Significance of APOB/APOA1 Ratio in the Prediction of Calcific Aortic Valve Disease.

Calcific aortic valve disease (CAVD) is a prevalent heart valve disease. The ratio of two apolipoproteins with distinct functions, Apolipoprotein B/Apolipoprotein A1 (APOB/APOA1), has been proposed as a novel assessment index for the evaluation of cardiovascular diseases. The aim of this article is to discuss the role of lipid parameters such as APOB/APOA1 in CAVD and the risk factors for CAVD, to develop a predictive model for CAVD, and to evaluate the sensitivity and specificity of this model. Patients who initially presented to the Department of Cardiology of the Second Affiliated Hospital of Dalian Medical University between 1 January 2023 and 31 December 2023 were retrospectively identified and included in the study. Patients were divided into an aortic valve calcification group (111 cases) and a control group (201 cases) based on computed tomography (CT) findings. The clinical data, laboratory examination results, and chest CT images of the patients were collected and analyzed. A variety of statistical methods were used to analyze risk factors for CAVD, to construct a CAVD prediction model, and to assess its sensitivity and specificity. Lipid parameters APOA1, APOB/APOA1, cumulative low-density lipoprotein (LDL) exposure, and non-high-density lipoprotein/high-density lipoprotein (non-HDL/HDL) were significantly associated with aortic valve calcification. Age, history of diabetes, diastolic blood pressure (DBP), APOB/APOA1, Cystatin C (Cys-c), and neutrophil-to-lymphocyte ratio (NLR) are identified as independent risk factors for CAVD, and the combined model achieved an AUC of 0.796 for CAVD prediction, corresponding to a sensitivity of 0.769 and a specificity of 0.755. The lipid parameters APOA1, APOB/APOA1, cumulative LDL exposure, and non-HDL/HDL have been demonstrated to be associated with aortic valve calcification. Furthermore, APOB/APOA1 can be used for the prediction of CAVD, and the combination of APOB/APOA1 with age, history of diabetes, DBP, Cys-c, and NLR has better prediction performance for CAVD.