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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 FrenoyVittorio PerducaGerman Cano-SanchoJean-Philippe AntignacGianluca SeveriFrancesca Romana Mancini
Published in: Environmental health : a global access science source (2022)
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.
Keyphrases
  • breast cancer risk
  • estrogen receptor
  • endoplasmic reticulum
  • ionic liquid
  • breast cancer cells
  • drinking water
  • deep learning
  • young adults