Michael Fralick, MD, PhD, SM; Sarah K. Chen, MD, MPH; Elisabetta Patorno, MD, DrPH; Seoyoung C. Kim, MD, ScD, MSCE
Financial Support: By the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School. Dr. Fralick received funding from the Eliot Phillipson Clinician-Scientist Training Program at the University of Toronto and the Canadian Institutes of Health Research through the Banting and Best PhD Award. Dr. Patorno is supported by a career development grant (K08AG055670) from the National Institute on Aging.
Disclosures: Dr. Patorno reports grants from the National Institute on Aging and Boehringer Ingelheim outside the submitted work. Dr. Kim reports grants from Pfizer, AbbVie, Roche, and Bristol-Myers Squibb outside the submitted work. Authors not named here have disclosed no conflicts of interest. Disclosures can also be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M19-2610.
Editors' Disclosures: Christine Laine, MD, MPH, Editor in Chief, reports that her spouse has stock options/holdings with Targeted Diagnostics and Therapeutics. Darren B. Taichman, MD, PhD, Executive Editor, reports that he has no financial relationships or interests to disclose. Cynthia D. Mulrow, MD, MSc, Senior Deputy Editor, reports that she has no relationships or interests to disclose. Jaya K. Rao, MD, MHS, Deputy Editor, reports that she has stock holdings/options in Eli Lilly and Pfizer. Christina C. Wee, MD, MPH, Deputy Editor, reports employment with Beth Israel Deaconess Medical Center. Sankey V. Williams, MD, Deputy Editor, reports that he has no financial relationships or interests to disclose. Yu-Xiao Yang, MD, MSCE, Deputy Editor, reports that he has no financial relationships or interest to disclose.
Reproducible Research Statement:Study protocol: Available from Dr. Fralick (e-mail, email@example.com). Statistical code: Not available. Data set: Available through IBM MarketScan (e-mail, firstname.lastname@example.org).
Corresponding Author: Michael Fralick, MD, PhD, SM, Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, 1620 Tremont Street, Suite 3030, Boston, MA 02120; e-mail, email@example.com.
Current Author Addresses: Drs. Fralick, Chen, Patorno, and Kim: Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, 1620 Tremont Street, Suite 3030, Boston, MA 02120.
Author Contributions: Conception and design: M. Fralick, S.K. Chen, E. Patorno, S.C. Kim.
Analysis and interpretation of the data: M. Fralick, S.K. Chen, S.C. Kim.
Drafting of the article: M. Fralick, S.K. Chen.
Critical revision for important intellectual content: M. Fralick, S.K. Chen, E. Patorno, S.C. Kim.
Final approval of the article: M. Fralick, S.K. Chen, E. Patorno, S.C. Kim.
Statistical expertise: S.C. Kim.
Administrative, technical, or logistic support: M. Fralick, S.C. Kim.
Collection and assembly of data: M. Fralick.
Hyperuricemia is common in patients with type 2 diabetes mellitus and is known to cause gout. Sodium–glucose cotransporter-2 (SGLT2) inhibitors prevent glucose reabsorption and lower serum uric acid levels.
To compare the rate of gout between adults prescribed an SGLT2 inhibitor and those prescribed a glucagon-like peptide-1 (GLP1) receptor agonist.
Population-based new-user cohort study.
A U.S. nationwide commercial insurance database from March 2013 to December 2017.
Persons with type 2 diabetes newly prescribed an SGLT2 inhibitor were 1:1 propensity score matched to patients newly prescribed a GLP1 agonist. Persons were excluded if they had a history of gout or had received gout-specific treatment previously.
The primary outcome was a new diagnosis of gout. Cox proportional hazards regression was used to estimate hazard ratios (HRs) of the primary outcome and 95% CIs.
The study identified 295 907 adults with type 2 diabetes mellitus who were newly prescribed an SGLT2 inhibitor or a GLP1 agonist. The gout incidence rate was lower among patients prescribed an SGLT2 inhibitor (4.9 events per 1000 person-years) than those prescribed a GLP1 agonist (7.8 events per 1000 person-years), with an HR of 0.64 (95% CI, 0.57 to 0.72) and a rate difference of −2.9 (CI, −3.6 to −2.1) per 1000 person-years.
Unmeasured confounding, missing data (namely incomplete laboratory data), and low baseline risk for gout.
Adults with type 2 diabetes prescribed an SGLT2 inhibitor had a lower rate of gout than those prescribed a GLP1 agonist. Sodium–glucose cotransporter-2 inhibitors may reduce the risk for gout among adults with type 2 diabetes mellitus, although future studies are necessary to confirm this observation.
Brigham and Women's Hospital.
Fralick M, Chen SK, Patorno E, et al. Assessing the Risk for Gout With Sodium–Glucose Cotransporter-2 Inhibitors in Patients With Type 2 Diabetes: A Population-Based Cohort Study. Ann Intern Med. 2020;172:186–194. [Epub ahead of print 14 January 2020]. doi: https://doi.org/10.7326/M19-2610
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Published: Ann Intern Med. 2020;172(3):186-194.
Published at www.annals.org on 14 January 2020
Cardiology, Coronary Risk Factors, Diabetes, Endocrine and Metabolism, Gout.
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