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Comparing the technical reliability and insulin dosing of a "do-it-yourself artificial pancreas" with a commercial hybrid closed-loop system in a "shadow-mode" scenario: An exploratory study Day-and-night glycaemic control with closed-loop insulin delivery versus conventional insulin pump therapy in free-living adults with well controlled type 1 diabetes: an open-label, randomised, crossover study. Keywords: CSII; insulin pump therapy; subcutaneous injection; type 1 diabetes EN CSII insulin pump therapy subcutaneous injection type 1 diabetes 2780 2783 4 08/03/23 20230901 NES 230901 INTRODUCTION Do-it-yourself artificial pancreas systems (DIYAPSs) are compound devices that autonomously adjust insulin dosage in people with diabetes. However, the DIYAPS was not delivering insulin, which might have caused an overcompensation of insulin adaptions if delivered insulin doses were lower than calculated by the DIYAPS. [Extracted from the article]

Open source artificial pancreas systems (OpenAPS) have gained considerable interest in the diabetes community. We analyzed continuous glucose monitoring (CGM) records of 80 OpenAPS users with type 1 diabetes (T1D). A total of 19 495 days (53.4 years) of CGM records were available. Mean glucose was 7.6 ± 1.1 mmol/L, time in range 3.9–10 mmol/L was 77.5 ± 10.5%, <3.9 mmol/L was 4.3 ± 3.6%, <3.0 mmol/L was 1.3 ± 1.9%, >10 mmol/L was 18.2 ± 11.0% and > 13.9 mmol/L was 4.1 ± 4.0%, respectively. In 34 OpenAPS users, additional CGM records were obtained while using sensor‐augmented pump therapy (SAP). After changing from SAP to OpenAPS, lower mean glucose (−0.6 ± 0.7; P < 0.0001), lower estimated HbA1c (−0.4 ± 0.5%; P < 0.0001), higher time in range 3.9–10 mmol/L (+9.3 ± 9.5%; P < 0.0001), less time < 3.0 mmol/L (−0.7 ± 2.2%; P = 0.0171), lower coefficient of variation (−2.4 ± 5.8; P = 0.0198) and lower mean of daily differences (−0.6 ± 0.9 mmol/L; P = 0.0005) was observed. Glycaemic control using OpenAPS was comparable with results of more rigorously developed and tested AP systems. However, OpenAPS was used by a highly selective, motivated and technology‐adept cohort, despite not being approved for the treatment of individuals with T1D. [ABSTRACT FROM AUTHOR]

Introduction: Real-world data provide insight into how medications perform in clinical practice. The PIONEER REAL Switzerland study aimed to understand clinical outcomes with oral semaglutide in adults with type 2 diabetes (T2D). Methods: PIONEER REAL Switzerland was a 34–44-week, multicentre, prospective, non-interventional, single-arm study of adults with T2D naïve to injectable glucose-lowering medication who were initiated on oral semaglutide in routine clinical practice. The primary endpoint was change in glycated haemoglobin (HbA1c) from baseline (BL) to end of study (EOS); secondary endpoints included change in body weight (BW) from BL to EOS and the proportion of participants achieving HbA1c < 7.0% and the composite endpoints HbA1c reduction ≥ 1%-points with BW reduction ≥ 3% or ≥ 5% at EOS. Safety was assessed in participants who received ≥ 1 dose of oral semaglutide. Results: Of the 185 participants (female/male, n = 67/118) initiating oral semaglutide, 168 (90.8%) completed the study and 143 (77.3%) remained on treatment with oral semaglutide at EOS. At BL, participants had a mean age of 62 years, diabetes duration of 6.4 years, HbA1c of 7.7%, BW of 95.6 kg and body mass index of 33.2 kg/m2; 56.2% of participants were receiving glucose-lowering medications. Significant reductions were observed for HbA1c (estimated change − 0.91%; 95% confidence interval [CI] − 1.10, − 0.71; p < 0.0001) and BW (estimated change − 4.85%; 95% CI − 5.70, − 4.00; p < 0.0001). In total, 139 adverse events (AEs) were reported in 65 (35.1%) participants; most were mild or moderate. The most frequent AEs were gastrointestinal disorders (27.0%); 31 AEs in 20 (10.8%) participants led to discontinuation of oral semaglutide. Six serious AEs were reported; all were considered unlikely to be related to oral semaglutide. Conclusion: People living with T2D treated with oral semaglutide in Switzerland achieved clinically significant reductions in HbA1c and BW, with no new safety signals. Clinical Trial Registration: ClinicalTrials.gov: NCT04537624. A graphical abstract is available for this article. [ABSTRACT FROM AUTHOR]

This article discusses the use of smartwatches and machine learning algorithms for detecting hypoglycemia in individuals with diabetes. The study conducted at the University Hospital Bern involved participants with type 1 diabetes who were subjected to hypoglycemia while driving in a simulator. The results showed that smartwatches can accurately detect pronounced hypoglycemia, but their performance in detecting mild hypoglycemia was less reliable. The study also found that electrodermal activity was a crucial feature for detecting hypoglycemia. Further research is needed to optimize the models for detecting milder hypoglycemia. The study was funded by various organizations and the authors declare no conflicts of interest. [Extracted from the article]

