Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Microbiome epidemiology and association studies in human health

Abstract

Studies of the human microbiome share both technical and conceptual similarities with genome-wide association studies and genetic epidemiology. However, the microbiome has many features that differ from genomes, such as its temporal and spatial variability, highly distinct genetic architecture and person-to-person variation. Moreover, there are various potential mechanisms by which distinct aspects of the human microbiome can relate to health outcomes. Recent advances, including next-generation sequencing and the proliferation of multi-omic data types, have enabled the exploration of the mechanisms that connect microbial communities to human health. Here, we review the ways in which features of the microbiome at various body sites can influence health outcomes, and we describe emerging opportunities and future directions for advanced microbiome epidemiology.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Similarities between microbiome epidemiology and genetic epidemiology.
Fig. 2: Differences between microbiome epidemiology and genetic epidemiology.
Fig. 3: Different features of the microbiome can influence human health outcomes.
Fig. 4: The microbiome has been associated with diverse body-wide health outcomes.
Fig. 5: Future directions for microbiome epidemiology.

References

  1. Eiseman, B., Silen, W., Bascom, G. S. & Kauvar, A. J. Fecal enema as an adjunct in the treatment of pseudomembranous enterocolitis. Surgery 44, 854–859 (1958).

    CAS  PubMed  Google Scholar 

  2. Peppercorn, M. A. & Goldman, P. The role of intestinal bacteria in the metabolism of salicylazosulfapyridine. J. Pharmacol. Exp. Ther. 181, 555–562 (1972).

    CAS  PubMed  Google Scholar 

  3. Wilson, K. H. & Blitchington, R. B. Human colonic biota studied by ribosomal DNA sequence analysis. Appl. Environ. Microbiol. 62, 2273–2278 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Eckburg, P. B. et al. Diversity of the human intestinal microbial flora. Science 308, 1635–1638 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Gill, S. R. et al. Metagenomic analysis of the human distal gut microbiome. Science 312, 1355–1359 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ley, R. E. et al. Obesity alters gut microbial ecology. Proc. Natl Acad. Sci. USA 102, 11070–11075 (2005). Early work demonstrating that not only does obesity influence gut microbial ecology, but manipulation thereof could have a role in regulating energy balance.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Turnbaugh, P. J. et al. The human microbiome project. Nature 449, 804–810 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012). At the time, the Human Microbiome Project was the largest and most comprehensive effort to characterize the typical human microbiome across body sites, a pioneering effort that demonstrated considerable variation in community structure despite relative stability in metabolic pathways between healthy individuals.

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kolde, R. et al. Host genetic variation and its microbiome interactions within the Human Microbiome Project. Genome Med. 10, 6 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019). One of a trio of initial manuscripts from the NIH Common Fund’s Integrative Human Microbiome Project, a large-scale initiative to densely phenotype and integrate clinical and multi-omic data in several conditions with established host–microbiome links (IBD, preterm labour and diabetes, respectively).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).

    Article  CAS  PubMed  Google Scholar 

  14. Mirzayi, C. et al. Reporting guidelines for human microbiome research: the STORMS checklist. Nat. Med. 27, 1885–1892 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Sinha, R. et al. Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium. Nat. Biotechnol. 35, 1077–1086 (2017). A multi-institutional effort to characterize the impact of heterogeneous upstream data generation protocols and bioinformatic workflows that, if not considered, can undermine the comparability of disparate population-scale microbiome studies.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Uffelmann, E. et al. Genome-wide association studies. Nat. Rev. Methods Prim. 1, 59 (2021).

    Article  CAS  Google Scholar 

  17. Mallick, H. et al. Experimental design and quantitative analysis of microbial community multiomics. Genome Biol. 18, 228 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Tsilimigras, M. C. B. & Fodor, A. A. Compositional data analysis of the microbiome: fundamentals, tools, and challenges. Ann. Epidemiol. 26, 330–335 (2016).

    Article  PubMed  Google Scholar 

  19. Hong, M.-G., Pawitan, Y., Magnusson, P. K. E. & Prince, J. A. Strategies and issues in the detection of pathway enrichment in genome-wide association studies. Hum. Genet. 126, 289–301 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Claussnitzer, M. et al. A brief history of human disease genetics. Nature 577, 179–189 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Lloyd-Price, J., Abu-Ali, G. & Huttenhower, C. The healthy human microbiome. Genome Med. 8, 51 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Franzosa, E. A. et al. Sequencing and beyond: integrating molecular “omics” for microbial community profiling. Nat. Rev. Microbiol. 13, 360–372 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bauermeister, A., Mannochio-Russo, H., Costa-Lotufo, L. V., Jarmusch, A. K. & Dorrestein, P. C. Mass spectrometry-based metabolomics in microbiome investigations. Nat. Rev. Microbiol. 20, 143–160 (2022).

