Journal of Diabetes Science and Technology最新文献

Diabetes is the leading cause of chronic kidney disease (CKD) and end-stage kidney disease in the world. It is known that maintaining optimal glycemic control can slow the progression of CKD. However, the failing kidney impacts glucose and insulin metabolism and contributes to increased glucose variability. Conventional methods of insulin delivery are not well equipped to adapt to this increased glycemic lability. Automated insulin delivery (AID) has been established as an effective treatment in patients with type 1 diabetes mellitus, and there is emerging evidence for their use in type 2 diabetes mellitus. However, few studies have examined their role in diabetes with concurrent advanced CKD. We discuss the potential benefits and challenges of AID use in patients with diabetes and advanced CKD, including those on dialysis.

Background: This study assessed changes in actigraphy-estimated sleep and glycemic outcomes after initiating automated insulin delivery (AID).

Methods: Ten adults with long-standing type 1 diabetes and impaired awareness of hypoglycemia (IAH) participated in an 18-month clinical trial assessing an AID intervention on hypoglycemia and counter-regulatory mechanisms. Data from eight participants (median age = 58 years) with concurrent wrist actigraph and continuous glucose monitoring (CGM) data were used in the present analyses. Actigraphs and CGM measured sleep and glycemic control at baseline (one week) and months 3, 6, 9, 12, 15, and 18 (three weeks) following AID initiation. HypoCount software integrated actigraphy with CGM data to separate wake and sleep-associated glycemic measures. Paired sample t-tests and Cohen's d effect sizes modeled changes and their magnitude in sleep, glycemic control, IAH (Clarke score), hypoglycemia severity (HYPO score), hypoglycemia exposure (CGM), and glycemic variability (lability index [LI]; CGM coefficient-of-variation [CV]) from baseline to 18 months.

Results: Sleep improved from baseline to 18 months (shorter sleep latency [P < .05, d = 1.74], later sleep offset [P < .05, d = 0.90], less wake after sleep onset [P < .01, d = 1.43]). Later sleep onset (d = 0.74) and sleep midpoint (d = 0.77) showed medium effect sizes. Sleep improvements were evident from 12 to 15 months after AID initiation and were preceded by improved hypoglycemia awareness (Clarke score [d = 1.18]), reduced hypoglycemia severity (HYPO score [d = 2.13]), reduced sleep-associated hypoglycemia (percent time glucose was < 54 mg/dL, < 60 mg/dL,< 70 mg/dL; d = 0.66-0.81), and reduced glucose variability (LI, d = 0.86; CV, d = 0.62).

Conclusion: AID improved sleep initiation and maintenance. Improved awareness of hypoglycemia, reduced hypoglycemia severity, hypoglycemia exposure, and glucose variability preceded sleep improvements.This trial is registered with ClinicalTrials.gov NCT03215914 https://clinicaltrials.gov/ct2/show/NCT03215914.

Background: The use of fructosamine to assess the glycemic control represents a new step in diagnostics, and it has been accompanied by the active scientific discussion in recent years. That is why the purpose of this work is to study the average level of fructosamine in apparently healthy individuals and individuals with diabetes mellitus (DM), as well as the possibility to use it when evaluating the effectiveness of inpatient treatment of patients with hyperglycemia on the seven to ten days of hospitalization.

Methods: This research work was carried out in Alma-Ata, Republic of Kazakhstan, based on the endocrinology department in the period from 2020 to 2022. The work consists of a retrospective analysis of previously examined patients and a prospective stage. The statistical evaluation was carried out with the calculation of reliability coefficient, confidence interval, and criteria for testing for normality. The level of fructosamine in healthy individuals in the corresponding region was analyzed in this article for the first time, and the correlation between this indicator and the level of glycated hemoglobin was found.

Results: The effectiveness of treatment of the Type 2 DM (according to the treatment protocol) has also been studied in stationary conditions for the seven to ten days, which makes it possible to judge the effectiveness of the prescribed therapy.

Conclusions: These results will allow identifying the irrationality of the prescribed therapy at an early stage, which is especially important for the correct management of patients with this pathology, and minimizing the possible complications.

Background: Control-IQ technology version 1.5 allows for a wider range of weight and total daily insulin (TDI) entry, in addition to other changes to enhance performance for users with high basal rates. This study evaluated the safety and performance of the updated Control-IQ system for users with basal rates >3 units/h and high TDI in a multicenter, single arm, prospective study.

