Stephen Twigg, Soo Lim, Seung-Hyun Yoo, Liming Chen, Yuqian Bao, Alice Kong, Ester Yeoh, Siew Pheng Chan, Jeremyjones Robles, Viswanathan Mohan, Neale Cohen, Margaret McGill, Linong Ji
{"title":"Asia-Pacific Perspectives on the Role of Continuous Glucose Monitoring in Optimizing Diabetes Management.","authors":"Stephen Twigg, Soo Lim, Seung-Hyun Yoo, Liming Chen, Yuqian Bao, Alice Kong, Ester Yeoh, Siew Pheng Chan, Jeremyjones Robles, Viswanathan Mohan, Neale Cohen, Margaret McGill, Linong Ji","doi":"10.1177/19322968231176533","DOIUrl":"10.1177/19322968231176533","url":null,"abstract":"<p><p>Diabetes is prevalent, and it imposes a substantial public health burden globally and in the Asia-Pacific (APAC) region. The cornerstone for optimizing diabetes management and treatment outcomes is glucose monitoring, the techniques of which have evolved from self-monitoring of blood glucose (SMBG) to glycated hemoglobin (HbA1c), and to continuous glucose monitoring (CGM). Contextual differences with Western populations and limited regionally generated clinical evidence warrant regional standards of diabetes care, including glucose monitoring in APAC. Hence, the APAC Diabetes Care Advisory Board convened to gather insights into clinician-reported CGM utilization for optimized glucose monitoring and diabetes management in the region. We discuss the findings from a pre-meeting survey and an expert panel meeting regarding glucose monitoring patterns and influencing factors, patient profiles for CGM initiation and continuation, CGM benefits, and CGM optimization challenges and potential solutions in APAC. While CGM is becoming the new standard of care and a useful adjunct to HbA1c and SMBG globally, glucose monitoring type, timing, and frequency should be individualized according to local and patient-specific contexts. The results of this APAC survey guide methods for the formulation of future APAC-specific consensus guidelines for the application of CGM in people living with diabetes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10152338","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":"Design for Manufacturing: Last Things First.","authors":"Michael Schoemaker, Lutz Heinemann","doi":"10.1177/19322968231222045","DOIUrl":"10.1177/19322968231222045","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139037694","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}
Catherine L Russon, Michael J Allen, Richard M Pulsford, Michael Saunby, Neil Vaughan, Matthew Cocks, Katie L Hesketh, Jonathan Low, Robert C Andrews
{"title":"A User-Friendly Web Tool for Custom Analysis of Continuous Glucose Monitoring Data.","authors":"Catherine L Russon, Michael J Allen, Richard M Pulsford, Michael Saunby, Neil Vaughan, Matthew Cocks, Katie L Hesketh, Jonathan Low, Robert C Andrews","doi":"10.1177/19322968241274322","DOIUrl":"10.1177/19322968241274322","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529048/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142288364","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}
Ming Yeh Lee, Tho D Pham, David M Maahs, Priya Prahalad
{"title":"Recall of High Bias Point-of-Care Hemoglobin A1c Test.","authors":"Ming Yeh Lee, Tho D Pham, David M Maahs, Priya Prahalad","doi":"10.1177/19322968241278744","DOIUrl":"10.1177/19322968241278744","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529059/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142107940","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}
Steven Setford, Stuart Phillips, Hilary Cameron, Mike Grady
{"title":"Clinical Accuracy of a Glucose Oxidase-Based Blood Glucose Test-Strip Across Extremes of Oxygen Partial Pressure.","authors":"Steven Setford, Stuart Phillips, Hilary Cameron, Mike Grady","doi":"10.1177/19322968231158663","DOIUrl":"10.1177/19322968231158663","url":null,"abstract":"<p><strong>Background: </strong>Glucose oxidase (GOx)-based blood glucose monitors (BGMs) are influenced by the partial pressure of oxygen (Po<sub>2</sub>) within the applied sample. Limited in-clinic data exists regarding the quantitative effect of Po<sub>2</sub> in unmanipulated capillary fingertip blood samples across physiologically representative glucose and Po<sub>2</sub> ranges.