Harnessing AI to Tailor Genotype-Based Diets: Impact on Metabolic Health and Gut Microbiome

Abstract: This work integrates gut microbiome and genomic data to maximize metabolic health outcomes, so investigating the possibilities of personalized nutrition driven by artificial intelligence. To create individualized dietary recommendations, a machine learning model was created combining genomic data (single nucleotide polymorphisms linked to metabolic pathways) with microbiome composition data (16S rRNA sequencing). 120 people participated in a randomized controlled trial (RCT), split into an AI-driven personalized diet group and a control group getting standard dietary recommendations. Measuring using HOMA-IR, the AI-driven diet group showed a 25% increase in insulin sensitivity over a 12-week period compared to a 10% improvement in the control group (p < 0.05). Comparatively to a 6% decrease in the control group (p < 0.05), cholesterol levels dropped by 15% in the personalized diet group. With an average drop of 3.5 kg/m² compared to 1.8 kg/m² in the control group, the personalized diet group also displayed a more significant decrease in body mass index (BMI). Significant changes in microbiome composition were also noted; increases in beneficial taxa including Firmicutes (6% vs 3% in the control group, p < 0.05) and Bacteroides (6% vs 2% in the control group, p < 0.05) linked with improvements in metabolic health markers. This work emphasizes the potential of combining genomic and microbiome data for precision nutrition and the efficiency of AI-driven personalized diets in maximizing metabolic health.