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Effectiveness of glucose-lowering medications on cardiovascular outcomes in patients with type 2 diabetes at moderate cardiovascular risk

Nature Cardiovascular Research volume 3, pages 431–440 (2024)Cite this article

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Abstract

Cardiovascular disease (CVD) is the leading cause of death among people with type 2 diabetes1,2,3,4,5, most of whom are at moderate CVD risk6, yet there is limited evidence on the preferred choice of glucose-lowering medication for CVD risk reduction in this population. Here, we report the results of a retrospective cohort study where data for US adults with type 2 diabetes and moderate risk for CVD are used to compare the risks of experiencing a major adverse cardiovascular event with initiation of glucagon-like peptide-1 receptor agonists (GLP-1RA; n = 44,188), sodium-glucose cotransporter 2 inhibitors (SGLT2i; n = 47,094), dipeptidyl peptidase-4 inhibitors (DPP4i; n = 84,315) and sulfonylureas (n = 210,679). Compared to DPP4i, GLP-1RA (hazard ratio (HR) 0.87; 95% confidence interval (CI) 0.82–0.93) and SGLT2i (HR 0.85; 95% CI 0.81–0.90) were associated with a lower risk of a major adverse cardiovascular event, whereas sulfonylureas were associated with a higher risk (HR 1.19; 95% CI 1.16–1.22). Thus, GLP-1RA and SGLT2i may be the preferred glucose-lowering agents for cardiovascular risk reduction in patients at moderate baseline risk for CVD. ClinicalTrials.gov registration: NCT05214573.

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Fig. 1: Cumulative incidence of study outcomes.

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Data availability

This study was conducted using deidentified data from OptumLabs Data Warehouse and linked 100% sample of Medicare fee-for-service claims. These data are third-party data owned by OptumLabs and contain sensitive patient information; therefore, the data is only available upon request. Interested researchers engaged in HIPAA compliant research may contact connected@optum.com for data access requests. The data use requires researchers to pay for rights to use and access the data. These data are subject to restrictions on sharing as a condition of access.

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Acknowledgements

We thank the Patient and Stakeholder Advisory Group convened in support of this work for their insight and feedback on model covariates and study design. Members of the Patient and Stakeholder Advisory Group include: J. P. W. Bynum (University of Michigan School of Medicine); J. K. Cuddeback (American Medical Group Association); W. B. DeHart (OptumLabs); R. A. Gabbay (American Diabetes Association); J. Gockerman (Grand Rapids, MI); E. H. Golembiewski (Mayo Clinic); J. Haag (Mayo Clinic); B. Labatte (Rochester, MN); R. J. Stroebel (Mayo Clinic); M. Tesulov (Rochester, MN) and S. Violette (UnitedHealth Group). Funding: research reported in this work was funded through a Patient-Centered Outcomes Research Institute Award PCS-1409-24099 (R.G.M.). The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Disclaimer: the statements in this report are solely the responsibility of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute, its Board of Governors or Methodology Committee.

