Diabetes Technology & Therapeutics

To examine the glycemic responses to two exercise modalities—high-intensity interval exercise (HIIE) and moderate-intensity continuous exercise (MICE)—when applying consensus guideline preexercise preparatory measures in adolescents with type 1 diabetes using automated insulin delivery (AID) systems.

In a two-period, crossover trial, 24 individuals (12 using MiniMed™ 780G and 12 using Tandem Control-IQ™) completed two experimental study days involving HIIE and MICE sessions. Participants were matched on age, sex, and total daily dose across AID systems. Continuous glucose monitoring (CGM) and insulin pump data were collected from participants’ AID systems. Plasma glucose (PG) was measured throughout the study days. Exercise was announced 60 min before MICE initiation, but not before HIIE.

No significant difference was observed for the primary outcome, CGM-derived time in range (TIR_CGM: 3.9–10.0 mmol/L [70–180 mg/dL]), between study visits (HIIE: 91.7 [58.3–100.0] % vs. MICE: 83.3 [75.0–100.0] %, P = 0.261). Participants developed hypoglycemia during both visits (HIIE: 8 vs. MICE 11). When analyzed by the AID system, TIR_PG (PG: 3.9–10.0 mmol/L [70–180 mg/dL]) was higher during MICE in the MiniMed™ 780G group, with no difference in the number of participants experiencing hypoglycemia at either visit.

When applying consensus guideline preexercise recommendations, both exercise modalities achieved generally stable glycemic outcomes; however, hypoglycemia remained a challenge across study visits. These findings highlight that current AID systems still fall short of ensuring safe glycemic responses during exercise.

This study evaluated the performance of Simplera Sync™ and Instinct™ sensors when paired with the MiniMed™ 780G (MM780G) system and assessed whether switching between sensors affected clinical outcomes of people with type 1 diabetes (PwT1D).

A single-center, two-arm, randomized crossover study in adult PwT1D started with either the Simplera Sync sensor or Instinct sensor for a 6-week period, followed by crossover to the alternate sensor for an additional 6 weeks. The primary end point was noninferiority in time in range (TIR; 70–180 mg/dL), with a noninferiority margin of 7.5%. Secondary end points included HbA1c and other continuous glucose monitoring-derived metrics.

Twenty-five participants were assessed for eligibility, 24 were randomized, and 23 completed the study. TIR was 80.6 (9.8)% during the Simplera Sync sensor period and 79.1 (9.5)% during the Instinct sensor period. The mean difference was 1.5% (95% confidence interval: 0.18–2.79), and the noninferiority criteria were met. HbA1c at the end of the Simplera Sync and Instinct sensor periods was 6.8 (0.7)% and 6.7 (0.7)%, respectively. No serious adverse events related to sensor use were reported.

This randomized controlled crossover trial demonstrates that both Simplera Sync and Instinct sensors meet international consensus targets for glycemic control when used with the MM780G system, and that the sensors can be interchanged without meaningful differences in glycemic control. This study supports the concept that the automated insulin delivery algorithm, rather than the sensor, is the primary determinant of clinical outcomes in MM780G users.

Automated insulin delivery (AID) systems integrate continuous glucose monitoring (CGM) and insulin pumps with algorithms that adjust insulin delivery. While randomized controlled trials (RCTs) demonstrate improvements in glycemic outcomes with AID, large-scale real-world data (RWD) analyses are needed to evaluate performance in routine care.

This retrospective, registry-based cohort study included adults (≥18 years) with type 1 diabetes (T1D) in the Swedish National Diabetes Register (NDR) from 2014 to 2024. Hemoglobin (Hb)A1c values were averaged per person-year and aggregated by clinic. Insulin delivery/glucose monitoring combinations included multiple daily injections (MDIs) with blood glucose meter (MDI-BG), MDI with CGM (MDI-CGM, reference), conventional continuous subcutaneous insulin infusion with blood glucose meter (CSII-BG), CSII with CGM (CSII-CGM), and AID systems (Tandem Control-IQ technology, Medtronic 670G, and Medtronic 780G). Mixed-model regression assessed HbA1c outcomes, with treatment, year, age, diabetes duration, gender, body mass index, physical activity level, smoking habits, and clinic size as fixed effects; with random effects for clinics; and weighting by clinic sample size.

