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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7492273/ https://www.ncbi.nlm.nih.gov/pubmed/32934239 http://dx.doi.org/10.1038/s41467-020-18476-8 |
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author | Gupta, Vinod K. Kim, Minsuk Bakshi, Utpal Cunningham, Kevin Y. Davis, John M. Lazaridis, Konstantinos N. Nelson, Heidi Chia, Nicholas Sung, Jaeyun |
author_facet | Gupta, Vinod K. Kim, Minsuk Bakshi, Utpal Cunningham, Kevin Y. Davis, John M. Lazaridis, Konstantinos N. Nelson, Heidi Chia, Nicholas Sung, Jaeyun |
author_sort | Gupta, Vinod K. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7492273 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-74922732020-10-01 A predictive index for health status using species-level gut microbiome profiling Gupta, Vinod K. Kim, Minsuk Bakshi, Utpal Cunningham, Kevin Y. Davis, John M. Lazaridis, Konstantinos N. Nelson, Heidi Chia, Nicholas Sung, Jaeyun Nat Commun Article 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. Nature Publishing Group UK 2020-09-15 /pmc/articles/PMC7492273/ /pubmed/32934239 http://dx.doi.org/10.1038/s41467-020-18476-8 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Gupta, Vinod K. Kim, Minsuk Bakshi, Utpal Cunningham, Kevin Y. Davis, John M. Lazaridis, Konstantinos N. Nelson, Heidi Chia, Nicholas Sung, Jaeyun A predictive index for health status using species-level gut microbiome profiling |
title | A predictive index for health status using species-level gut microbiome profiling |
title_full | A predictive index for health status using species-level gut microbiome profiling |
title_fullStr | A predictive index for health status using species-level gut microbiome profiling |
title_full_unstemmed | A predictive index for health status using species-level gut microbiome profiling |
title_short | A predictive index for health status using species-level gut microbiome profiling |
title_sort | predictive index for health status using species-level gut microbiome profiling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7492273/ https://www.ncbi.nlm.nih.gov/pubmed/32934239 http://dx.doi.org/10.1038/s41467-020-18476-8 |
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