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We're excited to share our latest preprint led by Suguru Nishijima on a novel computational tool to predict fecal microbial load solely from relative species profiles! Our study revealed that fecal microbial load is a substantial confounder in #microbiome - disease association studies. https://www.biorxiv.org/content/10.1101/2024.03.18.584290v1 
bioRxiv · Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associationsThe microbiota in individual habitats differ both in relative composition and absolute abundance. While sequencing approaches determine only the relative abundances of taxa and genes, experimental techniques for absolute abundance determination are rarely applied to large-scale microbiome studies. Here, we developed a machine learning approach to predict fecal microbial loads (microbial cells per gram) solely from relative abundance data. Applied to large-scale datasets (n = 34,539), we demonstrate that microbial load is the major determinant of gut microbiome variation and associated with numerous host factors. We found that for several diseases, the altered microbial load, not the disease itself, was the main driver of the gut microbiome changes. Adjusting for this effect substantially reduced the significance of more than half of the 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.
### Competing Interest Statement
The authors have declared no competing interest.