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Article(id=1241022951592612458, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241022939957621542, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202410252, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1729094400000, receivedDateStr=2024-10-17, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773812528751, onlineDateStr=2026-03-18, pubDate=1742832000000, pubDateStr=2025-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773812528751, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773812528751, creator=13701087609, updateTime=1773812528751, updator=13701087609, issue=Issue{id=1241022939957621542, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='6', pageStart='961', pageEnd='1152', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773812525976, creator=13701087609, updateTime=1773815469296, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241035285174219432, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241022939957621542, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241035285174219433, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241022939957621542, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1024, endPage=1030, ext={EN=ArticleExt(id=1241022952095928980, articleId=1241022951592612458, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Role of lipid metabolism in the association of atmospheric PM2.5 and NO2 with type 2 diabetes, columnId=1228016570660745413, journalTitle=Modern Preventive Medicine, columnName=Environmental and Occupational Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the association between long-term exposure to atmospheric pollutants (PM2.5 and NO2) and the risk of developing type 2 diabetes, and the mediating role of abnormal lipid metabolism in the association.

Methods

Based on a China Multi-Ethnic Cohort and data collected from 2018—2019 on 15 573 participants in Sichuan Province and hospital discharge data in Sichuan Province from 2018—2022. Logistic regression and Cox proportional risk model were used to investigate the two-by-two associations between PM2.5 and NO2, dyslipidemia, and type 2 diabetes; regression-based causal mediator model was used to explore the mediating role of dyslipidemia in the association between PM2.5 and NO2 and type 2 diabetes.

Results

For each 1 standard deviation (SD) increase in atmospheric PM2.5 and NO2 concentrations, the population Odds ratio (OR) (95% CI)for dyslipidemia was 1.042 (1.002-1.084) and 1.047 (1.003-1.093), respectively and the Hazard ratio (HR)(95% CI) for developing type 2 diabetes was 1.159 (1.044-1.288) and 1.330 (1.173-1.509), respectively. Patients with dyslipidemia had a higher risk of developing type 2 diabetes with a HR(95% CI) of 1.777 (1.418-2.227). Dyslipidemia partially mediated the association of chronic exposure to PM2.5 and NO2 on developing type 2 diabetes mellitus, with natural indirect effects HR (95% CI) of 1.004 (1.000 - 1.008) and 1.005 (1.000 - 1.010), respectively, corresponding to 3.1% and 2.0% of the total effect, respectively.

Conclusion

Long-term exposure to PM2.5 and NO2 was positively associated with dyslipidemia and increased risk of type 2 diabetes, and dyslipidemia partially mediated the association of air pollution on the risk of type 2 diabetes.

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目的

探讨大气污染物（PM2.5和NO2）长期暴露与2型糖尿病发病风险的关联，以及血脂代谢异常在关联中的作用机制。

方法

基于2018—2019年中国西南区域自然人群队列四川地区的15 573名参与者的基线调查数据以及2018—2022年四川省医院病案首页数据。采用logistic回归和Cox比例风险模型研究PM2.5和NO2、血脂异常、2型糖尿病之间的两两关联关系；采用基于回归的因果中介模型探讨血脂代谢异常在PM2.5和NO2与2型糖尿病关联之间的中介作用。

结果

大气PM2.5和NO2浓度每增加1个标准差(s)，人群血脂异常OR (95% CI)分别为1.042（1.002~1.084）和1.047（1.003~1.093），患2型糖尿病HR(95% CI)分别为1.159（1.044~1.288）和1.330（1.173~1.509）。血脂异常者患2型糖尿病的风险较高，其HR(95% CI)为1.777（1.418~2.227）。血脂异常在PM2.5和NO2长期暴露对患2型糖尿病关联中起部分中介作用，自然间接效应HR(95% CI)分别为1.004（1.000~1.008）和1.005（1.000~1.010），对应的自然间接效应分别占总效应的3.1%和2.0%。

