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

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

This article has been updated


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.


  1. 1.

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

    CAS  Article  Google Scholar 

  2. 2.

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

    CAS  Article  Google Scholar 

  3. 3.

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

    CAS  Article  Google Scholar 

  4. 4.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  6. 6.

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

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  8. 8.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  11. 11.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

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

    CAS  PubMed  Google Scholar 

  17. 17.

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

    CAS  Article  Google Scholar 

  18. 18.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

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

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

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

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

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

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

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

    CAS  Article  PubMed  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  33. 33.

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

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

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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




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).

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