Journal of Diabetes Science and Technology最新文献

Introduction: Considerable efforts to standardize continuous glucose monitoring (CGM) have occurred in recent years. The aim was to perform an analysis of clinical studies in clinicaltrials.gov to evaluate trends in CGM endpoint adoption.

Methods: Clinicaltrials.gov was searched for studies of drugs, devices and combination products containing CGM terms posted from 2012 to 2023. 1269 studies were returned and 954 were excluded. 315 studies were divided into two periods (P1 [2012-2017] and P2 [2018-2023]) and differences analyzed using descriptive statistics and two-tailed t tests.

Results: There was a significant 60.3% increase in total clinical studies from P1 (121) to P2 (194). Phase 2 and Phase 3 Studies both saw significant increases of 125.8 and 169.2%, respectively, in P2. Adult-only studies predominated in both periods, with a 40.4% increase in P2. Studies that included pediatric populations, although smaller in number, increased significantly. Most studies were nonindustry-funded, and studies in this category saw a significant 80.0% increase in P2. However, industry-only funded studies also increased significantly by 78.4% in P2 in the same period. Studies of type 1 diabetes (T1DM) and type 2 diabetes (T2DM) increased by 55.8% and 26.9%, respectively, but increases were not statistically significant. Studies of nondiabetes-related indications did increase significantly (233.3%). 27.6% of studies used CGM-derived metrics as primary endpoints (PE). Studies that used time in range (TIR) increased by 222.4% in P2, which was significant. Conversely studies that used mean amplitude of glycemic excursions (MAGE) decreased significantly by 71.3%.

Conclusion: Our data provide evidence of significant increases in the application of CGM endpoints in clinical studies in the last six years, including studies with TIR as the PE. Increases have been driven largely by academia, but our data show that industry is starting to follow suit. The significant increase in studies that included pediatrics is encouraging.

For people with diabetes on insulin injection therapy, insulin pen dose accuracy and reliability are key features. Dose accuracy of the Tempo Pen^TM with attached Tempo Smart Button^TM [Module] (the system) was tested for three doses under standard atmospheric conditions. Reliability and the ability of the Module to detect, store, and transmit dose-related data were also tested. The system met the International Organization for Standardization 11608-1:2014 requirements for dose accuracy at all doses. Mean (standard deviation) doses were 0.010 mL (0.002), 0.299 mL (0.002), and 0.597 mL (0.004) for the 0.010-mL, 0.300-mL, and 0.600-mL doses, respectively. The Module also met requirements for data transfer after every injection. The system accurately delivered doses and reliably captured and stored insulin dosing information.

Rapid technological advancements, such as artificial intelligence, wearable technologies, and telehealth with remote monitoring, are transforming continuous education for health care providers (HCPs) in diabetes management. These technologies improve patient care and necessitate innovative educational approaches to prepare HCPs for clinical integration. Digital education offers real-time, scalable, and cost-effective solutions, especially in areas with health care workforce shortages. However, the effect of digital education on HCPs' knowledge, skills, attitudes, and patient outcomes remains under-researched and necessitates further study. As technologies advance, achieving precision in diabetes continuous education becomes feasible. The 2024 ADA Standards of Care emphasize early adoption of advanced technologies and proficiency among HCPs. This commentary explores transformative trends, discussing limitations and proposing solutions to revolutionize continuous education in diabetes care.

Background: Type 2 diabetes mellitus (T2DM) and dementia are two of the leading chronic diseases in aging and are known to influence each other's disease progression. There is well-established evidence that T2DM increases the risk for cognitive decline and dementia. At the same time, people with cognitive changes or dementia can find it difficult to manage their diabetes, resulting in hyper- or hypoglycemic events which can exacerbate the dementia disease progression further. Monitoring of glucose variability is, therefore, of critical importance during aging and when people with T2DM develop dementia. The advent of continuous glucose monitoring (CGM) has allowed the monitoring of glucose variability in T2DM more closely. The CGM seems to be highly feasible and acceptable to use in older people with T2DM and has been shown to significantly reduce their hypoglycemic events, often resulting in falls. Less is known as to whether CGM can have a similar beneficial effect on people with T2DM who have cognitive impairment or dementia in community or hospital settings.

Aims: The current perspective will explore how CGM has made an impact on T2DM management in older people and those with comorbid cognitive impairment or dementia. We will further explore opportunities and challenges of using CGM in comorbid T2DM and dementia in community and hospital settings.

