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
隐形曼青应助APS采纳,获得50
刚刚
zl完成签到,获得积分10
刚刚
2秒前
3秒前
NexusExplorer应助uuuuuuu采纳,获得10
3秒前
4秒前
科研通AI6.2应助啊哈哈采纳,获得10
4秒前
董科见应助董瘦子采纳,获得10
4秒前
科研通AI6.1应助Zpj采纳,获得10
4秒前
5秒前
rootree发布了新的文献求助10
6秒前
豪123456发布了新的文献求助10
7秒前
潘名超发布了新的文献求助10
7秒前
科研通AI6.1应助尧尧采纳,获得10
7秒前
张天完成签到,获得积分10
9秒前
zaniuzl发布了新的文献求助10
10秒前
田様应助江海客采纳,获得10
11秒前
ZSW发布了新的文献求助10
12秒前
13秒前
14秒前
bkagyin应助积极璎采纳,获得10
14秒前
xp发布了新的文献求助10
15秒前
CodeCraft应助只只采纳,获得10
16秒前
16秒前
陈平安完成签到,获得积分10
17秒前
18秒前
潘名超完成签到,获得积分10
18秒前
英姑应助赞zan采纳,获得10
18秒前
18秒前
今天做实验了吗完成签到,获得积分10
19秒前
万能图书馆应助潘名超采纳,获得10
20秒前
彭于晏应助LouisKing采纳,获得10
20秒前
lanmo完成签到,获得积分10
21秒前
jaykin发布了新的文献求助10
21秒前
充电宝应助豪123456采纳,获得10
21秒前
瑞哥哥完成签到 ,获得积分10
23秒前
23秒前
米崽发布了新的文献求助10
23秒前
hhh驳回了bkagyin应助
24秒前
27秒前
高分求助中
Adhesion Science: Principles & Practice 1234
Signals, Systems, and Signal Processing 610
Introduction to Cosmetic Formulation and Technology, 2nd Edition 400
Petrology and Plate Tectonics,2025 400
Burger's Medicinal Chemistry and Drug Discovery 400
A Step-by-Step Guide to Qualitative Data Coding 2nd Edition 400
Programming for Chemical Engineers Using C, C++, and MATLAB 320
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 6699191
求助须知:如何正确求助?哪些是违规求助? 8441355
关于积分的说明 18033382
捐赠科研通 5933109
什么是DOI,文献DOI怎么找? 2988245
邀请新用户注册赠送积分活动 1964045
关于科研通互助平台的介绍 1906504