Diabetes technology & therapeutics最新文献

{"title":"Fully Closed-Loop Improves Glycemic Control Compared with Pump with CGM in Adolescents with Type 1 Diabetes and HbA1c Above Target: A Two-Center, Randomized Crossover Study.","authors":"Nithya Kadiyala, Rama Lakshman, Janet Allen, Julia Ware, Charlotte K Boughton, Malgorzata E Wilinska, Ajay Thankamony, Sara Hartnell, Hood Thabit, Ruben H Willemsen, Pratik Shah, Roman Hovorka","doi":"10.1089/dia.2025.0062","DOIUrl":"https://doi.org/10.1089/dia.2025.0062","url":null,"abstract":"<p><p><b><i>Introduction:</i></b> Adolescents with type 1 diabetes (T1D) can struggle to reach recommended HbA1c targets more than other age groups. The safety and efficacy of fully closed-loop (FCL) insulin delivery, which does not require mealtime bolusing, have not been assessed in this cohort. We evaluated the use of FCL with faster insulin aspart (Fiasp) in adolescents with T1D whose HbA1c was above recommended targets. <b><i>Materials and Methods:</i></b> This two-center, randomized, crossover study included 24 adolescents (13-19 years) using insulin pump therapy with above-target HbA1c (mean age 16.2 years, median HbA1c 74 mmol/mol [8.9%]). Participants underwent two 8-week periods of unrestricted living, comparing FCL (CamAPS HX) with Fiasp, with standard nonautomated insulin pump therapy with continuous glucose monitoring (CGM), in random order. <b><i>Results:</i></b> In an intention-to-treat analysis, the percentage of time glucose was in target range (primary endpoint 3.9-10.0 mmol/L) was higher during FCL than during pump with CGM use (mean ± standard deviation [SD]) 45.2% ± 7.2% vs. 32.3% ± 12.8%, mean difference 12.9 percentage points, 95% confidence interval [CI] 8.5 to 17.3, <i>P</i> < 0.001). Time spent in hyperglycemia >13.9 mmol/L and mean glucose were lower with FCL compared with pump with CGM (median time >13.9 mmol/L 28.7% vs. 39.6%, difference -7.3 percentage points, 95% CI -11.1 to -3.5, <i>P</i> < 0.001; mean glucose 11.1 mmol/L vs. 12.7 mmol/L, difference -1.2 mmol/L, 95% CI -1.8 to -0.5, <i>P</i> < 0.001). Proportion of time with glucose <3.9 mmol/L was similar between interventions (median: FCL 2.78% vs. pump with CGM 2.97%, difference -0.34 percentage points, 95% CI -1.03 to 0.35, <i>P</i> = 0.322). There was no difference in HbA1c after FCL compared with pump with CGM (median: 71 mmol/mol (8.6%) vs. 74 mmol/mol (8.9%), <i>P</i> = 0.227). There was no difference in total daily insulin dose (<i>P</i> = 0.276). No severe hypoglycemia or ketoacidosis occurred. <b><i>Conclusions:</i></b> FCL insulin delivery with CamAPS HX improved glucose outcomes compared with insulin pump therapy with CGM in adolescents with T1D and HbA1c above target.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144186675","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":"Hybrid Closed-Loop Therapy in Adults with Type 1 Diabetes in England: Long-Term Outcomes from a Real-World Observational Study.","authors":"Alexandros L Liarakos, Thomas S J Crabtree, Tomás P Griffin, Sufyan Hussain, Geraldine Gallen, Jackie Elliott, Niall Furlong, Parth Narendran, Hood Thabit, Lalantha Leelarathna, Mark L Evans, Christopher Philbey, Iain Cranston, Shafie Kamaruddin, Zin Zin Htike, Lynn Sawyer, Louise Curtis, Jesina Kirby, Isy Douek, Ali J Chakera, Simon Saunders, Alex Bickerton, Zosanglura Bawlchhim, Clare Soar, Claire Wadham, Claire Williams, Mindy Levitt, Philip Weston, Partha Kar, Robert E J Ryder, Alistair Lumb, Pratik Choudhary, Emma G Wilmot","doi":"10.1089/dia.2025.0165","DOIUrl":"https://doi.org/10.1089/dia.2025.0165","url":null,"abstract":"<p><p><b><i>Objective:</i></b> To evaluate longitudinal real-world outcomes in adults with type 1 diabetes initiating hybrid closed loop (HCL). <b><i>Methods:</i></b> Adults with type 1 diabetes, managed with an insulin pump and intermittently scanned continuous glucose monitoring with hemoglobin A1c (HbA1c) ≥8.5% (69 mmol/mol), were started on HCL between August and December 2021 as part of the National