Skip to main content
SpringerLink
Log in

Drug–Drug Interaction of the Sodium Glucose Co-Transporter 2 Inhibitors with Statins and Myopathy: A Disproportionality Analysis Using Adverse Events Reporting Data

  • Original Research Article
  • Published:
Drug Safety Aims and scope Submit manuscript

A Letter to the Editor to this article was published on 15 April 2023

A Letter to the Editor to this article was published on 17 June 2022

Abstract

Introduction

An increased risk of myopathy due to a potential interaction between sodium glucose co-transporter-2 inhibitors (SGLT-2i) and HMG-CoA reductase inhibitors (statins) has been suggested by case reports.

Objective

We aimed to assess if the reporting of myopathy is disproportionally higher among people using both SGLT-2i and statins compared to using either SGLT-2i or statins alone.

Methods

We conducted a disproportionality analysis using data from the US Food and Drug Administration Adverse Event Reporting System (FAERS). We included reports with at least one antihyperglycemic agent. We compared the proportion of myopathy cases to non-cases between those not using SGLT-2i or statins, using SGLT-2i only, statins only, or both. We calculated the reporting odds ratio and 95% confidence interval. We further stratified by individual SGLT-2i and selected statins (rosuvastatin or atorvastatin).

Results

We included 688,388 reports with at least one antihyperglycemic agent recorded, of which 9.80% had at least one SGLT-2i agent. Among all included reports, there were a total of 2202 myopathy cases with the majority, 61.26%, occurring among those using statins alone and only 2.72% of myopathy cases were among those using both SGLT-2i and statins together. Reporting of myopathy was not disproportionally higher among those reporting the use of SGLT-2i with statins (reporting odds ratio 2.95, 95% confidence interval 2.27–3.85) compared to statins alone (reporting odds ratio 6.41, 95% confidence interval 5.86–7.02).

Conclusions

Reports of myopathy were not disproportionally higher among those using SGLT-2i with statins compared to SGLT-2i or statins alone at the class level. Further observational studies may be needed to better assess this interaction at the agent level.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Lupsa BC, Inzucchi SE. Use of SGLT2 inhibitors in type 2 diabetes: weighing the risks and benefits. Diabetologia. 2018;61(10):2118–25.

    Article  CAS  PubMed  Google Scholar 

  2. Ni L, Yuan C, Chen G, et al. SGLT2i: beyond the glucose-lowering effect. Cardiovasc Diabetol. 2020;19(1):98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Zinman B, Wanner C, Lachin JM, et al. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015;373(22):2117–28.

    Article  CAS  PubMed  Google Scholar 

  4. Neal B, Perkovic V, Mahaffey KW, et al. Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med. 2017;377(7):644–57.

    Article  CAS  PubMed  Google Scholar 

  5. Wiviott SD, Raz I, Bonaca MP, et al. Dapagliflozin and cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2019;380(4):347–57.

    Article  CAS  PubMed  Google Scholar 

  6. Cannon CP, Pratley R, Dagogo-Jack S, et al. Cardiovascular outcomes with ertugliflozin in type 2 diabetes. N Engl J Med. 2020;383(15):1425–35.

    Article  CAS  PubMed  Google Scholar 

  7. Bhatt DL, Szarek M, Pitt B, et al. Sotagliflozin in patients with diabetes and chronic kidney disease. N Engl J Med. 2021;384(2):129–39.

    Article  CAS  PubMed  Google Scholar 

  8. Packer M, Anker SD, Butler J, et al. Cardiovascular and renal outcomes with empagliflozin in heart failure. N Engl J Med. 2020;383(15):1413–24.

    Article  CAS  PubMed  Google Scholar 

  9. McMurray JJV, Solomon SD, Inzucchi SE, et al. Dapagliflozin in patients with heart failure and reduced ejection fraction. N Engl J Med. 2019;381(21):1995–2008.

    Article  CAS  PubMed  Google Scholar 

  10. Bhatt DL, Szarek M, Steg PG, et al. Sotagliflozin in patients with diabetes and recent worsening heart failure. N Engl J Med. 2021;384(2):117–28.

    Article  CAS  PubMed  Google Scholar 

  11. Qu H, Guo M, Chai H, et al. Effects of coenzyme Q10 on statin-induced myopathy: an updated meta-analysis of randomized controlled trials. J Am Heart Assoc. 2018;7(19):e009835.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Iwere RB, Hewitt J. Myopathy in older people receiving statin therapy: a systematic review and meta-analysis. Br J Clin Pharmacol. 2015;80(3):363–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhou Z, Albarqouni L, Breslin M, et al. Statin-associated muscle symptoms (SAMS) in primary prevention for cardiovascular disease in older adults: a protocol for a systematic review and meta-analysis of randomised controlled trials. BMJ Open. 2017;7(9):e017587.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Kamal-Bahl SJ, Burke T, Watson D, et al. Discontinuation of lipid modifying drugs among commercially insured United States patients in recent clinical practice. Am J Cardiol. 2007;99:530–4.

