Metabolomic Profiles of 67 Carotid Artery Plaques for Stroke Risk Prediction

Non-targeted metabolomics data from 67 carotid artery plaque samples, comprising 40 stable and 27 unstable cases. The dataset, created by Jia-Qi Ma and last updated in 2026, identifies 98 metabolites significantly associated with unstable plaques and uses four machine learning algorithms to screen for potential biomarkers. It includes results from KEGG enrichment analysis linking metabolites to specific signaling and metabolic pathways.

Use Cases

• Train machine learning models to classify plaque stability based on metabolic signatures.
• Identify potential metabolic biomarkers for unstable carotid plaques as described in the study.
• Validate findings related to cGMP-PKG and glucagon signaling pathways in plaque pathology.
• Conduct pathway analysis using the network relationships between 43 metabolites and their pathways.

Strengths

• Includes data from 67 clinical samples with clear classification into 40 stable and 27 unstable plaques.
• Identifies 98 significantly differential metabolites and 43 linked to specific metabolic pathways.
• Applies four distinct machine learning algorithms (RF, SVM, LASSO, LR) for feature analysis.

Limitations

• Row count and column-level documentation are unknown, requiring manual inspection after download.
• The dataset is very small at 15.1 KB, limiting its scope for large-scale model training.
• Data may reflect bias inherent to the specific sample collection and analysis methodology.

Provenance

Source

figshare

Collection Method

Non-targeted metabolomics analysis performed on surgically collected plaque tissue samples.

Freshness

Last updated 2026-05-12 04:20:10; freshness should be verified.