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.

Maturation of the gut microbiome during the first year of life contributes to the protective farm effect on childhood asthma

Abstract

Growing up on a farm is associated with an asthma-protective effect, but the mechanisms underlying this effect are largely unknown. In the Protection against Allergy: Study in Rural Environments (PASTURE) birth cohort, we modeled maturation using 16S rRNA sequence data of the human gut microbiome in infants from 2 to 12 months of age. The estimated microbiome age (EMA) in 12-month-old infants was associated with previous farm exposure (β = 0.27 (0.12–0.43), P = 0.001, n = 618) and reduced risk of asthma at school age (odds ratio (OR) = 0.72 (0.56–0.93), P = 0.011). EMA mediated the protective farm effect by 19%. In a nested case–control sample (n = 138), we found inverse associations of asthma with the measured level of fecal butyrate (OR = 0.28 (0.09–0.91), P = 0.034), bacterial taxa that predict butyrate production (OR = 0.38 (0.17–0.84), P = 0.017) and the relative abundance of the gene encoding butyryl–coenzyme A (CoA):acetate–CoA-transferase, a major enzyme in butyrate metabolism (OR = 0.43 (0.19–0.97), P = 0.042). The gut microbiome may contribute to asthma protection through metabolites, supporting the concept of a gut–lung axis in humans.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Composition of the bacterial gut microbiome at months 2 and 12.
Fig. 2: EMA as a measure of gut microbiome maturation.
Fig. 3: EMA and the farm effect on asthma.
Fig. 4: Bacterial metabolites and EMA.
Fig. 5: Network of single taxa and summary of findings.

Data availability

Taxonomy was assigned using the Greengenes database (http://greengenes.lbl.gov) for 16S rRNA sequences and the UNITE dynamic database (https://unite.ut.ee/) for ITS sequences. All 16S rRNA and ITS sequences are deposited in the Supplementary Information without metadata. PASTURE is an ongoing birth cohort with fieldwork still being executed. As long as the study is not anonymized, European data protection legislation prohibits sharing of individual data, even when pseudonymized. Upon request, the authors will share aggregate data that do not allow identification of individuals.

References

  1. 1.

    Barker, D. J. The fetal and infant origins of adult disease. BMJ301, 1111 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Tamburini, S., Shen, N., Wu, H. C. & Clemente, J. C. The microbiome in early life: implications for health outcomes. Nat. Med.22, 713–722 (2016).

    CAS  PubMed  Google Scholar 

  3. 3.

    de Steenhuijsen Piters, W. A. et al. Nasopharyngeal microbiota, host transcriptome, and disease severity in children with respiratory syncytial virus infection. Am. J. Respir. Crit. Care Med.194, 1104–1115 (2016).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Chung, H. et al. Gut immune maturation depends on colonization with a host-specific microbiota. Cell149, 1578–1593 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Ege, M. J. The hygiene hypothesis in the age of the microbiome. Ann. Am. Thorac. Soc.14, S348–S353 (2017).

    PubMed  Google Scholar 

  6. 6.

    Ege, M. J. et al. Exposure to environmental microorganisms and childhood asthma. N. Engl. J. Med.364, 701–709 (2011).

    CAS  PubMed  Google Scholar 

  7. 7.

    Stein, M. M. et al. Innate immunity and asthma risk in Amish and Hutterite farm children. N. Engl. J. Med.375, 411–421 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Illi, S. et al. Protection from childhood asthma and allergy in Alpine farm environments—the GABRIEL Advanced Studies. J. Allergy Clin. Immunol. 129, 1470–1477 (2012).

  9. 9.

    Birzele, L. T. et al. Environmental and mucosal microbiota and their role in childhood asthma. Allergy72, 109–119 (2017).

    CAS  PubMed  Google Scholar 

  10. 10.

    Depner, M. et al. Bacterial microbiota of the upper respiratory tract and childhood asthma. J. Allergy Clin. Immunol. 139, 826–834 (2017).

  11. 11.

    Trompette, A. et al. Gut microbiota metabolism of dietary fiber influences allergic airway disease and hematopoiesis. Nat. Med.20, 159–166 (2014).

