Closed-loop insulin delivery in adults with type 1 diabetes in real-life conditions: a 12-week multicentre, open-label randomised controlled crossover trial

The role of automated insulin delivery technology in diabetes

The role of automated insulin delivery systems in diabetes is expanding. Hybrid closed-loop systems are being used in routine clinical practice for treating people with type 1 diabetes. Encouragingly, real-world data reflects the performance and usability observed in clinical trials. We review the commercially available hybrid closed-loop systems, their distinctive features and the associated real-world data. We also consider emerging indications for closed-loop systems, including the treatment of type 2 diabetes where variability of day-to-day insulin requirements is high, and other challenging applications for this technology. We discuss issues around access and implementation of closed-loop technology, and consider the limitations of present closed-loop systems, as well as innovative approaches that are being evaluated to improve their performance.

A Glucose-Responsive Cannula for Automated and Electronics-Free Insulin Delivery

Automated delivery of insulin based on continuous glucose monitoring is revolutionizing the way insulin-dependent diabetes is treated. However, challenges remain for the widespread adoption of these systems, including the requirement of a separate glucose sensor, sophisticated electronics and algorithms, and the need for significant user input to operate these costly therapies. Herein, a user-centric glucose-responsive cannula is reported for electronics-free insulin delivery. The cannula-made from a tough, elastomer-hydrogel hybrid membrane formed through a one-pot solvent exchange method-changes permeability to release insulin rapidly upon physiologically relevant varying glucose levels, providing simple and automated insulin delivery with no additional hardware or software. Two prototypes of the cannula are evaluated in insulin-deficient diabetic mice. The first cannula-an ends-sealed, subcutaneously inserted prototype-normalizes blood glucose levels for 3 d and controls postprandial glucose levels. The second, more translational version-a cannula with the distal end sealed and the proximal end connected to a transcutaneous injection port-likewise demonstrates tight, 3-d regulation of blood glucose levels when refilled twice daily. This proof-of-concept study may aid in the development of "smart" cannulas and next-generation insulin therapies at a reduced burden-of-care toll and cost to end-users.

Lived Experience of Fully Closed-Loop Insulin Delivery in Adults with Type 1 Diabetes

Introduction: The Closing the Loop in Adults With Type 1 Diabetes (CLEAR) randomized crossover study compared a novel fully closed-loop insulin delivery system with no carbohydrate entry or mealtime bolusing (CamAPS HX), with standard insulin pump therapy and glucose sensor in adults with type 1 diabetes and suboptimal glycemic outcomes. This qualitative substudy aimed to understand the psychosocial impact of using the fully automated system. Materials and Methods: Adults participating in the CLEAR study were invited to take part in a virtual semistructured interview after they had completed 8 weeks using the fully closed-loop system. Recruitment continued until there was adequate representation and data saturation occurred. Interviews were anonymized and transcribed for in-depth thematic analysis using an inductive-deductive approach. Study participants were also asked to complete questionnaires assessing diabetes distress, hypoglycemia confidence, and closed-loop treatment satisfaction. Results: Eleven participants (eight male and three female; age range 26-66 years) were interviewed. After an initial adjustment period, interviewees reported enjoying a reduction in diabetes burden, freed-up mental capacity, and improved mood. All were happy with overnight glycemic outcomes, with the majority reporting benefits on sleep. Although experiences of postprandial glucose outcomes varied, all found mealtimes easier and less stressful, particularly when eating out. Negatives raised by participants predominantly related to the insulin pump hardware, but some also reported increased snacking and challenges around resuming carbohydrate counting at trial closeout. Conclusions: In adults with type 1 diabetes, use of a fully closed-loop insulin delivery system had significant quality-of-life benefits and provided a welcome break from the day-to-day demands of living with diabetes. Clinical Trial Registration: NCT04977908.

Population-Specific Glucose Prediction in Diabetes Care With Transformer-Based Deep Learning on the Edge

Leveraging continuous glucose monitoring (CGM) systems, real-time blood glucose (BG) forecasting is essential for proactive interventions, playing a crucial role in enhancing the management of type 1 diabetes (T1D) and type 2 diabetes (T2D). However, developing a model generalized to a population and subsequently embedding it within a microchip of a wearable device presents significant technical challenges. Furthermore, the domain of BG prediction in T2D remains under-explored in the literature. In light of this, we propose a population-specific BG prediction model, leveraging the capabilities of the temporal fusion Transformer (TFT) to adjust predictions based on personal demographic data. Then the trained model is embedded within a system-on-chip, integral to our low-power and low-cost customized wearable device. This device seamlessly communicates with CGM systems through Bluetooth and provides timely BG predictions using edge computing. When evaluated on two publicly available clinical datasets with a total of 124 participants with T1D or T2D, the embedded TFT model consistently demonstrated superior performance, achieving the lowest prediction errors when compared with a range of machine learning baseline methods. Executing the TFT model on our wearable device requires minimal memory and power consumption, enabling continuous decision support for more than 51 days on a single Li-Poly battery charge. These findings demonstrate the significant potential of the proposed TFT model and wearable device in enhancing the quality of life for people with diabetes and effectively addressing real-world challenges.

Early Real-World Performance of the MiniMed™ 780G Advanced Hybrid Closed-Loop System and Recommended Settings Use in the United States

Background: The MiniMed™ 780G system (MM780G) with Guardian™ 4 sensor includes a 100 mg/dL glucose target (GT) and automated insulin corrections up to every 5 min and was recently approved for use in the United States. In the present study, early real-world MM780G performance and the use of recommended system settings (100 mg/dL GT with an active insulin time of 2 h), by individuals with type 1 diabetes, were evaluated. Methods: CareLink™ personal data uploaded between the launch of the MM780G to August 22, 2023 were aggregated and underwent retrospective analysis (based on user consent) and if users had ≥10 days of continuous glucose monitoring (CGM) data. The 24-h day CGM metrics, including mean glucose, percentage of time spent in (%TIR), above (%TAR), and below (%TBR) target range (70-180 mg/dL), in addition to delivered insulin and closed-loop (CL) exits, were compared between an overall group (n = 7499) and individuals who used recommended settings (each, for >95% of the time). An analysis of the same metrics for MiniMed™ 770G system (MM770G) users (n = 3851) who upgraded to the MM780G was also conducted (paired t-test or Wilcoxon signed-rank test, P < 0.05 considered statistically significant). Results: For MM780G users, CGM use, and time in CL were >90% and all MM780G CGM metrics exceeded consensus-recommended goals. With recommended settings (22% of all users), mean %TIR and %TITR (70-140 mg/dL) were 81.4% and 56.4%, respectively. For individuals who upgraded from the MM770G, %TIR and %TITR increased from 73.2% to 78.3% and 45.8% to 52.6%, respectively, while %TAR reduced from 25.1% to 20.2% (P < 0.001, for all three). CL exits/week averaged <1, for all MM780G users. Conclusions: Early real-world MM780G use in the United States demonstrated a high percentage of time in range with low time above and below range. These outcomes are similar to those observed for real-world MM780G use in other countries.

Real-World Evidence of Automated Insulin Delivery System Use

Objective: Pivotal trials of automated insulin delivery (AID) closed-loop systems have demonstrated a consistent picture of glycemic benefit, supporting approval of multiple systems by the Food and Drug Administration or Conformité Européenne mark receipt. To assess how pivotal trial findings translate to commercial AID use, a systematic review of retrospective real-world studies was conducted. Methods: PubMed and EMBASE were searched for articles published after 2018 with more than five nonpregnant individuals with type 1 diabetes (T1D). Data were screened/extracted in duplicate for sample size, AID system, glycemic outcomes, and time in automation. Results: Of 80 studies identified, 20 met inclusion criteria representing 171,209 individuals. Time in target range 70-180 mg/dL (3.9-10.0 mmol/L) was the primary outcome in 65% of studies, with the majority of reports (71%) demonstrating a >10% change with AID use. Change in hemoglobin A1c (HbA1c) was reported in nine studies (range 0.1%-0.9%), whereas four reported changes in glucose management indicator (GMI) with a 0.1%-0.4% reduction noted. A decrease in HbA1c or GMI of >0.2% was achieved in two-thirds of the studies describing change in HbA1c and 80% of articles where GMI was described. Time below range <70 mg/dL (<3.9 mmol/L) was reported in 16 studies, with all but 1 study showing stable or reduced levels. Most systems had >90% time in automation. Conclusion: With larger and more diverse populations, and follow-up periods of longer duration (∼9 months vs. 3-6 months for pivotal trials), real-world retrospective analyses confirm pivotal trial findings. Given the glycemic benefits demonstrated, AID is rapidly becoming the standard of care for all people living with T1D. Individuals should be informed of these systems and differences between them, have access to and coverage for these technologies, and receive support as they integrate this mode of insulin delivery into their lives.

Efficacy of a Hybrid Closed-Loop Solution in Patients With Excessive Time in Hypoglycaemia: A Post Hoc Analysis of Trials With DBLG1 System

BACKGROUND

Automated insulin delivery is an efficient treatment for patients with type 1 diabetes. Little is known on its impact on patients with excessive time in hypoglycaemia.

METHODS

We performed a post hoc analysis of three randomized control trials that used the DBLG1 (Diabeloop Generation 1) hybrid closed-loop solution. Patients whose time below 70 mg/dL during baseline, open-loop phase exceeded 5% were selected. The outcomes were the differences between the closed-loop and the open-loop phases in time in various ranges and Glycemia Risk Index (GRI).

RESULTS

We identified 45 patients exhibiting ≥5% of time below 70 mg/dL during the open-loop phase. Under closed-loop, the time in hypoglycaemia (54 to <70 mg/dL) dropped from 7.9% (SD 2.4) to 3.2% (SD 1.6) (difference -4.7% [-5.3; -4.1], P < 10-4). The time below 54 mg/dL decreased from 1.9% (SD 1.3) to 0.8% (SD 0.7) (difference -0.9% [-1.4; -0.8], P < 10-4). The time in range (TIR 70-180 mg/dL) improved from 63.3 (SD 9.5) to 68.2% (SD 8.2) (difference 5.1% [2.9; 7.0], P < 10-4). The GRI improved from 51.2 (SD 12.4) to 38.0 (SD 10.9) (difference 13.2 [10.4; 16.0], P < 10-4).

CONCLUSION

DBLG1 decreased time in hypoglycaemia by more than 50% even in patients with excessive time in hypoglycaemia at baseline, while also improving both TIR and GRI, under real-life conditions. The improvement in GRI (13.2%) exceeded that of the improvement in TIR (5.1%) indicating that in this data set, GRI was more sensitive than TIR to the improvement in glycaemia achieved with closed-loop. These results support the safety and efficacy of this treatment.

Study protocol for a randomised open-label clinical trial examining the safety and efficacy of the Android Artificial Pancreas System (AAPS) with advanced bolus-free features in adults with type 1 diabetes: the 'CLOSE IT' (Closed Loop Open SourcE In Type 1 diabetes) trial

INTRODUCTION

Multiple automated insulin delivery (AID) systems have become commercially available following randomised controlled trials demonstrating benefits in people with type 1 diabetes (T1D). However, their real-world utility may be undermined by user-associated burdens, including the need to carbohydrate count and deliver manual insulin boluses. There is an important need for a 'fully automated closed loop' (FCL) AID system, without manual mealtime boluses. The (Closed Loop Open SourcE In Type 1 diabetes) trial is a randomised trial comparing an FCL AID system to the same system used as a hybrid closed loop (HCL) in people with T1D, in an outpatient setting over an extended time frame.

METHODS AND ANALYSIS

Randomised, open-label, parallel, non-inferiority trial comparing the Android Artificial Pancreas System (AAPS) AID algorithm used as FCL to the same algorithm used as HCL. Seventy-five participants aged 18-70 will be randomised (1:1) to one of two treatment arms for 12 weeks: (a) FCL-participants will be advised not to bolus for meals and (b) HCL-participants will use the AAPS AID algorithm as HCL with announced meals. The primary outcome is the percentage of time in target sensor glucose range (3.9-10.0 mmol/L). Secondary outcomes include other glycaemic metrics, safety, psychosocial factors, platform performance and user dietary factors. Twenty FCL arm participants will participate in a 4-week extension phase comparing glycaemic and dietary outcomes using NovoRapid (insulin aspart) to Fiasp (insulin aspart and niacinamide).

ETHICS AND DISSEMINATION

Approvals are by the Alfred Health Ethics Committee (615/22) (Australia) and Health and Disability Ethics Committees (2022 FULL 13832) (New Zealand). Each participant will provide written informed consent. Data protection and confidentiality will be ensured. Study results will be disseminated by publications, conferences and patient advocacy groups.

TRIAL REGISTRATION NUMBERS

ACTRN12622001400752 and ACTRN12622001401741.

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Effects of hybrid closed-loop system on glycemic control and psychological aspects in persons with type 1 diabetes treated with sensor-augmented pump: A prospective single-center observational study

AIMS/INTRODUCTION

This study evaluated the effects of the Medtronic MiniMed 770G hybrid closed-loop system on glycemic control and psychological aspects in persons with type 1 diabetes mellitus.

MATERIALS AND METHODS

This 3-month prospective observational study included 22 participants with type 1 diabetes mellitus who used the Medtronic MiniMed 640G predictive low-glucose suspend system and were switched to the 770G system. Time in the range of 70-180 mg/dL and glycated hemoglobin levels were evaluated; satisfaction, emotional distress and quality of life were assessed using self-reported questionnaires, including the Diabetes Treatment Satisfaction Questionnaire Status, Problem Area in Diabetes and Diabetes Therapy-Related Quality of Life.

RESULTS

Time in the range of 70-180 mg/dL increased (63.5 ± 13.4 to 73.0 ± 10.9% [mean ± standard deviation], P = 0.0010), and time above the range of 181-250 mg/dL decreased (26.9 ± 8.9 to 19.6 ± 7.1%, P < 0.0005). Glycated hemoglobin levels decreased (7.7 ± 1.0 to 7.2 ± 0.8%, P = 0.0021). The percentage of participants with time below the range of 54-69 mg/dL <4% of readings increased from 91% to 100% (P < 0.0005). No significant changes were detected in the satisfaction, emotional distress and quality of life levels, but increased sensor calibration might be related to worsened emotional distress and quality of life.

CONCLUSIONS

The hybrid closed-loop system decreased hyperglycemia and minimized hypoglycemia, but did not improve psychological aspects compared with the predictive low-glucose suspend system, probably because sensor calibration was increased.

Safety and performance of a hybrid closed-loop insulin delivery system with carbohydrate suggestion in adults with type 1 diabetes prone to hypoglycemia

AIMS

To evaluate the safety and performance of a hybrid closed-loop (HCL) system with automatic carbohydrate suggestion in adults with type 1 diabetes (T1D) prone to hypoglycemia.

