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

Tips for successful use of commercial automated insulin delivery systems: An expert paper of the Italian working group on diabetes and technology

In recent years, automated insulin delivery (AID) systems have transformed diabetes care with demonstrated benefits in glucose control, hypoglycemia risk, and psychosocial outcomes. Given that different systems show peculiarities in terms of components, approved indications of use, type of algorithm, modifiable settings, and additional features, with this expert paper, we aim to provide healthcare professionals with device-specific recommendations for the optimization of insulin therapy and diabetes self-management with the five commercial AID systems most commonly used in Italy. In detail, we provide educational tips and suggestions for adjustment of insulin dosing parameters to address specific glucose patterns as depicted by continuous glucose monitoring data and effectively manage physical activity or exercise.

Long-Term Glycemic Benefits and User Interaction Insights: Real-World Outcomes of Automated Insulin Delivery Use in a Pediatric Population

Background: Automated insulin delivery (AID) systems improve glycemic outcomes, but the roles of user interaction and insulin pump settings in these findings remain underexplored. Objective: To investigate how AID initiation influenced glycemic outcomes over a year and assess the impact of user behavior and insulin pump settings. Methods: This was a retrospective observational study on real-world data from 156 pediatric individuals initiating AID (Tandem Control-IQ or MiniMedTM 780G). Data collected at baseline and a year following AID initiation included measures of glycemic outcomes, user interaction (e.g., daily meals, carbohydrates, and user-initiated insulin bolus), and insulin pump settings. Results: Percentage of time in range (TIR: 3.9-10.0 mmol/L) improved after AID initiation and remained stable over the follow-up year (baseline: 61.9% vs. month 12: 69.1%, P < 0.001). The percentage of individuals reaching target (TIR >70%) declined after an initial increase (baseline: 29.5% vs. month 1: 60.0% vs. month 12: 43.7%, P < 0.005). The predefined measures for user interaction also increased over a year, such as user-initiated insulin boluses (baseline: 53.7% of total daily dose [TDD] vs. month 12: 59.9% of TDD, P = 0.034), reduced carbohydrate intakes relative to body weight (baseline: 5.0 g/[kg·d] vs. month 12: 4.6 g/[kg·d], P = 0.004), and longer active continuous glucose monitoring (CGM) wear time (baseline: 87.2% vs. month 12: 94.1%, P = 0.011). A positive association between TIR and daily registered meals (P < 0.001) and daily registered carbohydrates (P = 0.003) was found in the multivariate analysis while adjusting for insulin pump settings and total daily insulin dose. Conclusion: Glycemic outcomes improved 12 months after AID initiation and were positively associated with the number of meal announcements and daily carbohydrates registered in the pump. User-initiated bolus insulin and percentage of active CGM wear time had no impact on AID performance. Our findings emphasize the importance of continuous assessment of diabetes management, even with advanced technology, as user engagement remains crucial.

Precision medicine in type 1 diabetes: comparing metabolic outcomes of Control-IQ and MiniMed 780G according to patient characteristics

AIMS

This study aimed to compare 12-month metabolic outcomes in patients with type 1 diabetes (T1D) treated with either MiniMed 780G (Guardian 4) or Control-IQ (Dexcom G6) automated insulin delivery (AID) systems and identify interaction with patient characteristics.

MATERIALS AND METHODS

We conducted a single-centre, retrospective study including all patients (aged ≥16) with T1D who were started on either MiniMed 780G or Control-IQ between January 2021 and October 2022 and continued for ≥12 months. We used propensity score matching to compare the average marginal effects between MiniMed 780G and Control-IQ regarding the primary outcome (time in range [TIR]) and secondary outcomes (time below range [TBR], glucose monitoring indicator [GMI] and coefficient of variation [CV]) after 12 months. We tested for interaction effects between baseline characteristics (age, sex, socio-professional background, body mass index, insulin daily dose, carbohydrate counting practice) and treatment effect.

RESULTS

We included 245 patients (58% women): 178 treated with Control-IQ and 67 with MiniMed 780G. The mean ± SD age and haemoglobin A1c were 39 ± 15 years and 8.7 ± 1.8% (72 ± 20 mmol/mol) respectively. In the propensity score-matched sample (n = 221), we observed significant differences in 12-month TIR (MiniMed 780G minus Control-IQ [95% CI]: 6.4 [3.4;9.5]), GMI (-0.42 [-0.59; -0.25]) and CV (-2.12 [-3.68; -0.55]), while TBR showed no significant difference (-0.04 [-0.47; +0.40]). The 12-month TIR difference was consistent across subgroups, including baseline carbohydrate counting characteristics.

CONCLUSION

MiniMed 780G is associated with moderate metabolic superiority compared to Control-IQ, without interaction with patient characteristics. These results suggest that neither model is more appropriate for certain populations, particularly patients without carbohydrate counting practice.

Age-Dependent Bolus Settings: Insulin-to-Carbohydrate Ratios and Insulin Sensitivity Factors in Pediatric Patients with Type 1 Diabetes on Conventional CSII in the DPV Registry

Introduction: Effective bolus settings for insulin-to-carbohydrate ratios (ICRs) and insulin sensitivity factors (ISFs) are crucial for glycemic control in pediatric patients with type 1 diabetes on insulin pumps. Standard calculation methods based on the total daily insulin dose (TDD) often fall short for children. This study examined insulin pump settings for ICR and ISF in pediatric patients, considering diurnal variation, age, sex, and body mass index (BMI). The goal was to provide data-driven recommendations for initial bolus settings. Methods: We analyzed insulin pump settings in 7697 pediatric patients with type 1 diabetes in the DPV registry (Diabetes Prospective Follow-up) from Germany, Austria, Switzerland, and Luxembourg. Patients aged 1 to <18 years, postremission (diabetes duration >1 year, insulin dose ≥0.5 IU/kg/d), with good metabolic control (HbA1c ≤7.5%), using insulin pumps with short-acting analog insulin in 2023, were included. Automated insulin delivery system users were excluded. Patients were grouped by age (<6, 6 to <12, 12 to <18 years), BMI percentiles (P75), and sex. Results: Older children required more insulin, with lower ICRs and ISFs. Insulin requirements peaked in the morning with the lowest ICR and ISF, with medians (interquartile ranges): <6 years: 11.2 g carbs/IU (9.1-14.0) and 1:150 mg/dL (70-228); 6 to <12 years: 8.7 g carbs/IU (7.0-10.7) and 1:90 (50-140); and 12 to <18 years: 6.1 g carbs/IU (5.0-7.7) and 1:50 (40-80). ISF was highest in the late evening in all age groups, while a higher BMI-SDS was associated with a lower ISF. Girls above 6 years had lower ICR but similar ISF to that of boys. The factor obtained by multiplying ISF and TDD was comparable in all age groups and BMI categories. Conclusion: Our real-world findings on CSII settings in a large cohort of children with sufficient metabolic control highlight the inadequacy of a single TDD-based calculation formula, as insulin requirements varied by age, time of day, sex, and BMI. These findings may serve as a reference for commonly used age-dependent parameters for clinicians in establishing initial CSII settings before individualized dose titration and optimization.

Automated insulin delivery systems in the treatment of diabetes: Benefits, challenges, and practical considerations in pediatric patients

At present, the majority of patients with type 1 diabetes mellitus do not achieve the recommended glycemic control goals to reduce the risk of acute and chronic complications. Hybrid closed-loop systems or automated insulin infusion systems emerged as an opportunity to improve metabolic control, quality of life and reduce the psychosocial impact of type 1 diabetes. This article analyzes the evidence regarding their effectiveness and safety, the challenges they pose and best practices to optimize results when implemented in clinical practice.

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.

The role of automated insulin delivery technology in diabetes

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

Real-World Evidence of Omnipod® 5 Automated Insulin Delivery System Use in 69,902 People with Type 1 Diabetes

Background: The Omnipod® 5 Automated Insulin Delivery System was associated with favorable glycemic outcomes for people with type 1 diabetes (T1D) in two pivotal clinical trials. Real-world evidence is needed to explore effectiveness in nonstudy conditions. Methods: A retrospective analysis of the United States Omnipod 5 System users (aged ≥2 years) with T1D and sufficient data (≥90 days of data; ≥75% of days with ≥220 continuous glucose monitor readings/day) available in Insulet Corporation's device and person-reported datasets as of July 2023 was performed. Target glucose setting usage (i.e., 110-150 mg/dL in 10 mg/dL increments) was summarized and glycemic outcomes were examined. Subgroup analyses of those using the lowest average glucose target (110 mg/dL) and stratification by baseline characteristics (e.g., age, prior therapy, health insurance coverage) were conducted. Results: In total, 69,902 users were included. Multiple and higher glucose targets were more commonly used in younger age groups. Median percentage of time in range (TIR; 70-180 mg/dL) was 68.8%, 61.3%, and 53.6% for users with average glucose targets of 110, 120, and 130-150 mg/dL, respectively, with minimal time <70 mg/dL (all median <1.13%). Among those with an average glucose target of 110 mg/dL (n = 37,640), median TIR was 65.0% in children and adolescents (2-17 years) and 69.9% in adults (≥18 years). Subgroup analyses of users transitioning from Omnipod DASH or multiple daily injections and of Medicaid/Medicare users demonstrated favorable glycemic outcomes among these groups. Conclusion: These glycemic outcomes from a large and diverse sample of nearly 70,000 children and adults demonstrate effective use of the Omnipod 5 System under real-world conditions.

Postprandial Glucose Control With Different Hybrid Closed-Loop Systems According to Type of Meal in Adults With Type 1 Diabetes

BACKGROUND

Hybrid Closed-Loop Systems (HCLs) may not perform optimally on postprandial glucose control. We evaluated how first-generation and advanced HCLs manage meals varying in carbohydrates, fat, and protein.

METHOD

According to a cross-sectional design, seven-day food records and HCLs reports from 120 adults with type 1 diabetes (MiniMed670G: n = 40, MiniMed780G: n = 49, Control-IQ [C-IQ]: n = 31) were analyzed. Breakfasts (n = 570), lunches (n = 658), and dinners (n = 619) were divided according to the median of their carbohydrate (g)/fat (g) plus protein (g) ratio (C/FP). After breakfast (4-hour), lunch (6-hour), and dinner (6-hour), continuous glucose monitoring (CGM) metrics and early and late glucose incremental area under the curves (iAUCs) and delivered insulin doses were evaluated. The association of C/FP and HCLs with postprandial glucose and insulin patterns was analyzed by univariate analysis of variance (ANOVA) with a two-factor design.

RESULTS

Postprandial glucose time-in-range 70 to 180 mg/dL was optimal after breakfast (78.3 ± 26.9%), lunch (72.7 ± 26.1%), and dinner (70.8 ± 27.3%), with no significant differences between HCLs. Independent of C/FP, late glucose-iAUC after lunch was significantly lower in C-IQ users than 670G and 780G (P < .05), with no significant differences at breakfast and dinner. Postprandial insulin pattern (Ins3-6h minus Ins0-3h) differed by type of HCLs at lunch (P = .026) and dinner (P < .001), being the early insulin dose (Ins0-3h) higher than the late dose (Ins3-6h) in 670G and 780G users with an opposite pattern in C-IQ users.

CONCLUSIONS

Independent of different proportions of dietary carbohydrates, fat, and protein, postprandial glucose response was similar in users of different HCLs, although obtained through different automatic insulin delivery patterns.