Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning.

The 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.

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Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Interests F.G., P.M., and A.Z. are affiliated with Deep Longevity, Inc, a for-profit company developing biomarkers of aging and longevity commonly referred to as the “aging clocks. A.A., E.P., V.M., and A.Z. work for Insilico Medicine, a for-profit longevity biotechnology company developing the end-to-end target identification and drug discovery pipeline for a broad spectrum of age-related diseases. The microbiome aging model described in this article is a patent pending technology (application number 20200075127). The microbiomic aging clock is integrated in the Aging.AI system operated by the company. The company may have commercial interests in this publication.