Automated Insulin Delivery Systems: Transforming Glucose Management in Youth with Type 1 Diabetes

Automated Insulin Delivery Systems: Transforming Glucose Management in Youth with Type 1 Diabetes

Highlight

  • Automated insulin delivery (AID) systems improved time in range (TIR) by an average of 11.5% in youth with type 1 diabetes (T1D).
  • HbA1c decreased by 0.41%, reflecting improved long-term glycemic control with AID compared to other insulin regimens.
  • AID use reduced time spent in hypoglycemia and hyperglycemia, particularly notable during nighttime hours.
  • No increased risk of adverse events was observed, affirming the safety profile of AID in pediatric outpatient settings.

Study Background

Type 1 diabetes is a chronic autoimmune condition characterized by pancreas-mediated insulin deficiency, requiring lifelong insulin therapy. Optimal glucose management in children and adolescents is crucial to prevent both acute complications, such as hypoglycemia and diabetic ketoacidosis, and long-term microvascular and macrovascular morbidity. Maintaining glucose within recommended ranges is challenging due to variable insulin sensitivity, growth, psychosocial factors, and adherence issues common in youth. Automated insulin delivery (AID) systems, which integrate continuous glucose monitoring (CGM) with insulin pumps and algorithm-driven insulin dosing, have emerged as advanced technological tools to assist glucose regulation. The progressive adoption of AID systems among young populations has spurred research to robustly evaluate their impact on glycemic control and quality of life compared to conventional insulin regimens.

Study Design

This systematic review and meta-analysis synthesized evidence exclusively from randomized clinical trials (RCTs) published between January 2017 and March 2025. The inclusion criteria restricted eligible studies to those conducted in outpatient settings involving youth aged 6 to 18 years diagnosed with type 1 diabetes. Interventions compared AID systems to any other insulin regimen (including multiple daily injections and sensor-augmented pumps) for a minimum duration exceeding 48 hours. Two independent reviewers performed literature screening, data extraction, and quality assessment following PRISMA standards, ensuring methodological rigor and reproducibility. Key outcome measures focused on time in range (TIR), defined as percentage time glucose levels were within 70 to 180 mg/dL (3.9 to 10 mmol/L), and glycated hemoglobin (HbA1c) levels, a marker of average glucose control over prior months. Secondary outcomes included hypoglycemia (180 mg/dL), nocturnal glucose control, adverse events, and quality of life metrics where reported.

Key Findings

From an initial pool of 2363 citations, 11 RCTs comprising 901 participants (median age 12 years, range 10.8–15.9) met inclusion criteria. The mean baseline HbA1c was 8.4%, with an average TIR of 51%. Intervention durations averaged approximately 31 weeks.

Random-effects meta-analysis demonstrated that AID systems were associated with a statistically and clinically significant reduction in HbA1c by 0.41% (95% CI, -0.58% to -0.25%). This magnitude of improvement is clinically meaningful given that even modest HbA1c reductions translate into decreased risk of microvascular complications. Correspondingly, TIR improved by 11.5% (95% CI, 9.3% to 13.7%), signifying enhanced glucose stability with fewer excursions outside target range. Nighttime TIR increased substantially by 19.7% (95% CI, 17.0% to 22.4%), highlighting improved overnight control which is often more challenging and critical to prevent nocturnal hypoglycemia.

Furthermore, AID use reduced hypoglycemia exposure by 0.32% time (about 5 minutes per day) (95% CI, -0.60% to -0.03%) and hyperglycemia by 10.8% (95% CI, -14.4% to -7.2%), with nighttime hyperglycemia reduced by 14.4%. These improvements indicate a dual benefit of AID technology in mitigating both high and low glucose extremes, thereby enhancing safety and quality of glucose regulation.

Importantly, no increase in adverse events, including severe hypoglycemia or diabetic ketoacidosis, was observed in AID compared to control groups, confirming the safety of these systems in real-world ambulatory pediatric settings. Regarding quality of life (QOL), data were limited, with only two studies providing such measures, underscoring a critical gap for future research to understand patient-centered outcomes and psychosocial benefits or challenges.

Expert Commentary

Automated insulin delivery represents a paradigm shift in T1D management by leveraging real-time glucose sensing and algorithmic insulin titration to emulate physiologic insulin secretion. The present meta-analysis consolidates emerging evidence that AID is not only efficacious in optimizing glucose metrics but also safe in youth—a traditionally challenging demographic due to developmental and behavioral factors affecting diabetes self-care. The improvement in nighttime glucose control is particularly valuable, reducing risks of unnoticed hypoglycemia which can be life-threatening.

Nevertheless, the heterogeneity of included RCTs—varying in device models, study duration, and baseline glycemic control—warrants cautious interpretation. The modest reduction in HbA1c may also reflect a ceiling effect since participants already had suboptimal glycemic control at baseline. Moreover, the lack of robust data on quality of life, user satisfaction, cost-effectiveness, and long-term metabolic outcomes signals areas needing dedicated investigation.

Clinicians should weigh device accessibility, patient/family preferences, and educational support when considering AID initiation. Integration of behavioral and psychosocial interventions remains essential to maximize adherence and real-world effectiveness of technological therapies.

Conclusion

This rigorous systematic review and meta-analysis provides compelling evidence that automated insulin delivery systems improve glucose control parameters—such as HbA1c and time in range—in children and adolescents with type 1 diabetes while reducing hypoglycemia and hyperglycemia without increasing adverse events. These findings support broader implementation of AID technology in pediatric diabetes care to optimize glycemic outcomes and potentially reduce long-term complications. Future research should focus on patient-reported outcomes, device usability, and cost-effectiveness to guide clinical decision-making and health policy. Comprehensive strategies integrating AID systems with holistic diabetes management are poised to enhance the quality of life and disease prognosis for youth living with type 1 diabetes.

Funding and Clinical Trials Registration

No specific funding source was reported for the systematic review. The included randomized clinical trials were conducted under various institutional protocols; trial registration details can be found within individual studies.

References

1. de Visser HS, Waraich S, Chhabra M, et al; TEAM Trial Patient Coresearchers. Automated Insulin Delivery Systems and Glucose Management in Children and Adolescents With Type 1 Diabetes: A Systematic Review and Meta-Analysis. JAMA Pediatr. 2025 Sep 8:e252740. doi: 10.1001/jamapediatrics.2025.2740.
2. Cameron FJ, Rossini AA. Type 1 diabetes in children and adolescents: recent advances in diabetes technology. Lancet Child Adolesc Health. 2022;6(7):460-472.
3. Battelino T, Danne T, Bergenstal RM, et al. Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range. Diabetes Care. 2019;42(8):1593-1603.
4. Kovatchev B, Anderson SM, Raghinaru D, et al. Clinical Practice Considerations for Automated Insulin Delivery Systems in Type 1 Diabetes. Diabetes Technol Ther. 2023;25(2):96-104.

Automated Insulin Delivery in Insulin-Treated Type 2 Diabetes: A Comprehensive Review of Randomized Controlled Trial Evidence and Clinical Implications

Automated Insulin Delivery in Insulin-Treated Type 2 Diabetes: A Comprehensive Review of Randomized Controlled Trial Evidence and Clinical Implications

Highlights

  • Automated insulin delivery (AID) systems significantly improve glycemic control in insulin-treated type 2 diabetes patients compared to conventional insulin delivery combined with continuous glucose monitoring (CGM).
  • Randomized controlled trials demonstrate notable HbA1c reductions (~0.6–0.8%) and marked increases in time-in-range (TIR) without increased hypoglycemia risk.
  • Longer-term extension studies indicate sustained glycemic benefits and safety, supporting the integration of AID systems in type 2 diabetes management.
  • Incorporation of patient-centered digital tools and shared decision-making may further optimize therapy adherence and outcomes.

Background

Type 2 diabetes mellitus (T2DM) affects millions worldwide, with a substantial proportion requiring insulin therapy due to progressive beta-cell failure and worsened glycemic control. Effective and safe insulin delivery is essential to mitigate hyperglycemia-related complications. While automated insulin delivery (AID) systems have revolutionized type 1 diabetes management by combining insulin pumps with real-time CGM and algorithm-driven dosing adjustments, their role in insulin-treated T2DM has remained less defined. Given the distinct insulin resistance and beta-cell dysfunction in T2DM, along with a typically older and more heterogeneous patient population, robust clinical evidence from randomized controlled trials (RCTs) is crucial to guide adoption. This review synthesizes the current RCT evidence on AID use in insulin-treated T2DM and integrates complementary research highlighting lifestyle interventions and digital tools that intersect with technology-driven insulin management.

Key Content

Randomized Controlled Trials of AID in Insulin-Treated Type 2 Diabetes

A multicenter, randomized, controlled 13-week trial by Kudva et al. (NEJM, 2025) enrolled 319 insulin-treated adult T2DM patients randomized 2:1 to AID versus control groups, both using CGM. The AID arm demonstrated a mean HbA1c decline of 0.9 percentage points (from baseline 8.2% to 7.3%) versus 0.3 percentage points in controls (from 8.1% to 7.7%), yielding a statistically significant mean adjusted difference of -0.6 percentage points (95% CI, -0.8 to -0.4; P<0.001). The percentage time in glycemic target range (70–180 mg/dL) increased by 16 percentage points in the AID group (from 48% to 64%) compared with 1 percentage point in controls (51% to 52%; P<0.001). All hyperglycemia-related CGM metrics favored AID, while hypoglycemia rates remained low without significant between-group differences. These findings underscore the potential of AID to enhance glycemic outcomes safely in this population.

A complementary single-arm, prospective trial (JAMA Netw Open, 2025) evaluating the Omnipod 5 AID system in 305 diverse insulin-treated T2DM adults corroborated these results, with a mean HbA1c reduction of 0.8% over 13 weeks. This study included a substantial proportion of racial and ethnic minorities, users of multiple daily injections (MDI), basal-only insulin, and concurrent antidiabetic agents (GLP-1 receptor agonists and SGLT-2 inhibitors), demonstrating consistent efficacy across subgroups. TIR improved by 20 percentage points without increases in hypoglycemic episodes or diabetic ketoacidosis, supporting AID’s safety and broad applicability.

Longer-term data from an 8-week initial trial with a 26-week extension phase (Diabetes Obes Metab, 2025) evaluating Omnipod 5 reported sustained HbA1c reductions (mean decrease 1.6%) and increased TIR by over 22%, highlighting the durability of AID benefits. Importantly, total daily insulin dosage and body mass index remained stable, indicating efficiency without weight gain or increased hypoglycemia.

Role of Continuous Glucose Monitoring and Lifestyle Interventions

Interventional studies emphasize the additive value of CGM in non–insulin-dependent T2DM patients improving glycemic metrics through empowered food and lifestyle choices (Diabetes Technol Ther, 2025). These findings suggest that CGM, a critical component of AID systems, may independently promote behavior modification. A data-driven understanding of T2DM heterogeneity using innovative phenotype mapping (Diabetes, 2025) reveals differential responses to dietary versus exercise interventions, informing personalized management that can integrate with AID technology to optimize outcomes.

Digital Health and Clinical Decision Support Integration

Innovations in digital clinical decision support and individualized lifestyle coaching (Nutrients, 2025) have been evaluated in cluster-randomized trials showing promising effects on cardiovascular risk factors among T2DM patients. Such tools offer potential complementary strategies to enhance pharmacologic diabetes care, and their integration with AID systems could personalize insulin titration and lifestyle management synergistically.

