Impact of Missed and Late Meal Boluses on Glycemic Outcomes in Automated Insulin Delivery-Treated Children and Adolescents with Type 1 Diabetes: A Two-Center, Population-Based Cohort Study

Objective: To evaluate the impact of missed or late meal boluses (MLBs) on glycemic outcomes in children and adolescents with type 1 diabetes using automated insulin delivery (AID) systems. Research Design and Methods: AID-treated (Tandem Control-IQ or Medtronic MiniMed 780G) children and adolescents (aged 6-21 years) from Stanford Medical Center and Steno Diabetes Center Copenhagen with ≥10 days of data were included in this two-center, binational, population-based, retrospective, 1-month cohort study. The primary outcome was the association between the number of algorithm-detected MLBs and time in target glucose range (TIR; 70-180 mg/dL). Results: The study included 189 children and adolescents (48% females with a mean ± standard deviation age of 13 ± 4 years). Overall, the mean number of MLBs per day in the cohort was 2.2 ± 0.9. For each additional MLB per day, TIR decreased by 9.7% points (95% confidence interval [CI] 11.3; 8.1), and compared with the quartile with fewest MLBs (Q1), the quartile with most (Q4) had 22.9% less TIR (95% CI: 27.2; 18.6). The age-, sex-, and treatment modality-adjusted probability of achieving a TIR of >70% in Q4 was 1.4% compared with 74.8% in Q1 (P < 0.001). Conclusions: MLBs significantly impacted glycemic outcomes in AID-treated children and adolescents. The results emphasize the importance of maintaining a focus on bolus behavior to achieve a higher TIR and support the need for further research in technological or behavioral support tools to handle MLBs.

Real-world glycaemic outcomes of automated insulin delivery in type 1 diabetes: A meta-analysis

AIM

To evaluate the real-world effectiveness of automated insulin delivery (AID) systems in patients with type 1 diabetes (T1D).

MATERIALS AND METHODS

PubMed, Embase, the Cochrane Library, and ClinicalTrials.gov were searched for studies published up until 2 March 2024. We included pragmatic randomized controlled trials (RCTs), cohort studies, and before-after studies that compared AID systems with conventional insulin therapy in real-world settings and reported continuous glucose monitoring outcomes. Percent time in range (TIR; 3.9-10 mmol/L), time below range (TBR; <3.9 mmol/L), time above range (TAR; >10 mmol/L), and glycated haemoglobin (HbA1c) level were extracted. Data were summarized as mean differences (MDs) with 95% confidence interval.

RESULTS

A total of 23 before-after studies (101 704 participants) were included in the meta-analysis. AID systems were associated with an increased percentage of TIR (11.61%, 10.47 to 12.76; p < 0.001). The favourable effect of AID systems was consistently observed when used continuously for 6 (11.76%) or 12 months (11.33%), and in both children (12.16%) and adults (11.04%). AID systems also showed favourable effects on TBR (-0.53%, -0.63 to -0.42), TAR (-9.65%, -10.63 to -8.67) and HbA1c level (-0.42%, -0.47 to -0.37) when compared with previous treatments.

CONCLUSIONS

Similar improvements in glycaemic parameters were observed in real-world settings in RCTs using AID systems in T1D. AID systems benefit both children and adults by increasing TIR for both short- and long-term interventions.

Recommendations for recognizing, risk stratifying, treating, and managing children and adolescents with hypoglycemia

There has been continuous progress in diabetes management over the last few decades, not least due to the widespread dissemination of continuous glucose monitoring (CGM) and automated insulin delivery systems. These technological advances have radically changed the daily lives of people living with diabetes, improving the quality of life of both children and their families. Despite this, hypoglycemia remains the primary side-effect of insulin therapy. Based on a systematic review of the available scientific evidence, this paper aims to provide evidence-based recommendations for recognizing, risk stratifying, treating, and managing patients with hypoglycemia. The objective of these recommendations is to unify the behavior of pediatric diabetologists with respect to the timely recognition and prevention of hypoglycemic episodes and the correct treatment of hypoglycemia, especially in patients using CGM or advanced hybrid closed-loop systems. All authors have long experience in the specialty and are members of the Italian Society of Pediatric Endocrinology and Diabetology. The goal of treating hypoglycemia is to raise blood glucose above 70 mg/dL (3.9 mmol/L) and to prevent further decreases. Oral glucose at a dose of 0.3 g/kg (0.1 g/kg for children using "smart pumps" or hybrid closed loop systems in automated mode) is the preferred treatment for the conscious individual with blood glucose <70 mg/dL (3.9 mmol/L), although any form of carbohydrate (e.g., sucrose, which consists of glucose and fructose, or honey, sugary soft drinks, or fruit juice) containing glucose may be used. Using automatic insulin delivery systems, the oral glucose dose can be decreased to 0.1 g/kg. Practical flow charts are included to aid clinical decision-making. Although representing the official position of the Italian Society of Pediatric Endocrinology and Diabetology (ISPED), these guidelines are applicable to the global audience and are especially pertinent in the era of CGM and other advanced technologies.

