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

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PubMed ID: 30778224

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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

Nat Genet. Feb 2019. doi: 10.1038/s41588-019-0350-x

COMMENT: This work provides a new workflow, based on Mendelian Randomization (MR) methods, that is able to integrate GWAS, microbiome and metabolic results to obtain causal inferences. This methodology is applied to the study of the relationships between diabetes, microbiome and short-chain fatty acids.


Using an MR approach, we set out to identify whether any bacterial species or pathways, i.e., sets of species grouped according to their specific functions in the gut, have a causal effect on metabolic traits. We and others have recently shown that it is possible to detect variants in the host genome that influence the composition of the gut microbiota. These findings allowed us to deploy an MR approach to infer causal relationships by asking whether genetic predictors of microbiome content influence metabolic traits—or the reverse. This formulation holds even though the quantitative contribution of host genetics to variations in microbiome composition may be limited

This is schematically the new Workflow of analysis defined in this work:

1. Which microbiome features correlate with metabolic traits?

2. What are the genetic predictors of those individual microbiome features?

3. Do changes in microbiome features causally affect metabolic traits or vice versa?

Bidirectional Mendelian randomization

Genetic predictors of microbiome feature X  -> Microbiome feature X  ->  Metabolic trait Y

Genetic predictors of metabolic trait Y  ->  Metabolic trait Y  ->  Microbiome feature X

4. Can we replicate causal relationships?

Mendelian randomization

Genetic predictors of microbiome feature X  ->  Microbiome feature X  ->  Metabolic trait Y

These results suggest a causal role of gut-produced butyrate that is focused on the dynamic insulin response to food ingestion rather than on the homeostatic mechanisms involved in the maintenance of glucose metabolism in the fasted state


In summary, these data are consistent with a causal role of gut produced SCFAs, specifically butyrate and propionate, with respect to energy balance and glucose homeostasis in humans. We showed that a genetically influenced shift in the gut microbiome toward increased production of butyrate has beneficial effects on beta-cell function, although we did not detect an effect on T2D risk.

We also demonstrated that host genetic variation resulting in increased fecal propionate levels (reflecting some combination of increased production or impaired absorption) affects T2D risk.

Nevertheless, this study demonstrates that microbiome GWAS provide a route to causal inference that can guide and complement more direct experimental approaches, such as those based on fecal transplantation and animal models. We predict that with expanded microbiome-genetic studies (for example, the MiBioGen consortium), MR will become a standard tool for systematically screening a large number of hypotheses generated in current and future microbiome-wide association studies.


Raquel Tobes