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.

Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases

Abstract

Microbiome-wide association studies on large population cohorts have highlighted associations between the gut microbiome and complex traits, including type 2 diabetes (T2D) and obesity1. However, the causal relationships remain largely unresolved. We leveraged information from 952 normoglycemic individuals for whom genome-wide genotyping, gut metagenomic sequence and fecal short-chain fatty acid (SCFA) levels were available2, then combined this information with genome-wide-association summary statistics for 17 metabolic and anthropometric traits. Using bidirectional Mendelian randomization (MR) analyses to assess causality3, we found that the host-genetic-driven increase in gut production of the SCFA butyrate was associated with improved insulin response after an oral glucose-tolerance test (P = 9.8 × 10−5), whereas abnormalities in the production or absorption of another SCFA, propionate, were causally related to an increased risk of T2D (P = 0.004). These data provide evidence of a causal effect of the gut microbiome on metabolic traits and support the use of MR as a means to elucidate causal relationships from microbiome-wide association findings.

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: Schematic representation of the study.
Fig. 2: Causal effect of butyrate-producing activity of the gut on the glucose-stimulated insulin response.
Fig. 3: Causal effect of fecal propionate on T2D.

Data availability

The LifeLines-DEEP metagenomic sequencing data are available at the European Genome-phenome Archive (EGA) under accession code EGAS00001001704. Genotype and phenotype data can be requested from the Lifelines Biobank at https://www.lifelines.nl/researcher/biobank-lifelines/application-process/. Summary statistics for metabolic traits were downloaded from the MAGIC, GIANT and DIAGRAM websites (see URLs).

References

  1. 1.

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

    CAS  Article  Google Scholar 

  2. 2.

    Bonder, M. J. et al. The effect of host genetics on the gut microbiome. Nat. Genet. 48, 1407–1412 (2016).

    CAS  Article  Google Scholar 

  3. 3.

    Evans, D. M. & Davey Smith, G. Mendelian randomization: new applications in the coming age of hypothesis-free causality. Annu. Rev. Genomics Hum. Genet. 16, 327–350 (2015).

    CAS  Article  Google Scholar 

  4. 4.

    Larsen, N. et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One 5, e9085 (2010).

    Article  Google Scholar 

  5. 5.

    Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013).

    CAS  Article  Google Scholar 

  6. 6.

    Ley, R. E. et al. Obesity alters gut microbial ecology. Proc. Natl Acad. Sci. USA 102, 11070–11075 (2005).

    CAS  Article  Google Scholar 

  7. 7.

    Kreznar, J. H. et al. Host genotype and gut microbiome modulate insulin secretion and diet-induced metabolic phenotypes. Cell Rep. 18, 1739–1750 (2017).

    CAS  Article  Google Scholar 

  8. 8.

    Brunkwall, L. & Orho-Melander, M. The gut microbiome as a target for prevention and treatment of hyperglycaemia in type 2 diabetes: from current human evidence to future possibilities. Diabetologia 60, 943–951 (2017).

    CAS  Article  Google Scholar 

  9. 9.

    Kootte, R. S. et al. Improvement of insulin sensitivity after lean donor feces in metabolic syndrome is driven by baseline intestinal microbiota composition. Cell Metab. 26, 611–619.e6 (2017).

    CAS  Article  Google Scholar 

  10. 10.

    Zhang, X. et al. Human gut microbiota changes reveal the progression of glucose intolerance. PLoS One 8, e71108 (2013).

    CAS  Article  Google Scholar 

  11. 11.

    Ríos-Covián, D. et al. Intestinal short chain fatty acids and their link with diet and human health. Front. Microbiol. 7, 185 (2016).

    Article  Google Scholar 

  12. 12.

    Pingitore, A. et al. The diet-derived short chain fatty acid propionate improves beta-cell function in humans and stimulates insulin secretion from human islets in vitro. Diabetes Obes. Metab. 19, 257–265 (2017).

    CAS  Article  Google Scholar 

  13. 13.

    Chambers, E. S. et al. Effects of targeted delivery of propionate to the human colon on appetite regulation, body weight maintenance and adiposity in overweight adults. Gut 64, 1744–1754 (2015).

    CAS  Article  Google Scholar 

  14. 14.

    Zhao, L. et al. Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes. Science 359, 1151–1156 (2018).

    CAS  Article  Google Scholar 

  15. 15.