结论

PM2.5和NO2长期暴露与血脂异常和2型糖尿病发病风险增加呈正相关，血脂异常在大气污染对2型糖尿病发病风险的关联中起部分中介作用。

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许欢，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=hH3t5pHgno58NBOIwomfYA==, magXml=Y8YXc+ngcz59Ne2G6F3D0g==, pdfUrl=null, pdf=cr7R3sOx2vDQS19YdJVQSw==, pdfFileSize=785125, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=XeQhNLM21LVL/eRxDXkL6A==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=qRE5OnYLjRyIWmsvfiX0Lw==, mapNumber=null, authorCompany=null, fund=null, authors=

唐钰铃（1999—），女，硕士在读，研究方向：流行病与卫生统计学

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唐钰铃（1999—），女，硕士在读，研究方向：流行病与卫生统计学

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注：图A为PM2.5长期暴露-血脂异常-新发2型糖尿病的中介效应；图B为NO2长期暴露-血脂异常-新发2型糖尿病的中介效应。

, figureFileSmall=O3UJcsn0UtdLr06DVJV9GQ==, figureFileBig=BNBzL1Uxen2th8lWvIX9Ew==, tableContent=null), ArticleFig(id=1241022962615243012, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022951592612458, language=EN, label=Table 1, caption=

Baseline characteristics and long-term exposure to air pollution levels of participants

, figureFileSmall=null, figureFileBig=null, tableContent=
变量参与者(n=15 573)2型糖尿病发病
是(n=320)否(n=15 253)
年龄（岁）50.23±12.1459.68±11.4750.04±12.08
性别
男6 712(43.1)179(55.9)6 533(42.8)
女8 861(56.9)141(44.1)8 720(57.2)
居住地
城市8 431(54.1)174(54.4)8 257(54.1)
农村7 142(45.9)146(45.6)6 996(45.9)
教育程度
本科以上学历1 082(6.9)7(2.2)1 075(7.0)
大专1 848(11.9)19(5.9)1 829(12.0)
高中学历2 756(17.7)45(14.1)2 711(17.8)
初中5 010(32.2)91(28.4)4 919(32.2)
小学3 032(19.5)90(28.1)2 942(19.3)
文盲1 845(11.8)68(21.2)1 777(11.7)
家庭年收入（元）
<12 0001 451(9.3)40(12.5)1 411(9.3)
12 000~19 9991 814(11.6)55(17.2)1 759(11.5)
20 000~59 9995 805(37.3)123(38.4)5 682(37.3)
60 000~99 9993 202(20.6)60(18.8)3 142(20.6)
≥100 0003 301(21.2)42(13.1)3 259(21.4)
吸烟状况
从不吸烟10 688(68.6)181(56.6)10 507(68.9)
戒烟1 020(6.5)39(12.2)981(6.4)
当前吸烟3 865(24.8)100(31.2)3 765(24.7)
饮酒状况
从不饮酒7 396(47.5)190(59.4)7 206(47.2)
偶尔饮酒5 276(33.9)66(20.6)5 210(34.2)
经常饮酒2 901(18.6)64(20.0)2 837(18.6)
DASH得分21.99±4.3721.00±4.6322.01±4.37
体力活动（METs/d）21.96±14.9918.40±15.7722.03±14.97
BMI(kg/m2)24.13±3.2726.08±3.9624.09±3.24
是否患高血压
否11 464(73.6)145(45.3)11 319(74.2)
是4 109(26.4)175(54.7)3 934(25.8)
3年平均大气PM2.5暴露（μg/m3）60.76±7.8760.84±7.5260.76±7.88
3年平均大气NO2暴露（μg/m3）32.34±8.7732.88±8.5532.33±8.78
是否血脂异常
否11 555(74.2)178(55.6)11 377(74.6)
是4 018(25.8)142(44.4)3 876(25.4)
TC(mmol/L)4.91±1.035.07±1.054.90±1.02
TG(mmol/L)1.64±1.542.20±1.811.62±1.53
HDL-C(mmol/L)1.35±0.311.29±0.331.35±0.31
LDL-C(mmol/L)3.02±0.703.14±0.773.02±0.70
), ArticleFig(id=1241022962745266439, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022951592612458, language=CN, label=表1, caption=

