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

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 22, 2024

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

Язык: Английский

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SDOH Influences on Chronic Stress: The Physiological Cost That Increases Risk of Heart Disease in Early Midlife African American Women

Опубликована: Янв. 1, 2024

Язык: Английский

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Cardiovascular consequences of androgen gender affirming therapy in transmasculine individuals

Elsevier eBooks, Год журнала: 2024, Номер unknown, С. 183 - 202

Опубликована: Дек. 4, 2024

Язык: Английский

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Social Determinants of Health and Cumulative Incidence of Mortality Among US Adults Without Major Chronic Diseases

Journal of General Internal Medicine, Год журнала: 2024, Номер unknown

Опубликована: Дек. 11, 2024

Язык: Английский

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