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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  • 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.


  1. 1.

    Lynch, S. V. & Pedersen, O. The human intestinal microbiome in health and disease. N. Engl. J. Med. 375, 2369–2379 (2016).

  2. 2.

    Gilbert, J. A. et al. Microbiome-wide association studies link dynamic microbial consortia to disease. Nature 535, 94–103 (2016).

  3. 3.

    Gilbert, J. A. et al. Current understanding of the human microbiome. Nat. Med. 24, 392–400 (2018).

  4. 4.

    Dubinsky, M. & Braun, J. Diagnostic and prognostic microbial biomarkers in inflammatory bowel diseases. Gastroenterology 149, 1265–1274.e3 (2015).

  5. 5.

    Konstantinov, S. R., Kuipers, E. J. & Peppelenbosch, M. P. Functional genomic analyses of the gut microbiota for CRC screening. Nat. Rev. Gastroenterol. Hepatol. 10, 741–745 (2013).

  6. 6.

    Routy, B. et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018).

  7. 7.

    Bouter, K. E., van Raalte, D. H., Groen, A. K. & Nieuwdorp, M. Role of the gut microbiome in the pathogenesis of obesity and obesity-related metabolic dysfunction. Gastroenterology 152, 1671–1678 (2017).

  8. 8.

    Tang, W. H. et al. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N. Engl. J. Med. 368, 1575–1584 (2013).

  9. 9.

    Graham, C., Mullen, A. & Whelan, K. Obesity and the gastrointestinal microbiota: a review of associations and mechanisms. Nutr. Rev. 73, 376–385 (2015).

  10. 10.

    Wieland, A., Frank, D. N., Harnke, B. & Bambha, K. Systematic review: microbial dysbiosis and nonalcoholic fatty liver disease. Aliment. Pharmacol. Ther. 42, 1051–1063 (2015).

  11. 11.

    Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015).

  12. 12.

    Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).

  13. 13.

    Martínez, I. et al. The gut microbiota of rural papua new guineans: composition, diversity patterns, and ecological processes. Cell Reports 11, 527–538 (2015).

  14. 14.

    Gupta, V. K., Paul, S. & Dutta, C. Geography, ethnicity or subsistence-specific variations in human microbiome composition and diversity. Front. Microbiol. 8, 1162 (2017).

  15. 15.

    Winglee, K. et al. Recent urbanization in China is correlated with a Westernized microbiome encoding increased virulence and antibiotic resistance genes. Microbiome 5, 121 (2017).

  16. 16.

    Costea, P. I. et al. Towards standards for human fecal sample processing in metagenomic studies. Nat. Biotechnol. 35, 1069–1076 (2017).

  17. 17.

    Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).

  18. 18.

    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

  19. 19.

    Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016).

  20. 20.

    Anderson, M. J. & Walsh, D. C. I. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing? Ecol. Monogr. 83, 557–574 (2013).

  21. 21.

    Zhou, Y. et al. Gut microbiota offers universal biomarkers across ethnicity in inflammatory bowel disease diagnosis and infliximab response prediction. mSystems 3, e00188–e00117 (2018).

  22. 22.

    Yu, J. et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut 66, 70–78 (2017).

  23. 23.

    Walters, W. A., Xu, Z. & Knight, R. Meta-analyses of human gut microbes associated with obesity and IBD. FEBS Lett. 588, 4223–4233 (2014).

  24. 24.

    McDonald, D. et al. American Gut: an open platform for citizen science microbiome research. mSystems 3, e00031–18 (2018).

  25. 25.

    Stagaman, K. et al. Market integration predicts human gut microbiome attributes across a gradient of economic development. mSystems 3, e00122–17 (2018).

  26. 26.

    Costello, E. K., Stagaman, K., Dethlefsen, L., Bohannan, B. J. & Relman, D. A. The application of ecological theory toward an understanding of the human microbiome. Science 336, 1255–1262 (2012).

  27. 27.

    Liao, M. et al. Comparative analyses of fecal microbiota in Chinese isolated Yao population, minority Zhuang and rural Han by 16sRNA sequencing. Sci. Rep. 8, 1142 (2018).

  28. 28.

    Walters, W. et al. Improved bacterial 16S rRNA Gene (V4 and V4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. mSystems 1, e00009–e00015 (2015).

  29. 29.

    Frank, D. N. BARCRAWL and BARTAB: software tools for the design and implementation of barcoded primers for highly multiplexed DNA sequencing. BMC Bioinformatics 10, 362 (2009).

  30. 30.

    McDonald, D., Birmingham, A. & Knight, R. Context and the human microbiome. Microbiome 3, 52 (2015).

  31. 31.

    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

  32. 32.

    McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME. J. 6, 610–618 (2012).

  33. 33.

    He, Y. et al. Stability of operational taxonomic units: an important but neglected property for analyzing microbial diversity. Microbiome 3, 20 (2015).

  34. 34.

    McDonald, D. et al. The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome. Gigascience 1, 7 (2012).

  35. 35.

    Morgan, X. C. et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome. Biol. 13, R79 (2012).

  36. 36.

    Xie, H. et al. Shotgun metagenomics of 250 adult twins reveals genetic and environmental impacts on the gut microbiome. Cell Syst. 3, 572–584 (2016).

  37. 37.

    Lim, M. Y. et al. The effect of heritability and host genetics on the gut microbiota and metabolic syndrome. Gut 66, 1031–1038 (2017).

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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.


  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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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.

Competing interests

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

Corresponding authors

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

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