JIA ZHAO, SHENGHUI LIANG, LILY GUO, MAJID MOJIBIAN, ROBERT K. BAKER, VIVIAN FUNG, MEGAN LEVINGS, ANDRAS NAGY, TIM KIEFFER
Introduction and Objective: Stem cell-derived islet (SC-islet) replacement therapies are currently being investigated in clinical trials and have shown great promise for diabetes treatment. However, challenges remain, including the use of chronic immunosuppressants to limit immune reactions to implanted cells. To address this issue, we hypothesize that genetically modifying stem cells to achieve localized immune evasion could enable functional and durable SC-islet engraftment in patients without systemic immunosuppression. Methods: A human embryonic stem cell (hESC) line was genetically modified with the goal of providing immune-evasiveness through the constitutive expression of transgenes encoding PD-L1, FASL, CD200, CD47, HLA-G, CCL21, SERPINB9 and MFGE8. An inducible kill switch was also integrated, whereby HSV-TK is linked to the cell division gene CDK1 such that dividing cells can be selectively eliminated by exposure to the pro-drug ganciclovir (GCV). Results: The genetically engineered hESCs efficiently differentiated into insulin-secreting SC-islets in vitro. When co-cultured with various immune cell types, these SC-islets suppressed immune cell activation and were resistant to immune cell-mediated killing. By individually antagonizing the immunomodulatory factors, we determined all eight contribute to such tolerance. When proliferation was induced in SC-islet cultures or SC-islets were purposely contaminated with undifferentiated stem cells, GCV treatment efficiently eliminated these dividing cells. Conclusion: Our data suggest that SC-islets engineered to overexpress these eight immunomodulatory factors enable immune evasion and the kill switch system is effective in removing proliferative cells present in cultures. Cell implant studies are underway to assess the immune-evasiveness and kill switch effectiveness in vivo. Ultimately, this approach could provide a universal source for SC-islets to treat diabetes without the use of immunosuppression. Disclosure J. Zhao: None. S. Liang: None. L. Guo: None. M. Mojibian: None. R.K. Baker: None. V. Fung: None. M. Levings: None. A. Nagy: None. T. Kieffer: Employee; Fractyl Health, Inc. Stock/Shareholder; Fractyl Health, Inc. Funding Breakthrough T1D (3-SRA-2022-1252-S-B)
{"title":"2139-LB: Immune-Shielded Islets from Engineered Human Pluripotent Stem Cells for Potential Allogeneic Therapy","authors":"JIA ZHAO, SHENGHUI LIANG, LILY GUO, MAJID MOJIBIAN, ROBERT K. BAKER, VIVIAN FUNG, MEGAN LEVINGS, ANDRAS NAGY, TIM KIEFFER","doi":"10.2337/db25-2139-lb","DOIUrl":"https://doi.org/10.2337/db25-2139-lb","url":null,"abstract":"Introduction and Objective: Stem cell-derived islet (SC-islet) replacement therapies are currently being investigated in clinical trials and have shown great promise for diabetes treatment. However, challenges remain, including the use of chronic immunosuppressants to limit immune reactions to implanted cells. To address this issue, we hypothesize that genetically modifying stem cells to achieve localized immune evasion could enable functional and durable SC-islet engraftment in patients without systemic immunosuppression. Methods: A human embryonic stem cell (hESC) line was genetically modified with the goal of providing immune-evasiveness through the constitutive expression of transgenes encoding PD-L1, FASL, CD200, CD47, HLA-G, CCL21, SERPINB9 and MFGE8. An inducible kill switch was also integrated, whereby HSV-TK is linked to the cell division gene CDK1 such that dividing cells can be selectively eliminated by exposure to the pro-drug ganciclovir (GCV). Results: The genetically engineered hESCs efficiently differentiated into insulin-secreting SC-islets in vitro. When co-cultured with various immune cell types, these SC-islets suppressed immune cell activation and were resistant to immune cell-mediated killing. By individually antagonizing the immunomodulatory factors, we determined all eight contribute to such tolerance. When proliferation was induced in SC-islet cultures or SC-islets were purposely contaminated with undifferentiated stem cells, GCV treatment efficiently eliminated these dividing cells. Conclusion: Our data suggest that SC-islets engineered to overexpress these eight immunomodulatory factors enable immune evasion and the kill switch system is effective in removing proliferative cells present in cultures. Cell implant studies are underway to assess the immune-evasiveness and kill switch effectiveness in vivo. Ultimately, this approach could provide a universal source for SC-islets to treat diabetes without the use of immunosuppression. Disclosure J. Zhao: None. S. Liang: None. L. Guo: None. M. Mojibian: None. R.K. Baker: None. V. Fung: None. M. Levings: None. A. Nagy: None. T. Kieffer: Employee; Fractyl Health, Inc. Stock/Shareholder; Fractyl Health, Inc. Funding Breakthrough T1D (3-SRA-2022-1252-S-B)","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"1 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334851","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}
YU MI KANG, ROBERT P. GIUGLIANO, XINHUI RAN, PRAKASH DEEDWANIA, GAETANO M. DE FERRARI, JYOTHIS T. GEORGE, IOANNA GOUNI-BERTHOLD, GABRIEL PAIVA DA SILVA LIMA, YEHUDA HANDELSMAN, BASIL S. LEWIS, E. MAGNUS OHMAN, ANTHONY C. KEECH, HUEI WANG, MARC S. SABATINE, LAWRENCE A. LEITER
Introduction and Objective: Despite the very high risk for macrovascular complications, there are scant data on the benefit of lipid-lowering in type 1 diabetes (T1D). We examined the clinical efficacy of intensive LDL-C lowering with the PCSK9 inhibitor evolocumab in T1D. Methods: FOURIER enrolled pts w/ stable atherosclerotic cardiovascular disease (ASCVD) on statin randomized to evolocumab or placebo (median FU 2.2y). The primary endpoint (PEP) was CV death, MI, stroke, hospitalization for unstable angina, or coronary revascularization. The key secondary endpoint (SEP) was CV death, MI, or stroke. Hazard ratios (HR) and absolute risk reductions (ARR) with evolocumab vs. placebo were compared among pts w/o diabetes (no DM), with type 2 diabetes (T2D), and with T1D. Results: Of 27,564 pts, 197 (0.7%) had T1D. Their median (IQR) age was 58 (53-64) yrs and duration of diabetes 28 yrs. In the placebo arm, there was a stepwise increase in the 2.5-y KM rate of the PEP, going from 11.0% to 15.2% to 20.4% in pts w/ no DM, T2D, and T1D, respectively (p<0.0001; Fig). Evolocumab reduced the risk of the PEP by 13% (HR 0.87; 95% CI 0.73-0.96), 16% (HR 0.84 [0.75-0.93]), and 34% (HR 0.66 [0.32-1.38]), respectively. Corresponding ARRs were 1.3%, 2.5%, and 7.3%. Similar trends were seen for the key SEP. Conclusion: T1D pts with ASCVD face elevated MACE risk, and intensive LDL-C lowering with evolocumab appears to provide substantial clinical benefit in this high-risk group. Disclosure Y. Kang: None. R.P. Giugliano: Research Support; Amgen Inc, Anthos Therapeutics, Daiichi Sankyo, Ionis Pharmaceuticals. Other Relationship; Amgen Inc, CADECI, Centrix, Daiichi Sankyo, Dr. Reddy's Laboratories, Korean Heart Rhythm Society, Medical Education Resources (MER), Menarini, Pfizer Inc, SHAKEHEART, SUMMEET. Consultant; Amgen Inc, AstraZeneca, Beckman Coulter, Daiichi Sankyo, Gilead Sciences, Inc, Inventiva Pharma, Novartis Pharmaceuticals Corporation, Perosphere, Samsung, Syneos Health. X. Ran: None. P. Deedwania: None. G.M. De Ferrari: Advisory Panel; Daiichi Sankyo. Board Member; Amgen Inc, Merck & Co., Inc, Novartis AG. J.T. George: Employee; Amgen Inc. I. Gouni-Berthold: Speaker's Bureau; Amgen Inc, Sanofi-Aventis Deutschland GmbH. Advisory Panel; Daiichi Sankyo. Speaker's Bureau; Novartis AG. Advisory Panel; Novartis AG. Speaker's Bureau; Ultragenyx, Daiichi Sankyo. G. Paiva da Silva Lima: Employee; Amgen Inc. Stock/Shareholder; Amgen Inc. Y. Handelsman: Research Support; Amgen Inc. Consultant; Amgen Inc. Research Support; Applied Therapeutics. Consultant; Applied Therapeutics. Research Support; Corcept Therapeutics. Consultant; Corcept Therapeutics. Research Support; Ionis Pharmaceuticals, Lilly Diabetes, Merck Sharp & Dohme Corp, Regeneron Pharmaceuticals. B.S. Lewis: Consultant; Janssen Pharmaceuticals, Inc. E. Ohman: Employee; Amgen Inc. A.C. Keech: Research Support; Abbott, Amgen Inc, ASPEN Australia, Mylan. Speaker's Bureau; Novartis AG, Pfizer I
