Journal of Diabetes Science and Technology最新文献

The postprandial glycemic response (PPGR) is associated with diabetes and cardiovascular disease and is highly individualized. The PPGR is affected by both physiological and behavioral factors. Attention to the PPGR has dramatically increased recently with the widespread use of continuous glucose monitors. It is expected that individualized control of PPGRs will be important in the prevention of diabetes and its associated complications. In this article, we discuss six modifiable factors associated with the PPGRs, including (1) the glucoregulatory hormones, (2) gastric emptying, (3) salivary or pancreatic amylase, (4) diet, (5) physical exercise, and (6) sleep and circadian rhythm. Modifying these factors may allow for personalized intervention strategies to control the PPGR-to reduce the risk for cardiovascular disease in individuals with varying degrees of glycemia.

Background: Effective glucose management of hospitalized individuals is essential for improving outcomes such as wound healing and reducing complications including hypoglycemia. There are many models of effective management, including a Virtual Glucose Management Service (VGMS), where a team of diabetes specialists reviews patient data remotely and recommends interventions to optimize glucose management in hospitalized individuals. The objective of our study was to assess the effectiveness of VGMS for glycemic management in hospitalized individuals in a large academic medical center in New York City.

Methods: We conducted a prospective, quality improvement intervention-control study on individuals ≥ 18 years old with at least one inpatient point-of-care blood glucose (POC BG) value <70 mg/dL and/or > 200 mg/dL admitted to the hospital from January 1, 2022, to December 31, 2023. VGMS was implemented across four intervention units and differences in glycemic outcomes were measured against two control units.

Results: A total of 1338 individuals were included in the intervention and 1019 individuals were included in the control group. Average glucose values in the control and intervention groups were similar [Mean mg/dL: 174.1 (CI: 172.0, 176.7) vs Mean mg/dL: 175.9 (CI: 173.8, 178.0), P = .1798], however the proportion of individuals with hyperglycemia (POC BG >180 mg/dL) was significantly higher in the control group [72.2% (69.5%, 74.9%) vs 65.5% (63.5%, 67.7%), P ≤ .0001].

Conclusion: The implementation of a VGMS team significantly reduced hyperglycemia in hospitalized individuals. This study shows that VGMS is an effective and efficient process to implement in hospital settings to improve inpatient glycemic management.

In-hospital standard of care for people living with diabetes (PLWD) is based on capillary blood glucose to activate hypoglycemia treatment protocols. PLWD on non-critical care wards often prefer to keep their continuous glucose monitor (CGM) on for their sense of agency. This systematic review assessed the CGM accuracy in the hypoglycemic range for these PLWD. Databases were searched from 2012 to August 2025. We included studies of adult PLWD on non-critical care wards, with CGM levels below 70 mg/dL (3.9 mmol/L) that were compared with paired reference blood glucose levels. Nine included studies reported on 465 hypoglycemic CGM and reference blood glucose pairs. The mean and median absolute relative differences ranged from 7.6% to 53.3%, and from 11.7% to 38.5%, respectively. The methods for pairing CGM with reference blood glucose varied. In eight studies, the mean absolute relative differences between hypoglycemia range CGM and paired reference blood glucose results were greater than 15%. These high mean absolute relative differences suggest that hypoglycemic range CGM results are too inaccurate to guide in-hospital diabetes therapy.

Introduction: Automated insulin delivery (AID) systems for type 1 diabetes (T1D) improve HbA1C, increase time-in-range, and reduce hypoglycemia. However, starting AID systems involves multiple steps, from decision to initiation. This study quantified time to AID initiation (TT-AID) and factors influencing the timeline.

Methods: This retrospective study included adults with T1D at an academic diabetes center in Baltimore, Maryland who were on multiple daily injections and initiated an AID system for the first time since diagnosis from May 2022 to March 2025. Demographics and dates of AID decision, AID selection visit (optional), prescription, training, and initiation were extracted from electronic medical records. Time to AID initiation was measured, with differences by insurance and AID selection visit assessed using Wilcoxon rank-sum and log-rank tests.

Results: Participants included 114 adults with T1D [median age 38.9 years, 57% male, 21% Black, 75% commercial insurance, median diabetes duration 10.2 years (IQR = 3.5, 18.1)]. The median TT-AID was 89.5 days (IQR = 49, 132). The longest delay was between decision and training [median: 82.5 days (IQR = 43, 122)]. Patients attending the optional AID selection visit had significantly longer TT-AID compared with those who did not [112 (IQR = 79, 144) vs 55 (IQR = 35, 98) days, P ≤ .0001]. Time to AID system initiation did not differ by AID type (P = .74). Patients with commercial insurance initiated AID systems sooner than those with public insurance, [86 days (IQR = 69, 98) vs 122 (IQR = 67, 195), P = .03] within 6 months of decision.