Reducing Inequity in the Use of Automated Insulin Delivery Systems by Adults With Type 1 Diabetes: Key Learnings From a Safety Net Diabetes Clinic Program

BACKGROUND

Recent advancements in diabetes technology have significantly improved Type 1 diabetes (T1D) management, but disparities persist, particularly in the adoption of automated insulin delivery (AID) systems within minoritized communities. We aimed to improve patient access to AID system training and overcome clinical inertia to referral.

METHODS

We report on a transformative program implemented at Boston Medical Center, the largest safety-net hospital in New England, aimed at reducing disparities in AID system utilization. We employed a multidisciplinary team and quality improvement principles to identify barriers and develop solutions. Strategies included increasing access to diabetes educators, creating a referral system, and developing telemedicine education classes. We also made efforts to raise clinician awareness and confidence in recommending AID therapy.

RESULTS

At baseline, 13.5% of our clinic T1D population was using an insulin pump. The population referred included 97 people with T1D (49% female, mean A1c 8.7%, 68% public insurance beneficiaries, 25% Hispanic and 25% non-Hispanic Black). Results from the first year showed a 166% increase in AID system use rates, with 64% of referred patients starting on AID. Notably, 78% of patients with A1c >8.5% adopted AID systems, addressing a gap in representation observed in clinical efficacy trials. The initiative successfully narrowed disparities in AID use among minoritized populations.

CONCLUSIONS

The program's success among minoritized patients underscores the significance of tailored, collaborative, team-based care and targeted educational initiatives. Our experience provides a foundation for future efforts to ensure equitable access to diabetes technologies, emphasizing the potential of local quality improvement interventions.

During an 18-month course of automated insulin delivery treatment, children aged 2 to 6 years achieve and maintain a higher time in tight range

AIMS

To investigate whether the positive effects on glycaemic outcomes of 3-month automated insulin delivery (AID) achieved in 2- to 6-year-old children endure over an extended duration and how AID treatment affects time in tight range (TITR), defined as 3.9-7.8 mmol/L.

RESEARCH DESIGN AND METHODS

We analysed 18 months of follow-up data from a non-randomized, prospective, single-arm clinical trial (n = 35) conducted between 2021 and 2023. The main outcome measures were changes in time in range (TIR), glycated haemoglobin (HbA1c), time above range (TAR), TITR, and mean sensor glucose (SG) value during follow-up visits (at 0, 6, 12 and 18 months). The MiniMed 780G AID system in SmartGuard Mode was used for 18 months. Parental diabetes distress was evaluated at 3 and 18 months with the validated Problem Areas in Diabetes-Parent, revised (PAID-PR) survey.

RESULTS

Between 0 and 6 months, TIR and TITR increased, and HbA1c, mean SG value and TAR decreased significantly (p < 0.001); the favourable effect persisted through 18 months of follow-up. Between 3 and 18 months, PAID-PR score declined significantly (0 months: mean score 37.5; 3 months: mean score 28.6 [p = 0.06]; 18 months: mean score 24.6 [p < 0.001]).

CONCLUSIONS

Treatment with AID significantly increased TITR and TIR in young children. The positive effect of AID on glycaemic control observed after 6 months persisted throughout the 18 months of follow-up. Similarly, parental diabetes distress remained reduced during 18 months follow-up. These findings are reassuring and suggest that AID treatment improves glycaemic control and reduces parental diabetes distress in young children over an extended 18-month follow-up.

Psychometric Properties of the Automated Insulin Delivery: Benefits and Burdens Scale for Adults with Type 1 Diabetes

Objective: To evaluate the psychometric properties of a patient-reported outcome measure, the Automated Insulin Delivery-Benefits and Burdens Scale (AID-BBS), which was designed to assess benefits and burdens of AID use in adults with type 1 diabetes (T1D). The measure was hypothesized to have validity, reliability, and clinical utility for predicting likelihood of continued use of an AID system. Research Design and Methods: A total of 217 adults with T1D (ages from 18 to 82 years) who were enrolled in an AID system research trial completed AID-BBS items at study midpoint (6 weeks) and at the end of the trial (13 weeks). Data were collected on pre-post glycemic outcomes. Participants completed other patient-reported psychosocial outcome measures (e.g., emotional well-being, diabetes distress, attitudes toward diabetes technology, diabetes treatment satisfaction) at Week 13. Likelihood of continued device use was assessed with three items at 13 weeks. Results: Exploratory factor analysis supported a one-factor structure for each subscale (15-item benefit and 9-item burden subscale) when evaluated separately. Convergent, discriminant, and predictive validity, internal consistency, and test-retest reliability were supported. Benefit and burden subscales at week 6 predicted usage intention above and beyond device impact on glycemic outcomes, also controlling for baseline glycemic outcomes. Conclusion: Findings support the AID-BBS as a psychometrically valid, reliable, and useful instrument for assessing burdens and benefits associated with AID system use in adults with T1D. The measure can be used to help health care providers set realistic expectations and proactively address modifiable burdens. Clinical Trial Registration Number: NCT04200313.

Insulin Requirements According to Sex and Weight in a Population of 9036 Adult Persons With Type 1 Diabetes Using Closed-Loop Insulin Delivery

BACKGROUND

The prediction of the individual insulin needs may facilitate the initiation of insulin therapy. Our aim was to explore the relationships between body weight, sex, and daily amounts of insulin delivered by a hybrid closed-loop system.

METHODS

We performed a retrospective data collection of all consenting adult patients with type 1 diabetes who were equipped in Europe with the Diabeloop Generation 1 (DBLG1) hybrid closed-loop insulin delivery device between March 1, 2021 and February 28, 2023.

RESULTS

A total of 9036 users (59% females, age 45.6 ± 14.3 years) were included, reaching a mean follow-up of 320 ± 143 days, an overall 2 887 188 days of data. We observed a mean insulin-weight ratio of 0.617 ± 0.207 U/kg (0.665 ± 0.217 for males and 0.584 ± 0.193 for females, P < .001). Exploratory analysis of a subset of 4066 patients reaching >70% Time in Range (70-180 mg/dL) showed a mean insulin-weight ratio of 0.55 ± 0.17 U/kg (P < .001) (0.59 ± 0.18 for the 1438 males and 0.53 ± 0.16 for the 2628 females).

CONCLUSION

This large real-world analysis provides a quantitative estimation of the daily insulin requirements in adult patients with type 1 diabetes and shows significant differences between sex. These findings have relevant implications in the practical management of insulin therapy.

Automated Insulin Delivery Systems in Pediatric Type 1 Diabetes: A Narrative Review

This narrative review assesses the use of automated insulin delivery (AID) systems in managing persons with type 1 diabetes (PWD) in the pediatric population. It outlines current research, the differences between various AID systems currently on the market and the challenges faced, and discusses potential opportunities for further advancements within this field. Furthermore, the narrative review includes various expert opinions on how different AID systems can be used in the event of challenges with rapidly changing insulin requirements. These include examples, such as during illness with increased or decreased insulin requirements and during physical activity of different intensities or durations. Case descriptions give examples of scenarios with added user-initiated actions depending on the type of AID system used. The authors also discuss how another AID system could have been used in these situations.

JL, Garcia-Tirado J, DeBoer MD, Breton MD. Adjusting Therapy Profiles When Switching to Ultra-Rapid Lispro in an Advanced Hybrid Closed-Loop System: An in Silico Study

BACKGROUND

It has been shown that insulin acceleration by itself might not be sufficient to see clear improvements in glycemic metrics, and insulin therapy may need to be adjusted to fully leverage the extra safety margin provided by faster pharmacokinetic (PK) and pharmacodynamic (PD) profiles. The objective of this work is to explore how to perform such adjustments on a commercially available automated insulin delivery (AID) system.

METHODS

Ultra-rapid lispro (URLi) is modeled within the UVA/Padova simulation platform using data from previously published clamp studies. The Control-IQ AID algorithm is selected as it leverages carbohydrate-to-insulin ratio (CR in g/U), correction factor (CF in mg/dL/U), and basal rate (BR in U/h) daily profiles that are fully customizable. An experiment roadmap is proposed to understand how to safely modify these profiles when switching from lispro to URLi.

RESULTS

Simulations show that a 7% decrease in CR (approximately an 8% increase in prandial insulin) and a 7.5% increase in BR lead to cumulative improvements in glucose control with URLi. Comparing with baseline metrics using lispro, a clinically significant increase in time in the range of 70 to 180 mg/dL (overall: 70.2%-75.2%, P < .001; 6 am-12 am: 62.4%-68.5%, P < .001) and a reduction in time below 70 mg/dL (overall: 1.8%-1.2%, P < .001; 6 am-12 am: 1.8%-1.3%, P < .001) were observed.

CONCLUSION

Properly adjusting therapy parameters allows to fully leverage glucose control benefits provided by faster insulin analogues, opening opportunities to take another step forward into a next generation of more effective AID solutions.

Adaptive Biobehavioral Control: A Pilot Analysis of Human-Machine Coadaptation in Type 1 Diabetes

Background: While it is well recognized that an automated insulin delivery (AID) algorithm should adapt to changes in physiology, it is less understood that the individual would also have to adapt to the AID system. The adaptive biobehavioral control (ABC) method presented here attempts to compensate for this deficiency by including AID into an information cloud-based ecosystem. Methods: The Web Information Tool (WIT) implements the ABC concept via the following: (1) a Physiological Adaptation Module (PAM) that tracks metabolic changes and adapts AID parameters accordingly and (2) a Behavioral Adaptation Module (BAM) that provides information feedback. The safety of WIT (primary outcome) was assessed in an 8-week randomized, two-arm parallel pilot study. All participants used the Control-IQ® AID system enhanced with PAM, but only those in the Experimental group had access to BAM. Secondary glycemic outcomes were computed using the 2-week baseline period and the last 2 weeks of treatment. Results: Thirty participants with type 1 diabetes (T1D) completed all study procedures (17 female/13 male; age: 40 ± 14 years; HbA1c: 6.6% ± 0.5%). No severe hypoglycemia, DKA, or other serious adverse events were reported. Comparing the Experimental and Control groups, no significant difference was observed in time in range (70-180 mg/dL): 74.6% vs 73.8%, adjusted mean difference: 2.65%, 95% CI (-1.12%,6.41%), P = 0.161. Time in 70-140 mg/dL was significantly higher in the Experimental group: 50.7% vs 49.2%, 5.71% (0.44%,10.97%), P = 0.035, without increased time below range: 0.54% (-0.09%,1.17%), P = 0.089. Conclusion: The results demonstrate that it is safe to integrate an AID system into the WIT ecosystem. Validation in a full-scale study is ongoing.

Automated Insulin Delivery in Adults With Type 1 Diabetes and Suboptimal HbA1c During Prior Use of Insulin Pump and Continuous Glucose Monitoring: A Randomized Controlled Trial

BACKGROUND

Automated insulin delivery (AID) systems offer promise in improving glycemic outcomes for individuals with type 1 diabetes. However, data on those who struggle with suboptimal glycemic levels despite insulin pump and continuous glucose monitoring (CGM) are limited. We conducted a randomized controlled trial to assess the effects of an AID system in this population.

METHODS

Participants with hemoglobin A1c (HbA1c) ≥ 58 mmol/mol (7.5%) were allocated 1:1 to 14 weeks of treatment with the MiniMed 780G system (AID) or continuation of usual care (UC). The primary endpoint was change in time in range (TIR: 3·9-10·0 mmol/L) from baseline to week 14. After this trial period, the UC group switched to AID treatment while the AID group continued using the system. Both groups were monitored for a total of 28 weeks.

RESULTS

Forty adults (mean ± SD: age 52 ± 11 years, HbA1c 67 ± 7 mmol/mol [8.3% ± 0.6%], diabetes duration 29 ±13 years) were included. After 14 weeks, TIR increased by 18.7% (95% confidence interval [CI] = 14.5, 22.9%) in the AID group and remained unchanged in the UC group (P < .0001). Hemoglobin A1c decreased by 10.0 mmol/mol (95% CI = 7.0, 13.0 mmol/mol) (0.9% [95% CI = 0.6%, 1.2%]) in the AID group but remained unchanged in the UC group (P < .0001). The glycemic benefits of AID treatment were reproduced after the 14-week extension phase. There were no episodes of severe hypoglycemia or diabetic ketoacidosis during the study.

CONCLUSIONS

For adults with type 1 diabetes not meeting glycemic targets despite use of insulin pump and CGM, transitioning to an AID system confers considerable glycemic benefits.

Comparative Effectiveness of Hybrid Closed-Loop Automated Insulin Delivery Systems Among Patients with Type 1 Diabetes

BACKGROUND

Clinical trials have demonstrated the efficacy and safety of hybrid closed-loop (HCL) systems, yet few studies have compared outcomes in the real-world setting.

METHOD

This retrospective study analyzed patients from an academic endocrinology practice between January 1, 2018, and November 18, 2022. The inclusion criteria were diagnosis code for type I diabetes (T1D), >18 years of age, new to any HCL system [Medtronic 670G/770G (MT), Tandem Control IQ (CIQ), or Omnipod 5 (OP5)], and availability of a pump download within three months. The outcomes included %time in range (TIR) of 70 to 180 mg/dL, %time below range (TBR) <70 mg/dL at 90 days, and HbA1c for 91 to 180 days.

RESULT

Of the 176 participants, 47 were MT, 74 CIQ, and 55 OP5. Median (25%, 75%) change in HbA1c was -0.1 (-0.8, 0.3), -0.6 (-1.1, -0.15), and -0.55 (-0.98, 0)% for MT, CIQ, and OP5, respectively, (P = .04). TIR was 70 (57, 76), 67 (59, 75), and 68 (60, 76)% (P = .95) at 90 days while TBR was 2 (1, 3), 1 (0, 2), and 1 (0, 1)%, respectively, (P = .002). The %time in automated delivery was associated with TIR and change in HbA1c. After controlling other factors including %time in automated delivery, HCL type was not an independent predictor of change in HbA1c nor TIR but remained a significant predictor of TBR.

CONCLUSION

There were significant reductions in HbA1c in CIQ and OP5. TIR was similar across pumps, but TBR was highest with MT. The %time in automated delivery likely explains differences in change in HbA1c but not TBR between HCL systems.

Real-world lived experience of older adults with type 1 diabetes after an automated insulin delivery trial

AIMS

First-generation closed-loop automated insulin delivery improves glycaemia and psychosocial outcomes among older adults with type 1 diabetes in clinical trials. However, no study has previously assessed real-world lived experience of older adults using closed-loop therapy outside a trial environment.

METHODS

Semi-structured interviews were conducted with older adults who were pre-existing insulin pump users and previously completed the OldeR Adult Closed-Loop (ORACL) randomised trial. Interviews focused on perceptions of diabetes technology use, and factors influencing decisions regarding continuation.

RESULTS

Twenty-eight participants, mean age 70 years (SD 5), were interviewed at median 650 days (IQR 608-694) after their final ORACL trial visit. At interview, 23 participants (82%) were still using a commercial closed-loop system (requiring manual input for prandial insulin bolus doses). Themes discussed in interviews relating to closed-loop system use included sustained psychosocial benefits, cost and retirement considerations and usability frustrations relating to sensor accuracy and system alarms. Of the five participants who had discontinued, reasons included cost, continuous glucose monitoring-associated difficulties and usability frustrations. Cost was the largest consideration regarding continued use; most participants considered the increased ease of diabetes management to be worth the associated costs, though cost was prohibitive for some.

CONCLUSIONS

Almost 2 years after completing a closed-loop clinical trial, closed-loop automated insulin delivery remains the preferred type 1 diabetes therapy for the majority of older adult participants. Chronological age is not a barrier to real-world successful use of diabetes technology. Identifying age-related barriers, and solutions, to diabetes technology use among older adults is warranted.

Characteristics Associated With Elevated Time Below Range in Elderly Patients With Type 1 Diabetes Using an Automated Insulin Delivery System

BACKGROUND

This study investigated the characteristics associated with an increased risk of hypoglycemia, in elderly patients with type 1 diabetes mellitus (T1D) using automated insulin delivery (AID) systems.

METHODS

Cross-sectional observational study including patients >60 years, using sensor-augmented insulin pump therapy with predictive low-glucose management (SAPT-PLGM), hybrid closed-loop (HCL), and advanced hybrid closed-loop (AHCL), for more than three months. A geriatric assessment was performed, and body composition was determined to investigate its association with achieving time below range (TBR) <70 mg/dL goals.

RESULTS

The study included 59 patients (47.5% of men, mean age of 67.6 years, glycated hemoglobin [HbA1c] of 7.5 ± 0.6%, time in range (TIR) 77.8 ± 9.9%). Time below range <70 and <54 mg/dL were 2.2 ± 2.3% and 0.4 ± 0.81%, respectively. Patients with elevated TBR <70 mg/dL (>1%) had higher HbA1c levels, lower TIR, elevated time above range (TAR), and high glycemic variability. Regarding body composition, greater muscle mass, grip strength, and visceral fat were associated with a lower TBR <70 mg/dL. These factors were independent of the type of technology used, but TIR was higher when using AHCL systems compared with SAPT-PLGM and HCL systems.

CONCLUSIONS

In elderly patients treated with AID systems with good functional status, lower lean mass, lower grip strength, and lower visceral fat percentage were associated with TBR greater than 1%, regardless of the device used. A similar finding along was found with CGM indicators such as higher HbA1c levels, lower TIR, higher TAR, and higher CV. Geriatric assessment is crucial for personalizing patient management.

Time in Range Analysis in Automated Insulin Delivery Era: Should Day and Nighttime Targets be the Same?

