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Article(id=1241522923945653178, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522919977841545, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202309249, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1694707200000, receivedDateStr=2023-09-15, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773931731448, onlineDateStr=2026-03-19, pubDate=1710000000000, pubDateStr=2024-03-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773931731448, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773931731448, creator=13701087609, updateTime=1773931731448, updator=13701087609, issue=Issue{id=1241522919977841545, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='5', pageStart='769', pageEnd='960', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773931730503, creator=13701087609, updateTime=1773931880386, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241523548695622547, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522919977841545, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241523548695622548, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522919977841545, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=811, endPage=816, ext={EN=ArticleExt(id=1241522924339917774, articleId=1241522923945653178, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of the relationship between night shift work and metabolic syndrome in railway workers, columnId=1228016570660745413, journalTitle=Modern Preventive Medicine, columnName=Environmental and Occupational Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the effect of night shift work on metabolic syndrome (MS) in railway workers.

Methods

In total 7 635 railway workers who took part in the physical examination in the First Affiliated Hospital of Guangdong Pharmaceutical University from September to December 2021 were selected as the research subjects. Logistic regression was used for correlation analysis and restricted cubic spline (RCS) was used to analyze the dose-response relationship.

Results

The prevalence rate of metabolic syndrome in railway workers was 15.1%. After adjusting the confounding factors, former night shift (OR=1.198, 95%CI: 1.002-1.431) and current night shift (OR=1.219, 95%CI: 1.048-1.417), night shift duration of 6.0 ~ < 11.0 years (OR=1.323, 95%CI: 1.092-1.603) and ≥ 11.0 years (OR=1.256, 95%CI: 1.029-1.534), night shift twice a week (OR=1.269, 95%CI: 1.074-1.498), 3 times (OR=1.217, 95%CI: 1.001-1.479), and ≥ 4 times (OR=1.295, 95%CI:1.032-1.623) were significantly associated with the prevalence of MS in railway workers. The results of RCS showed that there was a linear positive correlation between the working years of night shift and the risk of MS (P total < 0.001, P non-linear=0.0760). The risk of MS began to increase after 1 year of night shift and reached the highest after 14.1 years.

Conclusion

Night shift work will lead to an increase in the risk of MS among railway workers, and the higher the number of years of night shift work, the higher the risk.

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目的

探讨夜班工作对铁路工人代谢综合征(metabolic syndrome，MS)的影响。

方法

选取2021年9—12月在广东药科大学附属第一医院参加职工体检的7 635名铁路职工为研究对象。采用logistic回归进行关联性分析，限制性立方样条(restricted cubic spline，RCS)分析剂量反应关系。

结果

铁路职工MS患病率为15.1%。调整混杂因素后，曾经夜班工作(OR=1.198, 95%CI:1.002～1.431)、现在夜班工作(OR=1.219, 95%CI:1.048～1.417)、夜班工作6.0～<11.0年(OR=1.323, 95%CI:1.092～1.603)、夜班工作≥11.0年(OR=1.256, 95%CI:1.029～1.534)、每周夜班2次(OR=1.269, 95%CI:1.074～1.498)、每周夜班3次(OR=1.217, 95%CI:1.001～1.479)、每周夜班≥4次(OR=1.295, 95%CI:1.032～1.623)与铁路职工MS患病具有统计学意义。RCS结果显示，夜班工作年限与MS患病风险存在线性的正相关关系(Ptotal<0.001，Pnon-linear=0.076 0)，MS的患病风险在夜班1年后开始增加，在夜班达14.1年时风险达到最高。

结论

夜班工作会导致铁路工人患MS的风险升高，夜班工作年限越高风险越高。

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陈青松，E-mail:；
吴珊，E-mail:
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杜懿涵（1998—），女，硕士在读，研究方向：职业病防治

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Shift work and its effect on sleeping quality and metabolic diseases in retired workers[D]. Wuhan: Huazhong University of Science and Technology, 2016., articleTitle=Shift work and its effect on sleeping quality and metabolic diseases in retired workers, refAbstract=null), Reference(id=1241678345658749704, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, doi=null, pmid=null, pmcid=null, year=2014, volume=63, issue=6, pageStart=1860, pageEnd=1869, url=null, language=null, rfNumber=[24], rfOrder=36, authorNames=Leproult R, Holmbäck U, Van cauter E, journalName=Diabetes, refType=null, unstructuredReference=Leproult R, Holmbäck U, Van cauter E. Circadian misalignment augments markers of insulin resistance and inflammation,independently of sleep loss[J]. Diabetes, 2014, 63(6): 1860-1869., articleTitle=Circadian misalignment augments markers of insulin resistance and inflammation,independently of sleep loss, refAbstract=null), Reference(id=1241678345994294029, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, doi=null, pmid=null, pmcid=null, year=2011, volume=12, issue=1, pageStart=14, pageEnd=25, url=null, language=null, rfNumber=[25], rfOrder=37, authorNames=Ekmekcioglu C, Touitou Y, journalName=Obesity Reviews, refType=null, unstructuredReference=Ekmekcioglu C, Touitou Y. Chronobiological aspects of food intake and metabolism and their relevance on energy balance and weight regulation[J]. Obesity Reviews, 2011, 12(1): 14-25., articleTitle=Chronobiological aspects of food intake and metabolism and their relevance on energy balance and weight regulation, refAbstract=null)], funds=[Fund(id=1241678323814814177, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, awardId=C202209, language=CN, fundingSource=广东省医学科学技术研究基金(C202209), fundOrder=null, country=null), Fund(id=1241678324410405355, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, awardId=2019GCZX012, language=CN, fundingSource=广东省公共卫生检测与评价工程技术研究中心(2019GCZX012), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241678285239799825, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, xref=1., ext=[AuthorCompanyExt(id=1241678285252382739, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, companyId=1241678285239799825, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong 510310, China), AuthorCompanyExt(id=1241678285277548564, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, companyId=1241678285239799825, 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，广东 广州 510310)]), AuthorCompany(id=1241678285864751131, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, xref=2., ext=[AuthorCompanyExt(id=1241678285868945436, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, companyId=1241678285864751131, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.广东药科大学附属第一医院健康管理部)]), AuthorCompany(id=1241678286128992288, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, xref=3., ext=[AuthorCompanyExt(id=1241678286137380897, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, companyId=1241678286128992288, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.广东省公共卫生检测与评价工程技术研究中心，广东 广州 510310)])], figs=[ArticleFig(id=1241678315849830789, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, language=EN, label=Figure 1, caption=Restricted cubic spline plot of participants’ years of night shift work associated with MS, figureFileSmall=cvjslhklTjsRm9zoKuEdkw==, figureFileBig=WYW99g8eLeA/MKFwURbMcQ==, tableContent=null), ArticleFig(id=1241678316806132112, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, language=CN, label=图1, caption=参与者夜班工作年限与MS的RCS图, figureFileSmall=cvjslhklTjsRm9zoKuEdkw==, figureFileBig=WYW99g8eLeA/MKFwURbMcQ==, tableContent=null), ArticleFig(id=1241678319599538591, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, language=EN, label=Table 1, caption=

Participant demographics and related characteristics［ n（%），M（P25，P75）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总人数
(n=7 635)		无MS组
(n=6 481)		MS组
(n=1 154)		χ2/Z值P值
性别3.0640.080
男6 437(84.3)5 484(85.2)953(14.8)
女1 198(15.7)997(83.2)201(16.8)
当前夜班状态2.0110.366
无夜班3 290(43.1)2 814(85.5)476(14.5)
曾经夜班工作1 461(19.1)1 237(84.7)224(15.3)
现在夜班工作2 884(37.8)2 430(84.3)454(15.7)
夜班工作年限（年）16.3160.004
无夜班3 290(43.1)2 814(85.5)476(14.5)
第一分位(<2.5)1 142(15.0)992(86.9)150(13.1)
第二分位（2.5～<6.0）1 024(13.4)879(85.8)145(14.2)
第三分位（6.0～<11.0）1 133(14.8)941(83.1)192(16.9)
第四分位（≥ 11.0）1 046(13.7)855(81.7)191(18.3)
每周夜班次数（次）-1.5800.114
无夜班3 290(43.1)2 814(85.5)476(14.5)
每周≤1609(8.0)528(86.7)81(13.3)
每周21 766(23.1)1 476(83.6)290(16.4)
每周31 216(15.9)1 033(85.0)183(15.0)
每周≥4754(9.9)630(83.6)124(16.4)
教育程度22.0490.502
高中及以下1 517(19.9)1 243(81.9)274(18.1)
大专3 621(47.4)3 142(86.9)479(13.2)
本科及以上2 497(32.7)2 096(83.9)401(16.1)
婚姻状况19.191<0.001
未婚2 163(28.3)1 895(87.6)268(12.4)
已婚或同居5 249(68.7)4 406(83.9)843(16.1)
离婚/分居或丧偶223(2.9)180(80.7)43(19.3)
每月家庭人均收入（元）7.5930.405
≤4 000758(9.9)643(84.8)115(15.2)
>4 001～6 00097(1.3)76(78.4)21(21.6)
>6 000～10 0001 581(20.7)1 323(83.7)258(16.3)
>10 000～20 0003 139(41.1)2 696(85.9)443(14.1)
>20 0002 060(27.0)1 743(84.6)317(15.4)
吸烟0.7920.673
从无4 631(60.7)3 944(85.2)687(14.8)
已戒烟378(5.0)321(84.9)57(15.1)
是2 626(34.4)2 216(84.4)410(15.6)
饮酒8.1350.004
否6 551(85.8)5 592(85.4)959(14.6)
是1 084(114.2)889(82.0)195(18.0)
每日直接饮水量（ml）11.2020.015
<500207(2.7)186(89.9)21(10.1)
500～<1 0001 734(22.7)1 484(85.6)250(14.4)
1 000～<1 5002 353(30.8)1 999(85.0)354(15.0)
1 500～<2 0002 003(26.2)1 709(85.3)294(14.7)
≥ 2 0001 338(17.5)1 103(82.4)235(17.6)
BMI8.5370.458
偏瘦215(2.8)193(89.8)22(10.2)
正常3 265(42.5)2 738(83.9)527(16.1)
超重3 046(39.9)2 612(85.8)434(14.2)
肥胖1 109(14.5)938(84.6)171(15.4)
腹型肥胖0.2500.617
否5 031(65.9)4 278(85.0)753(15.0)
是2 604(34.1)2 203(84.6)401(15.4)
高血压3.3110.069
否6 587(86.3)5 611(85.2)976(14.8)
是1 048(13.7)870(83.0)178(17.0)
糖尿病0.0900.765
否7 206(94.4)6 119(84.9)1 087(15.1)
是429(5.6)362(84.4)67(15.6)
年龄(岁)41(28, 49)41(27, 49)44(30, 50)-5.987<0.001
夜班工作年限(年)1.0(0.0, 7.0)1.0(0.0, 7.0)1.5(0.0, 9.0)-3.1160.002
每天坐着的时间(h)6.0(4.0, 8.0)6.0(4.0, 8.0)6.0(4.0, 8.0)-0.1020.918
运动频率(次/周)2.0(1.0, 4.0)2.0(1.0, 4.0)2.0(1.0, 4.0)-2.1220.034
睡眠时间(h)7.5(7.0,8.0)7.5(7.0, 8.0)7.5(7.0, 8.0)-1.5230.128
平均每月含糖饮料饮用量(ml)0.0(0.0, 660.0)0.0(0.0, 660.0)0.0(0.0, 350)-3.925<0.001
甘油三酯(mmol/L)1.24(0.82, 1.89)1.23(0.82, 1.86)1.30(0.86, 2.05)-2.8120.005
高密度脂蛋白胆固醇(mmol/L)1.23(1.03, 1.45)1.23(1.03, 1.45)1.24(1.03, 1.47)-0.9970.319
总胆固醇(mmol/L)5.34(4.26, 6.06)5.33(4.60, 6.04)5.41(4.74, 6.18)-3.615<0.001
低密度脂蛋白胆固醇(mmol/L)3.33(2.69, 3.99)3.32(2.68, 3.97)3.37(2.73, 4.06)-2.2350.025
), ArticleFig(id=1241678319914111401, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, language=CN, label=表1, caption=

参与者人口统计学及相关特征［n（%），M（P25，P75）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总人数
(n=7 635)		无MS组
(n=6 481)		MS组
(n=1 154)		χ2/Z值P值
性别3.0640.080
男6 437(84.3)5 484(85.2)953(14.8)
女1 198(15.7)997(83.2)201(16.8)
当前夜班状态2.0110.366
无夜班3 290(43.1)2 814(85.5)476(14.5)
曾经夜班工作1 461(19.1)1 237(84.7)224(15.3)
现在夜班工作2 884(37.8)2 430(84.3)454(15.7)
夜班工作年限（年）16.3160.004
无夜班3 290(43.1)2 814(85.5)476(14.5)
第一分位(<2.5)1 142(15.0)992(86.9)150(13.1)
第二分位（2.5～<6.0）1 024(13.4)879(85.8)145(14.2)
第三分位（6.0～<11.0）1 133(14.8)941(83.1)192(16.9)
第四分位（≥ 11.0）1 046(13.7)855(81.7)191(18.3)
每周夜班次数（次）-1.5800.114
无夜班3 290(43.1)2 814(85.5)476(14.5)
每周≤1609(8.0)528(86.7)81(13.3)
每周21 766(23.1)1 476(83.6)290(16.4)
每周31 216(15.9)1 033(85.0)183(15.0)
每周≥4754(9.9)630(83.6)124(16.4)
教育程度22.0490.502
高中及以下1 517(19.9)1 243(81.9)274(18.1)
大专3 621(47.4)3 142(86.9)479(13.2)
本科及以上2 497(32.7)2 096(83.9)401(16.1)
婚姻状况19.191<0.001
未婚2 163(28.3)1 895(87.6)268(12.4)
已婚或同居5 249(68.7)4 406(83.9)843(16.1)
离婚/分居或丧偶223(2.9)180(80.7)43(19.3)
每月家庭人均收入（元）7.5930.405
≤4 000758(9.9)643(84.8)115(15.2)
>4 001～6 00097(1.3)76(78.4)21(21.6)
>6 000～10 0001 581(20.7)1 323(83.7)258(16.3)
>10 000～20 0003 139(41.1)2 696(85.9)443(14.1)
>20 0002 060(27.0)1 743(84.6)317(15.4)
吸烟0.7920.673
从无4 631(60.7)3 944(85.2)687(14.8)
已戒烟378(5.0)321(84.9)57(15.1)
是2 626(34.4)2 216(84.4)410(15.6)
饮酒8.1350.004
否6 551(85.8)5 592(85.4)959(14.6)
是1 084(114.2)889(82.0)195(18.0)
每日直接饮水量（ml）11.2020.015
<500207(2.7)186(89.9)21(10.1)
500～<1 0001 734(22.7)1 484(85.6)250(14.4)
1 000～<1 5002 353(30.8)1 999(85.0)354(15.0)
1 500～<2 0002 003(26.2)1 709(85.3)294(14.7)
≥ 2 0001 338(17.5)1 103(82.4)235(17.6)
BMI8.5370.458
偏瘦215(2.8)193(89.8)22(10.2)
正常3 265(42.5)2 738(83.9)527(16.1)
超重3 046(39.9)2 612(85.8)434(14.2)
肥胖1 109(14.5)938(84.6)171(15.4)
腹型肥胖0.2500.617
否5 031(65.9)4 278(85.0)753(15.0)
是2 604(34.1)2 203(84.6)401(15.4)
高血压3.3110.069
否6 587(86.3)5 611(85.2)976(14.8)
是1 048(13.7)870(83.0)178(17.0)
糖尿病0.0900.765
否7 206(94.4)6 119(84.9)1 087(15.1)
是429(5.6)362(84.4)67(15.6)
年龄(岁)41(28, 49)41(27, 49)44(30, 50)-5.987<0.001
夜班工作年限(年)1.0(0.0, 7.0)1.0(0.0, 7.0)1.5(0.0, 9.0)-3.1160.002
每天坐着的时间(h)6.0(4.0, 8.0)6.0(4.0, 8.0)6.0(4.0, 8.0)-0.1020.918
运动频率(次/周)2.0(1.0, 4.0)2.0(1.0, 4.0)2.0(1.0, 4.0)-2.1220.034
睡眠时间(h)7.5(7.0,8.0)7.5(7.0, 8.0)7.5(7.0, 8.0)-1.5230.128
平均每月含糖饮料饮用量(ml)0.0(0.0, 660.0)0.0(0.0, 660.0)0.0(0.0, 350)-3.925<0.001
甘油三酯(mmol/L)1.24(0.82, 1.89)1.23(0.82, 1.86)1.30(0.86, 2.05)-2.8120.005
高密度脂蛋白胆固醇(mmol/L)1.23(1.03, 1.45)1.23(1.03, 1.45)1.24(1.03, 1.47)-0.9970.319
总胆固醇(mmol/L)5.34(4.26, 6.06)5.33(4.60, 6.04)5.41(4.74, 6.18)-3.615<0.001
低密度脂蛋白胆固醇(mmol/L)3.33(2.69, 3.99)3.32(2.68, 3.97)3.37(2.73, 4.06)-2.2350.025
), ArticleFig(id=1241678320329347505, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, language=EN, label=Table 2, caption=

