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Context: Ageing is a dynamic process depending on intrinsic and extrinsic factors and its evolution is a continuum of transitions, involving multifaceted processes at multiple levels. It is recognized that frailty and sarcopenia are shared by the major age-related diseases thus contributing to elderly morbidity and mortality. They are major health issues in aging populations, given their high prevalence and association with several adverse outcomes. Pre-frailty is still not well understood but it has been associated with changes in several physiological systems, including inflammation as well as changes in the balance of micronutrients and vitamins. Due to the complex phenotypes and underlying pathophysiology, the need for robust and multidimensional biomarkers is now essential to move towards more personalized care and prevention. Objective: The objective of the present study was to better characterize the complexity of pre-frailty phenotype using untargeted metabolomics in order to identify specific biomarkers, and study their stability over time. Research design and methods: The approach was based on the NU-AGE project (FP7 EU programme; clinicaltrials.gov, NCT01754012) that regrouped 1,250 free-living elderly people (65-79 y.o., men and women), free of major diseases, recruited within five European centres. Half of the volunteers were randomly assigned to an intervention group (1-year Mediterranean type diet). Presence of frailty was assessed by the criteria proposed by Fried et al (Fried et al. J Gerontol A Biol Sci Med Sci, 2001). In this study, a sub-cohort consisting in 212 subjects (pre-frail and non-frail) from the Italian and Polish centres were selected for mass spectrometry-based untargeted metabolomics. Metabolic profiles were determined from serum samples at T0 (baseline) and T1 (follow-up). All data were processed under the Galaxy web-based platform Worflow4metabolomics, guaranteeing their reproducibility (Giacomoni et al. Bioinformatics, 2015). Univariate statistical analyses were performed to identify discriminant metabolites regarding pre-frailty status. Predictive models were then built using linear logistic regression and ROC curve analyses were used to evaluate multivariate biomarkers. Results: Presence of sub-phenotypes of pre-frailty both at the gender level and depending on the pre-frailty progression and reversibility were revealed by untargeted metabolomics. Additionally, early markers, able to predict the evolution towards pre-frailty within one year, were identified for both genders. Moreover, some of these early biomarkers were found to be still relevant for classification of a ‘light pre-frail’ phenotype after its clinical appearance. Conclusion: These results open the door, through multivariate strategies, to the possibility of monitoring the disease progression over time at a very early stage. Longitudinal analysis of individual time trajectories to detect early deviations of health status would indeed contribute to a better disease prevention.

Introduction: Metabolic syndrome (MetS) is characterized by a cluster of risk factors including obesity, metabolic dysregulations such as insulin resistance, hypertension, and dyslipidemia, raising the risk for type 2 diabetes development and its complications. It involves multifaceted processes at multiple levels that are still far from being understood. New tools are therefore necessary to bring new knowledge about MetS, better stratify populations and customise strategies for its prevention and/or reversal. Methods: The Quebec Longitudinal Study on Nutrition and Successful Aging (NuAge) regroups 853 men and 940 women, aged 68–82 at recruitment in 2003–2005 (T1) and followed up annually for three years (T2-T4). In the present study, a nested case-control study on MetS was designed to identify a metabolomic/lipidomic signature of MetS in older men, reflecting its phenotypic spectrum. An optimized participant selection strategy was developed based on presence and number of MetS criteria, including medication, their stability over 3 years, as well as the identification of outliers. Results: The final selection included 123 men, 61 cases and 62 controls, with similar age and partial overlap of values defining MetS. This design is necessary to precisely detect and estimate the amplitude of metabolic deviations among the massive data sets, at an individual metabolite level as well as for a multivariate description. Conclusion: This selection process, optimized to limit cofounding effects, will allow identifying specific metabolomic/lipidomic signatures along with significant features for sample classification. Thus, one complex molecular phenotyping will provide a new approach/tool for a better MetS stratification in elderly

Introduction: Human ageing is a dynamic process depending on intrinsic and extrinsic factors and its evolution is a continuum of transitions, involving multifaceted processes at multiple levels. It is recognized that frailty and sarcopenia are shared by the major age-related diseases, thus contributing to elderly morbidity and mortality. They are major health issues in elderly populations, given their high prevalence and association with several adverse outcomes. Due to their complex phenotypes and underlying pathophysiology, the need for robust and multidimensional biomarkers is now essential to move towards a more personalized care and prevention. Methods: The NU-AGE project [1] regroups 1250 free-living elderly people (65–79 y.o., men and women), free of major diseases, recruited within 5 European centres. Twenty percent of the subjects were pre-frail as defined by the criteria proposed by Fried et al.[2]. Six hundred twenty five volunteers were randomly assigned to an intervention group (1-year Mediterranean diet). A sub-cohort consisting in first, 120 subjects, half pre-frail randomly selected from the Italian and Polish centres, and secondly, 92 subjects shifting their frailty status were included for untargeted serum metabolomics at T0 (recruitment) and T1 (after diet intervention). Results: Metabolomics enables to discriminate sub-phenotypes of pre-frailty both at the gender level and depending on the pre-frailty progression and reversibility. Additionally, early and/or predictive markers of pre-frailty were identified in both populations. Conclusion: These results open the door, through multivariate strategies, to a possibility of monitoring the disease progression over time and/or in response to interventions at a very early stage

