Your browser doesn't support javascript.
loading
Comparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes: A Multinational, Federated Analysis of LEGEND-T2DM.
Khera, Rohan; Aminorroaya, Arya; Dhingra, Lovedeep Singh; Thangaraj, Phyllis M; Pedroso Camargos, Aline; Bu, Fan; Ding, Xiyu; Nishimura, Akihiko; Anand, Tara V; Arshad, Faaizah; Blacketer, Clair; Chai, Yi; Chattopadhyay, Shounak; Cook, Michael; Dorr, David A; Duarte-Salles, Talita; DuVall, Scott L; Falconer, Thomas; French, Tina E; Hanchrow, Elizabeth E; Kaur, Guneet; Lau, Wallis C Y; Li, Jing; Li, Kelly; Liu, Yuntian; Lu, Yuan; Man, Kenneth K C; Matheny, Michael E; Mathioudakis, Nestoras; McLeggon, Jody-Ann; McLemore, Michael F; Minty, Evan; Morales, Daniel R; Nagy, Paul; Ostropolets, Anna; Pistillo, Andrea; Phan, Thanh-Phuc; Pratt, Nicole; Reyes, Carlen; Richter, Lauren; Ross, Joseph S; Ruan, Elise; Seager, Sarah L; Simon, Katherine R; Viernes, Benjamin; Yang, Jianxiao; Yin, Can; You, Seng Chan; Zhou, Jin J; Ryan, Patrick B.
Afiliación
  • Khera R; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, Connecticut, USA; Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Heal
  • Aminorroaya A; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, Connecticut, USA.
  • Dhingra LS; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, Connecticut, USA.
  • Thangaraj PM; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, Connecticut, USA.
  • Pedroso Camargos A; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, Connecticut, USA.
  • Bu F; Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
  • Ding X; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
  • Nishimura A; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
  • Anand TV; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA.
  • Arshad F; Department of Biostatistics, Fielding School of Public Health, University of California-Los Angeles, Los Angeles, California, USA.
  • Blacketer C; Observational Health Data Analytics, Janssen Research and Development, Titusville, New Jersey, USA.
  • Chai Y; Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
  • Chattopadhyay S; Department of Biostatistics, Fielding School of Public Health, University of California-Los Angeles, Los Angeles, California, USA.
  • Cook M; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
  • Dorr DA; Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.
  • Duarte-Salles T; Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands.
  • DuVall SL; Veterans Affairs Informatics and Computing Infrastructure, U.S. Department of Veterans Affairs, Salt Lake City, Utah, USA; Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA.
  • Falconer T; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA.
  • French TE; Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, Tennessee, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Hanchrow EE; Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, Tennessee, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Kaur G; Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom.
  • Lau WCY; Research Department of Practice and Policy, School of Pharmacy, University College London, London, United Kingdom; Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom; Centre for Safe Medication Practice and Resea
  • Li J; Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Durham, North Carolina, USA.
  • Li K; Department of Biostatistics, Fielding School of Public Health, University of California-Los Angeles, Los Angeles, California, USA.
  • Liu Y; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, Connecticut, USA; Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA.
  • Lu Y; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, Connecticut, USA.
  • Man KKC; Research Department of Practice and Policy, School of Pharmacy, University College London, London, United Kingdom; Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom; Centre for Safe Medication Practice and Resea
  • Matheny ME; Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, Tennessee, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Mathioudakis N; Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • McLeggon JA; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA.
  • McLemore MF; Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, Tennessee, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Minty E; Faculty of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada.
  • Morales DR; Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom.
  • Nagy P; Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Ostropolets A; Observational Health Data Analytics, Janssen Research and Development, Titusville, New Jersey, USA.
  • Pistillo A; Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain.
  • Phan TP; School of Pharmacy, Taipei Medical University, Taipei, Taiwan.
  • Pratt N; Quality Use of Medicines and Pharmacy Research Centre, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, Australia.
  • Reyes C; Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain.
  • Richter L; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA.
  • Ross JS; Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut, USA; Section of General Medicine and National Clinician Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
  • Ruan E; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA.
  • Seager SL; Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, London, United Kingdom.
  • Simon KR; Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, Tennessee, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Viernes B; Veterans Affairs Informatics and Computing Infrastructure, U.S. Department of Veterans Affairs, Salt Lake City, Utah, USA; Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA.
  • Yang J; Department of Computational Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California, USA.
  • Yin C; Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Shanghai, China.
  • You SC; Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea; Institute for Innovation in Digital Healthcare, Yonsei University College of Medicine, Seoul, South Korea.
  • Zhou JJ; Department of Biostatistics, Fielding School of Public Health, University of California-Los Angeles, Los Angeles, California, USA; Department of Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California, USA.
  • Ryan PB; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA.
J Am Coll Cardiol ; 84(10): 904-917, 2024 Sep 03.
Article en En | MEDLINE | ID: mdl-39197980
ABSTRACT

BACKGROUND:

Sodium-glucose cotransporter 2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) reduce the risk of major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head clinical trials.

OBJECTIVES:

The aim of this study was to compare the cardiovascular effectiveness of SGLT2is, GLP-1 RAs, dipeptidyl peptidase-4 inhibitors (DPP4is), and clinical sulfonylureas (SUs) as second-line antihyperglycemic agents in T2DM.

METHODS:

Across the LEGEND-T2DM (Large-Scale Evidence Generation and Evaluation Across a Network of Databases for Type 2 Diabetes Mellitus) network, 10 federated international data sources were included, spanning 1992 to 2021. In total, 1,492,855 patients with T2DM and cardiovascular disease (CVD) on metformin monotherapy were identified who initiated 1 of 4 second-line agents (SGLT2is, GLP-1 RAs, DPP4is, or SUs). Large-scale propensity score models were used to conduct an active-comparator target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, on-treatment Cox proportional hazards models were fit for 3-point MACE (myocardial infarction, stroke, and death) and 4-point MACE (3-point MACE plus heart failure hospitalization) risk and HR estimates were combined using random-effects meta-analysis.

RESULTS:

Over 5.2 million patient-years of follow-up and 489 million patient-days of time at risk, patients experienced 25,982 3-point MACE and 41,447 4-point MACE. SGLT2is and GLP-1 RAs were associated with lower 3-point MACE risk than DPP4is (HR 0.89 [95% CI 0.79-1.00] and 0.83 [95% CI 0.70-0.98]) and SUs (HR 0.76 [95% CI 0.65-0.89] and 0.72 [95% CI 0.58-0.88]). DPP4is were associated with lower 3-point MACE risk than SUs (HR 0.87; 95% CI 0.79-0.95). The pattern for 3-point MACE was also observed for the 4-point MACE outcome. There were no significant differences between SGLT2is and GLP-1 RAs for 3-point or 4-point MACE (HR 1.06 [95% CI 0.96-1.17] and 1.05 [95% CI 0.97-1.13]).

CONCLUSIONS:

In patients with T2DM and CVD, comparable cardiovascular risk reduction was found with SGLT2is and GLP-1 RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of SGLT2is and GLP-1 RAs should be prioritized as second-line agents in those with established CVD.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedades Cardiovasculares / Diabetes Mellitus Tipo 2 / Inhibidores del Cotransportador de Sodio-Glucosa 2 / Hipoglucemiantes Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: J Am Coll Cardiol Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedades Cardiovasculares / Diabetes Mellitus Tipo 2 / Inhibidores del Cotransportador de Sodio-Glucosa 2 / Hipoglucemiantes Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: J Am Coll Cardiol Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos