Keijer, JaapEscoté, XavierGalmés, SebastiàPalou, AndreuSerra, FranciscaAldubayan, Mona AdnanPigsborg, KristinaMagkos, FaidonBaker, Ella JCalder, Philip CGóralska, JoannaRazny, UrszulaMalczewska-Malec, MalgorzataSuñol, DavidGalofré, MarRodríguez, Miguel ACanela, NúriaMalcic, Radu GBosch, MontserratFavari, ClaudiaMena, PedroDel Rio, DanieleCaimari, AntoniGutierrez, Biotzadel Bas, Josep MPREVENTOMICS Consortium: Empowering Consumers to Prevent diet-related diseases through-OMIC sciences2024-10-092024-10-092023-04-19Keijer J, Escoté X, Galmés S, Palou-March A, Serra F, Aldubayan MA, et al. Omics biomarkers and an approach for their practical implementation to delineate health status for personalized nutrition strategies. Crit Rev Food Sci Nutr. 2023 Apr 19;1-29.https://hdl.handle.net/20.500.13003/18955https://hdl.handle.net/20.500.12105/23731Personalized nutrition (PN) has gained much attention as a tool for empowerment of consumers to promote changes in dietary behavior, optimizing health status and preventing diet related diseases. Generalized implementation of PN faces different obstacles, one of the most relevant being metabolic characterization of the individual. Although omics technologies allow for assessment the dynamics of metabolism with unprecedented detail, its translatability as affordable and simple PN protocols is still difficult due to the complexity of metabolic regulation and to different technical and economical constrains. In this work, we propose a conceptual framework that considers the dysregulation of a few overarching processes, namely Carbohydrate metabolism, lipid metabolism, inflammation, oxidative stress and microbiota-derived metabolites, as the basis of the onset of several non-communicable diseases. These processes can be assessed and characterized by specific sets of proteomic, metabolomic and genetic markers that minimize operational constrains and maximize the information obtained at the individual level. Current machine learning and data analysis methodologies allow the development of algorithms to integrate omics and genetic markers. Reduction of dimensionality of variables facilitates the implementation of omics and genetic information in digital tools. This framework is exemplified by presenting the EU-Funded project PREVENTOMICS as a use case.enghttp://creativecommons.org/licenses/by-nc/4.0/research articleAtribución-NoComercial 4.0 Internacional37077157110.1080/10408398.2023.21986051549-7852Critical reviews in food science and nutritionopen access2-s2.0-85153745135972798400001L641076658