Diabetes最新文献

{"title":"81-OR: Hide and Seek—Modulating PDL1 and HLA Class I Expression to Protect Human β-Cells in T1D","authors":"SAYRO JAWUREK, ALEXANDRA C. TITLE, CHANTAL RUFER, FELIX FORSCHLER, DECIO L. EIZIRIK, BURCAK YESILDAG","doi":"10.2337/db25-81-or","DOIUrl":"https://doi.org/10.2337/db25-81-or","url":null,"abstract":"Introduction and Objective: In type 1 diabetes (T1D), β-cells co-orchestrate their own demise by increasing their visibility to the immune system. The balance between HLA Class I and PDL1 expression is particularly critical: β-cells in T1D overexpress HLA Class I, increasing antigen presentation and immune targeting, but also upregulate PDL1, which promotes immune self-tolerance. Strategies to dissociate these mechanisms may aid development of novel T1D therapies. Methods: To study the misguided immune and β-cell dialogue in a relevant and scalable manner, we established three in vitro islet-immune injury models by culturing spheroids from primary human islets with proinflammatory cytokines, activated peripheral blood mononuclear cells or HLA-A2-restricted preproinsulin-specific cytotoxic T lymphocytes. We then modulated activity or expression of putative targets to investigate their effects on PDL1 and HLA Class I expression, β-cell function, survival and T cell infiltration. Results: In the established models, declining β-cell health manifested as increased basal and decreased glucose-stimulated insulin release, reduced intracellular insulin, loss of the first-phase insulin response and elevated proinsulin-to-insulin ratios. 3D microscopy revealed increased HLA Class I and PDL1 expression, and β-cell death. Extensive T cell infiltration and proinflammatory cytokine secretion confirmed immune activation in co-culture models. Liraglutide and HLA Class I blocking antibodies demonstrated anti-inflammatory and immune-protective effects, serving as controls for future studies. We are currently investigating TYK2-inhibitors and STAT2 knockdown as strategies for decoupling HLA Class I and PDL1 expression and immune protection. Our preliminary results shows TYK2 inhibition can protect islets from proinflammatory cytokine damage. Conclusion: The described biomimetic islet-immune assays provide scalable in vitro tools for studying interventions that can protect the β-cells from the immune-mediated attack that leads to T1D. Disclosure S. Jawurek: None. A.C. Title: None. C. Rufer: None. F. Forschler: Employee; InSphero AG. D.L. Eizirik: Advisory Panel; InSphero. B. Yesildag: Other Relationship; Novo Nordisk, Boehringer-Ingelheim, Eli Lilly and Company, Biomea Fusion, Biosplice Therapeutics, Abata Therapeutics, AstraZeneca, Amgen Inc.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"11 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"542-P: Development and Baseline Characteristics of a Novel Community-Based Nutrition-Lifestyle Therapy for Pregnant Women with Diabetes","authors":"RACHYL PINES, CEARA AXELROD, SARINTHA BELL, KRISTEN NELSON, ELIZABETH ROMERO GUTIERREZ, KATHERINE VICTORIA, KRISTIN N. CASTORINO","doi":"10.2337/db25-542-p","DOIUrl":"https://doi.org/10.2337/db25-542-p","url":null,"abstract":"Introduction and Objective: Socioeconomically disadvantaged (SED) pregnant Latina women are disproportionately burdened by type 2 and gestational diabetes. While diet and lifestyle therapy can help maintain glycemic control in pregnancy and plant-forward interventions have been successful in Latino populations, there is little data in pregnancy. This study evaluates a culturally tailored, plant-forward, dietary-lifestyle intervention delivered by community health workers (CHWs). We hypothesize that it will improve glycemic control in pregnant Latina women with diabetes, including time in continuous glucose monitoring (CGM) pregnancy range (TIR) 63-140 mg/dL, average glucose and glycemic variability. Methods: We performed a baseline analysis in the 12 out of target 30 participants currently enrolled. Participants are randomized to either plant-forward dietary-lifestyle intervention or to standard of care. The intervention arm receives 12 CHW-led classes. Standard of care group receives their regular pregnancy care visits. Both groups wear CGM unblinded as best practice for diabetes in pregnancy. Results: Of the 12 participants mean age was 33.17 ± 6.27 years and mean gestational age at enrollment was 22.05 ± 5.73 weeks. 