Diabetes technology & therapeutics最新文献

{"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}

{"title":"The Impact of Public Policy on Equitable Access to Technology for Children and Youth Living with Type 1 Diabetes in British Columbia, Canada.","authors":"Jeffrey Bone, Courtney Leach, Ananta Addala, Shazhan Amed","doi":"10.1089/dia.2024.0366","DOIUrl":"https://doi.org/10.1089/dia.2024.0366","url":null,"abstract":"<p><p><b><i>Objective:</i></b> Structural inequities impede technology uptake in marginalized populations living with type 1 diabetes (T1D). Our objective was to describe hemoglobin A1c (HbA_1c), time in range (TIR), and pump use to evaluate the impact of a universal funding policy for continuous glucose monitoring (CGM) across levels of deprivation in children with T1D in the Canadian province of British Columbia (BC). <b><i>Methods:</i></b> Patients with T1D and at least one outpatient visit after June 10, 2020 (1-year before universal CGM funding) who were enrolled in the BC Pediatric Diabetes Registry were included (<i>n</i> = 477). The Canadian Index of Multiple Deprivation (quintile 1 = least deprived; quintile 5 = most deprived) was determined using postal code. Mixed effects models were used to describe HbA_1c, TIR, and pump use, and an interrupted time series generalized additive model estimated the change in CGM use pre- and postintroduction of universal coverage. <b><i>Results:</i></b> No differences were observed among the five levels of deprivation for HbA_1c and TIR; however, for residential instability, those with the highest level of deprivation had a lower probability of pump use (-18.9%, 95% confidence interval [CI] = -26.1% to -11.7% for quintile 5 vs. 1). There was an increase in CGM uptake across all levels of deprivation 1-year after introduction of universal CGM funding. For example, the difference in sensor use from the most to least deprived situational group was -21.0% (-35.4%, -6.6%) at the time of universal coverage and shrank to -4.6% (-21.6%, 12.4%) after 12 months of coverage. However, an equity gap in CGM use persisted between the least and most deprived groups (-21.9, 95% CI = -34.5 to -9.4 for quintile 5 vs. 1 in economic dependency). <b><i>Conclusions:</i></b> Universal coverage of CGM improved uptake; however, equity gaps persisted. More research is needed to explore nonfinancial barriers to diabetes technology use in marginalized populations.</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":"142715656","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 Continuous Glucose Monitoring Versus Blood Glucose Monitoring to Support a Carbohydrate-Restricted Nutrition Intervention in People with Type 2 Diabetes.","authors":"Holly J Willis, Stephen E Asche, Amy L McKenzie, Rebecca N Adams, Caroline G P Roberts, Brittanie M Volk, Shannon Krizka, Shaminie J Athinarayanan, Alison R Zoller, Richard M Bergenstal","doi":"10.1089/dia.2024.0406","DOIUrl":"https://doi.org/10.1089/dia.2024.0406","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> Low- and very-low-carbohydrate eating patterns, including ketogenic eating, can reduce glycated hemoglobin (HbA1c) in people with type 2 diabetes (T2D). Continuous glucose monitoring (CGM) has also been shown to improve glycemic outcomes, such as time in range (TIR; % time with glucose 70-180 mg/dL), more than blood glucose monitoring (BGM). CGM-guided nutrition interventions are sparse. The primary objective of this study was to compare differences in change in TIR when people with T2D used either CGM or BGM to guide dietary intake and medication management during a medically supervised ketogenic diet program (MSKDP) delivered via continuous remote care. <b><i>Methods:</i></b> IGNITE (Impact of Glucose moNitoring and nutrItion on Time in rangE) study participants were randomized to use CGM (<i>n</i> = 81) or BGM (<i>n</i> = 82) as part of a MSKDP. Participants and their care team used CGM and BGM data to support dietary choices and medication management. Glycemia, medication use, ketones, dietary intake, and weight were assessed at baseline (Base), month 1 (M1), and month 3 (M3); differences between arms and timepoints were evaluated. <b><i>Results:</i></b> Adults (<i>n</i> = 163) with a mean (standard deviation) T2D duration of 9.7 (7.7) years and HbA1c of 8.1% (1.2%) participated. TIR improved from Base to M3, 61-89% for CGM and 63%-85% for BGM (<i>P</i> < 0.001), with no difference in change between arms (<i>P</i> = 0.26). Additional CGM metrics also improved by M1, and improvements were sustained through M3. HbA1c decreased by ≥1.5% from Base to M3 for both CGM and BGM arms (<i>P</i> < 0.001). Diabetes medications were de-intensified based on change in medication effect scores from Base to M3 (<i>P</i> < 0.001). Total energy and carbohydrate intake decreased (<i>P</i> < 0.001), and participants in both arms lost clinically significant weight (<i>P</i> < 0.001). <b><i>Conclusion:</i></b> Both the CGM and BGM arms saw similar and significant improvements in glycemia and other diabetes-related outcomes during this MSKDP. Additional CGM-guided nutrition intervention research is needed.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616493","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":"Comparison of Computational Statistical Packages for the Analysis of Continuous Glucose Monitoring Data with a Reference Software, \"Ambulatory Glucose Profile,\" in Type 1 Diabetes.","authors":"Kagan E Karakus, Janet K Snell-Bergeon, Halis K Akturk","doi":"10.1089/dia.2024.0410","DOIUrl":"https://doi.org/10.1089/dia.2024.0410","url":null,"abstract":"<p><p><b><i>Objective:</i></b> To compare the accuracy of commonly used continuous glucose monitoring (CGM) analysis programs with ambulatory glucose profile (AGP) and Dexcom Clarity (DC) in analyzing CGM metrics in patients with type 1 diabetes (T1D). <b><i>Research Methods:</i></b> CGM data up to 90 days from 152 adults using the same CGM and automated insulin delivery system with T1D were collected. Six of the 19 CGM analysis programs (CDGA, cgmanalysis, Glyculator, iglu, EasyGV, and GLU) were selected to compare with AGP and DC. Metrics were compared etween all tools with two one-sided <i>t</i>-tests equivalence testing. For the equivalence test, the acceptable range of deviation was set as ±2 mg/dL for mean glucose, ±2% for time in range (TIR), ±1% for time above range (TAR), time above range level 1 (TAR1), time above range level 2 (TAR2), and coefficient of variation (CV). <b><i>Results:</i></b> All packages were compared with each other for all CGM metrics, and most of them had statistically significant differences for at least some metrics. All tools were equivalent to AGP for mean glucose, TIR, TAR, TAR1, and TAR2 within ±2 mg/dL, ±2%, ±1%, ±1% and 1%, respectively. CDGA, Glyculator, cgmanalysis, and iglu were not equivalent to AGP for CV within ±1%. All tools were equivalent to DC for mean glucose, TIR, and TAR2 within ±2 mg/dL, ±2%, and ±1%, respectively. Glyculator was not equivalent for TAR1, TAR, and CV. CGDA, cgmanalysis, and iglu were not equivalent to DC for TAR1 and TAR. EasyGV and GLU were not equivalent for TAR within ±1%. <b><i>Conclusions:</i></b> CGM analysis programs reported CGM metrics statistically differently, but these differences may not be applicable in clinical practice. The equivalence test also confirmed that the differences are negligible for TIR and mean glucose, while they can be important for hyperglycemic ranges and CV. A standardization for CGM data handling and analysis is necessary for clinical studies reporting CGM-generated outcomes.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142603294","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":"Continuous Glucose Monitoring and Recreational Scuba Diving in Type 1 Diabetes: Head-to-Head Comparison Between Free Style Libre 3 and Dexcom G7 Performance.","authors":"Elena Gamarra, Giovanni Careddu, Andrea Fazi, Valentina Turra, Ambra Morelli, Chiara Camponovo, Pierpaolo Trimboli","doi":"10.1089/dia.2024.0126","DOIUrl":"10.1089/dia.2024.0126","url":null,"abstract":"<p><p><b><i>Background:</i></b> Scuba diving was previously excluded because of hypoglycemic risks for patients with type 1 diabetes mellitus(T1DM). Specific eligibility criteria and a safety protocol have been defined, whereas continuous glucose monitoring (CGM) systems have enhanced diabetes management. This study aims to assess the feasibility and accuracy of CGM Dexcom G7 and Free Style Libre 3 in a setting of repetitive scuba diving in T1DM, exploring the possibility of nonadjunctive use. <b><i>Material and Methods:</i></b> The study was conducted during an event of <i>Diabete Sommerso^®</i> association in 2023. Participants followed a safety protocol, with capillary glucose as reference standard (Beurer GL50Evo). Sensors' accuracy was evaluated through median and mean absolute relative difference (MeARD, MARD) and surveillance error grid (SEG). Data distribution and correlation were estimated by Spearman test and Bland-Altman plots. The ability of sensors to identify hypoglycemia was assessed by contingency tables. <b><i>Results:</i></b> Data from 202 dives of 13 patients were collected. The overall MARD was 31% (Dexcom G7) and 14.2% (Free Style Libre 3) and MeARD was 19.7% and 11.6%, respectively. Free Style Libre 3 exhibited better accuracy in normoglycemic and hyperglycemic ranges. SEG analysis showed 82.1% (Dexcom G7) and 97.4% (Free Style Libre 3) data on no-risk zone. Free Style Libre 3 better performed on hypoglycemia identification (diagnostic odds ratio of 254.10 vs. 58.95). Neither of the sensors reached the MARD for nonadjunctive use. <b><i>Conclusions:</i></b> The study reveals Free Style Libre 3 superior accuracy compared with Dexcom G7 in a setting of repetitive scuba diving in T1DM, except for hypoglycemic range. Both sensors fail to achieve accuracy for nonadjunctive use. Capillary tests remain crucial for safe dive planning, and sensor data should be interpreted cautiously. We suggest exploring additional factors potentially influencing sensor performance.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"829-841"},"PeriodicalIF":5.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141070356","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":"Continuous Glucose Monitoring Prediction of Gestational Diabetes Mellitus and Perinatal Complications.","authors":"Zoey Li, Roy Beck, Celeste Durnwald, Anders Carlson, Elizabeth Norton, Richard Bergenstal, Mary Johnson, Sean Dunnigan, Matthew Banfield, Katie Krumwiede, Judy Sibayan, Peter Calhoun","doi":"10.1089/dia.2024.0080","DOIUrl":"10.1089/dia.2024.0080","url":null,"abstract":"<p><p><b><i>Objective:</i></b> To assess the performance of continuous glucose monitoring (CGM)-measured glycemic metrics in predicting development of gestational diabetes mellitus (GDM) and select perinatal complications. <b><i>Research Methods:</i></b> In a prospective observational study, CGM data were collected from 760 pregnant females throughout gestation after study enrollment. GDM was diagnosed using the oral glucose tolerance test (OGTT) at 24-34 weeks of gestation. Predictive models were built using logistic and elastic net regression. Predictive performance was assessed by the area under the receiver-operating characteristic (AUROC) curve. <b><i>Results:</i></b> The AUROCs of using second trimester percent time >140 mg/dL (TA140) and week 13-14 TA140 in predicting GDM were 0.81 and 0.74, respectively. The AUROCs for predicting large-for-gestational-age (LGA) births and hypertensive disorders of pregnancy (HDP) using second trimester TA140 were both 0.58. When matching the specificity of OGTT, a model using TA140 in weeks 13-14 achieved similar sensitivity to OGTT in predicting HDP (13% vs. 10%, respectively) and LGA (6% for both methods). Elastic net also demonstrated similar AUROC and diagnostic performance with no meaningful improvement by using multiple predictors. <b><i>Conclusion:</i></b> CGM-measured hyperglycemic metrics such as TA140 predicted GDM with high AUROCs as early as 13-14 weeks of gestation. These metrics were also similar statistically to the OGTT at 24-34 weeks in predicting perinatal complications, although sensitivity was low for both. CGM could potentially be used as an early screening tool for elevated hyperglycemia during gestation, which could be used in addition to or instead of the OGTT.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"787-796"},"PeriodicalIF":5.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141751340","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":"Continuous Glucose Monitoring Metrics and Hemoglobin A1c Relationship in Patients with Type 2 Diabetes Treated by Hemodialysis.","authors":"Rodolfo J Galindo, Bobak Moazzami, Katherine R Tuttle, Richard M Bergenstal, Limin Peng, Guillermo E Umpierrez","doi":"10.1089/dia.2024.0145","DOIUrl":"10.1089/dia.2024.0145","url":null,"abstract":"<p><p><b><i>Background:</i></b> There is a need for accurate glycemic control metrics in patients with diabetes and end-stage kidney disease (ESKD). Hence, we assessed the relationship of continuous glucose monitoring (CGM) metrics and laboratory-measured hemoglobin A1c (HbA1c) in patients with type 2 diabetes (T2D) treated by hemodialysis. <b><i>Methods:</i></b> This prospective observational