Comparative Study

. 2024 Sep 3;84(10):904-917.

doi: 10.1016/j.jacc.2024.05.069.

Comparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes: A Multinational, Federated Analysis of LEGEND-T2DM

Rohan Khera¹, Arya Aminorroaya², Lovedeep Singh Dhingra², Phyllis M Thangaraj², Aline Pedroso Camargos², Fan Bu³, Xiyu Ding⁴, Akihiko Nishimura⁴, Tara V Anand⁵, Faaizah Arshad⁶, Clair Blacketer⁷, Yi Chai⁸, Shounak Chattopadhyay⁶, Michael Cook⁴, David A Dorr⁹, Talita Duarte-Salles¹⁰, Scott L DuVall¹¹, Thomas Falconer⁵, Tina E French¹², Elizabeth E Hanchrow¹², Guneet Kaur¹³, Wallis C Y Lau¹⁴, Jing Li¹⁵, Kelly Li⁶, Yuntian Liu¹⁶, Yuan Lu², Kenneth K C Man¹⁴, Michael E Matheny¹², Nestoras Mathioudakis¹⁷, Jody-Ann McLeggon⁵, Michael F McLemore¹², Evan Minty¹⁸, Daniel R Morales¹³, Paul Nagy¹⁷, Anna Ostropolets⁷, Andrea Pistillo¹⁹, Thanh-Phuc Phan²⁰, Nicole Pratt²¹, Carlen Reyes¹⁹, Lauren Richter⁵, Joseph S Ross²², Elise Ruan⁵, Sarah L Seager²³, Katherine R Simon¹², Benjamin Viernes¹¹, Jianxiao Yang²⁴, Can Yin²⁵, Seng Chan You²⁶, Jin J Zhou²⁷, Patrick B Ryan⁵, Martijn J Schuemie²⁸, Harlan M Krumholz²⁹, George Hripcsak⁵, Marc A Suchard³⁰

Affiliations

¹ 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 Health, New Haven, Connecticut, USA. Electronic address: rohan.khera@yale.edu.
² Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, Connecticut, USA.
³ Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
⁴ Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
⁵ Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA.
⁶ Department of Biostatistics, Fielding School of Public Health, University of California-Los Angeles, Los Angeles, California, USA.
⁷ Observational Health Data Analytics, Janssen Research and Development, Titusville, New Jersey, USA.
⁸ Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
⁹ Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.
¹⁰ 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.
¹¹ 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.
¹² Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, Tennessee, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
¹³ Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom.
¹⁴ 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 Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China; Laboratory of Data Discovery for Health, Hong Kong Science Park, Hong Kong, China.
¹⁵ Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Durham, North Carolina, USA.
¹⁶ 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.
¹⁷ Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
¹⁸ Faculty of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada.
¹⁹ Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain.
²⁰ School of Pharmacy, Taipei Medical University, Taipei, Taiwan.
²¹ Quality Use of Medicines and Pharmacy Research Centre, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, Australia.
²² 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.
²³ Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, London, United Kingdom.
²⁴ Department of Computational Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California, USA.
²⁵ Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Shanghai, China.
²⁶ 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.
²⁷ 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.
²⁸ Epidemiology, Office of the Chief Medical Officer, Johnson & Johnson, Titusville, New Jersey, USA.
²⁹ 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 Cardiovascular Medicine, Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, USA.
³⁰ Department of Biostatistics, Fielding School of Public Health, University of California-Los Angeles, Los Angeles, California, USA; Veterans Affairs Informatics and Computing Infrastructure, U.S. Department of Veterans Affairs, Salt Lake City, Utah, USA; Department of Biomathematics, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California, USA; Department of Human Genetics, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California, USA. Electronic address: msuchard@ucla.edu.

PMID: 39197980
PMCID: PMC12045554
DOI: 10.1016/j.jacc.2024.05.069

Comparative Study

Comparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes: A Multinational, Federated Analysis of LEGEND-T2DM

Rohan Khera et al. J Am Coll Cardiol. 2024.

. 2024 Sep 3;84(10):904-917.

doi: 10.1016/j.jacc.2024.05.069.

Authors

Affiliations

¹ 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 Health, New Haven, Connecticut, USA. Electronic address: rohan.khera@yale.edu.
² Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, Connecticut, USA.
³ Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
⁴ Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
⁵ Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA.
⁶ Department of Biostatistics, Fielding School of Public Health, University of California-Los Angeles, Los Angeles, California, USA.
⁷ Observational Health Data Analytics, Janssen Research and Development, Titusville, New Jersey, USA.
⁸ Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
⁹ Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.
¹⁰ 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.
¹¹ 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.
¹² Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, Tennessee, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
¹³ Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom.
¹⁴ 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 Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China; Laboratory of Data Discovery for Health, Hong Kong Science Park, Hong Kong, China.
¹⁵ Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Durham, North Carolina, USA.
¹⁶ 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.
¹⁷ Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
¹⁸ Faculty of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada.
¹⁹ Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain.
²⁰ School of Pharmacy, Taipei Medical University, Taipei, Taiwan.
²¹ Quality Use of Medicines and Pharmacy Research Centre, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, Australia.
²² 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.
²³ Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, London, United Kingdom.
²⁴ Department of Computational Medicine, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California, USA.
²⁵ Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Shanghai, China.
²⁶ 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.
²⁷ 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.
²⁸ Epidemiology, Office of the Chief Medical Officer, Johnson & Johnson, Titusville, New Jersey, USA.
²⁹ 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 Cardiovascular Medicine, Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, USA.
³⁰ Department of Biostatistics, Fielding School of Public Health, University of California-Los Angeles, Los Angeles, California, USA; Veterans Affairs Informatics and Computing Infrastructure, U.S. Department of Veterans Affairs, Salt Lake City, Utah, USA; Department of Biomathematics, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California, USA; Department of Human Genetics, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California, USA. Electronic address: msuchard@ucla.edu.

PMID: 39197980
PMCID: PMC12045554
DOI: 10.1016/j.jacc.2024.05.069

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.

Keywords: cardiovascular diseases; comparative effectiveness research; glucagon-like peptide-1 receptor agonists; hypoglycemic agents; sodium-glucose transporter 2 inhibitors; type 2 diabetes mellitus.

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Conflict of interest statement

Funding Support and Author Disclosures This study was partially funded through National Institutes of Health grants R01 HL167858, K23 HL153775, R01 LM006910, R01 HG006139, and R01 HL169954 and the U.S. Department of Veterans Affairs under the research priority to “Put VA Data to Work for Veterans” (VA ORD 22-D4V). The funders had no role in the design and conduct of the protocol; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Dr Khera is an associate editor at JAMA; and has received support from the National Heart, Lung, and Blood Institute of the National Institutes of Health (awards R01 HL167858 and K23 HL153775) and the Doris Duke Charitable Foundation (award 2022060); has received research support, through Yale University, from Bristol Myers Squibb, Novo Nordisk, and BridgeBio; is a coinventor of U.S. provisional patent applications WO2023230345A1, US20220336048A1, 63/346,610, 63/428,569, 63/484,426, 63/508,315, 63/580,137, 63/606,203, 63/619,241, and 63/562,335, unrelated to the present work; and is a cofounder of Evidence2Health and Ensight-AI, precision health platforms to improve evidence-based cardiovascular care and cardiovascular diagnostics. Dr DuVall has received grants from Alnylam Pharmaceuticals, AstraZeneca Pharmaceuticals, Biodesix, Celgene, Cerner Enviza, GlaxoSmithKline, Janssen Pharmaceuticals, Novartis International, and Parexel International through the University of Utah or the Western Institute for Veteran Research (outside the submitted work). Dr Man has received support from the C.W. Maplethorpe Fellowship, the National Institute of Health Research, European Commission Framework Horizon 2020, the Hong Kong Research Grant Council, and the Innovation and Technology Commission of the Hong Kong Special Administration Region Government outside the submitted work). Dr Morales is supported by a Wellcome Trust Clinical Research Fellowship (214588/Z/18/Z). In the past 3 years, Dr Krumholz has received expenses and/or personal fees from UnitedHealth, Element Science, Aetna, Reality Labs, Tesseract/4Catalyst, F-Prime, the Siegfried and Jensen Law Firm, the Arnold and Porter Law Firm, and the Martin/Baughman Law Firm; is a cofounder of Refactor Health, HugoHealth, and Ensight-AI; and is associated with contracts, through Yale New Haven Hospital, from the Centers for Medicare and Medicaid Services and, through Yale University, from Johnson & Johnson. Dr You is chief technology officer of PHI Digital Healthcare. Dr Ross has received research support through Yale University from Johnson & Johnson to develop methods of clinical trial data sharing, from the Food and Drug Administration for the Yale-Mayo Clinic Center for Excellence in Regulatory Science and Innovation program (grant U01 FD005938), from the Agency for Healthcare Research and Quality (grant R01 HS022882), and from Arnold Ventures; has received research support from the Medical Device Innovation Consortium as part of the National Evaluation System for Health Technology and from the National Heart, Lung, and Blood Institute of the National Institutes of Health (grants R01 HS025164 and R01 HL144644); and was an expert witness at the request of the relator’s attorneys, the Greene Law Firm, in a qui tam suit alleging violations of the False Claims Act and Anti-Kickback Statute against Biogen that was settled September 2022. Dr Thangaraj is a coinventor on provisional patent 63/606,203, unrelated to the present work, and is funded by grant 5T32 HL155000-03. Ms Blacketer, Dr Ostropolets, and Dr Ryan are employees of Johnson & Johnson. Dr Schuemie is an employee and shareholder of Johnson & Johnson. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Figures

**Central Illustration:. Study Design and Analytical Methodology to Evaluate the Comparative Effectiveness of SGLT2is, GLP1-RAs, DPP4is, and SUs for Cardiovascular Outcomes.**
We leveraged ten federated data sources from four different countries comprising more than 4.7 million individuals with T2DM on metformin monotherapy initiating one of the four second-line agents. We included a subset of 1.5 million patients with established CVD to assess the comparative cardiovascular effectiveness of these agents. We mapped the data sources to the OMOP CDM and leveraged robust state-of-the-art methodological and analytic strategies to minimize residual confounding, publication bias, and p-hacking. Abbreviations: CCAE, IBM MarketScan^® Commercial Claims and Encounters Data; CVD, cardiovascular disease; GDA, Germany Disease Analyser; IMRD, UK-IQVIA Medical Research Data; MACE, major adverse cardiovascular events; MDCD, IBM Health MarketScan^® Multi-State Medicaid Database; MDCR, IBM Health MarketScan^® Medicare Supplemental and Coordination of Benefits Database; OCEDM, Optum© Clinformatics Extended Data Mart - Date of Death; OEHR, Optum© de-identified Electronic Health Record Dataset; OMOP-CDM, Observational Medical Outcomes Partnership-common data model; SIDIAP, Information System for Research in Primary Care; T2DM, type 2 diabetes mellitus; USOC, United States Open Claims; VA, Department of Veterans Affairs Healthcare System.

**Figure 1:. Maximum Standardized Mean Difference Before and After Propensity Score Stratification for All Covariates Across Target-Comparator-Database Combinations.**
This figure shows to what extent the propensity score stratification reduced the maximum SMD for all covariates across six target-comparator pairs and ten data sources. Abbreviations: CCAE, IBM MarketScan^® Commercial Claims and Encounters Data; DPP4i, dipeptidyl peptidase 4 inhibitor; GDA, Germany Disease Analyzer; GLP1-RA, glucagon-like peptide-1 receptor agonist; IMRD, UK-IQVIA Medical Research Data; MDCD, IBM Health MarketScan^® Multi-State Medicaid Database; IQR, interquartile range; MDCR, IBM Health MarketScan^® Medicare Supplemental and Coordination of Benefits Database; OCEDM, Optum© Clinformatics Extended Data Mart - Date of Death; OEHR, Optum© de-identified Electronic Health Record Dataset; PS, propensity score; SGLT2i, sodium-glucose co-transporter-2 inhibitor; SIDIAP, Information System for Research in Primary Care; SMD, standardized mean difference; SU, Sulfonylurea; USOC, United States Open Claims; VA, Department of Veterans Affairs Healthcare System.

**Figure 2:. Meta-analytic Calibrated Hazard Ratio Estimates for Comparative Effectiveness of SGLT2is, GLP1-RAs, DPP4is, and SUs for Cardiovascular Outcomes.**
The figure shows the comparative effectiveness of SGLT2is, GLP1-RAs, DPP4is, and SUs for composite and individual cardiovascular outcomes across six target-comparator pairs with the y-axis representing target and the x-axis showing comparators. Points on the left side of the dashed line represent HR that favors the target (vertical agent) and points on the right side of the dashed line represent HR that favors the comparator (horizontal agent). Abbreviations: DPP4i, dipeptidyl peptidase 4 inhibitor; GLP1-RA, glucagon-like peptide-1 receptor agonist; HF, heart failure; HR, hazard ratio; MACE, major adverse cardiovascular events; MI, myocardial infarction; SGLT2i, sodium-glucose co-transporter-2 inhibitor; SU, Sulfonylurea.

**Figure 3:. Swarm Plot of Calibrated Hazard Ratio Estimates of Major Outcome Meta-analysis and Leave-one-out Meta-analysis.**
Circles depict the calibrated relative risk of each leave-one-out study in which one original data source is removed from the meta-analysis. Diamonds depict the original meta-analysis effect estimate with all data sources. Points are color-coded by major outcomes, the y-axis represents six target-comparator pairs, and the x-axis on log-scale measures the calibrated relative risk of each outcome. Abbreviations: DPP4i, dipeptidyl peptidase 4 inhibitor; GLP1-RA, glucagon-like peptide-1 receptor agonist; HF, heart failure; MACE, major adverse cardiovascular events; MI, myocardial infarction; SGLT2i, sodium-glucose co-transporter-2 inhibitor; SU, Sulfonylurea.

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Update of

Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study.
Khera R, Aminorroaya A, Dhingra LS, Thangaraj PM, Camargos AP, Bu F, Ding X, Nishimura A, Anand TV, Arshad F, Blacketer C, Chai Y, Chattopadhyay S, Cook M, Dorr DA, Duarte-Salles T, DuVall SL, Falconer T, French TE, Hanchrow EE, Kaur G, Lau WC, Li J, Li K, Liu Y, Lu Y, Man KK, Matheny ME, Mathioudakis N, McLeggon JA, McLemore MF, Minty E, Morales DR, Nagy P, Ostropolets A, Pistillo A, Phan TP, Pratt N, Reyes C, Richter L, Ross J, Ruan E, Seager SL, Simon KR, Viernes B, Yang J, Yin C, You SC, Zhou JJ, Ryan PB, Schuemie MJ, Krumholz HM, Hripcsak G, Suchard MA. Khera R, et al. medRxiv [Preprint]. 2024 Feb 8:2024.02.05.24302354. doi: 10.1101/2024.02.05.24302354. medRxiv. 2024. Update in: J Am Coll Cardiol. 2024 Sep 3;84(10):904-917. doi: 10.1016/j.jacc.2024.05.069. PMID: 38370787 Free PMC article. Updated. Preprint.

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Comparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes: A Multinational, Federated Analysis of LEGEND-T2DM

Affiliations

Comparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes: A Multinational, Federated Analysis of LEGEND-T2DM

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Conflict of interest statement

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