Diabetes technology & therapeutics最新文献_第9页

IF 5.4 2区医学

Diabetes technology & therapeutics Pub Date : 2024-05-01 Epub Date: 2024-03-01 DOI: 10.1089/dia.2023.0416

Hye Jin Kwon, Steven Trawley, Sara Vogrin, Andisheh Mohammad Alipoor, Peter G Colman, Spiros Fourlanos, Charlotte A Grills, Melissa H Lee, Richard J MacIsaac, David N O'Neal, Niamh A O'Regan, Vijaya Sundararajan, Glenn M Ward, Sybil A McAuley

{"title":"Driving-Related Glucose Patterns Among Older Adults with Type 1 Diabetes.","authors":"Hye Jin Kwon, Steven Trawley, Sara Vogrin, Andisheh Mohammad Alipoor, Peter G Colman, Spiros Fourlanos, Charlotte A Grills, Melissa H Lee, Richard J MacIsaac, David N O'Neal, Niamh A O'Regan, Vijaya Sundararajan, Glenn M Ward, Sybil A McAuley","doi":"10.1089/dia.2023.0416","DOIUrl":"10.1089/dia.2023.0416","url":null,"abstract":"Older adults with type 1 diabetes may face challenges driving safely. Glucose \"above-5-to-drive\" is often recommended for insulin-treated diabetes to minimize hypoglycemia while driving. However, the effectiveness of this recommendation among older adults has not been evaluated. Older drivers with type 1 diabetes were assessed while using sensor-augmented insulin pumps during a 2-week clinical trial run-in. Twenty-three drivers (median age 69 years [interquartile range; IQR 65-72]; diabetes duration 37 years [20-45]) undertook 618 trips (duration 10 min [5-21]). Most trips (n = 535; 87%) were <30 min duration; 9 trips (1.5%) exceeded 90 min and 3 trips (0.5%) exceeded 120 min. Pre-trip continuous glucose monitoring (CGM) was >5.0 mmol/L for 577 trips (93%) and none of these had CGM <3.9 mmol/L during driving (including 8 trips >90 min and 3 trips >120 min). During 41 trips with pre-trip CGM ≤5.0 mmol/L, 11 trips had CGM <3.9 mmol/L. Seventy-one CGM alerts occurred during 60 trips (10%), of which 54 of 71 alerts (76%) were unrelated to hypoglycemia. Our findings support a glucose \"above-5-to-drive\" recommendation to avoid CGM-detected hypoglycemia among older drivers, including for prolonged drives, and highlight the importance of active CGM low-glucose alerts to prevent hypoglycemia during driving. Driving-related CGM usability and alert functionality warrant investigation. Clinical trial ACTRN1261900515190.","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139691383","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}

引用次数: 0

Continuous Glucose Monitor Metrics Are Associated with Emergency Department Visits and Hospitalizations for Hypoglycemia and Hyperglycemia, but Have Low Predictive Value. 连续血糖监测仪指标与因低血糖和高血糖导致的急诊就诊和住院治疗有关，但预测价值较低。

IF 5.7 2区医学

Diabetes technology & therapeutics Pub Date : 2024-05-01 Epub Date: 2024-03-22 DOI: 10.1089/dia.2023.0493

Lisa K Gilliam, Melissa M Parker, Howard H Moffet, Alexandra K Lee, Andrew J Karter

{"title":"Continuous Glucose Monitor Metrics Are Associated with Emergency Department Visits and Hospitalizations for Hypoglycemia and Hyperglycemia, but Have Low Predictive Value.","authors":"Lisa K Gilliam, Melissa M Parker, Howard H Moffet, Alexandra K Lee, Andrew J Karter","doi":"10.1089/dia.2023.0493","DOIUrl":"10.1089/dia.2023.0493","url":null,"abstract":"Objective: Determine whether continuous glucose monitor (CGM) metrics can provide actionable advance warning of an emergency department (ED) visit or hospitalization for hypoglycemic or hyperglycemic (dysglycemic) events. Research Design and Methods: Two nested case-control studies were conducted among insulin-treated diabetes patients at Kaiser Permanente, who shared their CGM data with their providers. Cases included dysglycemic events identified from ED and hospital records (2016-2021). Controls were selected using incidence density sampling. Multiple CGM metrics were calculated among patients using CGM >70% of the time, using CGM data from two lookback periods (0-7 and 8-14 days) before each event. Generalized estimating equations were specified to estimate odds ratios and C-statistics. Results: Among 3626 CGM users, 108 patients had 154 hypoglycemic events and 165 patients had 335 hyperglycemic events. Approximately 25% of patients had no CGM data during either lookback; these patients had >2 × the odds of a hypoglycemic event and 3-4 × the odds of a hyperglycemic event. While several metrics were strongly associated with a dysglycemic event, none had good discrimination. Conclusion: Several CGM metrics were strongly associated with risk of dysglycemic events, and these can be used to identify higher risk patients. Also, patients who are not using their CGM device may be at elevated risk of adverse outcomes. However, no CGM metric or absence of CGM data had adequate discrimination to reliably provide actionable advance warning of an event and thus justify a rapid intervention.","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11058412/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139562935","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}

引用次数: 0

The Relationship Between Glycated Hemoglobin and Time in Range in a Pediatric Population. 儿科人群中 HbA1c 与时间范围之间的关系。

IF 5.4 2区医学

Diabetes technology & therapeutics Pub Date : 2024-05-01 Epub Date: 2024-01-04 DOI: 10.1089/dia.2023.0482

Mathilde Vandenbempt, Hanne Matheussen, Sara Charleer, Anne Rochtus, Kristina Casteels

{"title":"The Relationship Between Glycated Hemoglobin and Time in Range in a Pediatric Population.","authors":"Mathilde Vandenbempt, Hanne Matheussen, Sara Charleer, Anne Rochtus, Kristina Casteels","doi":"10.1089/dia.2023.0482","DOIUrl":"10.1089/dia.2023.0482","url":null,"abstract":"In adults with type 1 diabetes (T1D), time in range (TIR) [70-180 mg/dL] has been proposed as an additional metric besides glycated hemoglobin (HbA1c). This retrospective monocentric cohort study determined the correlation between HbA1c and TIR during the 2, 4, and 12 weeks (TIR2w, TIR4w, and TIR12w) before consultation in a pediatric T1D population. A total of 168 children with T1D were included. Continuous glucose monitoring data, HbA1c, and demographic variables were collected. We found strong linear correlations between HbA1c and TIR2w (R = -0.571), HbA1c and TIR4w (R = -0.603), and between HbA1c and TIR12w (R = -0.624). A strong correlation exists between TIR2w and TIR12w, HbA1c and time above range (TAR), and between TIR and TAR at different time points. In conclusion, a strong correlation was found between HbA1c and TIR, making TIR a potentially complementary metric to HbA1c. TIR2w seems a viable alternative to TIR12w. TAR also seems promising in assessing glycemic control.","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138828698","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}

引用次数: 0

MiniMed 780G System Outperforms Other Automated Insulin Systems Due to Algorithm Design, Not Bias: Response to Inaccurate Allegations. MiniMed 780G 系统性能优于其他自动胰岛素系统是由于算法设计，而非偏见 - 对不准确指控的回应。

IF 5.4 2区医学

Diabetes technology & therapeutics Pub Date : 2024-04-25 DOI: 10.1089/dia.2024.0121

Tim van den Heuvel, Javier Castaneda, Isabeau Thijs, Arcelia Arrieta, Lou Lintereur, John Shin, Ohad Cohen

引用次数: 0

Validation of the UVA Simulation Replay Methodology Using Clinical Data: Reproducing A Randomized Clinical Trial. 利用临床数据验证 UVA 模拟回放方法：再现随机临床试验。