41.67% have food insecurity and 8.13% have housing insecurity. 50% are primarily Spanish speaking and 67.77% have a high school education or less. Average baseline A1c was 5.78 ± 1.25%, range 4.5-9.3%. Mean CGM TIR 63-140 mg/dL was 78.39 ± 24.28%. Conclusion: This baseline data suggests a need for an effective, culturally and linguistically tailored dietary-lifestyle intervention in SED pregnant Latina women with diabetes. While on average goal pregnancy TIR of >70% was met, there is significant variation and several participants are not meeting this goal. Overall, results of this pilot study will increase understanding of the effect of high-quality CHW-led education interventions on glycemic profiles, and maternal and fetal pregnancy outcomes of this vulnerable population. Disclosure R. Pines: None. C. Axelrod: None. S. Bell: None. K. Nelson: None. E. Romero Gutierrez: None. K. Victoria: None. K.N. Castorino: Research Support; Abbott, Medtronic, Dexcom, Inc., Lilly Diabetes, MannKind Corporation, Eli Lilly and Company, Insulet Corporation. Speaker's Bureau; Dexcom, Inc. Advisory Panel; MannKind Corporation. Speaker's Bureau; Insulet Corporation. Funding National Institutes of Health NIMHD (P50MD017344 subaward SCON-00003510)","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"139 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"1756-P: Metabolic Bariatric Surgery Effects on Skeletal Muscles and Adipose Tissues","authors":"MARINA CARDELLINI, DANIELE MOROSETTI, VALENTINA SPINOZZI, FRANCESCA DAVATO, PAOLO GENTILESCHI, ALESSANDRO CARINI, FRANCESCO GARACI, ROSSELLA MENGHINI, MASSIMO FEDERICI","doi":"10.2337/db25-1756-p","DOIUrl":"https://doi.org/10.2337/db25-1756-p","url":null,"abstract":"Introduction and Objective: Visceral adipose tissue and skeletal muscles play a key role in the onset of Metabolic Syndrome and Atherosclerotic Diseases. To understand their changes after Metabolic Bariatric Surgery (MBS) we performed a 10 years follow up of the FLORINASH cohort. Methods: Results were obtained on 44 obese subjects: 23 underwent to lifestyle changes and 21 to MBS with 5 years as time since surgery for 7 patients and 10 years for 14 patients. All patients underwent abdominal CT scan: subcutaneous and visceral adipose tissue volumes were measured between D12 vertebra and femoral heads. Areas of skeletal muscle tissue (SMT), ​​subcutaneous (SAT) and visceral adipose tissue (VAT) were calculated on a slice passing through the L3 vertebra. Results: A significant reduction in VAT and SMT were found in post-bariatric patients, while there weren't differences in SAT and total adipose tissue (TAT) volume. Dividing patients on the basis of time since surgery, we observed that those operated since 5 years showed a greater reduction in VAT compared to 10 years post-surgery patients, while the last had enhanced sarcopenia. VAT and TAT were significantly correlated with weight, BMI, waist circumference, adiposity evaluation by BIA and DEXA, but not with metabolic parameters. SM area was correlated with hand grip strength (p=0,011). VAT area was not associated with age but significantly correlates with BMI (p=0,003), waist (p<0,001), body weight (p=0,013), adipose tissue (BIA: % Fat mass p<0,001, kg Fat mass p<0,001, DEXA: Fat p<0,001). VAT correlates with components of metabolic syndrome such as glycemia (p=0.007), insulinemia (p=0.001), triglycerides (p=0.039), HBA1c (p<0.001), insulin resistance (HOMA p=0.002), intima-media thickness (p=0.007) and degree of hepatic steatosis (p<0.001). Conclusion: The exactly measure VAT and SM by CT scan slice might contribute to really understand and predict the cardiometabolic risk of overweight or obese patients and could be performed simply and routinely. Disclosure M. Cardellini: None. D. Morosetti: None. V. Spinozzi: None. F. Davato: None. P. Gentileschi: None. A. Carini: None. F. Garaci: None. R. Menghini: None. M. Federici: None.