{"title":"1991-LB: Cardiovascular Efficacy of Evolocumab in Persons with Type 1 Diabetes Mellitus—Insights from FOURIER Trial","authors":"YU MI KANG, ROBERT P. GIUGLIANO, XINHUI RAN, PRAKASH DEEDWANIA, GAETANO M. DE FERRARI, JYOTHIS T. GEORGE, IOANNA GOUNI-BERTHOLD, GABRIEL PAIVA DA SILVA LIMA, YEHUDA HANDELSMAN, BASIL S. LEWIS, E. MAGNUS OHMAN, ANTHONY C. KEECH, HUEI WANG, MARC S. SABATINE, LAWRENCE A. LEITER","doi":"10.2337/db25-1991-lb","DOIUrl":"https://doi.org/10.2337/db25-1991-lb","url":null,"abstract":"Introduction and Objective: Despite the very high risk for macrovascular complications, there are scant data on the benefit of lipid-lowering in type 1 diabetes (T1D). We examined the clinical efficacy of intensive LDL-C lowering with the PCSK9 inhibitor evolocumab in T1D. Methods: FOURIER enrolled pts w/ stable atherosclerotic cardiovascular disease (ASCVD) on statin randomized to evolocumab or placebo (median FU 2.2y). The primary endpoint (PEP) was CV death, MI, stroke, hospitalization for unstable angina, or coronary revascularization. The key secondary endpoint (SEP) was CV death, MI, or stroke. Hazard ratios (HR) and absolute risk reductions (ARR) with evolocumab vs. placebo were compared among pts w/o diabetes (no DM), with type 2 diabetes (T2D), and with T1D. Results: Of 27,564 pts, 197 (0.7%) had T1D. Their median (IQR) age was 58 (53-64) yrs and duration of diabetes 28 yrs. In the placebo arm, there was a stepwise increase in the 2.5-y KM rate of the PEP, going from 11.0% to 15.2% to 20.4% in pts w/ no DM, T2D, and T1D, respectively (p&lt;0.0001; Fig). Evolocumab reduced the risk of the PEP by 13% (HR 0.87; 95% CI 0.73-0.96), 16% (HR 0.84 [0.75-0.93]), and 34% (HR 0.66 [0.32-1.38]), respectively. Corresponding ARRs were 1.3%, 2.5%, and 7.3%. Similar trends were seen for the key SEP. Conclusion: T1D pts with ASCVD face elevated MACE risk, and intensive LDL-C lowering with evolocumab appears to provide substantial clinical benefit in this high-risk group. Disclosure Y. Kang: None. R.P. Giugliano: Research Support; Amgen Inc, Anthos Therapeutics, Daiichi Sankyo, Ionis Pharmaceuticals. Other Relationship; Amgen Inc, CADECI, Centrix, Daiichi Sankyo, Dr. Reddy's Laboratories, Korean Heart Rhythm Society, Medical Education Resources (MER), Menarini, Pfizer Inc, SHAKEHEART, SUMMEET. Consultant; Amgen Inc, AstraZeneca, Beckman Coulter, Daiichi Sankyo, Gilead Sciences, Inc, Inventiva Pharma, Novartis Pharmaceuticals Corporation, Perosphere, Samsung, Syneos Health. X. Ran: None. P. Deedwania: None. G.M. De Ferrari: Advisory Panel; Daiichi Sankyo. Board Member; Amgen Inc, Merck & Co., Inc, Novartis AG. J.T. George: Employee; Amgen Inc. I. Gouni-Berthold: Speaker's Bureau; Amgen Inc, Sanofi-Aventis Deutschland GmbH. Advisory Panel; Daiichi Sankyo. Speaker's Bureau; Novartis AG. Advisory Panel; Novartis AG. Speaker's Bureau; Ultragenyx, Daiichi Sankyo. G. Paiva da Silva Lima: Employee; Amgen Inc. Stock/Shareholder; Amgen Inc. Y. Handelsman: Research Support; Amgen Inc. Consultant; Amgen Inc. Research Support; Applied Therapeutics. Consultant; Applied Therapeutics. Research Support; Corcept Therapeutics. Consultant; Corcept Therapeutics. Research Support; Ionis Pharmaceuticals, Lilly Diabetes, Merck Sharp & Dohme Corp, Regeneron Pharmaceuticals. B.S. Lewis: Consultant; Janssen Pharmaceuticals, Inc. E. Ohman: Employee; Amgen Inc. A.C. Keech: Research Support; Abbott, Amgen Inc, ASPEN Australia, Mylan. Speaker's Bureau; Novartis AG, Pfizer I","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"30 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334849","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}
FELIX LAM, VINAYAK TIWARI, GIULIANO MION, DISHA KHEDEKAR, PRATEEP MUKHERJEE, SHAILJA PANDEY, DHARMI DESAI, LAURA WILSON, JESSICA DUNNE, LICHEN HAO, MATTIAS WIELOCH, JULIA H. ZACCAI, ROBERT B. MCQUEEN, KIMBER M. SIMMONS, EMILY K. SIMS
Introduction and Objective: Autoimmune type 1 diabetes (T1D) often goes undiagnosed until a major clinical event triggers disease recognition. Identifying individuals in early T1D stages remains a clinical challenge given inefficient screening thus limiting opportunities for early intervention. This study aimed to develop a predictive machine learning model that identified individuals before the onset of stage 3 T1D. Methods: This was a retrospective cohort study that utilized medical claims data and lab test results from the US Managed Markets Insight & Technology (MMIT) dataset to develop two age specific AI/ML Models (0-24 years and 25+ years) for identifying individuals with presumed early stage T1D at least one year from first observed T1D diagnosis. Confirmed stage 3 T1D cases, used to train and validate the model, were required to have ≥2 claims for T1D, a ratio of T1D : type 2 diabetes claims of ≥0.5, ≥1 claim for insulin or continuous glucose monitoring, and claims activity of at least 1 medical and 1 pharmacy claim in each year for two years before first observed T1D diagnosis or treatment (index). The model was trained on patient data >12 months prior to index to identify patients at least one year before the appearance of a T1D diagnosis or treatment. Variables included T1D and non-T1D associated clinical variables, autoimmune markers, comorbidities, demographic factors, and sequential medical events. Results: Both models were able to detect diagnosed T1D patients (~80% sensitivity in the 0-24 model; ~92% in the 25+model) at ~8% precision in the 0-24 model (~14k true positives in ~167k predicted positives) and ~10% in the 25+ model (~16k in ~169k). Conclusion: The study demonstrates the potential clinical utility of machine learning models for the early detection of type 1 diabetes. This may enable earlier diagnosis through increased screening efficiency and yield, allowing for timely intervention and better management of T1D, ultimately improving patient outcomes. Disclosure F. Lam: Consultant; Sanofi. V. Tiwari: Consultant; Sanofi. G. Mion: Consultant; Sanofi. D. Khedekar: Consultant; Sanofi. P. Mukherjee: Consultant; Sanofi. S. Pandey: Consultant; Sanofi. D. Desai: Consultant; Sanofi. L. Wilson: Employee; Sanofi-Aventis U.S. Stock/Shareholder; Sanofi-Aventis U.S. J. Dunne: Employee; Sanofi, Novo Nordisk. L. Hao: Employee; Sanofi. M. Wieloch: Employee; Sanofi. Stock/Shareholder; Sanofi. J.H. Zaccai: Employee; Sanofi. R.B. McQueen: Speaker's Bureau; Sanofi. Other Relationship; Sanofi. K.M. Simmons: Consultant; Sanofi. Research Support; Sanofi. Advisory Panel; Sanofi, Shoreline Biosciences. E.K. Sims: Consultant; Sanofi. Speaker's Bureau; Med Learning Group. Other Relationship; American Diabetes Association. Funding This study was funded by Sanofi.
{"title":"2058-LB: Identification of Earlier Stage Autoimmune Type 1 Diabetes Using Machine Learning Algorithms","authors":"FELIX LAM, VINAYAK TIWARI, GIULIANO MION, DISHA KHEDEKAR, PRATEEP MUKHERJEE, SHAILJA PANDEY, DHARMI DESAI, LAURA WILSON, JESSICA DUNNE, LICHEN HAO, MATTIAS WIELOCH, JULIA H. ZACCAI, ROBERT B. MCQUEEN, KIMBER M. SIMMONS, EMILY K. SIMS","doi":"10.2337/db25-2058-lb","DOIUrl":"https://doi.org/10.2337/db25-2058-lb","url":null,"abstract":"Introduction and Objective: Autoimmune type 1 diabetes (T1D) often goes undiagnosed until a major clinical event triggers disease recognition. Identifying individuals in early T1D stages remains a clinical challenge given inefficient screening thus limiting opportunities for early intervention. This study aimed to develop a predictive machine learning model that identified individuals before the onset of stage 3 T1D. Methods: This was a retrospective cohort study that utilized medical claims data and lab test results from the US Managed Markets Insight & Technology (MMIT) dataset to develop two age specific AI/ML Models (0-24 years and 25+ years) for identifying individuals with presumed early stage T1D at least one year from first observed T1D diagnosis. Confirmed stage 3 T1D cases, used to train and validate the model, were required to have ≥2 claims for T1D, a ratio of T1D : type 2 diabetes claims of ≥0.5, ≥1 claim for insulin or continuous glucose monitoring, and claims activity of at least 1 medical and 1 pharmacy claim in each year for two years before first observed T1D diagnosis or treatment (index). The model was trained on patient data &gt;12 months prior to index to identify patients at least one year before the appearance of a T1D diagnosis or treatment. Variables included T1D and non-T1D associated clinical variables, autoimmune markers, comorbidities, demographic factors, and sequential medical events. Results: Both models were able to detect diagnosed T1D patients (~80% sensitivity in the 0-24 model; ~92% in the 25+model) at ~8% precision in the 0-24 model (~14k true positives in ~167k predicted positives) and ~10% in the 25+ model (~16k in ~169k). Conclusion: The study demonstrates the potential clinical utility of machine learning models for the early detection of type 1 diabetes. This may enable earlier diagnosis through increased screening efficiency and yield, allowing for timely intervention and better management of T1D, ultimately improving patient outcomes. Disclosure F. Lam: Consultant; Sanofi. V. Tiwari: Consultant; Sanofi. G. Mion: Consultant; Sanofi. D. Khedekar: Consultant; Sanofi. P. Mukherjee: Consultant; Sanofi. S. Pandey: Consultant; Sanofi. D. Desai: Consultant; Sanofi. L. Wilson: Employee; Sanofi-Aventis U.S. Stock/Shareholder; Sanofi-Aventis U.S. J. Dunne: Employee; Sanofi, Novo Nordisk. L. Hao: Employee; Sanofi. M. Wieloch: Employee; Sanofi. Stock/Shareholder; Sanofi. J.H. Zaccai: Employee; Sanofi. R.B. McQueen: Speaker's Bureau; Sanofi. Other Relationship; Sanofi. K.M. Simmons: Consultant; Sanofi. Research Support; Sanofi. Advisory Panel; Sanofi, Shoreline Biosciences. E.K. Sims: Consultant; Sanofi. Speaker's Bureau; Med Learning Group. Other Relationship; American Diabetes Association. Funding This study was funded by Sanofi.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"6 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334850","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}
REBECCA GOTTLIEB, BO WANG, JONATHAN E. KAVNER, KYLE MALLIRES, JARED R. TANGNEY
Introduction and Objective: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are highly effective in both weight loss and glycemic control for people with Obesity and/or Diabetes but can result in sarcopenia - loss of lean muscle mass (LLMM) (1). LLMM effects (2) can be overcome through exercise and increased protein consumption. Phenylalanine (phe) is an essential amino acid released from skeletal muscle breakdown and exogenous protein ingestion. A wearable phenylalanine sensor with activity monitor could track LLMM and protein ingestion for use with these transformative medications. This sensor utilizes a new methodology that relies on an engineered phe bioreceptor using a short nucleic acid sequence (aptamer) labeled with a methylene blue redox probe. This aptamer is attached to an electrode surface where the binding and concentration of the phe is measured through the electrochemical technique square wave voltammetry. Methods: The aptamer bioreceptor was applied to microneedle electrodes and a calibration (0-1500 µM/L) was performed in phosphate buffer solution (PBS). Results: The phe sensor showed log-linear calibration on day 1 and 7 (Fig 1), R^2 d1: 0.986, d7: 0.994, with low limit of detection d1: 3.9 µM/L, d7: 6.4 µM/L. Conclusion: This work demonstrated performance of a proof-of-concept continuous protein monitor utilizing an engineered bioreceptor on a microsensor array that could be used in conjunction with GLP-1 RA therapy. Disclosure R. Gottlieb: Employee; Biolinq. B. Wang: Employee; Biolinq. J.E. Kavner: Employee; Biolinq. K. Mallires: Employee; Biolinq. J.R. Tangney: None.