METHODS

A 32-hour in-hospital pilot study, including a night period, 4 meals and 2 vigorous unannounced 45-minute aerobic sessions, was conducted in 11 adults with T1D prone to hypoglycemia. The primary outcome was the percentage of time in range 70-180 mg/dL (TIR). Main secondary outcomes were time below range < 70 mg/dL (TBR < 70) and < 54 (TBR < 54). Data are presented as median (10th-90th percentile ranges).

RESULTS

The participants, 6 (54.5%) men, were 24 (22-48) years old, and had 22 (9-32) years of T1D duration. All of them regularly used an insulin pump and a continuous glucose monitoring system. The median TIR was 78.7% (75.6-91.2): 92.7% (68.2-100.0) during exercise and recovery period, 79.3% (34.9-100.0) during postprandial period, and 95.4% (66.4-100.0) during overnight period. The TBR < 70 and TBR < 54 were 0.0% (0.0-6.6) and 0.0% (0.0-1.2), respectively. A total of 4 (3-9) 15-g carbohydrate suggestions were administered per person. No severe acute complications occurred during the study.

CONCLUSIONS

The HCL system with automatic carbohydrate suggestion performed well and was safe in this population during challenging conditions in a hospital setting.

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

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

Utility and precision evidence of technology in the treatment of type 1 diabetes: a systematic review

BACKGROUND

The greatest change in the treatment of people living with type 1 diabetes in the last decade has been the explosion of technology assisting in all aspects of diabetes therapy, from glucose monitoring to insulin delivery and decision making. As such, the aim of our systematic review was to assess the utility of these technologies as well as identify any precision medicine-directed findings to personalize care.

METHODS

Screening of 835 peer-reviewed articles was followed by systematic review of 70 of them (focusing on randomized trials and extension studies with ≥50 participants from the past 10 years).

RESULTS

We find that novel technologies, ranging from continuous glucose monitoring systems, insulin pumps and decision support tools to the most advanced hybrid closed loop systems, improve important measures like HbA1c, time in range, and glycemic variability, while reducing hypoglycemia risk. Several studies included person-reported outcomes, allowing assessment of the burden or benefit of the technology in the lives of those with type 1 diabetes, demonstrating positive results or, at a minimum, no increase in self-care burden compared with standard care. Important limitations of the trials to date are their small size, the scarcity of pre-planned or powered analyses in sub-populations such as children, racial/ethnic minorities, people with advanced complications, and variations in baseline glycemic levels. In addition, confounders including education with device initiation, concomitant behavioral modifications, and frequent contact with the healthcare team are rarely described in enough detail to assess their impact.

CONCLUSIONS

Our review highlights the potential of technology in the treatment of people living with type 1 diabetes and provides suggestions for optimization of outcomes and areas of further study for precision medicine-directed technology use in type 1 diabetes.

The Advanced Hybrid Closed Loop Improves Glycemia Risk Index, Continuous Glucose Monitoring Index, and Time in Range in Children with Type 1 Diabetes: Real-World Data from a Single Center Study

Introduction: The Glycemia Risk Index (GRI) and Continuous Glucose Monitoring Index (COGI) are newly defined composite metric parameters derived from continuous glucose monitoring (CGM) data. GRI is divided into five separate risk zones (from lowest to highest: A-E). In this study, the effect of the advanced hybrid closed loop (AHCL) system on GRI and COGI in children with type 1 diabetes was evaluated. Materials and Methods: Forty-five children who had started using the AHCL and whose baseline and sixth-month CGM data were available were analyzed in terms of achievement of CGM consensus goals and changes in GRI scores and zones. The paired t-test was used for the analyses. Results: The mean age and duration of diabetes of the participants were 10.95 ± 3.41 and 3.85 ± 2.67 years, respectively. The mean GRI score significantly decreased from 35.66 ± 17.46 at baseline to 22.83 ± 9.08 at 6 months (P < 0.001). Although the proportion of those in the A zone was 20% at baseline, it increased to 42% at 6 months. AHCL also improved COGI from 72.59 ± 12.44 to 82.90 ± 7.72 (P < 0.001). Time in range (TIR) increased significantly from 70.54% to 80.51% (P < 0.001) at 6 months. Conclusion: AHCL provides not only an improvement in TIR but also a significant improvement in both GRI and COGI at 6 months. The incorporation of GRI and COGI alongside TIR may enhance the assessment of the glycemic profile by providing a more comprehensive and in-depth analysis.

Glucose control and psychosocial outcomes with use of automated insulin delivery for 12 to 96 weeks in type 1 diabetes: a meta-analysis of randomised controlled trials

BACKGROUND

Glycaemic control of Type 1 Diabetes Mellitus (T1DM) remains a challenge due to hypoglycaemic episodes and the burden of insulin self-management. Advancements have been made with the development of automated insulin delivery (AID) devices, yet, previous reviews have only assessed the use of AID over days or weeks, and potential benefits with longer time of AID use in this population remain unclear.

METHODS

 We performed a systematic review and meta-analysis of randomised controlled trials comparing AID (hybrid and fully closed-loop systems) to usual care (sensor augmented pumps, multiple daily insulin injections, continuous glucose monitoring and predictive low-glucose suspend) for adults and children with T1DM with a minimum duration of 3 months. We searched PubMed, Embase, Cochrane Central, and Clinicaltrials.gov for studies published up until April 4, 2023. Main outcomes included time in range 70-180 mg/dL as the primary outcome, and change in HbA1c (%, mmol/mol), glucose variability, and psychosocial impact (diabetes distress, treatment satisfaction and fear of hypoglycaemia) as secondary outcomes. Adverse events included diabetic ketoacidosis (DKA) and severe hypoglycaemia. Statistical analyses were conducted using mean differences and odds ratios. Sensitivity analyses were performed according to age, study duration and type of AID device. The protocol was registered in PROSPERO, CRD42022366710.

RESULTS

We identified 25 comparisons from 22 studies (six crossover and 16 parallel designs) including a total of 2376 participants (721 in adult studies, 621 in paediatric studies, and 1034 in combined studies) which were eligible for analysis. Use of AID devices ranged from 12 to 96 weeks. Patients using AID had 10.87% higher time in range [95% CI 9.38 to 12.37; p < 0.0001, I2 = 87%) and 0.37% (4.77 mmol/mol) lower HbA1c (95% CI - 0.49% (- 6.39 mmol/mol) to - 0.26 (- 3.14 mmol/mol); p < 0·0001, I2 = 77%]. AID systems decreased night hypoglycaemia, time in hypoglycaemia and hyperglycaemia and improved patient distress, with no increase in the risk of DKA or severe hypoglycaemia. No difference was found regarding treatment satisfaction or fear of hypoglycaemia. Among children, there was no difference in glucose variability or time spent in hypoglycaemia between the use of AID systems or usual care. In sensitivity analyses, results remained consistent with the overall analysis favouring AID.

CONCLUSION

The use of AID systems over 12 weeks, regardless of technical or clinical differences, improved glycaemic outcomes and diabetes distress without increasing the risk of adverse events in adults and children with T1DM.

Communication Protocols Integrating Wearables, Ingestibles, and Implantables for Closed-Loop Therapies

Body-conformal sensors and tissue interfacing robotic therapeutics enable the real-time monitoring and treatment of diabetes, wound healing, and other critical conditions. By integrating sensors and drug delivery devices, scientists and engineers have developed closed-loop drug delivery systems with on-demand therapeutic capabilities to provide just-in-time treatments that correspond to chemical, electrical, and physical signals of a target morbidity. To enable closed-loop functionality in vivo, engineers utilize various low-power means of communication that reduce the size of implants by orders of magnitude, increase device lifetime from hours to months, and ensure the secure high-speed transfer of data. In this review, we highlight how communication protocols used to integrate sensors and drug delivery devices, such as radio frequency communication (e.g., Bluetooth, near-field communication), in-body communication, and ultrasound, enable improved treatment outcomes.

