Distinctive Microbiome Type Distribution in a Young Adult Balinese Cohort May Reflect Environmental Changes Associated with Modernization


An important public health question is understanding how changes in human environments can drive changes in the gut microbiota that influence risks associated with human health and wellbeing. It is well-documented that the modernization of societies is strongly correlated with intergenerational change in the frequency of nutrition-related chronic diseases in which microbial dysbiosis is implicated. The population of Bali, Indonesia, is well-positioned to study the interconnection between a changing food environment and microbiome patterns in its early stages, because of a recent history of modernization. Here, we characterize the fecal microbiota and diet history of the young adult women in Bali, Indonesia (n = 41) in order to compare microbial patterns in this generation with those of other populations with different histories of a modern food environment (industrialized supply chain). We found strong support for two distinct fecal microbiota community types in our study cohort at similar frequency: a Prevotella-rich (Type-P) and a Bacteroides-rich (Type-B) community (p < 0.001, analysis of similarity, Wilcoxon test). Although Type-P individuals had lower alpha diversity (p < 0.001, Shannon) and higher incidence of obesity, multivariate analyses with diet data showed that community types significantly influenced associations with BMI. In a multi-country dataset (n = 257), we confirmed that microbial beta diversity across subsistent and industrial populations was significantly associated with Prevotella and Bacteroides abundance (p < 0.001, generalized additive model) and that the prevalence of community types differs between societies. The young adult Balinese microbiota was distinctive in having an equal prevalence of two community types. Collectively, our study showed that the incorporation of community types as an explanatory factor into study design or modeling improved the ability to identify microbiome associations with diet and health metrics.

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Data availability

The Balinese dataset supporting the conclusions of this article is available in the European Nucleotide Archive repository, project code PRJEB32385. Datasets for other populations are available in the MG-RAST repository, project numbers mgp401 and mgp7058.

Code Availability

Complete accounts of statistical analyses and R scripts are provided in the Online Resource 1 of this publication.


  1. 1.

    Filippo CD, Cavalieri D, Paola MD et al (2010) Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. PNAS 107(33):14691–6. https://doi.org/10.1073/pnas.1005963107

    Article  PubMed  Google Scholar 

  2. 2.

    Yatsunenko T, Rey FE, Manary MJ et al (2012) Human gut microbiome viewed across age and geography. Nature 486:222–227. https://doi.org/10.1038/nature11053

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Schnorr SL, Candela M, Rampelli S et al (2014) Gut microbiome of the Hadza hunter-gatherers. Nat Commun 5:3654. https://doi.org/10.1038/ncomms4654

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Martínez I, Stegen JC, Maldonado-Gómez MX et al (2015) The gut microbiota of rural Papua New Guineans: composition, diversity patterns, and ecological processes. Cell Rep 11:527–538. https://doi.org/10.1016/j.celrep.2015.03.049

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Gorvitovskaia A, Holmes SP, Huse SM (2016) Interpreting Prevotella and Bacteroides as biomarkers of diet and lifestyle. Microbiome 4:15. https://doi.org/10.1186/s40168-016-0160-7

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Gomez A, Petrzelkova KJ, Burns MB et al (2016) Gut microbiome of coexisting BaAka pygmies and Bantu reflects gradients of traditional subsistence patterns. Cell Rep 14:2142–2153. https://doi.org/10.1016/j.celrep.2016.02.013

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Mancabelli L, Milani C, Lugli GA et al (2017) Meta-analysis of the human gut microbiome from urbanized and pre-agricultural populations. Environ Microbiol 19:1379–1390. https://doi.org/10.1111/1462-2920.13692

    Article  PubMed  Google Scholar 

  8. 8.

    Jha AR, Davenport ER, Gautam Y et al (2018) Gut microbiome transition across a lifestyle gradient in Himalaya. PLoS Biol 16(11):e2005396. https://doi.org/10.1371/journal.pbio.2005396

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Obregon-Tito AJ, Tito RY, Metcalf J et al (2015) Subsistence strategies in traditional societies distinguish gut microbiomes. Nat Commun 6:1–9. https://doi.org/10.1038/ncomms7505

    CAS  Article  Google Scholar 

  10. 10.

    Human Microbiome Project Consortium T (2012) Structure, function and diversity of the healthy human microbiome. Nature 486:207–214. https://doi.org/10.1038/nature11234

    CAS  Article  Google Scholar 

  11. 11.

    Vangay P, Johnson AJ, Ward TL et al (2018) US immigration westernizes the human gut microbiome. Cell 175:962-972.e10. https://doi.org/10.1016/j.cell.2018.10.029

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    David LA, Maurice CF, Carmody RN et al (2014) Diet rapidly and reproducibly alters the human gut microbiome. Nature 505:559–563. https://doi.org/10.1038/nature12820

    CAS  Article  Google Scholar 

  13. 13.

