Environmental Dioxin Exposure Index And Breast Cancer Risk In A Case-Control Study Nested Within The French E3N Prospective Cohort: Considering Time Of Exposure In The Risk Estimate
Aurélie DANJOU, Centre Léon Bérard, France
COUDON T. 1,2
, LEFFONDRE K. 3
, LEVEQUE E. 3
, FAURE E. 1
, BOUTRON-RUAULT M. 4,5,6
, CLAVEL-CHAPELON F. 4,5,6
, DOSSUS L. 7
, FERVERS B. 1,2
1 Département Cancer et Environnement, Centre Léon Bérard, Lyon, France
2 Université Claude Bernard Lyon 1, Villeurbanne, France
3 Université de Bordeaux, Institut de Santé Publique, d'Epidémiologie et de Développement, Centre INSERM U1219 Epidemiology and Biostatistics, Bordeaux, France
4 Inserm, Center for Research in Epidemiology and Population Health (CESP), U1018, team "Lifestyle, genes and health", Villejuif, France
5 Université Paris-Sud, UMRS 1018, Villejuif, France
6 Gustave Roussy, Villejuif, France
7 Nutritional Epidemiology Group, Nutrition and Metabolism Section, International Agency for Research on Cancer, Lyon, France
Purpose: To investigate breast cancer (BC) risk associated with environmental dioxin exposure, using statistical methods to consider temporal dimensions of exposure in the risk estimate.
Population: We designed a case-control study, nested within the E3N prospective cohort, that involves 98,995 French female volunteers, adherent to a health insurance plan (MGEN) and regularly followed-up since 1990 by self-administered questionnaires. Between 1990 and 2008, we identified 5,455 invasive BC cases that were matched to randomly selected controls on age, department of residence, menopausal status and existence of biological sample. For the present analysis, the study population was restricted to 525 cases and 952 controls living in the Rhône-Alpes region at baseline.
Methods: Assessment of environmental dioxin exposure was based on a detailed inventory of dioxin emitting sources and residential history of study subjects. For each participant, exposure was evaluated using a GIS (geographic information system)-based environmental dioxin exposure index including proximity to and technical characteristics of dioxin emitting sources, exposure duration, and wind direction and speed. First, BC risk associated with the environmental dioxin exposure index will be estimated with conditional logistic regression models. Second, using B-spline functions, we will analyze the relative weight of the exposure dose with respect to time and its association with BC risk. Models will be adjusted for BC risk factors and, when relevant, further adjusted for the number of years living in an urban area from birth to baseline.
Results: Results will be presented and discussed at the IARC 50th Anniversary Conference.
Conclusions: Our study may provide new ways of considering temporal dimensions for environmental exposures in disease risk assessment.
Funding sources: Lyon-UCBL, CLARA, ADEME. E3N: MGEN, LNCC, IGR, Inserm.