Nishijima Suguru, Stankevic Evelina, Aasmets Oliver, Schmidt Thomas S B, Nagata Naoyoshi, Keller Marisa Isabell, Ferretti Pamela, Juel Helene Bæk, Fullam Anthony, Robbani Shahriyar Mahdi, Schudoma Christian, Hansen Johanne Kragh, Holm Louise Aas, Israelsen Mads, Schierwagen Robert, Torp Nikolaj, Telzerow Anja, Hercog Rajna, Kandels Stefanie, Hazenbrink Diënty H M, Arumugam Manimozhiyan, Bendtsen Flemming, Brøns Charlotte, Fonvig Cilius Esmann, Holm Jens-Christian, Nielsen Trine, Pedersen Julie Steen, Thiele Maja Sofie, Trebicka Jonel, Org Elin, Krag Aleksander, Hansen Torben, Kuhn Michael, Bork Peer
Molecular Systems Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.
Cell. 2025 Jan 9;188(1):222-236.e15. doi: 10.1016/j.cell.2024.10.022. Epub 2024 Nov 13.
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.
个体栖息地中的微生物群在相对组成和绝对丰度上都有所不同。虽然测序方法可以确定分类群和基因的相对丰度，但它们无法提供关于其绝对丰度的信息。在这里，我们开发了一种机器学习方法，仅根据相对丰度数据来预测粪便微生物负荷（每克微生物细胞数）。将我们的预测模型应用于一个大规模宏基因组数据集（n = 34,539），我们证明微生物负荷是肠道微生物组变异的主要决定因素，并且与众多宿主因素相关，包括年龄、饮食和药物治疗。我们进一步发现，对于几种疾病，微生物负荷的变化而非疾病状况本身，更能有力地解释患者肠道微生物组的改变。校正这种效应会大幅降低大多数疾病相关物种的统计显著性。我们的分析表明，粪便微生物负荷是微生物组研究中的一个主要混杂因素，凸显了其对于理解健康和疾病中微生物组变异的重要性。