After adjusting for the covariates, AID systems were associated with significantly lower HbA1c compared with MDI-CGM: β = −4.0 mmol/mol (95% confidence interval [CI] −4.3 to −3.7, P < 0.001), with individual system effects, Tandem Control-IQ technology β = −4.8 mmol/mol (95% CI −5.2 to −4.5, P < 0.001), Medtronic 780G β = −3.1 (−3.5 to −2.7, P < 0.001), and Medtronic 670G β = −2.9 (95% CI −3.5 to −2.4, P < 0.001). CSII-CGM also outperformed MDI-CGM: β = −1.7 mmol/mol (95% CI −1.9 to −1.4, P < 0.001). Differences between the Tandem and Medtronic AID systems were significant (P < 0.001).

In a nationwide RWD analysis, AID use was consistently associated with clinically and statistically significant HbA1c reductions in adults with T1D, with the greatest effect for Tandem Control-IQ technology. These findings align with RCTs and international RWD, supporting AID as a preferred technology in routine diabetes care for T1D.

To evaluate the relative sensitivity of several available CGM metrics for the detection of the effects of clinical interventions in people with type 1 diabetes (T1D) and type 2 diabetes (T2D).

Real-world data from people with poor glycemic control (hemoglobin A1c 8.2 ± 1.3%) for 120 people with T1D and 92 people with T2D, using Libre 2 CGM. Analysis of CGM data from 3 days prior to admission and 2 days immediately before discharge from ∼8 days of in-hospital care with changes in therapy as prescribed by hospital-based diabetes specialists. CGM metrics included: quality-score (Q-Score), Time in Range (TIR) (3.9–10 mmol/L), Time Above Range (>10 mmol/L), Time Below Range (<3.9 mmol/L), Mean Sensor Glucose, Glucose Management Indicator, Glycemia Risk Index, Glucose Daily Range, and Mean of Absolute Daily Differences (MODD). We evaluated the paired differences in all metrics pre- and postintervention within subjects using classical paired Student’s t tests.

The Q-Score showed the largest effects in terms of Student’s t-values for T1D, for T2D, and for all (T1D and T2D) subjects after pooling, indicating better sensitivity for detection of an effect than TIR or seven other metrics. One of the five components of the Q-Score, MODD, a classical measure of stability of glucose patterns from day to day, showed the second-best sensitivity in evaluating changes within subjects specifically for people with T1D.

We observed consistent differences in sensitivity for the detection of the effects of therapeutic interventions, with Q-Score being superior to eight alternatives. This study needs replication using additional patient populations and multiple types of interventions to evaluate its generalizability and applicability to both randomized controlled clinical trials and real-world clinical data.

The objective of the study was to evaluate the prevalence of cardiovascular, renal, and hepatic biomarkers that fall outside guideline-based thresholds in adults with type 1 diabetes (T1D), and to identify demographic and clinical factors associated with these biomarkers.

This observational study linked data on 2,923 adults with T1D from the T1D Exchange Registry and NorstellaLinQ’s laboratory data (2022–2023). Prevalence of biomarkers outside guideline-recommended thresholds was calculated. Generalized estimating equations (GEE) were used to model associations with age, sex, race/ethnicity, insurance, T1D duration, body mass index (BMI), and hemoglobin A1c (HbA1c), while accounting for within-subject correlation due to repeated measures.

Elevated low-density lipoprotein (LDL) cholesterol (≥70 mg/dL, above the recommended goal for individuals with diabetes at high risk for atherosclerotic cardiovascular disease) was common (80.2%), as were overweight (33.8%) and obesity (30.3%). Additional findings included high total cholesterol (≥200 mg/dL, 22.6%), low high-density lipoprotein (HDL) cholesterol (<40 mg/dL males, <50 mg/dL females, 13.4%), elevated urinary albumin-to-creatinine ratio (≥30 mg/g, 13.0%), reduced estimated glomerular filtration rate (<60 mL/min/1.73 m², 6.2%), and elevated fibrosis-4 index (FIB-4) (≥1.3, 14.4%). In adjusted GEE models, higher HbA1c was significantly associated with increased odds of abnormal cardiovascular and renal biomarkers across most measures. Male sex and BMI were linked to lower HDL cholesterol, while older age was strongly associated with elevated FIB-4.

Obesity and elevated LDL cholesterol are highly prevalent in adults with T1D, indicating substantial cardiometabolic risk. The lack of outcome trials in this population underscores the need for studies to evaluate strategies to improve weight and lipid management and thereby reduce cardiovascular and renal risk in adults with T1D.

Daily use of continuous glucose monitoring (CGM) has been shown to reduce diabetes-related events and associated costs in individuals with type 2 diabetes (T2D) regardless of their therapy. However, adoption of CGM among the large majority of T2D adults in the United States who are treated with noninsulin therapies has been limited.

This retrospective database study assessed the effects of CGM acquisition on health care resource utilization (HCRU) in a large cohort of T2D adults treated with noninsulin, antidiabetes therapies. Inclusion criteria were T2D diagnosis, age ≥18 years, treated with noninsulin therapies, CGM-naive before CGM acquisition, and continuous medical/pharmacy insurance coverage during the 12-month preindex and postindex periods. The primary outcome measures were changes in all-cause hospitalizations (ACH), emergency department (ED) visits, acute diabetes complications, hyperglycemic events (HGE), and diabetic ketoacidosis (DKA) during the 12 months following CGM acquisition.

A total of 20,468 adults with T2D were included in this analysis. CGM acquisition was associated with significant reductions in event rates in the postindex period compared with the preindex period HCRU at 12 months: ACH (−25%), ED visits (−7%), HGE (−7%), DKA (−86%), and acute diabetes complications (−7%), all P < 0.0001. Similar reductions in events per person were also observed: ACH (−20.6%), ED visits (−7.2%), HGE (−6.2%), DKA (−63.0%), and acute diabetes complications (−6%), all P < 0.0001. Significant reductions were also seen in patients with cardiovascular disease. ACH and ED visits decreased by 35% and 12%, respectively; in those with liver disease by 25% and 13%; in those with renal disease, ACH decreased by 33%; and in those with hypertension, ACH and ED visits decreased by 26% and 7%, respectively. All reductions were statistically significant (P < 0.01).

This analysis demonstrated an association between CGM acquisition and reductions in HCRU in adults with T2D treated with noninsulin therapies.

The efficacy of adding various noninsulin hypoglycemic drugs to automated insulin delivery (AID) systems in patients with type 1 diabetes (T1D) was investigated in randomized controlled trials (RCTs), yet no meta-analysis has been conducted. This study aimed to systematically analyze the existing evidence.

Four datasets were searched up to August 31, 2025. Inclusion criteria were as follows: T1D populations of any age; comparing any type of noninsulin hypoglycemic drug added to AID systems or not; and reporting primary outcomes (time in range [TIR] = 70–180 mg/dL, 3.9–10.0 mmol/L). Results were pooled using a random-effect meta-analysis. Risk of bias was assessed using the Cochrane RoB2 tool. Quality of evidence was assessed by the Grading of Recommendations Assessment, Development, and Evaluation approach (Registered number: CRD420251107996).

Nine RCTs met the inclusion criteria, investigating sodium–glucose cotransporter-2 inhibitor (SGLT-2i) (N = 5), glucagon-like peptide-1 receptor agonist (GLP-1 RA) (N = 2), and dipeptidyl peptidase-4 inhibitor (DPP-4i) (N = 2), respectively. Overall, these drugs with AID systems improved TIR by +10.0% (7.4%–12.6%) and time in tight range (TITR; 70–140 mg/dL, 3.9–7.8 mmol/L) by +8.9% (6.8%–11.0%) with I² of 60.4% and 15.4% (P < 0.001). These improvements were primarily driven by reductions in time above range (TAR) >250 mg/dL (>13.9 mmol/L; −4.3 [−5.8 to −2.8] %), TAR >180 mg/dL (>10.0 mmol/L; −11.6 [−14.7 to −8.5] %), and coefficient of variation (−2.9 [−4.5 to −1.4] %) without increased hypoglycemia. Daily insulin dose decreased by 8.9 units. Among these, SGLT-2i conferred the greatest TIR improvement (+12.5%), followed by GLP-1 RA (+7.1%) and DPP-4i (+6.4%). No significant differences were found in severe hypoglycemia (SH) and diabetic ketoacidosis (DKA).

SGLT-2i, DPP-4i, and GLP-1 RA may serve as effective and safe adjuncts for T1D individuals using AID systems, offering improvements in TIR, reductions in hyperglycemia, and reduced insulin requirements without evidence of increasing DKA and SH.

This study aimed to investigate the associations between glycemic outcomes and a range of clinical and demographic factors, including treatment modality, sex, age, diabetes duration, and body mass index, in youth with type 1 diabetes in an international registry.

This observational, cross-sectional cohort study included youth <21 years from 23 countries. Proportions of individuals using different treatment modalities (continuous glucose monitoring [CGM] with injections, CGM with pump, automated insulin delivery [AID]) and achieving recommended time in tight range (TITR >50%), time in range (TIR >70%), and glycated hemoglobin (HbA1c) (≤6.5% [48 mmol/mol] and ≤7% [53 mmol/mol]) were assessed using mixed-effects fractional logistic and linear regression models. Sex, age (categorized), diabetes duration (categorized), body mass index standard deviation score (categorized), and treatment modality were included as covariates.

Data of 7691 individuals (mean [standard deviation] age of 13.7 [4.3] years, diabetes duration 6.3 [4.2] years, 47.8% female) were included. AID users were the most likely to achieve TITR target (adjusted mean [standard error of the mean] 41.2% [2.9]), followed by CGM with insulin pump (25.2% [2.3]) and CGM with injections (13.7% [1.5], P < 0.001). A similar association was observed for proportions of individuals achieving TIR and HbA1c targets (P < 0.001). Age <6 years was associated with a higher coefficient of variation (CV) (P < 0.001) and a lower probability of achieving both TITR and TIR targets compared with other age groups (P < 0.001). In analyses of TITR associated with mean sensor glucose stratified by CV, a higher TITR for a lower CV was observed only at mean glucose levels below 150–160 mg/dL; above this threshold, the pattern reversed, with lower CV associated with lower TITR at a given mean glucose level.

Children younger than 6 years and individuals not using glucose-responsive insulin therapy were less likely to meet the recommended glycemic targets. It is imperative to minimize these disadvantages.

To evaluate long-term trends in metabolic outcomes among people living with type 1 diabetes (T1D) within a nationwide structured care program in Belgium with full reimbursement of diabetes technologies.

Repeated cross-sectional analyses were performed across three audit periods (2010–2011, 2017–2018, and 2023–2024). Outcomes included glycated hemoglobin (HbA1c), low-density lipoprotein (LDL) cholesterol, systolic blood pressure (SBP), and body mass index. Trends were analyzed using generalized estimating equations adjusted for age, sex, diabetes duration, insulin delivery, and glucose monitoring modalities. In 2023–2024, glycemic outcomes were compared across technology use, including hybrid closed-loop (HCL) therapy.

In adults, HbA1c decreased from 7.9% (62.8 mmol/mol) to 7.5% (58.5 mmol/mol, P < 0.001), with target attainment (<7%; <53 mmol/mol) increasing from 22.3% to 35.1%. In children and adolescents, HbA1c declined from 8.0% (63.5 mmol/mol) to 7.6% (59.0 mmol/mol, P < 0.01), and target attainment rose from 19.7% to 33.1% (P < 0.001). Continuous glucose monitoring (CGM) exceeded 90% by 2023–2024, while insulin pump use reached 25% in adults and 47% in younger individuals. In adults, HCL therapy was associated with lower HbA1c (7.0%; 53.5 mmol/mol), higher time-in-range (73%), and lower time-below-range (1.5%) compared with other modalities (P < 0.001). LDL cholesterol improved, with target attainment (<70 mg/dL; <1.8 mmol/L) increasing from 18.3% to 37.4% (P < 0.001), and from 15.5% to 46.5% among those with cardiovascular disease (<55 mg/dL; <1.4 mmol/L, P < 0.001). SBP remained stable. Obesity prevalence increased in all age groups.

Over 14 years, glycemic and lipid outcomes improved alongside near-universal CGM use and increasing adoption of HCL therapy. However, rising obesity and persistent smoking-related risks highlight the need for targeted prevention strategies. These real-world data confirm that benefits seen in clinical trials translate into durable population-level improvements and support integrated care models for implementing diabetes technology effectively.

To assess postprandial glucose excursions following a meal bolus of inhaled technosphere insulin (TI) in youth with type 1 diabetes.

As part of a multicenter randomized controlled trial, 217 youth 4–17 years old with type 1 diabetes using multiple daily injections (MDI) completed an in-clinic standardized meal challenge with their first TI dose. Glucose levels were monitored for 2 h. Outcomes were compared with outcomes in MDI-using adults with T1D who received a TI (N = 51) or a rapid-acting analogue (RAA, N = 25) bolus for a similar meal challenge in a separate trial.

Following TI inhalation, the glucose excursion and glycemic metrics were similar in the pediatric cohort to those of the adult TI cohort. In contrast, compared with the adult RAA cohort, the pediatric TI cohort had a smaller glucose excursion (P < 0.001), lower peak glucose (P < 0.001), smaller AUC180 (P < 0.001), and higher time-in-range 70–180 mg/dL (P = 0.002). A glucose value < 70 mg/dL occurred in 14 (6%) of the pediatric TI cohort versus 1 (2%) of the adult TI cohort. Children 4–12 years old had a significantly higher peak glucose and excursion (P < 0.001) than those 13–17 years old. No significant difference in response to meal challenges was observed between groups with baseline HbA1c < 8.5% and HbA1c ≥ 8.5%. The mean ratio of TI inhaled units to the calculated RAA bolus that would be given for the number of carbohydrates was 1.9 ± 0.6 in the pediatric cohort and 1.9 ± 0.2 in the adult cohort.

In children with type 1 diabetes, the postmeal glucose excursion following a TI bolus was similar to the postmeal glucose excursion that occurs with TI in adults and significantly lower than the excursion that has been observed with RAA in adults. These findings are consistent with the known pharmacokinetic profile of TI.

Challenges with infusion sets and skin sites may hinder optimal glycemic levels in automated insulin delivery (AID). This study investigated how infusion set wear time affects glycemic control, insulin doses, and infusion site tissue characteristics and evaluated the impact of ultrasound-guided site rotation.

Children and adolescents (6–18 years) using AID completed a 28-day prospective study. Skin assessments and ultrasound imaging were performed at baseline, day 14, and day 28. Hyperechogenicity was interpreted as early lipohypertrophy. Analyses were intention-to-treat. Time in range (TIR) and insulin dose/kg/day were modeled as piecewise functions by days since infusion set change.

Of 40 participants, 31 had sufficient continuous glucose monitoring data (mean age 11.3 years, diabetes duration 5.6 years, 55% male, HbA1c 54 mmol/L [7.1%]). After guiding, TIR increased by 2.5% points/day for 2 days post set change (P < 0.0001) and then decreased by 4.7% points/day (P < 0.0001). Insulin doses rose by 0.036 U/kg/day after day 2 (P = 0.009). Hyperechogenicity was associated with lower TIR after day 2 and correlated with clinical lipohypertrophy (P < 0.001, risk ratio = 3.63). Participants with ultrasound-only tissue changes had higher TIR than those with both ultrasound and clinical signs (73% vs. 67%, P < 0.0001).

TIR declines from day 2 of infusion set wear despite rising insulin doses, indicating reduced absorption not fully compensated by AID. Ultrasound-guided site selection improves glycemic outcomes, likely by supporting more consistent and effective site rotation.

Type 2 diabetes mellitus (T2DM) is one of the most common chronic diseases in Sweden. Continuous glucose monitoring (CGM) is an increasingly important alternative to self-monitoring of blood glucose (SMBG), particularly for people receiving insulin.

To summarize the evidence for CGM in insulin-treated T2DM and conduct a policy landscape analysis of national and local guidelines to explore access to CGM in Sweden relative to other countries.

A scoping review was conducted to identify evidence on the clinical, patient, and economic value of CGM, supplemented with searches of Swedish and European guidelines. Current recommendations for CGM in T2DM in Sweden were compared with European-wide and country-specific recommendations. Regional recommendations and funding processes within Sweden were reviewed to examine heterogeneity in local access to CGM.

