Performance of an Automated Insulin Delivery System in People Living With Type 2 Diabetes and Insulin Resistance: First Real-World Evidence in 26 427 Users.
Isabeau Thijs, Arcelia Arrieta, Javier Castañeda, Michael Joubert, Francesco Giorgino, Benedikt Voelker, Tim Van den Heuvel, Jeremy Basset-Sagarminaga, Goran Petrovski, John Shin, Robert Vigersky, Ohad Cohen
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引用次数: 0
Abstract
Background: Type 2 diabetes (T2D) is a phenotypically heterogeneous disease. The use of insulin is required in a significant portion of people with T2D, despite recent developments in antidiabetic medications. This study analyzes glycemic outcomes in automated insulin delivery (AID) users with T2D with different insulin requirements.
Methods: This is a retrospective, real-world analysis including MiniMed 780G (MM780G) data uploaded to CareLink Personal (January 2020 to April 2024). Four cohorts were identified based on phenotypes of T2D: (A) users with total daily dose of insulin (TDD) ≥ 100 IU, (B) users with self-reported T2D, (C) users with self-reported T2D and TDD ≥ 100 IU, and (D) users with self-reported T2D and TDD <100 IU. Glycemic outcomes and insulin use were assessed post-AID, pre-AID versus post-AID, and six-month longitudinal post-AID.
Results: A total of 26 427 users were included in this study, of which 18 466 in cohort A, 10 795 in cohort B, 2 834 in cohort C, and 7 961 in cohort D. Mean time in range (TIR) was 71.1% ± 12.2 for cohort A, 75.1% ±14.1 for cohort B, 72.2% ± 15.0 for cohort C, and 76.1% ± 13.6 for cohort D. Mean time below range (TBR) <70 mg/dL was ≤1% in all cohorts. The users in cohort C using the recommended optimal settings (glucose target [GT] of 100 mg/dL and active insulin time [AIT] of two hours) had a greater TIR with 78.7% ± 10.8. All cohorts increased ≥10% post-AID compared with pre-AID.
Conclusions: The use of this AID is associated with effective therapy outcomes, as indicated by over 70% TIR, and appears to be safe, as demonstrated by a low TBR in a large cohort of real-life users with self-reported T2D and high or low TDD.
期刊介绍:
The Journal of Diabetes Science and Technology (JDST) is a bi-monthly, peer-reviewed scientific journal published by the Diabetes Technology Society. JDST covers scientific and clinical aspects of diabetes technology including glucose monitoring, insulin and metabolic peptide delivery, the artificial pancreas, digital health, precision medicine, social media, cybersecurity, software for modeling, physiologic monitoring, technology for managing obesity, and diagnostic tests of glycation. The journal also covers the development and use of mobile applications and wireless communication, as well as bioengineered tools such as MEMS, new biomaterials, and nanotechnology to develop new sensors. Articles in JDST cover both basic research and clinical applications of technologies being developed to help people with diabetes.