    Article  CAS  PubMed  Google Scholar 

  25. Zhang, Y. et al. Metatranscriptomics for the human microbiome and microbial community functional profiling. Annu. Rev. Biomed. Data Sci. 4, 279–311 (2021).

    Article  PubMed  Google Scholar 

  26. Hamady, M. & Knight, R. Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res. 19, 1141–1152 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Morgan, X. C. & Huttenhower, C. Chapter 12: human microbiome analysis. PLoS Comput. Biol. 8, e1002808 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).

    Article  CAS  PubMed  Google Scholar 

  29. Nishiwaki, H. et al. Meta-analysis of gut dysbiosis in Parkinson’s disease. Mov. Disord. 35, 1626–1635 (2020).

    Article  CAS  PubMed  Google Scholar 

  30. Asnicar, F. et al. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat. Med. 27, 321–332 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Johnson, A. J. et al. Daily sampling reveals personalized diet-microbiome associations in humans. Cell Host Microbe 25, 789–802.e5 (2019). An in-depth exploration of the personalized links between dietary intake and gut microbial communities.

    Article  CAS  PubMed  Google Scholar 

  32. Choi, Y., Hoops, S. L., Thoma, C. J. & Johnson, A. J. A guide to dietary pattern-microbiome data integration. J. Nutr. 152, 1187–1199 (2022).

    Article  PubMed  Google Scholar 

  33. Qin, Y. et al. Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort. Nat. Genet. 54, 134–142 (2022).

    Article  CAS  PubMed  Google Scholar 

  34. Lopera-Maya, E. A. et al. Effect of host genetics on the gut microbiome in 7738 participants of the Dutch Microbiome Project. Nat. Genet. 54, 143–151 (2022).

    Article  CAS  PubMed  Google Scholar 

  35. Liu, X. et al. Mendelian randomization analyses support causal relationships between blood metabolites and the gut microbiome. Nat. Genet. 54, 52–61 (2022).

    Article  CAS  PubMed  Google Scholar 

  36. David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).

    Article  CAS  PubMed  Google Scholar 

  37. von Schwartzenberg, R. J. et al. Caloric restriction disrupts the microbiota and colonization resistance. Nature 595, 272–277 (2021).

    Article  Google Scholar 

  38. Sonnenburg, E. D. et al. Diet-induced extinctions in the gut microbiota compound over generations. Nature 529, 212–215 (2016). This study captures the interplay between dietary chemistry, microbial ecology and host health by demonstrating ways in which evolutionarily typical relationships can be disrupted (and restored).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Brennan, C. A. & Garrett, W. S. Fusobacterium nucleatum — symbiont, opportunist and oncobacterium. Nat. Rev. Microbiol. 17, 156–166 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Thomas, A. M. et al. Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat. Med. 25, 667–678 (2019).

    Article  CAS  PubMed  Google Scholar 

  41. Mehta, R. S. et al. Association of dietary patterns with risk of colorectal cancer subtypes classified by Fusobacterium nucleatum in tumor tissue. JAMA Oncol. 3, 921–927 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  43. Vojinovic, D. et al. Relationship between gut microbiota and circulating metabolites in population-based cohorts. Nat. Commun. 10, 5813 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  45. Fettweis, J. M. et al. The vaginal microbiome and preterm birth. Nat. Med. 25, 1012–1021 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Stelzer, I. A. et al. Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset. Sci. Transl. Med. 13, eabd9898 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Knights, D., Costello, E. K. & Knight, R. Supervised classification of human microbiota. FEMS Microbiol. Rev. 35, 343–359 (2011).

    Article  CAS  PubMed  Google Scholar 

  48. Pasolli, E., Truong, D. T., Malik, F., Waldron, L. & Segata, N. Machine learning meta-analysis of large metagenomic datasets: tools and biological insights. PLoS Comput. Biol. 12, e1004977 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Mazidi, M. et al. Meal-induced inflammation: postprandial insights from the personalised responses to dietary composition trial (PREDICT) study in 1000 participants. Am. J. Clin. Nutr. 114, 1028–1038 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & Bäckhed, F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell 165, 1332–1345 (2016).

