Journal of Diabetes Science and Technology最新文献

Introduction: Automated insulin delivery (AID) systems have improved outpatient glycemic control, reduced hypoglycemia, and enhanced quality of life for many people with diabetes. Interest is growing in translating these systems to inpatient care, where conventional insulin regimens are frequently associated with glycemic excursions and highly variable insulin requirements.

Methods: We conducted a narrative review of peer-reviewed studies evaluating inpatient use of AID systems, including both hospital-initiated AID and continuation of personal outpatient AID devices during hospitalization, across medical and surgical populations.

Results: As outpatient adoption of AID systems increases, hospital clinicians are encountering patients using these technologies more frequently during admission. Available studies of both hospital-initiated AID and continued personal AID use during hospitalization suggest feasibility and safety, with improvements in time in range and no clear increase in hypoglycemia, although large-scale data remain limited. Broader implementation is constrained by important operational and regulatory barriers, including limited electronic health record integration, lack of device interoperability, regulatory clearance, and the absence of standardized institutional policies.

Conclusion: As inpatient exposure to AID systems continues to expand, hospitals must develop infrastructure and operational policies to support safe use of personal devices and to evaluate hospital-initiated AID for patients with difficult-to-manage hyperglycemia. This review summarizes current evidence and highlights key challenges to integrating AID systems into routine inpatient diabetes care.

Millions of people with diabetes have no or only limited access to electricity that limits their ability to store insulin according to the recommendations of the insulin manufacturer in general. At the same time, environmental temperatures are increasing in many countries which have a negative impact on the glucose-lowering effect of insulin not stored adequately. Therefore, the availability of heat-stable insulin formulations that do not require constant cooling would be of help for many patients; however, despite this clinical need, apparently not many are in clinical development. This commentary discusses the different aspects and approaches that are of relevance in this context.

Hybrid closed-loop (HCL) systems have transformed outpatient diabetes management, yet their application in complex inpatient and hospital-based scenarios remains underexplored. This article examines the utilization of HCL systems in three challenging clinical contexts: the perioperative period, dialysis (hemodialysis and peritoneal dialysis), and during glucocorticoid therapy. Our article and case series examples provide an overview of the current available literature, preliminary data, and practical guidance for clinicians on HCL systems in these settings. Further research is urgently needed to establish the evidence base in this high-risk cohort.

Background: Perioperative hyperglycemia in people with diabetes is associated with increased morbidity, mortality, and health care costs. Despite guideline recommendations to institute interventions to reduce hyperglycemia, standardized protocols that integrate into clinical workflows are lacking. In this article, we evaluate the efficacy of a digitally embedded, glycemic management protocol in people with diabetes undergoing surgery.

Methods: We conducted a retrospective analysis of a quality improvement study conducted at a tertiary-care academic hospital. Adults with diabetes undergoing noncardiac surgery with more than two hours of procedure time were included. A multidisciplinary protocol was implemented guiding insulin administration and glucose monitoring across preoperative, intraoperative, and post-anesthesia care unit (PACU) phases. People undergoing surgery during one year before protocol implementation were compared with those in the year after. The primary outcome was the proportion of intraoperative glucose readings within 70 to 180 mg/dL. Secondary outcomes included glucose control in other perioperative phases, hypoglycemia incidence, and 30-day postoperative complications.

Results: Among 1254 adults (634 pre-intervention, 620 post-intervention), the mean proportion of intraoperative glucose values in the target range of 70 to 180 mg/dL showed a modest yet statistically significant improvement after protocol implementation (0.65 vs 0.72, P = .021). We found a reduced risk of hypoglycemia in the preoperative phase (3.7% vs 1.3%, P = .007) and no increased risk of hypoglycemia in the intraoperative or PACU phases. An increase in glucose monitoring and intravenous insulin use was noted across all phases of care (P < .001).

Conclusions: Implementation of a digitally embedded perioperative glycemic management protocol improved glucose monitoring and intraoperative glucose control without increasing hypoglycemia. These findings support the safe and effective use of the protocol across surgical specialties and case urgencies, supporting the value of integrating decision support tools into clinical workflows.

Inpatient hyperglycemia remains a challenge, as conventional insulin regimens often lead to both hyperglycemia and hypoglycemia. Traditional glucose monitoring methods, such as point-of-care testing, fail to detect diurnal and nocturnal glycemic fluctuations, contributing to suboptimal control. This review examines the effectiveness of continuous glucose monitoring (CGM) and automated insulin delivery (AID) systems in managing diabetes in hospitalized patients, including those with additional challenges such as end-stage kidney disease (ESKD), pregnancy, and steroid use. In patients with ESKD, CGM has demonstrated reliable glucose measurements and improved glycemic control, particularly in those undergoing hemodialysis. It has been shown to increase time in range (TIR) and reduce hypoglycemia, with clinical accuracy verified in multiple studies. Existing evidence shows that AID systems may offer improved outcomes in this population, with increased TIR and reduced glycemic variability compared with conventional insulin therapy. Continuous glucose monitor use has been beneficial for maternal glycemic control in pregnancy, leading to lower HbA1c levels, increased TIR, reduced maternal hypoglycemia, reduced neonatal hypoglycemia, and admissions to intensive care. Limited studies have evaluated AID system use during labor. In addition, CGM helps identify postprandial hyperglycemia in patients with glucocorticoid-induced hyperglycemia, which is crucial for managing glucose fluctuations. Studies in patients receiving glucocorticoids have shown that continuous glucose monitoring improves glycemic control without significantly increasing hypoglycemic events. In conclusion, limited studies have shown the role of CGM and AID systems and their effects on glycemic outcomes in hospitalized patients with diabetes, particularly those with ESKD, in pregnancy, and those receiving glucocorticoids. These technologies used for glucose monitoring and insulin delivery could offer an alternative method of diabetes management in certain inpatient populations.

