Journal of Diabetes Science and Technology最新文献

{"title":"Combining Glucose Monitoring and Insulin Infusion in an Integrated Device: A Narrative Review of Challenges and Proposed Solutions.","authors":"Michael Schoemaker, Anna Martensson, Julia K Mader, Kirsten Nørgaard, Guido Freckmann, Pierre-Yves Benhamou, Peter Diem, Lutz Heinemann","doi":"10.1177/19322968231203237","DOIUrl":"10.1177/19322968231203237","url":null,"abstract":"<p><p>The introduction of automated insulin delivery (AID) systems has enabled increasing numbers of individuals with type 1 diabetes (T1D) to improve their glycemic control largely. However, use of AID systems is limited due to their complexity and costs associated. The user must wear both a continuously monitoring glucose system and an insulin infusion pump. The glucose sensor and the insulin catheter must be inserted at two different body sites using different insertion devices. In addition, the user must pair and manage the different systems. These communicate with the AID software implemented on the pump or on a third device such as a dedicated display device or smart phone application. These components might be developed and commercialized by different manufacturers, which in turn can cause difficulties for patients seeking technical support. A possible solution to these challenges would be to integrate the glucose sensor and insulin catheter into a single device. This would allow the glucose sensor and insulin catheter to be inserted simultaneously, eliminating the need for pairing, and simplifying system management. In recent years, different technologies have been developed and evaluated in clinical investigations that combine the glucose sensor and the insulin catheter in one platform. The consistent finding of all these studies is that integration has no adverse effect on insulin infusion and glucose measurements provided that certain conditions are met. In this review, we discuss the perceived challenges of such an approach and discuss possible solutions that have been proposed.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"441-451"},"PeriodicalIF":4.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41126726","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 End Products (AGEs) Webinar Meeting Report.","authors":"Cindy N Ho, Alessandra T Ayers, Paul Beisswenger, Stuart Chalew, Ann Marie Schmidt, Ambarish Pandey, Pankaj Kapahi, Alexander Fleming, David C Klonoff","doi":"10.1177/19322968241296541","DOIUrl":"10.1177/19322968241296541","url":null,"abstract":"<p><p>The advanced glycation end products (AGEs) Webinar was co-hosted by Diabetes Technology Society and Kitalys Institute on August 8, 2024, with the goal of reviewing progress made in the measurement and use of AGEs in clinical practice. Meeting topics included (1) AGEs as predictors of diabetic nephropathy (DKD), (2) hemoglobin glycation index (HGI) and the glycation gap (GG), (3) formation and structure of AGEs, (4) AGEs as a risk factor of cardiovascular disease (CVD), and (5) approaches to limit or prevent AGE formation.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"576-581"},"PeriodicalIF":4.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142604945","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":"Utilizing a Novel Telemedicine Clinic for Managing Patients With Type 2 Diabetes: A Six-Month Randomized Control Trial Pilot Study.","authors":"Fiorella Sotomayor, Reynier Hernandez, Chikara Gothong, Monica Y Choe, Garrett I Ash, William Scott, Lillian Pinault, Fernando Gomez-Peralta, Marc R Blackman, Lakshmi G Singh, John D Sorkin, Elias K Spanakis","doi":"10.1177/19322968241305627","DOIUrl":"10.1177/19322968241305627","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"595-597"},"PeriodicalIF":4.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11635784/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142812982","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":"Efficacy and Safety of Tirzepatide in Adults With Type 1 Diabetes: A Proof of Concept Observational Study.","authors":"Halis Kaan Akturk, Fran Dong, Janet K Snell-Bergeon, Kagan Ege Karakus, Viral N Shah","doi":"10.1177/19322968231223991","DOIUrl":"10.1177/19322968231223991","url":null,"abstract":"<p><strong>Background: </strong>Tirzepatide is approved by the United States Food and Drug Administration (FDA) for the management of type 2 diabetes. The efficacy and safety of this drug have not been studied in people with type 1 diabetes (T1D).</p><p><strong>Methods: </strong>In this single-center, retrospective, observational study, hemoglobin A1c (HbA1c), weight, body mass index (BMI), and continuous glucose monitoring (CGM) data were collected from electronic health records of adults with T1D at initiation of tirzepatide and at subsequent clinic visits over 8 months. Primary outcomes were reduction in HbA1c and percent change in body weight and secondary outcomes were change in CGM metrics and BMI over 8 months from baseline.