SUMMARY 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, 72 of which had mild/moderate (stage 0–2 fibrosis) NAFLD, and 14 had advanced fibrosis (stage 3 or 4 fibrosis). We identified a set of forty features (p-value <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 novel fecal-microbiome derived metagenomic signature to detect advanced fibrosis in NAFLD.

The human gut microbiome plays a key role in human health1, but 16S characterization lacks quantitative functional annotation2. The fecal metabolome provides a functional readout of microbial activity and could be used as an intermediate phenotype mediating these interactions3. In the first comprehensive description of the fecal metabolome, examining 1116 metabolites of 786 individuals from a population-based twin study (TwinsUK), the fecal metabolome was found to be only modestly influenced by host genetics (h2=17.9%). One replicated locus at the NAT2 gene was associated with fecal metabolic traits. The fecal metabolome largely reflects gut microbial composition explaining on average 67.7% (±18.8%) of its variance. It is strongly associated with visceral fat mass, illustrating potential mechanisms underlying the well-established microbial influence on abdominal obesity. Fecal metabolic profiling appears as a novel tool to explore links between microbiome composition, host phenotypes, and heritable complex traits.

Genetic factors modifying the blood metabolome have been investigated through genome-wide association studies (GWAS) of common genetic variants and through exome sequencing. We conducted a whole-genome sequencing study of common, low-frequency and rare variants to associate genetic variations with blood metabolite levels using comprehensive metabolite profiling in 1,960 adults. We focused the analysis on 644 metabolites with consistent levels across three longitudinal data collections. Genetic sequence variations at 101 loci were associated with the levels of 246 (38%) metabolites (P ≤ 1.9 × 10). We identified 113 (10.7%) among 1,054 unrelated individuals in the cohort who carried heterozygous rare variants likely influencing the function of 17 genes. Thirteen of the 17 genes are associated with inborn errors of metabolism or other pediatric genetic conditions. This study extends the map of loci influencing the metabolome and highlights the importance of heterozygous rare variants in determining abnormal blood metabolic phenotypes in adults.