TY - JOUR

T1 - Omics biomarkers and an approach for their practical implementation to delineate health status for personalized nutrition strategies

AU - Keijer, Jaap

AU - Escoté, Xavier

AU - Galmés, Sebastià

AU - Palou-March, Andreu

AU - Serra, Francisca

AU - Aldubayan, Mona Adnan

AU - Pigsborg, Kristina

AU - Magkos, Faidon

AU - Baker, Ella J

AU - Calder, Philip C

AU - Góralska, Joanna

AU - Razny, Urszula

AU - Malczewska-Malec, Malgorzata

AU - Suñol, David

AU - Galofré, Mar

AU - Rodríguez, Miguel A

AU - Canela, Núria

AU - Malcic, Radu G.

AU - Bosch, Montserrat

AU - Favari, Claudia

AU - Mena, Pedro

AU - Rio, Daniele Del

AU - Caimari, Antoni

AU - Gutierrez, Biotza

AU - Bas, Josep M. del

N1 - CURIS 2023 NEXS 098

PY - 2023

Y1 - 2023

N2 - Personalized 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 thedysregulation 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.

AB - Personalized 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 thedysregulation 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.

KW - Faculty of Science

KW - Biomarkers

KW - Diet

KW - Health

KW - Omics

KW - Personalized nutrition

U2 - 10.1080/10408398.2023.2198605

DO - 10.1080/10408398.2023.2198605

M3 - Review

C2 - 37077157

JO - Critical Reviews in Food Science and Nutrition

JF - Critical Reviews in Food Science and Nutrition

SN - 1040-8398

ER -