{"title":"2041-LB: Continuous Protein Sensor for Sarcopenia Management during GLP-1RA Therapy","authors":"REBECCA GOTTLIEB, BO WANG, JONATHAN E. KAVNER, KYLE MALLIRES, JARED R. TANGNEY","doi":"10.2337/db25-2041-lb","DOIUrl":"https://doi.org/10.2337/db25-2041-lb","url":null,"abstract":"Introduction and Objective: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are highly effective in both weight loss and glycemic control for people with Obesity and/or Diabetes but can result in sarcopenia - loss of lean muscle mass (LLMM) (1). LLMM effects (2) can be overcome through exercise and increased protein consumption. Phenylalanine (phe) is an essential amino acid released from skeletal muscle breakdown and exogenous protein ingestion. A wearable phenylalanine sensor with activity monitor could track LLMM and protein ingestion for use with these transformative medications. This sensor utilizes a new methodology that relies on an engineered phe bioreceptor using a short nucleic acid sequence (aptamer) labeled with a methylene blue redox probe. This aptamer is attached to an electrode surface where the binding and concentration of the phe is measured through the electrochemical technique square wave voltammetry. Methods: The aptamer bioreceptor was applied to microneedle electrodes and a calibration (0-1500 µM/L) was performed in phosphate buffer solution (PBS). Results: The phe sensor showed log-linear calibration on day 1 and 7 (Fig 1), R^2 d1: 0.986, d7: 0.994, with low limit of detection d1: 3.9 µM/L, d7: 6.4 µM/L. Conclusion: This work demonstrated performance of a proof-of-concept continuous protein monitor utilizing an engineered bioreceptor on a microsensor array that could be used in conjunction with GLP-1 RA therapy. Disclosure R. Gottlieb: Employee; Biolinq. B. Wang: Employee; Biolinq. J.E. Kavner: Employee; Biolinq. K. Mallires: Employee; Biolinq. J.R. Tangney: None.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"36 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335127","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}
LOIS E. DONOVAN, PATRICIA LEMIEUX, AMY DUNLOP, JENNIFER M. YAMAMOTO, HELEN R. MURPHY, DAVID SIMMONS, RHONDA C. BELL, KATHLEEN CHAPUT, JAMIE L. BENHAM, GLYNIS P. ROSS, KARA NERENBERG, KHORSHID MOHAMMAD, BRUCE A. PERKINS, JANE E. BOOTH, HENRY N. NTANDA, GEORGE TOMLINSON, DENICE FEIG
Introduction and Objective: The efficacy of hybrid closed-loop insulin therapy (HCL) in pregnancy varies by system. Our objective was to assess the efficacy in pregnancy of a HCL that is in common use outside of pregnancy. Methods: This multicenter, open-label trial randomized pregnant women with type 1 diabetes (T1D) and early pregnancy A1C of 6.2-10%, at 14 sites in Canada and Australia, to start HCL (t:slim X2 insulin pump with Control-IQ technology with Dexcom G6 sensor) by 16 weeks gestation or continue with standard care with continuous glucose monitoring (CGM). Use of the lowest target range (sleep activity) was recommended throughout the day and night with the optional use of the highest target range for exercise. The primary outcome was the percentage of time that glucose was in the pregnancy range of 63 to 140mg/dL [3.5 to 7.8mmol/L] (TIRp) as measured by CGM from 16 to 34weeks +6days gestation adjusted for site, baseline TIRp and baseline mode of insulin delivery, using intention-to-treat principles. Secondary outcomes included time above 140mg/dL (7.8mmol/L), time below 63mg/dL (3.5mmol/L), mean glucose, safety events and pregnancy outcomes. Results: A total of 91 women (mean ± SD: age 31.7 ± 5.2 years, diabetes duration 19.0 ± 8.1 years, early pregnancy A1C 7.4 ± 1.0%) were randomized (46 to HCL and 45 to standard care). The mean adjusted TIRp was 12.6 percentage points (95% CI 9.9,15.2; p<0.001) higher in HCL than standard care (65.4 ± 9.5% versus 50.3 ± 13.9%, respectively) with 11.4 percentage points less time above range (95% CI 8.6,14.2 p<0.001) and 1.04 percentage points less time below 63mg/dL (95%CI 0.6,1.48 p<0.001). The adjusted mean glucose was 11.2mg/dL (0.62 mmol/L) lower with HCL versus standard care (95%CI 7.2,16.2 p<0.001). There was 1 episode of severe hypoglycemia with HCL and 0 with standard care and 2 episodes of diabetic ketoacidosis in each of the trial arms. Conclusion: This HCL resulted in 3 more hours/day spent in TIRp in T1D pregnancy compared to standard care. No safety concerns arose. Disclosure L.E. Donovan: Other Relationship; Medtronic, Dexcom, Inc., Tandem Diabetes Care, Inc, Inner Analytics. P. Lemieux: Advisory Panel; Dexcom, Inc. A. Dunlop: None. J.M. Yamamoto: Other Relationship; Abbott. H.R. Murphy: Research Support; Abbott, Dexcom, Inc. Advisory Panel; Medtronic, Ypsomed AG. Speaker's Bureau; Eli Lilly and Company, Dexcom, Inc., Ypsomed AG, Novo Nordisk, Sanofi. D. Simmons: Research Support; Novo Nordisk, AMSL. Other Relationship; Abbott, Abbott, Boehringer-Ingelheim. Speaker's Bureau; Ascensia Diabetes Care. R.C. Bell: None. K. Chaput: None. J.L. Benham: None. G.P. Ross: None. K. Nerenberg: None. K. Mohammad: None. B.A. Perkins: Other Relationship; Abbott, Novo Nordisk, Sanofi. Advisory Panel; Abbott, Insulet Corporation, Sanofi, Novo Nordisk, Nephris, Vertex Pharmaceuticals Incorporated. Research Support; Novo Nordisk. J.E. Booth: None. H.N. Ntanda: None. G. Tomlinso
{"title":"2084-LB: A Randomized Multicenter Trial of Hybrid Closed-Loop Insulin Therapy with Control-IQ Technology in Type 1 Diabetes in Pregnancy","authors":"LOIS E. DONOVAN, PATRICIA LEMIEUX, AMY DUNLOP, JENNIFER M. YAMAMOTO, HELEN R. MURPHY, DAVID SIMMONS, RHONDA C. BELL, KATHLEEN CHAPUT, JAMIE L. BENHAM, GLYNIS P. ROSS, KARA NERENBERG, KHORSHID MOHAMMAD, BRUCE A. PERKINS, JANE E. BOOTH, HENRY N. NTANDA, GEORGE TOMLINSON, DENICE FEIG","doi":"10.2337/db25-2084-lb","DOIUrl":"https://doi.org/10.2337/db25-2084-lb","url":null,"abstract":"Introduction and Objective: The efficacy of hybrid closed-loop insulin therapy (HCL) in pregnancy varies by system. Our objective was to assess the efficacy in pregnancy of a HCL that is in common use outside of pregnancy. Methods: This multicenter, open-label trial randomized pregnant women with type 1 diabetes (T1D) and early pregnancy A1C of 6.2-10%, at 14 sites in Canada and Australia, to start HCL (t:slim X2 insulin pump with Control-IQ technology with Dexcom G6 sensor) by 16 weeks gestation or continue with standard care with continuous glucose monitoring (CGM). Use of the lowest target range (sleep activity) was recommended throughout the day and night with the optional use of the highest target range for exercise. The primary outcome was the percentage of time that glucose was in the pregnancy range of 63 to 140mg/dL [3.5 to 7.8mmol/L] (TIRp) as measured by CGM from 16 to 34weeks +6days gestation adjusted for site, baseline TIRp and baseline mode of insulin delivery, using intention-to-treat principles. Secondary outcomes included time above 140mg/dL (7.8mmol/L), time below 63mg/dL (3.5mmol/L), mean glucose, safety events and pregnancy outcomes. Results: A total of 91 women (mean ± SD: age 31.7 ± 5.2 years, diabetes duration 19.0 ± 8.1 years, early pregnancy A1C 7.4 ± 1.0%) were randomized (46 to HCL and 45 to standard care). The mean adjusted TIRp was 12.6 percentage points (95% CI 9.9,15.2; p&lt;0.001) higher in HCL than standard care (65.4 ± 9.5% versus 50.3 ± 13.9%, respectively) with 11.4 percentage points less time above range (95% CI 8.6,14.2 p&lt;0.001) and 1.04 percentage points less time below 63mg/dL (95%CI 0.6,1.48 p&lt;0.001). The adjusted mean glucose was 11.2mg/dL (0.62 mmol/L) lower with HCL versus standard care (95%CI 7.2,16.2 p&lt;0.001). There was 1 episode of severe hypoglycemia with HCL and 0 with standard care and 2 episodes of diabetic ketoacidosis in each of the trial arms. Conclusion: This HCL resulted in 3 more hours/day spent in TIRp in T1D pregnancy compared to standard care. No safety concerns arose. Disclosure L.E. Donovan: Other Relationship; Medtronic, Dexcom, Inc., Tandem Diabetes Care, Inc, Inner Analytics. P. Lemieux: Advisory Panel; Dexcom, Inc. A. Dunlop: None. J.M. Yamamoto: Other Relationship; Abbott. H.R. Murphy: Research Support; Abbott, Dexcom, Inc. Advisory Panel; Medtronic, Ypsomed AG. Speaker's Bureau; Eli Lilly and Company, Dexcom, Inc., Ypsomed AG, Novo Nordisk, Sanofi. D. Simmons: Research Support; Novo Nordisk, AMSL. Other Relationship; Abbott, Abbott, Boehringer-Ingelheim. Speaker's Bureau; Ascensia Diabetes Care. R.C. Bell: None. K. Chaput: None. J.L. Benham: None. G.P. Ross: None. K. Nerenberg: None. K. Mohammad: None. B.A. Perkins: Other Relationship; Abbott, Novo Nordisk, Sanofi. Advisory Panel; Abbott, Insulet Corporation, Sanofi, Novo Nordisk, Nephris, Vertex Pharmaceuticals Incorporated. Research Support; Novo Nordisk. J.E. Booth: None. H.N. Ntanda: None. G. Tomlinso","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"8 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335129","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}