Methods: Adults with type 1 diabetes (T1D) using continuous subcutaneous insulin infusion (CSII) and at least one basal rate over 3 units/h (N = 34, mean age = 39.9 years, 41.2% female, diabetes duration = 21.8 years) used the t:slim X2 insulin pump with Control-IQ technology version 1.5 for 13 weeks. Primary outcome was safety events (severe hypoglycemia and diabetic ketoacidosis (DKA)). Central laboratory hemoglobin A_1c (HbA_1c) was measured at system initiation and 13 weeks. Participants continued using glucagon-like peptide-1 (GLP-1) receptor agonists, sodium-glucose transport protein 2 (SGLT-2) inhibitors, or other medications for glycemic control and/or weight loss if on a stable dose.

Results: All 34 participants completed the study. Fifteen participants used a basal rate >3 units/h for all 24 hours of the day. Nine participants used >300 units TDI on at least one day during the study. There were no severe hypoglycemia or DKA events. Time in range 70-180 mg/dL was 64.8% over the 13 weeks, with 1.0% time <70 mg/dL. Hemoglobin A_1c decreased from 7.69% at baseline to 6.87% at 13 weeks (-0.82%, P < .001).

Conclusions: Control-IQ technology version 1.5, with wider range of weight and TDI input and enhancements for users with high insulin requirements, was safe in individuals with T1D in this study.

Introduction: Insulin pump therapy can be adversely affected by interruption of insulin flow, leading to a rise in blood glucose (BG) and subsequently of blood beta-hydroxybutyrate (BHB) ketone levels.

Methods: We performed a PubMed search for English language reports (January 1982 to July 2024) estimating the rate of rise in BG and/or BHB after ≥ 60 minutes of interruption of continuous subcutaneous insulin infusion (CSII) in persons with type 1 diabetes (PwT1D). We also simulated the rise in BG in a virtual population of 100 adults with T1D following suspension of continuous subcutaneous insulin infusion.

Results: We identified eight relevant studies where BG and BHB (seven of these eight studies) were measured following suspension of CSII as a model for occlusion. After 60 minutes post-suspension, the mean extracted rates of rise averaged 0.62 mg/dL/min (37 mg/dL/h) for BG and 0.0038 mmol/L/min (0.20 mmol/L/h) for BHB. Mean estimated time to moderately/severely elevated BG (300/400 mg/dL) or BHB (1.6/3.0 mmol/L) was, respectively, 5.8/8.5 and 8.0/14.2 hours. The simulation model predicted moderately/severely elevated BG (300/400 mg/dL) after 9.25/12, 6.75/8.75, and 4.75/5.75 hours in the virtual subjects post-interruption with small (5th percentile), medium (50th percentile), and large (95th percentile) hyperglycemic changes.

Discussion: Clinical studies and a simulation model similarly predicted that, following CSII interruption, moderate/severe hyperglycemia can occur within 5-9/6-14 hours, and clinical studies predicted that moderate/severe ketonemia can occur within 7-12/13-21 hours. Patients and clinicians should be aware of this timing when considering the risks of developing metabolic complications after insulin pump occlusion.

Background: A noninvasive, wearable continuous glucose monitor would be a major advancement in diabetes therapy. This trial investigated a novel noninvasive glucose monitor which analyzes spectral variations in radio frequency/microwave signals reflected from the wrist.

Methods: A single-arm, open-label, experimental study compared glucose values from a prototype investigational device with laboratory glucose measurements from venous blood samples (Super GL Glucose Analyzer, Dr. Müller Gerätebau GmbH) at varying levels of glycemia. The study included 29 male participants with type 1 diabetes (age range = 19-56 years). The study comprised three stages with the following aims: (1) demonstrate initial proof-of-principle, (2) test an improved device design, and (3) test performance on two consecutive days without device recalibration. The co-primary endpoints in all trial stages were median and mean absolute relative difference (ARD) calculated across all data points.

Results: In stage 1, the median and mean ARDs were 30% and 46%, respectively. Stage 2 produced marked performance improvements with a median and mean ARD of 22% and 28%, respectively. Stage 3 showed that, without recalibration, the device performed as well as the initial prototype (stage 1) with a median and mean ARD of 35% and 44%, respectively.

Conclusion: This proof-of-concept study shows that a novel noninvasive continuous glucose monitor was capable of detecting glucose levels. Furthermore, the ARD results are comparable to first models of commercially available minimally invasive products without the need to insert a needle. The prototype has been further developed and is being tested in subsequent studies.

Trial registration number: NCT05023798.

Introduction: An error grid compares measured versus reference glucose concentrations to assign clinical risk values to observed errors. Widely used error grids for blood glucose monitors (BGMs) have limited value because they do not also reflect clinical accuracy of continuous glucose monitors (CGMs).