    Wu GD, Chen J, Hoffmann C et al (2011) Linking long-term dietary patterns with gut microbial enterotypes. Science 334:105–108. https://doi.org/10.1126/science.1208344

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Arumugam M, Raes J, Pelletier E et al (2011) Enterotypes of the human gut microbiome. Nature 473:174–180. https://doi.org/10.1038/nature09944

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Costea PI, Hildebrand F, Arumugam M et al (2018) Enterotypes in the landscape of gut microbial community composition. Nat Microbiol 3:8–16. https://doi.org/10.1038/s41564-017-0072-8

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Roager HM, Licht TR, Poulsen SK et al (2014) Microbial enterotypes, inferred by the Prevotella-to-Bacteroides ratio, remained stable during a 6-month randomized controlled diet intervention with the new Nordic diet. Appl Environ Microbiol 80:1142–1149. https://doi.org/10.1128/AEM.03549-13

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Rajilić-Stojanović M, Heilig HGHJ, Tims S et al (2013) Long-term monitoring of the human intestinal microbiota composition. Environ Microbiol 15:1146–1159. https://doi.org/10.1111/1462-2920.12023

    CAS  Article  Google Scholar 

  18. 18.

    Wang J, Linnenbrink M, Künzel S et al (2014) Dietary history contributes to enterotype-like clustering and functional metagenomic content in the intestinal microbiome of wild mice. Proc Natl Acad Sci USA 111:E2703-2710. https://doi.org/10.1073/pnas.1402342111

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Sonnenburg ED, Sonnenburg JL (2014) Starving our microbial self: the deleterious consequences of a diet deficient in microbiota-accessible carbohydrates. Cell Metab 20:779–786. https://doi.org/10.1016/j.cmet.2014.07.003

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Sonnenburg ED, Smits SA, Tikhonov M et al (2016) Diet-induced extinctions in the gut microbiota compound over generations. Nature 529:212–215. https://doi.org/10.1038/nature16504

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Marzuki S, Sudoyo H, Suryadi H et al (2003) Human genome diversity and disease on the island Southeast Asia. In: Marzuki S, Verhoef J, Snippe H (eds) Tropical diseases: from molecule to bedside. Springer, US, Boston, MA, pp 3–18

    Chapter  Google Scholar 

  22. 22.

    Thedja MD, Muljono DH, Nurainy N et al (2011) Ethnogeographical structure of hepatitis B virus genotype distribution in Indonesia and discovery of a new subgenotype, B9. Arch Virol 156:855–868. https://doi.org/10.1007/s00705-011-0926-y

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Herningtyas EH, Ng TS (2019) Prevalence and distribution of metabolic syndrome and its components among provinces and ethnic groups in Indonesia. BMC Public Health 19:377. https://doi.org/10.1186/s12889-019-6711-7

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Statistics Indonesia (2015) Number and growth rate of populations in Denpasar Municipality, 2001–2015. https://denpasarkota.bps.go.id/statictable/2016/07/25/157/jumlah-dan-laju-pertumbuhan-penduduk-kota-denpasar-2001-2015.html. Accessed 11 Jun 2020

  25. 25.

    Statistics Indonesia (2018) Regional GDP of Denpasar 2010–2018. https://denpasarkota.bps.go.id/dynamictable/2019/07/29/86/pdrb-kota-denpasar-atas-dasar-harga-berlaku-menurut-lapangan-usaha-tahun-2010-2018-juta-rupiah-.html. Accessed 11 Jun 2020

  26. 26.

    Statistics Indonesia (2020) Gross domestic regional income per capita 2018–2020. In: Statistics of Bali Province. https://bali.bps.go.id/indicator/52/172/1/pdrb-perkapita-atas-dasar-harga-berlaku-kabupaten-kota-di-provinsi-bali.html. Accessed 26 Apr 2021

  27. 27.

    Erhardt J (2010) NutriSurvey: nutrition surveys and calculations [computer software]. EBISpro, Germany

    Google Scholar 

  28. 28.

    Caporaso JG, Lauber CL, Walters WA et al (2012) Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J 6:1621–1624. https://doi.org/10.1038/ismej.2012.8

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Rognes T, Flouri T, Nichols B et al (2016) VSEARCH: a versatile open source tool for metagenomics. PeerJ 18(4):e2584. https://doi.org/10.7717/peerj.2584

    Article  Google Scholar 

  30. 30.

    Bolyen E, Rideout JR, Dillon MR et al (2019) Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37:852–857. https://doi.org/10.1038/s41587-019-0209-9

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Callahan BJ, McMurdie PJ, Rosen MJ et al (2016) DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods 13:581–583. https://doi.org/10.1038/nmeth.3869

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    Google Scholar 

  33. 33.

    Quast C, Pruesse E, Yilmaz P et al (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:D590–D596. https://doi.org/10.1093/nar/gks1219

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Hall M, Beiko RG (2018) 16S rRNA gene analysis with QIIME2. Methods Mol Biol 1849:113–129. https://doi.org/10.1007/978-1-4939-8728-3_8

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Murtagh F, Legendre P (2014) Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? J Classif 31:274–295. https://doi.org/10.1007/s00357-014-9161-z

    Article  Google Scholar 

  36. 36.

    Oksanen J, Blanchet FG, Friendly M, et al (2017) vegan: community ecology package. R package version 2.4–3. Finland

  37. 37.

    Kloke JD, McKean JW (2012) Rfit: rank-based estimation for linear models. The R Journal 4:8

    Article  Google Scholar 

  38. 38.

    Wilke A, Bischof J, Gerlach W et al (2016) The MG-RAST metagenomics database and portal in 2015. Nucleic Acids Res 44:D590-594. https://doi.org/10.1093/nar/gkv1322

    CAS  Article  PubMed  Google Scholar 

  39. 39.

    Koninck RD, Déry S (1997) Agricultural expansion as a tool of population redistribution in Southeast Asia. J Southeast Asian Stud 28:1–26. https://doi.org/10.1017/S0022463400015150

    Article  Google Scholar 

  40. 40.

    Antara M, Sumarniasih MS (2017) Role of tourism in economy of Bali and Indonesia. J. Hosp. Tour. Manag 5:33–44. https://doi.org/10.15640/jthm.v5n2a4

    Article  Google Scholar 

  41. 41.

    Knights D, Ward TL, McKinlay CE et al (2014) Rethinking “enterotypes.” Cell Host Microbe 16:433–437. https://doi.org/10.1016/j.chom.2014.09.013

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Le Chatelier E, Nielsen T, Qin J et al (2013) Richness of human gut microbiome correlates with metabolic markers. Nature 500:541–546. https://doi.org/10.1038/nature12506

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Turnbaugh PJ, Hamady M, Yatsunenko T et al (2009) A core gut microbiome in obese and lean twins. Nature 457:480–484. https://doi.org/10.1038/nature07540

    CAS  Article  PubMed  Google Scholar 

  44. 44.

    Wang J, Li W, Wang C, et al (2020) Enterotype Bacteroides is associated with a high risk in patients with diabetes: a pilot study. J Diabetes Res 2020. https://doi.org/10.1155/2020/6047145https://doi.org/10.1155/2020/6047145

  45. 45.

    de Moraes ACF, Fernandes GR, da Silva IT et al (2017) Enterotype may drive the dietary-associated cardiometabolic risk factors. Front Cell Infect Microbiol 7:47. https://doi.org/10.3389/fcimb.2017.00047

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Koren O, Knights D, Gonzalez A et al (2013) A guide to enterotypes across the human body: meta-analysis of microbial community structures in human microbiome datasets. PLoS Comput Biol 9(1):e1002863. https://doi.org/10.1371/journal.pcbi.1002863

    CAS  Article  PubMed  PubMed Central  Google Scholar 

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We are grateful to university students at the Faculty of Medicine, Udayana University, Denpasar (Tjokorda Istri Pramitasuri and colleagues), who had assisted the study during the recruitment, enrolment, and data collection stage in Denpasar. We thank Prodia Laboratory in Denpasar for supporting the collection and initial storage of the samples. We are grateful to Prof. Sangkot Marzuki at Indonesia Science Academy (Akademi Ilmu Pengetahuan Indonesia) for facilitating and supporting the commencement of this study. We thank our colleagues, Sukma Oktavianthi, Lidwina Priliani, Hidayat Trimarsanto, Eline Klaassens, and Mark Read for their input and guidance.


This research was partially supported by the Indonesia Ministry of Research and Technology/National Agency for Research and Innovation through the Eijkman Institute for Molecular Biology; Australia’s Department of Foreign Affairs and Trade through the Australia Awards Scholarship; and the International Program Development Fund (2013 Round).

Author information




CAF, SGM, AJH, IWW, DMW, and HS performed sampling. CAF proposed the study, did laboratory work, analyzed the data, and drafted the manuscript. SGM, HS, and AJH designed, directed, and facilitated the study and provided major support in the data interpretation. DMW and IWW coordinated and facilitated the study enrolment, including the collection and interpretation of diet and demographic data. RD and RM provided support for the analysis and interpretation of diet data.

Corresponding author

Correspondence to Andrew J. Holmes.

Ethics declarations

Ethics Approval

The ethical permit for this study was granted by the Udayana University Faculty of Medicine and Sanglah Hospital Ethics Commission on 18 September 2014 in Denpasar, Indonesia (No. 1286/UN.14.2/Litbang/2014). The permit was endorsed by the Eijkman Institute Research Ethics Commission on 24 December 2014 in Jakarta, Indonesia (Permit No. 80).

Consent to Participate

Written consent for participation in this study was obtained from all study participants.

Consent for Publication

Written consent for publication of data was obtained from all study participants. All data has been de-identified to protect the privacy of the study participants.

Competing Interests

The authors declare no competing interests.

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Febinia, C.A., Malik, S.G., Djuwita, R. et al. Distinctive Microbiome Type Distribution in a Young Adult Balinese Cohort May Reflect Environmental Changes Associated with Modernization. Microb Ecol (2021). https://doi.org/10.1007/s00248-021-01786-9

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  • Microbiome
  • Fecal
  • Community assembly
  • Bali
  • Lifestyle
  • Population
  • Heterogeneity