Author information

Authors and Affiliations

  1. Division of Endocrinology, Diabetes, & Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA

    Rozalina G. McCoy

  2. University of Maryland Institute for Health Computing, Bethesda, MD, USA

    Rozalina G. McCoy

  3. Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA

    Rozalina G. McCoy

  4. OptumLabs, Eden Prairie, MN, USA

    Rozalina G. McCoy, Kavya Sindhu Swarna & Yihong Deng

  5. Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT, USA

    Jeph Herrin

  6. Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Rochester, MN, USA

    Kavya Sindhu Swarna, Yihong Deng, Bijan J. Borah & Mindy M. Mickelson

  7. Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA

    David M. Kent

  8. Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA

    Joseph S. Ross

  9. Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA

    Joseph S. Ross

  10. Division of Endocrinology, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA

    Guillermo E. Umpierrez

  11. Division of Endocrinology, Department of Medicine, University of Miami Miller School of Medicine, Miami, FL, USA

    Rodolfo J. Galindo

  12. Florence Heller Graduate School, Brandeis University, Waltham, MA, USA

    William H. Crown

  13. Division of Endocrinology, Department of Medicine, Mayo Clinic, Rochester, MN, USA

    Victor M. Montori & Juan P. Brito

  14. Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, MN, USA

    Victor M. Montori & Juan P. Brito

  15. Department of Pharmacotherapy, Washington State University, Spokane, WA, USA

    Joshua J. Neumiller

  16. Providence Medical Research Center, Spokane, WA, USA

    Joshua J. Neumiller

  17. Department of Public Health Sciences, University of Chicago, Chicago, IL, USA

    Eric C. Polley

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  1. Rozalina G. McCoy
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Contributions

R.G.M. conceived and designed the study, interpreted the data, drafted the manuscript, supervised the study and secured funding. J.H. conducted analyses, interpreted the data and revised the manuscript. K.S.S. managed the data, conducted analyses, interpreted the data and revised the manuscript. Y.D. assisted with analyses, interpreted the data and revised the manuscript. D.M.K. interpreted the data and revised the manuscript. J.S.R. contributed to study design, interpreted the data and revised the manuscript. G.E.U. interpreted the data and revised the manuscript. R.J.G. interpreted the data and revised the manuscript. W.H.C. interpreted the data and revised the manuscript. B.J.B. interpreted the data and revised the manuscript. V.M.M. interpreted the data and revised the manuscript. J.P.B. interpreted the data and revised the manuscript. J.J.N. interpreted the data and revised the manuscript. M.M.M. provided administrative support and supervision and revised the manuscript. E.C.P. contributed to study design, supervised analyses, interpreted the data and revised the manuscript.

Corresponding author

Correspondence to Rozalina G. McCoy.

Ethics declarations

Competing interests

G.E.U. reports unrestricted support for research studies to Emory University from Dexcom, Abbott and Bayer, and serves on the advisory board of Directors for GlyCare. R.J.G. has received unrestricted research support (to Emory University) from Novo Nordisk, Eli Lilly and Dexcom, and consulting fees from Sanofi, Novo Nordisk, Eli Lilly, Pfizer, Boehringer, Bayer and Weight Watchers. W.H.C. has received unrestricted research consulting support from Janssen Scientific Affairs LLC, Viatris, Merck and Optum. J.J.N. reports serving as a consultant to Sanofi, Bayer, Eli Lilly and Boehringer Ingelheim. The other authors declare no competing interests.

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Nature Cardiovascular Research thanks Hans-Peter Brunner–La Rocca, Koos Zwinderman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Table 1 Association between glucose-lowering treatment and cardiovascular outcomes in the unweighted study cohort
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Extended Data Table 2 Association between glucose-lowering treatment and cardiovascular outcomes by duration of treatment: intention-to-treat analysis
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Extended Data Table 3 Number needed to treat (NNT) to experience one fewer adverse health outcome compared to treatment with sulfonylurea
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Extended Data Table 4 Association between glucose-lowering treatment and cardiovascular outcomes: as-treated analysis, censored upon discontinuation of the study drug
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Extended Data Table 5 Association between glucose-lowering treatment and cardiovascular outcomes: as-treated analysis, censored upon discontinuation of the study drug or addition of another drug class, whichever came first
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Extended Data Table 6 Association between glucose-lowering treatment and cardiovascular outcomes: intention-to-treat analysis adjusted for baseline cardiovascular disease risk
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Extended Data Table 7 Age-based heterogeneity of treatment effects analysis
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Extended Data Table 8 Association between glucose-lowering treatment and falsification endpoints of pneumonia, appendicitis hospitalizations, and screening colonoscopy
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Extended Data Fig. 1 Study Design.

CVD, cardiovascular disease. MACE, major adverse cardiovascular event.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2, Tables 1–6 and References.

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McCoy, R.G., Herrin, J., Swarna, K.S. et al. Effectiveness of glucose-lowering medications on cardiovascular outcomes in patients with type 2 diabetes at moderate cardiovascular risk. Nat Cardiovasc Res 3, 431–440 (2024). https://doi.org/10.1038/s44161-024-00453-9

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