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Article(id=1322590092368139223, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202410106, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1728489600000, receivedDateStr=2024-10-10, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793259650556, onlineDateStr=2026-10-29, pubDate=1736438400000, pubDateStr=2025-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793259650556, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793259650556, creator=13701087609, updateTime=1793259650556, updator=13701087609, issue=Issue{id=1322589954702688372, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='1', pageStart='1', pageEnd='192', issueExtLink='null', onlineDate='null', pubDate='1736438400000', pubDateStr='2025-01-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793259617734, creator='13701087609', updateTime=1793260023378, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322591656147276425, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322591656147276426, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=155, endPage=159, ext={EN=ArticleExt(id=1322590093756453848, articleId=1322590092368139223, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Study on the health benefits of ozone simulation interventions for diabetes based on a prospective cohort, columnId=1228016573156360233, journalTitle=Modern Preventive Medicine, columnName=Disease Control and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To quantify the health benefits of interventions targeting ozone exposure on diabetes prevalence under different strategies, providing scientific support for policies aimed at reducing atmospheric ozone levels to mitigate diabetes.

Methods

Utilizing questionnaire, physical examination data, and atmospheric data from a cohort of 16 607 participants in Chengdu, Southwest China, we employed a Logistic regression model to explore the association between ozone and diabetes risk. We simulated ratio interventions, fixed-value interventions, and threshold interventions for ozone concentration using the parametric G formula method to quantify the reduction in diabetes prevalence under each intervention strategy.

Results

An increase of 1 mg/m3 in ozone corresponded to an odds ratio (OR) of 1.07 (95%CI: 1.01-1.13). Reducing ozone concentration by 5%, controlling it at P50 (85.20 mg/m3), or lowering it to P75(86.12 mg/m3) or below could respectively reduce diabetes prevalence by 27.38% (23.47%-31.28%), 8.35% (4.01%-12.70%), and 10.50% (6.00%-15.01%).

Conclusion

Various strategies to intervene on ozone concentration can reduce diabetes prevalence, with ratio interventions showing the most significant effectiveness. Future measures should consider local ozone levels and comprehensively account for seasonal variations, regional differences, and pollution sources to implement interventions aimed at percentage reductions in overall ozone concentration, thereby reducing diabetes risk.

, authors=Yin-ling BAI, Shao-kun YANG, Bing GUO, Xing ZHAO, Gong-hua WU, Ju-ying ZHANG, authorsList=Yin-ling BAI, Shao-kun YANG, Bing GUO, Xing ZHAO, Gong-hua WU, Ju-ying ZHANG, authorCompany=null, correspAuthors=Gong-hua WU, 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=1322590098680566766, articleId=1322590092368139223, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=基于前瞻性队列的臭氧模拟干预对糖尿病的健康收益研究, columnId=1228016573844226129, journalTitle=现代预防医学, columnName=疾病预防控制, runingTitle=null, highlight=null, articleAbstract=
目的

量化不同策略下干预臭氧对糖尿病患病的健康收益，为降低大气臭氧浓度以减少糖尿病的政策提供科学支持。

方法

基于西南区域自然人群队列基线的16 607名成都市研究对象的问卷、体检数据和大气数据，使用logistic回归模型探究臭氧与糖尿病患病风险的关联，并采用参数G formula法模拟比值干预、定值干预和阈值干预臭氧浓度，量化各项干预策略下糖尿病患病率的降低程度。

结果

臭氧每增加1 g/m3对应OR(95%CI)为1.07 (1.01～1.13)，将臭氧浓度降低5%、控制在P50 (85.20 g/m3)或降低至P75 (86.12 g/m3)及以下可分别减少27.38% (23.47%～31.28%)、8.35%(4.01%～12.70%)和10.50% (6.00%～15.01%)的糖尿病患病。

结论

使用各种策略干预臭氧浓度均可减少糖尿病，其中比值干预效果较好，未来应基于当地臭氧浓度，综合考虑不同季节、地区和污染来源采取干预措施，按百分比降低整体的臭氧浓度，改善糖尿病患病风险。

, authors=白银铃, 杨绍坤, 郭冰, 赵星, 吴功华, 张菊英, authorsList=白银铃, 杨绍坤, 郭冰, 赵星, 吴功华, 张菊英, authorCompany=null, correspAuthors=吴功华, authorNote=