    CAS  PubMed  Google Scholar 

  12. 12.

    Stewart, C. J. et al. Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature562, 583–588 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Yassour, M. et al. Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci. Transl. Med.8, 343ra381 (2016).

    Google Scholar 

  14. 14.

    Depner, M. et al. Clinical and epidemiologic phenotypes of childhood asthma. Am. J. Respir. Crit. Care Med.189, 129–138 (2014).

    PubMed  Google Scholar 

  15. 15.

    Kumbhare, S. V., Patangia, D. V., Patil, R. H., Shouche, Y. S. & Patil, N. P. Factors influencing the gut microbiome in children: from infancy to childhood. J. Biosci.44, 49 (2019).

    PubMed  Google Scholar 

  16. 16.

    Backhed, F. et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe17, 690–703 (2015).

    PubMed  Google Scholar 

  17. 17.

    Bokulich, N. A. et al. Antibiotics, birth mode, and diet shape microbiome maturation during early life. Sci. Transl. Med.8, 343ra382 (2016).

    Google Scholar 

  18. 18.

    Borewicz, K. et al. The effect of prebiotic fortified infant formulas on microbiota composition and dynamics in early life. Sci. Rep.9, 2434 (2019).

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Abrahamsson, T. R. et al. Low gut microbiota diversity in early infancy precedes asthma at school age. Clin. Exp. Allergy44, 842–850 (2014).

    CAS  PubMed  Google Scholar 

  20. 20.

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

    PubMed  Google Scholar 

  21. 21.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Galazzo, G. et al. Development of the microbiota and associations with birth mode, diet, and atopic disorders in a longitudinal analysis of stool samples, collected from infancy through early childhood. Gastroenterology158, 1584–1596 (2020).

    CAS  PubMed  Google Scholar 

  23. 23.

    Ege, M. J. et al. Environmental bacteria and childhood asthma. Allergy67, 1565–1571 (2012).

    CAS  PubMed  Google Scholar 

  24. 24.

    Gardiner, B. J. et al. Clinical and microbiological characteristics of Eggerthella lenta bacteremia. J. Clin. Microbiol.53, 626–635 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Wang, Q. et al. A metagenome-wide association study of gut microbiota in asthma in UK adults. BMC Microbiol.18, 114 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Stiemsma, L. T. et al. Shifts in Lachnospira and Clostridium sp. in the 3-month stool microbiome are associated with preschool age asthma. Clin. Sci.130, 2199–2207 (2016).

    CAS  Google Scholar 

  27. 27.

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

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Simonyte Sjodin, K. et al. Temporal and long-term gut microbiota variation in allergic disease: a prospective study from infancy to school age. Allergy74, 176–185 (2019).

    CAS  PubMed  Google Scholar 

  29. 29.

    Yang, B. et al. A meta-analysis of the effects of probiotics and synbiotics in children with acute diarrhea. Medicine98, e16618 (2019).

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Derrien, M., Alvarez, A. S. & de Vos, W. M. The gut microbiota in the first decade of life. Trends Microbiol.27, 997–1010 (2019).

    CAS  PubMed  Google Scholar 

  31. 31.

    Theiler, A. et al. Butyrate ameliorates allergic airway inflammation by limiting eosinophil trafficking and survival. J. Allergy Clin. Immunol.144, 764–776 (2019).

    CAS  PubMed  Google Scholar 

  32. 32.

    Roduit, C. et al. High levels of butyrate and propionate in early life are associated with protection against atopy. Allergy74, 799–809 (2019).

    CAS  PubMed  Google Scholar 

  33. 33.

    Louis, P., Young, P., Holtrop, G. & Flint, H. J. Diversity of human colonic butyrate-producing bacteria revealed by analysis of the butyryl–CoA:acetate CoA-transferase gene. Environ. Microbiol.12, 304–314 (2010).

    CAS  PubMed  Google Scholar 

  34. 34.

    Riviere, A., Selak, M., Lantin, D., Leroy, F. & De Vuyst, L. Bifidobacteria and butyrate-producing colon bacteria: importance and strategies for their stimulation in the human gut. Front. Microbiol.7, 979 (2016).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Reichardt, N. et al. Specific substrate-driven changes in human faecal microbiota composition contrast with functional redundancy in short-chain fatty acid production. ISME J.12, 610–622 (2018).

