Journal of Diabetes Science and Technology最新文献

{"title":"Explainable Machine-Learning Models to Predict Weekly Risk of Hyperglycemia, Hypoglycemia, and Glycemic Variability in Patients With Type 1 Diabetes Based on Continuous Glucose Monitoring.","authors":"Simon Lebech Cichosz, Søren Schou Olesen, Morten Hasselstrøm Jensen","doi":"10.1177/19322968241286907","DOIUrl":"https://doi.org/10.1177/19322968241286907","url":null,"abstract":"<p><strong>Background and objective: </strong>The aim of this study was to develop and validate explainable prediction models based on continuous glucose monitoring (CGM) and baseline data to identify a week-to-week risk of CGM key metrics (hyperglycemia, hypoglycemia, glycemic variability). By having a weekly prediction of CGM key metrics, it is possible for the patient or health care personnel to take immediate preemptive action.</p><p><strong>Methods: </strong>We analyzed, trained, and internally tested three prediction models (Logistic regression, XGBoost, and TabNet) using CGM data from 187 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach combined with feature engineering deployed on the CGM signals was used to predict hyperglycemia, hypoglycemia, and glycemic variability based on consensus targets (time above range ≥5%, time below range ≥4%, coefficient of variation ≥36%). The models were validated in two independent cohorts with a total of 223 additional patients of varying ages.</p><p><strong>Results: </strong>A total of 46 593 weeks of CGM data were included in the analysis. For the best model (XGBoost), the area under the receiver operating characteristic curve (ROC-AUC) was 0.9 [95% confidence interval (CI) = 0.89-0.91], 0.89 [95% CI = 0.88-0.9], and 0.8 [95% CI = 0.79-0.81] for predicting hyperglycemia, hypoglycemia, and glycemic variability in the interval validation, respectively. The validation test showed good generalizability of the models with ROC-AUC of 0.88 to 0.95, 0.84 to 0.89, and 0.80 to 0.82 for predicting the glycemic outcomes.</p><p><strong>Conclusion: </strong>Prediction models based on real-world CGM data can be used to predict the risk of unstable glycemic control in the forthcoming week. The models showed good performance in both internal and external validation cohorts.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390908","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":"Correlation Between Presepsin Levels and Continuous Glucose Monitoring Metrics in Infection-Free Individuals With Type 1 Diabetes.","authors":"Ioanna Zografou, Dimitrios Kouroupis, Georgios Dimakopoulos, Panagiotis Doukelis, Michael Doumas, Theocharis Koufakis","doi":"10.1177/19322968241288865","DOIUrl":"https://doi.org/10.1177/19322968241288865","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390907","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":"High-Frequency 10-kHz Spinal Cord Stimulation Provides Long-term (24-Month) Improvements in Diabetes-Related Pain and Quality of Life for Patients with Painful Diabetic Neuropathy.","authors":"Erika A Petersen, Thomas G Stauss, James A Scowcroft, Michael J Jaasma, Deborah R Edgar, Judith L White, Shawn M Sills, Kasra Amirdelfan, Maged N Guirguis, Jijun Xu, Cong Yu, Ali Nairizi, Denis G Patterson, Michael J Creamer, Vincent Galan, Richard H Bundschu, Neel D Mehta, Dawood Sayed, Shivanand P Lad, David J DiBenedetto, Khalid A Sethi, Johnathan H Goree, Matthew T Bennett, Nathan J Harrison, Atef F Israel, Paul Chang, Paul W Wu, Charles E Argoff, Christian E Nasr, Rod S Taylor, David L Caraway, Nagy A Mekhail","doi":"10.1177/19322968241268547","DOIUrl":"https://doi.org/10.1177/19322968241268547","url":null,"abstract":"<p><strong>Background: </strong>The SENZA-PDN study evaluated high-frequency 10-kHz spinal cord stimulation (SCS) for the treatment of painful diabetic neuropathy (PDN). Over 24 months, 10-kHz SCS provided sustained pain relief and improved health-related quality of life. This report presents additional outcomes from the SENZA-PDN study, focusing on diabetes-related pain and quality of life outcomes.</p><p><strong>Methods: </strong>The SENZA-PDN study randomized 216 participants with refractory PDN to receive either conventional medical management (CMM) or 10-kHz SCS plus CMM (10-kHz SCS + CMM), allowing crossover after six months if pain relief was insufficient. Postimplantation assessments at 24 months were completed by 142 participants with a permanent 10-kHz SCS implant, comprising 84 initial and 58 crossover recipients. Measures included the Brief Pain Inventory for Diabetic Peripheral Neuropathy (BPI-DPN), Diabetes-Related Quality of Life (DQOL), Global Assessment of Functioning (GAF), and treatment satisfaction.</p><p><strong>Results: </strong>Over 24 months, 10-kHz SCS treatment significantly reduced pain severity by 66.9% (<i>P</i> < .001; BPI-DPN) and pain interference with mood and daily activities by 65.8% (<i>P</i> < .001; BPI-DPN). Significant improvements were also observed in overall DQOL score (<i>P</i> < .001) and GAF score (<i>P</i> < .001), and 91.5% of participants reported satisfaction with treatment.</p><p><strong>Conclusions: </strong>High-frequency 10-kHz SCS significantly decreased pain severity and provided additional clinically meaningful improvements in DQOL and overall functioning for patients with PDN. The robust and sustained benefits over 24 months, coupled with high participant satisfaction, highlight that 10-kHz SCS is an efficacious and comprehensive therapy for patients with PDN.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377876","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":"Discordance Between Clinician and Person-With-Diabetes Perceptions Regarding Technology Barriers and Benefits.","authors":"Ananta Addala, Kelsey R Howard, Yasaman Hosseinipour, Laya Ekhlaspour","doi":"10.1177/19322968241285045","DOIUrl":"https://doi.org/10.1177/19322968241285045","url":null,"abstract":"<p><p>The quality of clinician-patient relationship is integral to patient health and well-being. This article is a narrative review of published literature on concordance between clinician and patient perspectives on barriers to diabetes technology use. The goals of this manuscript were to review published literature on concordance and to provide practical recommendations for clinicians and researchers. In this review, we discuss the qualitative and quantitative methods that can be applied to measure clinician and patient concordance. There is variability in how concordance is defined, with some studies using questionnaires related to working alliance, while others use a dichotomous variable. We also explore the impact of concordance and discordance on diabetes care, barriers to technology adoption, and disparities in technology use. Published literature has emphasized that physicians may not be aware of their patients' perspectives and values. Discordance between clinicians and patients can be a barrier to diabetes management and technology use. Future directions for research in diabetes technology including strategies for recruiting and retaining representative samples, are discussed. Recommendations are given for clinical care, including shared decision-making frameworks, establishing social support groups optimizing clinician-patient communication, and using patient-reported outcomes to measure patient perspectives on outcomes of interest.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377875","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":"Estimating Breakfast Characteristics Using Continuous Glucose Monitoring and Machine Learning in Adults With or at Risk of Type 2 Diabetes.","authors":"Ryan Pai, Souptik Barua, Bo Sung Kim, Maya McDonald, Raven A Wierzchowska-McNew, Amruta Pai, Nicolaas E P Deutz, David Kerr, Ashutosh Sabharwal","doi":"10.1177/19322968241274800","DOIUrl":"https://doi.org/10.1177/19322968241274800","url":null,"abstract":"<p><strong>Background: </strong>Continuous glucose monitoring (CGM) systems allow detailed assessment of postprandial glucose responses (PPGR), offering new insights into food choices' impact on dysglycemia. However, current approaches to analyze PPGR using a CGM require manual meal logging, limiting the scalability of CGM-driven applications like personalized nutrition and at-home diabetes risk assessment.</p><p><strong>Objective: </strong>We propose a machine learning (ML) framework to automatically identify and characterize breakfast-related PPGRs from CGM profiles in adults at risk of or living with noninsulin-treated type 2 diabetes (T2D).</p><p><strong>Methods: </strong>Our PPGR estimation framework uses a random forest ML algorithm trained on 15 adults without diabetes who wore a CGM for up to four weeks. The algorithm performance was evaluated on a held-out subset of the participants' CGM data as well as on an external validation data set of 36 individuals at risk for or with noninsulin-treated T2D.</p><p><strong>Results: </strong>Our algorithm's estimations of breakfast PPGRs displayed no statistically significant differences to annotated PPGRs, in terms of incremental area under the curve and glucose rise (<i>P</i> > .05 for both data sets), while a small difference in prebreakfast glucose was found in the nondiabetes data set (<i>P</i> = .005) but not in the validation T2D data set (<i>P</i> = .18).</p><p><strong>Conclusions: </strong>We designed an ML framework to automatically estimate the timing of meal events from CGM data in individuals without diabetes and in individuals at risk or with T2D. This could provide a more scalable approach for analyzing postprandial glycemia, increasing the feasibility of CGM-based precision nutrition and diabetes risk assessment applications.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142288368","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":"HIT4HYPOS Continuous Glucose Monitoring Data Analysis: The Effects of High-Intensity Interval Training on Hypoglycemia in People With Type 1 Diabetes and Impaired Awareness of Hypoglycemia.","authors":"Catriona M Farrell, Giacomo Cappon, Daniel J West, Andrea Facchinetti, Rory J McCrimmon","doi":"10.1177/19322968241273845","DOIUrl":"https://doi.org/10.1177/19322968241273845","url":null,"abstract":"<p><strong>Aims: </strong>To assess the impact of high-intensity interval training (HIIT) on hypoglycemia frequency and duration in people with type 1 diabetes (T1D) with impaired awareness of hypoglycemia (IAH).</p><p><strong>Methods: </strong>Post hoc analysis of four weeks of continuous glucose monitoring (CGM) data from HIT4HYPOS; a parallel-group study comparing HIIT + CGM versus no exercise + CGM in 18 participants with T1D and IAH.</p><p><strong>Results: </strong>When compared with those participating individuals not exercising, HIIT did not increase total hypoglycemia frequency, <i>T_Hypo(L1)</i> 1.44 [1.00-2.77]% versus 2.53 [1.46-4.23]%; <i>P</i> = .335, <i>T_Hypo(L2)</i> 0.25 [0.09-0.37]% versus 0.45 [0.20-0.78]%; <i>P</i> = .146, HIIT + CGM versus CGM, respectively, rate (<i>EventPerWeek_Hypo</i> 5.30 [3.35-8.27] #/week vs 7.45 [3.54-10.81] #/week, <i>P</i> = .340) or duration (<i>Duration_Hypo</i> 33.33 [27.60-39.10] minutes vs 39.56 [31.00-48.38] minutes; <i>P</i> = .219, HIIT + CGM vs CGM, respectively). There was a reduction in nocturnal hypoglycemia in those who carried out HIIT, <i>T_Hypo</i>_(L1) 0.50 [0.13-0.97]% versus 2.45 [0.77-4.74]%; <i>P</i> = .076; <i>T_Hypo</i>_(L2) 0.00 [0.00-0.03]% versus 0.49 [0.13-0.74]%; <i>P</i> = .006, HIIT + CGM versus CGM, respectively.</p><p><strong>Conclusions/interpretation: </strong>Based on CGM data collected from a real-world study of four weeks of HIIT versus no exercise in individuals with T1D and IAH, we conclude that HIIT does not increase hypoglycemia, and in fact reduces exposure to nocturnal hypoglycemia.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142288370","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 People With Type 2 Diabetes Not Achieving HbA1c Target After Initiation of Fast-Acting Insulin Therapy: Using Machine Learning Framework on Clinical Trial Data.","authors":"Carsten Wridt Stoltenberg, Stine Hangaard, Ole Hejlesen, Thomas Kronborg, Peter Vestergaard, Morten Hasselstrøm Jensen","doi":"10.1177/19322968241280096","DOIUrl":"https://doi.org/10.1177/19322968241280096","url":null,"abstract":"<p><strong>Background and aims: </strong>Glycemic control is crucial for people with type 2 diabetes. However, only about half achieve the advocated HbA1c target of ≤7%. Identifying those who will probably struggle to reach this target may be valuable as they require additional support. Thus, the aim of this study was to develop a model to predict people with type 2 diabetes not achieving HbA1c target after initiating fast-acting insulin.</p><p><strong>Methods: </strong>Data from a randomized controlled trial (NCT01819129) of participants with type 2 diabetes initiating fast-acting insulin were used. Data included demographics, clinical laboratory values, self-monitored blood glucose (SMBG), health-related quality of life (SF-36), and body measurements. A logistic regression was developed to predict HbA1c target nonachievers. A potential of 196 features was input for a forward feature selection. To assess the performance, a 20-repeated stratified 5-fold cross-validation with area under the receiver operating characteristics curve (AUROC) was used.</p><p><strong>Results: </strong>Out of the 467 included participants, 98 (21%) did not achieve HbA1c target of ≤7%. The forward selection identified 7 features: baseline HbA1c (%), mean postprandial SMBG at all meals 3 consecutive days before baseline (mmol/L), sex, no ketones in urine, baseline albumin (g/dL), baseline low-density lipoprotein cholesterol (mmol/L), and traces of protein in urine. The model had an AUROC of 0.745 [95% CI = 0.734, 0.756].</p><p><strong>Conclusions: </strong>The model was able to predict those who did not achieve HbA1c target with promising performance, potentially enabling early identification of people with type 2 diabetes who require additional support to reach glycemic control.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142288371","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":"Diabetic Peripheral Neuropathy: Emerging Treatments of Neuropathic Pain and Novel Diagnostic Methods.","authors":"Johan Røikjer, Mette Krabsmark Borbjerg, Trine Andresen, Rocco Giordano, Claus Vinter Bødker Hviid, Carsten Dahl Mørch, Pall Karlsson, David C Klonoff, Lars Arendt-Nielsen, Niels Ejskjaer","doi":"10.1177/19322968241279553","DOIUrl":"https://doi.org/10.1177/19322968241279553","url":null,"abstract":"<p><strong>Background: </strong>Diabetic peripheral neuropathy (DPN) is a prevalent and debilitating complication of diabetes, often leading to severe neuropathic pain. Although other diabetes-related complications have witnessed a surge of emerging treatments in recent years, DPN has seen minimal progression. This stagnation stems from various factors, including insensitive diagnostic methods and inadequate treatment options for neuropathic pain.