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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

  • Ippazio Cosimo Antonazzo
  • Elisabetta Poluzzi
  • Emanuele Forcesi
  • Francesco Salvo
  • Antoine Pariente
  • Giulio Marchesini
  • Fabrizio De Ponti
  • Emanuel RaschiEmail author
Original Article

Abstract

Aims

To investigate the occurrence of myopathy with oral glucose-lowering drugs (OGLDs) and statins, with a focus on dipeptidyl peptidase-4 inhibitors (DPP4-is).

Methods

The FDA adverse event reporting system (FAERS) was queried (2004–2016) to compare the proportion of adverse events with OGLDs, alone and in combination with statins, using the reporting odds ratio (ROR) with relevant 95% confidence interval (95%Cl), adjusted for sex, age and concomitant presence of other OGLDs/lipid-lowering drugs. Drug–drug interaction is claimed whenever the frequency of an event is enhanced by combination treatment. Consistency/robustness of findings was tested by applying additive/multiplicative models and accounting for competition bias (i.e., adverse events previously known to be associated with OGLDs were removed).

Results

Over a 13-year period, we retrieved 142,888 cases of myopathy. The use of DPP4-is alone was not associated with higher reporting of myopathy (no. of cases = 4898; adjusted ROR = 1.00; 95%CI = 0.96–1.04), with the notable exclusion of vildagliptin (262; 1.64; 1.42–1.88). No increased occurrence emerged when used in combination with statins, with consistent findings from additive/multiplicative models for all DPP4-is. Likewise, no increased reporting was found for other OGLDs (28,964; 0.64; 0.62–0.67); data on the interaction with statins were consistent/robust across analyses only for sulfonylureas (the interaction is likely and biologically plausible) and sodium glucose cotransporter-2 inhibitors.

Conclusions

Real-world FAERS data do not raise concern for muscular toxicity with DPP4-is in combination with statins, making a drug interaction very unlikely.

Keywords

Myopathy FAERS Drug interactions Antidiabetic drugs 

Notes

Acknowledgements

Authors at the University of Bologna are supported by Institutional funds.

Compliance with ethical standards

Conflict of interest

ICA, EP, EF, FS, AP, FDP and ER have no conflicts of interest relevant to the content of the present work. GM reports personal fees and other from Sanofi, personal fees and other from NOVO Nordisk, personal fees and other from Eli-Lilly, other from Astra Zeneca, other from Glaxo, personal fees and other from Janssen, outside the submitted work.

Ethical approval

For this type of study, ethical approval is not required.

Informed Consent

For this type of study, formal consent is not required.

Supplementary material

592_2019_1378_MOESM1_ESM.docx (34 kb)
Supplementary material 1 (DOCX 33 kb)

