Diabetes technology & therapeutics最新文献

{"title":"Glycemic Risk Index in a Cohort of Patients with Type 1 Diabetes Mellitus Stratified by the Coefficient of Variation: A Real-Life Study.","authors":"Sandra Herranz-Antolín, Clara Coton-Batres, María Covadonga López-Virgos, Verónica Esteban-Monge, Visitación Álvarez-de Frutos, Leonel Pekarek, Miguel Torralba","doi":"10.1089/dia.2024.0181","DOIUrl":"10.1089/dia.2024.0181","url":null,"abstract":"<p><p><b><i>Objective</i></b>: To analyze the Glycemic Risk Index (GRI) and assess their possible differences according to coefficient of variation (CV) in a cohort of real-life type 1 diabetes mellitus (DM) patient users of intermittently scanned continuous glucose monitoring (isCGM). <b><i>Patients and Methods</i></b>: In total, 447 adult users of isCGM with an adherence ≥70% were included in a cross-sectional study. GRI was calculated with its hypoglycemia (CHypo) and hyperglycemia (CHyper) components. Multivariate linear regression analysis was performed to evaluate the factors associated with GRI. <b><i>Results:</i></b> Mean age was 44.6 years (standard deviation [SD] 13.7), 57.7% being male; age of DM onset was 24.5 years (SD 14.3) and time of evolution was 20.6 years (SD 12.3). In patients with CV >36% (52.8%) versus CV ≤36% (47.2%), differences were observed in relation to GRI (18.8% [SD 1.9]; <i>P</i> < 0.001), CHypo (2.9% [SD 0.3]; <i>P</i> < 0.001), CHyper (6.3% [SD 1.4]; <i>P</i> < 0.001), and all classical glucometric parameters except time above range level 1. The variables that were independently associated with GRI in patient with CV >36% were time in range (TIR) (β = -1.49; confidence interval [CI:] 95% -1.63 to -1.37; <i>P</i> < 0.001), glucose management indicator (GMI) (β = -7.22; CI: 95% -9.53 to -4.91; <i>P</i> < 0.001), and CV (β = 0.85; CI: 95% 0.69 to 1.02; <i>P</i> < 0.001). However, in patients with CV ≤36%, the variables were age (β = 0.15; CI: 95% 0.03 to 0.28; <i>P</i> = 0.019), age of onset (β = -0.15; CI: 95% -0.28 to -0.02; <i>P</i> = 0.023), TIR (β = -1.35; CI: 95% -1.46 to -1.23; <i>P</i> < 0.001), GMI (β = -6.67; CI: 95% -9.18 to -4.15; <i>P</i> < 0.001), and CV (β = 0.33; CI: 95% 0.11 to 0.56; <i>P</i> = 0.004). <b><i>Conclusions:</i></b> In this study, the factors independently associated with metabolic control according to GRI are modified by glycemic variability.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"960-967"},"PeriodicalIF":5.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141476164","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":"An Update on the iglu Software Package for Interpreting Continuous Glucose Monitoring Data.","authors":"Elizabeth Chun, Nathaniel J Fernandes, Irina Gaynanova","doi":"10.1089/dia.2024.0154","DOIUrl":"10.1089/dia.2024.0154","url":null,"abstract":"<p><p><b><i>Background:</i></b> Continuous glucose monitors (CGMs) are increasingly used to provide detailed quantification of glycemic control and glucose variability. An open-source R package iglu has been developed to assist with automatic CGM metrics computation and data visualization, providing a comprehensive list of implemented CGM metrics. Motivated by the recent international consensus statement on CGM metrics and recommendations from recent reviews of available CGM software, we present an updated version of iglu with improved accessibility and expanded functionality. <b><i>Methods:</i></b> The functionality was expanded to include automated computation of hypo- and hyperglycemia episodes with corresponding visualizations, composite metrics of glycemic control (glycemia risk index and personal glycemic state), and glycemic metrics associated with postprandial excursions. The algorithm for mean amplitude of glycemic excursions has been updated for improved accuracy, and the corresponding visualization has been added. Automated hierarchical clustering capabilities have been added to facilitate statistical analysis. Accessibility was improved by providing support for the automatic processing of common data formats, expanding the graphical user interface, and providing mirrored functionality in