Logistic regression analysis of MS among participants with different night shift work status

, figureFileSmall=null, figureFileBig=null, tableContent=
模型无夜班OR值(95%CI)
曾经夜班工作现在夜班工作
模型一11.071(0.901～1.272)1.105(0.961～1.270)
模型二11.183(0.990～1.413)1.213(1.044～1.409)a
模型三11.198(1.002～1.431)a1.219(1.048～1.417)a
), ArticleFig(id=1241678320799109559, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, language=CN, label=表2, caption=

铁路职工夜班状态与MS的关联logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
模型无夜班OR值(95%CI)
曾经夜班工作现在夜班工作
模型一11.071(0.901～1.272)1.105(0.961～1.270)
模型二11.183(0.990～1.413)1.213(1.044～1.409)a
模型三11.198(1.002～1.431)a1.219(1.048～1.417)a
), ArticleFig(id=1241678321570861504, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, language=EN, label=Table 3, caption=

Logistic regression analysis of MS among participants with different years of night shift work

, figureFileSmall=null, figureFileBig=null, tableContent=
模型无夜班OR值(95%CI)趋势性P值
第一分位
(<2.5年)		第二分位
(2.5～<6.0年)		第三分位
(6.0～<11.0年)		第四分位
（≥ 11.0年)
模型一10.894(0.734 ～ 1.089)0.975(0.798 ～ 1.192)1.206(1.004 ～ 1.449)a1.321(1.098 ～ 1.588)a0.001a
模型二11.076(0.871 ～ 1.329)1.146(0.928 ～ 1.416)1.317(1.087 ～ 1.595)a1.245(1.020 ～ 1.519)a0.003a
模型三11.086(0.879 ～ 1.343)1.155(0.935 ～ 1.426)1.323(1.092 ～ 1.603)a1.256(1.029 ～ 1.534)a0.002a
), ArticleFig(id=1241678321973514690, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, language=CN, label=表3, caption=

铁路职工夜班工作年限与MS的关联logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
模型无夜班OR值(95%CI)趋势性P值
第一分位
(<2.5年)		第二分位
(2.5～<6.0年)		第三分位
(6.0～<11.0年)		第四分位
（≥ 11.0年)
模型一10.894(0.734 ～ 1.089)0.975(0.798 ～ 1.192)1.206(1.004 ～ 1.449)a1.321(1.098 ～ 1.588)a0.001a
模型二11.076(0.871 ～ 1.329)1.146(0.928 ～ 1.416)1.317(1.087 ～ 1.595)a1.245(1.020 ～ 1.519)a0.003a
模型三11.086(0.879 ～ 1.343)1.155(0.935 ～ 1.426)1.323(1.092 ～ 1.603)a1.256(1.029 ～ 1.534)a0.002a
), ArticleFig(id=1241678322405528013, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, language=EN, label=Table 4, caption=

Logistic regression analysis of the association between the number of night shift days per week and MS among railway workers

, figureFileSmall=null, figureFileBig=null, tableContent=
模型无夜班OR值(95%CI)趋势性P值
每周夜班≤1 d每周夜班2 d每周夜班3 d每周夜班≥4 d
模型一10.907(0.704～1.168)1.162(0.991～1.362)b1.047(0.871～1.260)1.164(0.938～1.444)0.101
模型二10.967(0.749～1.250)1.259(1.067～1.487)a1.204(0.991～1.463)b1.295(1.034～1.623)a0.003a
模型三10.980(0.758～1.267)1.269(1.074～1.498)a1.217(1.001～1.479)a1.295(1.032～1.623)a0.003a
), ArticleFig(id=1241678323030479317, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522923945653178, language=CN, label=表4, caption=

铁路职工每周夜班天数与MS的关联logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
模型无夜班OR值(95%CI)趋势性P值
每周夜班≤1 d每周夜班2 d每周夜班3 d每周夜班≥4 d
模型一10.907(0.704～1.168)1.162(0.991～1.362)b1.047(0.871～1.260)1.164(0.938～1.444)0.101
模型二10.967(0.749～1.250)1.259(1.067～1.487)a1.204(0.991～1.463)b1.295(1.034～1.623)a0.003a
模型三10.980(0.758～1.267)1.269(1.074～1.498)a1.217(1.001～1.479)a1.295(1.032～1.623)a0.003a
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铁路职工夜班工作与代谢综合征的关联性分析
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杜懿涵 1 , 陆崇 2 , 冯小燕 1 , 莫枫欣 1 , 陈青松 1, 3 , 吴珊 1
现代预防医学 | 环境与职业卫生 2024,51(5): 811-816
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现代预防医学 | 环境与职业卫生 2024, 51(5): 811-816
铁路职工夜班工作与代谢综合征的关联性分析
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杜懿涵1, 陆崇2, 冯小燕1, 莫枫欣1, 陈青松1, 3 , 吴珊1
作者信息
  • 1.广东药科大学公共卫生学院，广东 广州 510310
  • 2.广东药科大学附属第一医院健康管理部
  • 3.广东省公共卫生检测与评价工程技术研究中心，广东 广州 510310

    • 杜懿涵（1998—），女，硕士在读，研究方向：职业病防治

通讯作者:

陈青松，E-mail:；
吴珊，E-mail:
Analysis of the relationship between night shift work and metabolic syndrome in railway workers
Yi-han DU1, Chong LU2, Xiao-yan FENG1, Feng-xin MO1, Qing-song CHEN1, 3 , Shan WU1
Affiliations
  • School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong 510310, China
出版时间: 2024-03-10 doi: 10.20043/j.cnki.MPM.202309249
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摘要
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目的

探讨夜班工作对铁路工人代谢综合征(metabolic syndrome，MS)的影响。

方法

选取2021年9—12月在广东药科大学附属第一医院参加职工体检的7 635名铁路职工为研究对象。采用logistic回归进行关联性分析，限制性立方样条(restricted cubic spline，RCS)分析剂量反应关系。

结果

铁路职工MS患病率为15.1%。调整混杂因素后，曾经夜班工作(OR=1.198, 95%CI:1.002～1.431)、现在夜班工作(OR=1.219, 95%CI:1.048～1.417)、夜班工作6.0～<11.0年(OR=1.323, 95%CI:1.092～1.603)、夜班工作≥11.0年(OR=1.256, 95%CI:1.029～1.534)、每周夜班2次(OR=1.269, 95%CI:1.074～1.498)、每周夜班3次(OR=1.217, 95%CI:1.001～1.479)、每周夜班≥4次(OR=1.295, 95%CI:1.032～1.623)与铁路职工MS患病具有统计学意义。RCS结果显示，夜班工作年限与MS患病风险存在线性的正相关关系(Ptotal<0.001，Pnon-linear=0.076 0)，MS的患病风险在夜班1年后开始增加，在夜班达14.1年时风险达到最高。

结论

夜班工作会导致铁路工人患MS的风险升高，夜班工作年限越高风险越高。

夜班工作  /  铁路职工  /  代谢综合征  /  剂量反应关系
Objective

To investigate the effect of night shift work on metabolic syndrome (MS) in railway workers.

Methods

In total 7 635 railway workers who took part in the physical examination in the First Affiliated Hospital of Guangdong Pharmaceutical University from September to December 2021 were selected as the research subjects. Logistic regression was used for correlation analysis and restricted cubic spline (RCS) was used to analyze the dose-response relationship.

