Why we need it
Reasons to establish the EIRENE
The European health policy framework Health 2020 recognized that complex problems of chronic diseases and growing health inequalities as the most challenging health problems of the 21st century cannot be effectively solved without addressing the upstream determinants of health including environmental exposures. A sum of exposures from gestation to death has been recently described in the theoretical concept of the exposome as a complex of direct and indirect exposures mediated through the environmental and socioeconomic interactions and lifestyle choices.
Assessment of the exposome, however, requires the development of high throughput methods enabling quantification of biomarkers of exposure (through inhalation, ingestions and dermal contact) and characterization of on-going processes in human body (gut microbiome, infection, inflammation, oxidative stress) as markers of susceptibility, effect, or additional exposure. These methods are recently only being developed.
Therefore, we aim to establish a sustainable research infrastructure enabling the advancement of exposome research in Europe by bringing together complementary capacities available in the member states, harmonizing them and upgrading to address current scientific and societal challenges in the areas of chemical exposures and population health. We want to mediate an open access to the infrastructures supporting a world-class research expanding the scientific knowledge in the area of human exposome, supporting the development of new technologies and translation of the research results to the daily lives of citizens via public-private (industry, spin-offs) or public-public (policy-making) partnerships in order to tackle a problem of non- genetic factors behind the development of chronic conditions and to improve the population health.
In the short term, launching these methods in the high-throughput facilities will enable a new generation of large-scale studies focused on better understanding of the impact of various factors on the onset and development of diseases. Such infrastructural capacity will then be applied to screen the sets of well-designed and harmonized cohort studies to ensure adequate statistical power needed for health impact assessment of combined exposures. Better coordination of existing and future cohort studies including parent-child, adult, occupation, and ageing cohorts is needed to capture exposures throughout the life course. In the long term, harmonized sample and data collection methods would produce comparable data, enabling also meta-analyses and comparisons with different statistical approaches to improve our knowledge and understanding of disease aetiology and to help identifying factors affecting health.
The integration of the outcomes of the human health-relevant toxicological models and the state-of-the-art exposure models will further contribute to identification of the major drivers of toxicity. In vitro, ex vivo and in silico studies will help to identify causal exposure-response relationships and to better understand the underlying biological and biochemical processes. Translational human in vitro cell model systems (organs-on‐a‐chip) as well as cellular models will be used and combined with adverse outcome pathways (AOPs) modelling to explore connection between molecular initiation events, key events, and adverse outcomes. This will contribute to identification of early markers of disbalance and effect potentially applicable for the diagnosis of preclinical stages of selected diseases.
Improved data sharing and integration between various human and environmental surveys is another important impact on future infrastructural landscape enabling joint interpretation of data across sectors, disciplines and multiple stakeholders, and development of new prediction models, prevention, and intervention strategies. The outcomes of such efforts will be available to both the scientific community and policy makers to inform evidence-based decision taking for the adequate protection of human health and better healthcare. It should be noted that interpretation of high‐r esolution exposure and metabolic profiles will require advanced statistical and bioinformatics approaches. The objectives of such studies cannot be reached without development of computational tools that maximize information gain from the experimental work. The statistical pipelines will be established together with sufficient data storage, management, analysis, and interpretation capacities enabling FAIR data management and integrative analysis across multidimensional omics (exposomics, metabolomics, genomics, epigenomics, metagenomics, transcriptomics, proteomics) data.