Journal of Diabetes Science and Technology最新文献

As diabetes technologies continue to advance, their use is expanding beyond type 1 diabetes to include populations with type 2 diabetes, older adults, pregnant individuals, those with psychiatric conditions, and hospitalized patients. This review examines the psychosocial outcomes of these technologies across these diverse groups, with a focus on treatment satisfaction, quality of life, and self-management behaviors. Despite demonstrated benefits in glycemic outcomes, the adoption and sustained use of these technologies face unique challenges in each population. By highlighting existing research and identifying gaps, this review seeks to emphasize the need for targeted studies and tailored support strategies to understand and optimize psychosocial outcomes and well-being.

Background: The glycemic characterization of congenital hyperinsulinism (HI), a rare disease causing severe hypoglycemia in childhood, is incomplete. Continuous glucose monitoring (CGM) offers deep glycemic phenotyping to understand disease burden and individualize patient care. Typically, CGM has been restricted to severe HI only, with performance being described in short-term, retrospective studies. We have described CGM-derived phenotyping in a prospective, unselected national cohort providing comprehensive baseline information for future therapeutic trials.

Methods: Glycemic frequency and trends, point accuracy, and patient experiences were drawn from a prospective, nationwide, observational study of unselected patients with persistent HI using the Dexcom G6 CGM device for 12 months as an additional monitoring tool alongside standard of care self- monitoring blood glucose (SMBG).

Findings: Among 45 patients with HI, mean age was six years and 53% carried a genetic diagnosis. Data confirmed higher risk of early morning (03:00-07:00 h) hypoglycemia throughout the study period and demonstrated no longitudinal reduction in hypoglycemia with CGM use. Device accuracy was suboptimal; 17 500 glucose levels paired with SMBG demonstrated mean absolute relative difference (MARD) 25% and hypoglycemia detection of 40%. Patient/parent dissatisfaction with CGM was high; 50% of patients discontinued use, citing inaccuracy and pain. However, qualitative feedback was also positive and families reported improved understanding of glycemic patterns to inform changes in behavior to reduce hypoglycemia.

Interpretation: This comprehensive study provides unbiased insights into glycemic frequency and long-term trends among patients with HI; such data are likely to influence and inform clinical priorities and future therapeutic trials.

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.

Objectives: Time in range (TIR, 70-180 mg/dL) is an established marker of glycemic control. More recently, time in tight range (TTR, 70-140 mg/dL) has been proposed as well. The aim of this study was to examine the relationship between TIR, TTR, and HbA1c in youth and young adults with type 1 diabetes (T1D) in the German/Austrian/Luxembourgian/Swiss Diabetes Prospective Follow-up (DPV) registry.

Methods: Data of youth and young adults aged ≤25 years with T1D for >3 months, documented in the DPV registry between 2019 and 2022 were analyzed. The most recent available HbA1c and corresponding continuous glucose monitoring (CGM) profiles in the 12 preceding weeks with at least 80% completeness were included. Associations were investigated using correlation and adjusted regression models.

Results: 1901 individuals (median age 14.0 years [IQR 10.4-16.9]) were included in the analysis. TIR and TTR correlated strongly, r = 0.965 (95% CI [0.962, 0.968]), P < .001. TTR estimates predicted from TIR were significantly higher in the group with high coefficient of variation (CV group ≥ 36%), P < .001. Correlations between TIR or TTR and HbA1c were both strong, r = -0.764 (95% CI [-0.782, -0.745]) and r = -0.777 (95% CI [-0.795, -0.759]), both P < .001, with no significant difference (P = .312) However, adjusted regression models indicated a slightly better fit for the prediction of HbA1c from TIR compared with TTR.

Conclusions: Based on large, real-world data from a multinational registry, TIR and TTR correlated strongly, and both showed a good prediction of HbA1c. TTR estimates predicted from TIR were significantly higher in people with high glucose variability (CV).