白银铃（1998—），女，硕士在读，研究方向：卫生统计方法与应用

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吴功华，E-mail:
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Science of the Total Environment, 2019, 674: 189-199., articleTitle=Spatial-temporal variations and source contributions to forest ozone exposure in China, refAbstract=null), Reference(id=1322590107920617526, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322590092368139223, doi=null, pmid=null, pmcid=null, year=2022, volume=72, issue=8, pageStart=876, pageEnd=894, url=null, language=null, rfNumber=[22], rfOrder=21, authorNames=Wang XJ, Ling C, Ke G, journalName=Journal of the Air & Waste Management Association, refType=null, unstructuredReference=Wang XJ, Ling C, Ke G, et al. Spatio-temporal trajectory evolution and cause analysis of air pollution in Chengdu, China[J]. 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Baseline characteristics [（），n (%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
基本特征总人数（n=16 607）糖尿病P值
未发病（n=15 153）发病（n=1 454）
年龄(岁)51.00±12.1450.35±12.0357.80±11.21<0.001
性别<0.001
男性7 303 (44.0)6 472 (42.7)831 (57.2)
女性9 304 (56.0)8 681 (57.3)623 (42.8)
家庭年收入(元)<0.001
<12 0001 569 (9.4)1 401 (9.2)168 (11.6)
12 000～19 9991 958 (11.8)1 763 (11.6)195 (13.4)
20 000～59 9996 175 (37.2)5 585 (36.9)590 (40.6)
60 000～99 9993 430 (20.7)3 153 (20.8)277 (19.1)
≥100 0003 475 (20.9)3 251 (21.5)224 (15.4)
教育水平<0.001
文盲2 008 (12.1)1 744 (11.5)264 (18.2)
小学3 254 (19.6)2 903 (19.2)351 (24.1)
初中5 347 (32.2)4 885 (32.2)462 (31.8)
高中2 931 (17.6)2 705 (17.9)226 (15.5)
大学及以上3 067 (18.5)2 916 (19.2)151 (10.4)
婚姻状况<0.001
已婚15 016 (90.4)13 705 (90.4)1 311 (90.2)
丧偶689 ( 4.1)646 ( 4.3)43 ( 3.0)
离异707 ( 4.3)616 ( 4.1)91 ( 6.3)
未婚195 ( 1.2)186 ( 1.2)9 ( 0.6)
居住地0.096
城镇10 789 (65.0)9 815 (64.8)974 (67.0)
农村5 818 (35.0)5 338 (35.2)480 (33.0)
吸烟情况<0.001
从不11 274 (67.9)10 463 (69.0)811 (55.8)
吸烟4 174 (25.1)3 685 (24.3)489 (33.6)
戒烟1 159 ( 7.0)1 005 ( 6.6)154 (10.6)
饮酒情况<0.001
从不7 920 (47.7)7 212 (47.6)708 (48.7)
偶尔6 323 (38.1)5 875 (38.8)448 (30.8)
经常2 364 (14.2)2 066 (13.6)298 (20.5)
被动吸烟0.182
否7 906 (47.6)7 189 (47.4)717 (49.3)
是8 701 (52.4)7 964 (52.6)737 (50.7)
室内空气污染0.687
否2 538 (15.3)2 310 (15.2)228 (15.7)
是14 069 (84.7)12 843 (84.8)1 226 (84.3)
BMI分级<0.001
偏瘦390 ( 2.3)377 ( 2.5)13 ( 0.9)
正常7 770 (46.8)7 327 (48.4)443 (30.5)
超重6 318 (38.0)5 668 (37.4)650 (44.7)
肥胖2 129 (12.8)1 781 (11.8)348 (23.9)
代谢当量21.56±14.9221.72±14.8619.97±15.46<0.001
DASH评分22.19±4.5722.26±4.5421.51±4.80<0.001
NO2(g/m3)36.87±11.6136.89±11.5936.69±11.830.529
PM2.5(g/m3)50.44±6.8850.46±6.8850.23±6.920.226
气温(℃)16.72±0.4816.72±0.4816.78±0.48<0.001
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基线特征[（），n (%)]

