Diabetes technology & therapeutics最新文献

{"title":"GLP-1 Receptor Agonist Therapy in Cystic Fibrosis-Related Diabetes: A Case Report.","authors":"Gida Ayada, Sagit Zolotov, Raya Cohen, Tal Lavi, Muhammad Abdul-Ghani, Naim Shehadeh, Afif Nakhleh","doi":"10.1089/dia.2024.0367","DOIUrl":"https://doi.org/10.1089/dia.2024.0367","url":null,"abstract":"","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142364771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Initiating GLP-1 Therapy in Combination with FreeStyle Libre Provides Greater Benefit Compared with GLP-1 Therapy Alone.","authors":"Eugene E Wright, Gregory J Roberts, Joyce S Chuang, Yelena Nabutovsky, Naunihal Virdi, Eden Miller","doi":"10.1089/dia.2024.0015","DOIUrl":"10.1089/dia.2024.0015","url":null,"abstract":"<p><p><b><i>Background and Aim:</i></b> Glucagon-like peptide-1 receptor agonists (GLP-1 RA) therapy provides glycemic benefits to individuals with type 2 diabetes (T2D). However, the effects of GLP-1 RA therapy in combination with FreeStyle Libre systems (FSL) are unknown. This study aimed to compare changes in hemoglobin A1c (HbA1c) between people acquiring GLP-1 with FSL (GLP-1+FSL) versus GLP-1 without FSL (GLP-1). <b><i>Methods:</i></b> This real-world study used Optum's de-identified Market Clarity Data, a linked electronic health records (EHR)-claims database, and included adults with T2D and HbA1c ≥8% who acquired their first GLP-1 RA medication between 2018 and 2022. GLP-1+FSL subjects acquired their first FSL within ±30 days of their first GLP-1 acquisition. Cohorts were matched 1:5 on baseline insulin therapy, age, sex, baseline HbA1c, and GLP-1 type. Paired changes in HbA1c were compared between unmatched and matched groups at 6 months. <b><i>Results:</i></b> The study included 24,724 adults in the unmatched cohort (GLP-1+FSL, <i>n</i> = 478; GLP-1, <i>n</i> = 24,246). The matched cohort included 478 GLP-1+FSL users and 2,390 GLP-1 users: mean age 53.5 ± 11.8 and 53.5 ± 11.3 years, HbA1c 10.25 ± 1.68% and 10.22 ± 1.69%, respectively. HbA1c reduction was greater in the GLP-1+FSL group compared with the GLP-1 group in the unmatched cohort (-2.43% vs. -1.73%, difference 0.70%, <i>P</i> < 0.001, respectively) and in the matched cohort (-2.43% vs. -2.06%, difference 0.37%, <i>P</i> < 0.001). GLP-1+FSL vs. GLP-1 treatment was associated with greater HbA1c reduction in the intensive insulin (-2.32% vs. -1.50%), nonintensive insulin (-2.50% vs. -1.74%), and noninsulin group (-2.46% vs. -1.78%), as well as in patients using semaglutide (-2.73% vs. -1.92%) and dulaglutide (-2.45% vs. -1.71%) GLP-1 RA, all <i>P</i> < 0.001. <b><i>Conclusions:</i></b> Adults with suboptimally controlled T2D, initiating GLP-1 RA with FreeStyle Libre, had greater improvement in HbA1c compared with those treated with GLP-1 RA only. These results suggest an additional glycemic benefit of FSL when used with a GLP-1 RA in T2D treatment.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"754-762"},"PeriodicalIF":5.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140848149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Point-Counterpoint: The Need for Do-It-Yourself (DIY) Open Source (OS) AID Systems in Type 1 Diabetes Management.","authors":"Gregory P Forlenza, Ideen Tabatabai, Dana M Lewis","doi":"10.1089/dia.2024.0073","DOIUrl":"10.1089/dia.2024.0073","url":null,"abstract":"<p><p>In the last decade, technology developed by people with diabetes and their loved ones has added to the options for diabetes management. One such example is that of automated insulin delivery (AID) algorithms, which were created and shared as open source by people living with type 1 diabetes (T1D) years before commercial systems were first available. Now, numerous options for commercial systems exist in some countries, yet tens of thousands of people with diabetes are still choosing Open-Source AID (OS-AID), previously called \"do-it-yourself\" (DIY) systems, which are noncommercial versions of these open-source AID systems. In this article, we provide point and counterpoint perspectives regarding (1) safety and efficacy, (2) regulation and support, (3) user choice and flexibility, (4) access and affordability, and (5) patient and provider education, for open source and commercial AID systems. The perspectives reflected here include that of a person living with T1D who uses and has developed OS-AID systems, a physician-researcher based in the United States who conducts clinical trials to support development of commercial AID systems and supports people with diabetes using all types of AID, and an endocrinologist with T1D who uses both systems and treats people with diabetes using all types of AID.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"689-699"},"PeriodicalIF":5.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140862491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MiniMed 780G System Outperforms Other Automated Insulin Systems Due to Algorithm Design, Not Bias: Response to Inaccurate Allegations.","authors":"Tim van den Heuvel, Javier Castaneda, Isabeau Thijs, Arcelia Arrieta, Lou Lintereur, John Shin, Ohad Cohen","doi":"10.1089/dia.2024.0121","DOIUrl":"10.1089/dia.2024.0121","url":null,"abstract":"","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"783-784"},"PeriodicalIF":5.