Association of cognitive decline, diabetes mellitus and cancer with statins use: gaining insight through the FDA pharmacovigilance database

Abstract

Abstract: AIM: To analyze the association between statins use and cognitive decline, diabetes mellitus and cancer through the US Food and Drug Administration (FDA) Adverse Event Reporting System (AERS). METHODS: Widely used pharmacovigilance tools were adopted for quantitative detection of signals, i.e. drug-associated adverse events, including the proportional reporting ratio (PRR), the reporting odds ratio (ROR), the Bayesian confidence propagation neural network (BCPNN) and the gamma possion shrinker (GPS). RESULTS: As for the association between statins use and cognitive decline, results of four algorithms are respectively 2.03,2.03,0.80,1.77, PRR,ROR and BCPNN were indicative of a definite risk, especially for atorvastatin and fluvastatin; as for the association between statin use and diabetes mellitus, results of four algorithms are respectively 2.35,2.42,1.09,2.12, PRR,ROR,BCPNN and GPS were all indicative of a definite risk, especially for simvastatin, atorvastatin and lovastatin; as for the association between statin use and cancer, results of four algorithms are respectively 0.90,0.89,-0.17,1.00, PRR,ROR,BCPNN and GPS all found no convincing evidence for risk of cancer after statins use. CONCLUSION: Data mining of the FDA's adverse event reporting system, AERS, is useful for examining statins-associated cognitive decline, diabetes mellitus and cancer. The data strongly suggest the necessity of well-organized clinical studies with respect to statins-associated adverse events.

Key words: statins, cognitive decline, diabetes mellitus, cancer, adverse event, pharmacovigilance

CLC Number:

R972⁺.6

JIANG Peng-li, WU Jing, SUN He. Association of cognitive decline, diabetes mellitus and cancer with statins use: gaining insight through the FDA pharmacovigilance database[J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2014, 19(10): 1132-1138.

References 25

[1]	MRC/BHF Heart Protection Study of cholesterol lowering with simvastatinin 20,536 high-risk individuals: a randomised placebo-controlled trial[J]. Lancet, 2002, 360(9326): 7-22.
[2]	Nissen SE, Nicholls SJ, Sipahi I, et al.Effect of very high-intensity statin therapy on regression of coronary atherosclerosis: the ASTEROID trial[J]. Jama, 2006, 295(13): 1556-1565.
[3]	李静远,蔡家利,戴仁科,等.他汀类药物在高脂血症大鼠体内中的PK-PD关系研究[J].中国临床药理学与治疗学,2011,16(12):1321-1325.
[4]	Jukema JW, Cannon CP, de Craen AJ, et al. The controversies of statin therapy: weighing the evidence[J]. J Am Coll Cardiol, 2012, 60(10): 875-881.
[5]	Thompson PD, Clarkson PM, Rosenson RS, et al.An assessment of statin safety by muscle experts[J]. Am J Cardiol, 2006, 97(8A), 69C-76C.
[6]	Wagstaff LR, Mitton MW, Arvik BM, et al.Statin-associated memory loss: analysis of 60 case reports and review of the literature[J]. Pharmacotherapy, 2003, 23(7): 871-880.
[7]	Poluzzi E, Raschi E, Motola D, et al.Antimicrobials and the risk of torsades de pointes: the contribution from data mining of the US FDA Adverse Event Reporting System[J]. Drug Saf, 2010, 33(4): 303-314.
[8]	Noren GN BA, Orre R, Edwards, IR.Duplicate detection in adverse drug reaction surveillance[J]. Data Min Knowl Disc, 2007, 14(3): 305-328.
[9]	http://www.fda.gov/Drugs/InformationOnDrugs/ucm079750.htm.
[10]	EUROMEDSTAT. http://www.euromedstat.cnr.it/. 2007.
[11]	Raschi E, Piccinni C, Poluzzi E,et al.The association of pancreatitis with antidiabetic drug use: gaining insight through the FDA pharmacovigilance database[J]. Acta Diabetol, 2011, 50(4):569-577.
[12]	Staffa JA, Chang J, Green L.Cerivastatin and reports of fatal rhabdomyolysis[J]. N Engl J Med, 2002, 346(7): 539-540.
[13]	http://www.meddramsso.com/files_acrobat/MSSO_EU_user_group_10Mar05_SMQs.pdf.
[14]	Bate A, Evans SJ.Quantitative signal detection using spontaneous ADR reporting[J]. Pharmacoepidemiol Drug Saf, 2009, 18(6): 427-436.
[15]	Hauben M, Madigan D, Gerrits CM,et al.The role of data mining in pharmacovigilance[J]. Expert Opin Drug Saf, 2005, 4(5): 929-948.
[16]	Almenoff JS, Pattishall EN, Gibbs TG,et al.Novel statistical tools for monitoring the safety of marketed drugs[J]. Clin Pharmacol Ther, 2007, 82(2): 157-166.
[17]	Joshi RR, Samant VV.Bayesian data mining of protein domains gives an efficient predictive algorithm and new insight[J]. J Mol Model, 2007, 13(1): 275-282.
[18]	Bate A, Lindquist M, Edwards IR, et al.A Bayesian neural network method for adverse drug reaction signal generation[J]. Eur J Clin Pharmacol, 1998, 54(4): 315-321.
[19]	Orrea R, Lansner A.Bayesian neural networks with confidence estimations applied to data mining[J]. Comput Stat Data An, 2000, 34: 473-493.
[20]	van Puijenbroek EP, Bate A, Leufkens HG, et al. A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions[J]. Pharmacoepidemiol Drug Saf, 2002, 11(1): 3-10.
[21]	Strandell J, Bate A, Hagg S, et al.Rhabdomyolysis a result of azithromycin and statins: an unrecognized interaction[J]. Br J Clin Pharmacol, 2009, 68(3): 427-434.
[22]	Stephenson WP, Hauben M.Data mining for signals in spontaneous reporting databases: proceed with caution[J]. Pharmacoepidemiol Drug Saf, 2007, 16(4): 359-365.
[23]	Pariente A, Gregoire F, Fourrier-Reglat A, et al.Impact of safety alerts on measures of disproportionality in spontaneous reporting databases: the notoriety bias[J]. Drug Saf,2007, 30(10): 891-898.
[24]	Tatonetti NP, Fernald GH, Altman RB.A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports[J]. J Am Med Inform Assoc, 2012, 19(1): 79-85.
[25]	Astrup A, Finer N.Redefining type 2 diabetes: 'diabesity' or 'obesity dependent diabetes mellitus'[J] ?Obes Rev, 2000, 1(2): 57-59.