基于BKMR和机器学习方法的尿中金属混合物与异常血压的关联及氧化应激的介导作用

IF 6.1 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecotoxicology and Environmental Safety Pub Date : 2025-06-12 DOI:10.1016/j.ecoenv.2025.118478

Junjie Chen , Hao Zeng , Zhanglei Pan , Miao Li , Qingfeng Zhou , Kaichen Chen , Yulan Hao , Xiangke Cao , Lei Zhang , Qian Wang

{"title":"基于BKMR和机器学习方法的尿中金属混合物与异常血压的关联及氧化应激的介导作用","authors":"Junjie Chen , Hao Zeng , Zhanglei Pan , Miao Li , Qingfeng Zhou , Kaichen Chen , Yulan Hao , Xiangke Cao , Lei Zhang , Qian Wang","doi":"10.1016/j.ecoenv.2025.118478","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Exposure to heavy metals represents a significant risk factor for hypertension and blood pressure disorders. Notably, current evidence indicates that the key biological processes of oxidative stress, inflammation, and endothelial dysfunction are related to metal exposure and blood pressure dysregulation, ultimately contributing to cardiovascular pathogenesis. However, their underlying biological mechanisms remain incompletely characterized.</div></div><div><h3>Methods</h3><div>A longitudinal study was performed among 45 healthy university students in Caofeidian, China. These participants were followed up in 4 seasons for physical examination and blood and urine samples collection between December 2017 and October 2018. we employed linear mixed effect model (LME), Bayesian kernel-machine regression (BKMR) and Machine learning (ML) to evaluate complex exposure-response relationships between multi-metal mixtures and blood pressure outcomes. Finally, we constructed the mediation analyses to analyze the potential intermediary roles of indicators in these association.</div></div><div><h3>Results</h3><div>The analysis revealed significant associations between Cr, Mn, and Mo and elevated levels of 8-iso-prostaglandin-F2α (8-iso-PGF2α) and blood pressure (all <em>P</em> < 0.05), respectively. BKMR and ML further demonstrated both cumulative effects and interaction patterns within the metal mixture that collectively influenced blood pressure. Additionally, 8-iso-PGF2α is significantly positively correlated with SBP and was subsequently identified as a candidate mediator. Eventually, we found that the metals of Mn, Cr, and Mo were associated with SBP mediated by 8-iso-PGF2α with 24.6 %, 17.4 %, and 20.7 %, respectively.</div></div><div><h3>Conclusions</h3><div>These findings establish a mechanistic link between metal exposure and blood pressure dysregulation in young adults. Notably, the application of machine learning demonstrates novel utility in quantifying mixed metals on blood pressure and predicting the development of cardiovascular injury, providing a novel insight into environmental risk assessment methodologies</div></div>","PeriodicalId":303,"journal":{"name":"Ecotoxicology and Environmental Safety","volume":"301 ","pages":"Article 118478"},"PeriodicalIF":6.1000,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Association between metal mixture in urine and abnormal blood pressure and mediated effect of oxidative stress based on BKMR and Machine learning method\",\"authors\":\"Junjie Chen , Hao Zeng , Zhanglei Pan , Miao Li , Qingfeng Zhou , Kaichen Chen , Yulan Hao , Xiangke Cao , Lei Zhang , Qian Wang\",\"doi\":\"10.1016/j.ecoenv.2025.118478\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Exposure to heavy metals represents a significant risk factor for hypertension and blood pressure disorders. Notably, current evidence indicates that the key biological processes of oxidative stress, inflammation, and endothelial dysfunction are related to metal exposure and blood pressure dysregulation, ultimately contributing to cardiovascular pathogenesis. However, their underlying biological mechanisms remain incompletely characterized.</div></div><div><h3>Methods</h3><div>A longitudinal study was performed among 45 healthy university students in Caofeidian, China. These participants were followed up in 4 seasons for physical examination and blood and urine samples collection between December 2017 and October 2018. we employed linear mixed effect model (LME), Bayesian kernel-machine regression (BKMR) and Machine learning (ML) to evaluate complex exposure-response relationships between multi-metal mixtures and blood pressure outcomes. Finally, we constructed the mediation analyses to analyze the potential intermediary roles of indicators in these association.</div></div><div><h3>Results</h3><div>The analysis revealed significant associations between Cr, Mn, and Mo and elevated levels of 8-iso-prostaglandin-F2α (8-iso-PGF2α) and blood pressure (all <em>P</em> < 0.05), respectively. BKMR and ML further demonstrated both cumulative effects and interaction patterns within the metal mixture that collectively influenced blood pressure. Additionally, 8-iso-PGF2α is significantly positively correlated with SBP and was subsequently identified as a candidate mediator. Eventually, we found that the metals of Mn, Cr, and Mo were associated with SBP mediated by 8-iso-PGF2α with 24.6 %, 17.4 %, and 20.7 %, respectively.</div></div><div><h3>Conclusions</h3><div>These findings establish a mechanistic link between metal exposure and blood pressure dysregulation in young adults. Notably, the application of machine learning demonstrates novel utility in quantifying mixed metals on blood pressure and predicting the development of cardiovascular injury, providing a novel insight into environmental risk assessment methodologies</div></div>\",\"PeriodicalId\":303,\"journal\":{\"name\":\"Ecotoxicology and Environmental Safety\",\"volume\":\"301 \",\"pages\":\"Article 118478\"},\"PeriodicalIF\":6.1000,\"publicationDate\":\"2025-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecotoxicology and Environmental Safety\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0147651325008188\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecotoxicology and Environmental Safety","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0147651325008188","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}

