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PERSONALIZED NUTRITIONAL STRATEGIES FROM THE INTEGRATION OF METABOLIC, GENETIC, AND BEHAVIORAL DATA: ADVANCES, APPLICATIONS, AND PERSPECTIVES IN PRECISION NUTRITION

Camila Maria Silva Leite Letícia Lourenço de Araújo Tamires da Cunha Soares Janete da Costa Barbosa de Melo Maria Alíssia Costa Carvalho Tercília Menezes Monteiro Bruna Furtado Sena de Queiroz Gisele Viana de Moura Éllen Bianca Costa Gomes Idalice Viviane de Araújo Myllena Amanda Carvalho de Almeida Edna Lara Vasconcelos Gomes Monteiro Ramos Joice Moreira Vaz Ana Karoline Sousa Soares Leal Sarah Yasmim Vaz de Lima

Sep 2026 · Revista de Estudos Interdisciplinares · 0 citations

Abstract

Personalized nutrition seeks to overcome the limitations of recommendations based on population averages by considering individual variability determined by metabolic, genetic, behavioral, and environmental factors. This study aimed to analyze how metabolic, genetic, and behavioral data can contribute to the personalization of nutritional strategies and to more favorable metabolic, clinical, anthropometric, and behavioral responses. An integrative literature review was conducted in PubMed, Medline, Scopus, Web of Science, Embase, Lilacs and Scielo,, considering studies published between January 2015 and August 2026. A total of 857 studies were identified; after removal of duplicates, title and abstract screening, and full-text assessment, eight studies met the eligibility criteria. The findings indicated favorable effects of personalized nutrition mainly on diet quality and some metabolic parameters. Metabolic data showed more consistent results, whereas evidence related to genetic information was heterogeneous. The behavioral dimension was relevant to adherence and intervention effectiveness. Integrating different dimensions broadens individual characterization but does not, by itself, guarantee better outcomes. Precision nutrition remains promising, but biomarker validation, methodological standardization, and longer studies are needed to confirm its clinical effectiveness.

DOI Semantic Scholar

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