Personalized nutrition to mitigate inflammation in genetically predisposed individuals: a secondary analysis of the Danish PREVENTOMICS intervention - Galmés, Sebastià; Palou-March, Andreu; Pigsborg, Kristina; Aldubayan, Mona Adnan; Gormsen, Sophia M.O.; Calleja, Alberto; Trabal, Joan; Martínez, Vanesa; Gutiérrez, Biotza; Del Bas, Josep M.; Magkos, Faidon; Serra, Francisca (Food science and human wellness, 2025)
Deep phenotyping and genetic characterization of individuals are fundamental to assessing the metabolic status and determining nutrition-specific requirements. This study aimed to ascertain the utmost effectiveness of personalized interventions by aligning dietary adjustments with both the genotype and metabolotype of individuals. Therefore, we assessed here the usefulness of a polygenic score (PGS) characterizing a potential pro-inflammatory profile (PGSi) as a nutrigenetic tool to discern individuals from the Danish PREVENTOMICS cohort that could better respond to precision nutrition (PN) plans, specifically targeted at counteracting the low-grade inflammatory profile typically found in obesity. The cohort followed a PN plan to counteract the pro-inflammatory profile (PNi group) or generic dietary recommendations (Control) for 10 weeks. PGSi was applied for genetic stratification (Low/High). The effects of the intervention on anthropometrics and biomarkers related to inflammatory profile and carbohydrate metabolism were assessed. Around 30% of subjects had a high genetic predisposition to pro-inflammatory status (high-PGSi). These individuals demonstrated the most effective response to the dietary plan, experiencing improved body composition, with significant decreases in body weight (∆: –4.84%; P = 0.039) and body fat (∆: –4.86%; P = 0.007), and beneficial changes in pro- and anti-inflammatory biomarkers, with significant increases in IL-10 (∆: 71.3%; P = 0.025) and decreases in TNF-α (∆: –3.0%; P = 0.048), CRP (∆: –31.1%), ICAM1 (∆: –5.8%), and MCP1 (∆: –4.2%) circulating levels, compared to low-PGSi individuals. Both phenotypic and genetic stratification contributed to a better understanding of metabolic heterogeneity in response to diet. This approach allows for refinement of the prediction of individual requirements and potentially for better management of obesity.