    CAS  PubMed  Google Scholar 

  36. 36.

    Reichardt, N. et al. Phylogenetic distribution of three pathways for propionate production within the human gut microbiota. ISME J.8, 1323–1335 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Tamanai-Shacoori, Z. et al. Roseburia spp.: a marker of health? Future Microbiol. 12, 157–170 (2017).

    CAS  PubMed  Google Scholar 

  38. 38.

    Frati, F. et al. The role of the microbiome in asthma: the gut–lung axis. Int. J. Mol. Sci.20, 123 (2018).

    PubMed Central  Google Scholar 

  39. 39.

    Marsland, B. J., Trompette, A. & Gollwitzer, E. S. The gut–lung axis in respiratory disease. Ann. Am. Thorac. Soc.12(Suppl. 2), S150–S156 (2015).

    PubMed  Google Scholar 

  40. 40.

    Singh, P. et al. Intestinal microbial communities associated with acute enteric infections and disease recovery. Microbiome3, 45 (2015).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Forbes, J. D. et al. A comparative study of the gut microbiota in immune-mediated inflammatory diseases—does a common dysbiosis exist? Microbiome6, 221 (2018).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Kemter, A. M. & Nagler, C. R. Influences on allergic mechanisms through gut, lung, and skin microbiome exposures. J. Clin. Invest.130, 1483–1492 (2019).

    Google Scholar 

  43. 43.

    Arrieta, M. C. et al. Associations between infant fungal and bacterial dysbiosis and childhood atopic wheeze in a nonindustrialized setting. J. Allergy Clin. Immunol.142, 424–434 (2018).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Zhai, S. et al. Dietary butyrate suppresses inflammation through modulating gut microbiota in high-fat diet-fed mice. FEMS Microbiol. Lett.366, fnz153 (2019).

    CAS  PubMed  Google Scholar 

  45. 45.

    Kepert, I. et al. d-Tryptophan from probiotic bacteria influences the gut microbiome and allergic airway disease. J. Allergy Clin. Immunol.139, 1525–1535 (2017).

    CAS  PubMed  Google Scholar 

  46. 46.

    Cait, A. et al. Reduced genetic potential for butyrate fermentation in the gut microbiome of infants who develop allergic sensitization. J. Allergy Clin. Immunol.144, 1638–1647 (2019).

    CAS  PubMed  Google Scholar 

  47. 47.

    Bonaz, B., Bazin, T. & Pellissier, S. The vagus nerve at the interface of the microbiota–gut–brain axis. Front. Neurosci.12, 49 (2018).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Osadchiy, V., Martin, C. R. & Mayer, E. A. The gut–brain axis and the microbiome: mechanisms and clinical implications. Clin. Gastroenterol. Hepatol.17, 322–332 (2019).

    CAS  PubMed  Google Scholar 

  49. 49.

    Kabata, H. & Artis, D. Neuro-immune crosstalk and allergic inflammation. J. Clin. Invest.130, 1475–1482 (2019).

    Google Scholar 

  50. 50.

    von Mutius, E., Schmid, S. & Group, P. S. The PASTURE project: EU support for the improvement of knowledge about risk factors and preventive factors for atopy in Europe. Allergy61, 407–413 (2006).

    Google Scholar 

  51. 51.

    Loss, G. et al. Consumption of unprocessed cow’s milk protects infants from common respiratory infections. J. Allergy Clin. Immunol.135, 56–62 (2015).

    PubMed  Google Scholar 

  52. 52.

    Loss, G. J. et al. The early development of wheeze. Environmental determinants and genetic susceptibility at 17q21. Am. J. Respir. Crit. Care Med.193, 889–897 (2016).

    PubMed  Google Scholar 

  53. 53.

    Hose, A. J. et al. Latent class analysis reveals clinically relevant atopy phenotypes in 2 birth cohorts. J. Allergy Clin. Immunol.139, 1935–1945 (2017).

    PubMed  Google Scholar 

  54. 54.