Background: The Glycemia Risk Index (GRI) is a composite score designed to simplify continuous glucose monitoring (CGM) interpretation by quantifying risks associated with hypoglycemia, hyperglycemia, and glucose variability in a single number. Although proposed as a decision-support tool, its clinical utility has not been well studied yet.

Objective: To evaluate how Diabetes Care and Education Specialists (DCESs) and other health care professionals (HCPs) perceive the GRI and its usefulness in clinical practice, and to assess its perceived advantages, limitations, and potential for integration into the care of individuals with diabetes.

Methods: In this observational study, 28 DCESs and other HCPs participated in a virtual educational session about the GRI and then completed an online survey. The survey collected demographic information, preferences for using GRI versus the Ambulatory Glucose Profile (AGP) to evaluate glycemic management, and feedback on the GRI's usefulness. Open-ended qualitative responses were rated independently by investigators on a 5-point Likert scale (1-5, with 1 being least positive/most negative and 5 being most positive/least negative) and analyzed thematically.

Results: Most participants preferred using the GRI alongside the AGP rather than either tool alone. When tracking individual progress over time, 50% preferred using both tools, while 39% preferred the GRI alone, and 11% preferred the AGP alone. The majority (75%) were willing to integrate the GRI into their clinical workflows. Participants rated the GRI highly for its advantages (4.57 ± 0.84) and usefulness for primary care practitioners (4.5 ± 0.96) and diabetes specialists (4.18 ± 1.28), while concerns about disadvantages were moderate (3.04 ± 1.20). Participants discussed in free-text four themes, including how GRI (1) simplifies data, (2) helps clinical decision support, (3) promotes better understanding of CGM data, and (4) needs wider dissemination.

Conclusions: The GRI is perceived as a valuable complement to traditional CGM reports, particularly in facilitating quick clinical assessments and furthering diabetes care and education. While enthusiasm for broader integration is high, barriers such as lack of standardization, limited guideline adoption, and HCP training must be addressed to support its clinical uptake. Future work should assess the GRI's impact on clinical outcomes and explore implementation strategies.

Background: Digital health lifestyle interventions (DHLI) may offer scalable solutions to manage prediabetes in clinical practice; however, their effectiveness on people with prediabetes has not been systematically investigated and reviewed. Hence, in this systematic review, meta-analysis, and meta-regression the effectiveness of DHLI on prediabetes-related outcomes was investigated.

Methods: Four databases were searched to identify randomized controlled trials investigating the effectiveness of DHLI on adults with prediabetes published before 23 February 2024. The primary outcome was the change in body weight, with secondary outcomes including, among others, glycemic status, body composition, and feasibility outcomes. Meta-analyses were conducted to provide overall effect estimates of outcomes. In addition, meta-regressions on the primary outcome were conducted. The study quality was assessed using the Cochrane Risk of Bias tool, and the certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.

Results: A total of 33 studies were included (n = 14 398). The study duration ranged from 3 to 60 months. The digital interventions varied from in-person meetings combined with pedometers and telephone calls to fully digital interventions. The overall estimated treatment difference in change in body weight favored the intervention (mean difference: -1.74 kg; 95% confidence interval: -2.37, -1.11; P < .01) with moderate certainty. Statistically significant overall effect estimates favoring the intervention were also found for secondary outcomes with very low to moderate certainty.

Conclusion: Digital health lifestyle interventions can result in statistically significant change in body weight and other secondary outcomes among people with prediabetes.

Background: Managing glycemia during Ramadan is challenging for individuals with type 1 diabetes (T1D) due to prolonged fasting and altered eating patterns. While many are exempt from fasting, some choose to fast, necessitating careful monitoring. The glycemia risk index (GRI) is valuable for assessing glycemic quality and interpreting continuous glucose monitoring (CGM) data to identify individuals needing closer clinical attention. This study investigates the effects of Ramadan fasting on glycemic control in T1D, focusing on GRI and its components for hypoglycemia (CHypo) and hyperglycemia (CHyper).

Method: An ambispective study involved 186 individuals with T1D using intermittent scanning CGM (isCGM). Data were retrospectively collected for one month before Ramadan and prospectively during and one month after Ramadan. Clinical, metabolic, and glycemic data were collected, with GRI calculated alongside its components.

Results: During Ramadan, GRI improved by 54.6% (from 56.4 to 25.6), CHypo decreased by 60% (from 6 to 2.4), and CHyper dropped by 40.5% (from 21 to 12.5). However, these benefits were temporary, as glycemic measures increased after Ramadan, reflecting a return to pre-Ramadan patterns once normal routines resumed. No participants were admitted for diabetes emergencies during Ramadan. Adolescents and patients on insulin pumps had more favorable outcomes. GRI and its components significantly correlated with other CGM metrics, with these relationships maintained during and after Ramadan.

Conclusions: Ramadan fasting significantly improved GRI and its components in individuals with T1D. Incorporating GRI as a novel metric alongside classical CGM metrics could enhance glycemic control, highlighting the need for personalized diabetes management strategies.