Article(id=1241319155601822490, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241319148798669160, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202410478, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1730217600000, receivedDateStr=2024-10-30, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773883149289, onlineDateStr=2026-03-19, pubDate=1750780800000, pubDateStr=2025-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773883149289, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773883149289, creator=13701087609, updateTime=1773883149289, updator=13701087609, issue=Issue{id=1241319148798669160, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='12', pageStart='2113', pageEnd='2304', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773883147667, creator=13701087609, updateTime=1773885555254, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241329247004971040, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241319148798669160, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241329247004971041, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241319148798669160, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2172, endPage=2178, ext={EN=ArticleExt(id=1241319156402934595, articleId=1241319155601822490, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Comprehensive metabolomics analysis of the association between combined exposure to metals and phthalates with blood lipids, columnId=1228016570660745413, journalTitle=Modern Preventive Medicine, columnName=Environmental and Occupational Health, runingTitle=null, highlight=null, articleAbstract=
Objective To investigate the overall association between co-exposure to metals and phthalates (PAEs) and blood lipid indicators, as well as the role of metabolic disturbances and endogenous metabolites as mediators in this association.
Methods A total of 74 residents from Guangzhou (n=35) and Qingyuan (n=39) were randomly selected, and their urine samples were analyzed for 15 metal elements and 9 PAE metabolites. Generalized linear models and generalized weighted quantile sum (gWQS) regression models were used to assess the association between individual pollutants, combined exposure, and blood lipid levels. Non-targeted metabolomics, meet-in-the-Middle (MITM) approach, and mediation analysis were performed to identify endogenous metabolites and metabolic pathways linking combined exposure with blood lipid changes, and to explore the mediating effects of metabolites.
Results The concentrations of seven metals, including arsenic and cadmium, and four PAE metabolites, including mono-isobutyl phthalate (MiBP), were significantly positively correlated with increased total cholesterol (TC). The weighted quantile sum (WQS) index, which represented the overall load of metal and PAE combined exposure, showed a dose-dependent relationship with TC increase. For each one-unit increase in the WQS index, TC increased by 0.43 (95% CI: 0.05-0.82) mmol/L. Eight metabolites, including perilla acid, dehydroepiandrosterone, octanoylcarnitine, arachidonic acid, estrone, kynurenine, glucose-6-phosphate, and ferulic acid, were significantly associated with the WQS index and TC levels, and exhibited significant mediating effects, and the corresponding mediation effects were 0.09 (95% CI: 0.02-0.27)，0.18 (95% CI: 0.04-0.35)、0.21 (95% CI: 0.02-0.44)、0.12 (95% CI: 0.02-0.24)、0.08 (95% CI: 0.02-0.21)、0.08 (95% CI: 0.01-0.20) and 0.09 (95% CI: 0.01-0.19), respectively. The biosynthesis pathway of steroid hormones was significantly enriched.