    Fuchs, O. et al. Farming environments and childhood atopy, wheeze, lung function, and exhaled nitric oxide. J. Allergy Clin. Immunol.130, 382–388 (2012).

    CAS  PubMed  Google Scholar 

  55. 55.

    Roduit, C. et al. Increased food diversity in the first year of life is inversely associated with allergic diseases. J. Allergy Clin. Immunol.133, 1056–1064 (2014).

    PubMed  Google Scholar 

  56. 56.

    Depner, M. et al. Atopic sensitization in the first year of life. J. Allergy Clin. Immunol.131, 781–788 (2013).

    PubMed  Google Scholar 

  57. 57.

    Bokulich, N. A., Thorngate, J. H., Richardson, P. M. & Mills, D. A. Microbial biogeography of wine grapes is conditioned by cultivar, vintage, and climate. Proc. Natl Acad. Sci. USA111, E139–E148 (2014).

    CAS  PubMed  Google Scholar 

  58. 58.

    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. USA108(Suppl. 1), 4516–4522 (2011).

    CAS  PubMed  Google Scholar 

  59. 59.

    Bokulich, N. A. & Mills, D. A. Improved selection of internal transcribed spacer-specific primers enables quantitative, ultra-high-throughput profiling of fungal communities. Appl. Environ. Microbiol.79, 2519–2526 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods7, 335–336 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods13, 581–583 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome6, 90 (2018).

    PubMed  PubMed Central  Google Scholar 

  63. 63.

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

    Google Scholar 

  64. 64.

    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  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Abarenkov, K. et al. The UNITE database for molecular identification of fungi—recent updates and future perspectives. N. Phytol.186, 281–285 (2010).

    Google Scholar 

  66. 66.

    Kormann, M. S. et al. G-protein-coupled receptor polymorphisms are associated with asthma in a large German population. Am. J. Respir. Crit. Care Med.171, 1358–1362 (2005).

    PubMed  Google Scholar 

  67. 67.

    Moffatt, M. F. et al. A large-scale, consortium-based genomewide association study of asthma. N. Engl. J. Med.363, 1211–1221 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Moffatt, M. F. et al. Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature448, 470–473 (2007).

    CAS  PubMed  Google Scholar 

  69. 69.

    Torgerson, D. G. et al. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat. Genet.43, 887–892 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Bouzigon, E. et al. Effect of 17q21 variants and smoking exposure in early-onset asthma. N. Engl. J. Med.359, 1985–1994 (2008).

    CAS  PubMed  Google Scholar 

  71. 71.

    Caliskan, M. et al. Rhinovirus wheezing illness and genetic risk of childhood-onset asthma. N. Engl. J. Med. 368, 1398–1407 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Schedel, M. et al. Polymorphisms related to ORMDL3 are associated with asthma susceptibility, alterations in transcriptional regulation of ORMDL3, and changes in TH2 cytokine levels. J. Allergy Clin. Immunol.136, 893–903 (2015).

    CAS  PubMed  Google Scholar 

  73. 73.

    Dostal, A. et al. Effects of iron supplementation on dominant bacterial groups in the gut, faecal SCFA and gut inflammation: a randomised, placebo-controlled intervention trial in South African children. Br. J. Nutr.112, 547–556 (2014).

    CAS  PubMed  Google Scholar 

  74. 74.

    Pham, V. T., Lacroix, C., Braegger, C. P. & Chassard, C. Early colonization of functional groups of microbes in the infant gut. Environ. Microbiol.18, 2246–2258 (2016).

    CAS  PubMed  Google Scholar 

  75. 75.

    Ramirez-Farias, C. et al. Effect of inulin on the human gut microbiota: stimulation of Bifidobacterium adolescentis and Faecalibacterium prausnitzii. Br. J. Nutr.101, 541–550 (2009).

    CAS  PubMed  Google Scholar 

  76. 76.

    Muthén, L. K. & Muthén, B. O. Mplus User’s Guide 7th edn (Muthén & Muthén, 1998–2012).

  77. 77.

    Holmes, I., Harris, K. & Quince, C. Dirichlet multinomial mixtures: generative models for microbial metagenomics. PloS ONE7, e30126 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Breiman, L. Random forests. Mach. Learn.45, 5–32 (2001).

