Journal of Diabetes Science and Technology最新文献

Background: Complexity of glucose regulation in persons with type 1 diabetes (PWDs) necessitates increased automation of insulin delivery (AID). This study aimed to analyze real-world data over 12 months from PWDs who started using the MiniMed 780G (MM780G) advanced hybrid closed-loop (aHCL) AID system at the Diabeter clinic, focusing on glucometrics and clinical outcomes.

Methods: Persons with type 1 diabetes switching to the MM780G system were included. Clinical data (e.g. HbA1c, previous modality) was collected from Diabeter's electronic health records and glucometrics (time in range [TIR], time in tight range [TITR], time above range [TAR], time below range [TBR], glucose management indicator [GMI]) from CareLink Personal for a 12-month post-initiation period of the MM780G system. Outcomes were age-stratified, and the MM780G system was compared with previous use of older systems (MM640G and MM670G). Longitudinal changes in glucometrics were also evaluated.

Results: A total of 481 PWDs were included, with 219 having prior pump/sensor system data and 334 having monthly longitudinal data. After MM780G initiation, HbA1c decreased from 7.6 to 7.1% (P < .0001) and the percentage of PWDs with HbA1c <7% increased from 30% to 50%. Glucose management indicator and TIR remained stable with mean GMI of 6.9% and TIR >70% over 12 months. Age-stratified analysis showed consistent improvements of glycemic control across all age groups, with older participants achieving better outcomes. Participants using recommended system settings achieved better glycemic outcomes, reaching TIR up to 77% and TTIR up to 55%.

Conclusions: Use of MM780G system results in significant and sustained glycemic improvements, consistent across age groups and irrespective of previous treatment modalities.

Background: Early detection and intervention are crucial for preventing vision-threatening diabetic retinopathy (DR) in adults with type 1 diabetes (T1D). This exploratory study uses machine learning on continuous glucose monitoring (CGM) data to identify factors influencing DR and predict high-risk individuals for timely intervention.

Methods: Between June 2018 and March 2022, adults with T1D with incident DR or no retinopathy (control) were identified. The CGM data were collected retrospectively for up to seven years before the date of defining incident DR or no retinopathy. A mixture of three machine learning algorithms was trained and evaluated in two different scenarios, using different glycemic features extracted from CGM traces (scenario 1), and the two principal components (two PCs; exposure to hyperglycemia and hypoglycemia risk) of those features (scenario 2). Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model.

Results: The CGM data of 30 adults with incident DR (mean±SD age of 21.2±9.4 years, glycated hemoglobin [HbA_1c] of 8.6%±1.0%, and body mass index [BMI] of 24.5±4.8 kg/m²) and 30 adults without DR (age of 41.8±14.7 years, HbA_1c of 7.0%±0.9%, and BMI of 26.2±3.6 kg/m²) were included in this analysis. In scenario 2, classifiers outperformed scenario 1, resulting in an average AUC-ROC increase to 0.92 for two of three models, indicating that the two PCs captured vital classification data, representing the most discriminative aspects and enhancing model performance.

Conclusion: Machine learning approaches using CGM data may have potential to aid in identifying adults with T1D at risk of DR.

Diabetes technologies, including continuous glucose monitors, insulin pumps, and automated insulin delivery systems offer the possibility of improving glycemic outcomes, including reduced hemoglobin A1c, increased time in range, and reduced hypoglycemia. Given the rapid expansion in the use of diabetes technology over the past few years, and touted promise of these devices for improving both clinical and psychosocial outcomes, it is critically important to understand issues in technology adoption, equity in access, maintaining long-term usage, opportunities for expanded device benefit, and limitations of the existing evidence base. We provide a brief overview of the status of the literature-with a focus on psychosocial outcomes-and provide recommendations for future work and considerations in clinical applications. Despite the wealth of the existing literature exploring psychosocial outcomes, there is substantial room to expand our current knowledge base to more comprehensively address reasons for differential effects, with increased attention to issues of health equity and data harmonization around patient-reported outcomes.

Objective: The objective was to evaluate the safety and efficacy of ultra-rapid-acting insulin with the Boost and Ease-off features of the Cambridge hybrid closed-loop system.

Methods: A secondary analysis of Boost and Ease-off from two double-blind, randomized, crossover hybrid closed-loop studies comparing (1) Fiasp to insulin aspart (n = 25), and (2) Lyumjev to insulin lispro (n = 26) was carried out. Mean glucose on initialization of Boost and Ease-off, change in glucose 60 and 120 minutes after initialization, duration and frequency of use, mean glucose, and time in, above, and below target glucose range were calculated for periods of Boost use, Ease-off use, or neither.

Results: Participants used Boost for longer with Fiasp than insulin aspart (median [interquartile range, IQR] = 75 [53-125] minutes vs 60 [49-75] minutes; P = .01). Mean glucose on Boost initialization with Fiasp was 238 ± 62 mg/dL compared with 218 ± 45 mg/dL with insulin aspart (P = .08). Fiasp use resulted in a greater glucose reduction 120 minutes after Boost initialization [-59 ± 34 mg/dL vs -43 ± 31 mg/dL; P = .02]. There were no statistically significant differences in sensor glucose endpoints during Boost or Ease-off periods between Fiasp and aspart. There were no statistically significant differences during Boost or Ease-off periods when comparing Lyumjev with insulin lispro. There were no safety issues when using Boost and Ease-off with ultra-rapid insulins.

