Journal of Diabetes Science and Technology最新文献

Pregnancy is a unique stage of life characterized by continuous maternal physiologic adaptations from conception to postpartum. Understanding the dynamic metabolic requirements of pregnancy can inform the effective use of current automated insulin delivery (AID) tools and aid in developing future diabetes technology to support diabetes management in this critical life period. In this review, we detail physiologic changes affecting early pregnancy, late pregnancy, intrapartum, and postpartum and discuss implications for using and designing AID systems.

Background: Comorbidities such as cardiovascular disease (CVD) and diabetic kidney disease (DKD) are major burdens of type 1 diabetes (T1D). Predicting people at high risk of developing comorbidities would enable early intervention. This study aimed to develop models incorporating socioeconomic status (SES) to predict CVD, DKD, and mortality in adults with T1D to improve early identification of comorbidities.

Methods: Nationwide Danish registry data were used. Logistic regression models were developed to predict the development of CVD, DKD, and mortality within five years of T1D diagnosis. Features included age, sex, personal income, and education. Performance was evaluated by five-fold cross-validation with area under the receiver operating characteristic curve (AUROC) and the precision-recall area under the curve (PR-AUC). The importance of SES was assessed from feature importance plots.

Results: Of the 6572 included adults (≥21 years) with T1D, 379 (6%) developed CVD, 668 (10%) developed DKD, and 921 (14%) died within the five-year follow-up. The AUROC (±SD) was 0.79 (±0.03) for CVD, 0.61 (±0.03) for DKD, and 0.87 (±0.01) for mortality. The PR-AUC was 0.18 (±0.01), 0.15 (±0.03), and 0.49 (±0.02), respectively. Based on feature importance plots, SES was the most important feature in the DKD model but had minimal impact on models for CVD and mortality.

Conclusions: The developed models showed good performance for predicting CVD and mortality, suggesting they could help in the early identification of these outcomes in individuals with T1D. The importance of SES in individual prediction within diabetes remains uncertain.

Background: The objective of this work is to document performance of automated insulin delivery (AID) during real-life use in type 2 diabetes (T2D).

Methods: A retrospective analysis was performed of continuous glucose monitoring and insulin delivery data from 796 individuals with T2D, who transitioned from 1-month predictive low-glucose suspend (PLGS) use to 3-month AID use, in real-life settings. Primary outcome was change of time in range (TIR = 70-180 mg/dL) from PLGS to AID. Secondary outcomes included time above/below range (TAR/TBR) and total daily insulin (TDI).

Results: Compared with PLGS, AID increased TIR on average from 63.2% to 72.6%, decreased TAR from 36.2% to 26.8%, and increased TDI from 70.2 to 76.3 U (all P < .001), without significant change to TBR. Glycemic improvements were more pronounced in those with worse glycemic control during PLGS use (P < .001).

Conclusions: Real-life use of AID led to a rapid and sustained improvement of glycemic control in individuals with T2D.

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: 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.

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.

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.