Explainable Artificial Intelligence-Guided Discovery of Plant Polysaccharide–Gut Microbiota Interactions for Precision Intervention in Metabolic Syndrome

Abstract

Metabolic syndrome represents a global health crisis in which gut microbiota dysbiosis plays a central role, and plant-derived polysaccharides have emerged as promising modulators capable of restoring microbial equilibrium and improving host metabolic health. However, the immense structural diversity of plant polysaccharides, the inter-individual variability of gut microbial communities, and the nonlinear, multi-layered nature of host–microbe interactions render conventional discovery and intervention paradigms insufficient for precision applications. This paper presents a systems-level framework that leverages explainable artificial intelligence to guide the discovery of plant polysaccharide–gut microbiota interactions, with the explicit goal of enabling precision nutritional interventions against metabolic syndrome. We examine the architectural requirements for integrating multi-omics data, polysaccharide structural descriptors, and clinical phenotypes within a transparent machine learning pipeline. The discussion spans data infrastructure, model interpretability methods such as feature attribution and concept-based explanations, deployment architectures for clinical decision support, and the governance and policy dimensions that must accompany the translation of algorithmic insights into real-world dietary strategies. By situating explainable artificial intelligence as an orchestrating layer across molecular, microbial, and population scales, the paper articulates how structural trade-offs in model complexity, data privacy, fairness, sustainability, and regulatory accountability shape the design of trustworthy systems. The analysis reveals that explainability is not merely a technical add-on but a foundational prerequisite for building credible causal models, enabling reproducible mechanistic inference, and earning acceptance from clinicians, regulators, and diverse populations. We conclude by identifying cross-domain synergies and future directions that integrate ecological modeling, federated learning, and policy frameworks to create a robust pipeline from polysaccharide characterization to culturally adapted, evidence-based metabolic syndrome management.