IF 5.4 2区医学

Diabetes technology & therapeutics Pub Date : 2024-04-25 DOI: 10.1089/dia.2023.0595

María F Villa-Tamayo, Patricio Colmegna, Marc D Breton

{"title":"Validation of the UVA Simulation Replay Methodology Using Clinical Data: Reproducing A Randomized Clinical Trial.","authors":"María F Villa-Tamayo, Patricio Colmegna, Marc D Breton","doi":"10.1089/dia.2023.0595","DOIUrl":"https://doi.org/10.1089/dia.2023.0595","url":null,"abstract":"BACKGROUND\u0000Computer simulators of human metabolism are powerful tools to design and validate new diabetes treatments. However, these platforms are often limited in the diversity of behaviors and glycemic conditions they can reproduce. Replay methodologies leverage field-collected data to create ad-hoc simulation environments representative of real-life conditions. After formal validations of our method in prior publications, we demonstrate its capacity to reproduce a recent clinical trial.\u0000\u0000\u0000METHODS\u0000Using the replay methodology, an ensemble of replay simulators was generated using data from a randomized crossover clinical trial comparing hybrid closed loop (HCL) and fully closed loop (FCL) control modalities in automated insulin delivery (AID), creating 64 subject/modality pairs. Each virtual subject was exposed to the alternate AID modality to compare the simulated vs observed glycemic outcomes. Equivalence tests were performed for time in, below, and above range (TIR, TBR, TAR) and glucose indexes (LBGI, HBGI) considering equivalence margins corresponding to clinical significance.\u0000\u0000\u0000RESULTS\u0000TIR, TAR, LBGI, and HBGI showed statistical and clinical equivalence between the original and the simulated data, TBR failed the equivalence test. For example, in HCL mode, simulated TIR was 84.89% vs. an observed 84.31% (p=0.0170, CI [-3.96,2.79]), and for FCL mode, TIR was 76.58% versus 77.41% (p=0.0222, CI [-2.54,4.20]).\u0000\u0000\u0000CONCLUSION\u0000Clinical trial data confirms the prior in-silico validation of the UVA replay method in predicting the glycemic impact of modified insulin treatments. This in-vivo demonstration justifies the application of the replay method to the personalization and adaptation of treatment strategies in people with T1D.","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140653757","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}

引用次数: 0

Mitigating Severe Hypoglycemia in Users of Advanced Diabetes Technologies: Impaired Awareness of Hypoglycemia and Unhelpful Hypoglycemia Beliefs as Targets for Interventions. 减轻先进糖尿病技术用户的严重低血糖症：作为干预目标的低血糖意识受损和无益的低血糖信念。

IF 5.4 2区医学

Diabetes technology & therapeutics Pub Date : 2024-04-25 DOI: 10.1089/dia.2024.0039

Yu Kuei Lin, Wen Ye, Helen Rogers, A. Brooks, Elena Toschi, D. Kariyawasam, Simon R Heller, N. de Zoysa, Stephanie A Amiel

