Diabetes最新文献

{"title":"23-PUB: Reduction of Residual Lipid Risk in Patients with Type 2 Diabetes and Mixed Dyslipidemia Treated with a Fixed-Dose Combination of Atorvastatin/Fenofibrate","authors":"JOSE G. SANDER-PADILLA, LAURA A. LUGO-SÁNCHEZ, KEVIN F. RIOS-BRITO, KARLA E. RODRIGUEZ-ROCANDIO, MARÍA M. ARGUEDAS, DIANA FLORES-HUANOSTA, ILEANA C. RODRIGUEZ-VAZQUEZ, JORGE GONZALEZ-CANUDAS, YULIA ROMERO","doi":"10.2337/db24-23-pub","DOIUrl":"https://doi.org/10.2337/db24-23-pub","url":null,"abstract":"Introduction and Objective: The persistence of a high cardiovascular risk not dependent on LDL-C levels constitutes the residual cardiovascular risk of lipid origin (RLR). Lipid-lowering treatment does not always consider the modification of RLR. The present study aimed to evaluate the changes in RRL profile in patients with T2D and dyslipidemia treated with a fixed-dose combination (FDC) of Atorvastatin/Fenofibrate. Methods: A phase IIIb, randomized, prospective, double-blind, multicenter study in the Mexican population with diagnosis of T2D and mixed dyslipidemia. Patients were randomized to the Atorvastatin/Fenofibrate 20 mg/160 mg or Atorvastatin 20 mg once daily for four months. As part of the RLR evaluation, Triglycerides/HDL-C, residual cholesterol, Total cholesterol/HDL-C, and the triglycerides-to-glucose index (TyG) were estimated. Student's t-test and McNemar test (differences within groups) χ2, and independent samples Student's t-test were applied. Results: We included 65 patients with an average age of 56.8 ± 10.4 years. After two months of follow-up, there was a triglycerides reduction of -132.7 ± 145.3 mg/dL in the FDC group and of -68.7 ± 75.7 mg/dL with Atorvastatin therapy, while for the fourth month, the reduction was -138.3 ± 123.7 mg/dL and -60.5 ± 80.1 mg/dL, respectively. When evaluating RLR, both groups experienced a reduction in this profile, after 2 and 4 months of evaluation. However, when comparing the mean change reductions between groups of Triglycerides/HDL-C ratio (-3.9 vs -1.3, p=0.020), residual cholesterol (-19 vs -9.4, p= 0.018), and TyG (-0.7 vs -0.2, p=0.002) a superior outcome was found in FDC compared with the monotherapy group. Conclusions: Patients receiving Atorvastatin/Fenofibrate FDC had a better reduction than monotherapy in non-LDL cholesterol-dependent RRL markers, which translates to a decrease in overall cardiovascular risk for this treatment group. Disclosure J.G. Sander-Padilla: Employee; Laboratorios Silanes. L.A. Lugo-Sánchez: Employee; Laboratorios Silanes S.A. de C.V. K.F. Rios-Brito: Employee; Laboratorios Silanes S.A. de C.V. K.E. Rodriguez-Rocandio: Employee; Laboratorios Silanes S.A. de C.V. M.M. Arguedas: Employee; Laboratorios Silanes S.A. de C.V. D. Flores-Huanosta: Employee; Laboratorios Silanes. I.C. Rodriguez-Vazquez: Employee; Laboratorios Silanes S.A. de C.V. J. Gonzalez-Canudas: Employee; Silanes SA CV. Y. Romero: Employee; Laboratorios Silanes. Funding Laboratorios Silanes","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"161 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"1073-P: Trends in Guideline-Directed Medication Therapy for Type 2 Diabetes in a Statewide Quality Collaborative between 2018–2023","authors":"KARA R. MIZOKAMI-STOUT, LAUREN OSHMAN, HEIDI L. DIEZ, DINA H. GRIAUZDE, JOYCE M. LEE, KATHERINE L. KHOSROVANEH, NEHA BHOMIA, NOA KIM, JACQUELINE RAU, JACOB REISS, RODICA BUSUI","doi":"10.2337/db24-1073-p","DOIUrl":"https://doi.org/10.2337/db24-1073-p","url":null,"abstract":"Introduction and Objective: Since 2021, the Michigan Collaborative for Type 2 Diabetes (MCT2D) aims to improve guideline-directed