</p><p><strong>Results: </strong>The mean (±SD) age of the 26 adults (54% female) with T1D was 42 ± 8 years with a mean BMI of 36.7 ± 5.3 kg/m². There was significant reduction in HbA1c by 0.45% at 3 months and 0.59% at 8 months, and a significant reduction in body weight by 3.4%, 10.5%, and 10.1% at 3, 6, and 8 months after starting tirzepatide. Time in target range (TIR = 70-180 mg/dL) and time in tight target range (TITR = 70-140 mg/dL) increased (+12.6%, <i>P</i> = .002; +10.7%, <i>P</i> = .0016, respectively) and time above range (TAR >180 mg/dL) decreased (-12.6%, <i>P</i> = .002) at 3 months, and these changes were sustained over 8 months. The drug was relatively safe and well tolerated with only 2 patients discontinuing the medication.</p><p><strong>Conclusions: </strong>Tirzepatide significantly reduced HbA1c and body weight in adults with T1D. A randomized controlled trial is needed to establish efficacy and safety of this drug in T1D.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"292-296"},"PeriodicalIF":4.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139691986","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":"Implications of the Glycemia Risk Index in Assessing Metabolic Control and its Correlation With Therapy-Related Quality of Life During-Ramadan Fasting in Adults With Type 2 Diabetes.","authors":"Ayman Al Hayek, Wael M Al Zahrani, Mohammed A Al Dawish","doi":"10.1177/19322968251321860","DOIUrl":"10.1177/19322968251321860","url":null,"abstract":"<p><strong>Background: </strong>Ramadan fasting presents unique challenges for individuals with type 2 diabetes (T2D) due to alterations in diet and medication regimens. This study evaluates the effects of Ramadan fasting on glycemia by utilizing the glycemia risk index (GRI), which integrates both hypoglycemic and hyperglycemic risks into a unified metric, alongside continuous glucose monitoring (CGM) data. In addition, the study examines the correlation between GRI and diabetes therapy-related quality of life (DTR-QOL) to understand the broader impact on patient outcomes.</p><p><strong>Methods: </strong>An ambispective, one-group pre-post design was employed at a tertiary diabetes treatment center, involving 111 adults with T2D. Data were collected across three periods: one month before Ramadan, during, and one month after. Clinical, metabolic, and glycemic parameters were recorded. The CGM-based calculations included GRI, with its hypoglycemia component (CHypo) and hyperglycemia component (CHyper). The DTR-QOL was measured to evaluate therapy-related quality of life (QoL).</p><p><strong>Results: </strong>During Ramadan, GRI significantly decreased (median = 30.5) compared to before (35.2) and after (37.4; P < .001), indicating improved glycemic stability. Both CHypo and CHyper were significantly reduced during fasting. The %TIR_70-180 increased from 42% before to 66% during (<i>P</i> < .001), accompanied by a notable decrease in glycemic variability. The DTR-QOL scores were high across all domains, reflecting a positive therapy-related QoL (scale score: 78.3 [interquartile range = 75.4-81.3]). No significant differences were observed across GRI zones.</p><p><strong>Conclusions: </strong>With tailored education and CGM-based monitoring, Ramadan fasting can improve glycemia in individuals with T2D, enhancing GRI and related glycometric parameters for safer, more stable glycemic patterns.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251321860"},"PeriodicalIF":4.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11871576/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143531533","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":"Hypoglycemia Prediction in Type 1 Diabetes With Electrocardiography Beat Ensembles.","authors":"Mu-Ruei Tseng, Kathan Vyas, Anurag Das, Waris Quamer, Darpit Dave, Madhav Erranguntla, Carolina Villegas, Daniel DeSalvo, Siripoom McKay, Gerard Cote, Ricardo Gutierrez-Osuna","doi":"10.1177/19322968251319347","DOIUrl":"10.1177/19322968251319347","url":null,"abstract":"<p><strong>Introduction: </strong>Current methods to detect hypoglycemia in type 1 diabetes (T1D) require invasive sensors (ie, continuous glucose monitors, CGMs) that generally have low accuracy in the hypoglycemic range. A forward-looking alternative is to monitor physiological changes induced by hypoglycemia that can be measured non-invasively using, eg, electrocardiography (ECG). However, current methods require extraction of fiduciary points in the ECG signal (eg, to estimate QT interval), which is challenging in ambulatory settings.