Association of multiple metals with lipid markers against different exposure profiles: A population-based cross-sectional study in China

血脂谱 化学 人口 线性回归 载脂蛋白B 血脂 内科学 胆固醇 医学 生物化学 环境卫生 计算机科学 机器学习 有机化学
作者
Zhaoyang Li,Yali Xu,Zhijun Huang,Yue Wei,Jian Hou,Tengfei Long,Fei Wang,Xu Cheng,Yanying Duan,Xiang Chen,Hong Yuan,Minxue Shen,Meian He
出处
期刊:Chemosphere [Elsevier BV]
卷期号:264: 128505-128505 被引量:47
标识
DOI:10.1016/j.chemosphere.2020.128505
摘要

We sought to evaluate whether essential and toxic metals are cross-sectionally related to blood lipid levels using data among adults from Shimen (n = 564) and Huayuan (n = 637), two counties with different exposure profiles in Hunan province of China. Traditional and grouped weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) were performed to assess association between exposure to a mixture of 22 metals measured in urine or plasma, and lipid markers. Most of the exposure levels of metals were significantly higher in Shimen area than those in Huayuan area (all P-values < 0.001). Traditional WQS regression analyses revealed that the WQS index were both significantly associated with lipid markers in two areas, except for the HDL-C. Grouped WQS revealed that essential metals group showed significantly positive associations with lipid markers except for HDL-C in Huayuan area, while toxic metals group showed significantly negative associations except for HDL-C and LDL-C in Huayuan area. There were no significant joint effects, but potential non-linear relationships between metals mixture and TC or LDL-C levels were observed in BKMR analyses. Although consistent significantly associations of zinc and titanium with TG levels were found in both areas, the metals closely related to other lipid markers were varied by sites. Additionally, the BKMR analyses revealed an inverse U shaped association of iron with LDL-C levels and interaction effects of zinc and cadmium on LDL-C in Huayuan area. The relationship between metal exposure and blood lipid were not identical against different exposure profiles.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
宇文向雪发布了新的文献求助20
刚刚
YONG完成签到,获得积分10
刚刚
arniu2008发布了新的文献求助30
1秒前
扣子完成签到 ,获得积分10
1秒前
4秒前
fishswim1完成签到,获得积分10
5秒前
菜猫完成签到,获得积分10
5秒前
ycy完成签到 ,获得积分10
6秒前
范六六完成签到,获得积分20
6秒前
10秒前
花卷完成签到,获得积分10
10秒前
oo发布了新的文献求助10
11秒前
12秒前
cgliuhx完成签到,获得积分10
13秒前
Yuan完成签到,获得积分0
13秒前
王王完成签到 ,获得积分10
14秒前
LIVE完成签到,获得积分10
14秒前
clz完成签到,获得积分20
15秒前
Abdurrahman完成签到,获得积分10
16秒前
dadaup发布了新的文献求助10
16秒前
机智马里奥完成签到 ,获得积分10
19秒前
罗格朗因完成签到 ,获得积分10
20秒前
CipherSage应助百百采纳,获得10
20秒前
czzlancer完成签到,获得积分0
21秒前
白白不喽完成签到 ,获得积分10
21秒前
22秒前
科研通AI6.4应助范六六采纳,获得30
24秒前
贪玩飞机完成签到,获得积分10
24秒前
24秒前
林千万完成签到,获得积分10
25秒前
9527完成签到,获得积分10
25秒前
banana完成签到 ,获得积分10
25秒前
飞虎完成签到,获得积分10
26秒前
兰亭序完成签到 ,获得积分10
28秒前
lu2025发布了新的文献求助10
28秒前
lee1992发布了新的文献求助10
30秒前
无辜靖巧完成签到 ,获得积分10
30秒前
桃花岛主完成签到,获得积分10
30秒前
MMTI完成签到,获得积分10
31秒前
炙热香寒完成签到,获得积分10
31秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1000
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Photodetectors: From Ultraviolet to Infrared 500
Cancer Targets: Novel Therapies and Emerging Research Directions (Part 1) 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6359032
求助须知:如何正确求助?哪些是违规求助? 8173002
关于积分的说明 17212025
捐赠科研通 5414024
什么是DOI,文献DOI怎么找? 2865338
邀请新用户注册赠送积分活动 1842737
关于科研通互助平台的介绍 1690836