Diabetes technology & therapeutics最新文献

Objective: Pediatric prediabetes is common among youth with obesity, yet most do not progress to type 2 diabetes, and many regress to normoglycemia. Youth-onset type 2 diabetes has a severe course not well predicted by degree of adiposity or hemoglobin A1c (HbA1c) alone. This study used continuous glucose monitoring (CGM) to characterize glycemic patterns in youth with prediabetes and to determine whether baseline CGM metrics identify those at highest risk for HbA1c progression over 6 months.

Research design and methods: Youth aged 10-18 years with obesity and HbA1c 5.7%-6.4% were enrolled in a prospective 6-month observational cohort study, with baseline and follow-up assessments including HbA1c and 14 days of CGM wear. CGM metrics were derived using the R iglu package. Changes over time and associations with follow-up HbA1c and HbA1c change were analyzed using paired t-tests and PRESS-based forward-selected linear regression models, adjusting for clinical covariates.

Results: Among 29 youth with prediabetes, HbA1c and most CGM metrics remained stable over 6 months, with 32% regressing to normoglycemia. One participant progressed to HbA1c 6.5% who demonstrated markedly elevated baseline CGM glycemic variability and hyperglycemia. While multiple baseline CGM metrics were associated with follow-up HbA1c and HbA1c change in univariable analyses, PRESS-based multivariable models identified time spent above 140 mg/dL as the strongest and only consistent independent predictor of both higher follow-up HbA1c and worsening glycemic trajectory.

Conclusions: CGM metrics outperformed HbA1c in predicting short-term glycemic progression in youth with prediabetes, supporting CGM as a promising adjunctive tool for early risk stratification.

Continuous glucose monitoring (CGM) generates dense physiological time-series data sampled every 5 min across 24-h periods. Standard analytical approaches impose calendar-based temporal boundaries-treating midnight as a natural segmentation point-despite glucose homeostasis operating through continuous circadian oscillators that recognize no such delimiter. This structural misalignment introduces a systematic measurement artifact: biologically continuous overnight patterns are bisected at 00:00, artificially inflating glycemic variability estimates and obscuring individual circadian phase relationships. We analyzed approximately 60 days of CGM data, comparing three binning strategies: (1) 24 linear hour-bins (conventional), (2) 36 linear 40-min bins (resolution control), and (3) 36 angular 10° bins (circular topology). Shannon entropy with variance-weighted probabilities quantified information content. Bootstrap resampling (1000 iterations) and null topology permutation (random within-day time-permutation, 1000 iterations) distinguished genuine temporal structure from mathematical artifact. Circular representation demonstrated 12.1% higher information entropy compared to linear binning at matched resolution (3.56 vs. 3.18 bits, P < 0.001, bootstrap percentile method), with nonoverlapping confidence intervals (95% CI: 3.41-3.71 vs. 3.06-3.30). Increasing from 24 to 36 bins in linear space produced zero entropy change (3.18→3.18), isolating topological continuity as the information-preserving factor. Midnight boundary created 2.8-fold reduction in continuity correlation (r = 0.31 vs. r = 0.87, P < 0.001) for biologically adjacent timepoints. Analysis showed nonrandom angular variance structure (P < 0.001), with 1.97-fold variance differential between evening (21:20-00:00) and midday (11:20-14:00) zones. Midnight segmentation introduces quantifiable information loss through temporal discontinuity. Circular time representation-mapping 24-h cycles onto angular coordinates using established directional statistics-eliminates this artifact while preserving temporal information. Current glycemic variability metrics (coefficient of variation, time-in-range) calculated within midnight-bounded periods inherit discontinuity artifacts, potentially misclassifying normal circadian oscillations as pathological variability. Adoption of circular frameworks would align CGM analytics with chronobiological principles and enable individual circadian phenotyping without data manipulation. This represents methodological infrastructure requiring prospective validation for clinical utility.

Objective: 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.

Research design and methods: 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.

Results: 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).

Conclusions: 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.

Objective: To evaluate how continuous glucose monitoring (CGM)-derived metrics relate to severe hypoglycemia (SH) events in individuals with type 1 diabetes by utilizing a multistep machine-learning approach to generate virtual CGM profiles from glycemic data in the Diabetes Control and Complications Trial (DCCT).