Across international and Swedish studies, CGM was associated with improved clinical outcomes compared with SMBG, both for people with intensive insulin-treated T2DM and those on basal insulin only. The clinical benefits likely translate into fewer long-term diabetes complications and reduced resource utilization and budget impact versus SMBG. Health economic evaluations show that CGM can be considered a cost-effective intervention for all individuals treated with insulin in Sweden. European-wide guidance supports consideration of CGM for all insulin-treated individuals, but access in Sweden remains limited outside of the high-risk intensive insulin-treated population. This is exacerbated by regional heterogeneity in access, which partly stems from the attribution of budget responsibility to individual primary care units.

The benefits associated with CGM show that an expansion of the Swedish recommendations to cover all insulin-treated people with T2DM is warranted. Persistent regional disparities must be addressed to ensure equitable access to care for people with T2DM in Sweden. The establishment of dedicated funding mechanisms within primary care should be considered to promote more equitable and sustainable access.

Cross-sectional associations between continuous glucose monitoring (CGM) metrics and diabetic kidney disease (DKD) have been reported, but evidence based on longitudinal CGM metrics collected over extended periods has remained limited.

To evaluate the relationship between longitudinal CGM metrics and albuminuria in diabetes.

A single-center, retrospective, longitudinal observational study was conducted for 190 individuals with insulin-treated diabetes who had at least 3 years of CGM data. The primary exposure was time in range (TIR) across follow-up, with secondary exposures being time in tight range (TITR) and mean glycated hemoglobin (HbA1c) level. The primary outcome was the urinary albumin-to-creatinine ratio (UACR) measured closest to the end of follow-up. Multivariable linear regression and restricted cubic spline analyses were performed.

Over a median follow-up of 7.3 years, each 10-percentage-point increase in TIR was associated with an 11.8% decrease in UACR (95% confidence interval [CI], 1.5%–21.0% decrease). TITR showed a similar direction of association, but this association was not statistically significant. Each 1-percentage-point decrease in mean HbA1c level was associated with a 21.0% decrease in UACR (95% CI, 4.4%–34.6% decrease). Restricted cubic spline analysis suggested possible nonlinear associations for TIR and TITR, although these patterns were attenuated in sensitivity analyses.

Longitudinal CGM metrics, in particular TIR, were associated with albuminuria in diabetes, providing support for their clinical relevance for DKD.

Mobile health applications (apps) have been associated with improved glycemic outcomes in people with diabetes, but the behavioral factors leading to these outcomes remain poorly understood. The aim of this study was to quantify the impact of a diabetes app (mySugr^®) on glycemic and non-glycemic outcomes and to explore the relationship between behavior and glycemic control by investigating app usage patterns.

Glycemic (blood glucose [BG]) and non-glycemic (body mass index [BMI] and “tags” indicating hypoglycemia and hyperglycemia feelings) outcomes of mySugr users with type 1 (T1DM) or type 2 (T2DM) diabetes mellitus were collected in the app over 90 days and assessed through population analysis, including stratification by frequency of app usage. Linear modeling was employed to identify characteristics of app users that may predict glycemic outcomes.

Data from 80,044 mySugr users were analyzed. Improved glycemic control was observed (change in mean BG = −12.5 mg/dL; P < 0.001), correlating with app usage and was greatest in users with >16 app sessions/day. In users with T2DM, a 2.4% reduction from baseline was observed in mean BMI (−0.76 kg/m²; P < 0.001; n = 6910). Users with T1DM or T2DM reported reductions of 16% and 27% in hypoglycemia and hyperglycemia feelings, respectively (143,600 tags). Linear models showed a significant fit (P < 0.001) for both T1DM (n = 17,465) and T2DM (n = 39,069) users and explained a large degree of the variability in glycemic outcomes (R² = 0.355 for T1DM and R² = 0.444 for T2DM). Baseline glycemic control was the strongest predictor of glycemic outcomes, followed by number of BG logs and app engagement.

Using a digital diabetes app can improve glycemic and non-glycemic outcomes in people with diabetes. This work also establishes a methodology to correlate user behavior with glycemic control.