Gut Microbiome-Based Metagenomic Signature for Non-invasive Detection of Advanced Fibrosis in Human Nonalcoholic Fatty Liver Disease

Abstract

The presence of advanced fibrosis in nonalcoholic fatty liver disease (NAFLD) is the most important predictor of liver mortality. There are limited data on the diagnostic accuracy of gut microbiota-derived signature for predicting the presence of advanced fibrosis. In this prospective study, we characterized the gut microbiome compositions using whole-genome shotgun sequencing of DNA extracted from stool samples. This study included 86 uniquely well-characterized patients with biopsy-proven NAFLD, of which 72 had mild/moderate (stage 0-2 fibrosis) NAFLD, and 14 had advanced fibrosis (stage 3 or 4 fibrosis). We identified a set of 40 features (p < 0.006), which included 37 bacterial species that were used to construct a Random Forest classifier model to distinguish mild/moderate NAFLD from advanced fibrosis. The model had a robust diagnostic accuracy (AUC 0.936) for detecting advanced fibrosis. This study provides preliminary evidence for a fecal-microbiome-derived metagenomic signature to detect advanced fibrosis in NAFLD.

Keywords: NASH; biomarker; cirrhosis; fatty liver; fibrosis; hepatic steatosis; hepatitis; liver disease; microbiome; non-invasive.

Figure 1

Boxplots of the relative abundances of 37 species selected for the random forest model used to distinguish samples in the mild/moderate group (G1) from those in the advanced fibrosis group (G2). Sample diversity and patient age and BMI were also selected as important features by the random forest model (boxplots not shown).

Figure 2

Performance of the Random Forest model and Ordination of the NAFLD samples. (A) Receiver Operating Characteristic (ROC) curve of the final Random Forest model constructed using the relative abundances of the 37 selected species together with Shannon diversity, age, and BMI, which were also selected as important features by the training process. (B) Principal Component Analysis ordination of the NAFLD samples. Samples from the mild/moderate group (G1) are in brown while samples from the advanced fibrosis group (G2) are in blue. The samples were plotted using the first two principal components PC1 and PC2. These components, respectively, account for 34.3% and 19.6% of the total variance.

Figure 3

Overview of metabolomic and metagenomic analyses of Biopsy-proven NAFLD patients. Serum and stool samples from a cohort of 86 patients were analyzed for their metabolic and functional content. [Left] The metabolic profiles of 56 serum samples detected several differentially abundant metabolites, after multiple test correction. These are highlighted, with G1 enriched in brown and G2 enriched in blue. [Center] ORF sequences identified from whole genome sequencing of 86 stool samples were used to compute relative abundances of enzymes involved in SCFA production. Several enzymes were enriched in either G1 (brown) or G2 (blue), though they were not statistically significant after multiple test correction. [Right] Metabolic pathways were reconstructed from whole genome sequencing of 86 stool samples. Pathway abundance was calculated by summing the abundances of species in which the pathway was reconstructed. Several pathways were enriched in G1 (brown) or G2 (blue), though these were not statistically significant after multiple test correction.