A multivariate approach to investigate the combined biological effects of multiple exposures

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• dc.contributor.author Jain, Pooja
• dc.contributor.author Vineis, Paolo
• dc.contributor.author Liquet, Benoît
• dc.contributor.author Vlaanderen, Jelle
• dc.contributor.author Bodinier, Barbara
• dc.contributor.author van Veldhoven, Karin
• dc.contributor.author Kogevinas, Manolis
• dc.contributor.author Athersuch, Toby J.
• dc.contributor.author Font-Ribera, Laia
• dc.contributor.author Villanueva Belmonte, Cristina
• dc.contributor.author Vermeulen, Roel
• dc.contributor.author Chadeau-Hyam, Marc
• dc.date.accessioned 2018-11-26T08:28:23Z
• dc.date.available 2018-11-26T08:28:23Z
• dc.date.issued 2018
• dc.description.abstract Epidemiological studies provide evidence that environmental exposures may affect health through complex mixtures. Formal investigation of the effect of exposure mixtures is usually achieved by modelling interactions, which relies on strong assumptions relating to the identity and the number of the exposures involved in such interactions, and on the order and parametric form of these interactions. These hypotheses become difficult to formulate and justify in an exposome context, where influential exposures are numerous and heterogeneous. To capture both the complexity of the exposome and its possibly pleiotropic effects, models handling multivariate predictors and responses, such as partial least squares (PLS) algorithms, can prove useful. As an illustrative example, we applied PLS models to data from a study investigating the inflammatory response (blood concentration of 13 immune markers) to the exposure to four disinfection by-products (one brominated and three chlorinated compounds), while swimming in a pool. To accommodate the multiple observations per participant (n=60; before and after the swim), we adopted a multilevel extension of PLS algorithms, including sparse PLS models shrinking loadings coefficients of unimportant predictors (exposures) and/or responses (protein levels). Despite the strong correlation among co-occurring exposures, our approach identified a subset of exposures (n=3/4) affecting the exhaled levels of 8 (out of 13) immune markers. PLS algorithms can easily scale to high-dimensional exposures and responses, and prove useful for exposome research to identify sparse sets of exposures jointly affecting a set of (selected) biological markers. Our descriptive work may guide these extensions for higher dimensional data.
• dc.description.sponsorship This work was carried out within the EXPOsOMICS Project, which was funded by the European Commission (grant agreement 308610-FP7 European Commission, to PV). The Centre for Environment and Health is supported by the Medical Research Council and Public Health England (MR/L01341X/1). MC-H acknowledges support from Cancer Research UK, Population Research Committee Project grant ‘Mechanomics’ (project 22184)
• dc.format.mimetype application/pdf
• dc.identifier.citation Jain P, Vineis P, Liquet B, Vlaanderen J, Bodinier B, van Veldhoven K et al. A multivariate approach to investigate the combined biological effects of multiple exposures. J Epidemiol Community Health. 2018 Jul;72(7):564-71. DOI: 10.1136/jech-2017-210061
• dc.identifier.doi http://dx.doi.org/10.1136/jech-2017-210061
• dc.identifier.issn 0143-005X
• dc.identifier.uri http://hdl.handle.net/10230/35848
• dc.language.iso eng
• dc.publisher BMJ Publishing Group
• dc.relation.ispartof Journal of Epidemiology and Community Health. 2018 Jul;72(7):564-71
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/308610
• dc.rights Copyright © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted. This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword OMICs data
• dc.subject.keyword Exposome
• dc.subject.keyword Multi-level sparse PLS models
• dc.subject.keyword Multiple exposures
• dc.subject.keyword Multivariate response
• dc.subject.other Medi ambient
• dc.subject.other Epidemiologia
• dc.title A multivariate approach to investigate the combined biological effects of multiple exposures
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion