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Meta-analysis of gut microbiome studies identifies disease-specific and shared responses

Author

Listed:
  • Claire Duvallet

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Sean M. Gibbons

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    The Broad Institute of MIT and Harvard)

  • Thomas Gurry

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    The Broad Institute of MIT and Harvard)

  • Rafael A. Irizarry

    (Dana-Farber Cancer Institute
    Harvard T.H. Chan School of Public Health)

  • Eric J. Alm

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    The Broad Institute of MIT and Harvard)

Abstract

Hundreds of clinical studies have demonstrated associations between the human microbiome and disease, yet fundamental questions remain on how we can generalize this knowledge. Results from individual studies can be inconsistent, and comparing published data is further complicated by a lack of standard processing and analysis methods. Here we introduce the MicrobiomeHD database, which includes 28 published case–control gut microbiome studies spanning ten diseases. We perform a cross-disease meta-analysis of these studies using standardized methods. We find consistent patterns characterizing disease-associated microbiome changes. Some diseases are associated with over 50 genera, while most show only 10–15 genus-level changes. Some diseases are marked by the presence of potentially pathogenic microbes, whereas others are characterized by a depletion of health-associated bacteria. Furthermore, we show that about half of genera associated with individual studies are bacteria that respond to more than one disease. Thus, many associations found in case–control studies are likely not disease-specific but rather part of a non-specific, shared response to health and disease.

Suggested Citation

  • Claire Duvallet & Sean M. Gibbons & Thomas Gurry & Rafael A. Irizarry & Eric J. Alm, 2017. "Meta-analysis of gut microbiome studies identifies disease-specific and shared responses," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-01973-8
    DOI: 10.1038/s41467-017-01973-8
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    Cited by:

    1. 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.
    2. Sean M Gibbons & Claire Duvallet & Eric J Alm, 2018. "Correcting for batch effects in case-control microbiome studies," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-17, April.
    3. Lee Su Mun & See Wye Lum & Genevieve Kong Yuiin Sze & Cheong Hock Yoong & Kwek Ching Yung & Liong Kah Lok & Divya Gopinath, 2021. "Association of Microbiome with Oral Squamous Cell Carcinoma: A Systematic Review of the Metagenomic Studies," IJERPH, MDPI, vol. 18(14), pages 1-21, July.
    4. Antonino Malacrinò & Victoria A Sadowski & Tvisha K Martin & Nathalia Cavichiolli de Oliveira & Ian J Brackett & James D Feller & Kristian J Harris & Orlando Combita Heredia & Rosa Vescio & Alison E B, 2020. "Biological invasions alter environmental microbiomes: A meta-analysis," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-12, October.
    5. Alan Le Goallec & Braden T Tierney & Jacob M Luber & Evan M Cofer & Aleksandar D Kostic & Chirag J Patel, 2020. "A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery type," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-21, May.
    6. Braden T Tierney & Yingxuan Tan & Zhen Yang & Bing Shui & Michaela J Walker & Benjamin M Kent & Aleksandar D Kostic & Chirag J Patel, 2022. "Systematically assessing microbiome–disease associations identifies drivers of inconsistency in metagenomic research," PLOS Biology, Public Library of Science, vol. 20(3), pages 1-18, March.

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