Conclusions: The use of Fiasp and Lyumjev during Boost or Ease-off resulted in comparable safety and efficacy to using insulin aspart and lispro.

Background: The Glycemia Risk Index (GRI) describes the quality of glycemic control, emphasizing extreme hypoglycemia and hyperglycemia more than less extreme values. However, a pregnancy-specific GRI (pGRI), tailored to the tighter target glucose range required during pregnancy, has not been established.

Methods: We retrospectively evaluated clinical, metabolic, and Continuous Glucose Monitoring (CGM) data across pregnancy in women with insulin-treated diabetes, managed between September 2021 and March 2024 at the University Hospital of Pisa. First and second levels of hyperglycemia (TAR1: 140-180 mg/dL, TAR2: >180 mg/dL) and hypoglycemia (TBR1: 63-54 mg/dL, TBR2: <54 mg/dL) were used to calculate the pGRI at each trimester. Logistic regression analysis investigated the association between pGRI and risk of at least one adverse neonatal outcome (among preterm delivery, macrosomia, large for gestational age, small for gestational age, neonatal hypoglycemia, neonatal jaundice, and neonatal intensive care unit admission).

Results: Of 45 pregnant women, 25 (56%) experienced at least one adverse neonatal outcome. In the third trimester, women with adverse outcomes had significantly higher total TAR (26 [12-32]% vs 10 [4-23]%, P = .018) and lower TIR (71 [64-83]% vs 88 [75-92]%, P = .007). Specifically, the difference was notable in TAR2 (6 [2-15]% vs 1 [0-4]%, P = .004), whereas TAR1 was comparable between the 2 groups. Accordingly, third trimester pGRI was higher in women with adverse neonatal outcomes (38 [18-49]% vs 18 [10-31]%, P = .013) and, at logistic regression, slightly but significantly increased the risk of adverse neonatal outcomes (1.044 [1.004-1.086], P = .024).

Conclusions: Pregnant women with insulin-treated diabetes reporting adverse neonatal outcomes spent more time in hyperglycemia, particularly in extreme hyperglycemia. Therefore, the level of hyperglycemia should always be assessed during pregnancy. The pGRI, emphasizing extreme hyperglycemia, may be a novel comprehensive tool for assessing the risk of adverse neonatal outcomes.

Introduction: Automated insulin delivery (AID) systems have improved glycemic control in individuals with type 1 diabetes (T1D) but overweight and increased cardiovascular risk remain a challenge. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are associated with improved cardiometabolic profile but are currently not approved for the treatment of T1D.

Material and methods: Individuals with T1D at Steno Diabetes Center Copenhagen, Denmark, treated with AID and off-label GLP-1 RA for at least six months between January 2017 and May 2024 were included in a retrospective chart review study.

Results: Nineteen individuals with (median [range]) age 42 (24-60) years were included. At GLP-1 RA initiation, hemoglobin A1c (HbA1c) was 7.3% (6.1%-8.7%), HbA1c 56 (43-72) mmol/mol, body weight 91.5 (78.0-115.0) kg, and body mass index 35.4 (27.0-42.0) kg/m². Time in range was 74% (29%-82%), time above range 25% (18%-71%) while time below range was 1% (0%-5%). After six months of treatment, body weight changed -11% (-22% to -3%; P = .001) and total daily insulin dose changed -15.1 (-32.5 to -8.2) IU (P = .004). There were no significant changes in HbA1c or other glucose measures. One person developed ketoacidosis caused by infusion set failure, but none reported severe hypoglycemia.

Conclusion: Glucagon-like peptide-1 receptor agonist as add-on therapy for six months in individuals with obesity and AID-treated T1D led to considerable weight loss and a reduction in insulin dose.

Background: Glucagon-like peptide 1 (GLP-1) is a hormone that promotes insulin secretion, delays gastric emptying, and inhibits glucagon secretion. The GLP-1 receptor agonists have been developed as adjunctive therapies for type 2 diabetes to improve glucose control. Recently, there has been an interest in introducing GLP-1 receptor agonists as adjunctive therapies in type 1 diabetes alongside automatic insulin delivery systems. The preclinical validation of these systems often relies on mathematical simulators that replicate the glucose dynamics of a person with diabetes. This review aims to explore mathematical models available in the literature to describe GLP-1 effects to be used in a type 1 diabetes simulator.

Methods: Three databases were examined in the search for GLP-1 mathematical models. More than 1500 works were found after searching for specific keywords that were narrowed down to 39 works for full-text assessment.

Results: A total of 23 works were selected describing GLP-1 pharmacokinetics and pharmacodynamics. However, none of the found models was designed for type 1 diabetes. An analysis is included of the available models' features that could be translated into a GLP-1 receptor agonist model for type 1 diabetes.

Conclusion: There is a gap in research in GLP-1 receptor agonists mathematical models for type 1 diabetes, which could be incorporated into type 1 diabetes simulators, providing a safe and inexpensive tool to carry out preclinical validations using these therapies.