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

End-to-end offline reinforcement learning for glycemia control

The development of closed-loop systems for glycemia control in type I diabetes relies heavily on simulated patients. Improving the performances and adaptability of these close-loops raises the risk of over-fitting the simulator. This may have dire consequences, especially in unusual cases which were not faithfully - if at all - captured by the simulator. To address this, we propose to use model-free offline RL agents, trained on real patient data, to perform the glycemia control. To further improve the performances, we propose an end-to-end personalization pipeline, which leverages offline-policy evaluation methods to remove altogether the need of a simulator, while still enabling an estimation of clinically relevant metrics for diabetes.

Approaches of wearable and implantable biosensor towards of developing in precision medicine

In the relentless pursuit of precision medicine, the intersection of cutting-edge technology and healthcare has given rise to a transformative era. At the forefront of this revolution stands the burgeoning field of wearable and implantable biosensors, promising a paradigm shift in how we monitor, analyze, and tailor medical interventions. As these miniature marvels seamlessly integrate with the human body, they weave a tapestry of real-time health data, offering unprecedented insights into individual physiological landscapes. This log embarks on a journey into the realm of wearable and implantable biosensors, where the convergence of biology and technology heralds a new dawn in personalized healthcare. Here, we explore the intricate web of innovations, challenges, and the immense potential these bioelectronics sentinels hold in sculpting the future of precision medicine.

A Real-World Prospective Study of the Effectiveness and Safety of Automated Insulin Delivery Compared With Other Modalities of Type 1 Diabetes Treatment During Ramadan Intermittent Fasting

OBJECTIVE

To compare the fasting experience and glycemic control during Ramadan among people with type 1 diabetes (PWT1D) who use automated insulin delivery (AID) versus other modalities of treatment.

RESEARCH DESIGN AND METHODS

A total of 294 PWT1D who attempted fasting during Ramadan in 2022 were categorized on the basis of treatment modality into one of five groups: 1) AID (n = 62); 2) conventional pump + continuous glucose monitoring (CGM; n = 37); 3) pump + self-monitoring of blood glucose (SMBG; n = 8); 4) multiple daily injections (MDI) + CGM (n = 155); and 5) MDI + SMBG (n = 32). Predictors of fasting most days of Ramadan (i.e., breaking fast ≤2 days because of diabetes) were analyzed using uni- and multivariable logistic regression.

RESULTS

The median numbers of days when fasting was broken because of diabetes were 2, 5, 3, 3.5, and 2.5 for AID, conventional pump + CGM, MDI + CGM, pump + SMBG, and MDI + SMBG users, respectively (P = 0.047). Users of AID had a significantly greater time in range (TIR) and lower glycemia risk index, time below range, and time above range compared with users of conventional pumps and MDI (both P < 0.05). Likewise, 53% of AID users attained the double target of 1) breaking fast ≤2 days because of diabetes and 2) maintaining TIR ≥70% during Ramadan compared with only 3% of the conventional pump users and 44% of the MDI + CGM users (both P < 0.05). Compared with MDI + CGM users, AID users were twice as likely to complete fasting most days of Ramadan.

CONCLUSIONS

Use of AID is associated with the highest rates of fasting and best glycemic control during Ramadan fasting.

Emerging Technologies and Therapeutics for Type 1 Diabetes

Recent years witnessed advancements in diabetes technologies and therapeutics. People with type 1 diabetes have more options to control their blood glucose, prevent hypoglycemia, and spend more time with their loved ones. Newer diabetes technologies and therapeutics improve the quality of life and boost the confidence of people with type 1 diabetes. In parallel to changes in the diabetes technology field, stem cell research has been evolving. Gene editing and production of β cells from stem cells are ongoing. The current focus of cure studies is how to increase the survival of cells produced with stem cells. New adjunctive therapies are under development.

