Journal of Diabetes Science and Technology最新文献

Pregnancy adds unique medical and psychosocial complexity to the management of type 1 diabetes (TID). Automated insulin delivery (AID) use in pregnancy increasingly shows promise both in improving clinical outcomes and the patient experience for individuals living with T1D. Survey and qualitative data on psychosocial correlates of AID use in pregnancy demonstrate patient benefits compared with other glucose management strategies (such as multiple daily injections, continuous subcutaneous insulin infusion, or sensor-augmented pump therapy). Benefits include improved patient well-being, flexibility, and improved collaboration with health care provider teams. However, burdens have also been identified, including technical glitches, device maintenance, device bulk/visibility, frequent alarms, and the overwhelming quantity of available data. This review describes the lived experiences and perspectives of pregnant individuals with T1D using AID systems. Ongoing education and support for both patients and providers may help to maximize the psychosocial benefits of AID use and reduce potentially negative aspects for pregnant individuals with T1D. While AID represents a significant opportunity for optimizing glucose management for individuals with T1D, both patients and providers need to have realistic expectations based on evidence of what such systems can and cannot do.

Background: Lactate is not considered just a "waste product" of anaerobic glycolysis anymore. It has been proved to play a key role in several metabolic diseases, such as in the metabolic dysfunction-associated steatotic liver disease, obesity, and diabetes. The capability of simulating glucose-insulin-lactate interaction would be useful to design and test drugs targeting lactate metabolism in such pathological conditions. Minimal models are available, which describe and quantify glucose-lactate interaction but models to simulate postprandial glucose-insulin-C-peptide-lactate time courses are missing. The aim of this study is to fill this gap.

Methods: Starting from the Padova Type 2 Diabetes Simulator (T2DS), we first added a description of glucose-lactate kinetics and then created a population of 100 in silico subjects to match glucose-insulin-C-peptide-lactate data of 44 adolescents with/without obesity who underwent a standard oral glucose tolerance test (OGTT) of 75 g.

Results: The developed model accurately predicts all molecules time courses, guaranteeing precise model parameter estimates (percent coefficient of variation [CV%] median [25th-75th percentile] = 19 [9-29]%). The generated in silico population shows good agreement with the clinical data in terms of area under the curve (AUC) (P = .6, .6, .9, .6 for glucose, insulin, C-peptide, and lactate, respectively) and parameter distributions (P > .1).

Conclusions: We have developed a simulator to describe glucose, insulin, C-peptide, and lactate kinetics during an OGTT, which captures the behavior of a real population of adolescents with/without obesity both in terms of average and intersubject variability. Such simulator can be used to investigate the pharmacodynamics of drugs targeting lactate metabolic pathway in various pathological conditions.

Background and aim: Continuous glucose monitoring systems (CGMs) have been commercially available since 1999. However, automated insulin delivery systems may benefit from real-time inputs in addition to glucose. Continuous multi-analyte sensing platforms will meet this area of potential growth without increasing the burden of additional devices. We aimed to generate pilot data regarding the safety and function of a first-in-human, single-probe glucose/lactate multi-analyte continuous sensor.

Methods: The investigational glucose/lactate continuous multi-analyte sensor (PercuSense Inc, Valencia, California) was inserted to the upper arms of 16 adults with diabetes, and data were available for analysis from 11 of these participants (seven female; mean [SD] = age 43 years [16]; body mass index [BMI] = 27 kg/m² [5]). A commercially available Guardian 3 CGM (Medtronic, Northridge, California) was also inserted into the abdomen for comparison. All participants underwent a meal-test followed by an exercise challenge on day 1 and day 4 of wear. Performance was benchmarked against venous blood YSI glucose and lactate values.

Results: The investigational glucose sensor had an overall mean absolute relative difference (MARD) of 14.5% (median = 11.2%) which improved on day 4 compared with day 1 (13.9% vs 15.2%). The Guardian 3 CGM had an overall MARD of 13.9% (median = 9.4%). The lactate sensor readings within 20/20% and 40/40% of YSI values were 59.7% and 83.1%, respectively.

Conclusions: Our initial data support safety and functionality of a novel glucose/lactate continuous multi-analyte sensor. Further sensor refinement will improve run-in performance and accuracy.

Background: Continuous glucose monitoring (CGM) has emerged as an important tool for managing gestational diabetes mellitus (GDM), offering real-time glucose data and the potential for improved glycemic control. Unlike traditional self-monitoring of blood glucose (SMBG), which provides intermittent readings, CGM captures continuous glucose fluctuations, including postprandial and nocturnal changes, which are critical in GDM management.

Objective: This systematic review aimed to assess the effectiveness of CGM compared with SMBG in managing glycemic control in women with GDM, focusing on key glycemic metrics such as time in range (TIR) and glycemic variability (GV), and exploring their associations with maternal and neonatal outcomes.

Methods: A comprehensive search of PubMed and Google Scholar was conducted, adhering to PRISMA guidelines. Studies included randomized controlled trials, observational studies, and prospective cohort studies comparing CGM and SMBG, with 35 studies ultimately reviewed.

Results: Compared with SMBG, CGM demonstrated significant improvements in maintaining TIR and reducing GV, which correlated with favorable maternal and neonatal outcomes, including lower rates of large-for-gestational-age (LGA) infants, preterm birth, and NICU (neonatal intensive care unit) admissions. Furthermore, CGM detected more hyperglycemic and hypoglycemic events, particularly nocturnal fluctuations. However, the studies also highlighted the need for standardized metrics to optimize CGM use in GDM management.

Conclusion: Continuous glucose monitoring offers substantial advantages over SMBG for managing GDM by providing continuous, real-time glucose data, enabling timely treatment adjustments. These findings support the integration of CGM into clinical practice to improve maternal and neonatal outcomes in GDM. Further research is needed to establish standardized CGM metrics specific to GDM management.

Introduction: Growing research suggests continuous glucose monitoring (CGM) may help improve glycemic outcomes in noninsulin-using people with type 2 diabetes (T2D). The continuous biofeedback from CGM provides considerable opportunities to support personalized behavior changes; however, limited research exists to describe what happens to glycemia in this population when CGM is removed. The purpose of this follow-up study is to evaluate the effects of CGM discontinuation in noninsulin-using people with T2D.

Methods: The effects of CGM discontinuation were assessed using data from the UNITE study (NCT05928572). Phase 1 of UNITE was a two-month intervention that evaluated the impact of using a nutrition-focused approach during CGM initiation on glycemic measures in people with T2D. In Phase 2, after discontinuing CGM use for four months, blinded CGM data and other measures were collected at Follow-up and compared to data from the post-intervention (Post) period.

Results: The percent time in range 70 to 180 mg/dL decreased from 77% in the Phase 1 Post period to 60% during the Phase 2 Follow-up period (95% confidence interval [CI] = -22%, -12%; P < .0001). Several additional glycemic metrics also worsened significantly from Post to Follow-up (P < .05). Dietary intake and exercise at Follow-up were not statistically different from Post (P > .05), but physical activity decreased (P = .01).

Conclusion: In noninsulin-using people with T2D, glycemic measures improved with real-time CGM use, but these improvements deteriorated substantially and significantly when CGM use was discontinued. More research and more sensitive behavioral assessments are needed to better understand which factors and behavior changes may account for the glycemic decline.

Background: There is room for improvement in the outcome of automated insulin delivery. Our aim was to explore the impact on glucose metrics of algorithmic modifications of a DBLG1 hybrid closed-loop system, targeting the management of meal periods, hypoglycemia and hyperglycemia.

Methods: We performed a two-step analysis of CGM data of all consenting adult patients with type 1 diabetes who were equipped in Europe with DBLG1 between November 1, 2023 and January 31, 2025, comparing three successive versions of the algorithm: v1.12 vs. v1.16 (first step, retrospective comparison), then v1.16 vs. v1.17 (second step, ambispective before/after analysis). Time in range of 70 to 180 mg/dL was the primary endpoint.

Results: The first step (duration 319 days, 937 patients) compared 269 users of 1.12 version and 668 users of 1.16 version. Median TIR improved from 65.3% [IQR 58.4%-72.1%] to 71.3 [63.3%-78.0%]. Time in Tight Range 70 to 140 mg/dL increased from 37.5% [30.6%-43.1%] to 40.4% [31.7%-48.5%]. Time in Hypoglycemia was stable. Time >250 mg/dL decreased from 8.9% to 5.4%, GMI from 7.3% to 7.1%, CV from 30.4% to 27.4%, and GRI from 38.0 to 30.0. The second step (1212 patients, 120 days) showed a further improvement of TIR from 68.8% [59.6%-76.8%] to 70.8% [63.0%-77.6%] when upgrading from v1.16 to v1.17, with marginal changes in other glucose metrics. The incidence rates of severe hypoglycemia or hyperglycemia remained very low.

Conclusion: This large post-market report illustrates the margin of improvement in AID performances through algorithmic refinements that improve the efficacy without deteriorating the safety of closed-loop insulin delivery.

The global burden of diabetes continues to rise, resulting in a concomitant increase in the global demand for insulin replacement therapies. Subcutaneous insulin administration using pen injectors is one of the most common insulin delivery methods; these devices are often preferred over vial and syringe administration by individuals with diabetes owing to their convenience and ease of use, and by clinicians owing to enhanced user adherence and high dose accuracy. However, prefilled insulin pen injectors are commonly made of plastic and many are disposable; consequently, both their production and use are associated with a high carbon footprint and substantial medical waste through poor recycling infrastructures, posing a growing environmental concern. Historically, producing durable insulin pen injectors required high-quality metal components, which are more environmentally taxing despite being reusable. Conversely, plastic has a lower environmental footprint per unit but conventional plastic pen injectors have lacked durability, leading to greater consumption overall. Therefore, reusable insulin pen injectors that can be used repeatedly for several years, while also being easy to use and accurate, are essential for sustainable diabetes management. Novo Nordisk has developed several insulin pen injectors, such as NovoPen (NP) 4 (a reusable pen part of the NP family), FlexTouch (a disposable pen) and, more recently, DuraTouch (DT; a reusable pen). Here, we describe the technical attributes of DT using other pen injectors manufactured by Novo Nordisk as contextual comparators to illustrate how DT may meet users' functional needs, while helping to reduce the environmental burden associated with the use of insulin pen injectors.

The University of Chicago Monogenic Diabetes Registry (UCMDR) developed a participant portal to enhance engagement and data completeness in a large, longitudinal research study. Built in collaboration with the Center for Research Informatics (CRI), the portal integrates with REDCap to provide secure survey access, document exchange, and communication. Since its launch, 40% of invited participants have activated accounts. Among new enrollees, 88% of portal users completed their baseline survey, up from a historical 60%. Portal activation was higher among female participants (P < .01), with no significant differences by age or income. Overall portal feature use was modest, possibly reflecting lower adoption among historical participants accustomed to legacy processes. These findings support the feasibility of research participant portals while highlighting opportunities to improve equitable engagement and functionality.