Python. <b><i>Results:</i></b> The updated version of iglu has been released to the Comprehensive R Archive Network (CRAN) as version 4. The corresponding Python wrapper has been released to the Python Package Index (PyPI) as version 1. The new functionality has been demonstrated using CGM data from 19 subjects with prediabetes and type 2 diabetes. <b><i>Conclusions:</i></b> An updated version of iglu provides comprehensive and accessible software for analyses of CGM data that meets the needs of researchers with varying levels of programming experience. It is freely available on CRAN and on GitHub at https://github.com/irinagain/iglu.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"939-950"},"PeriodicalIF":5.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141418272","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 Glycemic Status Classification Model Using a Radiofrequency Noninvasive Blood Glucose Monitor.","authors":"Fazle Karim, James H Anderson, Kaptain Currie, Connor Bui, Dominic Klyve, Virend K Somers","doi":"10.1089/dia.2024.0170","DOIUrl":"10.1089/dia.2024.0170","url":null,"abstract":"<p><p>Despite significant efforts in the development of noninvasive blood glucose (BG) monitoring solutions, delivering an accurate, real-time BG measurement remains challenging. We sought to address this by using a novel radiofrequency (RF) glucose sensor to noninvasively classify glycemic status. The study included 31 participants aged 18-65 with prediabetes or type 2 diabetes and no other significant medical history. During control sessions and oral glucose tolerance test sessions, data were collected from both a RF sensor that rapidly scans thousands of frequencies and concurrently from a venous blood draw measured with an US Food and Drug Administration (FDA)-cleared glucose hospital meter system to create paired observations. We trained a time series forest machine learning model on 80% of the paired observations and reported results from applying the model to the remaining 20%. Our findings show that the model correctly classified glycemic status 93.37% of the time as high, normal, or low.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"979-983"},"PeriodicalIF":5.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141476163","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":"Starting Insulin Algorithms for Noncritical Illness: A Survey of 32 Academic Hospitals in the United States.","authors":"Hou-Hsien Chiang, Steven E Kahn, Irl B Hirsch","doi":"10.1089/dia.2024.0120","DOIUrl":"10.1089/dia.2024.0120","url":null,"abstract":"<p><p>Glycemic control immediately upon hospitalization is difficult. Endocrine Society guidelines suggest starting scheduled insulin therapy at 0.2-0.5 units/kg/day, but there has been no rigorous study to support this recommendation. To understand the variability of current practice, we surveyed starting insulin algorithms for noncritically ill patients among the top-ranking academic hospitals in the United States. Among the 20 hospitals with reported algorithms, 12 specified which patients should start with basal/nutritional insulin, whereas 5 specified who should start with only correction insulin. Weight-based and/or home-dose-based calculations were used to estimate the initial insulin requirements with various modifiers. In addition, various factors were considered when choosing among the correction dose algorithms. In summary, among the U.S. academic hospitals, there is variability in methods for determining insulin dosing on admission for noncritically ill patients. This inconsistency suggests that future studies to estimate initial insulin requirements are required.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"968-978"},"PeriodicalIF":5.