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

• 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
• DOI: 10.1038/s41467-020-18476-8

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.