A multi-domain algorithm towards precision nutrition: integrating genetic risk scores and phenotypic diversity in the
Cristina Álvarez-Martín1, María Benavent1, Natalia Úbeda1
1Research Group "Food and Nutrition in Health Promotion (CEU-NutriFOOD)", Departamento de Ciencias Farmacéuticas y de la Salud, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, Boadilla del Monte, 28660, Spain.
Background And Aims:
Older adults show substantial heterogeneity in health status, making it necessary to develop integrative tools for personalized nutrition assessment. This study aimed to develop and describe a preliminary multi-domain algorithm towards precision nutrition for older adults to generate personalized dietary and physical activity recommendations, and to evaluate its feasibility in a selected cohort of community-dwelling, healthy retirees.
Methods:
The algorithm was developed through a multi-step process. A literature review identified age-related conditions of interest and supported the definition of six health domains: Glucose Regulation, Nutritional Sufficiency, Cardiovascular Health, Physical Status, Digestive Health, and Circadian Rhythms. A nutrigenetic test was then designed, and relevant biochemical, anthropometric, dietary, lifestyle, physical activity, and genetic variables were selected and integrated into a multi-domain scoring system, summing up to an Overall Score. The algorithm was applied to participants from the MyFOOD4Senior study (n = 120).
Results:
The nutrigenetic test comprised 26 single nucleotide polymorphisms (SNPs), from which four candidate-SNP genetic risk scores (GRS) were derived: type 2 diabetes, vitamin D deficiency, cardiovascular disease, and strength deficiency. In addition to genetic information, the algorithm integrated dietary intake, micronutrient biomarkers, anthropometry, physical performance and digestive function into domain‑specific scores, providing an Overall Score (mean: 79.86 ± 10.02 points). Cluster analysis identified three health phenotypes: Reference/Healthy (n = 40), Frailty-like (n = 36), and Cardiovascular Risk-like (n = 44). The Frailty phenotype showed the lowest scores for Physical Status, Glucose Regulation, and Nutritional Sufficiency, whereas the Cardiovascular Risk-like phenotype was characterized by the lowest Cardiovascular Health score.
Conclusion:
This study presents the development of a multi-domain algorithm intended to advance the implementation of precision nutrition for active, community-dwelling older adults. By integrating multidimensional biological, functional, and nutritional variables, this framework provides a comprehensive approach to health phenotyping, establishing a solid basis for future longitudinal validation before its potential application in personalized geriatric nutrition.
Trial Registration:
ClinicalTrials.gov identifier NCT07461883 (Registration date 03102026).
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