    Peng, L., He, Z., Chen, W., Holzman, I. R. & Lin, J. Effects of butyrate on intestinal barrier function in a Caco-2 cell monolayer model of intestinal barrier. Pediatr. Res. 61, 37–41 (2007).

    CAS  Article  Google Scholar 

  16. 16.

    Schwiertz, A. et al. Microbiota and SCFA in lean and overweight healthy subjects. Obesity (Silver Spring) 18, 190–195 (2010).

    Article  Google Scholar 

  17. 17.

    Turpin, W. et al. Association of host genome with intestinal microbial composition in a large healthy cohort. Nat. Genet. 48, 1413–1417 (2016).

    CAS  Article  Google Scholar 

  18. 18.

    Goodrich, J. K. et al. Genetic determinants of the gut microbiome in UK Twins. Cell Host Microbe 19, 731–743 (2016).

    CAS  Article  Google Scholar 

  19. 19.

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

    CAS  Article  Google Scholar 

  20. 20.

    Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    CAS  Article  Google Scholar 

  21. 21.

    Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015).

    CAS  Article  Google Scholar 

  22. 22.

    Manning, A. K. et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat. Genet. 44, 659–669 (2012).

    CAS  Article  Google Scholar 

  23. 23.

    Strawbridge, R. J. et al. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes 60, 2624–2634 (2011).

    CAS  Article  Google Scholar 

  24. 24.

    Soranzo, N. et al. Common variants at 10 genomic loci influence hemoglobin A1(C) levels via glycemic and nonglycemic pathways. Diabetes 59, 3229–3239 (2010).

    CAS  Article  Google Scholar 

  25. 25.

    Prokopenko, I. et al. A central role for GRB10 in regulation of islet function in man. PLoS Genet. 10, e1004235 (2014).

    Article  Google Scholar 

  26. 26.

    Saxena, R. et al. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat. Genet. 42, 142–148 (2010).

    CAS  Article  Google Scholar 

  27. 27.

    Scott, R. A. et al. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66, 2888–2902 (2017).

    CAS  Article  Google Scholar 

  28. 28.

    Li, Y. et al. A functional genomics approach to understand variation in cytokine production in humans. Cell 167, 1099–1110.e14 (2016).

    CAS  Article  Google Scholar 

  29. 29.

    Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665 (2013).

    Article  Google Scholar 

  30. 30.

    Verbanck, M., Chen, C. Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).

    CAS  Article  Google Scholar 

  31. 31.

    Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).

    Article  Google Scholar 

  32. 32.

    Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

    Article  Google Scholar 

  33. 33.

    Bowden, J. et al. Improving the accuracy of two-sample summary data Mendelian randomization: moving beyond the NOME assumption. Preprint at https://www.biorxiv.org/content/early/2018/10/11/159442 (2018).

  34. 34.

    Rücker, G., Schwarzer, G., Carpenter, J. R., Binder, H. & Schumacher, M. Treatment-effect estimates adjusted for small-study effects via a limit meta-analysis. Biostatistics 12, 122–142 (2011).

    Article  Google Scholar 

  35. 35.

    Bycroft, C. et al. TheUK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    CAS  Article  Google Scholar 

  36. 36.

    Duncan, S. H., Hold, G. L., Barcenilla, A., Stewart, C. S. & Flint, H. J. Roseburia intestinalis sp. nov., a novel saccharolytic, butyrate-producing bacterium from human faeces. Int. J. Syst. Evol. Microbiol. 52, 1615–1620 (2002).

    CAS  PubMed  Google Scholar 

  37. 37.

    Pryde, S. E., Duncan, S. H., Hold, G. L., Stewart, C. S. & Flint, H. J. The microbiology of butyrate formation in the human colon. FEMS Microbiol. Lett. 217, 133–139 (2002).

    CAS  Article  Google Scholar 

  38. 38.

    Jakobsdottir, G., Bjerregaard, J. H., Skovbjerg, H. & Nyman, M. Fasting serum concentration of short-chain fatty acids in subjects with microscopic colitis and celiac disease: no difference compared with controls, but between genders. Scand. J. Gastroenterol. 48, 696–701 (2013).

    CAS  Article  Google Scholar 

  39. 39.

    Liu, F. et al. Fructooligosaccharide (FOS) and galactooligosaccharide (GOS) increase Bifidobacterium but reduce butyrate producing bacteria with adverse glycemic metabolism in healthy young population. Sci. Rep. 7, 11789 (2017).

