Diabetes technology & therapeutics最新文献

Background: The Omnipod^® 5 Automated Insulin Delivery System was associated with favorable glycemic outcomes for people with type 1 diabetes (T1D) in two pivotal clinical trials. Real-world evidence is needed to explore effectiveness in nonstudy conditions. Methods: A retrospective analysis of the United States Omnipod 5 System users (aged ≥2 years) with T1D and sufficient data (≥90 days of data; ≥75% of days with ≥220 continuous glucose monitor readings/day) available in Insulet Corporation's device and person-reported datasets as of July 2023 was performed. Target glucose setting usage (i.e., 110-150 mg/dL in 10 mg/dL increments) was summarized and glycemic outcomes were examined. Subgroup analyses of those using the lowest average glucose target (110 mg/dL) and stratification by baseline characteristics (e.g., age, prior therapy, health insurance coverage) were conducted. Results: In total, 69,902 users were included. Multiple and higher glucose targets were more commonly used in younger age groups. Median percentage of time in range (TIR; 70-180 mg/dL) was 68.8%, 61.3%, and 53.6% for users with average glucose targets of 110, 120, and 130-150 mg/dL, respectively, with minimal time <70 mg/dL (all median <1.13%). Among those with an average glucose target of 110 mg/dL (n = 37,640), median TIR was 65.0% in children and adolescents (2-17 years) and 69.9% in adults (≥18 years). Subgroup analyses of users transitioning from Omnipod DASH or multiple daily injections and of Medicaid/Medicare users demonstrated favorable glycemic outcomes among these groups. Conclusion: These glycemic outcomes from a large and diverse sample of nearly 70,000 children and adults demonstrate effective use of the Omnipod 5 System under real-world conditions.

Background: Continuous glucose monitors (CGMs) are increasingly used to provide detailed quantification of glycemic control and glucose variability. An open-source R package iglu has been developed to assist with automatic CGM metrics computation and data visualization, providing a comprehensive list of implemented CGM metrics. Motivated by the recent international consensus statement on CGM metrics and recommendations from recent reviews of available CGM software, we present an updated version of iglu with improved accessibility and expanded functionality. Methods: The functionality was expanded to include automated computation of hypo- and hyperglycemia episodes with corresponding visualizations, composite metrics of glycemic control (glycemia risk index and personal glycemic state), and glycemic metrics associated with postprandial excursions. The algorithm for mean amplitude of glycemic excursions has been updated for improved accuracy, and the corresponding visualization has been added. Automated hierarchical clustering capabilities have been added to facilitate statistical analysis. Accessibility was improved by providing support for the automatic processing of common data formats, expanding the graphical user interface, and providing mirrored functionality in Python. Results: The updated version of iglu has been released to the Comprehensive R Archive Network (CRAN) as version 4. The corresponding Python wrapper has been released to the Python Package Index (PyPI) as version 1. The new functionality has been demonstrated using CGM data from 19 subjects with prediabetes and type 2 diabetes. Conclusions: An updated version of iglu provides comprehensive and accessible software for analyses of CGM data that meets the needs of researchers with varying levels of programming experience. It is freely available on CRAN and on GitHub at https://github.com/irinagain/iglu.

Glycemic control immediately upon hospitalization is difficult. Endocrine Society guidelines suggest starting scheduled insulin therapy at 0.2-0.5 units/kg/day, but there has been no rigorous study to support this recommendation. To understand the variability of current practice, we surveyed starting insulin algorithms for noncritically ill patients among the top-ranking academic hospitals in the United States. Among the 20 hospitals with reported algorithms, 12 specified which patients should start with basal/nutritional insulin, whereas 5 specified who should start with only correction insulin. Weight-based and/or home-dose-based calculations were used to estimate the initial insulin requirements with various modifiers. In addition, various factors were considered when choosing among the correction dose algorithms. In summary, among the U.S. academic hospitals, there is variability in methods for determining insulin dosing on admission for noncritically ill patients. This inconsistency suggests that future studies to estimate initial insulin requirements are required.