引用次数: 0

摘要

重金属暴露是高血压和血压疾病的重要危险因素。值得注意的是，目前的证据表明，氧化应激、炎症和内皮功能障碍等关键生物学过程与金属暴露和血压失调有关，最终导致心血管发病。然而，其潜在的生物学机制尚未完全确定。方法对曹妃甸地区45名健康大学生进行纵向调查。这些参与者在2017年12月至2018年10月期间进行了4个季节的身体检查和血样和尿样采集。我们采用线性混合效应模型（LME）、贝叶斯核机回归（BKMR）和机器学习（ML）来评估多种金属混合物与血压预后之间的复杂暴露-反应关系。最后，我们构建了中介分析来分析这些关联中指标的潜在中介作用。结果分析显示Cr、Mn和Mo与8-iso-前列腺素f2 α (8-iso-PGF2α)和血压升高之间存在显著相关性（P值分别为 <； 0.05）。BKMR和ML进一步证明了金属混合物内的累积效应和相互作用模式，它们共同影响血压。此外，8-iso-PGF2α与收缩压呈显著正相关，随后被确定为候选介质。最后，我们发现金属Mn、Cr和Mo与8-iso-PGF2α介导的SBP相关，分别为24.6 %、17.4 %和20.7 %。结论：这些发现建立了金属暴露与年轻人血压失调之间的机制联系。值得注意的是，机器学习的应用在量化混合金属对血压的影响和预测心血管损伤的发展方面展示了新的实用性，为环境风险评估方法提供了新的见解

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Association between metal mixture in urine and abnormal blood pressure and mediated effect of oxidative stress based on BKMR and Machine learning method

Background

Exposure to heavy metals represents a significant risk factor for hypertension and blood pressure disorders. Notably, current evidence indicates that the key biological processes of oxidative stress, inflammation, and endothelial dysfunction are related to metal exposure and blood pressure dysregulation, ultimately contributing to cardiovascular pathogenesis. However, their underlying biological mechanisms remain incompletely characterized.

Methods

A longitudinal study was performed among 45 healthy university students in Caofeidian, China. These participants were followed up in 4 seasons for physical examination and blood and urine samples collection between December 2017 and October 2018. we employed linear mixed effect model (LME), Bayesian kernel-machine regression (BKMR) and Machine learning (ML) to evaluate complex exposure-response relationships between multi-metal mixtures and blood pressure outcomes. Finally, we constructed the mediation analyses to analyze the potential intermediary roles of indicators in these association.

Results

The analysis revealed significant associations between Cr, Mn, and Mo and elevated levels of 8-iso-prostaglandin-F2α (8-iso-PGF2α) and blood pressure (all P < 0.05), respectively. BKMR and ML further demonstrated both cumulative effects and interaction patterns within the metal mixture that collectively influenced blood pressure. Additionally, 8-iso-PGF2α is significantly positively correlated with SBP and was subsequently identified as a candidate mediator. Eventually, we found that the metals of Mn, Cr, and Mo were associated with SBP mediated by 8-iso-PGF2α with 24.6 %, 17.4 %, and 20.7 %, respectively.

Conclusions

These findings establish a mechanistic link between metal exposure and blood pressure dysregulation in young adults. Notably, the application of machine learning demonstrates novel utility in quantifying mixed metals on blood pressure and predicting the development of cardiovascular injury, providing a novel insight into environmental risk assessment methodologies

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Ecotoxicology and Environmental Safety 环境科学-毒理学

CiteScore

12.10

自引率

5.90%

发文量

1234

审稿时长

88 days

期刊介绍： Ecotoxicology and Environmental Safety is a multi-disciplinary journal that focuses on understanding the exposure and effects of environmental contamination on organisms including human health. The scope of the journal covers three main themes. The topics within these themes, indicated below, include (but are not limited to) the following: Ecotoxicology、Environmental Chemistry、Environmental Safety etc.