HEALS Health and Environment-wide Associations based on Large population Surveys

 
HEALS is organized in a series of interlinked streams of activity focusing on the different aspects of individual assessment of exposure to conventional 
and emerging environmental stressors and on the prediction of the associated health outcomes. These streams bring together state-of-the-art advances in 
human biomonitoring and systems biology towards the development of an exposure biology paradigm, exposure monitoring technologies and advanced tools for 
computational analyses of the exposure-to-effect continuum. In fact, HEALS proposes the functional integration of -omics derived data and biochemical 
biomonitoring to create the internal exposome at the individual level. These data will be exploited using advanced bioinformatics tools for both descriptive 
and predictive data mining. HEALS will propose a novel bioinformatics strategy focusing on biomarker fusion, and direct coupling of physiology-based biokinetic 
models to metabolic regulatory networks derived from -omics analyses. In this way, the internal dose of environmental stressors will be coupled to the alterations 
they bring about to gene expression, protein-protein interactions and metabolic regulation and plausible hypotheses on the respective pathways of toxicity 
can be established.  
  
The external exposome will be derived using environmental, occupational and dietary data and model fusion using efficient algorithms for mining existing 
environmental monitoring datasets and ubiquitous sensing using geo-localized sensors and mobile phones. A key innovation here is the development and optimization 
of the necessary software apps for data integration and the coupling of these datasets with agent-based modeling incorporating the socio-economic determinants 
of population exposure to health stressors. Exposomic analysis will focus on critical stages in human life extrapolating to the whole lifespan using Bayesian 
statistical modeling to construct the integrated individual exposome.  
  
The internal and external exposome data above will be used to derive environment-wide associations between exposure and health. Novel mathematical and 
computational tools will be used to explore the association between different environmental, genetic and epigenetic determinants and identified biological 
perturbations and, eventually, disease phenotypes. In addition, using the HEALS methodology, a plausible pathway towards establishing causality in the 
observed associations between environmental stressors and health status will be tread (see figure 1.1).  
  
The HEALS approach and tools will be put to test through their application in a number of population studies (incl. twins studies) across different exposure 
settings tackling key health endpoints of the SCALE initiative and the Parma Declaration for both children and the elderly. The overall population size involved 
in these studies is up to ca. 335,000 individuals covering different age, gender and socio-economic status groups. The cohorts involved are dispersed across 
Europe to provide sufficient geographic coverage so as to facilitate drawing conclusions at the EU-wide scale. These cohorts have been carefully selected 
to comprise twins. The classical twin methods combined with novel technologies represent a powerful approach towards identifying and understanding the 
mechanisms and pathways that underlie complex traits. In this way, epigenetic modification of both exposure to and effect of co-exposure to chemical, physical 
and biological environmental stressors throughout a person’s lifetime will be easier to take into account when deducing environmental exposure and health 
associations.