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Article(id=1324044555804357138, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324044540067328424, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202511026, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1762099200000, receivedDateStr=2025-11-03, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793606421661, onlineDateStr=2026-11-02, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793606421661, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793606421661, creator=13701087609, updateTime=1793606421661, updator=13701087609, issue=Issue{id=1324044540067328424, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='8', pageStart='1345', pageEnd='1536', issueExtLink='null', onlineDate='null', pubDate='1777046400000', pubDateStr='2026-04-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793606417910, creator='13701087609', updateTime=1793670137580, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324311799788602160, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324044540067328424, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324311799788602161, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324044540067328424, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1392, endPage=1397, ext={EN=ArticleExt(id=1324044556039238163, articleId=1324044555804357138, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=A study on the association between estimated glucose disposal rate and metabolic syndrome in coal mining occupational populations, columnId=1228016570660745413, journalTitle=Modern Preventive Medicine, columnName=Environmental and Occupational Health, runingTitle=null, highlight=null, articleAbstract=
Objective

Analyzing and evaluating the association between estimated glucose disposal rate (eGDR) and metabolic syndrome (MetS) among coal miners.

Methods

A cross-sectional study design was employed, with 8 102 employees from coal mining groups who underwent occupational health examinations in 2023 serving as the study subjects. Individual eGDR values were calculated based on waist circumference, hypertension status, and glycated hemoglobin levels, with MetS defined according to the criteria outlined in the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2020 Edition). Unconditional logistic regression models and restricted cubic spline (RCS) models were employed to investigate the association and dose-response relationship between eGDR and MetS, whilst also analyzing the interaction between eGDR and occupational factors on MetS.

Results

The mean age of enrolled employees was (48.7±5.4) years, with 2 932 individuals diagnosed with MetS (prevalence: 36.1%). Fully adjusted unconditional logistic regression analysis revealed that each standard deviation increases in eGDR (SD=2.23) reduced the risk of MetS to 0.52. The high eGDR group exhibited significantly lower MetS risk than the low eGDR group (OR=0.01), with no significant evidence of occupational factor interaction. RCS modeling revealed a strong nonlinear negative dose-response relationship between eGDR and MetS prevalence (P<0.001).

Conclusion

Among coal miners, eGDR exhibits a significant association with Mets, presenting a non-linear negative dose-response relationship, and no occupational factor interactions have been identified.

, authors=Ze-yuan ZHANG1, Shi-ting YI1, Ying-shi DAI1, Qian WANG1, Si-tong LIN1, Hong-xia ZHAO2, Lin-hui PANG2, Ji-sheng NIE3, Xiao-hua YE1, Ying-jun CHEN1, authorsList=Ze-yuan ZHANG, Shi-ting YI, Ying-shi DAI, Qian WANG, Si-tong LIN, Hong-xia ZHAO, Lin-hui PANG, Ji-sheng NIE, Xiao-hua YE, Ying-jun CHEN, authorCompany=null, correspAuthors=Xiao-hua YE, Ying-jun CHEN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1324044560179016236, articleId=1324044555804357138, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=煤矿职业人群中预估葡萄糖处置率与代谢综合征的关联性研究, columnId=1228016571273113811, journalTitle=现代预防医学, columnName=环境与职业卫生, runingTitle=null, highlight=null, articleAbstract=
目的

分析与评估煤矿工人群体中预估葡萄糖处置率（eGDR）与代谢综合征（MetS）的关联程度。

方法

采用横断面研究设计，以2023年参与职业健康体检的8 102名煤矿集团职工作为研究对象。根据腰围、是否患高血压、糖化血红蛋白指标计算个体eGDR值，并根据《中国2型糖尿病防治指南（2020版）》的标准定义MetS。使用非条件logistic回归模型分析与限制性立方样条（RCS）模型探究eGDR与MetS的相关性与剂量-反应关系，并分析eGDR与职业因素对MetS的交互作用。

结果

纳入职工的平均年龄为（48.7±5.4）岁，其中2 932人检出MetS，患病率为36.1%。完全调整协变量后的非条件logistic回归分析显示：eGDR每升高1个标准差（SD=2.23），MetS患病风险降至原先的0.52，且高eGDR组的MetS患病风险显著低于低eGDR组（OR=0.01），但尚未发现职业因素的显著交互作用。RCS模型分析表明，eGDR与MetS患病率存在非线性强负相关剂量-反应关系（P<0.001）。

结论

在煤矿工人群体中eGDR与Mets存在显著关联，且呈非线性负相关剂量反应关系；暂未发现职业因素交互作用。

, authors=张泽远1, 易世婷1, 戴颖诗1, 王芊1, 林思彤1, 赵宏霞2, 庞琳荟2, 聂继盛3, 叶小华1, 陈颖君1, authorsList=张泽远, 易世婷, 戴颖诗, 王芊, 林思彤, 赵宏霞, 庞琳荟, 聂继盛, 叶小华, 陈颖君, authorCompany=null, correspAuthors=叶小华, 陈颖君, authorNote=

