Automated insulin delivery (AID) systems improve glycemic outcomes, but the roles of user interaction and insulin pump settings in these findings remain underexplored. To investigate how AID initiation influenced glycemic outcomes over a year and assess the impact of user behavior and insulin pump settings. This was a retrospective observational study on real-world data from 156 pediatric individuals initiating AID (Tandem Control-IQ or MiniMed 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. 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%, < 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%, < 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, = 0.034), reduced carbohydrate intakes relative to body weight (baseline: 5.0 g/[kg·d] vs. month 12: 4.6 g/[kg·d], = 0.004), and longer active continuous glucose monitoring (CGM) wear time (baseline: 87.2% vs. month 12: 94.1%, = 0.011). A positive association between TIR and daily registered meals ( < 0.001) and daily registered carbohydrates ( = 0.003) was found in the multivariate analysis while adjusting for insulin pump settings and total daily insulin dose. 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.

This study evaluated the effects of ultra-rapid lispro (URLi) insulin versus insulin lispro on postprandial glucose excursions in 176 individuals with type 1 diabetes using Control-IQ+ technology. Postprandial glycemia differed the most between URLi and lispro at 60 min (mean glucose 166 ± 69 mg/dL vs. 178 ± 70 mg/dL; adjusted mean difference [AMD] = -11 mg/dL; < 0.001). The URLi had slightly lower mean glucose excursion compared with lispro (AMD = -4 mg/dL; = 0.001), but the differences between treatments were larger following breakfast (AMD = -9 mg/dL) compared with lunch (AMD = -2 mg/dL) and dinner (AMD = -2 mg/dL). Participants with insulin-to-carbohydrate ratio (ICR) <5 g/U had a larger treatment group difference favoring URLi on mean glucose excursion (AMD = -11 mg/dL) compared with those with ICR 5-15 g/U (AMD = -2 mg/dL) and ICR >15 g/U (AMD = 1 mg/dL). In conclusion, compared with insulin lispro, the use of URLi with Control-IQ+ technology modestly improved postprandial glucose excursions with the greatest amount of improvement for breakfast and in those with insulin resistance.

Managing postprandial blood glucose levels in children and adolescents with type 1 diabetes remains challenging, particularly with high-carbohydrate meals. This study analyzed postprandial glycemic responses to Margherita pizza and a ham sandwich with similar macronutrient content in children using two different automated insulin delivery (AID) systems: MiniMed™ 780G and Tandem t:slim X2™ with Control-IQ. Thirty-four participants consumed both meals on separate occasions while maintaining standard insulin boluses. Results showed no significant differences in glycemic control between meals, suggesting that Margherita pizza can be effectively managed with standard bolus strategies. However, individuals using MiniMed 780G demonstrated a higher time in target range (70-140 mg/dL) in the 10 h following pizza consumption compared with Control-IQ users, possibly due to MiniMed 780G's multiple automatic correction boluses. No other significant differences in glucometrics or insulin delivery were observed. These findings highlight the need for personalized meal management strategies based on specific AID system algorithms. ClinicalTrials.gov, NCT05729776.

To diagnose gestational diabetes mellitus (GDM), clinicians typically rely on the oral glucose tolerance test (OGTT). Continuous glucose monitoring (CGM) is a tool that could possibly be used to complement or replace the OGTT. Our aim is to describe CGM-derived glycemic patterns observed concurrently during the administration of a diagnostic OGTT in pregnancy. In total, 119 pregnant females underwent OGTT testing while wearing a blinded CGM sensor. Blood glucose (BG) measurements collected during the OGTT were compared with CGM-measured glucose values obtained using a Dexcom G6 Pro sensor to determine the differences between CGM-measured and BG levels during the OGTT, measure glycemic excursion during the OGTT, and determine differences in GDM diagnosis using standard BG draws during OGTT versus CGM-measured glucose levels. CGM-measured glucose levels were on average higher than paired BG levels during the OGTT at each timed measurement (fasting, 1-, 2- and 3-h); fasting CGM-measured glucose levels in particular were higher than fasting BG levels by 6 ± 13 mg/dL. The median CGM minus BG-measured glycemic excursion during the OGTT was 12 and 4 mg/dL for the 75 g and 100 g OGTT, respectively. Of 28 participants diagnosed with GDM based on OGTT BG levels, 24 (86%) participants would have been diagnosed as GDM using CGM with BG-based thresholds; of 91 participants not diagnosed with GDM, 54 (59%) would also have not been diagnosed with GDM using CGM. CGM glucose measurements using Dexcom G6 Pro tended to be slightly higher than BG values during an OGTT, leading to more participants who would have been diagnosed with GDM if the BG-based OGTT thresholds were applied to these CGM-measured glucose values. When CGM is used for GDM diagnosis, diagnostic glucose criteria may need to be specific for the type of sensor used accounting for any bias in glucose measurement.

