The Exposome in Epidemiological Practice

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• Prof. Paolo Vineis - School of Public Health, Imperial College London
• Wednesday 28 February 2018, 16:15-17:15
• Lecture Theatre 1, Computer Laboratory.

If you have a question about this talk, please contact David Greaves.

Sadly today's seminar shall not take place owing to snowy travel conditions.

There are two broad interpretations of the exposome concept and they are complementary. One, called “top-down”, is mainly interested in identifying new causes of disease by an agnostic approach based on omic technologies, similar to what has been applied in genetics with the GWAS design. This first approach is sometimes called “EWAS”, or “exposome-wide association study”, and utilizes tools such as metabolomics or adductomics to generate new hypotheses on disease etiology. The second general approach is called “bottom-up” and starts with a set of exposures or environmental compartments to determine the pathways or networks by which such exposures lead to disease, i.e. which pathways/networks are perturbed. We have used the latter approach in the EXP OsOMICS investigation (Vineis et al, 2016) as we explain below.

In this project we have selected a few priorities for research, with relevant practical implications for policy making and stakeholders: can we consolidate our knowledge on the health effects of two important exposures, air pollution and water contaminants, reinforcing causal assessment? Can we detect variation in exposures in a finer way than with the usual tools of epidemiology? Can we detect the effects of low and very low levels of exposure using omic biomarkers? How can we exploit omic measurements to study mixtures? Can we use improved exposure assessment to calibrate estimates of risk and burden of disease? Finally, methodological aims include the validation of a set of five omics measured in the same subjects (for a total number of more than 2,000), and the development of statistical tools to allow the analysis of very complex datasets. A statistical “toolkit” has been developed.

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