Conclusion The biosynthesis pathway of steroid hormones and metabolites such as dehydroepiandrosterone and androstenone may mediate the overall association between combined exposure to metals and PAEs and the increase in TC levels, providing the metabolic perspective for the overall effect on blood lipids of co-exposure to metals and PAEs.
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目的 探究金属与邻苯二甲酸酯（Phthalates, PAEs）复合暴露与血脂指标间的总体关联以及在此关联中的代谢扰动和内源代谢物的中介作用。
方法 随机抽取广州市（n=35）和清远市（n=39）的74名居民测定其尿液样本中15种金属元素和9种PAEs代谢物水平，采用广义线性模型和广义加权分位数和（gWQS）回归模型分别评估单污染物和复合暴露与血脂水平间的关联；基于非靶向代谢组学、中间相遇方法和中介分析识别连接复合暴露与血脂改变的内源代谢物及代谢通路，并探索代谢物的中介效应。
结果 尿中砷、镉等7种金属及邻苯二甲酸单异丁酯（MiBP）等4种PAEs代谢物浓度与总胆固醇（TC）增加呈显著正相关；表征金属与PAEs复合暴露总体负荷的加权分位数和（WQS）指数与TC增加呈剂量依赖性相关，其每增加1个单位，TC增加0.43（95% CI: 0.05~0.82）mmol/L。紫苏酸、脱氢表雄酮、花生四烯酸、雄酮、犬尿氨酸、葡萄糖胺6-磷酸和异戊酸等8种代谢物与WQS指数和TC水平均显著相关并具有显著的中介效应，中介效应依次为0.09(95% CI:0.02~0.27)、0.18(95% CI:0.04~0.35)、0.21(95% CI:0.02~0.44)、0.12(95% CI:0.02~0.24)、0.08(95% CI:0.02~0.21)、0.08(95% CI:0.01~0.20)和0.09(95% CI:0.01~0.19)；类固醇激素的生物合成途径代谢通路被显著富集。
结论 类固醇激素生物合成通路及脱氢表雄酮和雄酮等代谢物可能介导金属PAEs复合暴露与TC增加的总体关联。研究结果为金属和PAEs复合暴露的健康风险和潜在生物学机制提供了代谢组学视角。
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本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=lLNnRC0YYWcO4yqV3YGR5Q==, magXml=NYCoMupuc3phTFcCOZQ5HA==, pdfUrl=null, pdf=t5kMz2Z1IL2BnXPgbf0WHw==, pdfFileSize=1510939, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=zatmYhJcLzMrdVB57+YnJQ==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=/4apVUBmsgn3J8qUmWalsg==, mapNumber=null, authorCompany=null, fund=null, authors=
廖光宇（2001—），男，硕士在读，研究方向：环境与健康风险评估
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1.贵州医科大学公共卫生与健康学院，环境污染与疾病监控教育部重点实验室，贵州 贵阳 561113
2.生态环境部华南环境科学研究所，新污染物研究中心，生态环境部环境污染健康风险评价重点实验室, bio={"content":"
廖光宇（2001—），男，硕士在读，研究方向：环境与健康风险评估
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1.贵州医科大学公共卫生与健康学院，环境污染与疾病监控教育部重点实验室，贵州 贵阳 561113)]), AuthorCompany(id=1241319160228139044, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, xref=2., ext=[AuthorCompanyExt(id=1241319160236527651, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, companyId=1241319160228139044, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
2.生态环境部华南环境科学研究所，新污染物研究中心，生态环境部环境污染健康风险评价重点实验室)])]), Author(id=1241319162304319585, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, orderNo=1, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1241319162430148714, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, authorId=1241319162304319585, language=EN, stringName=Li LI, firstName=Li, middleName=null, lastName=LI, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=
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1.贵州医科大学公共卫生与健康学院，环境污染与疾病监控教育部重点实验室，贵州 贵阳 561113
2.生态环境部华南环境科学研究所，新污染物研究中心，生态环境部环境污染健康风险评价重点实验室, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1241319160085532692, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, xref=1., ext=[AuthorCompanyExt(id=1241319160089726997, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, companyId=1241319160085532692, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang, Guizhou 561113, China), AuthorCompanyExt(id=1241319160098115606, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, companyId=1241319160085532692, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
1.贵州医科大学公共卫生与健康学院，环境污染与疾病监控教育部重点实验室，贵州 贵阳 561113)]), AuthorCompany(id=1241319160228139044, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, xref=2., ext=[AuthorCompanyExt(id=1241319160236527651, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, companyId=1241319160228139044, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
2.生态环境部华南环境科学研究所，新污染物研究中心，生态环境部环境污染健康风险评价重点实验室)])]), Author(id=1241319162631475326, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, orderNo=2, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1241319162765693063, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, authorId=1241319162631475326, language=EN, stringName=Hao-nan LI, firstName=Hao-nan, middleName=null, lastName=LI, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=
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2.生态环境部华南环境科学研究所，新污染物研究中心，生态环境部环境污染健康风险评价重点实验室
3.中国医科大学公共卫生学院劳动卫生与环境卫生学系, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1241319160228139044, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, xref=2., ext=[AuthorCompanyExt(id=1241319160236527651, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, companyId=1241319160228139044, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
2.生态环境部华南环境科学研究所，新污染物研究中心，生态环境部环境污染健康风险评价重点实验室)]), AuthorCompany(id=1241319160320413743, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, xref=3., ext=[AuthorCompanyExt(id=1241319160328802353, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, companyId=1241319160320413743, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
3.中国医科大学公共卫生学院劳动卫生与环境卫生学系)])]), Author(id=1241319162987991191, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, orderNo=3, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1241319163164151970, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, authorId=1241319162987991191, language=EN, stringName=Bin TANG, firstName=Bin, middleName=null, lastName=TANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=
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2.生态环境部华南环境科学研究所，新污染物研究中心，生态环境部环境污染健康风险评价重点实验室, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1241319160228139044, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, xref=2., ext=[AuthorCompanyExt(id=1241319160236527651, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, companyId=1241319160228139044, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
2.生态环境部华南环境科学研究所，新污染物研究中心，生态环境部环境污染健康风险评价重点实验室)])]), Author(id=1241319163436781745, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, orderNo=4, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1241319163570999482, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, authorId=1241319163436781745, language=EN, stringName=Qi-long LIAO, firstName=Qi-long, middleName=null, lastName=LIAO, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=
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1.贵州医科大学公共卫生与健康学院，环境污染与疾病监控教育部重点实验室，贵州 贵阳 561113)]), AuthorCompany(id=1241319160228139044, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, xref=2., ext=[AuthorCompanyExt(id=1241319160236527651, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, companyId=1241319160228139044, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