ELLA C. MORGAN, JASMIN M. ALVES, TING CHOW, ANNY XIANG, KATHLEEN A. PAGE
Introduction and Objective: Prenatal exposure to gestational diabetes mellitus (GDM) increases obesity risk. Animal models suggest GDM exposure alters hypothalamic development, increasing food intake and adiposity. This study examined how GDM exposure affects hypothalamic function, dietary intake, and body fat in children from the BrainChild Cohort. Methods: We analyzed 134 children (57% female; 70 GDM-exposed, 64 unexposed) at baseline (8.6±1 yrs, 90% pre-pubertal) and 1-year follow-up (66% pre-pubertal). Functional MRI assessed hypothalamic response to glucose at baseline. At both time points, dietary intake was measured using 24-hour recalls and body fat via bioelectrical impedance. Linear models examined group differences at baseline and year 1, while mixed-effects models analyzed pooled data across time points, adjusting for age and sex. Results: GDM-exposed children had a higher hypothalamic response to glucose than unexposed children (β=0.08±0.04, p=0.01). At both time points, GDM-exposed children had higher caloric intake and body fat (p<0.05). Pooled analyses from baseline and Y1 showed GDM exposure was associated with higher body fat (β=4±1.5%, p=0.01), total energy intake (β=177±62.4 kcal, p=0.02), carbohydrate (β=18.5±8.8 g, p=0.04), sugar (β=10.9±5.5 g, p=0.05), and fat intake (β=7.5±3.4 g, p=0.03), with no differences in protein or fiber. Greater hypothalamic response to glucose was linked to increased body fat (β=3.8±2.2, p=0.09). Adjusting for hypothalamic response attenuated the association between GDM exposure and body fat (β=4.0±1.5→β=2.9±1.6), with further attenuation after adjusting for diet (β=4.0±1.5 →β=2.6±1.7). Diet alone did not affect this relationship. Conclusion: By age 8.5 years, GDM-exposed children exhibit altered hypothalamic responses to glucose, higher energy intake (particularly sugar and fat), and greater body fat, with effects persisting over one year. These findings highlight the role of the hypothalamus in linking GDM exposure to adiposity in children. Disclosure E.C. Morgan: None. J.M. Alves: None. T. Chow: None. A. Xiang: None. K.A. Page: None. Funding American Diabetes Association (1-14-ACE-36); NIH (R01DK134079, RO1DK116858)
{"title":"2077-LB: Effects of Exposure to Gestational Diabetes on Hypothalamic Function, Food Intake, and Adiposity","authors":"ELLA C. MORGAN, JASMIN M. ALVES, TING CHOW, ANNY XIANG, KATHLEEN A. PAGE","doi":"10.2337/db25-2077-lb","DOIUrl":"https://doi.org/10.2337/db25-2077-lb","url":null,"abstract":"Introduction and Objective: Prenatal exposure to gestational diabetes mellitus (GDM) increases obesity risk. Animal models suggest GDM exposure alters hypothalamic development, increasing food intake and adiposity. This study examined how GDM exposure affects hypothalamic function, dietary intake, and body fat in children from the BrainChild Cohort. Methods: We analyzed 134 children (57% female; 70 GDM-exposed, 64 unexposed) at baseline (8.6±1 yrs, 90% pre-pubertal) and 1-year follow-up (66% pre-pubertal). Functional MRI assessed hypothalamic response to glucose at baseline. At both time points, dietary intake was measured using 24-hour recalls and body fat via bioelectrical impedance. Linear models examined group differences at baseline and year 1, while mixed-effects models analyzed pooled data across time points, adjusting for age and sex. Results: GDM-exposed children had a higher hypothalamic response to glucose than unexposed children (β=0.08±0.04, p=0.01). At both time points, GDM-exposed children had higher caloric intake and body fat (p&lt;0.05). Pooled analyses from baseline and Y1 showed GDM exposure was associated with higher body fat (β=4±1.5%, p=0.01), total energy intake (β=177±62.4 kcal, p=0.02), carbohydrate (β=18.5±8.8 g, p=0.04), sugar (β=10.9±5.5 g, p=0.05), and fat intake (β=7.5±3.4 g, p=0.03), with no differences in protein or fiber. Greater hypothalamic response to glucose was linked to increased body fat (β=3.8±2.2, p=0.09). Adjusting for hypothalamic response attenuated the association between GDM exposure and body fat (β=4.0±1.5→β=2.9±1.6), with further attenuation after adjusting for diet (β=4.0±1.5 →β=2.6±1.7). Diet alone did not affect this relationship. Conclusion: By age 8.5 years, GDM-exposed children exhibit altered hypothalamic responses to glucose, higher energy intake (particularly sugar and fat), and greater body fat, with effects persisting over one year. These findings highlight the role of the hypothalamus in linking GDM exposure to adiposity in children. Disclosure E.C. Morgan: None. J.M. Alves: None. T. Chow: None. A. Xiang: None. K.A. Page: None. Funding American Diabetes Association (1-14-ACE-36); NIH (R01DK134079, RO1DK116858)","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"25 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334852","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}
NESTORAS N. MATHIOUDAKIS, MOHAMMED S. ABUSAMAAN, MARY E. ALDERFER, DEFNE ALVER, ADRIAN S. DOBS, BRIAN KANE, BENJAMIN LALANI, JOHN MCGREADY, KRISTIN RIEKERT, BENJAMIN RINGHAM, FATMATA VANDI, AMAL A. WANIGATUNGA, DANIEL ZADE, NISA M. MARUTHUR
Introduction and Objective: Prediabetes is highly prevalent, yet few patients receive evidence-based behavioral lifestyle support. Artificial intelligence (AI) may offer a scalable approach to diabetes prevention. This study evaluated whether a fully automated AI-based diabetes prevention program (ai-DPP), consisting of a mobile app and digital body weight scale, is non-inferior to a traditional human coach-based DPP (h-DPP) in adults with prediabetes and overweight or obesity. Methods: We conducted a two-site, pragmatic, RCT involving adults with prediabetes and overweight or obesity (NCT05056376). Participants were randomly assigned (1:1) to either an ai-DPP (Sweetch Health, Ltd) or a CDC-recognized h-DPP for a 12-month intervention. Physical activity was objectively measured using actigraphy. The primary endpoint, assessed at 12 months, was the CDC-defined composite diabetes risk reduction outcome, including achieving 5% weight loss, 4% weight loss plus 150 minutes of weekly physical activity, or a 0.2 reduction in A1C. The pre-specified non-inferiority margin was 15 percentage points. The primary outcome was analyzed using a modified intention-to-treat (mITT) approach, including participants with available 12-month data who did not use prohibited medications. Results: Of 427 screened, 368 were enrolled (183 ai-DPP, 185 h-DPP). Trial completion (85.1%) and prohibited medication use (3.5%) were similar between arms, leaving 300 (151 ai-DPP, 149 h-DPP) in the mITT analysis. Achievement of the primary outcome was similar between groups (ai-DPP: 35.8%, h-DPP: 35.6%; p = 0.97). The age - and sex-adjusted risk difference was -2.6% (lower 95% CI: -11.6%), demonstrating non-inferiority. Individual endpoints in the composite outcome also showed non-inferiority. Conclusion: A fully autonomous AI-based DPP requiring no human coaching is non-inferior to the traditional human-coach based DPP, presenting a promising, scalable alternative for adults with prediabetes. Disclosure N.N. Mathioudakis: None. M.S. Abusamaan: None. M.E. Alderfer: None. D. Alver: None. A.S. Dobs: None. B. Kane: None. B. Lalani: None. J. McGready: None. K. Riekert: None. B. Ringham: None. F. Vandi: None. A.A. Wanigatunga: None. D. Zade: None. N.M. Maruthur: None. Funding The National Institute of Diabetes and Digestive and Kidney Diseases (R01DK125780).
