Nutritional index and nutritypes for precision nutrimetry

Nutritional index and nutritypes for precision nutrimetry

Resumen del trabajo presentado al XXXIII Congreso de la Sociedad Española de Nutrición, celebrado en Granada del 19 al 21 de junio de 2024.

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Main Authors: Martínez, J. Alfredo, Higuera-Gómez, Andrea
Format: comunicación de congreso biblioteca
Language:English
Published: 2024
Online Access:http://hdl.handle.net/10261/364861
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spelling dig-ictan-es-10261-3648612024-07-31T12:10:25Z Nutritional index and nutritypes for precision nutrimetry Martínez, J. Alfredo Higuera-Gómez, Andrea Resumen del trabajo presentado al XXXIII Congreso de la Sociedad Española de Nutrición, celebrado en Granada del 19 al 21 de junio de 2024. [Background and aims] Artificial intelligence, by utilizing exploratory factor analyses, clusterization, and computational machine-learning based algorithms offers a promising method for categorizing individuals into personalized nutritypes. Integration of metabolic traits, lifestyle factors, and medical data, is contributing to precision disease prevention and management. This study aimed to develop a tool for clustering an online population using straightforward questions supported on machine-learning techniques. [Methods] Data from the NUTRiMDEA online cohort (n=17332 participants adults, 64.1% female) provided responses to a survey comprising 62 questions describing features related to nutritional health and life quality. Participants were grouped according to 32 selected questions using supervised machine-learning approaches. [Results] Five distinct clusters emerged after the statistical categorization, including lifestyle, health-related quality of life (HRQoL) and health status information. "Westernized Millennials" (n=967) exhibited fair lifestyle habits and low HRQoL, "Healthy" group (n=10616) represented the healthiest subset with medium HRQoL, "Mediterranean Young Adult" (n=2013) demonstrated a highest adherence to Mediterranean diet (MD) and medium HRQoL, "Pre-morbid" (n=600) encompassed a population with low adherence to MD and low HRQoL, while "Pro-morbid" (n=312) displayed worse lifestyle habits and poorer HRQoL and health status. The devised computational algorithm facilitated an explicit cluster assignment based on the responses. [Conclusion] Machine-learning analyses from an online questionnaire enabled the phenotyping, clustering, and identification of nutritypes to implement personalized interventions for chronic disease prevention and confirm that online methods hold potential for precision health intervention. Peer reviewed 2024-07-31T12:10:24Z 2024-07-31T12:10:24Z 2024 comunicación de congreso XXXIII Congreso de la Sociedad Española de Nutrición (2024) http://hdl.handle.net/10261/364861 en Sí none
institution ICTAN ES
collection DSpace
country España
countrycode ES
component Bibliográfico
access En linea
databasecode dig-ictan-es
tag biblioteca
region Europa del Sur
libraryname Biblioteca del ICTAN España
language English
description Resumen del trabajo presentado al XXXIII Congreso de la Sociedad Española de Nutrición, celebrado en Granada del 19 al 21 de junio de 2024.
format comunicación de congreso
author Martínez, J. Alfredo
Higuera-Gómez, Andrea
spellingShingle Martínez, J. Alfredo
Higuera-Gómez, Andrea
Nutritional index and nutritypes for precision nutrimetry
author_facet Martínez, J. Alfredo
Higuera-Gómez, Andrea
author_sort Martínez, J. Alfredo
title Nutritional index and nutritypes for precision nutrimetry
title_short Nutritional index and nutritypes for precision nutrimetry
title_full Nutritional index and nutritypes for precision nutrimetry
title_fullStr Nutritional index and nutritypes for precision nutrimetry
title_full_unstemmed Nutritional index and nutritypes for precision nutrimetry
title_sort nutritional index and nutritypes for precision nutrimetry
publishDate 2024
url http://hdl.handle.net/10261/364861
work_keys_str_mv AT martinezjalfredo nutritionalindexandnutritypesforprecisionnutrimetry
AT higueragomezandrea nutritionalindexandnutritypesforprecisionnutrimetry
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