张泽远（2000—），男，硕士在读，研究方向：流行病与卫生统计学

, correspAuthorsNote=
叶小华与陈颖君为共同通信作者。叶小华，E-mail：
陈颖君，E-mail：
, copyrightStatement=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=lYigajOaRNH5fY4D7hPNsw==, magXml=o+s0ZbR5NSqwJG94gZXSAg==, pdfUrl=null, pdf=nJbEl7TaHw3TquxjhgG1xQ==, pdfFileSize=999669, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=CNW9rRTnwuShu9juqH3mzA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=2FsdFDJ/yrNSydegKj/oiA==, mapNumber=null, fund=null)}, authors=[Author(id=1324044562494272053, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, orderNo=0, 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=1324044562683015735, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, authorId=1324044562494272053, language=EN, stringName=Ze-yuan ZHANG, firstName=Ze-yuan, middleName=null, lastName=ZHANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=Department of Epidemiology and Health Statistics, School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong 510000, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1324044562775290424, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, authorId=1324044562494272053, language=CN, stringName=张泽远, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1.广东药科大学公共卫生学院流行病与卫生统计学系，广东　广州　510000, bio={"content":"

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Curr Environ Health Rep, 2017, 4(3): 364-372., articleTitle=The healthy worker survivor effect: target parameters and target populations, refAbstract=null)], funds=[Fund(id=1324044571449111160, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, awardId=2025A03J3722, language=CN, fundingSource=广州市科技计划项目(2025A03J3722), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324044560560697901, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, xref=1., ext=[AuthorCompanyExt(id=1324044560573280814, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, companyId=1324044560560697901, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Epidemiology and Health Statistics, School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong 510000, China), AuthorCompanyExt(id=1324044560598446639, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, companyId=1324044560560697901, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.广东药科大学公共卫生学院流行病与卫生统计学系，广东　广州　510000)]), AuthorCompany(id=1324044560673944112, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, xref=2., ext=[AuthorCompanyExt(id=1324044560682332721, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, companyId=1324044560673944112, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.西山煤电（集团）有限责任公司职业病防治所)]), AuthorCompany(id=1324044560824939058, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, xref=3., ext=[AuthorCompanyExt(id=1324044560833327667, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, companyId=1324044560824939058, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.山西医科大学公共卫生学院)])], figs=[ArticleFig(id=1324044568789922416, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, language=EN, label=Fig. 1, caption=Restricted cubic spline model for eGDR and MetS, figureFileSmall=f/bv+XB4hmLhmcDIg4BgqA==, figureFileBig=CNW9rRTnwuShu9juqH3mzA==, tableContent=null), ArticleFig(id=1324044568861225585, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, language=CN, label=图1, caption=eGDR与MetS的限制性立方样条模型, figureFileSmall=f/bv+XB4hmLhmcDIg4BgqA==, figureFileBig=CNW9rRTnwuShu9juqH3mzA==, tableContent=null), ArticleFig(id=1324044569125466738, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, language=EN, label=Table 1, caption=

Basic characteristic of the study subjects

, figureFileSmall=null, figureFileBig=null, tableContent=
变量调查结果（n=8 102）代谢综合征χ2/tP值
否（n=5 170）是（n=2 932）
一般情况
年龄（岁，）48.7±5.448.6±5.349.0±5.63.110.002
年龄组[n（%）]1.300.523
青年（<45岁）2 114（26.1）1 346（26.0）768（26.2）
中年（45~59岁）5 840（72.1）3 736（72.2）2 104（71.7）
老年（≥60岁）148（1.8）88（1.8）60（2.2）
性别[n（%）]159.28<0.001
男7 398（91.3）4 567（88.3）2 831（96.5）
女704（8.7）603（11.7）101（3.5）
BMI[n（%）]1 653.25<0.001
低体重64（0.8）62（1.2）2（0.1）
正常2 670（33.0）2 386（46.1）284（9.7）
超重3 720（45.9）2 238（43.3）1 482（50.5）
肥胖1 648（20.3）484（9.4）1 164（39.7）
教育程度[n（%）]1.950.583
小学及以下183（2.3）115（2.2）68（2.3）
初中2 083（25.7）1 335（25.8）748（25.5）
高中/中专3 470（42.8）2 236（43.2）1 234（42.0）
大专/本科及以上2 366（29.2）1 484（28.7）882（30.2）
工种[n（%）]43.26<0.001
井下作业人员4 396（54.3）2 945（57.0）1 451（26.2）
井上作业人员1 113（13.7）682（13.2）431（9.6）
辅助服务人员1 907（23.5）1 138（22.0）769（14.7）
技术管理人员686（8.5）405（7.8）281（49.5）
婚姻状况[n（%）]0.690.708
未婚/同居119（1.5）80（1.5）39（1.3）
已婚7 634（94.2）4 865（94.1）2 769（94.4）
离婚/丧偶/分居349（4.3）225（4.4）124（4.2）
生活、职业特征
吸烟[n（%）]29.55<0.001
否3 217（39.7）2 165（41.9）1 052（35.9）
是4 255（52.5）2 631（50.9）1 624（55.4）
已戒烟630（7.8）374（7.2）256（8.7）
饮酒[n（%）]25.51<0.001
否3 293（40.6）2 207（42.7）1 086（37.0）
是4 283（52.9）2 648（51.2）1 635（55.8）
已戒酒526（6.5）311（6.1）215（7.2）
饮茶[n（%）]59.72<0.001
否4 860（60.0）3 265（63.2）1 595（54.4）
是3 342（40.0）1 905（36.8）1 337（45.6）
高油饮食[n（%）]117.09<0.001
否6 493（80.1）4 330（83.8）2 163（73.8）
是1 609（19.9）840（16.2）769（26.2）
高盐饮食[n（%）]1.420.234
否3 995（49.3）2 575（49.8）1 420（48.4）
是4 107（50.7）2 595（50.2）1 512（51.6）
体育锻炼[n（%）]1.880.170
否6 419（79.2）4 072（78.8）2 347（80.0）
是1 683（20.8）1 098（21.2）585（20.0）
倒班工作[n（%）]0.130.937
无倒班3 356（41.4）2 141（41.4）1 215（41.4）
曾经有1 264（15.6）812（15.7）452（15.4）
现在有3 482（43.0）2 217（42.9）1 265（43.1）
噪声暴露[n（%）]0.110.745
否4 355（53.8）2 786（53.9）1 569（53.5）
是3 747（46.2）2 384（46.1）1 363（46.5）
粉尘暴露[n（%）]18.22<0.001
否4 291（53.0）2 646（51.2）1 645（56.1）
是3 811（47.0）2 524（48.8）1 287（43.9）
职业紧张[n（%）]3.730.053
否2 685（33.1）1 674（32.4）1 011（34.5）
是5 417（66.9）3 496（67.6）1 921（65.5）
生化指标
TG（mmol/L，）1.75±1.261.32±0.792.51±1.5545.46<0.001
TC（mmol/L，）4.42±0.804.35±0.764.54±0.8710.10<0.001
HDL-C（mmol/L，）1.14±0.251.22±0.251.01±0.1938.50<0.001
LDL-C（mmol/L，）2.56±0.602.50±0.572.68±0.6313.04<0.001
BMI（kg/m2，）25.4±3.324.2±2.927.5±2.948.04<0.001
WC（cm，）89.08±9.7685.31±8.8295.74±7.5153.87<0.001
血糖5.54±1.325.25±0.866.04±1.7826.68<0.001
糖化血红蛋白5.67±0.985.48±0.716.01±1.2624.24<0.001
ALB（g/L，）47.05±2.4346.80±2.4247.46±2.3911.78<0.001
BUN（mmol/L，）5.75±1.245.76±1.265.74±1.220.750.453
UA（μmol/L，）354.0±84.3337.7±78.7382.7±86.223.89<0.001
eGDR8.05±2.239.02±1.976.34±1.5563.33<0.001
eGDR分组<0.001
Q1（0~6.273）2 026（25.0）403（7.8）1 623（55.4）
Q2（6.274~7.461）2 025（25.0）1 192（23.2）827（28.2）
Q3（7.462~10.187）2 032（25.1）1 578（30.5）454（15.5）
Q4（>10.187）2 019（24.9）1 991（38.5）28（1.0）
), ArticleFig(id=1324044569217741427, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, language=CN, label=表1, caption=

研究对象的基本特征分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量调查结果（n=8 102）代谢综合征χ2/tP值
否（n=5 170）是（n=2 932）
一般情况
年龄（岁，）48.7±5.448.6±5.349.0±5.63.110.002
年龄组[n（%）]1.300.523
青年（<45岁）2 114（26.1）1 346（26.0）768（26.2）
中年（45~59岁）5 840（72.1）3 736（72.2）2 104（71.7）
老年（≥60岁）148（1.8）88（1.8）60（2.2）
性别[n（%）]159.28<0.001
男7 398（91.3）4 567（88.3）2 831（96.5）
女704（8.7）603（11.7）101（3.5）
BMI[n（%）]1 653.25<0.001
低体重64（0.8）62（1.2）2（0.1）
正常2 670（33.0）2 386（46.1）284（9.7）
超重3 720（45.9）2 238（43.3）1 482（50.5）
肥胖1 648（20.3）484（9.4）1 164（39.7）
教育程度[n（%）]1.950.583
小学及以下183（2.3）115（2.2）68（2.3）
初中2 083（25.7）1 335（25.8）748（25.5）
高中/中专3 470（42.8）2 236（43.2）1 234（42.0）
大专/本科及以上2 366（29.2）1 484（28.7）882（30.2）
工种[n（%）]43.26<0.001
井下作业人员4 396（54.3）2 945（57.0）1 451（26.2）
井上作业人员1 113（13.7）682（13.2）431（9.6）
辅助服务人员1 907（23.5）1 138（22.0）769（14.7）
技术管理人员686（8.5）405（7.8）281（49.5）
婚姻状况[n（%）]0.690.708
未婚/同居119（1.5）80（1.5）39（1.3）
已婚7 634（94.2）4 865（94.1）2 769（94.4）
离婚/丧偶/分居349（4.3）225（4.4）124（4.2）
生活、职业特征
吸烟[n（%）]29.55<0.001
否3 217（39.7）2 165（41.9）1 052（35.9）
是4 255（52.5）2 631（50.9）1 624（55.4）
已戒烟630（7.8）374（7.2）256（8.7）
饮酒[n（%）]25.51<0.001
否3 293（40.6）2 207（42.7）1 086（37.0）
是4 283（52.9）2 648（51.2）1 635（55.8）
已戒酒526（6.5）311（6.1）215（7.2）
饮茶[n（%）]59.72<0.001
否4 860（60.0）3 265（63.2）1 595（54.4）
是3 342（40.0）1 905（36.8）1 337（45.6）
高油饮食[n（%）]117.09<0.001
否6 493（80.1）4 330（83.8）2 163（73.8）
是1 609（19.9）840（16.2）769（26.2）
高盐饮食[n（%）]1.420.234
否3 995（49.3）2 575（49.8）1 420（48.4）
是4 107（50.7）2 595（50.2）1 512（51.6）
体育锻炼[n（%）]1.880.170
否6 419（79.2）4 072（78.8）2 347（80.0）
是1 683（20.8）1 098（21.2）585（20.0）
倒班工作[n（%）]0.130.937
无倒班3 356（41.4）2 141（41.4）1 215（41.4）
曾经有1 264（15.6）812（15.7）452（15.4）
现在有3 482（43.0）2 217（42.9）1 265（43.1）
噪声暴露[n（%）]0.110.745
否4 355（53.8）2 786（53.9）1 569（53.5）
是3 747（46.2）2 384（46.1）1 363（46.5）
粉尘暴露[n（%）]18.22<0.001
否4 291（53.0）2 646（51.2）1 645（56.1）
是3 811（47.0）2 524（48.8）1 287（43.9）
职业紧张[n（%）]3.730.053
否2 685（33.1）1 674（32.4）1 011（34.5）
是5 417（66.9）3 496（67.6）1 921（65.5）
生化指标
TG（mmol/L，）1.75±1.261.32±0.792.51±1.5545.46<0.001
TC（mmol/L，）4.42±0.804.35±0.764.54±0.8710.10<0.001
HDL-C（mmol/L，）1.14±0.251.22±0.251.01±0.1938.50<0.001
LDL-C（mmol/L，）2.56±0.602.50±0.572.68±0.6313.04<0.001
BMI（kg/m2，）25.4±3.324.2±2.927.5±2.948.04<0.001
WC（cm，）89.08±9.7685.31±8.8295.74±7.5153.87<0.001
血糖5.54±1.325.25±0.866.04±1.7826.68<0.001
糖化血红蛋白5.67±0.985.48±0.716.01±1.2624.24<0.001
ALB（g/L，）47.05±2.4346.80±2.4247.46±2.3911.78<0.001
BUN（mmol/L，）5.75±1.245.76±1.265.74±1.220.750.453
UA（μmol/L，）354.0±84.3337.7±78.7382.7±86.223.89<0.001
eGDR8.05±2.239.02±1.976.34±1.5563.33<0.001
eGDR分组<0.001
Q1（0~6.273）2 026（25.0）403（7.8）1 623（55.4）
Q2（6.274~7.461）2 025（25.0）1 192（23.2）827（28.2）
Q3（7.462~10.187）2 032（25.1）1 578（30.5）454（15.5）
Q4（>10.187）2 019（24.9）1 991（38.5）28（1.0）
), ArticleFig(id=1324044569360347764, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, language=EN, label=Table 2, caption=

Relationship between eGDR and MetS prevalence

, figureFileSmall=null, figureFileBig=null, tableContent=
MetS模型1模型2模型3
OR（95%CI）P值OR（95%CI）P值OR（95%CI）P值
eGDR
每增加1SD（SD=2.23）0.46（0.44~0.47）<0.0010.46（0.44~0.48）<0.0010.52（0.50~0.54）<0.001
eGDR分组
Q1（0~6.273）1.001.001.00
Q2（6.274~7.461）0.17（0.15~0.20）<0.0010.17（0.15~0.20）<0.0010.29（0.25~0.34）<0.001
Q3（7.462~10.187）0.07（0.06~0.08）<0.0010.07（0.06~0.09）<0.0010.11（0.10~0.13）<0.001
Q4（>10.187）0.00（0.00~0.01）<0.0010.00（0.00~0.01）<0.0010.01（0.01~0.01）<0.001
趋势P值<0.001<0.001<0.001
), ArticleFig(id=1324044569448428149, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, language=CN, label=表2, caption=

eGDR与MetS患病率之间的关系

, figureFileSmall=null, figureFileBig=null, tableContent=
MetS模型1模型2模型3
OR（95%CI）P值OR（95%CI）P值OR（95%CI）P值
eGDR
每增加1SD（SD=2.23）0.46（0.44~0.47）<0.0010.46（0.44~0.48）<0.0010.52（0.50~0.54）<0.001
eGDR分组
Q1（0~6.273）1.001.001.00
Q2（6.274~7.461）0.17（0.15~0.20）<0.0010.17（0.15~0.20）<0.0010.29（0.25~0.34）<0.001
Q3（7.462~10.187）0.07（0.06~0.08）<0.0010.07（0.06~0.09）<0.0010.11（0.10~0.13）<0.001
Q4（>10.187）0.00（0.00~0.01）<0.0010.00（0.00~0.01）<0.0010.01（0.01~0.01）<0.001
趋势P值<0.001<0.001<0.001
), ArticleFig(id=1324044569662337654, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, language=EN, label=Table 3, caption=

