Scientific Reports (Oct 2024)

Computational algorithm based on health and lifestyle traits to categorize lifemetabotypes in the NUTRiMDEA cohort

  • Andrea Higuera-Gómez,
  • Víctor de la O,
  • Rodrigo San-Cristobal,
  • Rosa Ribot-Rodríguez,
  • Isabel Espinosa-Salinas,
  • Alberto Dávalos,
  • María P. Portillo,
  • J. Alfredo Martínez

DOI
https://doi.org/10.1038/s41598-024-75110-z
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 13

Abstract

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Abstract Classifying individuals based on metabotypes and lifestyle phenotypes using exploratory factor analyses, cluster definition, and machine-learning algorithms is promising for precision chronic disease prevention and management. This study analyzed data from the NUTRiMDEA online cohort (baseline: n = 17332 and 62 questions) to develop a clustering tool based on 32 accessible questions using machine-learning strategies. Participants ranged from 18 to over 70 years old, with 64.1% female and 35.5% male. Five clusters were identified, combining metabolic, lifestyle, and personal data: Cluster 1 (“Westernized Millennial”, n = 967) included healthy young individuals with fair lifestyle habits; Cluster 2 (“Healthy”, n = 10616) consisted of healthy adults; Cluster 3 (“Mediterranean Young Adult”, n = 2013) represented healthy young adults with a healthy lifestyle and showed the highest adherence to the Mediterranean diet; Cluster 4 (“Pre-morbid”, n = 600) was characterized by healthy adults with declined mood; Cluster 5 (“Pro-morbid”, n = 312) comprised older individuals (47% >55 years) with poorer lifestyle habits, worse health, and a lower health-related quality of life. A computational algorithm was elicited, which allowed quick cluster assignment based on responses (“lifemetabotypes”). This machine-learning approach facilitates personalized interventions and precision lifestyle recommendations, supporting online methods for targeted health maintenance and chronic disease prevention.

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