Systems Metabolomics for Prediction of Metabolic Syndrome

We thank Yunfei Liu for his contribution concerning correlation network analyses. All metabolomics analyses were performed within the metaboHUB French infrastructure (ANR-INBS-0010). This study was supported by the INRA DID'IT Metaprogramme. The GAZEL cohort received approvals from the National Commission for Data Processing and Freedoms (CNIL), the National Medical Council and the National Consultative Committee of Ethics, and the INSERM IRB. The study protocol was approved by the GAZEL Scientific Committee. All volunteers gave written and informed consent for this study. All the experiments conformed to the principles set out in the WMA Declaration of Helsinki and the Department of Health and Human Services Belmont ReportThe 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 Met