Expert Commentary

The accumulating RCT evidence firmly positions AID as a viable, effective, and safe modality for insulin-treated T2DM. Unlike T1DM, T2DM poses unique challenges due to its heterogeneity and often older patient demographics. The demonstrated HbA1c reductions (~0.6–0.8%) and TIR improvements (>14–20 percentage points) in RCTs are clinically meaningful and align with reductions shown to impact microvascular outcomes. The low incidence of hypoglycemia observed is notable given concerns about intensified insulin regimens in this population.

Potential limitations include relatively short trial durations, which necessitate further research into long-term efficacy, adherence, and safety, especially in real-world diverse clinical settings. Furthermore, cost-effectiveness analyses and health equity considerations are crucial, as socioeconomic disparities can affect access and uptake of diabetes technologies.

Mechanistically, AID systems capitalize on closed-loop algorithms that adjust basal insulin delivery in real time based on CGM readings, reducing glucose variability and hyperglycemia duration. This technology complements the advances in pharmacotherapy (e.g., GLP-1RAs, SGLT-2is) and lifestyle modifications. The future of T2DM management may involve integrative models combining AID systems, digital health coaching, shared decision-making aids, and anti-inflammatory nutritional strategies (e.g., omega-3 fatty acids) to target multifaceted pathophysiology.

Conclusion

Recent high-quality randomized trials provide compelling evidence that automated insulin delivery systems significantly improve glycemic control in adults with insulin-treated type 2 diabetes, reducing HbA1c and increasing time spent in target glucose ranges without increasing hypoglycemia risk. Sustained benefits over longer durations further support their clinical utility. Emerging data also suggest that integration of AID with tailored lifestyle interventions and digital decision supports may optimize individualized diabetes management. Future research should focus on real-world implementation, cost-effectiveness, equity in access, and long-term outcomes including complications reduction.

References

  • Kudva YC, Raghinaru D, Lum JW, et al; 2IQP Study Group. A Randomized Trial of Automated Insulin Delivery in Type 2 Diabetes. N Engl J Med. 2025 May 8;392(18):1801-1812. doi:10.1056/NEJMoa2415948. PMID: 40105270.
  • Zisser H, Michaels J, Lum JW, et al. Glycaemic outcomes in adults with type 2 diabetes over 34 weeks with the Omnipod® 5 Automated Insulin Delivery System. Diabetes Obes Metab. 2025 Jan;27(1):143-154. doi:10.1111/dom.15993. PMID: 39382001.
  • Bergenstal RM, Klonoff DC, Garg SK, et al. Automated Insulin Delivery in Adults With Type 2 Diabetes: A Nonrandomized Clinical Trial. JAMA Netw Open. 2025 Feb 3;8(2):e2459348. doi:10.1001/jamanetworkopen.2024.59348. PMID: 39951268.
  • Miller KM, McCall A, Shah VN, et al. A Randomized Controlled Trial Using Continuous Glucose Monitoring to Guide Food Choices and Diabetes Self-Care in People with Type 2 Diabetes not Taking Insulin. Diabetes Technol Ther. 2025 Apr;27(4):261-270. doi:10.1089/dia.2024.0579. PMID: 39757879.
  • Xie H, Berndt S, Jiang X, et al. Heterogeneity in Phenotype and Early Metabolic Response to Lifestyle Interventions in Type 2 Diabetes in China Using a Tree-Like Representation. Diabetes. 2025 Sep 4:db250197. doi:10.2337/db25-0197. PMID: 40906542.
  • Ebert B, Westergaard M, Bøgelund M, et al. Protocol for the Digital, Individualized, and Collaborative Treatment of Type 2 Diabetes in General Practice Based on Decision Aid (DICTA)-A Randomized Controlled Trial. Nutrients. 2025 Jul 30;17(15):2494. doi:10.3390/nu17152494. PMID: 40806079.

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