Metabolite Profiles of Stable and Unstable Carotid Plaques for Stroke Risk Prediction

A non-targeted metabolomics analysis of 67 carotid artery plaque samples (40 stable and 27 unstable) was performed to investigate metabolic changes. The study, authored by Jia-Qi Ma and last updated in May 2026, identified 98 metabolites differentially associated with unstable plaques and used four machine learning algorithms to screen for potential biomarkers.

Use Cases

• Train machine learning models to classify plaque stability based on metabolite profiles.
• Identify potential metabolic biomarkers for unstable carotid plaques as described in the results.
• Perform pathway enrichment analysis on the 98 differentially expressed metabolites to understand biological mechanisms.
• Validate findings from the cGMP-PKG and glucagon signaling pathways mentioned in the KEGG analysis.

Strengths

• Dataset includes 67 clinical samples with a clear breakdown of 40 stable and 27 unstable plaques.
• Analysis identified 98 significantly differentially associated metabolites and 43 linked to specific metabolic pathways.
• Applies four distinct machine learning algorithms (RF, SVM, LASSO, LR) for feature analysis and biomarker prediction.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 28.7 KB, indicating limited scope.

Provenance

Source

Jia-Qi Ma via figshare.

Collection Method

Non-targeted metabolomics analysis performed on carotid artery plaque tissue samples.

Freshness

Last updated 2026-05-12 04:20:13; freshness should be verified.