Hybrid closed-loop systems can help patients with extreme fear of hypoglycemia or hyperglycemia

Fear of hypoglycemia and hyperglycemia can lead to inappropriate diabetes self-management and untoward health outcomes. We report two patients, representative of these opposite conditions, who benefited from hybrid closed-loop technology. In the patient with fear of hypoglycemia, time in range improved from 26 to 56% and the patient did not present with severe hypoglycemia. Meanwhile, the patient with hyperglycemia aversiveness had a drastic reduction in time below range, from 19 to 4%. We conclude that hybrid closed-loop technology was an effective tool for improvement of glucose values in two patients with fear of hypoglycemia and hyperglycemia aversiveness, respectively.

Intelligent Insulin vs. Artificial Intelligence for Type 1 Diabetes: Will the Real Winner Please Stand Up?

Research in the treatment of type 1 diabetes has been addressed into two main areas: the development of "intelligent insulins" capable of auto-regulating their own levels according to glucose concentrations, or the exploitation of artificial intelligence (AI) and its learning capacity, to provide decision support systems to improve automated insulin therapy. This review aims to provide a synthetic overview of the current state of these two research areas, providing an outline of the latest development in the search for "intelligent insulins," and the results of new and promising advances in the use of artificial intelligence to regulate automated insulin infusion and glucose control. The future of insulin treatment in type 1 diabetes appears promising with AI, with research nearly reaching the possibility of finally having a "closed-loop" artificial pancreas.

A simple modeling framework for prediction in the human glucose-insulin system

Forecasting blood glucose (BG) levels with routinely collected data is useful for glycemic management. BG dynamics are nonlinear, complex, and nonstationary, which can be represented by nonlinear models. However, the sparsity of routinely collected data creates parameter identifiability issues when high-fidelity complex models are used, thereby resulting in inaccurate forecasts. One can use models with reduced physiological fidelity for robust and accurate parameter estimation and forecasting with sparse data. For this purpose, we approximate the nonlinear dynamics of BG regulation by a linear stochastic differential equation: we develop a linear stochastic model, which can be specialized to different settings: type 2 diabetes mellitus (T2DM) and intensive care unit (ICU), with different choices of appropriate model functions. The model includes deterministic terms quantifying glucose removal from the bloodstream through the glycemic regulation system and representing the effect of nutrition and externally delivered insulin. The stochastic term encapsulates the BG oscillations. The model output is in the form of an expected value accompanied by a band around this value. The model parameters are estimated patient-specifically, leading to personalized models. The forecasts consist of values for BG mean and variation, quantifying possible high and low BG levels. Such predictions have potential use for glycemic management as part of control systems. We present experimental results on parameter estimation and forecasting in T2DM and ICU settings. We compare the model's predictive capability with two different nonlinear models built for T2DM and ICU contexts to have a sense of the level of prediction achieved by this model.

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.

Impact of Intensive Glycemic Treatment on Diabetes Complications-A Systematic Review

Diabetes complications can be related to the long duration of the disease or chronic hyperglycemia. The follow-up of diabetic patients is based on the control of chronic hyperglycemia, although this correction, if obtained rapidly in people living with severe chronic hyperglycemia, can paradoxically interfere with the disease or even induce complications. We reviewed the literature describing the impact of the rapid and intense treatment of hyperglycemia on diabetic complications. The literature review showed that worsening complications occurred significantly in diabetic microangiopathy with the onset of specific neuropathy induced by the correction of diabetes. The results for macroangiopathy were somewhat mixed with the intensive and rapid correction of chronic hyperglycemia having a neutral impact on stroke and myocardial infarction but a significant increase in cardiovascular mortality. The management of diabetes has now entered a new era with new therapeutic molecules, such as gliflozin for patients living with type 2 diabetes, or hybrid insulin delivery systems for patients with insulin-treated diabetes. Our manuscript provides evidence in support of these personalized and progressive algorithms for the control of chronic hyperglycemia.

The Artificial Pancreas and Type 1 Diabetes

Diabetes technologies represent a paradigm shift in type 1 diabetes care. Continuous subcutaneous insulin infusion (CSII) pumps and continuous glucose monitors (CGM) improve glycated hemoglobin (HbA1c) levels, enhance time in optimal glycemic range, limit severe hypoglycemia, and reduce diabetes distress. The artificial pancreas or closed-loop system connects these devices via a control algorithm programmed to maintain target glucose, partially relieving the person living with diabetes of this constant responsibility. Automating insulin delivery reduces the input required from those wearing the device, leading to better physiological and psychosocial outcomes. Hybrid closed-loop therapy systems, requiring user-initiated prandial insulin doses, are the most advanced closed-loop systems commercially available. Fully closed-loop systems, requiring no user-initiated insulin boluses, and dual hormone systems have been shown to be safe and efficacious in the research setting. Clinical adoption of closed-loop therapy remains in early stages despite recent technological advances. People living with diabetes, health care professionals, and regulatory agencies continue to navigate the complex path to equitable access. We review the available devices, evidence, clinical implications, and barriers regarding these innovatory technologies.

Switching from treatment with sensor augmented pump to hybrid closed loop system in type 1 diabetes: impact on glycemic control and neuropsychological tests in the real world

AIMS

The aim of this study is to assess in the real world the impact of initiating hybrid closed loop (HCL) on glycemic control and quality of life in patients using sensor-augmented pump (SAP).

METHODS

In this prospective study, patients using SAP changed to an HCL system in a specialized hospital. HCL devices used were Medtronic 780G®, Tandem Control-IQ® and Diabeloop® system. Glucometric data and hypoglycemia and neuropsychological tests were assessed at baseline and 3 months after initiating HCL.

RESULTS

A total of 66 consecutive patients were included (74% women, mean age 44±11 years, diabetes duration 27.2 ±11 years). Significant improvements were observed in coefficient of variation (from 35.6% to 33.1%), time in range (from 62.2 % to 73.8%), time above 180 mg/dl (from 26.9% to 18%), time below 70 mg/dl (from 3.3% to 2.1%) and time below 55 mg/dl (from 0.7% to 0.3%). In addition, significant improvements were observed in fear of hypoglycemia and grade of distress associated to treatment and to interpersonal sphere.

CONCLUSIONS

Switching from SAP to HCL system improves time in range and reduces time in hypoglycemia and glycemic variability at 3 months. These changes are accompanied by significant reduction of neuropsychological burden related to diabetes.

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.

The Evolution of Diabetes Technology - Options Towards Personalized Care

Advances in diabetes technology, especially in the last few decades, have transformed our ability to deliver care to persons with diabetes (PWD). Developments in glucose monitoring, especially continuous glucose monitoring systems (CGM), have revolutionized diabetes care and empowered our patients to manage their disease. CGM has also played an integral role in advancing automated insulin delivery systems. Currently available and upcoming advanced hybrid-closed loop systems aim to decrease patient involvement and are approaching the functionality of a fully automated artificial pancreas. Other advances, such as smart insulin pens and daily patch pumps, offer more options for patients and require less complicated and costly technology. Evidence to support the role of diabetes technology is growing, and PWD and clinicians must choose the right type of technology with a personalized strategy to manage diabetes effectively. Here, we review currently available diabetes technologies, summarize their individual features and highlight key patient factors to consider when creating a personalized treatment plan. We also address current challenges and barriers to the adoption of diabetes technologies.

