LongITools is a large EU project (longitools.org) to study the interactions between the environment, lifestyle and health in determining people’s risks of developing chronic cardiovascular and metabolic diseases. Several of the work packages entail the generation, processing and analysis of molecular Omics data from large-scale European cohorts. More specifically in this SNIC-SENS project we will perform i) pre-processing of raw instrument metabolomics data from three European early-life cohorts (Generation R, EDEN and PANIC, n=5000 samples) into actionable tabularized format, ii) machine learning-based identification of metabolomics profiles associated with environmental exposures in early life and iii) perform computer-assisted identification of key metabolite features of interest selected from those metabolomic profiles that associate with exposures. We will use in-house algorithms and workflows that we have previously used to generate data from similarly sized cohorts in the SIMPLER national research infrastructure (simpler4health.se) using the SNIC-SENS facilities.