medication therapies (GDMT) for type 2 diabetes (T2D). We examined trends in glucagon-like peptide-1 receptor agonist (GLP-1RA) and sodium-glucose transport protein 2 inhibitor (SGLT2i) prescribing rates comparing primary care (PC) and endocrinology (Endo) practices enrolled in MCT2D. Methods: We analyzed pharmacy claims data from adults with T2D insured by Blue Cross Blue Shield of Michigan Preferred Provider Organization and Medicare Advantage plans who received care in an MCT2D-participating practice (PC=300; Endo=19) between 2018-2023. Descriptive statistics were used to examine differences in pharmacy claims for anti-hyperglycemic medications. Results: From June 2022-June 2023, among 38,437 persons with T2D (PC=37,361; Endo=1,076), 26% and 41% had claims for GLP-1RA and 19% and 37% for SGLT2i, respectively. Compared to 2018 prescription rates, GLP-1RA increased by 17% and 22%, while SGLT2i prescriptions increased by 15% and 28% in PC and Endo practices respectively (Figure 1). Conclusion: Among practices participating in a statewide collaborative to improve treatment and outcomes for people with T2D, the use of GDMT has increased since 2018. SGLT2i use is similar and GLP-1RA use is 2-3-fold higher than rates reported in other studies. Disclosure K.R. Mizokami-Stout: None. L. Oshman: Stock/Shareholder; Procter & Gamble, Johnson & Johnson Medical Devices Companies, Merck & Co., Inc., AbbVie Inc., Eli Lilly and Company, Abbott. H.L. Diez: None. D.H. Griauzde: None. J.M. Lee: Board Member; GoodRx. Advisory Panel; Sanofi. Consultant; Tandem Diabetes Care, Inc. K.L. Khosrovaneh: None. N. Bhomia: None. N. Kim: None. J. Rau: None. J. Reiss: None. R. Busui: Board Member; American Diabetes Association. Consultant; Procter & Gamble, AstraZeneca, Averitas Pharma, Inc., Bayer Inc., Lexicon Pharmaceuticals, Inc., Nevro Corp., Ono Pharmaceutical Co., Ltd., Novo Nordisk, Roche Diagnostics. Advisory Panel; ADA/ACC Diabetes by Heart Program.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"92 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"197-OR: Algorithmic Identification May Improve Racial and Ethnic Diversity of Clinical Study Recruitment","authors":"SARA JANE CROMER, VICTORIA CHEN, MICAH KOSS, MELTON M. FAN, CRISTINA I. FERNÁNDEZ HERNÁNDEZ, WILLIAM G. MARSHALL, EVELYN GREAUX, MIRIAM UDLER","doi":"10.2337/db24-197-or","DOIUrl":"https://doi.org/10.2337/db24-197-or","url":null,"abstract":"Introduction: Early analyses of Rare and Atypical Diabetes Network (RADIANT) study recruitment suggested that goals to recruit underrepresented groups were not being met. We tested whether a validated electronic health record (EHR) algorithm to identify people with an atypical form of diabetes improved identification of racially and ethnically minoritized individuals who may be candidates for the RADIANT study. Methods: Individuals identified by the algorithm were reviewed by research assistants, then classified by endocrinologists as atypical diabetes, a known type of diabetes, or unable to classify (more information needed). Chi-squared tests were used to compare the proportion of self-reported non-Hispanic Black (NHB) and Hispanic or Spanish-speaking (H/SS) participants enrolled through the Mass General site prior to use of the algorithm (mainly through referral by expert clinicians) and proportion of potential individuals identified by the algorithm. Results: Prior to beginning recruitment through the EHR algorithm, 53% of participants enrolled in RADIANT from the Mass General Brigham site identified as NHW, 5% as NHB, 5% as H/SS, 20% as non-Hispanic Asian (NHA). The algorithm initially identified 