{"title":"Mitigating Severe Hypoglycemia in Users of Advanced Diabetes Technologies: Impaired Awareness of Hypoglycemia and Unhelpful Hypoglycemia Beliefs as Targets for Interventions.","authors":"Yu Kuei Lin, Wen Ye, Helen Rogers, A. Brooks, Elena Toschi, D. Kariyawasam, Simon R Heller, N. de Zoysa, Stephanie A Amiel","doi":"10.1089/dia.2024.0039","DOIUrl":"https://doi.org/10.1089/dia.2024.0039","url":null,"abstract":"OBJECTIVE\u0000A subgroup analysis of the Hypoglycemia Awareness Restoration Programme for people with type 1 diabetes and problematic hypoglycemia (HARPdoc) trial was conducted to explore the impact of Blood Glucose Awareness Training (BGAT, a hypoglycemia awareness training program) and the HARPdoc (a psychoeducation addressing unhelpful hypoglycemia beliefs) in reducing severe hypoglycemia (SH) in individuals using advanced diabetes technologies (ADTs).\u0000\u0000\u0000METHODS\u0000Data from trial participants who utilized ADTs including continuous glucose monitors or automated insulin delivery systems were extracted. Generalized linear mixed effects models with Poisson distribution or linear mixed effects models were employed to evaluate SH incidence, and Gold questionnaire, Attitudes to Awareness of Hypoglycemia (A2A), Problem Areas in Diabetes (PAID), Hospital Anxiety and Depress Scale (HADS)-anxiety, and HADS-depression scores as measures of hypoglycemia awareness, unhelpful hypoglycemia beliefs, diabetes distress, and anxiety and depression symptoms, respectively.\u0000\u0000\u0000RESULTS\u0000In the 45 participants using ADTs, the BGAT and HARPdoc interventions both reduced SH incidence by more than 50% (P<0.0001) and yielded improvements in hypoglycemia awareness (P<0.05). HARPdoc outperformed BGAT in reducing SH at month 24 (P=0.01). HARPdoc also mitigated unhelpful hypoglycemia beliefs (P<0.0001), diabetes distress (P<0.05), and anxiety symptoms (P<0.05); BGAT demonstrated no significant impacts in these respects. Neither HARPdoc nor BGAT had significant effects on depression symptoms.\u0000\u0000\u0000CONCLUSION\u0000Psychoeducation (BGAT and HARPdoc) was effective in reducing SH in people using ADTs. HARPdoc may also provide greater long-term SH reduction and improves psychological wellbeing in this patient group.","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140654395","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}

引用次数: 0

Adaptive bio-behavioral control: A pilot analysis of human-machine co-adaptation in type 1 diabetes. 适应性生物行为控制：1 型糖尿病人机共同适应试验分析。

IF 5.4 2区医学

Diabetes technology & therapeutics Pub Date : 2024-04-25 DOI: 10.1089/dia.2023.0399

Patricio Colmegna, Ryan Mcfadden, Chiara Fabris, Benjamin J. Lobo, Ralf Nass, Mary Clancy-Oliveri, Sue A Brown, B. Kovatchev

{"title":"Adaptive bio-behavioral control: A pilot analysis of human-machine co-adaptation in type 1 diabetes.","authors":"Patricio Colmegna, Ryan Mcfadden, Chiara Fabris, Benjamin J. Lobo, Ralf Nass, Mary Clancy-Oliveri, Sue A Brown, B. Kovatchev","doi":"10.1089/dia.2023.0399","DOIUrl":"https://doi.org/10.1089/dia.2023.0399","url":null,"abstract":"BACKGROUND\u0000While it is well recognized that an automated insulin delivery (AID) algorithm should adapt to changes in physiology, it is less understood that the individual would also have to adapt to the AID system. The adaptive bio-behavioral control (ABC) method presented here attempts to compensate for this deficiency by including AID into an information cloud-based ecosystem.\u0000\u0000\u0000METHODS\u0000The Web Information Tool (WIT) implements the ABC concept via: (1) A Physiological Adaptation Module (PAM) that tracks metabolic changes and adapts AID parameters accordingly, and (2) a Behavioral Adaptation Module (BAM) that provides information feedback. The safety of WIT (primary outcome) was assessed in an 8-week randomized, two-arm parallel pilot study. All participants used the Control-IQ® AID system enhanced with PAM, but only those in the Experimental group had access to BAM. Secondary glycemic outcomes were computed using the 2-week baseline period and the last 2 weeks of treatment.\u0000\u0000\u0000RESULTS\u0000Thirty participants with type 1 diabetes (T1D) completed all study procedures (17F/13M; age: 40±14y; HbA1c: 6.6%±0.5%). No severe hypoglycemia, DKA, or other serious adverse events were reported. Comparing Experimental and Control groups, no significant difference was observed in time in range (70-180 mg/dL): 74.6% vs 73.8%, adjusted mean difference: 2.65%, 95%¬¬¬¬¬CI (-1.12%,6.41%), P=0.161. Time in 70-140 mg/dL was significantly higher in the Experimental group: 50.7% vs 49.2%, 5.71% (0.44%,10.97%), P=0.035, without increased time below range: 0.54% (-0.09%,1.17%), P=0.089.\u0000\u0000\u0000CONCLUSION\u0000The results demonstrate it is safe to integrate an AID system into the WIT ecosystem. Validation in a full-scale study is ongoing.","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140658386","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}

