Diabetes technology & therapeutics最新文献

{"title":"Addressing Factors to Reduce Interruption of Continuous Glucose Monitor Use During Radiologic Procedures.","authors":"Nicholas Saad, Druti Shukla, Michael Rickson, Megan O'Neill","doi":"10.1089/dia.2024.0550","DOIUrl":"10.1089/dia.2024.0550","url":null,"abstract":"","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"334-335"},"PeriodicalIF":5.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142812675","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":"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":"261-270"},"PeriodicalIF":5.7,"publicationDate":"2025-04-01","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":"The Cost of Continuous Glucose Monitoring Versus Self-Monitoring of Blood Glucose in Type 1 Diabetes Pregnancies in the United States: A Cost-Consequences Analysis Using Real-World Evidence.","authors":"Michael J DiStefano, R Brett McQueen, Valerie Gao, Matthew P Klein, Janet K Snell-Bergeon, Sarit Polsky","doi":"10.1089/dia.2024.0478","DOIUrl":"10.1089/dia.2024.0478","url":null,"abstract":"<p><p>Data on the cost implications of continuous glucose monitoring (CGM) use in type 1 diabetes (T1D) pregnancies in the United States are sparse. Drawing on associations identified in real-world evidence from a retrospective chart review at the Barbara Davis Center for Diabetes, we conducted a cost-consequences analysis of CGM use versus self-monitoring of blood glucose (SMBG), inclusive of neonatal intensive care unit (NICU) spending. In the base-case analysis assuming per-label CGM use and per-guideline finger-stick frequency, the per-person cost was $16,254 for CGM versus $15,182 for SMBG. In a real-world scenario analysis, the per-person cost was $13,708 for CGM versus $14,524 for SMBG. In both analyses, there was a cost savings for NICU admissions of $2,903 with CGM use. In the probabilistic sensitivity analysis, CGM was cost saving in 25% of base-case model iterations and 46% of real-world model iterations. This study adds to the growing evidence base that the increased costs of CGM use versus SMBG to manage T1D pregnancies are likely offset by better neonatal health outcomes.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"329-333"},"PeriodicalIF":5.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853392","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, Zdeněk Šumník","doi":"10.1089/dia.2024.0472","DOIUrl":"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":"301-307"},"PeriodicalIF":5.7,"publicationDate":"2025-04-01","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":"Long-Term Improvements in Glycemia and User-Reported Outcomes Associated with Open-Source Automated Insulin Delivery Systems in Adults with Type 1 Diabetes in the United Kingdom: A Real-World Observational Study.","authors":"Alexandros L Liarakos, Thomas S J Crabtree, Sufyan Hussain, Rachel Patel, Anastasios Gazis, Buddhike Mendis, Roselle Herring, Adele Kennedy, Neil Black, Robert E J Ryder, Emma G Wilmot","doi":"10.1089/dia.2024.0556","DOIUrl":"10.1089/dia.2024.0556","url":null,"abstract":"<p><p><b><i>Objective:</i></b> To evaluate real-world outcomes in adults with type 1 diabetes initiating open-source automated insulin delivery systems (OS-AID). <b><i>Methods:</i></b> Adults with type 1 diabetes who commenced OS-AID, between May 2016 and April 2021, across 12 centers in the United Kingdom were included. Anonymized clinical data, collected during routine clinical care between December 2019 and November 2023, were submitted to a secure web-based tool within the National Health Service network. Outcomes included change in hemoglobin A1c (HbA1c), sensor glucometrics, diabetes distress score, Gold score (hypoglycemia awareness), user opinion of OS-AID, and event rates (hospital admissions, paramedic callouts, severe hypoglycemia, and adverse events) between baseline and follow-up. <b><i>Results:</i></b> In total, 81 OS-AID users were included (51.9% male; 90.1% White British; mean age 41.4 years; median diabetes duration 25 years [IQR 17-32]). Over a mean follow-up of 1.7 years, HbA1c reduced by 0.8% (9 mmol/mol) (7.3 ± 1.1% vs. 6.5 ± 0.7%; <i>P</i> < 0.001), and the percentage of individuals achieving HbA1c ≤ 7.0% (53 mmol/mol) increased from 48.6% to 75.7% (<i>P</i> < 0.001). Diabetes-related distress score reduced by 0.9 (95% confidence interval [CI] -0.3, -1.5; <i>P</i> = 0.006), and Gold score reduced by 0.7 (95% CI -0.1, -1.3; <i>P</i> = 0.022). The percentage of individuals with impaired hypoglycemia awareness (Gold score ≥4) reduced (27.8% at baseline vs. 8.3% at follow-up; <i>P</i> = 0.039). Of those asked, all participants stated that OS-AID had a positive impact on quality of life. The number of hospital admissions was low. <b><i>Conclusions:</i></b> The use of OS-AID is associated with long-term improvements in HbA1c, hypoglycemia awareness, and diabetes-related distress in type 1 diabetes. These benefits were achieved without increased rates of hospital admissions, diabetic ketoacidosis, or severe hypoglycemia.