539 individuals with potentially atypical forms of diabetes. Of these, 452 under the age of 85 were reviewed, and 93 (20.6%) were classified as atypical and possible RADIANT candidates (v. 65.7% with a known type of diabetes and 13.7% unable to be classified). Of those with likely atypical diabetes, 39.8% identified as NHW, 22.6% as NHB, 11.8% as H/SS, and 20.4% as NHA. The algorithm identified a higher percentage of NHB individuals (p<0.001) and H/SS individuals (p<0.001) when compared to previous recruitment methods. Conclusion: Use of a validated algorithm to identify individuals with atypical diabetes in the EHR led to improved identification of candidates for the RADIANT study who are historically underrepresented in clinical and genetic research studies. Disclosure S. Cromer: Other Relationship; Johnson & Johnson Medical Devices Companies. Advisory Panel; Alexion Pharmaceuticals, Inc. Other Relationship; Wolters Kluwer Health. V. Chen: None. M. Koss: None. M.M. Fan: None. C.I. Fernández Hernández: None. W.G. Marshall: Employee; Abbott. E. Greaux: None. M. Udler: Other Relationship; Up-To-Date. Funding American Diabetes Association (7-21-JDFM-005); NIDDK (1U54DK118612)","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"79 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"1388-P: Risk of Diabetes among Children and Young Adults after COVID-19 Infection—The DiCAYA Study","authors":"SARAH CONDERINO, H. LESTER KIRCHNER, LORNA THORPE, JASMIN DIVERS, ANNEMARIE G. HIRSCH, CARA M. NORDBERG, BRIAN S. SCHWARTZ, BO CAI, CAROLINE RUDISILL, JIHAD S. OBEID, ANGELA D. LIESE, BRIAN E. DIXON, DANA DABELEA, ANNA BELLATORRE, HUI SHAO, JIANG BIAN, YI GUO, KRISTI REYNOLDS, MATTHEW T. MEFFORD, MANMOHAN K. KAMBOJ, ENEIDA A. MENDONCA, KATIE ALLEN, SHAWNA BURGETT, EVA LUSTIGOVA, SARAH BOST, MITCH MALTENFORT, LEVON H. UTIDJIAN, MATT M. ZHOU, TESSA L. CRUME, ANDREA TITUS","doi":"10.2337/db24-1388-p","DOIUrl":"https://doi.org/10.2337/db24-1388-p","url":null,"abstract":"Introduction & Objective: The association between COVID-19 infection and incident diabetes remains unclear despite recent research. Using a multistate electronic health record-based surveillance approach, we examined the risk of new diabetes among children (<18) and young adults (18-44) post COVID-19 infection. Methods: Pooled fixed-effects meta-analyses were performed. Patients (n=5,412,604) with no evidence of diabetes who received care in 2018-2019 were followed through diabetes diagnosis, death, or end of follow-up (12/31/22). COVID-19 infection was defined using labs or diagnoses from 6/1/20-12/31/21. Person-time was calculated from infection date for cases or a randomly selected visit date for controls. Propensity score-weighted Cox regression models were run at each site individually to estimate hazard ratios (HR) for diabetes risk for children and young adults. Results: COVID-exposed individuals were at higher risk of incident diabetes compared to those with no documented infection (Children HR = 1.85 [1.69, 2.03]; Young Adult HR = 1.37 [1.31, 1.42]). All participating sites reported elevated risk but results were more heterogeneous across young adults (range 1.3-3.7, heterogeneity I2=94% vs. range 1.6-2.0, I2=0%, Figure 1). Conclusion: These preliminary findings suggest COVID-19 infection is associated with increased risk of incident diabetes among children and young adults. Disclosure S. Conderino: None. H. Kirchner: None. L. Thorpe: None. J. Divers: None. A.G. Hirsch: None. C.M. Nordberg: None. B.S. Schwartz: None. B. Cai: None. C. Rudisill: None. J.S. Obeid: None. A.D. Liese: None. B.E. Dixon: Other Relationship; Elsevier. D. Dabelea: None. A. Bellatorre: None. H. Shao: Consultant; Eli Lilly and Company. J. Bian: None. Y. Guo: None. K. Reynolds: Research Support; Merck Sharp & Dohme Corp. M.T. Mefford: Research Support; Merck & Co., Inc. M.K. Kamboj: None. E.A. Mendonca: None. K. Allen: None. S. Burgett: None. E. Lustigova: None. S. Bost: None. M. Maltenfort: None. L.H. Utidjian: None. M.M. Zhou: None. T.L. Crume: None. A. Titus: None.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"79 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"1559-P: Heterogeneity of Trajectories of Metabolic Parameters after 50% Beta-Cell Mass Loss by Pancreatectomy","authors":"ROBERTO BIZZOTTO, GIANFRANCO DI GIUSEPPE, LAURA SOLDOVIERI, FRANCESCA CINTI, SIMONA MOFFA, MICHELA BRUNETTI, GEA CICCARELLI, SERGIO ALFIERI, GIUSEPPE QUERO, ANDREA MARI, ANDREA GIACCARI, TERESA MEZZA","doi":"10.2337/db24-1559-p","DOIUrl":"https://doi.org/10.2337/db24-1559-p","url":null,"abstract":"Temporal trajectories of metabolic parameters in the onset of dysglycemia are heterogeneous. We aimed to characterize the temporal trajectories of metabolic parameters after β cell mass reduction by pancreatectomy and to study their heterogeneity. Individuals without known diabetes diagnosis (N = 83) underwent mixed-meal/oral glucose tolerance tests (MMTT/OGTT) and/or hyperglycemic/euglycemic clamp (HC/EC) procedures, before and after surgery. We performed stepwise multivariate linear regression analysis on the glucose tolerance (GT) class (treated as ordinal number, 1 to 3) after surgery, using as independent variables the baselines and changes with surgery of anthropometrics and MMTT- and HC-derived functional parameters of insulin secretion, clearance, and sensitivity (IS), imputed via missForest algorithm when missing. We used the variables selected in this analysis (p<0.01) as input for the reversed graphed embedding (RGE) framework, to identify groups of individuals with extreme combinations of the variables (archetypes). Independent associations with after-surgery GT class (cross-validated R2 = 0.57) were observed for changes in IS and β cell glucose sensitivity (GS), and for baseline IS, GS, 1st phase insulin secretion, insulin secretion at 6 mmol/L glucose, and insulin clearance. IS and the β cell function parameters showed different trajectories combinations in each of the 5 archetypes identified via RGE (median adjusted Rand index = 0.88; N = 16, 8, 15, 13, 18). After surgery, all archetypes included individuals in each of the 3 GT classes (all proportions > 0 at 95% CI). The same β cell mass reduction determines a variety of combinations in changes of IS and β cell functional mechanisms. We identified five archetypes underlying these combinations. The same final GT class can be reached by individuals in any of the archetypes, which shed light on the hidden heterogeneity of glycaemic deterioration. Disclosure R. Bizzotto: None. G. Di Giuseppe: None. L. Soldovieri: None. F. Cinti: None. S. Moffa: None. M. Brunetti: None. G. Ciccarelli: None. S. Alfieri: None. G. Quero: None. A. Mari: Consultant; Lilly Diabetes. A. Giaccari: None. T. Mezza: None.