Urinary Phytoestrogen Levels Are Associated with Female Hormonal Cancers: An Analysis of NHANES Data From 1999 to 2010.
Alice W LeeValerie PoynorArchana J McEligotPublished in: Nutrition and cancer (2022)
Phytoestrogens are plant-derived compounds that are structurally similar to endogenous estrogens. Studies have shown phytoestrogens to have possible health benefits although they could also act as endocrine disruptors. This is particularly relevant for estrogen-dependent cancers since estrogens increase risk of breast, endometrial, and ovarian cancer. Using data from the National Health and Nutritional Examination Survey (NHANES), we assessed the associations between urinary phytoestrogens (daidzein, equol, o-Desmethylangolensin (O-DMA), genistein, enterodiol, enterolactone) and breast, endometrial, and ovarian cancer using multivariate logistic regression with odds ratios (ORs) and 95% confidence intervals (CIs). Cancer diagnosis and other characteristics were collected via in-person questionnaires. We found women in the highest tertile for daidzein and enterodiol had over twice the odds of having breast cancer (OR = 2.51, 95% CI 1.44-4.36 for daidzein, OR = 2.78, 95% CI 1.44-5.37 for enterodiol). In addition, women in the highest tertiles for daidzein and genistein had three to four times the odds of having endometrial cancer, respectively (OR = 3.09, 95% CI 1.01-9.49 for daidzein, OR = 4.00, 95% CI 1.38-11.59 for genistein). Overall, phytoestrogens were positively associated with breast and endometrial cancer although the associations varied by phytoestrogen type. Additional studies are needed to further inform phytoestrogens' role in disease etiology.Supplemental data for this article is available online at at https://doi.org/10.1080/01635581.2021.2020304.
Keyphrases
- endometrial cancer
- polycystic ovary syndrome
- electronic health record
- big data
- public health
- healthcare
- health information
- data analysis
- mental health
- papillary thyroid
- pregnancy outcomes
- social media
- childhood cancer
- machine learning
- pregnant women
- climate change
- estrogen receptor
- skeletal muscle
- young adults
- health promotion