2.生态环境部华南环境科学研究所，新污染物研究中心，生态环境部环境污染健康风险评价重点实验室)]), AuthorCompany(id=1241319160320413743, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, xref=3., ext=[AuthorCompanyExt(id=1241319160328802353, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, companyId=1241319160320413743, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
3.中国医科大学公共卫生学院劳动卫生与环境卫生学系)])], figs=[ArticleFig(id=1241319167207461191, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, language=EN, label=Fig.1, caption=
Generalized linear models of urinary concentrations of metals and phthalate metabolites with serum lipid levels, figureFileSmall=Mg1f6/F2mpJwe7aHNGov0Q==, figureFileBig=zatmYhJcLzMrdVB57+YnJQ==, tableContent=null), ArticleFig(id=1241319167291347274, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, language=CN, label=图1, caption=
尿液中金属元素与邻苯二甲酸酯代谢物浓度与各血脂水平的广义线性模型分析注：A~D依次为金属元素-邻苯二甲酸酯代谢物单体与TC、TG、HDL-C和LDL-C的广义线性分析结果。
, figureFileSmall=Mg1f6/F2mpJwe7aHNGov0Q==, figureFileBig=zatmYhJcLzMrdVB57+YnJQ==, tableContent=null), ArticleFig(id=1241319167547199826, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, language=EN, label=Fig.2, caption=
Overall dose-effect of co-exposure to PAHs-metals mixture on renal function and the contribution of individual pollutant, figureFileSmall=ZXlsEZbGGAyxTKgSD45mCQ==, figureFileBig=LsZk5jtvHhr/kuArioDJqw==, tableContent=null), ArticleFig(id=1241319167668834646, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, language=CN, label=图2, caption=
金属元素-邻苯二甲酸酯代谢物混合暴露对血脂水平的总剂量效应及单个污染物的贡献注：A~D依次为金属元素-邻苯二甲酸酯代谢物混合暴露对TC、TG、HDL-C和LDL-C的总体剂量效应；E~F依次为金属元素-邻苯二甲酸酯代谢物单体的平均权重。
, figureFileSmall=ZXlsEZbGGAyxTKgSD45mCQ==, figureFileBig=LsZk5jtvHhr/kuArioDJqw==, tableContent=null), ArticleFig(id=1241319167790469468, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, language=EN, label=Fig.3, caption=
The mediation effect analyses for selected differential metabolites between wqs index and TC, figureFileSmall=R2LrjX/eQ2lPVgd1Hl3H2g==, figureFileBig=4BThkZCJbFaXtCBA1pYMHg==, tableContent=null), ArticleFig(id=1241319167903715682, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, language=CN, label=图3, caption=
WQS指数与总胆固醇水平相关的差异代谢产物的中介效应分析注：中介效应分析调整了研究对象的年龄、性别、BMI、吸烟状况、吸烟、饮酒、年收入和体育锻炼。
, figureFileSmall=R2LrjX/eQ2lPVgd1Hl3H2g==, figureFileBig=4BThkZCJbFaXtCBA1pYMHg==, tableContent=null), ArticleFig(id=1241319168008573287, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, language=EN, label=Fig.4, caption=
The pathway enrichment analyses based on selected differential metabolites using the MITM approaches, figureFileSmall=5NKsSmQz2zqGWqaVOWE/wg==, figureFileBig=aKu9iUM0OivqjKILE9HZqw==, tableContent=null), ArticleFig(id=1241319168134402415, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, language=CN, label=图4, caption=
基于MITM方法筛选的差异代谢物的途径富集分析, figureFileSmall=5NKsSmQz2zqGWqaVOWE/wg==, figureFileBig=aKu9iUM0OivqjKILE9HZqw==, tableContent=null), ArticleFig(id=1241319168243454323, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, language=EN, label=Table 1, caption=
Basical characteristics of participants(n=74)
, figureFileSmall=null, figureFileBig=null, tableContent=
| 人口学特征 | 全部人群
（n=74） | 广州人群
（n1=35) | 清远人群
（n2=39) | 统计量 | P值 |
|---|
年龄(岁， ) | 38.0±11.8 | 32.0±11.0 | 43.3±9.9 | -4.611 | <0.001 |
| 性别[男性,n(%)] | 55(74.3) | 20(57.1) | 35(89.7) | 10.273 | 0.001 |
BMI(Kg/m2， ) | 23.0±3.7 | 22.2±3.8 | 23.8±3.4 | -1.976 | 0.053 |
| 吸烟[是,n(%)] | 38(51.4) | 14(40.0) | 24(61.5) | 3.425 | 0.064 |
| 饮酒[是,n(%)] | 30(40.5) | 15(42.9) | 15(38.5) | 0.148 | 0.701 |
