Abstract
BACKGROUND AND AIMS: Coronary heart disease (CHD) remains a leading cause of mortality globally and is becoming increasingly prevalent in China. Growing evidence suggests that dyslipidemia plays a critical role in the pathogenesis of atherosclerosis. The primary objective of this study was to investigate the association between neck circumference (NC) and the atherogenic index of plasma (AIP) with the severity of CHD, and to evaluate the diagnostic utility of combining NC with AIP for identifying CHD. METHODS: This observational, cross-sectional study included 1652 patients with chest pain from the Department of Cardiology at Chengde Central Hospital who underwent coronary angiography (CAG). Restricted cubic splines (RCS) were used to examine the associations between NC, AIP, and CHD. Independent predictors of CHD risk were identified through univariate and multivariate regression analyses and subsequently integrated into a risk prediction nomogram. The model's performance was validated using receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA). RESULTS: The study demonstrates a robust association between NC and AIP with both the incidence and severity of CHD. After adjusting for sex and age, the ORs (95% CIs) for CHD increased progressively across the quartiles of NC: ORs were 1.24 (0.89-1.74), 1.42 (0.98-2.04), and 1.80 (1.22-2.66) for the second, third, and fourth quartiles, respectively, compared with the first quartile, indicating a significant increasing trend (p-trend = 0.030). The ORs for the severity of coronary lesions across higher NC quartile were 1.22 (0.89-1.65), 1.38 (1.00-1.90), and 1.41 (1.02-1.95), relative to the lowest quartile (p-trend = 0.039). The RCS curve demonstrated a significant positive linear relationship between NC, AIP, and CHD. The predictive accuracy of the nomogram model for CHD was substantial, evidenced by an area under the curve (AUC) of reaching 0.729 (95% CI 0.697-0.761, p <0.001). CONCLUSIONS: NC and AIP are positively correlate with CHD. Combined measurement of these variables provides significant predictive value for the diagnosis of CHD.