Monogenic diabetes is a group of diseases that encompasses a growing number of genetic abnormalities affecting pancreatic function/development leading to glycemic dysregulation. This includes conditions that have historically been referred to as maturity onset diabetes of the young or MODY in addition to neonatal diabetes mellitus. While recognition of a genetic or inherited form of diabetes has been known for decades, advances in molecular genetic testing have resulted in identification of specific forms of monogenic diabetes. Despite this, these genetic forms of diabetes remain widely underreported. It is important to be able to identify genetic forms of diabetes as treatment, monitoring for microvascular and macrovascular complications, and overall management varies for the different forms of monogenic diabetes. Furthermore, the identification of a specific monogenic form of diabetes can significantly impact the person's quality of life and other family members, as well as health care costs. This article highlights the identification, treatment, and management for various forms of monogenic diabetes and addresses some unmet needs in caring for people with monogenic forms of diabetes.

Devices for continuous glucose monitoring (CGM) have been developed to optimize blood glucose control and liberate people with diabetes from finger-prick glucose measurements. Since 2016, the devices have been reimbursed in Germany for people with diabetes receiving insulin therapy, resulting in their increased use among people with type 1 diabetes (T1D) and type 2 diabetes (T2D). We investigated the prevalence of CGM use and its associated factors among German adults with diabetes in 2017 and 2021/2022. Participants aged 18 years or older with diagnosed diabetes were identified from two nationwide population-based telephone surveys in 2017 ( = 1396) and 2021/2022 ( = 1456). Prevalence and dynamics of CGM use were examined overall and stratified by sociodemographic and diabetes-related characteristics. Factors associated with CGM use were obtained from logistic regression models. The overall prevalence of CGM use was 8.2% in 2017 and 16.6% in 2021/2022. An increase in CGM use was observed across all the subgroups except for those without antidiabetic medications. CGM use increased from 31.1% to 75.4% in adults with T1D, from 6.3% to 13.6% in adults with T2D, and from 14.6% to 36.7% in all insulin users. In both surveys, younger age, insulin use, T1D, and reporting hypoglycemia were associated with CGM use. In addition, in 2017, higher education level and absence of obesity were associated with CGM use, whereas in 2021/2022, participation in the diabetes self-management education program and higher self-assessed quality of diabetes care were associated with CGM use. Among adults with diabetes in Germany, CGM use increased about twofold within 5 years, irrespective of sociodemographic factors. Educational inequality in CGM use diminished over time. The higher self-rated quality of diabetes care associated with the recent use of CGM provides further evidence to support its use among all adults with diabetes in Germany.

Continuous glucose monitoring (CGM) enhances diabetes self-management in insulin-treated individuals. However, the feasibility, acceptability, and benefits/burdens in youth-onset type 2 diabetes (Y-T2D) who are on infrequent self-monitoring of blood glucose (SMBG) regimens remain unclear. In Y-T2D prescribed SMBG less than or equal to twice daily, we conducted a 12-week randomized 2:1 parallel pilot trial of CGM versus fingerstick monitoring (Control). Control participants had an optional 4-week extension period to use CGM (Control-CGM). Feasibility was defined as recruitment, study participation, and retention >60% of individuals. Acceptability was defined as an individual CGM wear time of ≥60% at the end of the study. Diabetes distress and the benefits/burdens of CGM scores, hemoglobin A1c (HbA1c), and CGM-derived glycemic variables were compared at baseline and at the end of the intervention. The recruitment rate was 54% (52 screened eligible, 18 CGM, 10 Control; 82% female, 68% Black, 14.9 ± 3.8 years, body mass index: 36.2 ± 7.7 kg/m, HbA1c: 7.4 ± 2.4% (mean ± standard deviation [SD]), and 8 entered the optional Control-CGM group. The most commonly cited reason for declining study participation was reluctance to wear the device (50%). The participation rate was 91% and 75%, and retention was 100% and 75% for CGM and Control-CGM, respectively. A majority of Y-T2D had ≥60% wear time at the end of the study (CGM: 56% and Control-CGM: 83%). Wear time declined during the study (1st month: 71 ± 31% vs. 2nd month: 55 ± 32% vs. 3rd month: 38 ± 34%, = 0.003). There were no significant changes in glycemia, CGM burden/benefits, or diabetes distress scores ( > 0.05). Minor sensor adhesion adverse events were common (75%) causes of reduced wear time. CGM was a feasible and acceptable adjunct to diabetes self-care among >50% of Y-T2D prescribed infrequent SMBG monitoring. Unwillingness to wear a device and social stigma impeded device use. Additional research is needed to mitigate the high rates of skin adhesion-related adverse events in this population.