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"75 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"344-OR: The MOTS-c/CK2 Pathway Is a Novel Molecular Target for Type 2 Diabetes in East Asians","authors":"HIROSHI KUMAGAI, SU JEONG KIM, BRENDAN MILLER, SHIN HYUNG LEE, HIROFUMI ZEMPO, TOSHIHARU NATSUME, JUNXIANG WAN, RICARDO RAMIREZ, HEMAL H. MEHTA, YUICHIRO NISHIDA, NORIYUKI FUKU, SHOHEI DOBASHI, ERI MIYAMOTO-MIKAMI, HISASHI NAITO, MEGUMI HARA, CHIHARU IWASAKA, YOSUKE YAMADA, YASUKI HIGAKI, KEITARO TANAKA, KELVIN YEN, PINCHAS COHEN","doi":"10.2337/db24-344-or","DOIUrl":"https://doi.org/10.2337/db24-344-or","url":null,"abstract":"Introduction & Objective: MOTS-c is a mitochondrial DNA-encoded microprotein that improves impaired glucose metabolism caused by aging and high fat diet. The presence of a naturally occurring genetic variant of MOTS-c, K14Q MOTS-c, increases the susceptibility to type 2 diabetes (T2D) in East Asians. Nevertheless, the precise mechanism of MOTS-c action has not been fully elucidated. Here, we demonstrate that the protein kinase CK2 is a functional and direct target of MOTS-c and that the reduced binding of K14Q MOTS-c to CK2 increases the risk of T2D. Methods: We performed in vitro experiments, including dot blot, kinase activity, and surface plasmon resonance assays, to investigate the direct interaction between MOTS-c and CK2. We also examined the impact of MOTS-c treatment on CK2 activity in skeletal muscle, as well as on muscle glucose uptake in young mice. Subsequently, we investigated the impact of a naturally occurring K14Q MOTS-c variant on the prevalence of T2D in 12,068 Japanese individuals. Results: Dot blot and cell-free kinase activity assays demonstrated that MOTS-c activated CK2 by binding directly to its α subunit, and this binding was confirmed using a surface plasmon resonance assay. Importantly, the binding affinity of K14Q MOTS-c to CK2α was 16-fold less than that of the wild type MOTS-c, and K14Q MOTS-c did not activate CK2 in the cell-free kinase activity assay. Skeletal muscle CK2 activity was lower in old mice and higher after exercise, and was increased by MOTS-c administration, but not K14Q MOTS-c. MOTS-c administration, but not K14Q MOTS-c, significantly enhanced muscle glucose uptake, which was blunted by a CK2 inhibitor. In humans, the K14Q MOTS-c carriers exhibited an increased risk of type 2 diabetes, particularly among individuals aged 60 and above, while this increased risk was mitigated by daily physical activity. Conclusion: Together, these findings provide evidence that CK2 is required for MOTS-c effects and that the MOTS-c/CK2 pathway is a potential therapeutic target for T2D.<u></u> Disclosure H. Kumagai: None. S. Kim: None. B. Miller: None. S. Lee: None. H. Zempo: None. T. Natsume: None. J. Wan: None. R. Ramirez II: None. H.H. Mehta: None. Y. Nishida: None. N. Fuku: None. S. Dobashi: None. E. Miyamoto-Mikami: None. H. Naito: None. M. Hara: None. C. Iwasaka: None. Y. Yamada: None. Y. Higaki: None. K. Tanaka: None. K. Yen: None. P. Cohen: None.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"335 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"1217-P: Insulin Sensitivity and Insulin Secretion Differentially Affect Birthweight and Postpartum Glucose","authors":"EMILY A. ROSENBERG, KAITLYN JAMES, DEEPTI PANT, SARAH HSU, ROBIN L. AZEVEDO, CHLOE MICHALOPOULOS, TANAYOTT THAWEETHAI, CAMILLE E. POWE, ARANTXA MEDINA BAEZ","doi":"10.2337/db24-1217-p","DOIUrl":"https://doi.org/10.2337/db24-1217-p","url":null,"abstract":"Background: Insulin deficiency and insulin resistance are two fundamental mechanisms that lead to hyperglycemia. Despite variation in the underlying physiology, individuals with hyperglycemia in pregnancy are treated similarly. Methods: Pregnant women with diabetes risk factors completed a 2-hr 75-gram oral glucose tolerance test (OGTT) at 10-15 weeks’ gestation, 24-32 weeks’ gestation, and postpartum. We tested for associations between insulin sensitivity (opposite of insulin resistance, Matsuda index) or insulin secretory response (Stumvoll estimate) in the 1st trimester with birthweight