Input of multidimensional phenotyping in the metabolic syndrome stratification

Metabolic syndrome (MetS) is defined by a cluster of cardio-metabolic factors including obesity, hypertension, dysglycemia, and dyslipidemia. It affects a growing number of persons, in particular older adults often suffering from multiple chronic diseases, and its prevalence is now a public health challenge. 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.A nested case-control study on MetS was designed within the Quebec Longitudinal Study on Nutrition and Successful Aging (NuAge). It includes 61 cases and 62 controls of similar age (68-82 y.o.), selected among the 853 men. Both targeted and untargeted metabolomic/lipidomic approaches, available within the MetaboHUB French infrastructure [1], will be performed on serum samples collected at recruitment 2003-2005 (T1) and three years later (T4). Data analysis will be performed using reproducible online Galaxy workflows [2].The metabolomic/lipidomic data will be processed to identify specific signatures of MetS and its components, and study their stability over time. Then, these data will be analysed for evaluation of a molecular reclassification of the MetS phenotype. Finally, they will be integrated with phenotypic and detailed nutritional data available to better characterize sub-phenotypes.The approach developed here will open a door for a more comprehensive understanding of the metabolic phenotype resulting from the complex interplay between intrinsic and extrinsic factors. Thus, this project will allow an improved description of MetS associated characteristics and will offer new tools for better patient stratification in elderly populations