Diabetes technology & therapeutics最新文献

Diabetic retinopathy (DR) is a common and potentially sight-threatening complication of diabetes. Early detection of DR through screening can prevent visual loss. Handheld fundus cameras combined with artificial intelligence (AI) technology may improve DR screening. We evaluated the Aireen AI algorithm's performance in grading DR in fundus images captured by the handheld Optomed Aurora. Two retina specialists and Aireen graded 624 fundus images for DR. Sensitivity, specificity, and predictive values were measured against the ophthalmologists' grading. Overall, 97% of images were sufficient for DR classification. Aireen demonstrated 94.8% sensitivity, 91.4% specificity, and 92.7% diagnostic accuracy for DR. Aireen showed high diagnostic accuracy in detecting DR in Optomed Aurora images, suggesting its potential for effective screening. The validated use of AI with a handheld fundus camera may streamline the screening process, reduce the burden on health care professionals, and improve access to screening and patient outcomes through enhanced diagnostic accuracy.

Objective: To evaluate trends in insulin delivery and day-to-day variability of insulin requirements over 48 months of hybrid closed-loop use following diagnosis of type 1 diabetes (T1D) in individuals aged 10-16 years. Methods: A secondary analysis of the closed-loop arm of an open-label, multicenter, randomized, parallel hybrid closed-loop trial assessing closed-loop insulin delivery in newly diagnosed children and adolescents with T1D was conducted. Mean total daily dose (TDD) over 24 h and during the night, as well as mean total basal and bolus insulin over 24 h, were calculated. Day-to-day variability of insulin requirements was evaluated over 24 h and at night. Results: TDD increased from 27.2 ± 16.1 units/d (mean ± standard deviation) at 0-3 months following diagnosis to 65.7 ± 24.9 units/d at 42-48 months. The proportion of total daily insulin delivered as basal insulin rose from 41% to 61% over 48 months. Day-to-day variability of insulin requirements after diagnosis was high (coefficient of variation at 0-3 months: 23.3 ± 0.9%) and remained stable over 48 months. No clinically relevant sex-based differences were observed in insulin requirements. Conclusions: During the first 48 months after diagnosis of T1D, insulin requirements in children and adolescents more than double with hybrid closed-loop insulin delivery. Over time, a greater proportion of insulin is administered via the closed-loop algorithm, and the high day-to-day variability in insulin needs underscores the importance of initiating adaptive closed-loop systems from diagnosis.

In this study, we aimed to explore the impact of meal carbohydrate (CHO) content on postprandial hyperglycemia in hospitalized patients receiving fully automated insulin delivery (AID). We performed a post-hoc analysis of two trials and analyzed 844 postprandial periods from 48 adults treated with fully AID (FlorenceD2W-T2 or CamAPS HX) in hospital using generalized additive regression models. Meal CHO content had a nonlinear effect on postprandial hyperglycemia risk (P < 0.001). Postprandial hyperglycemia was more likely at breakfast compared with lunch and dinner (odds ratio or OR [95% confidence interval or CI] 1.8 [1.2, 2.6], P = 0.006; and 1.5 [1.1, 2.2], P = 0.05, respectively) and more frequent on days with glucocorticoid administration (OR [95% CI] 3.3 [2.1, 5.1]; P < 0.001). In conclusion, during fully AID in hospitalized patients, the risk of postprandial hyperglycemia remained <50% for meals ≤50 g CHO. The CHO tolerance was lowest at breakfast and with concomitant glucocorticoid therapy across all meals.

