Gut microbiota signature for NAFLD
Non-alcoholic fatty liver disease (NAFLD) is a condition whereby there is excess fat accumulation in the liver of people who consume little to no alcohol. A healthy liver should have very minimal to no fat at all. There are four stages of NAFLD, including;
1) Steatosis – fat accumulates in liver cells without any inflammation or scarring
2) Non-alcoholic steatohepatitis (NASH) – when the liver becomes inflamed
3) Fibrosis – persistent inflammation causes scarring
4) Cirrhosis – further scarring and lumps form
The gut-liver axis is the system which describes the bi-directional communication between the gut (including the gut microbiota) and the liver, via the portal vein. The gut-liver axis has been suggested to be involved in NAFLD progression, with some evidence from animal studies describing a number of different mechanisms1. Although, this is likely to be complex and multifactorial.
A recent study2 (Caussy et al. 2019) has investigated the gut microbiota of subjects with NAFLD to try and identify key microbial features that could be used as a diagnostic tool for the condition.
Using subjects from the Familial Cirrhosis and Twins and Family cohorts, participants were divided into three groups (referred to as probands) and paired with first-degree relative(s) (i.e. sibling, child, or parent).
The three groups include;
• NAFLD-cirrhosis (n=26 probands, with n=37 relatives)
• NAFLD-without advanced fibrosis (AF) (n=18 probands, with n=17 relatives)
• Non-NAFLD controls (n=54 probands, with n=44 relatives)
All subjects underwent a medical history and physical examination, had a series of assessments to determine liver health, and also provided a stool sample.
Firstly, when the faecal microbiota of probands were compared to their relatives, a significant correlation was found at the phyla level (p=0.023), particularly when they are living within the same household (p=0.046).
The assessment of beta-diversity (species differences between samples) based on liver phenotype only showed significantly low phylogenetic dissimilarity in two groups: non-NAFLD controls and relatives (p=0.001) and NAFLD-without AF and relatives (p=0.015) when compared to unrelated pairs. This relationship was not seen in those with severe liver disease (NAFLD-cirrhosis) and their relatives.
When comparing the gut microbiota between the three groups of probands, a decline in alpha-diversity (species richness and evenness within a single sample) was observed with increasing liver damage (NAFLD cirrhosis) (p<0.05).
However, beta-diversity only decreased with moderate liver damage (NAFLD-without AF) (p<0.05) and then increased in the severe stage of liver damage (NAFLD-cirrhosis).
In particular, at the genus level, Streptococcus was enriched in both the NAFLD-cirrhosis and NAFLD-without AF groups, and Megasphaera was only enriched in the NAFLD-cirrhosis group. Lactococcus and Bacillus were enriched in both non-NAFLD controls and NAFLD-without AF, and Pseudomonas was only enriched in the non-NAFLD control group. These differences may suggest that the gut microbiota shifts as the disease progresses.
Another aim of the study was to identify a signature, from stool samples, that could accurately detect NAFLD-cirrhosis. In addition to age, sex, and body mass index, the presence of 27 bacterial features were identified as being the most important predictors of NAFLD-cirrhosis. The model was then validated in a derivation cohort of probands and relatives of NAFLD-cirrhosis probands, and was deemed to have a robust diagnostic accuracy of 0.92 and 0.87 (AUROC), respectively.
The authors highlight that the observed changes in the gut microbiota of those with NAFLD do not suggest causality, and we do not currently know if the particular species have an effect on gut permeability or NAFLD progression. However, the promising findings from this study warrants further investigation in this field.
In the future, analysis of faecal microbiota may serve as a useful alternative to current invasive diagnostic tools for NAFLD.
1. Kirpich, Marsano and McClain (2016) Clin Biochem 48(0): 923-930
2. Caussy et al. (2019) Nature Communications 10(1) :1406