Host genetics influences gut microbiome composition, which affects glucose metabolism
Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases
Introduction and methods
Evidence is accumulating that the human gut microbiome plays a role in metabolic disease [1-3]. This opens the road to manipulation of the gut microbiome as an alternative to pharmacological interventions. To that end, microbiome features that are causal for disease need to be distinguished from those that result from disease or treatment thereof, and from those that show a statistical correlation due to confounding or pleiotropy.
Animal studies have pointed at a causal role for the gut microbiome in the development of type 2 diabetes (T2DM), insulin resistance and obesity [4,5]. Translation of these findings to humans is challenging and the specific bacterial species responsible remain to be clarified , as results differ among studies. One consistent finding in T2DM subjects is, however, a shift in the microbiome composition away from species able to produce butyrate. Butyrate and other fecal short-chain fatty acid (SCFA) are produced by gut bacterial fermentation of undigested food components. SCFAs are absorbed by the colonocytes and are used locally as fuel or they enter the portal bloodstream . Most evidence suggests that higher SCFA production has antiobesity and antidiabetic effects for the host, but some other studies have suggested that increased energy accumulation through accumulation of SCFAs can lead to obesity [8,9].
To study aimed to shed more light on the causal relationships among gut-microbiome composition, SCFA abundance and host energy metabolism. It has been demonstrated that variants can be detected in the host genome that influence the composition of the gut microbiota. Using a mendelian randomization (MR) approach, it is then possible to assess whether genetic predictors of microbiome content influence metabolic traits, or the other way around. This study combined genome-wide genetic data, gut metagenomic sequencing, measurements of fecal SCFAs and clinical phenotypes of 952 normoglycemic individuals from the LifeLines-DEEP (LL-DEEP) cohort. In addition, publicly available genome-wide-association (GWAS) summary statistics for 17 anthropometric and glycemic traits were used. The study focused on 245 microbiome features that were correlated with at least one of the measured anthropometric and metabolic traits in LL-DEEP. Genetic predictors (independent genetic variants) of the microbiome features were identified, that associated with the features.
- Inverse-variance weighted (IVW) testing revealed a significant causal influence for one specific microbiome feature, a microbial pathway involved in 4-aminobutonoate (GABA) degradation, on increased insulin secretion, specifically the ratio of the areas under the curve for insulin and glucose (AUC-insulin/AUC-glucose) measured during an oral glucose-tolerance test.
- The reverse MR analysis that tests the relationship between genetic predictors of AUC-insulin/AUC-glucose and GABA abundance, was not significant.
- There was no evidence of causality between the GABA-pathway and 7 metabolic and anthropometric traits (BMI, body-fat %, waist-hip ratio, visceral adipose tissue, abdominal subcutaneous adipose tissue, obesity and T2DM).
- The GABA-pathway did show a causal association with other insulin-response parameters.
- The two bacterial species that correlated most with the abundance of GABA are butyrate-producing bacteria.
- An increase in the level of propionate, another SCFA, in feces was causally related to increased odds of T2DM. Reverse MR analysis testing the effect of T2DM on fecal propionate levels revealed no association.
These findings suggest a causal role of gut-produced butyrate on the dynamic insulin response to food ingestion rather than on the homeostatic glucose metabolism in the fasted state. The level of gut-produced propionate was causally related to the risk of T2DM. These data are in line with a model in which host genetic variation affects gut-microbiome composition, thereby modulating GABA degradation activity, which in turn increases the ability of the pancreatic islets to secrete insulin in response to a physiological glucose challenge.
It should be noted that, even though LL-DEEP is currently the largest population study on the genetics of the microbiome, it is still underpowered to capture the limited genetic component that has been estimated for microbiome features. This can be concluded from the observation that different studies show limited direct overlap. Standardized protocols for data analyses and for larger samples sizes are needed to identify more robust genetic predictors. Expansion of the current analyses, combined with measures of circulating SCFAs, will contribute to better understanding of the complex interplay between the gut microbiome and host metabolism.
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