes management in situations where the technology might fail or that they take a break from using it.