Global health risk assessment of antibiotic resistance in agricultural soils

Abstract Antibiotic resistance is posing a major threat to public health, yet there is little effort to quantitatively assess risks associated with antibiotic resistance genes (ARGs), particularly in agricultural soils. We therefore propose a risk assessment framework by integrating metagenomic profiling, quantitative health risk assessments, and machine learning to evaluate distribution and health risks of ARGs in a diverse set of global agricultural soil samples. Based on 985 selected metagenomic samples from diverse agricultural systems, we identified 1745 subtypes from 30 major ARG families, revealing patterns between major agricultural settings. Approximately 1% of global agricultural areas were classified as high-risk, primarily concentrated in regions with intensive farming practices and high antibiotic usage. Our framework enables the identification of risk hotspots which seem to be driven by socioeconomic, climatic, and land use factors. These findings facilitate a methodological advancement for predicting ARG risk through mechanism-driven models, rather than descriptive abundance metrics. The proposed framework will support targeted soil management strategies to mitigate antibiotic resistance propagation in agroecosystems.