Sodium–glucose cotransporter-2 inhibitors (SGLT-2i) have been endorsed as a preferred second-line treatment for type 2 diabetes (T2D). But data comparing SGLT-2i against metformin in the setting of first-line therapy are limited. This retrospective cohort study investigated the risk of cardiovascular events among patients who started SGLT-2i versus metformin as initial pharmacological therapy for T2D. This population-based study provides novel comparative-effectiveness data that could help optimize first-line therapy for T2D.
Background:
Evidence on the risk for cardiovascular events associated with use of first-line sodium–glucose cotransporter-2 inhibitors (SGLT-2i) compared with metformin is limited.
Objective:
To assess cardiovascular outcomes among adults with type 2 diabetes (T2D) who initiated first-line treatment with SGLT-2i versus metformin.
Design:
Population-based cohort study.
Setting:
Claims data from 2 large U.S. commercial and Medicare databases (April 2013 to March 2020).
Participants:
Patients with T2D aged 18 years and older (>65 years in Medicare) initiating treatment with SGLT-2i or metformin during April 2013 to March 2020, without any use of antidiabetic medications before cohort entry, were identified. After 1:2 propensity score matching in each database, pooled hazard ratios (HRs) and 95% CIs were reported.
Intervention:
First-line SGLT-2i (canagliflozin, empagliflozin, or dapagliflozin) or metformin.
Measurements:
Primary outcomes were a composite of hospitalization for myocardial infarction (MI), hospitalization for ischemic or hemorrhagic stroke or all-cause mortality (MI/stroke/mortality), and a composite of hospitalization for heart failure (HHF) or all-cause mortality (HHF/mortality). Safety outcomes including genital infections were assessed.
Results:
Among 8613 first-line SGLT-2i initiators matched to 17 226 metformin initiators, SGLT-2i initiators had a similar risk for MI/stroke/mortality (HR, 0.96; 95% CI, 0.77 to 1.19) and a lower risk for HHF/mortality (HR, 0.80; CI, 0.66 to 0.97) during a mean follow-up of 12 months. Initiators receiving SGLT-2i showed a lower risk for HHF (HR, 0.78; CI, 0.63 to 0.97), a numerically lower risk for MI (HR, 0.70; CI, 0.48 to 1.00), and similar risk for stroke, mortality, and MI/stroke/HHF/mortality compared with metformin. Initiators receiving SGLT-2i had a higher risk for genital infections (HR, 2.19; CI, 1.91 to 2.51) and otherwise similar safety as those receiving metformin.
Limitation:
Treatment selection was not randomized.
Conclusion:
As first-line T2D treatment, initiators receiving SGLT-2i showed a similar risk for MI/stroke/mortality, lower risk for HHF/mortality and HHF, and a similar safety profile except for an increased risk for genital infections compared with those receiving metformin.
Primary Funding Source:
Brigham and Women's Hospital and Harvard Medical School.
References
- 1. U.S. Food and Drug Administration. Guidance for Industry. Diabetes Mellitus—Evaluating Cardiovascular Risk in New Antidiabetic Therapies to Treat Type 2 Diabetes. December 2008. Accessed at www.fda.gov/media/71297/download on 13 January 2022. Google Scholar
- 2.
Zinman B ,Wanner C ,Lachin JM ,et al ;EMPA-REG OUTCOME Investigators . Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015;373:2117-28. [PMID:26378978 ] doi:10.1056/NEJMoa1504720 CrossrefMedlineGoogle Scholar - 3.
Neal B ,Perkovic V ,Mahaffey KW ,et al ;CANVAS Program Collaborative Group . Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med. 2017;377:644-657. [PMID:28605608 ] doi:10.1056/NEJMoa1611925 CrossrefMedlineGoogle Scholar - 4.
Wiviott SD ,Raz I ,Bonaca MP ,et al ;DECLARE–TIMI 58 Investigators . Dapagliflozin and cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2019;380:347-357. [PMID:30415602 ] doi:10.1056/NEJMoa1812389 CrossrefMedlineGoogle Scholar - 5.
Lin DS ,Lee JK ,Hung CS ,et al . The efficacy and safety of novel classes of glucose-lowering drugs for cardiovascular outcomes: a network meta-analysis of randomised clinical trials. Diabetologia. 2021;64:2676-2686. [PMID:34536085 ] doi:10.1007/s00125-021-05529-w CrossrefMedlineGoogle Scholar - 6. U.S. Food and Drug Administration. Supplemental approval letter for empagliflozin. Accessed at www.accessdata.fda.gov/drugsatfda_docs/appletter/2016/204629Orig1s008ltr.pdf on 13 January 2022. Google Scholar
- 7. U.S. Food and Drug Administration. Supplemental approval letter for canagliflozin. Accessed at www.accessdata.fda.gov/drugsatfda_docs/appletter/2018/204042Orig1s027ltr.pdf on 13 January 2022. Google Scholar
- 8. U.S. Food and Drug Administration. Supplemental approval letter for dapagliflozin. Accessed at www.accessdata.fda.gov/drugsatfda_docs/appletter/2019/202293Orig1s018,%20205649Orig1s011ltr.pdf on 13 January 2022. Google Scholar
- 9.
American Diabetes Association . 9. Pharmacologic approaches to glycemic treatment: standards of medical care in diabetes-2021. Diabetes Care. 2021;44:S111-S124. [PMID:33298420 ] doi:10.2337/dc21-S009 CrossrefMedlineGoogle Scholar - 10.
Davies MJ ,D’Alessio DA ,Fradkin J ,et al . Management of hyperglycemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care. 2018;41:2669-2701. [PMID:30291106 ] doi:10.2337/dci18-0033 CrossrefMedlineGoogle Scholar - 11.
Cosentino F ,Grant PJ ,Aboyans V ,et al ;ESC Scientific Document Group . 2019 ESC guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur Heart J. 2020;41:255-323. [PMID:31497854 ] doi:10.1093/eurheartj/ehz486 CrossrefMedlineGoogle Scholar - 12.
UK Prospective Diabetes Study (UKPDS) Group. Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34). Lancet. 1998;352:854-65. [PMID:9742977 ] doi:10.1016/S0140-6736(98)07037-8 CrossrefMedlineGoogle Scholar - 13.
Sundström J ,Kristófi R ,Östlund O ,et al ;SMARTEST Study Group . A registry-based randomised trial comparing an SGLT2 inhibitor and metformin as standard treatment of early stage type 2 diabetes (SMARTEST): rationale, design and protocol. J Diabetes Complications. 2021;35:107996. [PMID:34389234 ] doi:10.1016/j.jdiacomp.2021.107996 CrossrefMedlineGoogle Scholar - 14.
Barr EL ,Zimmet PZ ,Welborn TA ,et al . Risk of cardiovascular and all-cause mortality in individuals with diabetes mellitus, impaired fasting glucose, and impaired glucose tolerance: the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab). Circulation. 2007;116:151-7. [PMID:17576864 ] doi:10.1161/CIRCULATIONAHA.106.685628 CrossrefMedlineGoogle Scholar - 15.
Raebel MA ,Xu S ,Goodrich GK ,et al . Initial antihyperglycemic drug therapy among 241 327 adults with newly identified diabetes from 2005 through 2010: a Surveillance, Prevention, and Management of Diabetes Mellitus (SUPREME-DM) study. Ann Pharmacother. 2013;47:1280-91. [PMID:24259692 ] doi:10.1177/1060028013503624 CrossrefMedlineGoogle Scholar - 16. U.S. Food and Drug Administration. Framework for FDA's Real-World Evidence Program. Accessed at www.fda.gov/media/120060/download on 13 January 2022. Google Scholar
- 17.
Schneeweiss S . Improving therapeutic effectiveness and safety through big healthcare data. Clin Pharmacol Ther. 2016;99:262-5. [PMID:26659268 ] doi:10.1002/cpt.316 CrossrefMedlineGoogle Scholar - 18.
Hernán MA ,Robins JM . Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol. 2016;183:758-64. [PMID:26994063 ] doi:10.1093/aje/kwv254 CrossrefMedlineGoogle Scholar - 19.
Verbrugge FH . Role of SGLT2 inhibitors in patients with diabetes mellitus and heart failure. Curr Heart Fail Rep. 2017;14:275-283. [PMID:28647919 ] doi:10.1007/s11897-017-0340-1 CrossrefMedlineGoogle Scholar - 20.
Franklin JM ,Patorno E ,Desai RJ ,et al . Emulating randomized clinical trials with nonrandomized real-world evidence studies: first results from the RCT DUPLICATE initiative. Circulation. 2021;143:1002-1013. [PMID:33327727 ] doi:10.1161/CIRCULATIONAHA.120.051718 CrossrefMedlineGoogle Scholar - 21.