Results

The prevalence rate of metabolic syndrome in railway workers was 15.1%. After adjusting the confounding factors, former night shift (OR=1.198, 95%CI: 1.002-1.431) and current night shift (OR=1.219, 95%CI: 1.048-1.417), night shift duration of 6.0 ~ < 11.0 years (OR=1.323, 95%CI: 1.092-1.603) and ≥ 11.0 years (OR=1.256, 95%CI: 1.029-1.534), night shift twice a week (OR=1.269, 95%CI: 1.074-1.498), 3 times (OR=1.217, 95%CI: 1.001-1.479), and ≥ 4 times (OR=1.295, 95%CI:1.032-1.623) were significantly associated with the prevalence of MS in railway workers. The results of RCS showed that there was a linear positive correlation between the working years of night shift and the risk of MS (P total < 0.001, P non-linear=0.0760). The risk of MS began to increase after 1 year of night shift and reached the highest after 14.1 years.

Conclusion

Night shift work will lead to an increase in the risk of MS among railway workers, and the higher the number of years of night shift work, the higher the risk.

Night shift work  /  Railway workers  /  Metabolic syndrome  /  Dose-response relationship
杜懿涵, 陆崇, 冯小燕, 莫枫欣, 陈青松, 吴珊. 铁路职工夜班工作与代谢综合征的关联性分析. 现代预防医学, 2024 , 51 (5) : 811 -816 . DOI: 10.20043/j.cnki.MPM.202309249
Yi-han DU, Chong LU, Xiao-yan FENG, Feng-xin MO, Qing-song CHEN, Shan WU. Analysis of the relationship between night shift work and metabolic syndrome in railway workers[J]. Modern Preventive Medicine, 2024 , 51 (5) : 811 -816 . DOI: 10.20043/j.cnki.MPM.202309249
正文
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随着我国铁路网络的迅速发展，铁路职工数量不断增加。国家铁路年鉴显示，截至2019年，铁路职工就业人数约为193.99万人[1]。据2020年铁道统计公报报道，全国铁路营业里程达14.68万公里，旅客发送量达22.03亿人，不断增长的运输量给我国的铁路运输业人员带来了极大的工作量及工作压力[2]。夜班工作是铁路工人最突出的工作特点之一。与其他职业人群相比，铁路工人夜班工作时间更长，这可能会破坏他们正常的工作-生活规律，增加代谢综合征(metabolic syndrome，MS)的患病风险[3]。MS是一系列代谢危险因素集合在同一个体的症候群，包括中心性肥胖、高血压、高血糖和高血脂[4]，可增加一些癌症的易感性以及动脉硬化等心血管疾病的患病风险[5-6]，是近年来医学研究的热点问题。当前，国内外关于夜班工作与MS关联的研究大多集中在医护与钢铁制造等行业，对铁路职工的研究较少。因此，本研究将探讨铁路工人中夜班工作与MS的关系，为该人群MS的早期发现和防治提供重要依据。
1　对象与方法
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1.1　对象
本研究为横断面研究，选取于2021年9—12月期间在广东药科大学附属第一医院接受常规职工体检的8 107名铁路职工为研究对象。排除标准：（1）有严重疾病病史者，如恶性肿瘤和自身免疫性疾病；（2）身高、体重、腰围、体重指数（body mass index, BMI）等人体测量数据缺失者；（3）未完成问卷调查和血样采集者，最终共纳入7 635名成年人。本研究获得广东药科大学第一附属医院医学伦理委员会的伦理批准(医伦理［2019］第109号)。
1.2　方法
1.2.1　问卷调查
通过一对一的面对面访谈收集职工的人口学特征（包括年龄、性别、教育程度等)、职业史(当前是否夜班工作及夜班工作年限）、生活习惯（包括吸烟、饮酒、睡眠时间等）、既往疾病史和用药史。
1.2.2　健康体检
