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Regional variation limits applications of healthy gut microbiome reference ranges and disease models

Nature Medicine volume 24, pages1532–1535(2018)Cite this article

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An Author Correction to this article was published on 24 September 2018

This article has been updated

Abstract

Dysbiosis, departure of the gut microbiome from a healthy state, has been suggested to be a powerful biomarker of disease incidence and progression1,2,3. Diagnostic applications have been proposed for inflammatory bowel disease diagnosis and prognosis4, colorectal cancer prescreening5 and therapeutic choices in melanoma6. Noninvasive sampling could facilitate large-scale public health applications, including early diagnosis and risk assessment in metabolic7 and cardiovascular diseases8. To understand the generalizability of microbiota-based diagnostic models of metabolic disease, we characterized the gut microbiota of 7,009 individuals from 14 districts within 1 province in China. Among phenotypes, host location showed the strongest associations with microbiota variations. Microbiota-based metabolic disease models developed in one location failed when used elsewhere, suggesting that such models cannot be extrapolated. Interpolated models performed much better, especially in diseases with obvious microbiota-related characteristics. Interpolation efficiency decreased as geographic scale increased, indicating a need to build localized baseline and disease models to predict metabolic risks.

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Fig. 1: Overview of sampling regions and of regional variation in gut microbiota.
Fig. 2: Evaluating cross-applicability of gut microbiota–based disease models among locations.
Fig. 3: Illustration of the difficulty gradient used to interpolate and extrapolate the MetS model.

Change history

  • 24 September 2018

    In the version of this article originally published, in the sentence “Applying the same approach to obesity (Fig. 2b), MetS (Fig. 2c) and fatty liver (Fig. 2d) yielded similar results,” two figure panels were cited incorrectly. The data for obesity are in Fig. 2c, and the data for MetS are in Fig. 2b. The sentence has been updated with the correct citations in the print, PDF and HTML versions of the article.

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Acknowledgements

We acknowledge the contributions of the 308 local CDC investigators and registered nurses for their help with collection point maintenance and metadata and stool sample collection. We thank all volunteers who participated in this project. This study was supported by the National Projects of Major Infectious Disease Control and Prevention (2017ZX10103011 (H.W.Z.)), National Natural Science Foundation of China (NSFC31570497 (H.W.Z.), 31322003 (H.W.Z.), and 81671171 (J.Y.)), China Postdoctoral Science Foundation (C1090132 (Y.H.)) and the Science and Technology Planning Project of Guangdong Province, China (2015A030401055 (W.W.)).

Author information

Author notes

  1. These authors contributed equally to this work: Yan He, Wei Wu, Hui-Min Zheng, Pan Li.

Affiliations

  1. Division of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China

    Yan He, Hui-Min Zheng, Pan Li, Hua-Fang Sheng, Mu-Xuan Chen, Xiao-Jiao Chen, Zu-Hua Rong, Nan Yu & Hong-Wei Zhou

  2. Department of Environmental Health, School of Public Health, Southern Medical University, Guangzhou, China

    Wei Wu, Hui-Min Zheng, Pan Li, Zhong-Dai-Xi Zheng, Zu-Hua Rong & Hong-Wei Zhou

  3. Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China

    Wei Wu, Zi-Hui Chen, Gui-Yuan Ji & Wen-Jun Ma

  4. Department of Pediatrics, University of California San Diego, La Jolla, CA, USA

    Daniel McDonald & Rob Knight

  5. Gut Infection and Inflammation Biology Lab, UNESCO-Regional Center for Biotechnology, NCR Biotech Science Cluster, Faridabad, India

    Prabhakar Mujagond

  6. Department of Pathophysiology, Southern Medical University, Guangzhou, China

    Peng Chen

  7. Shenzhen Fun-Poo Biotech Co., Ltd., Shenzhen, China

    Li-Yi Lyu, Xian Wang & Chong-Bin Wu

  8. Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China

    Yan-Jun Xu

  9. Department of Neurology, NanFang Hospital, Southern Medical University, Guangzhou, China

    Jia Yin

  10. Department of Microbiology and Immunology, KU Leuven–University of Leuven, Leuven, Belgium

    Jeroen Raes

  11. VIB, Center for the Biology of Disease, Leuven, Belgium

    Jeroen Raes

  12. Vrije Universiteit Brussel, Faculty of Sciences and Bioengineering Sciences, Microbiology Unit, Brussels, Belgium

    Jeroen Raes

  13. Department of Computer Science & Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA

    Rob Knight

  14. Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA

    Rob Knight

  15. State Key Laboratory of Organ Failure Research, Southern Medical University, Guangzhou, China

    Hong-Wei Zhou

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  1. Yan He
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Contributions

Y.H., W.W., W.-J.M. and H.-W.Z. designed the study; W.W., C.-Z.H., Z.-H.C, G.-Y.J., Y.-J.X. and W.-J.M. organized data collection and trained local CDC investigators; M.-X.C., Z.-D.-X.Z., P.M., X.-J.C., Z.-H.R., L.-Y.L. and N.Y. processed the samples; Y.H., H.-M.Z., P.L., H.-F.S., X.W., C.-B.W., P.C., J.Y. and H.-W.Z. analyzed the data; Y.H., D.M., W.-J.M., R.K. and H.-W.Z. drafted the manuscript; and R.K. and J.R. offered advice regarding the design of the study, data analysis and manuscript writing.

Corresponding authors

Correspondence to Wen-Jun Ma or Hong-Wei Zhou.

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Competing interests

L.-Y.L., X.W. and C.-B.W. are employees of Shenzhen Fun-Poo Biotech Co., LTD.

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He, Y., Wu, W., Zheng, HM. et al. Regional variation limits applications of healthy gut microbiome reference ranges and disease models. Nat Med 24, 1532–1535 (2018). https://doi.org/10.1038/s41591-018-0164-x

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