Research design and methods: Virtual CGM profiles were created for each DCCT participant using previously validated methods. HbA1c values and CGM metrics were analyzed as predictors of SH events within the subsequent 90 days using Poisson regression models. Sensitivity, specificity, and positive predictive value of time-below-range (TBR) <70 mg/dL for SH prediction were also assessed.

Results: All CGM-derived measures, including TBR, level 2 hypoglycemia (glucose <54 mg/dL), time-in-range 70-180 mg/dL, time-in-tight-range 70-140 mg/dL, low blood glucose index, and coefficient of variation, were higher, while the mean HbA1c was lower for participants who experienced at least one SH event compared with participants who did not. Each 1% increase in TBR and each 0.5% increase in level 2 hypoglycemia were associated with rate ratios of 1.23 (95% CI, 1.20-1.27) and 1.36 (95% CI, 1.30-1.43) for SH events, respectively. A similar pattern was seen when assuming a 0.5 standard deviation change in these metrics. Despite this association, TBR threshold of >6% demonstrated only 13% positive predictive value for SH events.

Conclusion: Hypoglycemia-focused CGM metrics reproduced by virtual CGM data from the DCCT were strongly associated with SH events, although the positive predictive value was low.

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

Methods: 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.

Results: 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.

Conclusions: 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.

Postbariatric hypoglycemia (PBH) after Roux-en-Y gastric bypass predisposes to health risks and impaired quality of life. Given limited therapeutic options, we evaluated the efficacy of a continuous glucose monitoring (CGM)-guided forecasting algorithm to reduce PBH. In this randomized trial, 59 participants underwent a standardized meal test and were assigned to receive 5 g of glucose either upon the algorithm's predictive alert (intervention, n = 32) or when plasma glucose declined below 3.0 mmol/L (control, n = 27). Hypoglycemia incidence (<3.0 mmol/L) was 31% in the intervention group and 44% in the control group (P = 0.30). Nadir glucose and time spent below 3.9 and 3.0 mmol/L did not differ significantly between groups. Extrapolation based on previously published glucose dose-response data suggests that increasing the preventive glucose dose to 10 g could reduce hypoglycemia incidence to 9%. While a 5 g preventive dose was insufficient, these simulations indicate that CGM-guided hypoglycemia forecasting warrants evaluation as an approach for reducing postprandial hypoglycemia in individuals with PBH.

Background: Glucose predictions aim to empower continuous glucose monitoring (CGM) users by enabling preventive actions to reduce adverse glycemic events. The Accu-Chek^® SmartGuide Predict app offers several AI-enabled predictive features, driven by machine learning algorithms. These include notifications for a low glucose predict within 30 min (LGP) and for nighttime low glucose risk, as well as a 2-h continuous glucose forecast.

Aims: This study aimed to quantify the potential glycemic benefits of using the Predict app's predictive features in an adult population with type 1 diabetes (T1D).

Methods: A comparative in silico study was conducted using the clinically backed University of Virginia Replay digital twin simulator. A control arm, simulating standard hypoglycemia and hyperglycemia mitigation strategies in line with international guidelines, was compared against intervention arms that incorporated probabilistic user behavior models responding to the app's predictive features. The evaluation was performed on 204 digital twins, representing 29,929 days of data, generated from the REPLACE-BG clinical trial dataset.

Results: Results demonstrated that using the app's predictive features has the potential to improve glycemic control in adults with T1D. The simulated intervention led to an average 2.9 percentage point reduction in time below range (<70 mg/dL), and a clinically significant increase of more than 3.6 percentage points in time in range (70-180 mg/dL). Furthermore, the daily number of CGM hypoglycemia alarms (<70 mg/dL) was reduced by 67%. The findings also suggest that consuming 10 g of fast-acting carbohydrates in response to LGP notifications provides an optimal balance, effectively preventing hypoglycemia while limiting rebound hyperglycemia.

Conclusions: This in silico evaluation provides strong evidence supporting the potential clinical utility of the Accu-Chek SmartGuide Predict app for improving glycemic management in adults with T1D.

Objective: Adjunctive use of tirzepatide or semaglutide has demonstrated benefits in improving glucose control (HbA1c, Time in Range), reducing body weight, and insulin requirements, in overweight (OW) or obese (OB) adults with type 1 diabetes (T1D). However, the adverse event (AE) profiles with these agents in this population have not been documented. This study evaluated real-world AEs associated with tirzepatide or semaglutide use in OW/OB adults with T1D.