引用次数: 0

Progression from Prediabetes to Diabetes in a Diverse US Population: a Machine Learning Model. 美国不同人群从糖尿病前期发展为糖尿病的过程：一个机器学习模型。

IF 5.4 2区医学

Diabetes technology & therapeutics Pub Date : 2024-04-15 DOI: 10.1089/dia.2024.0052

Joseph Aoki, Omar Khalid, Cihan Kaya, Zoltan Nagymanyoki, Jerry Hussong, Mohamed Salama

{"title":"Progression from Prediabetes to Diabetes in a Diverse US Population: a Machine Learning Model.","authors":"Joseph Aoki, Omar Khalid, Cihan Kaya, Zoltan Nagymanyoki, Jerry Hussong, Mohamed Salama","doi":"10.1089/dia.2024.0052","DOIUrl":"https://doi.org/10.1089/dia.2024.0052","url":null,"abstract":"Objective To date, there are no widely implemented machine learning (ML) models that predict progression from prediabetes to diabetes. Addressing this knowledge gap would aid in identifying at-risk patients within this heterogeneous population who may benefit from targeted treatment and management in order to preserve glucose metabolism and prevent adverse outcomes. The objective of this study was to utilize readily available laboratory data to train and test the performance of ML-based predictive risk models for progression from prediabetes to diabetes. Methods The study population was composed of laboratory information services data procured from a large US outpatient laboratory network. The retrospective data set was composed of 15,029 adults over a five-year period with initial hemoglobin A1C (A1C) values between 5.0% - 6.4%. ML models were developed using random forest survival methods. The ground truth outcome was progression to A1C values indicative of diabetes (i.e., ≧ 6.5%) within 5 years. Results The prediabetes risk classifier model accurately predicted A1C ≧ 6.5% within 5 years and achieved an area under the curve receiver-operator characteristic of 0.87. The most important predictors of progression from prediabetes to diabetes were initial A1C, initial serum glucose, A1C slope, serum glucose slope, initial HDL, HDL slope, age, and sex. Conclusions Leveraging readily obtainable laboratory data, our ML risk classifier accurately predicts elevation in A1C associated with progression from prediabetes to diabetes. While prospective studies are warranted, the results support the clinical utility of the model to improve timely recognition, risk stratification, and optimal management for patients with prediabetes.","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140701528","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}

引用次数: 0

Response to: MiniMed 780G System Outperforms Other Automated Insulin Systems Due to Algorithm Design, Not Bias-Response to Inaccurate Allegations. 回应：MiniMed 780G 系统性能优于其他自动胰岛素系统是由于算法设计，而非偏见 - 对不准确指控的回应。

IF 5.4 2区医学

Diabetes technology & therapeutics Pub Date : 2024-04-05 DOI: 10.1089/dia.2024.0125

Gregory P Forlenza, Jennifer L Sherr

引用次数: 0

Contrasting glycemic outcomes in young people with diabetic ketoacidosis at onset of type 1 diabetes. 患有糖尿病酮症酸中毒的 1 型糖尿病患者的血糖结果截然不同。

IF 5.4 2区医学

Diabetes technology & therapeutics Pub Date : 2024-04-03 DOI: 10.1089/dia.2024.0147

Rama Lakshman, Mazin Najami, R. Hovorka, C. Boughton

引用次数: 0