Khoa A Nguyen1,2, Lang Li3, Deshun Lu4, Aida Yazdanparast5, Lei Wang3, Rolf P Kreutz6, Elizabeth C Whipple7, Titus K Schleyer8,9. 1. Center for Health Information and Communication, Department of Veterans Affairs (VA), Veterans Health Administration, Health Service Research and Development Service (CIN 13-416), Richard L. Roudebush VA Medical Center, D6004-02, 1481 West 10th Street, Indianapolis, IN, 46202, USA. ngkhoa@umich.edu. 2. Department of Pharmacy Practice, College of Pharmacy, Purdue University, Indianapolis, IN, USA. ngkhoa@umich.edu. 3. Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA. 4. Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA. 5. Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA. 6. Department of Medicine, Krannert Institute of Cardiology, Indiana University School of Medicine, Indianapolis, IN, USA. 7. Research and Translational Sciences, Indiana University School of Medicine, Indianapolis, IN, USA. 8. Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA. 9. Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, USA.
Abstract
PURPOSE: To aid prescribers in assessing a patient's risk for statin-induced myopathy (SIM), we performed a comprehensive review of currently known risk factors and calculated aggregated odds ratios for each risk factor through a meta-analysis. METHODS: This meta-analysis was done through four phases: (1) Identification of the relevant primary literature; (2) abstract screening using inclusion and exclusion criteria; (3) detailed review and data extraction; and (4) synthesis and statistical analysis. RESULTS: Out of 44 papers analyzed from 836 papers searched from MEDLINE, 18 different potential risk factors were collected, divided into three categories: three demographics (11 papers), ten clinical factors (31 papers), and five pharmacogenetics/biomarkers (12 papers). Risk factors significant for myopathy and/or rhabdomyolysis included age, gender, diabetes, renal impairment, cardiovascular disease, certain interacting drugs, and mutations of the SLCO1B1 gene, which encodes a transporter protein in the liver. Several factors, such as gender, race, cardiovascular disease, and the GATM gene, which encodes a protein for creatine synthesis, appeared to be protective in terms of the outcomes of interest. CONCLUSIONS: This comprehensive assessment of risk factors can help support clinicians in reducing the incidence of SIM in their patient population on statins.
PURPOSE: To aid prescribers in assessing a patient's risk for statin-induced myopathy (SIM), we performed a comprehensive review of currently known risk factors and calculated aggregated odds ratios for each risk factor through a meta-analysis. METHODS: This meta-analysis was done through four phases: (1) Identification of the relevant primary literature; (2) abstract screening using inclusion and exclusion criteria; (3) detailed review and data extraction; and (4) synthesis and statistical analysis. RESULTS: Out of 44 papers analyzed from 836 papers searched from MEDLINE, 18 different potential risk factors were collected, divided into three categories: three demographics (11 papers), ten clinical factors (31 papers), and five pharmacogenetics/biomarkers (12 papers). Risk factors significant for myopathy and/or rhabdomyolysis included age, gender, diabetes, renal impairment, cardiovascular disease, certain interacting drugs, and mutations of the SLCO1B1 gene, which encodes a transporter protein in the liver. Several factors, such as gender, race, cardiovascular disease, and the GATM gene, which encodes a protein for creatine synthesis, appeared to be protective in terms of the outcomes of interest. CONCLUSIONS: This comprehensive assessment of risk factors can help support clinicians in reducing the incidence of SIM in their patient population on statins.
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