Artificial Intelligence-Driven Multidimensional Phenotyping of Gut Metabolic States for Personalized Prebiotic,...

This study presents multidimensional physicochemical phenotyping of human gut metabolic states using integrated multimodal profiling and machine learning. A total of 680 stool samples were analyzed using time-resolved optical, electrochemical, acoustic, magnetic, and spectral measurements implemented in a compact 3D-printed screening platform. Multivariate analysis explained 78.4% of structured variance, and cluster optimization identified seven primary clusters and 26 structurally retained subclusters representing proteolytic, saccharolytic, bile/lipid-rich, oxidative, diarrheal, pigment-linked, and normobiotic-like profiles. Clinical categories were linked to 21 subclusters after descriptor-based structure definition. A separate supervised layer assessed out-of-fold reproduction of fixed primary CL1-CL7 assignments, achieving 81.7% accuracy and a 0.799 macro F1-score in repeated cross-validation. Separately, cohort-internal diagonal category-to-subcluster distribution reached 84.1% (572/680 diagonal assignments, Wilson 95% CI: 81.2%-86.7%). 16S rRNA/laboratory-marker profiling supported the physicochemical structure, showing high diversity and low dysbiosis in the normobiotic-like phenotype, acidic high-diversity behavior in the saccharolytic phenotype, alkaline proteolytic behavior with elevated phenols and ammonia, bile-associated functional enrichment, and high dysbiosis with reduced diversity in the pigment-rich phenotype. SHAP and LIME attributed CL1-CL7 assignment to coordinated multimodal contributions. The method represents standardized extractable stool matrix profiling associated with microbiome-supported functional states, not a stand-alone diagnostic test.