Microbiome-metabolome multi-omics biomarkers for infectious disease prognosis: Current evidence, AI-driven integration, and translational challenges.

Abstract

Microbiome-metabolome interactions are emerging as promising predictors of infectious disease, beyond conventional pathogen detection. Growing evidence shows that microbial dysbiosis, altered microbial-derived metabolites, and host metabolic reprogramming are associated with disease severity, immune dysfunction, treatment response, and mortality across infectious diseases. High-throughput sequencing, metagenomic next-generation sequencing, and nuclear magnetic resonance platforms have identified microbial and metabolic signatures that are prognostic for inflammatory activation, oxidative stress, mitochondrial dysfunction, and immune dysregulation. Integration of microbiome and metabolomic datasets with multi-omics frameworks may improve prognostic stratification compared to single-omics approaches. Artificial intelligence and machine-learning models, such as random forests, gradient boosting, and deep learning algorithms, have demonstrated promising potential for identifying high-dimensional prognostic patterns and aiding risk prediction. However, most of the available evidence remains exploratory and is hampered by cohort heterogeneity, small sample sizes, cross-sectional study designs, batch effects, limited external validation, and difficulties with model interpretability and reproducibility. Current evidence supports the potential of microbiome-metabolome biomarkers as complementary prognostic tools rather than routine clinical diagnostics. Future progress will require large, multicenter longitudinal studies, harmonized analytical frameworks, explainable artificial intelligence models, and equitable implementation strategies to enable clinically reliable precision infectious-disease prognostics.