Smartphone Bolus Feature Increases Number of Insulin Boluses in People With Low Bolus Frequency

BACKGROUND

The t:connect mobile app from Tandem Diabetes Care recently added a feature to allow t:slim X2 insulin pump users to initiate an insulin bolus from their personal smartphone. User experience and user interface considerations prioritized safety and ease of use, and we examined whether the smartphone bolus feature changed bolus behavior in individuals who bolused less than three times/day.

METHODS

We performed a retrospective analysis of t:slim X2 insulin pump users in the United States who had remotely updated their insulin pump software to be compatible with the smartphone bolus version of the app and who gave less than three boluses per day prior to the smartphone bolus update.

RESULTS

Of the 4470 early adopters who met these criteria, the median number of boluses was 2.2 per day (prior to smartphone bolus update) versus 2.7 per day (after smartphone bolus update), equating to approximately half a bolus more delivered per day (P < .001). Overall, a median of one bolus per day was administered by smartphone app as opposed to being initiated from the screen on the insulin pump.

CONCLUSION

This analysis found a significant increase in bolusing behavior among early adopters of the smartphone bolus feature of the t:connect mobile app.

Automated insulin delivery among adults with type 1 diabetes for up to 2 years: a real-world, multicentre study

BACKGROUND AND AIMS

Automated insulin delivery (AID) improves glycaemia among people with type 1 diabetes in clinical trials and overseas real-world studies. Whether improvements are sustained beyond 12 months in the real world, and whether they occur in the Australian context, has not yet been established. We aimed to observe, up to 2 years, the effectiveness of initiating first-generation AID for type 1 diabetes management.

METHODS

Retrospective, real-world, observational study using medical records, conducted across five sites in Australia. Adults with type 1 diabetes, who had AID initiated between February 2019 and December 2021, were observed for 6-24 months after initiation (until June 2022). Outcomes examined included glucose metrics assessed by glycated haemoglobin (HbA1c ) and continuous glucose monitoring (CGM), safety and therapy continuation.

RESULTS

Ninety-four adults were studied (median age 39 years (interquartile range, IQR: 31-51); pre-initiation HbA1c 7.8% (7.2-8.6)). After AID initiation, HbA1c decreased by mean 0.5 percentage points (95% confidence interval (CI): -0.7 to -0.2) at 3 months (P < 0.001); CGM time in range 3.9-10.0 mmol/L increased by 11 percentage points (9-14) at 1 month (P < 0.001); these improvements were maintained up to 24 months (all P < 0.02). Median CGM time below 3.9 mmol/L was <1.5% pre- and post-AID initiation. The subgroup with pre-initiation HbA1c above 8.5% had the greatest HbA1c improvement (-1.4 percentage points (-1.8 to -1.1) at 3 months). Twelve individuals (13%) discontinued AID, predominantly citing difficulties with CGM. During the 150 person-years observed, four diabetes-related emergencies were documented: three severe hypoglycaemic events and one hyperglycaemic event without ketoacidosis.

CONCLUSIONS

Early glucose improvements were observed after real-world AID initiation, sustained up to 2 years, without excess adverse events. The greatest benefits were observed among individuals with highest glycaemia before initiation. Future-generation systems with increased user-friendliness may enhance therapy continuation.

Evaluation of a New Clinical Tool to Enhance Clinical Care of Control-IQ Users

BACKGROUND

The purpose of this study was to develop and test a new Clinic Tool to assist health care professionals with clinical care of persons with diabetes using the Control-IQ system.

METHODS

A Clinic Tool was iteratively developed with input from diabetes clinicians, which outlined a systematic process for assessing data, reviewing insulin settings, providing education, and documenting the encounter. Diabetes clinicians were recruited to trial the Clinical Tool in up to five clinical encounters (in-person, telehealth, or telephone). Quantitative surveys and free-text responses, including a knowledge quiz and the System Usability Scale (SUS), were administered to determine clinician satisfaction, confidence, knowledge, and implications for practice.