Grâce à une vision intégrée des phénomènes post-génomes, les approches omiques devraient permettre de mieux comprendre l’ensemble des processus biologiques complexes ayant lieu au cours du développement des maladies métaboliques chroniques telles que le syndrome métabolique (SMet). Cet ensemble de désordres cardiovasculaires et métaboliques précède et prédit le diabète de type 2 (T2D) et la nutrition foetale est maintenant reconnue comme un facteur de risque important pour son développement et le T2D à l’âge adulte. Dans cette étude, notre objectif était d’identifier des biomarqueurs précoces et/ou prédictifs du SMet, associés ou non à l’empreinte foetale par une approche protéomique ciblée. Cette étude cas-témoins nichée au sein de la cohorte Haguenau utilise les données anthropométriques et sociales d’individus nés à la maternité d’Haguenau et identifiés avec ou non un petit poids de naissance ajusté pour l’âge gestationnel (petit poids=SGA, poids normal=AGA). Nous avons donc sélectionné des sujets SGA, sains à 20 ans (T1, n=46), ayant développé (SMet30, n=23) ou non (nonSMet30, n=23) un SMet à 30 ans (T2). Une analyse protéomique ciblée a été réalisée sur les échantillons biologiques prélevés à T1 en utilisant des biopuces ciblant des cytokines, chemokines, adipokines, facteurs de croissance, facteurs angiogéniques, protéases, récepteurs solubles, ou encore des molécules d’adhésion. Cette technique détecte simultanément 1000 protéines à partir d’une faible quantité de sérum. Les résultats ont été analysés par des méthodes statistiques uni- et multivariées (ANOVA, ACP et PLS-DA) pour mettre en évidence des modulations de protéines et identifier des biomarqueurs prédictifs de l’apparition du SMet à T2. Une étape de validation a été effectuée (n=11) pour tester la robustesse du modèle prédictif obtenu. Les résultats ont montré 60 protéines modulées de façon significative entre cas et témoins à T1, soit plus de 7 ans avant l’apparition du SMet. Parmi celles-ci, 31 protéines ont un ratio de variation supérieur à 1,5 (n=23) ou inférieur à 0,65 (n=8). Elles sont en majorité impliquées dans la régulation de l’angiogenèse (n=7), de l’inflammation (n=5) ou encore du métabolisme (n=3). D’autres participent à la prolifération cellulaire ou sont impliquées dans le développement foetal (n=5). Une approche réseau utilisant Ingenuity a permis d’explorer en détail leurs liens et corrélations. L’analyse statistique par PLS-DA a permis de sélectionner les protéines candidats biomarqueurs ayant les plus fortes capacités discriminantes. L’intégration d’autres types de données (socio-économiques, cliniques, métabolomiques) par Random Forest sera réalisée pour enrichir les modèles prédictifs. Ce projet contribuera à renforcer nos connaissances sur le SMet et l’empreinte foetale; il va permettre l’identification précoce des sujets à risque afin d’améliorer leur prise en charge et de proposer une stratégie préventive efficace

Metabolomics is a powerful tool in nutrition research for characterizing complex phenotypes and identifying biomarkers of specific physiological responses to different diets in the context of molecular pathways, physiology and health status. However, nutritional metabolomics is particularly challenging because of the low amplitude of effects induced by nutritional interventions or transitions, in comparison to the large variability in samples due to intra/inter-individual variations but also collection, storage and pre-treatment. Untargeted approaches using multiple analytical platforms are particularly of relevance to obtain a large analytical coverage of metabolites, which is essential because of the extreme chemical diversity of the endogenous and food metabolomes, ranging from sugars and lipids to polyphenols. However, in this context, important criteria for selection of an untargeted method are not only based on analytical coverage, but also on robustness of sample pre-treatment and on analytical reproducibility in terms of retention times, masses and signal intensities. We will illustrate the use of UPLC/HRMS and GC/MS based untargeted strategies for the study of food exposure, as well as for the characterization of metabolic phenotypes associated with development or prevention of chronic metabolic diseases. Indeed, the determination and the annotation of plasma and urine metabolic profiles constitute a powerful data-driven approach for the discovery of potential biomarkers of food intake and/or metabolic changes. However, irrespective of the type of analytical techniques used, these metabolomic strategies generate large amounts of data that need analyses and integration to extract the biologically meaningful information and enable the discovery of new knowledge. We will show that application of numerical and symbolic data mining methods based on clustering and pattern mining/formal concept analysis are particularly relevant to discover classes and sub-classes of individual phenotypes, as well as guide visualization and interpretation of data from nutritional metabolomics

Life course is a continuum of consecutive transitions with multifactor lifestyle changes potentially impacting well-being and health status. Among multiple factors, shifts in dietary pattern, food composition and processing, are causing nutritional transitions. In the context of deleterious behavior, these transitions led to energy imbalance with an increasing prevalence of non-communicable chronic diseases. The complexity of diseases brings challenges for an integrative understanding of the metabolic processes contributing to the development of these disorders. Metabolomics offers now the possibility of characterizing global alterations associated with disease conditions or nutritional exposition and transition, and of identifying early and/or predictive biomarkers of disease development. In this context, intervention studies are essential to analyse specific nutrition effects, to discover potential new biomarkers and identify their role in the pathophysiological mechanisms. Using this approach, we showed in normal and overweight volunteers submitted to two months of overfeeding, that metabolomics allows identifying trajectories that reveal early subtle metabolic dysregulations, not detected by classical biochemical approaches, linked to differences in metabolic plasticity. To obtain a more comprehensive picture of nutritional exposure as well as a better predictive capacity of (bio)markers, complementary study designs in cohorts are mandatory. Within the French Gazel cohort, we will show that multidimensional model building, consisting in integrating heterogeneous data (i.e. metabolic, metabolomic, clinical and functional but also socio-economic data and food patterns) will bring new tools to better predict metabolic syndrome development. These models will allow revealing determinants and modulators of changes, and evaluating the contribution of nutrition as a modifiable risk factor, in disease development. This work was performed through the technical and scientific support of MetaboHUB. It illustrates the constructive collaborations between the infrastructure and research units in order to tackle challenging scientific questions in nutrition