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141476165","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":"Association of Race and Ethnicity with Prescriptions for Continuous Glucose Monitoring Systems Among a National Sample of Veterans with Diabetes on Insulin Therapy.","authors":"Kasia J Lipska, Carol Oladele, Kelson Zawack, Barbara Gulanski, Pradeep Mutalik, Peter Reaven, Julie A Lynch, Kyung Min Lee, Mei-Chiung Shih, Jennifer S Lee, Mihaela Aslan","doi":"10.1089/dia.2024.0152","DOIUrl":"10.1089/dia.2024.0152","url":null,"abstract":"<p><p><b><i>Introduction and Objective:</i></b> Continuous glucose monitoring (CGM) can improve glycemic control in people with diabetes on insulin therapy. We assessed rates of prescriptions for CGM in a national sample of Veterans across subgroups defined by race and ethnicity. <b><i>Methods:</i></b> This cross-sectional analysis of data from the U.S. Veterans Health Administration included adults with type 1 or type 2 diabetes on insulin therapy. Main exposures included self-reported race and ethnicity, and primary outcome was the percentage of patients with at least one CGM prescription between January 1, 2020, and December 31, 2021. Association of race and ethnicity categories with CGM prescription was examined using multilevel, multivariable mixed-effects models. <b><i>Results:</i></b> Among 368,794 patients on insulin (mean age, 68.5 years; 96% male; 96.8% type 2 diabetes; 0.8% American Indian or Alaska Native, 0.7% Asian, 18.9% Black or African American, 0.9% Native Hawaiian or other Pacific Islander, 70.2% White, 2.8% multiracial, 5.7% with unknown race, and 7.0% Hispanic or Latino ethnicity), 11.2% were prescribed CGM. CGM was prescribed for 10.4% American Indian or Alaska Native, 9.7% Asian, 9.2% Black or African American, 9.3% Native Hawaiian or other Pacific Islander, 11.8% White, 11.8% multiracial, and 10.1% patients with unknown race. CGM was prescribed for 8.3% Hispanic or Latino, 11.4% non-Hispanic, and 11.5% of patients with unknown ethnicity. After accounting for patient-, clinical-, and system-level factors, Black or African American patients had significantly lower odds of CGM prescription compared with White patients (adjusted odds ratio [aOR] 0.62, 95% confidence interval [CI] 0.59-0.64), whereas Hispanic or Latino patients had significantly lower odds compared with non-Hispanic patients (aOR 0.79, 95% CI 0.74-0.84). Findings were consistent across subgroups with clinical indications for CGM use. <b><i>Conclusions:</i></b> Among Veterans with diabetes on insulin therapy, there were significant disparities in prescribing of CGM technology by race and ethnicity, which require further study and intervention.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"908-917"},"PeriodicalIF":5.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142035520","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":"Impact of Missed and Late Meal Boluses on Glycemic Outcomes in Automated Insulin Delivery-Treated Children and Adolescents with Type 1 Diabetes: A Two-Center, Population-Based Cohort Study.","authors":"Christian Laugesen, Tobias Ritschel, Ajenthen G Ranjan, Liana Hsu, John Bagterp Jørgensen, Jannet Svensson, Laya Ekhlaspour, Bruce Buckingham, Kirsten Nørgaard","doi":"10.1089/dia.2024.0022","DOIUrl":"10.1089/dia.2024.0022","url":null,"abstract":"<p><p><b><i>Objective:</i></b> To evaluate the impact of missed or late meal boluses (MLBs) on glycemic outcomes in children and adolescents with type 1 diabetes using automated insulin delivery (AID) systems. <b><i>Research Design and Methods:</i></b> AID-treated (Tandem Control-IQ or Medtronic MiniMed 780G) children and adolescents (aged 6-21 years) from Stanford Medical Center and Steno Diabetes Center Copenhagen with ≥10 days of data were included in this two-center, binational, population-based, retrospective, 1-month cohort study. The primary outcome was the association between the number of algorithm-detected MLBs and time in target glucose range (TIR; 70-180 mg/dL). <b><i>Results:</i></b> The study included 189 children and adolescents (48% females with a mean ± standard deviation age of 13 ± 4 years). Overall, the mean number of MLBs per day in the cohort was 2.2 ± 0.9. For each additional MLB per day, TIR decreased by 9.7% points (95% confidence interval [CI] 11.3; 8.1), and compared with the quartile with fewest MLBs (Q₁), the quartile with most (Q₄) had 22.9% less TIR (95% CI: 27.2; 18.6). The age-, sex-, and treatment modality-adjusted probability of achieving a TIR of >70% in Q₄ was 1.4% compared with 74.8% in Q₁ (<i>P</i> < 0.001). <b><i>Conclusions:</i></b> MLBs significantly impacted glycemic outcomes in AID-treated children and adolescents. The results emphasize the importance of maintaining a focus on bolus behavior to achieve a higher TIR and support the need for further research in technological or behavioral support tools to handle MLBs.