    Google Scholar 

  79. 79.

    Aitchison, J. The statistical analysis of compositional data. J. Royal Stat. Soc. B44, 139–177 (1982).

    Google Scholar 

  80. 80.

    Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol.8, 2224 (2017).

    PubMed  PubMed Central  Google Scholar 

  81. 81.

    Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol.8, e1002687 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Brandes, U. et al. On modularity clustering. IEEE Trans. Knowl. Data Eng.20, 172–188 (2008).

    Google Scholar 

  83. 83.

    Langfelder, P. & Horvath, S. Eigengene networks for studying the relationships between co-expression modules. BMC Syst. Biol.1, 54 (2007).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This paper is dedicated to Jean-Charles Dalphin, one of the founders of the PASTURE study, who passed away shortly after the submission of this manuscript. We are very grateful for his constant support of this successful collaboration. We thank B. Stecher and A.-L. Boulesteix for valuable input in discussions about biological and statistical issues, respectively. We are grateful to G. Pagani for technical support with graphs. The PASTURE study was supported by the European Commission (research grants QLK4-CT-2001-00250, FOOD-CT-2006-31708 and KBBE-2007-2-2-06) and the European Research Council (grant 250268). The current analyses were supported by the German Federal Ministry of Education and Research (BMBF; project German Center for Lung Research (DZL)). P.L. and F.M.F. receive funding from the Scottish Government’s Rural and Environment Sciences and Analytical Services Division (RESAS).

Author information

Affiliations

Authors

Consortia

Contributions

E.v.M., J.-C.D., J.R. and J.P. obtained funds, set up the PASTURE birth cohort and were responsible for data collection and management of the study. D.A.M., R.L., M.K., M.R., H.R., R.F. and C.R. were responsible for laboratory analyses. K.M.K. and D.A.M. performed sequencing analyses. D.H.T. performed bioinformatics, and C.R. and R.F. performed SCFA analyses. F.M.F. and P.L. designed and performed the butyryl–CoA:acetate–CoA-transferase assay. E.S.-H. was involved in data management and S.P. performed statistical network analysis. P.V.K., A.M.K. and A.D.-C. were involved in the acquisition and interpretation of data. M.D. performed statistical analyses and completed the background literature search, M.J.E. supervised statistical analyses, M.D. and M.J.E. drafted the manuscript, and all authors provided substantial revisions and approved the final version of the manuscript. The PASTURE study group was involved in the acquisition, management and interpretation of data in Austria, Finland, France, Germany and Switzerland. The members of the PASTURE study group contributed substantially to the design, conception and conduct of the study or the acquisition or analysis of data.

Corresponding author

Correspondence to Markus J. Ege.

Ethics declarations

Competing interests

D.A.M. is a co-founder of Evolve Biosystems, a company focused on diet-based manipulation of the gut microbiota, and BCD Biosciences, a company advancing novel bioactive glycans. Neither Evolve Biosystems nor BCD Biosciences had a role in the conceptualization, design, data collection, analysis or preparation of this paper. M.K. has a patent share on the diagnostic use of SNPs in ORMDL3 on chromosome 17q21. H.R. has received research support from DFG, BMBF, EU, Land Hessen, DAAD, ALK, Stiftung Pathobiochemie, Ernst-Wendt-Stiftung, Mead Johnson Nutritional and Beckman Coulter; speaker’s honoraria from Allergopharma, Novartis, Thermo Fisher, Danone, Mead Johnson Nutritional and Bencard; and consulting fees from Bencard and Sterna Biologicals. He is a cofounder of Sterna Biologicals. E.v.M. is listed as an inventor on the following patents: publication number EP 1411977, composition containing bacterial antigens used for the prophylaxis and treatment of allergic diseases, granted on 18 April 2007; publication number EP 1637147, stable dust extract for allergy protection, granted on 10 December 2008; publication number EP 1964570, pharmaceutical compound to protect against allergies and inflammatory diseases, granted on 21 November 2012. E.v.M. is listed as an inventor and has received royalties on the following patent: publication number EP 2361632, specific environmental bacteria for the protection from and/or treatment of allergic, chronic inflammatory and/or autoimmune disorders, granted on 19 March 2014. She has received funding and research support from FrieslandCampina; she has received consultation and speaker fees from OM Pharma, Böhringer Ingelheim International, Peptinnovate, Pharmaventures, Nestlé Deutschland (36 months before publication) and HiPP (future). M.J.E. is a co-inventor on patents for the use of environmental bacteria to prevent asthma (EP000001964570B1, US000009950017B2 and EP000002361632B1). His employer has received investigational products for an intervention study with minimally processed milk from FrieslandCampina.

