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

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Title: A multivariate approach to investigate the combined biological effects of multiple exposures
Authors: Chadeau, M
Jain, P
Vineis, P
Liquet, B
Vlaanderen, J
Bodinier, B
Van Veldhoven, C
Kogevinas, M
Athersuch, TJ
Font-Ribera, L
Villanueva, C
Vermeulen, R
Item Type: Journal Article
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 multi-level extension of the 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.
Issue Date: 1-Jul-2018
Date of Acceptance: 19-Feb-2018
URI: http://hdl.handle.net/10044/1/57297
DOI: https://dx.doi.org/10.1136/jech-2017-210061
ISSN: 0143-005X
Publisher: BMJ Publishing Group
Start Page: 564
End Page: 571
Journal / Book Title: Journal of Epidemiology and Community Health
Volume: 72
Copyright Statement: © 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/
Sponsor/Funder: Commission of the European Communities
Cancer Research UK
Funder's Grant Number: 308610
‘Mechanomics’ PRC project grant 22184
Keywords: OMICs data
exposome
multi-level sparse PLS models
multiple exposures
multivariate response
1117 Public Health And Health Services
1604 Human Geography
Epidemiology
Publication Status: Published
Online Publication Date: 2018-03-21
Appears in Collections:Division of Surgery
Faculty of Medicine
Epidemiology, Public Health and Primary Care



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