由经过培训的专业人员采用符合国家标准的设备进行体格测量(测量数值精确到0.1),主要包括身高、体重、血压等检查项目。参与者在隔夜禁食8 h及以上后进行血液采集以检测空腹血糖、总胆固醇、甘油三酯、高密度脂蛋白胆固醇和低密度脂蛋白胆固醇等。
1.2.3　相关变量定义
根据《中国2型糖尿病防治指南（2013年版）》的标准[7]，符合3项及以上者定义为MS：（1）高血压（血压≥130/85 mm Hg或已确诊高血压者）；（2）高血糖（空腹血糖≥6.1 mmol/L或餐后2 h血糖≥7.8 mmol/L或已确诊糖尿病者）；（3）空腹高密度脂蛋白胆固醇≤1.04 mmol/L；（4）空腹甘油三酯≥1.70 mmol/L；（5）腹型肥胖（男性腰围≥90 cm，女性腰围≥85 cm）。在未使用降压药物的情况下，SBP≥140 mm Hg和（或）DBP≥90 mm Hg者及自我报告已确诊者诊断为高血压[8]。空腹血糖≥7.0 mmol/L者及自我报告已确诊者诊断为糖尿病[9]。夜班工作是指除开在早晨7或8时至18时左右以外时间工作的不规则工作制度[10]。当前夜班状态根据近半年的夜班状况分为无夜班、曾经（调查时已半年无夜班工作且曾经夜班工作三个月及以上）及现在夜班工作（调查时已夜班至少三个月）。BMI=体重（kg）/身高的二次方（m2），分为：偏瘦（<18.5 kg/m2）、正常（18.5 ~ 23.9 kg/m2）、超重（24 ~ 27.9 kg/m2）和肥胖（≥28 kg/m2）。吸烟分为从不吸烟、已戒烟（调查时已半年无吸烟）、现在吸烟（每天≥1支烟且持续半年以上）。饮酒指每周喝酒≥1次。睡眠时间指近一个月平均夜晚睡眠的时间。运动频率指近半年每周进行中或高强度体育活动的次数。含糖饮料平均饮用量指近半年平均每月饮用含糖饮料的量。
1.3　统计学分析
采用SPSS 26软件对研究对象的基本情况进行描述性统计，连续变量采用中位数及上下四分位数描述。分类变量用频数和百分比描述。两组组间中位数比较采用Mann-Whitney U检验。分类变量中无序变量的比较采用χ2检验，有序变量的比较采用Mann-Whitney U检验。采用二分类多因素logistic回归分别分析夜班工作状态、年限及每周夜班工作天数与MS之间的关系。将每个夜班工作年限类别的中位数作为模型中的连续变量进行趋势检验，将每周夜班次数这一等级变量转换为1df线性项进行趋势检验。使用R4.1.1软件构建限制性立方样条（restricted cubic spline，RCS）模型分析夜班工作年限与MS患病风险之间的剂量反应关系。检验水准α=0.05（双侧）。
2　结果
收起
2.1　基本情况
7 635名研究对象的平均年龄为（39±12）岁，其中MS患者1 154例，MS患病率为15.1%；现在和曾经夜班工作职工MS患病率分别为15.7%和15.3%。研究对象参与夜班工作整体水平为1.0（0.0,7.0）年。与无MS组职工相比，患有MS的职工夜班工作的年限更长、年龄更大、教育程度更低，且更多人有饮酒的习惯，平均总胆固醇水平及低密度脂蛋白胆固醇水平均较高，但他们的平均每日直接饮水量更多，运动频率更高，平均每月含糖饮料饮用量更少（P<0.05）。见表1。
2.2　铁路职工夜班工作状态与MS的关联logistic回归分析
以是否患MS为因变量，以当前夜班工作状态为自变量，以表1中差异有统计学意义（P<0.05）的因素及常识选择为校正变量，进行多因素logistic回归分析。结果显示，调整混杂因素后，曾经及现在夜班工作组职工患MS的风险均高于无夜班组职工，OR（95%CI）分别为1.198（1.002～1.431）和1.219（1.048～1.417）。见表2。
2.3　铁路职工夜班工作年限与MS的关联logistic回归分析
以是否患MS为因变量，以夜班工作年限四分位数为自变量，以表1中差异有统计学意义（P<0.05)的因素及常识选择校正变量，进行多因素logistic回归分析。调整混杂因素后，夜班工作6.0～<11.0年及≥11.0年组职工患MS的风险均高于无夜班组职工（P<0.05)，OR（95%CI)分别为1.323（1.092～1.063)和1.256（1.029～1.534)。趋势性检验结果显示，随着夜班工作年限的增加，参与者患MS的风险随之增加（趋势性P=0.002）。见表3。
2.4　铁路职工每周夜班次数与MS的关联logistic回归分析
以是否患MS为因变量，以每周夜班次数为自变量，以表1中差异有统计学意义(P<0.05)的因素及常识选择校正变量，进行多因素logistic回归分析。调整混杂因素后，每周夜班2 d、3 d及≥4 d组职工患MS风险均高于无夜班组职工(P<0.05)，OR(95%CI)分别为1.269(1.074～1.498)、1.217(1.001～1.479)和1.295(1.032～1.623)。趋势性检验结果显示，随着每周夜班次数的增加，参与者患MS的风险随之增加（趋势性P=0.003）。见表4。
2.5　铁路职工夜班工作年限与MS患病风险的剂量-反应关系
如图1，在校正了年龄、性别、教育程度、婚姻状况、吸烟、饮酒、运动频率、平均每天直接饮水量、含糖饮料平均饮用量、BMI、总胆固醇和低密度脂蛋白胆固醇后，观察到夜班工作年限与MS患病风险在一定范围内呈正相关(P total<0.001)，并呈线性关系(P non-linear=0.076 0)。MS的患病风险在夜班工作1年后开始增加，在夜班工作达14.1年时风险达到最高，随后随夜班工作年限的增加MS的患病风险缓慢下降。在夜班工作达18.3年以上时，夜班工作与MS无关。
3　讨论
收起