    Article  PubMed  Google Scholar 

  15. Zhang H, Plutzky J, Skentzos S, et al. Discontinuation of statins in routine care settings: a cohort study. Ann Intern Med. 2013;158(7):526–34.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Knauer MJ. The role of drug transporters in statin-induced myopathy.In: Electronic Thesis and Dissertation Repository; 2012.

  17. Ballantyne CM, Corsini A, Davidson MH, et al. Risk for myopathy with statin therapy in high-risk patients. Arch Intern Med. 2003;163(5):553–64.

    Article  CAS  PubMed  Google Scholar 

  18. Joy TR, Hegele RA. Narrative review: statin-related myopathy. Ann Intern Med. 2009;150(12):858–68.

    Article  PubMed  Google Scholar 

  19. Kabadi U. Marked weight loss, muscle wasting and fatigue on administration of empagliflozin in a subject with type 2 diabetes. Br J Med Med Res. 2017;21(5):1–7.

    Article  Google Scholar 

  20. Gao F, Hall S, Bach LA. Myopathy secondary to empagliflozin therapy in type 2 diabetes. Endocrinol Diabetes Metab Case Rep. 2020;2020:20–0017.

    PubMed  PubMed Central  Google Scholar 

  21. Brailovksi E, Kim RB, Juurlink D. Rosuvastatin myotoxicity after starting canagliflozin treatment: a case report. Ann Intern Med. 2020;173(7):585–7.

    Article  Google Scholar 

  22. Antonazzo IC, Poluzzi E, Forcesi E, et al. Myopathy with DPP-4 inhibitors and statins in the real world: investigating the likelihood of drug-drug interactions through the FDA adverse event reporting system. Acta Diabetol. 2020;57(1):71–80.

    Article  CAS  PubMed  Google Scholar 

  23. Lalagkas PN, Poulentzas G, Kontogiorgis C, et al. Potential drug-drug interaction between sodium-glucose co-transporter 2 inhibitors and statins: pharmacological and clinical evidence. Expert Opin Drug Metab Toxicol. 2021;17(6):697–705.

    Article  CAS  PubMed  Google Scholar 

  24. Kasichayanula S, Chang M, Liu X, et al. Lack of pharmacokinetic interactions between dapagliflozin and simvastatin, valsartan, warfarin, or digoxin. Adv Ther. 2012;29(2):163–77.

    Article  CAS  PubMed  Google Scholar 

  25. Devineni D, Manitpisitkul P, Murphy J, et al. Effect of canagliflozin on the pharmacokinetics of glyburide, metformin, and simvastatin in healthy participants. Clin Pharmacol Drug Dev. 2015;4(3):226–36.

    Article  CAS  PubMed  Google Scholar 

  26. Macha S, Lang B, Pinnetti S, et al. Pharmacokinetics of empagliflozin, a sodium glucose cotransporter 2 inhibitor, and simvastatin following co-administration in healthy volunteers. Int J Clin Pharmacol Ther. 2014;52(11):973–80.

    Article  CAS  PubMed  Google Scholar 

  27. Dawra VK, Cutler DL, Zhou S, et al. Assessment of the drug inter-action potential of ertugliflozin with sitagliptin, metformin, glimepiride, or simvastatin in healthy subjects. Clin Pharmacol Drug Dev. 2019;8(3):314–25.

    Article  CAS  PubMed  Google Scholar 

  28. Fang M, Wang D, Coresh J, et al. Trends in diabetes treatment and control in US adults, 1999–2018. N Engl J Med. 2021;384(23):2219–28.

    Article  PubMed  PubMed Central  Google Scholar 

  29. US Food and Drug Administration. FDA AEs reporting system (FAERS) public dashboard. 2017. https://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/ucm070093.htm. Accessed 9 Jun 2021.

  30. Lavertu A, Vora B, Giacomini KM, et al. A new era in pharmacovigilance: toward real-world data and digital monitoring. Clin Pharmacol Ther. 2021;109(5):1197–202.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Wu B, Hu Q, Tian F, et al. A pharmacovigilance study of association between proton pump inhibitor and dementia event based on FDA adverse event reporting system data. Sci Rep. 2021;11(1):10709.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Poluzzi E, Raschi E, Piccinni C, et al. Data mining techniques in pharmacovigilance: analysis of the publicly accessible FDA adverse event reporting system (AERS). 2012. In: Karahoca A , editor. Data mining applications in engineering and medicine. Croatia: InTech; 2012: p. 265–302.

  33. Grundmark B, Holmberg L, Garmo H, et al. Reducing the noise in signal detection of adverse drug reactions by standardizing the background: a pilot study on analyses of proportional reporting ratios-by-therapeutic area. Eur J Clin Pharmacol. 2014;70(5):627–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Raschi E, Parisotto M, Forcesi E, et al. Adverse events with sodium-glucose co-transporter-2 inhibitors: a global analysis of international spontaneous reporting systems. Nutr Metab Cardiovasc Dis. 2017;27(12):1098–107.

    Article  CAS  PubMed  Google Scholar 

  35. Géniaux H, Assaf D, Miremont-Salamé G, et al. Performance of the standardised MedDRA® queries for case retrieval in the French spontaneous reporting database. Drug Saf. 2014;37(7):537–42.

    Article  PubMed  Google Scholar 

  36. Wisniewski AF, Bate A, Bousquet C, et al. Good signal detection practices: evidence from IMI PROTECT. Drug Saf. 2016;39(6):469–90.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Montastruc JL, Sommet A, Bagheri H, et al. Benefits and strengths of the disproportionality analysis for identification of adverse drug reactions in a pharmacovigilance database. Br J Clin Pharmacol. 2011;72(6):905–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Rothman KJ, Lanes S, Sacks ST. The reporting odds ratio and its advantages over the proportional reporting ratio. Pharmacoepidemiol Drug Saf. 2004;13(8):519–23.

    Article  PubMed  Google Scholar 

  39. Van Puijenbroek EP, Egberts AC, Heerdink ER, et al. Detecting drug-drug interactions using a database for spontaneous adverse drug reactions: an example with diuretics and non-steroidal anti-inflammatory drugs. Eur J Clin Pharmacol. 2000;56(9–10):733–8.

    Article  PubMed  Google Scholar 

  40. Thakrar BT, Grundschober SB, Doessegger L. Detecting signals of drug-drug interactions in a spontaneous reports database. Br J Clin Pharmacol. 2007;64(4):489–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wishart DS, Feunang YD, Guo AC, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46(D1):D1074–82.

  42. Beulah E, Reddy N, Subeesh V, et al. Weber effect: an extended analysis for ten years of reporting trends in FDA Adverse Event Reporting System (FAERS). Value Health. 2018;21:S369.

    Article  Google Scholar 

  43. Faillie JL. Case-non-case studies: principle, methods, bias and interpretation. Therapie. 2019;74(2):225–32.

    Article  PubMed  Google Scholar 

  44. Mamidi RNVS, Dallas S, Sensenhauser C, et al. In vitro and physiologically-based pharmacokinetic based assessment of drug-drug interaction potential of canagliflozin. Br J Clin Pharmacol. 2017;83(5):1082–96.

    Article  CAS  PubMed  Google Scholar 

  45. Teng C, Frei CR. Delirium associations with antibiotics: a pharmacovigilance study of the FDA Adverse Event Reporting System (FAERS). Drugs Real World Outcomes. 2021. Epub ahead of print.

  46. Bégaud B, Dangoumau J. Pharmacoepidemiology: definitions, problems, methodology. Therapie. 2000;55(1):113–7.

    PubMed  Google Scholar 

Download references

Acknowledgments

We thank Paul Malik PharmD, PhD for providing further insight into the pharmacokinetic literature.

Author information

Authors and Affiliations

  1. School of Pharmacy, Faculty of Science, University of Waterloo, 10A Victoria Street S, Kitchener, ON, N2G 1C5, Canada

    Wajd Alkabbani, Ryan Pelletier, Michael A. Beazely, Youssef Labib, Breanna Quan & John-Michael Gamble

Authors
  1. Wajd Alkabbani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ryan Pelletier
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael A. Beazely
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Youssef Labib
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Breanna Quan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. John-Michael Gamble
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to John-Michael Gamble.

Ethics declarations

Funding

No direct funding was received for this project.

Conflict of interest

Wajd Alkabbani, Ryan Pelletier, Michael A Beazely, Youssef Labib, Breanna Quan, and John-Michael Gamble declare that they have no potential conflicts of interest that might be relevant to the contents of this manuscript.

Ethics Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Availability of Data and Material

No additional data available.

Code Availability

Available upon request.

Authors Contributions

WA wrote the first draft of the manuscript and conducted the statistical analysis. WA, RP, and JMG were involved in the conception of the study. All authors were involved in the study design, interpretation of results, and critical review of the manuscript. All authors read and approved the final version. JMG affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 37 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alkabbani, W., Pelletier, R., Beazely, M.A. et al. Drug–Drug Interaction of the Sodium Glucose Co-Transporter 2 Inhibitors with Statins and Myopathy: A Disproportionality Analysis Using Adverse Events Reporting Data. Drug Saf 45, 287–295 (2022). https://doi.org/10.1007/s40264-022-01166-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40264-022-01166-3

Search

Navigation