The Exposome-NL research programme drives research across three research lines. Each of the three research lines is further divided into research projects, covering specific components and research objectives.
We are updating and expanding Exposome Maps by integrating street-level imagery, social/digital environment descriptors, and exposome scans. By leveraging behavioural models and panel studies, we analyse complex human-environment interactions.
This line integrates high-dimensional exposome data with health cohorts to uncover prospective changes and upstream risk factors. We incorporate polygenic risk scores and gene-environment interactions into causal inference models. To ensure unbiased results from these complex datasets, we deploy AI-assisted knowledge tools for robust interpretation.
We are scaling the complexity of our Agent-Based Models and linking them this to Social-Cost-Benefit-Analyses. Our multi-omics research tests whether nutritional or pharmaceutical interventions can mitigate adverse effects at the cellular level. Finally, we evaluate real-world 'natural interventions' within municipalities and existing datasets.
We advance parallel ethical research and build participatory frameworks to maximize societal impact. Statistically, we pioneer transparent causal discovery, multi-layered data analytics, and meta-analytical tools for multi-cohort machine learning, supported by AI-driven knowledge graphs for data synthesis.
The environment we live in has a dominant impact on our health. It explains an estimated seventy percent of the chronic disease burden. Where we live, what we eat, how much we exercise, the air we breathe and whom we associate with; all of these environmental factors play a role. The combination of these factors over the life course is called the exposome. There is general (scientific) consensus that understanding more about the exposome will help explain the current burden of disease and that it provides entry points for prevention and ...
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