RESULTS

Twenty-nine clinicians (43% endocrinologists, mean 10.7 years in practice) enrolled in the study and completed 89 encounters using the Control-IQ Clinic Tool. Participants spent an average of 10 minutes using the Tool and reported excellent SUS scores within the 90%-95% percentile for usability. Knowledge quiz scores increased in 42% of participants. Both familiarity with Control-IQ and confidence providing clinical care to Control-IQ users significantly improved (P = .009 and P < .001 respectively). Ninety percent of participants agreed that the Tool will change their clinical care going forward.

CONCLUSION

The Control-IQ Clinical Tool is highly usable and impacted clinical care delivery to Control-IQ users. Tools that serve to improve clinician confidence in delivery of care to diabetes device users should be expanded, leveraged, and studied to assess the impact on adherence and glycemic control for persons with diabetes.

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.

Performance Effect of Adjusting Insulin Sensitivity for Model-Based Automated Insulin Delivery Systems

BACKGROUND

Model predictive control (MPC) has become one of the most popular control strategies for automated insulin delivery (AID) in type 1 diabetes (T1D). These algorithms rely on a prediction model to determine the best insulin dosing every sampling time. Although these algorithms have been shown to be safe and effective for glucose management through clinical trials, managing the ever-fluctuating relationship between insulin delivery and resulting glucose uptake (aka insulin sensitivity, IS) remains a challenge. We aim to evaluate the effect of informing an AID system with IS on the performance of the system.

METHOD

The University of Virginia (UVA) MPC control-based hybrid closed-loop (HCL) and fully closed-loop (FCL) system was used. One-day simulations at varying levels of IS were run with the UVA/Padova T1D Simulator. The AID system was informed with an estimated value of IS obtained through a mixed meal glucose tolerance test. Relevant controller parameters are updated to inform insulin dosing of IS. Performance of the HCL/FCL system with and without information of the changing IS was assessed using a novel performance metric penalizing the time outside the target glucose range.

RESULTS

Feedback in AID systems provides a certain degree tolerance to changes in IS. However, IS-informed bolus and basal dosing improve glycemic outcomes, providing increased protection against hyperglycemia and hypoglycemia according to the individual's physiological state.

CONCLUSIONS

The proof-of-concept analysis presented here shows the potentially beneficial effects on system performance of informing the AID system with accurate estimates of IS. In particular, when considering reduced IS, the informed controller provides increased protection against hyperglycemia compared with the naïve controller. Similarly, reduced hypoglycemia is obtained for situations with increased IS. Further tailoring of the adaptation schemes proposed in this work is needed to overcome the increased hypoglycemia observed in the more resistant cases and to optimize the performance of the adaptation method.

Detection of Meals and Physical Activity Events From Free-Living Data of People With Diabetes

BACKGROUND

Predicting carbohydrate intake and physical activity in people with diabetes is crucial for improving blood glucose concentration regulation. Patterns of individual behavior can be detected from historical free-living data to predict meal and exercise times. Data collected in free-living may have missing values and forgotten manual entries. While machine learning (ML) can capture meal and exercise times, missing values, noise, and errors in data can reduce the accuracy of ML algorithms.

METHODS

Two recurrent neural networks (RNNs) are developed with original and imputed data sets to assess detection accuracy of meal and exercise events. Continuous glucose monitoring (CGM) data, insulin infused from pump data, and manual meal and exercise entries from free-living data are used to predict meals, exercise, and their concurrent occurrence. They contain missing values of various lengths in time, noise, and outliers.

RESULTS

The accuracy of RNN models range from 89.9% to 95.7% for identifying the state of event (meal, exercise, both, or neither) for various users. "No meal or exercise" state is determined with 94.58% accuracy by using the best RNN (long short-term memory [LSTM] with 1D Convolution). Detection accuracy with this RNN is 98.05% for meals, 93.42% for exercise, and 55.56% for concurrent meal-exercise events.

CONCLUSIONS

The meal and exercise times detected by the RNN models can be used to warn people for entering meal and exercise information to hybrid closed-loop automated insulin delivery systems. Reliable accuracy for event detection necessitates powerful ML and large data sets. The use of additional sensors and algorithms for detecting these events and their characteristics provides a more accurate alternative.

Multivariable Automated Insulin Delivery System for Handling Planned and Spontaneous Physical Activities

BACKGROUND

Hybrid closed-loop control of glucose levels in people with type 1 diabetes mellitus (T1D) is limited by the requirements on users to manually announce physical activity (PA) and meals to the artificial pancreas system. Multivariable automated insulin delivery (mvAID) systems that can handle unannounced PAs and meals without any manual announcements by the user can improve glycemic control by modulating insulin dosing in response to the occurrence and intensity of spontaneous physical activities.

METHODS

An mvAID system is developed to supplement the glucose measurements with additional physiological signals from a wristband device, with the signals analyzed using artificial intelligence algorithms to automatically detect the occurrence of PA and estimate its intensity. This additional information gained from the physiological signals enables more proactive insulin dosing adjustments in response to both planned exercise and spontaneous unanticipated physical activities.

RESULTS

In silico studies of the mvAID illustrate the safety and efficacy of the system. The mvAID is translated to pilot clinical studies to assess its performance, and the clinical experiments demonstrate an increased time in range and reduced risk of hypoglycemia following unannounced PA and meals.

CONCLUSIONS

The mvAID systems can increase the safety and efficacy of insulin delivery in the presence of unannounced physical activities and meals, leading to improved lives and less burden on people with T1D.

First Generation of a Modular Interoperable Closed-Loop System for Automated Insulin Delivery in Patients With Type 1 Diabetes: Lessons From Trials and Real-Life Data

BACKGROUND

DBLG1 (Diabeloop Generation 1) stands as one of the five commercially available closed-loop solution worldwide for patients with type 1 diabetes as of 2023. Our aim was to provide an overview of all data obtained with this system regarding outcomes and populations, with an emphasis on interoperability.

METHODS

This report includes all available sources of data (three randomized control trials and five surveys on real-life data). Collection ran from March 3, 2017 to April 30, 2022.

RESULTS

We gathered data from 6859 adult patients treated with closed-loop from three to 12 months. Overall, all sources of data showed that time in range (TIR) 70 to 180 mg/dL, starting from 47.4% to 56.6%, improved from 12.2 to 17.3 percentage points. Time in hypoglycemia was reduced by 48% in average (range: 26%-70%) and reached a level of 1.3% in the largest and most recent cohort. In patients with excessive time in hypoglycemia at baseline (≥5%), closed-loop allowed a reduction in time below range (TBR) by 59%. The comparison of days with declared physical activity versus days without physical activity did not show differences in TBR. The improvement in TIR observed with three different pump systems (Vicentra Kaleido, n = 117; Sooil Dana-I, n = 84; and Roche Insight, n = 6684) ranged from 15.4 to 17.3 percentage points.

DISCUSSION

These data obtained in different European countries were consistent throughout all reports, showing that this closed-loop system is efficient (high improvement in TIR), safe (remarkably low level of TBR), and interoperable (three pump settings so far).

Analysis of "Hybrid Closed Loop Using a Do-It-Yourself Artificial Pancreas System in Adults With Type 1 Diabetes"

In an article in Journal of Diabetes Science and Technology, Nanayakkara and colleagues assessed the glycemic efficacy and safety of AndroidAPS, an open-source automated delivery (AID) system, in a crossover randomized controlled trial. Although the trial included only 20 participants during a relatively short 4-week intervention period, glycemic outcomes attained were similar to commercial AID systems and there were no safety concerns. Validation of open-source AID systems in studies such as this should help address clinician hesitancy regarding these systems, and affirms the role of patient-centered innovation and self-management in diabetes care.

Combining Glucose Monitoring and Insulin Infusion in an Integrated Device: A Narrative Review of Challenges and Proposed Solutions

The introduction of automated insulin delivery (AID) systems has enabled increasing numbers of individuals with type 1 diabetes (T1D) to improve their glycemic control largely. However, use of AID systems is limited due to their complexity and costs associated. The user must wear both a continuously monitoring glucose system and an insulin infusion pump. The glucose sensor and the insulin catheter must be inserted at two different body sites using different insertion devices. In addition, the user must pair and manage the different systems. These communicate with the AID software implemented on the pump or on a third device such as a dedicated display device or smart phone application. These components might be developed and commercialized by different manufacturers, which in turn can cause difficulties for patients seeking technical support. A possible solution to these challenges would be to integrate the glucose sensor and insulin catheter into a single device. This would allow the glucose sensor and insulin catheter to be inserted simultaneously, eliminating the need for pairing, and simplifying system management. In recent years, different technologies have been developed and evaluated in clinical investigations that combine the glucose sensor and the insulin catheter in one platform. The consistent finding of all these studies is that integration has no adverse effect on insulin infusion and glucose measurements provided that certain conditions are met. In this review, we discuss the perceived challenges of such an approach and discuss possible solutions that have been proposed.

Validating and Establishing a Diagnostic Threshold for the Hypoglycemia Awareness Questionnaire Impaired Awareness Subscale

OBJECTIVE

To evaluate the discriminant and convergent validities of the Hypoglycemia Awareness Questionnaire Impaired Awareness (HypoA-Q IA) subscale and establish a diagnostic threshold for the classification of impaired awareness of hypoglycemia (IAH) in adults with type 1 diabetes (T1D).

METHODS

Twenty-one adults with T1D (male, 48%; median age, 36 years; and T1D duration, 21 years) completed the HypoA-Q IA subscale, Clarke, and hypoglycemia severity (HYPO) scores, continuous glucose monitoring, and hyperinsulinemic hypoglycemic clamp testing. Those with IAH defined by a Clarke score of ≥4 (n = 10) and who experienced severely problematic hypoglycemia and/or marked glycemic lability started automated insulin delivery as part of an 18-month intervention study with the 6-monthly paired assessment of the HypoA-Q IA subscale, Clarke score, HYPO score and continuous glucose monitoring, and hypoglycemic clamp testing at baseline and 6 and 18 months.

RESULTS

The HypoA-Q IA subscale discriminated between those with and without IAH defined by the Clarke score (W = 110.5; P <.001). During intervention, the HypoA-Q IA subscale demonstrated convergent validity via significant relationships with the Clarke (r = 0.72; P <.001) and HYPO (r = 0.60; P <.001) scores; hypoglycemia exposure below 70 (r = 0.53; P <.01), 60 (r = 0.50; P <.01), and 54 (r = 0.48; P <.01) mg/dL; and autonomic symptom (r = -0.53; P <.05), epinephrine (r = -0.68; P <.001), and pancreatic polypeptide (r = -0.52; P <.05) responses to insulin-induced hypoglycemia. The receiver operating characteristic curve analysis revealed that the HypoA-Q IA subscale was an excellent predictor of an abnormal symptom response to insulin-induced hypoglycemia (area under the curve, 0.86) with a score of 12, which was the optimal threshold for IAH classification (sensitivity, 83%; specificity, 80%).

CONCLUSION

These findings support the validity of the HypoA-Q IA subscale and propose a HypoA-Q IA diagnostic threshold to identify IAH in both clinical and research settings.

Experience with burdens of diabetes device use that affect uptake and optimal use in people with type 1 diabetes

Diabetes technology continues to advance, with more individuals with type 1 diabetes (T1D) adopting insulin pumps, continuous glucose monitoring (CGM), and automated insulin delivery (AID) systems that integrate real-time glucose data with an algorithm to assist with insulin dosing decisions. These technologies are linked with benefits to glycemic outcomes (e.g. increased time in target range), diabetes management behaviors, and quality of life. However, current devices and systems are not without barriers and hassles for the user. The intent of this review is to describe the personal challenges and reactions that users experience when interacting with current diabetes technologies, which can affect their acceptance and motivation to engage with their devices. This review will discuss user experiences and strategies to address three main areas: (i) the emotional burden of utilizing a wearable device; (ii) the perceived and experienced negative social consequences of device use; and (iii) the practical challenges of wearing devices.

Glucose Variability Analysis in Two Large-Scale and Real-World Data Sets of Open-Source Automated Insulin Delivery Systems

BACKGROUND

Open-source automated insulin delivery (OS-AID) systems combine commercially available insulin pumps and continuous glucose monitors with open-source algorithms to automate insulin dosing for people with insulin-requiring diabetes. Two data sets (OPEN and the OpenAPS Data Commons) contain anonymized OS-AID user data.