    Article  CAS  PubMed  Google Scholar 

  51. Haiser, H. J. et al. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science 341, 295–298 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Claesen, J. et al. A Cutibacterium acnes antibiotic modulates human skin microbiota composition in hair follicles. Sci. Transl. Med. 12, eaay5445 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Zimmermann, M., Zimmermann-Kogadeeva, M., Wegmann, R. & Goodman, A. L. Separating host and microbiome contributions to drug pharmacokinetics and toxicity. Science 363, eaat9931 (2019). This study probes the mechanisms and consequences of drug metabolism by the gut microbiome and provides a general strategy for disentangling host and microbial contributions to drug metabolism.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Maini Rekdal, V., Bess, E. N., Bisanz, J. E., Turnbaugh, P. J. & Balskus, E. P. Discovery and inhibition of an interspecies gut bacterial pathway for Levodopa metabolism. Science 364, eaau6323 (2019).

    Article  PubMed  Google Scholar 

  55. Mehta, R. S. et al. Stability of the human faecal microbiome in a cohort of adult men. Nat. Microbiol. 3, 347–355 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Mallick, H. et al. Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences. Nat. Commun. 10, 3136 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Mathewson, N. D. et al. Gut microbiome-derived metabolites modulate intestinal epithelial cell damage and mitigate graft-versus-host disease. Nat. Immunol. 17, 505–513 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wang, Z. et al. Prognostic value of choline and betaine depends on intestinal microbiota-generated metabolite trimethylamine-N-oxide. Eur. Heart J. 35, 904–910 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Chiang, J. Y. L. Bile acid metabolism and signaling. Compr. Physiol. 3, 1191–1212 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Jia, W., Xie, G. & Jia, W. Bile acid-microbiota crosstalk in gastrointestinal inflammation and carcinogenesis. Nat. Rev. Gastroenterol. Hepatol. 15, 111–128 (2018).

    Article  CAS  PubMed  Google Scholar 

  62. Schloissnig, S. et al. Genomic variation landscape of the human gut microbiome. Nature 493, 45–50 (2013). One of the first efforts to describe the variability of microbial strain genetics within the human gut microbiome, finding both personalization and temporal stability of SNP variation patterns.

    Article  PubMed  Google Scholar 

  63. Truong, D. T., Tett, A., Pasolli, E., Huttenhower, C. & Segata, N. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res. 27, 626–638 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Scholz, M. et al. Strain-level microbial epidemiology and population genomics from shotgun metagenomics. Nat. Methods 13, 435–438 (2016).

    Article  CAS  PubMed  Google Scholar 

  65. Van Rossum, T., Ferretti, P., Maistrenko, O. M. & Bork, P. Diversity within species: interpreting strains in microbiomes. Nat. Rev. Microbiol. 18, 491–506 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Faith, J. J. et al. The long-term stability of the human gut microbiota. Science 341, 1237439 (2013). This work demonstrates that the content of the adult gut microbial community is relatively stable across years and may be more similar between family members than between unrelated adults.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Nayfach, S., Rodriguez-Mueller, B., Garud, N. & Pollard, K. S. An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography. Genome Res. 26, 1612–1625 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Ferretti, P. et al. Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe 24, 133–145.e5 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Zeevi, D. et al. Structural variation in the gut microbiome associates with host health. Nature 568, 43–48 (2019).

    Article  CAS  PubMed  Google Scholar 

  70. Karcher, N. et al. Analysis of 1321 Eubacterium rectale genomes from metagenomes uncovers complex phylogeographic population structure and subspecies functional adaptations. Genome Biol. 21, 138 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Beghini, F. et al. Large-scale comparative metagenomics of Blastocystis, a common member of the human gut microbiome. ISME J. 11, 2848–2863 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Fehlner-Peach, H. et al. Distinct polysaccharide utilization profiles of human intestinal Prevotella copri isolates. Cell Host Microbe 26, 680–690.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. De Filippis, F. et al. Distinct genetic and functional traits of human intestinal Prevotella copri strains are associated with different habitual diets. Cell Host Microbe 25, 444–453.e3 (2019).

    Article  PubMed  Google Scholar 

  74. Spor, A., Koren, O. & Ley, R. Unravelling the effects of the environment and host genotype on the gut microbiome. Nat. Rev. Microbiol. 9, 279–290 (2011).

    Article  CAS  PubMed  Google Scholar 

  75. Hall, A. B., Tolonen, A. C. & Xavier, R. J. Human genetic variation and the gut microbiome in disease. Nat. Rev. Genet. 18, 690–699 (2017).

    Article  CAS  PubMed  Google Scholar 

  76. Attaye, I., Pinto-Sietsma, S.-J., Herrema, H. & Nieuwdorp, M. A crucial role for diet in the relationship between gut microbiota and cardiometabolic disease. Annu. Rev. Med. 71, 149–161 (2020).

    Article  CAS  PubMed  Google Scholar 

  77. Mallick, H. et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput. Biol. 17, e1009442 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Wang, C., Hu, J., Blaser, M. J. & Li, H. Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data. Bioinformatics 36, 347–355 (2020).

    Article  CAS  PubMed  Google Scholar 

  79. Zhang, J., Wei, Z. & Chen, J. A distance-based approach for testing the mediation effect of the human microbiome. Bioinformatics 34, 1875–1883 (2018).

    Article  CAS  PubMed  Google Scholar 

  80. Wang, D. D. et al. The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk. Nat. Med. 27, 333–343 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Ma, W. et al. Dietary fiber intake, the gut microbiome, and chronic systemic inflammation in a cohort of adult men. Genome Med. 13, 102 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Sender, R., Fuchs, S. & Milo, R. Are we really vastly outnumbered? Revisiting the ratio of bacterial to host cells in humans. Cell 164, 337–340 (2016).

    Article  CAS  PubMed  Google Scholar 

  83. Zheng, D., Liwinski, T. & Elinav, E. Interaction between microbiota and immunity in health and disease. Cell Res. 30, 492–506 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Knights, D. et al. Complex host genetics influence the microbiome in inflammatory bowel disease. Genome Med. 6, 107 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Wirbel, J. et al. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nat. Med. 25, 679–689 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Kostic, A. D. et al. Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res. 22, 292–298 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Chung, L. et al. Bacteroides fragilis toxin coordinates a pro-carcinogenic inflammatory cascade via targeting of colonic epithelial cells. Cell Host Microbe 23, 421 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Wilson, M. R. et al. The human gut bacterial genotoxin colibactin alkylates DNA. Science 363, eaar7785 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Mima, K. et al. Fusobacterium nucleatum in colorectal carcinoma tissue and patient prognosis. Gut 65, 1973–1980 (2016).

    Article  CAS  PubMed  Google Scholar 

  91. Kostic, A. D. et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe 14, 207–215 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).

    Article  Google Scholar 

  93. Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006). Setting the groundwork for other similarly foundational work, this study helps to establish the biological and biochemical underpinnings of microbiome-mediated energetics and their role in host homeostasis, as well as methods for microbiome analysis that parallel those in human genetic family-based association tests.

    Article  PubMed  Google Scholar 

  94. Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009).

    Article  CAS  PubMed  Google Scholar 

  95. Mbakwa, C. A. et al. Gut colonization with Methanobrevibacter smithii is associated with childhood weight development. Obesity 23, 2508–2516 (2015).

    Article  PubMed  Google Scholar 

  96. Million, M. et al. Correlation between body mass index and gut concentrations of Lactobacillus reuteri, Bifidobacterium animalis, Methanobrevibacter smithii and Escherichia coli. Int. J. Obes. 37, 1460–1466 (2013).

    Article  CAS  Google Scholar 

  97. Samuel, B. S. & Gordon, J. I. A humanized gnotobiotic mouse model of host-archaeal-bacterial mutualism. Proc. Natl Acad. Sci. USA 103, 10011–10016 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Liou, A. P. et al. Conserved shifts in the gut microbiota due to gastric bypass reduce host weight and adiposity. Sci. Transl. Med. 5, 178ra41 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Russell, W. R. et al. High-protein, reduced-carbohydrate weight-loss diets promote metabolite profiles likely to be detrimental to colonic health. Am. J. Clin. Nutr. 93, 1062–1072 (2011).

    Article  CAS  PubMed  Google Scholar 

  100. Cox, A. J., West, N. P. & Cripps, A. W. Obesity, inflammation, and the gut microbiota. Lancet Diabetes Endocrinol. 3, 207–215 (2015).

