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

Vinod K. Gupta, Minsuk Kim, Utpal Bakshi, Kevin Y. Cunningham, John M. Davis, Konstantinos N. Lazaridis, Heidi Nelson, Nicholas Chia and Jaeyun Sung ()
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Vinod K. Gupta: Mayo Clinic
Minsuk Kim: Mayo Clinic
Utpal Bakshi: Mayo Clinic
Kevin Y. Cunningham: Mayo Clinic
John M. Davis: Mayo Clinic
Konstantinos N. Lazaridis: Mayo Clinic College of Medicine and Science
Heidi Nelson: Mayo Clinic
Nicholas Chia: Mayo Clinic
Jaeyun Sung: Mayo Clinic

Nature Communications, 2020, vol. 11, issue 1, 1-16

Abstract: Abstract 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.

Date: 2020
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DOI: 10.1038/s41467-020-18476-8

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