OPTIPROT, Targeted Precision Nutrition for Protein Needs in Ageing

This multicenter, cross-sectional observational study aims to use precision nutrition tools to identify metabolic clusters in adults aged 50-75 years, enabling tailored nutritional and protein recommendations based on specific metabolic characteristics. Conducted at Eurecat-Reus (Catalonia) and AZTI (Bizkaia), the study will enroll 200 participants (75 at Eurecat, 125 at AZTI). Analyses will include metabolomics, gut microbiota profiling, epigenomics, and glycomics, with the goal of refining dietary guidelines and protein intake recommendations to address the specific needs of aging populations.

The study hypothesizes that metabolic characterization using precision nutrition tools in adults aged 50 to 75 will enable the identification of clusters with similar metabolic profiles, thereby enhancing our understanding of each group's nutritional needs and facilitating the development of tailored dietary recommendations, particularly regarding protein intake. This multicenter, cross-sectional observational study will recruit 200 participants, 75 at Eurecat-Reus (Catalonia) and 125 at AZTI (Bizkaia), and will integrate metabolic, clinical, anthropometric, lifestyle, dietary, and microbiota data to form these clusters. The primary objective is to establish metabolic profiles and clusters using precision nutrition tools, while secondary objectives include identifying biomarkers associated with health and aging, assessing the relationship between protein intake and metabolic health, and exploring the associations between gut microbiota composition, glycomic patterns, and metabolic status. Participants will attend two visits: an initial visit for eligibility confirmation and informed consent, followed by a second fasting visit for blood sampling, anthropometric measurements, collection of pre-obtained samples, and the administration of validated questionnaires on lifestyle, dietary intake, and physical activity. Ultimately, the integrated analysis of these data will refine nutritional recommendations to better address the specific metabolic needs of each cluster, thereby improving dietary guidance and overall metabolic health in the aging population.