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Theory and methods
A multivariate approach to investigate the combined biological effects of multiple exposures
  1. Pooja Jain1,
  2. Paolo Vineis1,2,
  3. Benoît Liquet3,4,
  4. Jelle Vlaanderen5,
  5. Barbara Bodinier1,
  6. Karin van Veldhoven1,
  7. Manolis Kogevinas6,7,8,9,
  8. Toby J Athersuch1,10,
  9. Laia Font-Ribera6,7,8,9,
  10. Cristina M Villanueva6,7,8,9,
  11. Roel Vermeulen1,5,
  12. Marc Chadeau-Hyam1,5
  1. 1 Department of Epidemiology and Biostatistics, School of Public Health, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
  2. 2 Molecular and Genetic Epidemiology Unit, Italian Institute for Genomic Medicine (IIGM), Turin, Italy
  3. 3 UMR CNRS 5142, Laboratoire de Mathématiques et de leurs Applications, Université de Pau et des Pays de l’Adour, Anglet, France
  4. 4 School of Mathematics, ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia
  5. 5 Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, Netherlands
  6. 6 ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
  7. 7 Universitat Pompeu Fabra (UPF), Barcelona, Spain
  8. 8 CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
  9. 9 IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
  10. 10 Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
  1. Correspondence to Dr Marc Chadeau-Hyam, MRC-PHE Centre for Environment and Health, Imperial College London, London W2 1PG, UK; m.chadeau{at}imperial.ac.uk

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.

  • exposome
  • multiple exposures
  • multivariate response
  • OMICs data
  • multi-level sparse PLS models

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/

https://doi.org/10.1136/jech-2017-210061

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    Footnotes

    • Contributors PJ, PV, RV and MC-H developed the idea of the study and drafted the manuscript. PJ and BB ran the analyses under the guidance of MC-H and BL. JV, KvV, MK, TJA, LF-R and CMV provided the data, pre-processed them and contributed to the results evaluation and interpretation. All authors contributed to the writing of the paper and have approved this final version.

    • Funding 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).

    • Competing interests None declared.

    • Patient consent Detail has been removed from this case description/these case descriptions to ensure anonymity. The editors and reviewers have seen the detailed information available and are satisfied that the information backs up the case the authors are making.

    • Ethics approval The study was approved by the ethics committee of the research centre following the international regulations, and all volunteers signed an informed consent before participation.

    • Provenance and peer review Commissioned; externally peer reviewed.