The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Sodium-glucose cotransporter-2 inhibitors versus glucagon-like peptide-1 receptor agonists and the risk for cardiovascular outcomes in routine care patients with diabetes across categories of cardiovascular disease
Open accessAbstract
BACKGROUND: Both sodium-glucose cotransporter-2 (SGLT2) inhibitors and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have shown cardiovascular benefits in placebo-controlled trials of patients with type 2 diabetes (T2D) and established cardiovascular disease (CVD). OBJECTIVE: To evaluate whether SGLT2 inhibitors and GLP-1 RAs are associated with differential cardiovascular benefit among T2D patients with and without CVD. DESIGN: Population-based cohort study. SETTING: Medicare and 2 U.S. commercial claims data sets (April 2013 to December 2017). PARTICIPANTS: 1:1 propensity score-matched adult T2D patients with and without CVD (52 901 and 133 139 matched pairs) initiating SGLT2 inhibitor versus GLP-1 RA therapy. MEASUREMENTS: Primary outcomes were myocardial infarction (MI) or stroke hospitalization and hospitalization for heart failure (HHF). Pooled hazard ratios (HRs) and rate differences (RDs) per 1000 person-years were estimated, with 95% CIs, controlling for 138 preexposure covariates. RESULTS: The initiation of SGLT2 inhibitor versus GLP-1 RA therapy was associated with a slightly lower risk for MI or stroke in patients with CVD (HR, 0.90 [95% CI, 0.82 to 0.98]; RD, -2.47 [CI, -4.45 to -0.50]) but similar risk in those without CVD (HR, 1.07 [CI, 0.97 to 1.18]; RD, 0.38 [CI, -0.30 to 1.07]). The initiation of SGLT2 inhibitor versus GLP-1 RA therapy was associated with reductions in HHF risk regardless of baseline CVD in patients with CVD (HR, 0.71 [CI, 0.64 to 0.79]; RD, -4.97 [CI, -6.55 to -3.39]) and in those without CVD (HR, 0.69 [CI, 0.56 to 0.85]; RD, -0.58 [CI, -0.91 to -0.25]). LIMITATION: Treatment selection was not randomized. CONCLUSION: Use of SGLT2 inhibitors versus GLP-1 RAs was associated with consistent reductions in HHF risk among T2D patients with and without CVD, although the absolute benefit was greater in patients with CVD. There were no large differences in risk for MI or stroke among T2D patients with and without CVD. PRIMARY FUNDING SOURCE: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“These conclusions are further supported by observational data from a large populationbased cohort study in the U.S., which showed that SGLT2i reduced HHF compared with GLP-1 RA in people both with CVD (hazard ratio [HR] 0.71; 95% CI 0.64, 0.79) and without CVD (HR 0.69; 95% CI 0.56, 0.81).”
From Management of Hyperglycemia in Type 2 Diabetes, 2022. A Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) (2022) · cites this paper as [261]
Experimental — accuracy not yet measuredIn the citing paper
“but data from a real-world cohort of 557 109 individuals suggested that SGLT2is and GLP-1RAs provide comparable protection against atherosclerotic CVD in people with T2DM.53”
From Benefits of GLP-1 (Glucagon-Like Peptide 1) Receptor Agonists for Stroke Reduction in Type 2 Diabetes: A Call to Action for Neurologists (2022) · cites this paper as [53]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Empagliflozin, Cardiovascular Outcomes, and Mortality in Type 2 Diabetes
Zinman, B., Wanner, C., Lachin, J. M., Fitchett, D., Bluhmki, E., Hantel, S., Mattheus, M., Devins, T., Johansen, O. E., Woerle, H. J., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1504720 · Cited by 12,315
Canagliflozin and Cardiovascular and Renal Events in Type 2 Diabetes
Neal, B., Perkovic, V., Mahaffey, K. W., de Zeeuw, D., Fulcher, G., Erondu, N., Shaw, W., Law, G., Desai, M., & Matthews, D. R. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1611925 · Cited by 7,830
Liraglutide and Cardiovascular Outcomes in Type 2 Diabetes
Marso, S. P., Daniels, G. H., Brown-Frandsen, K., Kristensen, P., Mann, J. F. E., Nauck, M. A., Nissen, S. E., Pocock, S., Poulter, N. R., Ravn, L. S., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1603827 · Cited by 7,629
Dapagliflozin in Patients with Heart Failure and Reduced Ejection Fraction
