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Gut Microbiome Wellness Index 2 enhances health status prediction from gut microbiome taxonomic profiles

Authors :
Daniel Chang
Vinod K. Gupta
Benjamin Hur
Sergio Cobo-López
Kevin Y. Cunningham
Nam Soo Han
Insuk Lee
Vanessa L. Kronzer
Levi M. Teigen
Lioudmila V. Karnatovskaia
Erin E. Longbrake
John M. Davis
Heidi Nelson
Jaeyun Sung
Source :
Nature Communications, Vol 15, Iss 1, Pp 1-14 (2024)
Publication Year :
2024
Publisher :
Nature Portfolio, 2024.

Abstract

Abstract Recent advancements in translational gut microbiome research have revealed its crucial role in shaping predictive healthcare applications. Herein, we introduce the Gut Microbiome Wellness Index 2 (GMWI2), an enhanced version of our original GMWI prototype, designed as a standardized disease-agnostic health status indicator based on gut microbiome taxonomic profiles. Our analysis involves pooling existing 8069 stool shotgun metagenomes from 54 published studies across a global demographic landscape (spanning 26 countries and six continents) to identify gut taxonomic signals linked to disease presence or absence. GMWI2 achieves a cross-validation balanced accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased) individuals and surpasses 90% accuracy for samples with higher confidence (i.e., outside the “reject option”). This performance exceeds that of the original GMWI model and traditional species-level α-diversity indices, indicating a more robust gut microbiome signature for differentiating between healthy and non-healthy phenotypes across multiple diseases. When assessed through inter-study validation and external validation cohorts, GMWI2 maintains an average accuracy of nearly 75%. Furthermore, by reevaluating previously published datasets, GMWI2 offers new insights into the effects of diet, antibiotic exposure, and fecal microbiota transplantation on gut health. Available as an open-source command-line tool, GMWI2 represents a timely, pivotal resource for evaluating health using an individual’s unique gut microbial composition.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
15
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.365f9678a1e44e6af7e60c3b2af0a02
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-024-51651-9