Diabetes technology & therapeutics最新文献

{"title":"Time in Range, Time in Tight Range, and Average Glucose Relationships Are Modulated by Glycemic Variability: Identification of a Glucose Distribution Model Connecting Glycemic Parameters Using Real-World Data.","authors":"Yongjin Xu, Timothy C Dunn, Richard M Bergenstal, Alan Cheng, Yaghoub Dabiri, Ramzi A Ajjan","doi":"10.1089/dia.2023.0564","DOIUrl":"10.1089/dia.2023.0564","url":null,"abstract":"<p><p><b><i>Background:</i></b> Time in range (TIR), time in tight range (TITR), and average glucose (AG) are used to adjust glycemic therapies in diabetes. However, TIR/TITR and AG can show a disconnect, which may create management difficulties. We aimed to understand the factors influencing the relationships between these glycemic markers. <b><i>Materials and Methods:</i></b> Real-world glucose data were collected from self-identified diabetes type 1 and type 2 diabetes (T1D and T2D) individuals using flash continuous glucose monitoring (FCGM). The effects of glycemic variability, assessed as glucose coefficient of variation (CV), on the relationship between AG and TIR/TITR were investigated together with the best-fit glucose distribution model that addresses these relationships. <b><i>Results:</i></b> Of 29,164 FCGM users (16,367 T1D, 11,061 T2D, and 1736 others), 38,259 glucose readings/individual were available. Comparing low and high CV tertiles, TIR at AG of 150 mg/dL varied from 80% ± 5.6% to 62% ± 6.8%, respectively (<i>P</i> < 0.001), while TITR at AG of 130 mg/dL varied from 65% ± 7.5% to 49% ± 7.0%, respectively (<i>P</i> < 0.001). In contrast, higher CV was associated with increased TIR and TITR at AG levels outside the upper limit of these ranges. Gamma distribution was superior to six other models at explaining AG and TIR/TITR interactions and demonstrated nonlinear interplay between these metrics. <b><i>Conclusions:</i></b> The gamma model accurately predicts interactions between CGM-derived glycemic metrics and reveals that glycemic variability can significantly influence the relationship between AG and TIR with opposing effects according to AG levels. Our findings potentially help with clinical diabetes management, particularly when AG and TIR appear mismatched.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139691388","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 Flash Glucose Monitoring on the Fear of Hypoglycemia Phenomenon in Adults with Type 1 Diabetes.","authors":"Pablo Rodríguez de Vera Gómez, Carmen Mateo Rodríguez, Beatriz Rodríguez Jiménez, Lucía Hidalgo Sotelo, Mercedes Peinado Ruiz, Eduardo Torrecillas Del Castillo, Desirée Ruiz-Aranda, Isabel Serrano Olmedo, Ángela Candau Martín, María Asunción Martínez-Brocca","doi":"10.1089/dia.2023.0370","DOIUrl":"10.1089/dia.2023.0370","url":null,"abstract":"<p><p><b><i>Objective:</i></b> To assess the clinical impact of flash glucose monitoring (FGM) systems on fear of hypoglycemia (FoH) and quality of life in adults with type 1 diabetes mellitus (T1DM). <b><i>Methods:</i></b> Prospective quasi-experimental study with a 12-month follow-up. People with T1DM (18-80 years old) and self-monitoring by blood capillary glycemia controls were included. The FH15 questionnaire, a survey validated in Spanish in a comparable study population, was used to diagnose FoH with a cutoff point of 28 points. <b><i>Results:</i></b> A total of 181 participants were included, with a FoH prevalence of 69% (<i>n</i> = 123). A mean reduction in FH15 score of -4 points (95% confidence interval [-5.5 to -3]; <i>P</i> < 0.001) was observed, along with an improvement in quality of life (EsDQOL-test (Diabetes Quality of Life, Spanish version), -7 points [-10; -4], <i>P</i> < 0.001) and satisfaction with treatment (Diabetes Treatment Satisfaction questionnaire, self-reported version [DTSQ-s] test, +4.5 points [4; 5.5], <i>P</i> < 0.001). At the end of the follow-up, 64.2% of the participants saw an improved FoH intensity, compared to 35.8% who scored the same or higher. This improvement in FoH status was associated with a higher time-in-range at the end of the follow-up (<i>P</i> = 0.003), as well as a lower time spent in hyperglycemia (<i>P</i> = 0.005). In addition, it was linked to participants with a high baseline FoH levels (<i>P</i> < 0.001) and those who were university degree holders (<i>P</i> = 0.07). <b><i>Conclusions:</i></b> FGM is associated with an overall reduction of FoH in adults with T1DM and with an increase in their quality of life. Nevertheless, a significant percentage of patients may experience an increase of this phenomenon leading to clinical repercussions and a profound impact on quality of life.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139691384","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":"Limitations of 14-Day Continuous Glucose Monitoring Sampling for Assessment of Hypoglycemia and Glycemic Variability in Type 1 Diabetes.","authors":"Halis Kaan Akturk","doi":"10.1089/dia.2023.0476","DOIUrl":"10.1089/dia.2023.0476","url":null,"abstract":"<p><p>Continuous glucose monitoring (CGM) has become the standard of care in diabetes management with the recent advances in technology and accessibility in the last decade. An International Consensus was established to define CGM metrics and its goals in diabetes care. The 2019 International Consensus suggested 14 days of CGM sampling for the assessment of CGM metrics stating the limitations that may occur for hypoglycemia and glycemic variability metrics. Since then, several studies assessed the correlation between CGM metrics and duration of the sampling period. This review summarized the studies that investigated the relationship between 14-day CGM sampling to 90-day CGM data in >70% CGM users for all CGM metrics and highlighted possible solutions for more accurate CGM sampling durations in type 1 diabetes (T1D). Accumulating evidence showed that 14-day CGM sampling correlates well with 90-day CGM data for mean glucose, time in 70-180 mg/dL, and hyperglycemia metrics; however, it correlates weakly for hypoglycemia and glycemic variability metrics. In the studies included in this review, in adults with T1D, minimum sampling duration was 14 days for mean glucose, time in 70-180 mg/dL, and time in hyperglycemia (>180 and >250 mg/dL); however, minimum sampling duration varied between 21 to 30 days for time <70 mg/dL, 30 to 35 days for time <54 mg/dL, and 28 to 35 days for coefficient of variation. Longer than 14 days of CGM, sampling was required to properly assess hypoglycemia and glycemic variability in T1D.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139650448","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 Comparison of the Rates of Clock-Based Nocturnal Hypoglycemia and Hypoglycemia While Asleep Among People Living with Diabetes: Findings from the Hypo-METRICS Study.","authors":"Gilberte Martine-Edith, Patrick Divilly, Natalie Zaremba, Uffe Søholm, Melanie Broadley, Petra Martina Baumann, Zeinab Mahmoudi, Mikel Gomes, Namam Ali, Evertine J Abbink, Bastiaan de Galan, Julie Brøsen, Ulrik Pedersen-Bjergaard, Allan A Vaag, Rory J McCrimmon, Eric Renard, Simon Heller, Mark Evans, Monika Cigler, Julia K Mader, Jane Speight, Frans Pouwer, Stephanie A Amiel, Pratik Choudhary, For The Hypo-Resolve","doi":"10.1089/dia.2023.0522","DOIUrl":"10.1089/dia.2023.0522","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> Nocturnal hypoglycemia is generally calculated between 00:00 and 06:00. However, those hours may not accurately reflect sleeping patterns and it is unknown whether this leads to bias. We therefore compared hypoglycemia rates while asleep with those of clock-based nocturnal hypoglycemia in adults with type 1 diabetes (T1D) or insulin-treated type 2 diabetes (T2D). <b><i>Methods:</i></b> Participants from the Hypo-METRICS study wore a blinded continuous glucose monitor and a Fitbit Charge 4 activity monitor for 10 weeks. They recorded details of episodes of hypoglycemia using a smartphone app. Sensor-detected hypoglycemia (SDH) and person-reported hypoglycemia (PRH) were categorized as nocturnal (00:00-06:00 h) versus diurnal and while asleep versus awake defined by Fitbit sleeping intervals. Paired-sample Wilcoxon tests were used to examine the differences in hypoglycemia rates. <b><i>Results:</i></b> A total of 574 participants [47% T1D, 45% women, 89% white, median (interquartile range) age 56 (45-66) years, and hemoglobin A1c 7.3% (6.8-8.0)] were included. Median sleep duration was 6.1 h (5.2-6.8), bedtime and waking time ∼23:30 and 07:30, respectively. There were higher median weekly rates of SDH and PRH while asleep than clock-based nocturnal SDH and PRH among people with T1D, especially for SDH <70 mg/dL (1.7 vs. 1.4, <i>P</i> < 0.001). Higher weekly rates of SDH while asleep than nocturnal SDH were found among people with T2D, especially for SDH <70 mg/dL (0.8 vs. 0.7, <i>P</i> < 0.001). <b><i>Conclusion:</i></b> Using 00:00 to 06:00 as a proxy for sleeping hours may underestimate hypoglycemia while asleep. Future hypoglycemia research should consider the use of sleep trackers to record sleep and reflect hypoglycemia while asleep more accurately. The trial registration number is NCT04304963.