Insulin Delivery Research Articles

This study evaluated the glycemic responses to a graded exercise test (GXT) performed by 24 adolescents with type 1 diabetes (T1D) using automated insulin delivery (AID) systems. Each participant partook in a GXT on a bicycle ergometer until volitional exhaustion. Plasma glucose and lactate levels were measured during the GXT, whereas sensor glucose was monitored in the hours thereafter. Plasma glucose levels were stable throughout the GXT (overall change of -0.26 mmol/L [-5 mg/dL], P = 0.593), with no hypoglycemic events. Sensor glucose levels also remained stable and within the recommended glucose target ranges after the GXT for the remaining day and night, with only a few episodes of mild hypoglycemia. This study highlights the glycemic safety of performing GXT for adolescents utilizing AID systems.

OBJECTIVE Automated insulin delivery (AID) systems improve glycemic outcomes in youth with type 1 diabetes and are now the recommended mode of insulin delivery. Previous studies highlighted racial disparities in the use of continuous glucose monitoring and insulin pump therapy. The purpose of this study was to evaluate the use of AID systems and A1C outcomes in youth with type 1 diabetes by race. RESEARCH DESIGN AND METHODS This was a single-center cross-sectional study. We included youth and young adults with type 1 diabetes aged 2–21 years who had at least two clinic visits between December 2022 and December 2023. Demographics, diabetes device use, and A1C data were gathered from chart review, based on the latest office visit records available in the electronic medical record system for 2023. RESULTS Out of 668 youth aged 3–20 years with type 1 diabetes, 435 (65%) were AID users. The prevalence of AID use was 70% (341 of 483) in White youth compared with 47% (60 of 129) among Black youth and 62% (34 of 55) among youth of other racial groups (P <0.001). Black youth using AID achieved significantly lower A1C levels (median 8%, interquartile range [IQR] 7.5–8.8%) compared with Black youth who did not use an AID system (median 9.6%, IQR 8.1–11.6%, (P <0.001). CONCLUSION These findings support the persistence of racial disparities in diabetes technology utilization.

Traditional treatment methods for the management of diabetes, such as oral hypoglycemic med-ications and insulin injections, include drawbacks like systemic adverse effects, inconsistent medication levels, and low compliance. To avoid difficulties, glycemic levels in diabetic patients, a long-term meta-bolic condition, must be precisely and consistently controlled. Smart therapeutic systems allow for precise, on-demand medication release in response to local physiological or environmental cues, such as glucose levels, pH, temperature, or enzyme activity. They provide a possible substitute for conventional diabetic therapies. As these systems only administer medications when and where needed, they reduce side effects while simultaneously increasing therapeutic efficacy and patient compliance. These systems are designed to respond to signals from external sources (such as light, ultrasound, or magnetic fields) or stimuli like temperature, pH, glucose levels, and enzymes. As they use glucose-sensitive substances like phenyl-boronic acid, glucose oxidase, or polymers to precisely release insulin in hyperglycemic circumstances, glucose-responsive delivery methods are essential for diabetes. This review discusses a stimuli-responsive drug delivery system designed for diabetes treatment, with a focus on the developments in biomaterials, nanotechnology, and engineering that improve its effectiveness and biocompatibility. Along with the pos-sibility of combining a stimuli-responsive drug delivery system with wearable technology for continuous glucose monitoring and intelligent insulin delivery, issues, such as manufacturing complexity, stability, and patient safety, are also addressed. The stimuli-responsive drug delivery system has the potential to revolutionize diabetes management by bridging the gap between physiological needs and therapeutic de-livery, providing better glucose control, fewer side effects, and an enhanced standard of living for patients.

(Abstracted from N Engl J Med 2025;392:1801-1812) Automated insulin delivery (AID) systems improve outcomes in type 1 diabetes mellitus (T1DM), but their role in type 2 diabetes mellitus (T2DM) remains uncertain due to limited, small, or uncontrolled studies. Many patients achieve glycated hemoglobin (HbA1c) <7% with glucagon-like peptide 1 agonists or sodium-glucose cotransporter 2 inhibitors, but those who do not may benefit from AID.

While automated insulin delivery (AIDs) systems have significantly improved glycemic control for individuals with type 1 diabetes (T1D), there remains a need for identifying and acting upon complex physiologic and behavioral patterns which consistently lead to hypo- and hyperglycemia. Prior methods have lacked the ability to automatically identify and extract patterns across mixed-type multidimensional data (eg, insulin, glucose, activity) without instilling bias from stipulations on time-lagged coupling, pattern length, or pre-defining patterns. We introduce a new pattern-detection technique-Block-based Recurrence Quantification Analysis (BlockRQA)-and preliminary results using BlockRQA in an AID on both in silico and in an outpatient feasibility study. We first introduce the BlockRQA algorithm, which extends Recurrence Quantification Analysis for use in categorical and continuous time-series data, while maintaining interpretable patterns in the domain of interest, in contrast to prior state-of-the-art approaches which require embeddings. Next, we demonstrate the feasibility of utilizing these patterns and BlockRQA with an existing AID system (BlockRQA+AID) to identify and dose for patterns leading to hyperglycemia in individuals with T1D. We demonstrate how BlockRQA+AID can improve glucose outcomes in patterns leading to hyperglycemia in silico. And we show real-world results using BlockRQA+AID to reduce hyperglycemic events (>250 mg/dL) via an interim safety analysis of a small outpatient pilot study. For all cases, we show BlockRQA efficiently identifies, aggregates, and scores behavioral patterns which can be targeted for clinical intervention. The BlockRQA is a powerful pattern recognition tool that may be used to identify glucose outcome patterns to guide AID dosing.

This Korean Diabetes Association (KDA) consensus statement bridges global evidence with the Korean clinical context, where large randomized and real-world data remain limited. Recommendations required ≥80% agreement by the committee of clinical practice guideline and approval by the board of directors. The statement comprises three domains: diabetes screening aligned with Korean epidemiology; pharmacologic management guided by pathophysiology and comorbidities; and a severity construct of “severe diabetes mellitus” that links complication-based staging with metabolic grading to match therapeutic intensity to disease complexity. Compared with prior KDA guidelines, this statement introduces substantive advances in three areas. First, screening recommendations are streamlined to emphasize risk-aligned, practical implementation rather than prescriptive test sequences. Second, pharmacologic management applies an individualized framework for drug selection that jointly considers pathophysiology and comorbidities. It operationalizes individualized selection by dominant pathophysiology (insulin resistance vs. insulin insufficiency) and coexisting conditions, and formalizes treatment dynamics—early combination, timely initiation of injectables, avoidance of overbasalization, and structured deintensification. It also prioritizes agents with proven cardiovascular and renal protection and elevates management of obesity and metabolic dysfunction-associated steatotic liver disease as central goals; clinically, insulin should be initiated promptly in hypercatabolic states or suspected islet failure, and technology-enabled care—including continuous glucose monitoring and automated insulin delivery—are integral across all stages. Third, the newly introduced severity construct underpins treatment-intensity decisions across domains without reiterating prescriptive algorithms. Collectively, these recommendations provide a coherent, context-appropriate framework for diabetes screening and management in Korea and identify priorities for future evidence generation.

Body weight trends in individuals with type 1 diabetes using automated insulin delivery vs. traditional insulin pumps.

This study aimed to examine glucose metrics and insulin delivery patterns in children, adolescents, and adults with type1 (T1D) or 2 (T2D) diabetes in France using the tubeless Omnipod DASH® pump with and a continuous glucose monitoring (CGM) sensor connected to myDiabby Healthcare® Data Management Platform (DMP). Time-stamped CGM and insulin data were extracted from the DMP on December 6, 2023 for 17,344 users whose first data point from the tubeless pump occurred after January 1, 2020. The study population included users with sufficient pump and CGM data (≥ 90days of use) and ≥ 15.5% of CGM use days reaching > 70% coverage. Analyses were performed by type of diabetes and age group. Among 14,757 users included in this analysis, most reported having T1D (93.7%), the median age was 33years (Q1-Q3, 16-51), and the median duration of pump use was 545days for people with T1D and 505days for people with T2D (1.49 and 1.38years, respectively). People with T1D spent a median of 52.5% (Q1-Q3, 43.4-62.5) of time in range (70-180mg/dL, TIR) and a TIR ≥ 70% was attained by 12.6% of users. The median time below range (TBR, < 70mg/dL) was 3.7% (Q1-Q3, 2.1-6.1). For users with T2D, median TIR was 66.9% (Q1-Q3, 54.0-77.8), with 42.8% of users achieving a TIR ≥ 70%. Over 90% of all users consumed less than 60UI/day. This robust and scalable analysis of a database of substantial quantity, density, and quality found that tubeless pump users achieved moderate glycemic outcomes overall with favorablesafety outcomes in particular, and used the pump consistently. Such databases could be useful for research and patient care, and further work will show how best to use them.

The rising global prevalence of Type 1 and Type 2 diabetes presents significant challenges to healthcare systems worldwide, necessitating innovative solutions for more effective management. This paper explores the development of smart insulin delivery systems, which utilize advanced technologies such as continuous glucose monitoring (CGM) and automated insulin delivery to optimize diabetes management. These systems hold promise for improving glycemic control, reducing the risk of complications, and enhancing patient outcomes. This study reviews current insulin delivery methods, investigates emerging smart insulin technologies, and analyzes the challenges and barriers to their widespread adoption. The paper highlights the potential of closed-loop insulin delivery systems, biosensors, and artificial pancreas systems in transforming diabetes care. Despite the substantial promise, several challenges remain, including technical limitations, cost implications, patient adherence, regulatory hurdles, and issues related to access in underserved populations. Additionally, the integration of these systems into existing healthcare infrastructure, particularly in low-resource settings, is a significant concern. The findings suggest that smart insulin delivery systems have the potential to revolutionize diabetes care, providing personalized, automated insulin delivery that could lead to better disease management and reduced healthcare costs. Future research should focus on improving sensor accuracy, enhancing system integration with mobile health applications, and exploring scalability across diverse populations. This paper underscores the need for policy support, funding, and strategic innovation to ensure that these technologies are accessible and effective in addressing the global diabetes epidemic.

Intranasal delivery of insulin: An update on status quo and challenges for diabetes treatment.

Automated insulin delivery (AID) systems continuously deliver insulin subcutaneously, reducing the burden of managing type 1 diabetes mellitus (T1D). However, there are limited data comparing different insulin delivery modalities, particularly regarding their impact on health-related quality of life (HRQoL). This study aimed to quantify the disutility associated with conventional insulin delivery modalities and utility gains associated with wearable, on-body, AID systems. Health state vignettes representing different insulin delivery modalities were developed based on interviews with people with T1D alongside published literature and validated by experts. Utility values were elicited via the time trade-off (TTO) method from the general population in the United Kingdom (UK) (n = 110). The lowest mean utility values were observed for tubed non-AID systems (0.727), while the highest mean utility value was observed for tubeless systems with AID (0.909). The use of tubeless systems rather than tubed systems was associated with a significant increase in utility between + 0.082 and + 0.086 (p < 0.005), and the use of AID was associated with a significant increase in utility of between +0.096 and +0.100 versus the corresponding alternatives (p < 0.0005). The use of a tubeless and AID system was associated with a significantly increased utility versus all other health states (p < 0.0001), indicating significantly higher HRQoL. This study elicited utility values for health states representing insulin delivery modalities in T1D. Results suggested that tubeless and AID systems are associated with higher health state utility in T1D, indicating that people with T1D using such systems may experience improved HRQoL.

Semaglutide use with automated insulin delivery in adults with type 1 diabetes: qualitative analyses and patient-reported outcomes from a randomized controlled trial.

Use of technology is central to the management of type 1 diabetes (T1D), while patient reported outcomes measures (PROMs) can support in the management of these individuals. To assess the effect of diabetes technologies on patient-reported outcome measures (PROMs) in type 1 diabetes (T1D). Cochrane Library CENTRAL, Embase, MEDLINE, Scopus, and Web of Science were searched for relevant articles from 2013 to August 2025. We included longitudinal diabetes technology studies assessing validated PROMs in nonpregnant adults with T1D. Study characteristics and PROM data were extracted, and standardized mean differences (SMDs) for PROs were pooled using a random-effects meta-analysis. We identified 4,885 articles, comprising 81 independent studies (n = 19,148 participants) and 70 different PROMs. The Hypoglycemia Fear Survey (HFS) was most commonly used (k = 39 studies), followed by the Diabetes Treatment Satisfaction Questionnaire (status [DTSQs] or change version [DTSQc]; k = 38), Diabetes Distress Scale (DDS; k = 25), and Problem Areas in Diabetes (PAID) scale (k = 24). Technology use was associated with lower HFS total score compared with control (SMD -0.177; 95% CI -0.319, -0.036; P = 0.014; I2 = 0.0%), with the largest effect observed in automated insulin device users. A moderate positive effect of diabetes technologies was observed on DTSQs and DTSQc scores (SMD 0.429; 95% CI 0.206, 0.653; P < 0.001; I2 = 72.3%), with a small to moderate reduction in DDS and PAID scores (SMD -0.265; 95% CI -0.363, -0.166; P < 0.001; I2 = 0.0%). Differences in type of technology, varied use and incomplete reporting of PROMs, and different duration of studies. Diabetes technologies offer psychological benefits in adults with T1D. The large number of reported PROMs suggests a need to standardize their use.

Artificial Intelligence (AI) is reshaping healthcare by enabling algorithms to learn from complex medical data and assist in clinical decision-making. In endocrinology and diabetes, AI holds promise for predicting disease risk, optimizing glucose control, automating insulin delivery, and screening for complications using patients’ clinical data and physiological signals. However, the use of AI also raises concerns about risks such as demographic bias, domain variability, explainability and fairness—particularly when models trained on Western populations are applied to diverse LMIC settings. This talk will outline key applications and future directions of AI in diabetes and endocrine care, highlighting the integration of multimodal data, emerging foundation models, and ethical challenges. Drawing from the mHealth Lab’s work at BUET, examples will demonstrate how locally developed AI tools—such as smartphone-based clinical decision systems and low-cost physiological monitoring platforms—can bridge the gap between cutting-edge algorithms and equitable, real-world healthcare delivery in Bangladesh. [J Assoc Clin Endocrinol Diabetol Bangladesh, 2025;4(Suppl 1): S3]

The current study sought to evaluate a family-based programme designed for adolescents with type 1 diabetes and their parents and identify which families benefited most. A randomized controlled trial with a waitlist control with 157 parent-adolescent dyads collected data via online surveys and glycaemic measures at baseline and 3- and 6-month post-baseline. Regression models tested main and moderated effects on primary outcomes of percent time-in-range (% TIR) and diabetes health-related quality of life (HRQOL) and secondary outcomes of HbA1c and adolescent and parent diabetes distress. Beneficial intervention effects included improved parent report of supportive parenting (β = 0.151, p = 0.007, d = 0.31) and both parent (β = -0.177, p = 0.002, d = -0.36) and adolescent (β = -0.150, p = 0.024, d = -0.30) report of unsupportive parenting immediately after the intervention for the full sample. Improvements in HRQOL (β = 0.308, p = 0.002, d = 0.81) at 3-month post-baseline among racial and ethnic minoritized adolescents, HRQOL at 3-month (β = 0.261, p = 0.003, d = 0.94) and 6-month (β = 0.220, p = 0.005, d = 0.58) post-baseline among adolescents not using automated insulin delivery systems. The intervention also reduced parent diabetes distress among parents with high baseline distress (β = -0.200, p = 0.006, d = -0.54). While family dynamics improved for all, adolescents' quality of life improved among youth with fewer resources and from marginalized racial and ethnic backgrounds, whereas parents with higher distress benefited most. For adolescents, socioeconomic context may drive intervention response.

IntroductionCystic fibrosis-related diabetes (CFRD) is one of the most clinically impactful comorbidities associated with cystic fibrosis (CF). Current recommended management with insulin therapy is challenging due to variable daily insulin requirements and adds to the significant burden of self-management. This study aims to determine if hybrid closed-loop insulin delivery can improve glucose outcomes compared with standard insulin therapy with continuous glucose monitoring (CGM) in young people (≥16 years) and adults with CFRD.Methods and analysisThis open-label, multicentre, randomised, two-arm, single-period parallel design study aims to randomise 114 young people (≥16 years) and adults with CFRD. Following a 2–3 weeks’ run-in period, during which time participants use a masked CGM, participants with time in target glucose range (3.9–10.0 mmol/L) <80% will be randomised to 26 weeks with hybrid closed-loop insulin delivery or standard insulin therapy with CGM. The primary outcome is the between-group difference in time in target glucose range (3.9–10.0 mmol/L) based on CGM levels during the 26-week study phase. Analyses will be conducted on an intention-to-treat basis. Key secondary outcomes are time above target glucose range (>10.0 mmol/L), mean glucose and HbA1c. Other secondary efficacy outcomes include glucose and insulin metrics, change in forced expiratory volume in 1 s and body mass index. Safety, utility, participant experiences and participant-reported outcome measures will also be evaluated. The trial is funded by the National Institute for Health and Care Research.Ethics and disseminationEthics approval has been obtained from East of England–Cambridge South Research Ethics Committee. Results will be disseminated by peer-reviewed publications and conference presentations, and findings will be shared with people living with CF, healthcare providers and relevant stakeholders.Trial registration numberNCT05562492.

ABSTRACTBackgroundThis systematic review and meta‐analysis evaluated the efficacy and safety of the iLet bionic pancreas (iLet BP), a novel automated insulin delivery (AID) system, in managing type 1 diabetes. Unlike conventional AID systems, which require user input for insulin dosing, the iLet BP autonomously determines insulin delivery based solely on body weight. The study synthesized data from five randomized controlled trials (RCTs), comprising a total of 1130 patients, comparing iLet BP with standard care (SC).Outcomes AssessedPrimary outcomes included changes in HbA1c, mean glucose levels, and time in target glucose range (70–180 mg/dL), measured via continuous glucose monitoring (CGM). Secondary outcomes assessed adverse events and hypoglycaemia.Key FindingsResults demonstrated that the iLet BP significantly improved glycaemic control. The pooled analysis showed a standardised mean difference (SMD) in HbA1c of −0.50 [−0.63, −0.38] and in mean glucose levels of −0.36 [−0.50, −0.21] favouring iLet BP. Time in target glucose range was significantly higher with iLet BP (SMD: 0.58 [0.43, 0.73]). However, the odds of adverse events were notably higher in the iLet BP group (OR: 15.48 [8.07, 29.70]), while the risk of hypoglycaemia (OR: 2.22 [0.83, 5.94]) was not statistically significant.ConclusionIn conclusion, the iLet BP shows strong potential in improving glycaemic outcomes in patients with type 1 diabetes. However, concerns remain regarding its safety profile, particularly related to adverse events. Further large‐scale, high‐quality studies are needed to confirm its effectiveness and ensure broader clinical applicability.

The use of automated insulin delivery systems (AID) is standard of care for people with type 1 diabetes. However, the limited capacity of insulin pump cartridges, which can hold 1.6-3.0mL or the equivalent of 160–300 units of U100 insulin, can be a barrier to AID use for individuals with high total daily insulin (TDI) requirements. With the rising prevalence of obesity, expansion of AID use to type 2 diabetes, and trends towards smaller cartridge volumes to decrease the size of devices, practical solutions to reduce barriers to AID use for those with high TDI requirements are needed. U200 concentrated rapid-acting insulin (U200) has a similar pharmacokinetic and pharmacodynamic profile to U100 insulin, provides the same dose of U100 insulin in half of the volume, and has been used off-label to facilitate AID use for those with high TDI needs. In this perspective piece we provide practical considerations for clinical implementation of U200 use in AID systems, including identification of candidates, unique considerations in filling pumps with U200 insulin, guidance on programming appropriate AID settings for the different algorithms, concepts to address in patient education, and recommendations for standardized documentation in the electronic health record.

Balancing continuity of care and liability concerns: One community pharmacy experience of a person with type 1 diabetes using a do-it-yourself automated insulin delivery system.

Factors Considered Important by Diabetes Providers Before Recommending Automated Insulin Delivery Systems: Observations From the T1D Exchange Quality Improvement Collaborative