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Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study.
Khera, Rohan; Aminorroaya, Arya; Dhingra, Lovedeep Singh; Thangaraj, Phyllis M; Camargos, Aline Pedroso; 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 Cy; Li, Jing; Li, Kelly; Liu, Yuntian; Lu, Yuan; Man, Kenneth Kc; 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; Ruan, Elise; Seager, Sarah L; Simon, Katherine R; Viernes, Benjamin; Yang, Jianxiao; Yin, Can; You, Seng Chan; Zhou, Jin J; Ryan, Patrick B.
Afiliação
  • Khera R; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA.
  • Aminorroaya A; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA.
  • Dhingra LS; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06520, USA.
  • Thangaraj PM; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA.
  • Camargos AP; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA.
  • Bu F; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA.
  • Ding X; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA.
  • Nishimura A; Department of Biostatistics, University of Michigan - Ann Arbor, Ann Arbor, MI, 48105, USA.
  • Anand TV; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA.
  • Arshad F; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA.
  • Blacketer C; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA.
  • Chai Y; Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
  • Chattopadhyay S; Observational Health Data Analytics, Janssen Research and Development, LLC, Titusville, NJ, 8560, USA.
  • Cook M; Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong.
  • Dorr DA; Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
  • Duarte-Salles T; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA.
  • DuVall SL; Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.
  • Falconer T; Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 8007, Spain.
  • French TE; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Hanchrow EE; Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA.
  • Kaur G; Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA.
  • Lau WC; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA.
  • Li J; Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.
  • Li K; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Liu Y; Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.
  • Lu Y; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Man KK; Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD1 9SY, United Kingdom.
  • Matheny ME; Research Department of Practice and Policy, School of Pharmacy, University College London, London, WC1H 9JP, United Kingdom.
  • Mathioudakis N; Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom.
  • McLeggon JA; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong.
  • McLemore MF; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, Hong Kong.
  • Minty E; Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Durham, NC, USA.
  • Morales DR; Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
  • Nagy P; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA.
  • Ostropolets A; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA.
  • Pistillo A; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA.
  • Phan TP; Research Department of Practice and Policy, School of Pharmacy, University College London, London, WC1H 9JP, United Kingdom.
  • Pratt N; Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom.
  • Reyes C; Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong.
  • Richter L; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, Hong Kong.
  • Ross J; Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.
  • Ruan E; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Seager SL; Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Simon KR; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA.
  • Viernes B; Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.
  • Yang J; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Yin C; Faculty of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, AB, T2N4N1, Canada.
  • You SC; Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD1 9SY, United Kingdom.
  • Zhou JJ; Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Ryan PB; Observational Health Data Analytics, Janssen Research and Development, LLC, Titusville, NJ, 8560, USA.
medRxiv ; 2024 Feb 08.
Article em En | MEDLINE | ID: mdl-38370787
ABSTRACT

Background:

SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1-RAs) reduce 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 trials.

Methods:

Across the LEGEND-T2DM network, we included ten federated international data sources, spanning 1992-2021. We identified 1,492,855 patients with T2DM and established cardiovascular disease (CVD) on metformin monotherapy who initiated one of four second-line agents (SGLT2is, GLP1-RAs, dipeptidyl peptidase 4 inhibitor [DPP4is], sulfonylureas [SUs]). We used large-scale propensity score models to conduct an active comparator, target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, we fit on-treatment Cox proportional hazard models for 3-point MACE (myocardial infarction, stroke, death) and 4-point MACE (3-point MACE + heart failure hospitalization) risk, and combined hazard ratio (HR) estimates in a random-effects meta-analysis.

Findings:

Across cohorts, 16·4%, 8·3%, 27·7%, and 47·6% of individuals with T2DM initiated SGLT2is, GLP1-RAs, DPP4is, and SUs, respectively. Over 5·2 million patient-years of follow-up and 489 million patient-days of time at-risk, there were 25,982 3-point MACE and 41,447 4-point MACE events. SGLT2is and GLP1-RAs were associated with a lower risk for 3-point MACE compared with DPP4is (HR 0·89 [95% CI, 0·79-1·00] and 0·83 [0·70-0·98]), and SUs (HR 0·76 [0·65-0·89] and 0·71 [0·59-0·86]). DPP4is were associated with a lower 3-point MACE risk versus SUs (HR 0·87 [0·79-0·95]). The pattern was consistent for 4-point MACE for the comparisons above. There were no significant differences between SGLT2is and GLP1-RAs for 3-point or 4-point MACE (HR 1·06 [0·96-1·17] and 1·05 [0·97-1·13]).

Interpretation:

In patients with T2DM and established CVD, we found comparable cardiovascular risk reduction with SGLT2is and GLP1-RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of GLP1-RAs and SGLT2is should be prioritized as second-line agents in those with established CVD.

Funding:

National Institutes of Health, United States Department of Veterans Affairs.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: MedRxiv Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: MedRxiv Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos