AI-Guided Multi-Omics Risk Prediction of Foodborne Pathogen–Induced Metabolic Dysregulation: Integrating Gut Microbiome Signatures, Natural Polysaccharide Intervention, and Rapid Microbial Detection Frameworks

Abstract

The rising incidence of foodborne infections, coupled with their long-term metabolic sequelae, demands integrated surveillance and intervention systems that transcend conventional food safety paradigms. This paper presents a system-level architecture for AI-guided multi-omics risk prediction of metabolic dysregulation induced by foodborne pathogens. The proposed framework synthesizes high-resolution gut microbiome signatures, metabolomic and proteomic profiles, natural polysaccharide intervention modeling, and rapid microbial detection networks into an interoperable, data-driven decision infrastructure. Core to the design is a federated learning backbone that ingests heterogeneous multi-omics data from clinical cohorts, environmental sampling, and food supply chain sensors, training ensemble deep learning models to predict individualized risk trajectories for insulin resistance, non-alcoholic fatty liver disease, and dyslipidemia following enteric infection. By embedding rapid detection frameworks capable of single-cell sensitivity for pathogens such as Salmonella, the system enables near-real-time alert generation and targeted polysaccharide-based dietary countermeasures, informed by mechanistic insights into glycolipid metabolism regulation. The paper foregrounds structural trade-offs between model accuracy, latency, and interpretability, and analyzes the socio-technical demands of deploying such a system across fragmented regulatory jurisdictions. Governance challenges, including data sovereignty, algorithmic fairness across demographic and nutritional strata, and the sustainability of multi-omics data pipelines, are examined in depth. A comparative analysis of centralized versus edge-based AI architectures demonstrates that hybrid deployments best balance computational load, privacy preservation, and responsiveness in field settings. The discussion extends to policy frameworks needed to ensure equitable access to predictive risk tools and to prevent algorithmic bias that could disproportionately burden marginalized populations reliant on informal food markets. By integrating technical design with institutional analysis, the paper delineates a roadmap for transforming foodborne disease surveillance from reactive outbreak management into proactive, AI-augmented prevention ecosystems.