Wastewater as a predictive resistome surveillance system: from genes to ecological risk

BACKGROUND: Wastewater resistome surveillance has emerged as a promising strategy for monitoring antimicrobial resistance (AMR) at the population level. However, translating complex environmental resistome datasets into operational indicators of resistance risk remains a major challenge because standardized frameworks capable of integrating ecological, genetic, and environmental determinants of AMR are lacking. OBJECTIVE: To propose the Resistome Risk Index (RRI), a conceptual framework for transforming wastewater resistome data into standardized indicators of antimicrobial resistance risk suitable for longitudinal and comparative surveillance. METHODS: The proposed framework integrates multiple surveillance layers, including antimicrobial resistance gene abundance, mobility potential, host association, temporal persistence, and environmental context, into a composite resistome risk indicator. It combines targeted molecular monitoring, metagenomic analyses, and environmental metadata within a unified analytical architecture linking microbial community composition, mobilome dynamics, and resistome structure. Methodological challenges associated with host attribution, mobility inference, and ecological background variation are also discussed. RESULTS: The proposed RRI framework integrates diverse wastewater surveillance datasets into a standardized conceptual architecture that captures the ecological structure and dissemination potential of antimicrobial resistance. The framework supports longitudinal monitoring, inter-site comparisons, and integration of wastewater surveillance into One Health AMR monitoring programs. CONCLUSION: The RRI represents a theory-driven framework for translating wastewater resistome measurements into operational indicators of resistance risk. Although the framework requires empirical validation and calibration using longitudinal and multicenter datasets before practical implementation, it provides a scalable foundation for the development of predictive wastewater resistome surveillance systems and standardized antimicrobial resistance risk assessment.