METHODS

We assessed glycemic variability (GV) outcomes in the OPEN data set and characterized it alongside a comparison to the n = 122 version of the OpenAPS Data Commons. Glucose data are analyzed using an unsupervised machine learning algorithm for clustering, and GV metrics are quantified using statistical tests for distribution comparison. Demographic data are also analyzed quantitatively.

RESULTS

The n = 75 OPEN data set contains 36 827 days worth of data. Mean TIR is 82.08% (TOR < 70: 3.66%; TOR > 180: 14.3%). LBGI (P < .05) differs by gender whereas HBGI distributions are similar (P > .05). GV metrics (except TOR < 70, LBGI) show a statistically significant difference (P < .05) between data sets.

CONCLUSIONS

Both the OPEN and OpenAPS Data Commons data sets show TOR < 70, TIR, and TOR > 180 within recommended goals, adding additional evidence of real-world efficacy of OS-AID. Future research should evaluate in more detail potential data set differences and relationships between individual patterns of user behaviors and GV outcomes.

Variability of Glycemic Outcomes and Insulin Requirements Throughout the Menstrual Cycle: A Qualitative Study on Women With Type 1 Diabetes Using an Open-Source Automated Insulin Delivery System

BACKGROUND

The impact of hormone dynamics throughout the menstrual cycle on insulin sensitivity represents a currently under-researched area. Despite therapeutic and technological advances, self-managing insulin therapy remains challenging for women with type 1 diabetes (T1D).

METHODS

To investigate perceived changes in glycemic levels and insulin requirements throughout the menstrual cycle and different phases of life, we performed semi-structured interviews with 12 women with T1D who are using personalized open-source automated insulin delivery (AID) systems. Transcripts were analyzed using thematic analysis with an inductive, hypothesis-generating approach.

RESULTS

Participants reported significant differences between the follicular phase, ovulation, and luteal phase of the menstrual cycle and also during puberty, pregnancy, and menopause. All participants reported increased comfort and safety since using AID, but were still required to manually adjust their therapy according to their cycle. A lack of information and awareness and limited guidance by health care providers were frequently mentioned. Although individual adjustment strategies exist, achieving optimum outcomes was still perceived as challenging.

CONCLUSIONS

This study highlights that scientific evidence, therapeutic options, and professional guidance on female health-related aspects in T1D are insufficient to date. Further efforts are required to better inform people with T1D, as well as for health care professionals, researchers, medical device manufacturers, and regulatory bodies to better address female health needs in therapeutic advances.

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

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

Patient-reported outcomes for older adults on CamAPS FX closed loop system

AIMS

Use of the CamAPS FX hybrid closed loop (CL) system is associated with improved time in range and glycated haemoglobin A1c across the age span, but little is known about its effects on patient-reported outcomes (PROs).

METHODS

This open-label, randomized, multi-site study compared CamAPS FX to sensor-augmented pump (SAP) in a sample of older adults (≥60 years) with type 1 diabetes (T1D). Thirty-five older adults completed PROs surveys at the start of the study and after each period of 16 weeks using either CL or SAP. At the end of the study, 19 participated in interviews about their experiences with CL.

RESULTS

Results examining the 16 weeks of CL use showed that the overall Diabetes Distress Scale score and two subscales (powerlessness and physician distress) improved significantly along with trust on the Glucose Monitoring Satisfaction Survey. User experience interview responses were consistent in noting benefits of 'improved glycaemic control' and 'worrying less about diabetes'.

CONCLUSION

In this sample of older adults with T1D who have previously shown glycaemic benefit, there are indicators of improved PROs and subjective user experience benefits.

Long-Term Benefits of an Integrated Continuous Glucose Monitoring and Insulin Pump System for Emergency Admissions, Hospitalization, and Metabolic Control in a Cohort of People With Diabetes: Retrospective Cohort Study

BACKGROUND

There is evidence in the literature that the use of sensor-augmented insulin pumps in patients with high-complexity diabetes improves metabolic control. However, there is no long-term information on clinical outcomes such as hospitalization or admission to the emergency room. This study describes outcomes for metabolic control, incidence of hospitalizations, and emergency room visits in a specific population using this technology.