</p><p><strong>Methods: </strong>To address this issue, we present a machine-learning model that uses (1) convolutional neural networks (CNNs) to extract morphological information from raw ECG signals without the need to identify fiduciary points and (2) ensemble learning to aggregate predictions from multiple ECG beats. We evaluate the model on an experimental data set that contains ECG and CGM recordings over a period of 14 days from ten participants with T1D. We consider two testing scenarios, one that divides ECG data according to CGM readings (CGM-split) and another that divides ECG data on a day-to-day basis (day-split).</p><p><strong>Results: </strong>We find that models trained using CGM-splits tend to produce overly optimistic estimates of hypoglycemia prediction, whereas day-splits provide more realistic estimates, which are consistent with the intrinsic accuracy of CGM devices. More importantly, we find that aggregating predictions from multiple ECG beats using ensemble learning significantly improves predictions at the beat level, though these improvements have large inter-individual differences.</p><p><strong>Conclusion: </strong>Deep learning models and ensemble learning can extract and aggregate morphological information in ECG signals that is predictive of hypoglycemia. Using two validation procedures, we estimate an upper bound on the accuracy of ECG hypoglycemia prediction of 81% equal error rate and a lower bound of 60%. Further improvements may be achieved using big-data approaches that require longitudinal data from a large cohort of participants.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251319347"},"PeriodicalIF":4.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11863193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143501466","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":"Toward Detection of Nocturnal Hypoglycemia in People With Diabetes Using Consumer-Grade Smartwatches and a Machine Learning Approach.","authors":"Camilo Mendez, Ceren Asli Kaykayoglu, Thiemo Bähler, Juri Künzler, Aritz Lizoain, Martina Rothenbühler, Markus H Schmidt, Markus Laimer, Lilian Witthauer","doi":"10.1177/19322968251319800","DOIUrl":"10.1177/19322968251319800","url":null,"abstract":"<p><strong>Background: </strong>Nocturnal hypoglycemia poses significant risks to individuals with insulin-treated diabetes, impacting health and quality of life. Although continuous glucose monitoring (CGM) systems reduce these risks, their poor accuracy at low glucose levels, high cost, and availability limit their use. This study examined physiological biomarkers associated with nocturnal hypoglycemia and evaluated the use of machine learning (ML) to detect hypoglycemia during nighttime sleep using data from consumer-grade smartwatches.</p><p><strong>Methods: </strong>This study analyzed 351 nights of 36 adults with insulin-treated diabetes. Participants wore two smartwatches alongside CGM systems. Linear mixed-effects models compared sleep and vital signs between nights with and without hypoglycemia during early and late sleep. A ML model was trained to detect hypoglycemia solely using smartwatch data.</p><p><strong>Results: </strong>Sixty-six nights with spontaneous hypoglycemia were recorded. Hypoglycemic nights showed increased wake periods, heart rate, stress levels, and activity during early sleep, with weaker effects during late sleep. In nights when hypoglycemia occurred during early sleep, the ML model performed comparable or better than prior studies with an area under the receiver operator curve of 0.78 for level 1 and 0.83 for level 2 hypoglycemia, with sensitivity of 0.78 and 0.89, specificity of 0.64 for both, negative predictive value of 0.94 and 0.99, and positive predictive value of 0.25 and 0.13 for level 1 and level 2 hypoglycemia, respectively.</p><p><strong>Conclusions: </strong>Consumer-grade smartwatches demonstrate promise for detecting nocturnal hypoglycemia, particularly during early sleep. Refining models to reduce false alarms could enhance their clinical utility as low-cost, accessible tools to complement CGM.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251319800"},"PeriodicalIF":4.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851596/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143492230","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":"Validation of the Diabetes Technology Society Error Grid.","authors":"Jan S Krouwer","doi":"10.1177/19322968251320653","DOIUrl":"10.1177/19322968251320653","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251320653"},"PeriodicalIF":4.