This project develops, evaluates, and applies methods to study the effect of time-varying environmental risk and exposure mixtures on health throughout the life span.
Using novel machine learning and artificial intelligence techniques, we will analyse longitudinal data from multiple data sources that vary in scope and size: 1) The large-scale Dutch administrative data (CBS) which provides complete nationwide residential histories, 2) deeply phenotyped and omics-enriched cohorts such as Lifelines and the UK Biobank, and 3) high-resolution individual measurements from the Exposome-NL panel study, including glucose and blood pressure sensors. The integration of these data sources will provide a lens through which we can precisely assess the impact of the environment on health.
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 ...
Read More
