Diabetes technology & therapeutics最新文献

{"title":"A Comparison of Glucose and Additional Signals for Three Different Exercise Types in Adolescents with Type 1 Diabetes Using a Hybrid Closed-Loop System.","authors":"Rowen Seckold, Carmel E Smart, David N O'Neal, Michael C Riddell, Jordan Rafferty, Dale Morrison, Varuni Obeyesekere, Judy L Gooley, Barbora Paldus, Sarah R Valkenborghs, Sara Vogrin, Dessi P Zaharieva, Bruce R King","doi":"10.1089/dia.2024.0254","DOIUrl":"https://doi.org/10.1089/dia.2024.0254","url":null,"abstract":"<p><p><b><i>Objective:</i></b> To compare glycemic outcomes during and following moderate-intensity exercise (MIE), high-intensity interval exercise (HIE), and resistance exercise (RE) in adolescents with type 1 diabetes (T1D) using a hybrid closed-loop (HCL) insulin pump while measuring additional physiological signals associated with activity. <b><i>Methods:</i></b> Twenty-eight adolescents (average age 16.3 ± 2.1 years, 50% females, average duration of T1D 9.4 ± 4 years) using HCL (Medtronic MiniMed 670G) undertook 40 min of MIE, HIE, and RE. A temporary glucose target (8.3 mmol/L, 150 mg/dL) was set for 2 h prior and during exercise. Heart rate, accelerometer, venous glucose, lactate, ketones, and counter-regulatory hormones were measured for 280 min postexercise commencement. The primary outcome was glucose percentage time in range (TIR): 3.9-10 mmol/L (70-180 mg/dL) for 14 h from exercise onset. <b><i>Results:</i></b> Median (interquartile range) TIR for HIE was 88 (78, 96)%, MIE 79 (63, 88)%, and RE 86 (72, 95)% for 14 h from exercise onset. For MIE compared with HIE, TIR was lower (<i>P</i> = 0.012) and time above range (TAR) was greater (18 [2.4, 28] vs. 6.9 [0.0, 14]%, <i>P</i> = 0.041). Hypoglycemia occurred in 13 (46%), 11 (39%), and 14 (50%) of participants for HIE, MIE, and RE, respectively, the majority following the meal after exercise. There were higher levels of lactate (<i>P</i> = 0.001), growth hormone (<i>P</i> = 0.001), noradrenaline (<i>P</i> = 0.001), and heart rate (<i>P</i> = 0.01) during HIE and RE compared with MIE. <b><i>Conclusions</i></b>: HCL use in adolescents with T1D results in excellent TIR during different forms of exercise when a temporary target is set 2 h before. Extending the temporary target after exercise may also be needed to help minimize postexercise hypoglycemia.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142946156","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":"<i>Correction to:</i> Continuous Glucose Monitoring Systems in Noninsulin-Treated People with Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials (doi: 10.1089/dia.2023.0390).","authors":"","doi":"10.1089/dia.2023.0390.correx","DOIUrl":"https://doi.org/10.1089/dia.2023.0390.correx","url":null,"abstract":"","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142946153","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 Virtual DCCT: Adding Continuous Glucose Monitoring to a Landmark Clinical Trial for Prediction of Microvascular Complications.","authors":"Boris P Kovatchev, Benjamin Lobo, Chiara Fabris, Mohammadreza Ganji, Anas El Fathi, Marc D Breton, Lauren Kanapka, Craig Kollman, Tadej Battelino, Roy W Beck","doi":"10.1089/dia.2024.0404","DOIUrl":"https://doi.org/10.1089/dia.2024.0404","url":null,"abstract":"<p><p><b><i>Objective:</i></b> Using a multistep machine-learning procedure, add virtual continuous glucose monitoring (CGM) traces to the original sparse data of the landmark Diabetes Control and Complications Trial (DCCT). Assess the association of CGM metrics with the microvascular complications of type 1 diabetes observed during the DCCT and establish time-in-range (TIR) as a viable marker of glycemic control. <b><i>Research Design and Methods:</i></b> Utilizing the DCCT glycated hemoglobin data obtained every 1 or 3 months plus quarterly 7-point blood glucose (BG) profiles in a multistep procedure: (i) utilized archival BG traces to model interday BG variability and estimate glycated hemoglobin; (ii) trained across the DCCT BG profiles and associated each profile with an archival BG trace; and (iii) used previously identified CGM \"motifs\" to associate a CGM trace to a BG trace, for each DCCT participant. <b><i>Results:</i></b> TIR (70-180 mg/dL) computed from virtual CGM data over 14 days prior to each glycated hemoglobin measurement reproduced the observed glycemic control differences between the intensive and conventional DCCT groups, with TIR generally >60% and <40% in these groups, respectively. Similar to glycated hemoglobin, TIR was associated with the risk of development or progression of retinopathy, nephropathy, and neuropathy (all <i>P</i>-values <0.0001). Poisson regressions indicated that TIR predicted retinopathy and microalbuminuria similarly to the original glycated hemoglobin data. <b><i>Conclusions:</i></b> The landmark DCCT was revisited using contemporary data science methods, which allowed adding individual CGM traces to the original data. Fourteen-day CGM metrics predicted microvascular diabetes complications similarly to glycated hemoglobin. <b><i>Clinical Trials Registration:</i></b> Not a clinical trial.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142946160","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":"A Randomized Controlled Trial Using Continuous Glucose Monitoring to Guide Food Choices and Diabetes Self-Care in People with Type 2 Diabetes not Taking Insulin.","authors":"Thomas W Martens, Holly J Willis, Richard M Bergenstal, Davida F Kruger, Esra Karslioglu-French, Devin W Steenkamp","doi":"10.1089/dia.2024.0579","DOIUrl":"https://doi.org/10.1089/dia.2024.0579","url":null,"abstract":"<p><p><b><i>Objective:</i></b> Continuous glucose monitoring (CGM) is an effective tool for individuals with type 2 diabetes (T2D) on insulin. This study evaluated the effect of using CGM to reduce hyperglycemia, by focusing on food and lifestyle choices, in people with T2D not taking insulin. <b><i>Methods:</i></b> A 6-month randomized, prospective four-center study was conducted. The primary end point was a within-group reduction in time above range >180 mg/dL (TAR180) at 3 months. Participants were asked not to make diabetes medication changes in the first 3 months. Seventy-two adults not on insulin or sulfonylurea therapy, with glycated hemoglobin (HbA1c) 7.5%-12%, were randomized to use CGM alone (<i>n</i> = 31) or CGM plus a food logging app (<i>n</i> = 41) to aid diabetes management. Participants attended guided education visits. Differences in CGM metrics, HbA1c, and body weight were compared. <b><i>Results:</i></b> The CGM alone group decreased TAR180 from 55% at baseline to 27% at 3 months (<i>P</i> < 0.001) and 21% at 6 months (<i>P</i> < 0.001); the CGM plus food logging app group decreased TAR180 from 53% at baseline to 30% at both 3 and 6 months (<i>P</i> < 0.001 for both). For all participants, time in range (70-180 mg/dL) increased from 46% at baseline to 71% at 3 months (<i>P</i> < 0.001) and to 72% at 6 months (<i>P</i> < 0.001). HbA1c and weight were reduced by 1.3% (<i>P</i> < 0.001) and 7 pounds (lbs.) (<i>P</i> < 0.001) for all participants at 6 months. <b><i>Conclusion:</i></b> People with T2D not taking insulin showed large, clinically significant improvements in CGM metrics and HbA1c when using either CGM alone or with a food logging app. This occurred with a near absence of medication changes in the first 3 months and were therefore likely due to changes in food and/or lifestyle choices.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930827","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":"A Glucose Fraction Independent of Insulin Secretion: Implications for Type 1 Diabetes Progression in Autoantibody-Positive Cohorts.","authors":"Jay M Sosenko, David Cuthbertson, Laura M Jacobsen, Maria J Redondo, Emily K Sims, Heba M Ismail, Kevan C Herold, Jay S Skyler, Brandon M Nathan","doi":"10.1089/dia.2024.0422","DOIUrl":"https://doi.org/10.1089/dia.2024.0422","url":null,"abstract":"<p><p><b><i>Objective:</i></b> We assessed whether there is an impactful glucose fraction independent of insulin secretion in autoantibody-positive individuals. <b><i>Research Design and Methods:</i></b> Baseline 2-h oral glucose tolerance test data from the TrialNet Pathway to Prevention (TNPTP; <i>n</i> = 6190) and Diabetes Prevention Trial-Type 1 (DPT-1; <i>n</i> = 705) studies were used. Linear regression of area under the curve (AUC) glucose versus Index60 was performed to identify two fractions: dependent (dAUCGLU) or independent (iAUCGLU) of insulin secretion. <b><i>Results:</i></b> The lack of correlation (<i>r</i> = 0.06) of iAUCGLU and the inverse correlation of dAUCGLU (<i>r</i> = -0.59) with the first-phase insulin response from DPT-1 were consistent with the independent and dependent designations of the glucose fractions. Correlations of AUC C-peptide were inverse with dAUCGLU and positive with iAUCGLU (TNPTP: <i>r</i> = -0.72, <i>r</i> = 0.57; DPT-1: <i>r</i> = -0.56, <i>r</i> = 0.60). The explained variance of AUC C-peptide increased markedly after separating AUC glucose into its fractions (from 4% to 85% in TNPTP; from 1% to 67% in DPT-1). The independent fraction contributed more to the increased glycemia of impaired glucose tolerance (IGT) than did the dependent fraction. Both dAUCGLU and iAUCGLU predicted IGT and type 1 diabetes (T1D) (<i>P</i> < 0.0001 for all). However, whereas dAUCGLU was more predictive of T1D (chi-square: 849 vs. 249), iAUCGLU was more predictive of IGT (chi-square: 451 vs. 176). <b><i>Conclusions:</i></b> A glucose fraction independent of insulin secretion was identified that was appreciable in autoantibody-positive individuals. It provides insight into the relation between glucose and C-peptide, contributes substantially to the glycemia of IGT, and predicts both T1D and IGT, particularly the latter.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930813","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 Longer, the Better: Continuous Glucose Monitoring Use for ≥90% Is Superior to 70%-89% in Achieving Tighter Glycemic Outcomes in Children with Type 1 Diabetes.","authors":"Alzbeta Santova, Vit Neuman, Lukas Plachy, Shenali Anne Amaratunga, Marketa Pavlikova, Martina Romanova, Petra Konecna, David Neumann, Kamila Kocourkova, Jiri Strnadel, Renata Pomahacova, Petra Venhacova, Jaroslav Skvor, Barbora Obermannova, Stepanka Pruhova, Ondrej Cinek, Zdenek Sumnik","doi":"10.1089/dia.2024.0472","DOIUrl":"https://doi.org/10.1089/dia.2024.0472","url":null,"abstract":"<p><p><b><i>Objective:</i></b> The recommended threshold for the time spent on continuous glucose monitoring (CGM) is established at 70%. However, glucose outcomes in children with type 1 diabetes (CwD) using CGM for a different proportion of time within this threshold have not been evaluated yet. The study aims to compare glycemic parameters among CwD who spent 70%-89% and ≥90% on CGM using the population-wide data from the Czech national pediatric diabetes registry ČENDA. <b><i>Methods:</i></b> CwD aged <19 years who used real-time CGM >70% of the time and did not change the type of therapy throughout the year 2023 were included and divided into two groups based on the time they spent on CGM-70%-89% versus ≥90%. HbA1c, times in standard glycemic ranges, mean glucose, and coefficient of variability (CV) were compared between the groups and by treatment modalities. <b><i>Results:</i></b> Data from 1977 CwD (1035 males and 942 females) were evaluated. Among them, 404 participants (20.4%) used CGM 70%-89% of the time, and 1573 participants (79.6%) ≥90% of the time. Compared with the 70-89% group, the ≥90% CGM users achieved significantly lower HbA1c levels (51 mmol/mol, 6.8% vs. 58 mmol/mol, 7.4%, <i>P</i> < 0.001), higher time in range (72% vs. 60%, <i>P</i> < 0.001), and lower mean glucose and CV (8.1 mmol/L, 146 mg/dL vs. 9.1 mmol/L, 164 mg/dL and 37% vs. 40%, respectively, both <i>P</i> < 0.001). Analogous results were seen irrespective of the treatment modality. The differences persisted after propensity score adjustment. <b><i>Conclusion:</i></b> CGM use for ≥90% is associated with tighter glycemic control compared with 70%-89% use. Therefore, it is essential to motivate CwD to use CGM for the longest possible time and search for suitable options to overcome barriers in uninterrupted CGM monitoring.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930832","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":"Predicting and Ranking Diabetic Ketoacidosis Risk among Youth with Type 1 Diabetes with a Clinic-to-Clinic Transferrable Machine Learning Model.","authors":"Craig Vandervelden, Brent Lockee, Mitchell Barnes, Erin M Tallon, David D Williams, Anna Kahkoska, Angelica Cristello Sarteau, Susana R Patton, Rona Y Sonabend, Jacob D Kohlenberg, Mark A Clements","doi":"10.1089/dia.2024.0484","DOIUrl":"10.1089/dia.2024.0484","url":null,"abstract":"<p><p><b><i>Aim:</i></b> To use electronic health record (EHR) data to develop a scalable and transferrable model to predict 6-month risk for diabetic ketoacidosis (DKA)-related hospitalization or emergency care in youth with type 1 diabetes (T1D). <b><i>Method:</i></b> To achieve a sharable predictive model, we engineered features using EHR data mapped to the T1D Exchange Quality Improvement Collaborative's (T1DX-QI) data schema used by 60+ U.S. diabetes centers and chose a compact set of 15 features (e.g., demographics, factors related to diabetes management, etc.) to yield \"explainable AI\" predictions for DKA risk on a 6-month horizon. We used an ensemble of gradient-boosted, tree-based models trained on data collected from September 1, 2017 to November 1, 2022 (3097 unique patients; 24,638 clinical encounters) from a tertiary care pediatric diabetes clinic network in the Midwest USA. <b><i>Results:</i></b> We rank-ordered the top 10, 25, 50, and 100 highest-risk youth in an out-of-sample testing set, which yielded an average precision of 0.96, 0.81, 0.75, and 0.70, respectively. The lift of the model (relative to random selection) for the top 100 individuals is 19. The model identified average time between DKA episodes, time since the last DKA episode, and T1D duration as the top three features for predicting DKA risk. <b><i>Conclusions:</i></b> Our DKA risk model effectively predicts youths' relative risk of experiencing hospitalization for DKA and is readily deployable to other diabetes centers that map diabetes data to the T1DX-QI schema. This model may facilitate the development of targeted interventions for youths at the highest risk for DKA. Future work will add novel features such as device data, social determinants of health, and diabetes self-management behaviors.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930829","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":"Safety of Options to \"Boost\" (Enhancing Insulin Infusion Rates) and \"Ease-Off\" (Reducing Insulin Infusion Rates) in CamAPS FX Hybrid Closed-Loop System: A Real-World Analysis.","authors":"Chloë Royston, Simon Bergford, Peter Calhoun, Judy Sibayan, Yue Ruan, Charlotte Boughton, Malgorzata E Wilinska, Roman Hovorka","doi":"10.1089/dia.2024.0298","DOIUrl":"10.1089/dia.2024.0298","url":null,"abstract":"<p><p>The usage and safety of the Boost and Ease-off features in the CamAPS FX hybrid closed-loop system were analyzed in a retrospective analysis of real-world data from 7,464 users over a 12-month period. Boost was used more frequently than Ease-off, but for a shorter duration per use. Mean starting glucose was above range for Boost (229 ± 51 mg/dL), and within range for Ease-off (114 ± 29 mg/dL). Time spent below 70 mg/dL was low during Boost periods [median (interquartile range; IQR) 0.0% (0.0, 0.5%)], and lower than during no Boost periods [2.1% (1.2, 3.4%)], while time spent above 180 mg/dL was lower during Ease-off periods (15 ± 14%) compared with no Ease-off periods (25 ± 12%). There were no episodes of severe hypoglycemia or diabetic ketoacidosis attributed to Boost or Ease-off use. Boost and Ease-off allow users to engage safely with CamAPS FX to manage their glucose levels during periods of more-than-usual and less-than-usual insulin needs.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"60-65"},"PeriodicalIF":5.