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基本特征总人数（n=16 607）糖尿病P值
未发病（n=15 153）发病（n=1 454）
年龄(岁)51.00±12.1450.35±12.0357.80±11.21<0.001
性别<0.001
男性7 303 (44.0)6 472 (42.7)831 (57.2)
女性9 304 (56.0)8 681 (57.3)623 (42.8)
家庭年收入(元)<0.001
<12 0001 569 (9.4)1 401 (9.2)168 (11.6)
12 000～19 9991 958 (11.8)1 763 (11.6)195 (13.4)
20 000～59 9996 175 (37.2)5 585 (36.9)590 (40.6)
60 000～99 9993 430 (20.7)3 153 (20.8)277 (19.1)
≥100 0003 475 (20.9)3 251 (21.5)224 (15.4)
教育水平<0.001
文盲2 008 (12.1)1 744 (11.5)264 (18.2)
小学3 254 (19.6)2 903 (19.2)351 (24.1)
初中5 347 (32.2)4 885 (32.2)462 (31.8)
高中2 931 (17.6)2 705 (17.9)226 (15.5)
大学及以上3 067 (18.5)2 916 (19.2)151 (10.4)
婚姻状况<0.001
已婚15 016 (90.4)13 705 (90.4)1 311 (90.2)
丧偶689 ( 4.1)646 ( 4.3)43 ( 3.0)
离异707 ( 4.3)616 ( 4.1)91 ( 6.3)
未婚195 ( 1.2)186 ( 1.2)9 ( 0.6)
居住地0.096
城镇10 789 (65.0)9 815 (64.8)974 (67.0)
农村5 818 (35.0)5 338 (35.2)480 (33.0)
吸烟情况<0.001
从不11 274 (67.9)10 463 (69.0)811 (55.8)
吸烟4 174 (25.1)3 685 (24.3)489 (33.6)
戒烟1 159 ( 7.0)1 005 ( 6.6)154 (10.6)
饮酒情况<0.001
从不7 920 (47.7)7 212 (47.6)708 (48.7)
偶尔6 323 (38.1)5 875 (38.8)448 (30.8)
经常2 364 (14.2)2 066 (13.6)298 (20.5)
被动吸烟0.182
否7 906 (47.6)7 189 (47.4)717 (49.3)
是8 701 (52.4)7 964 (52.6)737 (50.7)
室内空气污染0.687
否2 538 (15.3)2 310 (15.2)228 (15.7)
是14 069 (84.7)12 843 (84.8)1 226 (84.3)
BMI分级<0.001
偏瘦390 ( 2.3)377 ( 2.5)13 ( 0.9)
正常7 770 (46.8)7 327 (48.4)443 (30.5)
超重6 318 (38.0)5 668 (37.4)650 (44.7)
肥胖2 129 (12.8)1 781 (11.8)348 (23.9)
代谢当量21.56±14.9221.72±14.8619.97±15.46<0.001
DASH评分22.19±4.5722.26±4.5421.51±4.80<0.001
NO2(g/m3)36.87±11.6136.89±11.5936.69±11.830.529
PM2.5(g/m3)50.44±6.8850.46±6.8850.23±6.920.226
气温(℃)16.72±0.4816.72±0.4816.78±0.48<0.001
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Association of O3 and covariates with the prevalence of diabetes

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变量类别OR值(95%CI)
臭氧(g/m3) a1.07(1.01～1.13)*
年龄(岁)1.05(1.05～1.06)***
性别男性参照组
女性0.72(0.61～0.85)***
家庭年收入(元)<12 000参照组
12 000～19 9991.08(0.86～1.35)
20 000～59 9991.06(0.88～1.29)
60 000～99 9990.97(0.78～1.21)
≥100 0001.00(0.79～1.27)
教育水平文盲参照组
小学0.98(0.82～1.17)
初中1.02(0.85～1.23)
高中1.05(0.84～1.32)
大学及以上0.93(0.71～1.22)
婚姻状况已婚参照组
丧偶0.83(0.60～1.14)
离异0.84(0.65～1.07)
未婚0.85(0.43～1.70)
居住地城镇参照组
农村0.55(0.46～0.66)***
吸烟情况从不参照组
吸烟1.38(1.16～1.65)***
戒烟1.08(0.86～1.36)
饮酒情况从不参照组
偶尔0.85(0.74～0.97)*
经常0.94(0.79～1.11)
被动吸烟否参照组
是1.13(1.01～1.27)*
室内空气污染否参照组
是0.97(0.83～1.14)
BMI分级偏瘦参照组
正常1.71(0.97～3.02)
超重2.92(1.65～5.15)***
肥胖5.13(2.89～9.10)***
代谢当量 a1.00(1.00～1.00)
DASH评分 a0.98(0.97～1.00)**
NO2(g/m3) a0.98(0.97～1.00)*
PM2.5(g/m3) a1.01(0.99～1.02)
气温(℃) a1.03(0.82～1.30)
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O3及协变量与糖尿病患病的关联

