microbiomeinformed

Microbiomeinformed is an approach in health sciences, nutrition, agriculture, and environmental management that integrates information about microbial communities into decision-making and policy. It treats the microbiome—the diverse communities of bacteria, archaea, fungi, and viruses—as an active factor that can influence outcomes rather than a passive background variable. In practice, microbiomeinformed work combines sequencing data, such as 16S rRNA gene profiling and shotgun metagenomics, with metabolomics, host data, and clinical or environmental context to characterize microbiomes and infer their functions. Advanced analytics and machine learning may be used to relate microbiome features to phenotypes and to guide interventions.

Applications of microbiomeinformed approaches include disease risk stratification and prediction of treatment response, personalized nutrition and microbiome-directed therapies, and more targeted antibiotic stewardship. In addition, probiotics, prebiotics, and other microbiome-targeted products are often developed or evaluated within a microbiomeinformed framework. In agriculture and environmental management, these approaches aim to optimize soil and crop health, biogeochemical cycles, and bioremediation by shaping microbial communities.

Challenges facing microbiomeinformed work include substantial variability between individuals and over time, gaps in taxonomic and functional annotation, sampling biases, and the need for standardized methodologies and transparent reporting. Interpreting associations versus causation remains difficult, and data privacy and equity considerations are important when applying microbiomeinformed strategies in healthcare or public policy. Ongoing work seeks to strengthen longitudinal studies, causal inference, and multi-omics integration to advance this field.