Improved Risk Prediction of Acute Myocardial Infarction in Patients With Stable Coronary Artery Disease Using an...

Area of Science:

• Cardiovascular Medicine
• Metabolomics
• Biomarker Discovery

Background:

• Patients with stable coronary artery disease (CAD) face elevated risks for acute myocardial infarction (AMI), especially older individuals.
• Accurate prediction models for AMI in CAD patients are crucial for timely diagnosis and intervention.
• Current prediction models may benefit from incorporating novel biomarkers.

Purpose of the Study:

• To develop and validate a predictive model for AMI risk in CAD patients by integrating circulating amino acid profiles with clinical variables.
• To identify specific amino acids that serve as potential biomarkers for AMI in the context of stable CAD.

Main Methods:

• Analysis of plasma amino acid levels in 874 CAD patients from two independent centers using targeted metabolomics via liquid chromatography-tandem mass spectrometry (LC-MS/MS).
• Quantification of 27 amino acids using 13C isotope-labeled internal standards.
• Application of univariate logistic regression, receiver operating characteristic (ROC) curve analysis, and nomogram analysis for biomarker identification and model performance assessment.

Main Results:

• Five amino acids—lysine, methionine, tryptophan, tyrosine, and N6-trimethyllysine—were identified as significant potential biomarkers differentiating stable CAD from AMI patients (p < 0.05).
• A base model using 12 clinical variables achieved an AUC of 0.7387 (discovery) and 0.8205 (validation).
• Integrating the five identified amino acids into the model significantly improved AMI risk prediction, increasing AUC to 0.7651 (discovery) and 0.8958 (validation) (p < 0.05).

Conclusions:

• Circulating amino acid profiles, particularly lysine, methionine, tryptophan, tyrosine, and N6-trimethyllysine, can significantly enhance the prediction of AMI risk in patients with stable CAD.
• The developed amino acid-assisted model demonstrates improved predictive performance compared to clinical variables alone.
• This model holds potential clinical utility for earlier detection and intervention strategies in CAD patients at risk of AMI.