Comparative effectiveness of SGLT2 inhibitors, GLP-1 receptor agonists, DPP-4 inhibitors, and sulfonylureas on risk of major adverse cardiovascular events: emulation of a randomised target trial using electronic health records
Abstract
Background Randomised clinical trials showed that compared with placebo, SGLT2 inhibitors and GLP-1 receptor agonists reduced risk of adverse cardiovascular events. The evidence base for the older antihyperglycaemic drug classes (DPP-4 inhibitors and sulfonylureas) is generally less well developed. Because most randomised trials evaluated one antihyperglycaemic medication versus placebo, a head-to-head comparative effectiveness analysis of the newer drug classes (SGLT2 inhibitors vs GLP-1 receptor agonists) or newer (SGLT2 inhibitors or GLP-1 receptor agonists) versus older (DPP-4 inhibitors or sulfonylureas) drug classes on risk of major adverse cardiovascular events (MACE) is not available. In this study, we aimed to evaluate the comparative effectiveness of incident use of SGLT2 inhibitors, GLP-1 receptor agonists, DPP-4 inhibitors, or sulfonylureas on risk of MACE. Methods We first specified the protocol of a four-arm randomised pragmatic clinical trial and then emulated it using the health-care databases of the US Department of Veterans Affairs. We built a cohort of metformin users with incident use of SGLT2 inhibitors, GLP-1 receptor agonists, DPP-4 inhibitors, or sulfonylureas between Oct 1, 2016 and Sept 30, 2021, and followed up until Dec 31, 2022. We used the overlap weighting approach to balance the treatment groups using a battery of predefined variables and a set of algorithmically selected variables from high-dimensional data domains. Both intention-to-treat and per-protocol analyses (the latter estimated the effect of maintained use of the antihyperglycaemic throughout follow-up) were conducted to estimate risk of MACE-defined as a composite endpoint of stroke, myocardial infarction, and all-cause mortality. Findings The final cohort consisted of 283 998 new users of SGLT2 inhibitors (n=46 516), GLP-1 receptor agonists (n=26 038), DPP-4 inhibitors (n=55 310), or sulfonylureas (n=156 134). In intention-to-treat analyses, compared with sulfonylureas, SGLT2 inhibitors, GLP-1 receptor agonists, and DPP-4 inhibitors were associated with lower risk of MACE (hazard ratio [HR] 0·77 [95% CI 0·74-0.80], 0·78 [0·74-0·81), and 0·90 [0·86-0.93], respectively). Both SGLT2 inhibitors and GLP-1 receptor agonists were associated with a lower risk of MACE when compared with DPP-4 inhibitors (HR 0·86 [0·82-0·89] and 0·86 [0·82-0·90], respectively). The risk of MACE between SGLT2 inhibitors and GLP-1 receptor agonists yielded an HR of 0·99 (0·94-1·04). In per-protocol analyses, compared with sulfonylureas, SGLT2 inhibitors, GLP1 receptor agonists, and DPP-4 inhibitors were associated with reduced risk of MACE (HR 0·77 [95% CI 0·73-0·82], 0·77 [0·72-0·82], and 0·88 [0·83-0·93], respectively). Both SGLT2 inhibitors and GLP-1 receptor agonists were associated with a lower risk of MACE when compared with DPP-4 inhibitors (HR 0·88 [0·83-0·93] and 0·88 [0·82-0·93], respectively). The risk of MACE between SGLT2 inhibitors and GLP-1 receptor agonists yielded an HR of 1·01 (0·94-1·07). Interpretation Both SGLT2 inhibitors and GLP-1 receptor agonists were associated with reduced risk of MACE compared with DPP-4 inhibitors or sulfonylureas. DPP-4 inhibitors were associated with reduced risk of MACE compared with sulfonylureas. There was no statistically significant difference in risk of MACE between SGLT2 inhibitors and GLP-1 receptor agonists. The results provide evidence of the real-world comparative effectiveness of the four most commonly used second-line antihyperglycaemics and could guide choice of antihyperglycaemic therapy. Funding US Department of Veterans Affairs and the American Society of Nephrology.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Drug combinations may increase response rates and effectiveness of treatment, which has so far been limited with monotherapies.
“On the one hand, drug combinations may increase response rates and effectiveness of treatment, which has so far been limited with monotherapies [229].”
From Obesity and MASLD: Is weight loss the (only) key to treat metabolic liver disease? (2024) · cites this paper as [229]
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 · see citing works
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 · see citing works
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 · see citing works
Austin, P. C. (2009) · Statistics in Medicine · doi:10.1002/sim.3697 · Cited by 6,758 · see citing works
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 · see citing works
Naghavi, M., Abajobir, A. A., Abbafati, C., Abbas, K. M., Abd-Allah, F., Abera, S. F., Aboyans, V., Adetokunboh, O., Afshin, A., Agrawal, A., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)32152-9 · Cited by 4,821 · see citing works
Self-citationMaking Neighborhood-Disadvantage Metrics Accessible — The Neighborhood Atlas
Kind, A. J. H., & Buckingham, W. R. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1802313 · Cited by 3,310 · see citing works
Multiple imputation of discrete and continuous data by fully conditional specification
van Buuren, S. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074463 · Cited by 2,827 · see citing works
Effect of Sitagliptin on Cardiovascular Outcomes in Type 2 Diabetes
Green, J. B., Bethel, M. A., Armstrong, P. W., Buse, J. B., Engel, S. S., Garg, J., Josse, R., Kaufman, K. D., Koglin, J., Korn, S., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1501352 · Cited by 2,577 · see citing works
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 · see citing works
Schneeweiss, S., Rassen, J. A., Glynn, R. J., Avorn, J., Mogun, H., & Brookhart, M. A. (2009) · Epidemiology · doi:10.1097/ede.0b013e3181a663cc · Cited by 1,128 · see citing works
Long-term neurologic outcomes of COVID-19
Xu, E., Xie, Y., & Al-Aly, Z. (2022) · Nature Medicine · doi:10.1038/s41591-022-02001-z · Cited by 530 · see citing works
Self-citationGlycemia reduction in type 2 diabetes–microvascular and cardiovascular outcomes
The GRADE Study Research Group (2022) · New England Journal of Medicine · doi:10.1056/nejmoa2200436 · Cited by 159 · see citing works
Cardiovascular effects of Glucagon-like peptide 1 (GLP-1) receptor agonists
Saraiva, F. K., & Sposito, A. C. (2014) · Cardiovascular Diabetology · doi:10.1186/s12933-014-0142-7 · Cited by 143 · see citing works
McEwan, P., Bennett, H., Khunti, K., Wilding, J., Edmonds, C., Thuresson, M., Wittbrodt, E., Fenici, P., & Kosiborod, M. (2020) · Diabetes, Obesity and Metabolism · doi:10.1111/dom.14162 · Cited by 58 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Mapping the effectiveness and risks of GLP-1 receptor agonists
Xie, Y., Choi, T., & Al-Aly, Z. (2025) · Nature Medicine · doi:10.1038/s41591-024-03412-w · Cited by 300 · see citing works
Self-citationObesity and MASLD: Is weight loss the (only) key to treat metabolic liver disease?
Huttasch, M., Roden, M., & Kahl, S. (2024) · Metabolism · doi:10.1016/j.metabol.2024.155937 · Cited by 169 · see citing works
Huang, Y., Liao, W., Huang, J., Lin, Y., Yang, S., Huang, C., Wang, C., & Su, P. (2024) · Diabetes, Obesity and Metabolism · doi:10.1111/dom.15869 · Cited by 44 · see citing works
- Authors
- Xie, Y., Bowe, B., Xian, H., Loux, T., McGill, J. B., & Al-Aly, Z.
- Affiliations
- VA St. Louis Health Care System (US)
- Saint Louis University (US)
- Washington University in St. Louis (US)
- Venue
- The Lancet Diabetes & Endocrinology
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 11
- Issue
- 9
- Pages
- 644-656
- ISSN
- 2213-8587 (ISSN-L)
- OpenAlex
- W4385217262
- Cited by
- 104
- References
- 15
- Citations by year
- 2026: 35 · 2025: 38 · 2024: 20 · 2023: 11
- FWCI
- 21.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Lancet Diabetes & Endocrinology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Stanford University
- Health Services Research and Development
- U.S. Department of Veterans Affairs
- American Society of Nephrology
- 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, DPP-4 inhibitors, sulfonylureas, major adverse cardiovascular events, antidiabetic drugs, type 2 diabetes, metformin, head-to-head comparison, overlap weighting, stroke, myocardial infarction, all-cause mortality, veterans, electronic health records
- MeSH terms
- Glucagon-Like Peptide-1 Receptor, Sodium-Glucose Transporter 2 Inhibitors, Glucagon-Like Peptide-1 Receptor Agonists, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Humans, Hypoglycemic Agents, Sulfonylurea Compounds, Dipeptidyl-Peptidase IV Inhibitors, Comparative Effectiveness Research, Electronic Health Records, Pragmatic Clinical Trials as Topic
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