Imperial College London

Dr Karin van Veldhoven

Faculty of MedicineSchool of Public Health

Honorary Research Associate
 
 
 
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Contact

 

+44 (0)20 7594 8464k.van-veldhoven

 
 
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Location

 

512Medical SchoolSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Chadeau:2018:10.1136/jech-2017-210061,
author = {Chadeau, M and Jain, P and Vineis, P and Liquet, B and Vlaanderen, J and Bodinier, B and van, Veldhoven C and Kogevinas, M and Athersuch, TJ and Font-Ribera, L and Villanueva, C and Vermeulen, R},
doi = {10.1136/jech-2017-210061},
journal = {Journal of Epidemiology and Community Health},
pages = {564--571},
title = {A multivariate approach to investigate the combined biological effects of multiple exposures},
url = {http://dx.doi.org/10.1136/jech-2017-210061},
volume = {72},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - 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.
AU - Chadeau,M
AU - Jain,P
AU - Vineis,P
AU - Liquet,B
AU - Vlaanderen,J
AU - Bodinier,B
AU - van,Veldhoven C
AU - Kogevinas,M
AU - Athersuch,TJ
AU - Font-Ribera,L
AU - Villanueva,C
AU - Vermeulen,R
DO - 10.1136/jech-2017-210061
EP - 571
PY - 2018///
SN - 0143-005X
SP - 564
TI - A multivariate approach to investigate the combined biological effects of multiple exposures
T2 - Journal of Epidemiology and Community Health
UR - http://dx.doi.org/10.1136/jech-2017-210061
UR - http://hdl.handle.net/10044/1/57297
VL - 72
ER -