Introduction: Consensus guidelines recommend reviewing 14 days of continuous glucose monitor (CGM) data when assessing glycemia in people with type 1 diabetes (T1D). Adult studies have shown that 7 days of CGM data provide a reliable assessment of glycemia. Objectives: To understand the minimum amount of CGM data required to assess glycemia in the pediatric T1D population. Methods: Real-world Dexcom G6 CGM data were extracted from cloud-based CGM software for 8 time windows (3, 5, 7, 10, 14, 30, 60, and 90 days), all starting on March 1, 2023. Youth <21 years with T1D and ≥70% CGM active time in each window were included. Pearson correlation and interclass correlation coefficients (ICCs) between 14-day data and other windows were calculated. Differences in the percentage of youth within predetermined thresholds of 14-day CGM metrics (±0.3% glucose management indicator [GMI]; ±5% time in range [TIR]/time in tight range; ±1% time below range <70 and <54 mg/dL) were assessed using chi-squared analyses. Sub-analyses were conducted according to categorical groupings of 14-day TIR, coefficient of variation (CV), and age. Results: A total of 1316 youth were included (45.0% female, 76.9% non-Hispanic White, median age 14.6 years). Median 14-day CGM active time was 97.2% and GMI and TIR were 7.4% (7.0, 7.9) and 60.5% (48.6, 70.6), respectively. Pearson correlation coefficients and ICCs between 14-day and GMI and TIR for all 8 windows were >0.9; however, categorical agreement as defined by the percentage of subjects acceptable thresholds for GMI and TIR only exceeded 90% at 10 days. Although there was no difference in agreement for CGM metrics according to categorical groupings of age, agreement was stronger for youth with TIR ≥70% and CV <36%. Conclusions: Although 14 days of CGM data are considered the gold standard, assessing ∼9.6 days of data in youth with T1D provides a reliable assessment of glycemia. For youth with higher TIR (≥70%) and lower CV (<36%), 7-day CGM data may prove sufficient.

Background: Post hoc evaluation of ONWARDS 6 assessed continuous glucose monitoring (CGM) metrics and CGM-based hypoglycemia with once-weekly insulin icodec (icodec) and once-daily insulin degludec (degludec) in adults with type 1 diabetes. Methods: Open CGM data were collected throughout ONWARDS 6. During weeks 0-4, 22-26, and 48-52, time in range (TIR; 3.9-10.0 mmol/L), time above range (TAR; >10.0 mmol/L), and time below range (TBR; <3.9 and <3.0 mmol/L) were assessed by treatment day after icodec injection or degludec titration. Rates of CGM-based clinically significant hypoglycemic episodes (<3.0 mmol/L for ≥15 consecutive min) and durations of CGM-based hypoglycemic episodes (<3.9 mmol/L) and CGM-based periods <3.0 mmol/L were reported (baseline to weeks 26 and 57). Results: Although rates of overall CGM-based clinically significant hypoglycemia were statistically significantly higher with icodec than degludec from baseline to week 26 and week 57, estimated rate ratios (icodec/degludec) for CGM-based clinically significant hypoglycemic episodes were lower than those estimated from self-measured blood glucose (SMBG) data from baseline to week 26 (1.38 vs. 1.88) and to week 57 (1.28 vs. 1.79). Observed percentage of TIR was highest on days 2-4 after icodec injection with a concomitant reduction in TAR and an increase in TBR. Median duration of CGM-based hypoglycemic episodes was comparable between treatment arms from baseline to week 26 (icodec: 35 min; degludec: 30 min) and to week 57 (35 min for both treatments). Median duration of CGM-based periods <3.0 mmol/L was the same for both treatments at week 26 and week 57 (25 min). Conclusion: In adults with type 1 diabetes, estimated rate ratios for CGM-based clinically significant hypoglycemia were lower than those estimated from SMBG data, although still favoring degludec with estimated rate ratios of 1.28-1.38. CGM metrics varied by treatment day after icodec injection, but median duration of CGM-based hypoglycemia was comparable between treatment arms.

