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

• Published in: Scientific reports Vol. 14; no. 1; pp. 24835 - 13
• Main Authors: Higuera-Gómez, Andrea, de la O, Víctor, San-Cristobal, Rodrigo, Ribot-Rodríguez, Rosa, Espinosa-Salinas, Isabel, Dávalos, Alberto, Portillo, María P., Martínez, J. Alfredo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22-10-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/208; 692/308; 692/4019; 692/499; 692/699; 692/700; Adolescent; Adult; Aged; Algorithms; Chronic illnesses; Cluster Analysis; Clustering; Cohort Studies; Computer applications; Diet, Mediterranean; Disease prevention; Exploratory factor analyses; Female; Humanities and Social Sciences; Humans; Learning algorithms; Life Style; Lifestyle; Lifestyles; Machine Learning; Male; Middle Aged; multidisciplinary; Phenotype; Phenotypes; Precision medicine; Prevention; Public health; Quality of Life; Science; Science (multidisciplinary); Young Adult; Young adults; Lifestyle; Precision medicine; Clustering; Machine-learning; Exploratory factor analyses; Public health
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-75110-z

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.