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"593 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"1804-P: Beta-Cell Loss with Severe Hyperglycemia Is Responsible for Augmented Notch 1 Expression in the Pancreatic Exocrine Ductal Cells","authors":"YURY KRYVALAP, RIM HABTE, SHAWN Z. MENG, JAN CZYZYK","doi":"10.2337/db25-1804-p","DOIUrl":"https://doi.org/10.2337/db25-1804-p","url":null,"abstract":"Introduction and Objective: Functional crosstalk between pancreatic islets and exocrine pancreatic tissue may be a determining factor in the progression of type 1 diabetes (T1D). Supporting this premise, we found that inhibition of serpinB13, an inhibitor of cathepsin L (catL) proteinase expressed in the exocrine pancreatic ductal cells, has positive outcomes in T1D. We also found that cleavage of Notch1 (a gatekeeper of β-cell development) following inhibition of serpinB13 with a monoclonal antibody (mAb), is a key molecular event that connects endocrine and exocrine tissue in the pancreas. Based on our observations, we hypothesized that the Notch1 receptor is expressed in the pancreatic ductal tree. Methods: To examine this, we induced β-cell-specific ablation with diphtheria toxin (DT) in InsCre/Rosa26iDTR transgenic mice, which were treated with a wild-type islet transplant or left untreated and sacrificed for pancreatic examination 2 weeks after diabetes induction. To assess Notch1 in the pancreas we used Western blot and IF microscopy with the Visiopharm software. Results: Both methods revealed that severe insulin-dependent diabetes results in marked augmentation of Notch1 expression (p<0.0001 and p=0.0054, respectively). The marked increase in the Notch1 protein level was specific, as expression of presenilins (which are required for processing of Notch1) was intact. We also found that islet transplantation preventing hyperglycemia in DT-treated animals completely blocked Notch1 upregulation. Finally, histological analysis revealed that Notch1 expression is confined to the cytokeratin-19+ epithelial cells in the exocrine ducts. Conclusion: We conclude that high Notch1 expression in the exocrine pancreas may explain its regulation by serpinB13. Moreover, our results demonstarte that hyperglycemia, rather than direct sensing of β-cell loss by the exocrine pancreas, is responsible for Notch upregulation in ductal epithelium. Disclosure Y. Kryvalap: None. R. Habte: None. S.Z. Meng: None. J. Czyzyk: None.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"517 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"729-P: Retrospective Effects of GLP-1 Receptor Agonists with and without Concomitant SGLT2 Inhibitor Use in Diabetic Renal Transplant Recipients after 12 Months","authors":"FRANCISCA M. ACOSTA, BEVERLY HU, CHRIS MANIKKUTTIYIL, PAOLA FRAUSTO, MARIA ESCOBAR VASCO, CAROLINA SOLIS-HERRERA","doi":"10.2337/db25-729-p","DOIUrl":"https://doi.org/10.2337/db25-729-p","url":null,"abstract":"Introduction and Objective: GLP-1RAs & SGLT2i are treatments with benefits extending beyond glycemic control in kidney transplant recipients (KTR). These agents show promise in enhancing metabolic & cardiorenal outcomes in diabetic KTR, but evidence, especially for monotherapy vs. combination use, remains scarce. This study evaluates the effects of GLP-1RAs alone & combined with SGLT2i on metabolic, glycemic, renal, & cardiovascular outcomes in T2D KTR over 12 months. Methods: Retrospective cohort study of KTR with pre-transplant T2D. Baseline and 12-month data on metabolic, glycemic, renal, & CV outcomes were analyzed. The 2nd drug was initiated 3-9 months after the 1st. Paired t-tests were used, results expressed as mean±SEM. Results: 18 patients (59±2.7 yrs, 83% male) were studied. 12 (66.7%) received GLP-1RAs and 6 (33.3%) GLP-1RA+SGLT2i. GLP-1RA monotherapy significantly improved BMI (p<0.01), FPG (p=0.03), & A1c (p=0.07). No changes in BP, HR, Total Cholesterol, or HDL noted. Combination therapy showed significant improvements in LDL (p=0.08), eGFR (p=0.04), BUN (p<0.01), & Tacrolimus levels (p=0.05). No patients discontinued therapy due to side effects. Conclusion: GLP-1RAs & SGLT2i are effective & safe for T2D KTR, GLP-1RA improving glycemic & weight outcomes, while combination therapy offers added renal and lipid benefits, suggesting synergistic effects warranting further study. Disclosure F.M. Acosta: None. B. Hu: None. C. Manikkuttiyil: None. P. Frausto: None. M. Escobar Vasco: None. C. Solis-Herrera: Advisory Panel; Novo Nordisk, Bayer Pharmaceuticals, Inc. Funding UTHSA LSOM Pilot Grant","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"7 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"1368-P: A Prediction Model for Prediabetes and Diabetes Using Easily Obtainable Clinical Data","authors":"ALAN HUTCHISON, CELESTE THOMAS, ESRA TASALI, MARY E. RINELLA, RAGHAVENDRA G. MIRMIRA, WILLIAM F. PARKER","doi":"10.2337/db25-1368-p","DOIUrl":"https://doi.org/10.2337/db25-1368-p","url":null,"abstract":"Introduction and Objective: Chronic diseases such as cirrhosis can reduce the accuracy of the hemoglobin A1c as a diagnostic test for diabetes (DM). We aimed to determine if easily obtained clinical data could be used to improve the diagnosis of DM beyond A1c in the general population. Methods: We analyzed 13,800 subjects from NHANES from 2005-2016 who had an A1c and OGTT. Including standard labs and vital signs features with <12% missing data, we split the subjects and applied the machine learning (ML) approach XG Boost to identify predictive features of OGTT 2-hour glucose (2hG) levels ≥140 mg/dL (pre-DM) and ≥200 mg/dL (DM). Results: The rate of DM by A1c and OGTT was 5.3%, by A1c alone was 0.4%, and by OGTT alone was 3.5%. Of those with pre-DM by A1c, 11.8% had 2hG ≥ 200 mg/dL; 1.5% of those without pre-DM had 2hG ≥ 200 mg/dL (A). The most important variables were included in the model: age, height, arm and waist circumference, pulse, blood pressure, fasting glucose, insulin, iron, and triglycerides, A1c, cholesterol, platelets, GGT, creatinine, neutrophil percentage, urine albumin and creatinine, and Poverty Ratio. The AUC of the model vs. the A1c for pre-DM was 0.76 vs. 0.67 and for DM was 0.92 vs. 0.87, respectively (B). For A1c < 6.3% the model had a higher average positive predictive value (boxplots) than the A1c (blue lines) (C). Conclusion: Incorporation of easily obtainable clinical data into a ML model can improve diagnosis of pre-DM and DM. Disclosure A. Hutchison: None. C. Thomas: None. E. Tasali: None. M.E. Rinella: Consultant; 89bio, Inc, Akero Therapeutics, Inc, Boehringer-Ingelheim, Eli Lilly and Company, Cytodyn, Inventiva Pharma, Echosens, Novo Nordisk, Madrigal Pharmaceuticals, Inc, Intercept Pharmaceuticals, Inc, Sagimet Biosciences. R.G. Mirmira: None. W.F. Parker: None.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"22 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"392-P: Metabotypes to Define Cardiorenal Risk in People with Type 1 Diabetes","authors":"LYNN ANG, YIYUAN HUANG, FARSAD AFSHINNIA, MOUSUMI BANERJEE, CATHERINE MARTIN, PRIYANKA RAMKUMAR, MATTHIAS KRETZLER, SUBRAMANIAM PENNATHUR, RODICA POP-BUSUI","doi":"10.2337/db25-392-p","DOIUrl":"https://doi.org/10.2337/db25-392-p","url":null,"abstract":"Introduction and Objective: Cardio-renal complications are the leading source of morbidity in people with type 1 diabetes (T1D). While previous metabolomic studies have characterized incident and predictive metabolic signatures (metabotypes) of diabetic kidney disease (DKD) and cardiovascular autonomic neuropathy (CAN) in separate T1D cohorts, no previous study has investigated CAN and DKD in the same subset of T1D patients. In this study we sought to identify shared and unique metabotypes of T1DKD and CAN in 120 T1D subjects. Methods: Baseline blood samples were processed for central carbon and lipidomic LC/MS analysis. DKD and CAN were defined as eGFR < 60 mL/min/1.73m2 and/or elevated albumin/creatinine (ACR), and prior validated cut-offs for indices of heart rate variability (SDNN, RMSSD). We used logistic regression to assess ability of metabolic feature/classes associating with incident and progression of DKD and CAN before and after adjusting for age, gender, race, diabetes duration and BMI. Results: Among 120 T1D subjects (mean age 48 years, mean A1c 8.2%; 42 % women) a unique panel of metabolites associated with incident DKD (with amino acids showing AUC of > 0.7 for eGFR and ACR) and CAN (peptides AUC > 0.7). In a subset of subjects with available follow-up data in adjusted analysis baseline azolines and glycerophosphoinositols were associated DKD progression (new decline in eGFR at follow-up < 60 or elevation of ACR compared with baseline). Unadjusted data showed baseline azolines, peptides and steroids may associate with progression of CAN while in adjusted analysis this was no longer significant presumably due to small numbers of subjects that exhibited progression. Conclusion: These data suggest that distinct metabotypes associate with T1D DKD and CAN at baseline, a subset of which associate with DKD progression. Ongoing studies will focus on identifying predictive CAN progression markers as more follow-up data becomes available for risk stratification in people with T1D. Disclosure L. Ang: None. Y. Huang: None. F. Afshinnia: None. M. Banerjee: None. C. Martin: None. P. Ramkumar: None. M. Kretzler: Research Support; AstraZeneca, Boehringer-Ingelheim, Chinook Therapeutics, Inc, Eli Lilly and Company, Moderna, Inc, Janssen Pharmaceuticals, Inc, National Institutes of Health, European Union. Advisory Panel; Novartis Pharmaceuticals Corporation. Research Support; Novo Nordisk. Advisory Panel; Otsuka America Pharmaceutical, Inc. Research Support; Regeneron Pharmaceuticals, Renalytix, Roche Pharmaceuticals, Sanofi, Travere, Certa, Dimerix. S. Pennathur: Research Support; Aurinia. R. Pop-Busui: Board Member; American Diabetes Association. Consultant; Averitas Pharma, Inc. Research Support; Bayer Pharmaceuticals, Inc. Other Relationship; Biogen. Research Support; Juvenile Diabetes Research Foundation (JDRF). Advisory Panel; Lexicon Pharmaceuticals, Inc, Novo Nordisk. Research Support; Novo Nordisk, National Insti","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"44 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"813-P: Machine Learning–Based Prediction Models for Initial Insulin Pump Dosing in Type 2 Diabetes Patients","authors":"MINGGUANG TANG, XIAOYI WANG, XI RONG","doi":"10.2337/db25-813-p","DOIUrl":"https://doi.org/10.2337/db25-813-p","url":null,"abstract":"Introduction and Objective: Accurate initial insulin dosing is essential for optimal glycemic control in type 2 diabetes patients with insulin pumps. Traditional weight-based estimations lack precision due to the heterogeneity of type 2 diabetes, underscoring the need for advanced predictive approaches. This study developed machine learning models to enhance the accuracy of initial premeal and basal dose predictions. Methods: Data from 1,245 patients at the First Affiliated Hospital of Guangxi Medical University were used for model construction and internal validation, and 60 patients from Sun Yat-sen Memorial Hospital for external validation. Adults aged 18-79 years with type 2 diabetes who initiated insulin pump therapy were included, with data collected during the first 24 hours following admission. Patients with severe comorbidities, acute complications, or organ failure were excluded. A stacked ensemble framework combining random forest, XGBoost, GBM, SVM, and Bayesian regression was used. Model 1 predicts premeal insulin doses, and Model 2 basal doses based on Model 1’s outputs. Performance was evaluated using RMSE, MAE, and MAPE. Results: Model 1 achieved an RMSE of 1.10 IU, MAE of 0.79 IU, and MAPE of 19.10% for internal validation, and an RMSE of 1.21 IU, MAE of 0.88 IU, and MAPE of 17.83% for external validation. Model 2 achieved an RMSE of 2.31 IU, MAE of 1.80 