Abstract

BackgroundEndometriosis is an estrogen-dependent disease. Endocrine disrupting chemicals (EDCs) and their mixtures may play an etiologic role. ObjectivesWe evaluated an adipose-to-serum ratio (ASR) of lipophilic EDCs and their mixtures associated with incident endometriosis. MethodsWe quantified 13 polychlorinated biphenyl (PCB) congeners, 6 polybrominated diphenyl ether (PBDE) congeners, and 11 organochlorine pesticides (OCPs) in serum and omental fat among women from the ENDO Study (2007–2009) aged 18–44 years diagnosed with (n=190) or without (n=283) surgically-visualized incident endometriosis. Odds ratios (OR) and 95% confidence intervals (CI) between ASR and endometriosis were estimated using logistic regression models adjusted for age (years), body mass index (kg/m2), serum cotinine (ng/ml), and breastfeeding conditional on parity. Bayesian hierarchical models (BHM) compared estimated associations for adipose and ASR to serum. Bayesian kernel machine regression (BKMR) estimated change in latent health and 95% posterior intervals (PI) between chemical mixtures and endometriosis. ResultsSelect ASR for estrogenic PCBs and OCPs were associated with an increased odds of an endometriosis diagnosis, but not for anti-estrogenic PCBs or PBDEs. Across all chemicals, BHMs generated ORs that were on average 14% (95% PI: 6%, 22%) higher for adipose and 20% (95% PI: 12%, 29%) higher for ASR in comparison to serum. ORs from BHMs were greater for estrogenic PCBs and OCPs, with no differences for PBDEs. BKMR models comparing the 75th to 25th percentile were moderately associated with endometriosis for estrogenic PCBs [adipose 0.27 (95% PI: 0.18, 0.72) and ASR 0.37 (95% PI: 0.06, 0.80)] and OCPs [adipose 0.17 (95% PI: 0.21, 0.56) and ASR 0.26 (95% PI: 0.05, 0.57)], but not for antiestrogenic PCBs and PBDEs. DiscussionASR added little insight beyond adipose for lipophilic chemicals. BKMR results supported associations between ASR and adipose estrogenic PCB and OCP mixtures and incident endometriosis. These findings underscore the importance of choice of biospecimen and considering mixtures when assessing exposure-disease relationships.

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