Journal of Diabetes Science and Technology最新文献

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.

Batteries are an essential component of many medical products used for diabetes therapy. The increased use of such products comes along with millions of batteries that are disposed of every year. The design of these products should enable the recycling of batteries as they contain a significant number of valuable resources. Regulations in the United States and the European Union concerning batteries used in medical products are changing toward requiring and supporting establishing recycling procedures. Currently, respective programs are active only in some countries. A greener diabetes therapy would include more attention to reducing usage and disposing of batteries.

Background: This study demonstrates the difference between glucose management indicator (GMI) and glycated hemoglobin (HbA_1c) according to sensor mean glucose and HbA_1c status using 2 continuous glucose monitoring (CGM) sensors in people with type 1 diabetes.

Methods: A total of 275 subjects (117 Dexcom G6 [G6] and 158 FreeStyle Libre 1 [FL]) with type 1 diabetes was included. The G6 and FL sensors were used, respectively, over 90 days to analyze 682 and 515 glycemic profiles that coincide with HbA_1c.

Results: The mean HbA_1c was 6.6% in Dexcom G6 and 7.2% in FL profiles. In G6 profiles, GMI was significantly higher than HbA_1c irrespective of mean glucose (all P < .001, mean difference: 0.58% ± 0.49%). The GMI was significantly higher than the given HbA_1c when HbA_1c was below 8.0% (all P < .001). The discordance was the highest at 0.9% for lower HbA_1c values (5.0%-5.9%). The proportion of discordance >0.5% improved from 60.1% to 30.9% when using the revised GMI equation in G6 profiles. In FL profile, the overall mean difference between GMI and HbA_1c was 0 (P = .966). The GMI was significantly lower by 0.9% than HbA_1c of 9.0% to 9.9% and higher by 0.5% in HbA_1c of 5.0% to 5.9% (all P < .001).

Conclusions: The GMI is overestimated in G6 users, particularly those with well-controlled diabetes, but the GMI and HbA_1c discordance improved with a revised equation from the observed data. The FL profile showed greater discordance for lower HbA_1c levels or higher HbA_1c levels.

Background: We present a digital therapeutic (DTx) using continuous glucose monitoring (CGM) and an advanced artificial intelligence (AI) algorithm to digitally personalize lifestyle interventions for people with type 2 diabetes (T2D).

Method: A study of 118 participants with non-insulin-treated T2D (HbA_1c ≥ 6.5%) who were already receiving standard care and had a mean baseline (BL) HbA_1c of 7.46% (0.93) used the DTx for three months to evaluate clinical endpoints, such as HbA_1c, body weight, quality of life and app usage, for a pre-post comparison. The study also included an assessment of initial long-term data from a second use of the DTx.

Results: After three months of using the DTx, there was an improvement of 0.67% HbA_1c in the complete cohort and -1.08% HbA_1c in patients with poorly controlled diabetes (BL-HbA_1c ≥ 7.0%) compared with standard of care (P < .001). The number of patients within the therapeutic target range (< 7.0%) increased from 38% to 60%, and 33% were on the way to remission (< 6.5%). Patients who used the DTx a second time experienced a reduction of -0.76% in their HbA_1c levels and a mean weight loss of -6.84 kg after six months (P < .001) compared with BL.

Conclusions: These results indicate that the DTx has clinically relevant effects on glycemic control and weight reduction for patients with both well and poorly controlled diabetes, whether through single or repeated usage. It is a noteworthy improvement in T2D management, offering a non-pharmacological, fully digital solution that integrates biofeedback through CGM and an advanced AI algorithm.

Background: The glycemia risk index (GRI) is a new composite continuous glucose monitoring (CGM) metric for weighted hypoglycemia and hyperglycemia. We evaluated the association between the GRI and cardiovascular autonomic neuropathy (CAN) and compared the effects of the GRI and conventional CGM metrics on CAN.

Methods: For this cross-sectional study, three-month CGM data were retrospectively analyzed before autonomic function tests were performed in 165 patients with type 1 diabetes. CAN was defined as at least two abnormal results of parasympathetic tests according to an age-specific reference.

Results: The overall prevalence of CAN was 17.1%. Patients with CAN had significantly higher GRI scores, target above range (TAR), coefficient of variation (CV), and standard deviation (SD) but significantly lower time in range (TIR) than those without CAN. The prevalence of CAN increased across higher GRI zones (P for trend <.001). A multivariate logistic regression analysis, adjusted for covariates such as HbA1c, demonstrated that the odds ratio (OR) of CAN was 9.05 (95% confidence interval [CI]: 2.21-36.96, P = .002) per 1-SD increase in the GRI. TIR and CV were also significantly associated with CAN in the multivariate model. The area under the curve of GRI for the prediction of CAN (0.85, 95% CI: 0.76-0.94) was superior to that of TIR (0.80, 95% CI: 0.71-0.89, P for comparison = .046) or CV (0.71, 95% CI: 0.57-0.84, P for comparison = .049).

Conclusions: The GRI is significantly associated with CAN in patients with type 1 diabetes and may be a better CGM metric than TIR for predicting CAN.

Pregnancy in people with type 1 diabetes mellitus (T1D) is well-known to be linked to adverse maternal and neonatal outcomes. Although advancements in diabetes technology, especially hybrid closed-loop (HCL) and advanced hybrid closed-loop (AHCL) systems, have greatly enhanced management for nonpregnant individuals with T1D, pregnant patients still represent a high-risk group that requires further research. Existing trials have shown mixed data in terms of clinically meaningful benefits in glycemic control, but these may be specific to the closed-loop system. Currently, there is one AHCL system approved and available for use in pregnancies complicated by diabetes in the United Kingdom, Europe, and Australia. However, there are no Food and Drug Administration (FDA)-approved closed-loop systems for use during pregnancy in the United States. Existing HCL/AHCL system use is off-label for pregnancy in the United States and often requires assistive techniques to target the tighter glucose levels needed during pregnancy. For patients struggling on multiple daily injections (MDIs) or sensor-augmented pump therapy (SAPT), studies have shown that HCL/AHCLs can reduce the burden of care and enable some people to achieve tighter glucose levels. This review aims to provide an overview of the existing evidence of closed-loop systems in pregnancies complicated by T1D and to discuss their implications and considerations with system use.