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"897-907"},"PeriodicalIF":5.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141161184","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 Glycemic Control with Dexcom CGM Use in Adults with Noninsulin-Treated Type 2 Diabetes.","authors":"Jennifer E Layne, Lauren H Jepson, Alexander M Carite, Christopher G Parkin, Richard M Bergenstal","doi":"10.1089/dia.2024.0197","DOIUrl":"10.1089/dia.2024.0197","url":null,"abstract":"<p><p><b><i>Aims:</i></b> The objective of this real-world, observational study was to evaluate change in continuing glucose monitoring (CGM) metrics for 1 year after CGM initiation in adults with noninsulin-treated type 2 diabetes (T2D). <b><i>Methods:</i></b> Data were analyzed from Dexcom G6 and G7 users who self-reported: T2D, ≥18 years, gender, no insulin use, and had a baseline percent time in range (TIR) 70-180 mg/dL of ≤70%. Outcomes were change in CGM metrics from baseline to 6 and 12 months overall and for younger (<65 years) and older (≥65 years) cohorts. Additional analyses explored the relationship between use of the high alert feature and change in TIR and time in tight range (TITR) 70-140 mg/dL. <b><i>Results:</i></b> CGM users (<i>n</i> = 3,840) were mean (SD) 52.5 (11.2) years, 47.9% female, mean TIR was 41.7% (21.4%), and 12.4% of participants were ≥65 years. Significant improvement in all CGM metrics not meeting target values at baseline was observed at 6 months, with continued improvement at 12 months. Mean baseline TIR increased by 17.3% (32.1%) from 41.7% (21.4%) to 59.0% (28.9%), and mean glucose management indicator decreased by 0.5% (1.2%) from 8.1% (0.9%) to 7.6% (1.1%) (both <i>P</i> < 0.001). Participants who maintained or customized the high alert default setting of 250 mg/dL had a greater increase in TIR and TITR compared with participants who disabled the alert. Days of CGM use over 12 months were high in 84.7% (15.9%). <b><i>Conclusion:</i></b> In this large, real-world study of adults with suboptimally controlled T2D not using insulin, Dexcom CGM use was associated with meaningful improvements in glycemic control over 12 months. Use of the high alert system feature was positively associated with glycemic outcomes. High use of CGM over 12 months suggests benefits related to consistent CGM use in this population.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"925-931"},"PeriodicalIF":5.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141431655","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 Telehealth Program Using Continuous Glucose Monitoring and a Connected Insulin Pen Cap in Nursing Homes for Older Adults with Insulin-Treated Diabetes: The Trescasas Study.","authors":"Fernando Gomez-Peralta, Cristina Abreu, Estefanía Santos, Alvaro Da Silva, Ana San Frutos, Luisa Vega-Valderrama, Marta García-Galindo, Ana Franco-López, Cristina López Mardomingo, Benito Cañuelo, Guillermo Blazquez, Marcos Matabuena","doi":"10.1089/dia.2024.0356","DOIUrl":"https://doi.org/10.1089/dia.2024.0356","url":null,"abstract":"<p><p><b><i>Objective:</i></b> To assess the impact and feasibility of a telehealth program using continuous glucose monitoring (CGM) and a connected insulin pen cap (CIPC) in nursing homes for older adults with insulin-treated diabetes. <b><i>Research Methods:</i></b> This multicenter, prospective, sequential, single-arm study consisted of three phases: (1) baseline, blind CGM (<i>Freestyle Libre Pro®</i>); (2) intervention 1, CGM (<i>Freestyle Libre2®</i>) without alarms; and (3) intervention 2, CGM with alarms for hypo and hyperglycemia. Two telehealth visits from reference diabetes units were conducted to adjust antidiabetic treatments. Insulin treatment was tracked using the <i>Insulclock®</i> CIPC. The study's primary objective was to evaluate the reduction of hypoglycemia rate. <b><i>Results:</i></b> Of 82 eligible patients at seven nursing homes, 54 completed the study (age: 87.7 ± 7.1, 68-102 years, 56% women, duration of diabetes: 18.7 years, baseline glycated hemoglobin: 6.9% [52 mmol/mol]). The mean number of hypoglycemic events was significantly reduced from baseline (4.4) to intervention 1 (2.8; <i>P</i> = 0.060) and intervention 2 (2.1; <i>P</i> = 0.023). The time below range 70 mg/dL (3.9 mmol/L) significantly decreased from 3.7% at baseline to 1.4% at intervention 2 (<i>P</i> = 0.036). The number of insulin injections significantly decreased from baseline to intervention 1 (1.2 to 0.99; <i>P</i> = 0.027). Nursing home staff expressed a positive view of the program, greater convenience, and potential to reduce hypoglycemia with the <i>Freestyle Libre2®</i> CGM versus the glucometer. <b><i>Conclusions:</i></b> A telehealth program using CGM and a CIPC was associated with improved glycemic profiles among institutionalized older individuals with diabetes receiving insulin and was well perceived by professionals.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142715654","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":"Comment on Preechasuk et al: Switching from Intermittently-Scanned Continuous Glucose Monitoring to Real-Time Continuous Glucose Monitoring with a Predictive Urgent Low Soon Alert Reduces Exposure to Hypoglycemia.","authors":"Alexander Seibold","doi":"10.1089/dia.2024.0345","DOIUrl":"https://doi.org/10.1089/dia.2024.0345","url":null,"abstract":"","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142715655","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":"Randomized, Crossover Trial of Control-IQ Technology with a Lower Treatment Range and a Modified Meal Bolus Module in Adults, Adolescents, Children, and Preschoolers with Varying Levels of Baseline Glycemic Control.","authors":"Sue A Brown, Lori M Laffel, Halis K Akturk, Gregory P Forlenza, Viral N Shah, R Paul Wadwa, Erin C Cobry, Elvira Isganaitis, Melissa Schoelwer, Virginia S Lu, Ricardo Rueda, Nicholas Sherer, John P Corbett, Ravid Sasson-Katchalski, Jordan E Pinsker","doi":"10.1089/dia.2024.0501","DOIUrl":"https://doi.org/10.1089/dia.2024.0501","url":null,"abstract":"<p><p><b><i>Objective:</i></b> We evaluated a modified version of Control-IQ technology with a lower treatment range and a modified meal bolus module in adults, adolescents, children, and preschoolers with type 1 diabetes in a multicenter, randomized, and crossover trial. <b><i>Research Design and Methods:</i></b> After a 2-week run-in with Control-IQ technology v1.5, the modified system was evaluated for 2 weeks using treatment range of 112.5-160 mg/dL (standard range [SR]), and for 2 weeks using lower treatment range of 90-130 mg/dL (lower range, LR), at home in random order. Two late bolus meal challenges were performed in each 2-week period, bolusing 45 min after meals with and without a new late bolus feature. <b><i>Results:</i></b> Overall, 72 participants aged 3-57 years completed the study. There were no diabetic ketoacidosis or severe hypoglycemia events. All meal challenges were completed safely. Time in range (TIR) 70-180 mg/dL improved the most with LR to 68.0% (+3.1%, <i>P</i> < 0.001, for LR vs. run-in and +2.1%, <i>P</i> < 0.001, for LR vs. SR). Similar improvements were observed for time in tight range (TITR) 70-140 mg/dL (+3.3%, <i>P</i> < 0.001, for LR vs. run-in and +4.0%, <i>P</i> < 0.001, for LR vs. SR), time >180 mg/dL, and mean glucose. Participants with lower baseline hemoglobin A1c (HbA1c) achieved the highest TIR and TITR with LR use, while the greatest improvements in TIR and TITR were evident in those with higher baseline HbA1c. <b><i>Conclusions:</i></b> The lower treatment range and late bolus feature of the modified Control-IQ system were safe for use in all age-groups. TIR and TITR improved with LR regardless of baseline HbA1c.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142726882","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}