Combined Exposure to Multiple Endocrine Disruptors and Uterine Leiomyomata and Endometriosis in US Women

Abstract

Background: Uterine leiomyomata (UL) and endometriosis (EM) are common gynecological diseases damaging the reproductive health of fertile women. Among all the potential factors, environmental endocrine-disrupting chemicals are insufficiently addressed considering the multiple pollutants and mixture exposure.

Methods: Women aged 20 to 54 years old in the National Health and Nutrition Examination Survey (NHANES) 2001-2006, having a complete measurement of ten commonly exposed endocrine-disrupting chemicals (including urinary phthalate metabolites, equol, and whole blood heavy metals) and answered questions about UL and EM were included (N=1204). Multivariable logistic regression model, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models were implemented to analyze the combined effect of chemicals on the overall association with UL and EM.

Results: In single chemical analysis, equol (OR: 1.90, 95% CI: 1.11, 3.27) and mercury (Hg) (OR: 1.91, 95% CI: 1.14, 3.25) were found positively associated with UL in tertile 3 vs. tertile 1. In WQS regression and BKMR models, the significant positive association between WQS index and UL (OR: 2.54, 95% CI: 1.52, 4.29) was identified and the positive relationship between equol and Hg exposure and UL were further verified. Besides, the mixture evaluation models (WQS and BKMR) also found MEHP negatively associated with UL. Although none of the single chemicals in tertile 3 were significantly associated with EM, the WQS index had a marginally positive association with EM (OR: 2.01, 95% CI: 0.98, 4.15), and a significant positive association was identified in subanalysis with participants restricted to premenopausal women (OR: 2.18, 95% CI: 1.03, 4.70). MIBP and MBzP weighted high in model of EM and MEHP weighted the lowest.

Conclusion: Comparing results from these three statistical models, the associations between equol, Hg, and MEHP exposure with UL as well as the associations of MIBP, MBzP, and MEHP exposure with EM warrant further research.

Keywords: Bayesian kernel machine regression (BKMR); endometriosis; mixed exposure; uterine leiomyomata; weighted quantile sum (WQS) regression.

Figure 1

The associations between chemical exposure and uterine leiomyomata (A) or endometriosis (B) in women aged between 20 and 54 years old with multivariable logistic regression model. Models were adjusted for age, ethnicity, BMI group, ovary removal, female hormone usage, pregnant status, and menopause status. Models^# were further adjusted for log-transformed urinary creatinine levels. *Indicates P-value < 0.05.

Figure 2

The weights of each chemical in positive WQS model regression index for uterine leiomyomata (A) and endometriosis (B). Models were adjusted for age, ethnicity, BMI group, ovary removal, female hormone usage, pregnant status, menopause status, and log-transformed urinary creatinine levels.

Figure 3

Overall risk (95% CI) of the mixture on uterine leiomyomata (A) and endometriosis (B) when comparing all the chemicals at different percentiles with all of them fixed at the median level. Models were adjusted for age, ethnicity, BMI group, ovary removal, female hormone usage, pregnant status, menopause status, and log-transformed urinary creatinine levels.

Figure 4

Exposure-response function (95% CI) between selected chemical exposure and uterine leiomyomata (A) or and endometriosis (B) with fixing all the other chemicals at their median level. Models were adjusted for age, ethnicity, BMI group, ovary removal, female hormone usage, pregnant status, menopause status, and log-transformed urinary creatinine levels.