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

(Hybrid) Closed-Loop Systems: From Announced to Unannounced Exercise

Physical activity and exercise have many beneficial effects on general and type 1 diabetes (T1D) specific health and are recommended for individuals with T1D. Despite these health benefits, many people with T1D still avoid exercise since glycemic management during physical activity poses substantial glycemic and psychological challenges - which hold particularly true for unannounced exercise when using an AID system. Automated insulin delivery (AID) systems have demonstrated their efficacy in improving overall glycemia and in managing announced exercise in numerous studies. They are proven to increase time in range (70-180 mg/dL) and can especially counteract nocturnal hypoglycemia, even when evening exercise was performed. AID-systems consist of a pump administering insulin as well as a CGM sensor (plus transmitter), both communicating with a control algorithm integrated into a device (insulin pump, mobile phone/smart watch). Nevertheless, without manual pre-exercise adaptions, these systems still face a significant challenge around physical activity. Automatically adapting to the rapidly changing insulin requirements during unannounced exercise and physical activity is still the Achilles' heel of current AID systems. There is an urgent need for improving current AID-systems to safely and automatically maintain glucose management without causing derailments - so that going forward, exercise announcements will not be necessary in the future. Therefore, this narrative literature review aimed to discuss technological strategies to how current AID-systems can be improved in the future and become more proficient in overcoming the hurdle of unannounced exercise. For this purpose, the current state-of-the-art therapy recommendations for AID and exercise as well as novel research approaches are presented along with potential future solutions - in order to rectify their deficiencies in the endeavor to achieve fully automated AID-systems even around unannounced exercise.

Automated Insulin Delivery Systems in Children and Adolescents With Type 1 Diabetes: A Systematic Review and Meta-analysis of Outpatient Randomized Controlled Trials

BACKGROUND

The glycemic control of automated insulin delivery (AID) systems in outpatient children and adolescents with type 1 diabetes (T1D) has not been systematically evaluated.

PURPOSE

To evaluate the efficacy and safety of AID systems in children and adolescents in outpatient settings.

DATA SOURCES

PubMed, Embase, the Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov were searched until 4 May 2023. This study was registered with PROSPERO (2023, CRD42023395252).

STUDY SELECTION

Randomized controlled trials that compared AID systems with conventional insulin therapy in outpatient children and adolescents with T1D and reported continuous glucose monitoring outcomes were selected.

DATA EXTRACTION

Percent time in range (TIR) (3.9-10 mmol/L), time below range (TBR) (<3.9 mmol/L), and time above range (TAR) (>10 mmol/L) were extracted. Data were summarized as mean differences (MDs) with 95% CIs.

DATA SYNTHESIS

Twenty-five trials (1,345 participants) were included in the meta-analysis. AID systems were associated with an increased percentage of TIR (MD, 11.38% [95% CI 9.01-13.76], P < 0.001; high certainty). The favorable effect was consistent whether AID was used over 3 months (10.46% [8.71-12.20]) or 6 months (10.87% [7.11-14.63]). AID systems had a favorable effect on the proportion of TBR (-0.59% [-1.02 to -0.15], P = 0.008; low certainty) or TAR (-12.19% [-14.65 to -9.73], P < 0.001; high certainty) compared with control treatment.

LIMITATIONS

Substantial heterogeneity was observed in most analyses.

CONCLUSIONS

AID systems are more effective than conventional insulin therapy for children and adolescents with T1D in outpatient settings. The favorable effect is consistent both in the short term and long term.

Safety and Efficacy of Sustained Automated Insulin Delivery Compared With Sensor and Pump Therapy in Adults With Type 1 Diabetes at High Risk for Hypoglycemia: A Randomized Controlled Trial

OBJECTIVE

Assess the safety and efficacy of automated insulin delivery (AID) in adults with type 1 diabetes (T1D) at high risk for hypoglycemia.

RESEARCH DESIGN AND METHODS

Participants were 72 adults with T1D who used an insulin pump with Clarke Hypoglycemia Perception Awareness scale score >3 and/or had severe hypoglycemia during the previous 6 months confirmed by time below range (TBR; defined as sensor glucose [SG] reading <70 mg/dL) of at least 5% during 2 weeks of blinded continuous glucose monitoring (CGM). Parallel-arm, randomized trial (2:1) of AID (Tandem t:slim ×2 with Control-IQ technology) versus CGM and pump therapy for 12 weeks. The primary outcome was TBR change from baseline. Secondary outcomes included time in target range (TIR; 70-180 mg/dL), time above range (TAR), mean SG reading, and time with glucose level <54 mg/dL. An optional 12-week extension with AID was offered to all participants.

RESULTS

Compared with the sensor and pump (S&P), AID resulted in significant reduction of TBR by -3.7% (95% CI -4.8, -2.6), P < 0.001; an 8.6% increase in TIR (95% CI 5.2, 12.1), P < 0.001; and a -5.3% decrease in TAR (95% CI -87.7, -1.8), P = 0.004. Mean SG reading remained similar in the AID and S&P groups. During the 12-week extension, the effects of AID were sustained in the AID group and reproduced in the S&P group. Two severe hypoglycemic episodes occurred using AID.

CONCLUSIONS

In adults with T1D at high risk for hypoglycemia, AID reduced the risk for hypoglycemia more than twofold, as quantified by TBR, while improving TIR and reducing hyperglycemia. Hence, AID is strongly recommended for this specific population.

Therapy Settings Associated with Optimal Outcomes for t:slim X2 with Control-IQ Technology in Real-World Clinical Care

Objective: To determine insulin dosing parameters that are associated with and predict optimal outcomes for people using t:slim X2 with Control-IQ technology (CIQ). Methods: Retrospective deidentified data from CIQ users were analyzed to determine the effect of Correction Factor, Carbohydrate-to-Insulin (C:I) Ratio, and basal rate settings (standardized by total daily insulin [TDI]) on glycemic control. We performed an associative analysis followed by linear regressions to determine the relative importance of the settings and confounding variables (e.g., age or number of user-initiated boluses) in predicting consensus glycemic outcomes. Results: Data from 20,764 individuals were analyzed (median age 39 years [interquartile range 19, 59], 55% female, TDI 46.4 U [33-65.2]). More aggressive Correction Factor settings, C:I ratio settings, and basal programs were all strongly associated with higher time in range (TIR, 70-180 mg/dL) and to a lesser degree to higher time <70 mg/dL. In linear regression, more aggressive Correction Factor predicted higher TIR, lower coefficient of variation, and importantly had only negligible impact on time below range. Higher basal rate settings and lower C:I ratio predicted increased TIR as well as increased hypoglycemia. The most important predictor in all glycemic outcomes was the average number of user-given boluses per day. Conclusion: Basal rates, C:I ratios, and Correction Factor settings all impact glycemic outcomes in CIQ users in usual clinical care. The correction Factor setting may be the most impactful "lever to pull" for clinicians and CIQ users to optimize TIR while not increasing hypoglycemia.

A Nutritional Approach to Optimizing Pump Therapy in Type 1 Diabetes Mellitus

Achieving optimal glucose control in individuals with type 1 diabetes (T1DM) continues to pose a significant challenge. While continuous insulin infusion systems have shown promise as an alternative to conventional insulin therapy, there remains a crucial need for greater awareness regarding the necessary adaptations for various special circumstances. Nutritional choices play an essential role in the efficacy of diabetes management and overall health status for patients with T1DM. Factors such as effective carbohydrate counting, assessment of the macronutrient composition of meals, and comprehending the concept of the glycemic index of foods are paramount in making informed pre-meal adjustments when utilizing insulin pumps. Furthermore, the ability to handle such situations as physical exercise, illness, pregnancy, and lactation by making appropriate adjustments in nutrition and pump settings should be cultivated within the patient-practitioner relationship. This review aims to provide healthcare practitioners with practical guidance on optimizing care for individuals living with T1DM. It includes recommendations on carbohydrate counting, managing mixed meals and the glycemic index, addressing exercise-related challenges, coping with illness, and managing nutritional needs during pregnancy and lactation. Additionally, considerations relating to closed-loop systems with regard to nutrition are addressed. By implementing these strategies, healthcare providers can better equip themselves to support individuals with T1DM in achieving improved diabetes management and enhanced quality of life.

Roadmap to the Effective Use of Continuous Glucose Monitoring: Innovation, Investigation, and Implementation

For 25 years, continuous glucose monitoring (CGM) has been evolving into what it is now: a key tool to both measure individuals' glycemic status and to help guide their day-to-day management of diabetes. Through a series of engineering innovations, clinical investigations, and efforts to optimize workflow implementation, the use of CGM is helping to transform diabetes care. This article presents a roadmap to the effective use of CGM that outlines past, present, and possible future advances in harnessing the potential of CGM to improve the lives of many people with diabetes, with an emphasis on ensuring that CGM technology is available to all who could benefit from its use.

Results From a Virtual Clinic for the Follow-up of Patients Using the Advanced Hybrid Closed-Loop System

BACKGROUND

Evidence regarding the implementation of medium-term strategies in advanced hybrid closed-loop (AHCL) system users is limited. Therefore, this study aimed to describe the efficacy and safety of the AHCL system in patients with type 1 diabetes (T1D) on a six-month follow-up in a virtual diabetes clinic (VDC).

METHOD

A prospective cohort of adult patients with T1D treated using the AHCL system (Mini Med 780G; Medtronic, Northridge, California) in a VDC follow-up. Standardized training and follow-up were conducted virtually. Clinical data and metabolic control outcomes were reported at baseline, and at three and six months.

RESULTS

Sixty-four patients (mean age = 42 ± 14.6 years, 65% men, 54% with graduate education) were included. Percentage time in range (%TIR) increased significantly regardless of prior therapy with intermittently scanned continuous glucose monitoring + multiple daily injections and sensor-augmented pump therapy with predictive low-glucose management after starting AHCL and persisted during the follow-up period with no hypoglycemic events. The %TIR 70 to 180 mg/dL according to socioeconomic strata was 73.4% ± 5.3%, 78.1% ± 8.1%, and 84.2% ± 7.5% for the lower, middle, and upper strata, respectively. The sensor was used more frequently in the population with a higher education level. Adherence to sensor use and SmartGuard retention were higher in patients who underwent the VDC follow-up.

CONCLUSIONS

Medium-term follow-up of users of AHCL systems in a VDC contributes to safely achieving %TIR goals. Virtual diabetes clinic follow-up favored adherence to sensor use and continuous SmartGuard use. Socioeconomic strata were associated with a better glycemic profile and education level was associated with better adherence to sensor use.

The Role of Ultra-Rapid-Acting Insulin Analogs in Diabetes: An Expert Consensus

Ultra-rapid-acting insulin analogs (URAA) are a further development and refinement of rapid-acting insulin analogs. Because of their adapted formulation, URAA provide an even faster pharmacokinetics and thus an accelerated onset of insulin action than conventional rapid-acting insulin analogs, allowing for a more physiologic delivery of exogenously applied insulin. Clinical trials have confirmed the superiority of URAA in controlling postprandial glucose excursions, with a safety profile that is comparable to the rapid-acting insulins. Consequently, many individuals with diabetes mellitus may benefit from URAA in terms of prandial glycemic control. Unfortunately, there are only few available recommendations from authoritative sources for use of URAA in clinical practice. Therefore, this expert consensus report aims to define populations of people with diabetes mellitus for whom URAA may be beneficial and to provide health care professionals with concrete, practical recommendations on how best to use URAA in this context.

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.

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.

Expected Basal Insulin Requirement During Continuous Subcutaneous Insulin Infusion Therapy by Age Group, Sex, and Body Mass Index, Based on 25,718 Young People with Type 1 Diabetes in the DPV Registry

Background: Since the introduction of insulin pumps into the therapy of pediatric subjects, different approaches have been taken to find optimal basal rates. Previously, the DPV registry provided circadian basal rate patterns for different age groups. As the number of pump users has increased recently and short-acting insulin analogues are now predominant, we performed a new analysis with a larger data pool. Methods: We included all recent basal profiles from type 1 diabetes (T1D) patients between 1 and 25 years from the DPV 2021 data pool. We excluded night-time-only pump users, human regular insulin users, and daily basal rates <0.05 and >1.0 U/(kgBW·d). Results: In the analysis of profiles from 25,718 young persons with T1D, differences in the daily pattern of basal rates were found between age groups. In addition, we saw significant (P < 0.001) differences in total daily basal dose between genders in all age groups except adults. In addition, the shape of the expected basal-rate pattern differed by body mass index, HbA1c, and use of continuous glucose monitoring. Discussion: This analysis demonstrates multiple factors influencing basal patterns and insulin requirement, including age group, gender, overweight, HbA1c, bolus frequency, and sensor use. As circadian basal rates are still mandatory for initiating insulin pump therapy with or without automation, a multimodal approach is necessary to estimate optimal basal rates.

Key indices of glycaemic variability for application in diabetes clinical practice

Near normal glycaemic control in diabetes consists to target daily glucose fluctuations and quarterly HbA1c oscillations in addition to overall glucose exposure. Consequently, the prerequisite is to define simple, and mathematically undisputable key metrics for the short- and long-term variability in glucose homeostasis. As the standard deviations (SD) of either glucose or HbA1c are dependent on their means, the coefficient of variation (CV = SD/mean) should be applied instead as it that avoids the correlation between the SD and mean values. A CV glucose of 36% is the most appropriate threshold between those with stable versus labile glucose homeostasis. However, when near normal mean glucose concentrations are achieved a lower CV threshold of <27 % is necessary for reducing the risk for hypoglycaemia to a minimal rate. For the long-term variability in glucose homeostasis, a CVHbA1c < 5 % seems to be a relevant recommendation for preventing adverse clinical outcomes.

Automated Insulin Delivery in Women with Pregnancy Complicated by Type 1 Diabetes

BACKGROUND

Hybrid closed-loop insulin therapy has shown promise for management of type 1 diabetes during pregnancy; however, its efficacy is unclear.

METHODS

In this multicenter, controlled trial, we randomly assigned pregnant women with type 1 diabetes and a glycated hemoglobin level of at least 6.5% at nine sites in the United Kingdom to receive standard insulin therapy or hybrid closed-loop therapy, with both groups using continuous glucose monitoring. The primary outcome was the percentage of time in the pregnancy-specific target glucose range (63 to 140 mg per deciliter [3.5 to 7.8 mmol per liter]) as measured by continuous glucose monitoring from 16 weeks' gestation until delivery. Analyses were performed according to the intention-to-treat principle. Key secondary outcomes were the percentage of time spent in a hyperglycemic state (glucose level >140 mg per deciliter), overnight time in the target range, the glycated hemoglobin level, and safety events.

RESULTS

A total of 124 participants with a mean (±SD) age of 31.1±5.3 years and a mean baseline glycated hemoglobin level of 7.7±1.2% underwent randomization. The mean percentage of time that the maternal glucose level was in the target range was 68.2±10.5% in the closed-loop group and 55.6±12.5% in the standard-care group (mean adjusted difference, 10.5 percentage points; 95% confidence interval [CI], 7.0 to 14.0; P<0.001). Results for the secondary outcomes were consistent with those of the primary outcome; participants in the closed-loop group spent less time in a hyperglycemic state than those in the standard-care group (difference, -10.2 percentage points; 95% CI, -13.8 to -6.6); had more overnight time in the target range (difference, 12.3 percentage points; 95% CI, 8.3 to 16.2), and had lower glycated hemoglobin levels (difference, -0.31 percentage points; 95% CI, -0.50 to -0.12). Little time was spent in a hypoglycemic state. No unanticipated safety problems associated with the use of closed-loop therapy during pregnancy occurred (6 instances of severe hypoglycemia, vs. 5 in the standard-care group; 1 instance of diabetic ketoacidosis in each group; and 12 device-related adverse events in the closed-loop group, 7 related to closed-loop therapy).

CONCLUSIONS

Hybrid closed-loop therapy significantly improved maternal glycemic control during pregnancy complicated by type 1 diabetes. (Funded by the Efficacy and Mechanism Evaluation Program; AiDAPT ISRCTN Registry number, ISRCTN56898625.).

MiniMed 780G™ advanced hybrid closed-loop system performance in Egyptian patients with type 1 diabetes across different age groups: evidence from real-world users

BACKGROUND

Advanced hybrid closed loop (AHCL) system provides both automated basal rate and correction boluses to keep glycemic values in a target range.

OBJECTIVES

To evaluate the real-world performance of the MiniMed™ 780G system among different age groups of Egyptian patients with type 1diabetes.

METHODS

One-hundred seven AHCL system users aged from 3 to 71 years were enrolled. Data uploaded by patients were aggregated and analyzed. The mean glucose management indicator (GMI), percentage of time spent within glycemic ranges (TIR), time below range (TBR) and time above range (TAR) were determined.

RESULTS

Six months after initiating Auto Mode, patients spent a mean of 85.31 ± 22.04% of the time in Auto Mode (SmartGuard) and achieved a mean GMI of 6.95 ± 0.58% compared with 7.9 ± 2.1% before AHCL initiation (p < 0.001). TIR 70-180 mg/dL was increased post-AHCL initiation from 63.48 ± 10.14% to 81.54 ± 8.43% (p < 0.001) while TAR 180-250 mg/dL, TAR > 250 mg/dL, TBR < 70 mg/dL and TBR < 54 mg/dL were significantly decreased (p < 0.001). After initiating AHCL, TIR was greater in children and adults compared with adolescents (82.29 ± 7.22% and 83.86 ± 9.24% versus 78.4 ± 7.34%, respectively; p < 0.05). The total daily dose of insulin was increased in all age groups primarily due to increased system-initiated insulin delivery including auto correction boluses and basal insulin.

CONCLUSIONS

MiniMed™ 780G system users across different age groups achieved international consensus-recommended glycemic control with no serious adverse effects even in challenging age group as children and adolescents.

First International Survey on Diabetes Providers' Assessment of Skin Reactions in Youth With Type 1 Diabetes Using Technological Devices

BACKGROUND

Advances in diabetes technological devices led to optimization of diabetes care; however, long-lasting skin exposure to devices may be accompanied by an increasing occurrence of cutaneous reactions.

METHODS

We used an open-link web-based survey to evaluate diabetes-care providers' viewpoint on prevalence, management practices, and knowledge related to skin reactions with the use of diabetes technological devices. A post hoc analysis was applied to investigate differences in the level of awareness on this topic in relation to the experience in diabetes technology.

RESULTS

One hundred twenty-five responses from 39 different countries were collected. Most respondents (69%) routinely examine patients' skin at each visit. All the preventive measures are not clear and, mainly, homogenously put into clinical practice. Contact dermatitis was the most frequently reported cutaneous complication due to diabetes devices, and its most common provocative causes are not yet fully known by diabetes-care providers. Almost half of the respondents (42%) had discussed the presence of harmful allergens contained in adhesives with device manufacturers. There is general agreement on the need to strengthen knowledge on dermatological complications.

CONCLUSIONS

Although diabetes-care providers are quite aware of the chance to develop skin reactions in people with diabetes using technological devices, there are still some unmet needs. Large follow-up studies and further dissemination tools are awaited to address the gaps revealed by our survey.

Automated Insulin Delivery System and People With Type 2 Diabetes: A Topic With Many Facets

Optimizing glucose control is of interest also for patients with type 2 diabetes (T2D). While systems for automated insulin delivery are widely used for patients with type 1 diabetes, as documented by many publications, this is not the case with T2D. Because of the number of such patients, this will change drastically in the next years. Manufacturers can transfer many learnings from type 1 to type 2; however, specific clinical aspects have to be considered. This commentary will discuss these aspects and some of the current activities. Future automated insulin delivery (AID) systems will take data from multisensor systems into account to individualize the AID algorithm, supported by artificial intelligence. There is a high need to document the benefits of AID systems in this patient group.

Glycemic control metrics in a cohort of hospitalized patients with type 1 diabetes using hybrid closed-loop and advanced hybrid closed-loop systems

AIMS

To describe Hybrid closed-loop (HCL) and advanced hybrid closed-loop (AHCL) performance in the hospital setting based on the continuous glucose monitoring (CGM) metrics description.

METHODS

This was an observational study from a cohort of patients with T1D using HCL/AHCL with history of hospitalization. CGM metrics were analyzed during the hospital stay. CGM metrics sub-analysis of the population with active Automated Mode (AM) and SmartGuard (SG) during hospitalization and/or surgical procedure was performed.

RESULTS

Twenty-four patients were included (50 % women; mean age, 49 years [inter-quartile range (IQR), 39-62 years]). During hospitalization 70.8 % patients achieved %Time in Range (TIR) between 70 and 180 mg/dL ≥ 70 %. The overall %TIR was 75.5 % (IQR, 67.3-81.5 %), % time below range (TBR) < 70 mg/dL was 2.1 % (IQR, 0.7-5.4 %) and %TBR < 54 mg/dL was 0 % (IQR, 0-5.4 %). Users of the AHCL with active SG achieved a non-significant higher %TIR during hospitalization (79 % [73.8.88 %] vs. 76 % [72.81 %], p = 0.312) and had a shorter stay (3[IQR, 2.4] vs. 6 days[IQR, 5.7], p = 0.045) compared to the users of the HCL with AM active. No device-related serious adverse events occurred for users of either system.

CONCLUSIONS

HCL/AHCL systems with active AM/SG in patients with T1D in the hospital environment leads to %TIR > 70 % in ranges of 70-180 mg/dL in patients without increasing hypoglycemia.

Association Between Diabetes Technology Use and Glycemic Outcomes in Adults With Type 1 Diabetes Over a Decade

OBJECTIVE

To evaluate change in mean clinic HbA1c from 2014 to 2021 with diabetes technology use in adults with type 1 diabetes.

RESEARCH DESIGN AND METHODS

In this single-center study, we analyzed diabetes technology use and mean clinic HbA1c among unique adults (age ≥18 years) with type 1 diabetes (last visit of the year per patient) between 1 January 2014 and 31 December 2021 from the electronic medical record. Diabetes technology use was defined as the use of continuous glucose monitors (CGMs) without an automated insulin delivery (AID) system or an AID system. Diabetes technology use and HbA1c over time were analyzed using mixed models adjusted for age, sex, and visit year.

RESULTS

A total of 15,903 clinic visits over 8 years (mean 1,988 patients per year, 4,174 unique patients, 52.7% female, 80.0% Non-Hispanic White) showed significant increases in CGM and AID use (P < 0.001 for both), resulting in an increase of diabetes technology use from 26.9% in 2014 to 82.7% in 2021. These increases were associated with a lower mean clinic HbA1c (7.7-7.5%, P < 0.001) and a higher percentage of adults achieving an HbA1c <7.0% (32.3-41.7%, P < 0.001) from 2014 to 2021. The HbA1c difference between technology users and nonusers increased over time from 0.36% (95% CI 0.26-0.47%, P < 0.001) in 2014 to 0.93% (95% CI 0.80-1.06%, P < 0.001) in 2021.

CONCLUSIONS

Adopting diabetes technology in adults with type 1 diabetes decreased HbA1c and increased the number of people achieving an HbA1c <7.0%, supporting the current international recommendation to offer AID systems to most individuals with type 1 diabetes.

A Comparison of Faster Insulin Aspart with Standard Insulin Aspart Using Hybrid Automated Insulin Delivery System in Active Children and Adolescents with Type 1 Diabetes: A Randomized Double-Blind Crossover Trial

Objective: To evaluate the use of faster acting (FIA) and standard insulin aspart (SIA) with hybrid automated insulin delivery (AID) in active youth with type 1 diabetes. Research Design and Methods: In this double-blind multinational randomized crossover trial, 30 children and adolescents with type 1 diabetes (16 females; aged 15.0 ± 1.7 years; baseline HbA1c 7.5% ± 0.9% [58 ± 9.8 mmol/mol]) underwent two unrestricted 4-week periods using hybrid AID with either FIA or SIA in random order. During both interventions, participants were using the hybrid AID (investigational version of MiniMed™ 780G; Medtronic). Participants were encouraged to exercise as frequently as possible, capturing physical activity with an activity monitor. The primary outcome was the percentage of sensor glucose time above range (180 mg/dL [10.0 mmol/L]) measured by continuous glucose monitoring. Results: In an intention-to-treat analysis, mean time above range was 31% ± 15% at baseline, 19% ± 6% during FIA use, and 20% ± 6% during SIA use with no difference between treatments: mean difference = -0.9%; 95% CI: -2.4% to 0.6%; P = 0.23. Similarly, there was no difference in mean time in range (TIR) (78% and 77%) or median time below range (2.5% and 2.8%). Glycemic outcomes during exercise or postprandial periods were comparable for the two treatment arms. No severe hypoglycemia or diabetic ketoacidosis events occurred. Conclusions: FIA was not superior to SIA with hybrid AID system use in physically active children and adolescents with type 1 diabetes. Nonetheless, both insulin formulations enabled high overall TIR and low time above and below ranges, even during and after documented exercise. Trial Registration Clinicaltrials.gov: NCT04853030.

Glycemic Outcomes During Early Use of the MiniMed™ 780G Advanced Hybrid Closed-Loop System with Guardian™ 4 Sensor

Background: Safety and significant improvement in overall glycated hemoglobin (A1C) and percentage of time spent in (TIR), below (TBR), and above (TAR) glucose range were demonstrated in the pivotal trial of adolescents and adults using the MiniMed™ advanced hybrid closed-loop (AHCL) system with the adjunctive, calibration-required Guardian™ Sensor 3. The present study evaluated early outcomes of continued access study (CAS) participants who transitioned from the pivotal trial investigational system to the approved MiniMed™ 780G system with the non-adjunctive, calibration-free Guardian™ 4 Sensor (MM780G+G4S). Study data were presented alongside those of real-world MM780G+G4S users from Europe, the Middle East, and Africa. Methods: The CAS participants (N = 109, aged 7-17 years and N = 67, aged >17 years) used the MM780G+G4S for 3 months and data of real-world MM780G+G4S system users (N = 10,204 aged ≤15 years and N = 26,099 aged >15 years) were uploaded from September 22, 2021 to December 02, 2022. At least 10 days of real-world continuous glucose monitoring (CGM) data were required for analyses. Glycemic metrics, delivered insulin and system use/interactions underwent descriptive analyses. Results: Time in AHCL and CGM use were >90% for all groups. AHCL exits averaged 0.1/day and there were few blood glucose measurements (BGMs) (0.8/day-1.0/day). Adults in both cohorts met most consensus recommendations for glycemic targets. Pediatric groups met recommendations for %TIR and %TBR, although not those for mean glucose variability and %TAR, possibly due to low use of recommended glucose target (100 mg/dL) and active insulin time (2 h) settings (28.4% in the CAS cohort and 9.4% in the real-world cohort). The CAS pediatric and adult A1C were 7.2% ± 0.7% and 6.8% ± 0.7%, respectively, and there were no serious adverse events. Conclusions: Early clinical use of the MM780G+G4S was safe and involved minimal BGMs and AHCL exits. Consistent with real-world pediatric and adult use, outcomes were associated with achievement of recommended glycemic targets. Clinical Trial Registration number: NCT03959423.

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.

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.

Real-World Evidence Analysis of a Hybrid Closed-Loop System

BACKGROUND

We analyzed real-world evidence to assess the performance of the mylife CamAPS FX hybrid closed-loop system.

METHODS

Users from 15 countries across different age groups who used the system between May 9, 2022, and December 3, 2022, and who had ≥30 days of continuous glucose monitor data, and ≥30% of closed-loop usage were included in the current analysis (N = 1805).

RESULTS

Time in range (3.9-10 mmol/L) was 72.6 ± 11.5% (mean ± SD) for all users and increased by age from 66.9 ± 11.7% for users ≤6 years old to 81.8 ± 8.7% for users ≥65 years. Time spent in hypoglycemia (<3.9 mmol/L) was 2.3% [1.3, 3.6] (median [interquartile range]). Mean glucose and glucose management indicator were 8.4 ± 1.1 mmol/L and 6.9%, respectively. Time using closed-loop was high at 94.7% [90.0, 96.9].

CONCLUSIONS

Glycemic outcomes from the present real-world evidence are comparable to results obtained from previous randomized controlled studies and confirm the efficacy of this hybrid closed-loop system in real-world settings.

UK's Association of British Clinical Diabetologist's Diabetes Technology Network (ABCD-DTN): Best practice guide for hybrid closed-loop therapy

This best practice guide is written with the aim of providing an overview of current hybrid closed-loop (HCL) systems in use within the United Kingdom's (UK) National Health Service (NHS) and to provide education and advice for their management on both an individual and clinical service level. The environment of diabetes technology, and particularly HCL systems, is rapidly evolving. The past decade has seen unprecedented advances in the development of HCL systems. These systems improve glycaemic outcomes and reduce the burden of treatment for people with type 1 diabetes (pwT1D). It is anticipated that access to these systems will increase in England as a result of updates in National Institute of Health and Care Excellence (NICE) guidance providing broader support for the use of real-time continuous glucose monitoring (CGM) for pwT1D. NICE is currently undertaking multiple-technology appraisal into HCL systems. Based on experience from centres involved in supporting advanced technologies as well as from the recent NHS England HCL pilot, this guide is intended to provide healthcare professionals with UK expert consensus on the best practice for initiation, optimisation and ongoing management of HCL therapy.

Management of type 1 diabetes in pregnancy: update on lifestyle, pharmacological treatment, and novel technologies for achieving glycaemic targets

Glucose concentrations within target, appropriate gestational weight gain, adequate lifestyle, and, if necessary, antihypertensive treatment and low-dose aspirin reduces the risk of pre-eclampsia, preterm delivery, and other adverse pregnancy and neonatal outcomes in pregnancies complicated by type 1 diabetes. Despite the increasing use of diabetes technology (ie, continuous glucose monitoring and insulin pumps), the target of more than 70% time in range in pregnancy (TIRp 3·5-7·8 mmol/L) is often reached only in the final weeks of pregnancy, which is too late for beneficial effects on pregnancy outcomes. Hybrid closed-loop (HCL) insulin delivery systems are emerging as promising treatment options in pregnancy. In this Review, we discuss the latest evidence on pre-pregnancy care, management of diabetes-related complications, lifestyle recommendations, gestational weight gain, antihypertensive treatment, aspirin prophylaxis, and the use of novel technologies for achieving and maintaining glycaemic targets during pregnancy in women with type 1 diabetes. In addition, the importance of effective clinical and psychosocial support for pregnant women with type 1 diabetes is also highlighted. We also discuss the contemporary studies examining HCL systems in type 1 diabetes during pregnancies.