| 年收入[元,n(%)] | | | | 0.409 | 0.815 |
| ≤50 000 | 30(40.5) | 13(37.2) | 17(43.6) | | |
| 50 000~100 000 | 21(28.4) | 11(31.4) | 10(25.6) | | |
| ≥100 000 | 23(31.1) | 11(31.4) | 12(30.8) | | |
| 体育运动[是，n(%)] | 35(47.3) | 13(37.1) | 22(56.4) | 2.747 | 0.097 |
), ArticleFig(id=1241319168360894842, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, language=CN, label=表1, caption=
本研究参与者的基本人口学特征（n=74）
, figureFileSmall=null, figureFileBig=null, tableContent=
| 人口学特征 | 全部人群
（n=74） | 广州人群
（n1=35) | 清远人群
（n2=39) | 统计量 | P值 |
|---|
| 年龄(岁，) | 38.0±11.8 | 32.0±11.0 | 43.3±9.9 | -4.611 | <0.001 |
| 性别[男性,n(%)] | 55(74.3) | 20(57.1) | 35(89.7) | 10.273 | 0.001 |
| BMI(Kg/m2，) | 23.0±3.7 | 22.2±3.8 | 23.8±3.4 | -1.976 | 0.053 |
| 吸烟[是,n(%)] | 38(51.4) | 14(40.0) | 24(61.5) | 3.425 | 0.064 |
| 饮酒[是,n(%)] | 30(40.5) | 15(42.9) | 15(38.5) | 0.148 | 0.701 |
| 年收入[元,n(%)] | | | | 0.409 | 0.815 |
| ≤50 000 | 30(40.5) | 13(37.2) | 17(43.6) | | |
| 50 000~100 000 | 21(28.4) | 11(31.4) | 10(25.6) | | |
| ≥100 000 | 23(31.1) | 11(31.4) | 12(30.8) | | |
| 体育运动[是，n(%)] | 35(47.3) | 13(37.1) | 22(56.4) | 2.747 | 0.097 |
), ArticleFig(id=1241319168461558145, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, language=EN, label=Table 2, caption=
Urinary pollutantsmedian(P25, P75) concentrations(ng/mL) and serum lipids levels(mmol/L) of participants
, figureFileSmall=null, figureFileBig=null, tableContent=
| 目标物 | 检测限 | 检出频率 | 全部人群（n=74） | 广州人群（n1=35) | 清远人群（n2=39) | P(Mann-Whitney tests) |
|---|
| As | 0.015 | 100 | 23.7(11.9, 41.8) | 12.6(7.5, 28.4) | 33.1(20.9, 51.7) | <0.001 |
| Cd | 0.004 | 100 | 0.7(0.2, 1.9) | 0.3(0.1, 0.6) | 1.6(0.8, 2.6) | <0.001 |
| Cr | 0.021 | 99 | 0.4(0.3, 0.8) | 0.4(0.2, 0.9) | 0.5(0.3, 0.8) | 0.439 |
| Ni | 0.240 | 96 | 17.2(8.5, 30.6) | 16.4(10.5, 27.6) | 19.4(5.9, 41.4) | 0.681 |
| Pb | 0.010 | 100 | 13.6(7.6, 20.5) | 7.8(5.9, 13.5) | 17.9(13.4, 28.8) | <0.001 |
| V | 0.016 | 100 | 3.3(2.3, 5.4) | 2.5(1.8, 3.1) | 4.3(3.2, 6.2) | <0.001 |
| Mn | 0.016 | 51 | 0.1(0.013, 1.5) | 0.012(0.012, 0.8) | 0.4(0.013, 2.1) | 0.025 |
| Fe | 0.200 | 100 | 36.2(22.2, 61.0) | 24.1(15.6, 32.6) | 58.1(36.5, 91.2) | <0.001 |
| Cu | 0.050 | 100 | 19.5(13.5, 33.5) | 17.1(10.7, 26.3) | 20.8(15.8, 33.8) | 0.090 |
| Zn | 0.046 | 100 | 463.3(236.2, 748.3) | 299.2(178.6, 511.2) | 564.9(381.6, 812.3) | <0.001 |
| Se | 0.290 | 99 | 7.9(3.3, 13.3) | 4.5(2.9, 9.8) | 11.5(5.8, 19.6) | <0.001 |
| Rb | 0.038 | 100 | 1 458.1(628.4, 3 034.7) | 634.2(377.9, 1 118.2) | 2 818.0(1 789.8, 3 788.5) | <0.001 |
| Sr | 0.048 | 100 | 64.6(40.4, 104.0) | 45.3(26.5, 70.5) | 84.3(63.5, 152.2) | <0.001 |
| Mo | 0.004 | 100 | 35.3(17.9, 64.9) | 22.1(13.1, 53.8) | 51.2(33.1, 74.8) | 0.001 |
| Cs | 0.022 | 100 | 6.4(2.6, 12.0) | 2.6(1.8, 4.4) | 11.6(7.4, 15.4) | <0.001 |
| MMP | 0.180 | 100 | 8.5(4.8, 12.8) | 6.4(4.0, 10.3) | 10.1(5.5, 17.7) | 0.011 |
| MEP | 0.100 | 97 | 11.9(5.7, 30.5) | 11.2(5.3, 22.9) | 12.7(6.6, 33.9) | 0.449 |
| MiBP | 0.060 | 93 | 11.2(6.4, 21.9) | 7.0(5.0, 13.8) | 15.3(10.4, 32.7) | <0.001 |
| MnBP | 0.620 | 99 | 17.8(10.4, 36.7) | 11.3(7.9, 24.4) | 25.2(16.8, 53.1) | <0.001 |
| MBzP | 0.050 | 89 | 0.5(0.3, 0.7) | 0.5(0.4, 0.8) | 0.4(0.2, 0.7) | 0.267 |
| MEHP | 0.720 | 100 | 15.3(12.3, 26.4) | 14.2(10.8, 16.3) | 21.3(13.9, 41.2) | <0.001 |
| OHMEHP | 0.210 | 96 | 17.4(8.5, 33.1) | 8.5(6.2, 15.1) | 31.3(18.2, 62.6) | <0.001 |