References

  1. 1.
    Bhome R, Penn H (2012) Rhabdomyolysis precipitated by a sitagliptin-atorvastatin drug interaction. Diabet Med 29(5):693–694CrossRefGoogle Scholar
  2. 2.
    Khan MW, Kurian S, Bishnoi R (2016) Acute-onset rhabdomyolysis secondary to sitagliptin and atorvastatin interaction. Int J Gen Med 9:103–106CrossRefGoogle Scholar
  3. 3.
    Kao DP, Kohrt HE, Kugler J (2008) Renal failure and rhabdomyolysis associated with sitagliptin and simvastatin use. Diabet Med 25(10):1229–1230CrossRefGoogle Scholar
  4. 4.
    DiGregorio RV, Pasikhova Y (2009) Rhabdomyolysis caused by a potential sitagliptin-lovastatin interaction. Pharmacotherapy 29(3):352–356CrossRefGoogle Scholar
  5. 5.
    European Medicines Agency (2014) Vildagliptin; Vildagliptin, metformin—Rhabdomyolysis. In PRAC recommendations on signals. Adopted at the PRAC Meeting of 10–13 June 2014. London, U.K., European Medicines Agency, pp 8–9 [internet]. Available from: http://www.ema.europa.eu/docs/en_GB/document_library/PRAC_recommendation_on_signal/2014/07/WC500169486.pdf
  6. 6.
    Strandell J, Caster O, Bate A, Noren N, Edwards IR (2011) Reporting patterns indicative of adverse drug interactions: a systematic evaluation in VigiBase. Drug Saf 34(3):253–266CrossRefGoogle Scholar
  7. 7.
    Fadini GP, Bonora BM, Mayur S, Rigato M, Avogaro A (2018) Dipeptidyl peptidase-4 inhibitors moderate the risk of genitourinary tract infections associated with sodium-glucose co-transporter-2 inhibitors. Diabetes Obes Metab 20(3):740–744CrossRefGoogle Scholar
  8. 8.
    Fadini GP, Sarangdhar M, Avogaro A (2018) Pharmacovigilance evaluation of the association between DPP-4 inhibitors and heart failure: stimulated reporting and moderation by drug interactions. Diabet Ther 9(2):851–861CrossRefGoogle Scholar
  9. 9.
    Labat V, Arnaud M, Miremont-Salame G, Salvo F, Begaud B, Pariente A (2017) Risk of myopathy associated with DPP-4 inhibitors in combination with statins: a disproportionality analysis using data from the WHO and French spontaneous reporting databases. Diabet Care 40(3):e27–e29CrossRefGoogle Scholar
  10. 10.
    Tarapues M, Cereza G, Figueras A (2013) Association of musculoskeletal complaints and gliptin use: review of spontaneous reports. Pharmacoepidemiol Drug Saf 22(10):1115–1118PubMedGoogle Scholar
  11. 11.
    Raschi E, Moretti U, Salvo F et al (2018) Evolving roles of spontaneous reporting systems to assess and monitor drug safety. In: Kothari CS, Shah M, Patel RM (eds) Pharmacovigilance. IntechOpen.  https://doi.org/10.5772/intechopen.79986 Google Scholar
  12. 12.
    Raschi E, Poluzzi E, Salvo F, et al. (2018) Pharmacovigilance of sodium-glucose co-transporter-2 inhibitors: what a clinician should know on disproportionality analysis of spontaneous reporting systems. Nutr Metab Cardiovasc Dis 28(6):533–542CrossRefGoogle Scholar
  13. 13.
    Poluzzi E, Raschi E, Piccinni C, De Ponti F (2012) Data mining techniques in pharmacovigilance: analysis of the publicly accessible FDA adverse event reporting system (AERS). In: Karahoca A (ed) Data mining applications in engineering and medicine. InTech, Croatia, pp 265–302Google Scholar
  14. 14.
    Fukazawa C, Hinomura Y, Kaneko M, Narukawa M (2018) Significance of data mining in routine signal detection: analysis based on the safety signals identified by the FDA. Pharmacoepidemiol Drug Saf 27(12):1402–1408CrossRefGoogle Scholar
  15. 15.
    Harpaz R, DuMouchel W, LePendu P, et al. (2013) Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system. Clin Pharmacol Ther 93(6):539–546CrossRefGoogle Scholar
  16. 16.
    Bate A, Evans SJ (2009) Quantitative signal detection using spontaneous ADR reporting. Pharmacoepidemiol Drug Saf 18(6):427–436CrossRefGoogle Scholar
  17. 17.
    Sakaeda T, Tamon A, Kadoyama K, Okuno Y (2013) Data mining of the public version of the FDA adverse event reporting system. Int J Med Sci 10(7):796–803CrossRefGoogle Scholar
  18. 18.
    Geniaux H, Assaf D, Miremont-Salame G, Raspaud B, Gouverneur A, Robinson P et al (2014) Performance of the standardised MedDRA(R) queries for case retrieval in the French spontaneous reporting database. Drug Saf 37(7):537–542CrossRefGoogle Scholar
  19. 19.
    van Puijenbroek EP, Egberts AC, Heerdink ER, Leufkens HG (2000) 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 56(9–10):733–738CrossRefGoogle Scholar
  20. 20.
    Van Puijenbroek EP, Egberts AC, Meyboom RH, Leufkens HG (1999) Signalling possible drug-drug interactions in a spontaneous reporting system: delay of withdrawal bleeding during concomitant use of oral contraceptives and itraconazole. Br J Clin Pharmacol 47(6):689–693CrossRefGoogle Scholar
  21. 21.
    Yue Z, Shi J, Jiang P, Sun H (2014) Acute kidney injury during concomitant use of valacyclovir and loxoprofen: detecting drug-drug interactions in a spontaneous reporting system. Pharmacoepidemiol Drug Saf 23(11):1154–1159CrossRefGoogle Scholar
  22. 22.
    Capogrosso Sansone A, Convertino I, Galiulo MT, et al. (2017) Muscular adverse drug reactions associated with proton pump inhibitors: a disproportionality analysis using the Italian national network of pharmacovigilance database. Drug Saf 40(10):895–909CrossRefGoogle Scholar
  23. 23.
    van Puijenbroek EP, Bate A, Leufkens HG, Lindquist M, Orre R, Egberts AC (2002) A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiol Drug Saf 11(1):3–10CrossRefGoogle Scholar
  24. 24.
    Thakrar BT, Grundschober SB, Doessegger L (2007) Detecting signals of drug-drug interactions in a spontaneous reports database. Br J Clin Pharmacol 64(4):489–495CrossRefGoogle Scholar
  25. 25.
    Raschi E, Piccinni C, Poluzzi E, Marchesini G, De Ponti F (2013) The association of pancreatitis with antidiabetic drug use: gaining insight through the FDA pharmacovigilance database. Acta Diabetol 50(4):569–577CrossRefGoogle Scholar
  26. 26.
    Fadini GP, Bonora BM, Avogaro A (2017) SGLT2 inhibitors and diabetic ketoacidosis: data from the FDA adverse event reporting system. Diabetologia 60(8):1385–1389CrossRefGoogle Scholar
  27. 27.
    Motola D, Piccinni C, Biagi C, et al. (2012) Cardiovascular, ocular and bone adverse reactions associated with thiazolidinediones: a disproportionality analysis of the US FDA adverse event reporting system database. Drug Saf 35(4):315–323CrossRefGoogle Scholar
  28. 28.
    Piccinni C, Motola D, Marchesini G, Poluzzi E (2011) Assessing the association of pioglitazone use and bladder cancer through drug adverse event reporting. Diabet Care 34(6):1369–1371CrossRefGoogle Scholar
  29. 29.
    Salvo F, Leborgne F, Thiessard F, Moore N, Begaud B, Pariente A (2013) A potential event-competition bias in safety signal detection: results from a spontaneous reporting research database in France. Drug Saf 36(7):565–572CrossRefGoogle Scholar
  30. 30.
    Kalaitzoglou E, Fowlkes JL, Popescu I, Thrailkill KM (2018) Diabetes pharmacotherapy and effects on the musculoskeletal system. Diabet/Metab Res Rev 35:e3100CrossRefGoogle Scholar
  31. 31.
    Scheen AJ (2018) The safety of gliptins: updated data in 2018. Expert Opin Drug Saf 17(4):387–405CrossRefGoogle Scholar
  32. 32.
    Sesti G, Avogaro A, Belcastro S et al (2019) Ten years of experience with DPP-4 inhibitors for the treatment of type 2 diabetes mellitus. Acta Diabetol 56(6):605–617CrossRefGoogle Scholar
  33. 33.
    Mascolo A, Rafaniello C, Sportiello L, et al. (2016) Dipeptidyl Peptidase (DPP)-4 inhibitor-induced arthritis/arthralgia: a review of clinical cases. Drug Saf 39(5):401–407CrossRefGoogle Scholar
  34. 34.
    Bouchi R, Fukuda T, Takeuchi T, et al. (2018) Dipeptidyl peptidase 4 inhibitors attenuates the decline of skeletal muscle mass in patients with type 2 diabetes. Diabet/Metab Res Rev 34(2):e2957CrossRefGoogle Scholar
  35. 35.
    Rizzo MR, Barbieri M, Fava I, et al. (2016) Sarcopenia in elderly diabetic patients: role of dipeptidyl peptidase 4 inhibitors. J Am Med Dir Assoc 17(10):896–901CrossRefGoogle Scholar
  36. 36.
    Bergman AJ, Cote J, Maes A, et al. (2009) Effect of sitagliptin on the pharmacokinetics of simvastatin. J Clin Pharmacol 49(4):483–488CrossRefGoogle Scholar
  37. 37.
    Tornio A, Niemi M, Neuvonen PJ, Backman JT (2012) Drug interactions with oral antidiabetic agents: pharmacokinetic mechanisms and clinical implications. Trends Pharmacol Sci 33(6):312–322CrossRefGoogle Scholar
  38. 38.
    He YL (2012) Clinical pharmacokinetics and pharmacodynamics of vildagliptin. Clin Pharmacokinet 51(3):147–162CrossRefGoogle Scholar
  39. 39.
    Hoffmann P, Martin L, Keselica M, et al. (2017) Acute toxicity of Vildagliptin. Toxicol Pathol 45(1):76–83CrossRefGoogle Scholar
  40. 40.
    Schelleman H, Han X, Brensinger CM, et al. (2014) Pharmacoepidemiologic and in vitro evaluation of potential drug-drug interactions of sulfonylureas with fibrates and statins. Br J Clin Pharmacol 78(3):639–648CrossRefGoogle Scholar
  41. 41.
    Leonard CE, Bilker WB, Brensinger CM, et al. (2016) Severe hypoglycemia in users of sulfonylurea antidiabetic agents and antihyperlipidemics. Clin Pharmacol Ther 99(5):538–547CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Italia S.r.l., part of Springer Nature 2019

Authors and Affiliations

  • Ippazio Cosimo Antonazzo
    • 1
  • Elisabetta Poluzzi
    • 1
  • Emanuele Forcesi
    • 1
  • Francesco Salvo
    • 2
    • 3
    • 4
  • Antoine Pariente
    • 2
    • 3
    • 4
  • Giulio Marchesini
    • 5
  • Fabrizio De Ponti
    • 1
  • Emanuel Raschi
    • 1
    Email author
  1. 1.Pharmacology Unit, Department of Medical and Surgical Sciences, Alma Mater StudiorumUniversity of BolognaBolognaItaly
  2. 2.University of BordeauxBordeauxFrance
  3. 3.INSERM U657BordeauxFrance
  4. 4.CIC Bordeaux CICI1401BordeauxFrance
  5. 5.Unit of Metabolic Diseases and Clinical Dietetics, Department of Medical and Surgical Sciences, Alma Mater StudiorumUniversity of BolognaBolognaItaly

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