Multi-pollutant exposure profiles associated with breast cancer risk: A Bayesian profile regression analysis in the French E3N cohort.

Air pollutants Bayesian profile regression Breast cancer Cluster Correlated exposures Mixture

Journal

Environment international
ISSN: 1873-6750
Titre abrégé: Environ Int
Pays: Netherlands
ID NLM: 7807270

Informations de publication

Date de publication:
08 Aug 2024
Historique:
received: 25 04 2024
revised: 02 08 2024
accepted: 06 08 2024
medline: 14 8 2024
pubmed: 14 8 2024
entrez: 13 8 2024
Statut: aheadofprint

Résumé

Human exposure to air pollution involves complex mixtures of multiple correlated air pollutants. To date, very few studies have assessed the combined effects of exposure to multiple air pollutants on breast cancer (BC) risk. We aimed to assess the association between combined exposures to multiple air pollutants and breast cancer risk. The study was based on a case-control study nested within the French E3N cohort (5222 incident BC cases/5222 matched controls). For each woman, the average of the mean annual exposure to eight pollutants (benzo(a)oyrene, cadmium, dioxins, polychlorinated biphenyls (PCB153), nitrogen dioxide (NO Among the 21 clusters identified, the cluster characterised by low exposures to all pollutants, except ozone, was taken as reference. A consistent increase in BC risk compared to the reference cluster was observed for 3 clusters: cluster 9 (OR=1.61; CrI=1.13,2.26), cluster 16 (OR=1.59; CrI=1.10,2.30) and cluster 15 (OR=1.38; CrI=1.00,1.88) characterised by high levels of NO This is the first study assessing the effect of exposure to a mixture of eight air pollutants on BC risk, using the BPR approach. Overall, results showed evidence of a positive joint effect of exposure to high levels to most pollutants, particularly high for NO

Sections du résumé

BACKGROUND BACKGROUND
Human exposure to air pollution involves complex mixtures of multiple correlated air pollutants. To date, very few studies have assessed the combined effects of exposure to multiple air pollutants on breast cancer (BC) risk.
OBJECTIVES OBJECTIVE
We aimed to assess the association between combined exposures to multiple air pollutants and breast cancer risk.
METHODS METHODS
The study was based on a case-control study nested within the French E3N cohort (5222 incident BC cases/5222 matched controls). For each woman, the average of the mean annual exposure to eight pollutants (benzo(a)oyrene, cadmium, dioxins, polychlorinated biphenyls (PCB153), nitrogen dioxide (NO
RESULTS RESULTS
Among the 21 clusters identified, the cluster characterised by low exposures to all pollutants, except ozone, was taken as reference. A consistent increase in BC risk compared to the reference cluster was observed for 3 clusters: cluster 9 (OR=1.61; CrI=1.13,2.26), cluster 16 (OR=1.59; CrI=1.10,2.30) and cluster 15 (OR=1.38; CrI=1.00,1.88) characterised by high levels of NO
DISCUSSION CONCLUSIONS
This is the first study assessing the effect of exposure to a mixture of eight air pollutants on BC risk, using the BPR approach. Overall, results showed evidence of a positive joint effect of exposure to high levels to most pollutants, particularly high for NO

Identifiants

pubmed: 39137687
pii: S0160-4120(24)00529-4
doi: 10.1016/j.envint.2024.108943
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

108943

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.

Auteurs

Camille Giampiccolo (C)

Department of Prevention Cancer Environnent, Centre Léon Bérard, Lyon, France; Inserm U1296 Radiations : Défense, Santé, Environnement, Lyon, France; Laboratoire de Biométrie Et Biologie Evolutive, CNRS UMR 5558, Villeurbanne, France.

Amina Amadou (A)

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

Thomas Coudon (T)

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

Delphine Praud (D)

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

Lény Grassot (L)

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

Elodie Faure (E)

Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, Villejuif, France.

Florian Couvidat (F)

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

Gianluca Severi (G)

Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, Villejuif, France; Department of Statistics, Computer Science and Applications (DISIA), University of Florence, Florence, Italy.

Francesca Romana Mancini (F)

Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, Villejuif, France.

Béatrice Fervers (B)

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

Pascal Roy (P)

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

Classifications MeSH