Insight into continuous glucose monitoring: from medical basics to commercialized devices

According to the latest statistics, more than 537 million people around the world struggle with diabetes and its adverse consequences. As well as acute risks of hypo- or hyper- glycemia, long-term vascular complications may occur, including coronary heart disease or stroke, as well as diabetic nephropathy leading to end-stage disease, neuropathy or retinopathy. Therefore, there is an urgent need to improve diabetes management to reduce the risk of complications but also to improve patient's quality life. The impact of continuous glucose monitoring (CGM) is well recognized, in this regard. The current review aims at introducing the basic principles of glucose sensing, including electrochemical and optical detection, summarizing CGM technology, its requirements, advantages, and disadvantages. The role of CGM systems in the clinical diagnostics/personal testing, difficulties in their utilization, and recommendations are also discussed. In the end, challenges and prospects in future CGM systems are discussed and non-invasive, wearable glucose biosensors are introduced. Though the scope of this review is CGMs and provides information about medical issues and analytical principles, consideration of broader use will be critical in future if the right systems are to be selected for effective diabetes management.

Efficacy, Safety, and Satisfaction with the Accu-Chek Insight with Diabeloop Closed-Loop System in Subjects with Type 1 Diabetes: A Multicenter Real-World Study

Aim: To evaluate the efficacy, safety and satisfaction of the closed-loop system Accu-Chek^® Insight with Diabeloop™ (DBLG1™) in adults with type 1 diabetes (T1D) in real-world conditions. Methods: Patients with T1D using DBLG1 for at least 3 months were included. Glucometric parameters were analyzed at baseline, 1, 2, and 3 months after starting DBLG1. HbA_1c was measured before and at 3 months. Technical issues and acute complications were recorded and patients completed a satisfaction questionnaire. Results: Sixty-two patients were included (43 women; age 44.2 ± 11 years; diabetes duration 24.6 ± 12 years; 40 used flash and 22 continuous glucose monitoring (CGM); 45 were on insulin pump and 17 on multiple daily injections). A significant improvement was observed in the CGM-derived glucose metrics early in the first month: Time in range (%TIR) 70-180 mg/dL (54.86 ± 17 vs. 72.23 ± 10.11); time above range level 1 (%TAR1) 180-250 mg/dL (26.26 ± 13.3 vs. 19.48 ± 6.78), time above range level 2 (%TAR2) > 250 mg/dL (12.02 ± 13.09 vs. 6.14 ± 5.23), time below range level 1 (%TBR 1) 54-70 mg/dL (5.73 ± 11.5 vs. 1.67 ± 1.3), time below range level 2 (%TBR2) < 54 mg/dL (1.18 ± 1.97 vs.0.44 ± 0.49), %CV (38.66 ± 7.53 vs. 29.63 ± 3.74), median glucose (168.57 ± 36 mg/dL vs. 154.63 ± 17.55 mg/dL), and %GMI (7.37 ± 0.91 vs. 7.02 ± 0.42). Also, HbA_1c decreased significantly (7.45% ± 1.05% vs. 6.95% ± 0.7%). No acute complications or serious adverse events occurred. Similar improvement was observed regardless of prior therapy or the glucose monitoring system used. Three patients discontinued DBLG1 and 21 experienced technical issues. Overall, patient satisfaction was high. Adjustments of the settings were modified in general in the direction of greater aggressiveness. Conclusions: A significant improvement in glycemic control without serious adverse events and a high degree of patient satisfaction were observed in this first real-world study evaluating the closed-loop system, Accu-Chek Insight with Diabeloop.

Automated Insulin Delivery (AID) Systems: Use and Efficacy in Children and Adults with Type 1 Diabetes and Other Forms of Diabetes in Europe in Early 2023

Type 1 diabetes (T1D) patients' lifestyle and prognosis has remarkably changed over the years, especially after the introduction of insulin pumps, in particular advanced hybrid closed loop systems (AHCL). Emerging data in literature continuously confirm the improvement of glycemic control thanks to the technological evolution taking place in this disease. As stated in previous literature, T1D patients are seen to be more satisfied thanks to the use of these devices that ameliorate not only their health but their daily life routine as well. Limited findings regarding the use of new devices in different age groups and types of patients is their major limit. This review aims to highlight the main characteristics of each Automated Insulin Delivery (AID) system available for patients affected by Type 1 Diabetes Mellitus. Our main goal was to particularly focus on these systems' efficacy and use in different age groups and populations (i.e., children, pregnant women). Recent studies are emerging that demonstrate their efficacy and safety in younger patients and other forms of diabetes.

Long-Term Glucose Forecasting for Open-Source Automated Insulin Delivery Systems: A Machine Learning Study with Real-World Variability Analysis

Glucose forecasting serves as a backbone for several healthcare applications, including real-time insulin dosing in people with diabetes and physical activity optimization. This paper presents a study on the use of machine learning (ML) and deep learning (DL) methods for predicting glucose variability (GV) in individuals with open-source automated insulin delivery systems (AID). A three-stage experimental framework is employed in this work to systematically implement and evaluate ML/DL methods on a large-scale diabetes dataset collected from individuals with open-source AID. The first stage involves data collection, the second stage involves data preparation and exploratory analysis, and the third stage involves developing, fine-tuning, and evaluating ML/DL models. The performance and resource costs of the models are evaluated alongside relative and proportional errors for 17 GV metrics. Evaluation of fine-tuned ML/DL models shows considerable accuracy in glucose forecasting and variability analysis up to 48 h in advance. The average MAE ranges from 2.50 mg/dL for long short-term memory models (LSTM) to 4.94 mg/dL for autoregressive integrated moving average (ARIMA) models, and the RMSE ranges from 3.7 mg/dL for LSTM to 7.67 mg/dL for ARIMA. Model execution time is proportional to the amount of data used for training, with long short-term memory models having the lowest execution time but the highest memory consumption compared to other models. This work successfully incorporates the use of appropriate programming frameworks, concurrency-enhancing tools, and resource and storage cost estimators to encourage the sustainable use of ML/DL in real-world AID systems.

Consensus Recommendations for the Use of Automated Insulin Delivery Technologies in Clinical Practice

The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage.

One-year real-world performance of the DBLG1 closed-loop system: Data from 3706 adult users with type 1 diabetes in Germany

AIM

The Diabeloop Generation 1 (DBLG1) system is an interoperable hybrid closed-loop solution that was commercialized in Germany in March 2021. We report the longitudinal glycaemic outcomes among the first 3706 users in a real-world setting.

METHODS

We performed a retrospective data collection of all consenting adult patients with type 1 diabetes who were equipped in Germany with the DBLG1 system before 30 April 2022, and with a minimum 14 days of closed-loop usage.

RESULTS

In total, 3706 users (41% women, age 45.1 ± 14.5 years) met the inclusion criteria, reaching a mean follow-up of 131.0 ± 85.1 days, an overall 485 600 days of continuous glucose monitoring data, and a median time spent in closed-loop of 95.0% (IQR 89.1-97.4). The median percentage time in range (70-180 mg/dl) was 72.1% (IQR 65.0-78.9); the time below 70 mg/dl was 0.9% (0.5-1.7), the time below 54 mg/dl was 0.1% (0.1-0.3), and the median Glucose Management Index was 7.0% (6.8-7.3). Exploratory analysis of a subset of 2460 patients in whom baseline glycated haemoglobin (HbA1c) was available [7.4% (IQR 6.9-8.0)] showed that the achieved mean time in range was influenced by baseline HbA1c, ranging from 65.8 ± 9.9% (A1c ≥8.5%) to 81.3 ± 6.8% (A1c <6.5%).

