NEDA RASOULI, ECENUR GUDER ARSLAN, ANDREI-MIRCEA CATARIG, KIM HOULIND, BERNHARD LUDVIK, JOAKIM NORDANSTIG, HARALD SOURIJ, SEBASTIAN THOMAS, SUBODH VERMA, MARC P. BONACA
Introduction and Objective: The STRIDE trial (NCT04560998) demonstrated that once weekly semaglutide 1.0 mg significantly improved maximum walking distance (MWD) in people with type 2 diabetes (T2D) and symptomatic peripheral artery disease (PAD) vs. placebo. Whether the benefits are consistent across T2D characteristics has not been described. Methods: The primary outcome in STRIDE, MWD measured on a constant load treadmill at 52 weeks, was analyzed by T2D duration (≥10 vs. <10 years), obesity status (BMI (≥30 vs. <30 kg/m2)), glycemic control (HbA1c (≥7% vs. <7%)), and concomitant T2D medications (SGLT2i or insulin). A mixed model for repeated measurements was employed, incorporating treatment, region, and subgroup as fixed factors, along with the treatment-by-subgroup interaction. Baseline values were used as covariates, all nested within each visit. Results: Among 792 randomized STRIDE participants at baseline, median T2D duration was 12.2 years, BMI 28.7 kg/m2, HbA1c 7.1%, with 35.1% on SGLT2i and 31.7% on insulin. Semaglutide significantly improved MWD regardless of T2D duration, BMI, HbA1c and concomitant SGLT2i or insulin use (Figure). Conclusion: These findings support the efficacy of semaglutide in patients with symptomatic PAD across the spectrum of T2D including non-obese participants and those with HbA1c <7%. Disclosure N. Rasouli: Advisory Panel; Novo Nordisk. Research Support; Novo Nordisk. Advisory Panel; Eli Lilly and Company. Research Support; Eli Lilly and Company. E. Guder Arslan: Employee; Novo Nordisk A/S, Sanofi. A. Catarig: Employee; Novo Nordisk A/S. Stock/Shareholder; Novo Nordisk A/S. K. Houlind: Consultant; LeMaitre, Novo Nordisk. B. Ludvik: Research Support; Novo Nordisk. Speaker's Bureau; Novo Nordisk. Advisory Panel; Novo Nordisk, Boehringer-Ingelheim. Speaker's Bureau; Boehringer-Ingelheim. Research Support; Amgen Inc. Speaker's Bureau; AstraZeneca. Research Support; Eli Lilly and Company. Advisory Panel; Eli Lilly and Company. Speaker's Bureau; Eli Lilly and Company. J. Nordanstig: Advisory Panel; AstraZeneca, Novo Nordisk. Other Relationship; Novo Nordisk. H. Sourij: Advisory Panel; Eli Lilly and Company. Speaker's Bureau; Eli Lilly and Company. Research Support; Eli Lilly and Company. Advisory Panel; Boehringer-Ingelheim. Speaker's Bureau; Daiichi Sankyo. Advisory Panel; Novo Nordisk A/S. Speaker's Bureau; Novo Nordisk A/S. Advisory Panel; Novartis AG, Amarin Corporation, Amgen Inc. S. Thomas: None. S. Verma: Other Relationship; Various. M.P. Bonaca: Other Relationship; CPC Clinical Research. Funding The STRIDE trial was funded by Novo Nordisk A/S
STRIDE试验（NCT04560998）表明，与安慰剂相比，每周一次的semaglutide 1.0 mg可显著改善2型糖尿病（T2D）和症状性外周动脉疾病（PAD）患者的最大步行距离（MWD）。这些益处在T2D特征中是否一致尚未被描述。方法：STRIDE的主要终点，52周时在恒定负荷跑步机上测量的MWD，通过T2D持续时间（≥10 vs. <；10年）、肥胖状况（BMI(≥30 vs. <30 kg/m2)）、血糖控制(HbA1c（≥7% vs. <7%）和伴随的T2D药物（SGLT2i或胰岛素）进行分析。采用重复测量的混合模型，将治疗、地区和亚组作为固定因素，以及治疗与亚组之间的相互作用。基线值作为协变量，均嵌套在每次访问中。结果：在基线时792名STRIDE随机参与者中，T2D持续时间中位数为12.2年，BMI为28.7 kg/m2， HbA1c为7.1%，其中SGLT2i为35.1%，胰岛素为31.7%。无论t2dm持续时间、BMI、HbA1c和伴随的SGLT2i或胰岛素使用情况如何，Semaglutide都能显著改善MWD（图）。结论：这些发现支持了西马鲁肽对包括非肥胖和HbA1c≥7%的t2dm患者在内的有症状的PAD患者的疗效。N. Rasouli：顾问小组；诺和诺德公司。研究支持;诺和诺德公司。顾问小组;礼来公司。研究支持;礼来公司。E. Guder Arslan：雇员；诺和诺德公司，赛诺菲公司。A.配餐：员工；诺和诺德公司股票/股东;诺和诺德公司K. Houlind：顾问；LeMaitre，诺和诺德。B. Ludvik：研究支持；诺和诺德公司。演讲者的局;诺和诺德公司。顾问小组;诺和诺德，勃林格殷格翰。演讲者的局;勃林格殷格翰集团。研究支持;安进公司。演讲者的局;阿斯利康。研究支持;礼来公司。顾问小组;礼来公司。演讲者的局;礼来公司。J. Nordanstig：咨询小组；阿斯利康，诺和诺德。其他关系;诺和诺德公司。H. Sourij：咨询小组；礼来公司。演讲者的局;礼来公司。研究支持;礼来公司。顾问小组;勃林格殷格翰集团。演讲者的局;第一三共制药。顾问小组;诺和诺德公司演讲者的局;诺和诺德公司顾问小组;诺华公司、Amarin公司、安进公司托马斯：没有。S. Verma：其他关系；不同。博纳卡议员：其他关系；CPC临床研究。STRIDE试验由诺和诺德公司资助