Total pancreatectomy with islet autotransplantation (TPIAT) requires strict glycemic management for islet survival using insulin pumps and continuous glucose monitors (CGMs). Hydroxyurea prevents reactive thrombocytosis but interferes with the accuracy of the Dexcom CGM. Hydroxyurea is reported to not interfere with the Libre CGM but has not been studied after TPIAT. Seven patients wore both Dexcom and Libre starting approximately a week after TPIAT. Dexcom and Libre values were obtained with point-of-care testing blood glucose (POCT BG) at 560 unique time points. Descriptive statistics included median, interquartile range (IQR), absolute difference between CGM and POCT, and mean absolute relative difference (MARD) for each Dexcom and Libre. Wilcoxon-Mann-Whitney tests were performed to compare parameters between Dexcom and Libre, with two-sided significance of < 0.05. Clarke error grids and boxplots were constructed. In the 9 h after hydroxyurea, median POCT BG was 110 mg/dL (IQR 88-143), median Dexcom BG was 172 mg/dL (135-219), and median Libre BG was 106 mg/dL (76-138). MARD for Dexcom was 59.5% and for Libre was 14.8% ( < 0.001). Median absolute difference between Dexcom and POCT BG (56 mg/dL [32-88]) was greater than that for Libre (12 mg/dL [6-23]; < 0.001). In Clarke error grids, 98.3% of values fell within clinically acceptable Zones A/B for Libre; 77.9% of values fell within these zones for Dexcom. At all other times, median POCT BG was 110 mg/dL (86-133), median Dexcom BG was 124 mg/dL (97-154), and median Libre BG was 104 mg/dL (76-128). MARD for Dexcom was 19.8% and for Libre was 14.7% ( < 0.001). Median absolute difference between Dexcom and POCT BG (18 mg/dL [9-30]) was clinically similar to that for Libre (13 mg/dL [6-23], < 0.001). Hydroxyurea does not seem to interfere with the accuracy of Libre in contrast to Dexcom. Use of Libre after TPIAT could facilitate improved glycemic management.

Evaluate the adequacy of the coefficient of variation (CV) and standard deviation (SD) as metrics of glucose variability (GV) across mean glucose (MG) levels in individuals with type 1 diabetes. Data from the GOLD and SILVER trials were analyzed. Glucose metrics were derived from continuous glucose monitoring (CGM). Generalized estimating equations were used to assess the relationship between SD and MG, considering intraindividual correlations. Nonlinear associations were evaluated using restricted cubic splines, and glucose values outside the CGM detection range (<2.22 mmol/L and >22.2 mmol/L) were handled using a censored Gamma model. In total, 158 individuals with an MG of 10.6 (SD 1.7) mmol/L were included. The SD of glucose values exhibited a nonlinear relationship with the MG during CGM and self-monitoring of blood glucose (SMBG) (both < 0.001 vs. linear model). The lack of fit of the constant CV model was most distinct at high glucose levels >12 mmol/L. During SMBG, a 25% reduction in MG from 12 to 9 mmol/L was associated with a 16% (95% confidence interval [CI] 10%-21%) reduction in the SD of glucose values. Similar associations were observed during CGM. This deviation was attributed to the censoring of glucose values outside the detection range. After adjusting for censoring, the lack of fit was resolved. When transitioning from SMBG to CGM, the ordinary CV and SD underestimated the treatment effect on GV by 30% and 27%, respectively, compared to estimates adjusted for censoring. Similarly, ordinary CV underestimated the treatment effect by 11% compared with CV adjusted for the nonlinear SD-MG relationship in the GOLD study. The SD of glucose values does not increase linearly with the MG during glucose-lowering therapy, suggesting that CV is not an optimal measure of GV. After adjusting for censored glucose values, CV remains reliable. Alternatively, nonlinear SD adjustments relative to MG effectively evaluate glucose-lowering therapies' impact on GV.

