[Untargeted metabolomics-based exploration of potential metabolic biomarkers for subclinical atherosclerosis in metabolic-associated fatty liver disease].

OBJECTIVES

Cardiovascular disease is the most common extrahepatic complication and leading cause of death in metabolic dysfunction‑associated fatty liver disease (MAFLD). However, early metabolic biomarkers for subclinical atherosclerosis (SA) in MAFLD patients remain unclear. This study used untargeted metabolomics to investigate serum metabolite and pathway changes associated with SA in MAFLD patients and to screen for potential serum metabolic biomarkers.

METHODS

From December 2023 to December 2024, 64 MAFLD patients with SA enrolled in the "Pan-Vascular Cohort Study" at the Health Management Medical Center of the Third Xiangya Hospital of Central South University were included as an experimental group, and 50 age- and sex-matched MAFLD patients without SA were included as controls. MAFLD was diagnosed according to the Guidelines for the Prevention and Treatment of Metabolic (Non-Alcoholic) Fatty Liver Disease (2024 Edition). SA was defined by the presence of multi-segment arterial plaques and/or peripheral arterial obstruction. Untargeted metabolomics profiling was performed using ultra-high performance liquid chromatography coupled with mass spectrometry. Differential metabolites were screened using fold change (FC) analysis, principal component analysis, and orthogonal partial least squares discriminant analysis. Receiver operating characteristic (ROC) curves evaluated diagnostic performance of differential metabolites, and pathway enrichment analysis identified metabolic pathways associated with MAFLD and atherosclerosis.

RESULTS

Using selection criteria of FDR-corrected q<0.05 and FC >1.5 or <0.67, a total of 415 differential metabolites were identified between the 2 groups. The top 5 differential metabolites were lysophosphatidylcholine (18꞉3), chelerythrine, N,N-hexamethylene thiocarbamate S-ethyl ester, N-ethyl-o-crotonylmethylaniline, and lysophosphatidylcholine (18꞉1). ROC analysis based on support vector machine (SVM) modeling of these top 5 metabolites yielded an area under the curve of 1. Differential metabolites were mainly enriched in histidine metabolism, sphingolipid metabolism, glycerophospholipid metabolism, unsaturated fatty acid biosynthesis, tryptophan metabolism, and taurine/hypotaurine metabolism.

CONCLUSIONS

The metabolic profiles of MAFLD patients with and without SA differ significantly. Key differential metabolites may serve as potential biomarkers for predicting atherosclerosis in MAFLD patients.