study included adults (age 18-80 years) with T2D (HbA1c 5%-12%), treated by hemodialysis (for at least 90 days). Participants used a Dexcom G6 Pro CGM for 10 days. Correlation analyses between CGM metrics [mean glucose, glucose management indicator (GMI), and time-in-range (TIR 70-180 mg/dL)] and HbA1c were performed. <b><i>Results:</i></b> Among 59 participants (mean age was 57.7 ± 9.3 years, 58% were female, 86% were non-Hispanic blacks), the CGM mean glucose level was 188.9 ± 45 mg/dL (95% CI: 177.2, 200.7), the mean HbA1c and GMI were 7.1% ± 1.3% and 7.8% ± 1.1%, respectively (difference 0.74% ± 0.95). GMI had a strong negative correlation with TIR 70-180 mg/dL (r = -0.96). The correlation between GMI and HbA1c (r = 0.68) was moderate. Up to 29% of participants had a discordance between HbA1c and GMI of <0.5%, with 22% having a discordance between 0.5% and 1%, and 49% having a discordance of >1%. <b><i>Conclusions:</i></b> In patients with diabetes and ESKD treated by hemodialysis, the GMI has a strong correlation with TIR, while HbA1c underestimated the average glucose and GMI. Given the limitations of HbA1c in this population, GMI or mean glucose and TIR may be considered as more appropriate glucose control markers.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"862-868"},"PeriodicalIF":5.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11693958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141161181","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":"Expert Panel Recommendations for a Standardized Ambulatory Glucose Profile Report for Connected Insulin Pens.","authors":"Gregg D Simonson, Amy B Criego, Tadej Battelino, Anders L Carlson, Pratik Choudhary, Sylvia Franc, Dana Gershenoff, George Grunberger, Irl B Hirsch, Diana Isaacs, Mary L Johnson, David Kerr, Davida F Kruger, Chantal Mathieu, Thomas W Martens, Revital Nimri, Sean M Oser, Anne L Peters, Ruth S Weinstock, Eugene E Wright, Carol H Wysham, Richard M Bergenstal","doi":"10.1089/dia.2024.0107","DOIUrl":"10.1089/dia.2024.0107","url":null,"abstract":"<p><p><b><i>Background</i></b>: Connected insulin pens capture data on insulin dosing/timing and can integrate with continuous glucose monitoring (CGM) devices with essential insulin and glucose metrics combined into a single platform. Standardization of connected insulin pen reports is desirable to enhance clinical utility with a single report. <b><i>Methods</i></b>: An international expert panel was convened to develop a standardized connected insulin pen report incorporating insulin and glucose metrics into a single report containing clinically useful information. An extensive literature review and identification of examples of current connected insulin pen reports were performed serving as the basis for creation of a draft of a standardized connected insulin pen report. The expert panel participated in three virtual standardization meetings and online surveys. <b><i>Results</i></b>: The <i>Ambulatory Glucose Profile (AGP) Report: Connected Insulin Pen</i> brings all clinically relevant CGM-derived glucose and connected insulin pen metrics into a single simplified two-page report. The first page contains the time in ranges bar, summary of key insulin and glucose metrics, the AGP curve, and detailed basal (long-acting) insulin assessment. The second page contains the bolus (mealtime and correction) insulin assessment periods with information on meal timing, insulin-to-carbohydrate ratio, average bolus insulin dose, and number of days with bolus doses recorded. The report's second page contains daily glucose profiles with an overlay of the timing and amount of basal and bolus insulin administered. <b><i>Conclusion</i></b>: The <i>AGP Report: Connected Insulin Pen</i> is a standardized clinically useful report that should be considered by companies developing connected pen technology as part of their system reporting/output.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"814-822"},"PeriodicalIF":5.