Journal of Diabetes Science and Technology最新文献

Background: The SENZA-PDN study evaluated high-frequency 10-kHz spinal cord stimulation (SCS) for the treatment of painful diabetic neuropathy (PDN). Over 24 months, 10-kHz SCS provided sustained pain relief and improved health-related quality of life. This report presents additional outcomes from the SENZA-PDN study, focusing on diabetes-related pain and quality of life outcomes.

Methods: The SENZA-PDN study randomized 216 participants with refractory PDN to receive either conventional medical management (CMM) or 10-kHz SCS plus CMM (10-kHz SCS + CMM), allowing crossover after six months if pain relief was insufficient. Postimplantation assessments at 24 months were completed by 142 participants with a permanent 10-kHz SCS implant, comprising 84 initial and 58 crossover recipients. Measures included the Brief Pain Inventory for Diabetic Peripheral Neuropathy (BPI-DPN), Diabetes-Related Quality of Life (DQOL), Global Assessment of Functioning (GAF), and treatment satisfaction.

Results: Over 24 months, 10-kHz SCS treatment significantly reduced pain severity by 66.9% (P < .001; BPI-DPN) and pain interference with mood and daily activities by 65.8% (P < .001; BPI-DPN). Significant improvements were also observed in overall DQOL score (P < .001) and GAF score (P < .001), and 91.5% of participants reported satisfaction with treatment.

Conclusions: High-frequency 10-kHz SCS significantly decreased pain severity and provided additional clinically meaningful improvements in DQOL and overall functioning for patients with PDN. The robust and sustained benefits over 24 months, coupled with high participant satisfaction, highlight that 10-kHz SCS is an efficacious and comprehensive therapy for patients with PDN.

While automated insulin delivery (AID) systems have multiple well-established benefits outside of pregnancy and are widely used in non-pregnant individuals with type 1 diabetes (T1D), none of the commercially available AID systems in North America are approved for use during pregnancy. Use of commercially available AID systems off-label in pregnancy is currently limited by: (1) glucose targets higher than the fasting glucose target range recommended during pregnancy and (2) algorithms which were not designed for the dynamic changes in insulin resistance which occur across gestation. However, as AID use in the general population expands, many individuals will opt to continue using these systems off-label during pregnancy, and thus, guidance for providers regarding AID use and optimization during pregnancy is of the utmost importance. A cornerstone to the effective use of AID systems is the systematic and accurate interpretation of continuous glucose monitoring (CGM) data. One obstacle to the use of both CGM and AID systems by obstetric providers is the lack of comfort with CGM interpretation. We therefore present here: (1) a systematic approach to CGM interpretation during pregnancy and (2) practical guidance regarding AID use during pregnancy for individuals who opt to use commercially available AID systems off-label during pregnancy after consideration of individualized risks and benefits.

Background: State-of-the-art diabetes self-management includes the usage of (software) tools, such as Bolus Calculators, to support patients with their therapeutic decisions. The development of such medical devices comes with strict obligations to ensure the safety and performance for the user; however, it is also necessary to continue to evaluate such aspects after the products are introduced into the market. In addition, such aspects cannot always be sufficiently validated by clinical trials; they need real-world evaluation to systematically improve such tools while they are on the market.

Methods: The approach described here uses innovative ways of generating user-centric evidence to improve the bolus calculator, including (1) human factor engineering, (2) analysis of glycemic real-world data, (3) patient-reported outcomes, and (4) machine-generated behavioral measurements.

Results: The combination of the diverse techniques to optimize the bolus calculator triggered changes in the user experience: a significant reduction in hypoglycemic events, -0.52% (±0.05), P < .01, n=3480, an increased diabetes treatment satisfaction (Diabetes Treatment Satisfaction Questionnaire [DTSQ] +9.90, P < .01, n=217), as well as an increased acceptance rate of bolus calculations, +15.73 (±0.89), P < .01, n=3436, were observed.

Conclusions: Altogether, human factor engineering and different forms of real-world data support fast and direct adaptations and improvements in products used for diabetes therapy.

Background: While continuous glucose monitoring (CGM) has transformed the care of people with diabetes (PWD) in the ambulatory setting, there continue to be significant barriers to access. With CGM on the horizon in the acute care setting, it is important to consider the potential for this shift to improve ambulatory CGM access to those at the highest risk of morbidity and mortality.

Methods: In this commentary, we review the existing literature on the specific barriers to CGM access for individuals with diabetes in the United States including racial disparities, provider bias, cost and shortage of specialty diabetes care. Key areas explored include the importance of CGM in diabetes management, the consequences of disparities in access to CGM, and leveraging the inpatient setting to promote equitable care and better outcomes for PWD.

Results: We present a vision for a new care model, which leverages the transition of care from the hospital to successfully incorporate CGM into the discharge plan.

Conclusions: Given that CGM utilization is associated with improved outcomes and reduced rates of hospitalization and emergency department visits, a care model that facilitates CGM access upon transition from inpatient to ambulatory care can enhance health equity and quality of life for people with diabetes.

Achieving pregnancy-specific glucose targets is difficult in pregnant individuals with type 1 diabetes (T1D), and the rates of complications for mothers and their infants remain high. Currently marketed automated insulin delivery (AID) systems are hybrid closed-loop (HCL) systems in which basal insulin delivery (with or without automated correction boluses) is driven by algorithms, and users are required to initiate meal boluses. For non-pregnant people with T1D, HCL therapy has established benefits for glycemic outcomes and quality of life. While none of the currently available HCL systems were designed for pregnancy-specific glucose targets and outcomes, preliminary data suggest that the use of HCL systems may result in improved glycemia during pregnancy. There is an accumulating body of literature examining HCL systems in pregnancy, although there are still limited data regarding the impact of HCL systems on perinatal outcomes. Many individuals conceive while using clinically available HCL systems and may be hesitant to discontinue use during pregnancy, and clinicians may consider HCL therapy for pregnant individuals who are struggling to meet recommended glycemic levels during pregnancy. We therefore offer guidance on how to counsel patients on the risks and benefits of HCL therapy in pregnancy, how to identify appropriate candidates for HCL therapy in pregnancy, and how to manage commercially available HCL systems off-label throughout gestation.

Continuous glucose monitors (CGMs) improve glycemic outcomes and quality of life for many people with diabetes. Research and clinical practice efforts have focused on CGM initiation and uptake. There is limited understanding of how to sustain CGM use to realize these benefits and limited consideration for different reasons/goals for CGM use. Therefore, we apply the Information-Motivation-Behavioral Skills (IMB) model as an organizing framework to advance understanding of CGM use as a complex, ongoing self-management behavior. We present a person-centered, dynamic perspective with the central thesis that IMB predictors of optimal CGM use vary based on the CGM use goal of the person with diabetes. This reframe emphasizes the importance of identifying and articulating each person's goal for CGM use to inform education and support.

Pregnancy is a unique stage of life characterized by continuous maternal physiologic adaptations from conception to postpartum. Understanding the dynamic metabolic requirements of pregnancy can inform the effective use of current automated insulin delivery (AID) tools and aid in developing future diabetes technology to support diabetes management in this critical life period. In this review, we detail physiologic changes affecting early pregnancy, late pregnancy, intrapartum, and postpartum and discuss implications for using and designing AID systems.

Background: Comorbidities such as cardiovascular disease (CVD) and diabetic kidney disease (DKD) are major burdens of type 1 diabetes (T1D). Predicting people at high risk of developing comorbidities would enable early intervention. This study aimed to develop models incorporating socioeconomic status (SES) to predict CVD, DKD, and mortality in adults with T1D to improve early identification of comorbidities.

Methods: Nationwide Danish registry data were used. Logistic regression models were developed to predict the development of CVD, DKD, and mortality within five years of T1D diagnosis. Features included age, sex, personal income, and education. Performance was evaluated by five-fold cross-validation with area under the receiver operating characteristic curve (AUROC) and the precision-recall area under the curve (PR-AUC). The importance of SES was assessed from feature importance plots.

Results: Of the 6572 included adults (≥21 years) with T1D, 379 (6%) developed CVD, 668 (10%) developed DKD, and 921 (14%) died within the five-year follow-up. The AUROC (±SD) was 0.79 (±0.03) for CVD, 0.61 (±0.03) for DKD, and 0.87 (±0.01) for mortality. The PR-AUC was 0.18 (±0.01), 0.15 (±0.03), and 0.49 (±0.02), respectively. Based on feature importance plots, SES was the most important feature in the DKD model but had minimal impact on models for CVD and mortality.

Conclusions: The developed models showed good performance for predicting CVD and mortality, suggesting they could help in the early identification of these outcomes in individuals with T1D. The importance of SES in individual prediction within diabetes remains uncertain.

Background: The objective of this work is to document performance of automated insulin delivery (AID) during real-life use in type 2 diabetes (T2D).

Methods: A retrospective analysis was performed of continuous glucose monitoring and insulin delivery data from 796 individuals with T2D, who transitioned from 1-month predictive low-glucose suspend (PLGS) use to 3-month AID use, in real-life settings. Primary outcome was change of time in range (TIR = 70-180 mg/dL) from PLGS to AID. Secondary outcomes included time above/below range (TAR/TBR) and total daily insulin (TDI).

Results: Compared with PLGS, AID increased TIR on average from 63.2% to 72.6%, decreased TAR from 36.2% to 26.8%, and increased TDI from 70.2 to 76.3 U (all P < .001), without significant change to TBR. Glycemic improvements were more pronounced in those with worse glycemic control during PLGS use (P < .001).

Conclusions: Real-life use of AID led to a rapid and sustained improvement of glycemic control in individuals with T2D.