Zhou Jing-Xuan, Zheng Zi-Yi, Peng Zhao-Xing, Yang Yu-Ting, Ni Hong-Gang
School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
J Environ Manage. 2025 Jan;373:124001. doi: 10.1016/j.jenvman.2024.124001. Epub 2025 Jan 1.
Although the association between heavy metals in human and the development of metabolic syndrome (MetS) have been extensively studied, the pathogenic mechanism of MetS affected by metals is not clear to date. In this study, a predictive model was developed with machine learning base on the large-scale dataset. These proposed models were evaluated via comparatively analysis of their accuracy and robustness. With the optimal model, two metals significantly correlated with MetS were screened and were employed to infer the pathogenicity mechanism of MetS via molecular docking. Significant associations between heavy metals and MetS were found. Molecular docking provided insights into the interactions between metal ions and key protein receptors involved in metabolic regulation, suggesting a mechanism by which heavy metals interfere with metabolic functions. Specifically, Ba and Cd affect the development of MetS thru their interactions with insulin and estrogen receptors. This study attempted to explore heavy metals' potential roles in MetS at the molecular level. These findings emphasize the importance of addressing environmental exposures in the prevention and treatment of MetS.
尽管人类体内重金属与代谢综合征（MetS）的关联已得到广泛研究，但迄今为止，金属影响MetS的致病机制尚不清楚。在本研究中，基于大规模数据集利用机器学习开发了一个预测模型。通过对这些模型的准确性和稳健性进行比较分析来评估它们。利用最优模型，筛选出与MetS显著相关的两种金属，并通过分子对接来推断MetS的致病机制。发现了重金属与MetS之间的显著关联。分子对接揭示了金属离子与参与代谢调节的关键蛋白受体之间的相互作用，提示了重金属干扰代谢功能的一种机制。具体而言，钡（Ba）和镉（Cd）通过与胰岛素和雌激素受体的相互作用影响MetS的发展。本研究试图在分子水平上探索重金属在MetS中的潜在作用。这些发现强调了在MetS的预防和治疗中应对环境暴露的重要性。
