Li Meiyan, Di Yihong, Duan Siyu, Wang Rui, He Pei, Zhang Zhongyuan, Dai Yuqing, Shen Zhuoheng, Chen Yue, Yang Huifang, Li Xiaoyu, Sun Jian, Zhang Rui
School of Public Health, Department of Medical Record and Statistics, General Hospital of Ningxia Medical University, Ningxia Medical University, Yinchuan, Ningxia, 750004, People's Republic of China.
Ningxia Key Laboratory of Environmental Factors and Chronic Disease Control, Yinchuan, Ningxia, 750004, People's Republic of China.
Cardiovasc Toxicol. 2025 May 28. doi: 10.1007/s12012-025-10015-5.
Fewer studies have focused on the interaction of metal mixtures with hypertension, especially in Chinese community-dwelling elderly. In addition, the relationship between metal exposure and hypertension may be attenuated or strengthened by the presence of multiple chronic diseases in older adults. In this study, inductively coupled plasma mass spectrometry was used to detect the levels of 12 metals in the urine of 693 elderly people in the Yinchuan community. We employed Directed Acyclic Graphs (DAG) to select variables for adjustment in the model. Conditional logistic regression model and restricted cubic spline analysis (RCS) were used to explore the association between and dose-response relationship between metal concentrations in urine and hypertension. Quantile g-computation and Bayesian kernel machine regression (BKMR) to analyze the association of individual urinary metal concentrations and metal mixtures with hypertension risk. Urinary concentrations of 12 metals (vanadium, iron, cobalt, zinc, copper, arsenic, selenium, molybdenum, cadmium, tellurium, thallium, and lead) were higher in the hypertension group than in the non-hypertension group. In the RCS models, the urinary concentrations of vanadium, iron, and lead showed a linear dose-response relationship with hypertension risk. Quantile g-computation analyses showed cadmium contributed the largest positive weights. The BKMR models showed that the positive slope of lead became steep at higher concentrations of urinary iron when the other three metals were at the median. We found that exposure to metal mixtures was associated with the risk of hypertension and a significant positive interaction between urinary iron and lead. Further research is needed to confirm our findings and elucidate the underlying mechanisms of the interaction between metals and hypertension.
较少有研究关注金属混合物与高血压的相互作用,尤其是在中国社区居住的老年人中。此外,老年人中多种慢性病的存在可能会减弱或增强金属暴露与高血压之间的关系。在本研究中,采用电感耦合等离子体质谱法检测了银川社区693名老年人尿液中12种金属的含量。我们使用有向无环图(DAG)来选择模型中用于调整的变量。采用条件逻辑回归模型和受限立方样条分析(RCS)来探讨尿中金属浓度与高血压之间的关联及剂量反应关系。采用分位数g计算法和贝叶斯核机器回归(BKMR)分析个体尿金属浓度和金属混合物与高血压风险的关联。高血压组尿中12种金属(钒、铁、钴、锌、铜、砷、硒、钼、镉、碲、铊和铅)的浓度高于非高血压组。在RCS模型中,钒、铁和铅的尿浓度与高血压风险呈线性剂量反应关系。分位数g计算分析显示镉的正向权重最大。BKMR模型显示,当其他三种金属处于中位数时,尿铁浓度较高时铅的正斜率变得陡峭。我们发现,接触金属混合物与高血压风险相关,且尿铁和铅之间存在显著的正向相互作用。需要进一步的研究来证实我们的发现,并阐明金属与高血压相互作用的潜在机制。