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139930410","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":"Postprandial Glucose Excursions with Ultra-Rapid Insulin Analogs in Hybrid Closed-Loop Therapy for Adults with Type 1 Diabetes.","authors":"Belma Haliloglu, Charlotte K Boughton, Rama Lakshman, Julia Ware, Munachiso Nwokolo, Hood Thabit, Julia K Mader, Lia Bally, Lalantha Leelarathna, Malgorzata E Wilinska, Janet M Allen, Sara Hartnell, Mark L Evans, Roman Hovorka","doi":"10.1089/dia.2023.0509","DOIUrl":"10.1089/dia.2023.0509","url":null,"abstract":"<p><p><b><i>Objective:</i></b> To evaluate postprandial glucose control when applying (1) faster-acting insulin aspart (Fiasp) compared to insulin aspart and (2) ultra-rapid insulin lispro (Lyumjev) compared to insulin lispro using the CamAPS FX hybrid closed-loop algorithm. <b><i>Research Design and Methods:</i></b> We undertook a secondary analysis of postprandial glucose excursions from two double-blind, randomized, crossover hybrid closed-loop studies contrasting Fiasp to standard insulin aspart, and Lyumjev to standard insulin lispro. Endpoints included incremental area under curve (iAUC)-2h, iAUC-4h, 4 h postprandial time in target range, time above range, and time below range. It was approved by independent research ethics committees. <b><i>Results:</i></b> Two trials with 8 weeks of data from 51 adults with type 1 diabetes were analyzed and 7137 eligible meals were included. During Lyumjev compared with insulin lispro, iAUC-2h and iAUC-4h were significantly decreased following breakfast (mean difference 92 mmol/L per 2 h (95% confidence interval [CI]: 56 to 127); <i>P</i> < 0.001 and 151 mmol/L per 4 h (95% CI: 74 to 229); <i>P</i> < 0.001, respectively) and the evening meal (<i>P</i> < 0.001 and <i>P</i> = 0.011, respectively). Mean time in target range (3.9-10.0 mmol/L) for 4 h postprandially significantly increased during Lyumjev with a mean difference of 6.7 percentage points (95% CI: 3.3 to 10) and 5.7 percentage points (95% CI: 1.4 to 9.9) for breakfast and evening meal, respectively. In contrast, there were no significant differences in iAUC-2h, iAUC-4h, and the other measures of postprandial glucose control between insulin aspart and Fiasp during breakfast, lunch, and evening meal (<i>P</i> > 0.05). <b><i>Conclusion:</i></b> The use of Lyumjev with CamAPS FX closed-loop system improved postprandial glucose excursions compared with insulin lispro, while the use of Fiasp did not provide any advantage compared with insulin aspart. Clinical Trial Registration numbers: NCT04055480, NCT05257460.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139691386","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":"Use of the Community-Derived Open-Source Automated Insulin Delivery Loop System in Type 2 Diabetes.","authors":"Colleen Bauza, Lauren G Kanapka, Ellis Greene, Rayhan A Lal, Brandon Arbiter, Roy W Beck","doi":"10.1089/dia.2023.0569","DOIUrl":"10.1089/dia.2023.0569","url":null,"abstract":"<p><p><b><i>Background:</i></b> No published data are available on the use of the community-derived open-source Loop hybrid closed-loop controller (\"Loop\") by individuals with type 2 diabetes (T2D). <b><i>Methods:</i></b> Through social media postings, we invited individuals with T2D currently using the Loop system to join an observational study. Thirteen responded of whom seven were eligible for the study, were using the Loop algorithm, and provided data. <b><i>Results:</i></b> Mean (±standard deviation) age was 61 ± 13 years, and mean body mass index was 31 ± 5 kg/m². All but one participant were using noninsulin glucose-lowering medications. Self-reported mean hemoglobin A1c decreased from 7.3% ± 1.1% before starting Loop to 6.0% ± 0.5% on Loop. Time in range 70-180 mg/dL increased from 84% to 93%. The amount of time in hypoglycemia was extremely low before and with Loop (time <54 mg/dL was 0.04% ± 0.06% vs. 0.09% ± 0.07%, respectively). No severe hypoglycemia or diabetic ketoacidosis events were reported while using Loop. <b><i>Conclusion:</i></b> These data, though limited, suggest that the Loop system is likely to be effective when used by individuals with T2D and should be evaluated in large-scale studies. Clinical Trial Registration numbers: NCT05951569.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139930415","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":"Development and Validation of a Machine Learning Model to Predict Weekly Risk of Hypoglycemia in Patients with Type 1 Diabetes Based on Continuous Glucose Monitoring.","authors":"Simon Lebech Cichosz, Morten Hasselstrøm Jensen, Søren Schou Olesen","doi":"10.1089/dia.2023.0532","DOIUrl":"10.1089/dia.2023.0532","url":null,"abstract":"<p><p><b><i>Aim:</i></b> The aim of this study was to develop and validate a prediction model based on continuous glucose monitoring (CGM) data to identify a week-to-week risk profile of excessive hypoglycemia. <b><i>Methods:</i></b> We analyzed, trained, and internally tested two prediction models using CGM data from 205 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach (XGBoost) combined with feature engineering deployed on the CGM signals was utilized to predict excessive hypoglycemia risk defined by two targets (time below range [TBR] >4% and the upper TBR 90th percentile limit) of TBR the following week. The models were validated in two independent cohorts with a total of 253 additional patients. <b><i>Results:</i></b> A total of 61,470 weeks of CGM data were included in the analysis. The XGBoost models had an area under the receiver operating characteristic curve (ROC-AUC) of 0.83-0.87 (95% confidence interval; 0.83-0.88) in the test dataset. The external validation showed ROC-AUCs of 0.81-0.90. The most discriminative features included the low blood glucose index, the glycemic risk assessment diabetes equation (GRADE), hypoglycemia, the TBR, waveform length, the coefficient of variation and mean glucose during the previous week. This highlights that the pattern of hypoglycemia combined with glucose variability during the past week contains information on the risk of future hypoglycemia. <b><i>Conclusion:</i></b> Prediction models based on real-world CGM data can be used to predict the risk of hypoglycemia in the forthcoming week. The models showed good performance in both the internal and external validation cohorts.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139431944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causes and Consequences of Continuous Glucose Monitor \"Breaks\": Perspectives from Adolescents with Type 1 Diabetes.","authors":"Karishma Datye, Kemberlee Bonnet, Angelee Parmar, David Schlundt, Sarah Jaser","doi":"10.1089/dia.2024.0210","DOIUrl":"10.1089/dia.2024.0210","url":null,"abstract":"","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141418274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Partial Clinical Remission of Type 1 Diabetes in Swedish Children: A Longitudinal Study from the Swedish National Quality Register (SWEDIABKIDS) and the Better Diabetes Diagnosis (BDD) Study.","authors":"Anna-Lena Fureman, Marie Bladh, Annelie Carlsson, Gun Forsander, Mikael Lilja, Johnny Ludvigsson, Ulf Samuelsson, Stefan Särnblad, Torbjörn Lind","doi":"10.1089/dia.2024.0112","DOIUrl":"10.1089/dia.2024.0112","url":null,"abstract":"<p><p><b><i>Aims/Hypotheses:</i></b> To investigate the frequency and characteristics of partial remission in Swedish children with type 1 diabetes and whether the insulin delivery method, that is, continuous subcutaneous insulin infusion (CSII) or multiple daily injections (MDIs), affects incidence and duration of this period, 2007-2011. Factors that increase the proportion of subjects who enter partial remission and extend this period can improve long-term metabolic control and reduce the risk of severe hypoglycemia, improve quality of life, and, in the long run, reduce late complications. <b><i>Methods:</i></b> Longitudinal data from 2007 to 2020 were extracted from the Swedish National Quality Register (SWEDIABKIDS) with all reported newly diagnosed children. Data on C-peptide from the participants in the Better Diabetes Diagnosis study from 2007 to 2010 were used. The definition of partial remission was insulin dose-adjusted HbA1c: HbA1c (%) + [4 × total daily insulin dose (U/kg/day)] ≤9. <b><i>Results:</i></b> Of the 3887 patients, 56% were boys. More boys than girls were in partial remission throughout the follow-up period until 24 months after diabetes onset. Fewer children 0-6 years old had partial remission at 3 and 12 months but not at 24 months compared with older age-groups. A larger proportion of patients using CSII at 12 and 24 months remained in partial remission compared with those with MDI (37% vs. 33%, <i>P</i> = 0.02 and 31% vs. 27%, <i>P</i> = 0.01, respectively). The level of C-peptide was higher in the group with partial remission and mean HbA1c was lower (both <i>P</i> < 0.001). Partial remission at 12 months after diabetes onset was associated with CSII (odds ratio [OR]: 1.39, confidence interval [CI]:1.13, 1.71), shorter diabetes duration (OR: 0.80, CI: 0.76, 0.84), and male sex (OR: 1.23, CI: 1.04, 1.46). <b><i>Conclusions/Interpretation:</i></b> Insulin through MDI, longer duration of diabetes, and female sex were associated with lower frequency of partial remission. Use of CSII seems to contribute to longer partial remission among Swedish children with type 1 diabetes.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141261066","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":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-06-24","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}