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变量类别OR值(95%CI)
臭氧(g/m3) a1.07(1.01～1.13)*
年龄(岁)1.05(1.05～1.06)***
性别男性参照组
女性0.72(0.61～0.85)***
家庭年收入(元)<12 000参照组
12 000～19 9991.08(0.86～1.35)
20 000～59 9991.06(0.88～1.29)
60 000～99 9990.97(0.78～1.21)
≥100 0001.00(0.79～1.27)
教育水平文盲参照组
小学0.98(0.82～1.17)
初中1.02(0.85～1.23)
高中1.05(0.84～1.32)
大学及以上0.93(0.71～1.22)
婚姻状况已婚参照组
丧偶0.83(0.60～1.14)
离异0.84(0.65～1.07)
未婚0.85(0.43～1.70)
居住地城镇参照组
农村0.55(0.46～0.66)***
吸烟情况从不参照组
吸烟1.38(1.16～1.65)***
戒烟1.08(0.86～1.36)
饮酒情况从不参照组
偶尔0.85(0.74～0.97)*
经常0.94(0.79～1.11)
被动吸烟否参照组
是1.13(1.01～1.27)*
室内空气污染否参照组
是0.97(0.83～1.14)
BMI分级偏瘦参照组
正常1.71(0.97～3.02)
超重2.92(1.65～5.15)***
肥胖5.13(2.89～9.10)***
代谢当量 a1.00(1.00～1.00)
DASH评分 a0.98(0.97～1.00)**
NO2(g/m3) a0.98(0.97～1.00)*
PM2.5(g/m3) a1.01(0.99～1.02)
气温(℃) a1.03(0.82～1.30)
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The impact of intervention in O3 on diabetes

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干预策略患病率(95%CI)(95%CI)aGIF (95%CI)b
观察情景9.50%参照组参照组
比值干预
5%6.97%(6.59%～7.34%)2.63%(2.25%～3.00%)27.38%(23.47%～31.28%)
10%5.51%(5.17%～5.86%)4.08%(3.73%～4.43%)42.53%(38.90%～46.17%)
15%4.35%(4.05%～4.65%)5.25%(4.94%～5.55%)54.67%(51.52%～57.82%)
20%3.41%(3.13%～3.69%)6.18%(5.90%～6.46%)64.43%(61.52%～67.35%)
定值干预
P759.22%(8.80%～9.65%)0.37%(-0.05%～0.79%)3.86%(-0.53%～8.25%)
P508.79%(8.38%～9.21%)0.80%(0.38%～1.22%)8.35%(4.01%～12.70%)
P258.24%(7.83%～8.65%)1.35%(0.94%～1.77%)14.11%(9.81%～18.41%)
最小值5.20%(4.86%～5.53%)4.40%(4.07%～4.73%)45.85%(42.40%～49.31%)
阈值干预
P758.59%(8.16%～9.02%)1.01%(0.58%～1.44%)10.50%(6.00%～15.01%)
P508.42%(8.00%～8.84%)1.18%(0.76%～1.59%)12.25%(7.89%～16.61%)
P258.08%(7.68%～8.47%)1.52%(1.12%～1.91%)15.81%(11.70%～19.92%)
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O3干预对糖尿病的影响

