Journal of Diabetes Science and Technology最新文献

{"title":"Automated Oral Minimal Models for Rapid Estimation of Insulin Sensitivity and Beta-Cell Responsivity in Large-Scale Data Sets: A Validation Study.","authors":"Simone Perazzolo, Alfonso Galderisi, Alice Carr, Colin Dayan, Claudio Cobelli","doi":"10.1177/19322968251365274","DOIUrl":"10.1177/19322968251365274","url":null,"abstract":"<p><p>The Oral Minimal Model (OMM) analysis offers unique measures of glucose-insulin regulation during glucose challenges. However, its manual test-by-test implementation limits scalability in large studies. We introduce the Automated Oral Minimal Model (AOMM), a tool that streamlines and automates the entire OMM workflow while preserving analytical fidelity, enabling efficient batch processing of large datasets. Built on SAAM II software, AOMM was validated against manually extracted results from <i>Sunehag</i> et al (<i>Obesity (Silver Spring)</i>, 2008), accurately reproducing key parameters such as insulin sensitivity (<i>Si</i>) and beta-cell responsivity (<i>Φ</i>) with high precision and substantial time savings. AOMM, with its user-friendly interface, facilitates broader application of minimal modeling in research and clinical studies.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251365274"},"PeriodicalIF":3.7,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12408534/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144956134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wearable Diabetes Technology for Hospitalized People With Diabetes and End-Stage Kidney Disease, Peripartum State, and Steroid Use.","authors":"Shubham Agarwal, Andrew P Demidowich, Diana Soliman, Guillermo E Umpierrez, Rodolfo J Galindo","doi":"10.1177/19322968251364276","DOIUrl":"10.1177/19322968251364276","url":null,"abstract":"<p><p>Inpatient hyperglycemia remains a challenge, as conventional insulin regimens often lead to both hyperglycemia and hypoglycemia. Traditional glucose monitoring methods, such as point-of-care testing, fail to detect diurnal and nocturnal glycemic fluctuations, contributing to suboptimal control. This review examines the effectiveness of continuous glucose monitoring (CGM) and automated insulin delivery (AID) systems in managing diabetes in hospitalized patients, including those with additional challenges such as end-stage kidney disease (ESKD), pregnancy, and steroid use. In patients with ESKD, CGM has demonstrated reliable glucose measurements and improved glycemic control, particularly in those undergoing hemodialysis. It has been shown to increase time in range (TIR) and reduce hypoglycemia, with clinical accuracy verified in multiple studies. Existing evidence shows that AID systems may offer improved outcomes in this population, with increased TIR and reduced glycemic variability compared with conventional insulin therapy. Continuous glucose monitor use has been beneficial for maternal glycemic control in pregnancy, leading to lower HbA1c levels, increased TIR, reduced maternal hypoglycemia, reduced neonatal hypoglycemia, and admissions to intensive care. Limited studies have evaluated AID system use during labor. In addition, CGM helps identify postprandial hyperglycemia in patients with glucocorticoid-induced hyperglycemia, which is crucial for managing glucose fluctuations. Studies in patients receiving glucocorticoids have shown that continuous glucose monitoring improves glycemic control without significantly increasing hypoglycemic events. In conclusion, limited studies have shown the role of CGM and AID systems and their effects on glycemic outcomes in hospitalized patients with diabetes, particularly those with ESKD, in pregnancy, and those receiving glucocorticoids. These technologies used for glucose monitoring and insulin delivery could offer an alternative method of diabetes management in certain inpatient populations.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251364276"},"PeriodicalIF":3.7,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12479449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144956175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scoping Review-Diabetes Technology for Individuals on Kidney Replacement Therapy (Dialysis): Current Trends and Future Directions.","authors":"Hellena Hailu Habte-Asres, Joseph Ngmenesegre Suglo, Khuram Chaudhry, Angus Forbes, David C Wheeler, Janaka Karalliedde","doi":"10.1177/19322968251353811","DOIUrl":"10.1177/19322968251353811","url":null,"abstract":"<p><strong>Aim: </strong>This review aims to map the existing literature on the use of diabetes technology in people receiving dialysis, with a focus on utilization, accuracy, and effectiveness.