Systems Metabolomics for Prediction of Metabolic Syndrome

The evolution of human health is a continuum of transitions, involving multifaceted processes at multiple levels, and there is an urgent need for integrative biomarkers that can characterize and predict progression toward disease development. The objective of this work was to perform a systems metabolomics approach to predict metabolic syndrome (MetS) development. A case-control design was used within the French occupational GAZEL cohort (n = 112 males: discovery study; n = 94: replication/validation study). Our integrative strategy was to combine untargeted metabolomics with clinical, sociodemographic, and food habit parameters to describe early phenotypes and build multidimensional predictive models. Different models were built from the discriminant variables, and prediction performances were optimized either when reducing the number of metabolites used or when keeping the associated signature. We illustrated that a selected reduced metabolic profile was able to reveal subtle phenotypic differences 5 years before MetS occurrence. Moreover, resulting metabolomic markers, when combined with clinical characteristics, allowed improving the disease development prediction. The validation study showed that this predictive performance was specific to the MetS component. This work also demonstrates the interest of such an approach to discover subphenotypes that will need further characterization to be able to shift to molecular reclassification and targeting of MetS.