The association of GATM polymorphism with statin-induced myopathy: a systematic review and meta-analysis

Purpose Statin-induced myopathy (SIM) is the commonest reason for discontinuation of statin therapy. The aim of this present meta-analysis is to assess the relationship between glycine amidinotransferase gene (GATM) polymorphism and risk of SIM. Methods MEDLINE, EMBASE, Web of Science, and Cochrane Library databases were searched systematically for case-control studies investigating the relationship between GATM polymorphism and SIM. Retrieved articles were carefully reviewed and assessed according to the inclusion criteria. Associations were assessed in pooled data by calculating odds ratio with 95% confidence intervals. Subgroup analysis was performed according to comedications and severity of SIM. Results Six studies with 707 cases and 2321 controls were included in this meta-analysis. GATM rs9806699 G>A was associated with decreased risk of SIM (OR = 0.80, 95% CI 0.68–0.94, P = 0.006). This association remained significant in the subgroup with fibrates or niacin excluded. However, the association of rs9806699 G>A with severe SIM was not significant. In addition, another two variations at GATM, rs1719247 C>T, and rs1346268 T>C were also associated with declined risk of SIM. Conclusions GATM polymorphism including rs9806699 G>A, rs1719247 C>T, and rs1346268 T>C may be protective factors of SIM. GATM rs9806699 G>A may only exert protective effect on mild SIM cases. Our meta-analysis indicates that GATM polymorphism may represent a pharmacogenomics biomarker for predicting incidence of SIM, which contributes to risk stratification and optimizing statin adherence.

Pharmacogenomics, biomarker network and allele frequencies in colorectal cancer

ABSTRACTColorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Over the last decades, several studies have shown that tumor-related genomic alterations predict tumor prognosis, drug response and toxicity. These observations have led to the development of a number of precision therapies based on individual genomic profiles. As part of these approaches, pharmacogenomics analyses genomic alterations that may predict an efficient therapeutic response. Studying these mutations as biomarkers for predicting drug response is of a great interest to improve precision medicine. Here we conduct a comprehensive review of the main pharmacogenomics biomarkers and genomic alterations affecting enzyme activity, transporter capacity, channels and receptors, and therefore the new advances in CRC precision medicine to select the best therapeutic strategy in populations worldwide, with a focus on Latin America.