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140956737","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":"Temporary Target Versus Suspended Insulin Infusion in Patients with Type 1 Diabetes Using the MiniMed 780G Advanced Closed-Loop Hybrid System During Aerobic Exercise: A Randomized Crossover Clinical Trial.","authors":"Ana María Gómez, Diana Cristina Henao, Oscar Mauricio Muñoz, Diana Marcela Romero, Julio David Silva León, Pablo Esteban Jaramillo, Evelyn Moscoso, Darío A Parra Prieto, Sofía Robledo, Maira García Jaramillo, Martin Rondón Sepúlveda","doi":"10.1089/dia.2023.0589","DOIUrl":"10.1089/dia.2023.0589","url":null,"abstract":"<p><p><b><i>Aim:</i></b> To compare the safety in terms of hypoglycemic events and continuous glucose monitoring (CGM) metrics during aerobic exercise (AE) of using temporary target (TT) versus suspension of insulin infusion (SII) in adults with type 1 diabetes (T1D) using advanced hybrid closed-loop systems. <b><i>Methods:</i></b> This was a randomized crossover clinical trial. Two moderate-intensity AE sessions were performed, one with TT and one with SII. Hypoglycemic events and CGM metrics were analyzed during the immediate (baseline to 59 min), early (60 min to 6 h), and late (6 to 36 h) post-exercise phases. <b><i>Results:</i></b> In total, 33 patients were analyzed (44.6 ± 13.8 years), basal time in range (%TIR 70-180 mg/dL) was 79.4 ± 12%, and time below range (%TBR) <70 mg/dL was 1.8 ± 1.7% and %TBR <54 mg/dL was 0.5 ± 0.9%. No difference was found in the number of hypoglycemic events, %TBR <70 mg/dL and %TBR <54 mg/dL between TT and SII. Differences were found in the early phase, with better values when using TT for %TIR 70-180 mg/dL (83.0 vs. 65.3, <i>P</i> = 0.005), time in tight range (%TITR 70-140 mg/dL) (56.3 vs. 41.5, <i>P</i> = 0.04), and time above range (%TAR >180 mg/dL) (15.3 vs. 31.8, <i>P</i> = 0.01). In the diurnal period, again %TIR was better for TT use (82.1 vs. 73.1, <i>P</i> = 0.02) and %TAR (15.0 vs. 22.96, <i>P</i> = 0.04). No significant differences were found in the CGM metrics during the different phases of AE. <b><i>Conclusion:</i></b> Our data appear to show that the use of TT compared with SII is equally safe in all phases of AE. However, the use of TT allows for a better glycemic profile in the early phase of exercise.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"823-828"},"PeriodicalIF":5.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142281839","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":"Assessing Patterns of Continuous Glucose Monitoring Use and Metrics of Glycemic Control in Type 1 Diabetes and Type 2 Diabetes Patients in the Veterans Health Care System: Integrating Continuous Glucose Monitoring Device Data with Electronic Health Records Data.","authors":"Tomoki Okuno, Sharon A Macwan, Donald Miller, Gregory J Norman, Peter Reaven, Jin J Zhou","doi":"10.1089/dia.2024.0083","DOIUrl":"10.1089/dia.2024.0083","url":null,"abstract":"<p><p><b><i>Objective:</i></b> To integrate long-term daily continuous glucose monitoring (CGM) device data with electronic health records (EHR) for patients with type 1 and type 2 diabetes (T1D and T2D) in the national Veterans Affairs Healthcare System to assess real-world patterns of CGM use and the reliability of EHR-based CGM information. <b><i>Research Design and Methods:</i></b> This observational study used Dexcom CGM device data linked with EHR (from 2015 to 2020) for a large national cohort of patients with diabetes. We tracked the initiation and consistency of CGM use, assessed concordance of CGM use and measures of glucose control between CGM device data and EHR records, and examined results by age, ethnicity, and diabetes type. <b><i>Results:</i></b> The time from pharmacy release of CGM to patients to initiation of uploading CGM data to Dexcom servers averaged 3 weeks but demonstrated wide variation among individuals; importantly, this delay decreased markedly over the later years. The average daily wear time of CGM exceeded 22 h over nearly 3 years of follow-up. Patterns of CGM use were generally consistent across age, race/ethnicity groups, and diabetes type. There was strong concordance between EHR-based estimates of CGM use and Dexcom CGM wear time and between estimates of glucose control from both sources. <b><i>Conclusions:</i></b> The study demonstrates our ability to reliably integrate CGM devices and EHR data to provide valuable insights into CGM use patterns. The results indicate in the real-world environment that CGM is worn consistently over many years for both patients with T1D and T2D within the Veterans Affairs Healthcare System and is similar across major race/ethnic groups and age-groups.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"806-813"},"PeriodicalIF":5.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141070354","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}