本研究参与者的基线特征和大气污染长期暴露水平

, figureFileSmall=null, figureFileBig=null, tableContent=
变量参与者(n=15 573)2型糖尿病发病
是(n=320)否(n=15 253)
年龄（岁）50.23±12.1459.68±11.4750.04±12.08
性别
男6 712(43.1)179(55.9)6 533(42.8)
女8 861(56.9)141(44.1)8 720(57.2)
居住地
城市8 431(54.1)174(54.4)8 257(54.1)
农村7 142(45.9)146(45.6)6 996(45.9)
教育程度
本科以上学历1 082(6.9)7(2.2)1 075(7.0)
大专1 848(11.9)19(5.9)1 829(12.0)
高中学历2 756(17.7)45(14.1)2 711(17.8)
初中5 010(32.2)91(28.4)4 919(32.2)
小学3 032(19.5)90(28.1)2 942(19.3)
文盲1 845(11.8)68(21.2)1 777(11.7)
家庭年收入（元）
<12 0001 451(9.3)40(12.5)1 411(9.3)
12 000~19 9991 814(11.6)55(17.2)1 759(11.5)
20 000~59 9995 805(37.3)123(38.4)5 682(37.3)
60 000~99 9993 202(20.6)60(18.8)3 142(20.6)
≥100 0003 301(21.2)42(13.1)3 259(21.4)
吸烟状况
从不吸烟10 688(68.6)181(56.6)10 507(68.9)
戒烟1 020(6.5)39(12.2)981(6.4)
当前吸烟3 865(24.8)100(31.2)3 765(24.7)
饮酒状况
从不饮酒7 396(47.5)190(59.4)7 206(47.2)
偶尔饮酒5 276(33.9)66(20.6)5 210(34.2)
经常饮酒2 901(18.6)64(20.0)2 837(18.6)
DASH得分21.99±4.3721.00±4.6322.01±4.37
体力活动（METs/d）21.96±14.9918.40±15.7722.03±14.97
BMI(kg/m2)24.13±3.2726.08±3.9624.09±3.24
是否患高血压
否11 464(73.6)145(45.3)11 319(74.2)
是4 109(26.4)175(54.7)3 934(25.8)
3年平均大气PM2.5暴露（μg/m3）60.76±7.8760.84±7.5260.76±7.88
3年平均大气NO2暴露（μg/m3）32.34±8.7732.88±8.5532.33±8.78
是否血脂异常
否11 555(74.2)178(55.6)11 377(74.6)
是4 018(25.8)142(44.4)3 876(25.4)
TC(mmol/L)4.91±1.035.07±1.054.90±1.02
TG(mmol/L)1.64±1.542.20±1.811.62±1.53
HDL-C(mmol/L)1.35±0.311.29±0.331.35±0.31
LDL-C(mmol/L)3.02±0.703.14±0.773.02±0.70
), ArticleFig(id=1241022962917232911, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022951592612458, language=EN, label=Table 2, caption=

Pairwise association between air pollution, dyslipidemia, and type 2 diabetes

, figureFileSmall=null, figureFileBig=null, tableContent=
关联OR/HR (95% CI)
暴露 – 中介
大气PM2.5长期暴露 – 血脂异常1.042（1.002~1.084）a
大气NO2长期暴露 – 血脂异常1.047（1.003~1.093）a
暴露 – 结局
大气PM2.5长期暴露 – 新发2型糖尿病1.159（1.044~1.288）b
大气NO2长期暴露 – 新发2型糖尿病1.330（1.173~1.509）b
中介 – 结局
血脂异常 – 新发2型糖尿病1.777（1.418~2.227）b
), ArticleFig(id=1241022963013701906, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022951592612458, language=CN, label=表2, caption=

