What question did this study set out to answer?

This study aims to investigate metabolic changes in unstable carotid plaques and identify biomarkers for stroke risk using machine learning.

Integrating untargeted metabolomics and deep learning approaches to identify specific metabolic signatures and new mechanisms in unstable plaques

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Randomized trial identifies specific metabolic biomarkers in unstable plaques, suggesting improved stroke risk predictions.

This study aims to investigate metabolic changes in unstable carotid plaques and identify biomarkers for stroke risk using machine learning.
Conducted non-targeted metabolomics analysis on 67 samples (40 stable, 27 unstable) of carotid plaques.
Utilized four machine learning algorithms (RF, SVM, LASSO, LR) to construct models for feature analysis and biomarker prediction.
Performed KEGG enrichment analysis to identify associated metabolic pathways.
Identified 98 metabolites significantly associated with unstable plaques.
Key pathways linked to unstable plaques include cGMP-PKG signaling, glucagon signaling, cancer metabolism, and adipocyte lipolysis.
Screened metabolites that may serve as potential biomarkers for stroke risk.

Ma et al. (2026) studied this question.

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