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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/121301
A multivariate approach to investigate the combined biological effects of multiple exposures
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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.
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JAIN, Pooja, et al. A multivariate approach to investigate the combined biological
effects of multiple exposures. Journal of Epidemiology and Community Health. 2018. ISSN 0143-005X. [consulted: 17 of August of 2026]. Available at: https://hdl.handle.net/2445/121301