Multiple xenoestrogen air pollutants and breast cancer risk: statistical approaches to investigate combined exposures effect.

Bayesian kernel machine regression (BKMR) air pollution breast cancer multiple exposure weighted quantile sum (WQS) regression xenoestrogen

Journal

Environmental pollution (Barking, Essex : 1987)
ISSN: 1873-6424
Titre abrégé: Environ Pollut
Pays: England
ID NLM: 8804476

Informations de publication

Date de publication:
26 Apr 2024
Historique:
received: 07 08 2023
revised: 10 02 2024
accepted: 23 04 2024
medline: 29 4 2024
pubmed: 29 4 2024
entrez: 28 4 2024
Statut: aheadofprint

Résumé

Studies suggested that exposure to air pollutants, with endocrine disrupting (ED) properties, have a key role in breast cancer (BC) development. Although the population is exposed simultaneously to a mixture of multiple pollutants and ED pollutants may act via common biological mechanisms leading to synergic effects, epidemiological studies generally evaluate the effect of each pollutant separately. We aimed to assess the complex effect of exposure to a mixture of four xenoestrogen air pollutants (benzo-[a]-pyrene (BaP), cadmium, dioxin (2,3,7,8-Tétrachlorodibenzo-p-dioxin TCDD)), and polychlorinated biphenyl 153 (PCB153)) on the risk of BC, using three recent statistical methods, namely weighted quantile sum (WQS), quantile g-computation (QGC) and Bayesian kernel machine regression (BKMR). The study was conducted on 5,222 cases and 5,222 matched controls nested within the French prospective E3N cohort initiated in 1990. Annual average exposure estimates to the pollutants were assessed using a chemistry transport model, at the participants' residence address between 1990 and 2011. We found a positive association between the WQS index of the joint effect and the risk of overall BC (adjusted odds ratio (OR) = 1.10, 95% confidence intervals (CI): 1.03-1.19). Similar results were found for QGC (OR = 1.11, 95%CI: 1.03-1.19). Despite the association did not reach statistical significance in the BKMR model, we observed an increasing trend between the joint effect of the four pollutants and the risk of BC, when fixing other chemicals at their median concentrations. BaP, cadmium and PCB153 also showed positive trends in the multi-pollutant mixture, while dioxin showed a modest inverse trend. Despite we found a clear evidence of a positive association between the joint exposure to pollutants and BC risk only from WQS and QGC regression, we observed a similar suggestive trend using BKMR. This study makes a major contribution to the understanding of the joint effects of air pollution.

Identifiants

pubmed: 38679129
pii: S0269-7491(24)00757-7
doi: 10.1016/j.envpol.2024.124043
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

124043

Informations de copyright

Copyright © 2024. Published by Elsevier Ltd.

Déclaration de conflit d'intérêts

Declaration of Competing Interest ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Auteurs

Amina Amadou (A)

Department of Prevention Cancer Environment, Centre Léon Bérard, Lyon, France; Inserm U1296 Radiations : Défense, Santé, Environnement, Lyon, France. Electronic address: amina.amadou@lyon.unicancer.fr.

Camille Giampiccolo (C)

Department of Prevention Cancer Environment, Centre Léon Bérard, Lyon, France; Université Claude Bernard Lyon 1, Lyon, France; Service de Biostatistique-Bioinformatique, Pole Sante Publique, Hospices Civils de Lyon, Lyon, France; Laboratoire de Biometrie Et Biologie Evolutive, CNRS UMR 5558, Villeurbanne, France.

Fabiola Bibi Ngaleu (F)

Department of Prevention Cancer Environment, Centre Léon Bérard, Lyon, France; Inserm U1296 Radiations : Défense, Santé, Environnement, Lyon, France; Université Claude Bernard Lyon 1, Lyon, France.

Delphine Praud (D)

Department of Prevention Cancer Environment, Centre Léon Bérard, Lyon, France; Inserm U1296 Radiations : Défense, Santé, Environnement, Lyon, France.

Thomas Coudon (T)

Department of Prevention Cancer Environment, Centre Léon Bérard, Lyon, France; Inserm U1296 Radiations : Défense, Santé, Environnement, Lyon, France.

Lény Grassot (L)

Department of Prevention Cancer Environment, Centre Léon Bérard, Lyon, France; Inserm U1296 Radiations : Défense, Santé, Environnement, Lyon, France.

Elodie Faure (E)

Centre de Recherche en Epidémiologie et Santé des Populations (CESP, Inserm U1018), Facultés de Médecine, Université Paris-Saclay, UPS UVSQ, Gustave Roussy, Villejuif, France.

Florian Couvidat (F)

National Institute for industrial Environment and Risks (INERIS), Verneuil-en-Halatte, France.

Pauline Frenoy (P)

Centre de Recherche en Epidémiologie et Santé des Populations (CESP, Inserm U1018), Facultés de Médecine, Université Paris-Saclay, UPS UVSQ, Gustave Roussy, Villejuif, France.

Gianluca Severi (G)

Centre de Recherche en Epidémiologie et Santé des Populations (CESP, Inserm U1018), Facultés de Médecine, Université Paris-Saclay, UPS UVSQ, Gustave Roussy, Villejuif, France; Department of Statistics, Computer Science and Applications (DISIA), University of Florence, Italy.

Francesca Romana Mancini (F)

Centre de Recherche en Epidémiologie et Santé des Populations (CESP, Inserm U1018), Facultés de Médecine, Université Paris-Saclay, UPS UVSQ, Gustave Roussy, Villejuif, France.

Pascal Roy (P)

Université Claude Bernard Lyon 1, Lyon, France; Service de Biostatistique-Bioinformatique, Pole Sante Publique, Hospices Civils de Lyon, Lyon, France; Laboratoire de Biometrie Et Biologie Evolutive, CNRS UMR 5558, Villeurbanne, France.

Béatrice Fervers (B)

Department of Prevention Cancer Environment, Centre Léon Bérard, Lyon, France; Inserm U1296 Radiations : Défense, Santé, Environnement, Lyon, France.

Classifications MeSH