Systems metabolomics for prediction of metabolic syndrome
Résumé
Human health is a continuum of transitions, involving complex processes at multiple levels, and there is an urgent need for integrative biomarkers to characteri2e and predict 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 GAZEL cohort (n=112 men: discovery study; n=94: replication/validation). Our integrative strategy was to combine untargeted metabolomics with clinical, sociodemographic, and food parameters to describe early phenotypes and build multidimensional predictive models. Baseline serum samples were analyzed using mass spectrometry-based untargeted metabolomics. Data mining methods were used to select the best candidate for prediction. Different models were built using linear logistic regressions and prediction performances were optimized either when reducing the metabolite number or when keeping the associated signature. Metabolomic data were then integrated with parameters from the database to determine whether multidimensional models improve prediction and impact subject stratification. A selected reduced metabolic profile was able to reveal subtle phenotypic differences 5 years before Mets occurrence. The metabolite only model showed better performances than the clinical one: lower misclassification rate (18% vs 26%) and higher area under the ROC curve (AUC=0.82 vs 0.74). Metabolomic data integration allowed optimizing prediction performances (10.8% misclassification, AUC=0.89). 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.