JOURNAL ARTICLE
RESEARCH SUPPORT, NON-U.S. GOV'T
REVIEW
SYSTEMATIC REVIEW
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Putting the cardiovascular safety of aromatase inhibitors in patients with early breast cancer into perspective: a systematic review of the literature.

In the adjuvant setting, the third-generation aromatase inhibitors (AIs) anastrozole, letrozole and exemestane are recommended at some point during treatment, either in the upfront, switch after tamoxifen or extended treatment setting after tamoxifen in postmenopausal patients with hormone receptor-positive early breast cancer. AIs have demonstrated superior disease-free survival and overall benefit-to-risk profiles compared with tamoxifen. Potential adverse events, including cardiovascular (CV) side effects, should be considered in the long-term management of patients undergoing treatment with AIs. AIs reduce estrogen levels by inhibiting the aromatase enzyme, thus reducing the levels of circulating estrogen. This further reduction in estrogen levels may potentially increase the risk of developing CV disease. This systematic review evaluated published clinical data for changes in plasma lipoproteins and ischaemic CV events during adjuvant therapy with AIs in patients with hormone receptor-positive early breast cancer. The electronic databases MEDLINE, EMBASE, Derwent Drug File and BIOSIS were searched to identify English-language articles published from January 1998 to 15 April 2011 that reported data on AIs and plasma lipoproteins and/or ischaemic CV events. Overall, available data did not show any definitive patterns or suggest an unfavourable effect of AIs on plasma lipoproteins from baseline to follow-up assessment in patients with hormone receptor-positive early breast cancer. Changes that occurred in plasma lipoproteins were observed soon after initiation of AI therapy and generally remained stable throughout the studies. Available data do not support a substantial risk of ischaemic CV events associated with adjuvant AI therapy; however, studies with longer follow-up are required to better characterize the CV profile of AIs.

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