Metabolomics and machine learning approaches for diagnostic and prognostic biomarkers screening in sepsis.

ABSTRACT:

Background

Sepsis is a life-threatening disease with a poor prognosis, and metabolic disorders play a crucial role in its development. This study aims to identify key metabolites that may be associated with the accurate diagnosis and prognosis of sepsis.

Methods

Septic patients and healthy individuals were enrolled to investigate metabolic changes using non-targeted liquid chromatography-high-resolution mass spectrometry metabolomics. Machine learning algorithms were subsequently employed to identify key differentially expressed metabolites (DEMs). Prognostic-related DEMs were then identified using univariate and multivariate Cox regression analyses. The septic rat model was established to verify the effect of phenylalanine metabolism-related gene MAOA on survival and mean a

SUBMITTER: She H 

PROVIDER: S-EPMC10634148 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature