Multi-omics analyses are becoming a leading favorite for researchers interested in the biochemical characterization of large-scale epidemiological cohorts. While genome-wide association studies (GWAS) have been and continue to be applied for large cohorts, metabolite panels are attracting growing attention in population-based studies. Thanks to these approaches, epidemiological cohort studies at the scale of hundreds of thousands, or even millions of samples, are no longer an unsurmountable challenge.

This article classifies different metabolomics approaches using the FAIR principles for scientific data (i.e.findability, accessibility, interoperability and reusability), with a spotlight on data quality. Additionally, we discuss the scientific impact of a solution to scale analysis quickly and reliably.

You can learn

Why metabolomics is an important dimension in todays multi-omics cocktail.

How standardization of metabolomics shapes the comparability of results between different population-based studies and thereby creates long-lasting insights.

How metabolomics can become a feasible option for larger scale population-based studies without taking years to run the samples.