    Article  CAS  PubMed  Google Scholar 

  101. Schluter, J. et al. The gut microbiota is associated with immune cell dynamics in humans. Nature 588, 303–307 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Haak, B. W. & Wiersinga, W. J. The role of the gut microbiota in sepsis. Lancet Gastroenterol. Hepatol. 2, 135–143 (2017).

    Article  PubMed  Google Scholar 

  103. Thingholm, L. B. et al. Obese individuals with and without type 2 diabetes show different gut microbial functional capacity and composition. Cell Host Microbe 26, 252–264.e10 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Zhong, H. et al. Distinct gut metagenomics and metaproteomics signatures in prediabetics and treatment-naïve type 2 diabetics. EBioMedicine 47, 373–383 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Newgard, C. B. et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 9, 311–326 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Pedersen, H. K. et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535, 376–381 (2016).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Allin, K. H. et al. Aberrant intestinal microbiota in individuals with prediabetes. Diabetologia 61, 810–820 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  109. Knip, M. & Siljander, H. The role of the intestinal microbiota in type 1 diabetes mellitus. Nat. Rev. Endocrinol. 12, 154–167 (2016).

    Article  CAS  PubMed  Google Scholar 

  110. Kostic, A. D. et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe 17, 260–273 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Vatanen, T. et al. The human gut microbiome in early-onset type 1 diabetes from the TEDDY study. Nature 562, 589–594 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Stewart, C. J. et al. Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature 562, 583–588 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Bjornevik, K. et al. Longitudinal analysis reveals high prevalence of Epstein–Barr virus associated with multiple sclerosis. Science 375, 296–301 (2022).

    Article  CAS  PubMed  Google Scholar 

  114. Cekanaviciute, E. et al. Gut bacteria from multiple sclerosis patients modulate human T cells and exacerbate symptoms in mouse models. Proc. Natl Acad. Sci. USA 114, 10713–10718 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Hughes, L. E. et al. Cross-reactivity between related sequences found in Acinetobacter sp., Pseudomonas aeruginosa, myelin basic protein and myelin oligodendrocyte glycoprotein in multiple sclerosis. J. Neuroimmunol. 144, 105–115 (2003).

    Article  CAS  PubMed  Google Scholar 

  116. Liu, S. et al. Altered gut microbiota and short chain fatty acids in Chinese children with autism spectrum disorder. Sci. Rep. 9, 287 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Li, G. et al. Diet, microbe, and autism: cause or consequence? Cell Host Microbe 30, 5–7 (2022).

    Article  CAS  PubMed  Google Scholar 

  118. Stokholm, J. et al. Maturation of the gut microbiome and risk of asthma in childhood. Nat. Commun. 9, 141 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  119. Barcik, W., Boutin, R. C. T., Sokolowska, M. & Finlay, B. B. The role of lung and gut microbiota in the pathology of asthma. Immunity 52, 241–255 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Arrieta, M.-C. et al. Early infancy microbial and metabolic alterations affect risk of childhood asthma. Sci. Transl. Med. 7, 307ra152 (2015).

    Article  PubMed  Google Scholar 

  121. Fujimura, K. E. et al. Neonatal gut microbiota associates with childhood multisensitized atopy and T cell differentiation. Nat. Med. 22, 1187–1191 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Koeth, R. A. et al. Intestinal microbiota metabolism of l-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat. Med. 19, 576–585 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Jie, Z. et al. The gut microbiome in atherosclerotic cardiovascular disease. Nat. Commun. 8, 845 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Gopalakrishnan, V. et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359, 97–103 (2018).

    Article  CAS  PubMed  Google Scholar 

  125. Alexander, J. L. et al. Gut microbiota modulation of chemotherapy efficacy and toxicity. Nat. Rev. Gastroenterol. Hepatol. 14, 356–365 (2017).

    Article  CAS  PubMed  Google Scholar 

  126. Sepich-Poore, G. D. et al. The microbiome and human cancer. Science 371, eabc4552 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Geller, L. T. et al. Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine. Science 357, 1156–1160 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Dubin, K. et al. Intestinal microbiome analyses identify melanoma patients at risk for checkpoint-blockade-induced colitis. Nat. Commun. 7, 10391 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Andrews, M. C. et al. Gut microbiota signatures are associated with toxicity to combined CTLA-4 and PD-1 blockade. Nat. Med. 27, 1432–1441 (2021).

    Article  CAS  PubMed  Google Scholar 

  130. Lee, K. A. et al. Cross-cohort gut microbiome associations with immune checkpoint inhibitor response in advanced melanoma. Nat. Med. 28, 535–544 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. McCulloch, J. A. et al. Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1. Nat. Med. 28, 545–556 (2022).

    Article  CAS  PubMed  Google Scholar 

  132. Ravel, J. et al. Vaginal microbiome of reproductive-age women. Proc. Natl Acad. Sci. USA 108 (Suppl. 1), 4680–4687 (2011).

    Article  CAS  PubMed  Google Scholar 

  133. Gosmann, C. et al. Lactobacillus-deficient cervicovaginal bacterial communities are associated with increased HIV acquisition in young South African women. Immunity 46, 29–37 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Fredricks, D. N., Fiedler, T. L. & Marrazzo, J. M. Molecular identification of bacteria associated with bacterial vaginosis. N. Engl. J. Med. 353, 1899–1911 (2005).

    Article  CAS  PubMed  Google Scholar 

  135. Saheb Kashaf, S. et al. Integrating cultivation and metagenomics for a multi-kingdom view of skin microbiome diversity and functions. Nat. Microbiol. 7, 169–179 (2022).

    Article  CAS  PubMed  Google Scholar 

  136. Oh, J. et al. Temporal stability of the human skin microbiome. Cell 165, 854–866 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Findley, K. et al. Topographic diversity of fungal and bacterial communities in human skin. Nature 498, 367–370 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Byrd, A. L., Belkaid, Y. & Segre, J. A. The human skin microbiome. Nat. Rev. Microbiol. 16, 143–155 (2018).

    Article  CAS  PubMed  Google Scholar 

  139. Kang, D., Shi, B., Erfe, M. C., Craft, N. & Li, H. Vitamin B12 modulates the transcriptome of the skin microbiota in acne pathogenesis. Sci. Transl. Med. 7, 293ra103 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  140. Charlson, E. S. et al. Assessing bacterial populations in the lung by replicate analysis of samples from the upper and lower respiratory tracts. PLoS ONE 7, e42786 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Greathouse, K. L. et al. Interaction between the microbiome and TP53 in human lung cancer. Genome Biol. 19, 123 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  142. Jin, C. et al. Commensal microbiota promote lung cancer development via γδ T cells. Cell 176, 998–1013.e16 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Coburn, B. et al. Lung microbiota across age and disease stage in cystic fibrosis. Sci. Rep. 5, 10241 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Man, W. H., de Steenhuijsen Piters, W. A. A. & Bogaert, D. The microbiota of the respiratory tract: gatekeeper to respiratory health. Nat. Rev. Microbiol. 15, 259–270 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Segata, N. et al. Composition of the adult digestive tract bacterial microbiome based on seven mouth surfaces, tonsils, throat and stool samples. Genome Biol. 13, R42 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Valm, A. M. The structure of dental plaque microbial communities in the transition from health to dental caries and periodontal disease. J. Mol. Biol. 431, 2957–2969 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Goh, C. E. et al. Association between nitrate-reducing oral bacteria and cardiometabolic outcomes: results from ORIGINS. J. Am. Heart Assoc. 8, e013324 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  148. Kroese, J. M. et al. Differences in the oral microbiome in patients with early rheumatoid arthritis and individuals at risk of rheumatoid arthritis compared to healthy individuals. Arthritis Rheumatol. 73, 1986–1993 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Fan, X. et al. Human oral microbiome and prospective risk for pancreatic cancer: a population-based nested case-control study. Gut 67, 120–127 (2018).

    Article  CAS  PubMed  Google Scholar 

  150. Francies, H. E., McDermott, U. & Garnett, M. J. Genomics-guided pre-clinical development of cancer therapies. Nat. Cancer 1, 482–492 (2020).

    Article  PubMed  Google Scholar 

  151. Lindenbaum, J., Rund, D. G., Butler, V. P. Jr, Tse-Eng, D. & Saha, J. R. Inactivation of digoxin by the gut flora: reversal by antibiotic therapy. N. Engl. J. Med. 305, 789–794 (1981).

    Article  CAS  PubMed  Google Scholar 

  152. Ivanov, I. I., Tuganbaev, T., Skelly, A. N. & Honda, K. T cell responses to the microbiota. Annu. Rev. Immunol. 40, 559–587 (2022).