大气污染长期暴露、血脂异常和新发2型糖尿病之间的两两关联结果

, figureFileSmall=null, figureFileBig=null, tableContent=
关联OR/HR (95% CI)
暴露 – 中介
大气PM2.5长期暴露 – 血脂异常1.042（1.002~1.084）a
大气NO2长期暴露 – 血脂异常1.047（1.003~1.093）a
暴露 – 结局
大气PM2.5长期暴露 – 新发2型糖尿病1.159（1.044~1.288）b
大气NO2长期暴露 – 新发2型糖尿病1.330（1.173~1.509）b
中介 – 结局
血脂异常 – 新发2型糖尿病1.777（1.418~2.227）b
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血脂代谢异常在大气PM2.5和NO2与2型糖尿病关联中的中介作用研究
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唐钰铃 1 , 程盼 1 , 谭坤 2 , 韩旭 2 , 张菊英 1 , 郭冰 1 , 刘元元 1 , 邓燕 3 , 许欢 1 , 赵星 1
现代预防医学 | 环境与职业卫生 2025,52(6): 1024-1030
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现代预防医学 | 环境与职业卫生 2025, 52(6): 1024-1030
血脂代谢异常在大气PM2.5和NO2与2型糖尿病关联中的中介作用研究
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唐钰铃1, 程盼1, 谭坤2, 韩旭2, 张菊英1, 郭冰1, 刘元元1, 邓燕3, 许欢1 , 赵星1
作者信息
  • 1．四川大学华西公共卫生学院/四川大学华西第四医院，流行病与卫生统计学系，四川　成都　610041
  • 2．四川省卫生信息中心/四川省医疗大数据中心
  • 3．四川省人民医院（电子科技大学附属医院）超声心脏电生理学与生物力学四川省重点实验室

    • 唐钰铃（1999—），女，硕士在读，研究方向：流行病与卫生统计学

通讯作者:

许欢，E-mail:
Role of lipid metabolism in the association of atmospheric PM2.5 and NO2 with type 2 diabetes
Yu-ling TANG1, Pan CHENG1, Kun TAN2, Xu HAN2, Ju-ying ZHANG1, Bing GUO1, Yuan-yuan LIU1, Yan DENG3, Huan XU1 , Xing ZHAO1
Affiliations
  • Department of Epidemiology and Health Statistics, West China School of Public Health, Sichuan University/West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2025-03-25 doi: 10.20043/j.cnki.MPM.202410252
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摘要
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目的

探讨大气污染物（PM2.5和NO2）长期暴露与2型糖尿病发病风险的关联，以及血脂代谢异常在关联中的作用机制。

方法

基于2018—2019年中国西南区域自然人群队列四川地区的15 573名参与者的基线调查数据以及2018—2022年四川省医院病案首页数据。采用logistic回归和Cox比例风险模型研究PM2.5和NO2、血脂异常、2型糖尿病之间的两两关联关系；采用基于回归的因果中介模型探讨血脂代谢异常在PM2.5和NO2与2型糖尿病关联之间的中介作用。

结果

大气PM2.5和NO2浓度每增加1个标准差(s)，人群血脂异常OR (95% CI)分别为1.042（1.002~1.084）和1.047（1.003~1.093），患2型糖尿病HR(95% CI)分别为1.159（1.044~1.288）和1.330（1.173~1.509）。血脂异常者患2型糖尿病的风险较高，其HR(95% CI)为1.777（1.418~2.227）。血脂异常在PM2.5和NO2长期暴露对患2型糖尿病关联中起部分中介作用，自然间接效应HR(95% CI)分别为1.004（1.000~1.008）和1.005（1.000~1.010），对应的自然间接效应分别占总效应的3.1%和2.0%。

结论

PM2.5和NO2长期暴露与血脂异常和2型糖尿病发病风险增加呈正相关，血脂异常在大气污染对2型糖尿病发病风险的关联中起部分中介作用。

2型糖尿病  /  空气污染  /  血脂异常  /  中介分析
Objective

To investigate the association between long-term exposure to atmospheric pollutants (PM2.5 and NO2) and the risk of developing type 2 diabetes, and the mediating role of abnormal lipid metabolism in the association.

Methods

Based on a China Multi-Ethnic Cohort and data collected from 2018—2019 on 15 573 participants in Sichuan Province and hospital discharge data in Sichuan Province from 2018—2022. Logistic regression and Cox proportional risk model were used to investigate the two-by-two associations between PM2.5 and NO2, dyslipidemia, and type 2 diabetes; regression-based causal mediator model was used to explore the mediating role of dyslipidemia in the association between PM2.5 and NO2 and type 2 diabetes.

Results

For each 1 standard deviation (SD) increase in atmospheric PM2.5 and NO2 concentrations, the population Odds ratio (OR) (95% CI)for dyslipidemia was 1.042 (1.002-1.084) and 1.047 (1.003-1.093), respectively and the Hazard ratio (HR)(95% CI) for developing type 2 diabetes was 1.159 (1.044-1.288) and 1.330 (1.173-1.509), respectively. Patients with dyslipidemia had a higher risk of developing type 2 diabetes with a HR(95% CI) of 1.777 (1.418-2.227). Dyslipidemia partially mediated the association of chronic exposure to PM2.5 and NO2 on developing type 2 diabetes mellitus, with natural indirect effects HR (95% CI) of 1.004 (1.000 - 1.008) and 1.005 (1.000 - 1.010), respectively, corresponding to 3.1% and 2.0% of the total effect, respectively.

Conclusion

Long-term exposure to PM2.5 and NO2 was positively associated with dyslipidemia and increased risk of type 2 diabetes, and dyslipidemia partially mediated the association of air pollution on the risk of type 2 diabetes.

Type 2 diabetes  /  Air pollution  /  Lipid metabolism  /  Mediation analysis
唐钰铃, 程盼, 谭坤, 韩旭, 张菊英, 郭冰, 刘元元, 邓燕, 许欢, 赵星. 血脂代谢异常在大气PM2.5和NO2与2型糖尿病关联中的中介作用研究. 现代预防医学, 2025 , 52 (6) : 1024 -1030 . DOI: 10.20043/j.cnki.MPM.202410252
Yu-ling TANG, Pan CHENG, Kun TAN, Xu HAN, Ju-ying ZHANG, Bing GUO, Yuan-yuan LIU, Yan DENG, Huan XU, Xing ZHAO. Role of lipid metabolism in the association of atmospheric PM2.5 and NO2 with type 2 diabetes[J]. Modern Preventive Medicine, 2025 , 52 (6) : 1024 -1030 . DOI: 10.20043/j.cnki.MPM.202410252
正文
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糖尿病已经成为我国重大公共卫生挑战，给社会带来了沉重的负担。2021年，我国糖尿病患病超1.17亿人，约有17万人死于糖尿病，其患病率和疾病负担一直处于上升趋势[1]。因此，识别糖尿病的危险因素与致病机制，并加强预防措施以延缓其进程，具有重要意义。
越来越多的证据表明，接触空气污染是导致 2 型糖尿病的一个新的重要风险因素[2]。然而，在观察性研究和前瞻性研究中，关于空气污染与 2 型糖尿病之间关系的证据仍然存在争议。PM2.5和NO2是四川地区重要的大气污染物[3]，也是较为典型的颗粒物和气态大气污染物，其来源广泛，是交通与工业燃烧排放的重要标志性污染物，其浓度变化可有效反映区域污染特征[4]。其中PM2.5是多种有害物质组成的复杂混合物，NO2是次生污染物形成的关键前体物[3]，二者常共同作用，加剧空气污染问题，是空气质量监测与治理的核心指标。以往的研究已广泛证实PM2.5和NO2与呼吸系统和心血管疾病密切相关[5]。本研究探讨大气污染物PM2.5和NO2的长期暴露与2型糖尿病之间的前瞻性关联，将为未来降低2型糖尿病风险的干预措施提供新的可能性。
血脂是血浆中脂质的统称，其浓度水平可反映体内脂质的代谢状况，传统的血脂指标主要包括总胆固醇（Total cholesterol, TC）、甘油三酯（Triglyceride, TG）、高密度脂蛋白胆固醇（High density lipoprotein cholesterol, HDL-C）和低密度脂蛋白胆固醇（Low density lipoprotein cholesterol, LDL-C）。血浆脂质水平的改变反映了脂质代谢的变化。脂质在外周组织和肝脏之间的异常运输会影响组织中脂质的生物合成和降解，这与许多疾病的发生有关[6-7]。现有研究提示，脂质代谢可能参与了大气污染暴露与2型糖尿病之间关联的潜在途径。一方面，大气污染物可能会通过炎症反应和氧化应激干扰脂质代谢[8]。另一方面，实验研究表明，血脂变化可能通过影响胰岛素抵抗，进而干扰β细胞功能、胰岛素分泌和葡萄糖稳态，导致糖尿病的发生[9-10]。然而，在我国仍缺乏人群流行病学的证据验证这一潜在关联通路。
综上，本研究基于中国西南区域自然人群队列（China multi-ethnic cohort, CMEC）四川地区的前瞻性数据，探讨四川省大气PM2.5和NO2长期暴露与2型糖尿病发病风险的关联，以及脂代谢异常在这一关联中的介导作用。研究结果在一定程度上可为大气污染所致2型糖尿病的发生机制及防治提供新的思路。
1　资料与方法
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1.1　研究对象
研究对象信息来源于国家重点研发计划CMEC队列（后简称西南队列）的年龄在30~79岁的参与者的基线调查数据[11]。基线调查于2018年5月至2019年9月期间以多阶段整群抽样的方法，收集了每位参与者面对面的问卷访谈、体检和临床实验室检查信息。每阶段均采取了严格的质控措施。本研究的纳入排除标准如下：纳入标准：①四川地区人群；②调查当天年龄30~79岁的参与者；③永久居民，具有完成基线调查和后续研究的能力；④完成问卷访谈、体格检查和血液检查者。排除标准如下：①无法提供唯一的中国居民身份证者；②患有严重的身体或精神疾病（例如精神分裂症和双相情感障碍）者；③拒绝遵守研究要求者；④在当地居住时间小于3年者；⑤来自拉萨和阿坝的藏族参与者（他们是没有固定住所的游牧民族，生活在高海拔的缺氧环境中，可能会改变自主神经系统和内分泌功能）；⑥基线前三年患有高脂血症或基线时患有2型糖尿病的参与者。详细的纳排流程见图1。最后，共有15 573名参与者纳入研究。本研究经四川大学伦理委员会审查并批准（审批号：K2016038和K2020022），所有调查对象均在参与调查前签署了知情同意书。
1.2　主要暴露与结局变量定义
1.2.1　空气污染
大气污染数据来源于China High Air Pollutants数据库（详情参见：https://weijing-rs.github.io/product.html）。该数据库的日均PM2.5和NO2浓度估算方法在以往研究中已有描述[12-14]。本研究的主要暴露为大气PM2.5和NO2的长期暴露水平，利用参与者居住地调查前3年的PM2.5和NO2的平均暴露浓度（单位：μg/m3）作为个体大气污染长期暴露估计的替代指标。
1.2.2　血脂指标
在基线调查时，由训练有素的操作人员使用经过校准的AU5800自动化化学分析仪（贝克曼库尔特商业企业，中国上海）测量8小时以上空腹后采集的血样。本研究主要感兴趣的中介因子为是否出现血脂异常，即满足以下任一条件：TC ≥ 6.2 mmol/L, TG ≥ 2.3 mmol/L，HDL-C < 1.0 mmol/L，LDL-C ≥ 4.1 mmol/L[15]。
1.2.3　2型糖尿病
本研究感兴趣的结局变量为是否有新发2型糖尿病，结局资料来源于2018年1月1日至2022年12月31日四川省管辖范围医院病案首页记录中的入院诊断的疾病代码及入院时间。疾病代码编码规则基于国际疾病分类第10版（The 10th edition of the international Classification of diseases, ICD-10），2型糖尿病的诊断代码为E11。从基线调查的日期开始计算个人年随访时间，直至出现2型糖尿病首次发生、死亡、失访或随访截止日期（2022年12月31日）中的最早一项。
1.3　关键协变量定义
参考以往文献[16-20]，本研究调整的混杂协变量包括年龄（连续变量）、性别（男、女）、居住地（城市、农村）、教育程度（本科以上学历、大专、高中学历、初中、小学和文盲）、家庭年收入（<12 000元、12 000~19 999元、20 000~59 999元、60 000~99 999元以及≥10 0000元）、吸烟状况（从不吸烟、戒烟以及当前吸烟）、饮酒状况（从不饮酒、偶尔饮酒以及经常饮酒）、体力活动（连续变量）、DASH（Dietary approaches to stop hypertension）得分（连续变量）、BMI（Body mass index）（连续变量）和是否为高血压（是、否）。
其中，体力活动以代谢当量（Metabolic equivalents, MET）代替。MET值表示与安静坐着相比，进行任一体力活动时的代谢率。健康饮食指数依据食物频率问卷，为每位参与者计算出DASH得分来衡量。该评分涵盖七类食物：蔬菜、新鲜水果、豆类、全谷类、红肉及加工肉品、乳制品以及钠盐。每类食物按照其平均摄入量的五分位数进行评分，分值范围为1~5分。将各类食物评分汇总，得到DASH总评分，其范围介于7至35分之间。
1.4　统计分析
1.4.1　关联研究
描述性分析基于是否患2型糖尿病的两组人群，描述了其人口统计学特征、健康相关的生活习惯、身体健康状况、血脂水平以及大气污染长期暴露水平。对连续变量，采用均数±标准差的形式来展示其分布情况，而分类变量则以频数（百分比）形式呈现。
关联研究采用logistic回归模型探讨长期暴露于大气污染物与血脂异常之间的关联。同时，采用Cox比例风险模型评估长期暴露于大气污染物与新发2型糖尿病，以及血脂异常与新发2型糖尿病之间的关联。为了验证比例风险假设，本研究采用了Schoenfeld残差图进行评估，并确认了该假设的适用性。所有模型均对混杂协变量进行了调整。
1.4.2　中介分析
中介分析采用了VanderWeele提出的基于回归的因果中介模型[21]。其模型形式如下：假设E为大气污染长期暴露浓度，M为血脂异常（二分类变量），t为2型糖尿病发生的随访时间，λT(t)为给定时间t的风险，C为可能影响暴露-结局、中介-结局和暴露-中介关联的混杂协变量集合。中介模型（logistic回归模型）和结局模型（Cox比例风险模型）如下所示：
在当前研究中，结局是罕见事件（2型糖尿病发生率<10%）。因此，在HR尺度上的自然间接效应（Natural indirect effect, NIE）（即中介效应）和自然直接效应（Natural direct effect, NDE）如下：
因果效应的标准误是通过自助法（Bootstrap method）获得（抽样次数设置为1 000次）。因果效应的置信区间是通过自助法结果的百分位数获得的。
1.4.3　软件及检验水准
本研究的统计分析过程均通过R软件（4.1.3版）实现，并采用双侧检验，检验水准α=0.05，中介分析采用了R软件包“CMAverse”[22]。
2　结果
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2.1　描述性分析
