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      Chronic Low-Dose Exposure to Xenoestrogen Ambient Air Pollutants and Breast Cancer Risk: XENAIR Protocol for a Case-Control Study Nested Within the French E3N Cohort

      research-article
      , PhD 1 , 2 , , PhD 1 , 3 , , PhD 1 , 2 , , PhD 3 , , PhD 4 , , PhD 4 , , PhD 5 , , PhD 1 , , PhD 1 , , PhD 1 , 6 , , PhD 7 , , PhD 8 , , PhD 8 , , PhD 8 , , MPH 1 , , PhD 5 , , PhD 9 , , PhD 5 , , MD, PhD 1 , 2 ,
      (Reviewer), (Reviewer)
      JMIR Research Protocols
      JMIR Publications
      breast cancer, hormone receptor status, air pollution, endocrine disruptors, multipollutant, geographic information system, land use regression, chemistry-transport model, epigenetic, gene-environment interaction, prospective study

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          Abstract

          Background

          Breast cancer is the most frequent cancer in women in industrialized countries. Lifestyle and environmental factors, particularly endocrine-disrupting pollutants, have been suggested to play a role in breast cancer risk. Current epidemiological studies, although not fully consistent, suggest a positive association of breast cancer risk with exposure to several International Agency for Research on Cancer Group 1 air-pollutant carcinogens, such as particulate matter, polychlorinated biphenyls (PCB), dioxins, Benzo[a]pyrene (BaP), and cadmium. However, epidemiological studies remain scarce and inconsistent. It has been proposed that the menopausal status could modify the relationship between pollutants and breast cancer and that the association varies with hormone receptor status.

          Objective

          The XENAIR project will investigate the association of breast cancer risk (overall and by hormone receptor status) with chronic exposure to selected air pollutants, including particulate matter, nitrogen dioxide (NO2), ozone (O3), BaP, dioxins, PCB-153, and cadmium.

          Methods

          Our research is based on a case-control study nested within the French national E3N cohort of 5222 invasive breast cancer cases identified during follow-up from 1990 to 2011, and 5222 matched controls. A questionnaire was sent to all participants to collect their lifetime residential addresses and information on indoor pollution. We will assess these exposures using complementary models of land-use regression, atmospheric dispersion, and regional chemistry-transport (CHIMERE) models, via a Geographic Information System. Associations with breast cancer risk will be modeled using conditional logistic regression models. We will also study the impact of exposure on DNA methylation and interactions with genetic polymorphisms. Appropriate statistical methods, including Bayesian modeling, principal component analysis, and cluster analysis, will be used to assess the impact of multipollutant exposure. The fraction of breast cancer cases attributable to air pollution will be estimated.

          Results

          The XENAIR project will contribute to current knowledge on the health effects of air pollution and identify and understand environmental modifiable risk factors related to breast cancer risk.

          Conclusions

          The results will provide relevant evidence to governments and policy-makers to improve effective public health prevention strategies on air pollution. The XENAIR dataset can be used in future efforts to study the effects of exposure to air pollution associated with other chronic conditions.

          International Registered Report Identifier (IRRID)

          DERR1-10.2196/15167

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          Most cited references80

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          Complementing the genome with an "exposome": the outstanding challenge of environmental exposure measurement in molecular epidemiology.

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            Dose-response analyses using restricted cubic spline functions in public health research.

            Taking into account a continuous exposure in regression models by using categorization, when non-linear dose-response associations are expected, have been widely criticized. As one alternative, restricted cubic spline (RCS) functions are powerful tools (i) to characterize a dose-response association between a continuous exposure and an outcome, (ii) to visually and/or statistically check the assumption of linearity of the association, and (iii) to minimize residual confounding when adjusting for a continuous exposure. Because their implementation with SAS® software is limited, we developed and present here an SAS macro that (i) creates an RCS function of continuous exposures, (ii) displays graphs showing the dose-response association with 95 per cent confidence interval between one main continuous exposure and an outcome when performing linear, logistic, or Cox models, as well as linear and logistic-generalized estimating equations, and (iii) provides statistical tests for overall and non-linear associations. We illustrate the SAS macro using the third National Health and Nutrition Examination Survey data to investigate adjusted dose-response associations (with different models) between calcium intake and bone mineral density (linear regression), folate intake and hyperhomocysteinemia (logistic regression), and serum high-density lipoprotein cholesterol and cardiovascular mortality (Cox model). 2010 John Wiley & Sons, Ltd.
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              Global patterns of cancer incidence and mortality rates and trends.

              While incidence and mortality rates for most cancers (including lung, colorectum, female breast, and prostate) are decreasing in the United States and many other western countries, they are increasing in several less developed and economically transitioning countries because of adoption of unhealthy western lifestyles such as smoking and physical inactivity and consumption of calorie-dense food. Indeed, the rates for lung and colon cancers in a few of these countries have already surpassed those in the United States and other western countries. Most developing countries also continue to be disproportionately affected by cancers related to infectious agents, such as cervix, liver, and stomach cancers. The proportion of new cancer cases diagnosed in less developed countries is projected to increase from about 56% of the world total in 2008 to more than 60% in 2030 because of the increasing trends in cancer rates and expected increases in life expectancy and growth of the population. In this review, we describe these changing global incidence and mortality patterns for select common cancers and the opportunities for cancer prevention in developing countries. (c)2010 AACR.
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                Author and article information

                Contributors
                Journal
                JMIR Res Protoc
                ResProt
                JMIR Research Protocols
                JMIR Publications (Toronto, Canada )
                1929-0748
                September 2020
                15 September 2020
                : 9
                : 9
                : e15167
                Affiliations
                [1 ] Department of Prevention Cancer Environment Centre Léon Bérard Lyon France
                [2 ] Inserm UA 08 Radiations: Défense, Santé, Environnement Lyon France
                [3 ] Ecole Centrale de Lyon INSA Université Claude Bernard Lyon 1 Ecully France
                [4 ] ISPED, Inserm U1219 Bordeaux Population Health Center Université de Bordeaux Bordeaux France
                [5 ] Centre de Recherche en Epidémiologie et Santé des Populations (CESP, Inserm U1018) Faculté de Médecine Université Paris-Saclay Villejuif France
                [6 ] Division of Cancer Epidemiology German Cancer Research Center (DKFZ) Heidelberg Germany
                [7 ] Univ Lyon Centre Léon Bérard GATE L-SE UMR 5824 Lyon France
                [8 ] National Institute for industrial Environment and Risks (INERIS) Verneuil-en-Halatte France
                [9 ] Centre for Environmental Health and Sustainability School of Geography, Geology and the Environment University of Leicester Leicester United Kingdom
                Author notes
                Corresponding Author: Béatrice Fervers beatrice.fervers@ 123456lyon.unicancer.fr
                Author information
                https://orcid.org/0000-0001-6662-2089
                https://orcid.org/0000-0003-0339-3084
                https://orcid.org/0000-0002-0857-7889
                https://orcid.org/0000-0001-5987-9839
                https://orcid.org/0000-0002-1269-8069
                https://orcid.org/0000-0001-6741-2518
                https://orcid.org/0000-0002-5956-5693
                https://orcid.org/0000-0002-6958-0109
                https://orcid.org/0000-0001-9877-6058
                https://orcid.org/0000-0002-5291-7545
                https://orcid.org/0000-0003-4487-8723
                https://orcid.org/0000-0003-4485-3489
                https://orcid.org/0000-0003-2062-4681
                https://orcid.org/0000-0001-8939-9369
                https://orcid.org/0000-0003-3321-6679
                https://orcid.org/0000-0003-2297-3869
                https://orcid.org/0000-0003-3423-2013
                https://orcid.org/0000-0001-7157-419X
                https://orcid.org/0000-0003-3498-6499
                Article
                v9i9e15167
                10.2196/15167
                7525465
                32930673
                f578a768-42e7-4016-85ef-92d42ce30f7d
                ©Amina Amadou, Thomas Coudon, Delphine Praud, Pietro Salizzoni, Karen Leffondre, Emilie Lévêque, Marie-Christine Boutron-Ruault, Aurélie M N Danjou, Xavier Morelli, Charlotte Le Cornet, Lionel Perrier, Florian Couvidat, Bertrand Bessagnet, Julien Caudeville, Elodie Faure, Francesca Romana Mancini, John Gulliver, Gianluca Severi, Béatrice Fervers. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 15.09.2020.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.

                History
                : 25 June 2019
                : 6 November 2019
                : 14 January 2020
                : 22 January 2020
                Categories
                Protocol
                Protocol

                breast cancer,hormone receptor status,air pollution,endocrine disruptors,multipollutant,geographic information system,land use regression,chemistry-transport model,epigenetic,gene-environment interaction,prospective study

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