Gonadotropins have been indicted in ovarian carcinogenesis but direct evidence has been limited and inconsistent. The aim of this study was to determine the association between prediagnostic levels of follicle stimulating hormone (FSH) and subsequent development of invasive epithelial ovarian cancer. A nested case-control study was conducted using cases and controls drawn from three cohorts: CLUE I and CLUE II of Washington County, MD, and the Island of Guernsey Study, United Kingdom. In total, 67 incident invasive epithelial ovarian cancer cases were each matched to 1 to 2 controls on age, menopausal status, time since last menstrual period, current hormone use and other relevant factors. FSH concentrations were classified into ranked thirds of low, medium or high based on the distribution among controls. Conditional logistic regression was used to estimate the odds ratio (OR) across increasing thirds of FSH concentrations. Results of the analysis showed that ovarian cancer risk decreased with higher FSH concentrations (p-trend = 0.005). Compared with the lowest third of FSH concentrations, the OR among those in the middle and highest thirds were 0.45 [95% Confidence Interval (CI): 0.20-1.00] and 0.26 (95% CI: 0.10-0.70), respectively. Associations persisted after excluding cases diagnosed within 5 years of follow-up. In conclusion, a reduction in subsequent risk of invasive epithelial ovarian cancer was observed among women with higher circulating FSH concentrations. These findings contradict the hypothesized role of FSH as a risk factor in ovarian carcinogenesis.

Original publication

DOI

10.1002/ijc.24406

Type

Journal article

Journal

Int J Cancer

Publication Date

01/08/2009

Volume

125

Pages

674 - 679

Keywords

Adult, Aged, Biomarkers, Tumor, Carcinoma, Case-Control Studies, Female, Follicle Stimulating Hormone, Great Britain, Humans, Logistic Models, Maryland, Middle Aged, Neoplasm Invasiveness, Odds Ratio, Ovarian Neoplasms, Predictive Value of Tests, Prospective Studies, Risk Factors, Statistics, Nonparametric, Time Factors