Environ Monit Assess. 2026 May 11;198(6):574. doi: 10.1007/s10661-026-15415-w.
ABSTRACT
Antimicrobial resistance (AMR) is a paramount global health threat. While antibiotic misuse is a recognized driver, other environmental pollutants, particularly non-biodegradable heavy metals, are increasingly implicated in the selection and dissemination of resistance through co-selection mechanisms. This systematic review synthesizes evidence on the role of environmental heavy metal contamination as a driver of AMR evolution and spread. A systematic search was conducted across seven databases (PubMed, Web of Science, Scopus, Cochrane, Biomed Central, Google Scholar, and Embase) between November 2023 and January 2024, following PRISMA guidelines. Studies investigating the impact of heavy metals on AMR in environmental matrices were included. Study quality was assessed using the CASP checklist, and data were synthesized thematically. From 9513 records, 22 studies published between 2018 and 2024 were included. Evidence frequently reported strong associations between heavy metal pollution (e.g., Pb, Cd, Hg) and increased abundance and diversity of antibiotic resistance genes (ARGs) in wastewater, riverine, and soil ecosystems. The dominant mechanism identified in the reviewed studies was co-resistance, with metal resistance genes and ARGs co-located on mobile genetic elements, facilitating horizontal transfer. Cross-resistance and co-regulation were also reported. Importantly, metal pollution was linked to the environmental presence of high-risk multidrug-resistant pathogens. Methodological appraisal revealed a predominance of cross-sectional studies and limited data on metal speciation, constraining causal inference. The reviewed evidence suggests that environmental heavy metal pollution may be an important but underappreciated driver of AMR, potentially acting through co-selection. Effective AMR control requires integrated strategies combining antimicrobial stewardship with environmental governance. Future studies should adopt longitudinal designs and advanced molecular tools to establish causation and quantify risks.
PMID:42113096 | DOI:10.1007/s10661-026-15415-w