{"title":"291-OR: Effect of Type 2 Diabetes Characteristics on Semaglutide Treatment in People with Type 2 Diabetes and Peripheral Artery Disease—A Post Hoc Analysis of the STRIDE Trial","authors":"NEDA RASOULI, ECENUR GUDER ARSLAN, ANDREI-MIRCEA CATARIG, KIM HOULIND, BERNHARD LUDVIK, JOAKIM NORDANSTIG, HARALD SOURIJ, SEBASTIAN THOMAS, SUBODH VERMA, MARC P. BONACA","doi":"10.2337/db25-291-or","DOIUrl":"https://doi.org/10.2337/db25-291-or","url":null,"abstract":"Introduction and Objective: The STRIDE trial (NCT04560998) demonstrated that once weekly semaglutide 1.0 mg significantly improved maximum walking distance (MWD) in people with type 2 diabetes (T2D) and symptomatic peripheral artery disease (PAD) vs. placebo. Whether the benefits are consistent across T2D characteristics has not been described. Methods: The primary outcome in STRIDE, MWD measured on a constant load treadmill at 52 weeks, was analyzed by T2D duration (≥10 vs. &lt;10 years), obesity status (BMI (≥30 vs. &lt;30 kg/m2)), glycemic control (HbA1c (≥7% vs. &lt;7%)), and concomitant T2D medications (SGLT2i or insulin). A mixed model for repeated measurements was employed, incorporating treatment, region, and subgroup as fixed factors, along with the treatment-by-subgroup interaction. Baseline values were used as covariates, all nested within each visit. Results: Among 792 randomized STRIDE participants at baseline, median T2D duration was 12.2 years, BMI 28.7 kg/m2, HbA1c 7.1%, with 35.1% on SGLT2i and 31.7% on insulin. Semaglutide significantly improved MWD regardless of T2D duration, BMI, HbA1c and concomitant SGLT2i or insulin use (Figure). Conclusion: These findings support the efficacy of semaglutide in patients with symptomatic PAD across the spectrum of T2D including non-obese participants and those with HbA1c &lt;7%. Disclosure N. Rasouli: Advisory Panel; Novo Nordisk. Research Support; Novo Nordisk. Advisory Panel; Eli Lilly and Company. Research Support; Eli Lilly and Company. E. Guder Arslan: Employee; Novo Nordisk A/S, Sanofi. A. Catarig: Employee; Novo Nordisk A/S. Stock/Shareholder; Novo Nordisk A/S. K. Houlind: Consultant; LeMaitre, Novo Nordisk. B. Ludvik: Research Support; Novo Nordisk. Speaker's Bureau; Novo Nordisk. Advisory Panel; Novo Nordisk, Boehringer-Ingelheim. Speaker's Bureau; Boehringer-Ingelheim. Research Support; Amgen Inc. Speaker's Bureau; AstraZeneca. Research Support; Eli Lilly and Company. Advisory Panel; Eli Lilly and Company. Speaker's Bureau; Eli Lilly and Company. J. Nordanstig: Advisory Panel; AstraZeneca, Novo Nordisk. Other Relationship; Novo Nordisk. H. Sourij: Advisory Panel; Eli Lilly and Company. Speaker's Bureau; Eli Lilly and Company. Research Support; Eli Lilly and Company. Advisory Panel; Boehringer-Ingelheim. Speaker's Bureau; Daiichi Sankyo. Advisory Panel; Novo Nordisk A/S. Speaker's Bureau; Novo Nordisk A/S. Advisory Panel; Novartis AG, Amarin Corporation, Amgen Inc. S. Thomas: None. S. Verma: Other Relationship; Various. M.P. Bonaca: Other Relationship; CPC Clinical Research. Funding The STRIDE trial was funded by Novo Nordisk A/S","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"48 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337522","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}
Introduction and Objective: A recent trial of tirzepatide (SURMOUNT) found a 92% reduction of diabetes incidence among patients with overweight or obesity over three years. Our objective was to estimate the potential population, outcomes, and cost associated with use of tirzepatide for the primary prevention of DM. Methods: Data from the 2021-2023 cycle of the National Health and Nutrition Examination Survey (NHANES) were used to estimate risk reductions for the incidence of DM extrapolated from SURMOUNT results. Costs and savings (from saved diabetes care costs) of tirzepatide were estimated under current U.S. list prices, direct to patient prices, and UK list prices. Results: In total, 2,022 NHANES participants were eligible for tirzepatide therapy (51% female, 49% male, median age 53, IQR 37-66) translating to a weighted U.S. population estimate of 83,454,492 (95% CI 74,423,832 - 92,485,151). Across the lifespan, 30,522,710 cases of diabetes could be prevented if risk reduction was persistent. Projected costs/savings are shown in Table 1. Conclusion: Nearly one in three Americans are potentially eligible for tirzepatide for the primary prevention of DM. At current list price, treating the entire population could cost nearly 20 trillion dollars, offset by only 3.4 trillion dollars of savings, yielding a net cost of approximately 16 trillion dollars, or 60% of 2023 US gross domestic product; treatment at the UK list price would result in nearly 500 billion in net savings. Disclosure J.B. Lusk: None. S. Aymes: None. E. OBrien: Research Support; Pfizer Inc. F. Li: None.
简介和目的：最近的一项试验发现，三年内，替西帕肽（SURMOUNT）可使超重或肥胖患者的糖尿病发病率降低92%。我们的目的是估计使用替西肽用于糖尿病一级预防的潜在人群、结局和成本。方法：使用来自2021-2023年国家健康与营养检查调查（NHANES）周期的数据来估计从SURMOUNT结果推断的糖尿病发病率的风险降低。替西帕肽的成本和节省（从节省的糖尿病护理费用中）是根据当前美国目录价格、直接对患者价格和英国目录价格估计的。结果：总共有2,022名NHANES参与者符合替西肽治疗的条件（51%女性，49%男性，中位年龄53岁，IQR 37-66），转化为加权的美国人口估计为83,454,492 （95% CI 74,423,832 - 92,485,151）。在整个生命周期中，如果风险持续降低，可以预防30,522,710例糖尿病病例。预计费用/节余见表1。结论：近三分之一的美国人可能有资格使用替西肽进行糖尿病的一级预防。按照目前的定价，治疗整个人群可能花费近20万亿美元，仅抵消3.4万亿美元的节省，产生约16万亿美元的净成本，或2023年美国国内生产总值的60%；按照英国的定价进行治疗将节省近5000亿英镑的净开支。J.B. Lusk：没有。S. Aymes：没有。E. brien：研究支持；辉瑞公司F.李：没有。
{"title":"173-OR: Eligibility, Impact, and Costs for Tirzepatide for the Primary Prevention of Diabetes Mellitus","authors":"JAY B. LUSK, SHANNON AYMES, EMILY OBRIEN, FAN LI","doi":"10.2337/db25-173-or","DOIUrl":"https://doi.org/10.2337/db25-173-or","url":null,"abstract":"Introduction and Objective: A recent trial of tirzepatide (SURMOUNT) found a 92% reduction of diabetes incidence among patients with overweight or obesity over three years. Our objective was to estimate the potential population, outcomes, and cost associated with use of tirzepatide for the primary prevention of DM. Methods: Data from the 2021-2023 cycle of the National Health and Nutrition Examination Survey (NHANES) were used to estimate risk reductions for the incidence of DM extrapolated from SURMOUNT results. Costs and savings (from saved diabetes care costs) of tirzepatide were estimated under current U.S. list prices, direct to patient prices, and UK list prices. Results: In total, 2,022 NHANES participants were eligible for tirzepatide therapy (51% female, 49% male, median age 53, IQR 37-66) translating to a weighted U.S. population estimate of 83,454,492 (95% CI 74,423,832 - 92,485,151). Across the lifespan, 30,522,710 cases of diabetes could be prevented if risk reduction was persistent. Projected costs/savings are shown in Table 1. Conclusion: Nearly one in three Americans are potentially eligible for tirzepatide for the primary prevention of DM. At current list price, treating the entire population could cost nearly 20 trillion dollars, offset by only 3.4 trillion dollars of savings, yielding a net cost of approximately 16 trillion dollars, or 60% of 2023 US gross domestic product; treatment at the UK list price would result in nearly 500 billion in net savings. Disclosure J.B. Lusk: None. S. Aymes: None. E. OBrien: Research Support; Pfizer Inc. F. Li: None.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"275 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337511","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}
ELIZABETH STATON, PIAOPIAO LI, JIEUN LEE, K M VENKAT NARAYAN, MOHAMMED K. ALI, ILANA GRAETZ, HUI SHAO