Discovery of insulin 100 years on

The discovery of insulin 100 years ago ranks among the greatest medical achievements ever. This sparked a revolution of scientific discovery and therapeutic intervention to treat people suffering with diabetes. A light was shone for other areas of medicine to illuminate what was possible with detailed scientific endeavour. There followed a range of firsts leading to the current time in which we now know more about this peptide hormone than almost any other protein in existence. This has allowed therapeutic advancement from a positon of knowledge leading to stunning innovation. This innovation is likely to lead to more physiological insulin replacement reducing the disease burden to individuals and society as whole.

A Systematic Review of Commercial Hybrid Closed-Loop Automated Insulin Delivery Systems

INTRODUCTION

Several different forms of automated insulin delivery systems (AID systems) have recently been developed and are now licensed for type 1 diabetes (T1D). We undertook a systematic review of reported trials and real-world studies for commercial hybrid closed-loop (HCL) systems.

METHODS

Pivotal, phase III and real-world studies using commercial HCL systems that are currently approved for use in type 1 diabetes were reviewed with a devised protocol using the Medline database.

RESULTS

Fifty-nine studies were included in the systematic review (19 for 670G; 8 for 780G; 11 for Control-IQ; 14 for CamAPS FX; 4 for Diabeloop; and 3 for Omnipod 5). Twenty were real-world studies, and 39 were trials or sub-analyses. Twenty-three studies, including 17 additional studies, related to psychosocial outcomes and were analysed separately.

CONCLUSIONS

These studies highlighted that HCL systems improve time In range (TIR) and arouse minimal concerns around severe hypoglycaemia. HCL systems are an effective and safe option for improving diabetes care. Real-world comparisons between systems and their effects on psychological outcomes require further study.

Human Digital Twin for Personalized Elderly Type 2 Diabetes Management

Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and builds a suite of models exploiting the data for prediction and management to personalize diabetes treatment in E-T2D patients. These models include mathematical and deep-learning ones that capture different patient aspects. Consequently, the HDT virtualizes the patient from different viewpoints using an HDT that mimics the patient and has interfaces to update the virtual models simultaneously from measurements. Using these models the HDT obtains deeper insights about the patient. Further, an adaptive patient model fusing this information and a learning-based model predictive control (LB-MPC) algorithm are proposed. The geriatric conditions are captured as model parameters and constraints while solving the LB-MPC to personalize the insulin infusion for E-T2D management. The HDT is deployed on and illustrated with 15 patients using clinical trials and simulations. Our results show that HDT helps improve the time-in-range from 3–75% to 86–97% and reduces insulin infusion by 14–29%.

Continuous Glucose Monitor, Insulin Pump, and Automated Insulin Delivery Therapies for Type 1 Diabetes: An Update on Potential for Cardiovascular Benefits

PURPOSE OF REVIEW

The incidence of type 1 diabetes (T1D) is rising in all age groups. T1D is associated with chronic microvascular and macrovascular complications but improving glycemic trends can delay the onset and slow the progression of these complications. Utilization of technological devices for diabetes management, such as continuous glucose monitors (CGM) and insulin pumps, is increasing, and these devices are associated with improvements in glycemic trends. Thus, device use may be associated with long-term prevention of T1D complications, yet few studies have investigated the direct impacts of devices on chronic complications in T1D. This review will describe common diabetes devices and combination systems, as well as review relationships between device use and cardiovascular outcomes in T1D.

RECENT FINDINGS

Findings from existing cohort and national registry studies suggest that pump use may aid in improving cardiovascular risk factors such as hypertension and dyslipidemia. Furthermore, pump users have been shown to have lower arterial stiffness and better measures of myocardial function. In registry and case-control longitudinal data, pump use has been associated with fewer cardiovascular events and reduction of cardiovascular disease (CVD) and all-cause mortality. CVD is the leading cause of morbidity and mortality in T1D. Consistent use of diabetes devices may protect against the development and progression of macrovascular complications such as CVD through improvement in glycemic trends. Existing literature is limited, but findings suggest that pump use may reduce acute cardiovascular risk factors as well as chronic cardiovascular complications and overall mortality in T1D.

