Large-scale Metabolomic Analysis Reveals Potential Biomarkers for Early Stage Coronary Atherosclerosis

Area of Science:

• Biochemistry
• Cardiovascular Medicine
• Metabolomics

Background:

• Coronary atherosclerosis (CAS) is a leading cause of death, often diagnosed late after severe vascular occlusion.
• Early detection of CAS is crucial for timely intervention and improved patient outcomes.
• Current diagnostic methods may not identify early-stage disease effectively.

Purpose of the Study:

• To identify novel, sensitive, and specific biomarkers for the early diagnosis of coronary atherosclerosis (CAS).
• To investigate metabolic alterations associated with early-stage CAS using an untargeted metabolomics approach.

Main Methods:

• Untargeted metabolomics was employed to analyze serum samples from early-stage CAS patients and controls.
• Partial least-squares discriminant analysis (PLS-DA) was used for data analysis and pattern recognition.
• Binary logistic regression identified a panel of nine metabolites as potential diagnostic biomarkers.

Main Results:

• PLS-DA successfully differentiated early-stage CAS patients from healthy controls.
• Significant metabolic dysregulation was observed in phospholipid, sphingolipid, and fatty acid metabolism.
• A combinatorial biomarker panel of nine metabolites demonstrated high diagnostic accuracy (AUC = 0.890) in an independent cohort.

Conclusions:

• The study provides insights into the metabolic pathways involved in early coronary atherosclerosis.
• A novel panel of nine metabolites serves as a promising combinatorial biomarker for early CAS detection.
• These findings support the potential for improved clinical screening and diagnosis of early-stage coronary atherosclerosis.