{"title":"1956-LB: Artificial Intelligence vs. Human Coaching for Diabetes Prevention—Results from a 12-Month, Multicenter, Pragmatic Randomized Controlled Trial","authors":"NESTORAS N. MATHIOUDAKIS, MOHAMMED S. ABUSAMAAN, MARY E. ALDERFER, DEFNE ALVER, ADRIAN S. DOBS, BRIAN KANE, BENJAMIN LALANI, JOHN MCGREADY, KRISTIN RIEKERT, BENJAMIN RINGHAM, FATMATA VANDI, AMAL A. WANIGATUNGA, DANIEL ZADE, NISA M. MARUTHUR","doi":"10.2337/db25-1956-lb","DOIUrl":"https://doi.org/10.2337/db25-1956-lb","url":null,"abstract":"Introduction and Objective: Prediabetes is highly prevalent, yet few patients receive evidence-based behavioral lifestyle support. Artificial intelligence (AI) may offer a scalable approach to diabetes prevention. This study evaluated whether a fully automated AI-based diabetes prevention program (ai-DPP), consisting of a mobile app and digital body weight scale, is non-inferior to a traditional human coach-based DPP (h-DPP) in adults with prediabetes and overweight or obesity. Methods: We conducted a two-site, pragmatic, RCT involving adults with prediabetes and overweight or obesity (NCT05056376). Participants were randomly assigned (1:1) to either an ai-DPP (Sweetch Health, Ltd) or a CDC-recognized h-DPP for a 12-month intervention. Physical activity was objectively measured using actigraphy. The primary endpoint, assessed at 12 months, was the CDC-defined composite diabetes risk reduction outcome, including achieving 5% weight loss, 4% weight loss plus 150 minutes of weekly physical activity, or a 0.2 reduction in A1C. The pre-specified non-inferiority margin was 15 percentage points. The primary outcome was analyzed using a modified intention-to-treat (mITT) approach, including participants with available 12-month data who did not use prohibited medications. Results: Of 427 screened, 368 were enrolled (183 ai-DPP, 185 h-DPP). Trial completion (85.1%) and prohibited medication use (3.5%) were similar between arms, leaving 300 (151 ai-DPP, 149 h-DPP) in the mITT analysis. Achievement of the primary outcome was similar between groups (ai-DPP: 35.8%, h-DPP: 35.6%; p = 0.97). The age - and sex-adjusted risk difference was -2.6% (lower 95% CI: -11.6%), demonstrating non-inferiority. Individual endpoints in the composite outcome also showed non-inferiority. Conclusion: A fully autonomous AI-based DPP requiring no human coaching is non-inferior to the traditional human-coach based DPP, presenting a promising, scalable alternative for adults with prediabetes. Disclosure N.N. Mathioudakis: None. M.S. Abusamaan: None. M.E. Alderfer: None. D. Alver: None. A.S. Dobs: None. B. Kane: None. B. Lalani: None. J. McGready: None. K. Riekert: None. B. Ringham: None. F. Vandi: None. A.A. Wanigatunga: None. D. Zade: None. N.M. Maruthur: None. Funding The National Institute of Diabetes and Digestive and Kidney Diseases (R01DK125780).","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"45 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335119","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}
CAICHEN ZHONG, SETH EMONT, LIN XIE, SUNDAY IKPE, ZHUN CAO, CRAIG B. LIPKIN, JOSHUA NOONE, EMILY ZACHERLE, CHALAK MUHAMMAD, ADAM DE HAVENON
Introduction and Objective: While newly diagnosed type 2 diabetes (T2D) at the time of a stroke is associated with poorer outcomes, characteristics comparing individuals diagnosed with T2D during stroke hospitalization and those with previously established T2D are not very well documented. This study aimed to examine the differences between the two groups. Methods: This retrospective, observational cohort study included adults hospitalized with an ischemic stroke from 07/01/2017 to 03/31/2023 utilizing the PINC AI™ Healthcare Database. Descriptive statistics were used to compare sociodemographic and clinical characteristics during and after hospitalization among individuals with a T2D diagnosis or anti-diabetic medication use before hospitalization (estT2D) and those with a discharge T2D diagnosis or laboratory values indicating T2D during hospitalization without prior evidence of T2D diagnosis (newT2D). Results: Compared to those with estT2D (N=103,060), individuals with newT2D (N=127,286) were younger (mean±SD: 68.6±12.5 vs 71.0±12.5 years), more likely to be male (55.4% vs 49.3%) and less likely to be enrolled in Medicare (61.8% vs 74.8%). Individuals with newT2D had a lower Charlson comorbidity index (CCI) score (mean±SD: 4.7±2.1 vs 5.5±2.4) and were more likely to be in the highest quintile of social vulnerability index (23.5% vs 21.3%). Individuals with newT2D also had longer lengths of stay (mean±SD: 5.6±5.7 vs 5.2±5.0 days), higher all-cause mortality during hospitalization (4.4% vs 3.6%) and lower all-cause 30-day readmission post discharge (11.8% vs 16.6%), compared to those with estT2D. Conclusion: Individuals hospitalized with stroke and newT2D had lower CCI scores and 30-day readmission rates compared to those with estT2D. They also experienced longer hospital stays and higher inpatient mortality. Our results highlight the need for early diagnosis and management of T2D. Disclosure C. Zhong: None. S. Emont: Other Relationship; Novo Nordisk. Employee; Premier, Inc. L. Xie: None. S. Ikpe: None. Z. Cao: Other Relationship; Novo Nordisk. Employee; Premier Inc. C.B. Lipkin: Employee; Premier Inc. J. Noone: Employee; Novo Nordisk. E. Zacherle: Employee; Novo Nordisk. C. Muhammad: Employee; Novo Nordisk. A. de Havenon: Consultant; Novo Nordisk.