percentile (for gestational age and sex) or 2-hr post-OGTT glucose postpartum. We used linear regression, adjusting for age, race/ethnicity, education, gestational age, BMI, GDM treatment (birthweight analyses), weeks postpartum (glucose analyses) and insulin sensitivity (insulin secretory response analyses). Results: We studied N=151 pregnant women at a median [IQR] =12 [11-13] weeks’ gestation. N=107 had data at median [IQR] 9 [7-13] weeks postpartum. A 1-unit increase in 1st trimester insulin sensitivity (log Matsuda) was associated with a decrease of 6.9 in birthweight percentile (95% CI [-13.7, -0.05] P=0.045). 1st trimester insulin secretory response (log Stumvoll) was not associated with birthweight (β=-12.7 95% CI [-28.3, 2.9] P=0.11). 1st trimester insulin sensitivity was not associated with postpartum 2-hr OGTT glucose (β=-0.80 95% CI [-9.6, 8.0] mg/dL P=0.86), while a 1 unit increase in 1st trimester insulin secretory response was associated with a 36 mg/dL decrease in 2-hr OGTT glucose (95% CI [-53.7, -18.8] P=<0.001). Conclusion: Early pregnancy insulin sensitivity is more strongly associated with birthweight, while insulin secretory response has a stronger relationship with postpartum glycemia. Insulin resistance may confer more risk for perinatal complications, while insulin deficiency may confer more risk for postpartum hyperglycemia. Disclosure E.A. Rosenberg: None. K. James: None. D. Pant: None. S. Hsu: None. R.L. Azevedo: None. C. Michalopoulos: None. T. Thaweethai: None. C.E. Powe: Consultant; Mediflix. Other Relationship; Wolters Kluwer Health. A. Medina Baez: None.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"35 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"1245-P: GDM Patients and Prognostic Factors for Subsequent Type 2 Diabetes Mellitus—An Electronic Cohort Review","authors":"RYUNG S. KIM, LIHUA LI, CARMEN R. ISASI, ATHENA PHILIS-TSIMIKAS, JEE-YOUNG MOON, JUNXIU LIU, DIANA S. WOLFE, CAROL J. LEVY","doi":"10.2337/db24-1245-p","DOIUrl":"https://doi.org/10.2337/db24-1245-p","url":null,"abstract":"Introduction: GDM affects 8-10% of pregnancies in the US and nearly 50% of these women have subsequent diabetes diagnosis. However, research on the prognostic factors of T2D incidence among women with GDM is scarce, due to the limited sample sizes. We aim 1) to construct a large electronic cohort of GDM and 2) to build a prognostic model for T2D incidence among patients with GDM. Methods: We extracted EMRs of patients diagnosed with GDM between 2016 and 2022 from two health systems in NYC: Montefiore (MMC) and Mt. Sinai. Only MMC patients were analyzed in this report. Prognostic factors during pregnancy included 32 baseline & pregnancy characteristics, 76 office visit variables, 418 lab tests, and prescription of 31 drugs. Time from GDM diagnosis to T2D was analyzed using proportional hazards models. Results: We collected EMRs of 6,014 GDM patients at MMC who were racially diverse with a median age of 32, BMI of 31.8 kg/m2. Among them, 355 (5.9%) later developed T2D, yielding a high T2D incidence rate (21.1 per 1,000 PY). There was an immediate heightened risk: T2D incidence proportions were 3.8% by 1 year after GDM diagnosis, and 11.9% by 5 years. The risk was elevated in Hispanic White (HR=2.3), Hispanic Non-White (HR=2.0), and Black (HR=2.3) compared to non-Hispanic White (p<0.00001). The risk was associated with higher BMI during pregnancy, insulin or oral-agent control compared to diet therapy, younger gestational age at GDM diagnosis, and Caesarean delivery. Lab findings associated with T2D risk included maternal glucose levels, erythrocyte MCH, monocytes, and ketone. T2D incidence was also associated with prescription of insulin therapy, oral treatment, aspirin, and iron supplements likely indicating underlying obstetric complications. Conclusions: A large electronic cohort of GDM patients identified potential prognostic factors of subsequent T2D. Future directions include calibration of 2 cohorts to establish the largest electronic cohort of GDM to date and building prognostic models for T2D risk. Disclosure R.S. Kim: None. L. Li: None. C.R. Isasi: None. A. Philis-Tsimikas: Advisory Panel; Dexcom, Inc., Lilly Diabetes, Novo Nordisk, Sanofi, Medtronic, Bayer Inc. J. Moon: None. J. Liu: None. D.S. Wolfe: None. C.J. Levy: Research Support; Dexcom, Inc. Consultant; Dexcom, Inc. Research Support; MannKind Corporation, T1D Exchange, Tandem Diabetes Care, Inc., Abbott, Insulet Corporation. Funding New York Regional Center for Diabetes Translation Research Pilot & Feasibility Project","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"26 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"1304-P: MODY Calculator Overestimates MODY Probability in Multiethnic Cohort with Youth-Onset Type 2 Diabetes Phenotype","authors":"RAYMOND J. KREIENKAMP, BEVERLEY SHIELDS, TONI I. POLLIN, MUSTAFA TOSUR, AMY S. SHAH, ANGELA D. LIESE, CATHERINE PIHOKER, SHYLAJA SRINIVASAN, ANDREW T. HATTERSLEY, MIRIAM UDLER, MARIA J. REDONDO","doi":"10.2337/db24-1304-p","DOIUrl":"https://doi.org/10.2337/db24-1304-p","url":null,"abstract":"Introduction & Objective: Identifying monogenic diabetes (MODY) remains a challenge for clinicians. Shields and colleagues developed a widely used MODY probability calculator (https://www.diabetesgenes.org/) based on clinical measures to assist in this decision. Because the calculator was developed in a predominantly adult White European population without any pediatric T2D cases, we sought to test its accuracy in ProDiGY, a multiethnic cohort with clinician-diagnosed T2D under age 20. Methods: MODY calculator probabilities were computed for 615 youth with clinician-diagnosed T2D (n=147 in SEARCH, n=468 in TODAY; overall, >60% non-White), including 20 participants with MODY (3%). Given the longitudinal design of these studies, MODY probability was calculated for each participant at multiple time points, allowing maximum and median probabilities. Results: Of the 20 individuals with MODY, 19 (95%) had a >60% median probability of having MODY. However, this calculator overestimated the probability in participants with no MODY variant detected. In fact, 85% of individuals without MODY had a maximum probability >60% of having MODY, and 67% had a median probability >60%. Family history of diabetes did not discriminate diabetes type with more T2D patients having a parent affected (64% v 55% in MODY). In contrast, HbA1c and BMI each had discriminatory capacity (ROC AUC >0.67). Conclusion: In a group of multi-ethnic youth with diabetes, the MODY calculator correctly identified those with MODY but overestimated the probability for those with a clinical phenotype of T2D, likely due to young age at diagnosis and high proportion with positive family history of diabetes. Disclosure R.J. Kreienkamp: None. B. Shields: None. T.I. Pollin: None. M. Tosur: None. A.S. Shah: None. A.D. Liese: None. C. Pihoker: None. S. Srinivasan: None. A.T. Hattersley: None. M. Udler: Other Relationship; Up-To-Date. M.J. Redondo: None. Funding RJK is supported by NIH T32DK007699. SS is supported by NIH K23DK120932 and R03DK138213. MJR is supported by NIH NIDDK R01DK124395.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"159 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"34-OR: The Effect of Combined Activin A and Myostatin Blockade on Body Composition—A Phase 1 Trial","authors":"DINKO GONZALEZ TROTTER, STEPHEN DONAHUE, CHRIS WYNNE, SHAZIA ALI, PRODROMOS PARASOGLOU, ANITA BOYAPATI, KUSHA MOHAMMADI, BRET J. MUSSER, PRETTY MEIER, JASON MASTAITIS, EVELYN GASPARINO, JESUS TREJOS, JOHN D. DAVIS, GARY A. HERMAN, ROBERT PORDY","doi":"10.2337/db24-34-or","DOIUrl":"https://doi.org/10.2337/db24-34-or","url":null,"abstract":"Introduction: Preclinical data suggest myostatin and activin A are important negative regulators of muscle mass. Trevogrumab (a monoclonal antibody [mAb]) binds and blocks myostatin signalling, while garetosmab (a mAb) binds and blocks activin A, AB and AC signalling. Here, the effects of administering trevogrumab and garetosmab, alone or in combination, on body composition in healthy participants was assessed. Methods: This Phase 1, double-blind, placebo-controlled study randomized healthy males and postmenopausal females to single-dose or multiple-dose parts of the study. For single-dose, females received: trevogrumab 6 mg/kg (n=6); garetosmab 10 mg/kg (n=6); combination trevogrumab 6 mg/kg and garetosmab (1 mg/kg, n=6; 3 mg/kg, n=6; 10 mg/kg, n=12); or placebo (PBO; n=12). For multiple‑dose, females received: garetosmab 10 mg/kg every 4 weeks (Q4W; n=6) or PBO (n=2); combination trevogrumab 6 mg/kg and garetosmab 10 mg/kg every 2 weeks (n=6) or PBO (n=4). In the multiple dose part, males received garetosmab 10 mg/kg Q4W (n=8) or PBO (n=8). Results: Thigh muscle volume (TMV) increased from baseline 7.7% with trevogrumab 6 mg/kg + garetosmab 10 mg/kg (nominal P<0.001 vs PBO) and 4.6% with trevogrumab 6 mg/kg (nominal P<0.05 vs PBO) 8 weeks after single-dose. Total fat mass and android fat mass (AFM) decreased from baseline with trevogrumab 6 mg/kg + garetosmab 10 mg/kg (-4.6% and -6.7%; both nominal P<0.05 vs PBO). After multiple-dose, TMV initially increased after 3 doses of trevogrumab 6 mg/kg + garetosmab 10 mg/kg but decreased to similar levels as PBO at Week 28; AFM and visceral fat mass decreased from baseline by 14.3% and 20.1%, respectively (both nominal P<0.05 vs PBO). No safety concerns were identified in any active treatment groups. Conclusion: Combined administration of trevogrumab and garetosmab led to dose-dependent, greater‑than‑additive increases in TMV and lean mass, while decreasing fat mass in healthy participants. Disclosure D. Gonzalez Trotter: Employee; Regeneron Pharmaceuticals Inc. Stock/Shareholder; Regeneron Pharmaceuticals Inc. S. Donahue: Employee; Regeneron Pharmaceuticals Inc. Stock/Shareholder; Regeneron Pharmaceuticals Inc. C. Wynne: Employee; NZCR. Stock/Shareholder; NZCR. S. Ali: Employee; Regeneron Pharmaceuticals Inc. Stock/Shareholder; Regeneron Pharmaceuticals Inc. P. Parasoglou: Employee; Regeneron Pharmaceuticals Inc. Stock/Shareholder; Regeneron Pharmaceuticals Inc. A. Boyapati: Employee; Regeneron Pharmaceuticals Inc. Stock/Shareholder; Regeneron Pharmaceuticals Inc. K. Mohammadi: Employee; Regeneron Pharmaceuticals Inc. Stock/Shareholder; Regeneron Pharmaceuticals Inc. B.J. Musser: Employee; Regeneron Pharmaceuticals Inc. Stock/Shareholder; Merck Sharp & Dohme Corp. P. Meier: Employee; Regeneron Pharmaceuticals Inc. Stock/Shareholder; Regeneron Pharmaceuticals Inc. J. Mastaitis: Employee; Regeneron Pharmaceuticals Inc. E. Gasparino: Employee; Regeneron Pharmaceuticals Inc","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"160 1","pages":""},"PeriodicalIF":7.7,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730618","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}