Materials and methods: In this single-center study at the Barbara Davis Center for Diabetes, we surveyed 230 adults with T1D who were using tirzepatide or semaglutide as adjunctive therapies. Demographics, data for diabetes control metrics, details of tirzepatide or semaglutide use, and related AEs were collected.

Results: Male participants had a higher baseline mean body weight (107.8 ± 18.9 kg vs. 89.6 ± 19.2 kg, P < 0.01) and were older (44.2 ± 12.2 years vs. 40.4 ± 11.5 years, P < 0.05) compared to female participants using tirzepatide or semaglutide at the time of initiation. Symptomatic hypoglycemia was more frequent in the tirzepatide-treated group compared to the semaglutide-treated group (29% vs. 13.4%, P < 0.001). Gastrointestinal AEs did not differ between the two groups. Young adults and females were more likely to report gastrointestinal AEs regardless of the medication. The proportion of individuals who reduced their dose due to AEs was similar.

Conclusions: We conclude that symptomatic hypoglycemia was more commonly reported by the tirzepatide-treated group compared to the semaglutide-treated group, while gastrointestinal AEs were comparable between groups. We recommend that individualized risk assessment and close supervision on insulin dose changes are required when prescribing off-label tirzepatide or semaglutide in adults with T1D.

Introduction: Continuous ketone monitoring (CKM) could optimize diabetic ketoacidosis (DKA) treatment by monitoring beta-hydroxybutyrate (BOHB) continuously and minimally invasively. However, data on the agreement between interstitial and venous BOHB during DKA is lacking. Our objectives were to assess the feasibility of CKM during DKA and the agreement between interstitial and venous BOHB.

Methods: This was a prospective multicenter method-comparison study conducted at two U.S. emergency departments. Adults (>18 years) in DKA were included. BOHB was measured via SiBio CKM and compared with simultaneously collected venous BOHB every 2 h during DKA treatment. Following DKA resolution, study staff removed CKM and assessed for complications. The primary outcome was level of agreement via Bland-Altman analysis between simultaneously collected CKM and venous BOHB values. Additional outcomes included correlation (r) between concurrent CKM and venous BOHB values, first detection of DKA resolution, and feasibility (uncomplicated application and removal of CKM).

Results: Thirty-four patients were enrolled, with a mean age of 40.8 years, 56% male, 50% Black, 79% type I diabetes, and mean presenting BOHB 7.0 mmol/L (range 2.5-13.5). We analyzed 164 paired CKM and venous BOHB values (mean [standard deviation (SD)] paired values per patient: 4.8 [3.8], range 1-16). Bland-Altman analysis found the average difference between CKM and venous BOHB was -0.38 mmol/L (95% confidence interval [CI] -1.63, 0.88). CKM values were strongly correlated with venous BOHB (r = 0.96, p < 0.001). The CKM value was lower than venous for 79% of paired values. DKA resolution was detected 55 min earlier (mean, 95% CI 26-84, p = 0.001) via CKM than standard care. No device-related complications occurred, and CKM application and removal were well tolerated by all patients.

Conclusion: CKM during DKA treatment was feasible, provided clinically accurate BOHB readings, and detected DKA resolution earlier than standard care. CKM-guided DKA treatment is a promising strategy with the potential to improve the quality and value of DKA care.

Continuous glucose monitors (CGMs) are becoming increasingly available, yet the relationship between CGM metrics and hemoglobin A1c (HbA1c) among individuals with prediabetes and normoglycemia remains unclear. We examined associations between HbA1c and eight CGM metrics across glycemic status. Our cohort included 972 individuals: 421 (43.3%) with type 2 diabetes, 319 (32.8%) with prediabetes, and 232 (23.9%) with normoglycemia. Associations were strongest in type 2 diabetes, with mean glucose showing the strongest relationships (standardized β = 0.79, P < 0.001). In prediabetes, associations were substantially attenuated, with mean glucose showing moderate association (standardized β = 0.22, P < 0.001). Among individuals with normoglycemia, CGM metrics showed minimal associations with HbA1c, with mean glucose demonstrating a weak association (standardized β = 0.10, P = 0.022) and time in range showing no significant relationship. All interaction terms were statistically significant (P < 0.001). These findings suggest that standard CGM metrics should not be interpreted to reflect HbA1c for individuals with prediabetes and normoglycemia.