| CxMEPP | 0.020 | 100 | 0.8(0.3, 1.5) | 0.4(0.2, 0.6) | 1.3(0.8, 2.5) | <0.001 |
| OxoMEHP | 0.050 | 99 | 6.6(4.0, 11.4) | 4.3(2.4, 6.5) | 10.9(6.4, 21.3) | <0.001 |
| ∑mPAEs | / | / | 114.3(69.7, 213.3) | 71.4(59.8, 116.9) | 190.4(105.1, 247.7) | <0.001 |
| TC | / | / | 5.27(4.59, 5.99) | 4.80(4.19, 5.39) | 5..61(5.14, 6.29) | <0.001 |
| TG | / | / | 1.06(0.71, 1.73) | 0.78(0.64, 1.13) | 1.27(0.83, 2.83) | 0.001 |
| HDL-C | / | / | 1.24(1.04, 1.42) | 1.23(1.07, 1.44) | 1.24(1.00, 1.40) | 0.306 |
| LDL-C | / | / | 2.97(2.53, 3.39) | 2.98(2.54, 3.43) | 2.96(2.45, 3.39) | 0.395 |
), ArticleFig(id=1241319168574804360, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319155601822490, language=CN, label=表2, caption=
参与者尿污染物浓度（ng/mL）和血脂（mmol/L）中位数[M（P25，P75）]水平
, figureFileSmall=null, figureFileBig=null, tableContent=
| 目标物 | 检测限 | 检出频率 | 全部人群（n=74） | 广州人群（n1=35) | 清远人群（n2=39) | P(Mann-Whitney tests) |
|---|
| As | 0.015 | 100 | 23.7(11.9, 41.8) | 12.6(7.5, 28.4) | 33.1(20.9, 51.7) | <0.001 |
| Cd | 0.004 | 100 | 0.7(0.2, 1.9) | 0.3(0.1, 0.6) | 1.6(0.8, 2.6) | <0.001 |
| Cr | 0.021 | 99 | 0.4(0.3, 0.8) | 0.4(0.2, 0.9) | 0.5(0.3, 0.8) | 0.439 |
| Ni | 0.240 | 96 | 17.2(8.5, 30.6) | 16.4(10.5, 27.6) | 19.4(5.9, 41.4) | 0.681 |
| Pb | 0.010 | 100 | 13.6(7.6, 20.5) | 7.8(5.9, 13.5) | 17.9(13.4, 28.8) | <0.001 |
| V | 0.016 | 100 | 3.3(2.3, 5.4) | 2.5(1.8, 3.1) | 4.3(3.2, 6.2) | <0.001 |
| Mn | 0.016 | 51 | 0.1(0.013, 1.5) | 0.012(0.012, 0.8) | 0.4(0.013, 2.1) | 0.025 |
| Fe | 0.200 | 100 | 36.2(22.2, 61.0) | 24.1(15.6, 32.6) | 58.1(36.5, 91.2) | <0.001 |
| Cu | 0.050 | 100 | 19.5(13.5, 33.5) | 17.1(10.7, 26.3) | 20.8(15.8, 33.8) | 0.090 |
| Zn | 0.046 | 100 | 463.3(236.2, 748.3) | 299.2(178.6, 511.2) | 564.9(381.6, 812.3) | <0.001 |
| Se | 0.290 | 99 | 7.9(3.3, 13.3) | 4.5(2.9, 9.8) | 11.5(5.8, 19.6) | <0.001 |
| Rb | 0.038 | 100 | 1 458.1(628.4, 3 034.7) | 634.2(377.9, 1 118.2) | 2 818.0(1 789.8, 3 788.5) | <0.001 |
| Sr | 0.048 | 100 | 64.6(40.4, 104.0) | 45.3(26.5, 70.5) | 84.3(63.5, 152.2) | <0.001 |
| Mo | 0.004 | 100 | 35.3(17.9, 64.9) | 22.1(13.1, 53.8) | 51.2(33.1, 74.8) | 0.001 |
| Cs | 0.022 | 100 | 6.4(2.6, 12.0) | 2.6(1.8, 4.4) | 11.6(7.4, 15.4) | <0.001 |
| MMP | 0.180 | 100 | 8.5(4.8, 12.8) | 6.4(4.0, 10.3) | 10.1(5.5, 17.7) | 0.011 |
| MEP | 0.100 | 97 | 11.9(5.7, 30.5) | 11.2(5.3, 22.9) | 12.7(6.6, 33.9) | 0.449 |
| MiBP | 0.060 | 93 | 11.2(6.4, 21.9) | 7.0(5.0, 13.8) | 15.3(10.4, 32.7) | <0.001 |
| MnBP | 0.620 | 99 | 17.8(10.4, 36.7) | 11.3(7.9, 24.4) | 25.2(16.8, 53.1) | <0.001 |
| MBzP | 0.050 | 89 | 0.5(0.3, 0.7) | 0.5(0.4, 0.8) | 0.4(0.2, 0.7) | 0.267 |
| MEHP | 0.720 | 100 | 15.3(12.3, 26.4) | 14.2(10.8, 16.3) | 21.3(13.9, 41.2) | <0.001 |
| OHMEHP | 0.210 | 96 | 17.4(8.5, 33.1) | 8.5(6.2, 15.1) | 31.3(18.2, 62.6) | <0.001 |
| CxMEPP | 0.020 | 100 | 0.8(0.3, 1.5) | 0.4(0.2, 0.6) | 1.3(0.8, 2.5) | <0.001 |
| OxoMEHP | 0.050 | 99 | 6.6(4.0, 11.4) | 4.3(2.4, 6.5) | 10.9(6.4, 21.3) | <0.001 |
| ∑mPAEs | / | / | 114.3(69.7, 213.3) | 71.4(59.8, 116.9) | 190.4(105.1, 247.7) | <0.001 |
| TC | / | / | 5.27(4.59, 5.99) | 4.80(4.19, 5.39) | 5..61(5.14, 6.29) | <0.001 |
| TG | / | / | 1.06(0.71, 1.73) | 0.78(0.64, 1.13) | 1.27(0.83, 2.83) | 0.001 |
| HDL-C | / | / | 1.24(1.04, 1.42) | 1.23(1.07, 1.44) | 1.24(1.00, 1.40) | 0.306 |
| LDL-C | / | / | 2.97(2.53, 3.39) | 2.98(2.54, 3.43) | 2.96(2.45, 3.39) | 0.395 |
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