    Article  Google Scholar 

  40. 40.

    Louis, P. & Flint, H. J. Formation of propionate and butyrate by the human colonic microbiota. Environ. Microbiol. 19, 29–41 (2017).

    CAS  Article  Google Scholar 

  41. 41.

    den Besten, G. et al. The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J. Lipid Res. 54, 2325–2340 (2013).

    Article  Google Scholar 

  42. 42.

    Kurilshikov, A., Wijmenga, C., Fu, J. & Zhernakova, A. Host genetics and gut microbiome: challenges and perspectives. Trends Immunol. 38, 633–647 (2017).

    CAS  Article  Google Scholar 

  43. 43.

    Taylor, A. E. et al. Mendelian randomization in health research: using appropriate genetic variants and avoiding biased estimates. Econ. Hum. Biol. 13, 99–106 (2014).

    Article  Google Scholar 

  44. 44.

    Wang, J. et al. Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative. Microbiome 6, 101 (2018).

    Article  Google Scholar 

  45. 45.

    Tigchelaar, E. F. et al. Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open 5, e006772 (2015).

    Article  Google Scholar 

  46. 46.

    Li, N. et al. Pleiotropic effects of lipid genes on plasma glucose, HbA1c, and HOMA-IR levels. Diabetes 63, 3149–3158 (2014).

    Article  Google Scholar 

  47. 47.

    García-Villalba, R. et al. Alternative method for gas chromatography-mass spectrometry analysis of short-chain fatty acids in faecal samples. J. Sep. Sci. 35, 1906–1913 (2012).

    Article  Google Scholar 

  48. 48.

    Schirmer, M. et al. Linking the human gut microbiome to inflammatory cytokine production capacity. Cell 167, 1125–1136.e8 (2016).

    CAS  Article  Google Scholar 

  49. 49.

    McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    CAS  Article  Google Scholar 

  50. 50.

    Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018).

    CAS  Article  Google Scholar 

  51. 51.

    Vatanen, T. et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell 165, 842–853 (2016).

    CAS  Article  Google Scholar 

  52. 52.

    Kang, H. M. et al. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010).

    CAS  Article  Google Scholar 

  53. 53.

    Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    CAS  Article  Google Scholar 

  54. 54.

    Loh, P. R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    CAS  Article  Google Scholar 

  55. 55.

    Eastwood, S. V. et al. Algorithms for the capture and adjudication of prevalent and incident diabetes in UK Biobank. PLoS One 11, e0162388 (2016).

    Article  Google Scholar 

Download references

Acknowledgements

We thank the participants and staff of the LL-DEEP cohort for their collaboration, the UMCG Genomics Coordination center, the UG Center for Information Technology and their sponsors BBMRI-NL and TarGet for storage and compute infrastructure. We are also grateful to M. J. Bonder for help in formatting summary statistics; to R. K. Weersma and Y. Li for discussions; and to K. Mc Intyre for editing the manuscript. Part of this work was conducted by using the UK Biobank resource under application no. 9161. This project was funded by IN-CONTROL CVON grant CVON2012-03 to M.G.N., A.Z., L.A.B.J. and J.F.; Top Institute Food and Nutrition (TiFN, Wageningen, the Netherlands) grant TiFN GH001 to C.W.; the Netherlands Organization for Scientific Research (NWO) grants NWO-VENI 016.176.006 to M.O., NWO-VIDI 864.13.013 to J.F. and NWO-VIDI 016.Vidi.178.056 to A.Z.; NWO Spinoza Prizes SPI 92-266 to C.W. and SPI 94-212 to M.G.N.; European Research Council (ERC) starting grant ERC no. 715772 to A.Z.; FP7/2007-2013/ERC Advanced Grant (agreement 2012-322698) to C.W.; ERC Consolidator Grant ERC no. 310372 to M.G.N.; Tripartite Immunometabolism consortium (TrIC)–Novo Nordisk Foundation grant NNF15CC0018486 to M.I.M.; and Wellcome grants 090532, 098381, 106130 and 203141 to M.I.M. A.Z. is also supported by a Rosalind Franklin Fellowship from the University of Groningen. M.I.M. is supported as a Wellcome Senior Investigator and a National Institute of Health Research Senior Investigator. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The views expressed in this article are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

