The exposome represents the totality of exposures from conception onwards, simultaneously identifying, characterizing and quantifying the exogenous and endogenous exposures and modifiable risk factors that predispose to and predict diseases throughout a person’s life span. Unraveling the exposome implies that both
environmental exposures and genetic variation are reliably measured simultaneously.
HEALS (Health and Environment-wide Associations based on Large population Surveys) brings together a comprehensive array of novel technologies, data analysis and modeling tools that support efficient design and execution of exposome studies. The HEALS approach brings together and organizes environmental, socio-economic, exposure, biomarker and health effect data; in addition, it includes all the procedures and computational sequences necessary for applying advanced bioinformatics coupling advanced data mining, biological and exposure modeling so as to ensure that environmental exposure-health associations are studied comprehensively. The overall approach is being verified in a series of population studies across Europe, tackling various levels of environmental exposure, age windows and gender differentiation of exposure, and socio-economic and genetic variability.
The main objective of HEALS is the refinement of an integrated methodology and the application of the corresponding analytical and computational tools for performing environment-wide association studies in support of EU-wide environment and health assessments. The HEALS approach is being refined on the basis of pre-existing population data and then it is going to be applied in a pilot environment and health examination survey covering eighteen EU Member States. The lessons learned will be translated into scientific advice towards the development of protocols and guidelines for the setting up of a European environment and health examination survey.
Exposome studies is requiring novel tools to address the complexity of emerging environmental health issues. Critical for success are the ability to bring together existing geospatial, environmental, health and socioeconomic data, and to collect new high resolution data using innovative environmental micro-sensors, remote sensing or other community and omics/systems biology based approaches approaches to describe the exposome for e.g. endocrine disruption-related syndromes and sex-related changes (menopause), neurodegenerative or respiratory diseases.
Focus is being implemented on susceptibility windows during growth (including pregnancy) and development, and on the unequal distribution of the burden of epigenetically active food and environment-related disease to vulnerable populations such as the young, elderly, socio-economic disadvantaged, gender and ethnic minorities. Mapping the entire lifecycle of an individual may not be necessary if critical lifetime events where an individual’s geospatial lifeline crosses a noteworthy environmental event 2 are recognized and understood. Hence, the most relevant exposure episodes in an individual’s life can be reconstructed and linked to socio-economic conditions at critical life stages such as prenatal exposure, puberty, or the reproductively active period.
Whereas exposure during all life stages may entail adverse effects, children, pregnant women and the elderly are particularly susceptible; thus these population sub-groups are the focus of this project. Modelling the mobility patterns of the population at risk at the individual level is challenging. There are considerable conceptual and computational difficulties involved in intersecting data on the distributions of pollutants, and/or the patterns of movements of recipient individuals or groups, reflecting the limitations of available data on environmental conditions and human distributions. With the advent of geographic information systems (GIS), GPS to track individuals, and personal environmental monitoring, undertaking such analyses throughout an individual’s lifetime is now possible.