Journal of Diabetes Science and Technology最新文献

{"title":"Highly Miniaturized, Low-Power CMOS ASIC Chip for Long-Term Continuous Glucose Monitoring.","authors":"Raja Hari Gudlavalleti, Xiangyi Xi, Allen Legassey, Pik-Yiu Chan, Jin Li, Diane Burgess, Charles Giardina, Fotios Papadimitrakopoulos, Faquir Jain","doi":"10.1177/19322968231153419","DOIUrl":"10.1177/19322968231153419","url":null,"abstract":"<p><strong>Background: </strong>The objective of this work is to develop a highly miniaturized, low-power, biosensing platform for continuous glucose monitoring (CGM). This platform is based on an application-specific integrated circuit (ASIC) chip that interfaces with an amperometric glucose-sensing element. To reduce both size and power requirements, this custom ASIC chip was implemented using 65-nm complementary metal oxide semiconductor (CMOS) technology node. Interfacing this chip to a frequency-counting microprocessor with storage capabilities, a miniaturized transcutaneous CGM system can be constructed for small laboratory animals, with long battery life.</p><p><strong>Method: </strong>A 0.45 mm × 1.12 mm custom ASIC chip was first designed and implemented using the Taiwan Semiconductor Manufacturing Company (TSMC) 65-nm CMOS technology node. This ASIC chip was then interfaced with a multi-layer amperometric glucose-sensing element and a frequency-counting microprocessor with storage capabilities. Variation in glucose levels generates a linear increase in frequency response of this ASIC chip. In vivo experiments were conducted in healthy Sprague Dawley rats.</p><p><strong>Results: </strong>This highly miniaturized, 65-nm custom ASIC chip has an overall power consumption of circa 36 µW. In vitro testing shows that this ASIC chip produces a linear (<i>R</i>² = 99.5) frequency response to varying glucose levels (from 2 to 25 mM), with a sensitivity of 1278 Hz/mM. In vivo testing in unrestrained healthy rats demonstrated long-term CGM (six days/per charge) with rapid glucose response to glycemic variations induced by isoflurane anesthesia and tail vein injection.</p><p><strong>Conclusions: </strong>The miniature footprint of the biosensor platform, together with its low-power consumption, renders this CMOS ASIC chip a versatile platform for a variety of highly miniaturized devices, intended to improve the quality of life of patients with type 1 and type 2 diabetes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418493/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9242047","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":"Electronic Dashboard to Improve Outcomes in Pediatric Patients With Type 1 Diabetes Mellitus.","authors":"Lily Sandblom, Chirag Kapadia, Vinay Vaidya, Melissa Chambers, Rob Gonsalves, Lea Ann Holzmeister, Fran Hoekstra, Stewart Goldman","doi":"10.1177/19322968231159401","DOIUrl":"10.1177/19322968231159401","url":null,"abstract":"<p><strong>Background and objectives: </strong>Incidence of type 1 diabetes mellitus (T1DM) is increasing, and these patients often have poor glycemic control. Electronic dashboards summating patient data have been shown to improve patient outcomes in other conditions. In addition, educating patients on T1DM has shown to improve glycated hemoglobin (A1C) levels. We hypothesized that using data from the electronic dashboard to monitor defined diabetes management activities to implement population-based interventions would improve patient outcomes.</p><p><strong>Methods: </strong>Inclusion criteria included patients aged 0 to 18 years at Phoenix Children's Hospital with T1DM. Patient data were collected via the electronic dashboard, and both diabetes management activities (A1C, patient admissions, and visits to the emergency department) and patient outcomes (patient education, appointment compliance, follow-up after hospital admission) were analyzed.</p><p><strong>Results: </strong>This study revealed that following implementation of the electronic dashboard, the percentage of patients receiving appropriate education increased from 48% to 80% (Z-score = 23.55, <i>P</i> < .0001), the percentage of patients attending the appropriate number of appointments increased from 50% to 68.2%, and the percentage of patients receiving follow-up care within 40 days after a hospital admission increased from 43% to 70%. The median A1C level decreased from 9.1% to 8.2% (Z-score = -6.74, <i>P</i> < .0001), and patient admissions and visits to the emergency department decreased by 20%.</p><p><strong>Conclusions: </strong>This study shows, with the implementation of an electronic dashboard, we were able to improve outcomes for our pediatric patients with T1DM. This tool can be used at other institutions to improve care and outcomes for pediatric patients with T1DM and other chronic conditions.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418418/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9258388","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 Real-Time Advisor-a Decision Support Application for Insulin Therapy Coupled With the Continuous Glucose Monitoring: Impact on Glycemic Control on People With Type 1 Diabetes.","authors":"Robin Pflaum, Patricia Vaduva, Maurine Allard, Christèle Derrien, Marie-Anne Lefebvre, Maxime Esvan, Isabelle Guilhem, Agathe Guenego","doi":"10.1177/19322968241266826","DOIUrl":"10.1177/19322968241266826","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418411/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141751839","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":"Clinical Usage and Potential Benefits of a Continuous Glucose Monitoring Predict App.","authors":"Timor Glatzer, Dominic Ehrmann, Bernhard Gehr, Maria Teresa Penalba Martinez, Joannet Onvlee, Gabriela Bucklar, Michèle Hofer, Miriam Stangs, Nora Wolf","doi":"10.1177/19322968241268353","DOIUrl":"10.1177/19322968241268353","url":null,"abstract":"<p><p>Continuous glucose monitoring (CGM) has become an increasingly important tool for self-management in people with diabetes mellitus (DM). In this paper, we discuss recommendations on how to implement predictive features provided by the Accu-Chek SmartGuide Predict app in clinical practice. The Predict app's features are aimed at ultimately reducing diabetes stress and fear of hypoglycemia in people with DM. Furthermore, we explore the use cases and potential benefits of continuous glucose prediction, predictions of low glucose, and nocturnal hypoglycemia.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418506/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004340","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":"Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App.","authors":"Pau Herrero, Magí Andorrà, Nils Babion, Hendericus Bos, Matthias Koehler, Yannick Klopfenstein, Eemeli Leppäaho, Patrick Lustenberger, Ajandek Peak, Christian Ringemann, Timor Glatzer","doi":"10.1177/19322968241267818","DOIUrl":"10.1177/19322968241267818","url":null,"abstract":"<p><strong>Background: </strong>Despite abundant evidence demonstrating the benefits of continuous glucose monitoring (CGM) in diabetes management, a significant proportion of people using this technology still struggle to achieve glycemic targets. To address this challenge, we propose the Accu-Chek^® SmartGuide Predict app, an innovative CGM digital companion that incorporates a suite of advanced glucose predictive functionalities aiming to inform users earlier about acute glycemic situations.</p><p><strong>Methods: </strong>The app's functionalities, powered by three machine learning models, include a two-hour glucose forecast, a 30-minute low glucose detection, and a nighttime low glucose prediction for bedtime interventions. Evaluation of the models' performance included three data sets, comprising subjects with T1D on MDI (n = 21), subjects with type 2 diabetes (T2D) on MDI (n = 59), and subjects with T1D on insulin pump therapy (n = 226).</p><p><strong>Results: </strong>On an aggregated data set, the two-hour glucose prediction model, at a forecasting horizon of 30, 45, 60, and 120 minutes, achieved a percentage of data points in zones A and B of Consensus Error Grid of: 99.8%, 99.3%, 98.7%, and 96.3%, respectively. The 30-minute low glucose prediction model achieved an accuracy, sensitivity, specificity, mean lead time, and area under the receiver operating characteristic curve (ROC AUC) of: 98.9%, 95.2%, 98.9%, 16.2 minutes, and 0.958, respectively. The nighttime low glucose prediction model achieved an accuracy, sensitivity, specificity, and ROC AUC of: 86.5%, 55.3%, 91.6%, and 0.859, respectively.</p><p><strong>Conclusions: </strong>The consistency of the performance of the three predictive models when evaluated on different cohorts of subjects with T1D and T2D on different insulin therapies, including real-world data, offers reassurance for real-world efficacy.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004342","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":"Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study.","authors":"Anna R Kahkoska, Kushal S Shah, Michael R Kosorok, Kellee M Miller, Michael Rickels, Ruth S Weinstock, Laura A Young, Richard E Pratley","doi":"10.1177/19322968221149040","DOIUrl":"10.1177/19322968221149040","url":null,"abstract":"<p><strong>Background: </strong>The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitoring (BGM). We explored heterogeneous treatment effects of CGM on hypoglycemia by formulating a data-driven decision rule that selects an intervention (ie, CGM vs BGM) to minimize percentage of time <70 mg/dL for each individual WISDM participant.</p><p><strong>Method: </strong>The precision medicine analyses used data from participants with complete data (n = 194 older adults, including those who received CGM [n = 100] and BGM [n = 94] in the trial). Policy tree and decision list algorithms were fit with 14 baseline demographic, clinical, and laboratory measures. The primary outcome was CGM-measured percentage of time spent in hypoglycemic range (<70 mg/dL), and the decision rule assigned participants to a subgroup reflecting the treatment estimated to minimize this outcome across all follow-up visits.