Studies on CGM assume equal glycemic control between MDI and CSII. However, observation periods are often short and the impact of hypoglycemia on glycemic control and variability is unclear. In this study, we analyzed 12846 days (35 years; CSII 2232 days [6 years]; MDI 10614 days [29 years]) of CGM readings obtained from 99 patients with T1D. In the overall analysis mean glucose was 169.6 ± 18.9mg/dl for CSII and 175.0 ± 29.5 mg/dl for MDI. Estimated A1c was 7.54 ± 0.66% (CSII) and 7.7 ± 1.0% (MDI). Percentage of readings in target range (70-180mg/dL) was 54.0 ± 15.5% for CSII and 57.4 ± 11.4% for MDI. In total, 3.1/2.2% of readings were < 54 mg/dL and 14.9/12.4% were > 250mg/dL for CSII and MDI, respectively. In total, 464 hypoglycemic events (< 54mg/dL) occurred in CSII (day: 305 [64%]; night: 170 [36%]), corresponding to 0.21 hypoglycemic events daily. In MDI, 2297 hypoglycemic events occurred (day: 1507/66%; night: 783/34%), corresponding to 0.22 hypoglycemic events daily. Mean duration of hypoglycemia was 146.5 ± 65.9 minutes (CSII) and 132.8 ± 42.8 minutes (MDI); 233 (CSII) vs. 982 (MDI) events were prolonged (>120 minutes). CV was 41.1 ± 6.7% for CSII and 40.8 ± 3.8% for MDI. In the 24 hours prior to hypoglycemia, mean glucose was higher (182.9 ± 30.5 vs. 166.9 ± 26.6mg/dl; p<0.001 for CSII and 167.2 ± 14.7 vs. 152.7 ± 19.1mg/dl; p=0.013 for MDI) and CV was lower (p<0.001 for both treatments) compared to the subsequent 24 hours. Age correlated to CV (r=-.254; p=0.028 for MDI), HbA1c to eA1c (r=.611; p=0.009 for CSII; r=.482; p=0.028 for MDI), and C-peptide to CGM-readings in target range (r=.246; p=0.042 for MDI), which negatively correlated to HbA1c (r=-.484; p<0.001 for MDI). Glycemic control was comparable between MDI and CSII in T1D. Glycemic variability was lower prior to a hypoglycemic event and in older patients, while remaining insulin secretion was associated with longer time in target range. Disclosure: A. Melmer: None. T. Züger: None. T. Pöttler: None. H. Kojzar: None. M. Cigler: None. F. Aberer: None. M. Laimer: None. J.K. Mader: Advisory Panel; Self; Boehringer Ingelheim International GmbH, Eli Lilly and Company, Prediktor Medical, Roche Diabetes Care, Sanofi. Speaker's Bureau; Self; Abbott, AstraZeneca, Dexcom, Inc., Novo Nordisk Inc. Stock/Shareholder; Self; decide Clinical Software GmbH. [ABSTRACT FROM AUTHOR]

Background: Thousands with type 1 diabetes are estimated to be using open source Artificial Pancreas Systems (APS) with commercially available insulin pumps, continuous glucose monitors (CGM), and an open source control algorithm to process glucose readings and adjust insulin delivery. OpenAPS and similar do-it-yourself (DIY) closed loop systems gained considerable interest in the online diabetes community. Many using DIY closed loop systems have chosen to donate their data to a shared, anonymized data repository called the "OpenAPS Data Commons." The present study evaluated glycemic control and glycemic variability of CGM readings of 80 DIY closed loop users. Methods: We analyzed 19251 days (53 years) of CGM readings with a mean duration of 134 days per patient (min. 3 days, max. 917 days) after the patient started looping. Results: Mean glucose was 137 ± 20mg/dl and estimated glycated hemogloblin A1c (eA1c) was 6.40 ± 0.70%. Time in target range (70-180mg/dL) was 77.5 ± 10.5%, 4.3% of CGM readings were below 70mg/dL, 1.3% were below 54mg/dL, 18.2% were above 180mg/dL, and 4.1% of CGM readings were above 250mg/dL, respectively. A total of 6474 hypoglycemic events (CGM reading < 54mg/dL) was observed (daytime: 5004 [73.9%]; nighttime: 1765 [26.1%]), which corresponds to 0.34 hypoglycemic events per day. The mean duration of each hypoglycemia event was 65.4 ± 41.4 minutes, and 1484 events were prolonged (duration > 120 minutes; daytime: 1043 [76.30%]; nighttime: 324 [23.7%]). Coefficient of variation (CV) was 35.5 ± 5.9% (daytime: 35.4%; nighttime: 33.9%) and mean of daily differences (MODD) was 50.1 ± 13.5 mg/dL. Conclusion: Open source AP systems show potential to support stable glycemic control in people with T1D. This is the largest descriptive analysis of open source APS data to date. The results are promising, but open source APS should be investigated in additional detail before a conclusion about their safety and efficacy can be drawn. Disclosure: A. Melmer: None. T. Züger: None. D.M. Lewis: Consultant; Self; Diabeloop SA, Roche Diabetes Care. S.M. Leibrand: Consultant; Self; Diabeloop SA, Roche Diabetes Care. M. Laimer: None. [ABSTRACT FROM AUTHOR]