Metabolomic profile for the early detection of coronary artery disease by using UPLC-QTOF/MS

Area of Science:

• Cardiovascular Disease Research
• Metabolomics
• Biomarker Discovery

Background:

• Traditional risk factors have limited predictive power for coronary artery disease (CAD).
• Acute myocardial infarction (AMI) often presents without prior warning symptoms.
• Novel diagnostic strategies are crucial for identifying patients at risk of acute coronary syndrome.

Purpose of the Study:

• To investigate the potential of non-targeted metabolomic profiling for differentiating CAD patient groups.
• To identify specific metabolites associated with the progression of CAD.
• To develop improved predictive criteria for CAD diagnosis and treatment.

Main Methods:

• Serum samples from stable angina, AMI, and healthy controls were analyzed using ultra-performance liquid chromatography coupled to time of flight mass spectrometry (UPLC-QTOF/MS).
• Multivariate statistical models, including orthogonal partial least squares-discriminant analysis (OPLS-DA), were employed for data analysis.
• Metabolomic profiles were validated using internal cross-validation methods, achieving an area under the receiver operating characteristic curve (AUROC) of 0.983.

Main Results:

• Distinct metabolomic profiles were observed between stable angina and AMI patients.
• Altered metabolites identified include phospholipids, fatty acids, sphingolipids, glycerolipids, and steroids.
• Specific metabolites such as ceramides (Cer(t18:0/16:0), Cer(d18:0/12:0)) and dehydroepiandrosterone sulfate were strongly associated with CAD progression.

Conclusions:

• Metabolomic profiling offers a powerful approach for distinguishing CAD patient states.
• Certain metabolites, particularly ceramides and steroids, demonstrate significant potential as predictive biomarkers for CAD.
• These findings support the development of more stringent and predictive diagnostic criteria for CAD.