Low Levels of IgM Recognizing 4-Hydroxy-2-Nonenal-Modified Apolipoprotein A-I Peptide and Its Association with the Severity of Coronary Artery Disease in Taiwanese Patients.

Autoantibodies against apolipoprotein A-I (ApoA-I) are associated with cardiovascular disease risks. We aimed to examine the 4-hydroxy-2-nonenal (HNE) modification of ApoA-I in coronary artery disease (CAD) and evaluate the potential risk of autoantibodies against their unmodified and HNE-modified peptides. We assessed plasma levels of ApoA-I, HNE-protein adducts, and autoantibodies against unmodified and HNE-peptide adducts, and significant correlations and odds ratios (ORs) were examined. Two novel CAD-specific HNE-peptide adducts, ApoA-I251-262 and ApoA-I70-83, were identified. Notably, immunoglobulin G (IgG) anti-ApoA-I251-262 HNE, IgM anti-ApoA-I70-83 HNE, IgG anti-ApoA-I251-262, IgG anti-ApoA-I70-83, and HNE-protein adducts were significantly correlated with triglycerides, creatinine, or high-density lipoprotein in CAD with various degrees of stenosis (<30% or >70%). The HNE-protein adduct (OR = 2.208-fold, p = 0.020) and IgM anti-ApoA-I251-262 HNE (2.046-fold, p = 0.035) showed an increased risk of progression from >30% stenosis in CAD. HNE-protein adducts and IgM anti-ApoA-I251-262 HNE may increase the severity of CAD at high and low levels, respectively.

A Coronary Artery Disease Monitoring Model Built from Clinical Data and Alpha-1-Antichymotrypsin.

Coronary artery disease (CAD) is one of the most common subtypes of cardiovascular disease. The progression of CAD initiates from the plaque of atherosclerosis and coronary artery stenosis, and eventually turns into acute myocardial infarction (AMI) or stable CAD. Alpha-1-antichymotrypsin (AACT) has been highly associated with cardiac events. In this study, we proposed incorporating clinical data on AACT levels to establish a model for estimating the severity of CAD. Thirty-six healthy controls (HCs) and 162 CAD patients with stenosis rates of <30%, 30−70%, and >70% were included in this study. Plasma concentration of AACT was determined by enzyme-linked immunosorbent assay (ELISA). The receiver operating characteristic (ROC) curve analysis and associations were conducted. Further, five machine learning models, including decision tree, random forest, support vector machine, XGBoost, and lightGBM were implemented. The lightGBM model obtained a sensitivity of 81.4%, a specificity of 67.3%, and an area under the curve (AUC) of 0.822 for identifying CAD patients with a stenosis rate of <30% versus >30%. In this study, we provided a demonstration of a monitoring model with clinical data and AACT.