Evidence map›Paper›PMID 41984016›Full record

ArticleJournal of the American College of Cardiology2026

Comparative Cardiovascular Effectiveness of Glucagon-Like Peptide 1 Receptor Agonists and Sodium-Glucose Cotransporter 2 Inhibitors in Diabetes Mellitus.

Fan Bu, Ruopeng Wu, Anna Ostropolets, Arya Aminorroaya, Hsin Yi Chen, Yi Chai, Lovedeep Singh Dhingra, Thomas Falconer, Jason C Hsu, Chungsoo Kim and 11 more

Abstract readComparative StudyMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American College of Cardiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Fan BuDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Ruopeng WuDepartment of Mathematics, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, Michigan, USA.
Anna OstropoletsObservational Health Data Analytics, Janssen Research and Development, LLC, Titusville, New Jersey, USA.
Arya AminorroayaSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, Connecticut, USA; Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, Connecticut, USA.
Hsin Yi ChenDepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA.
Yi ChaiCentre 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; Shenzhen University Medical School, School of Public Health, Shenzhen University, Shenzhen, China.
Lovedeep Singh DhingraSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, Connecticut, USA; Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, Connecticut, USA.
Thomas FalconerDepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA.
Jason C HsuInternational PhD Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan.
Chungsoo KimSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, Connecticut, USA; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, Connecticut, USA.
Wallis C Y LauCentre 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 (D24H), Hong Kong Science Park, Hong Kong, China; 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.
Kenneth K C ManCentre 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 (D24H), Hong Kong Science Park, Hong Kong, China; 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.
Evan MintyFaculty of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada.
Daniel R MoralesUsher Institute, University of Edinburgh, Edinburgh, United Kingdom.
Akihiko NishimuraDepartment of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Phyllis ThangrarajSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, Connecticut, USA; Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, Connecticut, USA.
Mui Van ZandtReal World Solutions, IQVIA, Durham, North Carolina, USA.
Can YinReal World Solutions, IQVIA, Shanghai, China.
Rohan KheraSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, Connecticut, USA; Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, Connecticut, USA; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, Connecticut, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
George HripcsakDepartment of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA; Veterans Affairs Informatics and Computing Infrastructure, U.S. Department of Veterans Affairs, Salt Lake City, Utah, USA.
Marc A SuchardVeterans Affairs Informatics and Computing Infrastructure, U.S. Department of Veterans Affairs, Salt Lake City, Utah, USA; Department of Biostatistics, Fielding School of Public Health, University of California-Los Angeles, Los Angeles, California, 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.

Funding

DISCOVERING AND APPLYING KNOWLEDGE IN CLINICAL DATABASESR01LM006910 · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2000 to 2005
$2.4M
Translating Personalized Inference from Randomized Clinical Trials to Real-World Cardiovascular CareR01HL167858 · YALE UNIVERSITY · 2025 to 2025
$744k
Extreme Heat and Acute Myocardial Infarction: Effect Modifications by Sex, Medical History, and Air PollutionR01HL169171 · YALE UNIVERSITY · 2025 to 2025
$743k
Real-world Evidence to Inform Decisions for Hypertension Treatment EscalationR01HL169954 · YALE UNIVERSITY · 2025 to 2025
$644k
Towards precision risk stratification, diagnosis, and treatment: statistical and computational machinery for synthesizing information across massive, diverse sources of genomic and clinical dataR35GM160458 · JOHNS HOPKINS UNIVERSITY · 2025 to 2025
$426k
Evaluating and Improving Utilization of Evidence-Based Medical Therapy in Patients with Heart Failure using Automated Tools in the Electronic Health RecordK23HL153775 · YALE UNIVERSITY · 2025 to 2025
$147k
NHGRI NIH HHS R01 HG006139NHLBI NIH HHS K23 HL153775NHLBI NIH HHS R01 HL167858NHLBI NIH HHS R01 HL169171NHLBI NIH HHS R01 HL169954NIGMS NIH HHS R35 GM160458NLM NIH HHS R01 LM006910
6 · The paper itself

Abstract

backgroundGlucagon-like peptide 1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter 2 inhibitors (SGLT2Is) have established cardiovascular benefits for patients with type 2 diabetes mellitus (T2DM), with similar class-level effectiveness found in previous studies. However, real-world comparative effectiveness assessments of individual agents remain limited.

objectivesThe goal of this study was to compare the cardiovascular effectiveness of individual GLP-1RAs and SGLT2Is.

methodsWe conducted a multinational, retrospective, new-user active-comparator cohort study using 10 U.S. and non-U.S. administrative claims and electronic health record databases. The study included 1,245,211 adults with T2DM receiving metformin who initiated second-line therapy with 1 of 6 GLP-1RAs (albiglutide, dulaglutide, exenatide, liraglutide, lixisenatide, semaglutide) or 1 of 4 SGLT2Is (canagliflozin, dapagliflozin, empagliflozin, ertugliflozin). Empagliflozin (393,499; 31.6%), semaglutide (235,585; 18.9%), dapagliflozin (208,666; 16.8%), and dulaglutide (207,348; 16.8%) were most commonly used. A secondary subgroup analysis included 316,242 patients with established cardiovascular diseases (CVDs). Primary outcomes were 3-point major adverse cardiovascular events (MACEs) (acute myocardial infarction, stroke, sudden cardiac death) and 4-point MACE (adding hospitalization/emergency room visit with heart failure). Secondary outcomes included the individual components. HRs were estimated for pairwise agent comparisons while on-treatment (per-protocol) and over total follow-up using Cox proportional hazards models, with propensity score adjustments, negative control calibration, and prespecified study diagnostics to guard against potential confounding. Random-effects meta-analysis produced summary HR estimates across data sources that passed diagnostics.

resultsAcross the study cohort, individual GLP-1RAs and SGLT2Is demonstrated broadly similar cardiovascular effectiveness, both within and across drug classes. For example, semaglutide and empagliflozin showed comparable risks for 3-point MACE (meta-analytic HR: 1.05; 95% CI: 0.79-1.39) and 4-point MACE (meta-analytic HR: 0.95; 95% CI: 0.81-1.12), with consistent findings in the CVD subgroup. Study diagnostics confirmed adequate equipoise, covariate balance, and statistical power to detect similarity in HRs between 0.8 and 1.2 for commonly used agents.

conclusionsIn this large-scale real-world study, individual GLP-1RAs and SGLT2Is exhibited largely comparable cardiovascular benefits, including in patients with established CVD. These findings align with network meta-analytic estimates from major cardiovascular outcome trials and broadly support current treatment guidelines. Clinical choices should be guided by relevant factors such as safety, adherence, tolerability, cost, and patient preference, where further work is needed.

Indexed as

Cardiovascular DiseasesDiabetes Mellitus, Type 2Glucagon-Like Peptide-1 Receptor AgonistsHypoglycemic AgentsSodium-Glucose Transporter 2 InhibitorsAgedFemaleHumansMaleMiddle AgedRetrospective StudiesSemaglutideTreatment OutcomeGlucagon-Like Peptide-1 Receptor AgonistsHypoglycemic AgentsSemaglutideSodium-Glucose Transporter 2 Inhibitorscardiovascular outcomescomparative effectivenessGLP-1 receptor agonistsreal-world evidenceSGLT2 inhibitorstype 2 diabetes mellitus

Identifiers

PMID41984016
PMCPMC13191718

What Socratic holds

Texttitle and abstract
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.