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"283-291"},"PeriodicalIF":5.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045352","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":"271-282"},"PeriodicalIF":5.7,"publicationDate":"2025-04-01","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":"Algorithm-Driven Initiation and Adaptation of Hybrid Closed-Loop in Young Children with Type 1 Diabetes: A Pilot Study.","authors":"Jacopo Pavan, Erin Cobry, Zachariah W Reed, María F Villa-Tamayo, Jenny L Diaz C, Mark D DeBoer, Melissa Schoelwer, Emily Jost, Ryan Kingman, Viola Holmes, John W Lum, Chaitanya L K Koravi, Bruce Buckingham, Roy Beck, R Paul Wadwa, Marc D Breton","doi":"10.1089/dia.2024.0650","DOIUrl":"https://doi.org/10.1089/dia.2024.0650","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> Glucose regulation in young children is complicated by higher glycemic variability, unpredictable behaviors, and low insulin needs. While the benefits of automated insulin delivery (AID) for this population are established, how to initiate and adjust pump settings still represents a challenging task for health care providers. In this study, we investigate the safety and efficacy of using algorithm-driven initiation and adjustments of AID parameters in children aged 2-6 years. <b><i>Methods:</i></b> Participants used AID at home for 8 weeks. Initial settings and periodic adjustments of therapy profiles (basal rates, insulin-to-carbohydrate ratios, insulin-correction factors, and sleep schedules) were provided through a cloud-based investigational software. Investigators reviewed therapy recommendations and could adjust if necessary. Primary safety endpoints included the percentage of time <54 mg/dL and >250 mg/dL, tested for noninferiority with respect to baseline. Primary efficacy endpoints (tested in a hierarchical manner) were the percentage of time in 70-180 mg/dL, mean glucose, the percentage of time >250 mg/dL, <70 mg/dL, and <54 mg/dL. <b><i>Results:</i></b> Thirty-two participants (age range: 2.0-5.9 years) were recruited for the study; 29 had sufficient data for the analysis. Investigators overrode 15% of software recommendations. The percentage of time <54 mg/dL and >250 mg/dL was noninferior in the 8-week follow-up with respect to baseline (<i>P</i> < 0.001). Statistically significant improvements were observed in the percentage of time in 70-180 mg/dL (<i>P</i> = 0.005), >250 mg/dL (<i>P</i> = 0.003), and mean glucose (<i>P</i> = 0.02). No difference was observed in the percentage of time <70 mg/dL (<i>P</i> = 0.34). Furthermore, no difference was observed with respect to a similar study cohort (same age range, <i>n</i> = 86) with expert pediatric endocrinologists modifying pump settings. <b><i>Conclusions:</i></b> Findings from this pilot study suggest that the use of AID with algorithm-driven initiation and adjustment of pump parameters is safe and effective in young children with type 1 diabetes. Further study of the algorithm in a larger cohort is indicated. Clinical Trials Registration number: NCT06017089.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143729078","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":"Glucose Control in Type 1 Diabetes after Pancreas Transplantation: Does Automated Delivery Offer Comparable Results?","authors":"Martina Zahradnická, Lenka Nemétová, Michal Kahle, David Vávra, Robert Bém, Peter Girman, Martin Haluzík, František Saudek","doi":"10.1089/dia.2024.0606","DOIUrl":"https://doi.org/10.1089/dia.2024.0606","url":null,"abstract":"<p><p><b><i>Objectives:</i></b> Pancreas transplantation provides long-term near-normal glycemic control for recipients with type 1 diabetes, but it is unknown how this control compares with an automated insulin delivery (AID) system. <b><i>Methods:</i></b> In this prospective study, we compared parameters from 31 consecutive pancreas-kidney transplantation recipients versus from 377 people using an AID-either MiniMed^™ 780G (<i>n</i> = 200) or Tandem t:slim X2^™ Control-IQ^™ (<i>n</i> = 177). <b><i>Results:</i></b> Compared with the MiniMed and Tandem AID groups, transplant recipients at 1 month (mean ± standard deviation [SD]: 36 ± 12 days) after pancreas transplantation exhibited significantly lower glycated hemoglobin (38 mmol/mol [36, 40] vs. 55 [53, 56.5] and 56 [54.7, 57.2], respectively), lower mean glycemia (6.4 mmol/L [6, 6.8] vs. 8.5 [8.3, 8.7] and 8.2 [8.0, 8.4], respectively), and spent more time in range (90% [86, 93] vs. 72% [70, 74] and 75% [73, 77], respectively). Time in hypoglycemia did not differ significantly between the groups. <b><i>Conclusions:</i></b> Overall, compared with AID treatment, pancreas transplantation led to significantly better diabetes control parameters, with the exception of time below range. Clinical trials registration number is Eudra CT No. 2019-002240-24.