Additional information

Peer review information Alison Farrell was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Participant flow.

The current microbiome analysis population (n = 720) was selected based on available microbiome data at month 12. The French arm was not included because by design microbiome samples were not taken at month 2. The children with samples available at month 2 and 12 (n = 618) were quite equally distributed over the centers (Austria N = 139; Switzerland N = 205; Germany N = 136; Finland N = 138). For n = 102 children only 12 months samples were available. Subsamples are colored in red, blue, and white according to asthma status (yes, no, not available, respectively). The different subsamples colored in grey represent the basis of the respective figures of the main manuscript as indicated*. Samples with fungi data are a subsample of the 618 samples with bacteria, and only children with ITS samples at both time points available were analyzed**. Healthy controls were defined by no diarrhea between 2 and 12 months and no asthma / wheeze anytime; individuals with missing or implausible values for sampling time point were excluded (5 for bacteria, 1 for fungi)***. Butyryl-CoA:acetate CoA-transferase gene assay failed in 19 of the 157 samples (12%) for technical reasons.

Extended Data Fig. 2 Sensitivity analysis on the effect of estimated microbiome age (EMA).

a, Scatter plot of the first two axes of a principal coordinate analysis (PCoA) over both time points on ASV (amplicon sequence variants) levels. The values in brackets represent percentage of variance explained after correction of negative eigenvalues. b, Scatterplot of the first PCoA-axis against EMA. c,d, Associations of asthma phenotypes with EMA restricted to individuals not included when establishing the prediction model (n = 480 children), that is the 618 children with measurements at both time points minus the 138 healthy individuals. EMA is used as z-standardized continuous variable (c) and dichotomized at the lowest quartile (d).

Extended Data Fig. 3 Association of principal component axes of microbial composition at 2 and 12 months with asthma phenotypes and bacterial genera.

a, Associations of asthma phenotypes with the first five axes of a principle component analysis (PCA) at month 2. b, Correlation of the asthma-protective PCA-axis 3 at month 2 (7% variance explained) with single genera. c, Associations of asthma phenotypes with PCA-axes at month 12. d, Correlation of the asthma-protective PCA-axis 1 at month 12 (14% variance explained) with single genera. eg, Mutually adjusted associations of EMA and the asthma-protective axes at both time points with asthma (e), atopic asthma (f), and non-atopic asthma (g). Associations are shown as odds ratios for the z-standardized variables.

Extended Data Fig. 4 Association of principle coordinate axes of microbial composition at 2 and 12 months with asthma phenotypes and bacterial genera.

Associations of asthma phenotypes of the first five axes of a principle coordinate analysis (PCoA) at month 2 a, and 12 c, using unweighted UniFrac as distance measure. Spearman correlations of the 10 most positively and 10 most negatively correlated individual genera with the asthma-protective PCoA-axes at month 2 b, and 12 d. Mutually adjusted models for EMA and the asthma-protective PCoA-axes at month 2 and 12 for asthma e, atopic asthma f, and nonatopic asthma g. Associations are shown as odds ratios for the z-standardized variables.

Extended Data Fig. 5 Correlation of estimated microbiome age (EMA) with asthma-protective axes and richness.

Relationship between EMA (x-axis) and various microbial measures (y-axis) including asthma-protective PCA- a, and PCoA-axes b, and bacterial richness c. The left column relates to 2 months, the right column to 12 months. As correlation coefficient Spearman’s rho is given.

Extended Data Fig. 6 Association of duration of breastfeeding with estimated microbiome age (EMA).

