Journal of Diabetes Science and Technology最新文献

Background: Glucagon-like peptide 1 (GLP-1) is a hormone that promotes insulin secretion, delays gastric emptying, and inhibits glucagon secretion. The GLP-1 receptor agonists have been developed as adjunctive therapies for type 2 diabetes to improve glucose control. Recently, there has been an interest in introducing GLP-1 receptor agonists as adjunctive therapies in type 1 diabetes alongside automatic insulin delivery systems. The preclinical validation of these systems often relies on mathematical simulators that replicate the glucose dynamics of a person with diabetes. This review aims to explore mathematical models available in the literature to describe GLP-1 effects to be used in a type 1 diabetes simulator.

Methods: Three databases were examined in the search for GLP-1 mathematical models. More than 1500 works were found after searching for specific keywords that were narrowed down to 39 works for full-text assessment.

Results: A total of 23 works were selected describing GLP-1 pharmacokinetics and pharmacodynamics. However, none of the found models was designed for type 1 diabetes. An analysis is included of the available models' features that could be translated into a GLP-1 receptor agonist model for type 1 diabetes.

Conclusion: There is a gap in research in GLP-1 receptor agonists mathematical models for type 1 diabetes, which could be incorporated into type 1 diabetes simulators, providing a safe and inexpensive tool to carry out preclinical validations using these therapies.

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.

Background: A diagnosis of type 1 diabetes in a young person can create vulnerability for sleep. Historically it has been rare for young people to be offered a closed-loop system soon after diagnosis meaning that studies examining sleep under these circumstances in comparison with standard treatment have not been possible. In this study, we examine sleep in young people (and their parents) who were provided with hybrid closed-loop therapy at diagnosis of type 1 diabetes versus those who receive standard treatment over a 2-year period.

Methods: The sample comprised 97 participants (mean age = 12.0 years; SD = 1.7) from a multicenter, open-label, randomized, parallel trial, where young people were randomized to either hybrid closed-loop insulin delivery or standard care at diagnosis. Sleep was measured using actigraphy and the Pittsburgh Sleep Quality Index (PSQI) in the young people, and using the PSQI in parents.

Results: Sleep in young people using hybrid closed-loop insulin delivery did not differ significantly compared with those receiving standard care (although there were nonsignificant trends for better sleep in the closed-loop group for 4 of the 5 sleep actigraphy measures and PSQI). Similarly, there were nonsignificant differences for sleep between the groups at 24 months (with mixed direction of effects).

Conclusions: This study assessed for the first time sleep in young people using a closed-loop system soon after diagnosis. Although sleep was not significantly different for young people using closed-loop insulin delivery as compared with those receiving standard care, the direction of effects of the nonsignificant results indicates a possible tendency for better sleep quality in the hybrid closed-loop insulin delivery group at the beginning of the treatment.

Background: Continuous glucose monitor (CGM) usage improves glycemia in people with type 1 diabetes (PWD) and is accepted as the standard of care. The CGM utilization is lower in patients with public insurance and minorized ethnicities. In 2022, California Medicaid reduced its barriers to obtaining CGM coverage for PWD. It is unknown whether this policy change is sufficient to increase CGM usage. We hypothesize that the change in Medicaid coverage improved CGM uptake in children and young adults with T1D.

Methods: Data were extracted from electronic medical record of a large urban children's hospital in 2021 and 2022. The CGM usage was determined based on clinician documentation or the presence of CGM downloads. Kruskal-Wallis tests, Wald tests, and χ² tests were used to test hypothesis (P < .05). Mixed effects logistical regression analyses were performed.

Results: We included 878 and 892 PWD (age ≤ 21 years) in 2021 and 2022, respectively. In 2022, Medicaid insured 59.3% of patients. Between 2021 and 2022, CGM usage did not change for privately insured patients (84%) but increased from 41% to 58% for patients receiving Medicaid. In our mixed effects logistic regression model, CGM usage was higher in 2022 and in English speakers. Public insurance, black race, and patients' age were negatively associated with CGM usage.

Conclusion: Our results suggest that Medicaid expansion of CGM coverage increases its utilization for pediatric PWD but did not eliminate the disparity. Future studies are needed to identify barriers that preclude equity in technology uptake.

Background and objective: The aim of this study was to develop and validate explainable prediction models based on continuous glucose monitoring (CGM) and baseline data to identify a week-to-week risk of CGM key metrics (hyperglycemia, hypoglycemia, glycemic variability). By having a weekly prediction of CGM key metrics, it is possible for the patient or health care personnel to take immediate preemptive action.

Methods: We analyzed, trained, and internally tested three prediction models (Logistic regression, XGBoost, and TabNet) using CGM data from 187 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach combined with feature engineering deployed on the CGM signals was used to predict hyperglycemia, hypoglycemia, and glycemic variability based on consensus targets (time above range ≥5%, time below range ≥4%, coefficient of variation ≥36%). The models were validated in two independent cohorts with a total of 223 additional patients of varying ages.

Results: A total of 46 593 weeks of CGM data were included in the analysis. For the best model (XGBoost), the area under the receiver operating characteristic curve (ROC-AUC) was 0.9 [95% confidence interval (CI) = 0.89-0.91], 0.89 [95% CI = 0.88-0.9], and 0.8 [95% CI = 0.79-0.81] for predicting hyperglycemia, hypoglycemia, and glycemic variability in the interval validation, respectively. The validation test showed good generalizability of the models with ROC-AUC of 0.88 to 0.95, 0.84 to 0.89, and 0.80 to 0.82 for predicting the glycemic outcomes.

Conclusion: Prediction models based on real-world CGM data can be used to predict the risk of unstable glycemic control in the forthcoming week. The models showed good performance in both internal and external validation cohorts.

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.

The quality of clinician-patient relationship is integral to patient health and well-being. This article is a narrative review of published literature on concordance between clinician and patient perspectives on barriers to diabetes technology use. The goals of this manuscript were to review published literature on concordance and to provide practical recommendations for clinicians and researchers. In this review, we discuss the qualitative and quantitative methods that can be applied to measure clinician and patient concordance. There is variability in how concordance is defined, with some studies using questionnaires related to working alliance, while others use a dichotomous variable. We also explore the impact of concordance and discordance on diabetes care, barriers to technology adoption, and disparities in technology use. Published literature has emphasized that physicians may not be aware of their patients' perspectives and values. Discordance between clinicians and patients can be a barrier to diabetes management and technology use. Future directions for research in diabetes technology including strategies for recruiting and retaining representative samples, are discussed. Recommendations are given for clinical care, including shared decision-making frameworks, establishing social support groups optimizing clinician-patient communication, and using patient-reported outcomes to measure patient perspectives on outcomes of interest.