摘要：SummaryThe human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous metagenomic analyses of gut microflora revealed associations between specific microbes and host age. Nonetheless there was no reliable way to tell a host's age based on the gut community composition. Here we developed a method of predicting hosts' age based on microflora taxonomic profiles using a cross-study dataset and deep learning. Our best model has an architecture of a deep neural network that achieves the mean absolute error of 5.91 years when tested on external data. We further advance a procedure for inferring the role of particular microbes during human aging and defining them as potential aging biomarkers. The described intestinal clock represents a unique quantitative model of gut microflora aging and provides a starting point for building host aging and gut community succession into a single narrative.Graphical AbstractDisplay OmittedHighlights•DNNs are the most appropriate model to predict host age from gut microflora profiles•Our DNN models reach MAE of 5.9 years in independent verification•Feature importance analysis gives a starting point for anti-aging intervention designMicrobiology; Microbiome; Bioinformatics; Applied Computing in Medical Science; Artificial Intelligence; Deep Learning; Aging; Biogerontology; Aging Clock