Continuous glucose monitoring (CGM) has become the standard of care for outpatient diabetes management, yet its initiation during hospitalization-particularly at discharge-remains underutilized. The transition from hospital to home presents a unique opportunity to start CGM, educate patients, and improve glycemic outcomes. Although preliminary studies suggest that CGM initiation at discharge can increase time-in-range and reduce hypoglycemia and hospital readmissions, widespread adoption faces several challenges, including therapeutic inertia, patient selection, insurance barriers, and limited implementation guidance. At the time of this writing, CGMs are not yet US Food and Drug Administration-approved for inpatient use, but approval is anticipated. In this article, we present an actionable, stepwise protocol for CGM initiation at hospital discharge, developed by the Council for Clinical Excellence in Inpatient Diabetes at Johns Hopkins Medicine. The protocol includes multidisciplinary coordination, inclusive patient selection, structured education, designation of outpatient follow-up providers, and emphasis on consistent postdischarge care. We address common barriers such as impaired cognition during recovery and device compatibility with imaging studies. While further research is needed to confirm long-term cost-effectiveness and clinical outcomes, we believe our protocol can serve as a practical foundation for hospitals and providers seeking to safely and effectively integrate CGM initiation into discharge workflows.

Objective: The objective was to evaluate the safety and efficacy of ultra-rapid-acting insulin with the Boost and Ease-off features of the Cambridge hybrid closed-loop system.

Methods: A secondary analysis of Boost and Ease-off from two double-blind, randomized, crossover hybrid closed-loop studies comparing (1) Fiasp to insulin aspart (n = 25), and (2) Lyumjev to insulin lispro (n = 26) was carried out. Mean glucose on initialization of Boost and Ease-off, change in glucose 60 and 120 minutes after initialization, duration and frequency of use, mean glucose, and time in, above, and below target glucose range were calculated for periods of Boost use, Ease-off use, or neither.

Results: Participants used Boost for longer with Fiasp than insulin aspart (median [interquartile range, IQR] = 75 [53-125] minutes vs 60 [49-75] minutes; P = .01). Mean glucose on Boost initialization with Fiasp was 238 ± 62 mg/dL compared with 218 ± 45 mg/dL with insulin aspart (P = .08). Fiasp use resulted in a greater glucose reduction 120 minutes after Boost initialization [-59 ± 34 mg/dL vs -43 ± 31 mg/dL; P = .02]. There were no statistically significant differences in sensor glucose endpoints during Boost or Ease-off periods between Fiasp and aspart. There were no statistically significant differences during Boost or Ease-off periods when comparing Lyumjev with insulin lispro. There were no safety issues when using Boost and Ease-off with ultra-rapid insulins.

Conclusions: The use of Fiasp and Lyumjev during Boost or Ease-off resulted in comparable safety and efficacy to using insulin aspart and lispro.

In an article in the Journal of Diabetes Science and Technology, Backfish and coauthors examined the dose accuracy and reliability of the Tempo Pen and Tempo Smart Button connected insulin pen system. This study sponsored by Eli Lilly and Company found that this system met the International Organization for Standardization 11608-1:2014 requirements for dose accuracy at a range of doses, as well as the data transfer requirements after all injections. While these results are very encouraging, they were based on simulated human factors data while data from a human factors validation study where individuals successfully dialed and administered correct doses was not reported. There is a need for further studies in a variety of populations that would greatly benefit from this enhanced insulin pen technology to facilitate optimization of dose adjustments based on accurate and reliable insulin dosing data.

Introduction: Prior studies have not identified if continuous glucose monitoring (CGM) metrics at a critical gestational age window can discriminate risk of adverse pregnancy outcomes. We evaluated late second- and third-trimester CGM metrics by gestational age associated with pregnancy outcomes in gravidas with type 1 diabetes (T1DM).

Methods: Dexcom G6 CGM data from a retrospective cohort of singleton gestations with T1DM (2018-2022) at an academic medical center were analyzed. Time in, above, and below range 63 to 140 mg/dL (TIR, TAR, TBR), glycemic variability, and mean glucose concentration were computed in two-week CGM intervals from 24⁰ to 39⁶ weeks^days. Adverse pregnancy outcomes were hypertensive disorders of pregnancy (HDP), large-for-gestational age (LGA), and neonatal hypoglycemia. Linear mixed-effects models were fitted on CGM metrics computed from two-week CGM intervals, with gestational age, adverse pregnancy outcomes (i.e. presence/absence of HDP, LGA, and/or neonatal hypoglycemia), and their interaction as fixed effects.

Results: In 87 gravidas with preconception median hemoglobin A1c 6.5% (IQR 6.0, 7.1) and maternal body mass index 24.8 kg/m² (IQR 21.9, 27.1), 71% had at least one adverse pregnancy outcome. Between weeks 24⁰ and 37⁶, gravidas with HDP had higher TAR and mean glucose and lower TIR (P < .05). Gravidas with LGA had lower TBR between weeks 24⁰ and 35⁶. TIR, TAR, and mean glucose evolution differed by HDP status, with greatest divergence between groups at 28⁰ to 29⁶ weeks' gestation (P ≤ .001).

Conclusion: CGM metrics in the late second to early third trimester, a period of peak insulin resistance, may help to distinguish risk of HDP and LGA in gravidas with T1DM.