</p><p><strong>Method: </strong>Clinical accuracy data were collected as part of a BGM manufacturer's ongoing post-market surveillance program for a commercially available GOx-based BGM test-strip. The data set comprised 29 901 paired BGM-comparator readings and corresponding Po<sub>2</sub> values from 5 428 blood samples from a panel of 975 subjects.</p><p><strong>Results: </strong>A linear regression-calculated bias range of 5.22% (+0.72% [low Po<sub>2</sub>: 45 mm Hg] to -4.5% [high Po<sub>2</sub>: 105 mm Hg]); biases calculated as absolute at <100 mg/dL glucose was found. Below the nominal Po<sub>2</sub> of 75 mm Hg, a linear regression bias of +3.14% was calculated at low Po<sub>2</sub>, while negligible impact on bias (regression slope: +0.002%) was observed at higher than nominal levels (>75 mm Hg). When evaluating BGM performance under corner conditions of low (<70 mg/dL) and high (>180 mg/dL) glucose, combined with low and high Po<sub>2</sub>, linear regression biases ranged from +1.52% to -5.32% within this small group of subjects and with no readings recorded at <70 mg/dL glucose at low and high Po<sub>2</sub>.</p><p><strong>Conclusions: </strong>Data from this large-scale clinical study, performed on unmanipulated fingertip capillary bloods from a diverse diabetes population, indicate Po<sub>2</sub> sensitivity of the BGM to be markedly lower than published studies, which are mainly laboratory-based, requiring artificial manipulation of oxygen levels in aliquots of venous blood.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11531037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9396449","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":"Corrigendum to \"Hybrid Closed Loop Using a Do-It-Yourself Artificial Pancreas System in Adults With Type 1 Diabetes\".","authors":"","doi":"10.1177/19322968241295366","DOIUrl":"10.1177/19322968241295366","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528759/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142557993","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}
Pim Dekker, Tim van den Heuvel, Arcelia Arrieta, Javier Castañeda, Dick Mul, Henk Veeze, Ohad Cohen, Henk-Jan Aanstoot
{"title":"Twelve-Month Real-World Use of an Advanced Hybrid Closed-Loop System Versus Previous Therapy in a Dutch Center For Specialized Type 1 Diabetes Care.","authors":"Pim Dekker, Tim van den Heuvel, Arcelia Arrieta, Javier Castañeda, Dick Mul, Henk Veeze, Ohad Cohen, Henk-Jan Aanstoot","doi":"10.1177/19322968241290259","DOIUrl":"https://doi.org/10.1177/19322968241290259","url":null,"abstract":"<p><strong>Background: </strong>Complexity of glucose regulation in persons with type 1 diabetes (PWDs) necessitates increased automation of insulin delivery (AID). This study aimed to analyze real-world data over 12 months from PWDs who started using the MiniMed 780G (MM780G) advanced hybrid closed-loop (aHCL) AID system at the Diabeter clinic, focusing on glucometrics and clinical outcomes.</p><p><strong>Methods: </strong>Persons with type 1 diabetes switching to the MM780G system were included. Clinical data (e.g. HbA1c, previous modality) was collected from Diabeter's electronic health records and glucometrics (time in range [TIR], time in tight range [TITR], time above range [TAR], time below range [TBR], glucose management indicator [GMI]) from CareLink Personal for a 12-month post-initiation period of the MM780G system. Outcomes were age-stratified, and the MM780G system was compared with previous use of older systems (MM640G and MM670G). Longitudinal changes in glucometrics were also evaluated.</p><p><strong>Results: </strong>A total of 481 PWDs were included, with 219 having prior pump/sensor system data and 334 having monthly longitudinal data. After MM780G initiation, HbA1c decreased from 7.6 to 7.1% (<i>P</i> < .0001) and the percentage of PWDs with HbA1c <7% increased from 30% to 50%. Glucose management indicator and TIR remained stable with mean GMI of 6.9% and TIR >70% over 12 months. Age-stratified analysis showed consistent improvements of glycemic control across all age groups, with older participants achieving better outcomes. Participants using recommended system settings achieved better glycemic outcomes, reaching TIR up to 77% and TTIR up to 55%.</p><p><strong>Conclusions: </strong>Use of MM780G system results in significant and sustained glycemic improvements, consistent across age groups and irrespective of previous treatment modalities.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142501267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julia K Mader, Brian Huffman, Robert Sharon, Gabriela Bucklar, Julia Roetschke