OBJECTIVE

We aimed to assess long-term glycemic and clinical outcomes after the use of continuous subcutaneous insulin infusion and continuous glucose monitoring in people with diabetes.

METHODS

A retrospective cohort study was carried out in patients with diabetes previously treated with an intensive insulin regimen at a specialized diabetes treatment center who required a sensor-augmented insulin pump due to nonoptimal glycemic control. Glycated hemoglobin, severe hypoglycemic episodes, nonsevere hypoglycemic episodes, perception of hypoglycemia, and the incidence of emergency room visits and hospitalizations before and after treatment were evaluated.

RESULTS

Between January 2013 and August 2020, 74 patients with a median age of 36 (IQR 27-46) years were included in the study with a median 4 (IQR 2-7) years of follow-up. We found a statistically significant reduction in glycated hemoglobin (8.35% vs 7%; P<.001), nonsevere hypoglycemic episodes (71/74, 96% vs 62/74, 84%; P=.01), emergency room visits (42/73, 58% vs 4/62, 6%; P<.001), and hospitalizations (36/72, 50% vs 10/72, 14%; P<.001) after use of continuous subcutaneous insulin infusion.

CONCLUSIONS

The use of a sensor-augmented insulin pump associated with a strict follow-up program for patients with high-complexity diabetes led to a significant and sustained reduction in glycated hemoglobin and hypoglycemic episodes, as well as in the rate of emergency room visits and hospitalizations. These results encourage the adoption of this technology in patients who do not achieve metabolic control with optimal management of diabetes.

Mast Cell Deficiency in Mice Attenuates Insulin Phenolic Preservative-Induced Inflammation

One major obstacle that limits the lifespan of insulin infusion pumps is surmounting the tissue site reaction at the device implantation site. All commercial insulin formulations contain insulin phenolic preservatives (IPPs) designed to ensure insulin protein stability and prolong shelf-life. However, our laboratory demonstrated that these preservatives are cytotoxic and induce inflammation. Mature mast cells (MCs) reside in cutaneous tissue and are one of the first responders to an epidermal breach. Upon activation, MCs release proinflammatory and immunomodulatory prepacked mediators that exacerbate these inflammatory reactions. Thus, we hypothesized that once the epidermis is breached, cutaneous MCs are triggered inciting the inflammatory response to IPP-induced inflammation. This hypothesis was pursued utilizing our modified in vivo mouse air pouch model, including a c-kit dependent (C57BL/6J-kitW-sh/W-sh) and a c-kit independent (Cpa3-Cre; Mcl-1fl/fl) MC-deficient mouse model. Leukocytes were quantified in the mouse air pouch lavage fluid following flow cytometry analysis for IPP infusion under three different states, insulin-containing phenolic preservatives (Humalog®), insulin preservatives alone, and normal saline as a control. The air pouch wall was assessed using histopathological evaluations. Flow cytometry analysis demonstrated a statistically significant difference in inflammatory cell recruitment for both MC-deficient mouse models when compared to the control strain including infused control saline. Significantly less inflammation was observed at the site of infusion for the MC-deficient strains compared to the control strain. Overall, concordant results were obtained in both mouse types, C57Bl6-kitW-sh/W-sh and Cpa3-Cre; Mcl-1fl/fl. These findings in multiple model systems support the conclusion that MCs have important or possible unique roles in IPP-induced inflammation.

The changing landscape of automated insulin delivery in the management of type 1 diabetes

Automated insulin delivery systems, also known as closed-loop or 'artificial pancreas' systems, are transforming the management of type 1 diabetes. These systems consist of an algorithm which responds to real-time glucose sensor levels by automatically modulating insulin delivery through an insulin pump. We review the rapidly changing landscape of automated insulin-delivery systems over recent decades, from initial prototypes to the different hybrid closed-loop systems commercially available today. We discuss the growing body of clinical trials and real-world evidence demonstrating their glycaemic and psychosocial benefits. We also address future directions in automated insulin delivery such as dual-hormone systems and adjunct therapy as well as the challenges around ensuring equitable access to closed-loop technology.

