Abstract
Background: Coronary disease (CAD) is a multifactorial complex pathology characterized by excessive inflammatory activation and oxidative stress. Amino acids are among the potential biomarkers for cardiovascular pathology. The analysis aimed to investigate the possible relationship between proteomic profiling and coronary artery disease risk as novel markers of CAD. Methods: Patients with similar demographic and clinical profiles, including the prevalence of comorbidities such as arterial hypertension, dyslipidemia, and diabetes mellitus, were divided into two groups based on the results of their coronary angiograms. Serum amino acid levels were measured using liquid chromatography-tandem mass spectrometry. Results: Patients with significant coronary atherosclerosis confirmed in coronary angiograms were characterized by higher levels of circulating cystine, threonine, methionine, and proline. The number of involved coronary arteries in atherosclerotic processes revealed a correlation with circulating levels of threonine, methionine, and proline, not cystine. The multivariable logistic regression analysis for any significant coronary artery disease prediction revealed higher values of circulating threonine as a possible risk factor. Thereafter, a subanalysis was conducted to examine the relationship between amino acid levels and atherosclerotic risk in specific coronary arteries. The multivariate analysis revealed cystine and proline as potential risk factors for atherosclerosis of the left descending artery (LAD). Higher values of threonine were identified as a possible risk factor for atherosclerotic plaque location in the circumflex artery in multivariate regression analysis. Proline circulating levels were found to be prognostic for right coronary artery disease. Conclusions: Elevated circulating levels of amino acids, including cystine, threonine, methionine, and proline, were observed in patients with significant coronary artery disease in our exploratory pilot study. The high circulating amino acid levels can be predictive of coronary artery disease in our multivariate models.