</p><p><strong>Methods: </strong>A scoping review was conducted using the Joanna Briggs Institute methodology, with systematic searches of Medline, Embase, and CINAHL for studies on diabetes technologies in dialysis populations.</p><p><strong>Results: </strong>The search identified 1060 continuous glucose monitoring (CGM) and 1467 continuous subcutaneous insulin infusion or automated insulin delivery (CSII/AID) records, with 64 studies included. Eighteen studies assessed CGM accuracy, reporting mean absolute relative difference (MARD) values ranging from 8.1% to 29%, with over 97% of readings falling within Clarke error grid zones A or B. Thirteen studies compared glycemic markers, finding that HbA_1c underestimated glucose by 7.3 mmol/mol, while glycated albumin showed a stronger correlation (r = 0.508). Four studies reported on dialysis effects, showing that people on automated peritoneal dialysis (APD) had lower mean glucose levels (181 ± 64 mg/dL) compared to continuous ambulatory peritoneal dialysis (CAPD) (238 ± 67 mg/dL; <i>P</i> < .05). Eleven studies evaluating diabetes treatment efficacy using CGM found that dulaglutide significantly reduced glucose CV from 28.1% to 19.8% (<i>P</i> = .003). Twenty-two studies examining glycemic outcomes reported that TIR was lower on dialysis days (80.2%, <i>P</i> = .02). Finally, four AID studies reported TIR improvements of up to 37.6% and a 1.5 mmol/L reduction in glucose (<i>P</i> = .003).</p><p><strong>Conclusion: </strong>This review highlights the potential of CGM and AID to improve diabetes outcomes in people on dialysis. While their clinical utility is evident, broader access and further research are needed to optimize their use in this high-risk population.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251353811"},"PeriodicalIF":3.7,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12405195/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144956180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced Glycation Endproducts: A Marker of Long-term Exposure to Glycemia.","authors":"David C Klonoff, Rachel E Aaron, Tiffany Tian, Ashley Y DuNova, Ambarish Pandey, Connie Rhee, G Alexander Fleming, David B Sacks, Rodica Pop-Busui, David Kerr","doi":"10.1177/19322968241240436","DOIUrl":"10.1177/19322968241240436","url":null,"abstract":"<p><p>This article examines the importance of advanced glycation endproducts (AGEs) and summarizes the structure of AGEs, pathological changes associated with AGEs, the contribution of AGEs to metabolic memory, and the value of AGEs as a predictor of diabetic complications and cardiovascular disease in people with and without diabetes. As a practical focus, skin autofluorescence (SAF) is examined as an attractive approach for estimating AGE burden. The measurement of AGEs may be of significant value to specific individuals and groups, including Black and Hispanic/Latino Americans, as they appear to have higher concentrations of hemoglobin A_1c (HbA_1c) than would be predicted by other metrics of mean glycemia. We hypothesize that if the amount of glycation of HbA_1c is greater than expected from measured glucose levels, and if AGEs are accumulating, then this accumulation of AGEs might account for the increased rate of complications of diabetes in populations with high rates of vascular disease and other complications. Thus, identifying and modifying the burden of AGEs based on measurement of AGEs by SAF may turn out to be a worthwhile metric to determine individuals who are at high risk for the complications of diabetes as well as others without diabetes at risk of vascular disease. We conclude that available evidence supports SAF as both a clinical measurement and as a means of evaluating interventions aimed at reducing the risks of vascular disease and diabetic complications.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1407-1416"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11572222/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140207042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building Trustworthy Generative Artificial Intelligence for Diabetes Care and Limb Preservation: A Medical Knowledge Extraction Case.","authors":"Shayan Mashatian, David G Armstrong, Aaron Ritter, Jeffery Robbins, Shereen Aziz, Ilia Alenabi, Michelle Huo, Taneeka Anand, Kouhyar Tavakolian","doi":"10.1177/19322968241253568","DOIUrl":"10.1177/19322968241253568","url":null,"abstract":"<p><strong>Background: </strong>Large language models (LLMs) offer significant potential in medical information extraction but carry risks of generating incorrect information. This study aims to develop and validate a retriever-augmented generation (RAG) model that provides accurate medical knowledge about diabetes and diabetic foot care to laypersons with an eighth-grade literacy level. Improving health literacy through patient education is paramount to addressing the problem of limb loss in the diabetic population. In addition to affecting patient well-being through improved outcomes, improved physician well-being is an important outcome of a self-management model for patient health education.