Multi-omics analysis revealed biomarkers for coronary atherosclerosis: Occurrence and development

Area of Science:

• Cardiovascular Research
• Metabolomics
• Biomarker Discovery

Background:

• Coronary atherosclerosis (CA) is a major cause of cardiovascular disease with high mortality.
• Current diagnostic methods are often inadequate for early or subclinical CA detection.
• There is a need for sensitive and specific biomarkers for CA diagnosis and severity assessment.

Purpose of the Study:

• To discover sensitive and specific biomarkers for diagnosing coronary atherosclerosis (CA) severity.
• To evaluate the diagnostic performance of identified biomarker panels.
• To assess the potential for improving early diagnosis and personalized management of CA.

Main Methods:

• Multi-omics data integration from 443 participants across discovery, testing, and validation cohorts.
• Refinement of biomarker panels using random forest models.
• Evaluation of biomarker performance in blinded validation cohorts for CA occurrence and severity.

Main Results:

• Plasma metabolites strongly correlated with CA severity and distinguished different CA stages.
• A diagnostic model achieved an AUC > 0.933, with high sensitivity and specificity.
• Biomarker panels demonstrated good performance in a blinded validation cohort, with most remaining significant after adjusting for comorbidities.

Conclusions:

• Significant metabolic changes are associated with coronary atherosclerosis (CA) progression.
• Established biomarker panels show potential for diagnosing CA severity.
• Key metabolites identified may improve early diagnosis and personalized management of CA.