Plasma Metabolomics and Machine Learning-Driven Novel Diagnostic Signature for Non-Alcoholic Steatohepatitis.

ABSTRACT: We performed targeted metabolomics with machine learning (ML)-based interpretation to identify metabolites that distinguish the progression of nonalcoholic fatty liver disease (NAFLD) in a cohort. Plasma metabolomics analysis was conducted in healthy control subjects (n = 25) and patients with NAFL (n = 42) and nonalcoholic steatohepatitis (NASH, n = 19) by gas chromatography-tandem mass spectrometry (MS/MS) and liquid chromatography-MS/MS as well as RNA sequencing (RNA-seq) analyses on liver tissues from patients with varying stages of NAFLD (n = 12). The resulting metabolomic data were subjected to routine statistical and ML-based analyses and multi-omics interpretation with RNA-seq data. We found 6 metabolites that were significantly altered in NAFLD among 79

SUBMITTER: Ji M 

PROVIDER: S-EPMC9312563 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature