Journal of Diabetes Science and Technology最新文献

Background: The number of older adults with type 1 diabetes (T1D) is increasing. Use of automated insulin delivery (AID) may influence nutrition and eating behaviors. We explored how three eating styles (restrained, external, emotional) differ between older adults with T1D who use and do not use AID.

Methods: We administered a one-time electronic survey from September to November 2023 to adults ≥65 years with T1D receiving care through a university-affiliated hospital system. Clinical and demographic information was collected from medical records. Eating styles were characterized with the Dutch Eating Behavior Questionnaire.

Results: Our sample (n = 77, 95% non-Hispanic white) had mean (SD) age: 71.8 (4.1) years, diabetes duration: 33 (18) years, hemoglobin A1c (HbA1c): 6.83 (1.12%), and body mass index (BMI): 27.3 (4.7) kg/m². Respondents reported variable eating styles, with the highest median scores for external and restrained eating and lower scores for emotional eating. Older adults using AID systems had higher median scores for emotional and external eating, and more varied restrained eating scores compared to those not using AID systems. Weak correlations were found between eating styles and HbA1c (restrained: r = -0.14; external: r = 0.08; emotional: r = 0.15), as well as between restrained (r = 0.09) and external (r = 0.04) eating with BMI, with a small correlation between emotional eating and BMI (r = 0.27).

Conclusions: Eating styles may vary between older adult AID users and non-users. To our knowledge, this is the first study to characterize eating styles in this population, though generalizability is limited by a non-diverse and small sample with high technology use overall (eg, continuous glucose monitoring, insulin pumps).

Background: The study aimed to determine the relationship between basal insulin adherence and glycemic control evaluated by time in range (TIR) in people with insulin-treated type 2 diabetes (T2D), using data from both continuous glucose monitors (CGM) and connected insulin pens. Furthermore, the study aimed to determine the best basal insulin adherence metric.

Methods: CGM data and basal insulin data were collected from 106 insulin-treated people (aged ≥18 years) with T2D. Three different adherence metrics were employed (dose deviation, dose deviation ≤20%, and a traditional metric) and a three-step methodology was used to measure insulin adherence level. The coefficient of determination (R²), based on a univariate linear regression analysis, was used to determine the relationship between each adherence metric and TIR.

Results: A statistically significant relationship was observed between TIR and adherence quantified as the dose deviation ≤20% metric (R² = 0.67, P = .006). Neither the relationship between the dose deviation metric and TIR (R² = 0.43, P = .08) nor the relationship between the traditional metric and TIR (R² = 0.35, P =.23) was found to be statistically significant.

Conclusions: Our study indicates a relationship between basal insulin adherence and TIR in people with insulin-treated T2D. This seems to underscore the role of basal insulin adherence for optimal glycemic outcomes and utilizing TIR as a clinical marker. Furthermore, the results suggest that the magnitude of deviation from the recommended basal insulin dose impacts glycemic control, indicating dose deviation ≤20% as a more accurate metric for quantifying adherence.

With increasing prevalence of obesity and cardiovascular diseases, there is a growing interest in the use of glucagon-like peptide-1 receptor agonists (GLP-1RAs) as an adjunct therapy in type 1 diabetes (T1D). The GLP-1RAs are currently not approved by the US Food and Drug Administration for the treatment of T1D in the absence of randomized controlled trials documenting efficacy and safety of these agents in this population. The Diabetes Technology Society convened a series of three consensus meetings of clinicians and researchers with expertise in diabetes technology, GLP-1RA therapy, and T1D management. The project was aimed at synthesizing current literature and providing conclusions on the use of GLP-1RA therapy as an adjunct to automated insulin delivery (AID) systems in adults with T1D. The expert panel members met virtually three times on January 17, 2024, and April 24, 2024, and August 14, 2024, to discuss topics ranging from physiology and outcomes of GLP-1RAs in T1D to limitations of current sensors, algorithms, and insulin for AID systems. The panelists also identified research gaps and future directions for research. The panelists voted to in favor of 31 recommendations. This report presents the consensus opinions of the participants that, in adults with T1D using AID systems, GLP-1RAs have the potential to (1) provide effective adjunct therapy and (2) improve glycemic and metabolic outcomes without increasing the risk of severe hypoglycemia or diabetic ketoacidosis.

As medical devices become more integrated with wireless technologies, the risks of cyberattacks and data breaches increase, making stringent cybersecurity measures essential. The implementation of rigorous cybersecurity standards is essential for enhancing the cybersecurity of devices. This article examines the evolving cyber threats faced by the medical technology industry, the role of IEEE 2621 in providing comprehensive security benchmarks for medical devices, and the need for continuous risk assessments and adherence to regulatory standards to mitigate future cyber risks. Adherence to cybersecurity standards establishes ensures the effective protection of sensitive data and critical infrastructure.

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.

The advanced glycation end products (AGEs) Webinar was co-hosted by Diabetes Technology Society and Kitalys Institute on August 8, 2024, with the goal of reviewing progress made in the measurement and use of AGEs in clinical practice. Meeting topics included (1) AGEs as predictors of diabetic nephropathy (DKD), (2) hemoglobin glycation index (HGI) and the glycation gap (GG), (3) formation and structure of AGEs, (4) AGEs as a risk factor of cardiovascular disease (CVD), and (5) approaches to limit or prevent AGE formation.

Background: Older adults may be less comfortable with continuous glucose monitoring (CGM) technology or require additional education to support use. The Virtual Diabetes Specialty Clinic study provided the opportunity to understand glycemic outcomes and support needed for older versus younger adults living with diabetes and using CGM.

Methods: Prospective, virtual study of adults with type 1 diabetes (T1D, N = 160) or type 2 diabetes (T2D, N = 74) using basal-bolus insulin injections or insulin pump therapy. Remote CGM diabetes education (3 scheduled visits over 1 month) was provided by Certified Diabetes Care and Education Specialists with additional visits as needed. CGM-measured glycemic metrics, HbA1c and visit duration were evaluated by age (<40, 40-64 and ≥65 years).

Results: Median CGM use was ≥95% in all age groups. From baseline to 6 months, time 70 to 180 mg/dL improved from 45% ± 22 to 57% ± 16%; 50 ± 25 to 65 ± 18%; and 60 ± 28 to 69% ± 18% in the <40, 40-64, and ≥65-year groups, respectively (<40 vs 40-64 years P = 0.006). Corresponding values for HbA1c were 8.0% ± 1.6 to 7.3% ± 1.0%; 7.9 ± 1.6 to 7.0 ± 1.0%; and 7.4 ± 1.4 to 7.1% ± 0.9% (all P > 0.05). Visit duration was 41 min longer for ages ≥65 versus <40 years (P = 0.001).

Conclusions: Adults with diabetes experience glycemic benefit after remote CGM use training, but training time for those >65 years is longer compared with younger adults. Addressing individual training-related needs, including needs that may vary by age, should be considered.

Type 2 diabetes (T2D) is a risk factor for cognitive decline. In neurodegenerative disease research, remote digital cognitive assessments and unobtrusive sensors are gaining traction for their potential to improve early detection and monitoring of cognitive impairment. Given the high prevalence of cognitive impairments in T2D, these digital tools are highly relevant. Further research incorporating remote digital biomarkers of cognition, behavior, and motor functioning may enable comprehensive characterizations of patients with T2D and may ultimately improve clinical care and equitable access to research participation. The aim of this commentary article is to review the feasibility, validity, and limitations of using remote digital cognitive tests and unobtrusive detection methods to identify and monitor cognitive decline in neurodegenerative conditions and apply these insights to patients with T2D.