CONCLUSION

This large real-world analysis confirms the relevance of the DBLG1 automated insulin delivery solution for the achievement of standards of care in adult patients with type 1 diabetes.

The Medtronic 780G advanced hybrid closed-loop system achieves and maintains good glycaemic control in type 1 diabetes adults despite previous treatment

INTRODUCTION

Improvements in continuous glucose monitoring (CGM) in recent years have changed the treatment of type 1 diabetes (T1D) by permitting the automation of glucose control. The Minimed 780G advanced hybrid closed-loop (ACHL) system adapts basal infusion rates and delivers auto-correction boluses in order to achieve a user-decided glucose target (100, 110 or 120mg/dL). This study set out to evaluate the effectiveness of the Medtronic 780G system in real-life conditions over 6 months.

MATERIALS AND METHODS

Prospective study that included T1D subjects previously treated with insulin pump without CGM (pump group) or with sensor-augmented pump with predictive low-glucose suspend (SAP-PLGS group) who started with the Minimed 780G system. Sensor and pump data from baseline, and at 1, 3 and 6 months were downloaded and HbA1c was recorded at baseline and at 6 months.

RESULTS

Fifty T1D subjects were included; 25 were previous SAP-PLGS 640G users and 25 used 640G without CGM. 66% were female, 48.6 (40-57) years of age with 20 (12-31.5) years of diabetes duration. Time in range (TIR) improved in the total cohort from baseline to 6 months (69% (57.7-76) vs. 74% (70-82); p=0.01 as did HbA1c (7.6% (7.1-7.8) vs. 7.0% (6.8-7.5); p<0.001), with improvement in times <54, >180 and >250mg/dL. Outcomes at 6 months did not differ between groups, although the SAP-PLGS subjects were prone to hypoglycaemia and the pump group mainly presented suboptimal metabolic control.

CONCLUSION

The AHCL Medtronic Minimed 780G system achieves and maintains good glycaemic control over 6 months in real-life conditions in different profiles of T1D subjects.

Automated insulin delivery systems: from early research to routine care of type 1 diabetes

Automated insulin delivery (AID) systems, so-called closed-loop systems or artificial pancreas, are based upon the concept of insulin supply driven by blood glucose levels and their variations according to body glucose needs, glucose intakes and insulin action. They include a continuous glucose monitoring device which provides a signal to a control algorithm tuning insulin delivery from an infusion pump. The control algorithm is the key of the system since it commands insulin administration in order to maintain blood glucose in a predefined target range and close to a near-normal glucose level. The last two decades have shown dramatic advances toward the use in free life of AID systems for routine care of type 1 diabetes through step-by-step demonstrations of feasibility, safety and efficacy in successive hospital, transitional and outpatient trials. Because of the constraints of pharmacokinetics and dynamics of subcutaneous insulin delivery, the currently available AID systems are all 'hybrid' or 'semi-automated' insulin delivery systems with a need of meal and exercise announcements in order to anticipate rapid glucose variations through pre-meal bolus or pre-exercise reduction of infusion rate. Nevertheless, these AID systems significantly improve time spent in a near-normal range with a reduction of the risk of hypoglycemia and the mental load of managing diabetes in everyday life, representing a milestone in insulin therapy. Expected progression toward fully automated, further miniaturized and integrated, possibly implantable on long-term and more physiological closed-loop systems paves the way for a functional cure of type 1 diabetes.

Quality of life and psychological well-being among children and adolescents with diabetes and their caregivers using open-source automated insulin delivery systems: Findings from a multinational survey

BACKGROUND

Open-source automated insulin delivery (AID) systems have shown to be safe and effective in children and adolescents with type 1 diabetes (T1D) in real-world studies. However, there is a lack of evidence on the effect on their caregivers' quality-of-life (QoL) and well-being. The aim of this study was to assess the QoL of caregivers and children and adolescents using open-source AID systems using validated measures.

METHODS

In this cross-sectional online survey we examined the caregiver-reported QoL and well-being of users and non-users. Validated questionnaires assessed general well-being (WHO-5), diabetes-specific QoL (PAID, PedsQL) and sleep quality (PSQI).

RESULTS

168 caregivers from 27 countries completed at least one questionnaire, including 119 caregivers of children using open-source AID and 49 not using them. After inclusion of covariates, all measures but the PAID and one subscale of the PedsQL showed significant between-group differences with AID users reporting higher general (WHO-5: p = 0.003), sleep-related (PSQI: p = 0.001) and diabetes-related QoL (PedsQL: p < 0.05).

CONCLUSIONS

The results show the potential impact of open-source AID on QoL and psychological well-being of caregivers and children and adolescents with T1D, and can therefore help to inform academia, regulators, and policymakers about the psychosocial health implications of open-source AID.

Impact of glycaemic technologies on quality of life and related outcomes in adults with type 1 diabetes: A narrative review

AIMS

To explore the association between the use of glycaemic technologies and person-reported outcomes (PROs) in adults with type 1 diabetes (T1D).

METHODS

We included T1D and technology publications reporting on PROs since 2014. Only randomised controlled trials and cohort studies that used validated PRO measures (PROMs) were considered.

RESULTS

T1D studies reported on a broad range of validated PROMs, mainly as secondary outcome measures. Most studies examined continuous glucose monitoring (CGM), intermittently scanned CGM (isCGM), and the role of continuous subcutaneous insulin infusion (CSII), including sensor-augmented CSII and closed loop systems. Generally, studies demonstrated a positive impact of technology on hypoglycaemia-specific and diabetes-specific PROs, including reduced fear of hypoglycaemia and diabetes distress, and greater satisfaction with diabetes treatment. In contrast, generic PROMs (including measures of health/functional status, emotional well-being, depressive symptoms, and sleep quality) were less likely to demonstrate improvements associated with the use of glycaemic technologies. Several studies showed contradictory findings, which may relate to study design, population and length of follow-up. Differences in PRO findings were apparent between randomised controlled trials and cohort studies, which may be due to different populations studied and/or disparity between trial and real-world conditions.

CONCLUSIONS

PROs are usually assessed as secondary outcomes in glycaemic technology studies. Hypoglycaemia-specific and diabetes-specific, but not generic, PROs show the benefits of glycaemic technologies, and deserve a more central role in future studies as well as routine clinical care.

Increase Access, Reduce Disparities: Recommendations for Modifying Medicaid CGM Coverage Eligibility Criteria

Numerous studies have demonstrated the clinical value of continuous glucose monitoring (CGM) in type 1 diabetes (T1D) and type 2 diabetes (T2D) populations. However, the eligibility criteria for CGM coverage required by the Centers for Medicare & Medicaid Services (CMS) ignore the conclusive evidence that supports CGM use in various diabetes populations that are currently deemed ineligible. In an earlier article, we discussed the limitations and inconsistencies of the agency's CGM eligibility criteria relative to current scientific evidence and proposed practice solutions to address this issue and improve the safety and care of Medicare beneficiaries with diabetes. Although Medicaid is administered through CMS, there is no consistent Medicaid policy for CGM coverage in the United States. This article presents a rationale for modifying and standardizing Medicaid CGM coverage eligibility across the United States.

Effectiveness and safety of a model predictive control (MPC) algorithm for an artificial pancreas system in outpatients with type 1 diabetes (T1D): systematic review and meta-analysis

BACKGROUND

The purpose of this study was to assess the effectiveness and safety of a model predictive control (MPC) algorithm for an artificial pancreas system in outpatients with type 1 diabetes.