Health Service England HCL pilot. We collected outcomes, including change in HbA1c, sensor glucometrics, Gold score (hypoglycemia awareness), diabetes distress score, acute event rates, and user opinion of HCL. <b><i>Results:</i></b> In total, 420 HCL users across 30 diabetes centers in the United Kingdom were included (median age 40 [interquartile range or IQR 29-50] years, 68% female, 85% White British). Over a median follow-up of 12 months (IQR 8-28) (range 6-38 months), mean adjusted HbA1c reduced by 1.4% (95% confidence interval [CI] -1.5, -1.3; <i>P</i> < 0.001) (16 mmol/mol [95% CI -17, -14]; <i>P</i> < 0.001). Time in range (70-180mg/dL) increased from 33.7% to 60.4% (<i>P</i> < 0.001). The proportion of individuals achieving HbA1c ≤7.5% (58 mmol/mol) increased from 0% to 33.1% (<i>P</i> < 0.001). Diabetes distress score reduced (-1.1; 95% CI -1.3, -1.0; <i>P</i> < 0.001) and Gold score reduced (-0.4; 95% CI -0.5, -0.2; <i>P</i> < 0.001). The percentage of individuals with impaired hypoglycemia awareness (Gold score ≥4) decreased (16.6% [baseline] vs. 9.2% [follow-up]; <i>P</i> < 0.001). Almost all participants stated that HCL had a positive impact on quality of life (94.5%; 361/382). The number of hospital admissions was low. <b><i>Conclusions:</i></b> Long-term real-world use of HCL is associated with sustained improvements in glycemic and person-reported outcomes in adults with type 1 diabetes and above-target HbA1c levels.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144186676","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":"Beyond the Trend Arrow: Potential Value of Artificial Intelligence-Supported Glucose Predictions for People with Type 1 Diabetes Using Continuous Glucose Monitoring Systems.","authors":"Sufyan Hussain, William Polonsky, Renza Scibilia, Timor Glatzer","doi":"10.1089/dia.2025.0293","DOIUrl":"https://doi.org/10.1089/dia.2025.0293","url":null,"abstract":"<p><p>Advances in diabetes technologies such as continuous glucose monitoring (CGM) have provided significant opportunities to improve glycemic and quality-of-life outcomes for people with type 1 diabetes (T1D). The ambulatory glucose profile and the introduction of glucose thresholds helped a lot to identify patterns, which was the first step toward improving hyper-and hypoglycemia management. Despite these innovations, the relentless burden of day-to-day T1D management continues to be a challenge for individuals and their families. In particular, hypoglycemia remains a significant cause of morbidity and mortality, as well as a barrier to achieving optimal glycemia, contributing to anxiety, fear, worry, and distress. Algorithm developments have led to CGM device-based thresholds and predictive alarms to warn individuals of impending hypoglycemia. More recent developments with artificial intelligence technology now allow for forecasting glucose trends and values over longer time frames, thereby aiding therapy decision-making. In this article, we focus on hypoglycemia and summarize recent developments in glucose prediction from CGM devices. While not intended to be a comprehensive review, we provide an update, highlight anticipated developments, and speculate on potential pitfalls and the potential value from medical, psychosocial, and lived experience perspective.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144180935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>Letter:</i> Insight on Acceptability of Continuous Glucose Monitoring in Youth-Onset Type 2 Diabetes.","authors":"Shahzaib Shahzaib, Safi Ur Rehman, Arifa Arifa","doi":"10.1089/dia.2025.0178","DOIUrl":"https://doi.org/10.1089/dia.2025.0178","url":null,"abstract":"","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144172927","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":"Initiation of Continuous Glucose Monitoring and Mortality in Type 2 Diabetes.","authors":"Peter D Reaven, Sharon Macwan, Michelle Newell, Gayatri Arani, Gregory J Norman, Donald R Miller, Jin J