Real-World Evidence of the Cambridge Hybrid Closed-Loop App With a Novel Real-Time Continuous Glucose Monitoring System

We evaluated the performance of the interoperable Cambridge hybrid closed-loop app with FreeStyle Libre 3 glucose sensor, and YpsoPump insulin pump in a real-world setting. Data from 100 users (63 adults [mean ± SD age 41.9 ± 14.0 years], 15 children [8.6 ± 5.2 years)] and 22 users of unreported age) for a period of 28 days were analyzed. Time in range (3.91- 10.0mmol/L) was 72.6 ± 11.1% overall. Time below range (<3.9mmol/L) was 3.1% (1.4-5.1) (median [interquartile range]). Auto-mode was active for 95.8% (91.8-97.9) of time. This real-world analysis suggests that the performance of Cambridge hybrid closed-loop app with this glucose sensor is comparable to other commercially available hybrid closed-loop systems.

Pediatric Type 1 Diabetes: Mechanisms and Impact of Technologies on Comorbidities and Life Expectancy

Type 1 diabetes (T1D) is one of the most common chronic diseases in childhood, with a progressively increasing incidence. T1D management requires lifelong insulin treatment and ongoing health care support. The main goal of treatment is to maintain blood glucose levels as close to the physiological range as possible, particularly to avoid blood glucose fluctuations, which have been linked to morbidity and mortality in patients with T1D. Indeed, the guidelines of the International Society for Pediatric and Adolescent Diabetes (ISPAD) recommend a glycated hemoglobin (HbA1c) level < 53 mmol/mol (<7.0%) for young people with T1D to avoid comorbidities. Moreover, diabetic disease strongly influences the quality of life of young patients who must undergo continuous monitoring of glycemic values and the administration of subcutaneous insulin. In recent decades, the development of automated insulin delivery (AID) systems improved the metabolic control and the quality of life of T1D patients. Continuous subcutaneous insulin infusion (CSII) combined with continuous glucose monitoring (CGM) devices connected to smartphones represent a good therapeutic option, especially in young children. In this literature review, we revised the mechanisms of the currently available technologies for T1D in pediatric age and explored their effect on short- and long-term diabetes-related comorbidities, quality of life, and life expectation.

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.

Diabetes technology including automated insulin delivery systems to manage hyperglycemia in a failing pancreatic graft: Case series of people with type 1 diabetes and a pancreas kidney or pancreas-only transplant

We share our experience of using continuous subcutaneous insulin infusion (CSII) therapy and diabetes technology in six people (5 men) with type 1 diabetes (mean duration 36 years), who developed hyperglycemia post-simultaneous kidney/pancreas (n = 5) or pancreas only (n = 1) transplant. All were on immunosuppression and multiple daily injections of insulin prior to CSII. Four people were started on automated insulin delivery, and two people on CSII and intermittently scanned continuous glucose monitoring. With diabetes technology, the median time in range glucose improved from 37% (24-49%) to 56.6% (48-62%), and similarly, glycated hemoglobin fell from 72.7 mmol/mol (72-79 mmol/mol) to 64 mmol/mol (42-67 mmol/mol; P < 0.05 for both) with no concomitant increase in hypoglycemia. Use of diabetes technology improved glycemic parameters in people with type 1 diabetes with failing pancreatic graft function. Early use of such technology should be considered to improve diabetes control in this complex cohort.

Challenges of Glycemic Control in People With Diabetes and Advanced Kidney Disease and the Potential of Automated Insulin Delivery

Diabetes is the leading cause of chronic kidney disease (CKD) and end-stage kidney disease in the world. It is known that maintaining optimal glycemic control can slow the progression of CKD. However, the failing kidney impacts glucose and insulin metabolism and contributes to increased glucose variability. Conventional methods of insulin delivery are not well equipped to adapt to this increased glycemic lability. Automated insulin delivery (AID) has been established as an effective treatment in patients with type 1 diabetes mellitus, and there is emerging evidence for their use in type 2 diabetes mellitus. However, few studies have examined their role in diabetes with concurrent advanced CKD. We discuss the potential benefits and challenges of AID use in patients with diabetes and advanced CKD, including those on dialysis.