期刊
BREAST CANCER RESEARCH
卷 13, 期 5, 页码 -出版社
BMC
DOI: 10.1186/bcr3040
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资金
- National Institutes of Health [R01 CA49449, P01 CA87969]
- NATIONAL CANCER INSTITUTE [U01CA049449, R01CA049449, P01CA087969] Funding Source: NIH RePORTER
Introduction: Sex and growth hormones are positively associated with postmenopausal breast cancer risk. However, few studies have evaluated the influence of multiple hormones simultaneously. Methods: We considered the roles of estrone, estradiol, estrone sulfate, testosterone, androstenedione, dehydroepiandrosterone (DHEA), DHEA sulfate and prolactin and, secondarily, insulin-like growth factor 1 (IGF-1) and c-peptide in postmenopausal breast cancer risk among 265 cases and 541 controls in the prospective Nurses' Health Study. We created several hormone scores, including ranking women by the number of hormones above the age- and batch-adjusted geometric mean and weighting hormone values by their individual associations with breast cancer risk. Results: Women in the top versus bottom quintile of individual estrogen or androgen levels had approximately a doubling of postmenopausal breast cancer risk. Having seven or eight compared to zero hormones above the geometric mean level was associated with total (RR = 2.7, 95% CI = 1.3 to 5.7, P trend < 0.001) and estrogen receptor (ER)-positive (RR = 3.4, 95% CI = 1.3 to 9.4, P trend < 0.001) breast cancer risk. When comparing the top versus bottom quintiles of the score weighted by individual hormone associations, the RR for total breast cancer was 3.0 (95% CI = 1.8 to 5.0, P trend < 0.001) and the RR for ER-positive disease was 3.9 (95% CI = 2.0 to 7.5, P trend < 0.001). The risk further increased when IGF-1 and c-peptide were included in the scores. The results did not change with adjustment for body mass index. Conclusions: Overall, the results of our study suggest that multiple hormones with high circulating levels substantially increase the risk of breast cancer, particularly ER-positive disease. Additional research should consider the potential impact of developing risk prediction scores that incorporate multiple hormones.
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