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699972","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":"Personalized Hemoglobin A1c Shows Better Correlation with Mean Glucose than Laboratory Hemoglobin A1c in Ugandan Youth with Type 1 Diabetes, but Mean Glucose Is Not Clinically Useful in This Population Due to Extreme Glucose Variability.","authors":"Thereza Piloya-Were, Catherine Nyangabayaki, Timothy C Dunn, Daniel Malinga, Jemima Nambooze, Elizabeth Pappenfus, Lin Zhang, Anila Bindal, Shannon Beasley, Muna Sunni, Brandon M Nathan, Sandy Liu, Antoinette Moran","doi":"10.1089/dia.2024.0537","DOIUrl":"https://doi.org/10.1089/dia.2024.0537","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> Continuous glucose monitoring (CGM) is unaffordable in sub-Saharan Africa, and providers rely heavily on hemoglobin A1c (A1c) to guide insulin adjustment. The relationship between A1c and mean glucose (MG) varies between individuals and populations. We assessed this relationship in Ugandan youth of age 4-26 years with type 1 diabetes, and evaluated whether calculation of the personalized A1c (pA1c), which only requires a brief initial sensor wear, is clinically useful. <b><i>Materials and Methods:</i></b> CGM data were averaged across three blinded sensor wears (31-41 days). We calculated individual apparent glycation ratios using A1c after the second sensor, and applied these to A1cs collected after the third sensor to determine pA1c. Participants were evaluated for clinical factors that influence red blood cell (RBC) lifespan (malaria, G6PD deficiency, sickle-cell trait, hemolysis, iron deficiency). <b><i>Results:</i></b> Patients across the A1c spectrum experienced substantial time in both hyper- and hypoglycemia; average coefficient of variation was 44%. MG was >250 mg/dL (13.9 mmol/L) in 50% of participants, and 55% of participants spent ≥4% time with glucose <70 mg/dL (3.9 mmol/L). There was considerable variability in the A1c-MG relationship. The pA1c more accurately represented MG by significantly reducing variation in this relationship (<i>R</i>² = 0.84 vs. 0.40; <i>r</i> = 0.92 vs. 0.63), but MG is not useful in individuals with the wide glucose fluctuations seen in this population. Clinical factors did not impact the A1c-MG relationship. <b><i>Conclusions:</i></b> Neither the measured A1c nor the calculated pA1c provided reliable guidance for insulin adjustment in this population. No matter how accurately MG is measured or estimated, it is just an average, with limited clinical application in individuals with wide glycemic variation. These measures cannot replace the information available from CGM about glycemic excursion, daily glucose patterns, or percent time in various glucose ranges. Our data suggest that it is essential to find a way to make CGM at least periodically affordable in low-resource settings.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669248","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 Importance of Instigating Automated Insulin Delivery Systems at Onset of Type 1 Diabetes: 1-Year Follow-Up of Children and Adolescents from Two Tertiary Pediatric Diabetes Centers.","authors":"Ivana Rabbone, Silvia Savastio, Valeria Castorani, Eleonora Chiarle, Alessandra Ferrari, Erica Pozzi, Claudio Cavalli, Andrea Scaramuzza","doi":"10.1089/dia.2025.0057","DOIUrl":"https://doi.org/10.1089/dia.2025.0057","url":null,"abstract":"<p><p>To evaluate differences in glucometrics in children and adolescents assigned to automated insulin delivery (AID), predictive low-glucose suspend (PLGS), or multiple daily injections (MDI) in the first month of diabetes management. In this real-world prospective cohort study, all subjects aged 0-18 years with diabetes onset between January 1, 2020, and June 30, 2023, were assigned to MDI (<i>n</i> = 24), PLGS (<i>n</i> = 28), or AID (<i>n</i> = 32) but were allowed to switch after the first 3 months. The primary outcome was HbA1c after 12 months. The mean age (<i>n</i> = 84) was 7.9 ± 3.9 years (range 1-18 years), and 58 were male. After 12 months, HbA1c was significantly lower in the AID group than in the PLGS or MDI groups (AID 6.6% ± 0.6% vs. PLGS 7.4% ± 1.1% vs. MDI 7.6% ± 1.5%, <i>P</i> = 0.001), with better time in range (<i>P</i> = 0.001), time below range (<i>P</i> = 0.01), time above range (<i>P</i> = 0.001), coefficient of variation (<i>P</i> = 0.01), and glucose management indicator (<i>P</i> = 0.001). AID is best started at diabetes onset to optimize glucose control outcomes.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669249","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}