Inflammation Factors Mediate the Association between Heavy Metal and Homa-IR Index: an Integrated Approach from the NHANES (2011∼2016).

Qingsong Mao, Xinyi Zhang, Xiaoyi Zhu, Xinling Tian, Yuzhe Kong
{"title":"Inflammation Factors Mediate the Association between Heavy Metal and Homa-IR Index: an Integrated Approach from the NHANES (2011∼2016).","authors":"Qingsong Mao, Xinyi Zhang, Xiaoyi Zhu, Xinling Tian, Yuzhe Kong","doi":"10.1016/j.amjms.2025.03.013","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The interplay between heavy metals exposure and insulin resistance (IR), specifically through the mediation of inflammation factors, is crucial for understanding metabolic disturbances. This study utilizes data from the NHANES (2011∼2016) to investigate these relationships in a large, diverse U.S.</p><p><strong>Population: </strong></p><p><strong>Method: </strong>The study analyzed the associations between heavy metals (cadmium (Cd), lead (Pb), mercury (Hg), manganese (Mn)) and the Homeostatic Model Assessment for Insulin Resistance (Homa-IR) index. The analyses included descriptive statistics, Pearson's correlations, linear and non-linear regression models, and advanced statistical models such as Weighted Quantile Sum (WQS) regression and Bayesian Kernel Machine Regression (BKMR). Inflammation factors were assessed for their mediating role in these associations.</p><p><strong>Result: </strong>The findings highlighted significant positive correlations between specific heavy metals and the Homa-IR index. Both linear and non-linear associations were evident, with certain metals showing a more pronounced impact in the presence of high inflammation markers. It was found that the Homa-IR index was negatively associated with Pb (β (95%CI) = -0.0126 (-0.0238 ∼ -0.0015), P=0.0268) and Hg (β (95%CI) = -0.0090 (-0.0180 ∼ -0.0001), P=0.0487). The WQS regression indicated an overall positive relationship between heavy metal mixtures (Estimate: 0.0050, P<0.05) and the Homa-IR index where Cu had the highest weights (0.7741), while BKMR analyses detailed the varying effects of individual metals at different exposure levels. In the mediation analysis, it can be found that monocyte (Mono) mediated the association between Pb and Homa-IR index (direct effect: -0.0546, indirect effect: -0.0082) and neutrophil (Neu) (direct effect: -0.0521, indirect effect: -0.0047) can mediate the association between Hg and Homa-IR index.</p><p><strong>Conclusion: </strong>This study confirms that exposure to heavy metals is associated with increased insulin resistance and that inflammation significantly mediates this relationship. Understanding these pathways is essential for developing targeted interventions to mitigate the metabolic consequences of environmental exposures.</p>","PeriodicalId":94223,"journal":{"name":"The American journal of the medical sciences","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The American journal of the medical sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.amjms.2025.03.013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Background: The interplay between heavy metals exposure and insulin resistance (IR), specifically through the mediation of inflammation factors, is crucial for understanding metabolic disturbances. This study utilizes data from the NHANES (2011∼2016) to investigate these relationships in a large, diverse U.S.

Population:

Method: The study analyzed the associations between heavy metals (cadmium (Cd), lead (Pb), mercury (Hg), manganese (Mn)) and the Homeostatic Model Assessment for Insulin Resistance (Homa-IR) index. The analyses included descriptive statistics, Pearson's correlations, linear and non-linear regression models, and advanced statistical models such as Weighted Quantile Sum (WQS) regression and Bayesian Kernel Machine Regression (BKMR). Inflammation factors were assessed for their mediating role in these associations.

Result: The findings highlighted significant positive correlations between specific heavy metals and the Homa-IR index. Both linear and non-linear associations were evident, with certain metals showing a more pronounced impact in the presence of high inflammation markers. It was found that the Homa-IR index was negatively associated with Pb (β (95%CI) = -0.0126 (-0.0238 ∼ -0.0015), P=0.0268) and Hg (β (95%CI) = -0.0090 (-0.0180 ∼ -0.0001), P=0.0487). The WQS regression indicated an overall positive relationship between heavy metal mixtures (Estimate: 0.0050, P<0.05) and the Homa-IR index where Cu had the highest weights (0.7741), while BKMR analyses detailed the varying effects of individual metals at different exposure levels. In the mediation analysis, it can be found that monocyte (Mono) mediated the association between Pb and Homa-IR index (direct effect: -0.0546, indirect effect: -0.0082) and neutrophil (Neu) (direct effect: -0.0521, indirect effect: -0.0047) can mediate the association between Hg and Homa-IR index.

Conclusion: This study confirms that exposure to heavy metals is associated with increased insulin resistance and that inflammation significantly mediates this relationship. Understanding these pathways is essential for developing targeted interventions to mitigate the metabolic consequences of environmental exposures.

求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信