Key result
Integrating five amino acids improves AMI risk prediction in CAD patients to ~0.90 AUC.
Why the study?
Patients with stable CAD remain at increased risk of AMI, motivating the development of a reliable model incorporating circulating amino acid profiles to predict AMI risk.
Does a circulating amino acid-assisted model improve the prediction of acute myocardial infarction risk compared to a clinical variable-only model in patients with stable coronary artery disease?
Cohort (n=874)
Yes
Does a circulating amino acid-assisted model improve the prediction of acute myocardial infarction risk compared to a clinical variable-only model in patients with stable coronary artery disease?
Absolute Event Rate: 0.8958% vs 0.8205%
p-value: p=8.91e-03
Incorporating specific circulating amino acids into clinical prediction models significantly improves the risk prediction of acute myocardial infarction in patients with stable coronary artery disease.
May refine AMI prediction in stable CAD; leaves open prospective validation before clinical use.
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 13 C 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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Zhao et al. (2024) conducted a cohort in Stable coronary artery disease (n=874). Amino acid-assisted prediction model vs. Base clinical model was evaluated on Area under the curve (AUC) for AMI risk prediction in the validation set (p=8.91e-03). Integrating five amino acids into a clinical model improved acute myocardial infarction risk prediction in CAD patients, increasing the validation AUC from 0.8205 to 0.8958 (p=8.91e-03).
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