Interaction between eGDR group and occupational factor exposure on MetS

, figureFileSmall=null, figureFileBig=null, tableContent=
变量模型1OR（95%CI）模型2OR（95%CI）模型3OR（95%CI）
噪声（ref：不接触噪声暴露）
接触噪声暴露1.20（0.96，1.50）1.23（0.98，1.54）1.19（0.95，1.50）
粉尘（ref：不接触粉尘暴露）
接触粉尘暴露0.81（0.65，1.02）0.82（0.66，1.03）0.79（0.63，1.02）
职业紧张（ref：无职业紧张）
职业紧张0.97（0.77，1.23）0.95（0.75，1.20）0.94（0.74，1.19）
倒班工作（ref：无倒班工作）
曾经有倒班工作0.95（0.69，1.33）0.94（0.67，1.30）0.96（0.69，1.35）
现在有倒班工作0.90（0.71，1.14）0.88（0.69，1.12）0.88（0.68，1.12）
), ArticleFig(id=1324044569746223735, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044555804357138, language=CN, label=表3, caption=

eGDR组与职业因素暴露对MetS的交互作用

, figureFileSmall=null, figureFileBig=null, tableContent=
变量模型1OR（95%CI）模型2OR（95%CI）模型3OR（95%CI）
噪声（ref：不接触噪声暴露）
接触噪声暴露1.20（0.96，1.50）1.23（0.98，1.54）1.19（0.95，1.50）
粉尘（ref：不接触粉尘暴露）
接触粉尘暴露0.81（0.65，1.02）0.82（0.66，1.03）0.79（0.63，1.02）
职业紧张（ref：无职业紧张）
职业紧张0.97（0.77，1.23）0.95（0.75，1.20）0.94（0.74，1.19）
倒班工作（ref：无倒班工作）
曾经有倒班工作0.95（0.69，1.33）0.94（0.67，1.30）0.96（0.69，1.35）
现在有倒班工作0.90（0.71，1.14）0.88（0.69，1.12）0.88（0.68，1.12）
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煤矿职业人群中预估葡萄糖处置率与代谢综合征的关联性研究
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张泽远 1 , 易世婷 1 , 戴颖诗 1 , 王芊 1 , 林思彤 1 , 赵宏霞 2 , 庞琳荟 2 , 聂继盛 3 , 叶小华 1 , 陈颖君 1
现代预防医学 | 环境与职业卫生 2026,53(8): 1392-1397
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现代预防医学 |环境与职业卫生 2026 , 53 (8) : 1392 -1397
煤矿职业人群中预估葡萄糖处置率与代谢综合征的关联性研究
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张泽远1, 易世婷1, 戴颖诗1, 王芊1, 林思彤1, 赵宏霞2, 庞琳荟2, 聂继盛3, 叶小华1 , 陈颖君1
作者信息
  • 1.广东药科大学公共卫生学院流行病与卫生统计学系，广东　广州　510000
  • 2.西山煤电（集团）有限责任公司职业病防治所
  • 3.山西医科大学公共卫生学院
通讯作者:
叶小华与陈颖君为共同通信作者。叶小华，E-mail：
陈颖君，E-mail：
作者简介:

张泽远（2000—），男，硕士在读，研究方向：流行病与卫生统计学

A study on the association between estimated glucose disposal rate and metabolic syndrome in coal mining occupational populations
Ze-yuan ZHANG1, Shi-ting YI1, Ying-shi DAI1, Qian WANG1, Si-tong LIN1, Hong-xia ZHAO2, Lin-hui PANG2, Ji-sheng NIE3, Xiao-hua YE1 , Ying-jun CHEN1
Affiliations
  • Department of Epidemiology and Health Statistics, School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong 510000, China
出版时间: 2026-04-25 doi: 10.20043/j.cnki.MPM.202511026
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摘要
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目的

分析与评估煤矿工人群体中预估葡萄糖处置率（eGDR）与代谢综合征（MetS）的关联程度。

方法

采用横断面研究设计，以2023年参与职业健康体检的8 102名煤矿集团职工作为研究对象。根据腰围、是否患高血压、糖化血红蛋白指标计算个体eGDR值，并根据《中国2型糖尿病防治指南（2020版）》的标准定义MetS。使用非条件logistic回归模型分析与限制性立方样条（RCS）模型探究eGDR与MetS的相关性与剂量-反应关系，并分析eGDR与职业因素对MetS的交互作用。

结果

纳入职工的平均年龄为（48.7±5.4）岁，其中2 932人检出MetS，患病率为36.1%。完全调整协变量后的非条件logistic回归分析显示：eGDR每升高1个标准差（SD=2.23），MetS患病风险降至原先的0.52，且高eGDR组的MetS患病风险显著低于低eGDR组（OR=0.01），但尚未发现职业因素的显著交互作用。RCS模型分析表明，eGDR与MetS患病率存在非线性强负相关剂量-反应关系（P<0.001）。

结论

在煤矿工人群体中eGDR与Mets存在显著关联，且呈非线性负相关剂量反应关系；暂未发现职业因素交互作用。

估计葡萄糖处置率  /  代谢综合征  /  横断面研究  /  煤矿工人  /  交互作用
Objective

Analyzing and evaluating the association between estimated glucose disposal rate (eGDR) and metabolic syndrome (MetS) among coal miners.

