Diabetes technology & therapeutics最新文献

{"title":"Review of Monogenic Diabetes: Clinical Features and Precision Medicine in Genetic Forms of Diabetes.","authors":"Erin C Cobry, Andrea K Steck","doi":"10.1089/dia.2024.0602","DOIUrl":"https://doi.org/10.1089/dia.2024.0602","url":null,"abstract":"<p><p>Monogenic diabetes is a group of diseases that encompasses a growing number of genetic abnormalities affecting pancreatic function/development leading to glycemic dysregulation. This includes conditions that have historically been referred to as maturity onset diabetes of the young or MODY in addition to neonatal diabetes mellitus. While recognition of a genetic or inherited form of diabetes has been known for decades, advances in molecular genetic testing have resulted in identification of specific forms of monogenic diabetes. Despite this, these genetic forms of diabetes remain widely underreported. It is important to be able to identify genetic forms of diabetes as treatment, monitoring for microvascular and macrovascular complications, and overall management varies for the different forms of monogenic diabetes. Furthermore, the identification of a specific monogenic form of diabetes can significantly impact the person's quality of life and other family members, as well as health care costs. This article highlights the identification, treatment, and management for various forms of monogenic diabetes and addresses some unmet needs in caring for people with monogenic forms of diabetes.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143771540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-Term Glycemic Benefits and User Interaction Insights: Real-World Outcomes of Automated Insulin Delivery Use in a Pediatric Population.","authors":"Emilie B Lindkvist, Ajenthen G Ranjan, Kristen Nørgaard, Jannet Svensson","doi":"10.1089/dia.2025.0068","DOIUrl":"https://doi.org/10.1089/dia.2025.0068","url":null,"abstract":"<p><p><b><i>Background:</i></b> Automated insulin delivery (AID) systems improve glycemic outcomes, but the roles of user interaction and insulin pump settings in these findings remain underexplored. <b><i>Objective:</i></b> To investigate how AID initiation influenced glycemic outcomes over a year and assess the impact of user behavior and insulin pump settings. <b><i>Methods:</i></b> This was a retrospective observational study on real-world data from 156 pediatric individuals initiating AID (Tandem Control-IQ or MiniMed^TM 780G). Data collected at baseline and a year following AID initiation included measures of glycemic outcomes, user interaction (e.g., daily meals, carbohydrates, and user-initiated insulin bolus), and insulin pump settings. <b><i>Results:</i></b> Percentage of time in range (TIR: 3.9-10.0 mmol/L) improved after AID initiation and remained stable over the follow-up year (baseline: 61.9% vs. month 12: 69.1%, <i>P</i> < 0.001). The percentage of individuals reaching target (TIR >70%) declined after an initial increase (baseline: 29.5% vs. month 1: 60.0% vs. month 12: 43.7%, <i>P</i> < 0.005). The predefined measures for user interaction also increased over a year, such as user-initiated insulin boluses (baseline: 53.7% of total daily dose [TDD] vs. month 12: 59.9% of TDD, <i>P</i> = 0.034), reduced carbohydrate intakes relative to body weight (baseline: 5.0 g/[kg·d] vs. month 12: 4.6 g/[kg·d], <i>P</i> = 0.004), and longer active continuous glucose monitoring (CGM) wear time (baseline: 87.2% vs. month 12: 94.1%, <i>P</i> = 0.011). A positive association between TIR and daily registered meals (<i>P</i> < 0.001) and daily registered carbohydrates (<i>P</i> = 0.003) was found in the multivariate analysis while adjusting for insulin pump settings and total daily insulin dose. <b><i>Conclusion:</i></b> Glycemic outcomes improved 12 months after AID initiation and were positively associated with the number of meal announcements and daily carbohydrates registered in the pump. User-initiated bolus insulin and percentage of active CGM wear time had no impact on AID performance. Our findings emphasize the importance of continuous assessment of diabetes management, even with advanced technology, as user engagement remains crucial.