McMurray, J. J. V., Solomon, S. D., Inzucchi, S. E., Køber, L., Kosiborod, M. N., Martinez, F. A., Ponikowski, P., Sabatine, M. S., Anand, I. S., Bělohlávek, J., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1911303 · Cited by 7,140
Semaglutide and Cardiovascular Outcomes in Patients with Type 2 Diabetes
Marso, S. P., Bain, S. C., Consoli, A., Eliaschewitz, F. G., Jódar, E., Leiter, L. A., Lingvay, I., Rosenstock, J., Seufert, J., Warren, M. L., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1607141 · Cited by 6,900
Austin, P. C. (2009) · Statistics in Medicine · doi:10.1002/sim.3697 · Cited by 6,758
Dapagliflozin and Cardiovascular Outcomes in Type 2 Diabetes
Wiviott, S. D., Raz, I., Bonaca, M. P., Mosenzon, O., Kato, E. T., Cahn, A., Silverman, M. G., Zelniker, T. A., Kuder, J. F., Murphy, S. A., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1812389 · Cited by 6,382
Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy
Perkovic, V., Jardine, M. J., Neal, B., Bompoint, S., Heerspink, H. J. L., Charytan, D. M., Edwards, R., Agarwal, R., Bakris, G., Bull, S., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1811744 · Cited by 6,189
Cardiovascular and renal outcomes with empagliflozin in heart failure
Packer, M., Anker, S. D., Butler, J., Filippatos, G., Pocock, S. J., Carson, P., Januzzi, J., Verma, S., Tsutsui, H., Brueckmann, M., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2022190 · Cited by 5,206 · cited 14×
“Conclusion: Use of SGLT2 inhibitors versus GLP-1 RAs was associated with consistent reductions in HHF risk among T2D patients with and without CVD, although the absolute benefit was greater in patients with CVD.”
BackgroundInfluentialCosentino, F., Grant, P. J., Aboyans, V., Bailey, C. J., Ceriello, A., Delgado, V., Federici, M., Filippatos, G., Grobbee, D. E., Hansen, T. B., et al. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehz486 · Cited by 4,763
Davies, M. J., D’Alessio, D. A., Fradkin, J., Kernan, W. N., Mathieu, C., Mingrone, G., Rossing, P., Tsapas, A., Wexler, D. J., & Buse, J. B. (2018) · Diabetes Care · doi:10.2337/dci18-0033 · Cited by 3,518
Self-citationGerstein, H. C., Colhoun, H. M., Dagenais, G. R., Diaz, R., Lakshmanan, M., Pais, P., Probstfield, J., Riesmeyer, J. S., Riddle, M. C., Rydén, L., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(19)31149-3 · Cited by 3,163
Estimating Causal Effects from Large Data Sets Using Propensity Scores
Rubin, D. V. (1997) · Annals of Internal Medicine · doi:10.7326/0003-4819-127-8_part_2-199710151-00064 · Cited by 3,104
Zelniker, T. A., Wiviott, S. D., Raz, I., Im, K., Goodrich, E. L., Bonaca, M. P., Mosenzon, O., Kato, E. T., Cahn, A., Furtado, R. H. M., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(18)32590-x · Cited by 2,845 · cited 1×
“More recent trials have included patients with risk factors but no established CVD (11, 13), and meta-analyses have suggested that the MACE prevention benefits are confined to those with established CVD compared with just cardiovascular risk factors (16, 17, 37).”
BackgroundEconomic Costs of Diabetes in the U.S. in 2017
American Diabetes Association (2018) · Diabetes Care · doi:10.2337/dci18-0007 · Cited by 2,528
Lixisenatide in Patients with Type 2 Diabetes and Acute Coronary Syndrome
Pfeffer, M. A., Claggett, B., Diaz, R., Dickstein, K., Gerstein, H. C., Køber, L. V., Lawson, F. C., Ping, L., Wei, X., Lewis, E. F., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1509225 · Cited by 2,409
Sotagliflozin in patients with diabetes and recent worsening heart failure
Bhatt, D. L., Szarek, M., Steg, P. G., Cannon, C. P., Leiter, L. A., McGuire, D. K., Lewis, J. B., Riddle, M. C., Voors, A. A., Metra, M., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2030183 · Cited by 1,868
Kristensen, S. L., Rørth, R., Jhund, P. S., Docherty, K. F., Sattar, N., Preiss, D., Køber, L., Petrie, M. C., & McMurray, J. J. V. (2019) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(19)30249-9 · Cited by 1,663 · cited 2×
“99]) (16), so the data presented here represent the first direct comparison of SGLT2 inhibitors to GLP-1 RAs for HHF.”
