Journal of Diabetes Science and Technology最新文献

Background: To identify diurnal glycemic patterns in adults with type 2 diabetes (T2D) using continuous glucose monitoring (CGM)-based machine learning and examine their association with diabetes distress, a key psychosocial outcome.

Methods: In this observational study, 137 adults with T2D wore blinded CGM (FreeStyle Libre Pro), yielding 1657 days of data. Glycemic patterns were identified using unsupervised machine learning via Gaussian mixture modeling, validated with Bayesian information criterion and silhouette scores. Diabetes distress was assessed with the 17-item Diabetes Distress Scale and analyzed through analysis of covariance (ANCOVA), adjusting for age, sex, body mass index, diabetes duration, and glucose management indicator.

Results: Clustering identified four distinct glycemic profiles: Cluster 1 (suboptimal control, nocturnal hypoglycemia; 15.8%), Cluster 2 (suboptimal control, nocturnal hyperglycemia; 27.1%), Cluster 3 (poorly controlled, prolonged hyperglycemia; 21.1%), and Cluster 4 (well controlled; 36.1%). Diabetes distress scores varied significantly: participants in Cluster 3 reported the highest distress (mean = 2.37, 95% CI = 1.99-2.76), while Cluster 4 reported the lowest (mean = 1.67, 95% CI = 1.48-1.86; P = .03). Effect sizes indicated differences corresponded to clinically meaningful categories of "little or no distress" vs "moderate distress."

Conclusions: CGM-based machine learning identified physiologically distinct glycemic phenotypes that were also associated with psychosocial burden. This work demonstrates the added value of integrating CGM-derived profiles with patient-reported outcomes. These findings highlight the potential of CGM phenotyping to support precision diabetes care by enabling early identification of high-risk subgroups, guiding tailored behavioral and psychosocial interventions, and informing technology-enabled decision tools that connect physiological monitoring with emotional well-being in T2D management.

Background: Hypoglycemia is a critical challenge for insulin-dependent people with diabetes using multiple daily injections (MDI), who rely on reactive responses to continuous glucose monitoring (CGM) alerts. To meet the need for a proactive safety tool, we evaluated the performance of the Low Glucose Predict (LGP) feature in the Accu-Chek SmartGuide Predict App.

Methods: This retrospective analysis pooled data from three prospective trials, including 85 subjects over 2709 recording days. The LGP feature uses a XGBoost model to predict low glucose events up to 30 minutes in advance. Performance was assessed rigorously against both capillary blood glucose (BG) and CGM values, including an analysis with "close-call" predictions (+10 mg/dL above the threshold). Metrics included sensitivity, specificity, and ROC-AUC.

Results: Against the stringent capillary BG reference, LGP showed high performance: sensitivity of 87.13% and specificity of 97.43% (ROC-AUC 0.9787). Including close-call events improved sensitivity to 91.89% and specificity to 98.09%. Referenced against CGM, sensitivity was 94.40% and specificity was 98.25%. The system provided an actionable mean lead time of 14.71 ± 8.30 minutes (CGM reference), with a low average daily true notification rate of 1.31 (2.60 including close-calls).

Conclusion: The LGP feature is an accurate, highly sensitive, and specific tool for timely, proactive low glucose prediction, validated against both capillary BG and CGM. This predictive intelligence is a crucial mechanism for people with diabetes to safely mitigate hypoglycemia risk, addressing a significant clinical gap and potentially reducing fear of hypoglycemia and diabetes distress.

Background: The automated assessment and prediction of diabetic foot ulcer (DFU) severity depends heavily on precise segmentation of the ulcer region. This approach avoided reliance on built-in segmentation tools, which often lacked the accuracy needed to delineate wound boundaries effectively. The objective of this study was to develop and evaluate an artificial intelligence (AI)-driven method for ulcer segmentation and severity classification of DFU using Wagner's grading system.

Methods: A novel method was introduced for segmenting the boundaries of DFUs, paired with a lightweight classification model for predicting ulcer severity as per Wagner's grade. This method was developed using a retrospective cohort of patients in India. A total of 1339 ulcer images were collected from 510 patients and augmented to 6579 images for AI-model generalizability. It incorporated an enhanced active contour model, combined with Sobel edge detection, to achieve precise delineation of ulcer edges. An AI-powered mobile application was developed to facilitate the real-time and remote assessment of the severity of DFUs.

Results: The proposed segmentation approach successfully delineated ulcer regions, achieving a Dice similarity coefficient of 0.99. The classification model attained an accuracy of 95.58%, with a sensitivity of 95.58%, a specificity of 99.16%, and an F1 score of 95.53%. The method also recorded a false-positive rate of 0.84% and a false negative rate of 4.83%, reflecting improved classification performance compared to existing methods.

Conclusions: The comparative analysis demonstrated that the proposed method significantly improved both segmentation and classification of DFUs, thereby supporting enhanced clinical management of the condition.

Sharing research code in an open access version-controlled repository offers significant benefits for both science as a whole and for individual researchers. In this article, we focus on this practice, which is fully aligned with the NIH's Gold Standard Science (GSS) program as well as FAIR (findable, accessible, interoperable, reusable) and TRUST (transparency, responsibility, user focus, sustainability, technology) principles. Gold Standard Science supports open science by emphasizing transparency, reproducibility, and the use of best practices that enable others to verify and extend research. Pairing a research article's cited data snapshot with a versioned, environment-specific code release, deposited in a companion code repository, ensures that, upon submission to a medical journal, readers and reviewers can directly verify results. An executable and updatable companion code repository complements, rather than replaces, established research data repositories. When code underlying medical research results is made openly available, then other scientists can inspect, run, and validate analyses. These activities enhance reproducibility, which is a core aim of GSS. Shared code also facilitates collaborative innovation by allowing researchers to extend the utility of the code to new datasets and applications. For researchers, code sharing can increase visibility, credibility, and citation impact. Demonstrating transparency through shared executable and updatable code builds trust with journal readers, peer reviewers, funders, and peers. Shared code in an open access repository signals adherence to high standards of scientific integrity and attracts opportunities for collaboration. A researcher who shares code receives recognition as a leader in reproducible, trustworthy research consistent with NIH's GSS principles.

Background: Despite established guidelines and increasing national hospital quality metrics, achieving consistent inpatient glycemic control remains challenging. A system-wide glucose data monitoring dashboard can help consolidate and visualize key metrics to support quality improvement (QI) and standardize care.

Methods: A web-based diabetes dashboard was implemented across 7 hospitals within a large health care system to monitor monthly data from the electronic health record. Metrics included patient-days with hypoglycemia (<70 mg/dL), hyperglycemia (mean >180 mg/dL), in-hospital mortality, hospital length of stay (LOS), and 30-day readmissions to the emergency department (ED) or inpatient/observation (IP/OBS). A total of 455 303 admissions were analyzed between January 2018 and March 2025, comparing pre-implementation (2018-2022) to post-implementation (2023-2025). Statistical analyses included t tests or Wilcoxon rank-sum tests. Given differences between the large academic site and 6 community hospitals, a difference-in-differences analysis was performed to evaluate impact by hospital type.

Results: After implementation of the dashboard, patient-days with hypoglycemia decreased from 4.81% to 4.15%, hyperglycemia from 25.30% to 23.46%, mortality from 2.69% to 2.13%, and LOS from 7.56 to 7.29 days (all P < .01). Emergency department and IP/OBS readmissions increased slightly (P < .01 and P = .01, respectively). Comparing the community hospitals to the academic, statistically significant reductions were observed in hypoglycemia, hyperglycemia, and mortality but with increased ED readmissions. There were no differences in LOS or IP/OBS readmission.

Conclusions: Implementation of a system-wide electronic dashboard was associated with improved glycemic control, mortality, and LOS. Dashboards can effectively support multidisciplinary collaboration and QI in diverse hospital settings.

The downside of the success story of diabetes technology is the amount of waste generated by manufacturing and using such medical products. Quantitative surveys performed in the United States and Germany document how many pounds of plastic waste, batteries, and electronic parts are piling up per patient with diabetes each year; however, the actual environmental burden of diabetes care has not been assessed yet. Given the highly probable further increase in usage of diabetes technology devices, what are the options to change the situation? Ideally, one would avoid generating waste by optimizing the design of the products (Eco-Design); however, this approach faces a number of complex regulatory and economic hurdles. The issue of waste management is becoming increasingly important as the use of wearable devices increases. Stewardship of the environment will require all stakeholders to address waste management.

Background: Comparative assessment of therapeutic technologies is often biased due to the inability to blind interventions, especially when therapies differ in form or dosing. While double-dummy design, where participants receive both an active treatment and a matched placebo to maintain blinding, is well established in pharmacological trials, its applicability for medical devices requiring user interaction, such as automated insulin delivery (AID) systems is challenging.

Methods: We present the methodology by which two AID systems are used in a double-dummy, blinded, randomized trial, one system providing insulin therapy and the other, a diluent.Outcomes and conclusion:The study demonstrates the feasibility, of comparing 2 AID systems, without operartor bias.