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

# metadata.dc.description.abstract[].value
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.

# metadata.dc.relation.isformatof[].value
Reproducció del document publicat a:
                http://dx.doi.org/10.1136/jech-2017-210061

# metadata.dc.relation.ispartof[].value
Journal of Epidemiology and Community Health, 2018

# metadata.dc.source[].value
Articles publicats en revistes (ISGlobal)

# metadata.dc.title[].value
A multivariate approach to investigate the combined biological
                effects of multiple exposures