</p><p><strong>Results: </strong>The optimal decision rule was found to be a decision list with 3 steps. The first step moved WISDM participants with baseline time-below range >1.35% and no detectable C-peptide levels to the CGM subgroup (n = 139), and the second step moved WISDM participants with a baseline time-below range of >6.45% to the CGM subgroup (n = 18). The remaining participants (n = 37) were left in the BGM subgroup. Compared with the BGM subgroup (n = 37; 19%), the group for whom CGM minimized hypoglycemia (n = 157; 81%) had more baseline hypoglycemia, a lower proportion of detectable C-peptide, higher glycemic variability, longer disease duration, and higher proportion of insulin pump use.</p><p><strong>Conclusions: </strong>The decision rule underscores the benefits of CGM for older adults to reduce hypoglycemia. Diagnostic CGM and laboratory markers may inform decision-making surrounding therapeutic CGM and identify older adults for whom CGM may be a critical intervention to reduce hypoglycemia.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10515516","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":"The Promise of Hypoglycemia Risk Prediction.","authors":"Oliver Schnell, Ralph Ziegler","doi":"10.1177/19322968241267778","DOIUrl":"10.1177/19322968241267778","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004347","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":"Diabetes Technology Meeting 2023.","authors":"Tiffany Tian, Rachel E Aaron, Ashley Y DuNova, Johan H Jendle, David Kerr, Eda Cengiz, Andjela Drincic, John C Pickup, Kong Y Chen, Naomi Schwartz, Douglas B Muchmore, Halis K Akturk, Carol J Levy, Signe Schmidt, Riccardo Bellazzi, Alan H B Wu, Elias K Spanakis, Bijan Najafi, James Geoffrey Chase, Jane Jeffrie Seley, David C Klonoff","doi":"10.1177/19322968241235205","DOIUrl":"10.1177/19322968241235205","url":null,"abstract":"<p><p>Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 1 to November 4, 2023. Meeting topics included digital health; metrics of glycemia; the integration of glucose and insulin data into the electronic health record; technologies for insulin pumps, blood glucose monitors, and continuous glucose monitors; diabetes drugs and analytes; skin physiology; regulation of diabetes devices and drugs; and data science, artificial intelligence, and machine learning. A live demonstration of a personalized carbohydrate dispenser for people with diabetes was presented.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140287558","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":"Proposing a New Frontier in Diabetes Treatment: The Integration of Biotechnology and Artificial Intelligence.","authors":"Lysandro Pinto Borges, Marina Dos Santos Barreto, Ronaldy Santana Santos, Eloia Emanuelly Dias Silva, Deise Maria Rego Rodrigues Silva, Pedro Henrique Macedo Moura, Pamela Chaves de Jesus, Jessiane Bispo de Souza, Lucas Alves da Mota Santana, Adriana Gibara Guimarães","doi":"10.1177/19322968241259636","DOIUrl":"10.1177/19322968241259636","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418541/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141751840","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":"Use of Continuous Glucose Monitoring in Pump Therapy Sensor Augmented Pump or Automated Insulin Delivery in Different Age Groups (0.5 to <26 Years) With Type 1 Diabetes From 2018 to 2021: Analysis of the German/Austrian/Swiss/Luxemburg Diabetes Prospective Follow-up Database Registry.","authors":"Louisa van den Boom, Marie Auzanneau, Joachim Woelfle, Marina Sindichakis, Antje Herbst, Dagmar Meraner, Kathrin Hake, Christof Klinkert, Bettina Gohlke, Reinhard W Holl","doi":"10.1177/19322968231156601","DOIUrl":"10.1177/19322968231156601","url":null,"abstract":"<p><strong>Aim: </strong>Insulin pump, continuous glucose monitoring (CGM), and sensor augmented pump (SAP) technology have evolved continuously leading to the development of automated insulin delivery (AID) systems. Evaluation of the use of diabetes technologies in people with T1D from January 2018 to December 2021.</p><p><strong>Methods: </strong>A patient registry (Diabetes Prospective Follow-up Database [DPV]) was analyzed for use of SAP (insulin pump + CGM ≥90 days, no automated dose adjustment) and AID (HCL or LGS/PLGS). In total 46,043 people with T1D aged 0.5 to <26 years treated in 416 diabetes centers (Germany, Austria, Luxemburg, and Switzerland) were included and stratified into 4 groups A-D according to age. Additionally, TiR and HbA1c were analyzed.</p><p><strong>Results: </strong>From 2018 to 2021, there was a significant increase from 28.7% to 32.9% (sensor augmented pump [SAP]) and 3.5% to 16.6% (AID) across all age groups, with the most frequent use in group A (<7 years, 38.8%-40.2% and 10.3%-28.5%). A similar increase in SAP and AID use was observed in groups B (7 to <11 years) and C (11 to <16 years): B: +15.8 PP, C: +15.9 PP. HbA1c improved significantly in groups C and D (16 to <26 years) (both <i>P</i> < .01). Time in range (TiR) increased in all groups (A: +3 PP; B: +5 PP; C: +5 PP; D: +5 PP; <i>P</i> < 0.01 for each group). Insulin pumps (61.0% versus 53.4% male) and SAP (33.5% versus 28.9% male) are used more frequently in females.</p><p><strong>Conclusion: </strong>In recent years, we found an increasing use of new diabetes technologies and an improvement in metabolic control (TiR) across all age groups.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10830313","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}