Fertility treatments have become ever more popular in the UK, US and Canada in recent decades as infertility rates in these regions rise. As these regimens become increasingly common their public health impact should be assessed, especially in regards to their potential association with breast cancer. These treatments may affect breast cancer risk through their actions on reproductive hormone levels in treated women. In past decades, estrogen and progesterone have been shown to play an important role in the development of many breast cancers. 1. We propose to examine the association between three different fertility treatments, clomiphene, letrozole and in-vitro fertilization, and breast cancer. Common to these treatments is their effect on the ovulatory cycle as they stimulate the ovulation of oocytes, (cells in ovaries which may undergo division to form an ovum), yet cause the levels of circulating reproductive hormones to become higher than normal for brief but not-insignificant periods of time. While there have been prior studies examining this association, the results are so far contradictory and seemingly inconclusive. Results from our study will be important for both influencing current fertility treatment protocols as well as screening protocols for women who have undergone such treatments in the past.
Background: Fertility treatments whose mechanisms of action involve the induction of ovulation cause circulating levels of estrogen and progesterone to increase to supraphysiologic levels which may contribute to long-term risk of breast cancer due to the cancer's strong interplay with a woman's hormonal profile. Objectives: To examine the association between three different fertility treatments which aid in inducing ovulation and breast cancer risk. Methods: A population-based matched case control study using the CPRD will be performed to calculate odds ratios for the treatments of interest: clomiphene, letrozole and in-vitro fertilization. Incident cases of breast cancer will be ascertained through a validated algorithm used in prior breast cancer studies by our group using the same database. Controls will be population-based. Exposure will be assessed through the patient's medical history for evidence of being prescribed/having undergone one of the three aforementioned fertility treatments. Data Analysis: Conditional logistic regression will be used to calculate odds ratios and 95% CIs comparing the exposure of these three fertility treatments in breast cancer cases compared to the exposure levels in controls. Some confounding factors, notably age, calendar time of follow-up and socioeconomic status will be adjusted for through matching, while the remaining factors will be adjusted for through the logistic regression.
Samy Suissa - Chief Investigator - McGill University
Haim Abenhaim - Corresponding Applicant - McGill University
Togas Tulandi - Collaborator - McGill University