INTRODUCTION

Hybrid closed-loop systems (HCLS) use has shown that time in range (TIR) tends to improve more during the nighttime than during the day. This study aims to compare the conventional TIR, currently accepted as 70 to 180 mg/dL, with a proposed recalculated time in range (RTIR) considering a tighter glucose target of 70 to 140 mg/dL for the nighttime fasting period in T1DM patients under HCLS.

METHODS

We conducted a retrospective study that included adults patients receiving treatment with Tandem t:slim X2 Control-IQ. Daytime TIR was characterized as glucose values between 70 and 180 mg/dL during the 07:01 to 23:59 time frame. Nighttime fasting TIR was specified as glucose values from 70 to 140 mg/dL between 00:00 and 07:00. The combination of the daytime and nighttime fasting glucose targets results in an RTIR, which was compared with the conventional TIR for each patient. The 14 days Dexcom G6 CGM data were downloaded from Tidepool platform and analyzed.

RESULTS

We included 22 patients with a mean age of 49.7 years and diabetes duration of 24.7 years, who had been using automatic insulin delivery (AID) HCLS for a median of 305.3 days. We verified a mean conventional TIR of 68.7% vs a mean RTIR of 60.3%, with a mean percentage difference between these two metrics of -8.4%. A significant decrease in conventional TIR was verified when tighter glucose targets were considered during the nighttime period. No significant correlation was found between the percentage difference values and RTIR, even among the group of patients with the lowest conventional TIR.

CONCLUSIONS

Currently, meeting the conventional TIR metrics may fall short of achieving an ideal level of glycemic control. An individualized strategy should be adopted until further data become available for a precise definition of optimal glucose targets.

Control-IQ Technology Use in Individuals With High Insulin Requirements: Results From the Multicenter Higher-IQ Trial

BACKGROUND

Control-IQ technology version 1.5 allows for a wider range of weight and total daily insulin (TDI) entry, in addition to other changes to enhance performance for users with high basal rates. This study evaluated the safety and performance of the updated Control-IQ system for users with basal rates >3 units/h and high TDI in a multicenter, single arm, prospective study.

METHODS

Adults with type 1 diabetes (T1D) using continuous subcutaneous insulin infusion (CSII) and at least one basal rate over 3 units/h (N = 34, mean age = 39.9 years, 41.2% female, diabetes duration = 21.8 years) used the t:slim X2 insulin pump with Control-IQ technology version 1.5 for 13 weeks. Primary outcome was safety events (severe hypoglycemia and diabetic ketoacidosis (DKA)). Central laboratory hemoglobin A1c (HbA1c) was measured at system initiation and 13 weeks. Participants continued using glucagon-like peptide-1 (GLP-1) receptor agonists, sodium-glucose transport protein 2 (SGLT-2) inhibitors, or other medications for glycemic control and/or weight loss if on a stable dose.

RESULTS

All 34 participants completed the study. Fifteen participants used a basal rate >3 units/h for all 24 hours of the day. Nine participants used >300 units TDI on at least one day during the study. There were no severe hypoglycemia or DKA events. Time in range 70-180 mg/dL was 64.8% over the 13 weeks, with 1.0% time <70 mg/dL. Hemoglobin A1c decreased from 7.69% at baseline to 6.87% at 13 weeks (-0.82%, P < .001).

CONCLUSIONS

Control-IQ technology version 1.5, with wider range of weight and TDI input and enhancements for users with high insulin requirements, was safe in individuals with T1D in this study.

Feasibility and Preliminary Safety of Smartphone-Based Automated Insulin Delivery in Adolescents and Children With Type 1 Diabetes

BACKGROUND

A smartphone-based automated insulin delivery (AID) controller device can facilitate use of interoperable components and acceptance in adolescents and children.

METHODS

Pediatric participants (N = 20, 8F) with type 1 diabetes were enrolled in three sequential age-based cohorts: adolescents (12-<18 years, n = 8, 5F), school-age (8-<12 years, n = 7, 2F), and young children (2-<8 years, n = 5, 1F). Participants used the interoperable artificial pancreas system (iAPS) and zone model predictive control (MPC) on an unlocked smartphone for 48 hours, consumed unrestricted meals of their choice, and engaged in various unannounced exercises. Primary outcomes and stopping criteria were defined using fingerstick blood glucose (BG) data; secondary outcomes compared continuous glucose monitoring (CGM) data with preceding sensor augmented pump (SAP) therapy.