研究共纳入15 573名研究对象，如表1所示。在基线未发生2型糖尿病的研究对象中，随访期间共发生320例（2.0%）新发2型糖尿病，平均随访时间为4.23年。相比于未发生2型糖尿病的患者，2型糖尿病患者有较大的年龄、较低的教育程度和家庭年收入、更大概率吸烟、DASH健康膳食模式的依从性较低、较少的体力活动和较大的BMI指数。
2.2　关联和中介分析
两两关联的探索性分析结果见表2。大气PM2.5浓度和NO2浓度每增加1个标准差（s），人群血脂异常的风险增加，其OR（95% CI）分别为1.042（1.002~1.084）和1.047（1.003~1.093）。在暴露-结局关联中，大气PM2.5浓度和NO2浓度每增加1个s，人群新发2型糖尿病的风险增加，其HR（95% CI）分别为1.159（1.044~1.288）和1.330（1.173~1.509）。此外，在中介-结局关联中，本研究发现出现血脂异常的患者其新发2型糖尿病的风险较高，其HR为1.777（1.418~2.227）。
中介分析的结果见图2。血脂异常在大气PM2.5和NO2长期暴露对新发2型糖尿病关联中的中介效应HR (95% CI)分别为1.004（1.000~1.008）和1.005（1.000~1.010），其对应P值分别为0.035和0.047。
3　讨论
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在这项基于西南队列的四川地区研究中，我们发现PM2.5和NO2长期暴露与血脂异常和2型糖尿病发病风险增加呈正相关。中介效应分析提示，血脂异常在大气污染长期暴露对2型糖尿病发病风险的正向影响中起部分中介作用。该研究结果提供了脂代谢可能是大气污染物与2型糖尿病发病关联的潜在机制的人群流行病学证据。
长期暴露于PM2.5与糖尿病发病之间的正向关联与先前的研究相一致[2,16]。最新一项meta分析[2]汇总了2019年1月以前的有关大气污染长期暴露与2型糖尿病发病率关联的研究。结果显示，较高的PM2.5暴露水平与较高的2型糖尿病发病率呈现出统计学意义的相关，其PM2.5浓度每增加10 μg/m3，HR（95% CI）为1.10（1.04~1.16），而NO2与2型糖尿病发病的关联无统计学意义。本研究同时观察到了长期暴露于PM2.5和NO2与2型糖尿病发病风险的正向关联。此外，本研究还观察到了大气污染长期暴露与血脂异常风险之间的正向关联，相关研究结果与本研究发现是一致的[23-24]。
本研究聚焦于血脂代谢异常的潜在中介作用，结果提示血脂代谢异常在大气PM2.5和NO2长期暴露与2型糖尿病发病的关联中发挥了中介作用。既往生物学证据表明血脂代谢异常可能是大气污染长期暴露与2型糖尿病发病路径中的潜在中介途径。吸入的颗粒物可以诱导促炎介质、氧化应激以及自主神经系统的破坏，从而引起相关血脂谱变化[25-26]，如TC和LDL-C水平升高，以及HDL-C水平下降，影响了胆固醇的运输，继而促进了胰岛素抵抗。例如，HDL水平降低是胰岛素抵抗的代谢表现，因为HDL颗粒可能刺激骨骼肌和脂肪细胞的葡萄糖摄取，并直接改善β细胞功能和生存[27-28]。此外，脂蛋白和脂质也可能通过直接影响β细胞功能、胰岛素产生和葡萄糖稳态从而促进糖尿病的发生发展[9-10]。
本研究存在一定的局限性，首先，本研究的大气污染数据来源于住址的平均浓度，研究对象并非一直处于居住地址，可能由于工作、旅行等目的导致个体暴露存在误差，本研究纳入住址居住超过三年的研究对象，并通过日均污染物浓度计算三年平均个体暴露，可认为暴露水平误差在允许范围内。第二，研究的基线协变量来源于问卷，可能存在回忆偏倚。第三，本研究为观察性研究，存在一定的测量偏倚和混杂偏倚。
综上所述，通过控制空气污染，预防血脂异常或在血脂异常人群中开展干预措施，可以更好的降低2型糖尿病患病风险，提升人群生活质量。
基金
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  • 国家自然科学基金(81973151; 82103943; 82073667)
  • 四川省自然科学基金项目(2023NSFSC0038)
文献
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doi: 10.20043/j.cnki.MPM.202410252
  • 接收时间：2024-10-17
  • 首发时间：2026-03-18
  • 出版时间：2025-03-25
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  • 收稿日期：2024-10-17
基金
国家自然科学基金(81973151; 82103943; 82073667)
四川省自然科学基金项目(2023NSFSC0038)
作者信息
    1．四川大学华西公共卫生学院/四川大学华西第四医院，流行病与卫生统计学系，四川　成都　610041
    2．四川省卫生信息中心/四川省医疗大数据中心
    3．四川省人民医院（电子科技大学附属医院）超声心脏电生理学与生物力学四川省重点实验室

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