Limited insulin pump cartridge volumes can present challenges to automated insulin delivery (AID) system use for adolescents and young adults (AYA) with type 1 diabetes (T1D) and high insulin requirements. We assessed the real-world safety and effectiveness of U200 concentrated insulin use in AID (U200-AID) among AYAs with T1D. We conducted a two-center, retrospective cohort study assessing glycemia, pump utilization, and safety outcomes pre-/post-U200-AID. Among 50 AYAs initiating U200-AID (age 15.4 years, T1D duration 5.5 years, hemoglobin A1c 8.5%), time in range (70-180 mg/dL) increased (44.6% ± 12.6% vs. 48.9% ± 11.4%, P = 0.012) and time below range (<70 mg/dL) did not change significantly. Days between cartridge changes increased (2.2 ± 0.5 vs. 3.0 ± 0.5 days, P < 0.001) despite increased total daily insulin dose (102.6 ± 23.5 vs. 125.8 ± 38.9 U100 insulin units, P < 0.001). No severe hypoglycemia or diabetic ketoacidosis occurred (median follow-up 290 days [interquartile range 227, 476]). These data suggest that U200-AID is a viable option for individuals with T1D and high insulin requirements.

Objective: Qualitative meal size estimation (QMSE) could be an interesting alternative to precise carbohydrates counting (PCC) for patients equipped with hybrid closed-loop systems (HCL). The aim is to compare postprandial glycemic control following meals declared by QMSE of the DBLG1 system with PCC. Methods: We randomly selected a 20% sample of patients from the commercial database of type 1 diabetes patients equipped with the DBLG1 system in Europe. We assumed that when the carbohydrates (CHO) amount was identical to the predefined average meal value (small, medium, or large meal), the patient used the semiquantitative method, and the corresponding meals were assigned to the QMSE group. The others were assigned to the PCC group. The glucose metrics of the meals were computed during the postprandial period, defined as [t_meal; t_meal + 4 h], provided that there was no other meal during this 4-h period or during the previous 4 h. Results: A total of 1959 patients from seven Western European countries were included (mean HbA1c 7.6% ± 1.2%; mean age 43.9 ± 14.7 years). Overall, 287,000 meals (47%) were declared with PCC and 327,819 (53%) with QMSE and the mean meal size was 47.2 ± 32.5 g and 48.4 ± 28.6 g, respectively. The postprandial TIR was 62.39% ± 30.86% with QMSE and 63.21% ± 30.62% with PCC. The mean TIR difference of 0.81% was statistically significant but not clinically relevant. Time below range (TBR) was low for both methods of declaration (TBR < 70 mg/dL of 1.4% ± 5.0% with QMSE and 1.4% ± 4.8% with PCC). Conclusion: The semi-quantitative CHO declaration achieves similar glycemic results as CHO counting in this retrospective study. This method could help to reduce the burden of diabetes and offers an alternative to patients reluctant to use CHO counting.

Objective: Multiple daily injections therapy in older adults with diabetes can negatively impact glycemic control and comorbidities. This issue may be overcome with advanced diabetes technology that reduces hypoglycemia and hyperglycemia. The present study evaluated real-world glycemic outcomes of a United States (US) cohort ≥65 years using the MiniMed™ 780G (MM780G) advanced hybrid closed-loop system. Methods: CareLink™ personal data as of December 18, 2024, for U.S. MM780G system users ≥65 years, were de-identified and analyzed. Metrics, including time in range (TIR 70-180 mg/dL), time in tight range (TITR 70-140 mg/dL), time below range 70 mg/dL (TBR70), and time above range 180 mg/dL and 250 mg/dL (TAR180 and TAR250, respectively), with and without recommended optimal settings (ROS, 100 mg/dL glucose target with 2 h active insulin time) were determined. Subanalyses based on age group (≥75 years) and type 1 diabetes (T1D) or type 2 diabetes (T2D) were, also, conducted. Results: The overall cohort (n = 8542) had a mean TIR, TITR, TBR70, TAR180, and TAR250 of 78.4%, 51.4%, 0.9%, 20.7%, and 3.6%, respectively, with a 6.8% glucose management indicator. For ROS users (n = 2753), TIR and TITR were higher (81.9% and 55.9%, respectively, P < 0.001), and TAR180 and TAR250 were lower (17.2% and 2.5%, respectively, P < 0.001). Data trended similarly among the population aged ≥75 years, and no differences were observed between T1D and T2D. Conclusions: In a real-world setting, a U.S. cohort aged ≥65 years using the MM780G system achieved consensus-recommended glycemic targets. Use of ROS enabled more users to achieve an even higher level of glycemic control.