Abstract

-analysis performed using DiSc 1.4, Review Manager 5.4, and Stata SE 17.0 software to calculate diagnostic metrics such as overall sensitivity and specificity. The overall diagnostic performance of the AI was assessed. Meta-regression analysis explored sources of heterogeneity. Results: A total of 7 articles involving 1,669 GC patients were included. The analysis showed that AI had a sensitivity of 0.90 (95% CI: 0.84-0.94) and a specificity of 0.95 (95% CI: 0.91-0.98) for the diagnosis of GC LNM, with significant heterogeneity across studies. The area under the curve was 0.97, indicating an excellent diagnostic value. Meta-regression analysis showed that the precision nutrition. Summary: This document from the Ibero-American Network of Nutriomics and Precision Nutrition (RINN22; https://rinn22.com/) provides a comprehensive overview of the concepts of precision nutrition approaches to guide their application in clinical and public health as well as establish the position of RINN22 regarding the current and future state of precision nutrition. Key Messages: The progress and participation of nutriomics to precision nutrition is an essential pillar for addressing diet- related diseases and developing innovative managing strategies, which will be promoted by advances in bioinformatics, machine learning, and integrative software, as well as the description of specific novel biomarkers. In this context, synthesizing and critically evaluating the latest developments, potential applications, and future needs in the fi eld of nutrition is necessary with a holistic perspective, incorporating progress in omics technologies aimed at precision nutrition interventions. This approach must address and confront healthy, social, food security, physically active lifestyle, sanitation, and sustainability challenges with preventive, participatory, and predictive strategies of personalized, population, and planetary nutrition fora precision tailored health. (c) 2024 The Author(s). Published by S. Karger AG, Basel