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Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles
Recent advancements in human gut microbiome research have revealed its crucial role in shaping innovative predictive healthcare applications. We introduce Gut Microbiome Wellness Index 2 (GMWI2), an advanced iteration of our original GMWI prototype, designed as a robust, disease-agnostic health stat...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10592848/ https://www.ncbi.nlm.nih.gov/pubmed/37873265 http://dx.doi.org/10.1101/2023.09.30.560294 |
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author | Chang, Daniel Gupta, Vinod K. Hur, Benjamin Cobo-López, Sergio Cunningham, Kevin Y. Han, Nam Soo Lee, Insuk Kronzer, Vanessa L. Teigen, Levi M. Karnatovskaia, Lioudmila V. Longbrake, Erin E. Davis, John M. Nelson, Heidi Sung, Jaeyun |
author_facet | Chang, Daniel Gupta, Vinod K. Hur, Benjamin Cobo-López, Sergio Cunningham, Kevin Y. Han, Nam Soo Lee, Insuk Kronzer, Vanessa L. Teigen, Levi M. Karnatovskaia, Lioudmila V. Longbrake, Erin E. Davis, John M. Nelson, Heidi Sung, Jaeyun |
author_sort | Chang, Daniel |
collection | PubMed |
description | Recent advancements in human gut microbiome research have revealed its crucial role in shaping innovative predictive healthcare applications. We introduce Gut Microbiome Wellness Index 2 (GMWI2), an advanced iteration of our original GMWI prototype, designed as a robust, disease-agnostic health status indicator based on gut microbiome taxonomic profiles. Our analysis involved pooling existing 8069 stool shotgun metagenome data across a global demographic landscape to effectively capture biological signals linking gut taxonomies to health. 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”). The enhanced classification accuracy of GMWI2 outperforms both the original GMWI model and traditional species-level α-diversity indices, suggesting a more reliable tool for differentiating between healthy and non-healthy phenotypes using gut microbiome data. Furthermore, by reevaluating and reinterpreting previously published data, GMWI2 provides fresh insights into the established understanding of how diet, antibiotic exposure, and fecal microbiota transplantation influence gut health. Looking ahead, GMWI2 represents a timely pivotal tool for evaluating health based on an individual’s unique gut microbial composition, paving the way for the early screening of adverse gut health shifts. GMWI2 is offered as an open-source command-line tool, ensuring it is both accessible to and adaptable for researchers interested in the translational applications of human gut microbiome science. |
format | Online Article Text |
id | pubmed-10592848 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-105928482023-10-24 Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles Chang, Daniel Gupta, Vinod K. Hur, Benjamin Cobo-López, Sergio Cunningham, Kevin Y. Han, Nam Soo Lee, Insuk Kronzer, Vanessa L. Teigen, Levi M. Karnatovskaia, Lioudmila V. Longbrake, Erin E. Davis, John M. Nelson, Heidi Sung, Jaeyun bioRxiv Article Recent advancements in human gut microbiome research have revealed its crucial role in shaping innovative predictive healthcare applications. We introduce Gut Microbiome Wellness Index 2 (GMWI2), an advanced iteration of our original GMWI prototype, designed as a robust, disease-agnostic health status indicator based on gut microbiome taxonomic profiles. Our analysis involved pooling existing 8069 stool shotgun metagenome data across a global demographic landscape to effectively capture biological signals linking gut taxonomies to health. 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”). The enhanced classification accuracy of GMWI2 outperforms both the original GMWI model and traditional species-level α-diversity indices, suggesting a more reliable tool for differentiating between healthy and non-healthy phenotypes using gut microbiome data. Furthermore, by reevaluating and reinterpreting previously published data, GMWI2 provides fresh insights into the established understanding of how diet, antibiotic exposure, and fecal microbiota transplantation influence gut health. Looking ahead, GMWI2 represents a timely pivotal tool for evaluating health based on an individual’s unique gut microbial composition, paving the way for the early screening of adverse gut health shifts. GMWI2 is offered as an open-source command-line tool, ensuring it is both accessible to and adaptable for researchers interested in the translational applications of human gut microbiome science. Cold Spring Harbor Laboratory 2023-10-02 /pmc/articles/PMC10592848/ /pubmed/37873265 http://dx.doi.org/10.1101/2023.09.30.560294 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Chang, Daniel Gupta, Vinod K. Hur, Benjamin Cobo-López, Sergio Cunningham, Kevin Y. Han, Nam Soo Lee, Insuk Kronzer, Vanessa L. Teigen, Levi M. Karnatovskaia, Lioudmila V. Longbrake, Erin E. Davis, John M. Nelson, Heidi Sung, Jaeyun Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles |
title | Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles |
title_full | Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles |
title_fullStr | Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles |
title_full_unstemmed | Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles |
title_short | Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles |
title_sort | gut microbiome wellness index 2 for enhanced health status prediction from gut microbiome taxonomic profiles |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10592848/ https://www.ncbi.nlm.nih.gov/pubmed/37873265 http://dx.doi.org/10.1101/2023.09.30.560294 |
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