Novel Insights into the Adverse Health Effects of per- and Polyfluoroalkyl Substances on the Kidney via Human Urine Metabolomics

尿 人类健康 代谢组学 医学 不利影响 人肾 内科学 药理学 化学 环境卫生 生物化学 色谱法
作者
Anen He,Juan Li,Li Zhao,Yao Lu,Yong Liang,Zhen Zhou,Zhuo Man,Jitao Lv,Yawei Wang,Guibin Jiang
出处
期刊:Environmental Science & Technology [American Chemical Society]
卷期号:57 (43): 16244-16254 被引量:49
标识
DOI:10.1021/acs.est.3c06480
摘要

Per- and polyfluoroalkyl substances (PFAS) receive significant research attention due to their potential adverse effects on human health. Evidence shows that the kidney is one of the target organs of PFAS. In occupational exposure scenarios, high PFAS concentrations may adversely affect kidney metabolism, but whether this effect is reflected in the small metabolic molecules contained in urine remains unknown. In this study, 72 matched serum and urine samples from occupational workers of a fluorochemical manufactory as well as 153 urine samples from local residents were collected, and 23 PFAS levels were quantified. The concentrations of Σ23PFAS in the serum and urine samples of workers were 5.43 ± 1.02 μg/mL and 201 ± 46.9 ng/mL, respectively, while the Σ23PFAS concentration in the urine of the residents was 6.18 ± 0.76 ng/mL. For workers, high levels of urinary PFAS were strongly correlated with levels in serum (r = 0.57-0.93), indicating that urinary PFAS can be a good indicator for serum PFAS levels. Further, a urine nontargeted metabolomics study was conducted. The results of association models, including Bayesian kernel machine regression, demonstrated positive correlations between urinary PFAS levels and key small kidney molecules. A total of eight potential biomarkers associated with PFAS exposure were identified, and all of them showed significant positive correlations with markers of kidney function. These findings provide the first evidence that urine can serve as a matrix to indicate the adverse health effects of high levels of exposure to PFAS on the kidneys.
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
爱笑的山晴完成签到,获得积分10
2秒前
katha完成签到,获得积分20
2秒前
Legend发布了新的文献求助10
2秒前
王金金发布了新的文献求助10
4秒前
5秒前
5秒前
6秒前
丘比特应助katha采纳,获得10
6秒前
Shanks完成签到,获得积分10
9秒前
10秒前
10秒前
哈基米应助科研通管家采纳,获得10
10秒前
10秒前
10秒前
10秒前
wanci应助科研通管家采纳,获得10
11秒前
11秒前
11秒前
11秒前
11秒前
777发布了新的文献求助10
11秒前
Alfred发布了新的文献求助30
11秒前
13秒前
晨屿完成签到 ,获得积分10
13秒前
柳crystal完成签到,获得积分10
15秒前
汉堡包应助兰亭序采纳,获得10
16秒前
17秒前
kepler完成签到,获得积分10
18秒前
鸟窝发布了新的文献求助10
18秒前
20秒前
万能图书馆应助刘JX采纳,获得10
22秒前
安渝完成签到 ,获得积分10
23秒前
NexusExplorer应助超级铅笔采纳,获得10
23秒前
25秒前
研友_LmYM7L完成签到,获得积分10
26秒前
27秒前
顾矜应助sanch采纳,获得10
29秒前
31秒前
在水一方应助进步采纳,获得10
32秒前
lxy完成签到,获得积分10
33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
信任代码:AI 时代的传播重构 450
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 6358099
求助须知:如何正确求助?哪些是违规求助? 8172554
关于积分的说明 17208868
捐赠科研通 5413467
什么是DOI,文献DOI怎么找? 2865108
邀请新用户注册赠送积分活动 1842639
关于科研通互助平台的介绍 1690736