BackgroundAdvanced methods in meta‐analysis: multivariate approach and meta‐regression
van Houwelingen, H. C., Arends, L. R., & Stijnen, T. (2002) · Statistics in Medicine · doi:10.1002/sim.1040 · Cited by 1,599
Cardiovascular outcomes with ertugliflozin in type 2 diabetes
Cannon, C. P., Pratley, R., Dagogo-Jack, S., Mancuso, J., Huyck, S., Masiukiewicz, U., Charbonnel, B., Frederich, R., Gallo, S., Cosentino, F., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2004967 · Cited by 1,514
Mortality and cardiovascular disease in type 1 and type 2 diabetes
Rawshani, A., Rawshani, A., Franzén, S., Eliasson, B., Svensson, A.-M., Miftaraj, M., McGuire, D. K., Sattar, N., Rosengren, A., & Gudbjörnsdottir, S. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1608664 · Cited by 1,389
A review of uses of health care utilization databases for epidemiologic research on therapeutics
Schneeweiß, S., & Avorn, J. (2005) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2004.10.012 · Cited by 1,290
Buse, J. B., Wexler, D. J., Tsapas, A., Rossing, P., Mingrone, G., Mathieu, C., D’Alessio, D. A., & Davies, M. J. (2020) · Diabetes Care · doi:10.2337/dci19-0066 · Cited by 1,192
Self-citationA combined comorbidity score predicted mortality in elderly patients better than existing scores
Gagne, J. J., Glynn, R. J., Avorn, J., Levin, R., & Schneeweiss, S. (2011) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2010.10.004 · Cited by 995
Self-citation9. Pharmacologic Approaches to Glycemic Treatment: Standards of Medical Care in Diabetes—2021
American Diabetes Association (2021) · Diabetes Care · doi:10.2337/dc21-s009 · Cited by 972
Zelniker, T. A., Wiviott, S. D., Raz, I., Im, K., Goodrich, E. L., Furtado, R. H. M., Bonaca, M. P., Mosenzon, O., Kato, E. T., Cahn, A., et al. (2019) · Circulation · doi:10.1161/circulationaha.118.038868 · Cited by 781
Schneeweiß, S. (2006) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.1200 · Cited by 741
Validating Administrative Data in Stroke Research
Tirschwell, D., & Longstreth, W. T. (2002) · Stroke · doi:10.1161/01.str.0000032240.28636.bd · Cited by 664
Measuring Frailty in Medicare Data: Development and Validation of a Claims-Based Frailty Index
Kim, D. H., Schneeweiß, S., Glynn, R. J., Lipsitz, L. A., Rockwood, K. J., & Avorn, J. (2017) · The Journals of Gerontology Series A · doi:10.1093/gerona/glx229 · Cited by 628
Self-citationDiabetes Mellitus–Related All‐Cause and Cardiovascular Mortality in a National Cohort of Adults
Raghavan, S., Vassy, J. L., Ho, Y.-L. A., Song, R. J., GAGNON, D. R., Cho, K., Wilson, P. W. F., & Phillips, L. S. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.118.011295 · Cited by 576
Kiyota, Y., Schneeweiß, S., Glynn, R. J., Cannuscio, C. C., Avorn, J., & Solomon, D. H. (2004) · American Heart Journal · doi:10.1016/j.ahj.2004.02.013 · Cited by 533
Self-citation10. Cardiovascular Disease and Risk Management: Standards of Medical Care in Diabetes—2021
American Diabetes Association (2021) · Diabetes Care · doi:10.2337/dc21-s010 · Cited by 495
Gregg, E. W., Cheng, Y. J., Srinivasan, M., Lin, J., Geiss, L. S., Albright, A. L., & Imperatore, G. (2018) · The Lancet · doi:10.1016/s0140-6736(18)30314-3 · Cited by 480
Das, S. R., Everett, B. M., Birtcher, K. K., Brown, J. M., Januzzi, J. L., Kalyani, R. R., Kosiborod, M., Magwire, M., Morris, P. B., Neumiller, J. J., et al. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.05.037 · Cited by 466
Self-citationCardiovascular Events Associated With SGLT-2 Inhibitors Versus Other Glucose-Lowering Drugs
Kosiborod, M., Lam, C. S. P., Kohsaka, S., Kim, D. J., Karasik, A., Shaw, J. E., Tangri, N., Goh, S., Thuresson, M., Chen, H., et al. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.03.009 · Cited by 465
Risk of diabetic ketoacidosis after initiation of an SGLT2 inhibitor
Fralick, M., Schneeweiss, S., & Patorno, E. (2017) · New England Journal of Medicine · doi:10.1056/nejmc1701990 · Cited by 334
Self-citationA systematic review of validated methods for identifying heart failure using administrative data
Saczynski, J. S., Andrade, S. E., Harrold, L. R., Tjia, J., Cutrona, S. L., Dodd, K. S., Goldberg, R. J., & Gurwitz, J. H. (2012) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.2313 · Cited by 282
A basic study design for expedited safety signal evaluation based on electronic healthcare data
Schneeweiss, S. (2010) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.1926 · Cited by 255
Transparency and Reproducibility of Observational Cohort Studies Using Large Healthcare Databases.
Wang, S., Verpillat, P., Rassen, J. A., Patrick, A., Garry, E., & Bartels, D. (2015) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.329 · Cited by 201
Wahl, P. M., Rodgers, K., Schneeweiß, S., Gage, B. F., Butler, J., Wilmer, C. I., Nash, M., Esper, G. J., Gitlin, N., Osborn, N. K., et al. (2010) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.1924 · Cited by 174
Patorno, E., Goldfine, A. B., Schneeweiß, S., Everett, B. M., Glynn, R. J., Liu, J., & Kim, S. C. (2018) · BMJ · doi:10.1136/bmj.k119 · Cited by 174
Self-citationImplications of the Propensity Score Matching Paradox in Pharmacoepidemiology
Ripollone, J. E., Huybrechts, K. F., Rothman, K. J., Ferguson, R. E., & Franklin, J. M. (2018) · American Journal of Epidemiology · doi:10.1093/aje/kwy078 · Cited by 98
Dave, C. V., Schneeweiss, S., Wexler, D. J., Brill, G., & Patorno, E. (2020) · Diabetes Care · doi:10.2337/dc19-1943 · Cited by 97
Self-citationPatorno, E., Gopalakrishnan, C., Franklin, J. M., Brodovicz, K. G., Massó-González, E. L., Bartels, D. B., Liu, J., & Schneeweiß, S. (2017) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13184 · Cited by 85
Self-citation
Cited by
Sorted by citations
Davies, M. J., Aroda, V. R., Collins, B. S., Gabbay, R. A., Green, J., Maruthur, N. M., Rosas, S. E., Del Prato, S., Mathieu, C., Mingrone, G., et al. (2022) · Diabetes Care · doi:10.2337/dci22-0034 · Cited by 2,104
Davies, M. J., Aroda, V. R., Collins, B. S., Gabbay, R. A., Green, J., Maruthur, N. M., Rosas, S. E., Del Prato, S., Mathieu, C., Mingrone, G., et al. (2022) · Diabetologia · doi:10.1007/s00125-022-05787-2 · Cited by 1,048
Cardiometabolic Comorbidities in Cancer Survivors
Zullig, L. L., Sung, A. D., Khouri, M. G., Jazowski, S. A., Shah, N. P., Sitlinger, A. P., Blalock, D. V., Whitney, C. A., Kikuchi, R., Bosworth, H. B., et al. (2022) · JACC CardioOncology · doi:10.1016/j.jaccao.2022.03.005 · Cited by 105
D’Andrea, E., Wexler, D. J., Kim, S. C., Paik, J. M., Alt, E. M., & Patorno, E. (2023) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2022.6664 · Cited by 105
Self-citationCaruso, I., Cignarelli, A., Sorice, G. P., Natalicchio, A., Perrini, S., Laviola, L., & Giorgino, F. (2022) · Metabolites · doi:10.3390/metabo12020183 · Cited by 86
Metadata
- Authors
- Patorno, E., Htoo, P. T., Glynn, R. J., Schneeweiss, S., Wexler, D. J., Pawar, A., Bessette, L. G., Chin, K., Everett, B. M., & Kim, S. C.
- Affiliations
- Brigham and Women's Hospital (US)
- Harvard University (US)
- ORCID
- Venue
- Annals of Internal Medicine
- Publisher
- American College of Physicians
- Published
- 2021-11
- Volume
- 174
- Issue
- 11
- Pages
- 1528-1541
- ISSN
- 0003-4819 (ISSN-L)
- 1539-3704
- PMC
- PMC8969214
- OpenAlex
- W3201795055
- Cited by
- 103
- References
- 44
- Citations by year
- 2026: 9 · 2025: 17 · 2024: 24 · 2023: 27 · 2022: 23 · 2021: 3
- FWCI
- 14.37
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annals of Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Aging (K08AG055670)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Failure Treatment and Management
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- SGLT2 inhibitors, GLP-1 receptor agonists, type 2 diabetes, cardiovascular disease, cardiovascular outcomes, myocardial infarction, stroke, heart failure hospitalization, real-world evidence, antidiabetic drug comparison, Medicare beneficiaries, cardiovascular risk reduction, propensity score matching, claims data, glycemic control
- MeSH terms
- Glucagon-Like Peptide-1 Receptor, Sodium-Glucose Transporter 2 Inhibitors, Glucagon-Like Peptide-1 Receptor Agonists, Aged, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Female, Heart Failure, Hospitalization, Humans, Male, Middle Aged, Myocardial Infarction, United States, Cohort Studies, Matched-Pair Analysis, Stroke
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review