Introduction and Objective: GLP-1RA drugs have demonstrated benefits for people with type 2 diabetes mellitus (T2DM) or obesity, however, medication access is limited. This microsimulation study modeled the impact of increased access to semaglutide globally on the burden of 7 conditions. Methods: We developed and validated a microsimulation model incorporating age-, sex-, and country-specific prevalence and incidence data for T2DM, obesity, CVD, CKD, stroke, ESKD, and all-cause mortality, using data from the 2021 Global Burden of Disease and NCD-RisC studies. A simulation experiment was conducted to evaluate the effects of providing universal access to semaglutide on these key health outcomes over 5 years across 196 countries. Results: Of the worldwide adult population of approximately 6.1 billion, 19.0% met eligibility criteria for semaglutide use, based on a prevalence of 8.76% for type 2 diabetes and 16.4% for obesity. Universal access to semaglutide could reduce 5-year all-cause mortality by 7.41% (absolute change (AC): -0.29%, 95% CI: -0.47 to -0.10%; about 28M lives) and obesity prevalence by 21.9% (AC -5.42%, 95% CI: -6.48 to -4.36%; about 330M fewer obese individuals). Country specific results on all health outcomes are presented in Figure 1. Conclusion: Expanding access and affordability to GLP-1RA can significantly improve health worldwide. Disclosure E. Staton: None. P. Li: None. J. Lee: None. K. Narayan: None. M.K. Ali: Advisory Panel; Eli Lilly and Company. I. Graetz: Research Support; Pfizer Inc, PRIME Education, LLC. H. Shao: None. Funding National Institutes of Health (R01DK133465)
{"title":"2043-LB: Universal Access to GLP1-RAs Could Reduce Global Obesity Prevalence by 20% and Save 28 Million Lives over Five Years—A Microsimulation Study","authors":"ELIZABETH STATON, PIAOPIAO LI, JIEUN LEE, K M VENKAT NARAYAN, MOHAMMED K. ALI, ILANA GRAETZ, HUI SHAO","doi":"10.2337/db25-2043-lb","DOIUrl":"https://doi.org/10.2337/db25-2043-lb","url":null,"abstract":"Introduction and Objective: GLP-1RA drugs have demonstrated benefits for people with type 2 diabetes mellitus (T2DM) or obesity, however, medication access is limited. This microsimulation study modeled the impact of increased access to semaglutide globally on the burden of 7 conditions. Methods: We developed and validated a microsimulation model incorporating age-, sex-, and country-specific prevalence and incidence data for T2DM, obesity, CVD, CKD, stroke, ESKD, and all-cause mortality, using data from the 2021 Global Burden of Disease and NCD-RisC studies. A simulation experiment was conducted to evaluate the effects of providing universal access to semaglutide on these key health outcomes over 5 years across 196 countries. Results: Of the worldwide adult population of approximately 6.1 billion, 19.0% met eligibility criteria for semaglutide use, based on a prevalence of 8.76% for type 2 diabetes and 16.4% for obesity. Universal access to semaglutide could reduce 5-year all-cause mortality by 7.41% (absolute change (AC): -0.29%, 95% CI: -0.47 to -0.10%; about 28M lives) and obesity prevalence by 21.9% (AC -5.42%, 95% CI: -6.48 to -4.36%; about 330M fewer obese individuals). Country specific results on all health outcomes are presented in Figure 1. Conclusion: Expanding access and affordability to GLP-1RA can significantly improve health worldwide. Disclosure E. Staton: None. P. Li: None. J. Lee: None. K. Narayan: None. M.K. Ali: Advisory Panel; Eli Lilly and Company. I. Graetz: Research Support; Pfizer Inc, PRIME Education, LLC. H. Shao: None. Funding National Institutes of Health (R01DK133465)","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":"144335293","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}
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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：