Methods

A cross-sectional study design was employed, with 8 102 employees from coal mining groups who underwent occupational health examinations in 2023 serving as the study subjects. Individual eGDR values were calculated based on waist circumference, hypertension status, and glycated hemoglobin levels, with MetS defined according to the criteria outlined in the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2020 Edition). Unconditional logistic regression models and restricted cubic spline (RCS) models were employed to investigate the association and dose-response relationship between eGDR and MetS, whilst also analyzing the interaction between eGDR and occupational factors on MetS.

Results

The mean age of enrolled employees was (48.7±5.4) years, with 2 932 individuals diagnosed with MetS (prevalence: 36.1%). Fully adjusted unconditional logistic regression analysis revealed that each standard deviation increases in eGDR (SD=2.23) reduced the risk of MetS to 0.52. The high eGDR group exhibited significantly lower MetS risk than the low eGDR group (OR=0.01), with no significant evidence of occupational factor interaction. RCS modeling revealed a strong nonlinear negative dose-response relationship between eGDR and MetS prevalence (P<0.001).

Conclusion

Among coal miners, eGDR exhibits a significant association with Mets, presenting a non-linear negative dose-response relationship, and no occupational factor interactions have been identified.

Estimated glucose disposal rate  /  Metabolic syndrome  /  Cross-sectional study  /  Coal miners  /  Interaction
张泽远, 易世婷, 戴颖诗, 王芊, 林思彤, 赵宏霞, 庞琳荟, 聂继盛, 叶小华, 陈颖君. 煤矿职业人群中预估葡萄糖处置率与代谢综合征的关联性研究. 现代预防医学, 2026 , 53 (8) : 1392 -1397 . DOI: 10.20043/j.cnki.MPM.202511026
Ze-yuan ZHANG, Shi-ting YI, Ying-shi DAI, Qian WANG, Si-tong LIN, Hong-xia ZHAO, Lin-hui PANG, Ji-sheng NIE, Xiao-hua YE, Ying-jun CHEN. A study on the association between estimated glucose disposal rate and metabolic syndrome in coal mining occupational populations[J]. Modern Preventive Medicine, 2026 , 53 (8) : 1392 -1397 . DOI: 10.20043/j.cnki.MPM.202511026
正文
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代谢综合征（Metabolic Syndrome，MetS）是一种由高血压（HTN）、向心性肥胖、葡萄糖代谢受损和血脂异常等多种心血管代谢危险因素协同作用所致的临床症候群[1-2]。近年来，中国MetS患病率持续上升，18岁以上成人患者已增至约2.3亿，患病率从2002年的9.5%升至2010—2012年的18.7%[3]，带来沉重的健康负担，并显著增加慢性肾病（CKD）、心血管疾病（CVD）及早死风险，已成为我国重要的公共卫生问题[4-5]。
胰岛素抵抗（Insulin Resistance，IR）是MetS的核心病理机制，表现为胰岛素介导的葡萄糖摄取与利用效率下降，进而导致高血糖、高甘油三酯等代谢异常[6]。目前，高胰岛素-正常血糖钳夹技术是评估IR的金标准，但因其侵入性和高成本，难以在临床广泛应用[7]。作为替代指标，胰岛素-葡萄糖（TyG）指数简便、低成本，能反映IR及糖脂代谢紊乱[8]，但在合并中心性肥胖或高血压时可能无法全面识别IR[9-10]。