Zhou","doi":"10.1089/dia.2025.0227","DOIUrl":"https://doi.org/10.1089/dia.2025.0227","url":null,"abstract":"<p><p><b><i>Background:</i></b> Although use of continuous glucose monitoring (CGM) has been linked with improved glucose control, including reductions in hemoglobin A1c and episodes of hypoglycemia, there has been little investigation of its possible role in reducing other serious clinical events. <b><i>Objective:</i></b> To estimate the effect of starting CGM in patients with type 2 diabetes (T2D) on mortality. <b><i>Research Design:</i></b> A cohort study comparing mortality between propensity score-matched CGM users and non-CGM users over 18 months. <b><i>Setting:</i></b> Veterans Affairs Health Care System. <b><i>Participants:</i></b> Adult patients with T2D receiving insulin who were identified as CGM users or non-CGM users between January 1, 2015, and December 31, 2020. <b><i>Measurements:</i></b> Primary outcome of all-cause mortality; secondary outcomes of serious all-cause hospitalization, cardiovascular events, and admissions related to hyperglycemia and hypoglycemia. <b><i>Results:</i></b> A total of 12,729 patients with T2D (94% male with mean age 66) who were new CGM users were 1:1 matched with non-CGM users. Total follow-up time was 17,676 and 17,034 person-years for CGM and non-CGM users. Risk for mortality was lower in CGM users (hazard ratio or HR 0.79: 95% confidence interval or CI 0.73-0.86), as were risks for all-cause hospitalization (0.91: 0.86, 0.96), cardiovascular events (0.84: 0.73, 0.96), and admissions for hyperglycemia (0.88: 0.81, 0.95). Lower risk for mortality persisted after accounting for early deaths, COVID-19, recent onset of diabetes, subsequent use of insulin pumps or newer diabetes medications, or when stratifying by frequency of CGM use, frailty index or mortality risk (all HRs: 0.83 or less, range of CI: 0.60-0.94). No differences between CGM and non-CGM users were seen with negative control outcomes. <b><i>Limitations:</i></b> Unmeasured health factors, behaviors, or other confounders may exist. <b><i>Conclusion:</i></b> In a large national cohort, initiation of CGM was associated with lower mortality in T2D patients using insulin and indicates use of CGM may have benefits that extend beyond glucose lowering.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144156763","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":"Severe and Recurrent Diabetic Ketoacidosis in Children and Youth with Type 1 Diabetes: Risk and Protective Factors.","authors":"David D Schwartz, Madhav Erraguntla, Daniel J DeSalvo, Don Buckingham, Darpit Dave, Rona Sonabend, Sarah K Lyons","doi":"10.1089/dia.2025.0128","DOIUrl":"https://doi.org/10.1089/dia.2025.0128","url":null,"abstract":"<p><p><b><i>Objective:</i></b> To examine the risk and protective factors for severe and recurrent diabetic ketoacidosis (DKA) in a large sample of children in the Southwestern United States. <b><i>Methods:</i></b> Retrospective chart review of children age 0-18 years with type 1 diabetes (T1D) seen at a large children's hospital/integrated care delivery system between October 2019 and December 2022. Data from the preceding 2 years were used to predict postdiagnosis DKA in each subsequent year. Logistic regression and recursive feature elimination (RFE) were used to select significant predictors of any DKA, severe DKA, and recurrent DKA. Model performance was evaluated using fivefold cross-validation, with area under the curve in the receiver operating characteristic plot as the performance metric. <b><i>Results:</i></b> Records were obtained for 4649 encounters, representing 1850 patients and 846 prior DKA events. Based on RFE, single prior DKA, recurrent prior DKA, and hemoglobin A1c were significant shared predictors for subsequent DKA, severe DKA, and recurrent DKA, and female sex was positively associated with any DKA and recurrent DKA. The model for recurrent DKA also included age between 10 and 14 years as an unshared risk factor, and Hispanic ethnicity and use of an insulin pump (with or without automated insulin delivery) as unshared protective factors. Incidence of severe DKA was highly correlated (<i>r</i> = 0.95) with number of prior DKA events. Black and female patients were more likely to experience multiple recurrent DKA episodes and repeated episodes of severe DKA. <b><i>Conclusions:</i></b> Severe and recurrent DKA have both shared and unshared risk factors. Severe DKA may be a singular phenomenon in most cases, although a subset of patients (primarily Black and female) experience repeated severe events, placing them at high risk for adverse health outcomes. Recurrent DKA appears to be more of a chronic issue, although a number of variables emerged as protective factors, suggesting ways in which recurrent DKA might be prevented.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149591","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":"Evaluating Cardiovascular-Kidney-Metabolic Syndrome Knowledge in Large Language Models: A Comparative Study of ChatGPT, Gemini, and DeepSeek.","authors":"Luxiang Shang, Sha Sha, Yinglong Hou","doi":"10.1089/dia.2025.0216","DOIUrl":"https://doi.org/10.1089/dia.2025.0216","url":null,"abstract":"","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149589","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":"Efficacy and Safety of a Non-Carbohydrate Counting Meal Bolus Strategy in Adults with Type 1 Diabetes Using Open-Source Automated Insulin Delivery.","authors":"Yunying Cai, Le Ma, Lun Zhang, Qiongli Neng, Heng Su","doi":"10.1089/dia.2024.0591","DOIUrl":"https://doi.org/10.1089/dia.2024.0591","url":null,"abstract":"<p><p><b><i>Aims:</i></b> This study addressed the challenge of postprandial glycemic variability in type 1 diabetes (T1D), even with AID (automated insulin delivery). We evaluated the effectiveness of a non-carbohydrate counting (non-CC) meal bolus strategy in adults with T1D utilizing open-source AID. <b><i>Methods:</i></b> A total of 32 adults with T1D, aged 18 to 50 years, participated in a randomized crossover trial utilizing open-source AID. Following a 7-day run-in period, participants were randomly assigned to one of two groups: automatic mode (closed loop) or manual mode (open loop). After 2 weeks, the participants underwent a crossover to the alternate treatment mode. Prandial boluses were administered according to a sliding scale based on preprandial glucose levels, without utilizing either the exact carbohydrate content of meals or meal announcement buttons. The study compared the differences in time in range (TIR) and insulin dosage across the different phases. <b><i>Results:</i></b> Compared with the open-loop phase, the TIR for patients during the closed-loop phase increased significantly during the night (75.45% ± 12.08% vs. 83.05% ± 7.20%, <i>P</i> < 0.001) and 24 h (73.40% ± 9.98% vs. 79.21% ± 4.84%, <i>P</i> = 0.019), with a more pronounced effect observed at night. During the closed-loop phase, the frequency of 24-h hypoglycemic events (＜3.9 mmol/L) was reduced compared with the open-loop phase, with no difference in nocturnal hypoglycemic events. In addition, compared with the open-loop phase, there were no significant differences in average postprandial blood glucose and peak blood glucose levels during the closed-loop phase; however, the time to reach peak postprandial blood glucose was delayed (86.06 ± 20.80 min vs. 99.08 ± 15.05 min, <i>P</i> < 0.001). <b><i>Conclusions:</i></b> A non-CC meal bolus strategy based on preprandial glucose in adults with T1D utilizing open-source AID effectively prevents glycemic excursions and maintains a mean TIR over 70%.