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干预策略患病率(95%CI)(95%CI)aGIF (95%CI)b
观察情景9.50%参照组参照组
比值干预
5%6.97%(6.59%～7.34%)2.63%(2.25%～3.00%)27.38%(23.47%～31.28%)
10%5.51%(5.17%～5.86%)4.08%(3.73%～4.43%)42.53%(38.90%～46.17%)
15%4.35%(4.05%～4.65%)5.25%(4.94%～5.55%)54.67%(51.52%～57.82%)
20%3.41%(3.13%～3.69%)6.18%(5.90%～6.46%)64.43%(61.52%～67.35%)
定值干预
P759.22%(8.80%～9.65%)0.37%(-0.05%～0.79%)3.86%(-0.53%～8.25%)
P508.79%(8.38%～9.21%)0.80%(0.38%～1.22%)8.35%(4.01%～12.70%)
P258.24%(7.83%～8.65%)1.35%(0.94%～1.77%)14.11%(9.81%～18.41%)
最小值5.20%(4.86%～5.53%)4.40%(4.07%～4.73%)45.85%(42.40%～49.31%)
阈值干预
P758.59%(8.16%～9.02%)1.01%(0.58%～1.44%)10.50%(6.00%～15.01%)
P508.42%(8.00%～8.84%)1.18%(0.76%～1.59%)12.25%(7.89%～16.61%)
P258.08%(7.68%～8.47%)1.52%(1.12%～1.91%)15.81%(11.70%～19.92%)
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基于前瞻性队列的臭氧模拟干预对糖尿病的健康收益研究
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白银铃 , 杨绍坤 , 郭冰 , 赵星 , 吴功华 , 张菊英
现代预防医学 | 疾病预防控制 2025,52(1): 155-159
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现代预防医学 |疾病预防控制 2025 , 52 (1) : 155 -159
基于前瞻性队列的臭氧模拟干预对糖尿病的健康收益研究
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白银铃, 杨绍坤, 郭冰, 赵星, 吴功华 , 张菊英
作者信息
  • 四川大学华西公共卫生学院/华西第四医院，流行病与卫生统计学系，四川 成都 610041
通讯作者:
吴功华，E-mail:
作者简介:

白银铃（1998—），女，硕士在读，研究方向：卫生统计方法与应用

Study on the health benefits of ozone simulation interventions for diabetes based on a prospective cohort
Yin-ling BAI, Shao-kun YANG, Bing GUO, Xing ZHAO, Gong-hua WU , Ju-ying ZHANG
Affiliations
  • Department of Epidemiology and Health Statistics, West China School of Public Health / West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2025-01-10 doi: 10.20043/j.cnki.MPM.202410106
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摘要
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目的

量化不同策略下干预臭氧对糖尿病患病的健康收益，为降低大气臭氧浓度以减少糖尿病的政策提供科学支持。

方法

基于西南区域自然人群队列基线的16 607名成都市研究对象的问卷、体检数据和大气数据，使用logistic回归模型探究臭氧与糖尿病患病风险的关联，并采用参数G formula法模拟比值干预、定值干预和阈值干预臭氧浓度，量化各项干预策略下糖尿病患病率的降低程度。

结果

臭氧每增加1 g/m3对应OR(95%CI)为1.07 (1.01～1.13)，将臭氧浓度降低5%、控制在P50 (85.20 g/m3)或降低至P75 (86.12 g/m3)及以下可分别减少27.38% (23.47%～31.28%)、8.35%(4.01%～12.70%)和10.50% (6.00%～15.01%)的糖尿病患病。

结论

使用各种策略干预臭氧浓度均可减少糖尿病，其中比值干预效果较好，未来应基于当地臭氧浓度，综合考虑不同季节、地区和污染来源采取干预措施，按百分比降低整体的臭氧浓度，改善糖尿病患病风险。

臭氧  /  糖尿病  /  模拟干预  /  G formula
Objective

To quantify the health benefits of interventions targeting ozone exposure on diabetes prevalence under different strategies, providing scientific support for policies aimed at reducing atmospheric ozone levels to mitigate diabetes.

Methods

Utilizing questionnaire, physical examination data, and atmospheric data from a cohort of 16 607 participants in Chengdu, Southwest China, we employed a Logistic regression model to explore the association between ozone and diabetes risk. We simulated ratio interventions, fixed-value interventions, and threshold interventions for ozone concentration using the parametric G formula method to quantify the reduction in diabetes prevalence under each intervention strategy.

Results

An increase of 1 mg/m3 in ozone corresponded to an odds ratio (OR) of 1.07 (95%CI: 1.01-1.13). Reducing ozone concentration by 5%, controlling it at P50 (85.20 mg/m3), or lowering it to P75(86.12 mg/m3) or below could respectively reduce diabetes prevalence by 27.38% (23.47%-31.28%), 8.35% (4.01%-12.70%), and 10.50% (6.00%-15.01%).

Conclusion

Various strategies to intervene on ozone concentration can reduce diabetes prevalence, with ratio interventions showing the most significant effectiveness. Future measures should consider local ozone levels and comprehensively account for seasonal variations, regional differences, and pollution sources to implement interventions aimed at percentage reductions in overall ozone concentration, thereby reducing diabetes risk.

Ozone  /  Diabetes  /  Simulation intervention  /  G formula
白银铃, 杨绍坤, 郭冰, 赵星, 吴功华, 张菊英. 基于前瞻性队列的臭氧模拟干预对糖尿病的健康收益研究. 现代预防医学, 2025 , 52 (1) : 155 -159 . DOI: 10.20043/j.cnki.MPM.202410106
Yin-ling BAI, Shao-kun YANG, Bing GUO, Xing ZHAO, Gong-hua WU, Ju-ying ZHANG. Study on the health benefits of ozone simulation interventions for diabetes based on a prospective cohort[J]. Modern Preventive Medicine, 2025 , 52 (1) : 155 -159 . DOI: 10.20043/j.cnki.MPM.202410106
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全球糖尿病患病显著上升，至2021年约有5.37亿成年人患有糖尿病[1]，而中国糖尿病患者数量最多，估计超过1.40亿人，预计到2045年将超过1.74亿[2]。据估计，在2021年中国人因糖尿病损失了1.17千万个伤残调整生命年[3]。越来越多的证据支持糖尿病受空气污染的影响，26.1%的糖尿病残疾调整生命年可归因于空气污染[4]，其中臭氧(Ozone, O3)可导致糖代谢紊乱进而诱发糖尿病[5]。自2013年中国发布《大气污染防治行动计划》以来，中国空气污染物浓度总体降低，而O3浓度急剧上升，应尽快控制大气中O3浓度[6]。由于各种技术和社会等原因，将O3完全清除并不现实，因此探索合理的O3浓度以减少糖尿病至关重要。然而，实施干预措施将O3降低到特定浓度后，糖尿病的减少程度是未知的。因此，本研究基于中国西南区域自然人群队列（简称西南队列）数据，利用参数G formula方法模拟干预O3浓度，定量估计与不干预的情况相比糖尿病的减少程度，为控制O3污染预防糖尿病提供科学支持。
1　资料与方法
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1.1　研究对象
本文研究对象为西南队列成都地区的21 592名基线参与者。西南队列采用多阶段整群抽样的方法，在2018年5月—2019年9月抽取西南地区的居民，并收集了社会人口学、行为生活方式、疾病史等信息，以及体格检查和生化指标数据[7]。研究已获得四川大学伦理审查委员会的伦理批准（审批号：K2016038）。本研究采取以下排除标准：（1）基线时在当地居住时间小于3年；（2）患恶性肿瘤者；（3）孕妇；（4）年龄<30岁或≥80岁；（5）自报基线调查前3年已确诊糖尿病；（6）暴露、协变量或结局缺失。基于以上标准共纳入16 607名基线参与者，见图1。
1.2　变量来源及定义
1.2.1　暴露
O3数据来自于中国环境检测总站（https://www.cnemc.cn/），以0.1°（≈10 km）空间分辨率为单位，利用迭代随机森林模型估算所得[8]。根据基线调查时获得的住址，将O3的年暴露水平与每位研究对象相匹配，为每位研究对象单独计算前3年的暴露平均值。
1.2.2　结局
根据美国糖尿病协会的标准，糖尿病定义为空腹血糖（fasting plasma glucose, FPG）≥126 mg/dL（7.0 mmol/L）或糖化血红蛋白（hemoglobin A1c, HbA1c）≥48 mmol/mol（6.5%）[9]。FPG与HbA1c均来自西南队列基线调查的血生化数据。
1.2.3　关键协变量
综合烹饪频率、燃料类型和通风设备等变量判断是否有室内空气污染。以体重（kg）除以身高的二次方（m2）得到身体质量指数（body mass index, BMI），<18.5 kg/m2为偏瘦，18.5～24 kg/m2为正常，24～28 kg/m2为超重，≥28 kg/m2为肥胖。代谢当量是根据不同类型体力活动的频率和强度计算所得的每日代谢当量任务（metabolic equivalent tasks per day, MET-h/d），用来反映基线前1年平均每日的体力活动水平[10]。根据问卷中食物频率调查表计算DASH评分，反应研究对象的饮食情况[11]。空气细颗粒物（particulate matter 2.5, PM2.5）、二氧化氮（nitrogen dioxide, NO2）和气温来自于中国环境检测总站，其他协变量均来自于西南队列基线调查的问卷数据。