{"title":"GLP-1-Based Therapies Do Not Interfere With Blood Glucose Monitoring Systems.","authors":"Julia K Mader, Brian Huffman, Robert Sharon, Gabriela Bucklar, Julia Roetschke","doi":"10.1177/19322968241293810","DOIUrl":"https://doi.org/10.1177/19322968241293810","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142501265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Prediction of Incident Diabetic Retinopathy in Adults With Type 1 Diabetes Using Machine Learning Approach: An Exploratory Study.","authors":"Eslam Montaser, Viral N Shah","doi":"10.1177/19322968241292369","DOIUrl":"https://doi.org/10.1177/19322968241292369","url":null,"abstract":"<p><strong>Background: </strong>Early detection and intervention are crucial for preventing vision-threatening diabetic retinopathy (DR) in adults with type 1 diabetes (T1D). This exploratory study uses machine learning on continuous glucose monitoring (CGM) data to identify factors influencing DR and predict high-risk individuals for timely intervention.</p><p><strong>Methods: </strong>Between June 2018 and March 2022, adults with T1D with incident DR or no retinopathy (control) were identified. The CGM data were collected retrospectively for up to seven years before the date of defining incident DR or no retinopathy. A mixture of three machine learning algorithms was trained and evaluated in two different scenarios, using different glycemic features extracted from CGM traces (scenario 1), and the two principal components (two PCs; exposure to hyperglycemia and hypoglycemia risk) of those features (scenario 2). Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model.</p><p><strong>Results: </strong>The CGM data of 30 adults with incident DR (mean±SD age of 21.2±9.4 years, glycated hemoglobin [HbA<sub>1c</sub>] of 8.6%±1.0%, and body mass index [BMI] of 24.5±4.8 kg/m<sup>2</sup>) and 30 adults without DR (age of 41.8±14.7 years, HbA<sub>1c</sub> of 7.0%±0.9%, and BMI of 26.2±3.6 kg/m<sup>2</sup>) were included in this analysis. In scenario 2, classifiers outperformed scenario 1, resulting in an average AUC-ROC increase to 0.92 for two of three models, indicating that the two PCs captured vital classification data, representing the most discriminative aspects and enhancing model performance.</p><p><strong>Conclusion: </strong>Machine learning approaches using CGM data may have potential to aid in identifying adults with T1D at risk of DR.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142501266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Katharine Barnard-Kelly, Linda Gonder-Frederick, Jill Weissberg-Benchell, Lauren E Wisk
{"title":"Psychosocial Aspects of Diabetes Technologies: Commentary on the Current Status of the Evidence and Suggestions for Future Directions.","authors":"Katharine Barnard-Kelly, Linda Gonder-Frederick, Jill Weissberg-Benchell, Lauren E Wisk","doi":"10.1177/19322968241276550","DOIUrl":"https://doi.org/10.1177/19322968241276550","url":null,"abstract":"<p><p>Diabetes technologies, including continuous glucose monitors, insulin pumps, and automated insulin delivery systems offer the possibility of improving glycemic outcomes, including reduced hemoglobin A1c, increased time in range, and reduced hypoglycemia. Given the rapid expansion in the use of diabetes technology over the past few years, and touted promise of these devices for improving both clinical and psychosocial outcomes, it is critically important to understand issues in technology adoption, equity in access, maintaining long-term usage, opportunities for expanded device benefit, and limitations of the existing evidence base. We provide a brief overview of the status of the literature-with a focus on psychosocial outcomes-and provide recommendations for future work and considerations in clinical applications. Despite the wealth of the existing literature exploring psychosocial outcomes, there is substantial room to expand our current knowledge base to more comprehensively address reasons for differential effects, with increased attention to issues of health equity and data harmonization around patient-reported outcomes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142466697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}