简介和目的：虽然卒中时新诊断的2型糖尿病（T2D）与较差的预后相关，但在卒中住院期间诊断为T2D的个体与先前诊断为T2D的个体的特征比较并没有很好的文献记录。这项研究旨在检查两组之间的差异。方法：这项回顾性、观察性队列研究纳入了2017年1月7日至2023年3月31日期间因缺血性卒中住院的成年人，使用PINC AI™医疗保健数据库。描述性统计用于比较T2D诊断或住院前使用抗糖尿病药物的个体（estT2D）和出院T2D诊断或住院期间没有T2D诊断证据的实验室值显示T2D的个体（newT2D）住院期间和住院后的社会人口学和临床特征。结果：与estT2D患者（N=103,060）相比，newT2D患者（N=127,286）更年轻（平均±SD: 68.6±12.5岁vs 71.0±12.5岁），更可能是男性（55.4% vs 49.3%），更不可能参加医疗保险（61.8% vs 74.8%）。newT2D患者的Charlson共病指数（CCI）得分较低（平均±SD: 4.7±2.1比5.5±2.4），且更有可能处于社会脆弱性指数的最高五分位数（23.5%比21.3%）。与estT2D患者相比，newT2D患者的住院时间更长（平均±SD: 5.6±5.7 vs 5.2±5.0天），住院期间的全因死亡率更高（4.4% vs 3.6%），出院后30天的全因再入院率更低（11.8% vs 16.6%）。结论：与estT2D患者相比，卒中合并newT2D住院患者CCI评分和30天再入院率较低。他们还经历了更长的住院时间和更高的住院死亡率。我们的研究结果强调了早期诊断和治疗T2D的必要性。钟：没有。S. Emont：其他关系；诺和诺德公司。员工;总理Inc .)谢林：没有。艾克：没有。曹中：其他关系；诺和诺德公司。员工;总理公司。C.B.利普金：雇员；总理公司。J. Noone：雇员；诺和诺德公司。E. Zacherle：雇员；诺和诺德公司。C. Muhammad：雇员；诺和诺德公司。A. de Havenon：顾问；诺和诺德公司。
{"title":"1373-P: Comparison of Characteristics among Individuals with Established vs. Newly Diagnosed Type 2 Diabetes during Ischemic Stroke Hospitalization—A Retrospective Cohort Study","authors":"CAICHEN ZHONG, SETH EMONT, LIN XIE, SUNDAY IKPE, ZHUN CAO, CRAIG B. LIPKIN, JOSHUA NOONE, EMILY ZACHERLE, CHALAK MUHAMMAD, ADAM DE HAVENON","doi":"10.2337/db25-1373-p","DOIUrl":"https://doi.org/10.2337/db25-1373-p","url":null,"abstract":"Introduction and Objective: While newly diagnosed type 2 diabetes (T2D) at the time of a stroke is associated with poorer outcomes, characteristics comparing individuals diagnosed with T2D during stroke hospitalization and those with previously established T2D are not very well documented. This study aimed to examine the differences between the two groups. Methods: This retrospective, observational cohort study included adults hospitalized with an ischemic stroke from 07/01/2017 to 03/31/2023 utilizing the PINC AI™ Healthcare Database. Descriptive statistics were used to compare sociodemographic and clinical characteristics during and after hospitalization among individuals with a T2D diagnosis or anti-diabetic medication use before hospitalization (estT2D) and those with a discharge T2D diagnosis or laboratory values indicating T2D during hospitalization without prior evidence of T2D diagnosis (newT2D). Results: Compared to those with estT2D (N=103,060), individuals with newT2D (N=127,286) were younger (mean±SD: 68.6±12.5 vs 71.0±12.5 years), more likely to be male (55.4% vs 49.3%) and less likely to be enrolled in Medicare (61.8% vs 74.8%). Individuals with newT2D had a lower Charlson comorbidity index (CCI) score (mean±SD: 4.7±2.1 vs 5.5±2.4) and were more likely to be in the highest quintile of social vulnerability index (23.5% vs 21.3%). Individuals with newT2D also had longer lengths of stay (mean±SD: 5.6±5.7 vs 5.2±5.0 days), higher all-cause mortality during hospitalization (4.4% vs 3.6%) and lower all-cause 30-day readmission post discharge (11.8% vs 16.6%), compared to those with estT2D. Conclusion: Individuals hospitalized with stroke and newT2D had lower CCI scores and 30-day readmission rates compared to those with estT2D. They also experienced longer hospital stays and higher inpatient mortality. Our results highlight the need for early diagnosis and management of T2D. Disclosure C. Zhong: None. S. Emont: Other Relationship; Novo Nordisk. Employee; Premier, Inc. L. Xie: None. S. Ikpe: None. Z. Cao: Other Relationship; Novo Nordisk. Employee; Premier Inc. C.B. Lipkin: Employee; Premier Inc. J. Noone: Employee; Novo Nordisk. E. Zacherle: Employee; Novo Nordisk. C. Muhammad: Employee; Novo Nordisk. A. de Havenon: Consultant; Novo Nordisk.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"12 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335124","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}
MELISSA-ROSINA PASQUA, JOELLE DOUMAT, ADNAN JAFAR, MICHAEL TSOUKAS, AHMAD HAIDAR
Introduction and Objective: We assessed glycemia, insulin needs, and C-peptide levels with semaglutide after MMTT in type 1 diabetes. Methods: This is a sub-analysis of a randomized crossover trial assessing semaglutide vs. placebo with automated insulin delivery (AID) in adults with T1D (NCT05205928). Participants performed a MMTT with 6 mL/kg of Boost, while using fully-closed-loop AID, after 12 weeks of semaglutide and placebo, in random order. Plasma glucose and C-peptide levels were measured over 120 minutes. C-peptide levels <0.003 nmol/L assumed to be 0 nmol/L. Paired t-test was performed for parametric comparisons, with Wilcoxin signed-rank test for non-parametric comparisons. Results: Ten participants completed the MMTT, with 8 having C-peptide levels and 7 having pump data; 40% were female, with age 47 (SD 14) years and T1D duration 29 (11) years. All but one had baseline C-peptide of < 0.003 pmol/L. Semaglutide reduced glucose AUC compared to placebo (p=0.006), but C-peptide AUC was not different between arms (p=0.35). Despite having lower glucose AUC, the insulin delivery by the AID was lower for semaglutide than placebo (p = 0.024). Conclusion: Semaglutide reduced glucose AUC during fully closed-loop therapy after weight-adjusted meal replacement, with less insulin output required from the AID. Further studies are needed to understand mechanistic of effects. Disclosure M. Pasqua: Speaker's Bureau; Abbott, Sanofi, Medtronic. J. Doumat: None. A. Jafar: None. M. Tsoukas: Speaker's Bureau; Novo Nordisk, Eli Lilly and Company, Boehringer-Ingelheim, Janssen Pharmaceuticals, Inc, Sanofi. A. Haidar: Research Support; Tandem Diabetes Care, Inc. Consultant; Eli Lilly and Company, Abbott. Research Support; ADOCIA, Dexcom, Inc., Ypsomed AG, Bigfoot Biomedical, Inc. Funding Canada Research Chair in Artificial Pancreas Systems.
