Distinct Gut Microbial Signature and Host Genetic Variants in Association with Liver Fibrosis Severity in Patients with MASLD.
Nantawat SatthawiwatThananya JinatoSawannee SutheeworapongNatthaporn TanpowpongNatthaya ChuaypenPisit TangkijvanichPublished in: Nutrients (2024)
Gut microbiota might affect the severity and progression of metabolic dysfunction-associated steatotic liver disease (MASLD). We aimed to characterize gut dysbiosis and clinical parameters regarding fibrosis stages assessed by magnetic resonance elastography. This study included 156 patients with MASLD, stratified into no/mild fibrosis (F0-F1) and moderate/severe fibrosis (F2-F4). Fecal specimens were sequenced targeting the V4 region of the 16S rRNA gene and analyzed using bioinformatics. The genotyping of PNPLA3 , TM6SF2 , and HSD17B13 was assessed by allelic discrimination assays. Our data showed that gut microbial profiles between groups significantly differed in beta-diversity but not in alpha-diversity indices. Enriched Fusobacterium and Escherichia_Shigella , and depleted Lachnospira were found in the F2-F4 group versus the F0-F1 group. Compared to F0-F1, the F2-F4 group had elevated plasma surrogate markers of gut epithelial permeability and bacterial translocation. The bacterial genera, PNPLA3 polymorphisms, old age, and diabetes were independently associated with advanced fibrosis in multivariable analyses. Using the Random Forest classifier, the gut microbial signature of three genera could differentiate the groups with high diagnostic accuracy (AUC of 0.93). These results indicated that the imbalance of enriched pathogenic genera and decreased beneficial bacteria, in association with several clinical and genetic factors, were potential contributors to the pathogenesis and progression of MASLD.
Keyphrases
- liver fibrosis
- magnetic resonance
- microbial community
- genome wide
- type diabetes
- cardiovascular disease
- high throughput
- copy number
- climate change
- computed tomography
- endothelial cells
- gene expression
- adipose tissue
- cancer therapy
- risk assessment
- big data
- drug delivery
- transcription factor
- deep learning
- single cell
- artificial intelligence
- fine needle aspiration