Author information

Affiliations

Authors

Contributions

S.S. performed statistical analyses on the LifeLines and 500FG cohorts; N.R.v.Z. and A.M. performed statistical analyses on UK Biobank and DIAGRAM studies; A.K. and A.V.V. processed raw microbiome data in Lifelines-DEEP and 500FG; U.V. and L.F. downloaded and harmonized the summary statistics from the GIANT, MAGIC and DIAGRAM consortia; L.F., and C.W. provided LifeLines-DEEP data; Z.M., A.A.M.M. and D.M.A.E.J. provided critical input in manuscript revisions; M.O., L.A.B.J. and M.G.N. provided 500FG data; S.S., N.R.v.Z. and M.I.M. wrote the manuscript, to which J.F., A.Z. and C.W. provided critical input; S.S., N.R.v.Z., A.M., C.W. and M.I.M. designed the study. All authors read, revised and approved the manuscript.

Corresponding authors

Correspondence to Serena Sanna or Cisca Wijmenga or Mark I. McCarthy.

Ethics declarations

Competing interests

M.I.M. serves on advisory panels for Pfizer, NovoNordisk and Zoe Global; has received honoraria from Pfizer, NovoNordisk and Eli Lilly; has stock options in Zoe Global; and has received research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier and Takeda. All other authors declare no competing financial interests.

Additional information

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

Integrated supplementary information

Supplementary Figure 1 Distribution of variance explained for microbiome features in 500FG.

Distribution of variance explained of microbiome features in 445 normo-glycemic 500FG individuals by genetic predictors selected in LL-DEEP GWAS at different P-value thresholds, when predictors are selected from the full HRC imputed data (green), or when restricted to HapMap2 SNPs and proxies (red). Distributions are represented as boxplots, where the box hinges represent the 25th and 75th percentile, the central line of the box represents the median value, and the points outliers (default options in the boxplot() function in R).

Supplementary Figure 2 Clustering of metabolic traits through genetic correlation.

Clustering of metabolic traits generated using the R function hclust(“complete”) and a dissimilarity metric of (1-ρg)/2, where ρg is the genetic correlation.

Supplementary Figure 3 MR plot showing the causal effect of microbiome PWY-5022 on the glucose-stimulated insulin response.

In this plot, each point is a genetic predictor of PWY-5022. The x-axis plot shows their effect size on PWY-5022 as estimated in LL-DEEP cohort (952 samples), and the y-axis shows the allelic effect on glucose-stimulated insulin response as estimated in MAGIC (4213 samples). Whiskers represent 95% confidence intervals of these effects. The slope of the red line represents the causal effect estimated by the Mendelian Randomization analysis with the IVW test, with dashed lines corresponding to 95% confidence interval (as given in Supplementary Table 5)

Supplementary Figure 4 Forest plot of the PWY-5022 causal effect estimated with different statistical tests.

The forest plot shows the causal effect (in mU/mmol) of PWY-5022 on glucose-stimulated insulin response parameter AUCinsulin/AUCglucose estimated with different MR tests, using 8 genetic predictors and their effects from LL-DEEP (952 samples) and MAGIC (4213 samples) summary statistics. Bars represent 95% confidence intervals. The annexed table indicates the two-sided pvalues for the respective MR and pleiotropy statistical tests.

Supplementary Figure 5 Forest plot of the fecal propionate causal effect on T2D, estimated with different statistical tests.

The forest plot shows the causal effect (in log(OR) units) of fecal propionate on T2D estimated with different MR tests, using 3 genetic predictors and their effects from LL-DEEP (898 samples) and DIAGRAM (26,676 T2D cases and 132,532 controls) summary statistics. Bars represent 95% confidence intervals. The annexed table indicates the two-sided pvalues for the respective MR and pleiotropy statistical tests.

Supplementary Figure 6 Forest plot of the fecal propionate causal effect on BMI, estimated with different statistical tests.

The forest plot shows the causal effect of fecal propionate on BMI (in SD units) estimated with different MR tests using 3 genetic predictors and their effects from LL-DEEP (898 samples) and GIANT (339,224 samples) summary statistics. Bars represent 95% confidence intervals. The annexed table indicates the two-sided pvalues for the respective MR and pleiotropy statistical tests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6 and Supplementary Tables 1 and 3–11

Reporting Summary

Supplementary Table 2

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sanna, S., van Zuydam, N.R., Mahajan, A. et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases. Nat Genet 51, 600–605 (2019). https://doi.org/10.1038/s41588-019-0350-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