From Molecular Profiling to Precision Medicine in Metabolic Syndrome

Session: From Molecular Profiling to Precision Medicine in Metabolic SyndromeMetabolic syndrome (MetS), defined as a cluster of cardio-metabolic factors including obesity, hypertension, dysglycemia, and dyslipidemia, and mostly affecting older adults, is now a public health challenge because of its growing prevalence. In the context of personalized medicine/nutrition, new tools are necessary to bring additional knowledge about MetS etiology, better stratify populations and customise strategies for prevention. The objective of this study was to investigate the integration of data from complementary untargeted metabolomics platforms (HRMS, RMN) and technologies to characterize the MetS phenotypic spectrum. A case-control study was designed within the Quebec NuAge cohort1. Six complementary untargeted metabolomic/lipidomic approaches, available within the MetaboHUB infrastructure2, were performed on serum samples collected at recruitment and 3 years later. Standard operating procedures were designed to guaranty the inter-laboratory standardisationfrom sample preparation to data processing. Data analyses were performed using reproducible online Galaxy workflows3. A full feature selection strategy was developed to build a comprehensive molecular MetS signature, stable over time. Consistent cross-sectional and longitudinal data were observed with a wide range of metabolites (lipids, carbohydrates, amino-acids, peptides…) reflecting subject stability regarding MetS, and providingnew insights about underlying mechanisms. Correlation network analysis contributed to explore the links between the molecular signature and clinical parameters. Additionally, an optimized reduced signature was proposed, allowing good prediction performances (12% misclassification, AUC=0.96, CI:[0.94-0.98]), for future clinical application. These results demonstrated the interest of a multidimensional molecular phenotyping aspart of the next generation of medicine tools in the frame of non-communicable diseases