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143492231","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":"Feasibility of a Glucose Manipulation Procedure for the Standardized Performance Evaluation of Continuous Glucose Monitoring Systems.","authors":"Manuela Link, Manuel Eichenlaub, Delia Waldenmaier, Stephanie Wehrstedt, Stefan Pleus, Nina Jendrike, Sükrü Öter, Cornelia Haug, Stefanie Hossmann, Martina Rothenbühler, Derek Brandt, Guido Freckmann","doi":"10.1177/19322968251317526","DOIUrl":"10.1177/19322968251317526","url":null,"abstract":"<p><strong>Background: </strong>In continuous glucose monitoring (CGM) system performance studies, it is common to implement specific procedures for manipulating the participants' blood glucose (BG) levels during the collection of comparator BG measurements. Recently, such a procedure was proposed by a group of experts, and this study assessed its ability to produce combinations of BG levels and rates of change (RoCs) with certain characteristics.</p><p><strong>Methods: </strong>During three separate in-clinic sessions conducted over 15 days, capillary BG measurements were carried out every 15 minutes for 7 hours. Simultaneously, the participants' BG levels were manipulated by controlling food intake and insulin administration to induce transient hyperglycemia and hypoglycemia. Subsequently, the combinations of BG levels and RoCs were categorized into dynamic glucose regions distinguishing between rapidly increasing BG levels (Alert high), hyperglycemia (BG high), rapidly falling BG levels (Alert low), and hypoglycemia (BG low).</p><p><strong>Results: </strong>A total of 24 adult participants with type 1 diabetes were included. Capillary BG-RoC combinations showed 7.5% in the Alert high region, 13.3% in the BG high region, 9.8% in the Alert low region, and 11.0% in the BG low region. No adverse events related to the glucose manipulation procedure were documented.</p><p><strong>Conclusions: </strong>As recommended by the experts, the percentage of data points in regions was ≥7.5%, demonstrating the procedure's feasibility. However, given that the recommendation for the alert high region was only barely achieved, we suggest optimizations to the procedure and definition of dynamic glucose regions to facilitate the procedures' adoption in standardized CGM performance evaluations.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251317526"},"PeriodicalIF":4.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11848859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143483327","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":"Evaluation of the Effectiveness of Continuous Glucose Monitors on Glycemic Control in Patients With Type 2 Diabetes Receiving Institutional Financial Assistance.","authors":"Kevin K Cheng, Maxine F Vera Cruz, Tracy S Tylee, Mary S Kelly","doi":"10.1177/19322968251320122","DOIUrl":"10.1177/19322968251320122","url":null,"abstract":"<p><strong>Background: </strong>Current guidelines suggest utilizing continuous glucose monitoring (CGM) to improve hemoglobin A_1c (HbA_1c) in patients with diabetes. Financial cost remains a barrier to implementation. Medicare coverage criteria include all patients with diabetes treated with at least one injection of insulin per day, while Washington Medicaid is more restrictive. There remains a paucity of literature examining effectiveness of CGMs on clinical outcomes among patients with type 2 diabetes with lower incomes.</p><p><strong>Methods: </strong>This is a single-center, retrospective, observational study including adults with type 2 diabetes receiving institutional financial assistance for CGMs. A cohort with no CGM use is included for comparison. The primary outcome is change in HbA_1c approximately three months after CGM implementation from baseline. Secondary outcomes include mean differences in number of antidiabetic agents and changes in insulin dose prior to and after CGM implementation.</p><p><strong>Results: </strong>Among the CGM cohort, most patients were of Hispanic ethnicity (77%) and a majority had no insurance (77%). The average HbA_1c prior to CGM implementation was 8.3% and three months post-CGM was 7.7%, with a mean difference of -0.6% (<i>P</i> = .004). There were no statistically significant differences in the average number of antidiabetic agents, total daily dosages of insulin, or mean differences in the number of emergency room visits or hospitalizations prior to and post-implementation of a CGM.</p><p><strong>Conclusion: </strong>Overall, there is a statistical and clinical improvement in HbA_1c before and after implementation of CGMs in patients with type 2 diabetes who meet Medicaid criteria for CGM coverage receiving financial assistance.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251320122"},"PeriodicalIF":4.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11843561/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468223","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}