Approved Research

A phenome-wide association analysis and Mendelian Randomisation analysis on metabolic features detected by untargetted metabolomics

Lay summary

Advanced metabolomics methods have now made it possible to measure thousands of small molecules in body specimen including blood. These molecules are important since they could inform us regarding human health and could in some instances be used as target for prevention or treatment of diseases. Although many studies have investigated the relation of the blood levels of these molecules with diseases, its not clear whether the relation is causal. A third factor might be driving the relation or the blood levels of the metabolite might have been affected by the early stages of the disease. Assessment of the causal effect is normally done in a trial setting where participants receive - in random - medications of other interventions that  affect the blood levels of the metabolite. However, specific interventions for these molecules are not known and in many instances such studies are not ethical. An alternative approach is to use genetic information (so called Mendelian Randomisation approach). In this approach, genetic variants that are related to blood levels of the metabolite are studied for their relation with clinical traits and disorders. Given that genetic variants are inherited in random, any differences in the comparison group should be due to the causal effect of the metabilte.

In another project, we have identified genetic variants for a wide range of metabolies and are interested to test their associations with traits and diseases. In this project, we wil use data from UK bio bank to study the association of the genetic variants with a wide range of clinical traits and diseases. This will unravel the clinical importance of the studied metabolites in an agnostic approach and the results could be used either to build or improve risk prediction models or provide novel drug target candidates.