1.3　统计学分析
1.3.1　描述性分析
根据是否出现糖尿病将研究对象分为两组，对其基线特征进行描述。连续变量和分类变量分别使用（均数±标准差）和频数（构成比）进行描述，并使用t检验和χ2检验比较两组差异。另外，使用（均数±标准差）、百分位数和极值描述基线前3年研究对象O3的暴露情况。
1.3.2　模拟干预
使用参数G formula方法估计模拟干预O3对糖尿病患病的平均因果效应[12]。该方法分为四步：（1）根据干预策略设置暴露水平，创建新的分析数据集；（2）利用原始数据集，纳入O3和协变量，对是否患糖尿病建立logistic回归模型；（3）使用模型参数和分析数据集预测不同干预策略下糖尿病的边际患病率；（4）计算观察情景和模拟干预情景下的结局风险差异（）和广义影响分数（generalized impact fraction, GIF）。风险差异指观察情景下人群中糖尿病患病率（）与模拟干预情景下的糖尿病患病风险（）之差。广义影响分数是指通过降低当前暴露于某一感兴趣水平而导致人群中病例减少的比例[13]。公式如下：
其中P（·）表示事件发生概率，Y为结局，表示自然观察的暴露情况，表示模拟干预后的暴露量。95%置信区间通过1 000次蒙特卡罗模拟估算。
本研究建立回归模型，并调整人口特征、其他污染物及气温等，分析暴露O3与糖尿病患病的关联关系，使用比值比（odds ratio, OR）和95%置信区间（confidence interval, CI）展示结果。并在可识别性假设（可交换性、正数性和一致性）下，模拟3组干预措施对糖尿病的影响：使用比值干预，将整个人群暴露的污染物浓度按百分比降低（5%、10% 、15%、20%），以5%为例即aint=（1-5%）×anat；采用定值干预，将整体暴露污染物浓度保持在最小值或某百分位数（P25、P50、P75），以P25为例即aint=P25；以及使用阈值干预，仅对污染物浓度高于阈值（P25、P50、P75）的研究对象进行干预，以P25为例即。
本研究分析均在R 4.3.3中进行，采用双侧检验，检验水准α=0.05。
2　结果
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2.1　描述性分析结果
本研究共纳入了16 607名研究对象，1 454人在基线时被判断为糖尿病，患病率为9.50%。研究对象的平均年龄为（51.00±12.14）岁，男性糖尿病患者占比为57.2%，见表1。O3的3年平均浓度为（85.01±1.88）g/m3，第25、50、75百分位数（P25、P50、P75）分别为84.00、85.20和86.12 g/m3，最小值与最大值分别为75.56和96.93 g/m3。
2.2　回归模型结果
Logistic回归模型显示，O3浓度增加与糖尿病患病风险呈正相关。O3每增加1 g/m3，相应OR（95%CI）为1.07（1.01～1.13），见表2。
2.3　模拟干预结果
表3展示了对O3进行模拟干预后，糖尿病的患病改变。进行模拟干预前，研究人群的糖尿病患病率为9.50%。若所有研究对象的O3浓度都降低5%，糖尿病患病率将降低2.63%（2.25%～3.00%），可减少27.38%（23.47%～31.28%）的糖尿病发生。进一步降低O3浓度可减少更多糖尿病，当降低20%时，可减少64.43%（61.52%～67.35%）的糖尿病。如果将所有人的O3暴露浓度保持在P75（86.12 g/m3），研究人群中糖尿病患病率为9.22%（8.80%～9.65%），没有显著变化；如果控制在P50（85.20 g/m3）、P25（84.00 g/m3）或最小值（75.56 g/m3），可减少现有8.35%（4.01%～12.70%）、14.11%（9.81%～18.41%）或45.85%（42.40%～49.31%）的糖尿病发生。以现在O3浓度的P75、P50、P25为阈值进行干预后，对应的糖尿病患病率分别为8.59%（8.16%～9.02%）、8.42%（8.00%～8.84%）、8.08%（7.68%～8.47%），比现在降低1.01%（0.58%～1.44%）、1.18%（0.76%～1.59%）、1.52%（1.12%～1.91%），广义影响分数为10.50%（6.00%～15.01%）、12.25%（7.89%～16.61%）、15.81%（11.70%～19.92%）。
3　讨论
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现有研究表明O3会导致糖尿病，但对干预O3降低糖尿病患病率的定量评估研究有限。基于西南队列，本研究使用参数G formula估计了比值干预、定值干预和阈值干预三类策略下模拟干预O3的糖尿病健康收益。使用不同的干预策略对O3进行干预，均可使糖尿病患病率降低，得到不同的健康收益。
越来越多的研究探索了O3与糖尿病的关联。研究者通过动物实验和人群研究发现，O3可通过DNA甲基化或干扰机体糖异生、半乳糖代谢和碳代谢等葡萄糖代谢途径诱导糖尿病的发生[14-16]。Renzi等人[17]利用罗马大型队列发现O3与糖尿病发病率存在正向关联，每升高10 g/m3 O3糖尿病的风险为1.01（1.00～1.03）。Paul等人[18]发现O3浓度每增加一个四分位数间距可增加糖尿病0.5%（0.1%～0.9%）的发病风险和6.4%（3.7%～9.1%）的死亡风险。基于中国33个社区的大型横断面研究发现，O3浓度每增加22 g/m3，糖尿病患病风险增加14.0%（5.0%～25.0%）[19]。Wang等人[5]基于中国健康与养老追踪调查发现，O3的年平均浓度每增加10g/m3，老年人群的发病风险增加5.7%（0.4%～11.4%）。以上研究表明，O3暴露浓度的升高会导致糖尿病的发生增加，与本研究结果一致，均支持可通过降低O3浓度的干预来减少糖尿病。
在本研究中，使用三种干预策略均可减少糖尿病，尤其将现在O3浓度降低20%，可减少64.43%的糖尿病发生。虽然使用三种干预策略均能使糖尿病减少，但将O3浓度均干预至P75（86.12 g/m3）或及其以下，糖尿病无显著的健康收益。现在的O3浓度过高，仅设定较高限值稍降低平均O3浓度不能改善现有糖尿病的负担，需要更大力度地干预O3污染。Li等人[20]基于北京197万病例，估计了空气污染对心血管疾病合并糖尿病患者的住院负担，发现将O3浓度控制在日均60 g/m3时，仅0.22%（0.19%～0.26%）的入院，0.23%（0.04%～0.41%）的住院时长和0.52%（0.16%～0.89%）的住院费用可归因与O3污染。可见控制大气O3污染浓度以减少糖尿病负担具有很大潜力。
与需要设定很低界值的定值干预和阈值干预不同，使用比值干预策略，将每位研究对象的O3暴露浓度降低5%～20%均可使糖尿病显著减少。使用比值干预降低O3浓度以减少糖尿病的效率高于另外两种干预策略。成都市各地O3浓度分布不均，同时将其降低至某一界值是不可行的，较高的界值不足以降低全市糖尿病发生，或过低的界值使得措施难以落实。使用比值干预策略降低O3浓度，更易实现减少糖尿病的目的，针对不同时间、空间和来源分别对O3进行干预，让其在原有浓度上按百分比降低，以得到显著的糖尿病健康收益。调查发现，四川盆地的O3的浓度在春夏季最高，主要来源于工业和交通运输[21]。当地O3浓度还会受到其他地区的影响，如成都的O3污染可来自于重庆和贵州等地[22]。未来需在春夏季加强对工业和交通排放的干预，并与周围城市合作，共同降低O3污染以减少糖尿病负担。
本研究存在一定的局限性：第一，本研究人群仅来自成都市，而大气污染物在不同区域浓度及分布不同，结果外推时具有局限性；第二，不同季节臭氧浓度不一样，本研究只选取三年平均暴露值，不足以模拟臭氧动态变化的暴露情况；第三，本研究根据横断面数据进行分析，虽然大致保证了暴露与结局的顺序，但也可能存在因果倒置的问题。
综上所述，不同策略干预O3污染浓度，均可减少糖尿病，其中比值干预减少糖尿病的效果比定值干预和阈值干预更好。未来应针对不同季节、地区和来源，实施综合性干预措施，按百分比降低O3浓度，以减少糖尿病发生，提高居民健康质量。
基金
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  • 国家自然科学基金面上项目(82073667; 81973151)
  • 国家自然科学基金青年项目(82103943)
文献
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2025年第52卷第1期
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doi: 10.20043/j.cnki.MPM.202410106
  • 接收时间：2024-10-10
  • 首发时间：2026-10-29
  • 出版时间：2025-01-10
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  • 收稿日期：2024-10-10
基金
国家自然科学基金面上项目(82073667; 81973151)
国家自然科学基金青年项目(82103943)
作者信息
    四川大学华西公共卫生学院/华西第四医院，流行病与卫生统计学系，四川 成都 610041

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