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763282","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":"Real-Life Performance of Automated Insulin Delivery Systems for High-Carbohydrate Meals: Are All Systems the Same?","authors":"Angela Zanfardino, Giorgia Ippolito, Gulsum Ozen, Alfonso Galderisi, Assunta Serena Rollato, Antonietta Chianese, Veronica Testa, Emanuele Miraglia Del Giudice, Andrea E Scaramuzza, Dario Iafusco","doi":"10.1089/dia.2025.0106","DOIUrl":"https://doi.org/10.1089/dia.2025.0106","url":null,"abstract":"<p><p>Managing postprandial blood glucose levels in children and adolescents with type 1 diabetes remains challenging, particularly with high-carbohydrate meals. This study analyzed postprandial glycemic responses to Margherita pizza and a ham sandwich with similar macronutrient content in children using two different automated insulin delivery (AID) systems: MiniMed™ 780G and Tandem t:slim X2™ with Control-IQ. Thirty-four participants consumed both meals on separate occasions while maintaining standard insulin boluses. Results showed no significant differences in glycemic control between meals, suggesting that Margherita pizza can be effectively managed with standard bolus strategies. However, individuals using MiniMed 780G demonstrated a higher time in target range (70-140 mg/dL) in the 10 h following pizza consumption compared with Control-IQ users, possibly due to MiniMed 780G's multiple automatic correction boluses. No other significant differences in glucometrics or insulin delivery were observed. These findings highlight the need for personalized meal management strategies based on specific AID system algorithms. <b>Study registration:</b> ClinicalTrials.gov, NCT05729776.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763286","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":"Reduction of Postprandial Glucose Excursions in Adults, Adolescents, and Children with Type 1 Diabetes Using Ultra-Rapid Lispro Insulin and Control-IQ+ Technology.","authors":"Grazia Aleppo, Peter Calhoun, Ryan Bailey, Jordan E Pinsker, Carol J Levy, John W Lum, Roy W Beck","doi":"10.1089/dia.2025.0077","DOIUrl":"https://doi.org/10.1089/dia.2025.0077","url":null,"abstract":"<p><p>This study evaluated the effects of ultra-rapid lispro (URLi) insulin versus insulin lispro on postprandial glucose excursions in 176 individuals with type 1 diabetes using Control-IQ+ technology. Postprandial glycemia differed the most between URLi and lispro at 60 min (mean glucose 166 ± 69 mg/dL vs. 178 ± 70 mg/dL; adjusted mean difference [AMD] = -11 mg/dL; <i>P</i> < 0.001). The URLi had slightly lower mean glucose excursion compared with lispro (AMD = -4 mg/dL; <i>P</i> = 0.001), but the differences between treatments were larger following breakfast (AMD = -9 mg/dL) compared with lunch (AMD = -2 mg/dL) and dinner (AMD = -2 mg/dL). Participants with insulin-to-carbohydrate ratio (ICR) <5 g/U had a larger treatment group difference favoring URLi on mean glucose excursion (AMD = -11 mg/dL) compared with those with ICR 5-15 g/U (AMD = -2 mg/dL) and ICR >15 g/U (AMD = 1 mg/dL). In conclusion, compared with insulin lispro, the use of URLi with Control-IQ+ technology modestly improved postprandial glucose excursions with the greatest amount of improvement for breakfast and in those with insulin resistance.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763290","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-Measured Glucose Levels During Oral Glucose Tolerance Testing in Pregnancy.","authors":"Anders L Carlson, Roy W Beck, Zoey Li, Elizabeth Norton, Richard M Bergenstal, Mary Johnson, Sean Dunnigan, Matthew Banfield, Katie Krumwiede, Judy Sibayan, Peter Calhoun, Celeste Durnwald","doi":"10.1089/dia.2024.0563","DOIUrl":"https://doi.org/10.1089/dia.2024.0563","url":null,"abstract":"<p><p><b><i>Background:</i></b> To diagnose gestational diabetes mellitus (GDM), clinicians typically rely on the oral glucose tolerance test (OGTT). Continuous glucose monitoring (CGM) is a tool that could possibly be used to complement or replace the OGTT. Our aim is to describe CGM-derived glycemic patterns observed concurrently during the administration of a diagnostic OGTT in pregnancy. <b><i>Methods:</i></b> In total, 119 pregnant females underwent OGTT testing while wearing a blinded CGM sensor. Blood glucose (BG) measurements collected during the OGTT were compared with CGM-measured glucose values obtained using a Dexcom G6 Pro sensor to determine the differences between CGM-measured and BG levels during the OGTT, measure glycemic excursion during the OGTT, and determine differences in GDM diagnosis using standard BG draws during OGTT versus CGM-measured glucose levels. <b><i>Results:</i></b> CGM-measured glucose levels were on average higher than paired BG levels during the OGTT at each timed measurement (fasting, 1-, 2- and 3-h); fasting CGM-measured glucose levels in particular were higher than fasting BG levels by 6 ± 13 mg/dL. The median CGM minus BG-measured glycemic excursion during the OGTT was 12 and 4 mg/dL for the 75 g and 100 g OGTT, respectively. Of 28 participants diagnosed with GDM based on OGTT BG levels, 24 (86%) participants would have been diagnosed as GDM using CGM with BG-based thresholds; of 91 participants not diagnosed with GDM, 54 (59%) would also have not been diagnosed with GDM using CGM. <b><i>Conclusions:</i></b> CGM glucose measurements using Dexcom G6 Pro tended to be slightly higher than BG values during an OGTT, leading to more participants who would have been diagnosed with GDM if the BG-based OGTT thresholds were applied to these CGM-measured glucose values. When CGM is used for GDM diagnosis, diagnostic glucose criteria may need to be specific for the type of sensor used accounting for any bias in glucose measurement.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763277","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":"Enhancing Type 1 Diabetes Immunological Risk Prediction with Continuous Glucose Monitoring and Genetic Profiling.","authors":"Eslam Montaser, Leon S Farhy, Stephen S Rich","doi":"10.1089/dia.2024.0496","DOIUrl":"10.1089/dia.2024.0496","url":null,"abstract":"<p><p><b><i>Background:</i></b> Early identification of individuals at high risk for type 1 diabetes (T1D) is essential for timely intervention. Islet autoantibodies (AB) and continuous glucose monitoring (CGM) reveal early signs of glycemic dysregulation, while T1D genetic risk scores (GRS) further improve disease prediction. We use CGM data and T1D GRS to develop an AB classifier (1 AB vs. ≥2 AB) and predict early T1D risk. <b><i>Methods:</i></b> Thirty-nine AB-positive (18 with 1 and 21 with ≥2 AB) healthy relatives of T1D (mean age 22.1 ± 11.1 years, HbA1c 5.3 ± 0.3%, body mass index 24.1 ± 5.8 kg/m²) were enrolled in a National Institutes of Health's (NIH) TrialNet ancillary study. Participants wore CGMs for a week and consumed three standardized liquid mixed meals (SLMM). Post-SLMM CGM glycemic features and T1D GRS were used in a linear support vector machine (SVM) model with recursive feature elimination (RFE) for AB classification, evaluated via fivefold cross-validation using the receiver operating characteristic and precision-recall area under the curve (AUC-ROC/PR). <b><i>Results:</i></b> Significant differences between the AB groups were observed in the post-SLMM percent time of glucose >180 mg/dL and GRS (<i>P</i> = 0.020 and <i>P</i> = 0.001, respectively). An SVM model with two RFE-selected features (T1D GRS and incremental AUC) achieved the best performance, classifying 1 versus ≥2 AB individuals with an AUC-ROC of 0.93 (95% confidence interval [CI]: 0.83-1.00) and AUC-PR of 0.89 (95% CI: 0.71-0.99), compared with AUC-ROC of 0.80 (95% CI: 0.46-1.00) and AUC-PR of 0.82 (95% CI: 0.71-0.93) using all features. <b><i>Conclusions:</i></b> A machine learning approach combining a 1-week CGM home test and T1D GRS reliably assesses T1D immunological risk, enabling early intervention.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"292-300"},"PeriodicalIF":5.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142834419","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":"Seeing the Road Ahead-The Need to Address Accessibility of Diabetes Technology.","authors":"Rishika Kartik, Halis Kaan Akturk, Michael W Stewart, Gregory P Forlenza","doi":"10.1089/dia.2024.0412","DOIUrl":"10.1089/dia.2024.0412","url":null,"abstract":"","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"245-247"},"PeriodicalIF":5.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142881491","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 Glucose ContrOL Using a Next-GeneraTION Automated Insulin Delivery Algorithm in Patients with Type 1 and Type 2 Diabetes: The EVOLUTION Study.","authors":"Tom Wilkinson, Renee Meier, Niranjala Hewapathirana, Claire Lever, Solita Donnelly, Rachael Sampson, Jonathan Williman, Mert Sevil, Saeed Salavati, Sam Carl, Bonnie Dumais, Trang T Ly, Martin de Bock","doi":"10.1089/dia.2024.0463","DOIUrl":"10.1089/dia.2024.0463","url":null,"abstract":"<p><p>This study evaluated a next-generation automated insulin delivery (AID) algorithm for Omnipod in type 1 and type 2 diabetes across multiple phases: 14-day run-in with usual therapy, 48-h AID use in a hotel setting (type 1 only), and up to 6 weeks of outpatient AID use. Participants did, or did not, deliver manual boluses at alternating periods. Twelve adults with type 1 diabetes completed the hotel phase; 9 of those 12 plus 8 adults with type 2 diabetes completed the subsequent outpatient phase. Outpatient % continuous glucose monitor readings >250 mg/dL decreased from 33.5% at baseline to 9.4% with, and 14.3% without, manual boluses in type 1 diabetes and from 20.8% to 7.7% with, and 10.5% without, manual boluses in type 2 diabetes. Time below 70 mg/dL remained <4% during all phases. No adverse events occurred. In conclusion, a next-generation AID algorithm demonstrated feasibility in people with diabetes.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"323-328"},"PeriodicalIF":5.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045869","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 Glucose and Additional Signals for Three Different Exercise Types in Adolescents with Type 1 Diabetes Using a Hybrid Closed-Loop System.","authors":"Rowen Seckold, Carmel E Smart, David N O'Neal, Michael C Riddell, Jordan Rafferty, Dale Morrison, Varuni Obeyesekere, Judy L Gooley, Barbora Paldus, Sarah R Valkenborghs, Sara Vogrin, Dessi P Zaharieva, Bruce R King","doi":"10.1089/dia.2024.0254","DOIUrl":"10.1089/dia.2024.0254","url":null,"abstract":"<p><p><b><i>Objective:</i></b> To compare glycemic outcomes during and following moderate-intensity exercise (MIE), high-intensity interval exercise (HIE), and resistance exercise (RE) in adolescents with type 1 diabetes (T1D) using a hybrid closed-loop (HCL) insulin pump while measuring additional physiological signals associated with activity. <b><i>Methods:</i></b> Twenty-eight adolescents (average age 16.3 ± 2.1 years, 50% females, average duration of T1D 9.4 ± 4 years) using HCL (Medtronic MiniMed 670G) undertook 40 min of MIE, HIE, and RE. A temporary glucose target (8.3 mmol/L, 150 mg/dL) was set for 2 h prior and during exercise. Heart rate, accelerometer, venous glucose, lactate, ketones, and counter-regulatory hormones were measured for 280 min postexercise commencement. The primary outcome was glucose percentage time in range (TIR): 3.9-10 mmol/L (70-180 mg/dL) for 14 h from exercise onset. <b><i>Results:</i></b> Median (interquartile range) TIR for HIE was 88 (78, 96)%, MIE 79 (63, 88)%, and RE 86 (72, 95)% for 14 h from exercise onset. For MIE compared with HIE, TIR was lower (<i>P</i> = 0.012) and time above range (TAR) was greater (18 [2.4, 28] vs. 6.9 [0.0, 14]%, <i>P</i> = 0.041). Hypoglycemia occurred in 13 (46%), 11 (39%), and 14 (50%) of participants for HIE, MIE, and RE, respectively, the majority following the meal after exercise. There were higher levels of lactate (<i>P</i> = 0.001), growth hormone (<i>P</i> = 0.001), noradrenaline (<i>P</i> = 0.001), and heart rate (<i>P</i> = 0.01) during HIE and RE compared with MIE. <b><i>Conclusions</i></b>: HCL use in adolescents with T1D results in excellent TIR during different forms of exercise when a temporary target is set 2 h before. Extending the temporary target after exercise may also be needed to help minimize postexercise hypoglycemia.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"308-322"},"PeriodicalIF":5.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142946156","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":"Current Treatment Options for Diabetic Retinal Disease.","authors":"Ryan F Bloomquist, Doan Tam Bloomquist, Thomas W Gardner","doi":"10.1089/dia.2024.0548","DOIUrl":"10.1089/dia.2024.0548","url":null,"abstract":"<p><p>The global incidence of diabetes is rising steadily and with it the number of people living with diabetic retinal disease (DRD) is increasing. Like diabetes, DRD can be treated but not cured. In response, therapies to address DRD include targeted ocular and systemic medications. This review discusses diabetes and DRD in terms of current screening recommendations, treatments, and considerations related to those therapies and future drug targets and trials on the horizon. This discourse is targeted at all members of the diabetes care team, including primary care providers, optometrists, and ophthalmologists. The dynamic landscape of diabetic retinopathy treatment is promising for the prevention and improvement of visually significant disease.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"248-260"},"PeriodicalIF":5.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892936","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}