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140335123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causes and Consequences of Continuous Glucose Monitor \"Breaks\": Perspectives from Adolescents with Type 1 Diabetes.","authors":"Karishma Datye, Kemberlee Bonnet, Angelee Parmar, David Schlundt, Sarah Jaser","doi":"10.1089/dia.2024.0210","DOIUrl":"10.1089/dia.2024.0210","url":null,"abstract":"","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"780-782"},"PeriodicalIF":5.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11535451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141418274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Hypoglycemia and Hyperglycemia Risk During and After Activity for Adolescents with Type 1 Diabetes.","authors":"Simon Bergford, Michael C Riddell, Robin L Gal, Susana R Patton, Mark A Clements, Jennifer L Sherr, Peter Calhoun","doi":"10.1089/dia.2024.0061","DOIUrl":"10.1089/dia.2024.0061","url":null,"abstract":"<p><p><b><i>Objective:</i></b> To predict hypoglycemia and hyperglycemia risk during and after activity for adolescents with type 1 diabetes (T1D) using real-world data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study. <b><i>Methods:</i></b> Adolescents with T1D (<i>n</i> = 225; [mean ± SD] age = 14 ± 2 years; HbA1c = 7.1 ± 1.3%; T1D duration = 5 ± 4 years; 56% using hybrid closed loop), wearing continuous glucose monitors (CGMs), logged 3738 total activities over 10 days. Repeated Measures Random Forest (RMRF) and Repeated Measures Logistic Regression (RMLR) models were used to predict a composite risk of hypoglycemia (<70 mg/dL) and hyperglycemia (>250 mg/dL) within 2 h after starting exercise. <b><i>Results:</i></b> RMRF achieved high precision predicting composite risk and was more accurate than RMLR Area under the receiver operating characteristic curve (AUROC 0.737 vs. 0.661; <i>P</i> < 0.001). Activities with minimal composite risk had a starting glucose between 132 and 160 mg/dL and a glucose rate of change at activity start between -0.4 and -1.9 mg/dL/min. Time <70 mg/dL and time >250 mg/dL during the prior 24 h, HbA1c level, and insulin on board at activity start were also predictive. Separate models explored factors at the end of activity; activities with glucose between 128 and 133 mg/dL and glucose rate of change between 0.4 and -0.6 mg/dL/min had minimal composite risk. <b><i>Conclusions:</i></b> Physically active adolescents with T1D should aim to start exercise with an interstitial glucose between 130 and 160 mg/dL with a flat or slightly decreasing CGM trend to minimize risk for developing dysglycemia. Incorporating factors such as historical glucose and insulin can improve prediction modeling for the acute glucose responses to exercise.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"728-738"},"PeriodicalIF":5.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140848072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response to: MiniMed 780G System Outperforms Other Automated Insulin Systems Due to Algorithm Design, Not Bias-Response to Inaccurate Allegations.","authors":"Gregory P Forlenza, Jennifer L Sherr","doi":"10.1089/dia.2024.0125","DOIUrl":"10.1089/dia.2024.0125","url":null,"abstract":"","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"785-786"},"PeriodicalIF":5.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140184016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Type of Patient Training Does Not Impact Outcomes in the First 90 Days of Automated Insulin Delivery Use.","authors":"Seema Meighan, Julia L Douvas, Andrew Rearson, Robert Squaresky, Andrea Kelly, Brynn E Marks","doi":"10.1089/dia.2024.0096","DOIUrl":"10.1089/dia.2024.0096","url":null,"abstract":"<p><p><b><i>Background:</i></b> Youth starting Omnipod 5 (OP5) can onboard with a diabetes educator or self-start with support from online, industry-provided educational modules. We compared glycemic control and pump interaction by training type among youth initiating OP5. <b><i>Methods:</i></b> This retrospective review included 297 youth with type 1 diabetes (T1D) aged <22 years initiating OP5. We analyzed baseline continuous glucose monitor (CGM) data and pump and CGM data from the first 90 days of OP5 use. Multilevel mixed-effects regression assessed for changes in time in range (TIR) from baseline to 90 days by training type. <b><i>Results:</i></b> Of youth initiating OP5, 42.4% trained with a diabetes educator and 57.6% self-started. At baseline, self-starters had a longer T1D duration (5.0 (2.6,7.9) vs. 2.5 (1.3, 5.5) years, <i>P</i> = 0.001), more time <54 mg/dL (0.3% (0.1,1) vs. 0.15% (0,1), <i>P</i> = 0.01), and a higher coefficient of variation (40.2% (37, 44.4) vs. 38.7% (34.4, 42.4), <i>P</i> = 0.004). After 90 days of OP5 use, groups did not differ in time in automated mode or boluses per day. In a longitudinal model, after adjusting for baseline TIR and T1D duration, 90-day TIR was 10.5%-points higher (CI: 9.2-11.8, <i>P</i> < 0.0001), positively associated with baseline TIR (β = 0.82, CI: 0.78-0.85, <i>P</i> < 0.0001), and 1.1%-points greater among self-starters (CI: 0.06-2.2; <i>P</i> = 0.04). <b><i>Conclusions:</i></b> After 90 days of OP5 use, glycemic control and pump interactions were minimally different between youth who self-started and those who trained with a diabetes educator. For youth at a tertiary care center previously using an Omnipod system, online educational modules offered by industry provide sufficient training for use.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"773-779"},"PeriodicalIF":5.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141161189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Potential Impact of Continuous Glucose Monitoring Use on Diabetes-Related Attitudes and Behaviors in Adults with Type 2 Diabetes: A Qualitative Investigation of the Patient Experience.","authors":"Taylor L Clark, William H Polonsky, Emily C Soriano","doi":"10.1089/dia.2023.0612","DOIUrl":"10.1089/dia.2023.0612","url":null,"abstract":"<p><p><b><i>Background:</i></b> Despite the known glycemic benefits of continuous glucose monitoring (CGM) for adults with type 2 diabetes (T2D), the attitudinal and behavioral changes underlying these glycemic improvements remain understudied. This study aimed to qualitatively explore these changes among a sample of adults with T2D. <b><i>Methods:</i></b> In-depth, semistructured interviews were conducted with adults with T2D who had been using CGM for 3-6 months as part of a larger community project in Ohio. Thematic analysis was used to identify themes across participants' experiences. <b><i>Results:</i></b> A total of 34 participants provided insights into their experiences with CGM. Six primary themes emerged: (1) Making the Invisible Visible, highlighting the newfound awareness of T2D in daily life. (2) Effective Decision-Making, emphasizing the use of real-time glucose data for immediate and long-term choices. (3) Enhanced Self-Efficacy, describing a renewed sense of control and motivation. (4) Diabetes-Related Diet Modifications. (5) Changes in Physical Activity. (6) Changes in Medication Taking. <b><i>Conclusions:</i></b> Participants reported a far-reaching impact of CGM on their daily lives, with many stating that CGM fostered a greater understanding of diabetes and prompted positive behavior changes. The observed attitudinal and behavioral shifts likely contributed synergistically to the significant glycemic benefits observed over the study period. This study highlights the technology's potential to bring about meaningful attitudinal and behavioral changes.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"700-708"},"PeriodicalIF":5.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140206476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online Classification of Unstructured Free-Living Exercise Sessions in People with Type 1 Diabetes.","authors":"Emilia Fushimi, Eleonora M Aiello, Sunghyun Cho, Michael C Riddell, Robin L Gal, Corby K Martin, Susana R Patton, Michael R Rickels, Francis J Doyle","doi":"10.1089/dia.2023.0528","DOIUrl":"10.1089/dia.2023.0528","url":null,"abstract":"<p><p><b><i>Background:</i></b> Managing exercise in type 1 diabetes is challenging, in part, because different types of exercises can have diverging effects on glycemia. The aim of this work was to develop a classification model that can classify an exercise event (structured or unstructured) as aerobic, interval, or resistance for the purpose of incorporation into an automated insulin delivery (AID) system. <b><i>Methods:</i></b> A long short-term memory network model was developed with real-world data from 30-min structured sessions of at-home exercise (aerobic, resistance, or mixed) using triaxial accelerometer, heart rate, and activity duration information. The detection algorithm was used to classify 15 common free-living and unstructured activities and relate each to exercise-associated change in glucose. <b><i>Results:</i></b> A total of 1610 structured exercise sessions were used to train, validate, and test the model. The accuracy for the structured exercise sessions in the testing set was 72% for <i>aerobic</i>, 65% for <i>interval</i>, and 77% for <i>resistance</i>. In addition, we tested the classifier on 3328 unstructured sessions. We validated the session-associated change in glucose against the expected change during exercise for each type. Mean and standard deviation of the change in glucose of -20.8 (40.3) mg/dL were achieved for sessions classified as <i>aerobic</i>, -16.2 (39.0) mg/dL for sessions classified as <i>interval</i>, and -11.6 (38.8) mg/dL for sessions classified as <i>resistance</i>. <b><i>Conclusions:</i></b> The proposed algorithm reliably identified physical activity associated with expected change in glucose, which could be integrated into an AID system to manage the exercise disturbance in glycemia according to the predicted class.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"709-719"},"PeriodicalIF":5.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139989517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}