Journal of Diabetes Science and Technology最新文献

The downside of the success story of diabetes technology is the amount of waste generated by manufacturing and using such medical products. Quantitative surveys performed in the United States and Germany document how many pounds of plastic waste, batteries, and electronic parts are piling up per patient with diabetes each year; however, the actual environmental burden of diabetes care has not been assessed yet. Given the highly probable further increase in usage of diabetes technology devices, what are the options to change the situation? Ideally, one would avoid generating waste by optimizing the design of the products (Eco-Design); however, this approach faces a number of complex regulatory and economic hurdles. The issue of waste management is becoming increasingly important as the use of wearable devices increases. Stewardship of the environment will require all stakeholders to address waste management.

Background: Comparative assessment of therapeutic technologies is often biased due to the inability to blind interventions, especially when therapies differ in form or dosing. While double-dummy design, where participants receive both an active treatment and a matched placebo to maintain blinding, is well established in pharmacological trials, its applicability for medical devices requiring user interaction, such as automated insulin delivery (AID) systems is challenging.

Methods: We present the methodology by which two AID systems are used in a double-dummy, blinded, randomized trial, one system providing insulin therapy and the other, a diluent.Outcomes and conclusion:The study demonstrates the feasibility, of comparing 2 AID systems, without operartor bias.

Automated insulin delivery (AID) systems adapt insulin delivery via a predictive algorithm integrated with continuous glucose monitoring and an insulin pump. Automated insulin delivery has become standard of care for glycemic management of people with type 1 diabetes (T1D) outside pregnancy, leading to improvements in time in range, with lower risk for hypoglycemia and improved treatment satisfaction. The use of AID facilitates optimal preconception care, thus more women of reproductive age are becoming pregnant while using AID. The effectiveness and safety in pregnant populations of using AID systems with algorithms for non-pregnant populations may be impacted by requirements for lower glucose targets and existence of increased insulin resistance during gestation. The CamAPS FX is the only AID system approved for use in pregnancy. A large randomized controlled trial (RCT) with this AID system demonstrated a 10.5% increase in time in pregnancy range (an additional 2.5 hours/day) compared with standard insulin therapy in pregnant women with T1D with a baseline glycated hemoglobin A1c (HbA_1c) ≥48 mmol/mol (6.5%). A RCT of AID not approved for use in pregnancy (MiniMed 780G) has also demonstrated some benefits of AID compared with standard insulin therapy with improved time in pregnancy range overnight (24 minutes), less hypoglycemia, and improved treatment satisfaction. There is also increasing evidence that AID can be safely continued during delivery and postpartum, while maintaining glycemic goals with lower risk for hypoglycemia. More AID systems are needed with flexible glucose targets in the pregnancy range and possibly with algorithms that better adapt to changing insulin requirements. More evidence is needed on the impact of AID on maternal and neonatal outcomes. We review the current evidence on the use of AID in pregnancy and postpartum.

Metrics derived from continuous glucose monitoring (CGM) systems are often discordant between systems. A major cause is that CGM systems are not standardized; they use various algorithms and calibration methods, leading to discordant CGM readings across systems. This discordance can be addressed by standardizing CGM performance assessments: If manufacturers aim their CGM systems at the same target, then CGM readings will align across systems. This standardization should include the comparator device, sample origin, and study procedures. With better aligned CGM readings, CGM-derived metrics will subsequently also align better between systems.

The mainstay of type 1 diabetes (T1D) management in pregnancy is optimization of glucose levels in a tight range. Achieving euglycemia has been revolutionized by advances in diabetes technology, including the development of automated insulin delivery (AID) systems. A small but growing population of gravidas with T1D elects to pursue off-label use of AID systems in pregnancy, and their outcomes have been described in numerous observational cohorts. This review aims to aggregate data from all available observational studies examining glycemic, maternal, and neonatal outcomes associated with antenatal AID use. A total of 243 pregnancies managed antenatally with AID were described in 24 publications, with largely reassuring outcomes data. Time in range (TIR) with commercial AID systems was generally acceptable, with many patients reaching pregnancy target TIR > 70% by the third trimester. Time in range with open-source AID systems appeared even higher, although with the potential tradeoff of worse time below range (TBR). Clinically, there do not appear to be major differences in pregnancy outcomes between AID systems and other methods of insulin delivery, although this assumption is based largely on indirect comparisons with other population-level reports rather than direct comparisons within analytic observational cohorts. Clinical outcomes appear superior with open-source AID compared with commercial AID, although this should be interpreted with caution based on the small sample size of this subpopulation (n = 16) and potential confounding. The real-world evidence generated by these observational studies provides invaluable information for patients and providers seeking to improve outcomes for gravidas with T1D.

Background: The tight glycemia required to optimize type 1 diabetes (T1D) pregnancy outcomes is difficult to achieve with standard insulin therapies. Automated insulin delivery (AID) offers an avenue to improve glycemia, but most available systems are not configurable to tight pregnancy glucose targets. Open-source AID may meet the needs of some pregnant women with T1D, but available data on its efficacy and safety in pregnancy are limited.

Methods: This single-center retrospective study describes the glycemic and obstetric outcomes of pregnancies in which supported open-source AID (SOSAID) was used. Included patients had a pregnancy managed on SOSAID at BCDiabetes between January 2023 and October 2024 and consented for inclusion of their clinical data. Charts were reviewed to obtain comprehensive glycemic data, obstetric outcomes, and adverse events.

Results: Ten patients, mean age 33 years, had a mean pre-pregnancy A1c of 6.7% (range 5.8%-8.0%). There were no episodes of DKA or severe hypoglycemia. Mean time-in-range (TIR_{63-140 mg/dL}) was 68% in trimester 2 and 70% in trimester 3. Seven patients commenced SOSAID during pregnancy, with their median 14-day TIR rising from 52% pre-SOSAID to 71% immediately after commencing SOSAID. There were no perinatal deaths or congenital anomalies. Pre-term delivery occurred in 1/10 and hypertensive disorders of pregnancy occurred in 2/10 women. Birthweight above 4 kg was present in 3/10, and neonatal hypoglycemia occurred in 4/10.

Conclusions: SOSAID systems represent a promising tool for managing T1D in pregnancy and were successful in reaching target pregnancy glycemia in this single-center cohort.