TY - JOUR
T1 - Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations
AU - Nishijima, Suguru
AU - Stankevic, Evelina
AU - Aasmets, Oliver
AU - Schmidt, Thomas S B
AU - Nagata, Naoyoshi
AU - Keller, Marisa Isabell
AU - Ferretti, Pamela
AU - Juel, Helene Bæk
AU - Fullam, Anthony
AU - Robbani, Shahriyar Mahdi
AU - Schudoma, Christian
AU - Hansen, Johanne Kragh
AU - Holm, Louise Aas
AU - Israelsen, Mads
AU - Schierwagen, Robert
AU - Torp, Nikolaj
AU - Telzerow, Anja
AU - Hercog, Rajna
AU - Kandels, Stefanie
AU - Hazenbrink, Diënty H M
AU - Arumugam, Manimozhiyan
AU - Bendtsen, Flemming
AU - Brøns, Charlotte
AU - Fonvig, Cilius Esmann
AU - Holm, Jens-Christian
AU - Nielsen, Trine
AU - Pedersen, Julie Steen
AU - Thiele, Maja Sofie
AU - Trebicka, Jonel
AU - Org, Elin
AU - Krag, Aleksander
AU - Hansen, Torben
AU - Kuhn, Michael
AU - Bork, Peer
AU - GALAXY and MicrobLiver Consortia
PY - 2025/1/9
Y1 - 2025/1/9
N2 - 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.
AB - 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.
KW - absolute abundance
KW - disease associations
KW - gut microbiome
KW - machine learning
KW - microbial load
KW - shotgun metagenomics
U2 - 10.1016/j.cell.2024.10.022
DO - 10.1016/j.cell.2024.10.022
M3 - Journal article
C2 - 39541968
SN - 0092-8674
VL - 188
SP - 222-236.e15
JO - Cell
JF - Cell
IS - 1
ER -