    Article  PubMed  Google Scholar 

  153. Cardoso, F. et al. 70-Gene signature as an aid to treatment decisions in early-stage breast cancer. N. Engl. J. Med. 375, 717–729 (2016).

    Article  CAS  PubMed  Google Scholar 

  154. No authors listed. A stool DNA test (Cologuard) for colorectal cancer screening. JAMA 312, 2566 (2014).

    Article  Google Scholar 

  155. Sorbara, M. T. & Pamer, E. G. Microbiome-based therapeutics. Nat. Rev. Microbiol. 20, 365–380 (2022).

    Article  CAS  PubMed  Google Scholar 

  156. Prescott, S. L. History of medicine: origin of the term microbiome and why it matters. Hum. Microbiome J. 4, 24–25 (2017).

    Article  Google Scholar 

  157. Kaper, J. B., Nataro, J. P. & Mobley, H. L. Pathogenic Escherichia coli. Nat. Rev. Microbiol. 2, 123–140 (2004).

    Article  CAS  PubMed  Google Scholar 

  158. Xu, J.-G., Cheng, B.-K. & Jing, H.-Q. Escherichia coli O157 H7 and shiga-like-toxin- producing Escherichia coli in China. World J. Gastroenterol. 5, 191–194 (1999).

    Article  PubMed  PubMed Central  Google Scholar 

  159. Sonnenborn, U. Escherichia coli strain Nissle 1917-from bench to bedside and back: history of a special Escherichia coli strain with probiotic properties. FEMS Microbiol. Lett. 363, fnw212 (2016).

    Article  PubMed  Google Scholar 

  160. Grozdanov, L. et al. Analysis of the genome structure of the nonpathogenic probiotic Escherichia coli strain Nissle 1917. J. Bacteriol. 186, 5432–5441 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

E.A.F. drafted the figures. The authors’ work was supported in part by the NIH NIDDK (National Institute of Diabetes and Digestive and Kidney Diseases) grant T32DK007703 (H.V.), NIH K23DK125838 (L.H.N.), R24DK110499 (C.H.), American Gastroenterological Association Research Scholars Award (L.H.N.), the Crohn’s and Colitis Foundation Career Development Award and Research Fellowship Award (L.H.N.), and Takeda Pharmaceuticals research agreement 4100215484 (C.H.).

Author information

Authors and Affiliations

Authors

Contributions

The authors contributed equally to all aspects of the manuscript.

Corresponding authors

Correspondence to Long H. Nguyen or Curtis Huttenhower.

Ethics declarations

Competing interests

C.H. is a member of the scientific advisory boards of Seres Therapeutics and Empress Therapeutics. The other authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Genetics thanks Falk Hildebrand, Huijue Jia and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Glossary

Gnotobiotic

A term that denotes a condition of having a defined community of known microorganisms present, incuding germ-free.

Non-independence

In which observations are in some way related, such that the value of one observation affects the value of others.

Compositionality

Data in which measurements are proportions, that is, they must sum to a fixed constant such as 100%.

Beta-diversity

Ecological measure that captures similarity between communities.

Alpha-diversity

Ecological measure that captures aspects of the richness and/or evenness of features within a community.

Genetic risk score

A summary statistic of the effect of overall genetic variants on a phenotype of interest.

Foodomics

The application of multi-omic technologies to investigate and exploit food science, typically as used to improve host health.

Random forests

Machine learning algorithms that use multiple decision trees to solve classification or regression problems (for example, were samples derived from cases or controls?).

Support vector machines

(SVMs). Machine learning algorithms that classify data by maximizing class separation in a (possibly transformed) high-dimensional space.

Logistic regression

A predictive analysis that estimates the probability of a categorical, often binary, dependent variable given one or more independent variables.

Heteroscedasticity

A variable characteristic for which the variability is unequal across the range of another predictor.

Zero-inflation

Data (or a corresponding probability distribution) in which identically zero observations are frequent.

Omnibus test

A class of statistical tests that quantify the variance explained in a (typically high-dimensional) measurement by a single (continuous or categorical) variable, as well as its significance relative to overall (unexplained) variance.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

VanEvery, H., Franzosa, E.A., Nguyen, L.H. et al. Microbiome epidemiology and association studies in human health. Nat Rev Genet (2022). https://doi.org/10.1038/s41576-022-00529-x

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41576-022-00529-x

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing