Application of two statistical approaches (Bayesian Kernel Machine Regression and Principal Component Regression) to assess breast cancer risk in association to exposure to mixtures of brominated flame retardants and per- and polyfluorinated alkylated substances in the E3N cohort

Pauline Frenoy, Vittorio Perduca, German Cano-Sancho, Jean-Philippe Antignac, Gianluca Severi, Francesca Romana Mancini, Pauline Frenoy, Vittorio Perduca, German Cano-Sancho, Jean-Philippe Antignac, Gianluca Severi, Francesca Romana Mancini

Abstract

Background: Brominated flame retardants (BFR) and per- and polyfluorinated alkylated substances (PFAS) are two groups of substances suspected to act as endocrine disruptors. Such substances could therefore be implicated in the occurrence of breast cancer, nevertheless, previous studies have led to inconstant results. Due to the large correlation between these substances, and the possibly non-linear effects they exert, evaluating their joint impact as mixtures on health remains challenging. This exploratory study aimed to generate hypotheses on the relationship between circulating levels of 7 BFR (6 polybrominated diphenyl ethers and 1 polybrominated biphenyls) and 11 PFAS and the risk of breast cancer in a case-control study nested in the E3N French prospective cohort by performing two methods: Principal Component Regression (PCR) models, and Bayesian Kernel Machine Regression (BKMR) models.

Methods: 194 post-menopausal breast cancer cases and 194 controls were included in the present study. Circulating levels of BFR and PFAS were measured by gas chromatography coupled to high-resolution mass spectrometry and liquid chromatography coupled to tandem mass spectrometry, respectively. The first statistical approach was based on Principal Component Analysis (PCA) followed by logistic regression models that included the identified principal components as main exposure variables. The second approach used BKMR models with hierarchical variable selection, this latter being suitable for highly correlated exposures. Both approaches were also run separately for Estrogen Receptor positive (ER +) and Estrogen Receptor negative (ER-) breast cancer cases.

Results: PCA identified four principal components accounting for 67% of the total variance. Component 3 showed a marginal association with ER + breast cancer risk. No clear association between BFR and PFAS mixtures and breast cancer was identified using BKMR models, and the credible intervals obtained were very wide. Finally, the BKMR models suggested a negative cumulative effect of BFR and PFAS on ER- breast cancer risk, and a positive cumulative effect on ER + breast cancer risk.

Conclusion: Although globally no clear association was identified, both approaches suggested a differential effect of BFR and PFAS mixtures on ER + and ER- breast cancer risk. However, the results for ER- breast cancer should be interpreted carefully due to the small number of ER- cases included in the study. Further studies evaluating mixtures of substances on larger study populations are needed.

Trial registration: ClinicalTrials.gov NCT03285230.

Keywords: Bayesian Kernel Machine Regression (BKMR); Breast cancer; Brominated flame retardants (BFR); Per- and polyfluorinated alkylated substances (PFAS); Principal Component Regression (PCR).

Conflict of interest statement

The authors declare that they have no competing interests.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
Univariate exposure–response functions between exposure to each substance and probit of probability of: A: having a breast cancer, B: having an ER + breast cancer, C: having an ER- breast cancer, all other substances being fixed at their median value
Fig. 2
Fig. 2
Cumulative effect of PFAS and BFR for: A: All breast cancer risk. B: ER + breast cancer risk. C: ER- breast cancer risk

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Source: PubMed

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