Association between organophosphorus pesticide exposure and depression risk in adults: A cross-sectional study with NHANES data

IF 7.3 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Yudong Wu , Jian Song , Qin Zhang , Shuangshuang Yan , Xiaoni Sun , Weizhuo Yi , Rubing Pan , Jian Cheng , Zhiwei Xu , Hong Su
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引用次数: 10

Abstract

Organophosphorus pesticides (OPPs) are widely used pesticides, and previous studies showed that OPPs can increase the risk of central nervous system disorders (e.g., Parkinson's and Alzheimer's disease). However, few studies have comprehensively explored their association with depression in general adults. We analyzed data from 5206 participants aged 20 years or more based on four National Health and Nutrition Examination Survey (NHANES) cycles. OPPs exposure was estimated using measures of urinary concentrations for six OPPs metabolites. Survey-weighted generalized linear regression model (SWGLM) was used to explore the association of OPPs metabolites with depression. Subgroup analyses were performed by age (≦60 years and >60 years) and gender. The weighted quantile sum (WQS) regression model was used to explore the overall association of six OPPs metabolites with depression. In addition, The Bayesian kernel machine regression (BKMR) model was applied to investigate the interaction and joint effects of multiple OPPs metabolites with depression. The SWGLM showed that dimethyl phosphate (DMP) and dimethyl thiophosphate (DMTP), whether taken as continuous or quartile variables, had a positive correlation with depression. Diethyl phosphate (DEP) and dimethyl dithiophosphate (DMDTP) in the highest quartile were positively associated with depression compared to the lowest quartile. In subgroup analysis, we found that the effects of the above chemicals on depression existed in the male and young middle-aged population, while DMP was present in the female. There was a significant combined overall effect of six OPPs metabolites with depression [OR = 1.232, 95%CI: (1.011, 1.504)] in WQS. Furthermore, the BKMR model also showed a positive trend in the overall effect of six OPPs metabolites with depression. In conclusion, our results suggest that exposure to OPPs may increase the risk of depression in US adults. Men and young and middle-aged populations are more vulnerable to OPPs and the mixture of OPPs metabolites may induce depression.

Abstract Image

有机磷农药暴露与成人抑郁风险之间的关系:一项基于NHANES数据的横断面研究
有机磷农药(OPPs)是广泛使用的农药,既往研究表明,OPPs可增加中枢神经系统疾病(如帕金森病和阿尔茨海默病)的风险。然而,很少有研究全面探讨它们与普通成年人抑郁症的关系。我们分析了5206名年龄在20岁或以上的参与者的数据,这些数据基于四个国家健康和营养检查调查(NHANES)周期。通过测量尿中六种OPPs代谢物的浓度来估计OPPs暴露。采用调查加权广义线性回归模型(SWGLM)探讨OPPs代谢物与抑郁症的关系。按年龄(≤60岁及≤60岁)、性别进行亚组分析。采用加权分位数和(WQS)回归模型探讨6种OPPs代谢物与抑郁症的总体关系。此外,应用贝叶斯核机回归(BKMR)模型研究多种OPPs代谢物与抑郁症的相互作用和联合效应。SWGLM结果显示,无论是作为连续变量还是四分位数变量,磷酸二甲酯(DMP)和硫代磷酸二甲酯(DMTP)均与抑郁症呈正相关。与最低四分位数相比,最高四分位数的磷酸二乙酯(DEP)和二甲基二硫代磷酸(DMDTP)与抑郁症呈正相关。在亚组分析中,我们发现上述化学物质对抑郁症的影响存在于男性和中青年人群中,而DMP存在于女性人群中。在WQS中,6种OPPs代谢物与抑郁症的综合总体效应显著[OR = 1.232, 95%CI:(1.011, 1.504)]。此外,BKMR模型还显示,六种OPPs代谢物对抑郁症的总体影响呈阳性趋势。总之,我们的研究结果表明,暴露于OPPs可能会增加美国成年人患抑郁症的风险。男性和中青年人群更容易受到OPPs的影响,OPPs代谢物的混合可能诱发抑郁症。
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来源期刊
Environmental Pollution
Environmental Pollution 环境科学-环境科学
CiteScore
16.00
自引率
6.70%
发文量
2082
审稿时长
2.9 months
期刊介绍: Environmental Pollution is an international peer-reviewed journal that publishes high-quality research papers and review articles covering all aspects of environmental pollution and its impacts on ecosystems and human health. Subject areas include, but are not limited to: • Sources and occurrences of pollutants that are clearly defined and measured in environmental compartments, food and food-related items, and human bodies; • Interlinks between contaminant exposure and biological, ecological, and human health effects, including those of climate change; • Contaminants of emerging concerns (including but not limited to antibiotic resistant microorganisms or genes, microplastics/nanoplastics, electronic wastes, light, and noise) and/or their biological, ecological, or human health effects; • Laboratory and field studies on the remediation/mitigation of environmental pollution via new techniques and with clear links to biological, ecological, or human health effects; • Modeling of pollution processes, patterns, or trends that is of clear environmental and/or human health interest; • New techniques that measure and examine environmental occurrences, transport, behavior, and effects of pollutants within the environment or the laboratory, provided that they can be clearly used to address problems within regional or global environmental compartments.
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