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Updated: Jun 11, 2026
Acute Myocardial Infarction in Rats
Published on: February 16, 2011
Improved Risk Prediction of Acute Myocardial Infarction in Patients With Stable Coronary Artery Disease Using an
Yi-Jing Zhao1,2, Yong Li3, Feng-Xiang Wang4
1State Key Laboratory of Natural Medicines School of Traditional Chinese Pharmacy China Pharmaceutical University, Nanjing, China.
Insights
Predicting acute myocardial infarction (AMI) in coronary artery disease (CAD) patients is improved by a new model using amino acid profiles. This amino acid-assisted model enhances early diagnosis and intervention for patients with CAD.
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.
Abstract:
Patients with stable coronary artery disease (CAD) are at an increased risk of acute myocardial infarction (AMI), particularly among older individuals. Developing a reliable model to predict AMI occurrence in these patients holds the potential to expedite early diagnosis and intervention. This study is aimed at establishing a circulating amino acid-assisted model, incorporating amino acid profiles alongside clinical variables, to predict AMI risk. A cohort of 874 CAD patients from two independent centers was analyzed. Plasma amino acid levels were quantified using liquid chromatography tandem mass spectrometry (LC-MS/MS) employing a targeted metabolomics approach. This methodology incorporated 13C isotope-labeled internal standards for precise quantification of 27 amino acids. Univariate logistic regression was applied to identify differentially expressed amino acids that distinguished between stable CAD and AMI patients. To assess prediction performance, receiver operating characteristic (ROC) curve and nomogram analyses were utilized. Five amino acids-lysine, methionine, tryptophan, tyrosine, and N6-trimethyllysine-emerged as potential biomarkers (p < 0.05), exhibiting significant differences in their expression levels across the two centers when comparing stable CAD with AMI patients. For AMI risk prediction, the base model, utilizing 12 clinical variables, achieved areas under the curve (AUC) of 0.7387 in the discovery phase (n = 623) and 0.8205 in the external validation set (n = 251). Notably, the integration of these five amino acids into the prediction model significantly enhanced its performance, increasing the AUC to 0.7651 in the discovery phase (Delong's test, p = 1.43e-02) and to 0.8958 in the validation set (Delong's test, p = 8.91e-03). In conclusion, the circulating amino acid-assisted model effectively enhances the prediction of AMI risk among CAD patients, indicating its potential clinical utility in facilitating early detection and intervention.
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