Diabetes technology & therapeutics最新文献

Objective: CamAPS FX is a customizable hybrid closed-loop app with a default target glucose of 105 mg/dL. The personal glucose target is user-adjustable in 1 mg/dL increments between 80 and 198 mg/dL in 30-min segments over 24 h. We assessed the impact of different personal glucose targets on glycemic control during real-world use of CamAPS FX in different age-groups.

Methods: We retrospectively analyzed data from real-world CamAPS FX users from 11 countries across all age-groups (1 to 90 years), who used the system between December 1, 2022 and November 30, 2023, and had a minimum of 8 weeks of closed-loop use. Every sensor glucose reading was matched to the user-specified glucose target.

Results: In total, 8604 users (mean age 32 ± 19 years, median days of data 89 [IQR 59, 119]) were included. Personal glucose targets were most frequently used by very young children (>50%), followed by school-aged children (>40%). All other age-groups used the default target 65%-68% of the time. Overall, personal glucose targets >120 mg/dL were associated with time in target range <70%. Time <70 mg/dL remained <4% across targets, apart from at the lowest (80-89 mg/dL). Older adults achieved time in range ≥70% across all targets. Very young children and young adults were only able to achieve time in range >70% with targets set below the default, which was associated with time <70 mg/dL of >4% in very young children.

Conclusions: Personal glucose targets are frequently used, with clinical impact differing depending on user-age. Adjusting glucose targets may help to achieve recommended glycemic targets and individual glycemic goals.

Barriers to insulin therapy remain a critical challenge for individuals with insulin-treated diabetes, contributing to suboptimal clinical outcomes. The Afrezza® inhaled insulin system, with Technosphere® insulin (TI), is a promising alternative to subcutaneous insulin injections, offering a rapid pharmacokinetic profile and ease of use. Clinical studies demonstrate comparable HbA1c reductions, fewer hypoglycemic events, and improved treatment satisfaction with TI compared with rapid-acting insulin analogs. By addressing barriers such as needle aversion, injection-related pain, and variable insulin absorption, TI has the potential to enhance diabetes treatment and quality of life. Individual choice, supported by shared decision-making, is essential for individualized diabetes care, empowering people with diabetes and improving outcomes. Broader adoption requires increased clinician awareness and insurance coverage.

Continuous glucose monitoring (CGM) technology is becoming increasingly available to people with diabetes using insulin therapy; however, availability for people with type 2 diabetes (T2D) not on insulin remains limited. For people with T2D, there is strong evidence of glycemic benefit with CGM use for those treated with insulin, and CGM is accepted as standard of care. This review explores the impact of CGM use on glycemic and patient-reported outcomes in noninsulin treated populations with T2D, reporting outcomes from 10 identified randomized controlled trials and 15 nonrandomized studies. We report evidence that supports the use of this technology in people with T2D not using insulin.

Background: Continuous glucose monitoring (CGM) systems may assist in glucose management for patients in the cardiac intensive care unit (CICU). We aimed to assess the accuracy, feasibility, and tolerability of Dexcom One+ in comparison with standard blood glucose measurements.

Materials and methods: From September 2024 to May 2025, we included patients with known diabetes or hyperglycemia on admission >140 mg/dL who were hospitalized in the CICU for acute coronary syndrome and/or heart failure. Sensors were inserted into the upper arms, and glucose readings were obtained using a dedicated receiver. Glucose levels were measured with the Cobas Pulse glucometer (Roche Diagnostics) at the routine frequency in the CICU as part of standard care. Accuracy was evaluated by the mean absolute relative difference (MARD). Clinical performance was assessed through Consensus Error Grid analysis. The feasibility outcome included the number of early sensor detachments and sensor failures. Safety outcomes encompassed skin reactions.

Results: We obtained 999 CGM-reference glucose paired samples from 48 patients (39 with previously diagnosed type 2 diabetes aged 73.5 ± 9.6 years. The mean HbA1c was 7.1 ± 1.3%. Overall, 725 paired samples were obtained during oxygen therapy, 362 during vasopressor infusion, and 280 during combined oxygen therapy and vasopressor infusion. CGM use duration was 4.0 ± 3.2 days. There were 3 reference readings below 70 mg/dL, 658 within the 70-180 mg/dL range, and 338 above 180 mg/dL. Overall, MARD was 11.6% (95% CI: 10.9-12.2). 93.5% of readings were in Zone A, 6.1% in Zone B, and 0.4% in Zone C. No readings were found in Zone D + E. We observed one mild hematoma at the insertion site, three sensor failures, and three early detachments.

Conclusions: In patients in CICU, the Dexcom One+ system showed acceptable accuracy and could support glucose monitoring.

Introduction: Automated insulin delivery (AID) systems have improved treatment for people with type 1 diabetes (T1D). Treatment with AID may also improve sleep. However, there is a need to examine this further.

Objective: To systematically collect and synthesize data from available studies that have assessed subjective and objective sleep parameters in people with T1D transitioning to AID systems.

Methods: Searches were performed in multiple databases, and the systematic review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Meta-analyses were performed for randomized clinical trials (RCTs) and prospective studies when feasible; incompatible studies were reviewed narratively. Data from adults, children, and caregivers of children were analyzed separately.

Results: A total of 27 studies met eligibility criteria. Sufficient data for meta-analysis were available only for subjective sleep quality measured by the Pittsburgh Sleep Quality Index (PSQI). Among adult AID users, no significant change in PSQI scores was observed in RCTs, while prospective studies found a minor improvement with AID systems (study n = 7, mean -0.42 points, 95% confidence interval [CI]: -0.81, -0.04, p = 0.03). For caregivers of children with T1D, analyses of both RCTs and prospective studies found improved sleep quality with AID systems (RCTs: n = 2, mean -1.79 points, 95%CI: -2.96, -0.61, p = 0.003 | Prospective studies: n = 4, mean -1.24 points, 95%CI: -2.02, -0.46, p = 0.002). Data on children with T1D were not sufficiently available for meta-analysis. Studies varied considerably in terms of comparator treatments and populations of interest. Most studies were based on secondary or ad hoc analyses.

Conclusion: AID systems may improve perceived sleep quality in caregivers of children with T1D. The effect on adults with T1D is inconclusive, although prospective studies suggest a benefit. Studies reporting objective sleep measures and data regarding children with T1D are limited. Further research in these areas is needed.

The impacts of the rapid transition to telehealth care among Veterans on insulin pumps (VIPs) at the onset of the COVID-19 pandemic have not yet been studied. We performed a longitudinal analysis of VIP outcomes from October 1, 2018 to September 30, 2021 using mixed effects logistic regression to assess changes in VA care delivery and VIP outcomes. Among 11,758 VIPs, adjusted odds of VIPs experiencing diabetes specialty care appointment delays increased by 31% in the first pandemic quarter and 44% in subsequent pandemic quarters. Adjusted odds of VIPs completing HbA1c testing decreased by 75% in the first pandemic quarter and 23% in subsequent pandemic quarters. No increases were found in the percentage of VIPs with poor diabetes control, diabetes-related emergency department or inpatient admissions, or all-cause mortality. VA telehealth expansion during the COVID-19 pandemic was associated with disruptions in care delivery, but the percentage of VIPs with adverse diabetes outcomes did not increase.

We analyzed data from 2253 veterans with type 1 (34%) or type 2 (66%) diabetes who initiated Dexcom continuous glucose monitoring (CGM) between 2015 and 2022, all with ≥10 days of data over a 6-month landmark period. CGM data were merged with electronic health records (EHR). Diabetic retinopathy (DR) was defined from EHR diagnoses. Incident DR was assessed up to 4 years from CGM initiation. Cox models evaluated associations between CGM metrics (mean glucose [MG], time in range [TIR], time above range [TAR], glycemia risk index [GRI], coefficient of variation, and week-to-week average real variability [ARV]) and DR. There were 99 incident DR events. In separate multivariable models, higher MG, TAR, GRI, and ARV, and lower TIR were associated with DR risk; these associations (except ARV) remained significant after landmark HbA1c adjustment and were stronger than for landmark HbA1c itself, suggesting that CGM metrics provide more informative risk stratification than HbA1c alone.

Background: This study evaluated the accuracy and safety of the Anytime 5Pro continuous glucose monitoring (CGM) system, a real-time, factory-calibrated device, over a 16-day period in adults with diabetes.

Methods: Adult participants with type 1 or type 2 diabetes were recruited from three clinical sites in China. Each participant was equipped with four sensors (one on each upper arm and two on the abdomen) for a period of up to 16 days. To evaluate sensor performance, participants were randomly assigned to one of three 7-hour clinic sessions on days 1 or 2, days 7, 8 or 9, and day 16. During the sessions, the real-time glucose values measured by Anytime 5Pro CGM system were compared with venous blood glucose values measured by the EKF blood glucose detector. Primary endpoints for assessment were the mean absolute relative difference (MARD), the proportion of CGM values within ± 20%/±20 mg/dL of reference values, and the percentage of paired points within Zones A and B of the consensus error grids.

Results: In the cohort of 72 participants (287 sensors), the CGM system exhibited an overall MARD of 8.58%, with 96.59% of values within the ± 20%/±20 mg/dL criteria. Comparative analysis revealed similar accuracy between arm (MARD: 8.58%; agreement: 97.03%) and abdomen (MARD: 8.58%; agreement: 96.16%) sensor placements. Throughout the 16-day wear period, the ± 20%/±20 mg/dL agreement rates remained above 95%, with MARDs ranging from 8.30%-8.88%. Consensus error grid analysis showed 99.99% of points in Zones A and B. No serious adverse events were reported.

Conclusions: The system demonstrated accurate glucose measurements across the 16-day wear period, irrespective of sensor placement site or glucose concentration.