Journal of Diabetes Science and Technology最新文献

{"title":"Identifying Diabetes Related-Complications in a Real-World Free-Text Electronic Medical Records in Hebrew Using Natural Language Processing Techniques.","authors":"Mor Saban, Miri Lutski, Inbar Zucker, Moshe Uziel, Dror Ben-Moshe, Ariel Israel, Shlomo Vinker, Avivit Golan-Cohen, Izhar Laufer, Ilan Green, Roy Eldor, Eugene Merzon","doi":"10.1177/19322968241228555","DOIUrl":"10.1177/19322968241228555","url":null,"abstract":"<p><strong>Background: </strong>Studies have demonstrated that 50% to 80% of patients do not receive an International Classification of Diseases (ICD) code assigned to their medical encounter or condition. For these patients, their clinical information is mostly recorded as unstructured free-text narrative data in the medical record without standardized coding or extraction of structured data elements. Leumit Health Services (LHS) in collaboration with the Israeli Ministry of Health (MoH) conducted this study using electronic medical records (EMRs) to systematically extract meaningful clinical information about people with diabetes from the unstructured free-text notes.</p><p><strong>Objectives: </strong>To develop and validate natural language processing (NLP) algorithms to identify diabetes-related complications in the free-text medical records of patients who have LHS membership.</p><p><strong>Methods: </strong>The study data included 2.3 million records of 41 469 patients with diabetes aged 35 or older between the years 2012 and 2017. The diabetes related complications included cardiovascular disease, diabetic neuropathy, nephropathy, retinopathy, diabetic foot, cognitive impairments, mood disorders and hypoglycemia. A vocabulary list of terms was determined and adjudicated by two physicians who are experienced in diabetes care board certified diabetes specialist in endocrinology or family medicine. Two independent registered nurses with PhDs reviewed the free-text medical records. Both rule-based and machine learning techniques were used for the NLP algorithm development. Precision, recall, and <i>F</i>-score were calculated to compare the performance of (1) the NLP algorithm with the reviewers' comments and (2) the ICD codes with the reviewers' comments for each complication.</p><p><strong>Results: </strong>The NLP algorithm versus the reviewers (gold standard) achieved an overall good performance with a mean <i>F</i>-score of 86%. This was better than the ICD codes which achieved a mean <i>F</i>-score of only 51%.</p><p><strong>Conclusion: </strong>NLP algorithms and machine learning processes may enable more accurate identification of diabetes complications in EMR data.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"999-1007"},"PeriodicalIF":4.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139576110","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":"Investigation of the Effect of 70 Potential Interferents on Measurement Results of Two Blood Glucose Monitoring Systems.","authors":"Annette Baumstark, Stefan Pleus, Jochen Mende, Nina Jendrike, Martina Tesar, Sebastian Schauer, Takayuki Sugiyama, Takashi Aoki, Mako Sugiura, Guido Freckmann, Cornelia Haug","doi":"10.1177/19322968241231294","DOIUrl":"10.1177/19322968241231294","url":null,"abstract":"<p><strong>Background: </strong>Reliable blood glucose (BG) measurements are important for people with diabetes to manage their therapy as well as in point-of-care testing (POCT) performed by health care professionals to monitor BG of patients or even to diagnose diabetes. Among other factors, endogenous and exogenous substances present in blood samples can impact the measurement results. To ensure and prove that blood glucose monitoring systems (BGMSs) are robust in terms of potential interferents, manufacturers have to perform extensive evaluations.</p><p><strong>Method: </strong>An interference screening test was performed for three reagent system lots of a POCT system and of a BGMS for self-monitoring of BG. A paired-difference approach based on ISO 15197:2013 and CLSI guideline EP07 was used with venous whole blood samples at two different glucose concentrations. Seventy potential interferents expected to be common in people with diabetes were evaluated.</p><p><strong>Results: </strong>The interference effects were determined as normalized biases between test samples and corresponding control samples. For 69 of the 70 investigated potential interferents, both systems met the predefined acceptance criteria, with the normalized biases falling within ±10 mg/dL or ±10% at glucose concentrations ≤100 mg/dL or >100 mg/dL, respectively, for each of the three evaluated reagent system lots.</p><p><strong>Conclusions: </strong>The BGMS investigated in this study were found to be robust with respect to the 70 evaluated potential interferents. Interference effects were observed only for N-Acetyl-L-cysteine. Extensive evaluations of potential interfering factors can make an important contribution to ensure reliability of BGMS.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"971-981"},"PeriodicalIF":4.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139741184","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":"A Scoping Review of Wearable Technologies for Use in Individuals With Intellectual Disabilities and Diabetic Peripheral Neuropathy.","authors":"Ercole Barsotti, Bailey Goodman, Riley Samuelson, Martha L Carvour","doi":"10.1177/19322968241231279","DOIUrl":"10.1177/19322968241231279","url":null,"abstract":"<p><strong>Background: </strong>Individuals with intellectual disabilities (IDs) are at risk of diabetes mellitus (DM) and diabetic peripheral neuropathy (DPN), which can lead to foot ulcers and lower-extremity amputations. However, cognitive differences and communication barriers may impede some methods for screening and prevention of DPN. Wearable and mobile technologies-such as smartphone apps and pressure-sensitive insoles-could help to offset these barriers, yet little is known about the effectiveness of these technologies among individuals with ID.</p><p><strong>Methods: </strong>We conducted a scoping review of the databases Embase, PubMed, and Web of Science using search terms for DM, DPN, ID, and technology to diagnose or monitor DPN. Finding a lack of research in this area, we broadened our search terms to include any literature on technology to diagnose or monitor DPN and then applied these findings within the context of ID.</p><p><strong>Results: </strong>We identified 88 articles; 43 of 88 (48.9%) articles were concerned with gait mechanics or foot pressures. No articles explicitly included individuals with ID as the target population, although three articles involved individuals with other cognitive impairments (two among patients with a history of stroke, one among patients with hemodialysis-related cognitive changes).</p><p><strong>Conclusions: </strong>Individuals with ID are not represented in studies using technology to diagnose or monitor DPN. This is a concern given the risk of DM complications among patients with ID and the potential for added benefit of such technologies to reduce barriers to screening and prevention. More studies should investigate how wearable devices can be used among patients with ID.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1082-1096"},"PeriodicalIF":4.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140028135","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":"Should We Stop Glucagon-Like Peptide-1 Receptor Agonists Before Surgical or Endoscopic Procedures? Balancing Limited Evidence With Clinical Judgment.","authors":"Guillermo Umpierrez, Francisco J Pasquel, Elizabeth Duggan, Rodolfo J Galindo","doi":"10.1177/19322968241231565","DOIUrl":"10.1177/19322968241231565","url":null,"abstract":"<p><p>The American Society of Anesthesiologists (ASA) Task Force recently recommended discontinuing glucagon-like peptide-1 receptor agonist (GLP-1 RA) agents before surgery because of the potential risk of pulmonary aspiration. However, there is limited scientific evidence to support this recommendation, and holding GLP-1 RA treatment may worsen glycemic control in patients with diabetes. As we await further safety data to manage GLP-1 RA in the perioperative period, we suggest an alternative multidisciplinary approach to manage patients undergoing elective surgery. Well-conducted observational and prospective studies are needed to determine the risk of pulmonary aspiration in persons receiving GLP-1 RA for the treatment of diabetes and obesity, as well as the short-term impact of discontinuing GLP-1 RA on glycemic control before elective procedures in persons with diabetes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1128-1131"},"PeriodicalIF":4.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140094100","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":"Envisioning Tools to Help Classify Type 1 Diabetes and Type 2 Diabetes in New-Onset Adult Diabetes.","authors":"Alessandra T Ayers, Cindy N Ho, Liana K Billings, Shivani Misra, David C Klonoff","doi":"10.1177/19322968251329055","DOIUrl":"10.1177/19322968251329055","url":null,"abstract":"<p><p>A tool is needed to distinguish type 1 diabetes (T1D) and type 2 diabetes (T2D) in adults with new-onset diabetes because correct classification is needed for correct diagnoses and treatments. Current classification methods are usually applied to biomarkers using binary or quantitative classification with a cut point and may not be adequately nuanced. Combinations of clinical features are not necessarily specific for classifying and may not always indicate a single diagnosis. A probabilistic decision tree classification tool with multiple branches per decision node is needed for adults with new-onset diabetes to avoid misdiagnosis of actual T1D as T2D, misdiagnosis of actual T2D or monogenic diabetes as T1D, and misclassified patients in future population health studies which will lead to incorrect conclusions and suboptimal patient outcomes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1137-1143"},"PeriodicalIF":4.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11954134/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143730229","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":"Metabolic Models, in Silico Trials, and Algorithms.","authors":"Ali Cinar, Ananda Basu, B Wayne Bequette, Marc D Breton, Bruce Buckingham, Eda Cengiz, Claudio Cobelli, Eyal Dassau, Francis J Doyle, Chiara Fabris, Andrea Facchinetti, Irl Hirsch, Roman Hovorka, Peter G Jacobs, Boris P Kovatchev, Chiara Dalla Man, Laurie Quinn, Jay Skyler","doi":"10.1177/19322968251338300","DOIUrl":"10.1177/19322968251338300","url":null,"abstract":"<p><p>Artificial pancreas (AP) systems, also called automated insulin delivery systems, have improved the time in range of glucose levels, reduced the daily burden of the user for glucose regulation, and improved their quality of life. Several commercially available AP systems operate in hybrid closed-loop mode that requires manual information from the user for meals and exercise. This article summarizes the progress on mathematical models of glucose-insulin dynamics, continuous glucose monitoring systems, and insulin pumps that form the building blocks of AP systems, the shift from animal studies to in silico clinical trials that accelerated the rate of progress in AP technologies and the efforts for developing the next-generation AP systems, and the fully automated AP that eliminates manual inputs and mitigates the effects of disturbances to glucose homeostasis-meals, physical activities, acute stress, and variations in sleep characteristics. A section is devoted to discuss the unique glycemic management challenges faced by women with diabetes across the lifespan (menstrual cycle, menopause, pregnancy) and summarize progress made to reduce their impact on glycemic management.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":"19 4","pages":"895-907"},"PeriodicalIF":4.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12213549/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144540370","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":"A Novel High-Intensity Short Interval Dance Intervention (THANDAV) for Non-Communicable Disease Prevention Tailored to Asian Indian Adolescent Girls.","authors":"Ranjit Mohan Anjana, Sharma Nitika, Narayanaswamy Jagannathan, Deenadayalan Vinothini, Kanniyappan Yuvarani, Viswanathan Mohan, Rajendra Pradeepa, Colin N A Palmer, Melissa O'Shea, Shifalika Goenka, Ranjini Manian, Amrita Karthik Raj, Shiny Surendran, Ranjit Unnikrishnan, James F Sallis, Harish Ranjani","doi":"10.1177/19322968251332925","DOIUrl":"10.1177/19322968251332925","url":null,"abstract":"<p><strong>Background: </strong>A significant majority of adolescents from lower-middle-income countries do not meet recommendations for daily physical activity. THANDAV (Taking High-Intensity Interval Training [HIIT] ANd Dance to Adolescents for Victory over noncommunicable diseases [NCDs]) is a 10-minute dance intervention incorporating principles of HIIT. The present study evaluated the effect of THANDAV on cardiovascular risk factors and lifestyle behavior in Asian Indian adolescent girls.</p><p><strong>Methods: </strong>THANDAV was delivered as a 12-week pilot cluster randomized controlled trial (cRCT) in two schools, involving 108 schoolgirls aged 13 to 15 years in Chennai, India. The primary outcome was step counts, while secondary outcomes included metabolic, clinical, and lifestyle parameters. Focus group discussions and interviews were held to assess barriers to and acceptability of THANDAV intervention.</p><p><strong>Results: </strong>In the intention-to-treat analysis, the intervention group had significantly favorable changes in step counts (+1073 steps/day), skeletal muscle mass (+0.9 kg), body weight (-0.7 kg), body fat percentage (-2.0%), body mass index (-0.3 kg/m²), waist circumference (-1.0 cm), systolic (-4 mm Hg) and diastolic blood pressure (-4 mm Hg), resting heart rate (-3 bpm) and body fat mass (-1.7 kg), moderate -to-vigorous physical activity (+29.5 minutes/day), sleep (+46.4 minutes/day), sedentary time (-199.7 minutes/day), Adolescence Stress Scale (-6.6), and junk food consumption (-2.7) scores compared with controls. Qualitative interviews revealed that THANDAV routines were time-efficient, enjoyable, and easily fit into adolescents' busy schedules enabling feasible engagement in active leisure time.</p><p><strong>Conclusions: </strong>THANDAV is a culturally acceptable HIIT-based dance intervention that improves leisure-time physical activity and reduces cardiometabolic risk in Asian Indian adolescent girls.</p><p><strong>Trial registration: </strong>The trial is registered with the Central Trials Registry-India (CTRI/2020/02/023384; URL: https://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=MzgyMTQ=&Enc=&userName=).</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1035-1050"},"PeriodicalIF":3.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12012495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144009902","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":"Evolution of the Artificial Pancreas: Components and Integration-CGMs, Insulin, and AP Systems.","authors":"Richard M Bergenstal, Adam Heller, Marc D Breton, Robert Vigersky, Susan A Brown, Gregory P Forlenza, Jennifer L Sherr, Roman Hovorka, Steven J Russell, Edward R Damiano, Melissa S Putman, Roy W Beck, Eric Renard, Rayhan Lal, Claudio Cobelli, Eyal Dassau, Halis K Akturk, Michael A Weiss, Jay S Skyler","doi":"10.1177/19322968251342239","DOIUrl":"10.1177/19322968251342239","url":null,"abstract":"<p><p>The landmark Diabetes Control and Complications Trial (DCCT) showed that glucose control is critical to reducing the risk of diabetes-related complications. This chapter outlines a series of innovations and investigations that followed the DCCT, aimed at minimizing the risk of hypoglycemia while further improving glucose control. The chapter presents an example of innovations in wired enzyme technology that facilitated the movement from capillary glucose monitoring to continuous glucose monitoring (CGM) and ultimately, the first-factory calibrated CGM system. The next glycemic management innovation was to connect CGM data to an insulin pump containing an algorithm able to adjust insulin delivery based on the changing glucose levels and trends. The key features of automated insulin delivery (AID) systems, currently approved in the United States, are presented. The AID summary table includes type of pump, type and function of the insulin delivery algorithm, the data management system, and the indications for use. The next section explores the innovation of alternative routes of insulin delivery to move toward the goal of a fully automated insulin delivery system. The main trials in developing and implementing an implantable intraperitoneal programmable system are summarized. The last section explores if sensor input in addition to glucose levels such as continuous sensing of ketone, lactate, or insulin levels may provide valuable feedback to move us closer to a fully autonomous AID system. Much of this diabetes innovation and investigation work has been supported by the National Institute of Diabetes and Digestive and Kidney Diseases over that last 75 years.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":"19 4","pages":"883-894"},"PeriodicalIF":3.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12511942/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144540369","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":"Response to \"Auto-Correction Boluses Contribute to Hypoglycemia After Following Temporary Target Cessation During Hyperglycemia\".","authors":"Robert A Vigersky, Jennifer J McVean, Louis Lintereur","doi":"10.1177/19322968251337485","DOIUrl":"10.1177/19322968251337485","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1160"},"PeriodicalIF":4.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12031728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144002194","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":"Advancing Diabetes Management: The Future of Enzyme-Less Nanoparticle-Based Glucose Sensors-A Review.","authors":"Hana Boucheta, Emna Zouaoui, Hana Ferkous, Anis Madaci, Krishna Kumar Yadav, Yacine Benguerba","doi":"10.1177/19322968241236211","DOIUrl":"10.1177/19322968241236211","url":null,"abstract":"<p><strong>Background: </strong>Glucose is vital for biological processes, requiring blood sugar levels to be maintained between 3.88 and 6.1 mmol/L, especially during fasting. Elevated levels signal diabetes, a global concern affecting 537 million people, necessitating effective glucose-monitoring devices.</p><p><strong>Method: </strong>Enzyme-based sensors, though selective, are sensitive to environmental factors. Nonenzymatic sensors, especially those with nanoparticles, offer stability, high surface area, and cost-effectiveness. Existing literature supports their immediate glucose oxidation, showcasing exceptional sensitivity.</p><p><strong>Results: </strong>This review details nonenzymatic sensors, highlighting materials, detection limits, and the promise of nanoparticle-based designs, which exhibit enhanced sensitivity and selectivity in glucose detection.</p><p><strong>Conclusion: </strong>Nanoparticle-based sensors, as reviewed, show potential for glucose monitoring, overcoming enzyme-based limitations. The conclusion suggests future directions for advancing these sensors, emphasizing ongoing innovation in this critical research area.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1097-1108"},"PeriodicalIF":4.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140174914","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}