Conclusion: Adults took roughly 3 months to initiate AID, with longer delays among those with public insurance and those attending AID selection visits. Streamlining AID system initiation may reduce delays and optimize outcomes.

Background: Glycemic control (GC) is hard to implement safely in intensive care due to patient variability. GC has been wrongly blamed for increased hypoglycemic risk instead of protocol design, limiting its adoption. Stochastic TARgeted (STAR) is a model-based, patient-specific, risk-based GC framework modulating intravenous (IV) insulin and nutrition, accounting for both inter- and intra-patient variability. This study assesses STAR GC's ability to provide safe and effective control across a large cohort.

Methods: This study was performed in Christchurch Hospital Intensive Care Unit, New Zealand. Patients were treated with STAR GC between April 2019 and December 2024. The STAR GC episodes not complying with filtering criteria were excluded. Results are analyzed in terms of performance, safety, and workload.

Results: Of 1340 adult ICU patients totaling 1958 STAR GC episodes, 1085 patients and 1430 episodes (86 010 h of control) remained after filtering. In total, 71% of blood glucose (BG) measurements were in the target band for a median [interquartile range, IQR] BG of 124 [110-148] mg/dL. Only three (0.21%) severe hypoglycemia events (BG < 40 mg/dL) occurred, two unrelated to the control design. High median [IQR] nutrition delivery (89.0 [17.2-100.0]) %goal feed was achieved with median [IQR] insulin rate of 4.5 [2.0-6.0] U/h. Results were consistent per-patient and improved once in the target band.

Conclusions: STAR provides safe, effective control for all patients in this large cohort, with minimal hypoglycemia and high nutrition rates. The protocol adapts to patients' specific needs and tolerances, encouraging STAR's adoption in other ICUs. The quality of control also enables prospective assessment of the future of GC's impact on patient outcomes.

Background: This study aimed to investigate the decline over time in the proportion of total daily insulin delivered as boluses in newly diagnosed youth with type 1 diabetes using a hybrid closed-loop system.

Method: A secondary analysis was conducted using data from the CLOuD study, an open-label, multicenter, randomized, parallel hybrid closed-loop trial to investigate bolus patterns in youth with newly diagnosed type 1 diabetes.

Results: Over the 48-month trial period, the proportion of total daily insulin delivered as carbohydrate-related boluses decreased from 58% to 34%. There was a decreasing trend in the median (interquartile range) amount of carbohydrates entered per day from 236 (204, 253) g to 184 (127, 232) g, and the number of carbohydrate-related boluses per day from 5.5 (4.6, 6.5) to 3.7 (2.9, 5.2) over the 48 months. Mean ± SD daily carbohydrate-related bolus insulin increased from 15.1 ± 6.6 to 22.0 ± 9.0 units/d, and the amount of insulin delivered per 10 g of carbohydrate more than doubled from 0.6 (0.5, 0.8) units to 1.3 (0.9, 1.5) units. The postprandial change in glucose (measured as the difference between peak glucose 30 to 180 minutes post carbohydrate-related bolus and glucose on carbohydrate-related bolus delivery) changed from 49 (45, 54) to 59 (53, 66) mg/dL.

Conclusions: The decline in the proportion of total daily insulin delivered for as bolus is likely attributable to a combination of missed boluses and under-bolusing, while the closed-loop algorithm compensates for the missed or insufficient carbohydrate-related insulin delivery by increasing basal insulin delivery.

Aims: To evaluate the outcomes of prolonged religious Jewish fasting in individuals with type 1 diabetes using automated insulin delivery (AID) systems.

Methods: This cross-sectional, non-interventional study assessed the effects of a 25-hour Jewish fast in individuals using AID systems. Data was collected on the day of the fast, one week before, and one week after.

Results: The study included data from 109 fasting days involving 80 adolescents and young adults with type 1 diabetes. The mean age of participants was 17.4 ± 4.1 years; 47.5% were male, and the average duration of diabetes was 7.2 ± 4.3 years. A total of 67.6% of participants modified their AID system settings during the fasting period, with the most common modification being a change in the target glucose level. Overall, 71.5% completed the fast without complications. Fasts were primarily broken because of sensor-detected hypoglycemia. No cases of severe hypoglycemia or diabetic ketoacidosis were reported during or after the fasting period. During the fast, the mean blood glucose level was 135 ± 28.6 mg/dL, time in range (70-180 mg/dL) was 80.7%, and time spent in hypoglycemia (<70 mg/dL) was 2.6%.

Conclusions: Prolonged fasting appears to be safe for adolescents and young adults with type 1 diabetes using AID systems. However, individualized adjustments to system settings are often necessary to maintain glycemic stability during fasting. To our knowledge, this is the first report of the effects of using an AID system during Jewish religious fasting.