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

Author

Listed:
  • Vinod K. Gupta

    (Mayo Clinic
    Mayo Clinic)

  • Minsuk Kim

    (Mayo Clinic
    Mayo Clinic)

  • Utpal Bakshi

    (Mayo Clinic
    Mayo Clinic)

  • Kevin Y. Cunningham

    (Mayo Clinic
    University of Minnesota Twin-Cities)

  • John M. Davis

    (Mayo Clinic)

  • Konstantinos N. Lazaridis

    (Mayo Clinic College of Medicine and Science)

  • Heidi Nelson

    (Mayo Clinic)

  • Nicholas Chia

    (Mayo Clinic
    Mayo Clinic)

  • Jaeyun Sung

    (Mayo Clinic
    Mayo Clinic
    Mayo Clinic)

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.

Suggested Citation

  • Vinod K. Gupta & Minsuk Kim & Utpal Bakshi & Kevin Y. Cunningham & John M. Davis & Konstantinos N. Lazaridis & Heidi Nelson & Nicholas Chia & Jaeyun Sung, 2020. "A predictive index for health status using species-level gut microbiome profiling," Nature Communications, Nature, vol. 11(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18476-8
    DOI: 10.1038/s41467-020-18476-8
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    Cited by:

    1. Vanessa R. Marcelino & Caitlin Welsh & Christian Diener & Emily L. Gulliver & Emily L. Rutten & Remy B. Young & Edward M. Giles & Sean M. Gibbons & Chris Greening & Samuel C. Forster, 2023. "Disease-specific loss of microbial cross-feeding interactions in the human gut," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Daniel Chang & Vinod K. Gupta & Benjamin Hur & Sergio Cobo-López & Kevin Y. Cunningham & Nam Soo Han & Insuk Lee & Vanessa L. Kronzer & Levi M. Teigen & Lioudmila V. Karnatovskaia & Erin E. Longbrake , 2024. "Gut Microbiome Wellness Index 2 enhances health status prediction from gut microbiome taxonomic profiles," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Oliver Aasmets & Kertu Liis Krigul & Kreete Lüll & Andres Metspalu & Elin Org, 2022. "Gut metagenome associations with extensive digital health data in a volunteer-based Estonian microbiome cohort," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    4. Brandon Hickman & Anne Salonen & Alise J. Ponsero & Roosa Jokela & Kaija-Leena Kolho & Willem M. Vos & Katri Korpela, 2024. "Gut microbiota wellbeing index predicts overall health in a cohort of 1000 infants," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    5. Qi Su & Qin Liu & Raphaela Iris Lau & Jingwan Zhang & Zhilu Xu & Yun Kit Yeoh & Thomas W. H. Leung & Whitney Tang & Lin Zhang & Jessie Q. Y. Liang & Yuk Kam Yau & Jiaying Zheng & Chengyu Liu & Mengjin, 2022. "Faecal microbiome-based machine learning for multi-class disease diagnosis," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    6. Rhee, Chaeyoung & Park, Sung-Gwan & Yu, Sung Il & Dalantai, Tergel & Shin, Juhee & Chae, Kyu-Jung & Shin, Seung Gu, 2023. "Mapping microbial dynamics in anaerobic digestion system linked with organic composition of substrates: Protein and lipid," Energy, Elsevier, vol. 275(C).
    7. Wanting Dong & Xinyue Fan & Yaqiong Guo & Siyi Wang & Shulei Jia & Na Lv & Tao Yuan & Yuanlong Pan & Yong Xue & Xi Chen & Qian Xiong & Ruifu Yang & Weigang Zhao & Baoli Zhu, 2024. "An expanded database and analytical toolkit for identifying bacterial virulence factors and their associations with chronic diseases," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

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