Global freshwater resistomes reveal environmental signatures associated with the burden of drug-resistant tuberculosis.

Antimicrobial resistance (AMR) is a growing global health threat, yet the extent to which environmental resistomes reflect human disease burden remains unclear. In this study, we provide the first attempt to bridge freshwater resistomes with human disease burden using machine learning models, with a focus on identifying environmental signatures associated with drug-resistant tuberculosis (DR-TB) burden. By analyzing 1280 freshwater metagenomes from 45 countries, we characterized the compositional distribution of the resistomes and further developed an integrated environmental AMR Risk Score. Integrating the AMR Risk Score with socio-economic variables provided complementary information associated with variation in multidrug-resistant tuberculosis burden beyond socio-economic factors alone. In complementary income-stratified analyses, feature selection identified recurrent environmental signatures associated with different drug-resistant tuberculosis subgroups. These findings indicate that freshwater resistome characteristics contain geographically structured information associated with DR-TB burden within the sampled datasets and support the potential value of freshwater metagenomic surveillance for characterizing environmental AMR patterns.