Objective: To analyze the Glycemic Risk Index (GRI) and assess their possible differences according to coefficient of variation (CV) in a cohort of real-life type 1 diabetes mellitus (DM) patient users of intermittently scanned continuous glucose monitoring (isCGM). Patients and Methods: In total, 447 adult users of isCGM with an adherence ≥70% were included in a cross-sectional study. GRI was calculated with its hypoglycemia (CHypo) and hyperglycemia (CHyper) components. Multivariate linear regression analysis was performed to evaluate the factors associated with GRI. Results: Mean age was 44.6 years (standard deviation [SD] 13.7), 57.7% being male; age of DM onset was 24.5 years (SD 14.3) and time of evolution was 20.6 years (SD 12.3). In patients with CV >36% (52.8%) versus CV ≤36% (47.2%), differences were observed in relation to GRI (18.8% [SD 1.9]; P < 0.001), CHypo (2.9% [SD 0.3]; P < 0.001), CHyper (6.3% [SD 1.4]; P < 0.001), and all classical glucometric parameters except time above range level 1. The variables that were independently associated with GRI in patient with CV >36% were time in range (TIR) (β = -1.49; confidence interval [CI:] 95% -1.63 to -1.37; P < 0.001), glucose management indicator (GMI) (β = -7.22; CI: 95% -9.53 to -4.91; P < 0.001), and CV (β = 0.85; CI: 95% 0.69 to 1.02; P < 0.001). However, in patients with CV ≤36%, the variables were age (β = 0.15; CI: 95% 0.03 to 0.28; P = 0.019), age of onset (β = -0.15; CI: 95% -0.28 to -0.02; P = 0.023), TIR (β = -1.35; CI: 95% -1.46 to -1.23; P < 0.001), GMI (β = -6.67; CI: 95% -9.18 to -4.15; P < 0.001), and CV (β = 0.33; CI: 95% 0.11 to 0.56; P = 0.004). Conclusions: In this study, the factors independently associated with metabolic control according to GRI are modified by glycemic variability.

Despite significant efforts in the development of noninvasive blood glucose (BG) monitoring solutions, delivering an accurate, real-time BG measurement remains challenging. We sought to address this by using a novel radiofrequency (RF) glucose sensor to noninvasively classify glycemic status. The study included 31 participants aged 18-65 with prediabetes or type 2 diabetes and no other significant medical history. During control sessions and oral glucose tolerance test sessions, data were collected from both a RF sensor that rapidly scans thousands of frequencies and concurrently from a venous blood draw measured with an US Food and Drug Administration (FDA)-cleared glucose hospital meter system to create paired observations. We trained a time series forest machine learning model on 80% of the paired observations and reported results from applying the model to the remaining 20%. Our findings show that the model correctly classified glycemic status 93.37% of the time as high, normal, or low.

Background and Aims: There has been an evolving trend in the use of intermittently scanned continuous glucose monitoring (isCGM) among individuals with type 1 diabetes. Although isCGM is proven to be beneficial in the treatment of individuals with type 1 diabetes, its use leads to increasing device costs. This study aimed to investigate the long-term cost-effectiveness of isCGM. Methods: Long-term clinical outcomes and costs were projected using the IQVIA Core Diabetes Model (v10.0) based on the observed real-world outcomes of isCGM. The clinical input data for the analysis were sourced from a real-world patient cohort from Eastern Finland, including 877 adult individuals with type 1 diabetes with isCGM (i.e., Freestyle Libre 1 and 2). At the baseline, the patients' mean age was 48 years, and the mean duration of diabetes was 25.8 years. The mean baseline HbA1c was 8.6%, and the mean 12-month change from baseline in HbA1c was -0.37% after the initiation of isCGM. The cost-effectiveness analysis was performed over a lifetime time horizon. A discount rate of 3% was used for the future costs and health outcomes. Results: The projected use of isCGM was associated with improved quality-adjusted life year (QALY) expectancy of 0.84 QALYs after the start of isCGM. The direct lifetime costs were 7861 EUR higher with the use of isCGM, which resulted in an incremental cost-effectiveness ratio of 9396 EUR per QALY gained. Conclusions: According to the present analysis, the use of isCGM is considered cost-effective in adult individuals with type 1 diabetes in a real-world setting in Finland.

Objective: Glycemic management in people with type 2 diabetes mellitus (T2DM) on insulin-secretagogue regimens without insulin is of importance, as this group still represents a significant proportion of patients. Risks for acute diabetes events (ADEs), including diabetic ketoacidosis (DKA) or hypoglycemia, using insulin-secretagogue drugs are well established. Few studies have suggested that continuous glucose monitoring (CGM) could be useful for monitoring glucose dynamics associated with the use of such therapies. To document this point an exploratory analysis was conducted in a group of individuals with noninsulin treated T2DM in France who are managed with oral insulin-secretagogues and initiating the FreeStyle Libre^® system (FSL). Methods: A retrospective study of the French national SNDS reimbursement claims database (≈66 million French people) was conducted to identify people with T2DM on oral insulin-secretagogues and receiving a first reimbursement of FSL between August 1, 2017 and December 31, 2018. The analysis included data for the 12 months before and up to 24 months after FSL initiation. Hospitalizations for diabetes-related acute events were identified using ICD-10 codes as main or related diagnosis, for: hypoglycemic events; DKA events; comas; and hyperglycemia-related admissions. Results: A total of 1272 people with T2DM on insulin-secretagogues without insulin initiated FSL during the selection period. Of these, 7.15% had at least one hospitalization for any ADE in the year before FSL initiation, compared with 2.52% at 12 months and 2.83% at 24 months following FSL initiation. Reductions in ADEs were driven by -73% fewer admissions for ADEs related to diabetic ketoacidosis (DKA) or other hyperglycemia-related events. These patterns of reduced ADEs persisted after 2 years. Conclusions: This study suggests the value of the FSL system in reducing ADEs in some people with T2DM in France being treated with insulin-secretagogues without insulin. Characteristics of these patients remain to be documented.

Objective: We analyzed the effect of implementing a flash glucose monitoring (FGM) technology in a public health care system with universal coverage on the rate of severe hypoglycemia requiring urgent care in adults with type 1 diabetes mellitus (T1DM). Methods: Using a comprehensive regional dataset, we extracted emergency care codes with hypoglycemia in individuals with T1DM who initiated the use of FGM in Andalucia, Spain, from January 1, 2020, to December 31, 2021. Severe hypoglycemia was defined as a confirmed blood glucose <70 mg/dL, which required the urgent dispatch of an emergency medical service (EMS) for onsite management. We compared hypoglycemic events reported in the 12 months before and after the initiation of FGM to determine the population incidence rates. Results: A total of 13,616 participants with a mean age of 43.7 ± 13.5 years were included. The follow-up periods were 23.4 and 24.8 months before and after FGM. There were 969 and 737 cases of hypoglycemia before and after the initiation of FGM. The baseline incidence rate was 358.58 episodes per 10,000 person-years, which decreased to 260.9 at the end of the follow-up (rate-ratio 0.72 [0.66; 0.80]). The reduction in hypoglycemia was significant in individuals aged ≥60 years (rate-ratio 0.40 [0.28; 0.55]) and males (0.64 [0.56; 0.72]). In addition, there was a reduction in the overall median HbA1c of -0.35% (95% CI [-0.38; -0.33], P < 0.001). Conclusion: The implementation of FGM systems in a public health care system as a provision for adults with T1DM was associated with significant reductions in the rate of severe hypoglycemic events that required urgent EMS care.

Objectives: To study real-world effect of switching to Insulin Glargine 300 U/mL (Gla-300) on glucose metrics in people with type 1 diabetes. Methods: This retrospective secondary-use study compared 151 adults who switched to Gla-300 from first-generation long-acting insulins (Switchers) to 281 propensity-score matched controls (Non-switchers) who continued first-generation long-acting insulins. Primary endpoint was difference in time in range (TIR) evolution. A fictive "switching" date was assigned to Non-switchers to facilitate between-group comparisons. Results: In the period before switching, TIR decreased numerically for people in whom Gla-300 was eventually initiated (-0.05%/month [-0.16 to 0.07]), while it increased for matched controls (0.08%/month [0.02 to 0.015]; between-group difference P = 0.047). After Gla-300-initiation, Switchers had similar TIR increase compared to Non-switchers (P = 0.531). Switchers used higher basal dose than before switch (Δ0.012 U/[kg·d] [0.006 to 0.018]; P < 0.0001). Conclusion: In real-life, Gla-300 was typically initiated in people where TIR was decreasing, which was reversed after switch using slightly higher basal insulin dose. ClinicalTrials: ClinicalTrials.gov number NCT05109520.

Introduction: Most continuous subcutaneous insulin infusion (CSII) catheters (KT) are changed every 3 days. This study aims at evaluating whether KT changes impact glucose control while under open-loop (OL) or automated insulin delivery (AID) modes. Methods: We included patients with type 1 diabetes who used Tandem t:slim x2 insulin pump and Dexcom G6 glucose sensor for 20 days in OL, then as AID. CSII and sensor glucose data in OL and for the past 20 days of 3-month AID were retrospectively analyzed. The percentage of time spent with sensor glucose above 180 mg/dL (%TAR180) was compared between the calendar day of KT change (D0), the next day (D1), and 2 days later (D2). Values were adjusted for age, gender, body mass index (BMI), hemoglobin A1c (HbA1c) at inclusion, and %TAR180 for the 2 h before KT change. Results: A total of 1636 KT changes were analyzed in 134 patients: 72 women (54%), age: 35.6 ± 15.7 years, BMI: 25.2 ± 4.7 kg/m², and HbA1c: 7.5 ± 0.8%. %TAR180 in the 2 h before the KT change was 51.3 ± 37.0% in OL and 33.2 ± 30.0% in AID mode. In OL, significant absolute increases of %TAR180 at D0 versus D1 (+6.9%; P < 0.0001) or versus D2 (+6.8%; P < 0.0001) were observed. In AID, significant absolute increases of %TA180R at D0 versus D1 (+4.8%; P < 0.0001) or versus D2 (+4.2%; P < 0.0001) were also observed. Conclusion: This study shows an increase in time spent in hyperglycemia on the day of the KT change both in OL and AID modes. This additional information should be taken into account to improve current AID algorithms. ClinicalTrials.gov: NCT04939766.