METHODS

We searched PubMed, EMBASE, Cochrane Central, and the Web of Science to December 2021. The eligibility criteria for study selection were randomized controlled trials comparing artificial pancreas systems (MPC, PID, and fuzzy algorithms) with conventional insulin therapy in type 1 diabetes patients. The heterogeneity of the overall results was identified by subgroup analysis of two factors including the intervention duration (overnight and 24 h) and the follow-up periods (< 1 week, 1 week to 1 month, and > 1 month).

RESULTS

The meta-analysis included a total of 41 studies. Considering the effect on the percentage of time maintained in the target range between the MPC-based artificial pancreas and conventional insulin therapy, the results showed a statistically significantly higher percentage of time maintained in the target range in overnight use (10.03%, 95% CI [7.50, 12.56] p < 0.00001). When the follow-up period was considered, in overnight use, the MPC-based algorithm showed a statistically significantly lower percentage of time maintained in the hypoglycemic range (-1.34%, 95% CI [-1.87, -0.81] p < 0.00001) over a long period of use (> 1 month).

CONCLUSIONS

Overnight use of the MPC-based artificial pancreas system statistically significantly improved glucose control while increasing time maintained in the target range for outpatients with type 1 diabetes. Results of subgroup analysis revealed that MPC algorithm-based artificial pancreas system was safe while reducing the time maintained in the hypoglycemic range after an overnight intervention with a long follow-up period (more than 1 month).

From islet of Langerhans transplantation to the bioartificial pancreas

Type 1 diabetes is a disease resulting from autoimmune destruction of the insulin-producing beta cells in the pancreas. When type 1 diabetes develops into severe secondary complications, in particular end-stage nephropathy, or life-threatening severe hypoglycemia, the best therapeutic approach is pancreas transplantation, or more recently transplantation of the pancreatic islets of Langerhans. Islet transplantation is a cell therapy procedure, that is minimally invasive and has a low morbidity, but does not display the same rate of functional success as the more invasive pancreas transplantation because of suboptimal engraftment and survival. Another issue is that pancreas or islet transplantation (collectively known as beta cell replacement therapy) is limited by the shortage of organ donors and by the need for lifelong immunosuppression to prevent immune rejection and recurrence of autoimmunity. A bioartificial pancreas is a construct made of functional, insulin-producing tissue, embedded in an anti-inflammatory, immunomodulatory microenvironment and encapsulated in a perm-selective membrane allowing glucose sensing and insulin release, but isolating from attacks by cells of the immune system. A successful bioartificial pancreas would address the issues of engraftment, survival and rejection. Inclusion of unlimited sources of insulin-producing cells, such as xenogeneic porcine islets or stem cell-derived beta cells would further solve the problem of organ shortage. This article reviews the current status of clinical islet transplantation, the strategies aiming at developing a bioartificial pancreas, the clinical trials conducted in the field and the perspectives for further progress.

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.

Biobehavioral Changes Following Transition to Automated Insulin Delivery: A Large Real-life Database Analysis

OBJECTIVE

To document glycemic and user-initiated bolus changes following transition from predictive low glucose suspend (PLGS) system to automated insulin delivery (AID) system during real-life use.

RESEARCH DESIGN AND METHODS

We conducted analysis of 2,329,166 days (6,381 patient-years) of continuous glucose monitoring (CGM) and insulin therapy data for 19,354 individuals with type 1 Diabetes, during 1-month PLGS use (Basal-IQ technology) followed by 3-month AID use (Control-IQ technology). Baseline characteristics are as follows: 55.4% female, age (median/quartiles/range) 39/19-58/1-92 years, mean ± SD glucose management indicator (GMI) 7.5 ± 0.8. Primary outcome was time in target range (TIR) (70-180 mg/dL). Secondary outcomes included CGM-based glycemic control metrics and frequency of user-initiated boluses.

RESULTS

Compared with PLGS, AID increased TIR on average from 58.4 to 70.5%. GMI and percent time above and below target range improved as well: from 7.5 to 7.1, 39.9 to 28.1%, and 1.66 to 1.46%, respectively; all P values <0.0001. Stratification of outcomes by age and baseline GMI revealed clinically significant differences. Glycemic improvements were most pronounced in those <18 years old (TIR improvement 14.0 percentage points) and those with baseline GMI >8.0 (TIR improvement 13.2 percentage points). User-initiated correction boluses decreased from 2.7 to 1.8 per day, while user-initiated meal boluses remained stable at 3.6 to 3.8 per day.

CONCLUSIONS

Observed in real life of >19,000 individuals with type 1 diabetes, transitions from PLGS to AID resulted in improvement of all glycemic parameters, equivalent to improvements observed in randomized clinical trials, and reduced user-initiated boluses. However, glycemic and behavioral changes with AID use may differ greatly across different demographic and clinical groups.

Coordination of glucose monitoring, self-care behaviour and mental health: achieving precision monitoring in diabetes

Monitoring of glucose plays an essential role in the management of diabetes. However, to fully understand and meaningfully interpret glucose levels, additional information on context is necessary. Important contextual factors include data on behaviours such as eating, exercise, medication-taking and sleep, as well as data on mental health aspects such as stress, affect, diabetes distress and depressive symptoms. This narrative review provides an overview of the current state and future directions of precision monitoring in diabetes. Precision monitoring of glucose has made great progress over the last 5 years with the emergence of continuous glucose monitoring (CGM), automated analysis of new glucose variables and visualisation of CGM data via the ambulatory glucose profile. Interestingly, there has been little progress in the identification of subgroups of people with diabetes based on their glycaemic profile. The integration of behavioural and mental health data could enrich such identification of subgroups to stimulate precision medicine. There are a handful of studies that have used innovative methodology such as ecological momentary assessment to monitor behaviour and mental health in people's everyday life. These studies indicate the importance of the interplay between behaviour, mental health and glucose. However, automated integration and intelligent interpretation of these data sources are currently not available. Automated integration of behaviour, mental health and glucose could lead to the identification of certain subgroups that, for example, show a strong association between mental health and glucose in contrast to subgroups that show independence of mental health and glucose. This could inform precision diagnostics and precision therapeutics. We identified just-in-time adaptive interventions as a potential means by which precision monitoring could lead to precision therapeutics. Just-in-time adaptive interventions consist of micro-interventions that are triggered in people's everyday lives when a certain problem is identified using monitored behaviour, mental health and glucose variables. Thus, these micro-interventions are responsive to real-life circumstances and are adaptive to the specific needs of an individual with diabetes. We conclude that, with current developments in big data analysis, there is a huge potential for precision monitoring in diabetes.

Closed-loop insulin delivery: update on the state of the field and emerging technologies

INTRODUCTION

Over the last five years, closed-loop insulin delivery systems have transitioned from research-only to real-life use. A number of systems have been commercialized and are increasingly used in clinical practice. Given the rapidity of new developments in the field, understanding the capabilities and key similarities and differences of current systems can be challenging. This review aims to provide an update on the state of the field of closed-loop insulin delivery systems, including emerging technologies.

AREAS COVERED

We summarize key clinical safety and efficacy evidence of commercial and emerging insulin-only hybrid closed-loop systems for type 1 diabetes. A literature search was conducted and clinical trials using closed-loop systems during free-living conditions were identified to report on safety and efficacy data. We comment on emerging technologies and adjuncts for closed-loop systems, as well as non-technological priorities in closed-loop insulin delivery.

EXPERT OPINION

Commercial hybrid closed-loop insulin delivery systems are efficacious, consistently improving glycemic control when compared to standard therapy. Challenges remain in widespread adoption due to clinical inertia and the lack of resources to embrace technological developments by health care professionals.

