A predictive index for health status using species-level gut microbiome profiling

Providing insight into one’s health status from a gut microbiome sample is an important clinical goal in current human microbiome research. Herein, we introduce the Gut Microbiome Health Index (GMHI), a biologically-interpretable mathematical formula for predicting the likelihood of disease independ...

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Bibliographic Details
Published in:Nature communications Vol. 11; no. 1; pp. 4635 - 16
Main Authors: Gupta, Vinod K., Kim, Minsuk, Bakshi, Utpal, Cunningham, Kevin Y., Davis, John M., Lazaridis, Konstantinos N., Nelson, Heidi, Chia, Nicholas, Sung, Jaeyun
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 15.09.2020
Nature Publishing Group
Nature Portfolio
Subjects:
49/23
631/114/2785
631/326/2565/2134
692/53
Data retrieval
Datasets
Digestive system
Disease
Diversity indices
Gut microbiota
Human subjects
Humanities and Social Sciences
Independent study
Information sharing
Intestinal microflora
Introduced species
Mathematical models
Microbiomes
Microbiota
Microorganisms
multidisciplinary
Robustness (mathematics)
Science
Science (multidisciplinary)
Surgery
Taxonomy
ISSN:2041-1723, 2041-1723
Online Access:Get full text
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Summary:Providing insight into one’s health status from a gut microbiome sample is an important clinical goal in current human microbiome research. Herein, we introduce the Gut Microbiome Health Index (GMHI), a biologically-interpretable mathematical formula for predicting the likelihood of disease independent of the clinical diagnosis. GMHI is formulated upon 50 microbial species associated with healthy gut ecosystems. These species are identified through a multi-study, integrative analysis on 4347 human stool metagenomes from 34 published studies across healthy and 12 different nonhealthy conditions, i.e., disease or abnormal bodyweight. When demonstrated on our population-scale meta-dataset, GMHI is the most robust and consistent predictor of disease presence (or absence) compared to α-diversity indices. Validation on 679 samples from 9 additional studies results in a balanced accuracy of 73.7% in distinguishing healthy from non-healthy groups. Our findings suggest that gut taxonomic signatures can predict health status, and highlight how data sharing efforts can provide broadly applicable discoveries. A biologically-interpretable and robust metric that provides insight into one’s health status from a gut microbiome sample is an important clinical goal in current human microbiome research. Herein, the authors introduce a species-level index that predicts the likelihood of having a disease.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-020-18476-8