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PMID: 30778224 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't

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

Nature genetics ·Vol. 51 ·No. 4 ·2019-00-00 ·Pages 600-605

Sanna S, van Zuydam NR, Mahajan A, Kurilshikov A, Vich Vila A, Võsa U, Mujagic Z, Masclee AAM, Jonkers DMAE, Oosting M, Joosten LAB, Netea MG, Franke L, Zhernakova A, Fu J, Wijmenga C, McCarthy MI

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.

MeSH Terms
Adolescent Adult Aged Aged, 80 and over Diabetes Mellitus, Type 2/genetics Fatty Acids, Volatile/genetics Female Gastrointestinal Microbiome/genetics Genotype Glucose Tolerance Test/methods Humans Male Metabolic Diseases/genetics Middle Aged Netherlands Young Adult
Chemicals
Fatty Acids, Volatile
Authors & Affiliations
17 authors, click to expand affiliations / ORCID
Sanna Serena ORCID
Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. [email protected].
van Zuydam Natalie R
Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK. | Oxford Centre for Diabetes Endocrinology and Metabolism, Churchill Hospital, University of Oxford, Oxford, UK.
Mahajan Anubha ORCID
Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK. | Oxford Centre for Diabetes Endocrinology and Metabolism, Churchill Hospital, University of Oxford, Oxford, UK.
Kurilshikov Alexander ORCID
Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Vich Vila Arnau ORCID
Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. | Department of Gastroenterology and Hepatology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Võsa Urmo
Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Mujagic Zlatan
Maastricht University Medical Center, Division Gastroenterology-Hepatology, NUTRIM School for Nutrition, and Translational Research in Metabolism, Maastricht, the Netherlands.
Masclee Ad A M
Maastricht University Medical Center, Division Gastroenterology-Hepatology, NUTRIM School for Nutrition, and Translational Research in Metabolism, Maastricht, the Netherlands.
Jonkers Daisy M A E
Maastricht University Medical Center, Division Gastroenterology-Hepatology, NUTRIM School for Nutrition, and Translational Research in Metabolism, Maastricht, the Netherlands.
Oosting Marije
Department of Internal Medicine, Radboud Institute of Molecular Life Sciences (RIMLS) and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, the Netherlands.
Joosten Leo A B ORCID
Department of Internal Medicine, Radboud Institute of Molecular Life Sciences (RIMLS) and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, the Netherlands.
Netea Mihai G ORCID
Department of Internal Medicine, Radboud Institute of Molecular Life Sciences (RIMLS) and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, the Netherlands.
Franke Lude ORCID
Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Zhernakova Alexandra
Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Fu Jingyuan ORCID
Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. | Department of Pediatrics, Groningen, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Wijmenga Cisca ORCID
Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. [email protected]. | K.G. Jebsen Coeliac Disease Research Centre, Department of Immunology, University of Oslo, Oslo, Norway. [email protected].
McCarthy Mark I ORCID
Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK. [email protected]. | Oxford Centre for Diabetes Endocrinology and Metabolism, Churchill Hospital, University of Oxford, Oxford, UK. [email protected]. | Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK. [email protected].
References (55)
55 references, click to expand
  1. Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016). DOI
  2. Bonder, M. J. et al. The effect of host genetics on the gut microbiome. Nat. Genet. 48, 1407–1412 (2016). DOI
  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). DOI
  4. Larsen, N. et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One 5, e9085 (2010). DOI
  5. Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013). DOI
  6. Ley, R. E. et al. Obesity alters gut microbial ecology. Proc. Natl Acad. Sci. USA 102, 11070–11075 (2005). DOI
  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). DOI
  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). DOI
  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). DOI
  10. Zhang, X. et al. Human gut microbiota changes reveal the progression of glucose intolerance. PLoS One 8, e71108 (2013). DOI
  11. Ríos-Covián, D. et al. Intestinal short chain fatty acids and their link with diet and human health. Front. Microbiol. 7, 185 (2016). DOI
  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). DOI
  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). DOI
  14. Zhao, L. et al. Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes. Science 359, 1151–1156 (2018). DOI
  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). DOI
  16. Schwiertz, A. et al. Microbiota and SCFA in lean and overweight healthy subjects. Obesity (Silver Spring) 18, 190–195 (2010). DOI
  17. Turpin, W. et al. Association of host genome with intestinal microbial composition in a large healthy cohort. Nat. Genet. 48, 1413–1417 (2016). DOI
  18. Goodrich, J. K. et al. Genetic determinants of the gut microbiome in UK Twins. Cell Host Microbe 19, 731–743 (2016). DOI
  19. Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018). DOI
  20. Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015). DOI
  21. Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015). DOI
  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). DOI
  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). DOI
  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). DOI
  25. Prokopenko, I. et al. A central role for GRB10 in regulation of islet function in man. PLoS Genet. 10, e1004235 (2014). DOI
  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). DOI
  27. Scott, R. A. et al. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66, 2888–2902 (2017). DOI
  28. Li, Y. et al. A functional genomics approach to understand variation in cytokine production in humans. Cell 167, 1099–1110.e14 (2016). DOI
  29. Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665 (2013). DOI
  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). DOI
  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). DOI
  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). DOI
  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. 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). DOI
  35. Bycroft, C. et al. TheUK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018). DOI
  36. Roseburia intestinalis sp. nov., a novel saccharolytic, butyrate-producing bacterium from human faeces.
    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). PMID: 12361264
  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). DOI
  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). DOI
  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). DOI
  40. Louis, P. & Flint, H. J. Formation of propionate and butyrate by the human colonic microbiota. Environ. Microbiol. 19, 29–41 (2017). DOI
  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). DOI
  42. Kurilshikov, A., Wijmenga, C., Fu, J. & Zhernakova, A. Host genetics and gut microbiome: challenges and perspectives. Trends Immunol. 38, 633–647 (2017). DOI
  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). DOI
  44. Wang, J. et al. Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative. Microbiome 6, 101 (2018). DOI
  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). DOI
  46. Li, N. et al. Pleiotropic effects of lipid genes on plasma glucose, HbA1c, and HOMA-IR levels. Diabetes 63, 3149–3158 (2014). DOI
  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). DOI
  48. Schirmer, M. et al. Linking the human gut microbiome to inflammatory cytokine production capacity. Cell 167, 1125–1136.e8 (2016). DOI
  49. McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016). DOI
  50. Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018). DOI
  51. Vatanen, T. et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell 165, 842–853 (2016). DOI
  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). DOI
  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). DOI
  54. Loh, P. R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015). DOI
  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). DOI
Article Info
Journal
Nature genetics
Abbr.
Nat Genet
ISSN
1546-1718
DOI
10.1038/s41588-019-0350-x
Email
[email protected]
[email protected]
[email protected]
Published
2019-00-00
Epub
2019-00-18
Pages
600-605
Language
English
Region
United States
NLM ID
9216904
PMCID
PMC6441384
Subset
IM
Grants
NIDDK NIH HHS · U01 DK105535 · United States
Corrections
CommentIn
PMID: 30850821
CommentIn
PMID: 30918350
CommentIn
PMID: 30976118
External Links
PubMed source DOI: 10.1038/s41588-019-0350-x PMC Full Text View journal details
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