{"title":"798-P: Semaglutide Effect during Mixed-Meal Tolerance Test (MMTT) with Fully Closed-Loop Therapy in Type 1 Diabetes (T1D)","authors":"MELISSA-ROSINA PASQUA, JOELLE DOUMAT, ADNAN JAFAR, MICHAEL TSOUKAS, AHMAD HAIDAR","doi":"10.2337/db25-798-p","DOIUrl":"https://doi.org/10.2337/db25-798-p","url":null,"abstract":"Introduction and Objective: We assessed glycemia, insulin needs, and C-peptide levels with semaglutide after MMTT in type 1 diabetes. Methods: This is a sub-analysis of a randomized crossover trial assessing semaglutide vs. placebo with automated insulin delivery (AID) in adults with T1D (NCT05205928). Participants performed a MMTT with 6 mL/kg of Boost, while using fully-closed-loop AID, after 12 weeks of semaglutide and placebo, in random order. Plasma glucose and C-peptide levels were measured over 120 minutes. C-peptide levels &lt;0.003 nmol/L assumed to be 0 nmol/L. Paired t-test was performed for parametric comparisons, with Wilcoxin signed-rank test for non-parametric comparisons. Results: Ten participants completed the MMTT, with 8 having C-peptide levels and 7 having pump data; 40% were female, with age 47 (SD 14) years and T1D duration 29 (11) years. All but one had baseline C-peptide of &lt; 0.003 pmol/L. Semaglutide reduced glucose AUC compared to placebo (p=0.006), but C-peptide AUC was not different between arms (p=0.35). Despite having lower glucose AUC, the insulin delivery by the AID was lower for semaglutide than placebo (p = 0.024). Conclusion: Semaglutide reduced glucose AUC during fully closed-loop therapy after weight-adjusted meal replacement, with less insulin output required from the AID. Further studies are needed to understand mechanistic of effects. Disclosure M. Pasqua: Speaker's Bureau; Abbott, Sanofi, Medtronic. J. Doumat: None. A. Jafar: None. M. Tsoukas: Speaker's Bureau; Novo Nordisk, Eli Lilly and Company, Boehringer-Ingelheim, Janssen Pharmaceuticals, Inc, Sanofi. A. Haidar: Research Support; Tandem Diabetes Care, Inc. Consultant; Eli Lilly and Company, Abbott. Research Support; ADOCIA, Dexcom, Inc., Ypsomed AG, Bigfoot Biomedical, Inc. Funding Canada Research Chair in Artificial Pancreas Systems.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"25 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335126","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}
ANURADHA KRISHNAN, SYDNEY CHANEN, TREVOR BELL, TRACY L. BRISTOW, RICHARD WOOD
Introduction and Objective: Hormonal fluctuations associated with menstruation can significantly affect women’s insulin sensitivity and blood glucose levels, posing challenges for diabetes management. Despite affecting half of the population, the intersection of menstruation and diabetes is largely understudied. This study aimed to examine the perceived effects of menstruation on diabetes management and identify critical knowledge and care gaps among people with diabetes (PWD) in the United States (US) and Europe (EU). Methods: From Oct.-Nov. 2024, menstruating PWD in the US (n=686) and EU (n=899) completed an online survey in which they reported overall satisfaction with their glycemic control, the impact of menstruation on their diabetes management, and whether they have discussed these issues with a healthcare provider (HCP). They also provided open-ended feedback on desired changes in diabetes care. Results: Few women report high satisfaction with their overall glycemic control, especially in the EU (19% US vs 14% EU, p<0.05). In both regions, over half of respondents report worsened control during menstruation (56% US, 55% EU), driven by women with Type 1 diabetes (T1) (60% T1 vs 23% T2, p<0.05). Only 36% of women have discussed menstruation’s impact with their HCP, less so among T2 women—particularly non-insulin users (39% T1, 32% T2 on insulin, 17% T2 non-insulin; p<0.05). Qualitatively, many women with diabetes report a lack of information on how hormonal changes affect glycemic control and pump users also express a need for personalized technology that caters to both diabetes and menstruation. Conclusion: These findings highlight unmet needs in gender-based diabetes care. The hormonal changes associated with menstruation must be addressed as key factors in diabetes management and incorporated into clinical discussions, care strategies, and diabetes technologies. Future research should explore the hormonal mechanisms influencing blood glucose and the impact of different menstrual cycle stages on glycemic control. Disclosure A. Krishnan: Research Support; Abbott, Dexcom, Inc., Eli Lilly and Company, diaTribe, Insulet Corporation, Medtronic, Roche Diabetes Care, Tandem Diabetes Care, Inc, Ypsomed AG, LifeScan Diabetes Institute. S. Chanen: Research Support; Abbott, Dexcom, Inc., Eli Lilly and Company, diaTribe, Insulet Corporation, Medtronic, Roche Diabetes Care, Ypsomed AG, Tandem Diabetes Care, Inc, LifeScan Diabetes Institute. T. Bell: Research Support; Abbott, Dexcom, Inc., Tandem Diabetes Care, Inc, Medtronic, MannKind Corporation, Insulet Corporation, CeQur, Beta Bionics, Inc, Eli Lilly and Company, Ypsomed AG. T.L. Bristow: None. R. Wood: Other Relationship; Abbott, diaTribe, Glooko, Inc, Dexcom, Inc., Medtronic, Lilly Diabetes, Insulet Corporation, Sanofi-Aventis U.S., Tandem Diabetes Care, Inc, Zucara Therapeutics.
{"title":"1660-P: Examining the Effects of Menstruation on Diabetes Management among People with Diabetes in the U.S. and EU","authors":"ANURADHA KRISHNAN, SYDNEY CHANEN, TREVOR BELL, TRACY L. BRISTOW, RICHARD WOOD","doi":"10.2337/db25-1660-p","DOIUrl":"https://doi.org/10.2337/db25-1660-p","url":null,"abstract":"Introduction and Objective: Hormonal fluctuations associated with menstruation can significantly affect women’s insulin sensitivity and blood glucose levels, posing challenges for diabetes management. Despite affecting half of the population, the intersection of menstruation and diabetes is largely understudied. This study aimed to examine the perceived effects of menstruation on diabetes management and identify critical knowledge and care gaps among people with diabetes (PWD) in the United States (US) and Europe (EU). Methods: From Oct.-Nov. 2024, menstruating PWD in the US (n=686) and EU (n=899) completed an online survey in which they reported overall satisfaction with their glycemic control, the impact of menstruation on their diabetes management, and whether they have discussed these issues with a healthcare provider (HCP). They also provided open-ended feedback on desired changes in diabetes care. Results: Few women report high satisfaction with their overall glycemic control, especially in the EU (19% US vs 14% EU, p&lt;0.05). In both regions, over half of respondents report worsened control during menstruation (56% US, 55% EU), driven by women with Type 1 diabetes (T1) (60% T1 vs 23% T2, p&lt;0.05). Only 36% of women have discussed menstruation’s impact with their HCP, less so among T2 women—particularly non-insulin users (39% T1, 32% T2 on insulin, 17% T2 non-insulin; p&lt;0.05). Qualitatively, many women with diabetes report a lack of information on how hormonal changes affect glycemic control and pump users also express a need for personalized technology that caters to both diabetes and menstruation. Conclusion: These findings highlight unmet needs in gender-based diabetes care. The hormonal changes associated with menstruation must be addressed as key factors in diabetes management and incorporated into clinical discussions, care strategies, and diabetes technologies. Future research should explore the hormonal mechanisms influencing blood glucose and the impact of different menstrual cycle stages on glycemic control. Disclosure A. Krishnan: Research Support; Abbott, Dexcom, Inc., Eli Lilly and Company, diaTribe, Insulet Corporation, Medtronic, Roche Diabetes Care, Tandem Diabetes Care, Inc, Ypsomed AG, LifeScan Diabetes Institute. S. Chanen: Research Support; Abbott, Dexcom, Inc., Eli Lilly and Company, diaTribe, Insulet Corporation, Medtronic, Roche Diabetes Care, Ypsomed AG, Tandem Diabetes Care, Inc, LifeScan Diabetes Institute. T. Bell: Research Support; Abbott, Dexcom, Inc., Tandem Diabetes Care, Inc, Medtronic, MannKind Corporation, Insulet Corporation, CeQur, Beta Bionics, Inc, Eli Lilly and Company, Ypsomed AG. T.L. Bristow: None. R. Wood: Other Relationship; Abbott, diaTribe, Glooko, Inc, Dexcom, Inc., Medtronic, Lilly Diabetes, Insulet Corporation, Sanofi-Aventis U.S., Tandem Diabetes Care, Inc, Zucara Therapeutics.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"51 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335006","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}
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