Inhaled technosphere insulin (TI, Afrezza®) has a more rapid onset of action than rapid-acting insulin analogs (RAA). Forty-nine adults with type 1 diabetes (T1D) initiated a regimen of TI plus insulin degludec for 13 weeks after completing 17 weeks in the usual-care control group of a randomized trial. The initial TI dose, based on bioequivalence, was approximately two times the RAA dose being used. The primary outcome was noninferiority for daytime time-in-range (TIR) 70-180 mg/dL at 13 weeks. During the preceding 17-week period (baseline), 41% of the 49 participants were using automated insulin delivery (AID), 6% a predictive-low-glucose-suspend pump, 4% a sensor-augmented pump (SAP), and 49% multiple daily injections (MDI) plus continuous glucose monitoring. Daytime TIR increased from 50% ± 17% at baseline to 55% ± 20% after 13 weeks (mean change 5.1%, 95% confidence interval [CI]: 0.3% to 9.8%, noninferiority < 0.001, superiority = 0.04), with an increase of 8.6% compared with baseline MDI/SAP and no change compared with baseline AID. Mean HbA1c change from baseline was -0.23% (95% CI: -0.42% to -0.04%, noninferiority < 0.001, superiority = 0.02), with mean change of -0.36% compared with MDI/SAP and 0.0% compared with AID. Participants meeting the HbA1c target of <7.0% increased from 14% to 31% ( = 0.02). Among baseline AID users, overnight TIR decreased by 15.6% when switched to TI-degludec, whereas among baseline MDI/SAP users, overnight TIR increased by 2.0%. Mean time <54 mg/dL was 0.5% ± 0.7% at baseline and 0.7% ± 0.8% after 13 weeks (mean change 0.2%, 95% CI: -0.1% to 0.5%). After 13 weeks, 40% of participants indicated a desire to continue using TI. In adults with T1D, glycemic outcomes were comparable or slightly better with TI-degludec after switching from AID or MDI. TI should be considered as an option for individuals who want an alternative to using an insulin pump or MDI for insulin delivery.

Continuous glucose monitoring (CGM) is unaffordable in sub-Saharan Africa, and providers rely heavily on hemoglobin A1c (A1c) to guide insulin adjustment. The relationship between A1c and mean glucose (MG) varies between individuals and populations. We assessed this relationship in Ugandan youth of age 4-26 years with type 1 diabetes, and evaluated whether calculation of the personalized A1c (pA1c), which only requires a brief initial sensor wear, is clinically useful. CGM data were averaged across three blinded sensor wears (31-41 days). We calculated individual apparent glycation ratios using A1c after the second sensor, and applied these to A1cs collected after the third sensor to determine pA1c. Participants were evaluated for clinical factors that influence red blood cell (RBC) lifespan (malaria, G6PD deficiency, sickle-cell trait, hemolysis, iron deficiency). Patients across the A1c spectrum experienced substantial time in both hyper- and hypoglycemia; average coefficient of variation was 44%. MG was >250 mg/dL (13.9 mmol/L) in 50% of participants, and 55% of participants spent ≥4% time with glucose <70 mg/dL (3.9 mmol/L). There was considerable variability in the A1c-MG relationship. The pA1c more accurately represented MG by significantly reducing variation in this relationship ( = 0.84 vs. 0.40; = 0.92 vs. 0.63), but MG is not useful in individuals with the wide glucose fluctuations seen in this population. Clinical factors did not impact the A1c-MG relationship. Neither the measured A1c nor the calculated pA1c provided reliable guidance for insulin adjustment in this population. No matter how accurately MG is measured or estimated, it is just an average, with limited clinical application in individuals with wide glycemic variation. These measures cannot replace the information available from CGM about glycemic excursion, daily glucose patterns, or percent time in various glucose ranges. Our data suggest that it is essential to find a way to make CGM at least periodically affordable in low-resource settings.