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144126963","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":"Imputation Model for Glucose Values Above the Upper Detection Limit for Continuous Glucose Monitors.","authors":"Mikkel T Olsen, Maria Panagiotou, Knut J Strømmen, Carina K Klarskov, Peter L Kristensen, Stavroula Mougiakakou","doi":"10.1089/dia.2025.0092","DOIUrl":"https://doi.org/10.1089/dia.2025.0092","url":null,"abstract":"<p><p><b><i>Objective:</i></b> All continuous glucose monitors (CGMs) have an upper detection limit, typically of 22.2 mmol/L. This might bias CGM metrics. We aimed to develop and validate a statistical model for imputing values above this limit. <b><i>Methods:</i></b> We analyzed CGM data from 85 inpatients with type 2 diabetes, 705 outpatients with type 1 diabetes, and 27 outpatients with type 2 diabetes. A Bayesian nonparametric latent Gaussian process regression model was applied to the CGM data intentionally right censored for the top 5%, 10%, 20%, and 30% and compared with the uncensored CGM data by the bias, mean squared error (MSE), and coefficient of determination (<i>R</i>²) of mean glucose, standard deviation (SD), and coefficient of variation (CV). <b><i>Results:</i></b> In hospitalized patients with diabetes, outpatients with type 1 diabetes, and outpatients with type 2 diabetes for 5% to 30% right censoring, respectively, the bias on mean glucose after imputation ranged from -0.012 to 0.362, -0.018 to 0.485, and -0.008 to 0.130, respectively. Bias on SD ranged from -0.024 to 0.226, -0.033 to 0.381, and -0.016 to 0.138, respectively. Bias on CV ranged from -0.207 to 1.543, -0.316 to 2.609, and -0.222 to 1.721, respectively. Similar results indicating good performance of the imputation model were observed for MSE and <i>R</i>². <b><i>Conclusions:</i></b> An imputation model for glucose values above the upper detection limit of CGMs was developed and validated in various populations. This enables a more unbiased quantification of CGM metrics for patients with severe hyperglycemia.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119032","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 the Accuracy of Continuous Glucose Monitoring Metrics: The Role of Missing Data and Imputation Strategies.","authors":"Simon Lebech Cichosz, Thomas Kronborg, Stine Hangaard, Peter Vestergaard, Morten Hasselstrøm Jensen","doi":"10.1089/dia.2025.0102","DOIUrl":"https://doi.org/10.1089/dia.2025.0102","url":null,"abstract":"<p><p><b><i>Aim:</i></b> This study aims to evaluate the accuracy of continuous glucose monitoring (CGM)-derived metrics, particularly those related to glycemic variability, in the presence of missing data. It systematically examines the effects of different missing data patterns and imputation strategies on both standard glycemic metrics and complex variability metrics. <b><i>Methods:</i></b> The analysis modeled and compared the effects of three types of missing data patterns-missing completely at random, segmental, and block-wise gaps-with proportions ranging from 5% to 50% on CGM metrics derived from 14-day profiles of individuals with type 1 and type 2 diabetes. Six imputation strategies were assessed: data removal, linear interpolation, mean imputation, piecewise cubic Hermite interpolation, temporal alignment imputation, and random forest-based imputation. <b><i>Results:</i></b> A total of 933 14-day CGM profiles from 468 individuals with diabetes were analyzed. Across all metrics, the coefficient of determination (<i>R</i>²) improved as the proportion of missing data decreased, regardless of the missing data pattern. The impact of missing data on the agreement between imputed and reference metrics varied depending on the missing data pattern. To achieve high accuracy (<i>R</i>² > 0.95) in representing true metrics, at least 70% of the CGM data were required. While no imputation strategy fully compensated for high levels of missing data, simple removal outperformed others in most scenarios. <b><i>Conclusion:</i></b> This study examines the impact of missing data and imputation strategies on CGM-derived metrics. The findings suggest that while missing data may have varying effects depending on the metric and imputation method, removing periods without data is a general acceptable approach.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143967845","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}