IU, and MAPE of 18.66% for internal validation, and an RMSE of 3.89 IU, MAE of 3.21 IU, and MAPE of 23.47% for external validation. Compared to traditional methods, machine learning models significantly reduced RMSE, MAE, and MAPE in both premeal and basal dose predictions. The prediction models are available as a web-based calculator at https://rongxi.shinyapps.io/Pump/. Conclusion: The machine learning models accurately predict initial insulin pump dosing and outperform traditional methods, offering a practical tool for optimizing therapy in type 2 diabetes patients with insulin pump treatment. Disclosure M. Tang: None. X. Wang: None. X. Rong: None. Funding the Clinical Research 'Climbing' Program of the First Affiliated Hospital of Guangxi Medical University (YYZS2023010); Guangxi Medical University Student Innovation and Entrepreneurship Training Program Project (X202310598348 and S202410598192)","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"21 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"1151-P: Continuous Glucose Monitoring (CGM) User Experience in Pediatric Hospital Diabetes Management","authors":"MING YEH LEE, HECTOR ORTEGA, KAREN BELANGER, SHERIDAN M. JONES, JULIE J.H. LEE, CHRISTOPHER CHOW-PARMER, CRISTAL SUAREZ, DAVID M. MAAHS, PRIYA PRAHALAD, RAYHAN LAL, MICHAEL S. HUGHES, SEJAL SHAH","doi":"10.2337/db25-1151-p","DOIUrl":"https://doi.org/10.2337/db25-1151-p","url":null,"abstract":"Introduction and Objective: Guidelines endorse CGM use during hospitalizations with proper support. However, barriers and facilitators for optimal implementation in the pediatric hospital setting are unknown. We aimed to evaluate user experiences before and after implementing a pediatric hospital CGM policy. Methods: In September 2024, we implemented a hospital policy to support using personal CGM to reduce fingersticks. People with diabetes (PwDs) or their guardians, nurses, and non-endocrine providers (residents and advance practice providers) were surveyed about their experiences using CGM during hospital encounters before and after implementation. Surveys included custom Likert-scale and free response questions. Hospital staff also completed a modified System Usability Scale (SUS). SUS scores were compared using two-tailed t-tests. Results: Surveys were completed by 40 PwDs, 22 nurses, and 12 providers at baseline and 9 PwDs, 37 nurses, and 12 providers post-implementation. PwD satisfaction with hospital glucose management was high throughout (>90%). Post-implementation, PwDs reported increased confidence in nurses’ ability to use CGM, more preference for using CGM for insulin dosing, and fewer interruptions. SUS scores increased by 16.8 ± 5.0 (mean difference ± SE) for nurses (p=0.002) and by 13.1 ± 7.2 for providers (p=0.09). Staff reported improved perceptions about ease of use, system integration, consistency, and ability to learn the system quickly. Staff perception that CGM workflow adds unnecessary tasks remained a major barrier. Though PwDs and nurses both report high confidence in nurses’ ability to use CGM, both groups wanted more staff education. Conclusion: Implementation of a pediatric hospital CGM policy was associated with improved experiences for PwD and staff. Identifying and addressing barriers to user experience, along with assessment of CGM accuracy, safety events, and glycemic outcomes are needed to promote program sustainability. Disclosure M. Lee: None. H. Ortega: None. K. Belanger: None. S.M. Jones: None. J.J.H. Lee: None. C. Chow-Parmer: None. C. Suarez: None. D.M. Maahs: Advisory Panel; Abbott, Medtronic. Research Support; Dexcom, Inc. Consultant; Sanofi. P. Prahalad: Consultant; Sanofi. R. Lal: Consultant; Abbott, Biolinq, Capillary Biomedical, Inc, Gluroo, PhysioLogic Devices, Portal Insulin, Sanofi, Tidepool. Advisory Panel; Provention Bio, Inc, Provention Bio, Inc, Microbion, Microbion, Lilly Diabetes. Research Support; Insulet Corporation, Medtronic, Tandem Diabetes Care, Inc, Sinocare Inc. M.S. Hughes: Consultant; Dexcom, Inc. Research Support; Tandem Diabetes Care, Inc, Medtronic, Insulet Corporation, Sinocare Inc. S. Shah: Research Support; MannKind Corporation. Funding National Institute of Health (5K12DK122550-05, DK007217-47), Stanford Maternal & Child Health Research Institute","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"4 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"723-P: Achieving Prediabetes Remission during Lifestyle Intervention Is More Effective Than Weight Loss for Type 2 Diabetes Prevention","authors":"ARVID SANDFORTH, LEONTINE SANDFORTH, SARAH KATZENSTEIN, JOCHEN SEISSLER, NIKOLAOS PERAKAKIS, ROBERT WAGNER, ANDREAS PETER, RAINER LEHMANN, HUBERT PREISSL, IRYNA YURCHENKO, JULIA SZENDROEDI, MATTHIAS BLÜHER, ANNETTE SCHÜRMANN, STEFAN KABISCH, KNUT MAI, PETER E. SCHWARZ, MARTIN HENI, MICHAEL RODEN, NORBERT STEFAN, ANDREAS FRITSCHE, REINER JUMPERTZ VON SCHWARTZENBERG, ANDREAS L. BIRKENFELD","doi":"10.2337/db25-723-p","DOIUrl":"https://doi.org/10.2337/db25-723-p","url":null,"abstract":"Introduction and Objective: Current guidelines recommend weight loss targets for individuals at risk for type 2 diabetes (T2D). Prediabetes is a high-risk state for T2D, and remission of prediabetes during weight loss has additional benefits for T2D prevention. Thus, we hypothesized that reaching glycemic targets is a more effective strategy for T2D prevention than weight loss targets. Methods: We studied 903 individuals with prediabetes from the German Prediabetes Lifestyle Intervention Study for whom data for weight loss and glycemic category classification was available. Glucose regulation was assessed by a 75 g oral glucose tolerance test. Prediabetes remission was defined as return to normal glucose regulation and normalized HbA1c according to ADA criteria. T2D risk was compared between responders and non-responders (R and NR) who lost weight (WL, n=298; < -5% of initial body weight), remained weight stable (WS, n=371; -5-0%) and gained weight (WG, n=234; >0%). Cox regression models were fit with age, sex and intervention intensity as covariates. Results: At baseline, age (p=0.11), fasting glucose (p=0.09), 2-hour glucose (p=0.98) and beta cell function were comparable between all three responder groups. WL-, WS- and WG-response was similarly protective from developing future T2D (HR for WL R vs. WL NR 0.11 [95 CI: 0.03-0.36], p = 0.00026, HR for WS R vs. WS NR 0.40 [95 CI: 0.17-0.93], p = 0.033, HR for WG R vs. WG NR 0.25 [95 CI: 0.09 -0.69], p = 0.0072,). T2D risk did not differ between weight loss strata (HR 0.82 [95 CI: 0.54-1.25], p = 0.36 for WS-R vs WG-R; and HR 0.91 [0.64-1.30], p = 0.61 for WL-R vs WG-R). Conclusion: Prediabetes remission, i.e. glycemic targets rather than weight loss targets, should be the primary treatment goal for T2D prevention. Disclosure A. Sandforth: None. L. Sandforth: None. S. Katzenstein: None. J. Seissler: None. N. Perakakis: Other Relationship; Novo Nordisk, Lilly Diabetes. Advisory Panel; Bayer Pharmaceuticals, Inc. Other Relationship; APOGEPHA, Transmedac Innovations AG, GWT-TUD, Elbe-Gesundsheintszentrum GmbH, Open Exploration. R. Wagner: Speaker's Bureau; Boehringer-Ingelheim, Novo Nordisk. Advisory Panel; Sanofi. Speaker's Bureau; Sanofi. Advisory Panel; Lilly Diabetes. A. Peter: None. R. Lehmann: None. H. Preissl: None. I. Yurchenko: None. J. Szendroedi: Advisory Panel; Novo Nordisk, Lilly Diabetes, Novartis AG, Boehringer-Ingelheim. M. Blüher: Advisory Panel; AstraZeneca. Speaker's Bureau; Amgen Inc. Advisory Panel; Bayer Pharmaceuticals, Inc, Boehringer-Ingelheim. Speaker's Bureau; Daiichi Sankyo. Advisory Panel; Eli Lilly and Company, Novo Nordisk, Nestlé Health Science, Sanofi-Aventis Deutschland GmbH. A. Schürmann: None. S. Kabisch: Research Support; Almond Board California, California Walnut Commission. Other Relationship; JuZo-Akademie, Boehringer-Ingelheim. Research Support; J. Rettenmaier & Söhne. Other Relationship; Lilly Diabetes. Research Support; Wilhelm-Doerenkamp-Fo","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"225 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}