Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations

The microbiota in individual habitats differ in both relative composition and absolute abundance. While sequencing approaches determine the relative abundances of taxa and genes, they do not provide information on their absolute abundances. Here, we developed a machine-learning approach to predict fecal microbial loads (microbial cells per gram) solely from relative abundance data. Applying our prediction model to a large-scale metagenomic dataset (n = 34,539), we demonstrated that microbial load is the major determinant of gut microbiome variation and is associated with numerous host factors, including age, diet, and medication. We further found that for several diseases, changes in microbial load, rather than the disease condition itself, more strongly explained alterations in patients’ gut microbiome. Adjusting for this effect substantially reduced the statistical significance of the majority of disease-associated species. Our analysis reveals that the fecal microbial load is a major confounder in microbiome studies, highlighting its importance for understanding microbiome variation in health and disease. [Display omitted] •Machine-learning model predicts fecal microbial load from relative microbiome profile•Predicted loads correlate with host and environmental factors in a large-scale dataset•Disease-associated microbial signatures are linked to predicted microbial load•Predicted load adjustment reduces statistical significance of disease-associated species A machine-learning approach enables the quantification of microbial load (microbial cells per gram) in fecal samples based on the relative microbiome profile. The predicted microbial load emerged as a major determinant of microbiome variation, confounding various disease-microbe associations across more than 34,000 global metagenomes.