Exposure to Polybrominated Diphenyl Ethers and a Polybrominated Biphenyl and Risk of Thyroid Cancer in Women: Single and Multi-Pollutant Approaches

Abstract

Background: Thyroid cancer incidence is the most rapidly increasing malignancy; rates are three times higher in women than men. Thyroid hormone-disrupting flame-retardant chemicals, including polybrominated diphenyl ethers (PBDE) and polybrominated biphenyls (PBB), may contribute to this trend.

Methods: We investigated the relationship between PBDE/PBB exposure and papillary thyroid cancer (PTC) in 250 incident female papillary thyroid cancer cases and 250 female controls frequency-matched on age. Interviews and postdiagnostic serum samples were collected from 2010 to 2013. Serum samples were analyzed for 11 congeners. We calculated ORs and 95% confidence intervals (95% CI) using single-pollutant logistic regression models for continuous and categorical lipid-adjusted serum concentrations of PBDE/PBB, adjusted for age, alcohol consumption, and education. We applied three multi-pollutant approaches [standard multipollutant regression models, hierarchical Bayesian logistic regression modeling (HBLR), principal components analysis (PCA)] to investigate associations with PBDE/PBB mixtures.

Results: In single-pollutant models, a decreased risk was observed at the highest (>90th percentile) versus lowest (<median) category of BDE-209 concentrations (OR, 0.47; 95% CI, 0.23-0.98); an elevated PTC risk was observed at the highest versus lowest category of BB-153 concentrations (OR, 1.81; 95% CI, 0.96-3.39). In standard multi-pollutant models, an interquartile range increase in BDE-100 concentrations was associated with increased PTC risk (OR, 1.18; 95% CI, 1.01-1.38). HBLR and PCA yielded no statistically significant results.

Conclusions: Our results using single- and multi-pollutant modeling do not generally support a positive association with PBDE/PBB and PTC risk.

Impact: Prospective studies with more advanced statistical approaches to analyze mixtures and populations with higher exposures could reveal new insights.

Figure 1.

Parameter estimates and 95% confidence/credible interval results from the standard multiple logistic regression (MLR) analysis and the hierarchical Bayesian multiple logistic regression (HBLR) analysis. Posterior means and quantile-based credible intervals are displayed for HBLR. The odds ratios represent the increase in odds for an interquartile range increase in exposure.