Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations
Suguru Nishijima, Evelina Stankevič, Oliver Aasmets, Thomas Schmidt, Naoyoshi Nagata, M. Keller, Pamela Ferretti, Helene Bæk Juel, Anthony Fullam, Shahriyar Mahdi Robbani, Christian Schudoma, Johanne Kragh Hansen, Louise Aas Holm, Mads Israelsen, Robert Schierwagen, Nikolaj Torp, Anja Telzerow, Rajna Hercog, Stefanie Kandels‐Lewis, Dienty Hendrina Maria Johanna Hazenbrink, Manimozhiyan Arumugam, Flemming Bendtsen, Charlotte Brøns, Cilius Esmann Fonvig, Jens‐Christian Holm, Trine Nielsen, Julie Steen Pedersen, Maja Thiele, Jonel Trebicka, Elin Org, Aleksander Krag, Torben Hansen, Michael Kuhn, Peer Bork, Torben Hansen, Matthias Mann, Jelle Matthijnssens, Aleksander Krag, Peer Bork, Manimozhiyan Arumugam, Jonel Trebicka, M.A. Karsdal, Ema Anastasiadou, Hans Israelsen, Hans Olav Melberg, Cristina Legido‐Quigley, Maja Thiele, Cristina Legido‐Quigley, Maja Thiele
Abstract
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.