DOI: 10.5061/dryad.b2rbnzspv
title: Data from: Social and environmental predictors of gut microbiome age in wild baboons

description: Understanding why some individuals age faster than others is essential to
 evolutionary biology and geroscience, but measuring variation in
 biological age is difficult. One solution may lie in measuring gut
 microbiome composition because microbiota change with many age-related
 factors (e.g., immunity and behavior). Here we create a microbiome-based
 age predictor using 13,563 gut microbial profiles from 479 wild baboons
 collected over 14 years. The resulting “microbiome clock” predicts host
 chronological age. Deviations from the clock’s predictions are linked to
 demographic and socio-environmental factors that predict baboon health and
 survival: animals who appear old-for-age tend to be male, sampled in the
 dry season (for females), and high social status (both sexes). However, an
 individual’s “microbiome age” does not predict the attainment of
 developmental milestones or lifespan. Hence, the microbiome clock
 accurately reflects age and some social and environmental conditions, but
 not the pace of development or mortality risk.
description: # Social and environmental predictors of gut microbiome age in wild
 baboons
 [https://doi.org/10.5061/dryad.b2rbnzspv](https://doi.org/10.5061/dryad.b2rbnzspv) This dataset consists of anonymized baboon data originally used to understand how to predict host age from 16S rRNA gut microbiome data.    ## Description of the data and file structure I simplified the main data required for this paper into the following four files: * IDTAXA_silva_assignment.rds is all the taxonomic assignments from SILVA_SSU_r132_March2018.RData. The specific ASVs are the rows and the columns are the taxonomic classification for those ASVs.  * merged_baboon_data_with_metadata_preset_more_blanks_greater_1000.rds is a lightly processed ASV table. The rows correspond to the sample ID (named DADA_id in the metadata file), and the columns correspond to specific ASVs.  * metadata_full.rds is an extremely wide metadata table (13524x83) that should include all the associated demographic, social, and environmental data required to recreate these analyses. Each row is a sample and each column is a descriptive variable.  * metadata_full_descriptions.csv is a README file for the metadata_full.rds datafile, consisting of descriptions of the columns. ## Sharing/Access information Data represented in this dataset is a subset of that used in [Grieneisen et al. 2021](https://www.science.org/doi/10.1126/science.aba5483) and [Bjork et al. 2022](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271586/). Raw 16*S* rRNA gene sequences are deposited on EBI-ENA (project ERP119849) and Qiita [study 12949]. Data derived from this raw data and its corresponding R and Python code is available at [https://github.com/maunadasari/Dasari_etal-GutMicrobiomeAge](https://github.com/maunadasari/Dasari_etal-GutMicrobiomeAge). ## Code/Software This code was written in RMarkdown and Jupyter Notebook and simplified for publication at [https://github.com/maunadasari/Dasari_etal-GutMicrobiomeAge](https://github.com/maunadasari/Dasari_etal-GutMicrobiomeAge). All questions related to the data and code in this repository should be directed to Mauna Dasari ([mauna.dasari@gmail.com](mailto:mauna.dasari@gmail.com)).