Patorno E ,Goldfine AB ,Schneeweiss S ,et al . Cardiovascular outcomes associated with canagliflozin versus other non-gliflozin antidiabetic drugs: population based cohort study. BMJ. 2018;360:k119. [PMID:29437648 ] doi:10.1136/bmj.k119 CrossrefMedlineGoogle Scholar - 22.
Patorno E ,Pawar A ,Bessette LG ,et al . Comparative effectiveness and safety of sodium-glucose cotransporter 2 inhibitors versus glucagon-like peptide 1 receptor agonists in older adults. Diabetes Care. 2021;44:826-835. [PMID:33495295 ] doi:10.2337/dc20-1464 CrossrefMedlineGoogle Scholar - 23.
Xie Y ,Bowe B ,Gibson AK ,et al . Comparative effectiveness of sodium-glucose cotransporter 2 inhibitors vs sulfonylureas in patients with type 2 diabetes. JAMA Intern Med. 2021;181:1043-1053. [PMID:34180939 ] doi:10.1001/jamainternmed.2021.2488 CrossrefMedlineGoogle Scholar - 24.
Fralick M ,Schneeweiss S ,Redelmeier DA ,et al . Comparative effectiveness and safety of sodium-glucose cotransporter-2 inhibitors versus metformin in patients with type 2 diabetes: an observational study using data from routine care. Diabetes Obes Metab. 2021;23:2320-2328. [PMID:34169619 ] doi:10.1111/dom.14474 CrossrefMedlineGoogle Scholar - 25.
Chen TH ,Li YR ,Chen SW ,et al . Sodium-glucose cotransporter 2 inhibitor versus metformin as first-line therapy in patients with type 2 diabetes mellitus: a multi-institution database study. Cardiovasc Diabetol. 2020;19:189. [PMID:33167990 ] doi:10.1186/s12933-020-01169-3 CrossrefMedlineGoogle Scholar - 26.
Khokhar B ,Jette N ,Metcalfe A ,et al . Systematic review of validated case definitions for diabetes in ICD-9-coded and ICD-10-coded data in adult populations. BMJ Open. 2016;6:e009952. [PMID:27496226 ] doi:10.1136/bmjopen-2015-009952 CrossrefMedlineGoogle Scholar - 27.
Miller DR ,Safford MM ,Pogach LM . Who has diabetes? Best estimates of diabetes prevalence in the Department of Veterans Affairs based on computerized patient data. Diabetes Care. 2004;27 Suppl 2:B10-21. [PMID:15113777 ] doi:10.2337/diacare.27.suppl_2.b10 CrossrefMedlineGoogle Scholar - 28.
Glynn RJ ,Knight EL ,Levin R ,et al . Paradoxical relations of drug treatment with mortality in older persons. Epidemiology. 2001;12:682-9. [PMID:11679797 ] doi:10.1097/00001648-200111000-00017 CrossrefMedlineGoogle Scholar - 29.
Schneeweiss S ,Avorn J . A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol. 2005;58:323-37. [PMID:15862718 ] doi:10.1016/j.jclinepi.2004.10.012 CrossrefMedlineGoogle Scholar - 30.
Tchetgen Tchetgen E . The control outcome calibration approach for causal inference with unobserved confounding. Am J Epidemiol. 2014;179:633-40. [PMID:24363326 ] doi:10.1093/aje/kwt303 CrossrefMedlineGoogle Scholar - 31. DeSantis A. Sodium-glucose co-transporter 2 inhibitors for the treatment of hyperglycemia in type 2 diabetes mellitus. UptoDate. Accessed at www.uptodate.com/contents/sodium-glucose-co-transporter-2-inhibitors-for-the-treatment-of-hyperglycemia-in-type-2-diabetes-mellitus#H2325150020 on 13 January 2022. Google Scholar
- 32.
Dave CV ,Schneeweiss S ,Patorno E . Comparative risk of genital infections associated with sodium-glucose co-transporter-2 inhibitors. Diabetes Obes Metab. 2019;21:434-438. [PMID:30207042 ] doi:10.1111/dom.13531 CrossrefMedlineGoogle Scholar - 33.
Kiyota Y ,Schneeweiss S ,Glynn RJ ,et al . Accuracy of Medicare claims-based diagnosis of acute myocardial infarction: estimating positive predictive value on the basis of review of hospital records. Am Heart J. 2004;148:99-104. [PMID:15215798 ] doi:10.1016/j.ahj.2004.02.013 CrossrefMedlineGoogle Scholar - 34.
Wahl PM ,Rodgers K ,Schneeweiss S ,et al . Validation of claims-based diagnostic and procedure codes for cardiovascular and gastrointestinal serious adverse events in a commercially-insured population. Pharmacoepidemiol Drug Saf. 2010;19:596-603. [PMID:20140892 ] doi:10.1002/pds.1924 CrossrefMedlineGoogle Scholar - 35.
Tirschwell DL ,Longstreth WT . Validating administrative data in stroke research. Stroke. 2002;33:2465-70. [PMID:12364739 ] doi:10.1161/01.str.0000032240.28636.bd CrossrefMedlineGoogle Scholar - 36.
Saczynski JS ,Andrade SE ,Harrold LR ,et al . A systematic review of validated methods for identifying heart failure using administrative data. Pharmacoepidemiol Drug Saf. 2012;21 Suppl 1:129-40. [PMID:22262599 ] doi:10.1002/pds.2313 CrossrefMedlineGoogle Scholar - 37. Social Security Administration. Requesting SSA's Death Information. Accessed at www.ssa.gov/dataexchange/request_dmf.html on 13 January 2022. Google Scholar
- 38. IBM Watson Health. IBM MarketScan Research Databases for life sciences researchers. Accessed at www.ibm.com/downloads/cas/OWZWJ0QO on 13 January 2022. Google Scholar
- 39. Research Data Assistance Center (ResDAC). Master Beneficiary Summary File (MBSF) Base. Accessed at www.resdac.org/cms-data/files/mbsf-base on 13 January 2022. Google Scholar
- 40.
Waikar SS ,Wald R ,Chertow GM ,et al . Validity of International Classification of Diseases, Ninth Revision, Clinical Modification codes for acute renal failure. J Am Soc Nephrol. 2006;17:1688-94. [PMID:16641149 ] doi:10.1681/ASN.2006010073 CrossrefMedlineGoogle Scholar - 41.
Ray WA ,Griffin MR ,Fought RL ,et al . Identification of fractures from computerized Medicare files. J Clin Epidemiol. 1992;45:703-14. [PMID:1619449 ] doi:10.1016/0895-4356(92)90047-q CrossrefMedlineGoogle Scholar - 42.
Hudson M ,Avina-Zubieta A ,Lacaille D ,et al . The validity of administrative data to identify hip fractures is high---a systematic review. J Clin Epidemiol. 2013;66:278-85. [PMID:23347851 ] doi:10.1016/j.jclinepi.2012.10.004 CrossrefMedlineGoogle Scholar - 43.
Ginde AA ,Blanc PG ,Lieberman RM ,et al . Validation of ICD-9-CM coding algorithm for improved identification of hypoglycemia visits. BMC Endocr Disord. 2008;8:4. [PMID:18380903 ] doi:10.1186/1472-6823-8-4 CrossrefMedlineGoogle Scholar - 44.
Schelleman H ,Bilker WB ,Brensinger CM ,et al . Anti-infectives and the risk of severe hypoglycemia in users of glipizide or glyburide. Clin Pharmacol Ther. 2010;88:214-22. [PMID:20592722 ] doi:10.1038/clpt.2010.74 CrossrefMedlineGoogle Scholar - 45.
Bobo WV ,Cooper WO ,Epstein RA ,et al . Positive predictive value of automated database records for diabetic ketoacidosis (DKA) in children and youth exposed to antipsychotic drugs or control medications: a Tennessee Medicaid Study. BMC Med Res Methodol. 2011;11:157. [PMID:22112194 ] doi:10.1186/1471-2288-11-157 CrossrefMedlineGoogle Scholar - 46.
Dave CV ,Schneeweiss S ,Kim D ,et al . Sodium-glucose cotransporter-2 inhibitors and the risk for severe urinary tract infections: a population-based cohort study. Ann Intern Med. 2019;171:248-256. [PMID:31357213 ] doi:10.7326/M18-3136 LinkGoogle Scholar - 47.
Belatti DA ,Phisitkul P . Declines in lower extremity amputation in the US Medicare population, 2000-2010. Foot Ankle Int. 2013;34:923-31. [PMID:23386749 ] doi:10.1177/1071100713475357 CrossrefMedlineGoogle Scholar - 48.
Chen Y ,Sloan FA ,Yashkin AP . Adherence to diabetes guidelines for screening, physical activity and medication and onset of complications and death. J Diabetes Complications. 2015;29:1228-33. [PMID:26316423 ] doi:10.1016/j.jdiacomp.2015.07.005 CrossrefMedlineGoogle Scholar - 49. Patorno E, Gopalakrishnan C, Zorina OI, et al. 992. Dynamic channeling among initiators of a recently marketed medication for type 2 diabetes mellitus (T2DM) [Abstract]. Pharmacoepidemiol Drug Saf. 2015;24:565-566. doi:
10.1002/pds.3838 CrossrefGoogle Scholar - 50.
Shin H ,Schneeweiss S ,Glynn RJ ,et al . Trends in first-line glucose-lowering drug use in adults with type 2 diabetes in light of emerging evidence for SGLT-2i and GLP-1RA. Diabetes Care. 2021;44:1774-1782. [PMID:34385345 ] doi:10.2337/dc20-2926 CrossrefMedlineGoogle Scholar - 51.
Rubin DB . Estimating causal effects from large data sets using propensity scores. Ann Intern Med. 1997;127:757-63. [PMID:9382394 ] doi:10.7326/0003-4819-127-8_part_2-199710151-00064 LinkGoogle Scholar - 52.
Groenwold RH ,White IR ,Donders AR ,et al . Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis. CMAJ. 2012;184:1265-9. [PMID:22371511 ] doi:10.1503/cmaj.110977 CrossrefMedlineGoogle Scholar - 53.
Austin PC . Statistical criteria for selecting the optimal number of untreated subjects matched to each treated subject when using many-to-one matching on the propensity score. Am J Epidemiol. 2010;172:1092-7. [PMID:20802241 ] doi:10.1093/aje/kwq224 CrossrefMedlineGoogle Scholar - 54.
Wang SV ,Schneeweiss S ,Rassen JA . Optimal matching ratios in drug safety surveillance [Letter]. Epidemiology. 2014;25:772-3. [PMID:25076153 ] doi:10.1097/EDE.0000000000000148 CrossrefMedlineGoogle Scholar - 55.
Austin PC . Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 2011;10:150-61. [PMID:20925139 ] doi:10.1002/pst.433 CrossrefMedlineGoogle Scholar - 56.
Austin PC . Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009;28:3083-107. [PMID:19757444 ] doi:10.1002/sim.3697 CrossrefMedlineGoogle Scholar - 57. Austin PC. Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research. Commun Stat Simul Comput. 2009;38(6):1228-34. doi:
10.1080/03610910902859574 CrossrefGoogle Scholar - 58.
Wang SV ,Jin Y ,Fireman B ,et al . Relative performance of propensity score matching strategies for subgroup analyses. Am J Epidemiol. 2018;187:1799-1807. [PMID:29554199 ] doi:10.1093/aje/kwy049 CrossrefMedlineGoogle Scholar - 59. Cox DR. Regression models and life-tables. J R Stat Soc Series B Stat Methodol. 1972;34:187-202. Google Scholar
- 60. Lin E, Tong T, Chen Y, et al. Fixed-effects model: the most convincing model for meta-analysis with few studies. arXiv. Preprint posted online 11 February 2020. doi:
10.48550/arXiv.2002.04211 CrossrefGoogle Scholar - 61. Rothman K. Episheet. Spreadsheets for the Analysis of Epidemiologic Data. Accessed at http://017c85b.netsolhost.com//wp-content/uploads/2012/10/Episheet.xls on 13 January 2022. Google Scholar
- 62.
Shin H ,Schneeweiss S ,Glynn RJ ,et al . Evolving channeling in prescribing SGLT-2 inhibitors as first-line treatment for type 2 diabetes. Pharmacoepidemiol Drug Saf. 2022;31:566-576. [PMID:34985178 ] doi:10.1002/pds.5406 CrossrefMedlineGoogle Scholar - 63.
Schneeweiss S . A basic study design for expedited safety signal evaluation based on electronic healthcare data. Pharmacoepidemiol Drug Saf. 2010;19:858-68. [PMID:20681003 ] doi:10.1002/pds.1926 CrossrefMedlineGoogle Scholar - 64.
Schneeweiss S . Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf. 2006;15:291-303. [PMID:16447304 ] doi:10.1002/pds.1200 CrossrefMedlineGoogle Scholar - 65. RStudio Team. RStudio: Integrated Development Environment for R. 2020. Accessed at www.rstudio.com on 13 January 2022. Google Scholar
- 66. Aetion. Evidence Platform®. Software for real-world data analysis. Accessed at http://aetion.com on 13 January 2022. Google Scholar
- 67.
Wang SV ,Verpillat P ,Rassen JA ,et al . Transparency and reproducibility of observational cohort studies using large healthcare databases. Clin Pharmacol Ther. 2016;99:325-32. [PMID:26690726 ] doi:10.1002/cpt.329 CrossrefMedlineGoogle Scholar - 68.
Patorno E ,Schneeweiss S ,Gopalakrishnan C ,et al . Using real-world data to predict findings of an ongoing phase IV cardiovascular outcome trial: cardiovascular safety of linagliptin versus glimepiride. Diabetes Care. 2019;42:2204-2210. [PMID:31239281 ] doi:10.2337/dc19-0069 CrossrefMedlineGoogle Scholar - 69.
Scheen AJ . Cardiovascular effects of new oral glucose-lowering agents: DPP-4 and SGLT-2 inhibitors. Circ Res. 2018;122:1439-1459. [PMID:29748368 ] doi:10.1161/CIRCRESAHA.117.311588 CrossrefMedlineGoogle Scholar - 70.
Gilbert RE ,Connelly KA . Reduction in the incidence of myocardial infarction with sodium-glucose linked cotransporter-2 inhibitors: evident and plausible. Cardiovasc Diabetol. 2019;18:6. [PMID:30634959 ] doi:10.1186/s12933-019-0812-6 CrossrefMedlineGoogle Scholar - 71.
Giorgino F ,Vora J ,Fenici P ,et al . Cardiovascular protection with sodium-glucose co-transporter-2 inhibitors in type 2 diabetes: does it apply to all patients. Diabetes Obes Metab. 2020;22:1481-1495. [PMID:32285611 ] doi:10.1111/dom.14055 CrossrefMedlineGoogle Scholar - 72.
McGuire DK ,Shih WJ ,Cosentino F ,et al . Association of SGLT2 inhibitors with cardiovascular and kidney outcomes in patients with type 2 diabetes: a meta-analysis. JAMA Cardiol. 2021;6:148-158. [PMID:33031522 ] doi:10.1001/jamacardio.2020.4511 CrossrefMedlineGoogle Scholar - 73.
Patorno E ,Gopalakrishnan C ,Franklin JM ,et al . Claims-based studies of oral glucose-lowering medications can achieve balance in critical clinical variables only observed in electronic health records. Diabetes Obes Metab. 2018;20:974-984. [PMID:29206336 ] doi:10.1111/dom.13184 CrossrefMedlineGoogle Scholar - 74.
Bansal V ,Kalita J ,Misra UK . Diabetic neuropathy. Postgrad Med J. 2006;82:95-100. [PMID:16461471 ] doi:10.1136/pgmj.2005.036137 CrossrefMedlineGoogle Scholar - 75.
Suissa S . Mortality reduction in EMPA-REG OUTCOME trial: beyond the antidiabetes effect. Diabetes Care. 2018;41:219-223. [PMID:29358464 ] doi:10.2337/dc17-1059 CrossrefMedlineGoogle Scholar - 76.
Nyirjesy P ,Sobel JD . Genital mycotic infections in patients with diabetes. Postgrad Med. 2013;125:33-46. [PMID:23748505 ] doi:10.3810/pgm.2013.05.2650 CrossrefMedlineGoogle Scholar
Author, Article, and Disclosure Information
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts (H.S., R.J.G., E.P.)
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, and Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts (S.S.).
Grant Support: By the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School. Dr. Patorno was supported by a career development grant (K08AG055670) from the National Institute on Aging and a research grant from the Patient-Centered Outcomes Research Institute (PCORI; DB-2020C2-20326).
Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M21-4012.
Reproducible Research Statement: Study protocol and Statistical code: Available from Dr. Shin (e-mail, [email protected]
Corresponding Author: HoJin Shin, BPharm, PhD, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street (Suite 3030), Boston, MA 02120; e-mail, [email protected]
Author Contributions: Conception and design: H. Shin, S. Schneeweiss, E. Patorno.
Analysis and interpretation of the data: H. Shin, S. Schneeweiss, R.J. Glynn, E. Patorno.
Drafting of the article: H. Shin.
Critical revision of the article for important intellectual content: H. Shin, S. Schneeweiss, R.J. Glynn, E. Patorno.
Final approval of the article: H. Shin, S. Schneeweiss, R.J. Glynn, E. Patorno.
Provision of study materials or patients: H. Shin, S. Schneeweiss.
Statistical expertise: H. Shin, S. Schneeweiss, R.J. Glynn.
Administrative, technical, or logistic support: H. Shin, E. Patorno.
Collection and assembly of data: H. Shin, S. Schneeweiss, E. Patorno.
This article was published at Annals.org on 24 May 2022.
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