Pancreas transplantation provides long-term near-normal glycemic control for recipients with type 1 diabetes, but it is unknown how this control compares with an automated insulin delivery (AID) system. In this prospective study, we compared parameters from 31 consecutive pancreas-kidney transplantation recipients versus from 377 people using an AID-either MiniMed 780G ( = 200) or Tandem t:slim X2 Control-IQ ( = 177). Compared with the MiniMed and Tandem AID groups, transplant recipients at 1 month (mean ± standard deviation [SD]: 36 ± 12 days) after pancreas transplantation exhibited significantly lower glycated hemoglobin (38 mmol/mol [36, 40] vs. 55 [53, 56.5] and 56 [54.7, 57.2], respectively), lower mean glycemia (6.4 mmol/L [6, 6.8] vs. 8.5 [8.3, 8.7] and 8.2 [8.0, 8.4], respectively), and spent more time in range (90% [86, 93] vs. 72% [70, 74] and 75% [73, 77], respectively). Time in hypoglycemia did not differ significantly between the groups. Overall, compared with AID treatment, pancreas transplantation led to significantly better diabetes control parameters, with the exception of time below range. Clinical trials registration number is Eudra CT No. 2019-002240-24.

Glucose regulation in young children is complicated by higher glycemic variability, unpredictable behaviors, and low insulin needs. While the benefits of automated insulin delivery (AID) for this population are established, how to initiate and adjust pump settings still represents a challenging task for health care providers. In this study, we investigate the safety and efficacy of using algorithm-driven initiation and adjustments of AID parameters in children aged 2-6 years. Participants used AID at home for 8 weeks. Initial settings and periodic adjustments of therapy profiles (basal rates, insulin-to-carbohydrate ratios, insulin-correction factors, and sleep schedules) were provided through a cloud-based investigational software. Investigators reviewed therapy recommendations and could adjust if necessary. Primary safety endpoints included the percentage of time <54 mg/dL and >250 mg/dL, tested for noninferiority with respect to baseline. Primary efficacy endpoints (tested in a hierarchical manner) were the percentage of time in 70-180 mg/dL, mean glucose, the percentage of time >250 mg/dL, <70 mg/dL, and <54 mg/dL. Thirty-two participants (age range: 2.0-5.9 years) were recruited for the study; 29 had sufficient data for the analysis. Investigators overrode 15% of software recommendations. The percentage of time <54 mg/dL and >250 mg/dL was noninferior in the 8-week follow-up with respect to baseline ( < 0.001). Statistically significant improvements were observed in the percentage of time in 70-180 mg/dL ( = 0.005), >250 mg/dL ( = 0.003), and mean glucose ( = 0.02). No difference was observed in the percentage of time <70 mg/dL ( = 0.34). Furthermore, no difference was observed with respect to a similar study cohort (same age range, = 86) with expert pediatric endocrinologists modifying pump settings. Findings from this pilot study suggest that the use of AID with algorithm-driven initiation and adjustment of pump parameters is safe and effective in young children with type 1 diabetes. Further study of the algorithm in a larger cohort is indicated. Clinical Trials Registration number: NCT06017089.

Automated insulin delivery (AID) systems improve glycemic control in people living with type 1 diabetes (PwT1D). AID is cost-effective versus other management approaches in a range of country settings and populations. This cost-utility analysis adds an evaluation of the MiniMed 780G system versus standard of care (SoC) in PwT1D and baseline glycated hemoglobin (HbA1c) level <8% not reaching glycemic targets, conducted from a societal perspective in The Netherlands. The analysis was run using the IQVIA CORE Diabetes Model, over 50 years. Costs were discounted at 3% per year, effects at 1.5% per year. Baseline cohort characteristics and treatment effects were sourced from the MiniMed 780G arm of a prospective multicenter study. Costs and utility estimates were taken from Dutch databases and published sources. Sensitivity analyses were conducted to address uncertainty. AID improved life expectancy by 0.52 years and quality-adjusted life expectancy by 0.99 quality-adjusted life-years (QALYs) versus SoC. AID was associated with an incremental combined cost of EUR 28,635 due to higher acquisition costs, which were partially offset by reduced direct treatment costs for diabetes-related complications and reduced indirect costs due to less time off work. Based on combined costs, the MiniMed 780G system was associated with an incremental cost-utility ratio of EUR 29,836 per QALY gained. For PwT1D in The Netherlands, who had a baseline HbA1c <8% and do not reach glycemic targets, AID system initiation was projected to improve long-term clinical outcomes and reduce both direct costs for the treatment of diabetes-related complications and productivity losses. From a societal perspective, the MiniMed 780G likely represents good value for money in The Netherlands.