</p><p><strong>Methods: </strong>We used an RAG architecture and built a question-and-answer artificial intelligence (AI) model to extract knowledge in response to questions pertaining to diabetes and diabetic foot care. We utilized GPT-4 by OpenAI, with Pinecone as a vector database. The NIH National Standards for Diabetes Self-Management Education served as the basis for our knowledge base. The model's outputs were validated through expert review against established guidelines and literature. Fifty-eight keywords were used to select 295 articles and the model was tested against 175 questions across topics.</p><p><strong>Results: </strong>The study demonstrated that with appropriate content volume and few-shot learning prompts, the RAG model achieved 98% accuracy, confirming its capability to offer user-friendly and comprehensible medical information.</p><p><strong>Conclusion: </strong>The RAG model represents a promising tool for delivering reliable medical knowledge to the public which can be used for self-education and self-management for diabetes, highlighting the importance of content validation and innovative prompt engineering in AI applications.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1264-1270"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11572003/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141065622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Minimum requirements for continuous glucose monitors: What does it mean?","authors":"Jan S Krouwer","doi":"10.1177/19322968251343645","DOIUrl":"10.1177/19322968251343645","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1417-1418"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12085550/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144086293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sampling Interval Influences Glucose Rate-of-Change Metrics.","authors":"Ermina Bach, Sanne Fisker, Mia Christensen, Bo M Bibby, Klavs W Hansen","doi":"10.1177/19322968251353540","DOIUrl":"10.1177/19322968251353540","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1419-1420"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12213518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Importance of FDA-Integrated Continuous Glucose Monitors to Ensure Accuracy of Continuous Glucose Monitoring.","authors":"David C Klonoff, Monica Gabbay, Sun Joon Moon, Emma G Wilmot","doi":"10.1177/19322968241250357","DOIUrl":"10.1177/19322968241250357","url":null,"abstract":"<p><p>Continuous glucose monitoring (CGM) has been shown to improve glycemic control and self-monitoring, as well as to reduce the risk of hypoglycemia. Integrated CGM (iCGM) FDA-cleared systems with published performance data are established nonadjunctive and accurate CGM tools that can directly inform decision-making in the treatment of diabetes (i.e., insulin dosing). Studies have assessed accuracy and safety data of CGMs that were eventually cleared for iCGM by the FDA and that informed the recommendation for their nonadjunctive use. Subsequent robust clinical trials and real-world studies demonstrated clinical effectiveness with improvements in a range of patient outcomes. In recent years, a number of non-iCGM-approved CGM devices have entered the market outside the United States worldwide. Some of these non-iCGM-approved CGM devices require additional user verification of blood glucose levels to be performed for making treatment decisions, termed adjunctive. Moreover, in many non-iCGM-approved CGM devices, accuracy studies published in peer-reviewed journals are scarce or have many limitations. Consequently, non-iCGM-approved CGM devices cannot be automatically perceived as having the same performance or quality standards than those approved for iCGM by the FDA. As a result, although these devices tend to cost less than iCGMs that carry FDA clearance and could therefore be attractive from the point of view of a health care payer, it must be emphasized that evaluation of costs should not be limited to the device (such as the usability preference that patients have for nonadjunctive sensors compared to adjunctive sensors) but to the wider value of the total benefit that the product provides to the patient.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1392-1399"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140849769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time in Range Analysis in Automated Insulin Delivery Era: Should Day and Nighttime Targets be the Same?","authors":"Ariana Maia, David Subias Andujar, Cristina Yuste, Lara Albert, Joana Vilaverde, Maria Helena Cardoso, Mercedes Rigla","doi":"10.1177/19322968241236456","DOIUrl":"10.1177/19322968241236456","url":null,"abstract":"<p><strong>Introduction: </strong>Hybrid closed-loop systems (HCLS) use has shown that time in range (TIR) tends to improve more during the nighttime than during the day. This study aims to compare the conventional TIR, currently accepted as 70 to 180 mg/dL, with a proposed recalculated time in range (RTIR) considering a tighter glucose target of 70 to 140 mg/dL for the nighttime fasting period in T1DM patients under HCLS.</p><p><strong>Methods: </strong>We conducted a retrospective study that included adults patients receiving treatment with Tandem t:slim X2 Control-IQ. Daytime TIR was characterized as glucose values between 70 and 180 mg/dL during the 07:01 to 23:59 time frame. Nighttime fasting TIR was specified as glucose values from 70 to 140 mg/dL between 00:00 and 07:00. The combination of the daytime and nighttime fasting glucose targets results in an RTIR, which was compared with the conventional TIR for each patient. The 14 days Dexcom G6 CGM data were downloaded from Tidepool platform and analyzed.</p><p><strong>Results: </strong>We included 22 patients with a mean age of 49.7 years and diabetes duration of 24.7 years, who had been using automatic insulin delivery (AID) HCLS for a median of 305.3 days. We verified a mean conventional TIR of 68.7% vs a mean RTIR of 60.3%, with a mean percentage difference between these two metrics of -8.4%. A significant decrease in conventional TIR was verified when tighter glucose targets were considered during the nighttime period. No significant correlation was found between the percentage difference values and RTIR, even among the group of patients with the lowest conventional TIR.</p><p><strong>Conclusions: </strong>Currently, meeting the conventional TIR metrics may fall short of achieving an ideal level of glycemic control. An individualized strategy should be adopted until further data become available for a precise definition of optimal glucose targets.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1326-1330"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571497/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140158197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Insulin Requirements According to Sex and Weight in a Population of 9036 Adult Persons With Type 1 Diabetes Using Closed-Loop Insulin Delivery.","authors":"Hocine Lebbad, Chesner Desir, Aurélien Vesin, Alice Adenis, Guillaume Charpentier, Pierre-Yves Benhamou","doi":"10.1177/19322968241252366","DOIUrl":"10.1177/19322968241252366","url":null,"abstract":"<p><strong>Background: </strong>The prediction of the individual insulin needs may facilitate the initiation of insulin therapy. Our aim was to explore the relationships between body weight, sex, and daily amounts of insulin delivered by a hybrid closed-loop system.</p><p><strong>Methods: </strong>We performed a retrospective data collection of all consenting adult patients with type 1 diabetes who were equipped in Europe with the Diabeloop Generation 1 (DBLG1) hybrid closed-loop insulin delivery device between March 1, 2021 and February 28, 2023.</p><p><strong>Results: </strong>A total of 9036 users (59% females, age 45.6 ± 14.3 years) were included, reaching a mean follow-up of 320 ± 143 days, an overall 2 887 188 days of data. We observed a mean insulin-weight ratio of 0.617 ± 0.207 U/kg (0.665 ± 0.217 for males and 0.584 ± 0.193 for females, <i>P</i> < .001). Exploratory analysis of a subset of 4066 patients reaching >70% Time in Range (70-180 mg/dL) showed a mean insulin-weight ratio of 0.55 ± 0.17 U/kg (<i>P</i> < .001) (0.59 ± 0.18 for the 1438 males and 0.53 ± 0.16 for the 2628 females).</p><p><strong>Conclusion: </strong>This large real-world analysis provides a quantitative estimation of the daily insulin requirements in adult patients with type 1 diabetes and shows significant differences between sex. These findings have relevant implications in the practical management of insulin therapy.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1257-1263"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141158332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}