The association between lipid metabolism and coronary artery disease: A systematic Mendelian randomization study...

Area of Science:

• Cardiovascular Science
• Genetics
• Metabolomics

Background:

• Coronary artery disease (CAD) is a leading cause of mortality globally, characterized by chronic inflammation and significant economic impact.
• Understanding the genetic basis of lipid metabolism in CAD is essential for identifying novel therapeutic targets and improving disease management.

Purpose of the Study:

• To identify genes involved in lipid metabolism causally associated with coronary artery disease (CAD).
• To develop a predictive model for CAD risk stratification using identified genetic biomarkers.

Main Methods:

• Differential gene expression analysis of 700 genes in CAD patients versus healthy controls (GSE250283 dataset).
• Two-sample Mendelian randomization analysis using genome-wide association studies data for lipid exposures and CAD outcomes.
• Further Mendelian randomization integrating expression quantitative trait loci (eQTLs) with Kyoto Encyclopedia of Genes and Genomes (KEGG) lipid metabolism pathways.
• Machine learning algorithms to select 5 key biomarker genes (SCP2, TNFAIP8, HMGCR, AGPAT3, MAPKAPK2) and develop a predictive nomogram.

Main Results:

• A positive association between lipid exposure and CAD was confirmed.
• Nineteen key genes with lipid regulatory functions and causal links to CAD were identified.
• Five genes (SCP2, TNFAIP8, HMGCR, AGPAT3, MAPKAPK2) were selected as robust biomarkers.
• The developed nomogram incorporating these biomarkers demonstrated high predictive accuracy for CAD risk stratification.

Conclusions:

• The study identified five novel causal lipid metabolism biomarker genes for coronary artery disease (CAD).
• These biomarkers offer significant clinical potential for improving CAD risk assessment and guiding therapeutic strategies.