Beta estimates of linear regression model of EMA versus duration of breastfeeding dichotomized at the indicated time points.

Extended Data Fig. 7 Acetate score and propionate score in the case-control sample.

The upper panels a, refer to propionate, the lower panels b, to acetate. The left column gives proportion of asthma cases within quartiles of the respective short-chain fatty acid (SCFA) variables. The right column gives odds ratios with 95%-confidence intervals for the associations of asthma phenotypes with the respective dichotomous SCFA variables (upper quartiles versus lowest quartile). Propionate and acetate level designate measured SCFA levels, whereas the estimated scores refer to the prediction models of measured SCFA levels by the microbial composition.

Extended Data Fig. 8 The gut mycobiome and estimated fungal age (EFA).

a, Log-scaled box and whiskers plots of relative abundance of most common fungal taxa at month 2 and month 12 in 189 children. Lower and upper hinges of the boxes denote the first and third quartiles, respectively; the bold central line represents the median; the whiskers extend to the most extreme data point within 1.5 times the interquartile range from the hinges; extreme values lie beyond the whiskers and are marked by circles. Missing boxes indicate relative abundance < 0.5% at the respective time point. ‘(F)’, ‘(O)’, or ‘(P)’ stand for unclassified genera of the respective fungal family, order or phylum. b, Chronological age, that is the exact sampling time point in months plotted against estimated fungal age (EFA) illustrates that all chronologic information is largely removed from EFA. The density plot included in panel b reveals a skewed distribution of EFA. c, Fungal taxa most importantly predicting fungal age in the 35 healthy individuals. d, Determinants of EFA in the population with ITS data. Odds ratios are given with 95%-confidence intervals. Listed are determinants with p-values <0.01 in bivariate analyses; only consumption of any bread (shaded in gray) remains in a multivariable model. e, Associations of asthma phenotypes with EFA.

Extended Data Fig. 9 Association of asthma phenotypes with microbial measures.

Asthma was defined as a doctor’s diagnosis of asthma or recurrent obstructive bronchitis. Asthma after 3 years was defined as a doctor’s diagnosis of asthma or recurrent obstructive bronchitis after the age of 3 years. The atopic and nonatopic phenotypes of asthma were defined by presence or absence of concomitant sensitization to inhalant allergens with specific IgE ≥ 0.7 IU/ml at age 6 years. Wheeze phenotypes were defined by a latent class analysis as previously performed14. Transient and intermediate wheeze were milder forms with better lung function and less medication. Persistent wheeze was related to genetic risk encoded on chromosome 17q21 and displayed reduced lung function. Lateonset wheeze was particularly associated with atopic sensitization and fraction of exhaled nitric oxide. Seasonal IgE was defined as at least one specific IgE to alder, birch, hazel, grass pollen, rye, mugwort, plantain, or alternaria ≥ 0.7 IU/ml at age 6 years. Perennial IgE (D. pteronyssinus, D. farinae, cat, horse, or dog) and food IgE (hen’s egg, cow’s milk, peanut, hazelnut, carrot or wheat flour) were defined in analogy.

Extended Data Fig. 10 Distribution of the microbial variables over the study centers.

PCA=principal component analysis, EMA=estimated microbiome age, EFA=estimated fungal age; p-values are derived from two-sided Kruskal-Wallis tests. The analyses were performed in all 618 individuals with data available for the respective measures, except for EFA, where data was available only in 189 individuals. Lower and upper hinges of the boxes denote the first and third quartiles, respectively; the bold central line represents the median; the whiskers extend to the most extreme data point within 1.5 times the interquartile range from the hinges; extreme values lie beyond the whiskers and are marked by circles.

Supplementary information

Supplementary Information

Supplementary Tables 1–9.

Reporting Summary

Supplementary Data

16S rRNA and ITS digits and sequences.

Supplementary Software

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Depner, M., Taft, D.H., Kirjavainen, P.V. et al. Maturation of the gut microbiome during the first year of life contributes to the protective farm effect on childhood asthma. Nat Med 26, 1766–1775 (2020). https://doi.org/10.1038/s41591-020-1095-x

Download citation

Further reading

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