</p><p><strong>Methods: </strong>In this comprehensive review, we highlight promising novel diagnostic techniques for assessing DPN, elucidating their development, strengths, and limitations, and assessing their potential as future reliable clinical biomarkers and endpoints. In addition, we delve into the most promising emerging pharmacological and mechanistic treatments for managing neuropathic pain, an area currently characterized by inadequate pain relief and a notable burden of side effects.</p><p><strong>Results: </strong>Skin biopsies, corneal confocal microscopy, transcutaneous electrical stimulation, blood-derived biomarkers, and multi-omics emerge as some of the most promising new techniques, while low-dose naltrexone, selective sodium-channel blockers, calcitonin gene-related peptide antibodies, and angiotensin type 2 receptor antagonists emerge as some of the most promising new drug candidates.</p><p><strong>Conclusion: </strong>Our review concludes that although several promising diagnostic modalities and emerging treatments exist, an ongoing need persists for the further development of sensitive diagnostic tools and mechanism-based, personalized treatment approaches.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142288366","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":"Batteries Within Diabetes Devices: A Narrative Review on Recycling, Environmental, and Sustainability Perspective.","authors":"Parizad Avari, Yi Cai, Vivek Verma, Monika Reddy, Madhavi Srinivasan, Nick Oliver","doi":"10.1177/19322968241278374","DOIUrl":"https://doi.org/10.1177/19322968241278374","url":null,"abstract":"<p><p>The adoption of diabetes technology for the management of type 1 and insulin-treated type 2 diabetes has greatly increased. The annual volume of discarded continuous glucose monitoring (CGM) devices, considering only Dexcom and Freestyle Libre brands, totals more than 153 million units and Omnipod^® contributes an additional estimated 43.8 million units.Although these technologies are clinically effective, their environmental impact is unknown. Batteries are a pivotal, yet often overlooked, component in diabetes technologies and can exert a detrimental impact on the environment.In this commentary article, we explore the environmental impact of batteries used in diabetes devices. Furthermore, we highlight various strategies, including recycling of used batteries and alternative design approaches, that may reduce the environmental burden, as they become the ubiquitous standard of care for people with diabetes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142288365","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":"Electronic Health Record Alert With Heart Failure Risk and Sodium Glucose Cotransporter 2 Inhibitor Prescriptions in Diabetes: A Randomized Clinical Trial.","authors":"Matthew W Segar, Kershaw V Patel, Neil Keshvani, Vaishnavi Kannan, Duwayne Willett, David C Klonoff, Ambarish Pandey","doi":"10.1177/19322968241264747","DOIUrl":"https://doi.org/10.1177/19322968241264747","url":null,"abstract":"<p><strong>Background: </strong>Sodium glucose cotransporter 2 inhibitors (SGLT2i) prevent heart failure (HF) in patients with type 2 diabetes mellitus (T2DM) but prescription rates are low. The effect of an electronic health record (EHR) alert notifying providers of patients' estimated risk of developing HF on SGTL2i prescriptions is unknown.</p><p><strong>Methods: </strong>This was a pragmatic, randomized clinical trial that compared an EHR alert and usual care among patients with T2DM and no history of HF or SGLT2i use at a single center. The EHR alert notified providers of their patient's HF risk and recommended HF prevention strategies. Randomization was performed at the provider level across general and subspecialty internal medicine as well as family medicine outpatient clinics. The primary outcome was proportion of SGLT2i prescriptions within 30 days. Proportion of natriuretic peptide (NP) tests within 90 days was also assessed.</p><p><strong>Results: </strong>A total of 1524 patients (median age 75 years, 45% women, 23% Black) were enrolled between September 28, 2021, and April 29, 2022 from 189 outpatient clinics. SGLT2i were prescribed to 1.2% (9/780) of patients in the EHR alert group and 0% (0/744) of those in the usual care group (<i>P</i> value = 0.009). Natriuretic peptide testing was performed within 90 days among 10.8% (84/780) of patients in the EHR alert group and 7.3% (54/744) of patients in the usual care group (<i>P</i> value = 0.02).</p><p><strong>Conclusions: </strong>In a single-center trial with low overall SGLT2i use, an EHR alert incorporating HF risk information significantly increased SGLT2i prescriptions and NP testing although the absolute rates were low.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142288367","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}