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141987648","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":"Transitioning from Self-Monitoring of Blood Glucose to Continuous Glucose Monitoring in Combination with a mHealth App Improves Glycemic Control in People with Type 1 and Type 2 Diabetes.","authors":"Josip Zivkovic, Michael Mitter, Delphine Theodorou, Johanna Kober, Wiebke Mueller-Hoffmann, Heather Mikulski","doi":"10.1089/dia.2024.0169","DOIUrl":"10.1089/dia.2024.0169","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> Integrating mobile health (mHealth) apps into daily diabetes management allows users to monitor and track their health data, creating a comprehensive system for managing daily diabetes activities and generating valuable real-world data. This analysis investigates the impact of transitioning from traditional self-monitoring of blood glucose (SMBG) to real-time continuous glucose monitoring (rtCGM), alongside the use of a mHealth app, on users' glycemic control. <b><i>Methods:</i></b> Data were collected from 1271 diabetes type 1 and type 2 users of the mySugr^® app who made a minimum of 50 SMBG logs 1 month before transitioning to rtCGM and then used rtCGM for at least 6 months. The mean and coefficient of variation of glucose, along with the proportions of glycemic measurements in and out of range, were compared between baseline and 1, 2, 3, and 6 months of rtCGM use. A mixed-effects linear regression model was built to quantify the specific effects of transitioning to a rtCGM sensor in different subsamples. A novel validation analysis ensured that the aggregated metrics from SMBG and rtCGM were comparable. <b><i>Results:</i></b> Transitioning to a rtCGM sensor significantly improved glycemic control in the entire cohort, particularly among new users of the mySugr app. Additionally, the sustainability of the change in glucose in the entire cohort was confirmed throughout the observation period. People with type 1 and type 2 diabetes exhibited distinct variations, with type 1 experiencing a greater reduction in glycemic variance, while type 2 displayed a relatively larger decrease in monthly averages.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"10-18"},"PeriodicalIF":5.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142281840","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":"Effectiveness of Semaglutide and Tirzepatide in Overweight and Obese Adults with Type 1 Diabetes.","authors":"Janet K Snell-Bergeon, Gurleen Kaur, Drew Renner, Halis K Akturk, Christie Beatson, Satish K Garg","doi":"10.1089/dia.2024.0328","DOIUrl":"10.1089/dia.2024.0328","url":null,"abstract":"<p><p><b><i>Objective:</i></b> Adults with type 1 diabetes (T1D) are increasingly overweight or obese, in part due to intensive insulin therapy. Newer non-insulin medications targeting both hyperglycemia and weight loss are approved for people with type 2 diabetes. These drugs also reduce cardiovascular disease, the major cause of mortality in people with diabetes. We assessed the real-world use of semaglutide and tirzepatide, in adults with T1D followed in a specialty diabetes clinic. <b><i>Materials and Methods:</i></b> This retrospective chart review included 100 adults who were prescribed semaglutide or tirzepatide (50 each) and 50 controls frequency matched for age, sex, diabetes duration, body mass index, and glycosylated hemoglobin (HbA1c) and who did not receive any weight loss medications during the study period. Data were collected prior to initiation of weight loss medications (baseline) and then for up to 1 year for each patient. <b><i>Results:</i></b> Matching characteristics did not differ between cases and controls. There were declines in weight in both semaglutide (-19.2 ± standard error (SE) 2.9 lbs. [9.1% body weight lost]) and tirzepatide (-49.4 ± SE 3.0 lbs. [21.4% body weight lost]) groups, and HbA1c decreased in both semaglutide (-0.54 ± SE 0.14%, <i>P</i> = 0.0001) and tirzepatide users (-0.68 ± SE 0.16%, <i>P</i> < 0.0001) over 12 months. Weight and HbA1c didn't change in controls. <b><i>Conclusions:</i></b> We observed weight loss of 9.1% and 21.4% and improved glucose control in semaglutide and tirzepatide users, respectively, after 1 year of off-label use. As off-label use of these drugs is increasing in patients with T1D, larger, prospective safety and efficacy trials are needed.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"1-9"},"PeriodicalIF":5.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142914004","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}