Methods: Diabetes Technology Society (DTS) convened 89 international experts in glucose monitoring to (1) smooth the borders of the Surveillance Error Grid (SEG) zones and create a user-friendly tool-the DTS Error Grid; (2) define five risk zones of clinical point accuracy (A-E) to be identical for BGMs and CGMs; (3) determine a relationship between DTS Error Grid percent in Zone A and mean absolute relative difference (MARD) from analyzing 22 BGM and nine CGM accuracy studies; and (4) create trend risk categories (1-5) for CGM trend accuracy.

Results: The DTS Error Grid for point accuracy contains five risk zones (A-E) with straight-line borders that can be applied to both BGM and CGM accuracy data. In a data set combining point accuracy data from 18 BGMs, 2.6% of total data pairs equally moved from Zones A to B and vice versa (SEG compared with DTS Error Grid). For every 1% increase in percent data in Zone A, the MARD decreased by approximately 0.33%. We also created a DTS Trend Accuracy Matrix with five trend risk categories (1-5) for CGM-reported trend indicators compared with reference trends calculated from reference glucose.

Conclusion: The DTS Error Grid combines contemporary clinician input regarding clinical point accuracy for BGMs and CGMs. The DTS Trend Accuracy Matrix assesses accuracy of CGM trend indicators.

Background: Continuous glucose monitor (CGM) use improves type 1 diabetes (T1D) outcomes, yet children from diverse backgrounds and on public insurance have worse outcomes and lower CGM utilization. Using novel CGM data acquisition and analysis of two T1D cohorts, we test the hypothesis that T1D youth from different backgrounds experience disparities in meaningful CGM use following both T1D diagnosis and CGM uptake.

Methods: Cohorts drawn from a pediatric T1D program were followed for one year beginning at diagnosis (n = 815, 2016-2020) or CGM uptake (n = 1392, 2015-2020). Using chart and CGM data, CGM start and meaningful use outcomes between racial/ethnic and insurance groups were compared using median days, one-year proportions, and survival analysis.

Results: Publicly compared with privately insured were slower to start CGM (233, 151 days, P < .01), had fewer use-days in the year following uptake (232, 324, P < .001), and had faster first discontinuation rates (hazard ratio [HR] = 1.61, P < .001). Disparities were more pronounced among Hispanic and black compared with white subjects for CGM start time (312, 289, 149, P = .0013) and discontinuation rates (Hispanic HR = 2.17, P < .001; black HR = 1.45, P = .038), and remained even among privately insured (Hispanic/black HR = 1.44, P = .0286).

Conclusions: Given the impact of insurance and race/ethnicity on CGM initiation and use, it is imperative that we target interventions to support universal access and sustained CGM use to mitigate the potential impact of provider biases and systemic disadvantage and racism. By enabling more equitable and meaningful T1D technology use, such interventions will begin to alleviate outcome disparities between youth with T1D from different backgrounds.

Objective: To demonstrate bioequivalence and safety for a ready-to-use room-temperature liquid-stable glucagon administered subcutaneously (SC) through a glucagon autoinjector (GAI) or a glucagon vial and syringe kit (GVS), versus a glucagon prefilled syringe (G-PFS).

Methods: Healthy adults (N = 32) were randomly assigned to receive 1-mg glucagon as GAI or G-PFS, and then as the alternative three to seven days later. Other healthy adults (N = 40) were randomly assigned to receive 1-mg glucagon as GVS or G-PFS, and then as the alternative two days later. Samples for plasma glucagon were obtained through 240 minutes after glucagon injection. Bioequivalence was declared when the geometric mean estimate ratio of the area under-the-concentration-versus-time curve from 0 to 240 minutes (AUC_0-240) and maximum concentration (C_max) for plasma glucagon between treatment groups was contained within the bounds of 80% and 125%. Adverse events (AEs) were recorded.

Results: The 90% confidence intervals (CIs) for AUC_0-240 and C_max geometric mean ratios for G-PFS to GAI and GVS to G-PFS were contained within the bounds 80% to 125% (G-PFS:GAI AUC_0-240 95.05%, 119.67% and C_max 88.01%, 120.24%; GVS:G-PFS AUC_0-240 87.39%, 100.66% and C_max 89.08%, 106.08%). At least one AE occurred in 15.6% (5/32) participants with GAI, 25% (18/72) with G-PFS, and 32.5% (13/40) with GVS. Sixty-nine of 73 (94.5%) AEs were mild, and none were serious. Nausea was the most common (33/73 [45%]).

Conclusions: Bioequivalence and safety were established after 1 mg of this ready-to-use room-temperature liquid-stable glucagon, administered SC to healthy adults, by autoinjector, prefilled syringe, or vial and syringe kit.