预估葡萄糖处置率（eGDR）是一项基于腰围（WC）、高血压状态和糖化血红蛋白A1c（HbA1c）计算的新兴指标。多项研究证实其与MetS密切相关：Chen[11]等基于NHANES成人队列发现eGDR与MetS呈非线性关联，eGDR降低显著增加MetS患病率，且预测效能优于TyG指数和HOMA-IR；Xia[12]等进一步验证eGDR具有保护作用（OR=0.48，95%CI：0.44~0.52）；Zhang[13]等在中国中老年人群（CHARLS）中也报告eGDR对MetS的预测AUC达0.71（95%CI：0.69~0.73）。这些结果提示eGDR可作为一般人群MetS风险评估的有效工具。
然而，目前关于职业人群、尤其是煤矿工人的相关研究较少。煤矿工人长期暴露于高粉尘、高噪声、高压力及作息不规律的工作环境中[14]，而职业性空气污染与噪声暴露已被证实显著增加MetS风险，使其成为MetS高危群体[15-16]。因此，本研究基于山西省太原市煤矿工人群体，旨在探讨eGDR与MetS患病率之间的关联，并评估其对MetS的预测效能，为该特殊职业人群的早期识别与干预提供科学依据。
1　对象与方法
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1.1　对象
研究对象为2023年山西省太原市西山煤电集团煤矿行业工人。研究对象分别来自同一集团不同煤矿，于职业病防治所参加年度职业健康体检。从25 597名煤矿职工中排除伤退、退养、问卷调查不完整、未完成人体测量或血液检查不完整者，最终8 102人纳入研究进行分析。本研究已通过广东药科大学附属第一医院伦理委员会批准，批件号：医伦审【2024】KT第（98）号，且获得所有研究对象的知情同意。
1.2　方法
1.2.1　问卷调查
采用本课题组自行设计的《煤矿工人职业健康调查问卷》，收集研究对象的基本信息、日常生活行为方式、职业史和疾病史。调查者为经受专业培训的在读研究生，调查问卷经多位专家评审与修改，确保了问题的合理性与全面性。在开展正式调查之前，本研究通过预调查对问卷信度进行初步评估，Cronbach α系数为0.897，具有良好的信度。
1.2.2　体格检查与实验室检测
本研究采用标准化体格检查流程，由经过系统培训的医务人员进行各项检测工作。检测过程严格遵循国家规范，使用经校准的医用设备。主要检查项目为人体测量参数，具体包括：身高、体重、血压、腰围等。使用欧姆龙HBP1300型上臂式电子血压计对受检者进行血压测量。采用上禾科技SH-300G医用超声波全自动电子身高体重测量仪器对受检者进行身高、体重与BMI的测量。使用无弹性软尺对受试者腰围与臀围进行测量，精确到0.1 cm。采用欧姆龙HBF-371型体重身体脂肪测量器对受检者的体脂率和内脏脂肪指数进行测量。
检查者使用一次性真空采血管抽取其空腹静脉血5 ml，室温静置30 min，置于低速台式冷冻离心机以1790×g的重力加速度离心10 min，分离血清。采用西门子ADVIA 1800全自动生化分析仪（西门子医学诊断股份有限公司，德国）及原装配套试剂检测受检者的空腹血糖、血脂指标等。
1.2.3　变量定义与计算
个体满足以下3项或更多项条件时，即可诊断为代谢综合征[17]：（1）腹型肥胖（即中心型肥胖）：腰围男性≥90 cm，女性≥85 cm；（2）高血糖：空腹血糖（Fasting Plasma Glucose，FPG）≥6.1 mmol/L或糖负荷后2 h血糖（2hPG）≥7.8 mmol/L或已确诊为糖尿病并治疗者；（3）高血压：血压≥130/85 mm Hg或已确认为高血压并治疗者；（4）空腹甘油三酯（Triglyceride，TG）≥1.70 mmol/L；（5）空腹高密度脂蛋白胆固醇（High density lipoprotein cholesterol，HDL-C）<l.04 mmol/L。
预估葡萄糖处置率（eGDR）的计算公式为：21.158-（0.09×WC [cm]）-（3.407×HTN[1=是，0=否]）-（0.551×HbA1c%），作为IR的指标[18]。根据eGDR指数的四分位数，参与者被分为四组（Q1，Q2，Q3，Q4）。其他指标：血清白蛋白（ALB）；血尿素氮（BUN）；尿酸（UA）。
基本特征与生活方式调查：（1）根据世界卫生组织（WHO）的年龄分段标准，将研究对象分为青年组（<45岁）、中年组（45~59岁）和老年组（≥60岁）。（2）以不吸烟或累计吸烟量少于100支者为不吸烟组，超过100支者作为吸烟组、曾经吸烟但已停止半年以上为戒烟组。（3）饮酒分为不饮酒组、戒酒组（曾经饮酒但已停止饮酒半年及以上）、饮酒组（过去1年内每周饮酒≥1次）。（4）喝茶者为过去一年内每周至少喝两次茶。（5）依据中国居民膳食指南（2022），高油饮食者为每日食用油摄入量超30 g。高盐饮食者为每日食盐摄入量超5 g。（7）体育锻炼者为过去一周内，参与了持续时长不少于10 min的中等或高强度体育活动，或累计进行了不少于30 min的低强度体育活动。（8）夜班工作：根据近半年的夜班状况（17:00至次日9:00工作）分为无夜班组、曾经夜班组、现在夜班组。（9）职业紧张通过标准化工具评估工具：工作内容问卷（JDC）、付出-回报失衡问卷（ERI）量表进行判断。
1.3　统计方法
研究对符合近似正态的连续变量描述为，分类变量以频率和百分比表示，分别使用独立t检验或卡方检验进行组间比较。采用不同水平的调整构建了三个logistic回归模型以分析eGDR水平与MetS患病的关联：（1）模型1未调整；（2）模型2调整了年龄、性别、教育水平、婚姻状况、收入水平；（3）模型3补充了吸烟，饮酒，身体质量指数（BMI），粉尘暴露，职业紧张，高盐，高油饮食与体育锻炼。为评估各模型中eGDR与模型中变量之间的潜在多重共线性，使用方差膨胀因子（VIF）。所有变量的VIF值均低于5，表明无显著多重共线性问题。为了研究eGDR与MetS患病之间的剂量-反应关系，使用了基于logistic回归模型的限制性立方样条（RCS），拟合了3~5个节点的RCS模型，并选择具有最低Akaike信息准则（AIC）的模型来确定最佳节点数为5节点。最后，研究对eGDR与职业暴露因素对MetS的交互作用进行分析。研究采用R 4.3.1软件进行数据统计，检验水准α=0.05。
2　结果
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2.1　一般人口学特征
研究对象中男性有7 398人（91.3%），女性有704人（8.7%），平均年龄为（48.76±5.448）岁。本研究中代谢综合征总人群检出率为36.1%，其中男性检出率38.3%，女性检出率14.3%。年龄、性别、BMI、工种、吸烟、饮酒、饮茶、高油饮食和粉尘暴露，以及除BUN外的生化指标在MetS患病组与正常组间存在统计学差异。总人群的平均eGDR为8.05±2.23，患有MetS者平均eGDR为6.34±1.55，显著低于不患MetS者的水平（P<0.001）。见表1。
2.2　eGDR与MetS的关联与剂量-反应关系
在充分调整协变量后，logistic回归分析显示eGDR与MetS患病率呈显著负相关。作为连续型变量的分析结果发现，每增加1标准差（SD=2.23）的eGDR，MetS患病风险为原先的0.52（95%CI：0.50~0.54）。趋势性分析发现与Q1低水平组相比，eGDR更高水平组的个体表现出显著较低的MetS患病风险（趋势P<0.001）。具体而言，MetS患病率的比值比（OR）在Q2中等水平组中为0.29，在Q3较高水平组中为0.11，在Q4高水平组中为0.01，见表2。
调整协变量后RCS曲线分析发现，eGDR和MetS之间呈现非线性剂量反应关系，总体关联性检验与非线性检验均有统计学意义（P<0.001）。MetS患病风险随eGDR降低而增加，eGDR越高，患MetS的风险越低。当eGDR<7.45时，MetS患病风险随eGDR的减小而增大，于>7.45后趋于平缓且患病风险下降，见图1。
2.3　eGDR组与职业暴露因素对MetS影响的交互作用
研究人群作为职业人群，具有特定的职业暴露危害因素，通过调整潜在的混杂因素，对eGDR组与职业暴露因素包括噪声、粉尘、职业紧张与倒班工作对MetS影响的交互作用进行分析。结果发现各模型中eGDR组与噪声暴露、粉尘暴露、职业紧张与倒班工作对MetS影响的交互作用无统计学意义（P>0.05），见表3。
3　讨论
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本研究对山西省太原市西山煤电集团8 102名煤矿职工的eGDR与MetS患病率之间的关联进行了研究，校正协变量后，研究发现eGDR与MetS之间显著关联，随着eGDR的减少，MetS患病风险急剧上升，呈负相关非线性剂量-反应关系。
本研究中煤矿职工MetS综合患病率为36.7%，显著高于中国普通人群（24.5%）[19]，与成都市青羊区65岁及以上老年人群（35.12%）[20]、巴西成年人（33%）[21]、美国成人（34.7%）[22]患病率相近。该差异可能与煤矿职工长期高强度体力劳动及倒班导致的昼夜节律失调相关[23]：高强度体力活动引发持续性氧化应激，使SOD活性下降、IL-6等促炎因子升高，进而激活NF-κB通路加剧胰岛素抵抗，增加MetS风险[24]。既往研究显示，倒班工作与男性钢铁工人HUA呈非线性剂量反应关系[25]，且可通过扰乱生物钟诱发IR及脂代谢紊乱，进而引发MetS[26]。
基于NHANES人群的研究也发现eGDR与MetS的RCS模型呈非线性负相关剂量-反应关系，MetS风险随eGDR降低而急剧升高[11]，与本研究结果一致。本研究进一步证实该关联，且发现eGDR>7.45时风险曲线趋于平缓，提示存在“阈值效应”，即MetS风险不再随eGDR升高而显著降低[25]。作为IR的有效反映指标，eGDR与MetS的关联可通过IR相关机制佐证：IR一方面损害脂肪细胞糖吸收及储脂能力，导致FFAs升高，诱发代偿性高胰岛素血症并损伤胰岛β细胞[27]；另一方面通过NO减少、ROS增多引发血管内皮功能障碍，结合交感神经激活及肾素介导的钠潴留加重血管损伤，促进高血压发生[28-30]。
值得注意的是，本研究未发现eGDR与职业危险因素对MetS存在显著交互作用，提示职业危险因素的作用路径可能不依赖于IR（eGDR反映）通路。丹麦儿童研究未发现环境暴露与IR相关[31]，中国宁夏研究亦表明职业紧张通过影响HSP70基因、诱导转氨酶升高参与MetS发病，其通路独立于eGDR[32]。据此推测，粉尘、噪声、职业紧张等职业因素与eGDR对MetS的作用相互独立，其具体关联仍需进一步验证。
本研究也存在一定局限性。一是横断面研究设计无法明确eGDR、IR与MetS的因果关系；二是饮食、烟酒等变量调查不精确，遗传史、既往病史等残余混杂因素未完全排除；三是样本仅来自山西煤矿职工，代表性有限，结果外推需谨慎；四是未发现eGDR与MetS关联的职业特异性差异，可能受残余混杂及未观察长期关联影响。上述局限性需通过未来前瞻性队列研究解决，且重体力活动改善胰岛素功能及健康工人效应，也可能是特异性缺乏的原因[33-34]。
综上，在煤矿工人群体中eGDR与Mets存在显著关联，且呈非线性负相关剂量反应关系；暂未发现职业因素交互作用。
基金
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  • 广州市科技计划项目(2025A03J3722)
文献
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doi: 10.20043/j.cnki.MPM.202511026
  • 接收时间：2025-11-03
  • 首发时间：2026-11-02
  • 出版时间：2026-04-25
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  • 收稿日期：2025-11-03
基金
广州市科技计划项目(2025A03J3722)
作者信息
    1.广东药科大学公共卫生学院流行病与卫生统计学系，广东　广州　510000
    2.西山煤电（集团）有限责任公司职业病防治所
    3.山西医科大学公共卫生学院

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