Overweight (OW) and obesity (OB) affect nearly two thirds of adults with type 1 diabetes (T1D), contributing to suboptimal glucose control, cardiovascular disease (CVD), and diabetic kidney disease (DKD). Many newer drugs such as tirzepatide (a dual-incretin) and sodium-glucose cotransporter-2 inhibitors are approved for people with type 2 diabetes associated with CVD and DKD. We evaluated CVD and DKD biomarkers with off-label long-term (21 months) use of tirzepatide in OW/OB adults with T1D. In this retrospective chart review study, we analyzed data for 84 OW/OB adults with T1D who were prescribed tirzepatide since July 2022 and had used tirzepatide for at least 6 months. Controls ( = 38) were frequency matched for age, diabetes duration, sex, glycosylated hemoglobin (HbA1c), and body mass index (BMI). Data were collected from electronic medical records before initiating tirzepatide and over 21 months of treatment. Linear mixed effects models were used to examine the changes in lipids, blood pressure, and estimated glomerular filtration rate (eGFR) over time in tirzepatide-treated adults versus controls. Baseline characteristics were similar except that tirzepatide users had a slightly higher baseline BMI than controls; 35.2 ± 4.8 kg/m and 33.3 ± 4.2 kg/m ( = 0.03), respectively. Patients using tirzepatide lost significantly more weight (-59 ± 4.6 lbs [-23.4%]) compared with a gain of (+1.7 ± 5.0 lbs [+1.8%]) in controls over 21 months. The HbA1c decreased more in patients using tirzepatide than controls (-0.50 ± 0.07% and -0.24 ± 0.09%, respectively, = 0.017). Patients using tirzepatide significantly improved total and low-density lipoprotein cholesterol, triglycerides, systolic blood pressure, and eGFR; these changes remained significant even after adjusting for weight and HbA1c. The eGFR declined significantly in controls but not in the tirzepatide users. We conclude that long-term use of tirzepatide in OW/OB adults with T1D results in more than 23% weight loss and sustained improvement in glucose control. Irrespective of changes in weight and/or HbA1c, we observed significant improvement in cardiovascular biomarkers and preservation of kidney function. We strongly recommend a long-term randomized control trial with tirzepatide in patients with T1D.

To evaluate differences in glucometrics in children and adolescents assigned to automated insulin delivery (AID), predictive low-glucose suspend (PLGS), or multiple daily injections (MDI) in the first month of diabetes management. In this real-world prospective cohort study, all subjects aged 0-18 years with diabetes onset between January 1, 2020, and June 30, 2023, were assigned to MDI ( = 24), PLGS ( = 28), or AID ( = 32) but were allowed to switch after the first 3 months. The primary outcome was HbA1c after 12 months. The mean age ( = 84) was 7.9 ± 3.9 years (range 1-18 years), and 58 were male. After 12 months, HbA1c was significantly lower in the AID group than in the PLGS or MDI groups (AID 6.6% ± 0.6% vs. PLGS 7.4% ± 1.1% vs. MDI 7.6% ± 1.5%, = 0.001), with better time in range ( = 0.001), time below range ( = 0.01), time above range ( = 0.001), coefficient of variation ( = 0.01), and glucose management indicator ( = 0.001). AID is best started at diabetes onset to optimize glucose control outcomes.

Postmeal hyperglycemia is difficult to avoid even with automated insulin delivery (AID) due to the delayed effect of subcutaneously administered rapid-acting insulin analogs. Inhaled technosphere insulin (TI, Afrezza®) has a more rapid onset of action with the potential to reduce the postmeal glucose rise. We evaluated the effects of a regimen of TI and degludec over 30 weeks. In total, 123 adults with type 1 diabetes (T1D) participated in a 17-week multicenter randomized controlled trial comparing a regimen of TI plus insulin degludec versus usual care, which consisted predominantly of AID or multiple daily insulin injections (MDI). Interested participants in the TI-degludec group continued this regimen for an additional 13 weeks, with no scheduled visits prior to a final visit at 30 weeks to approximate real-world care. Of the 62 participants in the TI-degludec group, 58 completed the 17-week visit and 45 continued into the extension phase. Prior to the study, 44% were using AID, 9% a sensor-augmented pump without automation, and 47% MDI. Mean HbA1c was 7.6% ± 1.0% at baseline, 7.6% ± 1.0% at 17 weeks, and 7.4% ± 1.0% at 30 weeks. Mean HbA1c change from 17 weeks to 30 weeks was -0.21% (95% confidence interval -0.33% to -0.09%, < 0.001). HbA1c was <7.0% in 21% at baseline, 30% at 17 weeks, and 42% at 30 weeks. Mean time in range 70-180 mg/dL was 52% ± 18% at baseline, 53% ± 20% at 17 weeks, and 54% ± 20% at 30 weeks. Mean percent time <54 mg/dL was 0.4% ± 0.6%, 0.4% ± 0.8%, and 0.6% ± 1.0%, respectively. Mean total daily TI dose at 30 weeks was 53 ± 31 U/day, which was about twice the total daily rapid-acting insulin analog dose of 24 ± 12 U/day at baseline prior to switching to TI. HbA1c levels were sustained over 30 weeks using a TI-degludec regimen after switching from AID or MDI. TI should be considered an option for people with T1D.

Continuous glucose monitors (CGM) are increasingly being used to manage diabetes. We evaluated the performance and safety of an investigational 15-day G7 integrated CGM (iCGM; Dexcom) in adults with diabetes. This prospective, multicenter study enrolled adults (age ≥18 years) with type 1 diabetes (T1D) or type 2 diabetes (T2D) at six clinical sites in the United States. Four in-clinic visits were conducted on days 1-3, 4-7, 9-12, and 13-15.5, with frequent arterialized venous blood draws for comparator measurements using a Yellow Springs Instrument (YSI) 2300 Stat Plus glucose analyzer. Participants with T1D or T2D using intensive insulin therapy participated in clinic sessions with deliberate, closely monitored glucose manipulations. Accuracy evaluations included the mean absolute relative difference (MARD), proportion of CGM values within 15 mg/dL of YSI values <70 mg/dL or within 15% of YSI values ≥70 mg/dL (%15/15), as well as %20/20, %30/30, and %40/40 agreement rates. Performance related to iCGM special controls, user experience, and device safety were also assessed. The study enrolled 130 adults with diabetes (mean ± standard deviation age 43.0 ± 14.4 years, 53.1% female, 86.9% with T1D) and analyzed 20,310 CGM-YSI matched pairs from 130 15-day G7 CGM devices. The overall MARD was 8.0% and the %15/15, %20/20, %30/30, and %40/40 agreement rates were 87.7%, 94.2%, 98.9%, and 99.8%, respectively. The device exceeded iCGM performance goals, and user experiences were broadly positive. No serious adverse events were reported. The 15-day G7 iCGM was accurate and safe in adults with diabetes throughout the 15.5-day wear period. NCT05263258.

This study compares unregulated open-source (OS) automated insulin delivery (AID) systems and commercial-AID (C-AID) systems regarding glucose management, patient-reported outcomes (PROs), and safety among adults with type 1 diabetes (T1D). We conducted a 12-week, prospective, observational, noninferiority, comparative, real-world study involving 78 adults with T1D and having used an AID system for ≥3 months (26 OS-AID and 52 C-AID users). A total of 4-week data from a blinded continuous glucose monitor was used to assess the effectiveness in glucose management (primary outcome: 24 h time in range [TIR%] for 4 weeks, with a noninferiority margin of 5%). Our study suggested that OS-AIDs were noninferior to C-AIDs regarding the 24 h TIR% (78.3% [standard deviation or SD 11.0] vs. 71.2% [SD 10.9], mean difference 7.2% [95.08% confidence interval or CI: 1.9% to 12.5%], < 0.001), even after adjusting for various confounding factors. OS-AIDs spent more time in hypoglycemia (<3.9 mmol/L) than C-AIDs (3.9% [SD 3.1] vs. 1.8% [SD 1.3], < 0.001) yet within the recommended range. OS-AID users reported less fear of hypoglycemia, while other PRO measures (diabetes distress, hypoglycemia awareness, sleep, fear of hypoglycemia, treatment satisfaction, and overall quality of life) were not different between groups. No severe hypoglycemia or diabetic ketoacidosis was reported in either group, with a similar occurrence rate of technical issues during the 12-week study period. OS-AIDs are safe and noninferior to C-AIDs for TIR% among adults with T1D in real-world settings. Both OS-AID and C-AID systems can be considered for T1D management.