Aortic dissection (AD) is a severe cardiovascular disease with a high mortality rate. However, the associations between the serum levels of metals and the risk of AD remain unclear. One hundred twenty-seven patients with AD (type A and B) identified from 2017 to 2019 at the Second Affiliated Hospital of Nanjing Medical University were included; 183 controls that were also included. A logistic regression analysis was performed to determine the associations between serum levels of metals and the risk of AD. Weighted Quantile Sum (WQS) regression and Bayesian Kernel Machine Regression (BKMR) analyses were performed to explore the effects of mixtures of metals on the risk of AD. A linear regression analysis was performed to evaluate the relationships between the serum levels of metals and the white blood cells (WBCs) count and serum lipid levels and blood glucose. We conducted a mediation analysis to explore the contribution rates of WBC counts or serum lipid levels and blood glucose to the association between metal levels and the risk of AD. Exposure to serum levels of Cu (coefficient = 6.33; 95 % CI = 2.52, 10.14; p trend < 0.001) were significantly and positively associated with the risk of AD. In the WQS analysis, Cu (50.3 %), Ni (32.7 %) and Mo (17.1 %) contributed to the AD risk. In the BKMR analysis, Cu and Mo were shown to play important roles in the association with the AD risk. Moreover, serum concentrations of Cu were significantly and inversely associated with HDL-cholesterol levels. HDL-cholesterol levels mediated 7.42 % of the association between serum Cu levels and the prevalence of AD. Our study provided the first evidence that serum levels of mixtures of metals are associated with the AD risk in a Chinese population. Increased concentrations of metals, particularly Cu, may increase the risk of AD by reducing HDL-cholesterol levels.
Keywords: Aortic dissection; Bayesian kernel machine regression; Metals; Weighted quantile sum.
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