Off-label use of an automated insulin delivery system in pregnant women with type 1 diabetes

Using commercially available automated insulin delivery (AID) systems for treating type 1 diabetes during pregnancy remains controversial. This retrospective study assessed six pregnant women with type 1 diabetes who underwent AID therapy. Our observations revealed that AID treatment, in most cases, did not achieve the desired glycemic targets for pregnancy.

In silico design and validation of a time-varying PID controller for an artificial pancreas with intraperitoneal insulin delivery and glucose sensing

Type 1 diabetes (T1D) is a chronic autoimmune disease featured by the loss of beta cell function and the need for lifetime insulin replacement. Over the recent decade, the use of automated insulin delivery systems (AID) has shifted the paradigm of treatment: the availability of continuous subcutaneous (SC) glucose sensors to guide SC insulin delivery through a control algorithm has allowed, for the first time, to reduce the daily burden of the disease as well as to abate the risk for hypoglycemia. AID use is still limited by individual acceptance, local availability, coverage, and expertise. A major drawback of SC insulin delivery is the need for meal announcement and the peripheral hyperinsulinemia that, over time, contributes to macrovascular complications. Inpatient trials using intraperitoneal (IP) insulin pumps have demonstrated that glycemic control can be improved without meal announcement due to the faster insulin delivery through the peritoneal space. This calls for novel control algorithms able to account for the specificities of IP insulin kinetics. Recently, our group described a two-compartment model of IP insulin kinetics demonstrating that the peritoneal space acts as a virtual compartment and IP insulin delivery is virtually intraportal (intrahepatic), thus closely mimicking the physiology of insulin secretion. The FDA-accepted T1D simulator for SC insulin delivery and sensing has been updated for IP insulin delivery and sensing. Herein, we design and validate-in silico-a time-varying proportional integrative derivative controller to guide IP insulin delivery in a fully closed-loop mode without meal announcement.

Application of Machine Learning Algorithms to Predict Uncontrolled Diabetes Using the All of Us Research Program Data

There is a paucity of predictive models for uncontrolled diabetes mellitus. The present study applied different machine learning algorithms on multiple patient characteristics to predict uncontrolled diabetes. Patients with diabetes above the age of 18 from the All of Us Research Program were included. Random forest, extreme gradient boost, logistic regression, and weighted ensemble model algorithms were employed. Patients who had a record of uncontrolled diabetes based on the international classification of diseases code were identified as cases. A set of features including basic demographic, biomarkers and hematological indices were included in the model. The random forest model demonstrated high performance in predicting uncontrolled diabetes, yielding an accuracy of 0.80 (95% CI: 0.79-0.81) as compared to the extreme gradient boost 0.74 (95% CI: 0.73-0.75), the logistic regression 0.64 (95% CI: 0.63-0.65) and the weighted ensemble model 0.77 (95% CI: 0.76-0.79). The maximum area under the receiver characteristics curve value was 0.77 (random forest model), while the minimum value was 0.7 (logistic regression model). Potassium levels, body weight, aspartate aminotransferase, height, and heart rate were important predictors of uncontrolled diabetes. The random forest model demonstrated a high performance in predicting uncontrolled diabetes. Serum electrolytes and physical measurements were important features in predicting uncontrolled diabetes. Machine learning techniques may be used to predict uncontrolled diabetes by incorporating these clinical characteristics.

CGM accuracy: Contrasting CE marking with the governmental controls of the USA (FDA) and Australia (TGA): A narrative review

The National Institute for Clinical Excellence updated guidance for continuous glucose monitoring (CGM) in 2022, recommending that CGM be available to all people living with type 1 diabetes. Manufacturers can trade in the UK with Conformité Européenne (CE) marking without an initial national assessment. The regulatory process for CGM CE marking, in contrast to the Food and Drug Administration (FDA) and Australian Therapeutic Goods Administration (TGA) process, is described. Manufacturers operating in the UK provided clinical accuracy studies submitted for CE marking. Critical appraisal of the studies shows several CGM devices have CE marking for wide-ranging indications beyond available data, unlike FDA and TGA approval. The FDA and TGA use tighter controls, requiring comprehensive product-specific clinical data evaluation. In 2018, the FDA published the integrated CGM (iCGM) criteria permitting interoperability. Applying the iCGM criteria to clinical data provided by manufacturers trading in the UK identified several study protocols that minimized glucose variability, thereby improving CGM accuracy on all metrics. These results do not translate into real-life performance. Furthermore, for many CGM devices available in the UK, accuracy reported in the hypoglycaemic range is below iCGM standards, or measurement is absent. We offer a framework to evaluate CGM accuracy studies critically. The review concludes that FDA- and TGA-approved indications match the available clinical data, whereas CE marking indications can have discrepancies. The UK can bolster regulation with UK Conformity Assessed marking from January 2025. However, balanced regulation is needed to ensure innovation and timely technological access are not hindered.

Effect of Tight Glycemic Control on Pancreatic Beta Cell Function in Newly Diagnosed Pediatric Type 1 Diabetes: A Randomized Clinical Trial

Importance

Near normalization of glucose levels instituted immediately after diagnosis of type 1 diabetes has been postulated to preserve pancreatic beta cell function by reducing glucotoxicity. Previous studies have been hampered by an inability to achieve tight glycemic goals.

Objective

To determine the effectiveness of intensive diabetes management to achieve near normalization of glucose levels on preservation of pancreatic beta cell function in youth with newly diagnosed type 1 diabetes.

Design, Setting, and Participants

This randomized, double-blind, clinical trial was conducted at 6 centers in the US (randomizations from July 20, 2020, to October 13, 2021; follow-up completed September 15, 2022) and included youths with newly diagnosed type 1 diabetes aged 7 to 17 years.