本研究结果显示，此次参与体检的职工MS检出率为15.11%，低于2021年中国20岁及以上中国居民31.1%的患病率[11]，这可能是因为参与者平均年龄不高(39±12)岁，同时符合代谢综合征诊断要求者较少。此次参与体检的职工高血压患病率为13.7%，糖尿病患病率为5.6%，与我国18～44岁成年居民13.3%的高血压患病率[12]及甲状腺疾病、碘状况与糖尿病流行病学调查(the Thyroid disorders, Iodine status and Diabetes Epidemiological survey, TIDE研究)通过自我报告糖尿病或空腹血糖≥7 mmol/L诊断得出我国50岁以下成年居民4.5%的糖尿病患病率[13]无较大差距。MS的发病率在不同性别中无差异，这与以往的一些研究结果不同[14-15]，可能是本次研究纳入的女性样本较少造成的。同时，本次研究还发现，MS组人群虽年龄与夜班工作年限更长，教育程度更低，且更多人有饮酒的习惯，但BMI处于正常范围的人更多，平均每日直接饮水量更多，运动频率更高，平均每月含糖饮料饮用量更少，这可能是该人群在患MS后更加注重自身健康，改善了不良生活习惯[16]。
本研究发现，曾经或现在进行夜班工作的铁路职工患MS的风险更高，夜班工作年限越长及每周夜班天数更多的铁路职工患MS的风险更高，这与既往的一些研究报道一致[17-18]。一项长达8年的医院队列研究结果显示，夜班工作与MS的患病风险相关[OR及95%CI为1.36(1.04～1.78)][17]。一项对马来西亚制造业工人的横断面调查显示夜班工作与MS风险增加2倍独立相关[15]。一项5年的队列研究结果显示医护人员每月夜班工作≥4 d，患MS的风险增加[19]。
本研究RCS模型结果显示，MS的患病风险在夜班工作年限1年后开始增高，在夜班工作年限达14.1年时MS的患病风险达到最高，随后随夜班工作年限的增加MS的患病风险缓慢下降。但当夜班工作年限达18.3年以上时，夜班工作与MS的患病风险增加无关。一项对我国西南地区40岁以上且工作年限达10年及以上的11 023名男性铁路职工的调查发现，与不上夜班的工人相比，长期夜班工作的工人MS的患病率较高，但长期的夜班工作与该地区铁路工人MS的患病风险增加无相关性[20]，可能是因为长时间夜班工作的职工的身体已经对此工作模式产生适应，因此具有更大的暴露接触潜力，故罹患MS的风险降低，即健康工人幸存者偏差[21]。在本次研究中，铁路职工的平均年龄是41(28, 49)岁，夜班工作年限[1.0 (0.0, 7.0)年]的整体水平仍不长，夜班作业职工的夜班工作年限是6.0 (2.0,11.0)年，拥有10年以上夜班工作年限的职工较少，RCS模型结果可能会存在一定的偏移。此外，随着职工年龄的增加，慢性非传染性疾病的患病率可能增高，部分工人可能因为身体健康受损的原因而停止夜班工作[22]。目前，夜班工作年限与MS患病风险间剂量反应关系的研究甚少，需要更大样本量的纵向队列研究进一步证实两者之间的关联和连续性变化趋势。
目前，夜班工作与MS发生的相关机制尚不完全清晰。现有研究发现的可能机制有：夜班工作使人体正常的昼夜节律遭到破坏，导致核心温度上升，同时褪黑素、可的松以及去甲肾上腺素等激素的分泌受到抑制，使工人的睡眠质量下降，引起睡眠障碍。随着睡眠障碍的加剧，各种代谢性疾病如高血压、糖尿病等的患病风险随之升高[23-24]。此外，夜班人群多通过饮酒及浓茶等方式来提高注意力和工作效率[25]，昼夜节律紊乱和不规律的生活习惯扰乱胰岛素的正常作用，引发胰岛素抵抗和肥胖[20]，从而增加MS的发病风险。
本研究探讨了铁路职工夜班工作与MS的关联及剂量反应关系，为铁路工人MS的防治提供参考。我们的研究仍存在一些局限性。一是，本研究未纳入膳食信息，这可能会使结果出现偏差；二是，本研究是一项横断面研究，无法论证夜班工作与MS的因果关系，需要更多基于人群的纵向研究来进一步探讨铁路职工夜班工作与MS患病风险的关联。
综上所述，铁路职工夜班工作与MS的患病风险存在关联，且随着夜班工作年限的增加，MS的患病风险随之升高。提示应加强职工健康宣教，同时积极探索更加科学合理的夜班工作体系，减少对夜班工作职工的不利影响。
基金
收起
  • 广东省医学科学技术研究基金(C202209)
  • 广东省公共卫生检测与评价工程技术研究中心(2019GCZX012)
文献
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2024年第51卷第5期
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doi: 10.20043/j.cnki.MPM.202309249
  • 接收时间：2023-09-15
  • 首发时间：2026-03-19
  • 出版时间：2024-03-10
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  • 收稿日期：2023-09-15
基金
广东省医学科学技术研究基金(C202209)
广东省公共卫生检测与评价工程技术研究中心(2019GCZX012)
作者信息
    1.广东药科大学公共卫生学院，广东 广州 510310
    2.广东药科大学附属第一医院健康管理部
    3.广东省公共卫生检测与评价工程技术研究中心，广东 广州 510310

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Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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