RESULTS

During AID, there was no more than one BG <50 mg/dL except in one young child participant; no instance of more than two episodes of BG ≥300 mg/dL lasting longer than 2 hours; and no adverse events. Despite large meals (total of 404.9 grams of carbs) and unannounced exercise (total of 182 minutes), overall CGM percent time in range (TIR) of 70 to 180 mg/dL during AID was statistically similar to SAP (63.5% vs 57.3%, respectively, P = .145). Overnight glucose standard deviation was 43 mg/dL (vs SAP 57.9 mg/dL, P = .009) and coefficient of variation was 25.7% (vs SAP 34.9%, P < .001). The percent time in closed-loop mode and connected to the CGM was 92.7% and 99.6%, respectively. Surveys indicated that participants and parents/guardians were satisfied with the system.

CONCLUSIONS

The smartphone-based AID was feasible and safe in sequentially younger cohorts of adolescents and children.

CLINICALTRIALS.GOV

NCT04255381 (https://clinicaltrials.gov/ct2/show/NCT04255381).

Design and In Silico Evaluation of an Exercise Decision Support System Using Digital Twin Models

BACKGROUND

Managing glucose levels during exercise is challenging for individuals with type 1 diabetes (T1D) since multiple factors including activity type, duration, intensity and other factors must be considered. Current decision support tools lack personalized recommendations and fail to distinguish between aerobic and resistance exercise. We propose an exercise-aware decision support system (exDSS) that uses digital twins to deliver personalized recommendations to help people with T1D maintain safe glucose levels (70-180 mg/dL) and avoid low glucose (<70 mg/dL) during and after exercise.

METHODS

We evaluated exDSS using various exercise and meal scenarios recorded from a large, free-living study of aerobic and resistance exercise. The model inputs were heart rate, insulin, and meal data. Glucose responses were simulated during and after 30-minute exercise sessions (676 aerobic, 631 resistance) from 247 participants. Glucose outcomes were compared when participants followed exDSS recommendations, clinical guidelines, or did not modify behavior (no intervention).

RESULTS

exDSS significantly improved mean time in range for aerobic (80.2% to 92.3%, P < .0001) and resistance (72.3% to 87.3%, P < .0001) exercises compared with no intervention, and versus clinical guidelines (aerobic: 82.2%, P < .0001; resistance: 80.3%, P < .0001). exDSS reduced time spent in low glucose for both exercise types compared with no intervention (aerobic: 15.1% to 5.1%, P < .0001; resistance: 18.2% to 6.6%, P < .0001) and was comparable with following clinical guidelines (aerobic: 4.5%, resistance: 8.1%, P = N.S.).

CONCLUSIONS

The exDSS tool significantly improved glucose outcomes during and after exercise versus following clinical guidelines and no intervention providing motivation for clinical evaluation of the exDSS system.

Retrospective Comparison of Commercially Available Automated Insulin Delivery With Open-Source Automated Insulin Delivery Systems in Type 1 Diabetes

BACKGROUND

Automated insulin delivery (AID) systems have shown to improve glycemic control in a range of populations and settings. At the start of this study, only one commercial AID system had entered the Austrian market (MiniMed 670G, Medtronic). However, there is an ever-growing community of people living with type 1 diabetes (PWT1D) using open-source (OS) AID systems.

MATERIALS AND METHODS

A total of 144 PWT1D who used either the MiniMed 670G (670G) or OS-AID systems routinely for a period of at least three to a maximum of six months, between February 18, 2020 and January 15, 2023, were retrospectively analyzed (116 670G aged from 2.6 to 71.8 years and 28 OS-AID aged from 3.4 to 53.5 years). The goal is to evaluate and compare the quality of glycemic control of commercially available AID and OS-AID systems and to present all data by an in-depth descriptive analysis of the population. No statistical tests were performed.

RESULTS

The PWT1D using OS-AID systems spent more time in range (TIR)70-180 mg/dL (81.7% vs 73.9%), less time above range (TAR)181-250 mg/dL (11.1% vs 19.6%), less TAR>250 mg/dL (2.5% vs 4.3%), and more time below range (TBR)54-69 mg/dL (2.2% vs 1.7%) than PWT1D using the 670G system. The TBR<54 mg/dL was comparable in both groups (0.3% vs 0.4%). In the OS-AID group, median glucose level and glycated hemoglobin (HbA1c) were lower than in the 670G system group (130 vs 150 mg/dL; 6.2% vs 7.0%).

CONCLUSION

In conclusion, both groups were able to achieve satisfactory glycemic outcomes independent of age, gender, and diabetes duration. However, the PWT1D using OS-AID systems attained an even better glycemic control with no clinical safety concerns.

Safety and Feasibility Evaluation of Automated User Profile Settings Initialization and Adaptation With Control-IQ Technology

BACKGROUND

Optimization of automated insulin delivery (AID) settings is required to achieve desirable glycemic outcomes. We evaluated safety and efficacy of a computerized system to initialize and adjust insulin delivery settings for the t:slim X2 insulin pump with Control-IQ technology in adults with type 1 diabetes (T1D).

METHODS

After a 2-week continuous glucose monitoring (CGM) run-in period, adults with T1D using multiple daily injections (MDI) (N = 33, mean age 36.1 years, 57.6% female, diabetes duration 19.7 years) were transitioned to 13 weeks of Control-IQ technology usage. A computerized algorithm generated recommendations for initial pump settings (basal rate, insulin-to-carbohydrate ratio, and correction factor) and weekly follow-up settings to optimize glycemic outcomes. Physicians could override the automated settings changes for safety concerns.

RESULTS

Time in range 70 to 180 mg/dL improved from 45.7% during run-in to 69.1% during the last 30 days of Control-IQ use, a median improvement of 18.8% (95% confidence interval [CI]: 13.6-23.9, P < .001). This improvement was evident early in the study and was sustained over 13 weeks. Time <70 mg/dL showed a gradual decreasing trend over time. Percentage of participants achieving HbA1c <7% went from zero at baseline to 55% at study end (P < .001). Only six of the 318 automated settings adaptations (1.9%) were overridden by study investigators.

CONCLUSIONS

Computerized initiation and adaptation of Control-IQ technology settings from baseline MDI therapy was safe in adults with T1D. The use of this simplified system for onboarding and optimizing Control-IQ technology may be useful to increase uptake of AID and reduce staff and patient burden in clinical care.

Discordance between Glucose Management Indicator and Glycated Hemoglobin in a Pediatric Cohort with Type 1 Diabetes: A Real-World Study

The introduction of continuous glucose monitoring (CGM) systems in clinical practice has allowed a more detailed picture of the intra- and interdaily glycemic fluctuations of individuals with type 1 diabetes (T1D). However, CGM-measured glucose control indicators may be occasionally inaccurate. This study aims to assess the discrepancy between the glucose management indicator (GMI) and glycated hemoglobin (HbA1c) (ΔGMI-HbA1c) within a cohort of children and adolescents with T1D, exploring its correlation with other CGM metrics and blood count parameters. In this single-center, cross-sectional study, we gathered demographic and clinical data, including blood count parameters, HbA1c values, and CGM metrics, from 128 pediatric subjects with T1D (43% female; mean age, 13.4 ± 3.6 years). Our findings revealed higher levels of the coefficient of variation (CV) (p < 0.001) and time above range > 250 mg/dL (p = 0.033) among subjects with ΔGMI-HbA1c > 0.3%. No association was observed between blood count parameters and ΔGMI-HbA1c. In conclusion, despite the advancements and the widespread adoption of CGM systems, HbA1c remains an essential parameter for the assessment of glycemic control, especially in individuals with suboptimal metabolic control and extreme glycemic variability.

Should people with type 2 diabetes treated by multiple daily insulin injections with home health care support be switched to hybrid closed-loop? The CLOSE AP+ randomized controlled trial

AIM

The study aim was to evaluate the feasibility, safety and efficacy of automated insulin delivery (AID) assisted by home health care (HHC) services in people with type 2 diabetes unable to manage multiple daily insulin injections (MDI) at home on their own.

PATIENTS AND METHODS

This was an open label, multicentre, randomized, parallel group trial. In total, 30 adults with type 2 diabetes using MDI and requiring nursing support were randomly allocated to AID or kept their usual therapy over a 12-week period. Both treatments were managed with the support of HHC services. The primary outcome was the percentage time in the target glucose range of 70-180 mg/dl (TIR). Secondary outcomes included other continuous glucose monitoring metrics, glycated haemoglobin (HbA1c) levels, daily insulin doses, body weight, and of quality of life scores, fear of hypoglycaemia and satisfaction questionnaires.

RESULTS

Age (69.7 vs. 69.3 years) and HbA1c (9.25 vs. 9.0) did not differ in MDI and AID at baseline. Compared with MDI, AID resulted in a significant increase in TIR by 27.4% [95% CI (15.0-39.8); p < .001], a decrease in time above range by 27.7% and an unchanged time below range of <1%. A between-group difference in HbA1c was 1.3% favouring AID. Neither severe hypoglycaemia nor ketoacidosis occurred in either group. Patient and caregiver satisfaction with AID was high.

CONCLUSIONS

AID combined with tailored HHC services significantly improved glycaemic control with no safety issues in people with type 2 diabetes previously under an MDI regimen with HHC. AID should be considered a safe option in these people when lacking acceptable glucose control.