Interventions

Random assignment to intensive diabetes management, which included use of an automated insulin delivery system (n = 61), or standard care, which included use of a continuous glucose monitor (n = 52), as part of a factorial design in which participants weighing 30 kg or more also were assigned to receive either oral verapamil or placebo.

Main Outcomes and Measures

The primary outcome was mixed-meal tolerance test-stimulated C-peptide area under the curve (a measure of pancreatic beta cell function) 52 weeks from diagnosis.

Results

Among 113 participants (mean [SD] age, 11.8 [2.8] years; 49 females [43%]; mean [SD] time from diagnosis to randomization, 24 [5] days), 108 (96%) completed the trial. The mean C-peptide area under the curve decreased from 0.57 pmol/mL at baseline to 0.45 pmol/mL at 52 weeks in the intensive management group, and from 0.60 to 0.50 pmol/mL in the standard care group (treatment group difference, -0.01 [95% CI, -0.11 to 0.10]; P = .89). The mean time in the target range of 70 to 180 mg/dL, measured with continuous glucose monitoring, at 52 weeks was 78% in the intensive management group vs 64% in the standard care group (adjusted difference, 16% [95% CI, 10% to 22%]). One severe hypoglycemia event and 1 diabetic ketoacidosis event occurred in each group.

Conclusions and Relevance

In youths with newly diagnosed type 1 diabetes, intensive diabetes management, which included automated insulin delivery, achieved excellent glucose control but did not affect the decline in pancreatic C-peptide secretion at 52 weeks.

Trial Registration

ClinicalTrials.gov Identifier: NCT04233034.

Glycemia Risk Index Assessment in a Pediatric and Adult Patient Cohort With Type 1 Diabetes Mellitus

BACKGROUND

To evaluate the glycemia risk index (GRI) as a new glucometry in pediatric and adult populations with type 1 diabetes (T1D) in clinical practice.

METHODS

A cross-sectional study of 202 patients with T1D receiving intensive treatment with insulin (25.2% continuous subcutaneous insulin infusion [CSII]) and intermittent scanning (flash) glucose monitoring (isCGM). Clinical and glucometric isCGM data were collected, as well as the component of hypoglycemia (CHypo) and component of hyperglycemia (CHyper) of the GRI.

RESULTS

A total of 202 patients (53% males and 67.8% adults) with a mean age of 28.6 ± 15.7 years and 12.5 ± 10.9 years of T1D evolution were evaluated.Adult patients (>19 years) presented higher glycated hemoglobin (HbA1c) (7.4 ± 1.1 vs 6.7 ± 0.6%; P < .01) and lower time in range (TIR) (55.4 ± 17.5 vs 66.5 ± 13.1%; P < .01) values than the pediatric population, with lower coefficient of variation (CV) (38.6 ± 7.2 vs 42.4 ± 8.9%; P < .05). The GRI was significantly lower in pediatric patients (48.0 ± 22.2 vs 56.8 ± 23.4; P < .05) associated with higher CHypo (7.1 ± 5.1 vs 5.0 ± 4.5; P < .01) and lower CHyper (16.8 ± 9.8 vs 26.5 ± 15.1; P < .01) than in adults.When analyzing treatment with CSII compared with multiple doses of insulin (MDI), a nonsignificant trend to a lower GRI was observed in CSII (51.0 ± 15.3 vs 55.0 ± 25.4; P= .162), with higher levels of CHypo (6.5 ± 4.1 vs 5.4 ± 5.0; P < .01) and lower CHyper (19.6 ± 10.6 vs 24.6 ± 15.2; P < .05) compared with MDI.

CONCLUSIONS

In pediatric patients and in those with CSII treatment, despite a better control by classical and GRI parameters, higher overall CHypo was observed than in adults and MDI, respectively. The present study supports the usefulness of the GRI as a new glucometric parameter to evaluate the global risk of hypoglycemia-hyperglycemia in both pediatric and adult patients with T1D.

Real-world performance of the MiniMed 780G advanced hybrid closed loop system in Latin America: Substantial improvement in glycaemic control with each technology iteration of the MiniMed automated insulin delivery system

AIM

We studied real-world performance of MiniMed (MM) 780G system users from Argentina, Brazil, Colombia and Chile (geographical analysis), and the effect of each technology iteration of the MM system on glycaemic control (technology iteration analysis).

MATERIALS AND METHODS

CareLink data from August 2020 to September 2022 were extracted. Endpoints included continuous glucose monitoring metrics. For the geographical analysis, aggregated endpoints for MM780G system users were calculated. For the technology iteration analysis, MM780G system user outcomes were compared with outcomes when the same individuals were still using the MM640G or MM670G system.

RESULTS

On average, 1025 MM780G system users from the geographical analysis were followed for 136 (SD 135) days, spent 91.5 (14.3)% in advanced hybrid closed loop, showed a glucose management indicator (GMI) of 6.7 (0.3)%, a time in range between 70 and 180 mg/dl (TIR) of 76.5 (9.0)%, and a time below range 70 mg/dl (TBR) of 2.7 (2.1)%. The percentage of users reaching targets of GMI <7%, TIR >70% and TBR <4% was 80.8%, 78.1% and 80.1%, respectively. The technology iteration analysis on users transitioning from MM640G to MM780G system (N = 381) showed 0.4% decrease in GMI (7.1% to 6.7%, p < .0001), 10.7% increase in TIR (65.9% to 76.6%, p < .0001), while TBR remained. The percentage of insulin delivered automatically increased as well (47.5%-57.7%, p < .0001). Users transitioning from MM670G system (N = 78) showed a similar but less pronounced pattern.

CONCLUSIONS

Real-world Latin American MM780G users on average showed good glucose control, achieving international targets. Glycaemic control increased with every technology iteration of the MM system, providing more automation each time.

Performance of a dual-hormone closed-loop system versus insulin-only closed-loop system in adolescents with type 1 diabetes. A single-blind, randomized, controlled, crossover trial

Objective

To assess the efficacy and safety of a dual-hormone (DH [insulin and glucagon]) closed-loop system compared to a single-hormone (SH [insulin only]) closed-loop system in adolescents with type 1 diabetes.