Supporting the Use of a Person's Own Diabetes Technology in the Inpatient Setting

The use of diabetes technology, including insulin pumps, continuous glucose monitoring devices, and automated insulin delivery systems, has increased significantly in recent years. As more people with diabetes adopt technology in the outpatient setting, we are seeing these devices more frequently in the inpatient setting. This review offers best-practice guidelines for the continuation of personal diabetes technology use in the inpatient setting. It describes policy and guideline stipulations, roles and responsibilities, and device- and brand-specific considerations. Although these devices are not approved for inpatient use by the U.S. Food and Drug Administration, there is general expert consensus that the continuation of personal diabetes devices during hospitalization is appropriate for patients who have sufficient knowledge, are not critically ill, and retain sufficient mental capacity during an acute illness. Health care systems and inpatient providers need to understand the benefits and limitations of personal diabetes technology use during hospitalization.

Evaluation of real-life clinical outcomes in Australian youth with type 1 diabetes on hybrid closed-loop therapy: A retrospective study

AIM

To determine the clinical outcomes and evaluate the perspectives of children with Type 1 diabetes (T1D) and their parents managing their child on hybrid closed-loop (HCL) therapy.

METHODS

Children with T1D on HCL attending a tertiary diabetes centre between April 2019 and July 2021 were included. A retrospective analysis of glycaemic data was conducted to determine the clinical outcomes. Time spent in closed loop, time in target glucose range (TIR 3.9-10 mmol/L), hypoglycaemia and hyperglycaemia were collected at baseline, 4 weeks, 3 and 6 months post-HCL. User experience was assessed by questionnaires administered to parents of children with T1D.

RESULTS

Seventy-one children, mean (SD) age of 12.2 (3.2) years were commenced on HCL. Ten (14%) discontinued HCL use, with 60% discontinuing within the first 6 months. Glycaemic outcomes were analysed in 52 children. Time spent in closed loop was 78 (21) % at 4 weeks, declined to 69 (28) % at 3 months (P = 0.037) and 63 (34) % at 6 months (P = 0.001). The mean %TIR increased from 59.8 at baseline to 67.6 at 3 months and 65.6 at 6 months with a mean adjusted difference of 7.8% points [95% CI 3.6, 11.9] and 5.5% points [95% CI 1.4, 9.5], respectively. There was a reduction in time > 10 mmol/L and time < 3.9 mmol/L from baseline to 6 months. Although families faced challenges with technology, better glucose control with reduced glycaemic fluctuations were reported.

CONCLUSIONS

HCL therapy is associated with improved glycaemia; however, adequate support and education are required for best outcomes.

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

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

J. Schreiber L, Wallace EJ, Wylie R, O'Sullivan J, Dolan EB, Duffy GP. Medical devices, smart drug delivery, wearables and technology for the treatment of Diabetes Mellitus

Diabetes mellitus refers to a group of metabolic disorders which affect how the body uses glucose impacting approximately 9% of the population worldwide. This review covers the most recent technological advances envisioned to control and/or reverse Type 1 diabetes mellitus (T1DM), many of which will also prove effective in treating the other forms of diabetes mellitus. Current standard therapy for T1DM involves multiple daily glucose measurements and insulin injections. Advances in glucose monitors, hormone delivery systems, and control algorithms generate more autonomous and personalised treatments through hybrid and fully automated closed-loop systems, which significantly reduce hypo- and hyperglycaemic episodes and their subsequent complications. Bi-hormonal systems that co-deliver glucagon or amylin with insulin aim to reduce hypoglycaemic events or increase time spent in target glycaemic range, respectively. Stimuli responsive materials for the controlled delivery of insulin or glucagon are a promising alternative to glucose monitors and insulin pumps. By their self-regulated mechanism, these "smart" drugs modulate their potency, pharmacokinetics and dosing depending on patients' glucose levels. Islet transplantation is a potential cure for T1DM as it restores endogenous insulin and glucagon production, but its use is not yet widespread due to limited islet sources and risks of chronic immunosuppression. New encapsulation strategies that promote angiogenesis and oxygen delivery while protecting islets from recipients' immune response may overcome current limiting factors.

Meal and Physical Activity Detection from Free-Living Data for Discovering Disturbance Patterns of Glucose Levels in People with Diabetes

Objective: The interpretation of time series data collected in free-living has gained importance in chronic disease management. Some data are collected objectively from sensors and some are estimated and entered by the individual. In type 1 diabetes (T1D), blood glucose concentration (BGC) data measured by continuous glucose monitoring (CGM) systems and insulin doses administered can be used to detect the occurrences of meals and physical activities and generate the personal daily living patterns for use in automated insulin delivery (AID). Methods: Two challenges in time-series data collected in daily living are addressed: data quality improvement and the detection of unannounced disturbances of BGC. CGM data have missing values for varying periods of time and outliers. People may neglect reporting their meal and physical activity information. In this work, novel methods for preprocessing real-world data collected from people with T1D and the detection of meal and exercise events are presented. Four recurrent neural network (RNN) models are investigated to detect the occurrences of meals and physical activities disjointly or concurrently. Results: RNNs with long short-term memory (LSTM) with 1D convolution layers and bidirectional LSTM with 1D convolution layers have average accuracy scores of 92.32% and 92.29%, and outperform other RNN models. The F1 scores for each individual range from 96.06% to 91.41% for these two RNNs. Conclusions: RNNs with LSTM and 1D convolution layers and bidirectional LSTM with 1D convolution layers provide accurate personalized information about the daily routines of individuals. Significance: Capturing daily behavior patterns enables more accurate future BGC predictions in AID systems and improves BGC regulation.

Clinical Implementation of the Omnipod 5 Automated Insulin Delivery System: Key Considerations for Training and Onboarding People With Diabetes

Automated insulin delivery (AID) systems, which connect an insulin pump, continuous glucose monitoring system, and software algorithm to automate insulin delivery based on real-time glycemic data, hold promise for improving outcomes and reducing therapeutic burden for people with diabetes. This article reviews the features of the Omnipod 5 Automated Insulin Delivery System and how it compares to other AID systems available on or currently under review for the U.S. market. It also provides practical guidance for clinicians on how to effectively train and onboard people with diabetes on the Omnipod 5 System, including how to personalize therapy and optimize glycemia. Many people with diabetes receive their diabetes care in primary care settings rather than in a diabetes specialty clinic. Therefore, it is important that primary care providers have access to resources to support the adoption of AID technologies such as the Omnipod 5 System.

Empfehlungen zur Diabetes-Behandlung mit automatischen Insulin-Dosierungssystemen

Das Prinzip der automatischen Insulindosierung, kurz „AID“ genannt, zeigt in Zulassungsstudien und Real-World-Erfahrungen ausgezeichnete Behandlungsergebnisse. Beim AID wird eine Insulinpumpe mit einem System zur kontinuierlichen Glukosemessung zusammengeschaltet, während ein Rechenprogramm, der sogenannte Algorithmus, die Steuerung der Insulingabe nach Bedarf übernimmt. Idealerweise wäre das System ein geschlossener Kreis, bei dem die Menschen mit Diabetes keine Eingabe mehr machen müssten. Jedoch sind bei den heute verfügbaren Systemen verschiedene Grundeinstellungen und Eingaben erforderlich (insbesondere von Kohlenhydratmengen der Mahlzeiten oder körperlicher Aktivität), die sich von den bisherigen Empfehlungen der sensorunterstützten Pumpentherapie in einzelnen Aspekten unterscheiden. So werden die traditionellen Konzepte von „Basal“ und „Bolus“ mit AID weniger nützlich, da der Algorithmus beide Arten der Insulinabgabe verwendet, um die Glukosewerte dem eingestellten Zielwert zu nähern. Daher sollte bei diesen Systemen statt der Erfassung von „Basal“ und „Bolus“, zwischen einer „nutzerinitiierten“ und einer „automatischen“ Insulindosis unterschieden werden. Gemeinsame Therapieprinzipien der verschiedenen AID-Systeme umfassen die passgenaue Einstellung des Kohlenhydratverhältnisses, die Bedeutung des Timings der vom Anwender initiierten Insulinbolusgaben vor der Mahlzeit, den korrekten Umgang mit einem verzögerten oder versäumten Mahlzeitenbolus, neue Prinzipien im Umgang mit Sport oder Alkoholgenuss sowie den rechtzeitigen Umstieg von AID zu manuellem Modus bei Auftreten erhöhter Ketonwerte. Das Team vom Diabetes-Zentrum AUF DER BULT in Hannover hat aus eigenen Studienerfahrungen und der zugrunde liegenden internationalen Literatur praktische Empfehlungen zur Anwendung und Schulung der gegenwärtig und demnächst in Deutschland kommerziell erhältlichen Systeme zusammengestellt. Für den Erfolg der AID-Behandlung scheint das richtige Erwartungsmanagement sowohl beim Behandlungsteam und als auch beim Anwender von großer Bedeutung zu sein.

Barriers to Uptake of Insulin Technologies and Novel Solutions

Diabetes-related technology has undergone great advancement in recent years. These technological devices are more commonly utilized in the type 1 diabetes population, which requires insulin as the primary treatment modality. Available devices include insulin pumps, continuous glucose monitors, and hybrid systems referred to as automated insulin delivery systems or hybrid closed-loop systems, which combine those two devices along with software algorithms to achieve advanced therapeutic capabilities, including automatic modulation of insulin delivery based on sensor-derived glucose levels to minimize abnormal glucose trends. Use of diabetes technology is associated with significant positive health and psychosocial outcomes, yet utilization rates are generally lacking across both adult and pediatric type 1 diabetes populations in the United States and other countries. There are consistent themes in existing barriers to technology uptake reported by individuals with type 1 diabetes or parents of children with type 1 diabetes, including physical burdens associated with wearing the devices, concerns in navigating the technology and the devices’ abilities to meet user expectations, high cost, inadequate resources within the healthcare team to support device use, disparities in technology access, and psychosocial barriers. It is important to understand the common barriers to uptake of not only the automated insulin delivery systems but also their component devices (insulin pumps and continuous glucose monitors) to fully support individuals in utilizing these devices and optimizing health benefits. The purpose of this article is to summarize the current automated insulin delivery devices that are available for use in management of type 1 diabetes, review common barriers to uptake of those systems and their component devices, and provide expert opinion on existing and future solutions to identified barriers.

Real-world performance of hybrid closed loop in youth, young adults, adults and older adults with type 1 diabetes: Identifying a clinical target for hybrid closed-loop use

AIM

To describe real-world hybrid closed loop (HCL) use and glycaemic outcomes across the lifespan and identify a clinical threshold for HCL use associated with meeting the internationally recommended target of 70% sensor glucose time in range (TIR; 70-180 mg/dL).

MATERIALS AND METHODS

Mixed models examined MiniMed 670G HCL use and glycaemic outcomes in 276 people with type 1 diabetes from four age groups: youth (aged <18 years), young adults (18-25 years), adults (26-49 years) and older adults (≥50 years) for 1 year. ROC analysis identified the minimum percentage HCL use associated with meeting the TIR goal of 70%.

RESULTS

HCL use at month 1 was 70.7% ± 2.9% for youth, 71.0% ± 3.8% for young adults, 78.9% ± 2.1% for adults and 84.7% ± 3.8% in older adults. HCL use declined significantly at 12 months to 49.3% ± 3.2% in youth (P < .001) and 55.7% ± 4.3% in young adults (P = .002). HCL use was sustained at 12 months in adults (76.4% ± 2.2%, P = .36) and older adults (80.4% ± 3.9%, P = .36). HCL use of 70.6% was associated with 70% TIR (sensitivity 58.3%, specificity 85%, AUC 0.77). Older age, 80% or higher continuous glucose monitor use and four or more blood glucose checks per day were associated with attaining the HCL-use threshold.

CONCLUSIONS

HCL use of 70% or higher may be a useful target for clinicians to use to assist people with diabetes in attaining glycaemic goals. Youth may struggle with HCL use more than adults and require clinical intervention to help sustain HCL use across time.

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.