The Aging Metabolite Database with Summary Statistics

TL;DR

The paper introduces the Aging Metabolite Database, which consolidates and summarizes aging-related metabolomic data from numerous studies to facilitate cross-study comparisons and validate metabolic changes during aging.

Contribution

It presents a new comprehensive database that integrates data from multiple studies on aging metabolites, enabling easier querying and validation across species and studies.

Findings

Summarizes aging-related metabolite data for nearly 2,700 metabolites.

Includes 6,500 data points from 27 human studies.

Provides 163 summary statistics for 53 common metabolites.

Abstract

Thousands of metabolomic papers are published each year, creating challenges for scientists to combine results and yield conclusions that span across studies. Literature databases such as the Human Metabolome Database provide summaries of metabolite detections and relevance, but it does not focus on specific processes, such as aging. Another database, MetaboAge, focuses specifically on how metabolite concentrations change with age. However, both databases can only search metabolites individually. Both databases lack an easily displayed overview of all the information provided, and they do not combine datasets to validate results across studies. This project aims to design a database from data extracted from the literature that cover studies across species, include query options, and combine data to get statistically sound results. The Aging Metabolite Database summarizes aging-related information for almost 2,700 non-unique metabolites and was created from 104 publications. A sub-database was created to focus on metabolite plasma levels in human aging and, including metadata from 27 papers. This resulted in 6,500 data points from 1,167 metabolites. 12 of these papers were selected for the summary results section of the database. Across these 12 studies, 53 common metabolites were reported and can be queried in 163 summary statistics, for example by age and sex. Summary statistics are provided for all aging and sex combinations. This information assists users to compare the results of their own age-related studies to published literature. Combining multiple datasets results in a more solid understanding of validated metabolic changes during the aging process.