A Multicenter Randomized Trial Evaluating Fast-Acting Insulin Aspart in the Bionic Pancreas in Adults with Type 1 Diabetes

Objective: To evaluate the insulin-only configuration of the iLet® bionic pancreas (BP) using fast-acting insulin aspart (Fiasp®) in adults with type 1 diabetes (T1D). Research Design and Methods: In this multicenter, randomized trial, 275 adults with T1D (18-83 years old, baseline HbA1c 5.3%-14.9%) were randomly assigned 2:2:1 to use the BP with fast-acting insulin aspart (BP-F group, N = 114), BP with aspart or lispro (BP-A/L group, N = 107), or a control group using their standard-care insulin delivery (SC group, N = 54) plus real-time continuous glucose monitoring (CGM). The primary outcome was HbA1c at 13 weeks. The BP-F versus SC comparison was considered primary and BP-F versus BP-A/L secondary. Results: Mean ± standard deviation (SD) HbA1c decreased from 7.8% ± 1.2% at baseline to 7.1% ± 0.6% at 13 weeks with BP-F versus 7.6% ± 1.2% to 7.5% ± 0.9% with SC (adjusted difference = -0.5%, 95% CI -0.7 to -0.3, P < 0.001). CGM-measured percent time <54 mg/dL over 13 weeks with BP-F was noninferior to SC (adjusted difference = 0.00%, 95% CI -0.07 to 0.05, P < 0.001 for noninferiority based on a prespecified noninferiority limit of 1%). Over 13 weeks, mean time in range 70-180 mg/dL (TIR) increased by 14% (3.4 h/day) and mean CGM glucose was reduced by 18 mg/dL with BP-F compared with SC (P < 0.001). Analyses of time >180 mg/dL, time >250 mg/dL, and the SD of CGM glucose all favored BP-F compared with SC (P < 0.001). Differences between BP-F and BP-A/L were minimal, with no difference in HbA1c at 13 weeks (adjusted difference = -0.0%, 95% CI -0.2 to 0.1, P = 0.67) or mean glucose (adjusted difference = -2.0 mg/dL, 95% CI -4.3 to 0.4, P = 0.10). Mean TIR was 2% greater with BP-F than BP-A/L (95% CI 1 to 4, P = 0.005), but the percentages of participants improving TIR by ≥5% were not significantly different (P = 0.49) and there were no significant differences comparing BP-F versus BP-A/L across nine patient-reported outcome surveys. The rate of severe hypoglycemia events did not differ among the three groups. Conclusions: In adults with T1D, HbA1c was improved with the BP using fast-acting insulin aspart compared with standard care without increasing CGM-measured hypoglycemia. However, the effect was no better than the reduction observed with the BP using aspart or lispro. Clinical Trial Registry: clinicaltrials.gov; NCT04200313.

Algorithms for Automated Insulin Delivery: An Overview

The technology needed to "close the loop," that is, a system for continuous glucose monitoring and a pump that infuses insulin, are only 2 of the 3 components needed for each system for automated insulin delivery (AID), the other is a "translation" of the glucose information into the appropriate amount of insulin to be applied at a given point in time to keep glucose levels in the body in the target range. It might look straightforward to calculate the required insulin dose and control the pump to apply this immediately; however, once a given amount of insulin is in the body, it will be absorbed and become metabolically active. To avoid lowering glucose levels toward too low levels, the algorithms used to calculate the insulin dose have to take a number of other factors into account. This is needed to make sure that AID systems are not only efficient but also safe, that is, not only Time-in-Range should be maximal, also Time-below-Range should be minimal. The review characterizes the different types of AID algorithms that were developed in the last decades. Using a structured approach, the different algorithms are classified. A systematic evaluation of the performance of the different algorithms is missing, not only during the clinical development of AID systems, but also in daily practice. However, it might very well be that other factors determine which AID algorithms will be used in practice.

Multi-Timescale Rhythmicity of Blood Glucose and Insulin Delivery Reveals Key Advantages of Hybrid Closed Loop Therapy

BACKGROUND

Blood glucose and insulin exhibit coordinated daily and hourly rhythms in people without diabetes (nonT1D). Although the presence and stability of these rhythms are associated with euglycemia, it is unknown if they (1) are preserved in individuals with type 1 diabetes (T1D) and (2) vary by therapy type. In particular, Hybrid Closed Loop (HCL) systems improve glycemia in T1D compared to Sensor Augmented Pump (SAP) therapies, but the extent to which either recapitulates coupled glucose and insulin rhythmicity is not well described. In HCL systems, more rapid modulation of glucose via automated insulin delivery may result in greater rhythmic coordination and euglycemia. Such precision may not be possible in SAP systems. We hypothesized that HCL users would exhibit fewer hyperglycemic event, superior rhythmicity, and coordination relative to SAP users.

METHODS

Wavelet and coherence analyses were used to compare glucose and insulin delivery rate (IDR) within-day and daily rhythms, and their coordination, in 3 datasets: HCL (n = 150), SAP (n = 89), and nonT1D glucose (n = 16).

RESULTS

Glycemia, correlation between normalized glucose and IDR, daily coherence of glucose and IDR, and amplitude of glucose oscillations differed significantly between SAP and HCL users. Daily glucose rhythms differed significantly between SAP, but not HCL, users and nonT1D individuals.

CONCLUSIONS

SAP use is associated with greater hyperglycemia, higher amplitude glucose fluctuations, and a less stably coordinated rhythmic phenotype compared to HCL use. Improvements in glucose and IDR rhythmicity may contribute to the overall effectiveness of HCL systems.

Automated Insulin Delivery with SGLT2i Combination Therapy in Type 1 Diabetes

Background: Use of sodium-glucose cotransporter 2 inhibitors (SGLT2i) as adjunct therapy to insulin in type 1 diabetes (T1D) has been previously studied. In this study, we present data from the first free-living trial combining low-dose SGLT2i with commercial automated insulin delivery (AID) or predictive low glucose suspend (PLGS) systems. Methods: In an 8-week, randomized, controlled crossover trial, adults with T1D received 5 mg/day empagliflozin (EMPA) or no drug (NOEMPA) as adjunct to insulin therapy. Participants were also randomized to sequential orders of AID (Control-IQ) and PLGS (Basal-IQ) systems for 4 and 2 weeks, respectively. The primary endpoint was percent time-in-range (TIR) 70-180 mg/dL during daytime (7:00-23:00 h) while on AID (NCT04201496). Findings: A total of 39 subjects were enrolled, 35 were randomized, 34 (EMPA; n = 18 and NOEMPA n = 16) were analyzed according to the intention-to-treat principle, and 32 (EMPA; n = 16 and NOEMPA n = 16) completed the trial. On AID, EMPA versus NOEMPA had higher daytime TIR 81% versus 71% with a mean estimated difference of +9.9% (confidence interval [95% CI] 0.6-19.1); p = 0.04. On PLGS, the EMPA versus NOEMPA daytime TIR was 80% versus 63%, mean estimated difference of +16.5% (95% CI 7.3-25.7); p < 0.001. One subject on SGLT2i and AID had one episode of diabetic ketoacidosis with nonfunctioning insulin pump infusion site occlusion contributory. Interpretation: In an 8-week outpatient study, addition of 5 mg daily empagliflozin to commercially available AID or PLGS systems significantly improved daytime glucose control in individuals with T1D, without increased hypoglycemia risk. However, the risk of ketosis and ketoacidosis remains. Therefore, future studies with SGLT2i will need modifications to closed-loop control algorithms to enhance safety.