Methods

This was a 26-hour, two-period, randomized, crossover, inpatient study involving 11 adolescents with type 1 diabetes (nine males [82%], mean ± SD age 14.8 ± 1.4 years, diabetes duration 5.7 ± 2.3 years). Except for the treatment configuration of the DiaCon Artificial Pancreas: DH or SH, experimental visits were identical consisting of: an overnight stay (10:00 pm until 7:30 am), several meals/snacks, and a 45-minute bout of moderate intensity continuous exercise. The primary endpoint was percentage of time spent with sensor glucose values below range (TBR [<3.9 mmol/L]) during closed-loop control over the 26-h period (5:00 pm, day 1 to 7:00 pm, day 2).

Results

Overall, there were no differences between DH and SH for the following glycemic outcomes (median [IQR]): TBR 1.6 [0.0, 2.4] vs. 1.28 [0.16, 3.19]%, p=1.00; time in range (TIR [3.9-10.0 mmol/L]) 68.4 [48.7, 76.8] vs. 75.7 [69.8, 87.1]%, p=0.08; and time above range (TAR [>10.0 mmol/L]) 28.1 [18.1, 49.8] vs. 23.3 [12.3, 27.2]%, p=0.10. Mean ( ± SD) glucose was higher during DH than SH (8.7 ( ± 3.2) vs. 8.1 ( ± 3.0) mmol/L, p<0.001) but coefficient of variation was similar (34.8 ( ± 6.8) vs. 37.3 ( ± 8.6)%, p=0.20). The average amount of rescue carbohydrates was similar between DH and SH (6.8 ( ± 12.3) vs. 9.5 ( ± 15.4) grams/participant/visit, p=0.78). Overnight, TIR was higher, TAR was lower during the SH visit compared to DH. During and after exercise (4:30 pm until 7 pm) the SH configuration produced higher TIR, but similar TAR and TBR compared to the DH configuration.

Conclusions

DH and SH performed similarly in adolescents with type 1 diabetes during a 26-hour inpatient monitoring period involving several metabolic challenges including feeding and exercise. However, during the night and around exercise, the SH configuration outperformed DH.

Continuous glucose monitoring and metrics for clinical trials: an international consensus statement

Randomised controlled trials and other prospective clinical studies for novel medical interventions in people with diabetes have traditionally reported HbA1c as the measure of average blood glucose levels for the 3 months preceding the HbA1c test date. The use of this measure highlights the long-established correlation between HbA1c and relative risk of diabetes complications; the change in the measure, before and after the therapeutic intervention, is used by regulators for the approval of medications for diabetes. However, with the increasing use of continuous glucose monitoring (CGM) in clinical practice, prospective clinical studies are also increasingly using CGM devices to collect data and evaluate glucose profiles among study participants, complementing HbA1c findings, and further assess the effects of therapeutic interventions on HbA1c. Data is collected by CGM devices at 1-5 min intervals, which obtains data on glycaemic excursions and periods of asymptomatic hypoglycaemia or hyperglycaemia (ie, details of glycaemic control that are not provided by HbA1c concentrations alone that are measured continuously and can be analysed in daily, weekly, or monthly timeframes). These CGM-derived metrics are the subject of standardised, internationally agreed reporting formats and should, therefore, be considered for use in all clinical studies in diabetes. The purpose of this consensus statement is to recommend the ways CGM data might be used in prospective clinical studies, either as a specified study endpoint or as supportive complementary glucose metrics, to provide clinical information that can be considered by investigators, regulators, companies, clinicians, and individuals with diabetes who are stakeholders in trial outcomes. In this consensus statement, we provide recommendations on how to optimise CGM-derived glucose data collection in clinical studies, including the specific glucose metrics and specific glucose metrics that should be evaluated. These recommendations have been endorsed by the American Association of Clinical Endocrinologists, the American Diabetes Association, the Association of Diabetes Care and Education Specialists, DiabetesIndia, the European Association for the Study of Diabetes, the International Society for Pediatric and Adolescent Diabetes, the Japanese Diabetes Society, and the Juvenile Diabetes Research Foundation. A standardised approach to CGM data collection and reporting in clinical trials will encourage the use of these metrics and enhance the interpretability of CGM data, which could provide useful information other than HbA1c for informing therapeutic and treatment decisions, particularly related to hypoglycaemia, postprandial hyperglycaemia, and glucose variability.

Diabetestechnologie bei Kindern und Jugendlichen mit Diabetes mellitus Typ 1

Die Behandlung des Diabetes mellitus Typ 1 (T1D) im Kindes- und Jugendalter ist komplex und stellt eine Herausforderung für die betroffenen Kinder und Jugendlichen, deren Familien und das ganze Umfeld (Schule/Kindergarten) dar. Das Ziel der Diabetestherapie besteht darin, eine möglichst normoglykämische Blutzuckerkontrolle zu erreichen, um akuten und chronischen Komplikationen vorzubeugen. Laut Registerstudien können die metabolischen Therapieziele derzeit noch nicht erreicht werden, weswegen ein Risiko für Akut- und Spätkomplikationen besteht. Weitere Therapieziele sind eine normale Entwicklung, Inklusion, Flexibilität im Alltag sowie eine hohe Lebensqualität. Abgesehen von neueren Insulinanaloga gingen auch die Entwicklungen in der Diabetestechnologie in den letzten Jahren mit großen Veränderungen und Verbesserungen in der Behandlung und Lebensqualität der betroffenen Familien einher. Die Insulinpumpentherapie, die kontinuierliche Glukosemessung sowie die automatische Insulindosierung (AID) führten zu einer signifikanten Verbesserung der metabolischen Einstellung sowie einer Reduktion der schweren Hypoglykämien und Ketoazidosen. Die Diabetestechnologie entwickelt sich ständig weiter und erfordert eine umfassende Schulung und Fortbildung der betroffenen Familien, der Betreuungseinrichtungen sowie auch des multidisziplinären Behandlungsteams. Ziel sind eine Reduktion der glykämischen Variabilität und damit ein besseres Langzeitoutcome der jungen Menschen mit T1D. Die AID ist zunehmend die Therapie der Wahl bei Kindern und Jugendlichen mit T1D. Mit weiteren Innovationen im Bereich der Diabetestechnologie ist in naher Zukunft zu rechnen.