Metrics derived from continuous glucose monitoring (CGM) systems are often discordant between systems. A major cause is that CGM systems are not standardized; they use various algorithms and calibration methods, leading to discordant CGM readings across systems. This discordance can be addressed by standardizing CGM performance assessments: If manufacturers aim their CGM systems at the same target, then CGM readings will align across systems. This standardization should include the comparator device, sample origin, and study procedures. With better aligned CGM readings, CGM-derived metrics will subsequently also align better between systems.

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: We aimed to identify the normal range of glucose rates of change (RoC) observed in health and assess whether existing metrics of temporal glycemic variability (GV-timing), such as mean absolute glucose change (MAG) and continuous overlapping net glycemic action (CONGA), are predictive of abnormally rapid RoC in type 1 diabetes (T1D).

Methods: We identified the normal range of RoC over one-hour intervals from continuous glucose monitoring (CGM) data of healthy individuals. Rapidly rising glucose was defined as RoC values above percentiles 99% (level 1, L1) or 99.9% (level 2, L2), and rapidly falling glucose as below 1% (L1) or 0.1% (L2). The percentage of time these thresholds are exceeded in a given individual is referred to as time in fluctuation (TIF). In a separate CGM dataset of 736 T1D individuals, we calculated TIF-L1 and TIF-L2, and compared them against corresponding values of MAG and CONGA.

Results: The extremum percentiles of RoC observed in health are 0.1%: -80 mg/dL/h, 1%: -50 mg/dL, 99%: +56 mg/dL/h, and 99.9%: +89 mg/dL/h. The T1D individuals spend significantly more TIF at rates exceeding these thresholds (TIF-L1: median, 16.7% [interquartile range, 12.7-21.5], TIF-L2: 5.0% [3.1-7.8]) than healthy individuals (TIF-L1: 1.4% [0.6-2.8], TIF-L2: 0.0% [0.0-0.2]). Both MAG and CONGA are highly correlated with TIF-L1 and TIF-L2 (r > .95 in each pairwise comparison).

Conclusions: Individuals with T1D spend significant time with glucose RoC exceeding those observed in health. Existing GV-timing metrics are strongly correlated with time with abnormal RoC. Incorporation of a GV-timing metric in clinical practice is recommended.

Background: Diabetes technologies are valuable tools to reduce burden and enhance glycemic control, especially during adolescence. The current study sought to understand the factors associated with parent and adolescent diabetes device satisfaction.

Methods: This study used cross-sectional data from 175 adolescents living with type 1 diabetes and 176 parents. Adolescent ages ranged from 12 to 19 (M_age=14.7, SD=1.89) and were balanced by gender (48% male). Kendall's W examined concordance between parent and adolescent satisfaction and bivariate correlations and paired t-tests identified correlates of satisfaction.

Results: There was low concordance (Kendall's W = 0.13) between parent and adolescent device satisfaction. Automated insulin delivery (AID) use (vs non-use) was related to higher satisfaction for adolescents (4.52 [0.71] vs 4.20 [0.87], P = .008) and parents (4.25 [0.82] vs 3.71 [0.90], P < .001). Pump use was not significantly related. Parent satisfaction was correlated with hemoglobin A_1c (HbA_1c; R = -0.301, P < .001), percent time-in-range (R = 0.214, P = .007), and percent time-above-range (R = -0.193, P = .015). Adolescent satisfaction was unrelated to glycemic measures. Adolescent and parent satisfaction were both related to better psychosocial functioning. Significant associations between AID use, psychosocial functioning, and glycemic control and device satisfaction remained after accounting for one another. Demographic correlates were non-significant.

Conclusions: Adolescents and their parents have discrepant levels of satisfaction with devices. Although both adolescent and parent satisfaction are linked to use of automated technology and better psychosocial functioning, only parent satisfaction is associated with glycemia. This pattern suggests adolescents and parents hold varying priorities when it comes to device use. Acknowledging and addressing these differences may enhance the uptake and continued use of devices.

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.