The study in Cell highlights microbial load's role in gut microbiome variation, suggesting it as a confounder in disease research and a key health determinant.
By Dr. Chinta SidharthanReviewed by Danielle Ellis, B.Sc.Nov 15 2024 Using machine learning , researchers have developed a way to predict the total number of microbes in our gut from sequencing data, revealing that microbial density, influenced by factors like age and diet, is a major contributor to gut microbiome variation and could reshape how we study disease connections.
Background The gut microbiome has a major influence on human health, as its composition is linked to various physiological processes and diseases. Researchers have widely used metagenomics to study microbial communities by examining the relative abundances of species within the microbiome. However, this relative data lacks information on microbial load or the total microbial count, which can impact microbiome diversity and function.
About the study In the present study, researchers employed a machine-learning approach to predict microbial load from gut microbiome data, utilizing large metagenomic datasets from two primary cohorts — one consisting of a heterogenous study population that included healthy individuals, as well as patients with end-stage liver disease, and the other comprising healthy individuals and patients with cardiometabolic diseases.
Related StoriesIn parallel, the study also explored the technical impact of deoxyribonucleic acid extraction and sequencing methods on microbial load predictions by comparing paired samples processed through different protocols. Statistical analysis assessed the influence of predicted microbial load on disease associations and microbial diversity, adjusting for confounding factors such as antibiotic use and demographic variables.
Additionally, the analyses revealed that several diseases are associated with distinct microbial load patterns. For example, conditions such as Crohn's disease and liver cirrhosis showed lower microbial loads, while diseases such as multiple sclerosis and colorectal cancer exhibited higher loads.
Diet Liver Machine Learning Metagenomics Microbiome
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